NASA/TM-1999-209554

Models and Measurements

Intercomparison II

Edited by

Jae H. Park

NASA Langley Research Center, Hampton, Virginia

Malcohn K. W. Ko

Atmospheric and Environmental Research, Inc., Cambridge, Massachusetts

Charles H. Jackman

NASA Goddard Space Flight Center, Greenbelt, Maryland

R. Alan Plumb

Massachusetts Institute of Technology, Cambridge, Massachusetts

Jack A. Kaye

Headquarters, National Aeronautics and Space Administration, Washington, D.C.

Karen H. Sage

SAIC, Hampton, Virginia

September 1999

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NASA /TM-1999-209554

Models and Measurements

Intercomparison II

Edited by

Jae H. Park

NASA Langley Research Center, Hampton, Virginia

Malcolm K. W. Ko

Atmospheric and Environmental Research, Inc., Cambridge, Massachusetts

Charles H. Jackman

NASA Goddard Space Flight Center, Greenbelt, Maryland

R. Alan Plumb

Massachusetts Institute of Technology, Cambridge, Massachusetts

Jack A. Kaye

Headquarters, National Aeronautics and Space Administration, Washington, D.C.

Karen H. Sage

SAIC, Hampton, Virginia

National Aeronautics and

Space Administration

Langley Research Center

Hampton, Virginia 23681-2199

September 1999 __
=C

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7121 Standard Drive

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Preface

Jae H. Park, Malcolm K. W. Ko, Charles H. Jackman, R. Alan Plumb, Jack A. Kaye, and Karen H. Sage

The activity and publication of this "Models and Measurements Intercomparison II (MM II)" during

the three-year period starting in November of 1996 was made possible by the unselfish hard work of

many international modeling groups and atmospheric scientists. Altogether, 28 atmospheric simulation

models were used for model intercomparisons. The model run (starting) conditions were prescribed in

order to minimize the differences and identify the causes of differences in the models. The modelers

have reviewed their results critically and discussed the causes of differences between modeled and measured results as well as between results from different models. This MM II report should help in evaluating the results from various predictive models for assessing humankind perturbations of the

stratosphere.

A kick-off meeting was held on November 4, 1996, at NASA Headquarters, Washington, D.C., to

discuss the goals of a model and measurement intercomparison exercise. Several topics and topics'

leaders were chosen at this initial meeting. The next months were spent in preparation of the model

experiments, including much discussion at the Atmospheric Effects of Aviation Project (AEAP) Global

Modeling Initiative (GMI) meeting held February 24-26, 1997, in San Diego, California. The model

exercises were finalized by June 25, 1997, and sent to interested modeling groups. A preliminary analysis meeting was held at the end of the AEAP GMI meeting on August 27, 1997, in Fairfax, Virginia.

Further analysis continued at the more comprehensive MM II meeting held November 3-5, 1997, in

Williamsburg, Virginia. The final analysis meeting was held on March 4-5, 1998, in Satellite Beach,

Florida. Participating scientists attending the annual meeting for AEAP in Virginia Beach, Virginia,

also attended an evening meeting on April 27, 1998, to discuss the Executive Summary and publication

of the MM II report. The rest of 1998 and early 1999 was spent finalizing figures to be included and

writing and re-writing text for the final MM II report.

The editors would like to express sincere thanks to all of the participants who made the activity successful, especially in light of the fact that they were not directly supported financially. Also, the editors

would like to thank Kathy Wolfe (CSC) and her staff for meeting support, and NASA Langley's

Technical Editing and Publishing Services, who made publication of the MM II report possible. A list of

contributors, who participated directly, reviewed the report, and provided necessary data, is given at the

end of this report.

111

Table of Contents

!II

Preface .........................................................................

1

Executive Summary .................................................................

10

1. Model Experiments and Model Descriptions ........................................... 10

1.1 Model Experiments ................................................... .10

1.1.1 Introduction .............................................................. 1i

1.1.2 Information on the Transport and Chemistry Runs .................... 11

1.1.2.1 Baseline Transport Runs ..................................................

1.1.2.1.1 A- 1 Age Diagnostic .................................................. 11

1.1.2.1.2 A-2 Seasonal Transport Diagnostics ..................................... 12

1.1.2.1.3 A-3 Artificial Tracers with Stratospheric Sources; HSCT Emissions; Case I ...... 13

1.1.2.1.4 A-4 Artificial Tracers with Stratospheric Sources; HSCT Emissions, Case II ..... 13

1.1.2.1.5 A-5 Focused Simulation: SF 6 ........................................... 14

1.1.2.1.6 A-6 Focused Simulations: CO2 .............................. 1415

. . , , , ° ° .... * ...... 0 .........

1.1.2.2 Baseline Chemistry Runs ..................... . ..................... 15

1.1.2.2.1 B-1 Baseline Runs for 1992 ...................... 17

1.1.2.2.2 B-2 Constrained Runs: Fixed Inputs .....................................

!. 1.2.2.3 B-3 Constrained Runs: Prescribed Ozone P/L .............................. 20

1.1.2.2.4 B-4 High Speed Civil Transport (HSCT) .................................. 2124

1.1.2.2.5 E-1 Modified Runs of B-1 .............................................

1.1.3 The Langley Upper Atmosphere Data Pilot (UADP) Information ..................... 2527

1.1.4 Modeling Groups and Proposed Experiments ....................................

31

1.2 Model Descriptions ............................................................

1.2.1 AER 2D Photochemical Transport Model ....................................... 31

1.2.2 CSIRO 2D Chemical-Transport Model ......................................... 38

........................... 42

1.2.3 GSFC 2D Model ................................ .46

1.2.4 GSFC-INT 2D Model ..................................................... .49

1.2.5 HARVARD 2D Model ..................................................... 54

.................. ° ........

1.2.6 LLNL 2D Model ................................ 58

1.2.7 MGO/UIUC 2D Model ....................... 63

. . , , ............. * ........

1.2.8 NCAR 2D Model ................................ 64

1.2.9 NOCAR 2D Model .................................................... 65

1.2.10 SUNY-SPb 2D Model ......................................................

1.2.11 UNIVAQ-2D Photochemical Transport Model .................................. 69 ..71

......... ° • • 0 .... , • , ° •

1.2.12 WISCAR 2D Model ............................... 72

............. ° .......... * • • *

1.2.13 ECHAM3/CHEM 3D Model .................... 75

1.2.14 GISS GCMAM 3D Model ..................................................

............................ 78

1.2.15 GISS 8x10 3D CTM Model ..................... 81

. . , .... , ....................

1.2.16 GMI 3D Model ............................... 84

...... , ............. ° • • * • • •

1.2.17 GSFC 3D Model ............................... 88

........ ° .......... ° . , ° * • •

1.2.18 LARC 3D Model ................................ . ..................... 9 !

1.2.19 MONASH1 3D Model ............................... . .......... 93

1.2.20 MONASH2 3D Model ............................... .. 94

, , , , , , ....

1.2.21 NCAR 3D Model ............................................. 96

1.2.22 SLIMCAT 3D Model .......................................... 99

1.2.23 UCI23 3D Chemistry Transport Model ........................................
1.2.24UIUC-3DModel......................................................... I02

1.2.25UNIVAQ-3DPhotochemical TransportModel................................. 107

2.TransportExperiments........................................................... 110

2.1ExecutiveSummary.......................................................... 110

2.2Introduction.................................. •_............................. 112

2.3Age Spectrum ............................................................... 113

2.4 Observations

................................................................ I15

2.5 Tropical Transport in Models

................................................... 119

2.6 Global Mean Age in Models

.................................................... 123

2.7 Source-Transport Covariance

................................................... 126

2.8 HSCT "ANOy" Tracer Distributions .................. , ........................... 127

2.9 Summary and Conclusions

..................................................... 134

3. Chemistry Experiments .......................................................... 190

3.1 Summary of the MMII Intercomparisons for Chemistry .... , ......................... 190

3.2 Data Quality ................................................................ 193

3.3 Comparison Overview.

........................................................ 196

3.3.1 Introduction.

3.3.2 Results. " ............................................................ 196

3.3.3 Summary ................................................................ 197

................................................................ 201

3.4 Source Gases Comparisons ..................................................... 226

3.4.1 Introduction .............................................................. 226

3.4.2 Global and Stratospheric Lifetimes ........................................... 226

3.4.3 Source Gas Concentrations .................................................. 228

3.4.4 Conclusions .............................................................. 229

3.5 NOy and Cly Intercomparisons .................................................. 240

3.5.1 Nitrogen Compounds ...................................................... 240

3.5.2 Chlorine Compounds ...................................................... 243

3.5.3 Brief Summary ........................................................... 245

4. Ozone Climatology .............................................................. 307

4.1 Introduction

................................................................. 307

4.2 Observational Data

........................................................... 307

4.3 Comparison of Models with Observations

......................................... 309

4.4 Conclusions ................................................................. 312

5. Special Diagnostics ............................................................. 363

5.1 Introduction

................................................................. 363

5.2 Partitioning of the Radical Species ............................................... 364

5.3 Ozone Production and Loss from B-1 Runs

........................... ............. 366

5.4 Fixed Ozone Production and Loss

............................................... 384

5.5 Signatures of High Latitude Dynamical Processes and Denitrification ................... 397

5.5.1 Introduction .............................................................. 397

5.5.2 Vortex and Tropical Boundaries .............................................. 398

5.5.3 High Latitude Seasonal Cycle of N20 ......................................... 400

vi
5.5.4MeridionalN20 Gradientsat20kmfromER-2Observations....................... 402

5.5.5MeridionalClyGradientsInferredfromER-2N20Observations.................... 403

5.5.6HighLatitudeSeasonal CycleofHNO3........................................ 403

5.5.7SummaryandConclusions.................................................. 404

5.6ChlorinePartitioningandOzoneinPolarVortex.................................... 433

5.6.1Introduction.............................................................. 433

5.6.2SouthernHemisphere- Chlorine............................................. 433

5.6.3SouthernHemisphere- Ozone............................................... 434

5.6.4NorthernHemisphere- Chlorine............................................. 435

5.6.5NorthernHemisphere- Ozone............................................... 435

6.FutureAtmosphericPerturbationfromNOxInjectionintheLowerStratosphere............. 449

449 6.1ChangeinNOy ..............................................................

6.2ChangeinNOxand03 ........................................................ 450

Appendix--Listof Contributors...................................................... 493

vii

Executive Summary

Malcolm.K.W. Ko, l R.Alan Plumb, 2 and Charles H. Jackman 3

I. Atmospheric and Environmental Research, Inc.

2. Massachusetts Institute of Technology

3. NASA Goddard Space Flight Center

The Second Workshop on Stratospheric Models and Measurements Workshop (M&M II) is the

continuation of the effort previously started in the first Workshop (M&M I, Prather and Remsberg

[1993]) held in 1992. As originally stated, the aim of M&M is to provide a foundation for establishing

the credibility of stratospheric models used in environmental assessments of the ozone response to

chlorofluorocarbons, aircraft emissions, and other climate-chemistry interactions. To accomplish this, a

set of measurements of the present day atmosphere was selected. The intent was that successful simulations of the set of measurements should become the prerequisite for the acceptance of these models as

having a reliable prediction for future ozone behavior.

The choice of the measurements for M&M I was limited by data availability, and the emphasis on

2-D and 3-D assessment models. Other models such as climate models, air-trajectory models and assimilation models were not included in the consideration. In M&M II, the GSFC DAO assimilation model

provided results for a number of transport experiments. In this report, we emphasize what was accomplished beyond M&M I.

A. New Data Used for M&M II

Almost all the data sets that were used for M&M I have been revised or replaced with better and

more complete compilations. UARS satellite data sets are the primary ones being used for the middle

and upper stratosphere. We chose to use the 1992 data as the basis for our comparison because it is the

only full year for which there is CLAES data available, The CLAES data provide the very useful global

coverage of N20, CH 4, HNO3, and CIONO 2. In addition, data for 03, C10, H20 and CH 4 were available from MLS and HALOE. Many of the evaluations in M&M II relied on individuals to compile the

UARS datasets for comparisons with the models. Future model comparisons will benefit from the

climatological datasets constructed by the UARS Science Team, which were not available for M &M II

but are now accessible (see http:/hayperi°n'gsfc'nasa'g°v/Analysis/UARS/urap/h°me'html)"

The M&M II exercise has also benefited from additional data from various ER-2 aircraft campaigns

(AASE II 91/92; SPADE 92/93; ASHOE/MAESA 94; STRAT 95/96; POLARIS 96/97) and balloon

launches from the OMS program (Brazil, Alaska, Bill Brune, Geoff Toon). New data on SF 6, CO 2 and

H20 provide diagnostics for transport rates, mean age of air, and propagation of seasonal cycles into the

tropical lower stratosphere. Enhanced payload of the ER-2 provided in situ measured concentrations of

OH and HO 2. Data from the new C1ONO 2 instrument also place additional contraints on partitioning of

the chlorine species. Simultaneous measurements of NOx and NOy allow determination of the NOx/

NOy ratio. New data on aerosol surface areas and photolysis rates measurements provide additional

constraints on our understanding of photochemical partitionings.

A climatology for ozone, consisting of monthly zonal mean column ozone and monthly ozone profiles as a function of latitude, was put together for M&M II. The column ozone climatology is based on

ozone column data from 1988 to 1996 using TOMS on Nimbus-7, Meteor 3 and Earth Probe. The ozone

profile is based on ozone sonde data between 0 and 28 km and SAGE II data between 20 and 60 kin. In
theregionwherethetwodatasetsoverlap(20-30kin),aweightedaverageisusedwithaheavierweightingforthesondedataatIoweraltitudesandaheavierweightingfortheSAGEII dataathigheraltitudes.

B. Strategy for Model Testing

Ozone is the only species where there is long-term global coverage to derive a reliable climatology.

However, using ozone by itself as a guide to choose the best transport and chemistry representations in

a model is problematic since it is never clear whether a good ozone simulation in a particular model is

achieved by having the correct combination of transport and chemistry or simply good fortune. A wrong

transport rate in the model will give erroneous Cly and NOy, which will produce an incorrect local

ozone removal rate. The combination of the wrong transport with the wrong ozone removal rate could

fortuitously result in a "correct" ozone simulation. This makes it impossible to use the agreement

between observed and calculated ozone as the only criterion for having the correct transport.

Direct comparison of model simulated tracer distributions with observed distributions has limited

value. In the case of observations from satellite platforms, one must take into account that the observations represent a specific year while the model results represent a climatological mean. Comparison of

3-D data with 2-D model results requires additional work since straight zonal averaging of the observation may overlook the effects from the wave motions that should be taken into account. This is particularly important for the winter hemisphere where planetary wave activities are stronger. In those cases,

averaging by potential vorticity (PV) may help (see e.g. Randel et al., 1998). Finally, observations from

aircraft and balloon platforms may be affected by short term motions so that observations taken at a particular latitude and altitude may be sampling air that has been transported from another location. In

those cases, use of alternative co-ordinates such as N20 and PV would also help.

Comparison of radical species with model results are even more problematic. For example, discrepancies between model calculated and observed NO 2 concentration at a particular location could be due

either to differences in NOy concentrations, differences in partitioning (because of differences in local

temperature, overhead ozone or air trajectory, treatments of heterogeneous reactions), or a combination

of the two. In this report, the simulation is carried out for 1992, a period when the stratosphere was

heavily perturbed by Pinatubo aerosols. Hence, the exercise provides a test bed for volcanic perturbations to stratospheric chemistry and the ways in which it is described in the models. However, the

approaches to polar stratospheric cloud and volcanic heterogeneous chemistry were not 'standardized',

and each modelling group made their own choices.

A number of theoretical developments enable us to develop a new strategy for model testing. Successful simulation of ozone depends on several processes. Therefore, it is difficult to identify the causes

of the discrepancy between model results and observation. The current approach in M&M II identifies a

number of independent tests for individual process simulated in the model (see Figure 1 and discussion

below). Having the independent tests for the components provides a theoretical framework in which further adjustments could be made.

B.1 Focus on Transport

Current thinking considers the lower stratosphere as being separated into the tropics, extra-tropics

and the polar regions. The tropical lower stratosphere is dominated by upwelling from the tropopause. It

is the region where source gases from the troposphere are transported into the stratosphere. In this

region, the local concentration of ozone is maintained by a balance between net photochemical production and transport away from the region. Downward motions occur in the extra-tropics and the polar

2
Transport

Transport/

Chemistry

T

m t

Chemistry

Transport/

chemistry

Figure 1. Testing different components of the model.

region. In both regions, local ozone concentration is determined by the balance between photochemical

removal and transport into the region. In addition to the large scale motion as determined by advection,

neighboring regions also communicate by irreversible transport associated with wave motions. In 2-D

models, these are simulated by exchange of air between the two regions along isentropes whose rate is

related to the horizontal eddy diffusion coefficient (Kyy). If the mixing ratio of a species is different in

two neighboring regions, this exchange of air will lead to a net transport of the species. Vertical motion

(downward) and vertical mixing will carry material out of the extra-tropical lower stratosphere into the

troposphere. Exchange along isentropes across the middle world is also thought to be important. This

mixing connects the extra-tropical lower stratosphere and the tropical upper troposphere. Within this

framework, the transport can be described by specifying the upwelling in the tropics, downward

motions in the extra-tropics and polar regions, vertical mixing in each region, and the values of Kyy

linking neighboring regions.

Progress has been made on understanding the individual components of transport in 2-D models.

Heating rates calculated from observed ozone, temperature and aerosol (Eluszkiewicz et al., 1996;

Jackman et al., 1996; Rosenlof, 1995) can be used to verify the heating rates associated with the residual

circulation used in the models. In fact, several models (e.g. CSIRO, GSFC and LLNL) use heating rates

calculated using observed fields to derive the residual circulation. Work by Minschwaner et al. (1996),

Volk et al. (1996) and Schoeberl et al. (1997) provide a quantitative measure of the mixing rate between

the tropics and mid-latitudes. Work by Hall et al. (1997) and Mote et al. (1998) provide measures of Kzz

in the tropics in addition to upwelling velocity and mixing rate from mid-latitudes. With these works, a

conceptual framework has been developed to identify the key transport processes in the lower stratosphere. Some of these parameters are related directly to observations (e.g., SF 6, CO2) on the one hand,

and can be derived as diagnostics from model output. Comparison of these model diagnostics with the

values derived from observations provides a measure of how well the transport processes are simulated

in each model.

3
Thecombinationsof thedataonlong-livedtracersfromsatellite,aircraftandballoonplatformsprovideampleopportunityto study the correlation diagram for these species. As shown by Plumb and Ko

(1992) and Plumb (1996), the slope of the correlation curve can be related to the ratio of the stratospheric lifetimes of the two species. The knowledge on the tropospheric growth rate of SF 6 allows one

to translate relative lifetime to absolute lifetimes for comparison with model calculations (Volk et al.,

1997).

B.2 Focus on Chemistry

The local production and removal rates of long-lived species such as ozone, N20 , and CH 4 depend

on the local concentrations of the radicals and photolysis rates. Partitioning of the radicals in an air parcel is determined by the local concentration of ozone, H20 , Cly, NOy, Bry and CH4; and the solar illumination to which it is exposed. The solar illumination depends on the overhead column ozone and the

exact trajectory of the motion. This is particularly important near the terminator as excursions in latitude

would bring the parcel in and out of sunlight. Away from the terminator, the observed concentrations of

the radicals at a particular latitude and longitude are found to correspond to the partitioning calculated

for the air-parcel by assuming that the parcel is in photochemical equilibrium while executing exact

zonal motion at the same latitude and altitude.

In situ observations of atmospheric trace species provided data for process studies of the partitioning of the radical species over a range of conditions with different sulfate loading and solar illumination.

In a series of studies performed by Ross Salawitch (see e.g. Salawitch et al., 1994), it was demonstrated

that a photostationary box model constrained by observed values of sulfate surface area, temperature,

NOy, Cly, Bry, H20 , ozone, overhead column ozone and CH 4 can produce the observed partitioning of

the radical species under a range of conditions. This method has been validated in the lower stratosphere

using the data from the AASE II, ASHOE/MAESA, SPADE and POLARIS aircraft campaigns, and in

the mid- to upper stratosphere using balloon data.

The above approach used to analyze the observations can be modified to examine photochemical

partitioning in the models. Specifically, appropriate parameters are taken from the outputs of the assessment models to constrain the photostationary model. The radical concentrations calculated by the constrained photostationary model can be compared to the radical concentrations calculated by the

assessment models. To the extent that the photostationary model can simulate the observed radicals

using appropriate rate data, this provides verification of the photochemical solvers in the assessment

models. The production and removal rates of the reservoir species calculated by the assessment models

are also partially verified since they are determined by the radicals. The limitations of this approach are

discussed in section 5.2.

C. Results of M&M II

The numerical experiments for M&M II were chosen so that the results from different models could

be easiIy compared and that specific diagnostics which can be related to observations provide guidelines

for the correct answers. The experiments can be separated into two groups. The first group involves

simulations of chemical inert tracers and is used to provide diagnostics for transport. The second group

uses the distributions (as functions of latitude, height and seasons) of chemical tracers simulated in the

models (H20 , Cly, NOy, various source gases and ozone) for comparison with observations.

4
C.1 Transport Exercises

The transport exercises include the following:

(1) simulation of a special tracer for diagnosing the age spectrum, and seasonal variation of the

transport parameters (A-1 and A-2),

(2) simulation of the distribution of an inert tracer using the boundary condition corresponding to the

emission history of SF6 (A-5),

(3) simulation of the distribution of an inert tracer using the seasonally varying boundary condition

corresponding to that of CO 2 (A-6),

(4) simulation of the distributions of inert tracers released in the lower stratosphere, which represent

HSCT emissions (A-3 and A-4).

The age spectrum (Hall and Plumb, 1994) encapsulates all the information required to reconstruct

the stratospheric response to any tropospheric time series of a conserved tracer, and as such it summarizes transport in a chemistry-independent way. From this, the mean age can be directly determined and

compared with determinations from observations of SF6 (e.g., Elkins et al., 1996; Harnisch et al., 1996)

and from the observed trend in CO 2 (Boering et al., 1995, 1996). The response to a seasonally varying

source can also be reconstructed. In principle, the full seasonal variation of the age spectrum,

which cannot be fully determined from the single experiment A-1, is required to do this, and so experiment A-2 was designed to simulate seasonal tracers explicitly. However, it was found that the results of

the seasonal tracer experiments could be reconstructed with little error from the age spectrum derived

from experiment A-1.

The model calculations of mean age for the stratosphere were found to vary widely, by as much as a

factor of 4. Moreover, compared to mean ages determined from observed SF 6 and CO 2 data, most models produced air that is too young throughout the stratosphere. Theoretical arguments indicate that

stratospheric ages that are too young could result from a mean meridional circulation that is too strong,

horizontal mixing between tropics and middle latitudes that is too weak, or vertical diffusion that is too

strong [analyses of observed tracers indicates that vertical diffusion is negligible in the tropical atmosphere (Hall and Waugh 1997), but this may not be true of all the models]. Controlled comparisons

between models differing in one or more of these circulation features are consistent with these arguments. A contribution from numerical errors (to which age calculations may be sensitive) cannot be

ruled out, as no experiments were designed to compare transport schemes, but it is noteworthy that one

pair of models, essentially identical except for their advection schemes, produced very similar magnitudes and distributions of stratospheric age.

The observation of propagation of the seasonal cycle of CO 2 (Boering et at., 1996), and that of H20

(Mote et al., 1998) allows one to examine the upward propagation of a signal into the tropical lower

stratosphere. Most models attenuated the signal too quickly; those that did not showed too rapid upward

phase propagation. From the phase velocity and attenuation, and a third measurement such as mean age,

one can deduce the tropical upwelling rate, the vertical eddy diffusion coefficient, and the rate extratropical air mixes into the tropics (Hall and Waugh, 1997; Mote et al., 1998). Compared to such deductions

from observations, tropical upwelling in most models is too fast, consistent with the circulation being

too strong from the age comparison. Vertical diffusion is also too large in many 2D models. Compared

to the mixing rate time constant of about 15 months derived from several independent analyses of

5
observations, most models are mixing the tropics and mid-latitudes too fast. This suggests that weak

horizontal mixing is not the reason for the excessively young ages in most models.

Boering et al. (I996) suggested that the model-calculated mean age should be related to the residence time of materials deposited at the same rate (in mixing ratio unit) in the whole stratosphere.

Indeed, it was found that model simulations of trace gases, not only of those with stratospheric sources,

but also those with tropospheric sources, correlated extremely well with the age simulations. Model-tomodel variation for tropospheric source gases is modest, but that for gases with stratospheric sources is

substantial, being as large as a factor of 2 for Cly in the lower stratosphere and as much as a factor of 3

for the idealized HSCT emission simulation (experiments A-3 and A-4). Thus, model deficiencies in the

simulation of age are indicative of serious transport errors that may impact the models' ability to simulate stratospheric composition, especially the stratospheric burden of HSCT emissions. This is an

important step in the verification of model-computed changes in NOy and H20 due to aircraft emission,

and these results indicate that improvements in the simulation of stratospheric transport should be a high

priority in future model development.

C.2 Chemistry Partitioning

Testing chemistry partitioning in the models is performed as follows. First, the individual model

was asked to simulate the 1992 atmosphere providing the calculated concentrations of the trace gases

including the radical species. Appropriate parameters are taken from the outputs of the individual model

to constrain the photostationary model of Salawitch. The radical concentrations calculated by the constrained photostationary model are used to verify the radical concentrations calculated by the assessment models.

The M&M I report identified a 30% difference in model calculated partitioning. Two separate

photochemical benchmark exercises were carried out in 1994 to resolve this. As a result of these calculations, the participating models have identified the causes of these differences for their own models and

modified them accordingly so that they produce the correct benchmark answers. As expected, the comparison with the constrained photostationary model confirms that the photochemical solvers used in

most assessment models are in good agreement with the photostationary model.

In interpreting the chemistry test, one should be aware of the following two caveats. First, results of

the photostationary model depend on the reaction rate constants used. With the current JPL-97 recommendation, some discrepancies between calculated results and observed results still exist. Most notable

of these is the model ozone deficit at 40 km (Clancy et al. 1987 and references cited; Natarajan and

Callis, 1991; Eluszkiewicz and Allen, 1993; Crutzen et al., 1995; Dessler et al., 1996; Osterman et al.,

1997; Summers et al., 1997). There are indications that models may underestimate the NOx/NOy ratio

in the summer lower extra-tropical stratosphere (Sen et al., 1998, Gao et al., 1999, Danilin et al., 1999).

Recent laboratory measurements (Brown et al. 1999 a,b) suggest that the rate recommendations for

OH + NO 2 and OH + HNO 3 in JPL-97 may have to be revised. Second, the above procedure provides a

valid test only for situations where heterogeneous chemistry is easily parameterized and where local

photochemistry is rapid. It is well-known that behavior of the radicals depends on the air-parcel trajectory (temperature and solar illumination) in regions when the temperature is cold enough to trigger heterogeneous chemistry on surfaces. In addition, the long phtochemical lifetimes of some reservoirs in the

lowermost stratosphere (such as HNO 3 and HC1) imply that transport can influence chemical partitioning, and the local photostationary solution may not be appropriate. There remains a need to find ways to

verify the PSC treatments in the models.
C.3 Chemical Tracers

In the following comparison, we emphasize specific aspects rather than direct comparison with

observed concentrations:

• The model calculated atmospheric lifetimes of N20 and several CFCs from the models are longer

than those derived from observations by Volk et al. (1997) and Minschwaner et al. (1998). Assuming

that the photolysis rates are correct, the models would need a stronger upwelling in the tropics and/or

less mixing between the tropics and mid-latitudes to get longer lifetimes. Unfortunately, this conflicts

with the requirement to get greater mean age of air at mid-latitudes.

• Because of zonal asymmetric motions, computation of the zonal mean of observed N20 and CH 4

would underestimate the latitudinal gradient across the tropical barrier and polar vortex. The alternative way is to use PV to define an equivalent latitude. The gradient across the barrier defined this way

is more pronounced. It is likely that unsuccessful simulations of the correct gradients across the region

boundaries would imply incorrect exchange rates across the boundary.

• Analyses of the HNO 3 and N20 measurements in the polar vortex suggest that correlative measurements of those two species provides an indication of removal of gas-phase HNO 3 by heterogeneous

reactions and recovery of HNO 3 on the polar region.

• Model results show significant differences in NOy and Cly (>50% at mid-latitudes, larger in the polar

region) in the lower stratosphere. Differences in model calculated NOy and Cly computed using specified source gases (N20 for NOy, CFCs for Cly) are even larger, indicating differences in transport as

a major contributor.

• The UARS measurements provide information on seasonal behavior of NO, NO 2, HNO 3, HCI,

C1ONO2, and C10. The differences among model calculated concentrations for these species are

large, reflecting the differences in NOy and Cly, and different partitionings because of different local

ozone. Given the large ranges covered by the model calculated values, the observed values generally

lie within the model ranges most of the time. However, there is no one model that matches the observation in a consistent way.

C.4 Ozone Comparison

The comparison between model ozone and the ozone climatology shows the following:

• The model predicted columns are within +5% of the climatology in the tropics, _+15% at mid-latitudes,

and as much as 30% in polar region. The general tendency is to underestimate the tropical column and

overestimate the column in the extra-tropics, consistent with too strong a circulation.

• Models that follow the JPL recommendation underestimate the ozone amount above 40 km (about

10% too little around 40km, up to 30% less at 60kin). Calculations have shown that including a 6%

yield of HCI from OH + CIO will increase the model calculated ozone in this region.

• The calculated ozone is within 10% of the climatology between 25 km and 35 km in the tropics and

mid-latitudes. The differences are as large as 30% at high latitudes in some models. Larger differences

also occur in the lower stratosphere. The differences are largest in the extra tropics below 20 km

where some models overestimate ozone by as much as 100% around 14 km.

• Concentrations of tropospheric ozone are more than a factor of 2 too low for many of the models.

Several other exercises were also included. While these results cannot be easily compared to observations, they provide some useful insights.
• The local ozone production and removal rates for the simulation of the 1992 atmosphere from different models were compared to each other. In spite of the large differences in NOy, Cly, Bry and H20

concentrations, the calculated production and removal rates show many similarities. This suggests that

there is internal buffering of the system.

• In another exercise, all the models used the same fixed ozone production and loss rates to compute

ozone. This exercise highlighted how differences in transport affect the model computed ozone. Generally, the column ozone computed in this exercise for a model was fairly close to the column ozone

calculated by the same model for the 1992 atmosphere even though the ozone production and loss

rates used in two simulations were quite different.

References

Boering, K.A., E.J. Hinsta, S.C. Wofsy, J.G. Anderson, B.C. Daube, Jr., A.E. Dessler, M. Lowenstein, M.P.

McCormick, J.R. Podolske, E,M. Weinstock, and G.K. Yue, Measurements of stratospheric carbon dioxide

and water vapor at northern mid-latitudes: Implications for troposphere-to-stratosphere transport, Geophy.

Res. Lett., 22, 2737-2740, 1995.

Boering, K.A., S.C. Wofsey, B.C. Daube, H.R. Schneider, M. Lowenstein, and J.R. Podolske, Stratospheric transport rates and mean age distribution derived from observations of atmospheric CO 2 and N20, Science, 274,

1340-1343, [ 996.

Brown, S.S., R.K. Talukdar, and A.R. Ravishankara, Rate constants for the reaction OH + NO 2 + M --->HNO 3 + M

under atmospheric conditions, Chem. Phys. Lett., 299, 277-284, 1999a.

Brown, S.S., R.K. Talukdar, and A.R. Ravishankara, Reconsideration of the rate constant for the reaction of

hydroxyl radicals with nitric acid, J. Phys. Chem., in press, 1999b.

Clancy, R.T., D.W. Rusch, R.J. Thomas, M. Allen, and R.S. Eckman, Model ozone phtotochemistry on the basis

of Solar Mesospheric Explorers mesospheric observations, J. Geophys. Res., 92, 3067-308(" _987.

Crutzen, P.J., J.-U. Grooz, C. Bruhl, R. Muller, J.M. Russell III, A reevaluation of the 03 budget with HALOE

UARS data: no evidence for the 03 deficit, Science, 268, 705-708, 1995.

Danilin, M.Y., J.M. Rodriguez, W. Hu, M.K.W. Ko, D.K. Weisenstein, J.B. Kumer, J.L. Mergenthaler, J.M.

Russel lI], M. Koike, G.K. Yue, N.B Jones, and P.V. Johnston, Nitrogen species in the post-Pinatubo stratosphere: Model analysis utilizing UARS measurements, J. Geophys. Res., 104, 8247-8262, 1999.

Dessler, A.E., S.R. Kawa, D.B. Considine, J.W. Waters, L. Froidevaux, and J.B. Kumer, 393 UARS measurements of CIO and NO 2 at 40 km and 46 km and implications for the 394 model "ozone deficit", Geophy. Res.

Lett., 23, 339-342, 1996.

Elkins, J. W., D.W. Fahey, J.M. Gilligan, G.S. Dutton, T.J. Baring, C.M. Volk, R.E. Dunn, R.C. Meyers, S.A.

Montzka, P.R. Wamsley, A.H. Hayden, J.H. Butler, T. M. Thompson, T.H. Swanson, E.J. Dlugokencky, P.C.

Novelli, D.F. Hurst, J.M. Lobert, S.J. Ciciora, R.J., McLaughlin, T.L. Thompson, R.H., Winkler, P.J. Fraser,

L.P. Steele, and M.P. Lucarelli, Airborne gas chromatograph for in situ measurements of long-lived species in

the upper troposphere and lower stratosphere, Geophy. Res. Lett., 23, 347-350, 1996.

Eluszkiewicz, J., and M. Allen, A global analysis of ozone deficit in the upper stratosphere and lower mesosphere,

J. Geophys. Res., 98, 1069-1082, 1993.

Eluszkiewicz, J., D. Crisp, R. Zurek, L. Elson, E. Fishbein, L. Froidevaux, J. Waters, R.Grainger, A. Lambert,

R. Harwood, and G. Peckham, Residual circulation in the stratosphere and lower mesosphere as diagnosed

from Microwave Limb Sounder Data, J. Atmos. Sci., 53, 217-240, 1996.

Gao, R.S., D.W. Fahey, L.A. Del Negro, S. G. Donnelly, E.R. Keim. J.A. Neuman, E. Teverovski, P.O.

Wennberg, T.F. Hanisco, E.J. Lanzendorf, M.H. Proffitt, J.J. Margitan, J.C. Wilson, J.W. Elkins, R.M.

Stimpfle, R.C. Cohen, C.T. McElroy, T.P. Bui, R.J. Salawitch, S.S. Brown, A.R. Ravishankara, R.W.

Portman, M.K.W. Ko, D.K. Weisensten, and P.A. Newman, A comparison of observations and model simulations of NOx/NOy in the lower stratosphere, Geophys. Res. Lett., 26, 1153-1156, 1999.

8
Geller,L.S.,J.W.Elk:,as, J.M. Lobert, A.D. Clarke, D.F. Hurst, J.tt. Butler, and R.C. Meyers, Tropospheric SF6:

Observed latitudinal distribution and trends, derived emissions, and interhemispheric exchange time, Geophy.

Res. Lett., 24,675-678, 1997.

Hall, T.M., and R.A. Plumb, Age as a diagnostic of stratospheric transport, J. Geophys. Res., 99, 1059-1070, 1994.

Hall, T.M., and D.W. Waugh, Tracer transport in the tropical stratosphere due to vertical diffusion and horizontal

mixing, Geophy. Res. Left., 24, 1383-1386, 1997.

Jackman, C. H., E. L. Fleming, S. Chandra, D. B. Considine, and J. E. Rosenfield, Past, present, and future modeled ozone trends with comparisons to observed trends, ./. Geophys. Res., 101, 28,753- 28,767, 1996.

Minschwaner, K., A.E. Dessler, J.W. Elkins, C.M. Volk, D.W. Fahey, M. Lowenstein, J.R. Podolske, A.E. Roche,

and K.R. Chen, Bulk properties of isotropic mixing into the tropics in the lower stratosphere, J. Geophys. Res.,

101, 9433-9439, 1996.

Minschwaner, K.,R.W. Carver, and B.P. Briegleb, Infrared radiative forcing and atmospheric lifetimes of trace

species based on observations form UARS, J. Geophys. Res., 103, 23243-23253, 1998.

Mote, P.W., T.J. Dunkerton, M.E. Mclntyre, E.A. Ray, P.H. Haynes, and J.M. Russell Ill, Vertical velocity,

vertical diffusion, and dilution by mid-latitude air in the tropical lower stratosphere, J. Geophys. Reds., 103,

8651-8666, 1998.

Natarajan, M. and L.B. Callis, Stratospheric Photochemical studies with Atmospheric Trace Molecule Spectroscopy (ATMOS) measurements, J. Geophys. Res., 96, 9361-9370, 1991.

Osterman G.B., R.J. Salawitch, B. Sen, G.C. Toon, R.A. Stachnik, H.M. Pickett, J+J. Margitan, J.-F. Blavicr, and

D.B. Peterson, Balloon-borne measurements of stratospheric radicals and their precursors: hnplicalions for the

production and loss of ozone, Geophy. Res. Lett., 24, 1107-I 110, 1997.

Prather, M.J. and Remsberg, E.E., The atmospheric effects of stratospheric aircraft: Report of the 1992 models and

measurements workshop, 3 volumes, NASA Reference Publication 1292, 1993.

Randel, W.J., F. Wu, J.M. Ruussell, A. Roche, and J.W. Waters, Seasonal cycles and QBO variations in stratospheric CH4 and H20 observed in UARS HALOE data, J. Atmos. Sci., 55, 163-184, 1998.

Rosenlof, K.H., Seasonal cycle of residual mean meridional circulation the stratosphere, J. Geophys+ Res., 100,

5173-5191, 1995.

Salawitch, R.J., S.C. Wofsy, P.O. Wennberg, R.C. Cohen, J.G. Anderson, D.W. Fahey, R.S. Gao, E.R. Keim, E. L.

Woodbridge, R.M. Stimpfle, J.P. Koplow, D.W. Kohn, C.R. Webster, R.D. May, L. Pfister, E.W. Gottlieb,

H.A. Michelsen, G.K. Yue, J.C. Wilson, C.A. Brock, H.H. Jonsson, J.E. Dye, D. Baumgardner, M.H. Proffitt,

M. Loewenstein, J.R. Podolske, J.W. Elkins, G.S. Dutton, E.J. Hisntsa, A.E. Dessler, E.M.Wienstock, K.K.

kelly. K.A. Boering, B.C. Daube, K.R. Chan, S.W. Bowen, The distribution of hydrogen, nitrogen, and chlorine'radicals in the lower stratosphere: Implications for changes in 03 due to emission of NOy from supersonic

aircraft, Geophys. Res. Lett., 21, 2547-2550, 1994.

Sen, B., G.C. Toon, G.B. Osterman, J-F Blavier, J.J. Margitan, R.J. Salawitch, and G.K. Yue, Measurements of

reactive nitrogen in the stratosphere, J. Geophys. Res., 103, 3571-3585, 1998.

Schoeberl, M.R., A.E. Roche, J.M. Russell III, D. Ortland, P.B. Hays, and J.W. Waters, An estimation of the

dynamical isolation of the tropica[ lower stratosphere using UARS wind and trace gas observations of quasibiennial oscillation, Geophys. Res. Lett., 24, 53-56, 1997.

Summers, M.E., R.R. Conway, D.E. Siskind, M.H. Stevens, D. Offermann, M. Riese, P. Preusse, D.F. Strobel,

J.M. Russell Ill, Implications of Satellite OH observations for middle atmospheric H20 and Ozone, Science,

277, 1967-1970, 1997.

Volk, C. M., J.W. Elkins, D.W. Fahey, R.J. Salawitch, G.S. Dutton, J.M. Gilligan, M+H. Profitt, M. Lowenstein,

J.R. Podolske, K. Minschwaner, J.J. Margitan, and K.R. Chen, Quantifying transport between the tropical and

mid-latitude lower stratosphere, Science, 272, 1763-1768, 1996.

Volk, C.M J.W. Elkins, D.W. Fahey, G.S. Dutton, J.M. Gilligan, M. Lowenstein, J.R. Podolske, K.R. Chcn, and

M.R. Gunson, Evaluation of source gas lifetimes from stratospheric observations, J. Geophys. Res., 102,

25543-25564, 1997.

9
1. Model Experiments and Model Descriptions

Charles H. Jackman I and Malcolm K. W. Ko 2

1. NASA Goddard Space Flight Center

2. Atmospheric and Environmental Research, Inc.

1.1 Model Experiments

The planning of the Model and Measurement Workshop started with the kick-off meeting in

November 1996 at NASA Headquarters. Participants were given the charge to design a number of

experiments that would use observations to test whether models are using the correct mechanisms to

simulate the distributions of ozone and other trace gases in the atmosphere. The purpose is closely tied

to the needs to reduce the uncertainties in the model predicted responses of stratospheric ozone to perturbations. The set of numerical experiments was finalized in a meeting in February 1997. People were

identified with each experiment. One was given the responsibility of defining the boundary conditions

for the simulation and specifying the content and format of the model outputs for analyses. The others

were to lead the analysis of the results for the experiment. The specifications for the experiments were

sent out to the modeling community in June 1997. The list of experiments, along with the definition

leads and the analysis leads are given in Table 1.i.I. Descriptions of each experiment are given in

section 1.2.

Twenty eight modeling groups responded to the requests for input. Table 1.1.2 lists the participating modeling group, along with the experiments performed. Table 1.1.3 gives the short hand designations of each model that will be used in labeling the results. Brief descriptions of each model as

provided by the individual modeling groups are given in section 1.3. The model results were archived

in the specified format at UADP at Langley Research Laboratory. The collection of the results at one

location in a common format make it possible to analyze the results. Throughout the exercise, the

modelers worked closely with the analysis leads to examine the model results. The discussions of the

results took place in two meetings, August 1997 and November I997. A draft report was put together

by the analysis leads and the draft was discussed in a meeting held in March 1998. The draft report was

revised and sent to copy-edit at the end of 1998.

1.1.1 Introduction

Since the Models and Measurements Workshop held in 1992 (Prather and Remsberg, 1993), additional progress has been made in understanding model differences. A photolysis benchmark (Stolarski

et ai. 1995) and a chemistry benchmark have been instituted. The photolysis benchmark led to improvements in the computations in 2-D models of radiative transfer with a very large reduction in modelmodel differences (Stolarski et al. 1995). The chemistry benchmark has been used by modellers to

check their chemistry solvers with subsequent improvements in some models.

Some transp0rt:differences among the models Were highlighted in Prather and Remsberg (1993)

with mode! simulations of C-14, Sr-90, and two idealized tracers. Recent measurements of SF 6 and CO 2

with subsequent derivations of"age of air" have prompted a renewed interest in modelled transport. The

model exercises for Models and Measurements II include both "transport" and "chemistry" parts.

10
Theinformationabouthetransportandchemistryrunsisgivenbelowfortworeasons:1)to assist

thereaderinunderstandingtheenclosedanalysisof themodelsimulations;and2) to assistmodellers,

whodidnottakepartintheoriginalModelsandMeasurementsII workshops,in repeatingthemodel

simulationsfortheirownbenefit.

References

Prather, M. J., and E. E. Remsberg, The Atmospheric Effects of Stratospheric Aircraft: Report of the 1992 Models

and Measurements Workshop, 3 volumes, NASA Reference Publication 1292, 1993.

Stolarski, R. S., S. L. Baughcum, W. H. Brune, A. R. Douglass, D. W. Fahey, R. R. Friedl, S. C. Liu, R. A. Plumb,

L. R. Poole, H. L. Wesoky, and D. R. Worsnop, 1995 Scientific Assessment of the Atmospheric Effects of

Stratospheric Aircraft, NASA Reference Publication 1381, 1995.

1.1.2 Information on the Transport and Chemistry Runs

1.1.2.1 Baseline Transport Runs

1.1.2.1.1 A-1. Age Diagnostic

Definition Lead: R. Alan Plumb

The main purpose of this experiment is to have a delta function source in order to generate the age

spectrum in the atmospheric model. The age spectrum at a particular location is the distribution of transport times from the troposphere to that location; the age is the first moment of this spectrum (see Hall

and Plumb, JGR, 99, 1059, 1994). The age spectrum carries much more information about transport

than age alone.

Proposed Run A-I

The tracers should be exactly conserved, except within a source region near the surface. Within this

source region only, the mixing ratio is specified as a function of time. The precise spatial location of the

source is not really critical, but it should be consistent between models (and must be in the troposphere).

The target should be the lowest 2 km of the troposphere and 10 degrees width in latitude centered on the

equator. In terms of the UADP grid (see below), it will be the lowest grid boxes centered on 5S, equator

and 5N. Note that the edges of the region are at 7.5S and 7.5N. You should pick a box in your model

grid that is LARGER than the region defined in UADP grid, but MUST lie below 500 mbar and within

20 degrees of the equator. For 3D models, all longitudes should be specified identically. The model

should be run for 20 years, beginning on January 1 of year 1.

11
Theboundaryconditionforthisrunisasfollows:Forthewholefirstmonth(startingatt=0,Jan1,

year1),XI = 1(mixingratiooftracerwithinbox)ateverytimestep.All subsequent timestepsafterthe first month:XI = 0.0

Proposedoutput from Run A-I

Model fields should be output on the UADP standard grid (see below) every half-month (on the first

and 15th of the months) for the entire 20 years of the run. Having the full time series is essential to

deriving the age spectrum. 3D model results should first be zonally averaged2

Modellers need to provide Temperature, W* (in cm/s), v* (in m/s), Kyy (in cm2/s), Kzz (in cm2/s),

and Kyz (in cm2/s) twice monthly. Please output at the beginning and middle of the month for one year.

This information should help in the analysis of A-1 model results.

1.1.2.1.2 A-2. Seasonal Transport Diagnostics : :_ _

Definition Lead: R. Alan Plumb

These runs are designed to focus on the seasonal transport in the models, and will form a fundamental basis on which CO2 concentrations can be understood.

Proposed Run A-2

Two runs, with tropical sources (see note above on source regions - use same target box as for age

diagnostic run A-1) whose seasonal cycles are 90 degrees out of phase should be done:

a. X2(t) = 1 + sin(2*pi*t/T) where T = ! model year

b. X3(t)= 1 + cos(2*pi*t/'I')

The boundary condition will be applied for the full five years. The constant term of one is included

to avoid negative mixing ratios. The basic conditions for these runs will be the same as for the age diagnostic (A-1). These runs need only be integrated for five years, however.

Initial condition: Outside the target box, the mixing ratios should be set to X2=l, X3=l, at all latitudes and heights, at t=0.

Proposed output from Model Run A-2

Model fields should be output on the UADP standard grid (see below) every half-month (on the first

and 15 of the months) for the entire 5 years of the run. 3D model results should first be zonally

averaged.

Temperature would also be useful to have every half-month. If temperatures repeat annually, then

one year of temperature output is sufficient.

12
1.1.2.1.3 A-3. Artificial Tracers with Stratospheric Sources; HSCT Emission; Case I

Definition Lead: Donald J. Wuebbles

The idea of these experiments is to test model transports in general, and in particular to test transport from natural and aircraft sources in a chemistry-independent way. These experiments will also help

define model stratospheric residence times for various sources. One of these runs will help assess the

way in which material is transported from mid-latitudes into the tropics. It is intended that results from

these calculations, along with the associated model-model comparisons of results, will allow assesment

of model-to-model transport differences on the simulations of real tracers.

Proposed Run A-3

This scenario will involve the simulation of emissions of a hypothetical tracer from a projected fleet

of HSCTs. Model input will be mapped to the specified grid for your model and provided as a function

of longitude, latitude, and altitude. Emissions will be zonally averaged for 2-D models. The gridded

emission will correspond to the NOx emissions for the case of 500 HSCTs at Mach 2.4 and E.I.=10.

However, you are to treat the NOx emissions as if this were a tracer. The stratospheric sources are constant in time. Surface destruction is imposed by setting the mixing ratio of the hypothetical tracer below

6 km to zero at every time step. There are no other destruction processes for these tracers, however. The

experiments should be initialized on January l, year 1, with zero mixing ratio everywhere, and run to

equilibrium.

The supersonic fleet scenario needed for this model experiment is in your directories; it is named

hsct_2015_eil0_500.*. You may get the data from anonymous ftp on uadpl.larc.nasa.gov. If you are

doing only MMII then the file will be in mm 1997]MMII/your_model_name. If you are doing IPCC and

MMII, the file will be in mm1997/IPCC/your_model_name.

Proposed output from Run A-3

Output the steady state concentration of NOx for 1 year. 2D model output should be submitted on

the UADP latitude/altitude grid (defined below) and 3D model output on the UADP longitude/latitude/

altitude grid for each month. A midmonth value is preferred for 2D models.

Temperature would also be useful to have every mid-month. If temperatures repeat annually, then

one year of temperature output is sufficient.

1.1.2.1.4 A-4. Artifical Tracers with Stratospheric Sources; HSCT Emission; Case II

Proposed Run A-4

For case A-4, same as A-3 but include only emissions at latitudes greater than 30 degrees North.

The emission of the tracer from the HSCT scenario should be truncated so that there is no emission of

the tracer at latitudes south of 30 degrees north (no emission in tropics, northern sub-tropics, or in southern hemisphere). All other aspects of this run will be identical to the baseline HSCT scenario (above).

13
1.1.2.1.5A-5. FocusedSimulation: SF6

DefinitionLead:MalcolmK. W. Ko

Proposed Run A-5

Simulate SF 6 concentration between 1966 to 2000.

Emission: restrict emission to between 30N to 60N, uniform emission rate per unit area (independent of land/ocean for 3-D models), total annual emission set equal to 0.2[t-1966] Ggm/yr. 1 Ggm is

le9 gin. If you want to reduce the length of the run, you can start with year N, sum all emissions prior to

year N to be released in year N, then run to 1996. Sink: no sink, i.e. no in situ chemical removal, no

deposition. Therefore, your result should conserve mass.

The recommendation is to keep the emission constant through each year. But if you are particular,

you can go through the following: If you are updating your emission rate every DS(in second), you will

be changing your emission N=Sy/DS times a year where Sy is the number of seconds in a 365 day year,

the amount emitted during the nth interval in a particular year is given by

{0.2*(T- 1966+ I)/N + 0.2*[n-(N+ 1)/2]/(N'N) } Ggm

and the emission rate is

{0.2*(T- 1966+ 1)/N + 0.2*In-(N+ 1)/2]/(N*N) }/DS Ggm/sec.

In the above equation T is an integer for the year. "n" goes from 1 to N.

Proposed output from A-5

Output: zonal mean distribution of SF 6 in UADP (see below) grid 4 times a year (March 15,

June 15, September 15, December 15) for the entire model run; AND global averaged concentration of

SF6 in the troposphere and concentration of SF 6 at the tropical tropopause. Also provide your steady

state zonal-mean distribution of N20 for the same four months and the global average concentration of

N20 in the troposphere. We may use N20 as the vertical co-ordinate in our analysis. The global averaged tropospheric concentration of N20 provided by you will be used to scale the N20 concentrations in

the stratosphere if it is necessary to worry about long-term trend in N20.

Temperature would also be useful to have 4 times a year (March 15, June 15, September 15,

December 15). If temperatures repeat annually, then one year of temperature output is sufficient.

Anticipated analysis: compare tropospheric concentration of SF 6 with various data, stratospheric

concentration of SF 6 with ATMOS and aircraft data. We may do some age of air type comparison.

1.1.2.1.6 A-6. Focused Simulations: CO 2

Definition Lead: Timothy M. Hall

The main purpose of this run is to simulate CO 2 as realistically as possible.

14
Proposed Run A-6

Here is information on specification of the CO 2 surface boundary conditions (in mixing ratio):

l) Set the CO 2 in the atmosphere everywhere equal to 337 ppm at the beginning of 1979 and

before; and

2) Set the CO 2 in the atmosphere to boundary conditions based on CMDL maritime measurements

for 17 years from 1979 through 1995 (inclusive). These values are based on data provided by

Pieter Tans and Ken Masarie at NOAA CMDL and are given every 10 degrees latitude from 90S

to 90N (19 latitudes).

To get this CO 2 input data, grab Appendix A via ftp:

ftp uadp ! .larc.nasa.gov

Name: anonymous

Password: <your e-mail address>

tip> cd mm 1997/MMII/input_data

tip> get Appendix A CO2.dat

tip> bye

Modellers should aim to specify these mixing ratio values over the first 2 km (approximately) of the

model atmospheres. 3D models should use equal values at all longitudes.

There is no steady state condition, due to the nonrepeating boundary condition. However, from the

age spectrum runs, it should be possible to tell when the startup transients become negligible (possibly

by year 10 for most models).

Proposed output from Run A-6

Model fields of CO 2 should be output on the UADP standard grid (see previous message) every

half-month (on the first and 15th of the months) for the final 6 years of the 17 year run (i.e., 1990 to

1995 inclusive).

Temperature would also be useful to have every half-month. If temperatures repeat annually, then

one year of temperature output is sufficient.

1.1.2.2 Baseline Chemistry Runs

1.1.2.2.1 B-I Baseline Runs for 1992

Definition Lead: David B. Considine

The objective of this run is for each modeling group to provide their "best" simulation of 1992,

however they see fit.

15
Proposed Run B-I

.

.

Run B-1 should include PSCs. Groups willing to provide output for Run E-1, Polar Processes,

should repeat this run without PSCs (see run E-1 below).

Sulfate aerosol surface area densities for 1992 are very high due to the eruption of Mr. Pinatubo

in 1991. The surface area densities used in the models should not be a background distribution

but should be elevated. A three year zonal and monthly mean climatology (1990-1992) of sulfate

aerosol SAD derived from SAGE lI observations has been constructed and is available from

the UADP for those interested in using it (file sage2sad_uadp.dat). See information under

"Constrained Chemistry Run B-2" to retrieve this file.

.

.

Because the 1992 sulfate aerosol SAD is decaying from highly elevated values, running a 2D

model to an annually repeating steady state for 1992 would not be legitimate. 2D models should

therefore run to an annually repeating steady state for 1990, and then integrate an extra 2 years

through the eruption of Mt. Pinatubo. Output from the last year only is requested.

Model temperatures should correspond to 1992 if possible. A zonal and monthly mean temperature file taken from 1992 NCEP data and the 1986 CIRA reference model is available on the

UADP data base (Temperature_92.dat). See information under "Constrained Chemistry Run B2" to retrieve this file.

. Boundary Conditions: You may use whatever source gas boundary conditions you feel are appropriate lor 1992. Here is a table of source gas boundary conditions that are believed to be reasonably close to 1992 conditions. These are generally taken from Table 6-3, WMO (1994) for the

year 1990. Source gas CH3Br is taken from the IS92a scenario given in Table 2.2 of "Climate

Change 1995." The CO 2 boundary condition is taken from Figure 5, p.23, of "Climate Change

1995."

Species

CFC- 10 (CC14)

CFC-I 1 (CFCI3)

CFC- 12 (CF2C12)

CFC- 113 (C2F3C13)

HCFC-22 (CHF2Cl)

CH3CC13

H- 1301 (CF3Br)

H- 1211 (CF2CIBr)

Value

108 pptv

270 pptv

465 pptv

70 pptv

106 pptv

153 pptv

1.7 pptv

2.9 pptv

CH3CI

CH3Br

CH 4

N20

CO 2

600 pptv

10 pptv

1715 ppbv

310 ppbv

356 ppmv

6. Reaction rates, heterogeneous reaction rates, and photolysis cross sections should be consistent

with JPL-97 (see below to retrieve this information).

16
Proposed output from Run B-1

2D model output should be submitted on the UADP latitude/altitude grid (defined below) and 3D

model output on the UADP longitude/latitude/altitude grid at the beginning and middle of each month

of 1992. An output of the daytime average value is preferred for 2D models. An output at ! 2Z would be

best for 3D models. Please output the following, if calculated:

a. Source Gases/Tracers (in mixing ratio):

CF2C12 ' CF2CI3 ' CC14' CH3C1, CH3CC13, CHCIF2, C2C13F3, CC1FO, CF20, N20, CH4,

H20 , HF, CH3Br

b. Column ozone (in Dobson Units) - by latitude and month

c. Medium Lived/Reservoir (in mixing ratio):

03, HC1, C1ONO 2, HNO3, N205, HBr, BrONO 2, H202

d. Short Lived/Radical (in mixing ratio):

O(3p), O(I D), NO, NO 2, OH, HO 2, HOCI, ClO, BrO, C1202, HOBr

e. Families (in mixing ratio): NOy, Cly, Bry

f. Other: CH20 & CO (in mixing ratio),

M (total number density in #/cm+3),

Temperature (Kelvin)

g. Ozone loss rates (in mixing ratio per second) for NOx, HOx, C1Ox, BrOx, and Ox

h.

i.

Ozone production rates (in mixing ratio per second) for

1. J(O 2) and 2. a sum of the rest

For N20, CH 4, CFC13, CF2CI2, CC14, CH3CI, CH3Br, CH3CC13' C2C!3F3' CHC1F2' and any

other CFC or HCFC in your model simulation, please provide:

i. Global yearly average atmospheric abundance (in # of molecules);

2. total global yearly average atmospheric loss rate (in # of molecules per second);

3. total global yearly average stratospheric loss rate (in # of molecules per second).

1.1.2.2.2 B-2 Constrained Runs: Fixed Inputs

Definition Lead: Charles H. Jackman

Here is additional information on Constrained Chemistry Run B-2. The "Proposed Model Run" and

"Proposed Model Run Output" are given below.

Simulate 1992 with fixed 1) zonal-mean temperature; 2) aerosol; and 3) ozone, water vapor, and

several source gases. These model parameters will be fixed to values constrained as much as possible by

observations.

17
Data to Constrain the Run B-2

The data to constrain this run are located at the Upper Atmosphere Data Pilot (UADP) at NASA

Langley Research Center. To retrieve these data, do the following:

ftp uadp I.larc.nasa.gov

Name: anonymous

Password: <your e-mail address>

tip> cd mm 1997/MMII/input_data

tip> get README

tip> get Constrained_species_92.dat

tip> get Temperature_92.dat

ftp> get sage2sad_uadp.dat

tip> bye

1) Zonal-mean Temperature

These data are located in "Temperature_92.dat"

Temperature data for 1992 was taken from the National Centers for Environmental Prediction

(NCEP) for 1000-1 mbar and the CIRA (1986) empirical reference model for the mesosphere above

1 mbar.

2) Zonal-mean Aerosol

These data are located in "sage2sad_uadp.dat"

The 1990-1992 surface area density data set was constructed from calculations of aerosol surface

area density made by Larry Thomason of the NASA Langley Research Center using SAGE II satellite

data. These calculations are described in Thomason, L. W., L. R. Poole, and T. Deshler, "A global climatology of stratospheric aerosol surface area density deduced from Stratospheric Aerosol and Gas

Experiment II measurements: 1984-1994," J. Geophys. Res., 102, 8967-8976, 1997. The Thomason

et al. calculations have been modified in the following ways: 1. Only data from 1990, 1991, and 1992

are included in this data set; 2. A time interpolation was used between -65 and +65 degrees to eliminate

occasional data gaps; 3. In the areas where the SAGE II instrument was saturated after the eruption of

Mt. Pinatubo in June, 1991, midlatitude values of surface area density have been interpolated through

the tropics. This interpolation fills the region from -15 to +15 degrees and 5.5 to 25.5 kilometers during

the months from June, 1991 to August, 1992; 4. During months where there was no data in the -80

to -70 and 70 to 80 degree bins, values from the -70 to -60 and 60 to 70 degree bins were used; 5. The

-90 to -80 and 80 to 90 degree latitude bins were filled with values from the -80 to -70 and 70

to 80degree bins; 6. The resulting 36 month by 18 latitude (10 degree resolution) by 36 altitude

(1 kilometer resolution from 5.5 to 40.5 kilometers) was interpolated to the standard UADP 37 latitude

by 31 altitude grid.

3) Zonal-mean Ozone, Water Vapor, and Several Source Gases

These data are located in "Constrained_species 92.dat"

The GSFC 2D model (e.g., Jackman et_all i996) was used in a ""data assim_iation" mode_ The

UARS MLS instrument version 3 mraSurmnents were used for 03 and H20. The UARS CLAES instrument version 7 measurements were used for N20 , CH4, and CFC-i2.-The model Was fiXed_io these

measurements where they are dependable and was fixed to 1990 boundary conditions in Table 6-3 of

WMO (1994) at the ground for N20 , CH4, and CFC-12. The model with these constraints was run for

18
twentyyearstoa steady-stateannuallyrepeatingcondition.Gasamountswerethuscomputedoverthe

wholedomainof themodel.

TheconstrainedsourcegasesCFC-11,CC14,CH3CCI3,CFC-113,andCH3Brarecomputedby

usingtheN20 valuesasatranferstandard[e.g.,(ConstrainedCFC-I1)= (ModelCFC-11)* (CLAES

N20)/(ModelN20)].TheconstrainedsourcegasHCFC-22is notconstrainedin anywayby UARS

measurementsbut is computedin thefree-runningmodel.Boundaryconditionsfor CFC-11,CC14,

CH3CC13, CFC-113, CH3Br, and HCFC-22 are also taken from year 1990 of Table 6-3 of WMO

(1994).

References

Jackman, C. H., E. L. Fleming, S. Chandra, D. B. Considine, and J. E. Rosenfield, Past, Present, and Future

Modeled Ozone Trends With Comparisons to Observed Trends, J. Geophys. Res., 101, 28,753-28,767, 1996.

WMO, Scientific Assessment of Ozone Depletion: 1994, World Meteorological Organization, Global Ozone

Research and Monitoring Project, Report No. 37, 1994.

Special Instructions

Modellers are urged to linearly interpolate in time between the monthly intervals to get out these

gases on a finer interval.

Modellers are encouraged to run their model to an approximate steady-state condition with the fixed

temperature and source gases AND a near background sulfate surface area. Modellers are then encouraged to run their model in a time-dependent manner for three years (1990-2) using the monthly changing, but prescribed, sulfate surface areas.

Proposed output from Run B-2

2D model output should be submitted on the UADP latitude/altitude grid (defined below) and

3D model output on the UADP longitude/latitude/altitude grid for each month of 1992. A midmonth

daytime average value is preferred for 2D models. A midmonth day at 12Z would be best for 3D models. Please output the following, if calculated:

a. Medium Lived/Reservoir (in mixing ratio):

HC1, C1ONO 2, HNO 3, N205, HBr, BrONO 2, H20 2

b. Short Lived/Radical (in mixing ratio):

O(3p), O(1D), NO, NO 2, OH, HO 2, HOCi, CIO, BrO, C1202, HOBr

c. Families (in mixing ratio): NOy, Cly, Bry

d. Other: CH20 & CO (in mixing ratio),

M (total number density in #/cm+3),

Temperature (Kelvin)

e. Ozone loss rates (in mixing ratio per second) for NOx, HOx, C1Ox, BrOx, and Ox

19
f. Ozoneproductionrates(in mixingratiopersecond)for

l. J(O2)and2.asumof therest

g. For N20, CH4, CFC! 3, CF2C12, CC14, CH3C1 , CH3Br , CH3CCI3, C2CI3F3, CHCIF2 ' and any

other CFC or HCFC in your model simulation, please provide:

I. Global yearly average atmospheric abundance (in # of molecules);

2. total global yearly average atmospheric loss rate (in # of molecules per second);

3. total global yearly average stratospheric loss rate (in # of molecules per second).

1.1.2.2.3 B-3 Constrained Runs: Prescribed Ozone P/L

Definition Lead: Malcolm K. W. Ko

Here is additional information on Constrained Chemistry Run B-3. The "Proposed Model Run" and

"Proposed Model Run Output" are given below.

Proposed Run B-3

The input file provides:

03: ozone mixing ratio

O3P: in units of mixing ratio per sec

O3L: loss frequency in unit of 1/sec

O3Q: quadratic loss frequency in unit of 1/sec

Data to Constrain the Run -

The data to constrain this run are located at the Upper Atmosphere Data Pilot (UADP) at NASA

Langley Research Center. To retrieve these data, do the following:

ftp uadpl .larc.nasa.gov

Name: anonymous

Password: <your e-mail address>

tip> cd mm 1997/MMII/input_data

tip> get O3.dat

tip> get O3Prod.dat

tip> get O3LLoss.dat

tip> get O3QLoss.dat

tip> bye

We suggest that you use O3L and O3Q and write the chemical tendency in your model at each time step

as

O3P - O3model*O3L - O3model*O3model*O3Q (eq 1),

where O3model is the model calculated ozone at each time step.

The net chemical tendency in each time step can be reconstructed by

O3P - O3"O3L -O3"O3"O3Q (eq 2).

Some modelers may be tempted to use the values defined by eq 2 as the fixed net tendency term at each

time step and the given O3 distribution on January 1 as initial condition and solve the equation as an

initial value problem. Since we do not do our model that way, we are not sure how well this works.

2O
However,this maynot work becausethenettendencydependson thetransporttermsin theAER

model.Sinceyourmodelhasdifferentransporttendencies, thesystemmaynotreachsteadystate.

Proposed output from Run B-3

Proposed model output:

2D model output should be _ubmitted on the UADP latitude/altitude grid (defined below) and 3D

model output on the UADP longitude/latitude/altitude grid for each month of 1992. A mid-month

daytime average value is preferred for 2D models. A mid-month day at 12Z would be best for 3D

models. Please output the following, if calculated:

a. Column ozone (in Dobson units by month and latitude)

b. 0 3 mixing ratio

1.1.2.2.4 B-4 High Speed Civil Transport (HSCT)

Definition Lead: Donald J. Wuebbles

Here is additional information on HSCT Run B-4. The "Proposed Model Run" and "Proposed

Model Run Output" are given below.

Proposed Run B-4

The emissions input of HSCTs only will be based on the new NASA database for HSCTs projected

to 2015. We will consider NOx emissions only for the case of 500 HSCTs at Mach 2.4 and E.I.=I0. We

will consider only NOx emissions so as to avoid interpretation problems with the analyses of the results

and because some models may not be able to include water vapor emissions. The E.I. = 10 case is

chosen to try to entre a perturbation that is statistically meaningful. The recommended source gas

boundary conditions for 2015 are given below. Modellers are asked to do the 2015 perturbed run for

BOTH sulfate aerosol amounts of SA0 and SA 1.

Two runs will be needed:

I) A 2015 base run with no emissions of NOx.

II) A 2015 perturbed run with emissions of NOx.

The supersonic fleet scenario needed for this model experiment is in your directories; it is named

hsct_2015_eil0_500.*. You may get the data from anonymous ftp on uadpl.larc.nasa.gov. If you are

doing only MMII then the file will be in mm1997/MMII/your_model_name. If you are doing IPCC and

MMII, the file will be in mm1997]IPCC]your_model_name.

Proposed output from Run B-4

2D model output should be submitted on the uadp latitude/altitude grid (defined below) and 3D

model output on the uadp longitude/latitude/altitude grid for each month of 1992. A midmonth daytime

average value is preferred for 2D models. A midmonth day at 12Z would be best for 3D models. Please

output the following, if calculated:

a. Source Gases/Tracers (in mixing ratio):

21
N20,CH4,H20

b. Columnozone(inDobsonUnits)- bylatitudeandmonth

c. MediumLived/Reservoir(inmixingratio):

03,HCI,CIONO2,HNO3,N205,HBr,BrONO2,H202

d. ShortLived/Radical(in mixingratio):

O(3p),O(ID),NO,NO2,OH, HO 2, HOCI, C10, BrO, C1202, HOBr

e. Families (in mixing ratio): NOy, Cly, Bry

f. Other: CO (in mixing ratio),

M (total number density in #/cm+3),

Temperature (Kelvin)

g. Ozone loss rates (in mixing ratio per second) for NOx, HOx, C1Ox, BrOx, and Ox

h. Ozone production rates (in mixing ratio per second) for

I. J(O2) and 2. a sum of the rest

Recommendations for Source Gas Boundary Conditions

Use "2015 - 3 ppbv Cly" boundary conditions (see below; same as being used in IPCC assessment).

The halogen source gases are taken from the "1995 Scientific Assessment of the Atmospheric

Effects of Stratospheric Aircraft", NASA Reference Publication 1381, 1995. Source gases CH 4 and

N20 are taken from the IS92a scenario given in Tables 2.5a and 2.5b of"Climate Change 19957' Source

gas CO 2 is taken from the IS92a scenario given in Figure 5, p. 23, of "Climate Change 1995." Source

gas CH3Br is taken from the IS92a scenario given in Table 2.2 of "Climate Change 1995."

22
Table - Recommendations for species concentrations in 2015

Species

CFC- 1I

CFC- 12

CFC-113

CCI 4

HCFC-22

CH3CCI 3

aHCFC- 141b

Halon- 130 I

Halon-1211

CH3CI

CH3Br

CH 4

N20

CO2

2015

("3 ppbv Cly")

220 pptv

470 pptv

80 pptv

70 pptv

250 pptv

3 pptv

12 pptv

1.4 pptv

1.1 pptv

600 pptv

10 pptv

2052 ppbv

333 ppbv

405 ppmv

If your model cannot handle HCFC-141b, we

recommend putting it in as CH3CCI 3 after adjusting for the number of chlorine atoms per

molecule, i.e., multiply the HCFC-141b mixing

ratio by 2/3 and add to CH3CCI 3. The stratospheric lifetimes for CH3CC13 and HCFC-141b

are 50 years and 75 years, respectively. These

lifetimes are sufficiently close so supplementing

CH3CCI 3 with HCFC-141b should be a reasonable approximation.

Use sulfate surface area SA0 (labelled SAunep) and SA 1, which are available at the NASA Langley

UADP. SA0 (SAunep) is the WMO background sulfate surface area. SA1 corresponds to the surface

area obtained by adding to the WMO background sulfate surface area the increase in surface area calculated by Wiesenstein et al. (J. Geophys. Res., "A two dimensional model of sulfur species and aerosols,"

in press) 1997 caused by a 500 aircraft fleet with sulfur EI=0.4 and assuming 50% of the SO 2 is convetted to sulfate particles in the plume.

To access surface area SAi:

ftp uadp ! .larc.nasa.gov

Name: anonymous

Password: <your e-mail address>

ftp> cd mm 1997/IPCC/input to model_runs

ftp> get README

ftp> get SURFACE_AREA.dat

ftp> bye

23
l)

and

2)

1.1.2.2.5 E-I Modified Runs of B-1

Definition Lead: David B. Considine

Proposed Run E-1

This run is very sim;lar to run B-l, except heterogeneous chlorine activation SHOULD NOT BE

included. The reactions

N205 + H20 -> HNO 3 + HNO 3

BrONO 2 + H20 -> HOBr + HNO 3

SHOULD BE included in this run.

Proposed output from Run E-1

2D model output should be submitted on the UADP latitude/altitude grid (defined below) and 3D

model output on the UADP longitude/latitude/altitude grid at the beginning and middle of each

month of 1992. An output of the daytime average value is preferred for 2D models. An output at

12Z would be best for 3D models. Please output the following, if calculated:

a. Source Gases/Tracers (in mixing ratio):

CF2CI2, CFCI3, CCI4, CH3C1, CH3CCI3, CHCIF 2, C2C13F 3, CCIFO, CF20 , N20 , CH4, H20 '

HF, CH3Br

b. Column ozone (in Dobson Units) - by latitude and month

c. Medium Lived/Reservoir (in mixing ratio):

03, HCI, CIONO2, HNO3, N205, HBr, BrONO2, H202

d. Short Lived/Radical (in mixing ratio):

O(3p), O(ID), NO, NO2, OH, HO2, HOCI, CIO, BrO, C1202, HOBr

e. Families (in mixing ratio): NOy, Cly, Bry

f. Other: CH20 & CO (in mixing ratio),

M (total number density in #/cm+3),

Temperature (Kelvin)

g. Ozone loss rates (in mixing ratio per second) for NOx, HOx, C1Ox, BrOx, and Ox

h. Ozone production rates (in mixing ratio per second) for !. J(O2) and 2. a sum of the rest

24
ForN20, CH4, CFC13,CF2CI2, CC14,CH3C1,CH3Br,CH3CCI3, C2CI3F3,CHCIF2,andany

otherCFCorHCFCinyourmodelsimulation,pleaseprovide:

!. Globalyearlyaverageatmosphericabundance(in# of molecules);

2.totalglobalyearlyaverageatmosphericlossrate(in#of moleculespersecond);

3.totalglobalyearlyaveragestratosphericlossrate(in# of moleculespersecond).

Modellersshouldupdatetheir chemistryto JPLPublication97-4,publishedJanuary15,1997.

Copiesof thispublicationareavailablefrom:

JetPropulsionLaboratory

California Institute of Technology

Secondary Distribution, MS 512-110

4800 Oak Grove Drive Pasadena, CA 91109

Phone: (818)397-7952

The JPL Publication 97-4 kinetics data base can be obtained electronically at

http://remus.jpl.nasa.gov/jp197/or via ftp at host remus.jpl.nasa.gov.

This electronic means of receiving the 1997 kinetics data base would be much faster than through

the regular mail.

1.1.3 The Langley Upper Atmosphere Data Pilot (UADP) Information

The following information is similar to that sent out for the 1995 assessment. The file naming convention has been altered to fit the requested model runs. Don Brown will be working on this effort with

me. Linda Hunt is no longer with this project. Please review this document and if you have any questions, please let us know.

Here is some information about the model run results you will be returning to us. Since you have

done this before you probably know that we're not real picky about the file format used. What we do ask

is that once you start using a format that you stick with it.

The file format below is identical to the one used for supersonic aircraft scenario computations. If

you can use it easily, please do. If you have a format you have sent to us previously, feel free to use that

format. Once again, we will handle the data in whatever format you send. However, once you use a

particular format, please stick with that format for all the data submitted for a particular effort.

The file name should indicate the scenario involved. Here's a suggested file naming convention:

QUANTITY. YEAR. MODELrun. MODEL

where:

QUANTITY = o3coi

ox 03 ch4 noy n2o ...

P.total P.jo2 P.ch3o2+no ...

L.total L.ox L.noy L.o.no2 ...

etc

(column 03, DU)

(species mixing ratio)

(Ox production, mixing ratio/s)

(Ox loss, mixing ratio/s)

YEAR = 1992

25
MODELRUN= bl,b2,b3,b4

MODEL= csirocsirol csiro2csiro2b...

llnl ...gsfc...

etc

(for different model versions)

Hence a name might turn out to be something like:

O3col. i 992.b2.gsfc

It is not necessary for you to have each parameter in its own file, in which case you could use

"species" in the file name (or use some other indicator). However, column data should be in a separate

file from data on a lat/alt grid.

Please let us know if you have any questions or suggestions. Thanks!

General UADP Model Data Submission lnfo (Revised 5/95)

° GENERAL: Data can be transferred to the UADP via electronic file transfer (TCP/IP protocol).

Electronic addresses are as follows:

uadp i .larc.nasa.gov 146.165.21.96

uadp2.1arc.nasa.gov 128.155.17.46

Anonymous ftp accounts (user name anonymous or tip, password is your internet address) are

available on both machines.

***NOTE: Some of you are participating in the IPCC and the M&MII efforts. For those of you participating in BOTH efforts, we have made directories for your models on uadp 1 and uadp2 in mm 1997/

MMII. If you would please put your MMII results in those directories on one of the machines and send

us email letting us know they are there, we would appreciate it.

Those of you participating in,

directory under mml997/MMII on

files are there.

_I&MII effort exclusively, please put your results in your model

ler uadpl or uadp2. And Iet us know via email that the results

We are going to try to keep an eye on disk space, please let us know if you run into any problems

with "disk full" messages.

. DESCRIPTIVE TEXT: A set of free format character data records (up to 80 characters per line)

should begin each file. The following information should be included:

a. an initial record specifying the number of lines of descriptive text

b. brief (1 to 5 lines) description of the model and technique

c. names and institutions of principal investigator(s)

d. journal reference for model documentation (where available)

e. name, telephone number, and electronic address (if available) of data contact

f. units for each parameter (parameter name followed by units)

g. information about the time and range of time steps for the data set (i.e. mid-month data for

twelve months of 1990)

***If items b-e are the same for all data in the current submission, you can put this information in a separate file rather than repeating it in each file.

26
3. DATARECORDS:Theinformationbelowillustrateshow theserecords(parts3A and3B)

shouldbewritten.

4. DATAGRID:Datashouldbeprovidedonthefollowingstandardgrid:

Latitude

Longitude

Vertical

Time

- 37latitudes(90Sto90Nat5degreeincrements)

(if appropriate)- 19longitudes(180Wto 180Eat20degreeincrements)

- 31levelsinz*(z* = 16*logl0(1000/p))(0to60inincrementsof 2kin)

forprofiles

- mid-month(15thof monthpreferred,otherwisemonthlymean)

. INFORMATION/QUESTIONS: Questions or inquiries about use of the system, data submittal,

and the like should be adressed to either Don Brown (757-864-5855) or Karen Sage

(757-894-5857). Electronic mail to the UADP can be addressed to brown@uadpl.larc.nasa.gov

or sage@uadp2.1arc.nasa.gov. FAX service is available at 757-864-7790. Our mailing address is:

MS 401A

21 Langley Blvd

NASA Langley Research Center

Hampton, Virginia USA 23681-0001

Model Data Submission File Specification

. FILE DESCRIPTIVE TEXT

- Value indicating number of lines (up to 80 characters per line) of descriptive text

- Descriptive text

. FILE HEADER LINE

- Four values indicating number of parameters, number of latitudes (=37), number of levels (=3 I

for profiles or arrays, =1 for columns), and number of time steps. For example, this line would

be 20 37 31 12 for 20 parameters, 37 latitudes, 31 levels, and 12 months.

3A. PARAMETER DATA HEADER LINE

- Two values indicating parameter name and time step (for example CH 4 15-JAN-81) separated

by at least one blank

3B. PARAMETER DATA:

2d models (zonally averaged output)--

DATA FOR I LATITUDES FOR J LEVELS

- Array of parameter values written in order of

(((I,J), J= 1,Num. of Z* levels), I= 1,Num. of latitudes)

(Repeat 3A and 3B for each parameter)

1.1.4 Modeling Groups and Proposed Experiments

Individual model's running experiments are summarized in Table !.1.2, and shorthand designation

of each model is summarized in Table 1.1.3.

Descriptions of each model are given in the following section.

27
Table 1.1.1 List of Experiments, Definition and Analysis Leads

Experiments

Transport Experiment

A l Age Diagnostics: simulation the distributions of an inert

tracer with specific initial condition to generate model output for

age analysis

A2 Seasonal transport diagnostics: simulation of the distributions of an inert tracer with seasonally varying boundary condition to simulate the seasonal cycle of CO 2.

A3 NOy- I: simulation the steady state distribution of an inert

tracer with an injection similar to that of the projected HSCT

fleet

A4 NOy-2: similar to A3 except that the injection are limted to

mid-latitudes

A5 SF6: simulation of SF6 distributions from 1966-2000 using

surface concentrations from observations

A6 CO2: simulation of the atmospheric distribution of CO 2

1979-1995 using the surface concentrations from NOAA CMDL

iChemistry Experiment

B1 1992 atmosphere: simulation of the chemical trace gases in

the 1992 atmosphere

B2 constrained simulation for 1992: simulation of the 1992 atmosphere using fixed zonal-mean temperature, aerosol surface

area, ozone, water vapor, N20, CH 4, CFC- 11, CFC- 12,

CH3CCI 3, CFC- 113, and CH3Br

B3 constrained ozone: simulation of the ozone distribution using

prescribed production and removal rates

B4 ozone response to injected NOx: simulations of the 2015

atmosphere to determine the ozone response to injction of NOx

from HSCT

El PSC treatment: similar to B1 except assume no PSC formation and reactions

Definition

Lead

Plumb

Plumb

Wuebbles

Wuebbles

!Ko

Hall

Considine

Jackman

No

Wuebbles

Considine

Analysis

Lead(s)

Hall

Waugh, Hall

Hall

Boefing

Plumb

Results

2.3

2.6

2.3, 2.5

2.4

2.7

Rind, Hall, 2.8

Wuebbles

Rind, Hall, 2.8

Wuebbles

Hall 2.3

Boering 2.4

Waugh, Hall 2.6

Hall 2.3

Boering 2.4

Waugh, Hall 2.6

Waugh, Hall 2.6

' Remsberg,

Scott, Ko, Tie,

Brasseur, Huang

Logan,

McPeters

Ko, Salawitch

Jackman

Considine

Tie, Brasseur,

Huang

Jackman,

Fleming, Ko

Wuebbles

Douglass

Chapter 3

Chapter 4

5.2

5.3

5.5

3.5

5.4

Chapter 6

5.6

28
2D Model

AER

CSIRO

GSFC-2D

GSFC-2Dint

HARVARD

LLNL

MGO-UIUC-2D

NCAR-2D

NOCAR

SUNY_SPB

UNIVAQ-2D

WISCAR

3D Model

ECHAM3/CHEM

GISS-3D

GISS8X10

GMI-DAO

GMI-GISS

GMI-NCAR

GMI-NCARcon

GSFC-3D

LARC-3D

MONASHI

MONASH2

NCAR-3D

SLIMCAT

UCI2 I

UCI23

UIUC-3D

UNIVAQ-3D

T

-T-

-TT

T

T

T

T

T

T

T

T

X

Table 1.1.2 Matrix of models and experiments.

u

A_

--2

-2

--2

-2

-2

-2

-2

-2

-2

- x x

x

29
Table1.1.3 Model names and designations.

Four character (or less)

Model "official" designation shorthand designation

AER AER

CSIRO CSI

ECHAM3/CHEM E3CH

GISS-3D GIS3

GISS8XI0 GISS

GMI-DAO GMDA

GMI-GISS GMGI

GMI-NCAR GMNC

GMI-NCARcon GMND

GSFC-2D GS2

GSFC-2Dint GS2i

GSFC-3D GS3

HARVARD HARV

LARC-3D LARC

LLNL LLNL

MGO-U1uC-2D MUI2

MONASH 1 MON 1

MONASH2 MON2

NCAR-2D NCA2

NCAR-3D NCA3

NOCAR NOCR

SLIMCAT SLIM

SUNY SPB NYSP

UCI21 UCIA

UCI23 UCIB

UIUC-3D UIU3

UNIVAQ-2D

UNIVAQ-3D

UAQ2

UAQ3

WISCAR WCAR

3O
1.2 Model Descriptions

1.2.1 Description of the AER 2-D Photochemical Transport Model

Malcolm Ko, Debra Weisenstein, Courtney J. Scott,

Run-Lie Shia, Jose Rodriguez, N.D. Sze

Atmospheric and Environmental Research, Inc

840 Memorial Dr., Cambridge, MA 02139

(email: mko@aer.com, weisenstein@aer.com, scott@aer.com)

General Information

The AER 2-D photochemical transport model participated in both M&M I, M&M II, and photolysis

and chemical solver intercomparisions sponsored by the AESA project. References to the model formulation are listed near the end of this document.

Domain and Grid Sizes

The AER 2-D model domain extends from south pole to north pole and approximately from the

ground to 60 km. Latitude and log-pressure are used as coordinates, with horizontal resolution of 9.5

and vertical resolution of 0.167 in units of ln(1000/p(mb)), equivalent to approximately 1.2 kin. The

center of each latitude band in degrees is given by (180./19)*(i-10), where i is the latitude index

ranging from 1 to 19. The pressure in mb at the center of each altitude bin is given by

1000.*exp(-0.167"(j-0.5)), where j is the altitude index ranging from 1 to 51.

Transport and Temperature

The dynamical transport occurs through the zonal-mean diabatic circulation, by quasi-horizontal

diffusion along isentropic surfaces, and by vertical diffusion in the troposphere and upper stratosphere.

Circulation and diffusion in the AER model are non-interactive and are specified independently of each

other and the adopted temperature fields. The diabatic circulation used was based on the calculated heating rates of Murgatroyd and Singleton (1961) for the upper stratosphere and Dopplick (1979) for the

lower stratosphere, and resembles that derived by Rosenfield et al. (1987) in both structure and magnitude. Temperatures are an 8 year average of NMC monthly-mean zonal-mean temperatures for the

1979-1986 period. Temperature distributions, based on the same 8 years of NCEP/NCAR Reanalysis

data (Kalnay et al., 1996), are used to account for deviations from the zonal and monthly mean temperature in the calculation of reaction rates and PSC formation probabilities. Daily temperatures are interpolated from monthly mean fields. The shape of the temperature distribution is retained for a full month

but is shifted in temperature to match the zonal-mean temperature for that day. Tropopause heights are

based on temperature gradients and vary seasonally from 10.2 to 12.3 km at 45 N.

The horizontal eddy diffusion coefficient, Kyy, is a function of latitude, altitude, and season. It has

the value 1.5x101° cm_/sec in the troposphere. In the lower statosphere, we simulate a tropical barrier

circulation (Plumb, 1996) by employing low values of Kyy in the tropics. These values are based on fits

of model transport to tropical/midlatitude exchange rates derived from observations (Minschwaner et

al., 1996; Volk et al., 1996; Schoeberl et al., 1997). In the tropical lower stratosphere below 23 km, the

value of Kyy is 1.3x109 cm2/sec and from 23 to 35 km is 0.7x109 cm2/sec (Shia et al., 1998). Midlatirude values of Kyy in the stratosphere range from 3-10x109 cm2/sec. The value of Kyy in the

31
stratosphereabove35km is 3x109 cm2/sec for all latitudes and seasons, based on the work of Kida

(1983) and Tung (1984). A Ky z term is calculated based on a translation of the horizontal diffusion,

which is assumed to be acting along isentropic surfaces, to the model's log-pressure grid.

The vertical diffusion coefficient, Kzz, is lxl05 cm2/sec in the troposphere, lxl03 cm2/sec in the

stratosphere below 40 km and l xl04 cm2/sec above 40 km. The relatively large vertical diffusion coefficient in the troposphere simulates convective overturning and synoptic scale eddies. Stratospheric vertical diffusion was estimated by Kida (1983) to be lxl03 cm2/sec. Enhanced vertical mixing above

40 km is based on the work of Garcia and Solomon (1985) regarding gravity wave breaking.

Photochemistry

The model contains 62 chemical species, including complete diurnal chemistry for the NOy, Clx,

Brx, Fx, HOx, Ox, methyl and ethyl families. Source gases include N20 , CH4, C2H6, H2, CO, CC14,

CFC-! 1, CFC-12, CFC-113, HCFC-22, CH3C1, CH3CCI3, Halon-1211, Halon-1301, and CH3Br. The

family chemistry approach is used for the transport of NOy, Clx, Brx, and Ox, but HNO 3 is transported

separately from other members of the NOy family. When the lifetime of HNO 3 becomes short, it is

solved using chemical equilibrium, and then the total NOy family is transported together. Explicit diurnal calculations are performed for the short-lived species every day of model simulation. An iterative

Newton/Raphson scheme is used to solve for all the short-lived species simultaneously, using 12 time

intervals for the daylight hours and 5 intervals for the nighttime hours.

The kinetic reaction rates and absorption cross-sections are taken from NASA/JPL (1997). The

solar fluxes are from WMO/NASA (1982). The spectral resolution is 5 nm or less over the wavelength

range from 178 nm to 330 nm. Longer intervals are used from 330 nm up to 805 nm. Solar zenith angle

is a function of altitude. The photolysis code, which includes calculation of multiple scattering in a

spherical atmosphere, was provided by M. Prather (UCI) and is known to agree with the codes of

Anderson and Lloyd (1990) and Dahlback and Stamnes (1991). 02 absorption in the Schumann-Runge

band is based on Minschwaner et al. (1992). Washout and rainout provide removal of H20 2, CH3OOH,

CH20, HNO 3, HC1, HBr, and HF in the troposphere below 10 km with lifetimes ranging from 5 days at

the surface to 40 days at 10 kin.

Heterogeneous Chemistry

The following heterogeneous reactions on sulfate and PSC particles are included in the model:

1. N205 + H20 = 2HNO 3

2. CIONO 2 + H20 = HOC1 + HNO 3

3. C1ONO 2 + HCI = Ct 2 + HNO 3

4. HOCI + HCI = C12 + H20

5. HOBr + HC1 = BrCl + H20

6. BrONO 2 + H20 = HOBr + HNO 3

7. N205 + HCI = CINO 2 + HNO 3

Heterogeneous reaction rates, k, are calculated as first-order loss rates proportional to particle surface

area:

k = yvA/4,

32
where7isthereactionprobabilityor stickingcoefficient,v isthethermalvelocityof thegasmolecule,

andA isthesurfaceareadensityof theaerosolparticles.Onsulfateaerosols,reaction! usesaconstant

7of 0.1,whilereactions2-4followHansonetal.(1994).Reaction5followsHansonandRavishankara

(1995)andreaction6 followsHansonetal. (1996).Reactions4 and5 accountfor possiblereaction

withintheparticlevolume,with volumeobtainedfrom surfaceareaby assuminga particleradiusof

0.1ram.Reaction7 appliesonlytoPSCs.

ParameterizedPSCchemistryisincludedforNAT andice,assumingaunimodalradiusof 0.5mm

for NAT and7 mmforiceparticles.Thecalculationassumesthermodynamicequilibriumbetweenthe

gasandthecondensedphasefor HNO3andH20,with nosupersaturationrequired.Thetemperature

probabilitydistributionisemployedtoobtainPSCsurfaceareasforeachtemperatureinthedistribution.

PSCandsulfatesurfaceareasareindependent inthemodel,butsulfatereactionratesarereducedto zero

whenthePSCsurfaceareaexceeds50%of thesulfatesurfaceareato simulatecoatingof sulfateby

NAT or ice.RatesofPSCreactionsareobtainedfor eachtemperaturein thedistribution,thenetrate

beingobtainedastheintegralof theprobabilityof occurrenceof aparticulartemperaturemultipliedby

therateandthesurfaceareaatthattemperature. ReactionprobabilitiesforPSCreactionsaretakenfrom

NASA/JPL(1997).Reactions5 and6 donot occuronPSC-type1 particles.All PSCreactionsare

assumedtohaveaconstantgexceptforreaction3onPSC-type!.

Time Steps and Transport Scheme

Production and loss terms for the long-lived species are updated every day and are calculated as

diurnal averages of the product of the rate and the radical species densities over the 17 diurnal time

intervals. Lightning is included as a source of NOY in the tropical troposphere, with a source strength of

2 MT/yr. Concentrations of long-lived atmospheric species are integrated forward in time using the iterative upstream scheme of Smolarkiewicz (1984). The scheme is positive definite with small implicit diffusion. The advective time step is 6 hours. The water vapor concentration is calculated for the

stratosphere only. The tropospheric values of H20 vary seasonally depending on the parameterized

value of relative humidity and the assigned temperature.

Sulfate Aerosol Microphysics

Sulfate aerosol concentrations and surface area can be calculated by the AER sulfate aerosol microphysics model. This model is built on the same framework as the 2-D CTM but operates as a separate

model, interacting with the CTM by use of off-line ozone and OH fields. Gas phase species included in

the sulfate model are CS 2, HzS, DMS, MSA, OCS, SO 2, SO3, and H2SO4. Particles are resolved into 40

size bins ranging from 0.39 nm to 3.2 mm in radius by volume doubling. Each aerosol size bin is transported separately, with the appropriate sedimentation rate applied. New nanometer-sized sulfate particles are formed by homogeneous nucleation of H2SO4 and H20 in the tropopause and polar regions and

grow to micron-sized particles by condensation of H2SO 4 and H20 vapor in the lower stratosphere. The

aerosols interact through coagulation, which decreases aerosol number density while conserving volume. Sulfate particles which are transported above 35 km experience evaporation and release H2SO 4

vapor, which may dissociate into SO 3 and SO 2. Tropospheric washout applies to aerosol particles,

MSA, SO 2, and .H2SO4. Sulfate surface area density fields calculated by the sulfate model can be used

in the CTM to calculate rates of heterogeneous reactions which determine ozone concentration. For

M&M II exercises, sulfate surface area density was specified and therefore not calculated.

33
References Cited

Anderson, D.E., Jr., and S.A. Lloyd, Polar twilight UV-visible radiation field: Perturbations due to multiple scattering, ozone depletion, stratospheric clouds, and surface albedo, J. Geophys. Res., 95, 7429-7434, 1990.

DahIback, A., and K. Stamnes, A new spherical model for computing the radiation field available for photolysis

and heating at twilight, Planet. Space Sci., 39, 671-683, 1991.

Dopplick, T.G., Radiative heating of the global atmosphere: Corrigendum, J. Atmos. Sci., 36, 1812-1817, 1979.

Garcia, R.R., and S. Solomon, The effect of breaking gravity waves on the dynamics and chemical composition of

the mesosphere and lower thermosphere, J. Geophys. Res., 90, 3850, 1985.

7 :

Hanson, D.R., A.R. Ravishankara, and S. Solomon, Heterogeneous reactions in sulfuric acid aerosols: A framework for model calculations, J. Geophys. Res., 99, 3615-3629, 1994.

Hanson, D.R., and A.R. Ravishankara, Heterogeneous chemistry of bromine species in sulfuric acid uner stratospheric conditions, Geophys. Res. Lett., 22,385-388, 1995.

Hanson, D.R., A.R. Ravishankara, and E.R. Lovejoy, Reactions of BrONO 2 with H20 on submicron sulfuric acid

aerosol and the implications for the lower stratosphere, ,L Geophys. Res., 101, 9063-9069, 1996.

Kalnay, E., et al., The NCEPPNCAR 40-year reanalysis project, Bull. Am. Met. Soc., 77, 437-471, 1996.

Kida, H. General circulation of air parcels and transport characteristics derived from a hemispheric GCM, Part I,

A determination of advective mass flow in the lower stratosphere, J. Meteorol. Soc. Japan, 61, 171-185, 1983.

Minschwaner, K., G.P. Anderson, L.A. Hall, and K. Yoshino, Polynomial coefficients for calculating 02

Schumann-Runge cross sections at 0.5 cm -! resoltuion, Journal of Geophysical Research, 97, 10,103-10,108, 1992.

Minschwaner, K., A.E. Dessler, J.W. Elkins, CIM.V0ik, D.W. Fahey, M. Loewenstein, J.R. Podolske, A.E. Roche,

and K.R. Chan, Bulk properties of isentropic mixing into the tropics in the lower stratosphere, J. Geophys. Res., 101, 9433-9439, 1996.

Murgatroyd, R.J., and F. Singleton, Possible meridional circulation in the stratosphere and mesosphere, Quart. J.

Roy. Meteor. Sot., 87, 125-135, 1961.

NASA/JPL, Chemical kinetics and photochemical data for use in stratospheric modeling, Evaluation Number 12,

JPL Publication 97-4, 1997.

Plumb, R.A., A "tropical pipe" model of stratospheric transport, J. Geophys. Res., 101, 3957-3972, 1996

Rosenfield, J.E., M.R. Schoeberl, and M.A. Geller, A computation of the stratospheric diabatic residual circulation

using an accurate radiative transfer model, J. Atmos. Sci., 44, 859-876, 1987.

Schoeberl, M. R., et al., An estimation of the dynamical isolation of the tropical lower stratosphere using UARS

wind and trace gas observations of the quasi-biennial oscillation, Geophys. Res. Lett., 24, pp. 53-56, 1997.

_hia, R.L., M.K.w. Ko, D.K. Weisrnstein, C. Scott, and J. Rodriguez, Transport between the tropical and mid latitude lower stratosphere: Implications for ozone response to high-speed civil transport emissions, J. Geophvs.

Res., 103, 25435-25446, 1998.

Smolarkiewicz, P.K. A simple positive definite advection scheme with small implicit diffusion, Mort. Weather

Rev., Ill, 479-487, 1984.

Tung, K.K. Modeling of tracer transport in the middle atmosphere. In Dynamics of the Middle Atmosphere, J.R.

Holton and T. Matsuno (eds.), pp. 412-444, Terra Scientific Publishing, Tokyo, Japan, 1984.

Volk, C.M., et al., Quantifying transport between the tropical and mid-latitude lower stratosphere, Science, 272,

1763-1768, 1996.

WMO/NASA, The Stratosphere 1981: Theot3' and measurements, WMO Global Ozone Monitoring Project

Report No. I 1, 1982.

34
References to AER 2-D Model Formulation

Ko, M.K.W., N.D. Sze, M. Livshits, M.B. McElroy, J.A. Pyle, The seasonal and latitudinal behavior of trace gases

and 03 as simulated by a two-dimensional model of the atmosphere, J. Atmos. Sci., 41, 2381-2408, 1984

Ko, M.K.W., K.K. Tung, D.K. Weisenstein, N.D. Sze, A zonal mean model of stratospheric tracer transport in

isentropic co-ordinates: Numerical simulations for nitrous oxide and nitric acid, J. Geophys. Res., 90, 2313-

2329, 1985

Ko, M.K.W., M.B. McElroy, D.K. Weisenstein, and N.D. Sze, Lightning: A possible source of stratospheric odd

nitrogen, J. Geophys. Res., 91,5395-5404, 1986.

Ko, M.K.W., N.D. Sze and D.K. Weisenstein, The roles of dynamical and chemical processes in determining the

stratospheric concentration of ozone in one-dimensional and two-dimensional models, J. Geophys. Res., 94,

9889-9896, 1989.

Ko, M.K.W., N.D. Sze, and D.K. Weisenstein, Use of satellite data to constrain the model-calculated atmospheric

lifetime for N20: Implications for other trace gases, J. Geophys. Res., 96, 7547-7552, 1991.

Kotamarthi, V.R., M.K.W. Ko, D.K. Weisenstein, J.M. Rodriguez, and N.D. Sze, The effect of lightning on the

concentration of odd nitrogen species in the lower stratosphere: An update. J. of Geophys. Res., 99,

8167-8174, 1994.

Plumb, R.A. and M.K.W. Ko, Interrelationships between mixing ratios of long-lived stratospheric constituents, J.

Geophys. Res., 97, 10,145-10,156, 1992.

Rodriguez, J.M., M.K.W. Ko, N.D. Sze, Role of heterogeneous conversion of N205 on sulphate aerosols in global

ozone losses, Nature, 352, 134-137, 1991.

Rodriguez, J.M., M.K.W. Ko, N.D. Sze, C.W. Heisey, G.K. Yue, and M.P. McCormick, Ozone response to

enhanced heterogeneous processing after the eruption of Mt. Pinatubo, Geophys. Res. Lett., 2I, 209-212, 1994.

Shia, R.L., M.K.W. Ko, M. Zou, and V.R. Kotamarthi, Cross-tropopause transport of excess 14C in a twodimensional model, J. Geophys. Res., 98, 18,599-18,606, 1993.

Shia, R.-L., M. K. W. Ko, D. K. Weisenstein, C. Scott and J. Rodriguez, Transport between the tropical and midlatitude lower stratosphere: Implications for ozone response to HSCT emissions, J. Geophys. Res., 103,

25,435-25,446, 19978

Weisenstein, D.K., M.K.W. Ko, J.M. Rodriguez, and N.D. Sze, Impact of heterogeneous chemistry on modelcalculated ozone change due to high speed civil transport aircraft, Geophys. Res. Letters, 18, 1991-1994, 1991.

Weisenstein, D.K., M.K.W. Ko, N-D Sze, and J.M. Rodriguez, Potential impact of SO 2 emissions from stratospheric aircraft on ozone. Geophys. Res. Lett., 23, 161-164, 1996.

Weisentein, D.K., G.K. Yue, M.K.W. Ko, N.D. Sze, J.M. Rodriguez, and C.J. Scott, A two-dimensional model of

sulfur species and aerosols, J. Geophys. Res., 102, 13,019-13035, 1997.

References to AER 2-D Model Applications

Danilin, M.Y., J.M. Rodriguez, M.K.W. Ko, D.K. Weisenstein, R.C. Brown, R.C. Miake-lye, and M.R. Anderson,

Aerosol particle evolution in an aircraft wake: implications for the HSCT fleet impact on ozone J. Geophys.

Res. 102, 21,453-21,463, 1997.

Fahey et al. In situ observations of NOy, 03, and the NOy/O3 ratio in the lower stratosphere. Geophys. Res. Lett.,

23, 1653-1656, 1996.

Fisher, D.A., C.H. Hales, D.L. Filkin, M.K.W. Ko, N.D. Sze, P.S. Connell, D.J. Wuebbles, I.S.A. Isaksen and F.

Stordal, Model calculations of the relative effects of CFCs and their replacements on stratospheric ozone.

Nature, 344,508-512, 1990.

35
Gunson, M.R., M.C. Abrams, L.L. Lowes, E. Mahieu, R. Zander, C.P. Rinsland, M.K.W. Ko, N.D. Sze, and D.K.

Weisenstein, Increase in levels of stratospheric chlorine and fluorine loading between 1985 and 1992.

Geophys. Res. Lett., 21, 2,223-2,226, 1994.

Keim, E.R., M.Lowenstein, J.R. Podoiske, D.W. Fahey, R.S. Gao, E.L. Woodbridge, R.C. Wamsley, S.G.

Donnelly, L.A. Del Negro, C.D. Nevison, S. Solomon, K.H. Rosenslof, C.J. Scott, M.K.W. Ko, D.Weisensten,

and K.R. Chen, Measurements of NOy-N20 correlation in the lower stratopshere: latitudinal and seasonal

changes and model comparisons, J. Geophys. Res., 102, 13,193-13,212, 1997.

Ko, M.K.W., J.M. Rodriguez, N.D. Sze, M.HI Profitt, W.L. Start, A. Krueger, E. V. Browell, and M.P.

McCormick, Implications of AAOE observations for proposed chemical explanations of the seasonal and

interannual behavior of Antarctic ozone. J. Geophys. Res., 94, 16,705-16,715, 1989.

Ko, M.K.W., N-D. Sze, C. Scott, and D.K. Weisenstein, On the relation between stratospheric chlorine/bromine

loading and short-lived tropospheric source gases, J. Geophys. Res., 102, 25507-25517, 1997.

Plumb, R. A., M.K.W. Ko, and R.L. Shia, Representation of localized aircraft NOy emissions in a twodimensional model of stratospheric ozone. J. Geophys. Rex., 100, 20,901-20,91 I, 1995.

Prather, M.J., M.M. Garcia, A.R. Douglass, C.H. Jackman, M.K.W. Ko, and N.D. Sze, The space shuttle's impact

on the stratosphere. J. Geophys. Res., 95, 18,583-18,590, 1990.

Rinsland, C.P., J.S. Levine, A. Goldman, N.D. Sze, M.K.W. Ko, and D.W. Johnson, Infrared measurements of HF

and HC1 total column abundances above Kitt Peak, 1977-1990: Seasonal cycles, long-term increases, and

comparisons with model calculations. J. Geophys. Res., 96, 15,523-15,540, 1991.

Rinsland, C.P., M.R. Gunson, M.C. Abrams, R. Znader, E. Mahieu, A. Goldman, M.K.W. Ko, J. M. Rodriguez,

and N. D. Sze, Profiles of stratospheric chlorine nitrate (CIONO2) from atmospheric trace molecule

spectroscopy/ATLAS 1 infrared solar occultation spectra, J. Geophys. Res., 99, 18,895-18,900, 1994.

Rinsland, C.P., M.C. Abrams, L.L. Lowes, R. Zander, E. Mahieu, A. Goldman, M.K.W. Ko, J.M. Rodriguez, and

N.D. Sze, Heterogeneous conversion of N205 to HNO 3 in the post-Mount Pinatubo eruption stratosphere, J.

Geophys. Res., 99, 8213-8220, 1994.

Rinsland, C.P., M.R. Gunson, M.K.W. Ko, D.K. Weisenstein, R. Zander, M.C. Abrams, A. Goldman, N.D. Sze,

and G.K. Yue, H2SO 4 photolyis: A source of sulfur dioxide in the upper stratosphere. Geophys. Res. Lett., 22, 1109-11 i2, 1995.

Rodriguez, J.M., M.K.W. Ko, and N.D. Sze, Antarctic chlorine chemistry: Possible global implications, Geophys.

Res. Lett., 15, 257-260, 1988.

Rodriguez, J.M., M.K.W. Ko, N.D. Sze, S.D.. Pierce, J.G. Anderson, D.W. Fahey, K. Kelly, C.B. Farmer, G.C.

Toon, M.T. Coffey, L.E. Heidt, W.G. Mankin, K.R. Chan, W.L. Starr, J.F. Vedder, M.P. McCormick, Nitrogen and chlorine species in the spring Antarctic stratosphere: Comparison of models with AAOE observations. J. Geophys. Res., 94, 16,683-16,703, 1989.

Rodriguez, J.M., M.K.W. Ko, and N.D. Sze, Role of heterogeneous conversion of N205 on sulfate aerosols in global ozone losses. Nature, 352, 134-137, 1991.

Rodriguez, J.M., M.K.W. Ko, N.D. Sze, C.W. Heisey, G.K. Yue, and M.P. McCormick, Ozone response to

enhanced heterogeneous processing after the eruption of Mt. Pinatubo, Geophys. Res. Lett., 21,209-212, 1994.

Sze, N.D., M.K.W. Ko, D.K. Weisenstein, J.M. Rodriguez, R.S. Stolarski, and M.R. Schoeberl, Antarctic ozone

hole: Possible implications for ozone trends in the southern hemisphere, J. Geophys. Res., 94, 11521-11528, 1989.

Weisenstein, D., M.K. Ko, and N.D. Sze, The chlorine budget of the present day atmosphere: A modeling study,

J. Geophys. Res., 97, 2547-2559, I992.

Weisenstein, D.K., M.K.W. Ko, J.M. Rodriguez, and N.D. Sze, Effects on stratospheric ozone from high speed

civil transport: Sensitivity to stratospheric aerosol loading. J. Geophys. Res., 98, 23133-23140, 1993.

36
Weisenstein, D.K.,M.K.W.Ko,N-DSze,andJ.M.Rodriguez, Potential impactofSO2emissionsfromstratosphericaircraftonozone.Geophys. Res. Lett., 23, 161-164, 1996.

Weisenstein, D. K., M.K.W. Ko, I.G. Dyominov, G. Pitari, L. Ricciardulli, G. Visconti, and S. Bekki, The effect

of sulfur emissions from HSCT aircraft: A 2-D model intercomparison, J. Geophys. Res, 103, 1527-1547,

1998.

Zander, R., C.P. Rinsland, E. Mahieu, M.R. Gunson, C.B. Farmer, M.C. Abrams, and M.K.W. Ko, Increase of carbonyl fluoride (COFz) in the stratosphere and its contribution to the 1992 budget of inorganic fluorine in the

upper stratosphere. J. Geophys. Res., 99, 16,737-16,743, 1994.

37
1.2.2 CSIRO 2-D Chemical-Transport Model, Version Number 94, October

1998

Peter Vohralik, Lakshman Randeniya, Ian Plumb, Keith Ryan

CSIRO Telecommunications and Industrial Physics

P.O. Box 218, Lindfield, NSW 2070, Australia

For more information contact Dr Keith Ryan at: Keith.Ryan@tip.csiro.au

General Information

The CSIRO 2-D model has been described briefly by Vohralik et al. (1998) and in more detail by

Randeniya et al. (1997). An earlier version of the CSIRO 2-D model, which included a less rigorous

treatment of the eddy diffusion terms Kyy, was described by Randeniya et al. (1996) and in the 1995

report on the Atmospheric Effects of Stratospheric Aircraft (Stolarski et al. (1995)). The improved treatment of the eddy diffusion terms leads to a good representation of the mixing between the tropics and

midlatitudes, as reported by Vohralik et al. (1998). The current version of the CSIRO 2-D model, which

is described in detail below, was used to obtain the results reported in the M&M II Technical Report

(1999, ref?.), the IPCC Special Report on Aviation and the Global Atmosphere (1999, ref?.) the NASA

High Speed Research Program report on the Atmospheric Effects of Stratospheric Aircraft (1999, ref?),

and the WMO/UNEP 1998 Ozone Assessment (1999, ref?). The model domain extends from 90 S to

90 N in 5 degree steps and from the ground to 80 km with a vertical resolution of approximately 2 km

(log pressure). Zonal mean diabatic circulation, together with appropriate diffusion terms, provide the

model transport. The chemistry module is family based and employs full diurnal averaging methods.

Photodissociation

The solar radiation module in the CSIRO 2-D model includes fully interactive multiple scattering

calculations and allowance for the curvature of the Earth. The method of Meier et al. (1982) is

employed to account for the effects of multiple scattering, while the spherical atmosphere treatment

uses ray-tracing techniques. The model uses 158 wavelengths from 175 to 850 nm, as defined by WMO

(1986). Cross sections are from the JPL97 database (DeMore et al. (1997)).

Chemistry

The chemical module is family based, with a total of 67 species and 213 reactions. The species

included in the model and the family definitions are as follows:

Fixed Species: N2, 02, CO 2

Transported families: Ox, NOy, Cly, Bry, where:

Ox = O + O(1D) + 03

NOy = N + NO + NO 2 + NO 3 + HONO + HNO 3 + HNO4 + 2 x N205 + C1ONO 2 + BrONO2

Cly = CI + 2 x C12 + CIO + C102 + OCIO + 2 x CI202 + HOC1 + HC1 + CIONO 2

Bry = Br + BrO + HOBr + HBr + BrONO 2

Other transported species: N20 , CH4, CO, H20 , H2, CFCI3, CF2C12 ' CFC 113, CFC_114, CFC_! 15,

CC14, HCFC_22, CH3CC13, CH3CI, CF3Br, CFzC1Br, CH3Br, HF, CF20 , CC1FO

38
Equilibriumspeciesnotintransportedfamilies:H, OH,HO2,H202,HCO,CH20,CH3,CH30,CH302,

CH3OOH

Ratecoefficientsconformtotherecommendations inJPL97.

Themodelincludestheheterogeneoushydrolysisof N205,CIONO2andBrONO2with sulphuric

acidaerosols,withthefollowingreactionprobabilities.A reactionprobabilityof 0.1 is usedfor the

hydrolysisof N205.ForC1ONO2thereactionprobabilityiscalculatedusingequation(4)of Hansonand

Ravishankara(1991).A reactionprobabilityof 0.8isusedforBrONO2hydrolysis.Otherheterogeneous

reactions,includingthoseonpolarstratosphericclouds,arenotincludedintheversionof theCSIRO

2-DmodelusedtoobtaintheresultsreportedintheM&M II TechnicalReport.

Zonal-meantemperatures(discussedfurtherbelow)areusedwhencalculatingreactionrates.

Themixingratiosof N2,02 andCO2 areheldfixedthroughoutthemodelatmosphere, andtheir

concentrationsaredeterminedfrom thehydrostaticequation.Theconcentrationsof N20, CO,H2,

CH3C1,CC14,CH3Br,thehalons,thechlorofluorocarbonsandthehydrochlorofluorocarbons areall set

atthegroundatvaluesappropriatetotheparticularcalculation.TroposphericH20 issettovaluesdeterminedbytheOort(1983)climatologywhereavailableandSAGE-IIfromPratherandRemsberg(1992)

otherwise.Thestratosphericconcentrationsof H20 arecalculatedbysettingthevolumemixingratioat

thetropopauseto 3x10-6andthenallowingtheconcentrationto varyin thestratosphereasaresultof

chemistryandtransport.Themodelalsoincludesanuppertroposphericsourceof NOybetween30S

and30N from4 to 12kin,andbetween25Sand25Nfrom 12to 14kin.TheNOysourcestrengthused

is 1100cm-3s-I, correspondingtoatotalsourceof approximately2TgN y-1.

Productionandlosstermsforthelong-livedspeciesareupdatedevery10days.Thisisdonebyfirst

carryingoutafull diurnalintegrationto establishthe24-hourbehaviourof all of thespecies.Thediurnalcoefficientsderivedfrom thisintegrationarethenmultipliedby noonratecoefficientsto obtain

diurnally-averagedreactionratecoefficients.Theseareusedin aconstrainedequilibriumcalculationto

determinethediurnal-averageconcentrationsof speciesintheOx,NOy,Cly andBry families,together

withthoseof theequilibriumspeciesnotincludedintransportedfamilies.Duringtheconstrainedequilibriumcalculationthefamilytotalconcentrationsareheldconstant.

Rainout and Surface Deposition

Rainout rates are included for transported family totals NOy, Cly and Bry and for H202, CH20,

CH3OOH , HF, CC1FO and CF20 for model grid cells below 10 km which are also more than 3 km

below the tropopause. Rainout rates for individual members of NOy, Cly and Bry are not explicitly

included when partitioning these family totals. Rainout rates for model levels at z = 1,3, 5, 7 and 9 km

are 3.74xi0 -6, 3.98x 10-6, 2.96x10-6, 1.38x10 -6 and 0.42x10 -6 s-1 respectively. In addition to these rainout processes, the following surface deposition rates are included: 6.7x10 -6 s-_ for NOy, OH, HO 2,

H202, HCO, CH 3 and 3.3x10 -8 s-1 for CH20. (Surface deposition is not applied to Ox, Cly, Bry, H 2 and

CO as these species are held fixed at the ground by the model.)

Dynamics

The transport in the CSIRO 2-D model is derived diagnostically, using observed temperatures and

ozone mixing ratio profiles. Temperatures from the ground to 60 km are 8-year zonal and monthly mean

National Meteorological Center (NMC) values obtained from the 1992 Models and Measurements

39
database(PratherandRemsberg(I993)).From60to80 km,temperaturesarefromtheMiddleAtmosphereProgramdatabase(BarnettandCorney(1995)).Radiativeheatingratesfrom thegroundto

60kmareascalculatedbyRosenfieldandobtainedfromthe1992ModelsandMeasurementsdatabase.

Above60km, becauseof thebreakdownof localthermodynamicequilibriumassumptions, cooling

ratesarecalculatedusinga Newtoniancoolingapproximationwith Newtoniancoolingcoefficients

fromHoltonandWehrbein(1980),andheatingratesarecalculatedusingparameterisations developed

byStrobel(1978)andSchoeberl andStrobel(1978).Becausethereareinevitablediscontinuitiesinboth

temperaturesandheatingratesat60kin,valuesfor bothof thesequantitiesarejoinedsmoothlyovera

transitionregionwhichextendsfrom60to 70kmbylinearinterpolationbetweenthedatasetsoverthis

region.Numericalexperimentshaveshownthattheozonecolumnisquiteinsensitiveto detailsof this

matchingPrOcedure, asis to beexpectedsincethereis little masstransportat altitudesgreaterthan

60kin.In thetroposphere, latentandsensibleheatingratesareaddedto theradiativeheatingratesto

obtainnetheatingrates.SensibleandlatentheatingratesareobtainedfromDopplick(1974).

ResidualmeanverticalvelocitiesarecalculatedfromthetransformedEulerianmean(TEM)energy

conservationequation(includingthedT/dtterm),byfirstassumingzeromeridionalvelocities.Meridionalvelocitiesarethenderivedfromthecontinuityequation,andthesemeridionalvelocitiesareusedto

obtain improved estimates of vertical velocities, as described by Solomon et al. (1986). This process is

repeated until a self-consistent set of residual circulation velocities is obtained.

The Eliassen-Palm flux divergence required for the calculation of horizontal eddy diffusion coefficients Kyy is evaluated from the left-hand side of the TEM zonal momentum equation. Zonal winds are

calculated from the temperatures using gradient wind balance. Because of the breakdown of the geostrophic approximation in the tropics, zonal winds at latitudes less than 15 degrees latitude are calculated from the curvature expression. Stratospheric Kyy values are calculated diagnostically as the ratio

of the EP flux divergence to the meridional gradient of potential vorticity, where the E ux divergence

is obtained by evaluating the left-hand side of the TEM zonal momentum equation. Where calculated

values fall outside the range lxl0 4 < Kyy < 3x106 m 2 s -l, they are reset to these limiting values; Kyy values are also set to the lower limit where zonal winds are easterly, in line with current understanding of

planetary wave propagation. Tropospheric Kyy values are set to 1x 106 m 2 s-1 io simulate rapid convective mixing. Values of K_,z are obtained by projecting the Kyy from isentropic surfaces to pressure surfaces. The CSIRO model uses an annually averaged zonal mean tropopause height distribution obtained

by averaging daily NMC tropopause data for the period 1983-1993. Kzz values are set to 4 m 2 s-1 in the

troposphere, 0.25 m 2 s-I between 21 and 42 km and 0.1 m 2 s-1 throughout the remainder of the stratosphere. Off-diagonal components of the diffusion tensor are included by assuming that diffusion occurs

predominantly along isentropes.

Advection in the model uses the variable-order Bott (1989) scheme. This method, which is a highorder implementation of the upwind scheme, is conservative and positive definite and produces only

small numerical diffusion. Tests in this laboratory and elsewhere (Strand and Hov (1993)) have shown

that the Bott scheme compares favourably with other modern advection algorithms in terms of both

accuracy and computational efficiency. The calculations reported here used a second-order Bott scheme

with a 6-hour time step. For a given model run, vertical velocities are calculated for each of the

12 months of the year at the beginning of the run and stored in a three-dimensional array. Vertical

velocities and temperatures are obtained at 10-day intervals by interpolation of the monthly values. Horizontal velocities are then computed from the vertical velocities using continuity. Small mass conservation correction fac.tors are derived to correct for departures from mass conservation (< I%) due to the

process-splitting algorithm used to integrate the continuity equation. Eddy diffusion coefficients Kyy are

derived self-consistently with the residual circulation as described above. This procedure is repeated at

each transport time step.

4O
References

Barnett, J.J., and M. Corney, A middle atmosphere temperature reference model from satellite measurements,

Adv. Space Res., 5, 125-134, 1985.

Bott, A., A positive definite advection scheme obtained by nonlinear renormalization of the advective fluxes,

Mon. Weather Rev., 117, 1006-1015, 1989.

DeMoore et al., Chemical kinetics and photochemical data for use in stratospheric modeling, JPL Publication

97-4, 1997.

Dopplick, T.G., The heat budget, in "The General Circulation of the Tropical Atmosphere", vol. 2, edited by

R.E. Newell et al., pp. 27-94, MIT Press, Cambridge, Mass., 1974.

Hanson D.R., and A.R. Raviskankara, The reaction probabilities of C1ONO 2 and N205 on 40 to 75% sulfuric acid

solutions, J. Geophys. Res., 96, 17307-17314, 1991.

Hanson D.R. et al., Reaction of BrONO 2 with H20 on submicron sulfuric acid aerosol and the implications for the

lower stratosphere, J. Geophys. Res., ! 01, 9063-9070, 1996.

Holton, J.R. and W.M. Wehrbein, A numerical model of the zonal mean circulation of the middle atmosphere,

Pure Appl. Geophys., 118,284-306, 1980.

Meier, et al., Radiation field in the troposphere and stratosphere from 240-1000 rim, I, General analysis, Planet.

Space Sci., 30, 923-933, 1982.

Oort, Global Atmospheric Circulation Statistics, 1958-1973, NOAA NOAA Prof. Pap. 14, U.S. Dep. of Commer.,

Rockville, Md., 1983.

Prather, M., and E.E. Remsberg (Eds.), The atmospheric effects of stratospheric aircraft: Report of the 1992 models and measurements workshop, Nasa Ref. Pub1. 1292, 1993.

Randeniya, L.K., P.F. Vohralik, I.C. Plumb, and K.R. Ryan, Impact of the heterogeneous hydrolysis of BrONO 2

on calculated ozone changes due to HSCT aircraft and increased sulphate aerosol levels, Geophys. Res. Lett.,

23,343-346, 1996.

Randeniya, L.K., P.F. Vohralik, I.C. Plumb, and K.R. Ryan, Heterogeneous BrONO 2 hydrolysis: effect on NO 2

columns and ozone at high latitudes in summer, J. Geophys. Res., in press, 1997.

Solomon, S., J.T. Kiehl, R.R. Garcia, and W. Grose, Tracer transport by the diabatic circulation deduced from

satellite observations, J. Atmos. Sci., 43, 1603-1617, 1986.

Stolarski, R.S. et al., 1995 Scientific assessment of the atmospheric effects of stratospheric aircraft, Nasa Ref.

Publ. 1381, 1995.

Strand, A., and O. Hov, A two-dimensional zonally averaged transport model including convective motions and a

new strategy for the numerical solution, J. Geophys. Res., 98, 9023-9037, 1993.

Strobel, D.F., Parameterization of the atmospheric heating rate from 15 to 120 km due to O 2 and 03 absorption of

solar radiation, J. Geophys. Res., 83, 6225-6230, 1978.

Schoeberl, M.R. and D.F. Strobel, The zonally averaged circulation of the middle atmosphere, J. Atmos. Sci., 35,

577-59I, 1978.

Vohralik, P.F., L.K. Randeniya, I.C. Plumb, and K.R. Ryan, Use of correlations between long-lived atmospheric

species in assessment studies, J. Geophys. Res., 103,3611-3627, 1998.

WMO, Atmospheric Ozone 1985, WMO Report No. 16, 1986.

41
1.2.3 GSFC 2D Model Description

Charles H. Jackman

Code 916

NASA/GSFC

Greenbelt, MD 20771

E-mail: jackman@ assess.gsfc.nasa.gov

TEL: (301) 614-6053

FAX: (301) 614-5903

David B. Considine

same address and FAX as above

E-mail: dbc@ welkin.gsfc.nasa.gov

TEL: (301) 614-5992

Eric L. Fleming

same address and FAX as above

E-mail: fleming@ welkin.gsfc.nasa.gov

TEL: (301) 614-5983

General Information

Much information about the model used in these Models and Measurements II model experiments is

discussed in Jackman et al. [1996].

Domain and Grid Sizes

Latitude - From 85S to 85N with 16 "g latitude bands (85S,75S,...75N,85N)

Altitude - From the ground to 0.0024 nPa - Equally spaced in log pressure -

Approximately a 2 km spacing

Circulation and Diffusion

A meridional stream function is calculated to obtain the transformed Eulerian circulation. The coefficients of the elliptic stream function equation depend on the zonal mean temperature and zonal wind,

which are based on the 17-year average (1979-95) of temperature data from the National Centers for

Environmental Prediction (NCEP) for 1000 -1 mbar, and the CIRA [1986] empirical reference model for

the mesosphere above t mbar. Zonal wind is derived from temperature using the gradient wind relation.

Forcing of the stream function is proportional to the vertical gradient of the mechanical forcing from

planetary waves, gravity waves, and equatorial Kelvin waves and the latitudinal gradient of the total

heating rate. We have computed diabatic heating rates following Rosenfield et al. [1994] utilizing

climatological distributions of temperature, ozone, and water vapor. The climatological latent heating

rate distribution is adapted from Newell et al. [1974]. Forcing from plantary waves is proportional to the

Eliassen-Palm (E-P) flux divergence, which we have computed offiine from the 17-year NCEP threedimensional analyses for 1000 -1 mbar and from the CIRA [1986] planetary wave climatology for the

mesosphere above 1 mbar.

42
Latitudinal eddy diffusion (Kyy) is obtained self-consistently as the ratio of the E-P flux divergence

to the latitudinal gradient of zonal mean potential vorticity. The parameterization of Holton and Zhu

[1984] is used to obtain the distributions of vertical eddy diffusion (Kzz) and mechanical forcing from

the gravity waves. The empirical zonal mean temperature and zonal wind fields are used to compute the

latitudinal, seasonal, and vertical distributions of gravity wave drag and diffusion based on a given set

of gravity wave parameters. The equatorial semiannual oscillation (SAO) is approximated using the

methodology of Gray and Pyle [1987] along with the empirical zonal mean wind field to diagnose the

mechanical forcing from thermally damped equatorial Kelvin waves.

Among the features of source gases simulated in the model are the sharp gradients between the tropics and subtropics, a relatively weak gradient between the subtropics and mid-latitudes, and a strong

descent in the late winter polar vortex with large horizonal gradients across the vortex edge near about

60 deg.

Transport Scheme

The numerical advection scheme is based on Lin and Rood [1996] and utilizes the piecewise parabolic method (PPM). The PPM is highly accurate and is capable of preserving sharp tracer gradients.

Time Steps

The species continuity equations are solved by operator splitting; that is, by assuming that the continuity equation may be factored into a product of difference operators [McRae et al., 1982]. First the

advection field is applied to the constituent distributions; second the vertical eddy diffusion (Kzz); third

the horizontal eddy diffusion (Kyy); and fourth the chemistry operators are implemented. The time steps

vary for each operator such that the advection field is applied with a 1/2 day step, the Kzz with a 1/8 day

step, and the Kyy and chemistry both with a 1 day step.

Photochemistry

All photochemical reaction rates are updated at I day intervals and the rates are all taken from

JPL 97-4 [DeMore et al. 1997].

Heterogeneous Chemistry on Sulfate Aerosols and PSCs

The following reactions on aerosol particles are included in the model:

1. CIONO 2 + HC1 -> HNO 3 + C12

2. CIONO 2 + H20 -> HNO 3 + HOCI

3. N205 + H20 -> 2HNO 3

4. HOC1 + HC1 -> C12 + H20

5. BrONO 2 + H20 -> HNO 3 + HOBr

6. HOBr + HCI -> BrCI + H20

Heterogeneous reaction rates are calculated as first-order loss rates for C1ONO 2, N205, HOC1,

BrONO 2, and HOBr using the formula:

k--TvA/4,

43
where]' isa"stickingcoefficient"whichcanbeinterpretedastheprobabilityof uptaketo theaerosol

andsubsequent reaction,v is thethermalvelocityof thegasphasemolecule,andA isthesurfacearea

densityof theaerosolparticles.

Onsulfateaerosols,thestickingcoefficientsforreactionsl and2arecalculatedusingthemethodologyof HansonandRavishankara[t994]. Thestickingcoefficientfor reaction3 is setto 0.1.The

stickingcoefficientforreaction4 iscalculatedasinHansonet ai.[1994].Thestickingcoefficientsfor

reactions5and6are0.8and0.2respectively, asinDeMoreetal.,[1997].

Theabovereactionsarealsoincludedonthesurfacesof Type1(nitricacidtrihydrate)andType2

(waterice)PSCs.TheratesforthesereactionsareasspecifiedinDeMoreetal.,[1997].

Polar Stratospheric Clouds

Polar stratospheric clouds (PSCs) are included using the methodology explained in Considine et al.

[1994]. Both Type 1 and Type 2 PSCs are simulated. Type 1 PSCs are assumed to be composed of nitric

acid tryhydrate and Type 2 PSCs are assumed to be water ice. The amount of HNO 3 and H20 condensed into PSCs in each gridbox is calculated using saturation vapor pressure measurements of HNO 3

over HNO3-3H20 made by Hanson and Mauersberger [1988] and saturation vapor pressure measurements of H20 over H20-ice made by Marti and Mauersberger [1991]. Supersaturation ratios of 10 and

1.4 are requred before Type 1 and Type 2 PSCs form, resulting in a reduction in PSC nucleation temperatures of approximately 3K and 2K for Type 1 and Type 2 PSCs, respectively [Peter et al., 1991 ]. Integrals over climatological temperature probability distributions are used to account for variations of

temperature away from the zonal mean values in the calculation of condensed phase HNO 3 and H20.

The temperature distributions are monthly climatologies of temperature variation taken from 14 years

(1979 - 1992) of NCEP temperature data, and vary with latitude+ pressure, and month of year. Particle

size distributions are assumed for both Type 1 and 2 PSCs. Type 1 PSCs are assumed to follow a lognormal distribution with a mode radius of ! micron and a standard deviation of 1.8. Type 1 PSCs are

assumed to follow a lognormal distribution with a mode radius of 10 microns and a standard deviation

of 1.8. Sedimentation of Type 1 and Type 2 PSCs is included, with sedimentation velocities calculated

according to Kasten [1968].

Treatment of Solar Radiance and Photodissociation

A look-up table is used for the photolytic source term (PST) and photolysis of 0 2 [J(O2)]. This table

was developed by D. Anderson and coworkers at the Johns Hopkins University Applied Physics Laboratory [Anderson and Lloyd, 1990]. The PST and J(O2) table is a function of wavelength, solar zenith

angle, and column ozone and is given on the pressure grid of the GSFC 2D model. The PST is computed

for the particular wavelength, solar zenith angle, column ozone, and pressure of interest and is multiplied by the solar irradiance at the top of the model to compute the reduced (or enhanced) flux at the

point of interest. The reduced (or enhanced) flux is then used with the photodissociation cross section

for a particular constituent to derive the photolysis rate of interest.

Family Chemistry and Transported Constituents or Families

Well-known family chemistry approximations are used to reduce the number of transported species

in the model. Thirty-one chemical species or families are currently transported in our model simulations. These include Ox (03, O[ID], O[3p]), NOz (N, NO, NO 2, NO 3, HO2NO2), N205, CIONO2,

HNO3, Clz (CI, C10, HOC1, HCI), Bry (Br, BrO, HBr, BrONO 2, HOBr), N20, CH 4, H20, CO 2, H 2,

44
CO, CH3OOH, CFC13, CF2CI 2, CH3CI, CC14, CH3CCI 3, CH3Br, CHC1F 2, C2C13F 3, C2C12F 4, C2CIF 5,

CBrC1F2, CBrF3, HF, CC1FO, CF20, solid HNO 3, and solid H20. The HOx (H, OH, HO2) species,

H202, and the hydrocarbons CH 3, CH30, CH30 2, CH20, and CHO are calculated using photochemical

equilibrium assumptions.

Nighttime Chemistry

Some constituents that vary significantly over the diurnal time period are treated with a special

nighttime calculation. The nighttime calculation is applied to constituents N20 5, C1ONO 2, CIO, BrO,

and BrONO 2, utilizing 24 time steps during each night. This methodology is explained further in

Douglass et al. [ 1989].

References

Anderson, D. E., Jr., and S. A. Lloyd, Polar twilight UV-visible radiation field: Perturbations due to multiple scattering, ozone depletion, stratospheric clouds, and surface albedo, J. Geophys. Res., 95, 7429-7434, 1990.

Considine, D. B., A. R. Douglass, and C. H. Jackman, Effects of a polar stratospheric cloud parameterization on

ozone depletion due to stratospheric aircraft in a two-dimensional model, J. Geophys. Res., 99, 18,879- ! 8,894,

1994.

DeMore, W. B., S. P. Sander, D. M. Golden, R. F. Hampson, M. J. Kurylo, C. J. Howard, A. R. Ravishankara,

C. E. Kolb, and M. J. Molina, Chemical kinetics and photochemical data for use in stratospheric modeling,

Evaluation number 12, JPL Publication 97-4, Jet Propulsion Laboratory, NASA, January 15, 1997.

Douglass, A. R., C. H. Jackman, and R. S. Stolarski, Comparison of model results transporting the odd nitrogen

family with results transporting separate odd nitrogen species, J. Geophys. Res., 94, 9862-9872, 1989.

Gray, L. J., and J. A. Pyle, Two-dimensional model studies of equatorial dynamics and tracer distributions, Q. J.

R. Meteorol. Soc., 113,635-651, 1987.

Hanson, D., and K. Mauersberger, Laboratory studies of the nitric acid trihydrate: Implications for the south polar

stratosphere, Geophys. Res. Lett., 15, 855-858, 1988.

Hanson, D. R., and A. R. Ravishankara, Reactive uptake of CIONO 2 onto sulfuric acid due to reaction with HC1

and H20, J. Phys. Chem., 98, 5728-5735, 1994.

Hanson, D. R., A. R. Ravishankara, and S. Solomon, Heterogeneous reactions in sulfuric acid aerosols: A framework for model calculations, J. Geophys. Res., 99, 3615-3629, 1994.

Holton, J. R., and X. Zhu, A further study of gravity wave induced drag and diffusion in the mesosphere, J. Atmos.

Sci., 41,2653-2662, 1984.

Jackman, C. H., E. L. Fleming, S. Chandra, D. B. Considine, and J. E. Rosenfield, Past, present, and future modeled ozone trends with comparisons to observed trends, J. Geophys. Res., 101,28,753-28,767, 1996.

Kasten, F., Falling speed of aerosol particles, J. Appl. Meteorol., 7, 944-947, 1968.

Lin, S.-J., and R. B. Rood, Multidimentional flux-form semi-Lagrangian transport schemes, Mon. Weather Rev.,

124, 2046-2070, 1996.

McRae, G. J., W. R. Goodin, and J. H. Seinfeld, Numerial solution of the atmospheric diffusion equation for

chemially reacting flows, J. Comput. Phys., 45, 1-42, 1982.

Newell, R. E., J. W. Kidson, D. G. Vincent, and G. J. Boer, The General Circulations of the Tropical Atmosphere,

vol. 2, chap. 7, MIT Press, Cambridge, Mass., 1974.

Peter, Th., C. Bruhl, and P.J. Crutzen, Increase in the PSC-Formation Probability Caused by High-Flying Aircraft,

Geophys. Res. Lett., 18, 1465-1468, 1991.

Rosenfield, J. E., P. A. Newman, and M. R. Schoeberl, Computations of diabatic descent in the stratospheric polar

vortex, J. Geophys. Res., 99, 16,677-16,689, 1994.

45
1.2.4 GSFC-INT 2D Model Description

Joan E. Rosenfield, David B. Considine, Julio T. Bacmeister

Code 916

NASA/GSFC

Greenbelt, MD 20771

E-mail: rose@ euterpe.gsfc.nasa.gov

TEL: (301) 614-5986

FAX: (301) 614-5903

General Information

The model consists of three separate modules - chemistry (including the photolysis rate calculation), radiation (which computes the heating and cooling rates), and dynamics. The dynamics uses heating rates computed in the radiation module. The dynamics computes temperatures, residual circulation,

and diffusion coefficients which are used in the chemistry module. The radiation uses the temperatures

from the dynamics code and the ozone and water vapor from the chemistry module. References describing the model and its applications are Rosenfield et al. [1997], Rosenfield et aI. [1998a], and Rosenfieid

and Douglass [1998b].

Domain and Grid Sizes

Chemistry Module:

Latitude - From 85S to 85N with 10 deg latitude bands (85S,75S,...75N,85N)

Altitude - From the ground to 0.0024 hPa - Equally spaced in log pressure -

Approximately a 2 km spacing

Dynamics Module:

Latitude - From 85. IS to 85.1N with 4.86 deg latitude bands

Altitude - From 2.66 km to 106.4 kin, at a resolution of 2.66 km

Radiation Module:

Latitude - Same as dynamics module

Altitude - 45 levels from the ground to 0.0018 hPa - equally spaced in In p,

approximately a 2 km spacing

Circulation and Temperature

The dynamics model is similar to that described in Bacmeister et al. [1995]. The residual circulation

is driven by radiative heating and cooling rates which are calculated with the model predicted temperatures, ozone, and water vapor, using the parameterizations discussed in Rosenfietd et al. [1994]. Latitudinally dependent effective tropospheric cloud heights and amounts are specified [Rosenfield

et al., 1997] in such a way that the outgoing longwave flux agrees with satellite observations.

The linear model of planetary wave drag and horizontal diffusion is as described in Bacmeister et al.

[1995]. A new parameterization of gravity wave drag and vertical mixing [Summers et al., 1997] is

46
included.To obtainsimulationsthatarevalid in thetropics,bothzonallyaveragedmomentumand

potentialtemperatureareadvectedbythemeridionalresidualcirculation.

Time Steps and Transport Scheme

In the chemistry module, the numerical advection scheme is based on Lin and Rood [1996] and utilizes the piecewise parabolic method (PPM). The PPM is highly accurate and is capable of preserving

sharp tracer gradients. The species continuity equations are solved by operator splitting; that is, by

assuming that the continuity equation may be factored into a product of difference operators [McRae

et al., 1982]. First the advection field is applied to the constituent distributions; second the vertical eddy

diffusion (Kzz); third the horizontal eddy diffusion (Kyy); and fourth the chemistry operators are implemented. The time steps vary for each operator such that the advection field is applied with a 1/2 day

step, the Kzz with a 1/8 day step, and the Kyy and chemistry both with a 1 day step.

In the dynamics module, the numerical advection scheme is that of Prather [1986], modified for

atmospheres with exponentially decreasing density by Shia et al. [1990]. The heating rates are updated

once a day, while the dynamics calculation is done every two hours.

Photochemistry

Well-known family chemistry approximations are used to reduce the number of transported species

in the model. Thirty-one chemical species or families are currently transported in our model simulations. These include Ox (03, O[1D], O[3p]), NOz (N, NO, NO2, NO3, HO2NO2) , N205, CIONO2,

HNO 3, Clz (CI, CIO, HOCI, HC1), Bry (Br, BrO, HBr, BrONO2, HOBr), N20, CH4, H20 , CO2, H2,

CO, CH3OOH, CFCI3, CF2C12, CH3CI , CC14, CH3CCI3, CH3Br , CHCIF2, C2CI3F3, C2C12F4, C2C1F5,

CBrC1F2, CBrF3, HF, CC1FO, CF20, solid HNO3, and solid H20. The HOx (H, OH, HO2) species,

H202, and the hydrocarbons CH3, CH30 , CH302, CH20 , and CHO are calculated using photochemical

equilibrium assumptions.

Some constituents that vary significantly over the diurnal time period are treated with a special

nighttime calculation. The nighttime calculation is applied to constituents N205, C1ONO2, CIO, BrO,

and BrONO2, utilizing 24 time steps during each night. This methodology is explained further in

Douglass et al. [1989].

All photochemical reaction rates are updated at I day intervals and the rates are all taken from

JPL 94-26 [DeMore et al. 1994]. The photolysis rate is calculated at 39 wavelengths between 1215 and

7300 nm. The mean intensity, or photolytic source term, is the sum of the attenuated direct beam and the

diffuse beam. The attenuation of the direct beam is calculated by using spherical geometry, while the

diffuse beam is calculated with plane parallel geometry only. We use the two-stream Eddington approximation multiple-scattering algorithm described by Toon et al. [1989], together with the delta scalings

of Joseph et al. [1976] to treat the anisotropic scattering associated with aerosols. The photolysis computations use the model generated ozone.

Heterogeneous Chemistry

The heterogeneous chemistry parameterizations are the same as those used in the GSFC 2D fixed

transport model.

47
References

Bacmeister, J. T., M. R. Schoeberl, M. E. Summers, J. E. Rosenfield, and X. Zhu, Descent of long-lived trace

gases in the winter polar vortex, J. Geophys. Res., 100, 11,669-11,684, 1995.

DeMore, W. B., et al., Chemical kinetics and photochemical data for use in stratospheric modeling, Evaluation

number 11, JPL Publication 94-26, Jet Propulsion Laboratory, NASA, 1994.

Douglass, A. R., C. H. Jackman, and R. S. Stolarski, Comparison of model results transporting the odd nitrogen

family with results transporting separate odd nitrogen species, J. Geophys. Res., 94, 9862-9872, 1989.

Jackman, C. H., E. L. Fleming, S. Chandra, D. B. Considine, and J. E. Rosenfield, Past, present, and future

modeled ozone trends with comparisons to observed trends, J. Geophys. Res., 101,28,753-28,767, 1996.]

Joseph, J. H., W. J. Wiscombe, and J. A. Weinman, The delta-Eddington approximation for radiative flux transfer,

J. Atmos. Sci., 33, 2452-2459, 1976.

Lin, S.-5., and R. B. Rood, Multidimentional flux-form semi-Lagrangian transport schemes, Mon. Weather Rev.,

124, 2046-2070, 1996.

McRae, G. J., W. R. Goodin, and 5. H. Seinfeld, Numerial solution of the atmospheric diffusion equation for

chemially reacting flows, J. Compuc Phys., 45, 1-42, 1982.

Prather, M. J., Numerical advection by conservation of second-order moments, J. Geophys. Res., 91, 6671-668 i,

1986.

Rosenfield, J. E., P. A. Newman, and M. R. Schoeberl, Computations of diabatic descent in the stratospheric polar

vortex, J. Geophys. Res., 99, 16,677-16,689, 1994.

Rosenfield, J. E., D. B. Considine, P. A. Meade, J. T. Bacmeister, C. H. Jackman, and M. R. Schoeberl, Stratospheric effects of Mount Pinatubo aerosol studied with a coupled two-dimensional model, J. Geophys. Res.,

102, 3649-3670, 1997.

Rosenfield, 5. E., D. B. Considine, M. R. Schoeberl, and E. V. Browell, The impact of subvisible cirrus clouds

near the tropical tropopause on stratospheric water vapor, Geophys. Res. Lett., 25, 1883-1886, 1998a.

Rosenfield, 5. E., and A. R. Douglass, Doubled CO2 effects on NOy in a coupled 2D model, Geophys. Res. Lett.,

1998b, in press.

Shia, R. L., Y. L. Ha, J. S. Wen, and Y. L. Yung, Two-dimensional transport and chemistry model: Numerical

experiments with a new advection algorithm, 5. Geophys. Res., 95, 7467-7483, 1990.

Summers, M. E., D. E. Siskind, J. T. Bacmeister, and R. R. Conway, Seasonal variation of middle atmosphere

CH4 and H20 with a new chemical-dynamical model, J. Geophys. Res., 102, 3503-3526, 1997.

Toon, O. B., C. P. McKay, T. P. Ackerman, and K. Santhanam, Rapid calculation of radiative heating rates

and photodissociation rates in inhomogeneous multiple scattering atmospheres, J. Geophys. Res., 94,

16,287-16,301, 1989.

48
1.2.5 HARVARD 2D Model Description

Hans R. Schneider

Center for Earth & Planetary Physics

Pierce Hall

Harvard University

29 Oxford St.

Cambridge, MA 02138-290 l

E-mail: hrs@io.harvard.edu

TEL: (617) 495-08 l0

FAX: (617) 495-4551

Dylan B. A. Jones

same address and FAX as above

E-mail: dbj@io.harvard.edu

TEL: (301) 286-4299

Larissa Kogan

same address and FAX as above

E-mail: lar@io.harvard.edu

TEL: (30 l) 286-3923

General Information

The model is an interactive 2-D residual circulation model. Circulation and temperatures are calculated using heating rates derived from model temperatures and model ozone. Eddy diffusion coefficients, Kyy, are externally specified. The residual circulation calculated by the dynamics module is

consistent with the diffusive tracer transport.

Details and first applications of the model are described in forthcoming papers.

Domain and Grid Sizes

Latitude - Pole to pole with a resolution of 5 degrees. A staggered grid is used for the dynamics module,

the latitudes of the chemistry/transport grid are -87.5S, -82.5S ..... 82.5N, 87.5N.

Altitude - Equally spaced in log pressure, z*, from the ground to z* = 81km. Level spacing is dz*=2.0

km. The first chemistry level is at z* = 1 km, i.e. the center of the lowest grid box. Dynamical

quantities are staggered in height.

Circulation and Diffusion

The model residual mean meridional circulation and temperatures are calculated using a primitive

equation dynamics module (Schneider and Geller, 1985). Heating rates are calculated with a full radiative transfer scheme (an updated version of Shi, 1981) using model temperatures and model ozone as

well as CO 2. The calculated heating rates are not used in the troposphere. There, they are replaced by a

heating rate parameterization adopted from Cunnold et al. (1975) with some modifications to account

for asymmetries between the Northern and Southern hemisphere.

49
Thetransition from calculated to parameterized heating rates near the tropopause is somewhat

ambiguous. The altitude and depth of the transition region have been adjusted such that reasonable

tropical temperature profiles are produced by the model.

Nonzero diabatic heating rates and a finite residual circulation are only obtained in the presense of

time dependent and nonconservative processes. These processes are parameterized and we have tried to

keep the parameterization as simple as possible. The model includes two dissipative processes to drive

its circulation: (I) gravity wave drag, parameterized by Rayleigh friction and (2) the potential vorticity

flux associated with breaking planetary waves, parameterized as Kyy times the gradient of the zonal

mean potential vorticity. The parameters have been chosen as follows:

Gravity wave drag has to be strong enough to close the stratospheric jets in the mesosphere. Planetary waves add to the deceleration of the winter jet, the easterly summer jet is, however, only subject to

gravity wave drag. The Rayleigh friction is assumed to be independent of latitude. It is largest above

50 km in altitude. In the lower stratosphere, i.e. the altitude region between 10 and 20 km, coefficients

have been reduced to ensure a frictional damping time scale that is longer than a season (close to one

year in the middle of the layer).

The structure of the prescribed distribution of Kyy follows loosely the analysis of NMC data by

Newman et al. (1986, 1988). A maximum in Kyy of is specified near the center of the polar night jet in

winter. A second maximum in Kyy is specified in the lower stratosphere in summer to account for the

action of trapped planetary waves.

The magnitude of the upper maximum in the Northern Hemisphere winter varies from 3 x 106

m2/sec (low Kyy case) to 6 × 106 m2/sec (large Kyy case). The Kyy maximum in the Southern winter is

assumed to have half the amplitude in order to account for the difference in planetary wave structures

between the hemispheres. The lower stratospheric summer maxima are set to 0.75 x 106 for the low Kyy

case and 1.5 x 106 m2/sec for the high Kyy case. The strength of these maxima is the same in the Northern and Southern Hemisphere. The stength of Kyy falls off exponentially in latitude and altitude away

from the maxima. Values of Kyy for the Northern Hemisphere midlatitudes at 30 km in altitude are typically 5 x 105 m2/sec in winter.

The Kyy'S are set to zero equatorward of 15 degrees. This defines a 'pipe' structure in the model.

However, the walls of the pipe are not well defined as Kyy increases gradually poleward of 15 degrees.

Furthermore, the rate of increase varies with season. At some altitudes, the pipe extends all the way to

the pole in summer. Due to our choice of representing the distribution of Kyy as a superposition of seasonally varying maxima, the values of the diffusion coefficients are comparatively small in the high latitude lower stratosphere, resulting in a 'stagnation zone' with the oldest air in this region.

The mass exchange between tropics and midlatitudes is determined by the model calculated residual

circulation which depends on Kyy in midlatitudes. Increasing the extratropical large scale mixing

increases the flow across the subtropical regions.

All results submitted for this intercomparison workshop were obtained with the low values of Kyy

as described above.

Transport Scheme

The numerical advection scheme is the Square Root scheme (Schneider, 1985) using fourth order

spatial differencing.

5O
Thelargescaleeddydiffusioncoefficients,Kyy,areassumedto representtheone non-zero component of the diffusion tensor in isentropic coordinates. The diffusion tensor in pressure coordinates is

determined by calculating the slope of isentropic surfaces at every grid point and rotating the isentropic

center accordingly.

An additional vertical diffusion of 10. m2/sec is specified below the tropopause of the model to represent tropospheric vertical exchange processes. The timescale of diffusive transport between the surface and the tropopause is of the order of a month. In the stratosphere, a background vertical diffusion of

0.1 m2/sec is used to account for the vertical mixing introduced by breaking gravity waves.

The tropopause height is set to 16 km in the tropics. In midlatitudes, it is 12 km in the summer and

10 km in winter. No attempts have been made to fine tune tropopause heights in order to generate realistic ozone couImn amounts.

Time Steps

Starting from day N, the dynamics is integrated forward for 5 days, using a split explicit time stepping scheme. Time steps are half an hour for the external step and 8 minutes for the internal steps. The

dynamical fields are saved at the beginning and the end of the 5-day interval.

Using interpolated dynamical fields for the 5-day interval, the tracer transport equation is integrated

forward for each transported species, using 3 hour timesteps.

Using the updated transported species concentrations, the remaining species are determined by

requiring photochemical equilibrium. To calculate equilibrium, a diurnal cycle (24-hour) integration is

performed with all chemical species. Diurnal coefficients are then determined from the diurnal calculation and the system of equations (Pi - Li) = 0, (i=l,number of species) is solved using rates that have

been multiplied by the appropriate diurnal coefficients. New production rates and loss frequencies are

calculated for the transported species, including the families.

Using the updated ozone of day+5, heating rates are recalculated, and the dynamics is integrated

forward again.

Photochemistry

Reaction rates and photo dissociation cross sections are taken from JPL 97-4 [DeMore et al. 1997].

Heterogeneous Chemistry on Sulfate Aerosols and PSCs

The following heterogeneous reactions are included in the model:

N205 + H20 = 2 x HNO 3

C1ONO 2 + H20 = HNO 3 + HOCI

Polar Stratospheric Clouds

Polar stratospheric clouds are not included in the model.

51
Treatment of Solar Radiance and Photodissociation

For the photodissociation calculation we assume that the direct solar beam is attenuated by gas

absorption and in situ Rayleigh scattering. The radiative transfer equation is solved using a 6-stream

approximation for clear sky conditions in a multiple scattering atmosphere. We use the radiative transfer

code from the Harvard photochemical model (Salawitch et al. [1994]). The incident solar flux specified

at the top of the atmosphere was taken from WMO [ 1986]. Cross sections for Rayleigh scattering and

for absorption in the Herzberg continuum (for wavelengths greater than 202.5 nm) of 0 2 were taken

from WMO [1986]. For absorption in the Schumann-Runge bands of 02 we use the cross section

parameterization of Minchwaner et al. [1992], with the the underlying Herzberg continuum included in

the parameterization.

Family Chemistry and Transported Constituents or Families

The chemistry module solves for 71 species. Four of them are fixed (N 2, 02, H 2 and H20), 28 are

transported and 39 are assumed to be in photochemical equilibrium. Four of the transported species are

families:

Ox = O + O(1D) + 03 + NO 2 + CIO + BrO + HO 2

NOy = N + NO + NO 2 + NO 3 + HONO + HNO 3 + HNO 4 + 2 x N205 + C1ONO 2 + C1NO + CINO 2

+ CIONO + BrONO 2

Cly = C1 + C10 + HC1 + HOCI + C1ONO 2 + C1NO + 2 x Cl 2 + C1NO 2 + C1ONO + C1OO + 2 x

CIOOCI + OCIO + BrC1

Bry = Br + BrC1 + BrO + HBr + HOBr + BrONO 2

Members of the following two families are treated as equilibrium species.

HOx = OH + H + HO 2 + H20 2

CHx = HCO + CH20 + CH 3 + CH30 + CH30 2 + CH3OOH

Daytime Concentrations

The daytime average species concentrations, submitted to this workshop, have been diagnosed at

every timestep by calculating daytime averages in addition to the 24-hour averages needed for the

determination of our diurnal coefficients. These daytime averages do not represent an exact daytime

photochemical equilibrium state. They are reasonably close to equilibrium but should not be used to

recalculate production or loss rates.

References

Cunnold, D., F. Alyea, NI Phillips, and R. Prinn: A three-dimensional dynamical-chemical model of atmospheric

ozone, J. Atmos. Sci., 32, 170-194, 1975

DeMore, W. B., S. P. Sander, D. M. Golden, R. F. Hampson, M. J. Kurylo, C. J. Howard, A. R. Ravishankara,

C. E. Kolb, and M. J. Molina, Chemical kinetics and photochemical data for use in stratospheric modeling,

Evaluation number 12, JPL Publication 97-4, Jet Propulsion Laboratory, NASA, January 15, 1997.

Minschwaner, K., G. P. Anderson, L. A. Hall, and K Yoshino, Polynomial coefficients for calculating 02

Schumann-Runge cross sections at 0.5 cm l resolution, J. Geophys. Res., 97, 10103-10108, 1992.

52
Newman, P.A.,M.R.Schoeberl, andR.A.Plumb,Horizontal mixingcoefficientsfortwo-dimensional chemical

modelscalculatedfromNational Meteorological Centerdata,J.Geophys. Res.,91,7919-7924, 1986.

Newman, P.A.,M.R.Schoeberl, R.A.Plumb,andJ.E.Rosenfield, Mixingratescalculatedfrompotential vorticity,J.Geophys. Res.,93,5221-5240, 1988.

Salawitch, R.J.,etal.,The distribution of hydrogen, nitrogen, and chlorine radicals in the lower stratosphere:

Implications for changes in 03 due to emission of NOy from supersonic aircraft, Geophys. Res. Lett., 21,

2547-2550, 1994.

Schneider, H. R. and M. A. Geller, A comparison of two- and three-dimensional transport within a stratospheric

circulation model, J. Atmos. Sci., 42, 1792-1808, 1985.

Schneider, H. R., D. B. A. Jones, G.-Y. Shi, and M. B. McElroy: Analysis of residual mean transport in the stratosphere. Part 1: Model description and comparison with satellite data. Submitted to J. Geophys. Res., 1998.

Schneider, H. R., D. B. A. Jones, S. C. Wofsy, and M. B. McElroy: Analysis of residual mean transport in the

stratosphere. Part 2: Distributions of CO 2 and mean age. Submitted to J. Geophys. Res., 1998.

Shi, Guang-Yu, An accurate calculation and representation of the infrared transmission function of the atmospheric constitutes, Ph.D. thesis, Dept. of Science, Tohoku University of Japan, 191pp, 1981.

53
1.2.6 LLNL 2-D Model

Douglas E. Kinnison

Atmospheric Chemistry Division

National Center for Atmospheric Research

P. O. Box 3000

Boulder, CO 80307-3000

E-mail: dkin@acd.ucar.edu

Telephone: (303)497-1469

Fax: (303)497-1400

Peter S. Connell

Global Climate Research Division, L-240

Lawrence Livermore National Laboratory

7000 East Avenue

P. O. Box 808

Livermore, CA 94550-9900

E-mail: conneli2@llnl.gov

Telephone: (510)422-1811

Fax: (510)422-5844

General Information

The LLNL 2-D chemical-radiative-transport (CRT) model, or LOTUS (Livermore Operator-split

Two-dimensional Zonal-average (U) System), calculates zonal average atmospheric distributions of

chemically active trace constituents in the troposphere and stratosphere, based on numeric representations of chemical and physical atmospheric processes. The processes represented include; (a) thermal

kinetic chemical reactions with rate constants based on climatological zonal temperature distributions,

(b) photolytic chemical reactions, (c) advection and diffusion driven by climatological zonal average

temperature, radiative transfer of energy, and orographic forcing, (d) surface emission and in situ production of active trace constituents, and (e) dry and wet deposition or removal of active species.

The model has been developed partially from various components of the previous LLNL 2-D CRT

model (Kinnison et al., 1994; Patten et al., 1994), that have been improved and modularized. The model

domain extends from pole to pole, and from the surface to 85 km. The horizontal resolution is 5 degrees

in latitude and the vertical coordinate is logarithmic in pressure, with an approximate vertical resolution

of 1.5 km. As implemented for this application, the model treats 46 chemically active species.

Chemistry Mechanism and Solution

The model integration procedure is based on the split operator approach, with a central time step

that can be made appropriate for either a diell-average chemistry solution technique (e.g. 1 day) or a

direct dieI solution (e.g. 900 seconds). Chemistry, transport, and physical process operators interact on

multiples of the central time step. Within the chemistry operator, species are integrated with

SMVGEAR H, a grouped stiffness-ordered, sparse matrix implementation of the variable internal timestep, variable order, implicit, error-controlled Gear solution technique (Jacobson, 1995). The continuity

tThe word 'diel' denotes the 24 hour diurnal/nocturnal cycle.

54
equation is solved for each species individually; no lumping of species into chemical families or steadystate approximations is made. Although the model can be run either in direct diei mode or as a diel

average integration, diel averaging is based on scaling with coefficients determined in a direct diel run

that properly account for diel correlations among species concentrations and solar insolation.

The photochemical mechanism represents the tropospheric and stratospheric interactions of actinic

solar flux and the species families Ox, NOy, ClOy, HOy, BrOy, and CH 4 and its oxidation products.

The chemical mechanism incorporates 46 transported and chemically active trace constituent species.

There are 118 thermal, both homogenous and heterogeneous, and 38 photolytic reactions. Source gases

present in the model include N20, CH 4, the chlorine containing compounds CFC-11, -t2, -113,

HCFC-22, CC14, CH3CC13, CH3CI, and the bromine containing compounds CH3Br, CF2CIBr, and

CF3Br. Surface and in situ emissions of NOx, CO, and HC1 are included. CO 2 abundance is an input to

the radiative heating calculation. Most of the thermal reaction rate constants were taken from the NASA

Panel recommendations provided in JPL Publication 97-4 (DeMore et al., 1997). Direct hydrolysis of

CIONO2, N205, and BrONO 2on the surface of stratospheric sulfate aerosol is included, as are the reactions of CIONO 2, HOC1, and HOBr with HCl on aerosol surfaces. Representation of the rate constants

for these the above heterogeneous reactions follow the work of Hanson et aI. (1994).

Thermal kinetic processes with larger activation energies and heterogeneous processes with threshold temperatures have a nonlinear response to temperature variations, which is inconsistent with simple

rate constant determination based on a zonal average temperature. In calculating thermal rate constants

in LOTUS, and following the sense of Portmann et al. (1996), we account for both the climatological

zonal average temperature and for spatial/temporal variability in temperature in the zonal dimension.

Using output of the assimilation model of the NASA Data Assimilation Office for the year 1995-6

(DAO STRAT), we evaluated zonal temperature distributions on the LOTUS grid using ten variable

width temperature bins. The means of these distributions are co-located with the Fleming climatological

temperatures and the rate constants are determined as a distribution average over values calculated for

each temperature bin.

The recognition of the effects of zonal temperature variability appears to be able to account for a

significant portion of the heterogeneous activation of chlorine, even with climatological background

sulfate aerosol surface area density. However, satellite climatologies of aerosol are based on sparse

information near the poles, so we enforce a minimum surface area density of 1(-8) cm2/cm 3 within

25 degrees of the poles when the frequency of polar stratospheric cloud occurrence exceeds 0.08 in the

climatology of Poole and Pitts (1994). Loss of water and HNO 3 from the stratosphere through gravitational sedimentation of ice particles is represented through time constants for first order loss applied

whenever the partial pressure of water exceeds to saturation vapor pressure over ice.

Absorption cross section information was assembled from a variety of sources, including JPL 97-4.

These cross sections, as functions of temperature, are combined with a tabulated radiative source function as a function of wavelength, solar zenith angle, pressure, and overhead ozone column, provided by

Kawa (personal communication, 1997) to produce the photolysis frequencies. The wavelength bin

structure is more highly resolved at shorter wavelengths and includes 79 bins between 180 and 760 nm.

Kawa also provided tabulated oxygen photolysis frequencies directly, calculated by the method of

Minschwaner, et al. (1992 and 1993). The effect of clouds on tropospheric photolysis frequencies is

treated simply as a process that partially obscures the lowest atmospheric layers. Additionally, air parcels near the edge of polar night may be alternately exposed and shadowed. This is represented in the

model by a sinusoidal average over neighboring latitude zones for latitudes within 30 degrees of the

pole and in the winter season when waves are more active.

55
Dynamics Model

The dynamical representation is based on the approach of Garcia and Solomon (1983). Both the

temperature and zonal mean wind field vary continuously over the annual cycle. The temperature field

is based on climatology of Fleming (personal communication, 1997), while the zonal mean wind is

based on data from Fleming et al. (1988). The method used to obtain the circulation is based on the

combination of the zonal mean momentum equation and the thermodynamic equation into a form that,

along with the thermal wind equation, yields a second order diagnostic equation for the residual mean

meridional stream function. This stream function is defined so that the continuity equation is solved

exactly. The coefficients in front of the stream function terms on the left side of the equation are functions of known quantities, namely, the Coriolis force, zonal mean wind, scale height, temperature field,

a global reference altitude dependent temperature field, and the Brunt-Vaisala frequency. The model

seasonally averages the known temperature fields to provide an altitude dependent reference temperature field from which an altitude dependent Brunt-Vaisala frequency is calculated. The right hand side

of the stream function equation includes the net heating rate term and the Eliassen-Palm flux representing wave driving. The net heating rate is calculated knowing the temperature and radiative chemical

species distribution and includes latent heating. This is done by simply calculating the left hand side of

the zonal mean momentum equation using the known zonal mean wind field and approximate values of

v* and w*. These approximate values of v* and w* are obtained from a diagnostic approach of iteration

between the continuity and thermodynamic equations with a known temperature field. The right hand

side also includes a very small value of Rayleigh friction (value of about 0.1 day -l) to account for small

scale disturbances. With all terms known, the second order equation for the stream function is evaluated

from which the velocity fields are then obtained.

The transport of chemical species is accomplished through both advection and turbulent eddy transport. Advection terms are treated using the second order, two dimensional transpc., algorithm of

Smolarkiewicz (1984). The transport caused by sub-grid scale processes is parameterized through the

diffusion coefficients, Kyy and Kzz. The coefficients of this motion are derived from treatment of planetary waves (both wave number I and 2), based on the ground breaking work of Garcia (1991) and

Garcia et al. (1992), and the gravity wave effects following Lindzen (1981).

Radiative Transfer Models

The infrared model that we have been using for several years is a version of the model described by

Harshvardhan et ai. (1987). We have modified the model to improve the accuracy in the upper stratosphere. It includes absorption and emission by CO 2, 03, and H20. It is based on wide-band parameterizations fit to line-by-line calculations. Inhomogeneous absorption paths are included by pressure- and

temperature-weighted scaling of trace gas absorber amounts. The model provides for specification of

fractional cloud cover within each vertical model layer. Separate fractions can be specified for convective (deep, overlapping) and randomly overlapped clouds.

References

DeMore, W.B., S.P. Sanders, D.M. Golden, R.F. Hampson, M.J. Kurylo, C.J. Howard, A.R. Ravishankara, C.E.

Kolb, and M.J. Molina, Chemical Kinetics and Photochemical Data for Use in Stratospheric Modeling, Eval.

No. 12, JPL Publication 97-4, January 15, 1997.

Fleming, E.L., S. Chandra, M.R. Schoeberl, and J.J. Barnett, Monthy mean global climatology of temperature,

wind geopotential height, and pressure for 0-120 km, NASA Tech. Memo. 1000697, 1988.

56
Garcia, R.R., and S. Solomon, A numerical model of the zonally averaged dynamical and chemical structure of the

middle atmosphere, J. Geophys. Res., 88, 1379-1400, 1983.

Garcia, R.R., Parameterization of planetay wave breaking in the middle atmosphere, J. Atmos. Sci., 1991.

Garcia, R. R., F. Stordal, S. Solomon, and J.T. Kiehl, A new numerical model of the middle atmosphere, 1,

Dynamics and transport of tropospheric source gases, J. Geophys. Res., 97, 12967, 1992.

Hanson, D.R.., and A.R. Ravishankara, Reactive uptake of CIONO2 onto sulfuric acid due to reaction with HCI

and HzO, J. Phys. Chem., 98, 5728-5735, 1994.

Harshvardhan, R. Davis, D.A. Randall, and T.G. Corset, A fast radiation parameterization for atmospheric circulaton models, J. Geophys. Res., 92, 1009-1016, 1987.

Jacobson, M.Z., Computation of global photochemistry with SMVGEARII, Arm. Env., 29, 2541-2546, 1995.

Kinnison, D.E., K.E. Grant, P.S. Connell, D.A. Rotman, and D.J. Wuebbles, The chemical and radiative effects of

the Mt. Pinatubo eruption, J. Geophys. Res., 99, 25705-25731, 1994.

Lindzen, R.S., Tubulence and stress owing to gravity wave and tidal breakdown, J. Geophys. Res., 86, 9707,

1981.

Minschwaner, K., G.P. Anderson, L.A. Hall, and K. Yoshino, Polynomial coefficients for calculating 0 2 Schumann-Runge cross sections at 0.5 cm -! resolution, J. Geophys. Res., 97, 10103-10108, 1992.

Minschwaner K., R.J. Salawitch, and M.B. McElroy, Absorption of solar radiation by 02: implications for 03 and

lifetimes of N20, CFCI3, and CF2C12, J. Geophys. Res., 98, 10543-10561, 1993.

Patten, K.O., Jr., P.S. Connell, D.E. Kinnison, D.J. Wuebbles, T.G. Slanger, and L. Froidevaux, Effect of vibrationally excited oxygen on ozone production in the stratosphere, J. Geophys. Res., 99, 121 I-1224, 1994.

Poole, L.R., and M.C. Pitts, Polar stratospheric cloud climatology based on Stratospheric Aerosol Measurement 1I

observations from 1978 to 1989, J. Geophys. Res., 99, 13083-13089, 1994.

Portmann, R.W., S.Solomon, R.R. Garcia, L.W. Thomason, L.R. Poole, and M.P. McCormick, Role of aerosol

variations in anthropogenic ozone depletion in the polar regions, J. Geophys. Res., 101,22991-23006, 1996.

Smolarkiewicz, P.K., A fully multidimensional positive definitive advection transort algorithm with small implicit

diffusion, J. Comput. Phys., 54, 325-362, 1984.

57
1.2.7 MGO/UIUC: Description of the 2-D

Model of Photochemical Dynamical and

Troposphere and Stratosphere

VLADIMIR ZUBOV and IGOR KAROL

Main Geophysical observatory,

194021, Karbyshev 7, S.Petersburg, Russia.

(email: zubov@main.mgo.rssi.ru, karol@main.mgo.rssi.ru)

EUGENE ROZANOV

Department of Atmospheric Sciences

University of Illinois at Urbana-Champaign

105 S. Gregory Ave, Urbana, IL 61801, USA.

(email: rozanov@ atmos.uiuc.edu)

Domain and Grid Sizes

Zonally Averaged Transient

Radiative Processes in the

The MGOAYlUC 2-D model domain extends from south pole to north pole and approximately from

the ground to 66 kin. Latitude and log-pressure altitude are used as coordinates, with horizontal

resolution of 10 deg. and vertical resolution of 2 in units of 7xln(1013/p(mb)), equivalent to approximately 2 kin. The center of each latitude in degrees is given by -85+10x(i-1), where i is the latitude

index ranging from 1 to 18. The pressure in mb at the center of each altitude bin is given by

1013./exp(1 +2x(j-1)/7.) where j is the altitude index ranging from 1 to 33.

Transport and Temperature

The dynamical part includes the numerical solution of the dynamical equations system in the stratosphere by approach used in [Garcia and Solomon, 1983]. The stratospheric temperature fields are

obtained from the energy balance equation. The zonal winds are evaluated from the thermal wind relation and from the calculated temperature. The mean meridional stream-function of the residual circulation is determined by vertical gradient in dynamical forcing and by horizontal gradient of the radiative

heating and cooling from the elliptic stream-function equation. Dynamical forcing depend on the gravity and planetary wave dissipation. Gravity wave dissipation is treated as the Rayleigh friction as a function of latitude, height and day of the year. We have used the modified Rayleigh friction coefficient

from [Holton and Wehrbein, 1980]. The planetary wave dissipation is inferred from the generalized

Eliassen-Palm relation according of [Hitchman and Brasseur, 1988] parametrization. At the bottom of

the stratosphere, at 16 km in height, the vertical velocity has been specified as a function of latitude and

day of the year [Brasseur at al., 1990] and the stream function is obtained from vertical velocity field,

taking care of the integral mass conservation. The tropospheric circulation has been derived from these

specification by interpolating linearly down to the ground level. Temperatures in the troposphere are an

4 year average of MAP monthly-mean zonal-mean temperatures for the 1979-1982 period [Barnett and

Corney, 1985]. Daily temperatures are interpolated from monthly mean fields. Tropopause heights are

calculated from the temperature gradients. The tropospheric water vapor mixing ratios are prescribed on

the base of Oort's statistics [Oort 1983].

The horizontal eddy diffusion coefficient, Kyy, is a function of latitude, altitude, and season. The

background Kyy values in the stratosphere are 3x105 m2/s [Atmospheric Ozone,1985] or larger and

58
related to the Rossby wave dissipation [Hitchman and Brasseur, 1988]. In the troposphere value Kyy has

been adopted as lx106 m2/s.

The vertical diffusion coefficient, Kzz, is lxl0 m2/sec in the troposphere. Between the tropopause

and 65 km altitude Kzz increases from 0.1 m2/s up to 16 m2/s. The relatively large vertical diffusion

coefficient in the troposphere simulates convective overturning and synoptic scale eddies. Enhancing

vertical mixing with altitude in the stratosphere is based on the work of [Garcia and Solomon, 1985]

regarding gravity wave breaking.

Radiation

The radiation scheme is to supply the photolysis rates and the net heating rates.

Photolysis rates

Photolysis rates are calculated every seventh model day for solar zenith angles related to the two

hours time steps. The calculations use 61 spectral intervals in the UV and Visible region with boundaries every 2 nm between 175.4 and 204.1 nm in the Schumann-Runge bands, and every 5 nm between

205 and 410 nm. The Chappuis region is subdivided into 3 broad bands between 410, 470, 630, and

750 nm. Absorption cross sections and quantum yields are mainly taken from DeMore et a1.[1997].

Within the model atmosphere O2,O3,NO2 and clouds are considered as absorbers influencing the transmission down to ground and the upward reflection. Taking the distribution of the absorbing atmosphere

and the albedo of the ground, the radiation available for the photolysis processes is directly calculated

for intervals between I75.4 and 285 nm. For larger wave lengths the Rayleigh scattering is taken into

account and the photon fluxes are calculated using the Delta-Eddington scheme [Joseph et al., 1976].

The O2 and the NO photolysis rates calculations in the Schumann-Runge bands follow the special treatment in [Allen and Frederic, 1982].

Net heating

The radiation code calculates net heating rates every one model day according to the scheme

described in [Frolkis and Rozanov, 1993]. The net heating rates are obtained by calculating separately

the short wave heating in the UV plus Visible and the NIR regions, and the long wave cooling rates in

the IR region.

Short wave heating

The heating calculations in the UV and visible are done for 13 intervals, ranging from 197.5 to

750 nm. Interval boundaries are every 10 nm between 197.5 and 297.5 nm, 312.5,450, and 750 rim. For

the two larger intervals in the Huggins and Chappuis bands, the Rayleigh scattering is accounted for and

the Delta-Eddington scheme is used. For the 11 lower intervals the up- and down-fluxes are calculated

directly taking care of the albedo of clouds and of the ground as well as the absorption by 02,03, and

NO 2 as done for photolysis rates calculations. The heating from the NIP, radiation is calculated using 12

interval with boundaries 0.7519, 0.8929, 0.9259, 0.9804, 1.099, 1.163, 1.299, 1.315, 1.724, 2, 2.326,

2.941, and 4 mu. Proceeding in principle as for the UV heating calculations, the relative spectral solar

fluxes are calculated at the top of the model atmosphere for all intervals. The optical properties of

clouds and of air in terms of the integrated optical depths, the single scattering albedo and the asymmetry coefficients of all layers are determined and the Delta-Eddington scheme is used to find the transmission and reflection coefficients. The absorption and scattering by H20, CO2, CH4, and N20 are then

59
accounted for taking also care of the temperature dependent optical parameters of these species [Frolkis

and Rozanov, 1993].

Longwave radiation =

The radiative cooling in the absorbing atmosphere is caiculated according to [Frolkis and Rozanov,

1992] and take care of the gases H20 , CO2, 03, CH4, N20, O2' CFCI3, CF2CI2 ' and NO 2 as well as of

aerosols, clouds and surface albedo. The IR region is divided into 17 intervals with boundaries 4.425,

4.545, 5.952, 7.042, 7.353, 7.692, 8.065, 8.475, 9.434, 10.20, 13.16, 14.29, 15.63, 17.24, 20.83, 35.71,

71.43, and 1000 mu. The transmission function calculations account for the continuum and the temperature dependent band absorption [Frolkis and Rozanov, 1993] utilizing Goody's statistical model.

Photochemistry

We have adopted as our photochemical algorithm the algorithm developed by Karol et al. [1992],

which was used for a box model and later modified and introduced into a 2-D photochemical-radiativetransport model [Zubov et al., 1995]. The algorithm includes a total of 55 species from the oxygen,

hydrogen, nitrogen, carbon, chlorine and bromine families. More than 100 gas-phase and 5 heterogeneous reactions are included. The set of photochemical equations for the considered species is solved by

using the implicit Newton-Raphson numerical scheme proposed for atmospheric chemistry by Ozolin

[1992] and Stott and Harwood [1993]. This scheme has first-order accuracy in time, is very stable and

does not produce negative concentrations. The core of the photochemical module is a specially designed

Generator of Chemical Routines (GCR) which is based on non-numerical algorithms. The GCR was

designed to: (1) process the photochemical reaction set, (2) determine the set of independent variables,

and (3) create the FORTRAN code that performs the photochemical calculations in the model.

Heterogeneous Chemistry

The heterogeneous reactions in and on stratospheric sulfate aerosol and polar stratospheric cloud

(PSC) particles have been included according to state-of-the-art parameterizations [Hanson et al., 1994

and DeMore et al., 1997]. The diagnostic thermodynamic scheme for predicting PSC events has been

incorporated following Hanson and Maursberger [1988], Muller and Crutzen [1993], and Chipperfield

et al. (1993). This scheme describes the condensation, sedimentation and evaporation of PSC type I

and II particles in simplified form without any microphysical calculations. When the clouds evaporate,

the condensed species transform back to the gaseous phase. The following heterogeneous reactions on

sulfate and PSC particles are included in the model:

1. N205 + H20 = 2HNO3

2. CIONO 2 + H20 = HOC1 + HNO 3

3. C1ONO 2 + HC1 = CI2 + HNO 3

4. HOCI + HCl = Cl 2 + H20

5. N205 + He1 = ClNO 2 + HNO 3

Time Steps and Transport Scheme

The time step is 2 hours for photochemical and gas transport calculations, one day for the integration of the dynamics equations of the model and net heating rates. The diurnal course of photolysis rates

is recalculated every 7th day.

60
The fields of the temperature and long-lived species are advected by a residual mean meridional circulation. Advection terms are treated using the Prather's scheme [Prather, 1986]. Explicit centered differencing is used for computing diffusion [Fletcher, 1991].

Applications of the model

The model validation against UARS ozone data has been presented by Egorova et al. (1998). The

model has been used for the simulation of present day, pre-industrial and Last Glacial Minimum stratospheric species and temperature (KaroI et al, 1996, 1997), and for estimation of ozone changes due to

NOx emission from subsonic aircrafts (Rozanov et al., 1996, Egorova et al., 1997).

References Cited

Allen, M. and J.E. Frederic, Effective photodissociation cross section for molecular oxygen and nitric oxide in the

Shumann-Runge bands. J.Atm Sci.,39, 2066-2075, 1982.

Atmospheric Ozone 1985, World Meteorological Organization-Report, N 16, Geneva, World Meteorological

Organization, 1986, - 1181 p.

Barnett, J.J. and M.Comey, Middle atmosphere reference model derived from satellite data, Handbook for MAP,

16, 47-85, 1985.

Brasseur,G., M.H.Hitchman, S.Walters, M.Dymek, E.Falise and M.Pirre, An interactive chemical dynamical radiative two-dimensional model of the middle atmosphere. J.Geophys.Res., 95, 5639-5655, 1990.

Chipperfield, M., D. Cariolle, P. Simon, R. Ramaroson, and D. Lary, A three-dimensional modeling study of trace

species in the Arctic lower stratosphere during winter 1989-1990, J. Geophys. Res., 98, 7199-7218, 1993.

DeMore, W.B., S.P.Sander, D.M.Golden, R.F.Hampson, M.J.Kurilo, C.J.Horward, A.R.Ravishankara, C.E.Colb,

and M.J.Molina, Chemical kinetics and photochemical data for use in stratospheric modeling, Evaluation 12,

J.P.L. Publ., 1997.

Fletcher K., Numerical methods in the fluid dynamics, Moscow, Mir, v.% 1,2, (Russian translation), 1991.

Frolkis, V.A. and E.V. Rozanov, Radiation code for climate and general circulation models. In IRS'92: Current

problems in atmospheric radiation. Proceedings of the International radiation Symposium,Tallinn, Estonia,

August 1992, A. Deepak Publ., 101-105, 1993.

Garcia,R.R, and S. Solomon, A numerical model of the zonally averaged dynamical and chemical structure of the

middle atmosphere, J.Geophys.Res., 88, 1379-1400,1983.

Garcia, R.R., and S. Solomon, The effect of breaking gravity waves on the dynamics and chemical composition of

the mesosphere and lower thermosphere, J. Geophys. Res., 90, 3850, 1985.

Hanson, D., and K. Maursberger, Laboratory studies of the nitric acid trihydrate: Implications for the south polar

stratosphere, Geophys. Res. Lettr., 15, 855-858, 1988.

Hanson, D., A. Ravishankara, and S. Solomon, Heterogeneous reactions in sulfuric acid aerosols: A framework

for model calculations, J. Geophys. Res., 99 (D2), 3615-3629, 1994.

Hitchman, M.H., and G.Brasseur, Rossby wave activity in a two-dimensional model: closure for wave driving and

meridional eddy diffusivity, J.Geophys.Res., 93, 9405-9417, 1988.

Holton, J.R., and W.M. Wehrbein, The role of forced planetary waves in the annual cycle of the zonal mean circulation of the middle atmosphere, J.Atm. Sci., 37, 1968-1983, 1980.

Joseph ,J.H., W.J. Wiskombe, and J.A.Weinman, The Delta-Eddington approximation for radiative flux transfer,

J.Atm Sci., No. 12, 2452-2459, 1976.

61
Karol, I.L., (lEd.), Radiative-Photochemical models of the atmosphere (in Russian), Gidrometeoizdat, Leningrad,

1986, 192 p.

Karol, I. L., Y. E. Ozolin, and E. V. Rozanov, Effect of space rocket launches on ozone, Ann. Geophys., 10,

810-814, 1992.

Muller, R., and P. Crutzen, A possible role of galactic cosmic rays in chlorine activation during polar night, J.

Geophys. Res., 98 (D20), 20,483-20,490, 1993.

Oort A.N. Global atmospheric circulation statistics, 1958-1973. NOAA, Princeton N.J., Professional paper 14,

1983, 423 p.

Ozolin, Y. E., Modelling of diurnal variations of gas species in the atmosphere and diurnal averaging in photochemical models, Izv. RAN, PAO, 28 (12), 135-143, 1992.

Prather, M.J., Numerical advection by conservation of second-order moments, J. Geophys. Res., 91,6671-6681,

1986.

Rozanov, E. V., and V. A. Frolkis, Radiation code for climate and general circulation models, in International

Radiation Symposium, pp. 101-105, A. Deepak PubI., Tallinn, Estonia, 1992.

Stott, P. A., and R. S. Harwood, An implicit time-stepping scheme for chemical species in a global atmospheric

circulation model, Ann. Geophys., I 1,377-388, 1993.

Talukdar, R. K., C. A. Longfellow, M. K. Gilles, and A. R. Ravishankara, Quantum yields of O(1D) in the photolysis of ozone between 289 and 329 nm as a function of temperature, Geophys. Res. Lettr., 25 (143-146), 1998.

References to MGO/UIUC 2-D Model Formulation and Applications

Karol I.L., Yu.E. Ozolin, E.V. Rozanov and V.A. Zubov. The zonally-averadged model of the photochemical,

radiative and dynamical processes in the troposphere and stratosphere. Study of aircraft emission impact on

03. - In Research activities in Atmospheric and Oceanic Modeling" Ed. by G.J. Boer, WMO, Rep. No. 19,

p. 7.15, Feb. 1994.

Zubov V.A., I.L,Karol, Y.E.Ozolin, and E.V.Rozanov, Global zonally averaged non stationary model of photochemistry, radiative and dynamic processes in the troposphere and stratosphere. Part I: Description of the

model and its verification. (In Russian), Izvestia RAN, Physics of atmosphere and ocean, 31,469-476, 1995.

Karol l.L.,E.V.Rozanov and V.A.Zubov. Model reconstruction of stratospheric composition and air transport during the preindustrial and last glacial periods. Atmospheric ozone. Proceedings of the XVIII quadrennial ozone

symposium, L'Aquila, Italy 12-21 September 1996, Ed. R.Bojkov, G.Visconti,v.2, pp. 743-746, 1996.

Rozanov E.V., V.A.Zubov, T.A. Egorova and Y.E.Ozolin, Model evaluation of subsonic aircraft effect on the gas

distribution and radiative forcing.), Proceedings of International Colloquium: "Impact of Aircraft emissions

upon the Atmosphere", Paris, France, 1996, Oct. 15-18, vol. II, pp. 641-646.

Karol I.L., V.A. Zubov, E.V. Rozanov, Ch. Bruehl and A. Zieger Model reconstruction of seasonal and latitudinal

changes of temperature gas transport and composition in the stratosphere of the preindustrial and last glacial

periods., Doklady RAN, v. 357, N l, pp. 99-103, 1997

Egorova T. A., V.A. Zubov, E,V.Rozanov, Y. E. Ozolin and S. Jagovkina. The Model evaluation of Subsonic Aircraft Effect on Ozone and Radiative Forcing. Research Activities in atmospheric and ocean modeling, Ed. by

A. Staniforth, Rep. 25, WMO/TD - No. 792, pp. 7.58-7.59, 1997.

Egorova T., V. Zubov,S. Jagovkina, and E.Rozanov, Simulation of the atmospheric ozone distributions with the

2D model and validations against UARS and TOMS data, Physics and Chemistry of the Earth, (in press), 1998

62
1.2.8 NCAR 2D Model

G. Brasseur, X. Tie, and R. Khosravi

National Center for Atmospheric Research

Boulder, Colorado

T. Huang

Jet Propulsion Laboratory

Pasadena, California

The NCAR 2-D model, which is named SOCRATES (Simulation Of Chemistry, RAdiation, and

Transport of Environmentally important Species), is an interactive chemical, dynamical, radiative

model. The spatial domain of the model is from the surface to 120 km in altitude and from 85 S to 85 N

in latitude. The vertical and horizontal resolutions are 1 km and 5 degrees, respectively.

The model chemistry includes 52 species, and 137 gas phase, 5 heterogeneous (PSC/aerosol), and

46 photolysis reactions. The reaction rate coefficients and absorption cross sections are taken from JPL

1997 compilation. The chemistry solver is based on a Backward Euler scheme in which the full set of

nonlinear ordinary differential equations for the concentrations of the species is solved simultaneously

(i.e., the family method is not used). Diurnal variations in the density of species are also calculated.

The dynamical fields of velocity and potential temperature are obtained by solving the thermodynamic, zonal momentum, mass continuity, and thermal wind equations in the transformed Eulerian

mean formulation. For planetary wave forcing the model of Matsuno and Andrews (1987) is adopted,

and for gravity wave forcing the Lindzen parameterization (1981) is used (option to use the model of

Fritts and Lu (I993) exists).

Radiative cooling calculations in the IR are based on the broad band model of the NCAR climate

community model (CCM 3). Cooling by CO 2, H20, 03, N20 , CH4, CFC11, and CFC12 are accounted

for in the stratosphere, and the formulation of Fomichev (1998) for the NLTE cooling by CO 2 in the

mesosphere is included. The total heating rate calculations include multiple scattering by air molecules

and aerosol and cloud particles in the lower atmosphere, as well as chemical heating rates due to Ox and

HOx reactions in the mesosphere and lower thermosphere regions. Photolysis of O 2 in the SchumannRunge bands is based on Kockarts' (1994) parameterization.

63
1.2.9 NOCAR 2D Model

Rolando Garcia

National Center for Atmospheric Research

Boulder, Colorado

Susan Solomon

National Oceanic & Atmospheric Administration/Aeronomy Laboratory

Boulder, Colorado

This is a 2D, zonally-averaged model based on the diagnostic streamfunction equation plus prognostic equations for zonal wind and temperature. It uses the Transformed Eulerian Mean (TEM) formulation for all of the zonally-averaged equations. A wave- 1, quasi-geostrophic planetary wave model is

coupled to the zonal-mean model, so the interaction between this wave and the zonal mean state can be

calculated explicitly. Diffusion and zonal mean forcing due to gravity wave breaking are parameterized

after Lindzen (1981). Breaking of the explicitly computed planetary wave is parameterized using the

method of Garcia (1991), which yields estimates of the EP-flux and diffusion due to breaking. The

chemical scheme includes gas phase and heterogeneous reactions for all of the species/families of

importance in the middle atmosphere. The influence of wave temperature perturbations on reaction rates

is taken into account. Reaction rates are taken mainly from the JPL 1997 compilation.

References

Garcia, JAS, 48, 1405-1419 (1991)

Garcia et al., JGR, 97, 12967-12991 (1992)

Garcia and Solomon, JGR, 99, 12937-12951 (1994)

JPL Evaluation Number 12, JPL Pub. 97-4, 15 Jan 1997

Lindzen, R.S., JGR, 86, 9707-9714 (1981)

64
1.2.10 SUNY-SPb 2D Model Description

Sergey P. Smyshlyaev (i), Victor L. Dvortsov (2), Valery A. Yudin (! ,2), and Marvin A. Geller (2)

AFFILIATION: (1)

(2)

Russian State Hydrometeorological Institute

St. Petersburg, Russia

State University of New York at Stony Brook

Stony Brook, New York

DATA CONTACT: Victor Dvortsov

phone: (516) 632-8228

fax: (516) 632-6251

E-mail: vdvor@ dombai.uars.sunysb.edu

General Information

The SUNY-SPb 2-D photochemical transport model participated in photolysis and chemical solver

intercomparisons sponsored by the AESA project. References to the model description can be found at

the end of this document.

Domain and Grid Sizes

The model domain extends from the surface up to 0.027 mbar (approximately 74 km), and from

87.5 degrees South to 87.5 degrees North. Latitude is used as horizontal coordinate with 5 degree resolution. Vertical grid is equally spaced in log-pressure (approximately 2 km spacing).

Circulation and Diffusion

A self-consistent set of zonally-averaged transport parameters (residual circulation and diffusivity

tensor) was derived from MA CCM2 using the extension of Plumb and Mahlman [1987] technique

developed by Yudin et. al. [1997].

Briefly, this was done by applying flux-gradient relationship to the results of the 3-D transport

numerical experiments with the two artificial orthogonal tracers. We had shown that 2-D model with the

transport parameters derived in such a way is capable to reproduce reasonably well zonally-averaged

fields of "real" tracers obtained from the parent 3-D model [Yudin et. al., 1997].

Transport Scheme

Using operator splitting approach, we solve continuity equations separately for advection and diffusion plus chemistry. Prather's second-order moments scheme [Prather, 1986] is used to advect tracers in

the model. The equations for diffusion and chemistry are solved by the alternating directions implicit

method [Smyshlyaev, 1991]. All modeled species are transported independently, with a correction at

each time step to conserve mass within families.

65
Time Steps

The model time step is 1 day. To account for the diurnal variations of short-lived gases, the detailed

photochemical calculations are performed every 15 days with time steps varying from 15 rain to 2 hours

depending on the rate of change of the atmospheric optical depth. The derived concentrations of the

short-lived gases are then used to calculate daily mean rates of photochemical production and destruction. This procedure is also used when the change of daily mean optical depth exceeds 50 percent, and

during the transition between polar day and night.

Photochemistry

The model features sixty chemical species. The rates of chemical reactions included in the model

are based on the JPL 97-4 compilation [De More et a1.,1997]. Ox, NOy, Cly and Bry families are considered. Hydrogen species are calculated separately. To solve the stiff system of continuity equations

we use an original technique [Smyshlyaev,1991 and Smyshtyaev and Yudin,1995] which is based on

the "family conservation method" [Turco and Whitten,1974] and methods for solving stiff differential

equations [Hall and Watt, I976].

Boundary conditions are specified according to WMO [1994], and water vapor is taken from MA

CCM2 simulations. Lightning is included as a source of NOY in the tropical troposphere, with a source

strength of 2 MT/yr.

The model has passed chemical benchmark [Stolarski et. al., 1995].

Heterogeneous Chemistry on Sulfate Aerosols and PSCs

The following reactions on stratospheric sulfate aerosol (SAA) and PSC particles are included in the

model:

1. C1ONO 2 + HC1 --> HNO 3 + C12

2. C1ONO 2 + H20 --> HNO 3 + HOC1

3. N205 + H20 --> 2HNO 3

4. HOC1 + HC1 --> C12 + H20

5. HOBr + HC1 --> BrC1 + H20

6. BrONO 2 + H20 --> HNO 3 + HOBr

7. N205 + HCI --> CIONO + HNO 3

Heterogeneous reaction rates are calculated as first-order loss rates:

k=wA/4,

where y is a "sticking coefficient" which can be interpreted as the probability of uptake to the aerosol

and subsequent reaction, v is the thermal velocity of the gas phase molecule, and A is the surface area

density of the aerosol particles.

Sticking coefficients for heterogeneous reactions on the SSA are taken from JPL 97-4 [DeMore

et al.,1997], except for the reactions of C1ONO 2 and HOC1 with HC1 and H20 which are from Hanson

and Ravishankara [t994] and Hanson et al., [1994]. Under low temperatures, the SSA is assumed to be

66
frozenandthesurfaceareais recalculatedaccordingto [DanilinandMcConnell,1995].Thereaction

ratesforfrozensulfuricacidarecalculatedasdefinedbyHansonandRavishankara[1993].

Thelastreaction(7)occursonlyonPSCparticles.All theabovereactions(1)to(7)applytoPSCI

andPSCII.TheratesforthereactionsonPSCparticlesareasspecifiedin [DeMoreetal.,1997].

Polar Stratospheric Clouds

Calculations of PSCI and PSCII surface areas are based on the Danilin and McConnell [1994]

parameterizations. PSCI particles are assumed to be composed of nitric acid tryhydrate. PSCI are

formed when the temperature drops below the point at which air becomes supersaturated with respect to

NAT. The equilibrium value of HNO 3 is defined as a function of temperature according to Hanson and

Mauersberger [ 1988]. We used a supersaturation ratio 8 for the PSCI, which approximately corresponds

to 3K overcooling [Peter et al., 1991]. Type II PSCs start to form when the H20 concentration exceeds

the equilibrium value above ice given by Marti and Maursberger [1993]. The PSCII overcooling is

taken to be 1.8K according to Peter et al. [1991], which corresponds to a supersaturation ratio of 1.4.

NAT and ice sedimentation velocities are calculated according to Kasten [1968].

To account for temperature variations with longitude, we employ a superposition of planetary

waves with zonal wave number from one to three. The wave amplitudes and phases as function of day,

altitude and latitude are calculated using the Fourier transform of the MA CCM2 3-D daily mean temperature fields [Smyshlyaev et al., 1997].

Treatment of Solar Radiance and Photodissociation

Modified delta-Eddington method [Dvortsov et al., 1992] is used to calculate solar fluxes in 81 spectral intervals from 175 to 850 nm. The solar fluxes at the top of the atmosphere are from Solar Ultraviolet Spectral Irradiance Monitor (SUSIM) data [VanHoosier et. al., 1988] for wavelengths shorter than

400 nm, and from measurements of Neckel and Labs [1984] for the longer wavelengths, as recommended by JPL 97-4 [DeMore et al., 1997]. Absorption cross sections are also from [De More et al.,

1997]. Direct solar fluxes are calculated for spherical atmosphere; scattered radiation is obtained in

plane-parallel approximation. Our algorithm accounts for the effects of multiple scattering on air molecules, aerosol and cloud particles.

Solar fluxes in the Schumann-Runge bands are calculated for 500 cm -t intervals using Kockarts's

[ 1994] algorithm implemented by S. Madronich.

The model has passed photolysis benchmark [Stolarski et. al., 1995].

References

DeMore, W. B., S. P. Sander, D. M. Golden, R. F. Hampson, M. J. Kurylo, C. J. Howard, A. R. Ravishankara,

C. E. Kolb, and M. J. Molina, Chemical kinetics and photochemical data for use in stratospheric modeling,

Evaluation number 12, JPL Publication 97-4, Jet Propulsion Laboratory, NASA, January 15, 1997.

Danilin, M.Y., and J.C. McConnell, Heterogeneous reactions in a stratosperic box model: a sensitivity study,

J.Geophys.Res., 99, 25681-25696, 1994.

Dvortsov V.L., S.G.Zvenigorodsky, and S.P.Smyshlyaev, On the use of Isaksen-Luther method of computing photodissociation rates in photochemical models, J.Geophys.Res., 97, 7593-7601, 1992.

67
Hall, G., and J.M.Watt, Modem numerical methods for ordinary differential equations, Clarendon Press, Oxford,

312 pp., 1976.

Hanson, D., and K. Mauersberger, Laboratory studies of the nitric acid trihydrate: Implications for the south polar

stratosphere, Geophys. Res. Lett., 15,855-858, 1988.

Hanson, D. R., and A. R. Ravishankara, Reactive uptake of C1ONO2 onto sulfuric acid due to reaction with HCI

and H20, J. Phys. Chem., 98, 5728-5735, 1994.

Hanson, D. R., A. R. Ravishankara, and S. Solomon, Heterogeneous reactions in sulfuric acid aerosols: A framework for model calculations, J. Geophys. Res., 99, 3615-3629, 1994.

Hanson, D.R., and A.R.Ravishankara, Reaction of CIONO2 with HC1 on NAT, NAD, and frozen sulfuric acid and

hydrolysis of N205 and CIONO2 on frozen sulfuric acid, J.Geophys.Res., 98, 22931-22936, 1993.

Kasten, F., Falling speed of aerosol particles, J.Appl.Meteorol., 7, 944-947, 1968.

Kockarts G., Penetration of solar radiation in the Schumann-Runge bands of molecular oxygen: A robust approximation, Annales Geophysicae, v12, n12, pp. 1207 ff., Dec 1994.

Marti J., and K.Mauersberger, A survey and new measurements on ice vapor pressure at temperatures between

170 and 250 K, Geophys.Res.Lett., 20, 363-366, 1993.

Neckel H. and D.Labs, The Solar Radiation Between 3300 and 12500 A, Solar Physics v.90, pp.205-258, 1984.

Peter Th., C.Bruhl, and P.J.Crutzen, Increase in the PSC-formation probability caused by highflying aircraft,

Geophys.Res.Lett., 18, 1465-1468, 1991.

Plumb, R. A., and J. D. Mahlman, The zonally averaged transport characteristics of the GFDL general circulation/

transport model, J. Atmos. Sci., 44, 298-327.

Prather, M. J., Numerical advection by conservation of second-order moments, J. Geophys. Res., 91,6671-6681,

1986.

Smyshlyaev S,P. Optimization of the numerical solution algorithms of the two-dimensional zonaly average photochemical model, in Atmosfemyy Ozon, 45-54, Politekhnichesky Institut, Leningrad, 1991.

Smyshlyaev S.P., and V.A.Yudin, Numerical simulation of the impact of aircraft exhaust on the ozone layer,

Atmospheric and Occanic Physics, 31, 116-125, 1995.

Smyshlyaev S.P., V.L.Dvortsov, M.A.Geller and V.A.Yudin, A two-dimensional model with input parameters

from a GCM: Ozone sensitivity to different formulation for the longitudinal temperature variation, submitted

to J. Geophys. Res., 1997.

Stolarski, R. S., S. L. Baughcum, W. H. Brune, A. R. Douglass, D. W. Fahey, R. R. Friedl, S. C. Liu, R. A. Plumb,

L. R. Poole, H. L. Wesoky, and D. R. Worsnop: 1995 Scientific Assessment of the Atmospheric Effects of

Stratospheric Aircraft, NASA Reference Publication 1381.

VanHoosier, M. E., J.-D. F. Bartoe, G. E. Brueckner, and D. K. Prinz, Absolute solar spectral irradiance 120 nm -

400 nm (Results from the Solar Ultraviolet Spectral Irradiance Monitor - SUSIM- Experiment on board

Spacelab 2), Astro, Lett. and Communications, 1988, vol. 27, pp. 163-168.

World Meteorological Organization/United Nations Environment Programme (WMO/UNEP), Scientific assessment of ozone depletion: 1994, Rep.37, World Meteorol. Org., Geneva, 1994.

Yudin, V. A., S. P. Smyshlyaev, M. A. Geller, and V. Dvortsov, Transport diagnostics of GCMs and implications

for 2-D chemistry-transport model of troposphere and stratopshere, submitted to J. Atmos. Sci., 1997.

68
1.2.11 UNIVAQ-2D Photochemical Transport Model

Giovanni Pitari, Barbara Grassi, Lucrezia Ricciardulli

Universita' degli Studi L'Aquila

via Vetoio, 67010 Coppito L'Aquila, Italy

(email: pitari@ aquila.infn.it)

The University of L'Aquila (Italy) two-dimensional model ('univaq-2d') is a 2D photochemicaltransport model extending from pole to pole with a 10 degree latitudinal resolution, and from the ground

to about 71 Km altitude with 26 log-pressure levels correspondent to an approximate altitude resolution

of 2.84 Km, for a total of 19x26 grid-points. The pressure levels are specified as p(j)=p(j-I)/l.5, with

p(0)=1000 hPa. The main features of this model have been described in Pitari et al. (1993).

All transport fields are taken off-line and are not interactive. The advective transport formulation is

based on the paper of Holton (1981): a monthly-averaged residual circulation is calculated off-line from

the daily output of a low-resolution spectral GCM (Pitari et al., 1992), as well as the antisymmetric

components of the diffusion tensor. The symmetric components are calculated following the technique

described in Newman et al. (1986) for Kyy and Ky z, with the monthly-averaged flux-to-gradient ratio of

quasi-geostrophic potential vorticity calculated from the same GCM output. A constant value of 2E05

mZ]s is added everywhere to Kyy for removing negative Values (located mostly in the polar latitudes)

and for controlling too small Kyy values, except in the tropical lower stratosphere where the calculated

Kyy is kept (typically of the order of 0.1-0.5 x 1E05 m2/s). No special treatment is adopted for the polar

vortices. Kzz is assigned with large tropospheric values (10-30 m2/s) decreasing to about 0.1-0.3 m_/s in

the lower stratosphere. The effect of gravity wave breaking is simulated by increasing Kzz in the upper

stratosphere and mesosphere.

The numerical scheme for transport is Eulerian fully explicit, with a four-cycle time integration

scheme using a 2.0 hour time increment (Lorenz, 1971).

All chemical species are diurnally-averaged. Diurnal variations are parameterized using the scheme

described in Brasseur and Solomon (1984) for N205 and assuming that a number of species are only

present during daytime (O,O(1D),H,OH,HO2,NO,CI,CIO,Br,BrO) • The 24-hr averaged photodissociation rates are calculated daily (i.e. every 24 model hours) and include the effects of Rayleigh and Mie

scattering.

All medium and short-lived chemical species are grouped in families: Ox, NOy=NOx+HNO 3, NOx,

HOx, CHOx, Cly, Bry, SOx, aerosols. The chemical species in the families are the following:

Ox: O(ID),O,O3

NOx: NO,NO2,NO3,N205,HNO4,C1ONO2,BrONO2,PAN

HOx: H,OH,HO2,H202

CHOx: CH302,CH3OOH,CH20,

CH3CHO,CH3CO3,CH3COOOH,C2H502,C2H5OOH,

C3H6OHO2,C3H6OHOOH,C5H8OHO2

Cly: C1,CIO,C1OOCI,HOC1,C1ONO2,HC1

Bry: Br,B rO,HOBr,BrONO2,HBr

SOx: SO2,H2SO 4

Aerosols: Aitken-nuclei, Sulfate, NAT, Ice.

Long-lived and surface-flux species are the following:

NzO,H20,CH4,CO,

69
C2H6,C2H4,C3H6,C5H 8,C 1oH 16,other hydrocarbons,

CF2C12,CFCI3,CC14,CH3CC13,CFC- 113,CH3C1,HCFC-22,CH3Br,Halon- 121 ! ,Halon- 1301,

OCS,CS2,DMS,H2S.

All photochemical data are taken from JPL (i997). The heterogeneous reactions on sulfate aerosols

included in the model are the following:

N20 5 + H20 --> 2HNO 3

CIONO 2 + H20 --> HOC1 + HNO 3

C1ONO 2 + HC1 --> C12 + HNO 3

HOC1 + HCI --> C12 + H20

BrONO 2 + H20 --> HOBr + HNO 3

The size distribution of both sulfate and PSC aerosols (NAT + ice) may be calculated using a fully interactive and mass conserving microphysical code for aerosol formation and growth. Denitrification and

dehydration due to PSC sedimentation are calculated explicitly from the NAT and ice aerosols size

distribution.

The 2D temperature field is taken from time-zonally averaged NMC data and is used in all photochemical calculation; the effects of temperature zonal asymmetries are included only in the calculation

of the heterogeneous reaction rates, by means of temperature standard deviations taken from climatological data or again calculated from the output of the GCM (Pitari, 1993).

References

Brasseur, G. and S. Solomon: Aeronomy of the Middle Atmosphere, D.Reidel, 1984.

Holton, J.R.: An Advective Model for Two-Dimensional Transport of Stratospheric Trace Species, J. Geophys.

Res., 86, 11989-11994, 1981.

JPL: Chemical Kinetics and Photochemical Data for Use in Stratospheric Modeling, JPL publ. 97-4, ! 997.

Lorenz, E.: An N-Cycle Time Differencing Scheme for Stepwise Numerical Integration, Mon. Weather Rev., 99,

644-648, 1971.

Newman, P.A., M.R. Schoeberl, and R.A. Plumb: Horizontal Mixing Coefficients for Two-Dimensional Chemical

Models Calculated From National Meteorological Center Data, J. Geophys. Res., 91, 7919-7924, 1986.

Pitari, G., S. Palermi, G. Visconti and R.G. Prinn: Ozone Response to a CO2 Doubling: Results From a Stratospheric Circulation Model With Heterogeneous Chemistry, J.Geophys.Res., 97, 5953-5962, 1992.

Pitari, G., V. Rizi, L. Ricciardulli, and G. Visconti: High-Speed Civil Transport Impact: Role of Sulfate, Nitric

Acid Trihydrate, and Ice Aerosols Studied With a Two-Dimensional Model Including Aerosol Physics,

J.Geophys.Res., 98,23141-23164, 1993.

Pitari, G.: Contribution to th Ozone Trend of Heterogeneous Reactions of CIONO2 on the Sulfate Aerosol Layer,

Geophys.Res.Lett., 20, 263-2666, 1993.

7O
1.2.12 WISCAR 2D Model

M. H. Hitchman

University of Wisconsin

Madison, Wisconsin

The WISCAR model was designed at NCAR by Guy Brasseur and collaborators. Several additions

were made by Hitchman and collaborators at UW-Madison, hence the name. The zonal mean model

features interactive wave dynamics, climatological aerosol amounts by season and phase of the QBO,

and analytic QBO forcing methods. For this run, aersols and the QBO are turned off. The WISCAR

model extends from the surface to 85 km, and from 85S to 85N in latitude.

Brasseur, G., M.H. Hitchman, S. Walters, M. Dymek, E. Falise, and M. Pin'e, An interactive chemical dynamical

radiative two-dimensional model of the middle atmosphere. J. Geophys. Res., 95, 5639-5656, 1990.

Politowicz, P.A. and M.H. Hitchman, Exploring the effects of forcing quasibiennial oscillations in a twodimensional model, J. Geophys. Res., 102, 16,481-16,497, 1997.

71
1.2.13 ECHAM3/CHEM

Dr. Volker Grewe

DLR Oberpfaffenhofen

D-82234 Wessling

Germany

Volker.Grewe@dlr.de

1. MMII model name, affiliation(s), general references:

ECHAM3/CHEM

PIs: B. Steil 1, M. Dameris 2, Ch. Bruehl l, P.J. Crutzen l,

V. Grewe 2, M. Ponater 2, R. Sausen 2, E. Roeckner 3

I: MPI for chemistry, 55020 Mainz, Germany

2: DLR Oberpfaffenhofen, 82234 Wessling, Germany

3: MPI for meteorology, 20146 Hamburg, Germany

Model description and validation:

Steil et al., Development of a chemistry module for GCMs: First results of a multi-annual integration, Ann. Geophys., in press, 1997.

Roeckner et. al. 1992 for GCM ECHAM3.

Description of submitted experiments and interpretation:

Dameris et al., Impact of aircraft NOx-emissions on tropospheric and stratospheric ozone. Part II:

3-D model results, Atmos. Environm., in press, 1997.

2. Class of model (e.g., off-line CTM, free-running GCM, assimilation): coupled dynamic-chemical

GCM.

BUT: the feedback of H20 and 03 on the radiation is switched off. In this sense it is similar to a

CTM with a GCM input,

BUT: EXTENSIVE USE OF DETAILED MODEL PHYSICS in the chemistry module

(e.g. to simulate wash-out, separation of gaseous and liquid water content for OH calculations, clouds for photolysis rates)

3. Meteorological data source (if off-line CTM):

see 2. Each experiment consists of 4+10 different years representing the climatological situation of

1992 or 2015.

4 years spin up time and 10 years for interpretation.

4. Sub-grid scale mixing parameterization in troposphere:

Convection: according to Tiedtke (1989)

Hot. Diffusion: implicit in semi-lagrangian scheme (only for tracer)

Ver. Diffusion: Louis (1979)

5. Sub-grid scale mixing in stratosphere:

Hor./Ver. Diff. see 4.

72
6.If grid-pointmodel,numbergridpointslongitudebylatitude:

severalprocessesaredescribedinthegrid-space: 64x32

7.Latitudevaluesatcentersofgrid points:

85.76058712,80.26877907,74.74454037,69.21297617,63.67863556,

58.14295405,52.60652603,47.06964206,41.53246125,35.99507841,

30.45755396, 24.91992863,19.38223135,13.84448373,8.30670286,

2.768903O1

forbothhemispheres

8.If spectralmodel,truncation:

T21

9.Numberverticalevels:

19

10.Verticalcoordinate(e.g.,pressure,sigma,hybrid,theta):

hybridsigma-p

11.Ve_icalcoordinmevaluesatcentersoflevels:

1009hPa, 996hPa,968hPa,921hPa,857hPa,779hPa,

690hPa,594hPa,499hPa,409hPa,326hPa,253hPa,

191hPa,141hPa,103hPa,74hPa,51hPa,30hPa,10hPa.

12.Numericaladvectionscheme:

Semi-lagrange(Rasch& Williamsen,1990)

13.Advectiontimestep:

40min.

14.Natureof chemicalreactionfor activespecies

(e.g.,production/losstablesorexplicitinteraction?):

family-concept:explicitinteraction.

15.If production/losstables,source:

16.If explicitinteraction,

(a)numberof transportedspeciesandfamilies:

12

(b)Listtransportedspecies/families:

H20,CH4,N20,H202,CO,CH302H,CIONO2,HNO3+NAT,

PSCtype2(Ice),CIOx(--CI+C10+C1OH+2*CI202+2*CI2)'

NOx(=N+NO+NOz+NO3+2*NzOs+HNO4) ,andOx(O3+O(3p)+O(1D)).

(c)Reactionratesource:

DeMoreetal. 1994

73
(d)Numericalchemicalsolver:

Longlivedspeciesandfamilies:semi-implicitapproach

specieswithinfamilies("mediumlived"):semi-analyticexponential approach

short-lived:steadystate.

Thesethreeapproachesareusedto solve algebraic solutions of a coupled system of differential

equations, which take into account the interaction of various species.

17. Heterogeneous chemistry parameterization:

PSCs: Hanson & Mauersberger (1988) using T, vmr(H20 ) and vmr(HNO3), with nucleation barrier

(dT=3K) to account for observed supersaturation.

Assumptions:

Type I: spherical with monodisperse size distribution particle number density lcm-3

Existence only at radii above 0.07 micrometer.

Type 2: dT=I.8K (Nucleation barrier) panicle number density 0. lcm-3

Existence only at radii above 0.7 micrometer.

18. Aerosol physics:

Stratospheric aerosol is prescibed according to WMO(1992) background aerosol conditions. The

enlargement of the aerosol surface areas due to admittance of water at very low temperatures is considered interactively (Luo et al., 1996).

74
-90

i.e.,

9.If spectralmodel,truncation:

10.Numberverticalevels:31

1.2.14 GISS GCMAM

David Rind and Jean Lerner

NASA/Goddard Institute for Space Studies

New York

1. This particular model has been used, although not extensively described, in the following references:

Koch,D. and D. Rind, 1998: 10Be/7Be as a tracer of stratospheric transport. JGR, .103, 3907-3917. Hannegan,

B., S. Olsen, M. Prather, X. Zhu, D. Rind and J. Lemer, 1998: The dry stratosphere: a limit on cometary influx.

Geophys. Res. Lett.,25, 1649-1652.

The version used for these experiments is an update of the original model described in detail in the following references:

Rind, D., R. Suozzo, N.K. Balachandran, A. Lacis and G. L. Russell, 1988: The GISS Global Climate]Middle

Atmosphere Model Part I: Model structure and climatology. J. Atmos. Sci., 45,329-370.

Rind, D., R. Suozzo and N.K. Balachandran, 1988: The GISS Global Climate]Middle Atmosphere Model Part II:

Model variability due to interactions between planetary waves, the mean circulation and gravity wave drag. J.

Atmos. Sci., 45, 371-386.

2. Class of model (e.g., off-line CTM, free-running GCM, assimilation): free-running GMC

3. Meteorological data source (if off-line CTM):

4. Wind data frequency (off-line CTMs); e.g., instantaneous 6 hourly samples, averaged 12 hourly

samples:

5. Sub-grid scale mixing parameterization in troposphere: None

6. Sub-grid scale mixing in stratosphere: None

7. If grid-point model, number gridpoints longitude by latitude: 72 X 46

8. Latitude values at centers of grid points:

Our grid is 5 deg longitude by 4 deg latitude (72x46 points).

The center of the latitude zone J is located at:

+(J- 1)'4

-90,-86,-82,-78 ...... 78,82,86,90

NA

11. Vertical coordinate (e.g., pressure, sigma, hybrid, theta): sigma coordinates to 150 mb; constant

pressure above

75
12. Vertical coordinate values at centers of levels:

layer center

31 .00439

30 .00781

29 .0139

28 .0247

27 .0439

26 .0781

25 .139

24 .247

23 .439

22 .781

21 1.390

20 2.470

19 4.390

18 7.810

17 13.90

16 24.70

15 43.90

14 71.20

13 I01.6

12 133.5

11 17i.0

10 218.5

9 279.5

8 369.5

7 497.5

6 640.5

5 764.0

4 851.5

3 906.5

2 944.5

1 972.0

13. Numerical advection scheme: Quadratic upstream scheme (similar to the second order moments

scheme ofPrather, M., 1986: JGR, 91, 6671-6681

14. Advection timestep: Forward leapfrog timestep=i 12.5 sec

15. Nature of chemical reaction for active species production/loss tables

16. If production/loss tables, source: CTM results of Prather

17. If explicit interaction,

(a) number of transported species and families:

(b) List transported species/families:

76
(c)Reactionratesource:

(d)Numericalchemicalsolver:

18.Heterogeneouschemistryparameterization:

19.Aerosolphysics:

Modelincludesparameterizedgravitywavedragassociatedwithmodelresolvedsourcesin thetroposphere:flowovermountains,convection,shearandfrontogensis. Thereisnodiffusivemixingoftracers

associatedwiththeseparameterizations.

77
1.2.15 "GISS8xl0" 3D CTM

M. Prather

University of Califomia-Irvine

Irvine, California

I. MMII model name, affiliation(s), general references:

The "GISS8x 10" model was developed by Michael Prather, and is used at U.C. Irvine and the Goddard

Institute for Space Studies (GISS). A description of the tropospheric version is found in Prather et al,

JGR, 92, 6579, 1987. This troposphere/stratosphere version is discussed in Hall and Prather, JGR, 100,

16699, 1995.

2. Class of model (e.g., off-line CTM, free-running GCM,assimilation):

Off-line Chemical Transport Model (CTM).

3. Meteorological data source (if off-line CTM):

GISS troposphere/stratosphere GCM (Rind et al, JAS, 47,475, 1990)

4. Wind data frequency (off-line CTMs); e.g., instantaneous 6 hourly samples, averaged 12 hourly

samples:

Instantaneous, 8-hourly samples.

5. Sub-grid scale mixing parameterization in troposphere:

Monthly mean wet and dry convection statistics archived by GCM act to mix tracer every 8 hours in the

CTM (Prather et al, JGR, 92, 6579, 1987).

6. Sub-grid scale mixing in stratosphere:

None

7. If grid-point model, number gridpoints longitude by latitude:

36x24

J

1

2

3

4

5

6

7

8

8. Latitude values at centers of grid points:

latitude_j

-87.5

-82.2

-74.4

-66.5

-58.7

-50.9

-43.1

-35.2

78
9 -27.4

10 -19.6

11 -11.7

12 -3.9

13 3.9

14 11.7

15 19.6

16 27.4,

17 35.2

18 43.1

19 50.9

20 58.7

21 66.5

22 74.4

23 82.2

24 87.5

9. If spectral model, truncation:

10. Number vertical levels: 21

11. Vertical coordinate (e.g., pressure, sigma, hybrid, theta):

pure sigma surface to 100mbar (9 levels), pure pressure above 100mb (12 levels)

12. Vertical coordinate values at centers of levels:

sigma at layer edges: edge pressure = p_surf(mb)*sigma + 100mb

sigma = 1.000000 0.973233 0.938102 0.886877 0.788246 0.637943 0.459962 0.278312 0.116969

0.000000

sigma forleveledges atpressureslessthan 100mb:pressure = 100mb*sigma

sigma = 0.464147 0.215393 0.099976 0.046413 0.021550 0.010000 0.004642 0.002155 0.001000

0.000466 0.000215 0.000022

13. Numerical advection scheme:

Second-order moments (quadratic upstream). See Prather, JGR, 91,6671, 1986.

14. Advection timestep:

4 hours

15. Nature of chemical reaction for active species (e.g., production/loss tables or explicit

interaction?):

Production/Loss look-up tables (zonally-averaged, monthly-mean).

79
16.If production/losstables,source:

Photochemical modeldescribedinRemsbergandPrather,MMI report,NASARef.Publ.1292,1993.

!7.If explicitinteraction,

(a)numberof transportedspeciesandfamilies:

(b)Listtransportedspecies/families:

(c)Reactionratesource:

(d)Numericalchemicalsolver:

18.Heterogeneouschemistryparameterization:

19.Aerosol physics:

80
1.2.16 NASA Global Modeling Initiative (GMI) Assessment Model

Jose Rodriguez

University of Miami

Miami, Florida

Doug Rotman

Lawrence Livermore National Laboratory

Livermore, California

The NASA AEAP (Atmospheric Effects of Aviation Program) GMI model is an off-line chemical

transport model designed for the assessment of atmospheric chemical impacts from aircraft emissions

[Rotman, et al, 1999; Rodriguez, et al, 1999; Kinnison et al, 1999; Douglass et al, 1999; Considine,

et al, 1999; and NASA Report No. 1385, 1996]. It uses input meteorological fields from three datasets:

assimilated meteorological fields from the NASA GEOS (Goddard Earth Observing System) assimilation system (DAO, 1997); the output from the MACCM2 (Middle Atmospheric version of the NCAR

Community Climate Model, version 2); and output from the NASA GISS (Goddard Institute for Space

Studies) climate model, version 2 prime. All three meteorological datasets contain fields at 6 hour increments at 4 degrees by 5 degrees horizontal resolution. Vertical resolution varies with DAO data in

29 levels to 0.1 mb, MACCM2 data in 44 levels to 0.025 mb and GISS data in 28 levels to 0.01 mb. Cell

center pressure levels (in a global averaged sense) are as follows,

NCARMACCM2: 992.528,970.4456,930.0657,869.3587,792.5497,704.6883,

611.0972,516.8513,427.4154,349.9381,286.5047,234.5705,192.0498,

157.2374,128.7351,105.3994,86.2938,70.6514,57.8444,47.2408,

38.3885,31.0394,24.9721,19.9905,15.9229, 12.6197,9.9518,7.8088,

6.0968,4.7363,3.6611,2.8158,2.1549,1.6409,1.2433,0.9373,0.7031,

0.5248,0.3898,0.2747,0.1751,0.101,0.0527,0.0249

GISS II': 987.7698,959.7423,921.0133,864.9579,776.2889, 649.4005,

504.1665,373.7107, 281.9842,219.8143,171.403,132.4764,100.4261,

69.60201,42.14166,23.71667,13.34166,7.49667,4.21416, 2.371665,

1.33416,0.74967,0.42141,0.237165,0.13341,0.07497,0.042135,0.00807

NASA DAOSTRAT: 921.9549, 771.3622, 648.0737, 547.7227, 447.2133, 346.4099,

273.8615,223.6068,187.0824,162.0186,139.6422,118.0002,100.0002,

87.99999,77.00004,68.12923,56.99993,46.41585,31.62289,21.54425,

14.67804,10.00012, 6.81304,4.64152,3.16225, 2.15436,1.46783,0.68133,

0.20761

Transport

The model uses a fixed horizontal grid. Although resolution is dependent on input data, most often

model simulations are carried out at 4 by 5 degrees (46 points from -90 to +90 latitude and 72 points

from 0 to 355 longitude). Vertical coordinates use the hybrid ETA style vertical grid (i.e., pressure at

vertical level is given as a function of surface pressure and a constants). Advection is carried out using

the variable order multidimensional flux form semi-Lagrangian method (Lin and Rood, 1996) using

30 minute time steps. Additional capabilities exist for transport using semi-Lagranigan methods

(Williamson and Rasch, 1990) and the Second Order method of Prather (1986). Sub-grid scale mixing is

accomplished by convective transport and vertical diffusion.

81
Photochemistry

The chemistry equations are solved using either SMVGEAR II (Jacobson, 1995) or the ONERA

solver of Ramaroson (1989). SMVGEAR II is a version of the original predictor/corrector, backward

differentiation code of Gear (1971) and uses a variable time step, variable order, implicit technique for

solving stiff numerical systems with strict error control. ONERA is a semi-implicit solver that shows

very good comparison to the SMVGEAR II solution. Chemistry includes a complete interactive stratospheric chemical package using reaction rates of DeMore, et al 1997. Photolysis rates are obtained by a

clear-sky lookup table where normalized radiative fluxes are a function of cross section (a function of

temperature, species, and wavelength), pressure, solar zenith angle, and column ozone. Temperature

dependent cross sections, quantum yields and solar flux are tabulated separately (DeMore, et al, 1997).

The GMI model uses a chemical time step of 30 minutes.

Chemical species and transported species

List of 46 active chemical species

O, O(1D), 03, N20, N, NO, NO 2, NO 3, N205, HNO3, HO2NO2, H20, H, OH,

HO2, H202 ' H2 ' CH4, CH302, CH3OOH , CH20, CO, CI, CI 2, C10, OC10,

C1202, HCI, HOC1, C1ONO 2, Br, BrC1, BrO, HBr, HOBr, BrONO 2, CH3CI,

CH3Br , CFCI3, CF2C12, CFCI 13, CHC1F 2, CC14, CH3CCI3, CF3Br, CF2C1Br

List of transported species:

All of the above, except O, O(1D), N, H20, H

List of heterogeneous reactions

N205 + H20

C1ONO 2 + H20

CIONO 2 + HCI

HOBr + HCI

HOCI + HCI

BrONO 2 + H20

Possible surfaces are STS, NAT and ice.

The GMI model includes a parameterization of polar stratospheric clouds that will respond to

increases in HNO 3 and H20 produced by aircraft emissions. Both type 1 and type 2 PSCs are considered. For the HSCT assessment simulations, type 1 PSCs were assumed to be totally STS and type 2

were assumed as ice. The parameterization also accounts for PSC sedimentation, which can produce

denitrification and dehydration at the poles.

The GMI model currently does not include explicit aerosol microphysics.

References

Considine, et al, to be submitted to JGR

DAO, 1997, Algorithm and Theoretical basis documents of the Data Assimilation Office, 1997.

82
DeMore, W. B., S. P. Sander, D. M. Golden, R. F. Hampson, M.J. Kurylo, C.J. Howard, A.R. Ravishankara, C.E.

Kolb, and M.J. Molina, Chemical Kinetics and Photochemical Data for Use in Stratospheric Modeling, Eval.

12, JPL Publication 97-4, 1997.

Douglass, et al, submitted to JGR

Gear, C.W., 1971: Numerical initial value problems in ordinary differential equations. Prentice-Hall, Englewood

Cliffs, NJ.

Jacobson, M.A., 1995: Computation of global photochemistry with SMVGEAR II, Atmos. Environ., 29A,

2541-2546.

Kinnison, et al, to be submitted to JGR

Lin, S.J. and R.B. Rood, 1996: A fast flux form semi-Lagrangian transport scheme on the sphere, Mon. Wea. Rev.,

124, 2046-2070.

NASA Report, No. 1385, 1996, Atmospheric Effects of Aviation: First report of the subsonic assessment project,

May 1996

Prather, 1986: Numerical advection by conservation of second-order moments, J. Geophys. Res., 91,6671-6681.

Rasch and Williamson, 1990: Computational aspects of moisture transport in global models of the atmosphere,

Q.J.R Meteorol. Soc., 116, 1071-1090.

Rodriguez, et al, to be submitted to JGR

Rotman, et al, to be submitted to JGR

83
1.2.17 GSFC 3D Chemistry and Transport Model

Anne R. Douglass and S. Randy Kawa

Code 916 NASA Goddard Space Flight Center

Greenbelt MD 20771

(email: douglass@persephone.gsfc.nasa.gov kawa@maia.gsfc.nasa.gov)

The Goddard Space Flight Center Three Dimensional Chemistry and Transport ModeI (GSFC-3D)

is an off-line chemistry and transport model which uses winds and temperatures from the Goddard Earth

Observing System Data Assimilation System (GEOS DAS) (DAO, 1997). GSFC-3D, which contains a

complete representation of stratospheric chemistry, has evolved from a chemistry and transport model

which used parameterizations for chemical processes [e.g., Rood et al., 1989; Douglass et al., 1991;

Weaver et al., 1996; Douglass et al., 1996] and is described by Douglass et al. [ 1997].

Transport

Winds and temperatures are updated every 6 hours using instantaneous output from the assimilation

system. There is no explicit sub-grid scale paramaterization for mixing in the stratosphere or troposphere. The model uses a fixed horizontal grid at 2 degree latitude by 2.5 degree longitude resolution

(91 points from -90 to +90; 144 points from 0 to 357.5). There are 28 vertical levels, with 10 sigma levels in the troposphere and lower stratosphere,17 stratospheric levels, and a boundary at 130 hPa. The

spacing is about 1.5 km up to 20 km, and increases to 4 km above 30 kin. Advection is accomplished

using the numerical scheme developed by Lin and Rood [1996]. The transport and photochemical contributions to the constituent continuity equation in the model are solved sequentially (process splitting).

Photochemistry

The photolysis rate routine is based on the radiative transfer calculations from the model of

Anderson and Lloyd (1990), which are used to determine the solar flux at any model location and solar

zenith angle using a table lookup. Constituent cross sections which may be temperature dependent are

taken from DeMore et al. (1994). The photolysis rates calculated in this way compare favorably with the

photolysis benchmark which was developed as part of the Atmospheric Effects of Aircraft Program

(Stolarski et al., 1995). Reaction rates are taken from DeMote et al. (1994). The photochemical production and loss are calculated using a predictor-corrector scheme with a time step of 15 minutes.

Constituents

Transported Constituents and Families

Ox O(|D), O(3p), 03

NOx NO, NO 2, NO 3

HNO 3

N205

HO2NO2

CIONO 2

CIx C1, CIO, C1202

HC1

HOCI

84
H202

Br x Br, BrO

N20

C12

OCIO

BrC1

HBr

BrONO 2

CH 4

HOBr

CH3OOH

CO

Steady State

NAT, ICE

CH3, CH302, CH30, CH20, CHO

H, OH, HO 2

Constant

H20

SA

Heterogeneous Chemistry

The following heterogeneous reactions are included:

1. N205+H20

2. CIONO2+H20

3. CIONO2+HC!

4. N205+HCI

5. HOCI+HCI

6. BrONO2+H20

Possible surfaces are liquid sulfate, NAT, and ice.

The liquid sulfate aerosol surface area (SA) is specified based on SAGE II data [Thomason and

Poole, 1993] and (when appropriate) a climatology for volcanically clean air [Albritton and Watson,

1992], independent of model temperature variations. HNO 3 uptake is not considered. Particle composition is assumed to be liquid sulfate and water, depending on temperatures in the model, down to 1 K

below the equilibrium formation temperature for nitric acid trihydrate (NAT). At temperatures below

this, NAT and subsequently ice are assumed to occupy all of the particle surface. No externally mixed

particle compositions are possible. Sulfate mean particle size is calculated from the SA assuming a fixed

particle number density of 10 cm -3. Temperature and relative humidity-dependent sticking coefficients

are calculated from the model H20, T, and HC1 according to Hanson and Ravi [ 1994] for reactions 2

and 3 and Hanson et al. [1994] for reaction 5. Reactions 1 and 6 use fixed sticking coefficients of 0.2

and 0.4, respectively. The sticking coefficient for reaction 4 on sulfate is 0.

85
Solid particle volume growth occurs at vapor pressure equilibrium controlled by the model T,

HNO 3, and H20 distributions. Model temperatures are raised by l K for calculation of the HNO 3

saturation vapor pressure over NAT [Hanson and Mauersberger, 1988], simulating a 1 K temperature

barrier to NAT formation. The water saturation vapor pressure over ice is calculated directly from

model T using Marti and Mauersberger [1993]. Particle condensed mass is calculated from the instantaneous difference between the total ambient mixing ratio and the saturation mixing ratio for these molecules. Condensed HNO 3 is removed from the gas phase for chemical reactions; H20 is not. Surface area

is obtained from volume assuming a fixed particle number density of 10 cm 3, a variable nucleation

fraction (set to 1 in these cases), and particle densities. Sticking coefficients for reactions 1-6 on ice are

fixed according to JPL 94. The relative humidity dependence of the sticking coefficient for reaction 3

on NAT is from Hanson and Ravi [1993] while the remainder are fixed according to JPL 94.

Collision frequencies for all reactions depend on T, SA, and molecular weight. For stability, all heterogeneous reaction coefficients are limited to not exceed a rate equivalent to a reactant lifetime of

approximately two model time steps (30 minutes) to maintain stability in the photochemical integration.

References

Albritton, D. L., and R. T. Watson, Co-Chairs, Scientific Assessment of Ozone Depletion: 1991, World Meteorological Organization Global Ozone Research and Monitoring Project - Report No. 25, 1992.

Algorithm and Theoretical Basis Documents of the Data Assimilation Office, 1997

Anderson, D. E., Jr., and S. A. Loyd, Polar twilight UV visible radiation field: perturbations due to multiple scattering, ozone depletion, stratospheric clouds, and surface albedo, J. Geophys. Res., 95, 7429-7434, 1990.

Douglass, A. R., R. B. Rood, J. A. Kaye, R. S. Stolarski, D. J. Allen, E. M. Larson, The influence of polar heterogeneous processes in reactive chlorine at middle latitudes: three-dimensional model implications, Geophys.

Res. Lett., 18, 25-28, 1991.

Douglass, A. R., C. J. Weaver, R. B. Rood, and L. Coy, A three dimensional simulation of the ozone annual cycle

using winds from a data assimilation system, J. Geophys. Res., 101, 1463-1474, 1991.

Douglass, A. R., R. B. Rood, S. R. Kawa and D. J. Allen, A three-dimensional simulation of the evolution of the

middle latitude winter ozone in the middle stratosphere, J. Geophys. Res., 102, 19,217-19,232, 1997.

Hanson, D., and K. Mauersberger, Laboratory studies of the nitric acid trihydrate: implications for the south polar

stratosphere, Geophys. Res. Lett., 15, 855-859, 1988.

Hanson, D. R., and A. R. Ravishankara, Reaction of CIONO2 with HCl on NAT, NAD, and frozen sulfuric acid

and hydrolysis of N205 and CIONO2 on frozen sulfuric acid, J. Geophys. Res., 98, 22,931-22,936, 1993.

Hanson, D.R., A.R. Ravishankara, and S. Solomon, Heterogeneous reactions in sulfuric acid aerosols: A framework for model calculations, J. Geophys. Res., 99, 3615-3629, 1994.

Hanson, D. R., and A. R. Ravishankara, Reactive uptake of CIONO2 onto sulfuric acid due to reaction with HCl

and H20, J. Phys. Chem., 98, 5728-5735, 1994.

Lin, S. J., and R. B. Rood, Multidimensional flux form semi-Lagrangian transport schemes, Mon. Weather Rev.,

124, 2046-2070, I996.

Marti, J. J., and K. Mauersberger, Laboratory simulations of PSC particle formation, Goephys. Res. Lett., 20,

359-362, 1993.

86
Rood,R.B.,D.S.Alien,W.E.Baker,D.J.LamichandJ.A.Kaye,Theuseof assimilateddatainconstituent

transport experiments, J.Atmos.Sci,46,687-701,1989.

Thomason, L.J.,andL.R.Poole,Useof stratospheric aerosol proporetiesadiagnosticsofAntarcticvortexprocesses, J.Geophys. Res.,98,23,003-23,012,993.

Weaver,C.J.,A.R.Douglass, D.B.Considine, A 5-yearsimulationof supersonicaircraftemissiontransport

usingathree-dimensional model,J.Geophys. Res.,101,20,975-20,984, 1996.

87
1.2.18 Langley 3-D Model

William L. Grose, Richard S. Eckman, R. Bradley Pierce

NASA Langley Research Center

Hampton, Virginia

The Langley Interactive Modeling Project for Atmospheric Chemistry and Transport (IMPACT)

model is a general circulation model (GCM) with coupled chemistry extending from the surface (24 levels, sigma coordinates) to approximately 60 kilometers. The GCM is a global spectral model based on

the primitive equations. The dependent variables in the model are represented by a truncated series of

surface spherical harmonics at each model level (triangular truncation at wavenumber 32). Vertical differencing is approximated by finite difference techniques. The chemistry module consists of 23 mass

continuity equations for individual constituents and/or families. The computed ozone Jistribution is

used in the radiative heating calculation in the GCM.

The model includes full tropospheric physics with cloud-radiative feedbacks. The model can also be

run "off line" as a CTM driven either by wind and temperature fields from the GCM or by analyzed

meteorological fiel,'s.

Dynamics and Radiative Modules

The dynamics module is based on that described by Blackshear et al. [1987], Grose et al. [ 1989] and

Pierce et al. [1993], which was originally developed from the model of Hoskins and _ _mons [1975].

The semi-implicit integration scheme of Robert et al. [1972] is used with a 15-minute time step. A 1-2-1

time filter is applied every six time steps to control time splitting. Orographic forcing is represented by

a smoothed spectral representation of the Earth's topography. A biharmonic diffusion is applied to the

models temperature, vorticity and divergence fields to represent sub-grid scale diffusion and to inhibit

spectral blocking.

A recent major development includes incorporation of moist physics, explicit cloud liquid water,

and physical parameterizations for planetary boundary layer (PBL) exchange processes and radiative

heating, appropriate for full, seasonal-cycle simulations. The tropospheric radiation scheme is that of

the NCAR Community Climate Model (CCMI) [Williamson et al., 1987]. The planetary boundary layer

is a bulk parametrization scheme taken from the Goddard Laboratory for Atmospheric Science (GLAS)

4th-order GCM [Kalnay et al., 1983]. The large-scale explicit cloud scheme is based on the University

of Wisconsin hybrid model [Zapotocny et al., 1991]. The moist convection scheme is based on that of

Kuo [1974] with closure described by Molinari [1982]. The gravity wave drag scheme [McFarlane,

1987] is adapted from the Community Climate Model (Version 2), as is the nonlinear, timesplit, vertical

diffusion scheme.

In the stratosphere, radiative heating is computed using the scheme of Shine [1987]. This scheme

incorporates the absorption of solar UV radiation by ozone and molecular oxygen and the infrared contributions due to CO2, H20, and O 3. The stratospheric and tropospheric radiative heating rates are

smoothly blended together at 100 hPa. This procedure is currently being modified so that the stratospheric heating code will include contributions from the upwelling radiation from the troposphere,

rather than from a specified climatology as is presently done.

88
Chemistry Module

Chemical calculations span the altitude range from the surface to approximately 60 km in 24 levels.

Vertical resolution for the 14 stratospheric levels is approximately 3 km. Time integration of the constituent mass continuity equations is achieved explicitly with a leapfrog time-step of 15 minutes.

Twenty-three chemical families or individual constituents are represented, namely Ox, NOy, HNO 3,

Cly, N20 5, H20 2, HC1, CIONO 2, N20, CFCI3, CF2C12, CC14, CH3CI, CH3CCI3, NAT, Bry, CH3Br,

CF3Br ' CF2BrC1 , CH4, CF20 , CFCIO, and HF [Eckman et al., 1995]. The inclusion of bromine chemistry and the explicit transport of methane in the model are recent and noteworthy developments. The

chemical families are partitioned into 53 species using photochemical equilibrium assumptions with 41

photolytic reactions and 107 chemical reactions. Odd hydrogen (HOx) is assumed to be in photochemical equilibrium throughout the model domain. A simplified methane oxidation scheme is included

through the representation of CHx chemistry [Eckman et ai. 1995]. The explicit transport of H20 will be

added to the model this year.

Photodissociation rates are obtained using a table lookup method to reduce computational time

[Kawa, private communication, 1995]. Cross sections and quantum yields are being updated based on

the recommendations of DeMore et al. [1997]. The rates include the effects of multiple scattering and

surface reflection. Kinetic rates are taken from the compilation of DeMore et al. [1997].

The model includes a parameterization that represents the impact of polar stratospheric cloud chemistry [M. P. Chipperfield, private communication, 1996]. Sulfate aerosol chemistry is currently included

using a zonal-mean aerosol climatology [WMO, 1991] and the parameterization of Hanson et al. [1994].

An initial attempt at adding an NOx source due to lightning has been implemented in the model following the scheme of Ko et al. [1986]. More elaborate parameterizations are being studied.

References

Blackshear, W. T., W. L. Grose, and R. E. Turner, Q. J. R. Meteorol. Soc., 113,815-846, 1987.

DeMore, W. B., et al. JPL Publ. 97-4, Jet Propul. Lab., Pasadena, Calif., 1997.

Eckman, R. S., W. L. Grose, R. E. Turner, W. T. Blackshear, J. M. Russell III, L. Froidevaux, J. W. Waters, J. B.

Kumer, A. E. Roche, J. Geophys. Res., 100, 13951-13966, 1995.

Eckman, R. S., W. L. Grose, R. E. Turner, W. T. Blackshear, J. Geophys. Res., 101,22977-22989, 1996.

Grose, W. L., R. S. Eckman, R. E. Turner, and W. T. Blackshear, Global modeling of ozone and trace gases, in

Atmospheric Ozone Research and its Policy Implications, edited by T. Schneider et al., pp. 1021-1035,

Elsevier Science Publishers, Amsterdam. 1989.

Hanson, D. R., A. R. Ravishankara, and S. Solomon, J. Geophys. Res., 99, 3615-3629, 1994.

Hoskins, B.J., and A.J. Simmons, Q. J. R. Meteorol. Soc., 101,637-655, 1975.

Kalnay, E., R. Balgovind, W. Chao, D. Edelmann, J. Pfaendtner, L. Takacs, and K. Takano, NASA Tech. Mere.

86064, NASA Goddard Space Flight Center, Greenbelt, MD 20771.

Ko, M. K. W., M. B. McElroy, D. K. Weisenstein, and N. D. Sze, J. Geophys. Res., 5395-5404, 1986.

Kuo, H.L., J. Atmos. Sci., 31, 1232-1240, 1974.

McFarlane, N.A., J. Atmos. Sci., Vol. 44, No 14, 1775-1800, 1987.

Molinari, J., Mon. Wea. Rev., 110, 1527-1534, 1982.

89
Pierce, R. B., W. T. Blackshear, T. D. Fairlie, W. L. Grose, R. E. Turner, J. Atmos. Sci., 50, 3829-385 l, 1993.

Robert, A. J., J. Henderson, and C. Turnbull, Mon. Wea. Rev., 100, 329-335, 1972.

Shine, K.P., Q.J.R. Meteorol. Soc.,113, 605-633, 1987.

Williamson, D. L., et al., NCAR Technical Note 285, Boulder, Colo., 1987.

WorId Meteorological Organization (WMO), Rep. 25, World Meteoro|. Org., Geneva, 1991.

Zapotocny, T. H., D. R. Johnson, F. M. Reames, R. B. Pierce, and B. J. Wolf, J. Atmos. Sci., Vol. 48, 2025-2043,

1991.

90
1.2.19 "MONASHI" Model

Darryn Waugh (*)

Meteorology CRC, Monash University,

Victoria, Australia

(*) Now at

Department of Earth and Planetary Sciences

Johns Hopkins University

Baltimore MD 21218

Email: waugh@jhu.edu

General Information:

The MONASHI model is described in Waugh et al. (1997) (see also Hall & Waugh 1997). The

model is an off-line chemical transport model based on the model developed at National Center for

Atmospheric Research (NCAR) by [Rasch & Williamson 1990] and [Rasch et al. 1994]. The CTM is

driven by wind fields from the Middle Atmosphere version of the NCAR Community Climate Model

version 2 (MACCM2) [Boville-1995].

Specific Information:

This model uses a semi-Lagrangian transport scheme, which preserves shapes of tracer distributions

and minimizes numerical diffusion [Williamson & Rasch 1989]. The scheme is not implicitly mass conservative, and a "mass fixer" is employed after advection to keep global tracer mass constant (see

Appendix of [Rasch et al. 1995].

The CTM is driven using 1 year of winds from a MACCM2 simulation with T42 horizontal resolution (128 X 64 longitude by latitude points, giving an approximate horizontal resolution of 2.8 degrees),

and there are 44 vertical levels from the surface to p approx 0.01 hPa (80 kin). A hybrid vertical

co-ordinate system is used, with upper 28 levels (above 100 hPa) at fixed pressure; the vertical resolution in the lower stratosphere is approximately 1.4 kin. The climatology of the above configuration of

MACCM2 is described by [Boville 1995]. Note that this is not the same MACCM2 dataset used in the

GMI-NCAR simulations (see MONASH2 description).

Daily-averaged horizontal winds from MACCM2 are used, and vertical velocities are calculated

within the CTM (using continuity). A one hour time step is used. No sub-grid scale parameterizations

are used in the model (troposphere and stratosphere).

N20 simulations use a loss rate look up table, using loss rates from [Garcia & Solomon 1983]. This

is the same loss rates as used in the on-line MACCM2 N20 simulations [e.g., Randel et al. 1994].

References:

Boville, B. A., Middle atmosphere version of the CCM2 (MACCM2): Annual cycle and interannual variability,

J. Geophys. Res., 100, 9017-9039, 1995.

Garcia, R. R. and S. Solomon, A numerical model of the zonally averaged dynamical and chemical structure of the

middle atmosphere, J. Geophys. Res., 88, 1379-1400, 1983.

91
Hall, T. M. and D. W. Waugh, Timescales for the stratospheric circulation derived from tracers, J. Geophys. Res.,

102, 8991-9001, 1997.

Randel, W.J., et al., Simulation of stratospheric N20 in the NCAR CCM2: Comparison with CLAES data and

global budget analysis, J. Atmos. Sci., 51, 2834-2845, 1994.

Rasch, P. J. and D. L+ Williamson, On shape-preserving interpolation and semi-Lagrangian transport, SIAM,

J. Sci. Stat. Comput., l 1,656-687, 1990.

Rasch, P. J., et al., A three-dimensional transport model for the middle atmosphere, J. Geophys. Res., 99,

999-1017, 1994.

Rasch, P. J., et al., A three-dimensional general circulation model with coupled chemistry for the middle

atmosphere, J. Geophys. Res., 100, 9041-9071, 1995.

Waugh, D. W., et al., Three-dimensional simulations of long-lived tracers using winds from MACCM2, J.

Geophys. Res., 102, 21493-21513, 1997.

92
1.2.20 "MONASH2" Model

Darryn Waugh (*)

Meteorology CRC, Monash University,

Victoria, Australia

(*) Now at

Department of Earth and Planetary Sciences

Johns Hopkins University

Baltimore MD 21218

Email: waugh@jhu.edu

General Information:

The MONASH2 model is the same CTM as used in MONASHI simulations, but is driven by a

wind fields from a different version of the Middle Atmosphere version of the NCAR Community

Climate Model version 2 (MACCM2). Results from MONASH2 are shown in Figure 2 of Waugh et al.

(1997) and in Hall & Waugh (1998).

Specific Information:

Details are as in MONASH1 description except where mentioned below.

The winds used in MONASH2 come from a MACCM2 simulation with the same vertical grid as

that used in MONASHI but lower horizontal resolution (64 x 64 longitude by latitude points, giving an

approximate horizontal resolution of 5.6 x 2.8 degrees). This version of MACCM2 included addition

gravity wave parameterisations to that described in [Boville 1995], and used in MONASHI, and has a

significantly different circulation. A description of this version of MACCM2 is in preparation

(B. Boville, person, comm.)

The MACCM2 dataset used in MONASH2 is the same as used in the GMI-NCAR simulations.

Six-hourly averaged horizontal and vertical winds from MACCM2 are used, with a one hour time

step is used. Sub-grid scale parameterizations are used in the troposphere, using parameters for convection and vertical diffusion archived from MACCM2.

References:

Hall, T. M. & Waugh, D. W., The influence of nonlocal chemistry on tracer distributions: Inferring the mean age

of air from SF6. J. Geophys. Res, 103, 13327-13336, 1998.

Waugh, D. W., et al., Three-dimensional simulations of long-lived tracers using winds from MACCM2, J.

Geophys. Res., 102, 21493-21513, 1997.

93
1.2.21 NCAR-3D

Guy Brasseur and Xue Xi Tie

National Center for Atmospheric Research

Boulder, Colorado

The NCAR-3D chemical transport model is global and extends from the surface to the approximate

altitude of 80 km. The spatial resolution is 2.8 degrees in latitude and 5.6 degrees in longitude, with

44 levels in the vertical (approximately i3 in the troposphere, 25 in the stratosphere, and 6 in the mesosphere). The timestep used to solve the continuity equations associated with each chemical compound

or chemical family is 30 minutes.

The transport of tracers is performed by using the three-dimensional semi-Lagrangian formulation

with shape-preserving interpolation of Rasch and Williamson (1991). The meteorological fields used to

drive the model are taken from a simulation using the middle atmosphere version of CCM2 (Boville,

1995) that includes an important modification to the gravity wave drag formulation. In this version of

CCM2 the momentum deposition by gravity waves is modified to include not only the previous representation of stationary gravity waves which are forced by flow over orography (McFarlane, 1987), but

also a background gravity wave source designed to include non-stationary gravity wave arising from

other sources (convection, shear, frontal propagation). These modifications have resulted in dramatic

improvements to the model circulation in the southern polar region, which were critical to the success of

the chemical simulation described here. The chemical-transport model also includes a representation of

boundary layer exchanges (Holstag and Boville, 1993) and of convective transport (Hack, 1994), based

on the dynamical output of CCM2. As we are focussing here on the distribution of chemical compounds

in the stratosphere, the role of these tropospheric processes will not be further discussed in the present

study.

The chemical scheme implemented in the model includes 46 species with a representation of 154

chemical and photochemical reactions that affect the oxygen, hydrogen, nitrogen, chlorine, and bromine

families in the stratosphere (see e.g., Brasseur and Solomon, 1986). Major constituents (N 2 =78% and

02=21%) are fixed and the mixing ratio of H2 is prescribed (0.5 ppmv). The distribution of water vapor

in the troposphere is taken from the CCM2 simulation, while, in the stratosphere and mesosphere, it is

calculated explicitly by the chemical-transport model. Photodissociation frequencies are interpolated

from a pre-calculated look-up table as a function of pressure, ozone column abundance, and solar zenith

angle. Wash-out of soluble nitrogen and chlorine compounds in the troposphere is crudely parameterized by adding a linear loss process below the tropopause. Heterogeneous processes occurring on the

surface of sulfate aerosol particles (which play a considerable role in the lower stratosphere at all latitudes) and of PSC particles (which are of considerable importance in polar regions) are explicitly represented. Surface area densities associated with the presence of background (non volcanic) aerosol are

specified as a function of latitude and height (for non volcanic conditions) according to the satellite

observations by SAGE (WMO, 1994). In the case of PSC particles, surface area densities are calculated

as a function of temperature and water and nitric acid concentrations, using the simple microphysics

model developed by Chipperfield et al. (1993). The saturation pressure for type I (nitric acid trihydrate

or NAT) and type II (ice) particles is derived from the expression of Hanson and Mauersberger (1988)

and of Jansco et al., (1970), respectively. Condensation (evaporation) take place when ambient densities

of gas phase nitric acid and water become larger (smaller) than saturation conditions. It is assumed, for

the calculation of surface area density, that the effective radii of the particles are 1 um and 10 um for

type I and type II PSCs, respectively. Sedimentation velocities for these particles are expressed according to Kasten (1968). In MM2 calculations, only tracer type chemical species (such as NOy etc.) are

included in the model.

94
The chemical transport equations are solved by a splitting operator technique; at each timestep, the

mixing ratios of the chemical species are successively updated in the following order: (1) advection by

the semi-Lagrangian technique, (2) chemical transformations, (3) mixing accounting for vertical subgrid transport, and (4) redistribution by convection. Initial conditions for the concentration of chemical

compounds are representative of June, and are taken from zonally averaged concentration provided by

our 2-D model (Brasseur et al., 1990; Granier and Brasseur, 1992).

References

Boville B., Middle atmosphere version of community climate model 2: Annual cycle and interan nual variability,

J. Geophy. Res., 100, 9017-9040, 1995.

Brasseur, G., and P. C. Solomon, Aeronomy of the middle atmosphere, Second Edition, Published by D. Reidel

Publishing Company, P. O. Box 17, 3300 AA Dordrecht, Holland, 1986.

Brasseur, G., M. H. Hitchman, S. Waiters, M. Dymek, E. Falise, and M. Pirre, An interactive chemical dynamical

radiative two-dimensional model of the middle atmosphere, J. Geophys. Res., 95, 5639-5655, 1990.

Chipperfield, M. P., D. Carriolle, P. Simon, R. Ramaroson, and D. J. Lary, A three-dimensional modeling study of

trace species in the Arctic lower stratosphere during winter 1989-1990, J. Geo phys. Res., 98, 7199-7218,

1993.

DeMore, W. B., et al., Chemical kinetics and photochemical data for use in stratospheric modeling, JPL Publication 94-26, 1994.

Granier, C. and G. Brasseur, Impact of heterogeneous chemistry on model predictions of ozone changes, J. Geophys. Res., 97, 18015-18033, 1992.

Hack, 1. J., B. A. Boville, B. P. Briegleb, J. T. Kiehl, P. J. Rasch, and D. L. Williamson, Description of the NCAR

community climate model (CCM2), Tech Note, NCAR/TN-382+STR, 108 pp, Natl. Cent. for Atmos. Res.,

Boulder, CO, 1993.

Hanson, D. R., A. R. Ravishankara, and S. Solomon, Heterogeneous reactions in sulfuric acid aerosols: A framework for model calculations, J. Geophy. Res., 99, 3615-3629, 1994.

Holtslag, A. A. M., and B. A. Boville, Local versus nonlocal boundary-layer diffusion in a global climate model,

J. Clim., 6, 1825-1842, 1993.

Jancso, G., J. Pupezin, and W. A. Van Hook, The vapor pressure of ice between + 10-20 and 1020, J. Phys. Chem.,

74, 2984-2989, 1970.

Kasten, F., Falling speed of aerosol particles, J. Appl. Meteorol., 7, 944-947, 1968.

McFarlance, N. A., The effect of orographically excited gravity wave drag on the general circulation of lower

stratosphere and troposphere, J. Atmos. Sci., 44, 1775-1800, 1988.

Rasch, P.J., and D.L. Williamson, Sensitivity of a general circulation model climate to the moisture transport formulation, J. Geophy. Res., 96, 13123-13137, 1991.

Williamson, D. L., and P. J. Rasch, Two-dimensional semi-Lagrangian transport with shape preserving interpolation, Mon. Wea. Rev., 117, 102-129, 1989.

WMO, Scientific Assessment of ozone depletion: 1991, 1992.

95
1.2.22 SLIMCAT 3-D Chemical Transport Model

Martyn Chipperfield

University of Cambridge

Cambridge, UK

SLIMCAT is a 3D off-line stratospheric chemical transport model. The model uses an isentropic

vertical coordinate and has a lower boundary near 10 kin. Horizontal winds and temperatures are specified from meteorological analyses, while the vertical (diabatic) motion is calculated from a radiation

scheme within the model. The chemistry and transport (advection) are treated separately using an 'split

operator' approach.

Chemistry Mechanism and Solution

The model contains the principal stratospheric chemical species in the Ox, HOx, NOy, Cly, Bry

groups, as well as source gases (e.g. N20, CH 4, CFC13, CF2C12, CH3Br) and a treatment of CH4 oxidation. The model calculates the full diurnal cycle of chemical species with a timestep of 20 minutes. The

model uses a limited number of chemical families for species which are in rapid photochemical equilibrium, and for which the partitioning can be calculated even at night (i.e., N + NO + NO2; Br + BrO;

C1 + C10 + C1202; O(3p) + O(ID) + 03). Otherwise, all species are integrated separately, except for H,

OH, and HO 2 which are assumed to be in photochemical equilibrium (and are not transported). The

chemistry is integrated using the 'Semi-Implicit Symmetric' method of Ramaroson et al. [1992]. Gas

phase chemical data is taken from JPL 1997.

For heterogeneous chemistry, the model contains an equilibrium treatment of reactions on liquid

sulfuric acid aerosols and on solid nitric acid trihydrate (NAT) and ice particles. The treatment of liquid

aerosols requires the H2SO4 loading of atmosphere. This field is specified month-by-month from

detailed two-dimensional (latitude-height) time-dependent calculations [Bekki and Pyle, 1994] and

advected as a passive tracer. The composition of liquid H20/H2SO4/HNO3/HC1 aerosols is then calculated from the analytical scheme of Carslaw et al. [1995a, b] using this H2SO 4 field along with the CTM

fields of temperature and other chemical species. This scheme also calculates the solubilities of HBr,

HOBr, and HOCi.

For NAT PSCs (HNO3.3H20) the model mixing ratios of HNO 3, H20, and temperature are used

with the expression of Hanson and Mauersberger [1988] to predict when they are thermodynamically

possible. The available surface area is calculated from the amount of HNO 3 condensed assuming that

there are 10 NAT particles cm -3. Ice particles are assumed to form when they are thermodynamically

possible. HNO 3 is removed from the gas phase in the presence of ice particles using the equilibrium

NAT expression [Hanson and Mauersberger, 1988]. This assumes a NAT coating (as opposed to a liquid coating) to the ice particles. The available surface area is calculated from an estimate of the amount

of H20 which is condensed and assuming that the radius of the ice particles formed is 10 um.

The model has a simple treatment of denitrification and dehydration. When formed, NAT and ice

particles are sedimented from the model with fall velocities appropriate for particles of radius 1 and

10 urn, respectively. The NAT and ice particles are transported to the model level below where they

may evaporate, depending on the ambient temperature.

Details of the chemical rates used in the heterogeneous calculations are given in Chipperfield

[1999].

96
Thephotolysisrates(Jvalues)arecalculatedusingaschemebasedonLaryandPyle[1991], which

in turn was based on Meier et al. [1982] and Nicolet et al. [1982]. The scheme uses a four-dimensional

look-up table (which has coordinates of pressure altitude, temperature, 03 column, and zenith angle) to

interpolate precomputed J values to a particular location and time in the atmosphere. The scheme takes

account of multiple scattering and spherical geometry and can calculate photolysis rates for zenith

angles up to 96 degrees. The J rates are updated every 2 chemical timesteps (i.e. 2 times per hour).

Dynamics

The CTM uses meteorological analyses (or GCM output) to specify the horizontal winds and temperatures. These analyses are read in every meteorological cycle (e.g. every 6 or 24 hours) and averaged

from the (higher resolution) analysis grid to the CTM grid.

The standard model uses the MIDRAD radiation scheme [Shine, 1987] to calculate heating rates for

the vertical transport in isentropic coordinates. The thermal infrared scheme is as described by Shine

[1987] although Curtis matrices are not used and so nonlocal thermodynamic equilibrium (non-LTE) is

not accounted for. The solar scheme is that of Shine and Rickaby [1989]. The model radiation scheme

employs two dummy levels above and below the actual model domain to account for the rest of the

atmosphere. The bottom dummy level extends down to 700 hPa where monthly varying upward longwave fluxes are specified. The model-calculated 0 3 field can be used interactively with MIDRAD (as

in the simulations described below) allowing a degree of coupling between the model chemistry and

transport, otherwise a monthly varying climatological field is used.

The SLIMCAT CTM contains a range of tracer advection schemes. In the standard model (used

here) the chemical tracers are advected using the second-order moments scheme of Prather [1986]. This

scheme has low numerical diffusion and performs well in maintaining strong gradients. This is particularly useful at the low model resolutions.

The simulations used in this report used a horizontal resolution of 7.5 x 7.5 (TI5 Gaussian grid) and

were run on 12 isentropic levels from 335 to 2700 K. The vertical resolution in the lower stratosphere

was about 3 km. The runs were forced using the U.K. Meteorological Office (UKMO) Analyses

[Swinbank and O'Neill, 1994] for the year 1992. The model's dynamical time step was 60 rain.

References

Bekki, S., and J.A. Pyle, A two-dimensional modeling study of the volcanic eruption of Mount Pinatubo, J.

Geophys. Res., 99, 18,861-18,869, 1994.

Carslaw, K., B. Luo, and T. Peter, An analytic expression for the composition of aqueous HNO3-H2SO4 stratospheric aerosols including gas phase removal of HNO3, Geophys. Res. Lett., 22, 1877-1880, 1995a.

Carslaw, K.S., S.L. Clegg, and P. Brimblecome, A thermodynamic model of the system HCI-HNO3-H2SO4-H20,

including solubilities of HBr, from <200 K to 328 K, J. Phys. Chem., 99, 1i,557-11,574, 1995b.

Chipperfield, M.P., Multiannual Simulations with a Three-Dimensional Chemical Transport Model, J. Geophys.

Res., 104, 1781-1805, 1999.

Hanson, D., and K. Mauersberger, Laboratory studies of the nitric acid trihydrate: Implications for the south polar

stratosphere, Geophys. Res. Lett., 15,855-858, 1988.

Lary, D.J., and J.A. Pyle, Diffuse radiation, twilight and photochemistry, J. Atmos. Chem., 13, 373-392, 1991.

97
Meier, R.R., D.E. Anderson, and M. Nicolet, Radiation field in the troposphere and stratosphere from 240-1000

nm, I, General analysis, Planet. Space Sci., 30, 923-933, 1982.

Nicolet, M., R.R. Meier, and D.E. Anderson, Radiation field in the troposphere and stratosphere from 240-1000

nm, I, Numerical analysis, Planet. Space Sci., 30, 935-983, 1982.

Prather, M.J., Numerical advection by conservation of second-order moments, J. Geophys. Res., 91, 6671-6681,

1986.

Ramaroson, R., M. Pirre and D. Cariolle, A box model for on-line computations of diurnal variations in a ID

model: Potential for application to multidimensional cases, Ann. Geophysicae., 10, 416-428, 1992.

Shine, K.P., The middle atmosphere in the absence of dynamical heat fluxes, Q. J. R. Meteorol. Soc., 113,

603-633, 1987.

Shine, K.P., and J.A. Rickaby, Solar radiative heating due to absorption by ozone, in Proceedings of Quadrennial

Ozone Symposium, Gottingen, pp 597-600, A. Deepak, Hampton, Va., 1989.

Swinbank, R., and A. O'Neill, A stratosphere-troposphere data assimilation system, Mort. Weather Rev., 122,

686-702, 1994a.

98
1.2.23 UCI23 Chemistry Transport Model

Michael Prather, Xin Zhu, Bryan Hannegan

Department of Earth System Science, University of California, Irvine

220 Rowland Hall, Irvine, CA, 92697-3100

(email: mprather@uci.edu, xzhu@uci.edu, bjhanneg@uci.edu)

General Information:

The "UCI2Y' chemistry transport mode| is the latest in a generation of CTMs developed by Michael

Prather and used at UCI and the Goddard Institute for Space Studies (GISS). Previous versions of the

CTM have been used in studies of stratospheric trace gases over the past decade, including the fate of

meteoritic metals (Prather and Rodriguez, 1988), correlations between N20 and severaI long-lived trace

gases, including halocarbons (Hall and Prather, 1995; Avallone and Prather, 1997), and previous intercomparison projects and assessment studies (MMI, WCRP, IPCC).

Meteorological Input:

UCI23 is an offiine chemistry transport model which uses meteorological fields from the new GISS

general circulation middle-atmosphere model II' described in Koch and Rind (1998; see also documentation in Rind and Lemer, 1996). Meteorological data are saved every 6 hours as a combination of

instantaneous and averaged quantities.

Grid/Resolution:

UCI23 has a horizontal resolution of 4 degrees latitude by 5 degrees longitude, with 2 degree latitude boxes at each pole. This yields 72 longitudinal grid points and 46 latitudinal grid points, centered

as follows:

J Latitude J Latitude J Latitude J Latitude

1 -89 13 -42 25 6 37 54

2 -86 14 -38 26 10 38 58

3 -82 15 -34 27 14 39 62

4 -78 16 -30 28 18 40 66

5 -74 17 -26 29 22 41 70

6 -70 18 -22 30 26 42 74

7 -66 19 -18 31 30 43 78

8 -62 20 -14 32 34 44 82

9 -58 21 -10 33 38 45 86

10 -54 22 -6 34 42 46 89

11 -50 23 -2 35 46

12 -46 24 2 36 50

The model has 23 vertical layers extending from the surface to 90km (0.002 mbar), using a hybrid

sigma coordinate system with fixed pressure levels above 150 mbar. Pressure at the lower edge of each

vertical level is given by the expression p = rla + (rib * psfc).

99
L _a _b L _a = p(mbar)

1 .0000000 1.0000000 12 150.0000000

2 4.3165505 .9712230 13 116.9999994

3 9.8920763 .9340528 14 86.1999954

4 17.9856151 .8800959 15 56.2000005

5 29.6762556 .8021583 16 31.5999954

6 49.2805839 .6714628 17 17.8000009

7 74.4604290 .5035971 18 9.9999991

8 100.5395696 .3297362 19 5.6200021

9 120.5035977 .1966427 20 3.1600022

10 132.9136692 .1139089 21 1.7799953

11 142.4460433 .0503597 22 1.0000038

23 .5619941

TOP .0020576

Physics

UCI23 uses the UC Irvine transport shell (Prather et al., 1987; Jacob et al., 1997) as its numerical

advection scheme, with an advective timestep of 6 hours. Advective mass fluxes are provided from the

GCM, and a quadratic upstream numerical method is used for advection, utilizing the mean, first, and

second moments of the species distribution in each grid cell to ensure numerical stability and mass

conservation.

No explicit diffusion is included in either the troposphere or stratosphere. In each column, the planetary boundary layer is completely mixed up to the PBL height given in the GCM data. Archived convective mass fluxes allow for transport of trace species via wet and dry convection in the troposphere.

Removal of species can occur via wet deposition (a specified fraction of species mass is removed

from the updraft), or dry deposition (a specified deposition velocity is applied to the lowest model

layer).

Chemistry

Linearized stratospheric chemistry is parameterized using look-up production and loss tables from

the PRATMO photochemical box model (Remsberg and Prather, 1993). Mean SBUV ozone and NCEP

temperature climatologies are used to determine photochemical production and loss rates for species as

a function of time, latitude, and altitude. No heterogeneous chemistry is included, nor are aerosol

physics.

References

Avallone, L.M. and M.J. Prather, Tracer-tracer correlations: Three-dimensional model simulations and comparisons to observations, J. Geophys. Res., 102, 19233-19246, 1997.

Hall, T.M. and M.J. Prather, Seasonal evolutions of N20, 03 and CO2: Three-dimensional simulations of stratospheric correlations, J. Geophys. Res., 100, 16699-16720, 1995.

Jacob, D.J. et al., Evaluation and intercomparison of global atmospheric transport models using 222Rn and other

short-lived tracers, J. Geophys. Res., 102, 5953-5970, 1997.

Koch, D., and D. Rind, 10Be/7Be as a tracer of stratospheric transport, J. Geophys. Res., 103, 3907-3917, 1998.

100
Prather, M.J.,Numerical advectionbyconservationfsecond-order moments, J.Geophys. Res.,91,6671-6681,

1986.

Prather,M.J.andJ.M.Rodriguez, Antarcticozone:meteoriticcontrolofHNO3,Geophys. Res.Lett.,15,I-4,

1988.

Remsberg, E.E.andM.J.Prather, eds.,Theatmosphericeffectsofstratospheric arcraft:Reportofthe1992modelsandmeasurements workshop, NASARef.Publ.1292,1993.

Rind,D.andJ.Lerner,Useof on-linetracersasadiagnostictoolingeneral circulationmodeldevelopment, I,

Horizontal andverticaltransport inthetroposphere, J.Geophys. Res.,101,12667-12675, 1996.

101
1.2.24 UIUC-3D Model

E, V. Rozanov, M. E. Schlesinger, F. Yang, and N. Andronova

Climate Research Group, Department of Atmospheric Sciences

University of Illinois at Urbana-Champaign

105 S. Gregory Street

Urbana, Illinois 61801

V. A. Zubov

A. I. Voeikov Main Geophysical Observatory

7 Karbyshev Street

St. Petersburg, 19402 l, Russia

General Information

The UIUC 3-D photochemical transport model participated in the M&M II. References to the model

formulation are listed near the end of this document.

Domain and Grid Sizes

The UIUC 3-D model domain extends from the South Pole to the North Pole and from the ground to

approximately 50 km (1 mb). The horizontal resolution of the model is 4 ° latitude x 5° longitude, and in

the vertical direction the model uses sigma coordinate with sigma = 1 at the earth's surface and

sigma = 0 at the top of the model:

CY- p - Prop

Ps - Ptop

where p is atmospheric pressure in mb, p top = 1 mb is the pressure at the top of the model, and Ps is the

atmospheric pressure at the surface. For the calculation of sigma values, the pressures at the half-integer

levels above 100 mbar were set as follow:

d

Pk+l/2 = Pk-1/2 e ,

where d = ln(Plo+t/2) - In(Ptop) and Pl0+I/2 = 100 mb. The 14 pressure values below 100 mb are prescribed at 150, 200, 250, 350, 450, 550, 650, 750, 850 and 1000 mb.

Transport and Temperature

For the description of vertical transport in the troposphere, the eddy diffusion approach, with

Kzz = 0.1 m2s q, has been used to enable vertical mixing of tropospheric air. The transport of species in

the stratosphere is purely advective and we did not use any parameterization for horizontal diffusion.

The velocity, temperature and tropospheric water vapor mixing ratio required by the ACTM have been

taken from a simulation by our 24-layer stratosphere/troposphere general circulation model (Yang and

Schlesinger, 1998). These prescribed fields vary with latitude, longitude, altitude and time, and they are

102
redefined every day at midnight UTS. The monthly mean 3-D fields of trace-gas mixing ratios are

stored for each month of the simulation. Trace-gas mixing ratios are also stored for 12 UTS at the

middle of each month to analyze the instantaneous geographical distributions of the species.

Photochemistry

The current version of the photochemical routine (Rozanov et al., 1998a,b) takes into account

28 species of the oxygen, nitrogen, hydrogen, carbon and chlorine cycles with 03, O(3p), NO, NO2,

N205, HNO 3, HNO 4, N20, H202, H20, H 2, HCI, HOCI, C1NO 3, CI 2, C1202, F-1 l, F-12, CO, and CH 4

being transported. It describes the main chemical processes responsible for the production and destruction of ozone in the stratosphere. The photochemical routine includes more than 100 gas-phase reactions

(DeMore et al., 1997) and 4 heterogeneous reactions on the surface of sulfate aerosol and polar stratospheric clouds (PSC) particles, as well as a simple thermodynamic scheme for PSC diagnosis.

The core of the photochemical module is a specially designed Generator of Chemical Routines

(GCR) which is based on non-numerical algorithms. The GCR was designed to: (i) process the photochemical reaction set, (ii) determine the set of independent variables, and (iii) create the FORTRAN

code that performs the photochemical calculations in the model.

The implicit numerical scheme proposed for atmospheric chemistry by Ozolin (1992) and Stott and

Harwood (1993) and the backward implicit Eulerian scheme are applied to solve the photochemical

equation set. For long-lived species, such as CH 4 and N20, a simple explicit scheme is used instead of

the reference implicit scheme. For some of the short-lived species, such as O(ID) and OH, the steadystate approximation is applied. For the other gases the reference Newton-Raphson scheme has been

changed by simplifying the iterative routine such that, during 'quiet periods' when the changes of gas

concentrations are small, the maximum number of iterations is limited.

Photodissociation rates are parameterized as functions of 02 and 03 amounts. To take the multiple

scattering in the atmosphere into account, dissociation rates are calculated using the full scheme

described by Rozanov et al., (1998a) as Jxqx, where qx is the concentration of molecule x and Jx represents the coefficient or probability of photodissociation,

jx(z, O) = J_2 ex(_.)tSx(_.)s_(z, O)d_,,

kl

where (Xl,_2) is the spectral region where molecule x can be dissociated, sL(z, 0)d)_ is the photon

flux at altitude z and zenith angle 0, 6x()_) is the absorption cross-section in cm ", and ex(X) represents

the quantum yield or efficiency. To compute the photodissociation rates, the spectral interval from

175 nm to 750 nm is divided into 61 subintervals with a resolution of about 5 nm in the shortwave part

(from 175 to 400 nm) and a lower resolution in the visible part. Cross-sections and quantum yields are

taken from DeMore et al. (1997). Alien and Frederic's (1982) parameterization scheme is used for O 2

and NO in the Schumann-Runge region. Oxygen and ozone amounts are calculated for all model grid

cells, with their photodissociation rates being determined by bilinear interpolation of tabulated precalculated photodissociation rate vectors. The accuracy of this parameterization has been examined

(Schlesinger, 1976) for oxygen and ozone photodissociation. It was found that the error for ozone dissociation with production of atomic oxygen does not exceed 20%. Further examination indicates that with

modifications that partially include multiple scattering in the atmosphere, the error does not exceed

15%.

103
The following heterogeneous reactions on sulfate and PSC particles are included in the model:

1. N20 5 + H20 = HNO 3 + HNO 3

2. CINO 3 + H20 = HOCI + HNO 3

3. C1NO 3 + HCI = CI2 + HNO 3

4. HOC1 + HC! = CI_ t- H20

The heterogeneous reaction probabilities for PSC I & II are calculated according to DeMore et al.

(1997) and for sulfates according to the state-of-the-art parameterization by Hanson et al. (1994). The

diagnostic thermodynamic scheme for predicting PSC events has been incorporated following Hanson

and Maursberger (1988) and Mtiller and Crutzen (1993).

Time Steps and Transport Scheme

The ACT model is integrated with a 2-hour time step for the chemistry and transport calculations. A

hybrid transport (HT) model (Zubov et al., 1996, 1997, 1998) has been developed for the advective

transport calculations. The HT model is a combination of the Prather scheme (Prather, 1986), which is

used in the vertical direction, and a semi-Lagrangian (SL) scheme, which is used for horizontal advection on a sphere (Williamson and Rasch, 1989). The use of the SL scheme for the horizontal transport

allows a significantly larger time step, and smaller dependence on any increase of horizontal resolution,

than would use of the Prather scheme. Furthermore, use of the Prather scheme for transport in only a

single direction (vertical) instead of three directions reduces from 10 to 3 the number of moments that

define the distribution of species in each model grid box. Moreover, combination of the SL scheme with

the Prather scheme yields a significant economy in the transport calculation compared to using the

Prather scheme alone, but attains an accuracy higher than that of the semi-Lagrangian _ '_eme alone.

In the present version of the model the lower boundary conditions for source gases (N20, CFC- 1 i,

and CFC-12) are prescribed as fixed zonally homogeneous, seasonally independent mixing ratios based

on the observational data for 1995 (Hofman and Peterson, 1996). The surface mixing ratio of CFC's is

scaled by a factor of 1.8 to account for other sources of active chlorine. The CO 2 mixing ratio is set

equal 335 ppmv everywhere. Tropospheric water vapor is taken from the UIUC 24-layer AGCM. The

initial mixing ratio fields for other species in the model were taken mainly from a 10-year steady-state

run of our 2-D model (Zubov et al., 1995), except water vapor, N20 and methane in the stratosphere

whose zonal-mean distributions for January were taken from the UARS dataset (Park et al., 1996;

Roche et al., 1996). The annual cycle of the surface source of CO and NOx, and the NOx source from

lightning are prescribed according to RIVM datasets (Mtiller and Brasseur, 1995; Olivier et al., 1996).

The subsonic aircraft source of NOx is prescribed from a NASA dataset and the latitudinal distribution

of the surface mixing ratios of the source gas CH 4 is set according to the published data for 1995 (Hein

and Crutzen, 1997; Hofmann and Peterson, 1996).

References

Allen, M., and J. E. Frederick, Effective photodissociation cross section for molecular oxygen and nitric oxide in

the Schumann-Runge bands, J. Atmos. Sci., 39, 2066-20 75, 1982.

DeMore, W., et al., Chemical kinetics and photochemical data for use in stratospheric modeling, in Evaluation 12,

NASA Jet Propulsion Laboratory, Pasadcna, California, 1997.

Hanson, D., A. Ravishankara, and S. Solomon, Heterogeneous reactions in sulfuric acid aerosols: A framework

for model calculations, J. Geophys. Res., 99 (D2), 3615-3629, 1994.

: 104
Hanson, D., and K. Maursberger, Laboratory studies of the nitric acid trihydrate: Implications for the south polar

stratosphere, Geophys. Res. Lettr., 15,855-858, 1988.

Hein, R., and P. J. Crutzen, An inverse modeling approach to investigate the global atmospheric methane cycle,

Global Biogeochemical Cycles, 11 (1), 43-76, 1997.

Hofman, D. J., and J. T. Peterson (Eds.), Climate Monitoring and Diagnostics Laboratory, No. 23, Summary

Report 1994-1995, pp. 16I, NOAA Environmental Research Laboratories, Boulder, CO, 1996.

MOiler, R., and P. Crutzen, A possible role of galactic cosmic rays in chlorine activation during polar night,

J. Geophys. Res., 98 (D20), 20,483-20,490, 1993.

Muller, J.-F., and G. Brasseur (1995): IMAGES: A three-dimensional chemical transport model of the global troposphere, J. Geophys. Res., 100, 16,445-16,490.

Olivier, J. G. J., et al., (1996): Description of EDGAR version 2.0: A set of global emission inventories of greenborise gases and ozone depleting substances for all anthropogenic and most natural sources on a country bases

on lxl ° grid, RIVM rep 771060002/TNO MEP rep R96/119, Natl. Inst. for Public Health and Environ.,

Bilthoven, the Netherlands, 1996.

Ozolin, Y. E., Modelling of diurnal variations of gas species in the atmosphere and diurnal averaging in photochemical models, lzv. RAN, PAO, 28 (12), 135- 143, 1992.

Roche, A. E., J. B. Kummer, R. W. Nightingale, J. L. Mergenthaler, G. A. Ely, P. L. Bailey, S. T. Massie, J. C.

Gille, D. P. Edwards, M. R. Gunson, M. C. Abrams, G. C. Toon, C. R. Webster, W. A. Traub, K. W. Jucks,

D. G. Johnston, D. G. Murcray, F. H. Murcray, A. Goldman, and E. C. Zipf, Validation of CH 4 and N20 measurements by the cryogenic limb array Etalon spectrometer instrument on the Upper Atmosphere Research

Satellite, J. Geophys. Res., 101,9679-9710, 1996.

Rozanov, E., V. Zubov, M. Schlesinger, F. Yang and N. Andronova (1998a), Simulation of trace-gas distributions

with the UIUC 3-D atmospheric chemical transport model: Model description and comparison of the sourcegas distributions with observations. J. Geophys. Res., (submitted).

Rozanov, E., V. Zubov, M. Schlesinger, F. Yang and N. Andronova (1998b), Three-Dimensional Simulations of

Ozone in the Stratosphere and Comparison with UARS Data, submitted to Physics and Chemistry of the Earth.

Park, J. H., J. M. Russell III, L. L. Gordley, S. R. Drayson, D. C. Benner, J. M. Mclnerney, M. R. Gunson, G. C.

Toon, B. Sen, J.-F. Blavier, C. R. Webster, E. C. Zipf, P. Erdman, U. Schmidt, and C. Shiller, Validation of

Halogen Occultation Experiment CH 4 measurements from UARS, J. Geophys. Res., 101, 10,183-10,203,

1996.

Prather, M. J., Numerical advection by conservation of second-order moments, J. Geophys., Res., 91, 6671-6681,

1986.

Schlesinger, M. E., A numerical investigation of the general circulation of atmospheric ozone, 376 pp., Ph.D.

Dissertation thesis, University of California, Los Angeles, Los Angeles, 1976.

Stott, P. A., and R. S. Harwood, An implicit time-stepping scheme for chemical species in a global atmospheric

circulation model, Ann. Geophys., 11,377 - 388, 1993.

Williamson, D. L., and P. J. Rasch, Two-dimensional semi-Lagrangian transport with shape-preserving interpolation, Mon. Wea. Rev., 117, 102-129, 1989.

Yang, F., and M. E. Schlesinger, Description and performance of the UIUC 24-layer stratosphere/troposphere

general circulation model, J. Climate, (to be submitted), 1998.

Zubov, V. A., I. L. Karol, Y. E. Ozolin, and E. V. Rozanov, The zonally averaged model of the photochemical,

radiative and dynamical processes in the troposphere and stratosphere. Part 1: The description of the model

and model validation, Izvestiya RAN Physics of Atmosphere and Ocean, 31 (N4), 469-476, 1995.

Zubov, V. A., E. V. Rozanov, and M. E. Schlesinger, Hybrid scheme for 3-dimensional advective transport, Mon.

Wea. Rev. (in press), 1998.

105
Zubov, V., E. Rozanov, M. Schlesinger, and N. Andronova, Hybrid Scheme for 3-dimensional Transport of

Chemical Species, Proceedings of the First SPARC General Assembly, WCRP-99, WMO-TD-No. 814, 1997,

421-424.

Zubov, V., E. Rozanov, M. Schlesinger, and N. Andronova, Hybrid advection scheme for 3-dimensional atmospheric models: testing and application for a study of NOx transport, Proceedings of International Colloquium

"Impact of Aircraft emissions upon the Atmosphere", Paris, France, 1996, Oct. 15-18, v. II, pp. 647-652.

106
1.2.25 UNIVAQ-3D Photochemical Transport Model

Giovanni Pitari, Barbara Grassi, Guido Visconti

Universita' degli Studi L'Aquila

via Vetoio, 67010 Coppito L'Aquila, Italy

(email: gianni.pitari@ aquila.infn.it)

The University of L'Aquila (Italy) three-dimensional model ('univaq-3d') is a 3D photochemicaltransport model using a horizontal grid of 19 points in latitude and 16 in longitude (10 and 22.5 degrees

resoIution, respectively) and extending from the ground to about 71 Km altitude with 26 log-pressure

levels correspondent to an approximate altitude resolution of 2.84 Km, for a total of 19x16x26 gridpoints. The pressure levels are specified as pfj)=p(j-l)/1.5, with p(0)=1000 hPa. The main features of

this model have been described in Pitari et al. (1997).

All transport fields are taken off-line and are not interactive. The advective transport is formulated in

flux form with the horizontal velocity fields u,v calculated in terms of a velocity stream-function and a

velocity potential, both taken from the daily output of a low-resolution spectral GCM (Pitari et al.,

1992). The vertical velocity w is calculated from the mass continuity equation.

A small horizontal diffusion (1.E05 m2/s) is used for numerical stability purposes, while a vertical

diffusion coefficient Kzz is used for simulate those transport processes not explicitly included in the

model (i.e. tropospheric convection and mesospheric gravity wave breaking). Kzz is assigned with large

tropospheric values (10-30 m2/s) decreasing to about 0.[-0.3 m2/s in the lower stratosphere. The effect

of gravity wave breaking is simulated by increasing Kzz in the upper stratosphere and mesosphere.

The zonally-averaged temperature field is taken from time-zonally averaged NMC data, while the

eddy deviations are taken from the output of the GCM.

The numerical advection scheme is Eulerian fully explicit, with a four-cycle time integration

scheme using a 1.0 hour time increment (Lorenz, 1971).

All chemical species interact explicitly in the model and are diurnally-averaged. Diurnal variations

are parametefized using the scheme described in Brasseur and Solomon (1984) for N205 and assuming

that a number of species are only present during daytime (O,O(1D),H,OH,HO2,NO,CI,CIO,Br,BrO).

The 24-hr averaged photodissociation rates are calculated daily (i.e. every 24 model hours) and include

the effects of Rayleigh and Mie scattering.

All medium and short-lived chemical species are grouped in families:

Ox, NOy=NOx+HNO 3, NOx, HOx, CHOx, Cly, Bry, SOx, aerosols.

The chemical species in the families are the following:

Ox: O(ID),O,O3

NOx: NO,NO2,NO3,N205,HNO4,CIONO2,BrONO2,PAN

HOx: H,OH,HOz,H202

CHOx: CH3Oz,CH3OOH,CH20,

CH3CHO,CH3CO 3,CH3COOOH,CzHSOz,CzH5OOH,

C3H6OHOz,C3H6OHOOH,C5HsOHO2

Cly: CI,C10,CIOOC1,HOCI,CIONOz,HCI

107
Bry:Br,BrO,HOBr,BrONO2,HBr

SOx:SO2,H2804

Aerosols: Aitken-nuclei, Sulfate, NAT, Ice.

Long-lived and surface-flux species are the following:

N20,H20,CH4,CO,

C2H6,C2H4,C3H6,C5Hs,C toH 16,other hydrocarbons,

CFzClz,CFC13,CC14,CH3CCI3,CFC- 113,CH3C1,HCFC-22,

CH3Br,Halon- 1211 ,Halon- 1301,

OCS,CS2,DMS,H2S.

In total there are 40 transported species and families plus aerosol panicles (44 size categories in

total) and 26 species calculated at photochemical equilibrium. The chemical solver has been developed

independently. All photochemical data are taken from JPL (1997).

Photochemical equilibrium applies to HOx, CHOx species and in the partition of NOx, Ox, Cly, Bry

families:

H, OH, HO 2, CH302, CH3OOH, CH20, C2H502, C2H5OOH, CH3CHO, CH3CO3,

CH3COOOH, C3H6OHO 2, C3H6OHOOH, C5HsOHO2,

NO, NO 2, NO 3, O, O(1D), C1, C10, C1202, Br, BrO, BrONO 2, HOBr

The heterogeneous reactions included in the model are the following:

N205 + H20 --> 2HNO 3

CIONO 2 + H20 --> HOCI + HNO 3

CIONO 2 + HCI --> C12 + HNO 3

HOCl + HCi --> CI2 + H20

BrONO 2 + H20 --> HOBr + HNO 3

N205 + HCI --> C1NO 2 + HNO 3

Heterogeneous reaction rates are calculated as first-order loss rates proportional to particle surface

area (see JPL-97). On sulfate aerosols, reaction 1 uses a constant y of 0.1, while reactions 2-4 follow

Hanson et al. (1994). Reaction 5 follows follows Hanson et al. (1996). Reaction 6 applies only to PSCs.

The size distribution of both sulfate and PSC aerosols (NAT + ice) may be calculated using an interactive and mass conserving microphysical code for aerosol formation and growth. Denitrification and

dehydration due to PSC sedimentation are calculated explicitly from the NAT and ice aerosol size

distribution.

The microphysics code includes homogeneous/heterogeneous nucleation, condensation/evaporation, coagulation, sedimentation, rainout. The microphysics code is fully interactive with the CTM. Particles are resolved in 15 size bins ranging from 0.31 nm to 10 um in radius by doubling the particle

radius. Each aerosol size bin is transported separately, for a total of 44 aerosol size categories including

Aitken, sulfate and PSC particles. Tropospheric washout applies to all aerosol panicles and also to SO 2

and H2SO 4. Calculated sulfate and PSC surface area density fields can be used in the CTM to calculate

rates of heterogeneous reactions. For M&M II exercises sulfate surface area density was specified.

108
References

Brasseur, G. and S. Solomon: Aeronomy of the Middle Atmosphere, D. Reidel, 1984.

JPL: Chemical Kinetics and Photochemical Data for Use in Stratospheric Modeling, JPL publ. 97-4, 1997.

Hanson, D.R., A.R. Ravishankara, and S. Solomon, Heterogeneous reactions in sulfuric acid aerosols: A framework for model calculations, J. Geophys. Res., 99, 3615-3629, 1994.

Hanson, D.R., A.R. Ravishankara, and E.R. Lovejoy, Reactions of BrONO 2 with H20 on submicron sulfuric acid

aerosol and the implications for the lower stratosphere, J. Geophys. Res., 101, 9063-9069, 1996.

Lorenz, E.: An N-Cycle Time Differencing Scheme for Stepwise Numerical Integration, Mon. Weather Rev., 99,

644-648, 197 !.

Pitari, G., S. Palermi, G. Visconti and R.G. Prinn: Ozone Response to a CO2 Doubling: Results From a Stratospheric Circulation Model With Heterogeneous Chemistry, J. Geophys. Res., 97, 5953-5962, 1992.

Pitari, G., B. Grassi, and G. Visconti: Results of a Chemical-Transport Model with Interactive Aerosol Microphysics, R.D. Bojkov and G. Visconti Eds., XVIII O3symp Proc., 759, 1997.

109
2. Transport Experiments

Timothy M. Hall, 1 Donald J. Wuebbles, 2 Kristie A. Boering, 3 Richard S. Eckman, 4 Jean Lerner]

R. Alan Plumb, 5 David H. Rind] Curtis P. Rinsland, 4 Darryn W. Waugh, 6 Chu-feng Wei 7

I. NASA Goddard Institute for Space Studies

2. University of Illinois

3. University of Califomia-Berkley

4. NASA Langley Research Center

5. Massachusetts Institute of Technology

6. Johns Hopkins University

Note on Authorship

All of the authors are responsible for this chapter, which has been written as a single document

rather than separately authored sections. However, we have listed at the beginning of each section and

subsection an author or authors to whom comments and questions are most conveniently addressed.

Table of Contents

2.1 Executive Summary

2.2. Introduction

2.3. Age Spectrum

2.4. Observation

2.5. Tropical Transport in Models

2.6. Global Mean Age in Models

2.7. Source-Transport Covariance

2.8. HSCT "ANOy" Tracer Distributions

2.9. Summary and Conclusions

2.1 Executive Summary (Hall)

MM2 defined a series of experiments to better understand and characterize model transport and to

assess the realism of this transport by comparison to observations. Measurements from aircraft, balloon,

and satellite, not yet available at the time of MM1 [Prather and Remsberg, 1993], provide new and stringent constraints on model transport, and address the limits of our transport modeling abilities. Simulations of the idealized tracers the age spectrum, and propagating boundary conditions, and conserved

HSCT-Iike emissions probe the relative roles of different model transport mechanisms, while simulations of SF 6 and CO 2 make the connection to observations. Some of the tracers are related, and transport

diagnostics such as the mean age can be derived from more than one of the experiments for comparison

to observations.

We draw several general conclusions:

(1) There is large variation in transport among models, producing fields of mean age that vary by more

than a factor 2, and fields of"ANOy" (conserved HSCT emission tracer) that vary by more than a factor

4. These variations are evident both locally and globally.

110
(2)Model-to-model variationsinmeanagearereflected,althoughreduced,insimulationsof long-lived

photochemicallyactivespecies,suchasN20,NOyandCly. Thissuggeststhatuncertaintyin model

transportis amajorfactordetermininguncertaintyin thebackgroundchemicalstateof modelstratospheres,andlimitsourabilitytoassesstheenvironmental impactof HSCTfleets.

(3)Nearlyall modelshavemeanagemagnitudestoosmallthroughoutthelowerandmiddlestratospherecomparedtoobservations, in somemodelsandlocationsbyafactorgreaterthan2.

(4)Mostmodelshaveoneof severalunrealisticfeaturesintheshapesof theirzonal-averagedmeanage

contourshapes.

In addition,anumberofmorespecificconclusionsmaybemade:

(5)Some2Dmodelshavetoolargeexplicitverticaldiffusion(toolargeKzz).

(6)2Dmodelsthatcomputeplanetarywavepropagationandbreakinginteractivelywiththecirculation

havesmallquasi-horizontal mixingin themidlatitudestratosphere(smallKyy)comparedto other2D

models.Thismayberesponsibleforthesemodels'tendencytohavealowerstratospherehighlatitude

meanagemaximum,whichisnotobserved.

(7)Theverticalphasevelocityin thetropicsof anannualcycletracersuchasH20+2CH4 istoorapid

formostmodels,indicatingeithertoorapidverticaladvectioninthetropics,toomuchverticaldiffusion,

orboth.

(8)Manymodelshavetropicsthatarenotisolatedenough,asseenin theoverattenuationperwavelengthof theannualcycleandin theunrealistically"flat" meanagecontours.Forsomemodelsvertical

diffusionwithinthetropicsmayberesponsiblefortheoverattenuation.

(9)ApositivecorrelationexistsacrossmodelsbetweenmeanageandANOy tracer in the lower midlatitude stratosphere. Given (3) above, this suggests that most models underestimate the stratospheric residence time of HSCT emissions. This relationship needs to be explored further, however, before

conclusions may be drawn from it.

We have not systematically examined the impact on long-lived tracer transport of numerical aspects

of model formulation such as advection algorithm, grid resolution, and coordinate system. Such a study

would be worthwhile. Nonetheless, some conclusions can be drawn with regards to certain models. The

mean age distributions are similar for the three-dimensional models MONASH2 and GMI-NCAR,

which differ only in their advection algorithm. On the other hand, models having the same advection

algorithm, resolution, and other aspects of numerical formulation, but differing in their large scale circulation (e.g., MONASH1 and MONASH2), have significant mean age differences. This suggests that the

choice of advection algorithm plays only a minor role in long-lived tracer transport for at least some of

the models. As for model resolution, increasing the horizontal grid resolution in one model tested

(LARC) resulted in only a small increase of stratospheric residence time of the HSCT emission tracer

compared to the variation across models. However, simulations of long-lived tracers by other models,

for example those models having lower resolution, may be more sensitive to numerical formulation.

It is important to appreciate that components of transport, such as advection by the residual circulation and mixing of extratropical air into the tropics, are highly coupled. The coupling is dynamical for

models that compute the circulation in a self-consistent way. However, even for models that prescribe

the components explicitly, their effects on tracers are not separable. For example, slowing the residual

111
circulationandenhancingthemixingof extratropicatairintothetropicsbothincreasethemagnitudeof

meanageinmodels,whichisdesirable.However,changingeitherof thesetransportcomponentsindependently(whichmaynotevenbepossiblein dynamicallyself-consistent models)couldhavedetrimentaleffectsonothercharacteristicsof tracerdistributions.Thetropicalstratospherewouldbeless

isolated,whenit isalreadynotisolatedenoughfor manymodels,asseeninmeanageandN20contour

shapes.In addition,slowingtropicalupwellingwouldincreasetheglobalphotochemical lifetimesof

N20andcertainCFCs,whichmodelsalreadyoverestimate(seesectionC-2).

2.2 Introduction (Hall and Wuebbles)

A series of experiments have been designed to evaluate the treatment of stratospheric transport processes within current two-dimensional (2D) and three-dimensional (3D) models used to study the chemistry and physics of the troposphere and stratosphere. These experiments are designed to examine model

performance against observations and also to evaluate model-to-model differences. As shown in this

chapter, the current treatment of transport by models causes large differences in the distributions of inert

and chemically long-lived tracers among models and between models and observations.

The goals of the transport experiments are:

(1) To isolate the effects of transport in models from other processes;

(2) To assess model transport for realistic tracers (such as SF 6 and CO2) for comparison to observations;

(3) To use certain idealized tracers to isolate model mechanisms and relationships to atmospheric chemical perturbations;

(4) To identify strengths and weaknesses of the treatment of transport processes in the models;

(5) To relate evaluated shortcomings to aspects of model formulation.

Each of the participating models was asked to provide analysis for the following six transport experiments (see the section of run specifications for detailed definitions):

(A I) The "age spectrum," the stratospheric response to an approximate delta function time-dependent

mixing ratio boundary condition in the tropical troposphere near the surface.

(A2) Tropical troposphere and time-dependent mixing ratio boundary condition near the surface.

(A3) Inert tracer representing NOx emissions from 500 HSCTs. Source distribution provided by NASA

database. Zero boundary condition below 6 km in troposphere. Models run to steady-state. The resulting

distributions are referred to here as "ANOy."

(A4) As in (A3), but no emissions south of 30°N.

(A5) Simulated SF 6 concentrations from 1966 to 2000. Time-dependent surface flux boundary condition specified.

(A6) Simulated CO 2 concentrations from 1979 to 1995. Time and latitude-dependent surface mixing

ratio boundary condition specified, based on observations.

The model participating in the transport experiments are given in Table 2.1. In total, 24 different

models are represented, although in a number of cases different versions of the same basic model are

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countedseparately(e.g.,NCAR-3D,MONASH1,MONASH2,andGMI-NCAR;GISS4x5,UCI23,

andGMI-GISS;GSFC-3DandGMI-DAO).Thisinterdependenceof 3Dmodelsmeanstherearefewer

winddatasetsandnumericalmethodsemployedthantherearemodelslisted.It shouldthereforenotbe

surprisingif resultsfromcertainof thesemodelsaresimilar.Thereareseveralclassesof approachtaken

intreatingtransportamongtheparticipating2Dmodels,with somemodelshavingentirelyexternally

prescribedcirculationanddiffusion(e.g.,AER),whileothershaveinternallyconsistentderivations

(e.g.,LLNL), upto fully interactive (e.g., GSFC-2Dint). These differences affect the "tunability" of

transport among the models.

The transport information contained in simulations A1 through A6 is not mutually independent. As

we shall show, basic features of the SF 6, CO2, sin and cos simulations can be inferred from the age

spectrum. Given the wide range of model performances in these transport experiments, these first order

features deserve the greatest attention. Thus, in this report, not all experiments are given equal weight of

analysis, and features of model transport to be compared to SF 6 and CO 2 observations are most often

derived from the age spectrum. This choice of emphasis is driven primarily by convenience: similar

conclusions about model transport would be reached if, for example, the direct SF 6 and sin simulations,

or CO2, had been analyzed instead. The NOx tracers do not have tropospheric source and, therefore,

cannot be reconstructed from the age spectrum. They are treated separately.

In section 2.3 we examine model results of the age spectrum, and discuss its relationship to other

tracers. We then present, in section 2.4, a range of recent observations, with special attention given to

the mean age and propagation into the tropical stratosphere of annual cycles in tracer mixing ratio.

Section 2.5 focuses on the tropical stratosphere of models, comparing model transport properties to

inferences from observations, and discussing model deficiencies. In section 2.6 we present the global

mean age distribution, compare models to observations, and explore possible reasons for model inaccuracies. Section 2.7 examines effects due to seasonal covariance of transport and source. In section 2.8,

model results for the ANOy tracers are presented, and sensitivity to model resolution analyzed. Here, we

also discuss possible relationships between ANOy tracer and mean age. We conclude and summarize in

section 2.9.

2.3 Age Spectrum (Hall)

The age spectrum in the stratosphere G(x, t'[R, to) is a type of Green function that propagates a tropospheric mixing ratio boundary condition specified in a region R into the stratosphere, and thus it may

be used to reconstruct the stratospheric response to an arbitrary time variation of a conserved tracer in R

by convolution of G with that time-variation. (Here, x indicates a position, and R a region, in two or

three dimensions.) G(x, t'[R, to) may be interpreted as the probability distribution function for an irreducible fluid element at x and time t' in the stratosphere to have last made contact with the region R a

time t' - to ago. The age spectrum is discussed by Kida [1983], Hall and Plumb [1994], and Hall and

Waugh [1997a]. Here, we briefly focus on severaI features which have received less attention.

G(x, fiR, to) is determined in a model as the response to the time-dependent boundary condition

8(t'- to) specified over R in the troposphere (see section on run specification). The "forcing region" R

varies somewhat from model to model, depending on particular grid spacings. For stratospheric studies,

the details of R are unimportant as long as it is within the troposphere, as nearly all air and tracers enter

the stratosphere in the tropics, regardless of their sources within the troposphere. The delta function in

time is approximated by holding the forcing region at a finite value for one month, and zero for the

duration of the simulation. (Note that the magnitude of the first month value is arbitrary, as G is afterwards normalized such that ) G(x, t'lR, t0)dt' = 1.)

113
In generalG is a function of both t' and to (or t' - to and t0) separately• In this inter-comparison, the

month corresponding to to is January, and a different start month would yield a different result. Only for

stationary transport does G depend exclusively on the elapsed time t' - t0. However, as we will illustrate

below, seasonality of transport for these models plays only a secondary role in determining many of the

annually averaged properties of G. We would get similar results if a different start month had been chosen. Therefore, from here on we write G = G(x, t), where t = t' - to is the elapsed time, and the separate

dependence on to and R is suppressed. If it were our goal, however, to analyze seasonal variations in G,

we would need to perform separate simulations for several source times to throughout the year. For tropospheric studies, the dependence of the age spectrum on R and to is of comparable importance to x and

t' - t0, and a more complete version of G must be considered (e.g., Holzer and Hall [1999])•

Figure 2.1 shows examples of G(x, t) for (a) GSFC-3D, (b) GSFC-2Dint, and (c) MONASH2 at the

equator and 60°N and pressure altitudes z of 18 km and 26 km. (Note that we use as a vertical coordinate z = 16 log(1000/p), where p is in millibars and z in kilometers.) The age spectra are broad, indicating a range of transit times from the troposphere. The spectra tend to be most peaked, and have the

shortest transit times, near the tropical tropopause. The first moment, or mean age F(x), is shown by the

vertical dashed line. This quantity can be compared to mean ages derived from observations of temporally-increasing tracers such as CO 2 and SF 6. For example, to the extent that SF 6 is a conserved tracer

whose mixing ratio is increasing linearly in time, its time lag at x in the stratosphere from its tropospheric time series is F(x) [Hall and Plumb, 1994]. In fact, the tendency of SF 6 is slowly nonlinear, so

the stratospheric response is actually a convolution of G(x, t) with this this nonlinear trend, and the

stratospheric lag of SF6 from the tropospheric history is not identically equal to the mean age [Volk

et al., 1997]• However, for our purposes, the difference of SF 6 from linearity is small [Volk et al., 1997;

Hall and Waugh, 1998], and F in the stratosphere is well approximated as the simple time lag of SF 6

from the tropospheric trend [Elkins et al., 1996; Volk et al., 1997]• The same is true for CO 2, once the

annual cycle has damped out above about 19 km at midlatitudes [Bischof et at., 1985; Schmidt and

Khedim, 1991; Boering et al., 1996]. Annual cycles of conserved tracers (oscillations of period one

year, such as those of CO 2 or H20+2CH 4) in the model stratospheres may also reconstructed from

G(x, t).

Normal Mode Analysis of the Age Spectrum

Examination of the model results for G(x, t) reveals that all models assume a simple exponential

decay after several years, illustrated for several models in Figure 2•2, a logarithmic version of

Figure 2.1. While the timeconstant for this decay, "c0, varies from model to model, within a model it is

universal, i.e., "Codoes not depend on location in the model atmosphere. This universality may be understood by considering a normal mode analysis of models' G(x, t), similar to that used by Prather [1996]

to analyze coupled chemical systems in a simple one-dimensional model• If an annually averaged perspective is taken, so that transport may be considered stationary, G(x, t) can be decomposed as

N

qln(X)e t/T" In principle, N is as large as the number of degrees of freedom of the system, at least

0

the number of model grid-points. However, for large enough times, only the basest mode is significant;

-tl%

i.e., as t ---)oo, O(x, t) - a0_0(x)e . All points x decay at the same rate "c0.

Table 2.2 lists 'to for the MM2 models, showing a range of values from 1.40 years (GSFC-2Dint) to

5.41 years (MONASH1). As will be discussed in Section 5, 'co, _0(x) , and F(x) are related, and their

variations from model to model are comparable.

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Relation to Other Transport Experiments

As noted above, the formulation of the age spectrum employed here has neglected explicit dependence on time, leaving only dependence on elapsed time from the January source. In the real atmosphere, transport varies seasonally and inter-annually, while in these models, transport varies

seasonally. Thus, one may ask how faithfully the fields from explicit tracer simulations (e.g., SF 6 and

CO2) can be reconstructed from G(x, t), given the particular January start date. At best, G(x, t) reconstruction can provide an annual mean picture of the fields. At worst, strong covariances between transport and surface sources could create large differences between the actual field and the reconstruction.

Figures 2.3(a) through (e) show scatter plots for 5 models of F as derived from the annual mean troposphere-to-stratosphere lag time of a full seasonally varying simulation of SF 6 (see section on run

specifications for details) versus F as the first moment of G(x, t). The correlation is high. This near

equivalence can also be seen in Figures 2.4(e) and (f), which show the height-latitude distributions of

F(x) for MONASH2 derived from the two techniques. Close agreement of the annually-averaged SF6

lag and the mean of the age spectrum does not imply, however, that seasonal variations in F are small.

In Figure 2.3(f), the scatter plot of 3(e) (MONASH2) is repeated, except that the SF 6 lag is for January,

rather than an annual mean. The correlation breaks down due to large seasonal motions of the mean age

isopleths, which cannot be reproduced from the age spectrum for the single value t0. These isopleth

motions can be seen in Figures 2.4(a) through (d), the zonal-mean mean age from MONASH2 for

January, April, July, and October.

We have also compared the reconstruction of a sinusoid using G(x, t) with the actual sin and cos

runs of A2. The amplitude A(x) and phase lag time "cc0(x) from the sin and cos runs and as reconstructed

from G(x, t) are plotted as tropical profiles in Figure 2.5 for the AER, GMI-NCAR, and SUNY-SPB

results. The reconstructed profiles are not identical to those of the sin and cos simulations, as all models

have some covariance between the oscillating source and transport, resulting in a rectification of the

background mixing ratio, which cannot be reproduced from G(x, t) (see Section 6). However, we will

show that the differences are smaller than the spread among models, and also smaller than the modelobservation differences. This is true even for the models with the largest rectification, about 20% (i.e.,

the models' source-transport temporal covariance contributes to about 20% of the background state for

the sin and cos runs).

We conclude that age spectral reconstructions of model tracers may be compared to appropriate

annual mean observations, even though the age spectra have not been determined as a function of source

time t0. However, for situations in which transport seasonality is important and comparisons to observations at particular dates are made, model data from the explicit seasonally varying simulation for the

date in question will be used.

2.4 Observations (Boering)

Since the first Models and Measurements Report was published six years ago [Prather and

Remsberg, 1993], extensive observations have been made from satellites (e.g., UARS), from aircraft

(e.g., ER-2 flights during SPADE, ASHOE-MAESA, STRAT, and POLARIS), and from balloons (e.g.,

the OMS campaign). The measurements of long-lived tracers have significantly improved our understanding of transport in the stratosphere, allowing us to quantify transport timescales and to constrain

models more strictly.

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Twocategoriesof long-livedtracershavebeenparticularlyuseful:(1)tracersof tropospheric origin

whose mixing ratios at the tropical tropopause are increasing approximately linearly over time; and

(2) tracers whose mixing ratios at the tropical tropopause vary approximately periodically with a dominant annual cycle. SF 6 [Elkins et al., 1996; Maiss et al., 1996; Geller et al., 1997; Harnisch et al., 1996]

and annually-averaged CO 2 [Bischof et al., 1985; Schmidt and Khedim, 1991; Boering et al., 1996] are

examples of (1). The annual cycle of CO 2 [Boering et aI., 1994; Boering et al., 1995; Boering et al.,

1996], forced at the Earth's surface, and "total hydrogen" /2/=H20+2CH4 [Mote et al., 1995; Mote

et al., 1996], forced by the annual cycle in tropical tropopause temperatures, are examples of (2). The

fundamental features of linearly increasing and seasonally-varying tracers that constrain model transport are summarized by the mean age F (from SF 6 and CO 2) and the phase lag time _coand amplitude A

of the annual cycle (from /2/ and CO2). In this section, we summarize observations of CO 2, SF 6, and

/2/=H20+2CH4, against which models will be compared in subsequent sections. We focus first on the

tropics, and then on middle and high latitudes.

Tropics

From the time series of observations of vertical profiles for CO 2 and /_/ in the tropics, phase lag

times can be determined (i.e., the time required for the extrema in the periodically-varying tracer mixing

ratios to reach a given altitude from the tropical tropopause). The observed phase speeds are approximately equal to the upwelling rates in the tropical stratosphere [Hall and Waugh, 1997b; Mote et al.,

1996] and can therefore be used to test models' vertical transport rates. Tropical phase lag times as a

function of potential temperature and altitude are shown in Figure 2.6. The phase lag times shown by

the dotted line are based on 4 years of H20 and CH 4 "version 18 level 2" data (about 1.3 km resolution)

from the HALOE instrument on the UARS satellite by Mote et al. [1998], who used an empirical

orthogonal function (EOF) analysis to estimate the phase speed c(z) as a function of height (data provided courtesy of Philip Mote). We have integrated c-I(z) over z to obtain "_o_(z).These data represent

annual mean phase lag times and can therefore be compared to annual mean model results (or age spectral reconstructions). For comparison, the open square symbol is the 1-year phase lag time from

HALOE data derived from examination of Plate 1 from Mote et al. [ 1998], and the black square symbol

is the I 1-month phase lag time from HALOE data derived by Randel et al. [1998], who used a harmonic

regression analysis of the version 18 level 3a data (about 2.5 km resolution).

Phase lag times determined from in situ measurements of CO 2 from aircraft and balloons [Boering

et al., 1996; Boering et al., 1998] and of H20 and CH 4 from balloons [Voemel et al., 1998] are also

shown in Figure 2.6. The CO 2 transit times were derived from knowledge of the boundary condition for

CO 2 mixing ratios entering the tropical stratosphere [Boering et al., 1996; Boering et al., 1998] and

extrema observed in the vertical profiles measured up to 20 km in October 1994, November 1995,

February 1996, July 1996, and September 1997 (typically between 2°S and 4°N) and up to 26 km in

February 1997 at 7°S. The /_/ phase lag times were determined from extrema in vertical profiles up to

25 km in February 1997 and November 1997 at 7°S and assuming that minimum and maximum water

vapor mixing ratios enter the stratosphere January 15 and August 1, respectively. Note that these times

are consistent with the boundary condition determined by Weinstock et al. [1998] from ER-2 H20 measurements and with the lowest water vapor minimum for the OMS flight of 970214 having already

propagated to 417 K by February 14 (see Figure 2.6). However, balloon observations during the CEPEX

campaign in March 1993 [Voemel et al., 1995] and the HALOE time series suggests that minimum

water vapor mixing ratios may enter as late as mid-March. Reflecting these uncertainties, the horizontal

error bars for the highest altitude in situ/_/ minimum in Figure 2.6 include a +2 to -1 month uncertainty.

The in situ "t_oshow a clear seasonal dependence, with shorter "cooat a given altitude in northern winter than at other times of year. The magnitude of the seasonal difference is similar to that derived from

116
HALOE data by Mote et al. [I996]. The annually averaged phase lag times are very similar to the

HALOE data. Overall, despite the lower spatial resolution of the satellite data versus the noncontinuous

time series (in some cases single "snapshots") provided by the in situ observations, the agreement in

phase lag times between the different observations is remarkably good, and thL _bservations may therefore be used with confidence to evaluate model transport.

A comparison of _o_and F from tropical observations is given in Figure 2.7. Mean ages shown are

the average of mean ages derived from recent CO 2 [Boering et al., 1998] and SF 6 [Elkins et al., 1998]

measurements from the OMS campaign at 7°S in February and November 1997. Mean ages from CO 2

are derived as a time lag with respect to the linearized, deseasonalized boundary condition for CO 2 mixing ratios entering the stratosphere (see Boering et al. [1996], Boering et al. [1998]); mean ages from

SF 6 are derived as a time lag with respect to tropical tropopause values by the method described in Volk

et al. [1997]. The divergence of the phase lag times and mean ages increases with altitude, resulting

from the mixing of older midlatitude air into the tropics. Note that for mean ages less than i year, a rigorous comparison of the magnitude of the mean ages and phase lag times cannot be made due to the

precision limits of the SF 6 measurements and uncertainties in its stratospheric boundary condition and

due to seasonal and interannual variations in CO 2 mixing ratios. Constraints such as those provided by

empirical age spectra derived from a time series of CO 2 observations [Andrews et al., 1998] are

required to determine reliable mean ages for the lowest 3 km of the tropical stratosphere. Comparison of

the observations in Figure 2.7 with model results on the larger vertical scale, however, are

straightforward.

The degree to which the annual cycles are attenuated as a function of altitude has also been determined from the in situ CO 2 and/2/ and remote/2/ observations, and in principle can be used to test how

accurately the models simulate the relative rates of tropical upwelling, vertical diffusion, and entrainment of midlatitude air into the tropics. Figure 2.8 shows the observed amplitude attenuation, plotted as

a fraction of its peak-to-peak amplitude at the tropical tropopause (390K for the in situ measurements

and 100 mbar for the HALOE measurements). Although the remote and in situ phase lag times are in

agreement (Figure 2.6), the in situ CO 2 and/2/ observations indicate a much more pronounced attenuation with altitude above 20 km than the HALOE observations. It is not immediately clear which dataset

should be given more weight, as they both have potential complications. On the one hand, HALOE

retrievals below 20 km near the tropopause are difficult and more suspect than at higher altitudes, and

representing the amplitude as a fraction of that at the tropopause could be biasing the attenuation significantly. In particular, the HALOE analyses derive a peak-to-peak amplitude in the water vapor annual

cycle of only about i.5 ppmv at the tropical tropopause [Mote et al., 1998; Randel et ai., 1998], while in

situ measurements suggest an amplitude of about 3 ppm [Weinstock et al., 1998]. For example, the

HALOE H20 tropical profile shown by Mote et al. [1996] (their Figure 2.1) displays a significant

"compression" of the cycle maxima and minima compared to simultaneous ER-2 measurements, while

Randel et al. [I998] (their Figure 2.15) note that HALOE is not able to account for all of the observed

H20 variability near the tropical tropopause. On the other hand, the in situ data above 20 km (the data

points showing the most disagreement with the HALOE data) come from single profiles (the CO 2

datum from Feb 1997 and the/2/ datum from Nov 97) at 7°S in a region shown by other simultaneous

measurements to be perturbed by filamentary intrusions of midlatitude air [Jost et at., 1998], while the

HALOE values come from 5 years of continuous data across the entire tropical region. Thus, the possibility exists that interannual variations and/or the sampling of subtropical air masses have significantly

skewed the attenuations derived from the in situ observations. At the present time and for this report,

then, we choose to weight the ampltiude attenuation towards the HALOE data but recognize that uncertainties remain.

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Extratropics: the Global Mean Age Distribution

There are no global observations of tracers throughout the stratosphere from which mean age may

be directly inferred. Thus, we do not, as yet, have a complete picture of the mean age distribution in the

atmosphere. However, the in situ campaigns mentioned above have provided high quality, high resolution measurements of CO 2 and SF 6 from aircraft and balloon platforms over a variety of seasons and latitudes, from which mean age may be inferred. These measurements, taken together, form a picture of

the mean age in the lower stratosphere, and a partial picture in the middle stratosphere, allowing us to

rule out many model distributions as unrealistic.

Figure 2.9 shows the latitudinal variation of F in the lower stratosphere at 19 to 20 km inferred from

aircraft SF 6 and CO 2 measurements obtained over a 3-week period in October-November 1994. Ages

from the SF 6 measurements were calculated as a time lag with respect to the value at the tropical tropopause, about 0.8 yr as seen in the time lag from the global surface mean SF 6 time series, and include a

small correction for its quadratic growth. (The quadratic correction increases with age, reaching about

6 months for ages of 6 years [Volk et al., 1997].) Ages from CO 2 were derived by accounting for CH 4

oxidation, then calculating a time lag with respect to a linear fit to the deseasonalized stratospheric

boundary condition obtained from observations from 1992 to 1997 [Boering et al., 1996; Boering et al.,

1998]. Note that the annual cycle in CO 2 results in seasonal oscillations in the apparent mean age, but

that these are significantly attenuated by about 20 km in the tropics and 19 km in the extratropics (see

below).

Balloon observations at discrete latitudes allow extension of the mean age distribution to higher altitudes. Figures 2.10a and 2.10b show vertical profiles of F in the tropics and at middle and high latitudes,

inferred from SF 6 and CO 2 measurements from balloons. Ages from SF 6 measured in samples collected

by a balloon-borne cryogenic whole air sampler (CWAS) were reported by Harnisch et al. [1996] as a

time lag with respect to the northern hemisphere surface mean of SF 6 of Maiss et al. [1996]. In order to

compare these ages with the in situ data reported relative to the tropical tropopause, a value of 1.4 years

has been subtracted from the CWAS SF 6 ages (comprised of a 0.6 yr lag between the northern hemisphere and global surface SF 6 means [Geller et al., 1997] and a 0.8 yr lag between the global surface

mean and tropical tropopause SF 6 values [Volk et al., 1997]). Note that an intercomparison of SF 6 calibration standards for the NOAA/CMDL (i.e., OMS) and the University of Heidelberg (i.e., CWAS)

laboratories agreed to better than 1%, and that the adjusted CWAS ages at the tropopause and in the

upper troposphere agreed well with ER-2 SF 6 ages for the same latitudes and seasons. The data labeled

"Tropics" were obtained from OMS flights at 7°S in February and November 1997 and from a CWAS

flight at 17°N. The flight at 17°N should be considered as subtropical, particularly given its vertical

structure above 24 km (see below), and even the OMS flights at 7°S show some filamentary structure

that is likely air from the subtropics or midlatitudes [Jost et al., 1998]. The data labeled "Midlatitude"

were obtained from an OMS flight at 34°N in September 1996 and from a CWAS flight at 44°N in

1993. The data labeled "High Latitude" were obtained from an OMS flight at 62°N in June 1997, during

which some vortex remnants were still apparent, and from a series of CWAS flights at 68°N in winters

from 1992 to 1995, which often penetrated the polar vortex.

From the measurements, we can make the following observations about the global F distribution:

(1) At a given altitude in the lower and middle stratosphere, F is smallest in the tropics, i.e., a given F

contour is higher in the tropics than at midlatitudes.

(2) In the subtropics (20°N) and midlatitudes (40°N), above about 24 kin, F changes little with height,

although it increases with latitude; i.e., F contours are oriented near vertically.

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(3) However, in the subtropics and midlatitudes, below about 24 km, F increases with height. Thus, F

contours which may be vertically oriented aloft, tilt poleward below.

(4) At high northern latitudes, F increases monotonically with height, at least through to 25 kin, with the

exception of the presence of vortex remnants observed in summer.

(5) In the lower stratosphere, latitudinal F gradients are largest equatorward of about +--30°.

(6) The magnitude of F can be 4 or more years (with respect to the tropical tropopause) in the tropics at

30 km and at high latitudes at 20 km.

These observations suggest the picture of the mean age distribution in the lower and middle stratosphere

shown in Figure 2.11, which is derived by contouring the in situ data.

A complementary composite of the aircraft and balloon data can be derived by looking at the mean

ages in the extratropics (i.e., polewards of 20 °) on N20 surfaces. Except when mixing of vortex and

midlatitude air has occurred, creating "mixing lines" in the N20:CO 2 relationship [Waugh et at., 1997],

a reasonably compact relationship exists between N20 and mean age from SF 6 observations and for

CO 2 observations, once the annual cycle has damped out. Figure 2.12 shows mean ages from over 1400

in situ SF 6 measurements and over 82,000 in situ CO 2 measurements from both aircraft and balloons

(i.e., all extratropical measurements from SPADE, ASHOE-MAESA, STRAT, POLARIS, and OMS),

binned and averaged in 5 ppb intervals from l0 to 320 ppb ofN20 [Boering et al., 1998]. The CO 2 and

SF 6 ages are very similar, indicating that interannual variations in the CO 2 growth rate introduce negligible errors in determining a mean age. Thus, in the extratropical lower and middle atmosphere, the

relationship between mean age and N20 is evidently fairly constant, despite interhemispheric and seasonal differences in the N20 distribution.

2.5 Tropical Transport in Models (Hall)

Transport in the tropics of the stratosphere critically affects the distribution of trace gases and the

dispersal of aircraft pollutants, and observations of F(x), A(x), and zeo(x) represent stringent constraints

on modeled transport in this region. To appreciate this, consider the simplest one-dimensional "tropical

leaky pipe" (TLP) model of tropical transport for a conserved tracer mixing ratio Z(z, t)

_H _H zlH _)( -zlH .._g'_ 1

=-7 (x-H<) (1)

Here w is a vertical velocity, K a vertical diffusion coefficient, H the air density scale height, and "_a

relaxation time-constant to a specified extratropical value He, which summarizes the rate at which

upweiling tropical air entrains extra-tropical air. Such a model has been used by Minschwaner et al.

[1996] and Volk et al. [i 996] (with K assumed zero), by Hall and Waugh [1997b] (with constant coefficients, but K allowed to be nonzero), and Mote et al. [1998] (with w, K, and "_allowed to vary spatially).

For the simple TLP model of (1) with constant coefficients, given He and H, values of w, K, and _ completely specify F(z), A(z), and lo_(z) and vice-versa. Clearly, real atmospheric and 2D and 3D model

transport is more complicated. Nonetheless, this simple model is useful to guide interpretation of differences between models and observations.

Figure 2.13a shows tropical profiles of A(z) for models and for inferences from satellite and in situ

observations. The observations are also shown in Figure 2.8. Most models results are summarized by

the shaded region, while the heavy solid line is derived from the analysis of Mote et ai. [1998] of H

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time series from the UARS HALOE (courtesy Philip Mote). The symbols are in situ estimates, as

described in Section 3. From 16 km to about 20 km the wide range of model performances brackets the

HALOE amplitude, while above about 21 km most models have significantly smaller amplitude than

HALOE.

The observed and modeled "_0(z) are shown in Figure 2.13b. In situ and remote inferences are in

good agreement, both showing that most models propagate the annual cycle too rapidly in the vertical.

This has consequences for the models' annual cycle amplitude: although at first sight the models do not

appear to have a general bias in A(z) (Figure 2.13a), we argue below that after accounting for the unrealistically rapid phase speeds most models are seen to effectively over-attenuate the signal.

Figure 2.14 shows profiles of F(z) in the tropics. Models are summarized by the shaded region, and

mean ages inferred from CO 2 and SF 6 observations of three recent balloon flights at 7°S from the

Observations of the Middle Stratosphere (OMS) campaign are shown as symbols. These data are shown

and discussed in Section 3. Most models significantly underestimate F, which will be discussed further

in Section 5. MONASH1 and UCI23, plotted separately in Figure 2. t4, come closest to matching the

tropical observations. Note that the model F(z) profiles are derived from G(x, t), rather than the SF 6

simulations for the dates corresponding to the balloon flights in November and February. However, this

introduces little error, as models show only small seasonal variation in tropical F. Moreover, there is little variation in the observed profiles of F.

As a convenient way to summarize modeled and observed tropical characteristics, consider the

average behavior of F(z), A(z), and l:co(z) over the tropical stratosphere from 100 mbar (16 km) to

24 mbar (26 km). Each modeled A(z) may be fit to an exponential decay with height and each 3:c0(z) to a

linear increase with height (i.e., a constant phase velocity c). A convenient measure of the mean age

distribution is dz/dF, which gives units of velocity for ease of comparison to c. These choices of fits for

A(z) and _c0(z) are guided by the TLP model in which (assuming constant coefficients) the amplitude

decays exponentially as e-z/Ha and the phase speed c = dz/d_co is constant, with H a and c depending on

the model parameters w, K, z [Hall and Waugh, 1977b].

Table 2.2 shows the values of H a and c for MM2 models, the scale height fit to the HALOE analysis

of Mote et al. [1998] and the scale height fit to the in situ observations. Also shown are dz/dF for the

models and from OMS SF 6 and CO 2 balloon measurements, as seen in Figures 2.14 and 2.10a. The

models attenuate the cycle amplitude with scale heights ranging from 2.2 km to 8.7 kin, while for observations H a = 7.6 km for the HALOE analysis and H a = 3.8 km for the in situ analysis. Note that the data

used for the in situ analysis range only up to about 21 km, since the two highest altitude points have

been excluded as they are derived from very few balloon flights and are likely contaminated by intrusions of midlatitude air [Jost el al., 1998]. When only the HALOE points through this height are used,

the exponential fit produces H, = 5.4 kin, in somewhat better agreement with the in situ observations.

Greater attenuation in the lower region is consistent with estimates of entrainment by Minschwaner et

al. [1996], Schoeberl et al. [-1997], and Mote et al. [1998], which suggest more rapid rates near the

tropopause than above.

Using the HALOE "_0 up to 26 km, an average phase speed, c, of 0.33 mm/s is obtained (&z/Axe0

from Figure 2.13). When only HALOE "c0_data to 21 km are used, c = 0.22 mm/s is obtained, indicating

slower upwelling in the lower tropical stratosphere than aloft [Mote et al., 1998]. Fitting a straight line

through all the in situ data below 21 km yields c = 0.23 mm/s. (Recall that the vertical coordinate is

16 log(1000/p). A more accurate relationship between altitude and pressure results in somewhat different values of c.) Overall, there is good agreement between in situ and satellite observations of annual

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cyclephase,asseeninFigure2.13b.Thephasespeedsof themodelsfrom16kmto 26kmrangefrom

• 0.30mm/sto 1.09mm/s(Table2.2).Mostmodelspropagatetheannualcycletoorapidly.

Themodelvaluesof dz/dF range from 0.12 mm/s to 0.42 mm/s, all larger than the value of 0.11

mm/s deduced from OMS SF 6 and CO 2. In general, models predict younger air than observed, in some

cases by a factor of 2 or more. However, for all models, the phase speed c is at least larger than dz/dF, as

the observations show. In fact, Hall and Waugh [1997a] showed that c _>dz/dF as long as G(t) is positively asymmetric, or has a "long tail," as will generally be the case when diffusive flow components are

present. Note that for the TLP model, c _>co >_&/dF, which reduces to c = w = dz/dF for a perfectly isolated tropical pipe (1:= ,_ in (1)) with no vertical diffusion.

At first sight, although there is a wide range of modeled H a, there appears to be no strong overall

bias. However a better measure of the amplitude attenuation is R = Ha/)_, where )_ = c x (lyear) is the

vertical wavelength of the annual cycle. What we would most like to infer from A(z) are the rates of

processes such as diffusion and entrainment that cause the attenuation of A(z). Normalizing by the

wavelength allows each model an equivalent amount of time (one year) for its attenuating processes to

act. Values of R are also listed in Table 2.2 and range from 0.2 to 0.5, compared to 0.7 for the HALOE

analyses and 0.5 for the in situ analysis. On a per wavelength basis, most models over-attenuate the

annual cycle.

Figure 2.15 summarizes Table 2.2 graphically. The top panel is a scatter plot of model values of ,4,./

dF versus c, while the bottom panel shows R = Ha/_. versus c. The numbers indicate the models as listed

in the Table 2.2, while the symbols indicate the values estimated from satellite and in situ observations.

There is some correlation between model values of dz]dF and c, as both increase with the rate of vertical

advection in a model. However, the correlation is not perfect. The quantity dz]dF is strongly affected by

the transport of extratropical air into the upwelling tropical plume, while is only weakly affected.

In order to understand the difference between the model results and the tropical observations, we

look at the relative roles of specific transport mechanisms. A natural tool to aid interpretation is the TLP

model, the simplest model that still includes three relevant mechanisms: vertical advection, vertical diffusion, and mixing of extratropical air into the tropics. Hall and Waugh [ 1997b] solved the TLP model

analytically to estimate w, K, and "_,given HALOE /2/ values of H, and c, values of F deduced from

ER-2 aircraft SF6, and assuming no cycle amplitude in the extratropics (reasonable for this simple

model, given the rapid latitude attenuation of cycles of CO). ]Boering et aI., 1994] and/2/ [Randel et at.,

1998]). Hall and Waugh [1997b] deduced that vertical diffusion in the tropics plays nearly no role in

propagating/2/ from 16 km to 24 km, and a secondary role in attenuating the signal, implying values of

K << 0.1 m2/s. Most of the attenuation is due to extratropical air mixing into the tropics, with a

timescale x -- 1.3 years, in agreement with independent estimates [Minschwaner et al., 1996; Volk et aI.,

1996]. Nearly all vertical transport appears to be due to advection at the average rate w-_ 0.3 ram/s, also

in approximate agreement with independent heating rate calculations [Rosenlof, 1995; Eluszkiewicz

et al., 1996]. Thus, in the context of the TLP model, H a - "_w and c - w. (In this low diffusion limit,

therefore, R -'t/(l year), the entrainment timescale in years.)

Mote et al. [1998] matched a TLP model with height varying coefficients and a photochemical

model to HALOE H20 and CH 4. They observed that in the region 20 km to 24 km, the HALOE H

amplitude attenuates much less than above or below, leading the authors to deductions of "_ up to

6.7 years locally. Over this region, Mote et al. [1998] found that even a small K (- 0.03 m2/s) plays

a ,significant role in the little attenuation that does occur. However, taking the average

x--1 = I "c-1 w -I., d,/ _ n ,-I & from the tropopause to 26 km yields _= 1.6 year, where the w-l(z) factor

appropriately weigfits altitudes at which more time is allowed for entrainment to act (1: and w values

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courtesyPhilipMote).Thus,theaveragentrainmenttimescale,atleast, of Mote et al. [1998] is not so

different than the estimates of Minschwaner et al. [1996], Volk et al. [1996], and Hall and Waugh "

[1997b]. Overall, then, from 16 km through 32 kin, entrainment of extratropical air appears to be the

primary attenuating mechanism.

The role of vertical diffusion in propagating and attenuating a sinusoid of period 1 year is illustrated

explicitly in Figure 2.16 for the TLP model, from the solution of Hall and Waugh [1997b]. The curves

in the figure correspond to w = 0.3 ram/s, and would shift somewhat for different w. In Figure 2.16a,

c/w is plotted versus K for values of the entrainment timescale "rfrom 0.5 years to 8.0 years. For K < 0.1

m2/s, diffusion plays only a small role in the propagation. At higher K values (typical of several 2D

models, as shown below), diffusion plays a significant role. Entrainment has little effect; the extratropics adds air with no cycle amplitude, increasing c only to second order.

Figure 2.16b shows R = Ha/)_ versus K. For R, there are three diffusion regimes: (1) For K < 0.01

m2/s (and '_ < 2 years), diffusion plays little role in attenuating the signal. The attenuation is due mostly

to dilution by extratropicaI air. This is the regime of the estimate by Hall and Waugh [1997b], based on

HALOE data. (2) For 0.01 < K < 0.3 mZ/s, H a (and therefore the amplitude) decreases with increasing

diffusion. Here, the amplitude is attenuated by both diffusion and dilution, but it is more sensitive to the

dilution. (3) For K > 0.3 mZ/s, H a increases with diffusion, with little dilutional influence. In this "diffusive-regime," the TLP model's atmosphere gets progressively closer to an equilibrium with the source,

in which the signal diffuses outward to much of the atmosphere within one cycle period (1 year).

Although the diffusive regime is not realistic, several 2D models may be close to it (see below).

Figure 2.17 illustrates the relative roles vertical diffusion and advection play in transporting the

annual cycles in four 2D MM2 models. The quantities plotted are: (1), annually-averaged Kzz, the vertical component of the model's diffusivity tensor (interpreted as K in the context of the TLP model);

(2), annually-averaged model "c_0,whose slope is c; (3), "re0from the HALOE f/ analysis; and (4), the

timescale for advection by the models' vertical residual circulation _-'*. For GSFC-2D (Figure 2.17a),

Kzz < 0.1 m2/s throughout the tropical stratosphere, putting it in the tow diffusion regime of the TLP

model (Figure 2.16a), so that c = _-'*. Moreover, the upwelling rate agrees fairly well with HALOE/_/

analyses. For GSFC-2Dint (Figure 2.17b) Kzz >> 0.1 m2/s below 22 km. As a consequence, the "re0profile is significantly steeper than the advective time (i.e., c> _'*). In this regime, diffusion plays a large

role in transporting the signal (Figure 2.16a), contrary to the estimates from observations. CSIRO

(Figure 2.17c) is an intermediate case. The advective time is close to the phase lag from HALOE,

suggesting a realistic v_-_*.However, Kzz > 0.I mZ/s, and c> _-'*. SUNY-SPB (Figure 2.17d) has

Kzz = 0.5 m2/s, which again results in c > _-'*. (More typical implementations of the SUNY-SPB model

use much smaller Kzz, as discussed in Yudin et al. [1998].) Note that the 2D residual circulation of

SUNY-SPB has a zero point in the lower tropical stratosphere, visible as a flattening of the advective

time profile just below 24 km. Some type of diffusive transport, either vertical or horizontal, may be

necessary in SUNY-SPB to propagate through this region. The origin of the zero-point is not known,

but it can be seen in the residual circulation of the MACCM2 GCM (see Figure 2.25), from which the

SUNY-SPB fields are derived.

From this analysis, we conclude that several 2D models have explicit values of Kzz that are too

large, in some cases by more than an order of magnitude. For models whose diffusion coefficients are

explicitly set, this may be a straightforward correction. For models whose coefficients are determined

interactively with a representation of propagating and breaking planetary waves, the physical reasons

for such large values of vertical diffusion should be identified and changes considered.

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3D models have no explicit vertical diffusion. A few 3D models (UCI23, GMI-NCAR,

MONASH1) have values of c that are not too far from that observed (see Table 2.2), while others propagate the signal too quickly (GMI-DAO, GSFC-3D, MONASH2, UNIVAQ-3D, GISS8xI0). A large

values of c could be due to an overly vigorous residual circulation, too much diffusion associated with

resolved features of the flow, or too much diffusion implicit in the numerical advection scheme. Numerical diffusion depends on the particular advection scheme and resolution employed by the model. We

have not systematically tested the schemes employed by MM2 models, a study which would be valuable. However, one-dimensional tests show that the second-order moments scheme [Prather, 1986], at

least, does not significantly attenuate a sinusoid with as few as 4 grid points per wavelength. Thus, for

example, numerical diffusion should not be an issue for UCI23. Recently, Yudin et al. [1998] have

derived 2D transport fields from the 3D fields of MACCM2 (equivalent to MONASH2 and

GMI-NCAR). They deduce Kzz = 0.5 m2/s near the tropical tropopause, decreasing to less than 0.1 m2/s

above about 20 km, suggesting a small but non-negligible role for vertical diffusion in propagating the

annual cycle through the lower stratosphere in MACCM2.

2.6 Global Mean Age in Models (Waugh and Hall)

The MM2 models exhibit a wide range of mean age distributions for both 2D and 3D models, as

seen in Figures 2.18 and 2.19. These differences are reduced in N20 simulations (see Figures 2.28 and

2.29), thus illustrating the role of age in highlighting and assessing model transport. In the tropics at

40 km, F ranges from under 2 years (UIUC-3D, GSFC-2Dint, and models using DAO winds) to over

5 years (UCI23). Although all models show the upwelling in the tropics characteristic of long-lived

tracers of tropospheric origin, the tropicai-extratropical gradients vary widely. Vertical age gradients at

high latitudes don't everywhere agree on sign. Some models have local age maxima in the high latitude

lower stratosphere (which is not observed), while others have local maxima higher up at high latitudes,

or have age increasing monotonically with height throughout the atmosphere.

Figure 2.20 shows vertical profiles of models' F at different latitudes and a latitudinal profile in the

lower stratosphere. Also shown are the observations from Section 3 for comparison. Most model profiles are indicated by the shaded regions, but several profiles lie outside these regions, and are plotted

individually. Models are generally too young overall, in some places by more than factors of 2. The

lower stratosphere F maximum at middle and high latitude in several models is a feature not present in

observations, with the exception of a single polar vortex profile suggesting a weak maximum around

25 km. In latitudinal profiles, only three models (HARVARD, MONASH2, MONASH1) capture the

sharp gradients observed between 10 ° and 30 ° from the equator.

For a first attempt at understanding the variation among models, it is useful to categorize them

based on the orientation of their F contours. Figure 2.21 shows schematics of 3 conceivable global F

orientations. The middle schematic, "class B", is the most consistent with observed F(x) features (1)

through (5), discussed in Section 3 and shown in Figure 2.11. In class A, F has a lower stratospheric

maximum at high latitudes, in violation of feature (5). In class C, there are no regions of near zero dl"/

dz, in violation of feature (2).

The models are classified as follows:

Class A: GSFC-2Dint, HARVARD, LLNL, NOCAR

Class B: AER, CSIRO, GSFC-2D, SUNY, GMI-NCAR, MONASHI, MONASH2

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ClassC: NCAR-2D,MGO-UIUC-2D,UNIVAQ-2D,WISCAR,GISS8xl0,GMI-DAO,GSFC-3D,

UCI23,UIUC-3D,UNIVAQ-3D

ThereisacontinuumfromAtoBto C,makingtheclassificationof modelsarbitraryinsomecases.For

example,LLNLcouldbeeitherA orB,whileSUNYcouldbeBorC.Moreover,otherclassescouldbe

definedbasedondifferentcharacteristicsof theF distribution,andmodelswouldbegroupeddifferently.In particular,thepresentclassificationschemeisdeterminedonlybythecontourshapes,andnot

bythemagnitudeof For itsgradients,whichvarieswidelyamongmodels.Nonetheless, theclassdefinitionsA, B, andC suggestvariationsamongmodelsof certaintransportmechanisms, whichwenow

discuss.

Thepresenceofrelativelyflatcontours(classCinFigure2.21)isconsistentwithweakisolationof

thetropics.If waveactivity,parameterizedasdiffusionin2Dmodels,reachestoofarandtoooftenfrom

themidlatitude"surf-zone"intothetropics,theobservedsharpgradientsof themeanage(andother

long-livedtracers;seebelow)betweenthetropicsandextratropicswill notberealized.Forveryrapid

anduniformmixing,the"globaldiffuser"limit is reached[PlumbandKo, 1992;Plumb,1996].To

illustratethispoint,Figure2.22comparesthreeversionsof theAER model:the"tropicalpipe",the

"leakytropicalpipe",andthe"no-pipe"models[Weisensteinet al., 1996].In thetropicalpipemodel,

thetropicsareisolatedbysettinglatitudinaldiffusion(withcoefficientKyy)tonearzeroequatorwardof

20°, whereasintheno-pipemodel,transportbyKyyissignificantatall latitudes.Theleakypipemodel,

theAERversionsubmittedto MM2,isanintermediatecase.In thetropicalpipe,F contoursbulgeup

themostin thetropics,sothattheextratropicalcross-isoplethgradientsarethemostlatitudinallyoriented.

Mixingof extratropicalairintothetropicsalsoincreasesthemeanagethroughoutthestratosphere

byallowingmoreairtorecirculatebeforeexitingthestratosphere. In Figure2.22,theno-pipemodelhas

theoldestairoverall.Thus,addedmixingwouldseemadvantageous, asmostMM2 modelsaretoo

youngoverall.However,additionalextratropicalairin thetropicscanhavethedetrimentaleffectsof

(1)flatteningF contoursof modelsin theextratrol_ics,whosecontoursmay alreadynot be steep

enough;and(2)furtherattenuatingtheamplitudeof H and the CO 2 cycle, which may already be overattenuated (see Section 4).

Counterintuitively, mixing of extratropical air into the tropics has little effect on the magnitude of

latitudinal mean age gradients. For example, while the orientation of F contours varies, dl"/dy varies little among the three AER models in Figure 2.22. Recently, Neu and Plumb [1998] have shown for a simple model that AF (the difference between extratropical and tropical mean age) is independent of such

mixing. This can be appreciated heuristically in the following way: when there is no mixing of extratropical air into the tropics, AF is given by the transit time for one circuit about the Brewer-Dobson cell,

which is approximated by the residual circulation. If some mixing is added, a fraction 5 of tropical air

now has extratropical age, increasing F overall in the tropics. However, the extratropical mean age

increases by an equivalent amount, as the fraction 5 recirculates to the extratropics. AF is unchanged.

In contrast, horizontal mixing by Kyy diffusion in midlatitudes strongly affects the orientation of

mean age contours. Figure 2.23 shows latitudinal profiles of Kyy for several 2D models, averaged from

18 km to 24 km. There is wide variation in the magnitudes and gradients. For example, HARVARD and

GSFC-2Dint, both class A models, have much smaller midlatitude values than the others. To appreciate

the transport role of Kyy, compare the mean age distribution to the distribution of transit times obtained

from back-trajectories using the residual circulation alone. Figure 2.24 shows these two quantities for

GSFC-2Dint and GSFC-2D. For both models the oldest air from the trajectory calculations is in the high

latitude lower stratosphere, as this represents the longest trajectory along residual circulation

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streamlines from the tropical tropopause. This distribution is very different from the GSFC-2D mean

age, for which Kyy transport prevents isolation of old air at high latitudes. On the other hand, there is little mixing in GSFC-2Dint, the mean age is more similar to the transit time distribution, and the model

exhibits a high latitude lower stratospheric F maximum. We note that unrealistic features in the spatial

structure of the residual circulation could also cause a high latitude lower stratospheric F maximum.

The mean age distribution is also strongly affected by the magnitude of the residual circulation. Figure 2.25 compares the residual circulations and mean age distributions of MONASH1 and MONASH2.

These simulations use the same transport model, but wind data from two versions of the NCAR

MACCM2 differing in their gravity wave drag. MONASH2 has a more vigorous circulation, and as a

result, its mean age is lower throughout the atmosphere. Countour shapes appear to change only little

between the models, and the ratio of MONASH2 to MONASHI mean age, about 0.8, is fairly uniform

through the lower and middle stratosphere.

We have not systematically examined the impact on the mean age of numerical aspects of model

formulation. The simulation of advection introduces diffusion ("numerical diffusion") whose magnitude

depends on the advection algorithm, the grid resolution, the coordinate system, and the time step. Diffusion, in turn, affects the global distribution and magnitude of mean age and other tracers. However, at

least for MONASH2 and GMI-NCAR the advection algorithm plays only a small role in determing

mean age. These models are CTMs driven by the same set of GCM wind data (NCAR MACCM2) but

employ different advection algorithms ("semi-Lagrangian" [Rasch, 1994] for MONASH2 and "fluxform semi-Lagrangian" [Lin and Rood, 1996] for GMI-NCAR). Their mean age fields show differences

that are much smaller than differences with other models. However, numerical diffusion may play a role

in determing mean age in other models. For example, models with lower resolution than the CTMs

driven by MACCM2 wind data are likely more sensitive to the choice of advection algorithm. On the

other hand, it is clear that the large scale stratospheric circulation strongly affects mean age. As the

comparison discussed above between MONASHI and MONASH2 and among the three versions of

AER show, models that have identical numerical formulations but different circulations have much different mean age distributions.

Using the normal mode decompositions of G(x, t) (see Section 2.3) the first moment of G(x, t), the

mean age F(x), can also be expressed as a sum over normal modes. Because F(x) is strongly influenced

by the tail region of G(x, t), which is, in turn, dominated by a single exponential decay mode

_o(X)e -t/% (see Section 2), we expect a close connection between F(x), "_0,and _t0(x)_The timescale

"_0displays similar magnitude variation among models as F(x). In Figure 2.26 we plot F versus "c0 for

the models of Table 2.2, where F is the mass weighted integral of F(x), for z > 20 km. There is strong

correlation between the two transport timescales for 3D models (top panel). For 2D models (bottom

panel), there is more scatter, but the correlation is still evident. Apparently, in 2D models, the relative

roles of the basest and higher order modes vary more. Recall, however, that 3D MM2 models are not as

independent as 2D models. Many share the same or similar driving meteorological fields.

The spatial distribution of F(x) is for most models very similar to that of _0(x), as shown in

Figure 2.27 for LLNL and GMI-NCAR. Differences between F(x) and V0(x) are most significant at

high latitudes. _0(x) tends to have a local maximum in the high latitude lower stratosphere, which is

particularly pronounced for class A models. The maximum is either not present or weaker in F(x).

Comparison with Other Long-Lived Tracers (Waugh)

The previous discussion has shown there is a large variation in the simulated mean age between

models, and that the simulated mean age generally differs greatly from that observed. A similar can also

125
be seen in simulations of long-lived tracers with significant photochemical loss in the stratosphere,

although the magnitude of the differences is typically reduced.

Figures 2.28 and 2.29 show the annual-mean zonal-mean N20 from alI models. There is a large

variation in N20 between models, both in isopleth shape and magnitude. For example, the isopleths

bulge upward sharply in the tropics of HARVARD, while in UCI23, they are relatively flat. The N20

magnitude at 40 km in the tropics varies from 20 ppb (NCAR-2D) to 100 ppb (GMI-NCAR). The

observed N20 from the CLAES instrument on UARS [Randel et al., 1994], is shown at the bottom of

Figure 2.29. Comparison with the simulated fields shows that although there is some large-scale qualitative agreement for most models, there are large quantitative differences. For example, HARVARD has

too little N20 in the upper stratosphere and the lower high latitude stratosphere, while GMI-DAO has

too much in both places.

Comparing the N20 of Figures 2.28 and 2.29 to the mean age of Figures 2.18 and 2.19 shows that

the model-model variation of these two quantities is similar. Equatorward of 40" the N20 and mean age

isopleths for a given model are nearly parallel, and models with young air generally have high N20. At

high latitudes N20 and mean age isopleth shapes within a model differ more, particularly for class A

models (GSFC-2DINT, HARVARD, LLNL, NOCAR). Because of photochemical loss in the upper

stratosphere, N20 fails off with altitude in all models, whereas the sign of df'/dz varies among models.

Therefore the N20 loss hides some of the transport differences between models, and between models

and observations. Overall, however, N20 simulations display differences consistent with those of mean

age.

Other tong-lived tracers also show consistent model-model differences. For example, Figure 2.30

compares NOy and Cly for the AER and NCAR-2D models. These families of active chemical species

have isopleth shapes similar to those of N20 and 17:for AER they bulge up sharply in the tropics of the

lower and middle stratosphere, while for NCAR-2D they are more flat. Clearly, variations in transport

affect the chemical background state of the models.

2.7 Source-Transport Covariance (Plumb)

Seasonality of model transport causes large time-variations in tracer distributions, as is seen for

example in the seasonal motion of F contours (Figure 2.4). Such seasonality can also affect the annually

averaged tracer distribution through rectification due to covariance of transport and source. We have

argued that G(x, t) may be used to reconstruct annually averaged F, A, and "_o_,even though transport

seasonality is not properly included, given the single source time to (January). However, the ability to

reconstruct annual-mean F does not necessarily imply small rectification for other tracers. SF 6 has little

seasonality of source, and thus no opportunity for covariance with transport. As for A and "to_from a

periodic source, all that is required for faithful reconstruction by G for a single to is that the spatial gradients of rectification be small. In principle, the stratosphere could experience large uniform rectification due to covariance between source and cross-tropopause transport, which would not be evident in A

and r0JTo ascertain the degree of rectification in the models, we analyze the sin and cos simulations of

experiment A2. For these simulations the boundary condition fixes the annually-averaged mixing ratio

to be unity at the surface. If no rectification occurred, the annually-averaged mixing ratio everywhere in

a model's atmosphere would also be unity. In fact, rectification occurs to some degree throughout all the

model atmospheres. However, the magnitudes are small, typically several percent. Rectification is

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therefore most apparent relative to the propagating sin and cos signals where the signals' amplitudes are

smallest--at high latitudes and in the upper stratosphere.

Figure 2.31 illustrates two examples: the first 5 years from the 10 year AER simulation, and the last

5 years from GSFC-2Dint. The time series for the sin experiment and the cos experiment is shown at 22

km altitude and the latitudes 50°S, the equator, and 50°N. In the AER model, relatively little rectification is evident in the long term, though there are transient effects as the initial pulse is distributed

through the model, especially for the sin tracer. The stronger initial surge in the tropics of sin, as

opposed to cos, indicates seasonal variation of the upwelling. Note that at high latitudes, the initial pulse

of sin tracer builds up through the winter in each hemisphere. It subsequently decays, and after 3 to 4

years the annual mean offset is almost gone. One of the largest examples of rectification among the

MM2 models is in the GSFC-2Dint model at high northern latitudes: source-transport covariance results

in a rectification effect on tracer mixing ratio of about 20% for the cos experiment and 10% for the sin

experiment. Transport seasonality in the GSFC-2Dint model is also revealed by the nonharmonic

appearance of these signals at high latitude.

We have little basis to judge the realism of model rectification other than noting that the effect does

not appear to play a dominant role in determining the distribution of observed stratospheric tracers.

Among the MM2 models there is no obvious similarity in the spatial distribution of rectification, except

that for most models the magnitude is everywhere well under 10%. For both models and the real atmosphere, rectification introduces no complication in interpreting time series of annual cycles at fixed

locations. However, in regions of small cycle amplitude, spatial gradients of rectification could, in principle, complicate the interpretation in terms of annual cycle properties of tracer mixing ratio profiles

measured at a single time. In addition, for a tracer having an annual cycle and a long term trend in its

forcing (e.g., CO2), rectification in the stratosphere could, in principle, complicate interpretation of the

background state. It would be difficult to isolate the component of the annual mean distribution due to

the secular trend alone from the component due to rectification. For example, the mean age inferred

from the GSFC-2D model's simulation of CO 2 (not shown) appears smaller in the southern hemisphere

than the northern hemisphere due to covariance of the CO 2 annual cycle with transport seasonality. The

extent to which this is occurs in the real atmosphere is unknown, although close agreement in mean age

inferred from CO 2 and SF 6 (see Figure 2.20, as well as Boering et al. [1998] and Elkins et ai. [1998])

suggests a minor role at most in the stratosphere.

2.8 HSCT "ANOy" Tracer Distributions (Rind, Hall, and Wuebbles)

Emissions of a tracer were prescribed corresponding to NOx from a fleet of 500 High Speed Civil

Transport (HSCT) aircraft. The total release was 1.75 x 106 kg d-1 with a latitudinal distribution mimicking that of prospective HSCT flight paths, i.e., peaking at northern hemisphere middle latitudes over

the north Atlantic. Maximum releases occur around 18.5 km altitude. The participating models advect

the tracer through the atmosphere, and instantaneously remove it below approximately 6 kin. The experiments test model transport treatment of a stratospheric perturbation, highlighting in particular the role

of stratosphere-troposphere exchange. Experiment A3 includes the full source, while A4 only includes

emissions north of 30°N.

Twenty-three models performed the A3 experiment, and 21 performed A4. They all differed in their

vertical resolution, with the number of layers varying from 25 to 44. Horizontal resolution varied from

2° latitude in the finest 3D model to 10° latitude in some 2D models. All output was interpolated to a

grid of 37 latitudes (5° latitude) and 31 layers from the surface to 60 km (2 km spacing).

127
Although each modeling group was instructed to integrate until an annually-repeating steady-state

was achieved, a perusal of the monthly data shows that in several cases such a state was not quite

reached. When an explicit duration of run was indicated, it was generally in the range of 10 to 12 years.

Model output was most usually reported for the 15th of each month; some models reported values for

the 1st and 15th, while the GISS model, the only model with the ANOy tracers run on-line in the GCM,

reported monthly averaged values. GSFC-3D produced results only for June and December.

Effects of Grid Resolution (Wuebbles)

While only a single HSCT emission source scenario was specified, the effective source distribution

varied among models due to their differing vertical resolutions. Figure 2.32 shows the distribution of the

A3 (and A4) source for most of the models. The data are plotted as the value input to the respective

model layers. When mass weighted according to the thickness of the layers, each of the models is seen

to inject accurately the desired total amount; however, as shown in the figure, the vertical distribution of

the source differed.

The impact of horizontal resolution was examined for the LARC 3D model by performing two runs

of the HSCT tracer, one at TI 6 (triangular truncation to wave number 16) resolution and one at T32 resolution. These simulations correspond approximately to 5.5 ° x 5.5 ° at TI 6 and 2.8 ° × 2.8 ° at T32. Each

simulation was run in an off-line mode using the same annually repeating wind and temperature fields

derived from a T32 GCM simulation. For the TI6 run, the T32 input fields were degraded to the appropriate resolution. The runs were otherwise identical, with the exception that the numerical time-step in

the T32 model was 30 minutes, half that used in the TI6 model.

Figures 2.33 and 2.34 show the calculated ANOy tracer fields at 69 hPa resulting from the specified

HSCT injections. Both simulations were run for 27 model years to ensure a cyclically repeating steadystate solution. In Figure 2.33 (T32) the ANOy mixing ratio peaks above 1.8 ppbv in the northern midlatitudes. Comparison Figure 2.34 shows that the T16 ANOy distribution is up to 15% less in some

regions than T32. While the overall distribution of the ANOy tracer is very similar in both simulations,

the high resolution simulation retains about 10% more ANOy in the zonal mean. Globally integrated

ANOy is 9% larger in the T32 simulation. The results indicate that the differences in horizontal resolution yield a small but non-negligible impact on the distribution of ANOy from aircraft effluents.

Annually-Averaged Distributions (Rind)

The annual average latitude/altitude distributions of HSCT ANOy tracer concentrations are given in

Figures 2.35a and b for tracer A3 and Figure 2.36a and b for tracer A4. The 3D models are shown

together (35a, 36a), as are the 2D models (35b, 36b). Table 2.3 lists the total integrated mass for the

tracer in cases A3 and A4. Significant differences from model to model are apparent. For example, total

burdens of the tracer differ by nearly a factor of 4 for A3. The 3D models look more similar than the 2D

models; part of this is because a number of the 3D models use similar wind fields (e.g., MONASHI,

MONASH2, and GMI-NCAR all use variants of the NCAR 3D model wind fields; GSFC-3D and GMIDAO use the Goddard 3D assimilated wind data; and GISS-3D, GMI-GISS and UCI23 use the GISS II'

winds).

Transport Between the Stratosphere and Troposphere (Rind)

Also shown in Table 2.3 is the models' ANOy tracer mass below and above 100 mb, as well as the

ratio. These values are displayed graphically in Figures 2.39 and 2.40. (As the LARC T16 values are

128
close to those of the LARC T32, only the later are shown in the table; only A3 is available for the GMIGISS, whose values are often close to those of the UCI23 model using the GISS winds, so the GMIGISS results are not shown in all the figures.) The ratio of ANOy at altitudes above and below 100 mb

varies between 2.0 and 5.5, i.e., they vary by almost a factor of 3. For the individual regions, the amount

in the "stratosphere" (approximated as the amount above 100 mb in altitude) typically varies over a factor of 2, and the amount in the "troposphere" (below 100 mb in altitude) varies by a factor of 2 to 3.

Similar magnitudes of variations arise for A4. Several models differ by even greater anaaounts. GSFC2D has very large values of mass at all levels, while the HARVARD model has very high values in the

stratosphere (true for both A3 and A4). In addition, UNIVAQ-3D has extremely low values in the troposphere, and hence a very high strat/trop ratio.

The relationship between these results and the altitude distribution of the sources (Figure 2.35) is

complicated. The UNIVAQ model and NCAR-3D have sources in layers at the highest altitude. These

models do have large strat/trop ratios, but their stratospheric values are not particularly large, implying

rapid transport down through the tropopause. In comparison, GMI-NCAR has a somewhat lower altitude source, and does have a lower strat/trop ratio, but actually has a larger stratospheric mass of ANOy.

HARVARD and GSFC-2D have similarly large high altitude sources and large stratospheric loadings,

but their strat/trop ratios differ by more than 50%. NOCAR, LLNL, and GMI-DAO have somewhat

lower altitude sources; only the last two of these have low strat]trop ratios, and only the first and third

have relatively low stratospheric values. The layering seems to have some effect, but differing model

transport is also quite important.

The strat/trop ratio for the 2D models (3.78 average for A3) is not significantly different from that

of the 3D models (average 4.10). The 2D model ratios have a somewhat higher standard deviation, but

this may simply indicate results from the fact that a number of 3D models use similar wind fields. The

average strat/trop ratio for A3 (3.95) is higher than that for A4 (3.00). With the source extending equatorward of 30°N, the A3 tracer can be more easily caught in the tropical upwelling branch of the stratospheric circulation, resulting in a longer stratospheric residence time.

Latitudinal Transport (Rind)

Another important question is how much of the tracer is spread equatorward into the southern hemisphere. Table 2.4 gives the ratios between the two hemispheres at 20 km altitude, near the location of

the peak loading. The mean ratio and standard deviation among the models for A3 is 2.76 + 0.74; 19 of

the models are within one standard deviation of the mean. In comparison to some of the other results,

this A3 diagnostic shows relatively good agreement among the models. For A4 the mean and standard

deviations (21.6 + 32.9) are dominated by the outlier results, as several models have very little southern

hemisphere A4 tracer. The largest ratios for A4 generally occur with the 2D models, although not all of

the 2D models have large ratios. The 3D models generally have ratios between 2 and 3 for A3, and

between 6 and 9 for A4.

Also shown in the table is the season when the ratio is a maximum. One might expect that the wintertime residual circulation within the stratosphere to be associated with poleward advection of tracers,

and hence maximum ratio during that season, or perhaps cumulatively, the following spring. However,

there is no agreement on this whatsoever, with each season represented approximately equally.

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Stratospheric Vertical Transport (Rind)

To characterize the vertical distribution within the northern hemisphere stratosphere, we calculate

the ratio between the peak value at 30 km, 40 km, and 50 km to the absolute peak value, which occurs

near the source in the lower stratosphere. Values are given in Tables 2.5 for A3 and A4, taken from the

season of maximum concentration in the lowest stratosphere, and choosing the maximum value at the

given altitudes. The ratios are therefore meant to be primarily illustrative; different choices for season

and location yield different magnitudes. As seen through these ratios, the vertical distribution within the

stratosphere differs greatly between models. The mean value and the standard deviation of the ratio for

A3 (A4) at 30 km is 0.46 + 0.19 (0.27 + 0.15); at 40 km is 0.36 + 0.13 (0.17 _+0.10); and at 50 km is

0.33 + 0.14 (0.14 _+0.08). The ratio tends to be lower for 2D models, indicating overall slower upward

transport. The smaller value of the ratio for A4 is consistent with the lack of a source equatorward of

30°N, the region of maximum upward velocity in the stratosphere.

Other components of the vertical distribution within the stratosphere are visible in Figures 2.35

and 2.36. GSFC-2D has relatively large A3 values in the upper stratosphere/lower mesosphere in both

hemisphere, while the HARVARD model has relatively large values at these levels only in the northern

hemisphere. Only the HARVARD model maintains a strong hemispheric gradient above 30 km.

Also shown in Table 2.4 is the season of maximum upper to lower stratospheric ratio in the northern

hemisphere, a measure of the season of maximum vertical extent of ANOy tracer. Most models have an

autumn maximum or no significant seasonal variation at all, although a few models have peak summertime values, especially for A4. The tracer abundance in the middle and upper stratosphere is governed

by the upward velocity in the tropics. In the reaI world the season of maximum tropical upward velocity

in the lower stratosphere is the northern hemisphere winter, associated with its stronger residual circulation [Rosenlof, 1995]. In the GISS models for example, the vertical velocities in the tropical low-tomiddle stratosphere are on the order of 0.5 mm/s. To rise 10 km would then take about 7 months, so a

summer/fall maximum in the middle stratosphere may be realistic. The autumn maximum is also in

accordance with the seasonal variation of the peak lower stratosphere tracer mass in some models, as

discussed below.

Seasonal Variation (Rind)

Another diagnostic of model transport is the seasonal distribution of the total tracer mass, which, as

indicated in Table 2.4 and Figures 2.35 and 2.36, is dominated by the lower stratospheric amounts. The

season of maximum total ANOy mass is indicated in Figure 2.41 for A3; values are generally similar for

A4.

The predominant time of year for peak values to occur in the models is autumn to early winter. Only

one model had a peak in the summer (LARC) and only one in the late winter (GSFC-2Dint). As the

source was constant throughout the year, the peak in the lower stratosphere occurs when the cumulative

mixing into the troposphere is weakest. Since downward mixing in the extratropics is thought to occur

in conjunction with the stratospheric residual circulation and extratropical storms, both of which maximize in winter, a peak concentration in autumn is reasonable. Note that the peak ozone transport into the

troposphere, which occurs in spring, is not a suitable analog, since the lower stratospheric ozone source,

associated with transport to high latitudes, maximizes during the winter season, unlike the source used

here.

The magnitude of the seasonal variation also differs among models, as seen in Figure 2.41, which

shows as a percentage maximum minus minimum values divided by the annual mean. The average

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model amplitude is 11 _+9%; the fact that the standard deviation is almost as large as the mean illustrates

the great disparity in seasonal amplitudes. Note that the models using the NCAR-3D winds have larger

variations than the other 3D models. The GSFC-2D model has very little seasonal variation.

Summary of A3/A4 Distributions (Rind)

This section has quantified the large differences among model results really in just about every

aspect of transport: exchange between the stratosphere and troposphere, between the northern and

southern hemispheres, and within the stratosphere. In most of these areas, differences of a factor of 2 or

3 are evident. Given that there is no obvious "right" answer, this degree of uncertainty should be factored into any analysis of aircraft impacts.

What causes the model differences? Model vertical resolution likely plays some role; the differing

vertical distribution of the source shown in Figure 2.32 indicates how model layering differs. Model

vertical transport, either the diffusion coefficient and the residual circulation in 2D models or the winds

in 3D models, are undoubtedly also important. Vertical transport coefficients are for many 2D models

"tuned" to observations of long-lived tracer distributions, but apparently this does not result in agreement for the HSCT NOx source. These inert HSCT emission tracers test model transport severely and

are likely more sensitive to variations in model formulations than tracers which have tropospheric

source and photochemical loss in the middle and upper stratosphere. GCM winds used in the 3D models

are compared with observations during the course of development, but there is generally Iess room for

calibration, given the more complete physical representation of many transport processes.

An additional characteristic of importance is the numerical advection scheme utilized. When the

GMI-GISS model was run with two different vertical advection schemes, it produced a factor of 2 difference in the peak stratospheric loading of ANOy (Rotman, personal communication). This difference

is generally reduced at higher resolution (Rasch, personal communication).

The A3 and A4 tracers have no atmospheric counterpart, and thus there is no absolute "right"

answer. In the next section we will discuss suggestive relationships between these idealized HSCT tracers and mean age, so that model performance may be indirectly assessed, at least partially. First, however, some comments can be made directly. For troposphere/stratosphere exchange the latitudinal

spread of volcanic aerosols implies that high latitude sources have difficulty getting into the opposite

hemisphere; thus, especially for A4, a greater NH/SH ratio (within reason) is probably more accurate.

(Volcanic aerosols are not a perfect analog because their fall velocity limits their stratospheric lifetime.)

The upward vertical advection within the stratosphere should be dominated by tropical upwelling,

which appears to be the case in all the models. Given that this upwelling should maximize in the northem hemisphere winter, there should be a seasonal variation in middle stratospheric concentrations, perhaps delayed by the slow rate of transport until the summer or autumn. Some upward advection should

occur in any case, so exceedingly low values of tracer mass in the middle stratosphere are probably

unreasonable. The seasonal maximum in total concentration in most of the models occurs in late fall,

which is reasonable, given that the stratospheric residual circulation, which produces downwelling in

the northern hemisphere extratropics, and extratropicat storms with associated tropopause folds, should

maximize during northern hemisphere winter.

ANOy-F Relationship (Hall)

Despite the fact that the ANOy tracers of experiments A3 and A4 are not real atmospheric tracers,

there exist connections between their distributyons in model simulations and distributions of tracers that

do have real atmospheric counterparts, such as F(x). To the extent these relationships are robust,

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ANOy(x) distributions can be assessed indirectly, by comparing simulated and observed F(x), as has

been done in Sections 4 and 5. Boering et al. [1996] noted that in certain limits, F(x) and ANOy(x) are

closely related. In particular, if the mixing ratio ;((x 0, t) of a conserved tracer is linearly increasing in a

region x 0, as is SF 6 in the troposphere, then at some other x, _(x, t) _: t - F(x). Substituting into the

continuity equation for yields

OF

0--7+ L(F) = 1 (2)

where L is the linear transport operator, and the boundary condition is F(x 0) = 0. (F is a function of t,

because transport varies seasonally. If we restrict ourselves to an annually averaged perspective then

_F/Ot = 0). Thus, the lag time of a linearly increasing tracer (the mean age) is equivalent to the steady

state atmospheric response to a uniform, constant tracer source and a zero boundary condition at the

surface. This equivalence suggests a relationship between F(x) and the distributions of tracers of HSCT

emission, such as ANOy(x), which have stratospheric sources and tropospheric sinks [Boering et ai.,

1996].

The correspondence cannot be perfect. The source of ANOy(x) is not uniform, but is instead

strongly peaked in the midlatitude lower stratosphere of the northern hemisphere. The approximation of

uniform source is only valid to the extent the stratosphere is well-mixed compared to its exchange time

with the troposphere, which is not the case. Nonetheless, useful ANOy(x)-F(x) relationships may still

hold locally within some region about the source. In addition, the ANOy that is transported to the rest of

the stratosphere may have aspects of its distribution similar to F(x). The extent this is true will vary

among models.

On a global basis, certain similarities between F(x) and ANOy(x) are seen by comparing the zonal

mean plots in Figures 2.t8 and 2.19 and Figures 2.33 and 2.34. In the northern hemisphere lower stratosphere, just below the region of peak forcing, ANOy and F contours are roughly parallel. In the southern

hemisphere, although there is generally much less ANOy, the contours are also parallel.

The total amount of ANOy in the southern hemisphere is also related to the global F(x) distribution,

at least in certain instances. GSFC-2Dint, HARVARD, and NOCAR, the three models with the largest

ratios of northern to southern hemisphere ANOy for A3 and A4, are all "class A" models (Figure 2.21).

These models have small Kyy throughout the stratosphere (Figure 2.23), thereby allowing old air to

remain isoIated at high latitudes, resulting in the high latitude lower stratospheric F maxima, and preventing ANOy from reaching the southern hemisphere.

In Figures 2.42 (2D models) and 43 (3D models), we examine more closely northern hemisphere

F(x) and ANOy by plotting profiles through the upper troposphere and lower stratosphere. Values are

averages from 35°N to 55°N. For most models, F(x) and ANOy(x) are well correlated below the

ANOy(x) peak. Moreover, many models have similar F(x)/ANOy(x) ratios. This relationship is summarized in Figure 2.44, a scatter plot of F versus ANOy, where the overbar indicates averaging over the

region 16 km to 22 km and 35°N to 55°N. There is a strong correlation in 2D models (bottom panel). 3D

models show a somewhat weaker correlation.

The ANOy(x) distribution in the lower stratosphere is sensitive to several transport mechanisms,

and to understand the distributions within models, it is necessary to examine these mechanisms in detail.

One mechanism is the mixing of midlatitude air into the tropics, where it can upwell. As noted above,

models with weak horizontal mixing tend to have little ANOy in the southern hemisphere. Other factors

equal, these models will tend to have little ANOy in the stratosphere as a whole, because ANOy will be

transported through the midlatitude tropopause into the troposphere before it can reach other

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stratosphericregions.However,otherfactorsarerarelyequalamongmodels,andbeyondafewextreme

cases, there is not a strong relationship between total stratospheric ANOy and horizontal mixing rates, or

tropical entrainment of extratropical air.

In particular, the rate of transport across the midlatitude tropopause also varies among models. A

critical factor determining this transport is simply the height of the source (typically peaking at 18 km)

from the tropopause. For this purpose, in 2D models the tropopause is the height of onset of rapid vertical diffusion, which parameterizes vertical mixing processes in the troposphere. The closer the source to

the tropopause, the less ANOy can reach other stratospheric regions, other things equal. A second factor

is the average rate of downward vertical advection by the residual circulation, which carries ANOy to

tropopause. Again, other things equal, rapid downwelling means less ANOy elsewhere in the

stratosphere.

Figure 2.45 shows profiles of annually averaged -_-'* and Kzz for several 2D models. Values are

averaged from 35°N to 55°N, and panels in the figure correspond to those of Figure 2.42. For example,

GSFC-2D has small Kzz down to 14 kin, below which Kzz increases abruptly. This onset of larger Kzz is

significantly below the peak source level. Moreover, GSFC-2D has relatively slow downwelling. As a

consequence, GSFC-2D has one of the highest ANOy peak values, and the most ANOy over the whole

stratosphere. HARVARD has a somewhat higher onset of troposphere-like Kzz , but it is still below the

source. HARVARD's downwelling rate is the slowest among 2D models. As a consequence,

HARVARD has the highest peak value of ANOy. The very small values of Kzz in HARVARD help to

keep the peak ANOy high, but also prevent ANOy from reaching other pans of the atmosphere.

HARVARD's global stratospheric ANOy is high, but less than GSFC-2D.

On the other extreme, although GSFC-2Dint has relatively slow downwelling throughout the lower

stratosphere and upper troposphere, the transition from low to high Kzz is significantly higher and more

gradual than other models. In fact, it begins above 18 km, the height of peak ANOy source. The resulting ANOy peak is smaller than most models. The small peak, combined with small values of Kzz, results

in GSFC-2Dint having the least ANOy in the stratosphere among 2D models.

A similar comparison of 3D MM2 models would be more difficult, but worthwhile. The processes

controlling the lower stratospheric ANOy(x) distributions are both more varied and more difficult to

quantify. Stratospheric wave driving is believed to be responsible for the residual circulation ("downward control") [Haynes et al., 1991] and therefore is ultimately responsible for the transport of air from

the overworld (the region of exclusively stratospheric potential temperature, in which most HSCT gases

are emitted) to the lower-most stratosphere (the region of the stratosphere having potential temperature

values whose isopleths cross into the troposphere). However, the fate of air in the lower-most stratosphere is determined by complex tropospheric meteorology [Holton et al., 1995]. The transport across

the tropopause associated with upper tropospheric events is not modeled well by diffusion.

Although the processes governing ANOy(x) are complex, and difficult to assess individually in

models, these comparisons have shown a good correlation between ANOy(x) and F(x). This implies that

despite ANOy not being an observable tracer, we can partially assess the realism of its simulation by

assessing the realism of simulated F(x), for which observations exist. Figure 2.46 shows several model

F(x) profiles at 35°N through the upper troposphere and lower stratosphere. The symbols are F inferred

from SF 6 measurements from an OMS balloon flight at similar latitude (see Section 3). Here, F from

models is derived from the SF 6 simulations, rather than G(x, t), so that the values correspond to the

month of flight (September) rather than an annual mean. Most models underestimate dF/dz through the

region, and hence have air in the lower stratosphere that is too young. To the extent that the ANOy(x)-

F(x) relationship is robust, the F underestimate implies that most models also underestimate ANOy in

the lower stratosphere, as has been suggested by Boering et al. [1996].

133
3DmodelsmayhaveadifferentANOy(x)-F(x)relationshipthan2Dmodels.Forexample,transport

intothetropospherealongmiddle-worldisentropescarrieslowerstratospherictracersfurtherintothe

tropicsof the tropospherethandoesthe rapidverticaldiffusionof 2D models.Thiswouldimply

youngerairin thelowermidlatitudestratospherefor thesameamountof ANOy,asthepathwayto the

"agesink"(nearthetropicalsurfacefor experimentA1) wouldbemoredirect.Suchadifferenceis

hintedat inFigure2.42,whichshowsthe3Dmodelshavinga moreshallowANOy-Fslope(anddisplayingmorescatter).In ordertoestablishamorereliableANOy-F relationship, experiments should be

performed with more realistically prescribed tropospheric sources and sinks of ANOy and F.

2.9 Summary and Conclusions (Hall)

In this chapter we have reported on model simulations of idealized and realistic conserved tracers in

order to better understand model transport and evaluate model transport against observations. Two general observations may be made:

(1) There is large variation in model transport, producing mean age fields that vary by more than a

factor 2, both locally and integrated, and ANOy fields that vary by factors of 4 or more, both locally and

integrated.

(2) Model-to-model variations in mean age are reflected, although reduced, in simulations of long-lived

photochemically active species, such as N20, NOy, and Cly. This suggests that uncertainty in model

transport is a major factor determining uncertainty in the background chemical state of model

stratospheres.

In addition to these observations, we draw several more specific conclusions and isolate aspects of

model transport that need improving:

(3) All models have too low mean age throughout the lower and middle stratosphere compared to observations, in some models and locations by a factor more than 2.

(4) Several models (AER, CSIRO, GSFC-2D, SUNY, GMI-NCAR, MONASH1, MONASH2) have

realistic zonally-averaged contour shapes of mean age ("class B" in the nomenclature of Section 5), but

their mean age magnitude is still too small. Other models have unrealistic features in their contour

shapes ("class A" or "class C" depending on features), in addition to low mean age.

(5) Many 2D models have too large explicit vertical diffusion (too large Kzz).

(6) Models that compute planetary wave propagation and breaking interactively with the circulation

tend to have too little quasi-horizontal mixing in the stratosphere (too small Kyy).

(7) The vertical phase velocity in the tropics of the annual cycle tracer such as H20+2CH 4 is too rapid

for most models. This fact, combined with low mean age indicates too rapid vertical upwelling in the

tropics, although some of the phase velocity is due to the high vertical diffusion.

(8) Many models have tropics that are not isolated enough, as seen in the overattenuation (per wavelength) of the annual cycle tracer and in the unrealistically "flat" mean age contours (class C models).

For some models vertical diffusion may be responsible for the overattenuation.

134
(9) For most models, mean age should be increased by reducing the residual circulation magnitude.

Increased mixing of extratropical air into the tropics would also increase mean age overall, but this

would detrimentally affect the tropics for most model (point (8)).

We have not systematically examined the impact on long-lived tracer transport of numerical aspects

of model formulation such as advection algorithm, grid resolution, and coordinate system. Such a study

would be worthwhile. Nonetheless, some conclusions can be drawn with regards to certain models. The

mean age distributions are similar for the three-dimensional models MONASH2 and GMI-NCAR,

which differ only in their advection algorithm. On the other hand, models having the same advection

algorithm, resolution, and other aspects of numerical formulation, but differing in their large scale circulation (e.g., MONASHI and MONASH2), have significant mean age differences. This suggests that the

choice of advection algorithm plays only a minor role in long-lived tracer transport for at least some of

the models. As for model resolution, increasing the horizontal grid resolution in one model tested

(LARC) resulted in only a small increase of stratospheric residence time of the HSCT emission tracer

compared to the variation across models. However, simulations of long-lived tracers by other models,

for example those models having lower resolution, may be more sensitive to numerical formulation.

It is important to appreciate that transport "mechanisms," such as residual circulation advection and

mixing of extratropical air into the tropics, are highly coupled. The coupling is dynamical for models

that compute these phenomena in a self-consistent way. However, even for models which prescribe the

mechanisms explicitly, their effects on tracers are coupled. Thus, for example, one might strive to slow

the residual circulation in order to increase mean age, and end up with older air but a poorly isolated

tropics, as seen in the mean age contour shapes. One would need to find the proper balance of residual

circulation magnitude and extratropical to tropical transport. This is difficult enough for highly

"tunable" models, having explicit mechanism prescriptions. For more realistic models, in which the

mechanisms are not truly separable in a dynamic sense, it is still more difficult. Nonetheless, given

observation (2) above, this is the task ahead.

Finally, we observed a suggestive positive correlation across models between ANOy and mean age

in the lower midlatitude stratosphere. In fact, idealized arguments hint that such a relationship might be

expected. The "integrated mean age" F of the stratosphere (the mean time since air in the stratosphere

was last in contact with the troposphere) equals the residence time _R of the stratosphere (the mean

time since air in the stratosphere next makes contact with the troposphere). If the stratosphere were a

well-mixed reservoir in contact with a lower boundary (the troposphere), then a tracer emitted locally in

the stratosphere at steady flux F would have total stratospheric abundance given simply by F_ R, which

equals FF. Alternatively, instead of assuming "well-mixedness", an identical conclusion is reached if

the tracer is assumed to be emitted uniformly through the stratosphere [Boering et al., 1996].

Clearly, the stratosphere is not well mixed and HSCT emissions are highly localized. Nonetheless,

certain transport mechanisms push both the mean age and the residence time of tracers released in the

lower midlatitude stratosphere in the same direction. Other factors equal, the more rapid the mixing of

extratropical air into the tropics, the greater the overall mean age and the greater the residence time, as

air and emission tracer are more likely to recircutate about the Brewer-Dobson cell before exiting the

stratosphere. Again, other factors equal, the slower the rate of midlatitude stratosphere to troposphere

transport, the greater the mean age and residence time, as more time is available for air and tracer in the

lower midlatitude stratosphere to enter the tropics. Both effects push the stratosphere closer to being

well-mixed compared to timescales for transport into the troposphere. At the other extreme, in the limit

of pure advection by the residual circulation, the residence time and mean age must be very different, as

the tracer emitted in the lower midlatitude stratosphere is much closer to exiting the stratosphere than is

air newly entering the stratosphere in the tropics. Still, the slower the residual circulation, the greater

both the residence time and the mean age.

135
It is tempting, therefore, to extrapolate the ANOy-F correlation and conclude that because models

underestimate mean age, they underestimate ANOy. However, such a conclusion must presently be

treated with great caution. The idealized arguments above have not been quantified, and take no account

of the complex coupling between transport mechanisms. Moreover, we observed a poorer correlation

among 3D models than 2D models, which is not understood. Until the relationship between conserved

tracers of different point source locations has a more reliable theoretical underpinning, the ANOy-F

relationship must be viewed as merely suggestive.

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138
(A) GSFC-2D

i

0.0 26.0

0

i 1 i

0.0 18,0

60.0 26.0

............ a ............

I i i

i a l _

60.0 18.0

............ a ............

(B) GSFC-2Dint

,,

I 1 i i i i

..a i i

0.0 26.0

i

0 0

0.0 18.0

I

0

j t I L i i i i

!

I i i 1 _ i i

60.0 18.0

(C) MONASH2

t t - i t

t i i i

i I i i

0.0 26.0

to

w L 1, i i

0.0 18.0

60.0 26.0

i I i _ A i

t ........ L ........

i | i

60.0 18.0

Figure 2.1. The age spectra for 3 models at 4 locations in the latitude-height plane: the equator at 18 km and

26 km, and 60°N at 18 km and 26 km. The models are (a) GSFC-2D, (b) GSFC-2Dint, and (c) MONASH2. The

mean age is indicated by the vertical dashed line, and the spectral width by the horizontal dashed line.

139
-4

0

-4

(A) GSFC-2D

i i i t .

-4

0

-4

I r i i

"_ 60.0 26.0

_-_-_._..._..

,.t

i i : ! t i i I i i

-4

0

-4

(B) GSFC-2Dint

0.0 26.0

10

a___

0.0 18.0

0 ]' -_ i t _ i. i I ! i I

60.0 26.0

-4 , , 3

0 r - . _ I .......

--. 60.0 18.0

-4

0

-4

(C) MONASH2

i i i i i _ r i i

0.0 26.0

io

.... -_ .... fO

0 [ i i i i

60.0 26.0

0 I * ' ' ' ' '

-4f_ 3

Figure 2.2. The same as in Figure 1, but here the vertical axes are log scale to emphasize the universal exponential

decay time "c0. The decay mode "co, as listed in Table 2.2, is illustrated by the dashed lines.

140
(o) 6

ID

4

E

AER

i

/

.p

"° , i

/

/

/

/

/

/

(c) 6

co

4

E

o

E 2

(e) 6

ID

'_ 4

E

o

L

o

g

_2

2 4

mean age from age spectrum

GSFC-2D

i i

/

/._ "

jot

I ]

2 4

mean age from age spectrum

MONASH2

g/"

0 pl I I

0 2 4

mean age from age spectrum

/

/

/

I

/

/

/

/

(b) 6

ID

"_ 4

g

_2

0

0

(d) 6

co

g

_2

GISS-8xlO

i . i

0

0

(f)

u

v

to

E

o

o

2

E

/

/

/

I i

2 4

mean age from age spectrum

NOCAR

i i

/

/

/

2 4

mean age from age spectrum

MONASH2

I

/

/

/

• • .. . °. •°#.

• " " ":'_i;i'"

: . • "" ./... :

:. • • "i. " _."_

• 'I " "I "i

:" t " /" """:':_'

•. ,,,..:" ....;-.._

• .....,:..'!..-:.-...-:,

/°°%- • , •_+.

e/.

• •

/..: _.. .. • •

• o+°° ] ]

2 4

mean age from age spectrum

/

/

/

/

I

I

0

0 6

Figure 2.3. Scatter plots of mean age deduced from the SF 6 simulations (vertical axes) and the age spectrum

(horizontal axes). The models are (a) AER, (b) GISS8xI0, (c) GSFC-2D, (d) NOCAR, and (e) MONASH2. For

(a) through (e), the SF 6 age is an annual mean. Panel (e) represents MONASH2, but this time the SF 6 lag is for

January only.

141
4C I ........ I 40 ...... i ....

35 35

i i

Meon A_e, September Mean Ac_e, December

,o

-50 0 _O -50 Oud e 50 _tltu_ _t

Meon • Annuof Ideon MeonAQefromAaeSDectrum

40 i . . A_ , I ...... 40

20 20

-5O Oud e 5O -5O 0 50 lot; Iot[lu<_

Figure 2.4. Zonally-averaged mean age distribution from MONASH2 for the individual months March, June,

September, December, the annual mean, and the age spectral mean, as ]ndicated.

142
32 , i i i i i i

SUNY-SPB

30

28 L

26 _'*_'

_,

24 _"_,

22 _"'q..

%..

20 ""'<-.

18 "---..

16

3230 I! _............. GMI-NCAR )

28

26 ',, ,_

22

20 "'-_....

16 , , ,

32 ' v' ' ' .....

__ AER

30

28

26 ,,,

24 _,,

_',

22 ,<-,

20 _ _

18

16 --* , , ,

0.0 1.0

32

30

28

26

24

22

20

18

16

32

30

28

26

24

22

20

18

16

32

30

28

26

24

22

2O

18

16

0.0 O.5 1.0

SUNY-SPB //7

/' /

/" /

"//

," /

,//

//

//

z7

, , x , , , I I I / _ I I I

GMI-NCAR /,"_/_

//'

• it

/Z/.

i i i i i i i i i// i i i i

AER ,,/,,/

,"i

//

0.5 1.5

AMPLITUDE PHASE LAG (YEARS)

Figure 2.5. Profiles of A(z) (left column) and xo_(z) (right column) as reconstructed from the age spectrum (solid)

and from the sine (short-dash) and cosine (long-dash) experiments. Each row is a different model: SUNY-SPB

(top), GMI-NCAR (middle), and AER (bottom).

143
62O

58O

g

._ 540

E 500

1-

460

no

420

380

. S0a:; , /

- n HALOE H: lyr /

- i • CO2 max

• OMS H max

- o CO 2 min

- z_ OMSHmin /

/

- °o,fk

_ _oooOO'PD°°

-- A ,..""A •

•/oO7

/"

I _ I I t l I I I l

l

F

/ II

25.6

24.3

22.9

21.4

19.9

18.4

16.7

E

v

'-I

<

13_

O

>

0 0.2 0.4 0.6 0.8 1 1.2

Time (yr)

Figure 2.6. Tropical profiles of phase lag times ('_0_) from both remote (/2/) and in situ (/_/ and CO2) observations

(see text for details of the observations). For the in situ observations, the measured potential temperature is represented on the left y-axis and an average tropical pressure-altitude derived from the potential temperature:pressure

altitude relationships from the in situ campaigns is represented on the right y-axis. For the HALOE observations,

the observed pressure-altitudes have been converted into potential temperatures using the in situ potential

temperature:pressure altitude relationship.

144
,,¢,

v

O

"-1

03

O..

E

03

I'--

t_

.u

e--

03

..i,....a

O

IX.

8OO

700

600

5OO

400

°

--e- CO 2 and SF 6 Mean Ages -

• CO 2 Phase Lag

A OMS H Phase Lag

--e-- HALOE H Phase Lag

[ I 1 i t I I

32.9

30.7

E

v

03

28 =

,<

03

0_

24.9 m

.Q

©

03

21.4 03

>

<

17.6

0 1 2 3 4 5

Time (yr)

Figure 2.7. Tropical profiles of phase lag times (%_) from in situ and remote observations and mean age (F) from

in situ observations. The mean age profile (diamonds) is an average of the mean ages derived from in situ CO 2

observations (3 profiles: 970214, 971111,971120) and SF 6 observations (2 profiles: 970214, 971111) at 7S in

Juazeiro do None, Brazil. Dotted lines are the l-sigma standard deviation of the average of the 5 profiles. Note

that mean ages from SF6 were set to zero at the tropical tropopause and that seasonal variations in CO 2 in the lowest 4 km of the stratosphere largely cancel out between the November and February profiles.

145
x,"

v

!,.-

i...

E

0_

I-

.i

.i..a

¢.-

41..a

O

IX.

9O0

800

7O0

6O0

5OO

400

[]

@

w

• [C02] Attenuation

• CO 2 Age Spectra

• OMS H

-o- HALOE H

-32.9

- 30.7

- 28

- 24.9

- 21.4

17.6

i _.. dill.

....[] ...... •

I _ 1 R L i I I I J .....@

E

v,

v

"0

w

<

i,.._

13.

60

.(3

0

03

(1:1

>

<

0.0 0.2 0.4 0.6 0.8 1.0

Relative Amplitude

Figure 2.8. Tropical profiles of A(z). All amplitudes are normalized to unity at the tropical tropopau_ (taken as

390K for in situ data and 100 mb for HALOE). The open squares represent the analysis of HALOE H by Mote

et al. [1998]. The circles represent the attenuation of the CO 2 annual cycle from 6 aircraft and 2 balloon deployments between 1994 and 1997 [Boering et al., 1996; Boering et al., 1998]. Gray squares are the amplitude from

empirical age spectra determined from a time series of 9 profiles in the lower tropical stratosphere from 1994

through 1997 at 420, 440, and 460K [Andrews et al., 1998]. The triangles are attenuations derived from observations of H-hat from these same 2 balloon deployments in 1997 [Voemel et al., 1998].

146
t.D

tt')

< ¢o

(Xl

,T =-

O

B

• Age from CO 2

o o Age from SF6

Oo _

i.o ,!!

,f °

!_ o .

J 1 i I i 1 t...... L i I , I i

• 0

t

I

-60 -40 -20 0 20 40 60

Latitude (degrees)

Figure 2.9. Mean age variations with latitude at 19-20 km over a 3-week period in October-November 1994. Circles are mean ages derived from SF 6 observations and triangles are mean ages derived from CO 2 observations.

147
COO4

E

(a)

Tropics

&A •

Midlatitude

z_ OMS CO 2

o OMS SF 6

[] CWAS SF 6

J 1 i I t t

0 2 4 6

Age (yr)

Figure 2.10. Mean age profiles from balloon observations (a) in the tropics and middle latitudes and (b) in middle

and high latitudes, including vortex data. Data labeled "OMS" are in situ balloon measurements of CO 2 (triangles) and SF 6 (circles) while data labeled "CWAS" are laboratory measurements of SF 6 collected from a balloonborne cryogenic whole air sampler (squares). The color coding is as follows: (a) black points are from the OMS

flight of 970214 at 7°S and from a CWAS flight at 17°N in 1987; red points are from the OMS flight of 971111

and 981120 at 7°S; open symbols are for the OMS flight of 960921 at 34°N and for the CWAS flight at 44°N in

1993; (b) Open black symbols are the same as in (a); pink points are for the OMS flight of 970630 at 64°N and

from a CWAS flight at 68°N in 1995; red squares represent CWAS data obtained in the Arctic vortex at 68°N.

Note that filaments of Arctic vortex air were encountered in the OMS flight of 970630, although they are not

color-coded as such. The CWAS data have been shifted by -1.4 years (see text).

148
04

CO

(b)

Middle &

High Latitude

oO

04

E

v

.w

w

<

60

O3

13-

O

04

O

• O o Vortex

O4 ° o

_oo

oo

O0

0

0

1 I

2 4

Age (yr)

Figure 2. l 0. Concluded.

A OMS CO 2

0 OMS SF 6

[3 CWAS SF 6

149
35

3O

E

25

2O

annual-mean mean age

7 J

2 1 1 2

15 , , , I , i , , I , , , i I , ,

-50 0 50

Iotitude

Figure 2.] I. A schematic of the annually-averaged mean age distribution (in years) made from a composite of

balloon and aircraft observations.

150
O

O

oO

O

I..

.I..a

.m

Z

O

CXl

0

CO

ER2&OMS CO 2

--e-- ER2&OMS SF 6

-- o"

_ _......... o.O...........

,C)

I I I t I I

-1 0 1 2 3 4

Age (yr)

t I

5 6 7

Figure 2.12. Mean ages in the extratropics (polewards of 20 degrees in both hemispheres) derived from over 1400

in situ SF 6 measurements (open circles) and over 82,000 in situ CO 2 measurements (solid circles) from both aircraft and balloons (from SPADE, ASHOE-MAESA, STRAT, POLARIS, and OMS), binned and averaged in

5 ppb intervals of N20 from 10 to 320 ppb [Boering et al., 1998]. Horizontal bars represent the 2-sigma standard

deviations of the averages (solid=CO2; dotted=SF6). The right y-axis represents a mapping of N20 at midlatitudes

from ATMOS and CLAES data onto a pressure altitude coordinate.

151
26 26

24

22

v

N

20

18

16

0.0

26

24

22

N

20

18

0.5

FRACTIONAL AMPLITUDE

24

22

20

18

16

1,0

(b)

26

24

22

20

18

16 16

0.0 0.5 1.0 1.5

PHASE LAG (YEARS)

Figure 2.13. Equatorial profiles of (a) A(z) and (b) zo(z) for a range of models and for the observational analyses.

All amplitudes are normalized to unity and the phase lag taken as zero at 16 km. The shaded region indicates the

range covered by (a) 19 models and (b) 20 models, while the individual lines represent several models failing outside the range: in (a), GMI-DAO (solid), GSFC-2Dint (short-dash), and GSFC-3D (long-dash); and in

(b), HARVARD (solid), and GSFC-2D (short-dash). The heavy solid line repr_ents the analysis of HALOE /2/

[Mote et al., 1998]. The symbols represent analyses of in situ CO 2 (circleQ and H (triangles) measurements (CO 2

from 6 aircraft and 2 balloon deployments between 1994 and 1997 and H from the same 2 balloon deployments

in 1997).

152
0.0 1.0 2.0 3.0 4.0 5.0

32 u 32

N

30 30

22 ," 22

20 20

16 ....... 16 0.o 1.0 210 3.0 4'.o 5.0

AGE(years)

Figure 2.14. Tropical profiles of F(z) from models and observations. The shaded region represents the range covered by 20 model profiles, while the light individual lines indicate several models falling outside the range:

MONASHI (solid) and UCI23 (long-dashed). The connected symbols are the averages (over 10K potential temperature bins) of mean age values inferred from CO 2 and SF 6 measurements from OMS balloon flights on the

dates 970217, 971111, and 971120. The mean age has been referenced to zero at the tropical tropopause. Note that

seasonal variations in CO 2 between February and November (discernible to about 21 km during these flights)

largely cancel. Short-dashed lines indicate one standard deviation.

153
_D

<

0.2

1.0

0.5

7

538

4

4

3 21

87 1_

6

5 1821

9

2

6 1 tl 13 20

182t 6

17

17

14

14

0 tr.l[

• I I I I I I I I I I

0.0 0.5 1.0

c (mm/s)

22

22

I I I

1.5

i 0.6

0.4

0.2

1.0

0.5

, 0.0

1.5

Figure 2.15. Scatter plots of dz/dF versus c (top panel) and R versus c (bottom panel). Model values are indicated

by numbers, as listed in Table 2.2. The square symbols represent values derived from HALOE H (for R) and in

situ SF6 and CO 2 (for dz/dF), while the triangles represent estimates from in situ CO 2 and H (for R) and CO 2 and

SF 6 (for dz/dF).

154
.-k6

2.00.001

I

I

I

I

I

I

I

I

/

I

I

I

/

/

I

/

/

/

/

/

102. 0

(a)

1.5

1.0

(b) l 4.0

0.0 0.0

0.001 0.01 0.1 1.0 10

K(m2/s)

Figure 2.16. The dependence of (a) c/w and (b) R on K in the simple tropical leaky pipe model for '_= 0.5, 1.0, 2.0,

4.0, and 8.0 years (x = 0.5 years is indicated by a long-dash). For these curves, w = 0.3 mm/s.

155
32

_;0.

28.

26-

_24

22

20

t8,

0.0

log(Kzz(m2/s))

-7 I 0

(a) GSFC-2D

•/ I I

/,/ m •

•••11 Z"

| ,'" •

/ •

.

0'.5 1'.0

TIME (years)

+11 +2

, t2

- 30

20

t8

16

15

32

30

• 28 28

• 26 26

.24 ]24

22 22

20,

18.

16:

0.0

log(Kzz(m2/s))

-,2 -I ? +I!

',/ ..-"" ."

,," ••

", /"" I"'-

/ ". •" / /_,_,-"

/ ...-;,,,"-",,.

/ ..-:_'" "',

I .-'_'.'- ",

%

i i . . %,

0'.5 1',0

TIME (years)

m m

+2

32

30

• 28

• 26

.24

22

2O

18

i[6

1.5

Iog(Kzz(m2/s))

-3 -1 32 o

3O

28

.

26

_24.

20.

t6 :

0.0

-2 + I +2 i n Z

(c) CSIRO •

I ," •

I ,' •

I ," •

I ,' •

mm, " i

,' i

I / •

I /•

I ,,m

i,li

4"

/.i #" ,"

.,,,-,- -,

l..i• ',

i •

0.5 ' I'.0 1.5

TIME (years)

h,g(Kzz(m2/s))

32 32-3 -2 -} 0 +? +1232

(d) SUNY-SPB / ,.,." _

,o ,o{ /: ,'" ,o

1 /i ' 78 78 ," .78

l /i' i ,'

i .,"

26 26 ,," • 26

_4 "_941 / i'"'" "_

" _- / / ."l • ..4

/ / ..."..:."

22 22 1 / ..j;m,mn_ ,2

-t / -i_ "1"- ;

-II I

,(, 20_ /__,li e- '! -20

o ,o7........ ',:..... to

0.0 0.5 1.0 1.5

TIME (years)

Figure 2.17. Tropical^profiles of model annually-averaged Kzz (top log scale; dashed), model I:_o(bottom scale;

solid line), HALOE H "_co(bottom scale; dotted), and model advective time by w* (bottom scale; symbols). The

different panels correspond to different models: (a) GSFC-2D; (b) GSFC-2Dint; (c) CSIRO; and (d) SUNY-SPB.

156
• "y' "

-50

AER

\

o 511

GSFC 2D-INT

-_ o

-50

NCAR 2D

O 50

UIUC 2D

50 '

40

-50 0

'\i

5O

CSIRO

J ((

,o_./ . . , ._ • _

-,_0 0

HARVARD

...... , . . ,

40

2O

II

-_ 0

/

5O

, . , , .

°i

-50

NOCAR ]

0 511

UNIVAQ 2D

__ i"°-

,o[,, ,/

-50 0 50

GSFC 2D

-5t3 0 50

LLNL

i

-50 0

4o h

_ot

SUNY

, • J . ,

0 50

WlSCAR

40

!

-50 o 5o

Figure 2.18. Modeled mean age distributions in the latitude-height plane as derived from the age spectrum. Each

pane[ corresponds to an MM2 two-dimensional model, as labeled. Mean age is taken as zero at the equator and

18 km. Countour intervals are 0.5 years, and the heavy contours indicate 2 year intervals. Negative values are not

shown.

157
GISS-8xlO

50

40

2o

-5O 0 50

GMI DAO

4(

2_

t0

-SO O 50

,50L • ' ' * .

401-

GMI NCAR

3.0_

- 1.0 _

"o

.\ _

o 5o

5o

lO

GSFC 3D

-50 0 50

MONASH 1

_o .f , . . , ....

0 \ _

-50 0 50

40

-50

MONASH 2

,.

0 ._

40

30

2C

t(]

UCl 23

-50 0

"O

,_ "O...._

5O

UIUC 3D

5oi. - , .... , .... , - - .

-50 0 50

401"

-50

U N NAQ - 3 O

"3. 0 _

_ O.0 ' LO _

0 511

Figure 2.19. Modeled mean age distributions in the latitude-height plane as derived from the age spectrum. Each

panel corresponds to an MM2 three-dimensional model, as labeled. Mean age is taken as zero at the equator and

18 kin. Countour intervals are 0.5 years, and the heavy contours indicate 2 year intervals. Negative values are not

shown.

158
(a) 6

4

O

v

_0 3

o

(c)

30

-5O

z = 20 km latitude = 5S

25

20__

0 50 0 2 4 6 8

latitude (degrees) mean age (years)

latitude = 40N

(d)

30

_" 25

S

2O

latitude = 65N

0 2 4 6 8 0 2 4 6 8

mean age (years) mean age (years)

Figure 2.20. In all panels, the shaded region indicates the range of mean ages of models, with the exceptions of

HARVARD (dot), MONASH1 (solid), GSFC-2D (dash), UCI23 (dash dot dot dot), GSFC-3D (dash dot), and

UIUC (long dashes). The symbols represent observations: mean age from in situ CO 2 (triangles) and in situ SF 6

(diamonds). Observations are as follows: (a) Latitudinal profile of in situ aircraft measurements from SPADE,

ASHOE/MAESA, STRAT, and POLARIS for CO 2 and from ASHOE/MAESA (one deployment only), STRAT,

and POLARIS for SF 6. Data are averaged in 2.5 degree latitude bins (plotted at the midpoint) for both tracers, and

in 19.5-21.5 km for CO 2 and 19-21 km for SF 6. (b) Vertical OMS balloon profiles at 5°N averaged in 1 km altitude bins over three flights for in situ CO 2 (one February, two November, 1997) and over two flights for in situ

SF0 (February, November, 1997). (c) In situ SF 6 and CO 2 mean ages from a single OMS balloon flight of

September, 1996, from 35°N, binned in altitude as in 5b, and mean age from SF 6 whole air samples, September,

1993, from 44°N (asterisk) [Harnisch et al, 1996]. (d) In situ CO 2 and SF 6 mean age from the OMS balloon flight

of June, 1997, 65°N, and whole air SF 6 samples at 68°N inside (asterisk; average of four flights) and outside

(pluses; single flight) the winter polar vortex [Harnisch et al., 1996]. (OMS SF 6 data provided courtesy James W.

Elkins and Fred. L. Moore.) See text for further details.

159
Class A

II

Class B

Class C

Figure 2.21. Schematic representation of the variation of model global F distribution, and its categorization into

three classes.

160
6O

5O

4O

30.

20  ̧

10

(a) AER PIPE

' 60

50

40

30

20

10

_o...... Y "5...... 1_°

 Lsoi

4O

10

(c) AER NO PIPE

90S 60S ' 30S E'QU 30N 60N' 10N

Figure 2.22. Mean age distributions for: (a) the AER "tropical pipe" model; (b) the AER "leaky pipe" model (the

version submitted to MM2); and (c) the AER "no pipe" model.

161
1'5 t

I II

_1.0 /// .....

"_:,,.. / --"

°-_I_,,/............ .,_ ,'// \

°°__: :--:-_- ,,-,-__---__ ,,,,...._.:.:., ,, - _-_--- ___-.-_-

90S 60S 30S EQU

90S 60S 30S EIQU 30N 60N 90N

2.0i • I I I I I I I I I I . I i I I I f 2.0

1.5

1.0

0.5

0.0

30N 60N 90N

Figure 2.23. Latitudinal profiles of annual-mean Kyy averaged from 18 km to 24 km for 5 2D models: CSIRO

(solid); GSFC-2D (dotted); GSFC-2Dint (short-dash); SUNY-SPB (medium-dash); and HARVARD (long-dash).

162
6O

5O

30

90S' ' 60S

................. 60 60

(a)

• 50 50

• 40 40

• 30

• 20

30S EQU 30N 60N 90N

30

20

90s '6;s' '30s

i i i | | | i ,

(b) /

'iQu 3oN'

60

50

4O

30

2O

60N 90N

6O

5O

40

30

I I i i i I 60

(c/) v \

3ON''6;N'

• 5O

• 40

• 30

20 - 20

90S' ' 60S' ' 30S' ' E'QU 90N

,o]

] 2 0O

30

"t

2°t 

! .............

90S 60S 30S EQU 30N 60N

6O

5O

4O

30

2O

90N

Figure 2.24. (A) The latitude-height mean age distribution for GSFC-2D; (B) The mean age distribution for

GSFC-2Dint; (C) The transit time distribution, using only the GSFC-2D residual circulation for transport; (D) The

transit time distribution, using only the residual circulation of GSFC-2Dint for transport.

163
50

",,,,..-',,,,S',_III _')11 /, ,,,,,,,....

40 ..n- "-- ,, IIIk-",.J/ ,. _',_,',.":

/, / I I I _ / ', _ _',"'.

3s _,'n / l I\ /'_/ _ _,_' ....

t ,, / I I v V _ ',_' ......

30 /I/It'",. _ \.i

:5 /: II Ifl\ _ ',

--,,, I / /_\ / ,_,...,

20 _ I ( k "_ 1 r _ s

15

..................... 50 50

• 45 45

- 40 40

- 35 35

/.

90S' " 60S' ' 30S' ' EQI3 ' 30N" ' 60N' 90N

- 30 30

" 25 25

" 20 20

15 15

90S' ' 60S' ' 30S' 'EQLI ' 30N' ' 60N

• • i • _ x .......... , i

:::::_._',:9_5q,,m|t_UA\\ "4 _ I _ H/1/1//1, _'_'_,:._q"' I 50

:-:=_fl.F)/il/i/imll l| _\\\\\\ _ [ %/////if[lll "_ x,','_"-'llll, I

::::--:"/IHtilll\,,-x7/"ll I _'," ....r/40

_1-.... '/;//I I \ _f"X- / I I ,',\"'- =/

-.'"'l'tt'l I \ I \/ I _ ', _ _ "a I-

,'-" /ll| _ "" - / | ,_, / 35

-"....,,,l_,, c I_,,,, ',h Lk / ',", I3°

"//Gs); t

I _ I i ,,25

\ / _ 20

. .. _ _,._.--,,,._ "_" ' "*..... I •72, ¢ _ I _ '_ I / .... -f •

:,. /,,

90N

5O

45

4O

35

30

25

20

i | | i ....... 50 50 _ .................

, _ ,, ", " ....... ".'_..'/",_k\\_"/ _ 11 ', _ ', "- ......... :/l: ))_"',_-"_ /

/ -45 45 \ \\,,, ......... _"-";_ _

- 40 40 ',, /io\

• 30 30 _,

_5 ......... is is,

90S 60S 30S EQIJ 30N' 60N" 90N

5O

45

4O

35

30

25

20

15

90S' ' 60S' ' 30S" ' EQU ' 30N' ' 60N' 90N

5O

45

40

35

30

25

20

15

90S' " 60S" '30S' 'EQU '30N' '60N' '90N

, • i | i

-4040-1

• 35 35

• 3030

-• 252020 25 r_

15 15

90S

 o,0,i .....

, \

• 45 45]

..... L 4550

40

35

30

25

15

'6bs''30s' 'EQU '3by" '66N" '90N

Figure 2.25. The residual circulation for the two versions of the MACCM2 GCM, used by MONASH1 and

MONASH2, and the corresponding F distributions. The left column of panels is for MONASH1, and the right for

MONASH2. The top row is annual mean _"* in 0.5 mm/s contours (solid positive, dashed negative), the middle

row annual mean _-'* in 0.5 m/s contours (solid positive, dashed negative), and the bottom row mean age, in

0.5 year contours.

164
6.0

5.0

4.0

%,

>-,

3.0

[...,

6.0

5.0

4.0

>..,

v 3.0

2.0

2.0

1.0

1.0

0.0

0.0

Figure 2.26. Scatter plot of

for z > 20 km.

0.0 1.0 2.0 3.0 4.0

1 I I I I I a I I

241_1

23

18

14

0.0 1.0 2.0 3.0 4.0

<AGE>(yrs)

0.0 1.0 2.0 3.0 4.0

I I I I I I I I

0.0

15

12

8

3

5

10

11 2

1 6

5.0

!

19

5.0

5.0

I 1

6,0

' 6.0

5.0

4.0

3.0

2.0

1.0

0.0

6.0

6.0

6.0

5.0

4.0

3.0

2.0

1.0

, , • 0.0

1.0 2.0 3.0 4.0 5.0 6.0

<AGE> (yrs)

F' versus _0 for models listed in Table 2.2. F is the mass-weighted integral of F(x),

165
6O

LLNL

60

50 50

4O 4O

3O 3O

20 20

10 10

i

90S 60S 30S EQU 30N 60N 90N

GMI-NCAR

60 60

50 50

40 40

30 30

20 20

10 1 10

i '1 i i i I I i

90S 60S 3_)S EQU 30N 60N 90N

Figure 2.27. Contour plots of r(x) (dashed) and _o(X) (solid) for LLNL (top panel) and GMI-NCAR (bottom

panel). F(x) is contoured in 0.5 year intervals. _0(x) is scaled to unity at the equator and 60 kin. Its contours intervals are 0.1, decreasing toward the troposphere.

166
50

40

20

I0

aer

-50 0 50

lot;rude

,oi.. /,

-50

csiro

5O

20

10

-50 0 50

lot_tuae

5o ....... h.oor .....

rv d

.... , .... , . . .

0 50 -50 0 50

Iot_tvde Iot;I _.e

50! ' ' {" I

2O

-50

c)sfc-2d

2O

.... /. ...... ....

-50 0

Iot_ude

Ilnl

o 5o

Iollt ude

50 • ' ' , ........ ncof2d , ' • 50 nocor 50 sun

o o

20 20 i

10

50 un;voq-2d wlscor

40 1'/ ' ' ' ' " ' .... ' ' " i

2O 2O

lOf . . . I .... I .... I . , l lot * . . I .... I .... I i I I

-50 0 50 -50 0 50 -50 0 50

Iol;tude Io(;tud41 Iot_ude

Figure 2.28. Annual-mean N20 distributions in the latitude-height plane. Each panel corresponds to an MM2 2D

model model, as labeled. Contour intervals are 50 ppb, and the 25 ppb and 1 ppb contour are also shown. Empty

panels correspond to models for which data was not available and are included for ease of comparison to

Figure 2.18.

167
9;ss-8xtO

5O , • , , .... i .... i _ _ •

10 .... I • • • • , .... i . . .

-50 0 50

_otRude

=_30

gsfc- 3d

-50 0 50

Io(;tud_

uc;23

10

-50 0 50 -50 0 50

Iot_lude tot;Lude

CLAES

4C

20

10 , , , I , , • - t .... z . _

-50 0 5(]

_ol;tude

....... 9T;._?°_.,

-50 0 50

totRude

monosh2

-50 _ot,O 511

Figure 2.29. Same as Figure 2.28, but for 3D models. N20 data from the CLAES instrument on HALOE are

shown at bottom [Randei et al., 1994]. Empty panels correspond to models for which data was not available and

are included for ease of comparison to Figure 2.19.

168
50_

.:,t 30

v

aer, NOY

20

10

-50 0 50

Iotltude

ncor2d, NOY

,o !

20"

30

N

i i i I i , , i I i

-50 0

Iot_tude

i = . i , . ,

5O

oer, CLY

40

i _ 1.0-

- 50 0 50

Iot[tude

ncor2d, CLY

5O

40

_ 3o

20

-_ 0 50

Iot;tude

Figure 2.30. Annual-mean latitude-height distributions of NOy (left column) and ely (right column) for the

models AER and NCAR-2D, as labeled. These data are from the full chemistry runs of B4. Contour intervals are

2.5 ppb for NOy and 0.5 ppb for ely.

169
(a) AER

0 1 2 3 4 5

i ] ! i I I ! i I ! ! ! I ! I I ! I I

1.5 , , . 45N 22km i i i • i r , •

l.O "'" ,'""" 7""-" ,/"" ,'""" "_,

0.5, ........

45S 22km

1.5

1.0

0.5

1.5

1.0

0.5

(b) GSFC-2Dint

0 1 2 3 4 5

I I l i I I I i I I I i I I i l 1 t i

45N 22km

1.5 ..... , ..............

0.5 0 22km

_l i . I I I I I I t I ! I I I i I I

45S 22km

0 ' i 5

1.5

1.0

0.5

1.5

1.0

0.5

Figure 2.3 i. Time series of experiment A2a, the sine forcing (solid); and A2b, the cosine forcing (short dash), for

two models: AER (top) and GSFC-2Dint (bottom). For each model, three latitudes are plotted at 22 km: 45°N (top

time series), the equator (middle time series), and 45°S (bottom time series).

170
.<

25

20

l'i

15

10

NOx from 500 HSCTs; E.I.=10 (1000 Kg/day)

i t I t I t I i I 11 I I t I t I t I t

I

0

I

-- AER

100 200 300 400 500 600 700 800 900 1000 1100

NOx (1000 kg/day)

Figure 2.32. Vertical profile of integrated A3 input rates (kg/s) for various models.

I

1200

171
0.20

.... ..,......... _......... : ........ _ ......... •......... _..

Figure 2.33. Contours of HSCT ANOy tracer mixing ratio (ppbv) at 69 mb for August 23 in the LARC T32 model.

172
Figure2.34.Contoursof HSCT ANOy tracer mixing ratio (ppbv) at 69 mb for August 23 in the LARC TI 6 model.

173
AER

60

50

4C

30

20

A3 (ppbv) Annual Means

GSFC-2D

so_ ___ so

40 i_ _ ;_ =: 40

30 30

GSFC-2D-Int

10 10 10

o

HARVARD

50 .

40

30

2G

10

0

NCAR-2D

60

0

LLNL

5(]

40

30

2O

o

NOCAR

o

MGO-UIUC-2D

6C i j I /

5¢

40

30

20

10

0

SUNY-SPB

50

40

30

20

I0

0

6C

50

40

30

30

I0

0

-90

UNIVAQ-2D

o 90

4C

30

2O

lo

o

_1 I I I0 .2 .4 ,6 .8 1 1,21.41_B1.84.4

m__ 1_t .1 I J

0 .2 .4 .6 .8 1 1-21.41.81.82.9

Figure 2.35. Contour plots of the A3 HSCT ANOy tracer annual mean mixing ratio for (a) 2D models; and (b) 3D

models.

174
GMI-DAO

60

50

40

30

20

10

0

MONASH 1

60

50

40

30

20

10

0

GMI-NCAR

2O

IO

0

UNIVAQ-3D

6O

5O

4O

3O

2O

IO

0

-90 0 90

0 .2 .4 .6 .8 1 1.21,41,61.8 2

A3 (ppbv) Annual Means

GISS-3D Model-IF

60,

5o]

4O]

30]

i i i

GSFC-3D

1oi

0'

MONASH2

60

NCAR-3D

I0

0

UCI

80

50--

40--

30

2O

J I

=

lO

o

GMI-GISS

80 F I

50--

40--

30--

20

?:

m

lO

UIUC - 3D

LARC-T32

o

-90 o 90 90 o 90

.J 1 I 1 | F _ ] I I L _

0 .2 .4 .6 .8 I 1,21,41,61,8 2 0 .2 .4 ,6 .8 1 1.21.41.61.8 2

Figure 2.35. Concluded.

175
AER

6O

50

40

30

20

10

0

HARVARD

60

50

40

30

2C

1C

o

NCAR-2D

60

50

40

30

20

10

0

UNDTAQ- ZD

60

50

40

30

20

10

0 90

o.2.,_6._I1.21.41.61.83.3 i_

A4 (ppbv) Annual Means

GSFC-2D

6O

5O

4O

3O

2O

I0

0

LLNL

60

50

4O

3O

2O

i0

0

NOCAR

6O

5O

4O

3O

2O

I0

0

o.2.,._.hl1.21.41.61.82.2 kGSFC-2D-Int

MGO-UIUC-2D

SUNY-SPB

I

0 .2 .4::_:i_ ] 1!21At.61.82.2

Figure 2.36. Contour plots of the A4 HSCT ANOy tracer annual mean mixing ratio for (a) 2D models; and (b) 3D

models.

176
5O

40

3O

2O

10

0

DAO- GMI

MONASHI

5O

40

30

2O

10

0

NCAR-GMI

80

50

40

3O

2O

10

0

1 UNIVAQ - 3D

60

50

40

30

20

I0

0

-90 0 90

0 ,2 .4 .6 ,8 I 1.21.41,61.8 2

A4 (ppbv) Annual Means

GISS-3D Model-IF

60

50

40

30

20

10

0

MONASH2

60

50

40

3O

20

i0

0

UCI

80

50

40

30

20

I0

0

0 .2 .4 1.21.4161.8 2

Figure 2.36. Concluded.

GSFC-3D

N CAR - 3 D

UIU C - 3D

0 ,2 ,4 .6 .8 1 1,21.41,61,8 2

177
AER

DAOtGMI

GISS (3D}

GSFC (2D}

GSFC 2D INT.

GSFC 3D

HARVARD

LARC

LLNL

MONTH1

NCAR 3D

NCAR 2D

NCAR/GMI

NOCAR

SIJNY

UC! (3D)

UIUC (2D}

UIUC (3D}

Univaq 2D

Univaq 3D

-1

A3 MASS (IN TROPOSPHERE) BELOW 100 MB

i I I I t ' I l [l'l LI I i I I '

--.....llllJl[

--,,:FIIIILII I I

II iii III1 I

IlltlltiliJlilll_ .I _

-FFTI:_IIII.II i I-' ,

IIIlIIlIL,, lllllillJll ' I i ' 'I

IIIIIIII-III . I I I

LLILJ_IIIIII _ L I i I I

I-]TilIIILII i I i I I

[//11111 IIII [ 11 • T,_

F[ I I [ 1 I 1 I.I I II MEAN=,.41

MEDIAN=I 23 [IIIIIIIIILI L Jl .......

f l 1 I I I LI Ll f I I t1_ _ _?_/

II I I I I I_I I 1 I I L I I I I I I I

0 1 2 3

1 0aKG

AER

DAO/GMI

GISS (3D)

GSFC (2D)

GSFC 2D INT.

GSFC 3D

HARVARD

LARC

LLNL

(_ MONASHI MON,_I-'I2

NCAR 313

NCAR 2D

NCAF:I/GMI

NOC/_

SUNY

UCl (3D}

UIUC (2D)

UtUC (3D}

Univaq 2D

Univaq 3D

4

A3 MASS (IN STRATOSPHERE) ABOVE 100 MB

2 4 6 8 10

1 0eKG

AER

DAO]GMI

GISS [3D)

GSFC (2D}

GSFC 2D INT.

GSFC 3D

HARVARD

LARC

LLNL

MONASHt

MONASH2

NCAR 3D

NCAR 2D

NCAPJGMI

NOCAR

SUNY

UCI (3D)

UIUC (2D}

UIUC (3D}

Univaq 2D

Univaq 3D

0

A3 TOTAL MASS

3 6 9 12 15

1 08KG

AER

DAO/GMI

GISS (3D)

GSFC {2D)

GSFC 2D INT.

GSFC 3D

HARVARD

LARC

LLNL

MONASH1

MONASH2

NCAR 3D

NCAR 2D

NCAPJGMI

SUNY

UCI (3D)

UIUC (2D)

UIUC (3D}

Univaq 2D

Univaq 3D

A3 MASS RATIO (STRAT/TROP)

2 4 6 8 10

Figure 2.37. Histogram for the A3 experiment of total ANOy mass, troposphere mass, stratosphere mass, and strat/

trop ratio as listed in Table 2.3. Masses in units 108 kg.

178
AER

DAO]GMI

GISS(3D)

GSFC(2D)

GSFC 2D INT,

GSFC 3D

HARVARD

LLNL

MONASH1

MONASH2 NCAR 3D

NCAR 2D

NCAPdGMI

NOCAFI

SUNY

UCI(3D)

UIUC (2D)

UIUC (3D)

UNIVAQ2D

UNIVAQ3D U

0

A4 MASS (IN TROPOSPHERE) BELOW 100 MB

I!lii j T1-N£ i ii_K,'_]_

,,-+t,t , l-t

L+,=Il_i J _ii,:= t I_

LiiLi _!/lii 7_l'lINJli_i

i li;jl j i I_l II ]

,+,,,oi,tIl]I L/ [l_t!ll 1

'+",,++,,, I:ttillit:ilft!I:ft t _,+tnl t ' ' ' '

tttltl,i:i:.hil/i_,:&I It1_t t _+.o.

L11I tj ! "[I i /1 1i l] [] :N/i]r;I : +Ll+tmu :

0,5 1 1,5 2 2.5

1 0"KG

! t I L

+4 AER • i _ {"'t

DAO/GMI I i

GSFC(2D) t

' GSFC2DINT ioi

! J GSFC 3D

+_ LLNL

MONASH2

! -_ NCAR 3D _ +

!-I NCAR 2D /:i:i : j

+ +++

SUNY - ! - _-.4-4-

UCI(3D)- f i | |

i UlUC(2D) - t i I

,-i UlUC(3D) _ I d t

+-I UNIVAQ2D * : ,

i_, UN,V,O3O_ !td

--+-..L__J 1

3 1

A4 MASS (IN STRATOSPHERE) ABOVE 100 MB

].1,! t ]-I I l I] I i- [ _ 1

{:,_o,_i,::Ut:,I

,_,._tt,_u J,,., t

Li_ 1t i J i ! lot

/j i ii t

t l, t iti :J::Jtt:,:

l I.I+t_LI I l_1 ! 1 ! i

I [ I 1 I 11 LL_I .....

II 1t]1 LIL]]

i/il/ll/ILJl

i;I ] ] i .I ! I // /

1 Io/ t t o STRAT

/ I 1 1 i I 11 MEAN=2_,

• ! ! ° I ! _%_.%' '++

ill 1

] ] i _L± L l.J .I_L_

3 4

1 0BKG

AER

DAOIGMI

GISS(3D)

GSFC(2D)

GSFC 2D INT.

GSFC 3D

HARVARD

LLNL

MONASH1

_ASH2

NCAR 3D

NCAR 2D

NCAR,SGMt

NOChB

SUNY

UCI(3D)

UIUC (2D)

UlUC (3D)

UNIVAQ2D

UNIVAQ3D

A4 TOTAL MASS

l i i _ i i i i__i ..i i ]_T_

= i . o ill 1 1 111 i

r ! I i 1 t l-rI t _i_ -

- IL J1 i, I,. ° ....... i l] 1 l I R7 _

i.i i-J 1 1 I 1. 1_±_I I _-L__

i I 1 i L I I t L L t i I J..l _

1 I ]

: I t :::[ :::K+ot -_::iq.:i:t :i:_-

lolllii [ !

::i::IL::[L1 I i:::l 1 i l::-::}::,_;.:i_

i ]

] 1 _!_ t I t _ t

_4 £l I-J 1ot _kl ] L_I__.J__I_ ]]_1_1 ] 1 - I ' t t I I I_.

• 1 [. L L I I L ] ] o TOTALA4 I

/ I [ 11 / _ j ] MEAN=3.07 I

- e ..... MEDIAN=/61 i I ILtol 1 I 1 .11

- l STD=I.37 i

:t t::tk i:t ]::l: i_t_4_]

1 i i 1 _J. i t t:::_:::_:: ::tt t::iiJ

2 4 6 8

1 0BKG

AER

DAO/GMI

GISS(3D)

GSFC(2D)

GSFC 2D INT

GSFC 30

HAFtVARD

LLNL

_ MON,_H 1

_ MONTH2 NCAR 3D

NCAR 2D

NCAR/GMI

SUNY

UCI(3D)

uiuc (2D)

UIUC (3D)

UNtVAQ2D

UNIVAQ3D

A4 MASS RATIO (STRAT/TROP)

.......l o I i i J _ t........

......... _ l ......... _ t l t

.......] _ oi i 1 I i / ....

_ .] L _ _1 _ ] [ .......i. _

_ ]<>r I 1 1 1 1 1 _

{ l i L 1......1 1 1 _

....:i::.:- :::::[:: -r-:t ::::[:::-:::::

+ 1 io ! !.......!........!........ ! -

o l l L

- ! o] ti_:: l: :[::_::zt_:

- I i oi 1 1 £. 1......... _._

m _ o 1 I

];: ]t--l [_ t +t1 o R_O I]1

I / I t t I MEAN=3.00I

- I t ° i I I I MEDIAN=2.781-

-_ L t <>I +o i, ......t-t I STD-I54 - I-_

..... ]_1 ]ot 1 1 I 1.

_ 1 l _ L __1 l ! l l

___1 .......1....._1__ 1 ] _ 1.._

3 6 9

Figure 2.38. Histogram for the A4 experiment of total ANOy mass, troposphere mass, stratosphere mass, and strat/

trop ratio as listed in Table 2.3. Masses in units i 08 kg.

179
MODEL

AER

DAO

GISS

TIME OF MAXIMUM TOTAL CONCENTRATION OF A3 MASS

1998 1999

May Jun Jul Aug Sep Oct Nov D_cv.'_o/_ Jan Feb Mar Apt

I I

4.8%

I

6.3%

I

GSFC 2D

GSFC 2D INT

1.1%

13.7%

GSFC 3D

HARVARD

LARC T32

LLNL

MONASH1

MONASH2

NCAR 3D

NCAR 2D

NCAR/GMI

NOCAR

SUNY

UCI

UIUC 2D

UIUC 3D

UNIVAQ 2D

UNIVAQ 3D

4.8%

I I

2.6%

v

2.5%

1

16.8%

I

21.9%

!

Rn 4%

3.7%

I I

. 11.3%

T

32.2%

I I

9.5%

I

3,5%

I

7.0%

I I

10.7%

I

7.8%

I I

Figure 2.39. Seasonal distribution of the total atmospheric ANOy tracer mass for the A3 experiment. The numbers

indicate the peak to peak amplitude over the year as a percentage of the annual mean for each model.

180
20

18

16

14

12

10

8

6

NOy A3 (ppb)

0.0 !.0 2.0 3.0

| | | u • ! n

/

S t

0.0 1.0 2.0

AER

3.0

4.0

4.0

5.0

• 20

18

16

14

12

10

8

6

5.0

NOy A3 (ppb)

0.0 1.0 2.0 3.0 4.0

. , , m i i i , • •

20 .-

18 ."

16 j_" • "

14

12

10

8 GSFC-2D

6

0,0 1.0 2.0 3.0 4.0

5.0

20

18

16

14

12

10

8

6

5.0

20

18

16

14

12

t0

8

6

0.0 1.0 2.0

i iI i I

gO

0.0 1.0 2.0

3.0 4.0 5.0

[ I [ ' 1 20

18

16

14

12

10

8

GSFC-2Dint

6

3.0 4.0 5.0

20

18

16

14

12

1o

8

6

0,0 1.o 2.0 3.0 4.0

i i i I I I I ' I 1 !

s J

0

|

-m

I HARVARD

!

0.0 1.0 2.0 3.0 4.0

5.0

20

18

16

14

12

10

8

6

5.0

20

18

16

14

12

10

8

6

0,0 1.0 2.0

n i i m

/

t t

0.0 1.0 2.0

3.0 4.0 5.0

• "_ 20

t8 18

16 16

14 14

12 12

10 10

8 8

LLNL

i, 6 6

3.0 4.0 5.0 0.0 1.0 2.0

NCAR-2D

3.0 4,0

5.0

' 20

18

16

14

12

10

8

6

5.0

0.0

20

I8

16

14

12

10

8

6

1,0

/

2.0 3.0 4.0

1 i i • •

NOCAR

5.0

' 20 20

' 18 18

' 16 16

' 14 14

• 12 12

• 10 l0

• 8 8

6 6

5.0

0.0 1.0 2.0

n i n. m

/

js

0.0 ! .0 2.0 3.0 4.0 0.0 1.0

AGE (yrs)

3.0 4.0

i| 11

SUNY

2.0 3.0 4.0

AGE (yrs)

5.0

° 20

18

16

14

12

10

8

6

5.0

Figure 2.40. Profiles of F (dashed) and A3 ANOy (solid) for 2D models. The bottom axis is F in years, and the top

ANOy in ppb. Values have been averaged annually and over the latitude range 35°N to 55°N. F is referenced to

zero at 6 kin. Panels correspond to models, as labeled.

181
NOy/A3 (kg/1.0e9) NOy/A3 (kg/1.0e9)

0.0 1.0 2.0 3.0 4.0 5.0 0.0 1.0 2.0 3.0 4.0 5.0

_o__o _o/ , ,\,,_ , , , , , _o

,81,,,/ r,+ ,81 ./ ,+

'+] ;/ t '6 ,61/ +

'41i/ p14 ,41/ 14

'=I;/ r'= '=I_ '=

,oV [,o ,Oll ,o

8 8

6 _6 8-_ GMI-GISS 8

6 I ......... 6

0.0 1.0 2.0 3.0 4.0 5.0 0.0 1.0 2.0 3.0 4.0 5.0

0.0 1.0 2.0 3.0 4.0 5.0 0.0 1.0 2.0 3.0 4.0 5.0

_o_, '2 ...... i _o _o_ _o

181 / t ll_ 16'_1l /, /..-," Ip '_16 ,61/

,41 /(, p14 ,41/..." p14

'=1I P'= '=1l" t '_

,ol/ t,o ,ol/ t,o

: 1 ..... GMI-N,CAR [ : :0_ :

0.0 1.0 2.0 3.0 4.0 5.0 . .0 2.0 3.0 4.0 5.0

0.0 1.0 2.0 3.0 4.0 5.0 0.0 1.0 2.0 3.0 4.0 5.0

_°i ' f :."...... i + _°1......... ..r _o

18 1 /..'" t l8 18 1 ,,'y 18

,6 1 /.. r,6 161."/ 16

141;/ [ 14 14 f /" 14

'_l/ t '_ '_1_ ':

io_ t,o ,Ol/ ,o

6 _6 6 I ........ 6

0.0 1.0 2.0 3.0 4.0 5.0 0.0 1.0 2.0 3.0 4.0 5.0

AGE (yrs) AGE (yrs)

Figure 2.41. Same as Figure 2.42, but for 3D models.

182
\

\.

0.0 1.0

3.0 ' '

o_ 2.0

<

©

Z 1.0

0.0 .

o.o i.o

1.0

I

0.0

3.0 '

oe 2.0

8

<

©

Z 1.0

O.C

t.0

2.0 3.0 4.0

I i i

2215

16

23

20

14

19

i.0 3.0

<AGE> (yrs)

2.0 3.0

1 I i i

10

4

9

11

1

6

1'.0 i.0 3.0 4.0

<AGE> (yrs)

2.0

1.0

0.0

4.0 5.0

I 4.0I I 5"03.0

5

2.0

1.0

0.0

;.0

Figure 2.42. )kNOy versus F, where ANOy is the total ANOy tracer mass over the region 35°N to 55°N and

16 km to 22 km, and [" is the mass weighted average of the mean age over the same region. The top panel

shows 3D models, and the bottom panel 2D models, with numbers corresponding to models as listed in Table 2.2.

183
22

18

14

10

6

Kzz (m2/s)

0.001 0.01

0.0 015

0.1 1

! [

AER

10

lI_ z8

_" 14

i.

r6

1.0

Kzz (m2/s)

0.001 0.01 0.1 I

I

22: ] / ' GSFC-2D

14 _

10 " "''''.

0.0 0.5

I0

22

18

14

10

6

1.0

0.1301 0.01 0. I 1

! I 1

22 ', GSFC-2Dint

18 .

14 "

1

10 _

I

I

6 • [

0.0 0'.5

10

L

• 22

"18

• 14

10

6

1.0

0.001 0.0I 0.l I

....

22 , HARVARD

I

I

18 _

t

"t

t

,,

10 ] ,.

I I

,

6 _ , !

0.0 015

10

22

18

I 14

10

6

1.0

0.001

22

18

14

10

6

0.0

0.01 0.1 1

I I

i LLNL

!

, , , • I

0.5

10

1.0

0.001

!

22 22

1

18 I8

,,I

-I

14 14

t

,,I

I0 10

6 1

0.0

0.01 0.1 1

! ! I

0.5

10

22

18

14

10

1.0

0.001 0.01 0.I 1 I0

I I I

22

18

14

10

6

0.0 o15

-w* (mm/s)

1.0

0.001

22 22 ]

18 18

14 14

10 10

6 ,

0.0 015

-w* (mm/s)

10

22

18

14

10

6

1.0

Figure 2.43• Profiles of 2D model -_-'* (solid) and Kzz (dashed). The bottom axis is -_,* in mm/s, and the top

Kzz in m2/s. Values have been averaged annually and over the latitude range 35°N to 55°N. Panels correspond to

models, as labeled. Empty panels indicate models for which data was not available and are included for ease of

comparison to Figure 2.40.

184
40

35

30

25

20

15

10

I I I I

==

II

| I !

!

/

/

/

/

/

/

I

I I i 40

35

\

\

"/ 20

/

• OMS SF6 15

/

I. AER

............. MONASH2

....... GISS8xl0

.... GSFC-2D

GSFC-2Dint

m UCI23

2.0 3.0 4.0 5.0 6.0

MEAN AGE (years)

10

Figure 2.44. Profiles of F(x) from several models (solid, dashed, and dotted lines, as indicated) at 35°N through

the upper troposphere and lower stratosphere, and values inferred from SF 6 measured from an OMS balloon flight

at similar latitude. Here, the model F(x) are taken from the SF 6 simulations, so that the month corresponding to the

balloon flight (September) may be plotted. F(x) is referenced to zero at 6 kin. The shaded region indicates the

height of peak ANOy emissions for simulations A3 and A4.

185
Table 2. I. MM2 models that i)erformed transport experiments

2D models 3D models

AER

CSIRO

GSFC-2D

GSFC-2Dint

HARVARD

LLNL

MGO-UIUC-2D

NCAR-2D

NOCAR

SUNY-SPB

UNIVAQ-2D

WISCAR

GISS8 × 10

GISS4 x 5

GMI-DAO

GMI-GISS

GMI-NCAR

GSFC-3D

LARC T32

MONASH 1

MONASH2

NCAR-3D

UCI21

UCI23

UIUC-3D

UNIVAQ-3D

Table 2.2. Parameter values used by each model

1 AER

2 CSIRO

3 GSFC-2D

4 GSFC-2Dint

5 HARVARD

6 LLNL

7 MGO-UIUC-2D

8 NCAR-2D a

9 NOCAR

I0 SUNY-SPB

11 UNIVAQ-2D

12 WISCAR

13 GISS8 x 10

14 GMI-DAO

15 GMI-GISS

16 GMI-NCAR

17 GMI-NCARcon

18 GSFC-3D

I9 MONASHI

20 MONASH2

21 UCI21

22 UCI23

23 UIUC-3D

24 UNIVAQ-3D

"_o(yrs)

! .87

2.15

3.04

1.40

2.86

1.97

3.23

1.51

2,38

2.22

3.66

2,19

1.44

3.47

2.89

2.78

1.56

5.41

2.89

2.23

3.14

H. (kin)

6.62

5.97

4.07

7.72

2.18

3.78

3,83

3.43

5.26

3.76

3.09

3,97

4.43

8.73

2.31

4.61

4.41

8.35

3.03

4.43

5.74

2.34

c (mm/s)

0.48

0.45

0.30

0.51

0,26

0,42

0.36

0.33

0.59

0.54

0.54

0.54

0.48

0.95

0.40

0.48

0.51

0.85

0.40

0.59

0.63

0.45

dz/dF (mm/s)

0.24

0,28

0,14

0.42

0.15

0.23

0,18

0.14

0.31

0.17

0,25

0.19

0.22

0.38

0.12

0.15

0.16

0.35

0.12

0.18

0.22

0.13

1.74

2.36

6,24

3.55

1.09

0.59

0.32

0.20

HALOE h b 7.6 0.30

OMS H c 3.7 0.24

OMS SF 6 0.11

aFits may be corrupted by a high tropopause.

bDerived from analysis of Mote et al. [1998]; note that a fit from 16 km to 20 km yields H. = 5.4 km.

CData range from 16 km to 20 kin.

R

0.44

0.42

0.42

0.48

0.26

0.28

0.33

0.33

0.28

0.22

0.18

0.23

O.3O

0.29

0.I8

0.31

O.28

0.31

0.24

0.24

0,29

0.24

0.18

0.19

0.7

0.5

186
Table 2.3. Masses of the A3 and A4 tracer in the stratosphere and troposphere and the strat/trop ratio. All masses

in units 108 kg. Stratosphere and troposphere defined as above and below 100 mb pressure surface

Model

2D Models

AER

GSFC-2D

GSFC-2Dint

HARVARD

LLNL

MGO-UIUC-2D

NCAR-2D

NOCAR

SUNY-SPB

UNIVAQ-2D

3D Models

GISS4x5

GMI-DAO

GMI-GISS

GMI-NCAR

GSFC-3D

LARC

MONASH 1

MONASH2

NCAR-3D

UCI23

UIUC-3D

UNIVAQ-3D

A3 Trop

1.63

3.74

1.08

2.38

A3 Strat

5.78

9.98

2.42

10.54

A3 Total

7.41

13.72

3.49

12.92

A3 Ratio

3.55

2.67

2.24

4.42

A4 Trop

0.96

2.26

0.77

1.48

A4 Strat

2.42

4.68

1.14

4.45

2.69

1.09

1.48

0.83

1.05

0.68

1.79

1.23

1.84

1.57

1.42

1.27

5.84

5.93

3.86

3.15

5.79

3.66

6.10

2.94

5.08

5.09

3.23

4.00

8.54

7.02

5.35

3.99

6.84

4.34

7.89

4.17

6.93

6.66

4.65

5.27

2.17

5.44

2.61

3.80

5.51

5.38

3.41

2.39

2.76

3.24

2.27

3.15

1.61

0.80

0.59

0.64

0.46

0.98

0.76

0.92

0.85

2.56

1.28

1.65

2.94

1.58

2.70

1.60

2.40

1.70

0.98

0.84

0.73

1.76

1.04

0.31

4.54

3.50

3.68

5.17

3.46

3.28

5.52

4.34

4.41

6.93

4.50

3.58

4.63

4.17

5.04

2.94

3.33

10.58

0.51

0.44

0.43

i .09

0.70

0.20

1.64

1.40

1.66

2.72

1.97

1.73

A4 Total A5 Ratio

3.38 2.52

6.95 2.07

1.91 1.48

5.94 3.00

4.17 1.59

2.08 1.60

2.24 2.80

3.58 4.59

2.04 3.43

3.68 2.76

2.36 2.10

3.32 2.61

2.55 2.00

2.14 3.21

1.84 3.18

2.09 3.86

3.81 2.50

2.67 2.81

1.94 8.65

187
Table2.4.Ratioofnorthernhemisphereto southern hemisphere annual mean of the tracers at 20 km. Also given is

the northern hemisphere season of maximum ratio. "L" indicates "late."

Model

2D Models

AER

GSFC-2D

GSFC-2Dint

HARVARD

LLNL

MGO-UIUC-2D

NCAR-2D

NOCAR

SUNY-SPB

UNIVAQ-2D

3D Models

GISS4x5

GMI-DAO

GMI-GISS

GMI-NCAR

GSFC-3D

LARC

MONASH 1

MONASH2

NCAR-3D

UCI23

UIUC-3D

UNIVAQ-3D

A3 NH/SH

2.81

2.33

3.79

3.51

2.76

2.12

1.44

5.04

2.67

2.62

2.62

2.89

2.47

2.28

2.72

2.60

2.00

2.02

3.20

2.86

3.16

2.48

Season

LSUM/FA

FALL

SUMMER

SUMMER

LSUM/FA

FALL

SUMMER

LSUM/FA

NONE

LSUM/FA

WINTER

SPRING

WINTER

SUMMER

NA

WINTER

NONE

FALL

FALL

WINTER

NONE

LSUM/FA

A4 NH/SH

9.83

5.09

120.15

94.58

12.20

6.85

2.11

68.86

7.06

29.17

7.30

7.18

5.84

6.42

6.58

6.04

11.77

7.65

8.30

9.41

Season

LSUM/FA

NONE

SUMMER

SUMMER

LSUM/FA

SUMMER

SUMMER

LWIN/SU

FALL/SP

FALL

WINTER

SPRING

SUMMER

NA

FALL

FALL

FALL

WINTER

SPRING

SPRING

188
Table2.5.RatiosforA3andA4ofnorthernhemispherevalueat30kmtoabsolutepeak,40kmtoabsolutepeak

and50kmtoabsolutepeak.

A3 A4

Model 30/pk 40/pk 50]pk Season 30/pk 40/pk 50/pk Season

2DModels

AER 0.23 0.16 0.16 NONE 0.06 0.06 0.06 WINTER

GSFC-2D 0.48 0.42 0.42 FALL 0.32 0.23 0.23 NONE

GSFC-2Dint 0.10 0.10 0.07 SUM/FA 0.04 0.04 0.04 NONE

HARVARD 0.46 0.31 0.24 FALL 0.25 0.18 0.06 FALL

LLNL 0.40 0.36 0.28 FALL 0.25 0.19 0.06 FALL

MGO-UIUC-2D 0.39 0.39 0.39 NONE

NCAR-2D 0.36 0.36 0.29 FALL 0.09 0.09 0.09 NONE

NOCAR 0.22 0.13 0.09 FALL 0.07 0.04 0.04 SUMMER

SUNY-SPB 0.48 0.40 0.38 FALL 0.36 0.25 0.19 SUMMER

UNIVAQ-2D 0.31 0.31 0.31 NONE 0.11 0.07 0.07 NONE

3DModels

GISS4x5 0.52 0.43 0.43 NONE 0.37 0.22 0.22 NONE

GMI-DAO 0.77 0.55 0.44 FALL 0.66 0.38 0.22 FALL

GMI-GIS 0.50 0.41 0.41 SUMfFA

GMI-NCAR 0.56 0.44 0.44 NONE 0.33 0.25 0.25 NONE

GSFC-3D 0.42 0.33 0.25 NA 0.41 0.29 0.23 NA

LARC 0.50 0.50 0.50 FALL

MONASH1 0.64 0.64 0.60 FALL 0.33 0.11 0.11 FALL

MONASH2 0.42 0.42 0.42 NONE 0.22 0.Il 0.I! NONE

NCAR-3D 0.33 0.28 0.22 FALL 0.21 0.12 0.07 FALL

UCI23 0.50 0.43 0.43 NONE 0.32 0.24 0.24 NONE

UIUC-3D 0.50 0.33 SUM/FA 0.40 0.20 SUM]FA

UNIVAQ-3D 0.50 0.33 0.33 SUMMER 0.30 0.30 0.25 NONE

189
3. Chemistry Experiments

3.1 Summary of the MMII Intercomparisons for Chemistry

Guy Brasseur 1 and Ellis Remsberg 2

1. National Center for Atmospheric Research

2. NASA Langley Research Center

The purpose of the chemistry component of the model comparison is to assess to what extent differences in the formulation of chemical processes explain the variance between model results. Observed

concentrations of chemical compounds are used to estimate to what degree the various models represent

realistic situations.

For readability, the materials for the chemistry experiment are reported in three separate chapters.

Chapter 3 discussed the data used to evaluate the models in their simulation of the source gases and the

NOy and Cly species. Chapter 4 discussed the ozone climatoloty and comparison with the model

results. Chapter 5 reported on a number of special diagnostics.

Several concerns were expressed during the MM I exercise in 1993, regarding specifically the modeling of photochemical processes between 20 and 40 km altitude, unsatisfactory simulations of photolysis rates and chemical cycles, and large differences in Clx and Brx partitioning. Since this earlier

comparison, specific aspects of these model differences have been analyzed, and addressed. At the same

time, more complex models, with a higher degree of interactivity between chemisry and dynamics have

been devleoped, introducing new potential causes for disagreements between models. Overall, the new

comparison (which also includes 3-D models) tends to show a better degree of similarity regarding species like ozone, NOx, HNO 3, HCI, etc., even when polar stratospheric clouds are included in the models. Model/data differences tend to be larger in the lower stratosphere, where transport of source and

reservoir species tends to play a more important role. These deficencies represent potential problems for

an accurate asessment of the impact of projected high altitude aircraft.

The major findings resulting from the Chemistry comparisons can be summarized as follows:

1. Source Gases

The comparison between calculated and measured source gas concentrations provides primarily a

diagnostic for model transport, and should be related to the discussion presented in earlier sections of

this report. However, the success in simulating the global distribution of long-lived species has a direct

consequence for the simulation of fast-reactive gases that are produced by these long-lived source gases.

A general pattern is that the calculated lifetime of source gases differs among the participating models, and that models which tend to derive the smallest lifetimes also produce the steepest latitudinal gradients. This behavior is probably indicative of the strength of upwelling in the tropics and the

permeability of the tropical barrier. In most cases, calculated species cross-sections for nitrous oxide

and methane show larger meridional gradients across the tropical barrier than shown by UARS observations, while, conversely, calculated gradients are smaller than indicated by measurements of CFC-! 1

from the ATMOS and ER-2 observations. It is clear that the representation of transport in models

remains inaccurate in many cases, and that obtaining a more realistic transport will not be trivial.

190
2.NitrogenandChlorineCompounds

Thecomparisonof nitrogenspeciesbetweentheparticipatingmodelssuggeststhatcalculatedNOy

concentrationsdifferby30percentamongthemodels.ThisdifferenceisevenlargerwhenN20 isconstrained.ThecalculatedNOmixingratiosdifferwidelyamongmodels,andmostmodelsdonotproperly reproducethe seasonalvariationof theNO maximumobservedat 45 km by HALOE.The

calculatedseasonal variabilityof theNO2mixingratiomaximumat 34kmis in goodagreementwith

HALOEobservations. NO2 concentrationsin themid-troposphereat mid-latitudesaremoresimilar

amongmodelsthanisthecaseof NO,andareingoodagreement withHALOEdata.Theseasonal variationintheHNO3mixingratiomaximumat24kmathighlatitudesisinrelativelygoodagreementwith

CLAESdata,butthecalculatedmagnitudeof thismaximumshowsawidespreadamongmodels.

In thecaseof Cly concentrations, differencesof approximatelyl0 percentarefoundwhenthedifferentmodelsarecompared.Thisdifferenceis largerwhenCFCdistributionsareconstrainedin the

model.The CIO mixing ratios are characterized by a wide spread among models, both for the vertical

profile and the seasonal variation. In most cases, the model simulated C1ONO 2 agrees well with CLAES

and ATMOS measurements at mid-latitudes. However, the seasonal behavior in CIONO 2 is not well

reproduced by all models. The model estimates of HC1 are generally lower that the observations by

HALOE and ATMOS, with large differences in the concentrations between the participating models.

3. Ozone

The different models participating in the intercomparison usually provide similar results regarding

ozone in the upper stratosphere (above 25 km), where the ozone budget is mostly controlled by gasphase chemistry. Theoretical estimates at these heights are in good agreement with observations. The

models start to diverge below 25 km, where transport and heterogeneous chemistry plays an increasingly important role. The largest differences are located between 20 km and the tropopause, and are as

high as 50 percent in the models that show the best agreement with the observations. The ozone column

calculated by the models is within 10-20 percent of the climatological values. Most models show the

best agreement with observations in the tropics.

4. Chemical Partitioning

Partitioning of the radical species has been calculated by the Photo-Stationary-State model of

Salawitch using long-lived species concentrations from the individual output of the participating models. In many cases and for most situations the calculated radical concentrations are in fair agreement

with the measured concentrations, although in certain cases large differences were reported for specific

models.

5. Representation of Polar Processes

Because both chemical and dynamical conditions play an important role in the evolution of ozone

and other chemical compounds at high latitudes, polar processes are difficult to represent in 2-D and

even 3-D models. The model comparison suggests that the observed structure of long-lived atmospheric

tracers (such as N20 ) are not consistently well simulated by the participating 2-D and 3-D models. In

particular, most models (with some exceptions) underestimate the strength of the meridional gradients

in the vicinity of the polar votices. In addition, the seasonal cycle of species such as N20 is not well

simulated in most models. On the other hand, simulations of the seasonal evolution of nitric acid in the

polar lower stratosphere are in reasonable agreement with observations from CLAES. However, the

191
modelsdifferbyseveralmonthsin thetimingof theonsetof theHNO3decreaseassociatedwithPSC

formation.

Comparisonsof calculatedpolar(inorganic)Cly withestimatesinferredfromER-2observations

suggestthatseveralmodelsunderestimateCly in thepolarregionsof bothhemispheres, withlargedifferencesin theCly profilesamongmodels.In thesouthernhemisphere,themodelsalsodifferin the

extentto which chlorine is released from HC1 and CIONO 2. The comparison shows significant differences among the participating models regarding the vertical extent over which polar processes are

important, the intensity of the chlorine activation, and the partitioning among chlorine species. For

example, large variance is found in the partitioning between CI20 2 and C10. As a result the model C10

profiles (which determines the rate of ozone destruction in polar regions) also exhibit significant variance, and calculated polar ozone in the Southern hemisphere differs from observations for all models in

significant ways.

In the northern hemisphere, all models show less chlorine activation than in the Antarctic region,

but there are still significant differences among the models. Because the observations of C10 and ozone

by MLS in the Arctic exhibit much more variability than in the Antarctic, the comparison between models and measurements is difficult in the Northern hemisphere. For example, the large interannuat variability shown in the observations is not reproduced in climatological models. Models using assimilated

meteorological data are probably more appropriate to reproduce Arctic proceses.

6. Ozone Production/Destruction

Several models involved in the MM II comparison also provided calculated ozone production and

destruction rates. The losses due to the various chemical families, taking into account heterogeneous

processes at a time (1992) where the aerosol load was high (due to the Pinatubo eruption in 1991) were

analyzed and compared among the different models. All models suggest that the HOx loss dominates in

the lower stratosphere (below 20 km) and in the upper stratosphere/mesosphere (above 45 kin). The

NOx family dominates the ozone destruction in the middle stratosphere (20-40 kin), while the influence

of the ClOx family is quite large (more than 30 percent) in the middle and upper stratosphere

(35-50 kin). The BrOx family contributes significantly (more than 30 percent) to the ozone destruction

in the lower polar stratosphere. The relative contribution of these families varies with season, and with

chlorine and aerosol loading. In all models, the ozone production rate is approximateIy equal to the total

destruction rate in the upper stratosphere, suggesting that photochemical conditions apply to ozone in

this region of the atmosphere.

Calculations of the ozone abundance for prescribed ozone production and loss rates show significant differences among the models both in the ozone meridional distribution and in the ozone column,

suggesting that variance in transport formulation among models has a significant impact on calculated

ozone. When ozone production and destruction are prescribed, the agreement between models and

observations is usually worse than when production and destruction rates are self-consistently calculated in the model.

References

Hall, T.M., et al., Transport experiments, in MMII Report, pp. 110-189, 1999.

Mlynczak, M., C. J. Mertens, R. R. Garcia, and R. Portmann, A detailed evaluation of the stratospheric heat

budget. II. Global radiation balance and diabatic circulations, accepted by J. Geophys. Res., 1998.

Scott, C.J. and M.K.W. Ko, Source gases comparisons, in MMII Report, pp. 226-239, 1999.

192
3.2 Data Quality

Ellis E. Remsberg

NASA Langley Research Center

Almost all the data sets that were used for the models and measurements (or MM) comparisons

(Prather and Remsberg, 1993) have been revised or replaced with better and more complete

compilations. UARS satellite data sets are the primary ones being used for the middle and upper

stratosphere (Chapters 3.3, 3.4, 3.5, 5.4, and 5.5), although it was not possible to employ the observed

atmosphere that has recently been compiled from UARS for the April 1992 to April 1993 period (see

http://hyperion.gsfc.nasa.gov/AnalysisAJARS/urap/home.html). One particular form of those data is the

monthly zonal means of species in terms of pressure versus equivalent latitude coordinates--which

would be most suitable for comparisons with 2-D models (see e.g., Randel et aI., 1998). It is noted that

this reference atmosphere of Randel et al. is not so specific for the 1992 year, but then neither are the

transport circulations for many of the models.

It is important to note that new versions of the UARS data sets are still being developed and made

available. Presently, HALOE comparisons are conducted using its Version 18 data. A Version 19 will

be available in early 1999, but only minor changes from Version 18 are occurring for most species. The

largest changes will be for water vapor and NO 2 below about the 50 mb level. These changes are due to

improved estimates of the so-called solar lockdown position for sunrise and sunset scans. V19 sunrise

NO 2 (and NOx) will be sharply lower at 100 mb and at higher latitudes, while the sunset values will be

larger by at least 50 percent below the 50 mb level. Changes are within 10 percent between the 40 and

7 mb levels. V19 NO (and NOx) will be smaller in the upper stratosphere by about 10% in both the sunrise and sunset data during the 1997 period of the POLARIS field experiment because of the removal of

a false trend that had been assumed in VI8 for the NO gas cell content. The VI8 results for NO during

1992 and 1993 did not change much.

The operative CLAES data are from either Version 7 or 8; T(p), CH 4, and CFC-12 are more accurate on Version 7, and N205 and CFC-11 are more accurate in Version 8. A Version 9 is being prepared

but was not available for this MM2 exercise. In particular, temperature and NO 2 will be better in Version 9. The current ISAMS data are Version 10; an eventual Version 12 will have significant improvements in N20, N205, and CO. Finally, the MLS Version 3 data have been used for comparisons up to

now. The recent MLS Version 4 contains improvements in 03 and H20, plus new distributions of

HNO 3. MLS Version 5 will contain further improvements and will have retrievals at all UARS levels,

not only at every other level. As a result, the peak values of CIO in the winter high latitudes of the lower

stratosphere will be reduced and smoothed somewhat, leading to lower and more accurate values of Ciy.

This result will impact the polar chemistry comparisons in Chapter 5.5. MLS Version 5 data will be

available soon.

Estimates of stratospheric wave activity can be derived from daily analyses of both trace and minor

species from CLAES, ISAMS, MLS and to a lesser extent HALOE data and from the temperatures and

winds from the assimilated meteorological analyses (see Chapter 5.4). The standard deviation of this

wave activity about the zonal mean value of a given parameter is an excellent diagnostic of large-scale

atmospheric variations (and indirectly the fidelity of the transport) for the 3-d models, although it is

expected that there will be some interannual variability in those data values. Similar diagnostics were

developed from the Nimbus 7 LIMS data as part of a pre-UARS climatology.

A new ozone profile climatology has been prepared from Version 5.96 SAGE II data and from the

representative, higher quality ozonesonde profiles in each 10 degree latitude zone (see chapter 4). The

193
Version 7 TOMS total ozone is used as a data reference in Chapter 5.3. However, neither of these climatologies is specific to the 1992 year of the baseline model run (B1) for MM2.

Satellite data are also used for model comparison in the lower stratosphere, but their accuracy is

reduced for some species, especially early in the 1992 baseline year when the volcanic aerosols interfered with the measured signals. Thus, assessments of the models in the lower stratosphere for 1992 rely

more critically on data and correlation diagrams from the ER-2 flights during AASE II and SPADE.

Data from later aircraft campaigns have also been used for comparisons. The measured chemical radicals provide consistency checks on the specific proposed reaction mechanisms (Chapter 5.1). Data on

CO 2 and SF6 from the ER-2 and OMS platforms provide very good estimates of tracer transport and

determinations of the age of air. In general, all the aircraft and balloon in situ data are both precise and

accurate. The only concerns are with regard to how representative they are of a particular latitude or season, and whether enough measurements have been obtained for a good characterization of the enhanced

effects of heterogeneous chemistry during 1992 and for determining the chemical processing due to

PSCs for any given year.

One outstanding concern was reiterated in Chapter 3.3 regarding the NOx/NOy ratios in the mid

and lower stratosphere and the fact that the observed NO 2 concentrations become progressively higher

than model values below 30 km. It is interesting that when NOy and J(NO2) from the ER-2 are used to

constrain model estimates of the NOx/NOy ratio, there is generally good agreement with the observed

ratio (Gao et al., 1997). Model/data agreement is less satisfactory when the constraints are from remote

measurements (Sen et al., 1998).

The largest component of NOy in the lower stratosphere is HNO 3. Thus, if the observations of

HNO 3 have a bias, there will also be a bias in the model NOy and in the model/data comparisons for the

components of NOy. Observations of HNO 3 from the rernote sensors ATMOS, MklV, and UARS have

an accuracy of no better than 16 percent, but the systematic errors due to biases in the HNO 3 spectral

line parameters are much smaller than that (Aaron Goldman, private communication, 1998). During the

early period of UARS there was significant underlying absorption and/or emission from the volcanic

aerosols that also had to be accounted for. In situ determinations of NOy on the ER-2 have an accuracy

of 15 percent (Fahey et al., 1989). Therefore, it is unlikely that the estimates of NOy (or HNO3) can

explain all of the discrepancies between the observed and calculated NOx/NOy ratios. More profiles of

the observed ratios of selected components of NOy would be very instructive.

Solar occultation measurements of NO and NO 2 occur at sunrise and sunset, when both species are

varying rapidly. Corrections have been applied to the retrieved HALOE, ATMOS, and MklV profiles to

account for this diurnal variation. Those sunset corrections are small for NO 2, even in the lower stratosphere (Sen et al., 1998). The corrections are larger for NO, but Newchurch et al. (1996) report good

agreement between diurnally corrected ATMOS NO and ER-2 measurements of NO in November

1994. HALOE Vi8 NO is much greater than from MklV below the 40 mb level for their 1993 sunset

comparisons, while V18 sunset NO 2 is much less than that from ATMOS or MklV below the 40 mb

level. The combined HALOE NO + NO 2 is also less than from MklV at those altitudes.

Systematic errors for NOx for all three remote sensing data sets get large in the lower stratosphere.

In particular, HALOE NO 2 is sensitive to uncertainties in line half-widths for H20--its primary interfering species in the lower stratosphere. Therefore, model comparisons with the more plentiful and

more accurate in situ measurements of NO, NO 2, and NOx should be more instructive for the lower

stratosphere.

194
A possible criticism of MM2 is that we did not re-evaluate the quality of the present models for

duplicating a baseline atmosphere for a period (say, the mid 1990s) when the effects of heterogeneous

chemistry are less important. For example, HALOE, TOMS/SBUV, SAGE II, and MLS data are available for comparison for the mid 1990s, and there are data from the aircraft and balloon campaigns in the

mid 1990s, as well.

References

Fahey, D., et al., In situ measurements of total reactive nitrogen, total water, and aerosol in a polar stratospheric

cloud in the Antarctic, J. Geophys. Res., vol. 94, 11299-11315, 1989.

Gao, R. S., et al., Partitioning of the reactive nitrogen reservoir in the lower stratosphere of the southern hemisphere: Observations and modeling, J. Geophys. Res., vol. 102, 3935-3949, 1997.

Newchurch, M. J., et al., Stratospheric NO and NO 2 abundances from ATMOS solar-occultation measurements,

Geophys. Res. Lett., vol. 23, 2373-2376, 1996.

Prather, M. J., and E. E. Remsberg, Eds., The atmospheric effects of stratospheric aircraft: Report of the 1992

models and measurements workshop, NASA Ref. Publ. 1292, March 1993.

Randel, W. J., F. Wu, J. M. Russell III, A. Roche, and J. W. Waters, Seasonal cycles and QBO variations in stratospheric CH 4 and H20 observed in UARS HALOE data, J. Atmos. Sciences, vol. 55, 163-185, 1998.

Sen, B., G. C. Toon, G. B. Osterman, J.-F. Blavier, J. J. Margitan, R. J. Salawitch, and G. K. Yue, Measurements

of reactive nitrogen in the stratosphere, J. Geophys. Res., vol. 103, 3571-3585, 1998.

195
3.3 Comparison Overview

Ellis Remsberg, l Patrick Purcell, 2 Praful P. Bhatt, 2 Karen H. Sage, 2 and Donald E. Brown 2

I. NASA Langley Research Center

2. Science Applications International Corporation

3.3.1 Introduction

The focus of this analysis is model comparisons with the UARS, CLAES and HALOE data. In general, the model results for the Present-day Atmosphere Comparisons (Bl) are not specific to 1992,

except for the tropospheric source gas boundary conditions and an enhanced SAGE II stratospheric

aerosol surface area for the Pinatubo event. Because the transport in most of the models is more appropriate for a generic year, we expect some differences from that of early 1992, which had a stronger than

normal residual circulation and meridional mixing. Exceptions are the SLIMCAT and GSFC-3D models, which use assimilated meteorological data for their transport.

We have elected to look closely at the March/April and September/October periods of the B I simulation. A March/April period should reflect the wintertime accumulation of ozone in the northern hemisphere. September is an interesting but more dynamically active time at the end of southern hemisphere

winter. One should see the effects of heterogeneous chemistry on surfaces of PSC aerosols at high

southern latitudes in September. Comparisons are shown first for the source gases, N20 , CH4, and H20.

Then we compare the results for the chemically-generated species, NOx, HNO3, NOy, HCI, and 03.

N20 profiles were selected from March and September 1992 and HNO 3 profiles were selected from

September 1992 and March 1993 from CLAES Version 8 data. In order to get the maximum latitude

coverage we obtained data from two successive yaw cycles of the UARS spacecraft. Poleward of 34N

the N20 data cover the period March 1-21, 1992; poleward of 34S the data span is March 26 to April l,

1992. The lower latitudes were sampled nearly each day in March. A similar approach was used for

September- the southern hemisphere (SH) was sampled from Sept. 1-18 and the northern hemisphere

(NH) from Sept. 24-30. The HNO 3 data from 1993 are from March I-17 in the NH and March 22-31 in

the SH.

The HALOE Version 18 CH 4 and H20 data were considered from the sunrise sweep of March 10-

April 26, 1992, (poleward of 30N from March 14-April 8 and poleward of 20S from April 20-26) and

the sunset period of September 7-October 13, 1992, (the NH from Sept. 7-Oct. 5 and the SH from

Oct. 5-13). HALOE data for NO + NO2, HC1, and 03 were considered for the same September-October

period in 1992 and from the sunset period of March 6-April 2 (SH) and April 3-11 (NH) of 1993. The

1993 period was chosen for the more chemically reactive species because of the significant uncertainties in the CLAES and HALOE radiances of the lower stratosphere for early 1992 from just the

emission/absorption from the Pinatubo aerosols. Due to interannual variability in transport, there are

differences for all the species for March/April of 1992 versus 1993, but the differences for the tracers in

the middle and upper stratosphere are generally small compared to the model-to-model differences for

those species in a given year.

The HALOE sunset NO + NO 2 should be a good proxy for the daytime average NOx from the 2-d

models, particularly if we include 2"N205 with the model NO + NO 2. When combined with CLAES

HNO 3, the observed total ought to be a very good estimate of NOy in the stratosphere, at least for

regions outside of polar night. In an attempt to remove the effect of transport in the results, we also

196
show comparisons for the ratio NOx]NOy. HALOE sunrise NO + NO 2 is not as accurate in the lower

stratosphere because of its larger diurnal correction (Gordley et al., 1996) and should also be somewhat

smaller than the model daytime average NOx in the middle stratosphere (Morris et al., 1997). The

HALOE NOx results for March 1992 are also less accurate in the lower stratosphere, because of uncertainties in the correction for the large amount of aerosol interference at that time. Therefore, we have

avoided early 1992 in our comparisons of the chemically active species. Comparisons of HC1 provide a

first order validation for total chlorine in the upper stratosphere of the models. The daytime average 03

comparisons are a measure of the net results of both chemistry and transport.

Although we evaluated the distributions of the more chemically active species for all of the models,

we decided to focus on the results from four of the 2-d models which were involved in both the MM and

MMII comparison studies and which are being involved in many of the stratospheric assessment studies. Those modeling groups are AER, GSFC-2D, LLNL, and UNIVAQ-2D. Several of the other groups

did not report daytime average chemical distributions. These include the 3-d model groups which report

zonal averages at a single Greenwich hour (12Z for the LARC-3D model), i.e., a 24-hour average result.

The NOCAR group reported noontime chemistry. The HARVARD and CSIRO model runs did not

include PSC chemistry. Otherwise, the results of the chemistry calculations were very similar for all the

models, as demonstrated by their respective distributions for the NOx/NOy ratio.

3.3.2 Results

Source Gas Comparisons for March/April

The following comparisons are in terms of zonal average differences in percent, (model - data)/data.

Results are shown as 4 panels per page. Models are grouped according to: (a) AER, GSFC-2D, LLNL,

and UNIVAQ-2D; (b) SLIMCAT, GSFC-3D .......... and NCAR-2D; and (c) NOCAR,

SUNY-SPB, CSIRO, and HARVARD. The model output is for March 1992.

Figure 3.3.1a is the CLAES N20 above 100 mb (a Z* of 16 km) for March 1992. Single profile random errors are 6 to 8% from 4.6 to 46 rob, and increasing to 39% at ! rob. But the random errors are

much smaller for a monthly zonal average, so it is presumed that the relative spatial variations have

been measured well. Systematic errors are of order 15 to 20% from 1 to 46 mb (Roche et al., 1996), but

Figure 3.3. I a shows minimum mixing ratios near 0.5 mb (about 55 km) with higher values above--an

unrealistic prospect. Much larger systematic errors are likely there.

Figures 3.3.1(b-d) are plots of the model N20 differences versus the CLAE.S data. Some striking

features are characteristic of almost all the models: (1) only weak SAO transport occurs in the model

upper tropical stratosphere, and (2) there is a lack of strong meridional gradients in N20 at 30 km, 30N

and at 40 km, 60S. It is perhaps unreasonable to expect the models to duplicate those specific characteristics of the 1992 transport, and, in fact, the March 1993 CLAES N20 distribution shows evidence of a

northward advection of N20 at 35 km and midlatitudes compared with March 1992. Those models that

have residual circulations and mixing transports that are specific to 1992 ought to agree best with the

CLAES 1992 field. Accordingly with the exception of the northern extratropics of the upper stratosphere, the model/data difference patterns are in good agreement for the two chemical/transport models

(CTMs), SLIMCAT and GSFC-3D that use 1992 assimilated data. The reason for the notable differences in the northern extratropics is unclear, although the GSFC-3D model circulation is obtained with

Data Assimilation Office (DAO) analyses and SLIMCAT uses UKMO analyses. But because the

CLAES N20 values are less than 25 ppbv in that region, it is more likely that even small differences in

197
the model net transport are the problem. The difference patterns for those two models are more consistent between 20 and 40 km (see discussion below for CH 4, too).

The CLAES data in Figure 3.3. la indicate large asymmetries in the amount of poleward mixing and

descent for the two hemispheres of the high latitude upper stratosphere. Yet, it is clear that there are

larger differences in net transport of N20 among the models--i.e., the amount of poleward and downward transport. There is a lot a similarity between the the model/data differences of the CSIRO and

SLIMCAT models. In general though, the significant differences among the set of model simulations of

N20 agree with the findings from the more specific model transport exercises of the "A" runs of MMII.

If we avoid those regions of high vertical and meridional N20 gradients, we expect agreement with

CLAES to within +/-20%, and generally that is what we find for the lower stratosphere, at least outside

the polar vortex regions.

Several models seem to have deficiencies in their chemistry and/or photolysis of N20, as well. For

example, there is an excess of N20 in the upper stratospheric results (above 35 km) for the NOCAR

model. Alternatively, Kyy may be too weak. In about half of the models there are significant deficits of

N20 in the upper stratosphere. Without other guidance it is hard to know whether any model is correct

in that region, since the CLAES N20 data also have large errors near the stratopause.

Model comparisons with the HALOE CH 4 distribution of March 1992 are shown in

Figure 3.3.2(a-d). Precision of the HALOE CH 4 in Figure 3.3.2a is very high and its accuracy varies

from 6% to 11% from 0.4 to 10 mb and then 15% at 40 mb (Park et al., 1996). First, it is noted that the

HALOE CH 4 data have a similar pattern to that for CLAES N20 , except HALOE CH 4 has a much

weaker vertical gradient in the upper stratosphere. That difference in gradient is expected to some extent

because the chemical loss rate is much greater for N20 than for CH 4. However, because the HALOE

CH 4 decreases smoothly at the stratopause, as expected, this is indirect evidence that the CLAES N20

data have systematic errors at that level. Once again, the lack of a clear SAO in the model CH 4 causes

enhanced differences in the upper tropical stratosphere. If we consider the region between about 20 and

40 kin, the difference patterns for N20 and for CH 4 are more similar for a given model. The results for

CH 4 from SLIMCAT and GSFC-3D are very similar in the northern hemisphere upper stratosphere,

unlike the findings for N20. Those two models differ near 45 km and 30S for CH 4. Still, based on the

CH 4 comparisons, there seems to be no common transport deficiency among the set of models.

The accuracy of the methane oxidation mechanism must also be considered for those models that

include it. Many of the models have an excess of CH 4 throughout the lower stratosphere, possibly

because some model CH 4 fields are not calculated but are prescribed from the Nimbus 7 SAMS data

which are biased high below about 28 km. Conversely, the LARC-3D model has a prescribed CH 4 field

obtained from an early version of the NOCAR model. Comparisons with HALOE CH 4 for March/April

1993 are similar to those for 1992 below 40 km. The data differences between those 2 years are generally much smaller than the model-to-model variations for March of a single year.

Middle atmosphere water vapor is calculated in some models and prescribed in others. The comparisons with HALOE water vapor in Figure 3.3.3 generally reveal differences of less than +]-20%

throughout the stratosphere. Two models (UNIVAQ-2D, NCAR-2D) show differences of order +30 to

+50% at about 40 km; one model (SUNY-SPB) has 30% less H20 near the stratopause, where the HOx

chemical cycle is becoming more important for ozone. At 20 km there is more variability among the

models but generally no worse than 25%, which is also the uncertainty in the HALOE values in the

lower stratosphere (Harries et al., 1996). However, the precision and relative variations in the HALOE

H20 are known to better than 25%. Because the H20 differences for many models tend to vary with

198
latitudeat20km,thoseresultscanprovideanimportantcheckof thedegreeof meridionalmixingand

transportinagivenmodel.

Chemically-Reactive and Reservoir Species for March/April

Results for NOx show better uniformity among the models. The model daytime average results

from March/April 1992 (not shown) are much greater then the observed sunrise values in the mid stratosphere, in part because N205 is present at sunrise but was not included in our observed sum for NOx.

But N205 is not so important in the lower stratosphere, where model NOx is still much higher than

observed for early 1992. At least part of that NOx difference may be due to an underestimate of the conversion of NOx to HNO 3 for the models in the presence of the early 1992 aerosol levels, but it is more

likely that the March 1992 HALOE NOx is not so reliable below about 30 km due to its large and uncertain aerosol correction. Therefore, we make our comparisons with the sunset NOx data for March/April

1993.

The observed distribution of NOx for March 1993 is in Fig 3.3.4a, and the measurements are considered very good in the middle and upper stratosphere. The HALOE NOx data are still not highly accurate below 20 mb (or about 27 kin), and we note that there is an unrealistic minimum near 20 km, due

most likely to not accounting properly for interfering cloud and H20 absorption near the tropopause.

HALOE comparisons with correlative measurements do show large percentage differences in the lower

stratosphere, even though any correlative occultation data should be subject to similar sources of error

as HALOE (Gordley et al., 1996).

Sunset comparisons with model daytime average NO + NO 2 + 2"N205 from the four 2-d models

are shown in Figure 3.3.4b. Again the effect of a reduced SAO signature is apparent in the model upper

tropical stratosphere. Otherwise there is a general excess of NOx for most models near the stratopause

and a slight deficit of NOx in the middle stratosphere. Outliers from that pattern are the HARVARD and

NCAR-2D models, which have deficits of NOx in the extratropical upper stratosphere. In addition, the

NCAR-2D model has an excess of NOx at 35 km that is consistent with its deficit in N20 in the upper

stratosphere but which is not seen in the other models. HALOE NO + NO 2 errors are smaller than 30%

between 27 and 40 km (Gordley et al., 1996), but the differences for some models slightly exceed that

uncertainty from 27 to 30 km. This lack of agreement is likely because more of the atmospheric NOy is

in the form of NOx in 1993, whereas the NOy partitioning in the models is based on the heterogeneous

chemistry on the larger volcanic aerosol amounts of spring 1992. The models generally show less NOx

below 30 km when compared with March/April 1993 HALOE data--a clear difference from the 1992

HALOE comparisons. Because the NOy partitioning is critically time-dependent on the true aerosol surface area for several years after the Pinatubo eruption, the model/data differences in Figure 3.3.4b are

not surprising for the lower stratosphere. The model/data agreement for NOx for most models is much

better between 30 and 40 kin, where the NOx family has a primary effect on the ozone chemical cycle.

Figure 3.3.5 is the comparison with CLAES HNO 3 for 1993, and there is general consistency

among the models. (The CLAES differences for HNO 3 between 1992 and 1993 are not large.) Model

HNO 3 is in excess above about 25 kin, even though the CLAES systematic errors are estimated at about

10% (Kumer et ai., 1996). However, comparisons of CLAES HNO 3 with correlative measurement profiles indicate that the CLAES results are too low in the middle stratosphere. Thus, it is likely that the

models are performing better at those altitudes than indicated in Figure 3.3.5b. The differences for

March 1993 at 25 to 30 km may be explained by a preferred partitioning of NOy into HNO 3 in the models for their elevated aerosol surface areas of 1992, which is consistent with the model deficits of NOx

in Figure 3.3.4b at those same altitudes. At 20 km the CLAES HNO 3 is not as accurate (14%), and the

model results diverge sharply from one another (c.f., GSFC-2D and UNIVAQ-2D). A better diagnosis

199
of the model differences at 20 km must come from comparisons with the aircraft data on NOy and NOx

and from the simulations of the tracer lifetimes in the lower stratosphere.

A more meaningful result is obtained by generating the sum of HALOE sunset NO and NO 2 plus

the HNO 3 from CLAES and then comparing that total with the NOy from the models. That distribution

for the March 1993 data is given in Figure 3.3.6a, and the differences with the model NOy are given in

Figure 3.3.6b. It is easier to compare with model NOy than NOx, because the NOy should not be sensitive to the time-of-day for its reported average or affected by a partitioning. As expected though, there is

not a lot of similarity among the model distributions of NOy because of the model transport differences

and whether a given model NOy calculation has achieved near equilibrium.

When the NOx is referenced to NOy from the models (Figure 3.3.7), the results are very similar

from model-to-model and somewhat similar to that from the satellite data (Figure 3.3.7a). This is an

indication that the model partitioning of NOy is reasonable. The 0.2 and 0.4 contours occur near 23 and

26 km in the data of Figure 3.3.7a, whereas those ratios in Figure 3.3.7b from the models occur closer to

26 and 28 km. The differences at 30N are shown more clearly in Figure 3.3.7c. This discrepancy is in

line with the preferred conversion of NOx to HNO 3 for the models for March 1992 versus 1993, and the

fact that peak values of HNO 3 were 48% higher in 1992 than in 1993 (Kumer et al., 1996). Obviously,

during a period when volcanic aerosols are a major contributor to the surface area estimates, the model/

measurement comparisons of NOx/NOy must be conducted for similar conditions. The observed

monthly estimates of the NOx and NOy from the UARS data represent new and accurate estimates of

the stratospheric distributions of those chemical families, at least in the middle stratosphere. In this way

they are similar to the estimates of NOx and NOy obtained from the nighttime L/MS data on NO 2 and

HNO 3. Although retrievals of NOx in the lower stratosphere from HALOE are more certain for the middle than the early 1990s, the mid 1990s data should be compared with a baseline atmosphere run from

the models for the condition of low or background aerosol surface area.

About 80 to 90% of total chlorine is in the form of HCi in the upper stratosphere, and Figure 3.3.8

indicates good similarity among the model results where the gas-phase, chlorine catalytic cycle affects

the ozone chemistry. HALOE HCI has uncertainties of order 14% in the middle and upper stratosphere

(Russell et al., 1996). Almost all of the models are within that uncertainty in the upper stratosphere.

Model HC1 deficits in the middle stratosphere may be due to their daytime partitioning of Cly and, in

part, due to their overall Cly amounts. In the lower stratosphere, where the HCI mixing ratios are small,

the HALOE uncertainties are closer to 20%. Once again the model results at those altitudes are dissimilar from one another and often by much more than the estimate of HALOE bias. Part of the model differences may be due to a preferred partitioning of total chlorine in the presence of aerosol surfaces from

its reservoir HCI and into reactive forms of chlorine. Overall best agreement was obtained with the

CS1RO model.

Because reasonably good model/data agreement was found for those altitude regions where the

respective chemical families affect ozone, there ought to be similarly good agreement for March ozone.

Figure 3.3.9 shows the well-documented model deficit of ozone in the upper stratosphere of order 10 to

30%. HALOE ozone is believed to be accurate to better than 12% in that region (Bruhl et al., 1996). At

about 30 km there tends to be model/data agreement. Between 20 and 30 km many models have ozone

values that are too large at middle latitudes, indicating that the accumulation of ozone from above is too

great. At tropical latitudes at 25 km many models have an ozone deficit, indicating ascent that is too

rapid and/or meridional mixing that is too weak. Model 03 at 20 km is greater than observed at low latitudes for most models, indicating rapid meridional mixing of the accumulated ozone from higher latitudes. Exceptions are the UNIVAQ-2D and NOCAR models, which have ozone deficits that extend to

extratropical latitudes of the lower stratosphere, an indication of strong diabatic ascent at tropical

200
latitudesfoIlowed by efficient poleward transport at 20 to 25 kin. HALOE ozone is not very accurate

below 15 km, and most models give higher values. Finally, in the mesosphere the SLIMCAT and

LARC-3D models have high ozone values, perhaps due to their reported 24-hour averages.

The stratospheric results shown here are very similar to the model ozone comparisons with the April

ozone climatology for 1988 to 1996 (Logan and McPeters, 1999), indicating that the HALOE data of

1993 are representative of that multiyear average. However, the differences with HALOE become pronounced at about 15 km where the HALOE ozone is systematically lower than the ozonesonde climatology, most likely because the HALOE data have not been corrected and/or screened properly for cloud

contamination effects near the tropopause.

Comparisons for September/October 1992

The comparison results for September/October 1992 (not shown) were similar to those for March/

April 1993, except in the Antarctic lower stratosphere where heterogeneous chemistry on PSC aerosols

is a factor. But the interpretation of any success of the polar chemistry in the models is critically dependent on the exact timing of the comparisons. For the region poleward of 60S the sunset HALOE data

were actually obtained from October 9-13, while the CLAES data were measured during September

1-18. The amount of polar N205 must also be included in a comparison of data with model NOx and

NOy, and we have not added the N205 from CLAES. Nevertheless, the models are generally consistent

with one another, and show considerably less NOx, more HNO 3, and less HC1 than the data between 20

and 25 km at those high southern latitudes. The two models without PSC chemistry (HARVARD and

CSIRO), have the smallest differences for NOx and HC1, perhaps because there was an atmospheric

recovery to a more nearly gas phase partitioning of the Cly and NOy by mid October 1992. Interestingly, those two models are in general agreement with the other models in that they show more HNO 3

than was observed in early to mid September from CLAES, perhaps because of differences in the

amount of HNO 3 in the particle phase. Although we have not made a comparison here with the MLS

CIO measurement, most models do show relatively large amounts of reactive chlorine in the vortex

region at that time. Many models give less 03 than observed above 25 kin, in agreement with their differences for the other reactants. But below 23 km most models have more 03 as well as more CH 4 than

observed for the vortex, indicating that the effect of descent from higher altitudes in the models is not

extending to the lowest parts of the polar stratosphere. The polar descent in the HARVARD model

seems to be the exception; its CH 4 is less than observed in the Antarctic vortex region.

3.3.3 Summary

In general, the CLAES and HALOE data sets that were used for the model comparisons of the baseline atmosphere (experiment B 1) are of good quality by mid 1992. Due to the large aerosol corrections

in the satellite data in early 1992, it is best to rely on the balloon and aircraft data for the model verification in the lower stratosphere at that time. The CLAES N20 and HNO 3 data are not as accurate at the

low mixing ratios of the upper stratosphere, and the accuracy of HALOE NO + NO 2 also degrades in

the lower stratosphere.

Model/data differences for both the source and reservoir gases are due more to model differences in

transport than chemistry. Even so, the model chemicaI partitioning of NOy and Cly for early 1992 is

hard to verify with the satellite data because of their inaccuracies in the lower stratosphere. By 1993 the

measured data are more reliable but the aerosol surface area distribution used in the models is not representative of 1993. The aircraft measurements from the lower stratosphere are a better comparison data

set for 1992.

201
References

Bruhl, C., et al., Halogen Occultation Experiment ozone channel validation, J. Geophys. Res., voi. 101, pp. 10217-

10240, 1996.

Gordley, L. L., et al., Validation of nitric oxide and nitrogen dioxide measurements made by the Halogen Occultation Experiment for UARS platform, J. Geophys. Res., vol. 101, pp. 10241-10266, 1996.

Harries, J. E., et al., Validation of measurements of water vapor from the Halogen Occultation Experiment

(HALOE), J. Geophys. Res., vol. IOI, pp. 10205-10216, 1996.

Kumer, J. B., et al., Comparison of correlative data with HNO 3 version 7 from the CLAES instrument deployed on

the NASA Upper Atmosphere Research Satellite, J. Geophys. Res., vol. 101, pp. 9621-9656, 1996.

Logan, J., and R. McPeters, Ozone climatology, in MMII Report, Chapter 4, 1999.

Morris, G. A., D. B. Considine, A. E. Dessler, S. R. Kawa, J. Kumer, J. Mergenthaler, A. Roche, and J. M.

Russell III, Nitrogen partitioning in the middle stratosphere as observed by the Upper Atmosphere Research

Satellite, J. Geophys. Res., vol. 102, pp. 8955-8965, 1997.

Park, J. H., et al., Validation of Halogen Occultation Experiment CH 4 measurements from the UARS, J. Geophys.

Res., vol. 101, pp. 10183-10203, 1996.

Roche, A. E., et al., Validation of CH 4 and N20 measurements by the cryogenic limb array etalon spectrometer

instrument on the Upper Atmosphere Research Satellite, J. Geophys. Res., vol. 101, pp. 9679-9710, 1996.

Russell, J. M., III, et al., Validation of hydrogen chloride measurements made by the Halogen Occultation Experiment from the UARS platform, J. Geophys. Res., vol. 101, pp. 10151-10162, 1996.

202
6O

5O

40-

v 30-

20-

10-

0

-90

CLAES N20 MAR - APR 1992

__ _ I 1 I fS. _J_ I

I I I I I

-60 -30 0 30 60

LATITUDE (DEG)

90

Figure 3.3.1. Comparisons for N20 for March 1992. (a) zonal mean field from March, 1992, CLAES data--

contour increments are not uniform; (b) (Model-data)/data differences in percent for AER, GSFC-2D, LLNL,

UNIVAQ-2D; (c) as in (b) but for SLIMCAT, GSFC-3D, LARC-3D, NCAR-2D; (d) as in (b) but for NOCAR,

SUNY-SPB, CSIRO, HARVARD. In (b-d) the contour increments are 10% from 0 to 100 and 50% above that.

203
6O

4O

2O

0

-10(

N20 Mar-Apr Run B1 - CLAES 1992

AER

• '_" -'::L.' _ _. : :-4- ,.'_, ' ,

. :,?" .... .' ...'.,,,':, .' . : .- .... ::

_. , I i , , , ] I

-50 0 50 100

60

4O

2O

0

-100

GSFC-2D

' _.:, ,_.:.;::;.-::-.=-:v:.:- :--:.:-;.-.!:.,/,_:sb.'..-:-.-.

:,.;:!i:.:,,: !,"...

...... )o_ / _---,_,; .... ., /

-50 0 50 100

6O

40

2O

LLNL

-" ",,_,o..... •- :-: -:.-',-.'..:-:.::-:-, ""

.,,.,.:, , "-,.. ,.,'. ;,-, , \', ,

_":::_,":_i:::::" :.v:',,__o ....... ?t.q-"_,':'_',

_._:,-: :,.-,-,...... , .:,,:,;'-,:--'-_,',,%,.,,,

•- ,_ .,:,_ .... -,

-100 -50 0 50 10(

60t '

401-

2O}-

01 ....

-100

UNIVAQ-2D

i, ,_2_.'::.b.:..-:::_:;:":"::-:v:::::_

•, ":': ::: :,',.:_:.[-.:."

I_ I

-50 0

Figure 3.3.1. Continued.

1 , i r i

50 101

204
6O

4O

2O

6O

40

/

0 , i , , I

-100 -50

.. -: :: -._--... -- - _ ._ -.:.._.,....;.:..-... _ =..: ..:_- _ _ .-: -: ,

.;"_'=;-- "-"-'_" -=-= -_i

Jr "'--,',

. - o ... /,

2O

N20 Mar-Apr Run B1 - CLAES 1992

SLIMCAT GSFC-3D

601 .... ' .... ' .... ' ....

1-

401-

, , 01 ......... , ,

0 50 100 - 100 -50 0 50 100

LARC-3D

.... i .... i .... i ' ' '

:,_.. .'2-.-i:'-.".'-'::'.'.''.''. ',. ...

,:_.;,, , , :,','-. .. - .;...-: .-.-.-.-..;..-......-

6O

4O

2O

- 100 -50 0 50 100 -100

NCAR-2D

.... i .... s .... i ....

. '...._...'.... :-.. ;.......'-."--::'.'.- _-'-

:_.6 .........

-50 0 50 t 00

Figure 3.3.1. Continued.

205
6O

4O

2O

0

-100

N20 Mar-Apr Run B1 - CLAES 1992

NOCAR

i f ! , , , ,

-50 0 50 00

6O

40

2O

0

-10(

SUNY-SPB

_;,,.:--_.-"-:-:-:-;_:.;.!-i-!+:: :{.80-'..-. :: " "

k_:t-" : '. . ::,',:',,",','.',', ,.'--"

I1,:;-: -:," : -6o ..

_,," £'.' ",,, , •,, "" ,_:-'.'::G.'.-.-.-.--ZI

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,,,,I i .... I ....

-50 0 50 100

60

4O

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-;. i i',So:.:

0 i ....

-100 -50 0 50 100

HARVARD

60ff .... ' " '.-_.:-t.: .' : '

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".", :_5,'"', o ', ,:,::,,',--,

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n

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-100 -5O 0 50

Figure 3.3.1, Concluded.

lO(

206
60

5O

4O

_v30

N

2O

10

0

-90

HALOE CH4 March->April 1992

I I 1 I

-60 -30 0 30

LATITUDE (DEG)

m

I

60 )0

Figure 3.3.2. As in Figure 3.3.1 (a-d), but for HALOE CH 4 for March 10-April 26, 1992. CH 4 increments in (a) are

0.2 ppmv.

207
6O

4O

20

-100

CH4 March->April Run B1 - HALOE 1992

AER

-50 0 50 100

20 F

GSFC-2D

P_ ....

-100 -50 0 50 100

60

40

20  ̧

LLNL

-100 -50 0 50 100

UNIVAQ-2D

"'-" " "' " " ' 'l_"t i

' o ' "'" --. " "II((,Y

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407

_) ........ : -;_-. ...... .

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i

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-100 -50 0 50 100

Figure 3.3.2. Continued.

208
6O

4O

2O

0

-100

CH4 March->April Run B1 - HALOE 1992

SLIMCAT GSFC-3D

<i .... 7-:::.-1:1";

-50 0 50

60_

4O

2O

0 , , T i I I ! , ___ __

00 -100 -50 0 50 100

6O

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0 50 100

Figure 3.3.2• Continued•

209
6O

4O

2O

CH4 March->April Run B1 - HALOE 1992

NOCAR

0 .... _ .... z,,,,i ....

-100 -50 0 50

SUNY-SPB

100

60J

40 k

201-

,#.... -: .... ,..-.'.-"

L ;_3

0] .... i .... i .... r , i , i

-100 -50 0 50 100

6O

40

20

CSIRO

0 .... i .1 , , , t .... I ....

-100 -50 0 50 00

6O

4O

2O

HARVARD

0 i i i i I i i i i i , , . _ i ....

-100 -50 0 50 100

Figure 3.3.2. Concluded.

210
60

50

40

v 30

N

20

10

0 I I I I J__

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.3.3. Comparisons for H20. (a) zonal mean field from HALOE for March 10-April 26, 1992, contour

increment is 0.5 ppmv; (b) model comparisons for AER, GSFC-2D, LLNL, and UNIVAQ-2D and contour increment is 5%.

211
6O

4O

20

H20 March->April Run B1 - HALOE 1992

AER GSFC-2D

o-": " "-::.:\_----

, ,.,,...-.::-::,.,

. -.:. . ,. _:,

6O

4O

20

/. ....... .--:,....,:_-.

-:.: )

0 50 100 -100 -50 0 50 100

6O

40

2O

LLNL

0 .... I • , , , f , , | , I

-100 -50 0 50 100

6o

40

2O

UNIVAQ-2D

0 .... I .... I .... L ,

-100 -50 0 50 100

Figure 3.3.3. Concluded.

212
v

.Ic

N

60

50

40

30

20

10

HALOE NO+NO2 March 1993

, \, ' J

0 I I t I I

-90 -60 -30 0 30 60 9¢

LATITUDE (DEG)

Figure 3.3.4. As in Figure 3.3.3, but for [-]ALOE sunset NO + ]'40 2 from March 6-April 11, 1993. Contour increment for (a) is 2 ppbv above the 2 ppbv value. In (b) tile increment is 10%.

213
6O

40

20

NO+NO2 March Run B1 - HALOE 1993

AER

...... i .... i ....

_- "...,-.'-;-i-;-.- _ .,'/: :

- 100 -50 0 50 100

60l

40F

20F

i

01

GSFC-2D

':: : - " " 1"-:.-'., .." *_. •

-100 -50 0 50 100

60

40

20

ol

-100

LLNL

i , , , I , , , , I , f , , T

-50 0 50 00

UNIVAQ-2D

40F

"._:- - _.;:

, : .... -'".'-'-'--;'- ,;,'.':: 0-:-'i::;:"

20 F

f. ,.

01 , , , ....

- 100 -50 0 50

Figure 3.3.4. Concluded.

100

214
6O

50

4O

_" 30-

N

20-

10-

0

-90

CLAES HNO3 MAR 1993

_0.100_ _ 0'100 _--

0.500 0.500

I I 1 I I

-60 -30 0 30 60

LATITUDE (DEG)

9C

Figure 3.3.5. Comparisons for HNO 3. (a) zonal mean field from CLAES for March 1993--contour increment is

1 ppbv except for values less than I ppbv; (b) model comparisons for AER, GSFC-2D, LLNL, and UNIVAQ2_-contour increment is 10%.

215
60

4O

2O

HNO3 March Run B1 - CLAES 1993

AER

0 , , i , I I !

-100 -50 0 5O 100

60

4O

2O

0

-101

GSFC-2D

.... ! I i

_:-8"'_::::::: ...............

,,,,I ! i

-50 0 50 100

60

4O

2O

LLNL

.... I r I ' ' '

.. ..... --. ..... . ..... .

:::i-_-==========================: :-;::::::!.!:--:-._.

0 .... I , ,: _ , I I

- 100 -50 0 50

601 .... ,

L

40F

UNIVAQ-2D

0 .... t ' ,

20F

O[

100 -100 50 0 50 100

Figure 3,3.5. Concluded.

216
v

..k

N

60

50

40

30-

20-

10-

0

-90

MEASUREMENTS NOy MAR 1993

] I 1 I I

-60 -30 0 30 60 9(;

LATITUDE (DEG)

Figure 3.3.6. As in figures 3.3.4 and 3.3.5, but for NOy. Contour increment in 3.3.6a is 2 ppbv and in 3.3.6b it is

10% up to 100% and 50% above that.

217
6O

4O

20

0

-100

NOy March Run B1 - Measurements 1993

AER

'._/ _ , _ ;, ' IL__' '

. ,_

t .... i , , , , E ....

-50 0 50 100

GSFC-2D

L

0t , , , z I I .... I , ,

-100 -50 0 50 t 00

60

40

2O

0

-t00

LLNL

_il!i_i!

i., "_:.!!..':i'!', _

'.,, ':,iI,','

_o: "_

-50 0 50 100

UNIVAQ-2D

6o

-. ' :.:-'::,_:-:"-:-:-.,=}.i .:--:--:":- -'.-

O1 , ,

-100 -50 0

Figure 3.3.6. Concluded.

50 100

218
6O

50--

40-

___ 30--

20-

1(]--

0

-90

March 1993 (Haloe/CLAES) / (NOx/NOy)

\'

f_

_-" 0.80 0.80 _

I I t I I

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.3.7. (a and b) As in figure 3.3.6, but for the ratio NOx/NOy; Contour increment is 0.1 up to 0.9 and 0.02

above that. (c) NOx/NOy ratios at 30N.

219
60

50

40

3o

N

20

10

6O

50

4O

3o

N

March Run B1 NOx / NOy

20

10

0

-90

AER

V ,o P i i I i

- u.Lu_

0 I £ i I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

LLNL

I I I I i

0

6O

5O

40

2O

10

01

-90

6O

GSFC-2D

I I I i T

-60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-2D

I , t I

I I I

-60 60 90

I I

-30 0 30

LATITUDE (DEG)

_ 1.00_f/

101-

Ol I I I I I

-90 -60 -30 0 30 60

LATITUDE (DEG)

9O

Figure 3.3.7. Continued.

220
6O

5O

40

_. 30

20-

10-

0

0.0

NOx/NOy March Run B1 1993

30 Degrees North Latitude

[

,I_ HALOE/CLAES

+ AER-2D

GSFC-2D

x LLNL-2D

[] UNIVAQ-2D

I

0.4 0.6

Ratio

Figure 3.3.7. Concluded.

221
v

-Ic

N

60

50

40

30-

20-

10-

0

-90

HALOE HCL March 1993

I I I I I

I I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.3.8. As in Figure 3.3.4, but for HALOE HCI. Contour increment for (a) is 0.25 ppbv and for (b) it is 5%.

222
6O

5O

40

3O

20

10

0

-90

I

5O

40

30

20

10

0

-90

HCL March Run B1 - HALOE 1993

AER

L L I I L .....

-60 -30 0 30 60 90

LATITUDE (DEG)

LLNL

'")-;';"-, o . ',"';' ';"-:

: ',' "".----::i', .-f, "','-"-','-':

%: :!,:-i:: :" "-_._;";'.','-.... %;, ;::;

-6O

} i L __ _ ]_.....

-30 0 30 60 90

LATITUDE (DEG)

40t3o1-

t_

20_

10_

GSFC-2D

:." : , :::l_q--.-:::=:-;-...-." ...... ,: .':.

I t l_ I I

-90 -60 -30 0 30 60

LATITUDE (DEG)

UNIVAQ-2D

3ot :'.--;:--------::-::::::::;-;-;.;.:-:;-:",, .;i_

_ I .-_:- .:,--?.;-;:-;--.--;..: --:.".". i!i'_,i_'

, ,--.,.-_-_._-,";:?o.--_-_o-:..:,,ii_"_

O [ I 3 ___± __ I I

-90 -60 -30 0 30 60

LATITUDE (DEG)

Figure 3.3.8. Concluded.

90

90

223
v

N

60

50

40

30-

20-

10-

0

-90

HALOE 03 March 1993

08o_ _ l bo__

3.00- 3.00

_._._.._5.00

----------- 5.00

7.00

____------ 3.00

l I I 1 I

-60 -30 0 30 60 9(;

LATITUDE (DEG)

Figure 3.3.9. As in Figure 3.3.4, but for HALOE 0 3. Contour increment for (a) is 1 ppmv, but below l ppmv it is

0.1 ppmv. Contour increment for (b) is 5%.

224
60

5O

40

3O

2O

10

0

-90

60

5O

40

30

20

10

0

-90

03 March Run B1 - HALOE 1993

AER

_• L-.-.-•- -- -'-1_ ,t ....

I / ::

• k .°- .

_I.: . ..... .•" " " • - ." "'-'.'.•-'.• _,':'"

_--_---_-<.0•-...... , _ -.... _,_,,,:

I I I I I

-60 -30 0 30 60

LATITUDE (DEG)

9O

60i

5O

40F

3O

20F

10

ol

-90

GSFC-2D

' I- _.._{.'-:-, ,_:o.: .

• .."" 0:"

..-•_.:_; •.... ..... . •. _ .... ;

' ' . •',;--.;•'" " _::i

• - . :. :, ...::_:--"

:i::: 0-,:_ -•" .... "'...'...."..

I I I I _ .........

-60 -30 0 30 60 90

LATITUDE (DEG)

LLNL

• D " -. I - .- - .IZ.. . t ". I

-- -2o-.-- .. , :i. •

._.-_p-...-.- ...... ..:. ,• "_;..... :::-,,',;,:

_---'_"_- h" .... "'- -" '.'- " - °-;-:--"-•"" '_I:",'_,

I I ] I I ----

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

50

4O

30

N

2O

10

0

-90

UNIVAQ-2D

i_ii_8::i::::i:-::, i::::'_.-:-:-:.-.:.:-:-:,,

•--:--o " •.',

I I I l I --

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.3.9• Concluded•

225
3.4 Source Gases Comparisons

Courtney J. Scott and Malcolm K. W. Ko

Atmospheric and Environmental Research, Inc.

3.4.1 Introduction

Comparisons of measured and model calculated distributions and stratospheric lifetimes of longlived source gases emitted from the surface of the earth provide a useful diagnostic for model transport

and chemistry in the lower stratosphere. These long-lived species play an important role in the ozone

budget, affecting the concentrations of Cly and NOy, which influence ozone production and loss.

Source gases from the present day atmosphere were examined using calculations from participating

models and selected measurements. These gases, unlike many of the tracers discussed in Part A, are

dependent on chemistry as well as transport. Differences in chemical treatment among the models, such

as photolysis rate calculations and diurnal averaging, could create substantial differences in source gas

concentrations. A previous intercomparison of photolysis rates helped to remove much of the intermodel difference in this quantity, so we will assume that transport differences are responsible for the

bulk of the inter-model differences in source gases. If this is true, theh a consistent picture of model

transport should emerge based both on source gases and the inert tracer experiments in Part A.

Observed source gas concentrations, which are compared with model results, are from the version

7.0 CLAES data from the UARS satellite, zonally averaged ATMOS data from the November 1994

ATLAS3 mission (Gunson et. al., 1996), and data from numerous aircraft campaigns that obtained

in-situ measurements in the lower stratosphere between 1992 and 1997. Comparisons between measurements and model calculations can be difficult. Aerosols emitted during the Mt. Pinatubo eruption may

have compromised CLAES data from I991 and 1992. Aircraft campaigns do not always capture data

representative of the zonal and climatological mean and do not take samples at many altitudes. Nevertheless, the measurements do provide a yardstick for model comparison.

Model results for source gases were obtained from Scenario B l, the 1992 atmosphere calculation.

In this scenario, modelers were asked to calculate their best 1992 atmosphere. The models were initialized with a 1990 steady state calculation and then continued for an additional 2 years. Sulfate surface

areas for 1991 and 1992 from SAGE II were provided, along with 1992 temperatures from NCEP. Models were required to use the JPL-97 reaction and photolysis rates (DeMore et. al, 1997). Models participating in this study were: AER, GSFC-2D, LLNL, CSIRO, SUNY-SPB, UNIVAQ-2D, LARC-3D,

HARVARD, GSFC-3D, ECHAM3/CHEM and NOCAR. Eight of the eleven models provided both global and stratospheric calculated lifetimes for 10 source gas species, ranging from stratospheric lifetimes

of 35 years for CH3Br to around 200 years for CHC1F 2. Close examination of mixing ratios, both in altitude and latitude, for CH 4, N20 and CFCI 3 was also made. These particular species were selected based

on the availability of measured data and their stratospheric lifetimes.

3.4.2 Global and Stratospheric Lifetimes

The atmospheric residence time, or lifetime, of a species defines the mixing ratio expected due to a

given steady state emission rate. In our analysis, we examine the global lifetime, the total atmospheric

burden divided by the total atmospheric loss, and the stratospheric lifetime, the total atmospheric burden

divided by the stratospheric loss. Given the assumptions stated above, the global and stratospheric lifetimes of long-lived source gases may be useful in determining transport signatures within models.

226
Global and stratospheric lifetimes for 10 species and eight models are shown in Table 3.4.1. For comparison, global lifetimes recommended in Scientific Assessment of Ozone Depletion (WMO, 1995) and

stratospheric lifetimes derived from the ASHOE/MAESA (A/M) campaign (Volk et. al., 1997) are also

shown. Note that for CF2C12, CFCl 3, and CC14, the derived mean stratospheric lifetime is smaller than

the recommended global lifetime though the upper limit of the error range is comparable to the global

lifetime. This may reflect the limited data used to derive the stratospheric lifetimes or uncertainty in the

WMO global lifetimes.

The LARC-3D model, the only 3D CTM to submit lifetimes, calculates stratospheric lifetimes for

N20, CF2C12, and CH3CI that are substantially longer than the other models and 40% longer than the

derived stratospheric lifetimes from A/M. Comparison of LARC-3D with the WMO global lifetimes

also indicate the 3D CTM is calculating lifetimes that are 40% longer for N20 and CF2CI 2. It should be

noted that the unusually large lifetimes calculated by the LARC-3D model may be due to a

non-converged 1990 data set.

Comparison of the global lifetimes with the WMO lifetimes for the longer lived species, N20 ,

CF2CI 2 and C2C13F3, indicate the remaining seven models calculate lifetimes in good agreement with

the documented values, calculating mean global lifetimes that are within +/-5% of the WMO values.

The calculated global lifetimes of the shorter lived species show model means, with the exception of the

HARVARD model which calculates much longer OH reactive lifetimes than any of the other models, to

be within +/-10% of the WMO values for the OH reactive species and within +/-25% for the photolytically reactive species.

Comparison of the calculated stratospheric lifetimes for the long-lived species, N20, CF2C12 and

C2C13F 3, show most of the models to be in good agreement with the A/M lifetimes, calculating mean

lifetimes within the error bars noted. For N20 and CF2C12, the LARC-3D value is close to a factor of 2

larger than the A/M value. While the rest of the models are within +/- 10% of each other, around 100

years. Among the seven models, AER and LLNL calculate the shortest lifetimes while GSFC-2D

calculates the longest. All of the models calculate much longer lifetimes for CH 4 than the A/M values

indicate. The calculated lifetimes of the short-lived species do not represent the derived values as well

as the long-lived species. For CFCI 3, GSFC-2D, HARVARD and LARC-3D calculate lifetimes around

60 years, about 50% larger than the A/M value. The remaining five models calculate a mean lifetime

within +/-10% of each other around 47 years, well within the error bars noted. HARVARD and

GSFC-2D calculate lifetimes larger than 55 years for CC14, about 70% larger than the A/M values.

Values from the remaining six models calculate a mean of 44 years, very close to the A/M calculated

values. In this group, the UNIVAQ-2D calculates the shortest lifetimes closest to the A/M lifetimes.

The 10 species studied can be separated into two groups, those with stratospheric lifetimes less than

60 years and those with stratospheric lifetimes over 120 years. Species with shorter lifetimes such as

CC14 and CFC13 are more sensitive to the transport characteristics at 75 mb while those with longer lifetimes (such as N20 and CF2C12) are more sensitive to the model transport around 10 mb. The 2D CTMs

are in relatively good agreement with each other. Figure 3.4.1 gives a graphical representation of the

calculated stratospheric lifetimes for selected species. In Figure 3.4.1, patterns can be noted with certain

models. The HARVARD model tends to calculate lifetimes that are shorter for the longer lived species,

while they calculate large lifetimes for the shorter lived species we examined. The UNIVAQ-2D model

shows an opposite pattern, where the shorter lived species have lifetimes below the mean of all models.

The remaining models tend to be in the middle and show no obvious patterns. These behaviors apply to

species whose removals are both driven by photolysis and by OH chemistry and is interpreted as indicative of the transport differences in the models. A general pattern is noted that models that tend to calculate shorter lifetimes for a given species have steeper latitudinal gradients for that species. This behavior

may be indicative of the strength of the tropical upwelling and/or the tropical barrier. Comparison of the

model calculated stratospheric lifetimes shows AER and LLNL tend to calculate similar lifetimes.

227
Basedonthesecorrelations,AERandLLNL mayhavesimilarupwellingand/orhavesimiliartropical

barriersatthe75and10mbsurfaces.HARVARDandUNIVAQ-2Dmayhavesmallerupwellingrates

attheir75mbsurface.Thedifferencesin modelcalculatedlifetimesseemtoberelatedtotheageof air

fromtheSF6andthetracerexperiments,eepertinentsections.Modelsthatarecalculatinglongerlifetimestendtohaveolderair,givingmodelcalculatedagesthatareclosertotheageof airderivedfrom

mea_rementsof SF6(Volket.at.,1997)andCO2fromtheER-2aircraft.Thelongersourcegaslifetimesinthemodelscouldsuggestthattheupwellinginthelowertropicalstratosphereforthemodelsis

tooweakbutthisis inconsistentwiththefindingsfromtheCO2study,whichdeducesthatthemodel

calculatedseasonal signalforCO2isadvectedupinthetropicstoofastwhencomparedto observations.

Thisseeminglycontradictorysituation,thatthe model ages are too young from the SF 6 experiment, but

the model calculated lifetimes are too long compared to observations, could be an indication that the

meridional flux across the tropical barrier is too weak.

3.4.3 Source Gas Concentrations

In order to discern dynamical signatures of upwelling verses horizontal mixing in the lower stratosphere, results from the AER 2-D CTM using three different mixing rates ranging from a weak tropical

barrier to a strong tropical barrier were examined. The models are: the Global Diffuser (NO PIPE)

(Kotamarthi et al., 1994; Weisenstein, et al., 1993); the Partial Barrier (LEAKY PIPE) (Shia et. al.,

1998); and the Tropical Barrier (PIPE) (Weisenstein et. at., 1996). The three models have identical

chemistry and advection and differ only in the horizontal diffusion coefficients in the lower and middle

stratosphere. Three different properties of the source gases were examined: lifetimes, latitudinal cross

sections at specified altitudes, and profiles at specified latitudes. Table 3.4.2 illustrates calculated stratospheric lifetimes for the three AER models. In all species examined, the PIPE model calculates the

shortest lifetimes while the NOPIPE model calculates the largest lifetimes. This is true not only for the

photolyticalty driven species, but also for the OH reactive species, such as CH 4. Clearly, the strength of

the tropical barrier effects the lifetimes of the species, as the highest concentrations are found in the

tropical regions where chemical processing is most intense. Thus, the highest concentrations are seen

with the strongest tropical barriers, leading to greater overall loss and shorter lifetimes. Differences in

advective transport may also impact the permeability of the tropical barrier. If this is truly a transport

signature, the calculated species concentrations in the tropics should be correlated with lifetimes. Specifically, models which calculate longer lifetimes should also calculate smaller concentrations in the

lower tropical stratosphere. Additionally, one expects the gradients of the source gas isopleths to be

steeper across the tropical barrier if the model is calculating shorter lifetimes. To test this hypothesis,

three source gas species were examined: CH 4 and N20 at 10 mb and CFC13 at 75 mb. Figure 3.4.2

shows the latitudinal cross sections at 10 mb of CH 4 calculated for spring for all models. In Figure 3.4.2,

GSFC-2D, UNIVAQ-2D, SUNY, LARC and GSFC-3D are all grouped together peaking 20% below

the zonal mean UARS CH 4 in the tropics. The remaining models, AER, HARVARD, LLNL, NOCAR

and CSIRO, all fall within 10% of the UARS zonal mean CH 4 in the tropics. Spring and fall equatorial

profiles of CH 4 for all models, UARS and ATMOS are shown in Figure 3.4.3. In the lower stratosphere

all model calculated CH 4 falls below the measured UARS data. Note that the ATMOS data is lower in

magnitude and the models are higher than ATMOS data for the same altitude range. A large spread is

seen in calculated CH 4 concentrations in the middle and upper stratosphere.

Figure 3.4.4 shows the latitudinal cross section of spring N20 at l0 mb. As in Figure 3.4.2, UARS

zonal mean data are shown for 1992 and 1993. Much larger variation is seen in the UARS N20 data

than in the CH 4 data. This may be due to a small contamination by the Mt. Pinatubo aerosols effecting

the data retrieval. Again, the models fall into two separate groups. The first group, containing the models SUNY-SPB, LARC, UNIVAQ-2D and GSFC-3D, are approximately 10% below the UARS tropical

concentrations. The second group, consisting of the models AER, HARVARD, LLNL, NOCAR and

228
CSIRO are 15 to 40% too high in the tropics. The GSFC-2D model falls the closest to the UARS data.

Figure 3.4.5 shows spring and fall equatorial profiles for N20. In contrast to the CH 4, the calculated

N20 values more closely represent the UARS and ATMOS measured N20 in the lower stratosphere,

falling within +/-30%. The spread in the middle and upper stratosphere is as large for the N20 as the

CH 4.

Figures 3.4.6 and 3.4.7 show fall CFCt 3 calculated and measured data, with a latitudinal cross section at 75 mb and an equatorial profile, respectively. Examination of Figure 3.4.6 does not show the

same pattern that both the N20 and CH 4 at 10 mb did. This is indicative of a different pattern of mixing

that drives the concentrations at 75 rob. All model calculated tropical CFC13 mixing ratios are grouped

together closely and fall within the error bars of the ER-2 flight data examined. Zonal mean ATMOS

measurements are also shown in Figure 3.4.6. These data show larger tropical air concentrations than

the ER-2 data. Most of the calculated tropical CFC13 data falls well below the ATMOS data. UARS

CFCI 3 was not used due to a retrieval contamination by Mt. Pinatubo aerosols. It is worth noting that

GSFC-2D shows one of the flatter distributions and also calculates the longest lifetime for CFC13. Additionally, the spread of calculated lifetimes for CFCI 3 is smaller than the lifetimes seen from N20. The

calculated CFC! 3 from most of the models have flatter distributions across the tropical barrier than the

measured data examined. This may be indicative of too much meridional flux across the tropical barrier

at the 75 mb level. This would substantiate a younger age of air calculation seen in the SF 6 calculations.

At mid and high latitudes, the measured data, ATMOS, A/M and STRAT show a large spread. These

values are probably indicative of polar vortex air, which most of the models do not represent well. Figure 3.4.7 illustrates the same behavior seen in the N20 and CH 4, where a small spread in calculated concentrations is seen in the lower stratosphere with the spread increasing at higher altitudes.

In all species examined, a large spread of calculated concentrations is seen at high latitudes. This

may be indicative of different polar process or high latitude dynamics. LARC-3D is consistently calculating much smaller mixing ratios than other models in the tropical lower stratosphere and tends to have

one of the flatter distributions for N20, CH 4 and CFCI 3. This is consistent with the longer calculated

lifetimes for the source gases. Additionally, GSFC-3D, GSFC-2D, UNIVAQ-2D, SUNY-SPB, and

LARC-3D seem to group together for the longer lived species, showing flatter distributions at the 10 mb

surface than the other models. Examination of the calculated source gases from the LLNL and AER

models show mixing ratios in the tropical stratosphere that are higher than the UARS data and the other

models. This also seems to substantiate the shorter calculated lifetimes seen in Figure 3.4.1. Deviations

from these systematic dynamical signatures in the profiles and cross sections could be due to differences

in the photolysis rates and/or the chemistry.

3.4.4 Conclusions

We attempt to use calculated and measured source gas concentrations and lifetimes as a diagnostic

for model transport. Based on our examination of the AER NO PIPE, PIPE and LEAKY PIPE models,

we found evidence that a stronger tropical barrier will mean younger stratospheric lifetimes, higher

source gas mixing ratios in the equatorial region and lower source gas mixing ratios in the mid-latitudes

( all in the lower stratosphere). Examination of stratospheric lifetimes of 10 species and mixing ratios of

CH 4, N20 and CFCI 3 indicate that there are systematic dynamical signatures that may be seen in these

parameters. A general pattern is noted that models that tend to calculate shorter lifetimes have steeper

latitudinal gradients. This behavior may be indicative of the strength of the tropical upwelling and the

permeability of the tropical barrier. Comparison of the model calculated stratospheric lifetimes show

AER and LLNL tend to calculate similar lifetimes. Based on these correlations, AER and LLNL may

have similar upwelling on the 75 and 10 mb surfaces. HARVARD and UNIVAQ-2D may have smaller

upwelling rates at their 75 mb surface.

229
The differences in model calculated lifetimes seem to be related to the age of air from the SF 6 and

the tracer experiments. Models that are calculating longer lifetimes tend to have older air, giving model

calculated ages that are closer to the age of air derived from measurements of SF 6 and CO 2 from the

ER-2 aircraft. The longer source gas lifetimes in the models would suggest that the upwelling in the

lower tropical stratosphere for the models is too weak. This is inconsistent with the conclusion from the

CO 2 study, which shows that the model calculated seasonal signal for CO 2 is advected up in the tropics

faster than observations. Thus, we have the seemingly contradictory situation that the model ages are

too young from the SF 6 experiment, but the model calculated lifetimes are too long compared to

observations.

Two groups of models tend to be seen in the 10 mb analysis. SUNY-SPB, LARC-3D, UNIVAQ-2D

and GSFC-3D form the first group, which are consistently calculating the longest lifetimes and have the

lowest tropical concentrations at I0 mb. The second group, AER, LLNL, HARVARD, NOCAR and

CSIRO, are consistently calculating shorter lifetimes and have the highest concentrations in the tropics

at 10 rob. Evidence of these groupings in the 75 mb analysis is not seen. This may be attributed to a

smaller range of calculated species concentrations at 75 mb or an indication that transport across the

tropical barrier at 75 mb is inconsistent among the models. Overall, almost all models examined show

calculated mixing ratios of CH 4, N20 and CFCI 3 at the equator and 50°N that are smaller than those

measured by the UARS version 7.0 CLAES.

Calculated species cross sections of N20 and CH 4 show larger gradients across the tropical barrier

than the UARS data would indicate. This also substantiates mixing ratios that are too high and ages of

air that is to young. Conversely, measured data from the ATMOS and ER-2 flights for CFCI 3 indicate a

larger gradient at the 75 mb region than the models are exhibiting. This indicates the models may have

too much meridional mixing at the 75 mb level. Clearly, the transport in the models differs for each altitude examined and obtaining more realistic transport will not be trivial. Additionally, a large spread of

calculated mixing ratio values is seen for all species examined at high latitudes. This may be indicative

of different polar process or differences in high latitude dynamics. These differences should be considered when interpreting results concerning the HSCT impact on ozone.

References

DeMore, W.B, et at., Chemical Kinetics and Photochemical Data for Use in Stratospheric Modeling, JPL Publication 97-4, 1997.

M. R. Gunson et al. "The atmospheric trace molecule spectroscopy (ATMOS) experiment: Deployment on the

ATLAS Space Shuttle missions" Geophys. Res. Lett. 23, 2333-2336 (1996).

Kotamarthi, V.R., M.K.W. Ko, D.K. Weisenstein, J.M. Rodriguez, and N.D.Sze, Effect of Lightning on the concentration of odd nitrogen species in the lower stratosphere: An update, J. Geophys. Res., 99, 8167-8173,

1994.

Volk, C,M, Elkins, J.W., Fahey, D.W., Dutton, G.S., Gilligan, J.M., Loewenstein, M., Podolske, J.R., Chan, K.R.,

Gunson, M.R., Evaluation of source gas lifetimes from stratospheric observations, J. Geophys. Res., 102,

25,543-25,564, 1997.

Shia, R.-L., M. K. W. Ko, D. K. Weisenstein, C. Scott and J. Rodriguez, Transport between the tropical and midlatitude lower stratosphere: Implications for ozone response to HSCT emissions, J. Geophys. Res., 1998.

Weisenstein, D.K., Ko, M.K.W., Sze, N.D. and Rodriguez, J.M., Potential impact of SO 2 emissions from stratospheric aircraft on ozone, Geophys. Res. Lett., 23, 161-164, 1996.

Weisenstein, D.K., M.K.W.Ko, J.M. Rodriguez, and N.D. Sze, Effects on statospheric ozone from high-speed

civil transport: Sensitivity to stratospheric aerosol loading, J. Geophys. Res., 98, 23,133-23,140, 1993

World Meteorological Organization (WMO), Scientific assessment of ozone depletion:1994, Rep. 37, Global

Ozone Res. and Monit. Project., Geneva, 1995.

230
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data. Error bars represented the meridional variations of the CH 4.

232
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Figure 3.4.3. Calculated CH 4 at the equator between 20 and 50 km. Spring data is in the upper panel (a) while fall

values are given in the lower panel (b). Solid lines denote UARS and ATMOS data. The E3CH calculated points

represent the model's resolution. Values are shown at 20, 24 and 32 kin. The 32 km value represents the top layer

of the model, from 0 hPa to 20 hPa and centered at l0 hPa.

233
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data. Error bars represent the meridional variations of the N20.

234
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Figure 3.4.5. Calculated N20 at the equator between 20 and 50 km. Spring data is in the upper panel (a) while fail

values are given in the lower panel (b). Solild lines denote UARS and ATMOS data. The E3CH calculated points

represent the model's resolution. Values are shown at 20, 24 and 32 kin. The 32 km value represents the top layer

of the model, from 0 hPa to 20 hPa and centered at 10 hPa.

235
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and A/M data sets.

236
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237
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238
Table 3.4.2. Calculated stratospheric lifetimes in years of the AER-2-D CTM with

different mixing rates in the tropical and sub-tropical region

SPECIES

NOPIPE LEAKY

PIPE

PIPE

N20 132 110 103

C2C13F3 95 78 72

CHC1F: 224 205 201

CFC13 58 49 46

CF2C12 114 93 87

CCI 4 49 42 40

CH3C1 73 63 62

CH3Br 41 36 35

I,,,

CH3CC13 49 42 40

CH 4 151 133 128

239
3.5 NOy and Cly Intercomparisons

XueXi Tie, Guy P. Brasseur, and Theresa Huang

National Center for Atmospheric Research

3.5.1 Nitrogen Compounds

In this section, we compare model calculations of nitrogen compounds (specifically NO, NO 2,

NOx, and HNO3) with available data. NO, NO 2, and NOx are measured by the HALOE instrument

(version 18) on board UARS, while HNO 3 are provided by the UARS CLAES instrument (version 7).

Vertical profiles of nitrogen compounds at selected latitudes were also measured by the ATMOS instruments which flew at several occasions on the space shuttle as part of the ATLAS program. The information of participating models is presented in Table 3.5.1. Note that the results submitted by 3-D models

are 24-hour average, while most of the results submitted by 2-D models are day-time average. As a

results, the NO and NO 2 can not be compared directly.

Total Nitrogen (NOy)

Two case runs of NOy results are available for comparison. Case B I calculates NOy from model

dependent N20 simulations, and case B2 specifies N20 concentrations. Figure 3.5.1 shows the calculated NOy vertical profiles at various latitudes in July, for cases B I (Fig. 3.5. l a,b) and B2 (Fig. 3.5. I c).

The large increase in NOy concentrations at polar region in winter shown in NCAR and NOCAR models are due to the effect of downward transport of NOy from the mesosphere and the lower thermosphere, as both these models have their upper boundary in the lower thermosphere. Comparison of NOy

results of B 1 and B2 indicate that the deviation of the NOy results among different models does not

improve with the constraint in N20 concentrations (B2). In fact, the spread of NOy among models are

even larger in B2 runs than in B1 runs, especially in the tropics. In the following inter-comparisons,

only the B 1 results will be used to compare with measurements.

In the tropics and mid-latitudes, the calculated NOy mixing ratios are more similar among models

than at higher latitudes. The maximum mixing ratio located near 32 km in the mid-latitudes range from

15 to 20 ppbv; while in the tropics, the NOy maximum is near 40 km, with values ranging from 17 to

25 ppbv. The deviation among models are larger at high latitudes than at lower latitudes. For example,

the mixing ratios of NOy at 85N and 30 km varies from l0 to 30 ppbv from model to model.

Nitric Oxide (NO)

Figure 3.5.2 shows the sunset (2a) and sunrise (2b) concentrations of NO measured by HALOE for

four different seasons as a function of latitude and height. The NO concentrations between sunset and

sunrise are similar, expect that the sunset concentrations are slightly larger than the sunrise concentrations. In all seasons, NO mixing ratio increases with height above the tropopause (typically 2 ppbv at

30 km, 6 ppbv at 35 km, and l0 ppbv at 40 km in the tropics). In the tropics, it reaches a maximum of

about 15 ppbv at approximately 45 kin. The location of this maximum varies slightly with season (30S

in March, 20N in June, 30N in September, 5S in December). Above 45 km, the mixing ratio decreases

to about 3 ppbv at 58 kin.

240
For comparison with the HALOE measurement, Figure 3.5.3 shows the model calculated daytime

NO latitude-height cross section in March and September. In general, the model NO results exhibit similar features to the HALOE data. All models shows an increase in NO concentration from the tropopause to a maximum at around 45 km in the tropics. However, the NO maximum concentration are quite

different among different models. For example, during March at 45 kin, NO mixing ratios range from

12 ppbv (HARVARD) to 20 ppbv (GSFC-2D, NOCAR, and CSIRO). In addition, the observed seasonal variabilities of the shift in latitude location of the maximum at 45 km (15S in March and 20N in

September) are not simulated in the models.

To compare the seasonal variation of the NO maximum, Figure 3.5.4 shows the NO mixing ratio

(ppbv) at 40 km, 50N. Measurements of NO by HALOE indicates that it has a very small seasonal variation with a maximum concentration in November (9 ppbv), and a minimum concentration in August

(8 ppbv). The majority of the model results of NO do not exhibit this type of seasonal variation. In addition, large deviations in NO concentrations exhibit among models.

The NO vertical profiles at 50N are compared between models and HALOE and ATMOS measurement in November (Fig. 3.5.5). The vertical profile of NO was measured at 49N by ATMOS and corrected for the diurnal effects by Newchurch et al. (1996). The measured profiles indicate a daytime

mixing ratios of 0.10 ppbv at 15 km, 0.07 ppbv at 20 km, 1 ppbv at 25 kin, and a maximum of around

7.5 ppbv at 44 kin. The model results of NO exhibit a large deviation from model to model. In particular, at 44 kin, the NO values range from 5 ppbv to 15 ppbv. It is worth noting, that the NO from the

UNIVAQ-2D model show a closet comparison to measurements at this latitude location. Note that 3-D

models (LARC-3D and SLIMCAT) tend to underestimate NO concentrations. Since the 3-D model

results of NO represent diurnally averaged value instead of the daytime averaged value from the 2-D

model and the MLS measurements, their values tend to be lower. This is due to fact that the concentration of NO is smaller during the night.

Nitrogen Dioxide (NO 2)

Sunset NO 2 abundances derived from HALOE observations (Fig. 3.5.6a) as a function of latitude

and height in the stratosphere are characterized by mixing ratios of typically 1 ppbv at 20 km, 6 ppbv at

30 kin, and a maximum of 8-9 ppbv at 35 kin. The mixing ratio decreases with altitude to less than

1 ppbv above 48 km. The NO 2 concentration exhibit a pronounced diurnal cycle as a result of the effect

of NO 2 photolysis during the daytime; therefore, sunrise NO 2 abundances are smaller than sunset abundances. A seasonal cycle is visible, which tends to shift the mixing ratio maximum towards the summer

high latitudes. The model simulated NO 2 daytime-averaged mixing ratios in June and December are

shown in Figure 3.5.7. The model results have similar maximum locations (around 35 km) compared to

the HALOE measurements. However, the magnitude of the NO 2 maximum at 35 km is quite different

among different models, ranging from 5 ppbv (HARVARD) to 8 ppbv (NCAR-2D and NOCAR) in

June.

The seasonal variation of the maximum concentration of NO 2 at 40 km and 50N is compared

between the models and HALOE measurements (Figure 3.5.8). The comparison show that some models

(such as CSIRO, LLNL, GSFC-2D, and SUNT_SPB) produce a similar seasonal variation of NO 2 compared to HALOE measurements, exhibiting a very small seasonal variation. However, 3-D models are

the exceptions which produce a large seasonal variation compared to HALOE measurement.

Figure 3.5.9 shows the vertical profiles of NO 2 mixing ratio at 50N in November for the model

results and HALOE measurements. It indicates that most models produce NO 2 profiles that are in good

agreement with that of HALOE measurements. The 3-D model NO 2 results presented here is probably

241
higher since the results submitted are diurnal averaged value instead of the daytime averaged value submitted by the 2-D models. Because the concentration of NO 2 is higher during the night, the 3-D model

results tend to be higher than the 2-D model results and the observation.

NOx (NO+NO2)

Sunset NOx abundances derived from HALOE observations (Fig. 3.5.10) as a function of latitude

and height in the stratosphere are characterized by mixing ratios of typically 6 ppbv at 20 km, 10 ppbv

at 30 km, and a maximum of i 6 ppbv at 40 km. Unfortunately, there are no data at polar region in winter

to check the downward transport from the mesosphere. The model simulated NOx daytime-averaged

mixing ratios in June and December are shown in Figure 3.5.11. The model results show that the location and the magnitude of the NOx maximum are quite different among different models, e.g. the magnitude of NOx ranges from 14 ppbv (GSFC-3D) to 24 ppbv (CSIRO) in March.

To compare the seasonal variation of the NOx concentration, Figure 3.5.12 shows the NOx mixing

ratio (ppbv) at 40 km, 50N. Measurements of NOx by HALOE indicates that it has a very small seasonal variation. However, some models a large seasonal variability. The deviation of modeled NOx concentrations is very large, indicating that the differences among the modeled NO and NO 2 concentrations

mentioned above are not only due to the defences in the partitioning of NO and NO2, but also are due to

the differences in the calculation of NOx. Note that the NOx mixing ratio of 3-D models (SLIMCAT

and LARC-3D) is much consistent with measurements, which further explains that the underestimation

of NO and overestimation of NO 2 in the 3-D models are due to fact that the results submitted are diurnal

averaged value.

Figure 3.5.13 shows the vertical profiles of NOx mixing ratio at 50N in November for the model

results and HALOE measurements. The model results of NOx exhibit large deviations from model to

model, which is similar to NO results shown in Figure 3.5.5. This indicates that the large diversities of

NO among the models are the major contributor to the NOx differences.

Nitric Acid (HNO3)

Figure 3.5.14 shows the meridional distribution of HNO 3 as observed by CLAES during four seasons. In this case, the maximum mixing ratio is found at high latitudes (near 25 kin), and is most pronounced during winter, when HNO 3 photolysis is weak. During winter and early spring, in the lower

stratosphere, there is an indication of HNO 3 removal as a result of PSC formation and sedimentation.

Figure 3.5.15 shows the calculated HNO 3 meridional distributions in June and December. In general,

the meridional distributions between models and CLAES measurements are quite similar. For example,

both models and measurements shows two maxima at high latitudes and a minimum in the tropics in the

lower stratosphere. The high latitude maximum is larger in winter than in summer. In the Antarctic winter, some models (AER, GSFC-2D, and UNIVAQ-2D) simulates a decease in HNO 3 concentrations as a

resuIt of PSC formation.

Figure 3.5.16 shows the seas0naI variation of HNO 3 mixing ratio at 60S and 24 km for model calculations and measurements provided by CLAES. The model results of HNO 3 have a similar seasonal

variations as the measurements (higher in winter and lower in summer). However, the concentrations

among the models are very different, which range from 7 to 20 ppbv in winter, and 6 to 15 ppbv in

summer.

242
Figure3.5.17showstheverticalprofilesof HNO 3 at 60N in November for the model results and

CLAES measurements. Both model results and CLAES measurements are characterized by a maximum

centered at around 25 km. The CLAES maximum at this altitude is about 11 ppbv, while the model

results have a wide spread in values, ranging from 7 ppbv to 16 ppbv.

NOx/NOy

Finally, the ratio of NOx/NOy is compared among models. Figure 3.5.18a shows the seasonal variation of NOx/NOy at 34 km, 60S. The result indicates that the seasonal variations among models are

very similar, which exit a maximum in January and a minimum in July. The deviation of modeled NOx/

NOy is relatively small in January (from 0.7 to 0.8), and relatively large in July (from 0.25 to 0.55).

Figure 3.5.18b shows the vertical profiles of NOx/NOy at 40N in November for the model results. The

model results of NOx/NOy exhibit large deviations in the troposphere, the ratio of NOx/NOy is relatively high from the model results of UNIVAQ-2D, NCAR-2D, and GSFC-2D, and relatively low from

the model result of CSIRO. In the stratosphere, the model results are similar, except that in the lower

stratosphere the SUNY-SPB result is slightly higher than the other models.

3.5.2 Chlorine Compounds

Satellite observations of the most abundant inorganic chlorine compounds HCI, C1ONO 2, and CIO,

are available from UARS (C10 from MLS of version 3, HCI from HALOE of version 18, and CIONO 2

from CLAES of version 7). Vertical profiles of the same three compounds were also measured by

ATMOS at selected locations. Note that the results submitted by 3-D models are 24-hour average, while

the results submitted by 2-D models are day-time average. As a results, the C10 and C1ONO 2 can not be

compared directly.

Total Inorganic Chlorine (Cly)

The model simulated Cly for two case runs B I and B2 are compared. Case B I calculated Cly

according to the model simulated CFCs concentrations, and case B2 uses a specified CFCs concentrations. Figure 3.5.19 shows the calculated Cly vertical profiles at 85S, 45S, equator, and 85N in July for

cases B1 (Fig. 3.5.19a,b) and B2 (Fig. 3.5.19c). Similar to the comparisons of NOy, the comparison of

NOy from B 1 and B2 indicates that a constraint of CFCs on the model does not produce a better comparison of Cly between different models. The spread of values among models are actually greater for B2

than the B 1 runs. In the following inter-comparisons, only the B 1 results will be used to compared with

measurements.

Chlorine Monoxide (CIO)

The MLS observations of CIO for the months of March, June, September and December is shown in

Figure 3.5.20. In March, the C10 maximum at 40 to 45 km reaches approximately 0.6 to 0.7 ppbv at

higher latitudes. In the tropics, the maximum mixing ratio of C10 is on the order of 0.2 ppbv.

Figure3.5.21 shows the calculated C10 meridionaI distribution in March (Fig. 3.5.21a,b) and in

September (Fig. 3.5.20c,d). In general, the meridional distribution of the model CIO results compares

well with MLS measurements, which indicates two maxima in high latitudes at 45 km and a minimum

in the tropics at 40 km. In September, large C10 concentrations are produced in the southern polar latitudes between 15 and 30 km as a results of the formation of PSCs in most of the models. The large C10

concentration in the polar reg!on is consistent with MLS measurements.

243
In Figure 3.5.22 the seasonal variation of CIO is compared between the models and MLS measurements at 44km and 45S. The seasonal change exhibit in the MLS data implies a higher concentration in

winter (0.7 ppbv in August) and a lower concentration in summer (0.55 ppbv in January). Model results

indicates a somewhat similar seasonal variation compared to the MLS observations, except for the

LARC-3D model which shows a CIO seasonal variation that is very uniform in time. Some models also

show large discrepancies from the observed values. In August, the calculated C10 mixing ratios range

from 0.4 to 1.1 ppbv, and in January, C10 range from 0.3 to 0.75 ppbv. Note that 3-D models

(LARC-3D and SLIMCAT) tend to underestimate CIO concentrations. Since the 3-D model results of

C10 represent diurnally averaged value instead of the daytime averaged value from the 2-D model and

the MLS measurements, their values tend to be lower. This is due to fact that the concentration of C10 is

smaller during the night. The SLIMCAT model has included C10+OH -> HCI+O 2 as a 5% channel.

This reaction tends to reduce C10 concentration in the upper stratosphere.

Figure 3.5.23 shows the vertical profile of C10 at 40N in November by model calculations and by

measurements from MLS and ATMOS. Mixing ratio of C10 derived by Zander et al. (1996) for the

1994 November ATMOS flight at 39-49N is approximately 0.2 ppbv at 30 km, 0.5 ppbv at 37 km (maximum value), and less than 0.1 ppbv at 50 km. Mixing ratio provided by MLS at 40N is of similar values

below 35 km. However, above 35 kin, the MLS C10 mixing ratios are slightly higher than the ATMOS

measurements. Both the model CIO results and the measurements have maximum mixing ratio located

between 37 and 40 km, except for the SLIMCAT model that has the maximum located at 45 km. The

magnitudes of the maxima are widely spread, from to 0.3 ppbv (LARC-3D) to 1.3 ppbv (UNIVAQ-2D).

This in contrast to the values of 0.55 to 0.6 ppbv as measured by ATMOS and MLS.

Chlorine Nitrate (CIONO 2)

Figure 3.5.24 shows the C1ONO2 mixing ratio measured by CLAES. It shows a C1ONO 2 maximum

at high latitudes between 25 and 30 kin. This maximum is most pronounced in winter, where it reaches

a value between 1.4 and 1.6 ppbv, when the photolysis of C1ONO 2 is at its weakest. On the other hand,

a typical mixing ratio in the tropics is 0. ! ppbv at 20 km, 0.6 ppbv at 30 km, and 0.2 ppbv at 40 km.

Lower C1ONO 2 mixing ratio (less than 0.2 ppbv) is seen below 20 km in the southern hemisphere polar

region from June to September when heterogeneous chemistry converts chlorine reservoirs into reactive

chlorine radicals. Figure 3.5.25 shows the calculated C1ONO 2 meridional distributions in June (24a)

and December (24b). The meridional distribution of the model results and the CLAES measurement

compares well, in particular the two maxima in high latitudes and a minimum in the tropics in the lower

stratosphere. However, the observed seasonal variations of C1ONO 2 is not reproduced well in models,

particularly the maximum concentrations in high latitudes that is higher in winter than in summer. In

winter Antarctic, most of the model simulate a depletion of C1ONO 2 as a result of heterogeneous reactions on the surface of PSC, but with large differences in the depletion magnitude. In particular, the

SUNY_SPB model simulates a very small heterogeneous depletion of C1ONO 2, while the AER and

LLNL models simulate the strongest heterogeneous depletion.

The seasonal variation of CIONO 2 at 60S and 30km is shown in Figure 3.5.26 from model calculations and CLAES observation. The measured seasonal variability provided by CLAES is rather pronounced. It is highest in July (12 ppbv) and lowest in January (0.5ppbv). None of the model can

reproduce this seasonal variability. In general, all the models overestimate its concentration in summer.

For example, in January, the calculated C1ONO 2 mixing ratio range from 0.8 to 1.3 ppbv compared to

the observed 0.5 ppbv by CLAES. In winter, the observed value falls within the range of calculated

C1ONO 2 concentrations, but the spread of CIONO 2 value among the models is great, with values ranging from 0.5 ppbv to 1.5 ppbv.

244
The vertical profiles of CIONO2 at 40N in December is shown in Figure 3.5.27 for the model calculations and the profile measured at 35-49N by Zander et al. (1996) during the ATMOS/ATLAS

mission, and by CLAES. The ATMOS profile is characterized by mixing ratios of 0.4 ppbv at 20 km,

0.9 ppbv at 27 km (maximum value), 0.1 ppbv at 40 km. Mixing ratio provided by CLAES at 40N is

very similar to ATMOS below 25 km. However, above 25 km, the CLAES C1ONO2 mixing ratio is

slightly higher than the ATMOS measurements. Both the model results and the measurements have

maximum mixing ratio located between 22 and 30 km. The magnitudes of maxima are widely spread

among the models, ranging from 0.8 ppbv (LLNL) to 1.6 ppbv (NCAR-2D). Compared with the measured maxima (0.9 ppbv by the ATMOS and 1.0 ppbv by the CLAES), most models simulate CIONO2

value that are consistent with the observed values, especially the LLNL, CSIRO, NOCAR, HARVARD,

UNIVAQ-2D, and SUNY_SPB models.

Hydrogen Chloride (HCi)

Shown in Figure 3.5.28 is the mixing ratio of HC1 as measured by HALOE. The HC1 mixing ratio is

relatively uniform in latitude above 30 km, and its value reaches around 2.8 ppbv at 45 km. Its mixing

ratio increase with altitude, with value of typically 0.2 ppbv at the tropopause, 1.6 ppbv at 30 kin, and

2.5 ppbv at 40 km. In the lower stratosphere, the HCI mixing ratio increases with latitude. At 20 kin, its

value ranges from 0.4 ppbv at the equator to about 1.4 ppbv at 60 N. In Figure 3.5.29, the calculated

HC1 mixing ratio of June (29a,b) and December (29c,d) is shown. In general, the calculated mixing

ratios of HCI in the tropics and in mid-latitudes decrease with altitude between the tropopause and

50 kin. However, at high latitudes, the disparity among models are more complex. In particular, some

model results (AER, GSFC-2D, NOCAR, LLNL, and SLIMCAT) suggest the depletion of HC1 as a

result of the formation of PSCs. Note that SUNY_SPB model submits day-time mean data everywhere

except polar night regions, so that polar night results are included in the figures.

Figure 3.5.30 shows the seasonal variation of HC1 for models and from HALOE measurements at

60S and 34 km. The HALOE measurement of HC1 shows a weak seasonal variation. For most of the

model, the calculated seasonal variation of HCI is also very uniformed in time, except the HARVARD

model in which a strong seasonal variation is produced. The magnitude of HC1 mixing ratio from models is in general smaller then that measured by HALOE.

Figure 3.5.31 shows the vertical profile of HCI mixing ratio from model estimates and that measured during the ATMOS/ATLAS mission in November, 1994 (35-49 N) by Zander et al. (1996). The

measured HC! is 1.2 ppbv at 20 km, 2 ppbv at 27 kin, 2.9 ppbv at 40 km, and 3.4 ppbv at 48 kin. At

48 kin, nearly all the chlorine loading is in the form of HC1. Compared to these observation values,

model results also show a decrease with latitude for the mixing ratio of HCI (see Fig. 3.5.30), but overall

the calculated mixing ratios of HC1 are lower than the ATMOS results. The calculated HC1 concentrations are widely spread among the models. For instance, at 35 km, the calculated highest value of HCI is

about 2.8 ppbv which is close to the measured value, while the calculated lowest value of HC1 is about

1.5 ppbv which is about half of the measured value.

3.5.3 Brief Summary

Following is the conclusion drawn from the comparison of NOy and Cly between models and measurements by UARS and ATMOS: (1) The calculated NOy differ by 30% among different models. The

difference among the models are even larger with constrained N20 distributions. (2) The calculated NO

mixing ratios differ widely among models, while most of the models fail to reproduce the HALOE seasonal variabilities of NO maximum at 45 kin. (3) The calculated seasonal variability of NO 2 maximum

245
at 34km in mid-latitudesagreeswell withHALOEmeasurements. ThemodelNO2concentrationsis

moresimilaramongmodelsthantheNOconcentrations. At 50Nin November,thecalculatedvertical

profilesagreewellwiththeHALOEmeasurement. (4)Themodelsimulatedseasonal variabilityof the

HNO3maximumat24km inhighlatitudesagreewellwithCLAESmeasurements, butthecalculated

magnitudeinHNO3showsa widespreadin valuesamongmodels.(5)ThecalculatedCly differ by

10%amongdifferentmodels.The difference is even greater with constrained CFCs distributions.

(6) The CIO mixing ratio shows a wide spread in values among different models in both the profile and

the seasonal variation. (7) The model simulated CIONO 2 agrees well with CLAES and ATMOS

measurements in mid-latitudes. However, the seasonal variation of CLAES measurement cannot be

reproduced by all models. (8) The HCI mixing ratio varies widely among models. The model estimates

are generally lower than HALOE and ATMOS measurements.

Acknowledgements

We thank K. Sage for making all plots for this analysis. The UARS data are analyzed by W. Randel

and F. Wu at NCAR Atmospheric Chemistry Division. We also thank all modelers for providing their

model results.

References

Zander, R, et al., The 1994 northern midlatitude budget of stratospheric chlorine derived from ATMOS/ATLAS-3

observations, Geophys. Res. Lett., 23, 2357-2360, 1996.

Michelsen et al., Stratospheric chlorine partitioning: Constraints from shuttle-borne measurements of HCf, CINO3,

and C10, Geophys. Res. Lett., 23, 2361-2364, 1996.

Newchurch, M., et al., Stratospheric NO and NO2 abundances from ATMOS solar-occulation measurements,

Geophys. Res. Lett., 23, 2373-2376, 1996.

Aelig, C.P., et al., Latitudinal distribution of upper stratospheric CIO as derived from space bound microwave

spectroscopy, Geophys. Res. Lett., 23, 2321-2334, 1996.

246
Table 3.5. I. Participating models

AER

CSIRO

ECHAM3

GSFC-2D

GSFC-3D

HARVARD

LARC-3D

LLNL

NCAR-2D

NOCAR

SLIMCAT

SUNY_SPB

UNIVAQ-2D

2-D

2-D

3-D

2-D

3-D

2-D

3-D

2-D

2-D

2-D

3-D

2-D

2-D

247
"O

<

B1. NOY JULY

85S

60 _, _ ' $ AER '

54 _ _- • CSIRO

-_ _ GSFC-2D

48 [_Lm % x LLNL

36

30-

24

18

12

6

0 ! I _ I

0 5 10 15 20 25

Mixing Ratio (ppbv)

30

60

54

48

42

36

3o

_ 24

18

12

6

0

0

45S

-- I

• CSIRO

_ GSFC-2D

( x LLNL

A SUNY-SPB

u UNIVAQ-2D

I t I 1 1_

5 10 15 20 25 30

Mixing Ratio (ppbv)

EQUATOR

54

48

42

36

30

24

18i • CSIRO

.

x LLNL

A SUNY-SPB

u UNIVAO-2D

t I I I I

0 5 10 15 20 25

Mixing Ratio (ppbv)

<

85N

54

48

42

36

30

 46o

18

12

0 5 10

1 I t

30 3O

+ AER

• CSIRO

GSFC-2D

x LLNL

A SUNY-SPB

[] UNIVAQ-2D

I I I

15 20 25

Mixing Ratio (ppbv)

Figure 3.5. la. Vertical profiles of model calculated NOy at 85S, 45S, equator, and 85N in July for case (a-b) B1

and (c) B2.

248
B1. NOY JULY

85S 45S

60 L , , , , , 60 "_%' ' ' '

48 - 48 -

42 " " 42!

36 _ 36

30}- __ " _ 301 __

24_ " < 24t ,___

_oL ./,, _,.._of_ * E3CH

._Q!_ _ * E3CH "-' l_.....e.._ o HARVARD

• '-'_'- o HARVARD

t0_ _ • LARC-3D 4o_ • LARC-3D •or • NCAR-2D

"'- Ir_ _v • NCAR-2D _ | o NOCAR

| o NOCAR "_ • SLIMCAT

"_ • SLIMCAT 0| i . I l I i

O| I I ' • I

0 5 10 15 20 25 30 0 5 10 15 20 25 30

Mixing Ratio (ppbv) Mixing Ratio (ppbv)

EQUATOR 85N

36 "_ 36

30r 1 301- 1

'_ 24_ I < 24_-_ 1

taL _ll_/'_ * E3CH 4

a _ * E3CH q ""/ _,_'_ o HARVARD /

'' r o HARVARD | .i2 _ v LAR.C-3D 4

to| • LARC-3D 4

""- | • NCAR-2D | " '_"" • NCAR-2D |

| o NOCAR 4 _l o NOCAR 4

o | • SI-IlVlCAT | v | • SLIMCAT |

0! _ _ i i i J 0L L i , l _ 30

0 5 10 15 20 25 30 0 5 10 15 20 25

Mixing Ratio (ppbv) Mixing Ratio (ppbv)

Figure 3.5.1b. Continued.

249
B2. NOY JULY

85S 45S

60[ _ , , , , / 60 "_, , , ,

54 541

48 48

42 42

36

30- _ 36

24 _ 30

24

18 : _Im_- + AER 4 18

_' • CSIRO | | _ • CSIRO

1 •

I x LLNL

6 A SUNY-SPB 4 61 A SUNY-SPB

,-,I [] UNIVAQ-2D | _,|: a UNIVAQ-2D

UI I I t f 1 / U li ,,c.-.l__ l I | I I

0 5 10 15 20 25 30 0 5 10 I5 20 25 30

Mixing Ratio (ppmv) Mixing Ratio (ppmv)

EQUATOR

60 _ , , , | 6o, 85N ,\_, , ,

8 48 •

42 , . i, 42

-- 30

_ 24 24

18 18 _Y • CSlRO

12 _ GSFC-2D 4 12 _ _ GSFC-2D

x LLNL J _ x LLNL

6 A SUNY-SPB t 6 A SUNY-SPB

o , , ,_u"IvAQ-,2DI o , , ,_U"IVAQ-,2D

0 5 10 15 20 25 30 0 5 10 15 20 25 30

Mixing Ratio (ppmv) Mixing Ratio (ppmv)

Figure 3.5. Ic. Concluded.

250
(./3

==

o

z

i,i

o

,..I

<

-1-

_E

o

"1"

(qw) aJns_Jd

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o

0

_Z

0

lo

(/1

t,/)

0 0 0 0

(_>1) tq6!eH

Z

0

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0

..J

o_

(qua) aanss_ad

_ _ _ _ _.

"" /.2.'. 0 I

ft.) ,,_ _ f'q

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on

(qw) aJnssaJa

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i i

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140

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(/1

0

(ll

(--

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V1

o

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(qw) aJ ns_g._ d

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Ix1

o o N °

cn

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f./)

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0

to

6

©

©

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251
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l,o

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r'3 0 0

.'..\[_t_[ L/////// /

: "

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252
60

50

4O

30

t,4

2O

10

0

-90

B1. NO MARCH

AER

I I i I I

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

_ 30

20

CSIRO

10

0

-9o -6O

I I I [

-30 0 30 60 90

LATITUDE (DEG)

GSFC-2D

50

4oi 1

201 . t,4

11[ I , , v'" 0''_ I_

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

GSFC-3D

60 .... I

40

30 __l

1(1

0 I I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

HARVARD LARC-3D_

6o //t , , \_.o__ 6o .....

40 40

v 30 _ 30

20 - 20

10 10

0 , , , _ _ 0 , , , , ,

-90 -60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

Figure 3.5.3. Model simulated daytime averaged NO latitude-height cross section for (a-b) March and (c-d)

September.

253
6O

50

40

_. 30

2O

10

ol

-90

LLNL

BI. NO MARCH

6O

5O

4O

_: 3O

20

-60 -30 0 30 60

LATITUDE (DEG)

9O

10

0

-90

NCAR-2D

__ _.o..L/i /

__

-60 -30 0 30 60 90

LATITUDE (DEG)

NOCAR

oo___,__

40-

30

t'4

20

'i

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

SLIMCAT

6o_,_, ,o, , , __

_4o___ 30 " ._..:

N

2O

1

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

50

4O

30

t_

2O

SUNY-SPB

10

0

-90

i i 1 _ i

-60 -30 0 30 60

LATITUDE (DEG)

9O

UNIVAQ-2D

oo_ 5O

40 __

_._o_

>YT I 10

0

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.3• Continued.

254
AER

60 , , ,

4O

30_

1

i

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

BI. NO SEPTEMBER

CSIRO

6o_ ,

4o

3o

20

10

0 1

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

50

4O

_. 30

2O

10

0

-90

GSFC-2D

.... ,.--0.1 ,---_

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_. 30

t_

2O

10

0

-90

HARVARD

i I l _ i

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

4O

3o

N

20

10

0

-90

LARC-3D

,

-6O

i I T i

-30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.3. Continued.

255
B1. NO SEPTEMBER

LLNL

60 / f / ....

50- " I -

t,4

_o_ _ _ _)=o_-_=

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

NCAR-2D

50

°-_

-

O/ , ; , ;

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

50

4O

30

N

2O

1F-,- 6.0 NOCAR _ ,

lO

o

-90

I i I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

SLIMCAT

ooy/jj.C_ ' , -2,of

4O

_o '__

0 | 2,, I I , I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

SUNY-SPB

,o

0I _ , _ _ ,

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-2D

3o_

t,,4

2o

lO

o , , ./-"d, \ ,/-"_,

-90 -60 -30 o 30 60

LATITUDE (DEG)

9O

Figure 3.5.3. Concluded.

256
>

Q.

O.,.

v

O.m

no')

t-

>,<

25

20

15

10

5

BI. NO 40km.

50N

l t t I t t -I l- I l

+ AER • LARC-3D o NOCAR

• CSIRO x LLNL • SLIMCAT

GSFC-2D • NCAR-2D /x SUNY-SPB

- <> HARVARD [] UNIVAQ-2D

®

__1 I I L I I 1 I I I 1.....

J F M A M J J A S O N D J

Time

Figure 3.5.4. Seasonal variation of NO at 40 km and 50N for model simulations and HALOE measurement. (Filled

circle: HALOE)

257
"O

"'I

°n

<

6O

55

50

45

40

35

30

25

20

15

10

B1. NO NOVEMBER

50N

1 I

x LLNL

+ AER • NCAR-2D

• CSIRO o NOCAR

GSFC-2D • SLIMCAT

o HARVARD A SUNY-SPB

• LARC-3D [] UNIVAQ-2D

I I I

0 5 10 15 20

Mixing Ratio (ppbv)

Figure 3.5.5. Vertical profiles of model calculated and measured NO at 50N in November. (Filled circle: HALOE,

white circle: ATMOS)

258
4_

E

(qw) aJnssaJa (q_u) aJnssaJa

(qw) 8JnssaJd

_ _

z z _

(W_l) ;q6teH (w_) _qSteH k

259
"F_

t"

t-

-3

¢,q

0

Z

LJ

0

.J

.<

"1-

r ..... .

>

ILk1\u._//////

(_>l) _Ul6!aH

0

o

_Z

• -4 0

O_

nO

uo

z

0

"0

o

0

,o

e"

r_

0

z

W

0

_.1

,<

"1-

(quo) ajngsajcl

0 0 0 0

(uu >1) _W6!eH

o

o

o_

(quJ) aJnssa_ a

z

-1-

• i L _ ,

o 0 0

u3 _ t_

(q_) eJnssaJcl

o o o - _. o o

I:• _J (/I

z

N

"lll)?

o ,0 o 0 0 0 _:n

(ua)_) lq6!eH (cU.'l) ,lq_!e H

_5

"6

0

_j

_5

1¢3

bib

b:

260
B1. NO2 JUNE

AER

60_ ,_ , , , ,

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_._ 30

20

10

0

-90

I I I I 1

-60 -30 0 30 60 90

LATITUDE (DEG)

GSFC-2D

°°t;[ ....

50-..__ - - -

 Oo' i -

0 I I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_v 30

2O

10

HARVARD

0 I I I 1 |

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

LARC-3D

5O

40

_30

1

I I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.7. Model simulated daytime averaged NO 2 latitude-height cross section for (a-b) June and (c-d)

December•

261
B1. NO2 JUNE

LLNL NCAR-2D

5o-Ill---,:__: i _ 1

0 I i i

- - ;:AT,TUDE<DEG) -90-00-&0T,TuO E_DE_00 90

6o , , NOCAa ,

SLIMCAT

6o_-_L_.-_, , ' L.

50 50 "

40 40

_.-30 N

.o:

-90 -60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

SUNY-SPB UNIVAQ-2D

60 ..... 60 t ....

so _ sot-- _:_ _ .

40 40 : ._ --

N h

2O

t0 10

0 0 _..... _,

-90 -60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG}

Figure 3.5.7. Continued.

262
6O

5O

4O

30

20

10

BI. NO2 DECEMBER

AER

0 I [ I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

4O

30

2O

10

601

GSFC-2D

0 I I I 1 I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

50

CSIRO

60[ .....

5o - . -

10

0 _ I I, I

-90 -60 -30 0 30

LATITUDE (DEG)

40

_. 30

2O

I

60 90

10

HARVARD

, , , i

0

-90 90

I I I I I

-60 -30 0 30 60

LATITUDE (DEG)

LARC-3D

50

4O

30-

N

2O

10

0 l I I I l

-90 -60 -30 0 30 60

LATITUDE (DEG)

Figure 3.5.7. Continued.

9O

263
30

B1. NO2 DECEMBER

LLNL

oo \

5O

4O

2O

10

0 L I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

NCAR-2D

60 _'4 ' '

v 30-

ol , , ,

-90 -60 -30 0 30 60

LATITUDE (DEG)

9O

NOCAR

6o/ ....

lO

0 I I I t I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

5O

4o_

30 =-____

20

lO

0

-90

SLIMCAT

J I L 1 r

-60 -30 0 30 60 90

LATITUDE (DEG)

SUNY-SPB

60 .....

-90 -60 -30 0 30 60

LATITUDE (DEG)

90

60

5O

4O

3o

2O

10

0

-90

UNIVAQ-2D

-6O -30 0 30 60

LATITUDE (DEG)

9O

Figure 3.5.7. Concluded.

Z

264
12

10

8

o)

c

x

4

B1. NO2 40km.

50N

I 1 t I I 1 1 I I I 1

A + AER • LARC-3D SUNY-SPB

• CSIRO x LLNL [] UNIVAQ-2D

GSFC-2D • NCAR-2D Y_

-<> HARVARD O NOCAR / X -

• SLIMCAT /

2

0 I 1 1 I 1 I l I I I 1

J F M A M J J A S O N D J

Time

Figure 3.5.8. Seasonal variation of NO 2 at 40 km and 50N for model simulations and HALOE measurement.

(Filled circle: HALOE)

265
60

55

5C

45

40

35

25

20

1

10

0 2

B1. NO2 November

50N

1 1 I 1

+ AER

• CSIRO

GSFC-2D

o HARVARD

LARC-3D

1

4

x LLNL

• NCAR-2D

o NOCAR

• SLIMCAT

SUNY-SPB

[] UNIVAQ-2D

I I

6 8 10

Mixing Ratio (ppbv)

Figure 3.5.9. Vertical profiles of model calculated and measured NO 2 at 50N in November. (Filled circle:

HALOE)

266
IfJ

E

D

)g

O

Z

Ld

O

J

<

-l-

(qw) aJnsse_

r

"_ ,'

_°I___';.#;_ Ill2// '

O O O O

(LU>1) _qS]aFl

O

<3

_Z

Z

0

Z

0

0

..J

0

(qLu) aJnssajc_

O O O

(Lug) lq6!a H

C,

O

--Z

O

O_

Z

C_

_O

Z

O

_O

"O

ii--°

___.4.o

O

('4

(qcu) _Jnss_

_ _ o _ _

z -_ A

Q Q Q _

(cu:_) _.LI6!aH

£

_J

_0

(quJ) eJnssaJd

,)_ ' o

_Z

< "w _J

267
B1. NOx MARCH

AER

60 _ " ' , .i , !

2o__-

10

0 1 1 I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

CSIRO

ool40,ol 

_2o_-

1°of , , , ,

I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60'

50

40

30

2O

10

0 1

-90 -60

GSFC-2D

------6.o

, , %0.1

-30 0 30 60 90

LATITUDE (DEG)

GSFC-3D

60 ..... l

_o- l___-_ i°o

01 _ , _ i _ i

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

HARVARD LARC-3D

50 ! 50 _"

40 40 "

""v 30 v 30- "

20 - 20 _"

10 10

0 _ , _ , , 0 , _ , ,

-90 -60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

Figure 3.5.11. Model simulated daytime averaged NOx latitude-height cross section for (a-b) March and (c-d)

September.

268
BI. NOx MARCH

LLNL

20

0 ,..--0.1

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

NCAR-2D

601 _ ' '

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

NOCAR

60 ....

_ 40_

_ 3O I _

_ 20_

10

-90 -60 -30 0 30 60

LATITUDE (DEG)

6O

50

9O

40

_. 30

20

SLIMCAT

10

0

-90

I I _. I I

-60 -30 0 30 60 90

LATITUDE (DEG)

SUNY-SPB

60 _'_..._-' ' '

10

0 I I I I I __

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-2D

50

40

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.11. Continued.

269
6O

5O

4O

30

2O

10

0

-90

AER

-60' -30' 0

' 3'; go 90

LATITUDE (DEG)

B1. NOx SEPTEMBER

CSIRO

40

_.30

20-

10

0 I I 1 ;,. I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_. 30

2O

10

0

-90

GSFC-2D

, , .... x"U__:1

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

30

2O

10

0

-90

HARVARD

1 I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

30

2O

LARC-3D

- _

10

0 i

-90 -60

I ! I I

-30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.11. Continued.

270
60

5O

40

30

2O

10

0

-90

B1. NOx SEPTEMBER

f..-0.1 ,-_ t

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

4O

_v 30

20

10

0

-90

NCAR-2D

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

30

N

2O

10

0

-90

,:,_ NOCAR

• _ 6.0 , _ ,-,,,,,_j/

-

I i i I ....

-60 -30 0 30 60 90

LATITUDE (DEG)

)

N

ii5O

40

20

10

0

-90

SLIMCAT

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

3o

2O

SUNY-SPB

10

0

-9o

I t 1 i i

-60 -30 0 30 60 90

LATITUDE (DEG)

60

50

40

30

20

10

0

-90

UNIVAQ-2D

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5. l I. Concluded.

271
.t3

t_

Q.

v

O

cr

O3

tT_

x

30

25-

20

15

10

B1. NOx 40km.

50N

t 1 I I I t I

+ AER • LARC-3D

" CSIRO x LLNL

GSFC-2D A NCAR-2D

O HARVARD o NOCAR

• SLIMCAT

1 I I I

A SUNY-SPB

u UNIVAQ-2D

5

0 I I I t I I I I I .... I I

J F M A M J J A S O N D J

Time

Figure 3.5.12. Seasonal variation of NOx at 40 km and 50N for model simulations and HALOE measurement.

0_illed circle: HALOE)

272
6O

B1. NOx November

50N

I

55

5O

45

40

35

25

®

@

2O

15

10

x LLNL

+ AER • NCAR-2D

• CSIRO o NOCAR

GSFC-2D • SLIMCAT

<> HARVARD A SUNY_SPB

• LARC-3D [] UNIVAQ-2D

I I I 0

0 5 10 15 20

Mixing Ratio (ppbv)

Figure 3.5.13. Vertical profiles of model calculated and measured NOx at 50N in November. (Filled circle:

HALOE).

273
c3

0

E

tU]

ILl

£3

0

r"

.E

i.i

<

J

..... i

0

(quJ) _Jnss_Jd

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(w>i) tq6!oH

0

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g

tO

2

(qw) e.lnssaJ d

J*_ 0

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0

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0

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(UJ>l) tq6!aH

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0

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0

r"

1"

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£3

(q_) eJnssaJd

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0

0

0

g

ID

4-

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8

(qw) eJnssaJ d

...... ]

0 0

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0"1

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(w)i) tq6]eH

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0

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9

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el')

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£

1

c_

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274
AER

6O5o__ ....

4o - :

Ol , _ , , ,

B1. HNO3 JUNE

6o

50

4o

30

20

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

CSIRO

10

0

-90

__o --_._____._ 0 _

I I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

GSFC-2D

40[ 

_0[_ _o_"

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

40

_. 30

2O

10

0__

-90

HARVARD

r r 1 m 6O

5O

4O

_30

v

20

10

£_____ L _ 0

-60 -30 0 30 60 90 -90

LATITUDE (DEG)

LARC-3D

i t i

I l L I

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.15. Model simulated zonally-averaged HNO 3 latitude-height cross section for (a-b) June and

(c-d) December.

275
60

50

4O

30

t_

20

10

0

-90

B1. HNO3 JUNE

LLNL

-60 -30 0 30- 60 90

LATITUDE (DEG)

60

5O

40

30

20

10

0

-90

NCAR-2D

:___ 0.05

__L _ !

-60 -30 0 30 60

LATITUDE (DEG)

90

N

60

5O

40

30

2O

10

0

-90

NOCAR

i I _ _ I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_. 30

20

10

0

-90

SLIMCAT

----L I i i I

-60 -30 0 30 60 90

LATITUDE (DEG)

60

50

4O

_v 30

ka

2O

10

0

-90

SUNY-SPB

_---0_ 5

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

30

kj

2O

10

0!__

-90

UNIVAQ-2D

, t

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.15. Continued.

276
6O

5O

4O

30

t,4

20

10

o

-9o

B1. HNO3 DECEMBER

AER

--o.95----f,,

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

30

2O

CSIRO

10

0

-90

I I I _ I

-60 -30 0 30 60 90

LATITUDE (DEG)

GSFC-2D

°°l ..... /

_J I i _ U_U_ m I"_--'_"*'I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

4O

30

2O

10

0

-90

HARVARD

Z Z 1

i I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

4O

20

10

0

-90

LARC-3D

t I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.15. Continued.

277
60

50

40

_. 30

t'4

2O

10

0

-90

BI. HNO3 DECEMBER

LLNL

-60 -30 0 30 60 90

LATITUDE (DEG)

NCAR-2D

60 ....

2010- -_-

0 i i i I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

N

NOCAR

60 ....

50

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

SLIMCAT

60 ....

N

20

0 _ i i l I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

SUNY-SPB

60 .....

5O

4O

3o

N

2O

10

0

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

30

2O

10

0

-90

UNIVAQ-2D

-6O -30 0 3O 6O 9O

LATITUDE (DEG)

Figure 3.5.15. Concluded.

278
20

B1. HNO3 24km.

60S

t I I I I I

>

O..

v

O

cr

t-

.w

x

15

10

x LLNL

-+ AER • NCAR-2D

• CSIRO o NOCAR

GSFC-2D • SLIMCAT

<> HARVARD A SUNY-SPB

• LARC-3D [] UNIVAQ-2D

_ I I I [ I t I t

J F M A M J J A S

Time

1

O

I

N

1

D

Figure 3.5.16. Seasonal variation of HNO 3 at 24 km and 60S for model simulations, and CLAES, MLS measurements. (Filled circle: CLAES).

279
60

55

5O

45

40

B1. HNO3 December

t I

+ AER

[] CSIRO

GSFC-2D

¢ HARVARD

• LARC-3D

60N

1 I I

x LLNL

• NCAR-2D

o NOCAR

• SLIMCAT

z_ SUNY-SPB

[] UNIVAQ-2D

1 1

35

25

20

15

10

5

0__1 I I 1 t I t I

0 2 4 6 8 10 12 14 16 18

Mixing Ratio (ppbv)

Figure 3.5.17. Vertical profiles of model calculated and measured HNO 3 at 60N in December. (Filled circle:

CLAES)

280
>-

O

z

O

z

1.00

0.90

0.80

0.70

B1. NOX/NOY 34km.

60S

I 1 I I I I L

+ AER • NCAR-2D

• CSlRO o NOCAR

GSFC-2D • SLIMCAT

o HARVARD A SUNY-SPB

• LARC-3D [] UNIVAQ-2D

x LLNL

I I 1

I I I 1 I 1 I 1 I I I

F M A M J J A S O N D J

Time

Figure 3.5.18. Model simulated NOx/NOy (a) Seasonal variation at 34 km and 60S, and (b) vertical profiles of

model calculated at 40N in November.

281
60

5O

4O

+ AER

• CSIRO

GSFC-2D

<> HARVARD

• LARC-3D

x LLNL

B1. NOX/NOY November

40N

1 I

20

10

• NCAR-2D

o NOCAR

• SLIMCAT

z_ SUNY-SPB

[] UNIVAQ-2D

0

0.0 0.2 0.4 0.6 0.8 1.0

NOX/NOY

Figure 3.5.18. Concluded.

282
<

60

54

48

42

36

30

24

18

12

6

0

0

85S

I I

•* ECHAM3/CHEM

o HARVARD

v LARC-3D

• NCAR-2D

.o NOCAR

• SLIMCAT

I I . !

1 2 3

Mixing Ratio (ppmv)

BI. CLY JULY

45S

60 ' ' ' _$iE

54-* ECHAM3/CHEM

o HARVARD _,_

48 v LARC-3D

• NCAR-2D _

42, ° NOCAR _

36

._ 30

< 24

18

12

6'

OR i t....... ,

4 0 I 2 3

Mixing Ratio (ppmv)

4

.9. =

EQUATOR

ool. i i

54 ECHAM3/CHEM _ b -

48 "T_- HARVARD LARC-3D _ I

-

I • NCAR-2D ,_'

36

30

24

18

12[ 6

0

<

J I m___

0 1 2 3 4 4

Mixing Ratio (ppmv)

85N

60 , , ,

54-* ECHAM3/CHEM _<]_

o HARVARD _

48 .v LARC-3D i_<_l

NCAR-2D _

42 .o NOCAR _;_II

36 -• SLIMCAT _L_j_

30-

24-

18-

12

Oo

0 1 2 3

Mixing Ratio (ppmv)

Figure 3.5.19. Vertical profiles of model calculated Cly at 85S, 45S, equator, and 85N in July for (a-b) case B I

and (c) case B2.

283
6O

54

48

42  ̧

36!

O)

._ 30

._

_: 24

18

12

6

0

0

85S

+ AER' ' ' _I_

-" CSIRO

GSFC-2D

x LLNL _il

I I I

I 2 3

Mixing Ratio (ppmv)

BI. CLY JULY

45S

60 + AER' ' '_

54-" CSIRO $

GSFC-2D _

48 ;x LLNL _

42

36

< 24

18

12

6

0 | I l

4 0 1 2 3

Mixing Ratio (ppmv)

4

60

54

48

42

36

"13

._ 30

<

24

18

12

0

0

EQUATOR

+ AER' ' '_

-" CSlRO $_jl

GSFC-2D _,_

_x LLNL _j_

z_ SUNY-SPB z_-_!

I I I

I 2 3

Mixing Ratio (ppmv)

6O

54

48

42

36

30

24

18

12

6

0

0

Figure 3.5.19. Continued.

85N

+ AER'

I CSIRO

GSFC-2D

.x LLNL

z_ SUNY-SPB

n UNIVAQ-2D

L

L

L

L

I I I

1 2 3

Mixing Ratio (ppmv)

284
36

"O

30

<

24

85S

54 AER

CSIRO .;; .

48t_ GSFC-2D -:_,! -

LLNL '

42

18

12

6

0

0

1 I I

1 2 3

Mixing Ratio (ppbv)

i

]

]

B2. CLY JULY

45S

601 ' , _ I .-'I

l+ CS,RO

48[- _ GSFC-2D _ -iL_

/ x LLNL 1-_ Z

42 .A SUNY-SPB _-_;/' _

u UNIVAQ-2D _,t-_+

36_

"13

.._ 30

<

24

18

12

6

0 I 1 I

4 0 1 2 3

Mixing Ratio (ppbv)

.,i,,

z_ -

Jr,

.,r,

,&

4

EQUATOR

60 / , , ._

/ X

_K 54 x

48 _ ._

42

36

_ 24

18 R

CSIRO

12 _t)< S FC-2D

lle x LLNL

6 If. A SUNY-SPB

I

[] UNIVAQ-2D

0 I I I

it,

0 1 2 3

Mixing Ratio (ppbv)

,9,

60

54

48

42

36

"O

_ 3o

<

24

18

12

6

0

0

85N

, , _ '____ _:

p $-

- ,,.i ,#_

_- J /1%

1 /1",

u UNIVAQ-2D

! I I

1 2 3

Mixing Ratio (ppbv)

Figure 3.5.19. Concluded.

285
C

0

_J

=E

0

-6

._J

(qw) a_nss_a

_ O 0

I ,

O

....)

i

0

• i , i

0 0

0

0

_Z

0

O=

0

0

I,D

(W_l) ;4B!oH

,O

o_

O

J

(qw) aJnssgJ d

_Z

0 0 0 0

g

0

O_

R

0

_D

,O

C_

J

6"

LIJ

(qLU) eJnsseJ d

_0 O 0 0

_ _ 0 z

%77 )lti / i

o i

0 0 0 0

(W>l) 4.q6!aH

(qw) gJnsssJ d

0

_,_ 0 0 __

.......... 17

O9 _o_

-_ lr,

0 0 0 0

t=

0

a

0J

6

"0

r,.)

"13

M

N

C',I

286
6O

5O

4O

z_

_, 30

t,4

2O

B1. CLO MARCH

AER

I I I | ...... t

-60 -30 0 30 60 90

LATITUDE (DEG)

10

0

-90

CSIRO

30 -

'iI .....

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

50

40

30

2O

10

0

-90

GSFC-2D

I I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

60i

50

4O

30

_4

2O

10

0

-90

GSFC-3D

i i i

-6O

I L ....... _ I

-30 0 30 60 90

LATITUDE (DEG)

HARVARD

60 0 ....

40

30

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

z_

_. 30

t,4

2O

10

o

-90

LARC-3D

I I I i I

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.21. Model simulated daytime averaged C10 latitude-height cross section for (a-b) March and (c-d)

September.

287
60

5O

4030

t_

2O

10

0

-90

BI. CLO MARCH

LLNL

_--_-"_ j_.__ .

-60'-3o' o 3'0 6'0 9o

LATITUDE (DEG)

6O

5O

4O

3O

h

2O

NCAR-2D

-------- o.o_______s _ _-__

10

0

-90 -60' -30 0 30 6O' 9O

LATITUDE (DEG)

NOCAR

60 ......

30_

Ol , B _, i ,

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

30

2O

SLtMCAT

10

0

-90 -60

O.os_:

-_o o 3'0 60' 9o

LATITUDE (DEG)

6O

5O

4O

A

3O

KJ

2O

10

0

-90

SUNY-SPB

i-.- 0.'05 ' ' ' '

o'1-

I I i I I

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_--_30

20

UNIVAQ-2D

o.o_.... '----___C -' '-

lO

0

-9O -6o'._o o 3'0 6'0 9o

LATITUDE (DEG)

Figure 3.5.21. Continued.

288
B1. CLO SEPTEMBER

AER

40

_ 30

20

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

CSIRO

1 , I ! " t

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

30

20

GSFC-2D

_

10

0

-9O

___ I I I T

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_. 30

k_

20

10

0

-90

HARVARD

s_6.o5 ' ' ' ---:-----

_"0.05.J

-6o

I I I I

-30 0 30 60

LATITUDE (DEG)

9O

LARC-3D

801 , , , , , _

__ 40___

_v_ 30_

2O

I I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.21. Continued.

289
5O

40_

_._ 30_

20

10.

0

-90

B1. CLO SEPTEMBER

LLNL

I I I I |

-60 -30 0 30 60 90

LATITUDE (DEG)

60

50

4O

30

h

2O

10

o

-9o

NCAR-2D

/'_ 0105_ ' -"-- u._5 ' ,

| I I I J

-60 -30 0 30 60 90

LATITUDE (DEG)

60

50

40

_ ao

20

10

NOCAR

J i

5

0.05,., "

0 I I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

SLIMCAT

6°F---

-90 -60 -30

I

0

LATITUDE (DEG)

I

30 60 90

60

50

4O

30

20

SUNY-SPB

10

0

-90

I I ]_ I I

-60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-2D

6oj -0.05_ ' ' '

/

_ 30

N

'it .....

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.21. Concluded.

290
B1. CLO 44km.

45S

2.0 I I t I I I I [-- I 1 ] --

+ AER • NCAR-2D

1.8- • CSIRO o NOCAR -

GSFC-2D • SLIMCAT

1.6- <> HARVARD A SUNY-SPB -

• LARC-3D [] UNIVAQ-2D

1.4- x LLNL

1.2- -

o

1.orr

0.8-

0.6_-

0.2- I

0.0 I I I I I t I I I

J F M A M A S O N D J

I I

J J

Time

Figure 3.5.22. Seasonal variation of CIO at 44 km and 45S for model simulations and MLS measurement (Filled

circle: MLS).

291
"13

<

60

55

50

45

40

35

30

25

2O

15

10

0

0,0

B1. CLO November

40N

+ AER

• CSIRO

GSFC-2D

o HARVARD

• LARC-3D

I

0.5

x LLNL

• NCAR-2D

o NOCAR

• SLIMCAT

z_ SUNY-SPB

[] UNIVAQ-2D

I

1.0

Mixing Ratio (ppbv)

1.5

Figure 3.5.23. Vertical profiles of model calculated and measured CIO at 50N in November (Filled circle: MLS,

white circle: ATMOS/MAS).

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o

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£

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(q_) a_nsseJcl (q_) eJnssaJcl

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M

(uJ_1)lq6!eH (U_>l)lqB!eH

293
60

5O

40

30

20

10

0

-90

B1. CLONO2 JUNE

AER

I I ] I I

-60 -30 0 30 60 90

LATITUDE (DEG)

CSIRO

50

'

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

30

20

10

o

-90

GSFC-2D

I I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_- 30

2O

10

HARVARD LARC-3D

6o

5O

4o

30

2O

10

0

-90

0 I I ! ..... Z I I [ I l f

-90 -60 -30 0 30 60 90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

Figure 3.5.25. Model simulated CIONO 2 latitude-height cross section for (a-b) June and (c-d) December.

294
6O

5O

40

30

F,I

20

6O

5O

40

30

20

10

0

-90

LLNL

J i i i

I I I I I

-6O -3O 0 3O 6O

LATITUDE (DEG)

B1. CLONO2 JUNE

NCAR-2D

60 'Jl .....

50 .L. O

_ 30

20

10

0 I I I | I

90 -90 -60 -30 0 30 60 90

LATITUDE (DEG)

NOCAR

10

0

-9o

60

5o

4o

30

2O

lO

o

-90

I I t I I

-60 -30 0 30 60

LATITUDE (DEG)

9O

SLIMCAT

60 i i i i i

4O

3O

20

t0

0 I I ,

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

SUNY-SPB

-6O

I I I t

-30 0 30 60

LATITUDE (DEG)

90

60

5O

40

30

20

UNIVAQ-2D

i i i

10

0

-90

• i I i I

-60 -30 0 30 60

LATITUDE (DEG)

9O

Figure 3.5.25. Continued.

295
6O

5O

4O

30

_4

2O

10

0

-90

B1. CLONO2 DECEMBER

AER

i _ i I I

-60 -30 0 30 60 90

LATITUDE (DEG)

CSIRO

ooI .... [j

10

0 i J I I 1

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

30

2O

10

0

-90

GSFC-2D

I I I I f

-60 -30 0 30 60 90

LATITUDE (DEG)

HARVARD

60 .....

4O

30_

2O

10

0

-90 -6O -30 0 30 60 90

LATITUDE (DEG)

LARC-3D

6°[ __0 .....

50

40 ..0..5

11, z , , _

-90 -60 -3O 0 30 6O 90

LATITUDE (DEG)

Figure 3.5.25. Continued.

296
6O

5O

4O

30

2O

LLNL

i _ t b

10

0

-90

I I i r I

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

30

20

NCAR-2D

i i , i \

10

0

-90

I

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

3o

2O

NOCAR

10

0

-9o

I I I i 1

-60 -30 0 30 60 90

LATITUDE (DEG)

SLIMCAT

60 ....

so

2O

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

50

4O

_. 30

20

SUNY-SPB

10

0

-9o

I I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

3o

2O

UNIVAQ-2D

lO

0 t z I _ I --

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.25. Concluded.

297
>

..Q

13_

CL

v

O.w

.,I.-,

rr"

c'-

.w

x

.I

B1. CLONO2 30km.

60S

I I I 1 t I I 1

AER • NCAR-2D

CSIRO o NOCAR

GSFC-2D • SLIMCAT

HARVARD A SUNY-SPB

LARC-3D [] UNIVAQ-2D

LLNL

®

@

I I I

I I t 1 I I I I I I I

F M A M J J A S O N D J

Time

Figure 3.5.26. Seasonal variation of CIONO 2 at 30 km and 60S for model simulations and CLAES measurement

(Filled circle: CLAES).

298
<

60

55

50

45

40

35

30

25

20

15

B1. CLONO2 November

40N

t I

+ AER x LLNL

• CSIRO • NCAR-2D

GSFC-2D o NOCAR

o HARVARD • SLIMCAT

• LARC-3D A SUNY-SPB

[] UNIVAQ-2D

10

5

0 I I L

0.0 0.5 1.0 1.5 2.0

Mixing Ratio (ppbv)

Figure 3.5.27. Vertical profiles of model calculated and measured CIONO 2 at 40N in November (Filled circle:

CLAES, white circle: ATMOS/ATLAS)

299
¢-

_.1

L_

0

-.]

(quu) _JnsseJ d

c_

O

_z

o

V. o

_D

O

"O

O

O

(/1

O

O

O O O O

(w>i) lq6!eI-I

(qw) aJnssaJd

_b

_J

(.2

"1"

Ld

o

-1-

(qw) eJnssgJ d

0 o

(w>l) _q6!OH

(qw) aJnsseJ d

o

o

_z "-"

zo

2 E

O

(J

=,

0 _ _ 0 0 o _3

- ,.o 2 o Oz _ _ o 2 -- Z "_

....... o ........ o _

2 8

"-1- -r

, , ,o := ..... b:

0 0 0 0 0 0 0 0

300
B1. HCL JUNE

AER

50

_ 40__

_ _o

S

20

10

0 I i I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

50

4O

_ _o

N

20

10

0

-90

CSIRO

i t i i

i i i i L

-60 -30 0 30 60 90

LATITUDE (DEG)

GSFC-2D

4o i

,1

0 I , I J

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

40

_ 30

20

10

HARVARD

i = _ J

O I A 1 I I ......

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

LARC-3D

50

40

3o__-8 -_

20

10

0 i i 1 i 1

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.29. Model simulated HCI latitude-height cross section for (a-b) June and (c-d) December.

301
60

50

Jt

40

3O

2O

10_

0

-90

6O

5o

4O

30

20

10

6O

50

40

3O

20

10

B1. HCL JUNE

LLNL

t i _ i 1

-60

NCAR-2D

.L I [ I

-30 0 30 60 90

LATITUDE (DEG)

NOCAR

1/ ,

3.0

20

10

0-90 -60' -30' 0' 30' 60 90

LATITUDE (DEG)

0 1 I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

SUNY-SPB

_3.0 L.J ' ' '

SLIMCAT

°°/ ..... /

4O

2O

10

0-90 -60, -30, 0' 3'0 6'0 90

LATITUDE (DEG)

0 I I I I I

-90 -60 -30 0 30 60

LATITUDE (DEG)

9O

6o!

5O

40

2O

UNIVAQ-2D

n i n i t

10

0 I I [ I !

-90 -60 -30 0 30 60

LATITUDE (DEG)

9O

Figure 3.5.29. Continued.

302
6O

5O

4O

20

10

AER

* t i i i

0 I I I I ...... J_

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

BI. HCL DECEMBER

CSIRO

60 .....

40

_ ao

20

lO

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_ 3o

20

10

GSFC-2D

0 I I I | |

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

HARVARD

601 .....

40 _-

_v30 -

2o-_

0 1 I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

LARC-3D

50 • -

:£ 3o

20

1

I I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.29. Continued.

303
6O

5O

4O

z_

_v 30

2O

10

B1

LLNL

i i i _ i I

__2___ --_-----_ __ _

_o-

• HCL DECEMBER

NCAR-2D

60 i _ i i i

40 _'"_" 3.0

10

0 , , , | i 0 . , . . .

-90 -60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

60

5O

40

_ _o

N

20

10

0 I

-90 -60

NOCAR SLIMCAT

60, .....

_°_ • _ _ 40

_o__

lO

I I I I 0 .... 1= I I | I

-30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

6O

5O

40

_. 30

_4

20

SUNY-SPB

10

0 t

-90 -60

t. , ,t l

-30 0 30 60

LATITUDE (DEG)

9O

UNIVAQ-2D

601 .... _

_°_

1

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 3.5.29• Concluded.

3O4
4.0

B1. HCL 34km.

60S

I I t 1 t I I I I

AER '_' NCAR-2D

CSIRO o NOCAR

GSFC-2D • SLIMCAT

HARVARD A SUNY-SPB

LARC-3D [] UNIVAQ-2D

LLNL

@

I I

• @ ® • 0

I [ I I I i I 1 1 I I

F M A M J J A S O N D J

Time

Figure 3.5.30. Seasonal variation of HC1 at 34 km and 60 S for model simulations and HALOE measurement.

(Filled circle: HALOE).

305
B1. HCL November

40N

60 I T

55

50

45

40

35

7O

= 30

<

©

+ AER

• CSIRO

GSFC-2D

25 _ HARVARD

20

• LARC-3D

x LLNL

15 A NCAR-2D

o NOCAR

10 • SLIMCAT

t A SUNY-SPB _

5 [] UNIVAQ-2D

0 t I I I

0 1 2 3 4 5

Mixing Ratio (ppbv)

Figure 3.5.31. Vertical profiles of model calculated and measured HCI at 40N in November (white circle:

HALOE)

306
4. Ozone Climatology

Jennifer A. Logan I and Richard D. McPeters 2

1. Harvard University

2. NASA/Goddard Space Flight Center

4.1 Introduction

The primary role of models in the assessment process is to predict changes to ozone. It is crucial

therefore that the ability of the models to reproduce the actual distribution of ozone be tested. Historically, maps of the ozone column (latitude by month) have been used for this purpose. In MM I a climatology was developed for the vertical distribution of ozone for 15-60 km, based on SBUV data for

1979-80. SBUV profiles are reported with vertical resolution of -5 km, but the true resolution is lower,

-8 km above the ozone maximum and -15 km for 10-25 km. The climatology was considered valid to

about 20-30% at 20 km and to 50% at 15 km. Comparisons were made with models in mixing ratio

(ppm), which emphasizes the middle and upper stratosphere.

A new ozone climatology was developed for the vertical distribution of ozone for MM II. Our goal

was to develop a product that could be used to evaluate models in the lower stratosphere, the region

where most of the ozone column resides and where most of the ozone loss is occurring, as well as the

middle and upper stratosphere.

The data used for the climatology are from ozone sonde stations and from SAGE II. We derived the

climatology for 1988-96. This period was centered on 1992, the base year for the B1 model runs. Several years of sonde data are required to develop a climatology, since many stations make measurements

weekly. Also, the SAGE data could not be used for all altitudes for up to 2 years after the Pinatubo eruption in 1991. A several year average is thus needed to obtain a realistic climatology. Using the period

1988-96 dampens biases caused for example by anomalously low values at northern high latitudes in

recent years in late winter and starting the period as late as 1988 allows the inclusion of several sonde

stations that started in the late 1980s. The modeling groups were asked to provide their best simulation

for 1992 in terms of both the tropospheric trace gas boundary conditions and the aerosol distribution

(see Chapter 1). We used sonde data for 40-60 N, where there are stations that make measurements

three times per week, to evaluate any bias between the 9 year average for ozone and data for 1992

exclusively. For these latitudes, ozone in 1992 was lower than the 9-year mean by 10-20% from about

30 to 200 mbar in January and December (40-50 N only); by less than 5% to 15% in February, May,

June, August, September and November: and by less than 5% in March, April, July and October. For

most months and altitudes, the 9-year climatology is within 10% (or less) of the values observed in the

lower stratosphere at northern mid-latitudes in 1992. There may be similar smalI differences between

1992 and the mean for 1988-96 at higher latitudes_ but the weekly soundings at the few high latitude stations are inadequate to define monthly anomalies for 1992.

4.2 Observational Data

The climatology using SAGE II and sonde data can be developed with some confidence, based on

the results of the SPARC Ozone Trends Assessment (WMO, 1998). As part of that activity, SAGE II

ozone profiles (Version 5.96) were compared with MLS, HALOE, SBUV, iidar, sonde, and Umkehr

307
data. The comparison with MLS data showed clearly when the SAGE II data were corrupted by aerosols

following the Pinatubo eruption, and allowed the development of time filtering criteria (as a function of

altitude and latitude) for SAGE II data. It was recommended that data be omitted for 6 months at 30 km,

up to 1.5 years at 24 km, and up to 2.5 years at 19 kin. One problem identified with SAGE II data

(v5.96) is that there is a difference of-I0% between sunrise and sunset measurements above 45 km; it

was recommended that these measurements be combined, as there is no reason to prefer one over the

other. Comparison of SAGE II overpasses with sonde profiles showed agreement to within 10% for

20-30 km (with SAGE larger), but differences increased with decreasing altitudes and latitudes, to as

much as 30% at 15 km. Lidar data also agree with SAGE II profiles to within about 5% for 22-35 km,

with SAGE larger; SAGE II and HALOE agree to within 5% for 25-50 km; MLS concentrations are a

few percent larger than SAGE II from about 26-42 km, and a few percent smaller than SAGE II at

48 km. Evaluation of the sonde data indicated that all three sonde types in use today provide consistent

results between the tropopause and -28 km. Comparison of sonde and lidar data in the stratosphere

showed agreement to within _+3-5%.

Based on results of the SPARC data evaluation, we chose to use sonde data for 0-28 km, and

SAGE II data for 22-60 km. Monthly means were formed for each type of data in 10° latitude bins for

80°N to 80°S, and with resolution of 2 km in pressure altitude. The SAGE data were converted from

altitude to pressure using NCEP temperatures. Data from 2-4 sonde stations are available for each latitude band from 10° to 70°N, but there are only 1-2 stations in most southern hemisphere latitude bands,

and none for 0°-10°N and 50°-60°S. Monthly means for all stations in a given latitude band were

weighted equally. Data from 0°-I0°S were used for 0°-10°N, and values for 50°-60°S were derived

from neighboring latitude bands. The mean sonde and SAGE II profiles were compared (see Figure 4.1)

and agreement between the two found to be excellent, except at 60°-70°S in September to December.

The disagreement appears to be caused by a bias in the location of the sonde stations with respect to the

position of the ozone hole, compared to the zonal mean measured by SAGE II. The method used here to

form the climatology leads to a major problem in the depiction of tropospheric ozone - the longitudinal

gradient in the southern tropics (Samoa, 14°S, 170°W, versus Natal, 6°S, 35°W) is translated into an

unrealistic latitudinal gradient. The tropospheric part of the climatology in the tropics will be improved

in future work.

The climatology was formed as follows: sonde data were used exclusively for 0-20 km, SAGE II

data exclusively for 30-60 km, and weighted averages for 22-28 km (80% sonde and 20% SAGE at

22 km, 60% sonde and 40% SAGE at 24 km etc). An error analysis of the climatology was not made

because of time constraints, but will be part of future work. We emphasize however the agreement

between sonde and SAGE II for 20 to 30 kin, generally better than 5-10%. Another important point to

note is that the spread of monthly mean ozone profiles for each station in a given latitude band in the

lower stratosphere (e.g., stations in the North America, Europe, and Japan) is much smaller than the

spread among model results for the same altitude (Figure 4.2). The ozone climatology is shown for midseason months in partial pressure and mixing ratio in Figures 4.3 and 4.4.

Ozone column data for 1988-96 were also compiled into a climatology, using TOMS measurements

from Nimbus-7, Meteor 3 and Earth Probe (Figure 4.5a). This was compared to the integrated ozone

profile climatology. Differences are generally less than 5% except for small regions in the northern

tropics, southern subtropics, and near 60°S in October-November (Figure 4.5b). The reasons for these

differences are understood.

308
4.3 Comparison of Models with Observations

Model results were available from 13 models for the 1992 baseline atmosphere run, B 1 of MM II.

These included six 2-d models that participated in MM 1, AER, GSFC-2D, LLNL, NCAR-2D, NOCAR,

and UNIVAQ-2D, as well as 3 other 2-D models, CSIRO, HARVARD and SUNY-SPB. In addition,

four 3-d models provided results, GSFC-3D (January-May only), LARC-3D, SLIMCAT, and

ECHAM3/CHEM. Comparisons were made with model results in mixing ratio (ppm) and partial pressure (nbars). A variety of formats were used to compare model and observation. These included:

(1) Plots of the ozone column (latitude x month), and difference plots for January, April, July and

October.

(2) Altitude by latitude plots of ozone for the same months in ppm and nbars, and difference plots.

(3) Vertical profiles of ozone for the same months at selected latitudes in ppm and nbars.

(4) The annual cycle of ozone at 12, 16, 20, 26, 32, and 40 km.

The comparisons were made with 13 models in three arbitrary groups:

1. GSFC-3D, LARC-3D, SLIMCAT, ECHAM3/CHEM

2. GSFC-2D, HARVARD, LLNL, NOCAR, SUNY-SPB

3. AER, CSIRO, NCAR-2D, UNIVAQ-2D

Only a selection of these plots are shown here; the complete set may be found at the anonymous ftp

site, uadp 1.1arc.nasa.gov in the directory "logan".

It was apparent on first inspection of the profiles in nbars that the models differed widely in the lowermost stratosphere. It was unclear if some of these differences were due to different tropopause

heights, so a test of the tropopause height was devised based on the ozone gradient. This test was based

on a prior analysis of the ozone gradient at the thermal tropopause (using sonde data) by Logan. The

ozone tropopause is the first pressure altitude above 6 km at which the ozone gradient exceeds a threshold of 60 ppb/km for February to August and 45 ppb/km for September to January. This definition is

specific to the northern hemisphere, and was applied there to both the climatology and to the model

results. Limited sonde data for the southern hemisphere suggests the thresholds are somewhat different

in southern mid-latitudes. Figures of the monthly tropopause heights at selected latitudes are also on the

ftp site.

Ozone Column

Differences between model ozone columns and the climatology for 1988-96 are shown in

Figure 4.6. Excluding the region affected by the Antarctic ozone hole (not all models include PSC

chemistry), the agreement between models and the data is generally better than 10% for GSFC-2D,

HARVARD, LLNL, SUNY-SPB and UNIVAQ-2D; better than 15% for AER and GSFC-3D; and better than 20% for CSIRO. Differences exceed 20% for some regions for ECHAM3/CHEM, LARC-3D,

NCAR-2D, and SLIMCAT. The SLIMCAT model has no troposphere, which means the column

amounts are actually biased low by about 10%. Column results were not available for NOCAR. Most

3O9
modelsoverestimatethe ozone column, particularly at middle and high latitudes; exceptions are

GSFC-2D and HARVARD which show regions of over and underestimate at these latitudes. Most models show best agreement with the column in the tropics. Even for models that include PSCs there are

large discrepancies between model and observations at high southern latitudes. The columns plots can

be misleading about how well a model reproduces ozone, because of cancellation of errors, as discussed

below.

Height-latitude Cross Sections

Differences between observations and all models are shown in Figure 4.7 as height-latitude cross

sections for January, April, July, and October. Note that contours are given every 10% from -50 to 50,

then at 75, 100, 150, and 200%. These figures show clearly that all models except HARVARD and

SLIMCAT underestimate ozone from about 35 km to 55 km by 10-30%. This is an improvement over

MM I, where discrepancies were about 20-40%. The HARVARD and SLIMCAT models inadvertently

included the reaction path OH + C10 -> HCI + 0 2 with a 5% yield, which increases ozone values. The

SLIMCAT model uses meteorology for 1992 from the UKMO analysis, and 1992 was a cold year; the

model also uses isentropic levels, and errors in the approximate conversion from pressure to altitude

could lead to apparently higher ozone values in the region of strong vertical gradients (Chipperfield,

pers. comm).

Several of the models agree with the observations within _+10% between 25 km and 35 km for all

latitudes, with a tendency for the models to be high by about 10% near 25-30 km (CSIRO, ECHAM3/

CHEM, GSFC-2D, GSFC-3D, LLNL). About half of the models agree within 10% except at higher latitudes, where there are differences of 20-30% or more (AER, HARVARD, LARC-3D, NCAR-2D,

SLIMCAT, SUNY-SPB, UNIVAQ-2D). The NOCAR model is unusual in underestimating ozone by up

to 30% at 25 km in the tropics; it is within 10% of the observations at high latitudes.

Differences between model and observations start to increase rapidly below 25 km, and are largest

between 20 km and the tropopause. The group of models with the best agreement with the ozone column (within 10%) tend to show the best agreement with observations in the lowest part of the stratosphere, but even for these models there are differences up to (and in small regions exceeding) 50% in

the ozone profile (GSFC-2D, LLNL, HARVARD, SUNY-SPB and UNIVAQ-2D). For the AER and

GSFC-3D models, for which the columns agree within 15%, there are regions between about 12 and

18 km where the models exceed the observations by 50-100%. There are differences exceeding 100% in

this altitude region for CSIRO, SLIMCAT, ECHAM3/CHEM, NCAR-2D and LARC-3D. The LARC3D, NOCAR, and UNIVAQ-2D models are unusual in having large regions in both the tropics and midlatitudes where the models are too low between the tropopause and 25 km, by as much as 30-40%.

For many of the models concentrations of tropospheric ozone are more than a factor of 2 too low,

which can contribute to an underestimate of over 5% in the ozone column, compensating in part for the

general overestimate of stratospheric ozone. The LARC-3D, NCAR-2D, and UNIVAQ-2D models have

regions where tropospheric ozone is too high.

Vertical Profiles of Ozone

Comparisons between models and observations are shown in partial pressure for selected latitudes

in Figure 4.8. The vertical profiles reveal features of the models that are difficult to discern from the difference plots discussed above.

310
These figures emphasize again that the variation among models tends to be largest and the lack of

agreement with data tends to be worst in the lowermost stratosphere (12-18 km) and at the higher latitudes. The range of model values near I6 km is as large as a factor of 4 at middle and high latitudes,

while the range is smaller, at most 30-50%, near the partial pressure maximum (22-24 km). The models

tend to be larger than observations at these altitudes. For mid-latitudes, the models agree best with the

observations and with each other above the partial pressure maximum.

Figure 4.8 shows that the shape of the vertical profile below the partial pressure maximum is often

different from the observations at middle and high latitudes, while the shape is about the same as the

observations in the tropics. One prominent feature in the northern hemisphere is the accumulation of

ozone in the lowermost stratosphere in winter and early spring, and the appearance of a secondary maximum below 100 mbar (17 km); this maximum is apparent until about July and disappears by fall (see

Figure 4.2). Many of the models have no such structure, perhaps because the vertical resolution is too

coarse. LARC-3D and SLIMCAT have a secondary maximum with about the right phase, while GSFC2D, LLNL-2D and CSIRO have a secondary maximum that persists too long.

The ECHAM3/CHEM model is prominent in having vastly too much ozone, with too low a peak, at

most latitudes. The AER model has too broad an ozone peak at too low an altitude in summer at the

higher northern latitudes. The SUNY model has unusually low ozone in the mid-winter months at

700-80 ° , but nowhere else.

The ozone profiles for 20°-30°N are tropical in nature, with a tropopause near 16 km, and most

models capture this feature. Both GSFC-3D and ECHAM3]CHEM have too low a tropopause (as

defined by ozone) by 2-4 km in January to April, and the ozone profiles are much too high at 16 km. It

might be expected that models using assimilated winds would have the tropopause in the correct location. The only two models with too high a tropopause in the tropics, NOCAR and LARC-3D, have less

ozone than observed in the tropical lower troposphere.

A number of the models either have a seasonally invariant tropopause at mid-latitudes (AER,

GSFC-2D, LLNL, NCAR-2D), or a model that does not have the correct seasonal variation

(HARVARD, NOCAR, SLIMCAT). In some cases, the tropopause being too low by 4 km (e.g.,

GSFC-2D and LLNL in October at 50°-60°N) may be contributing to the large overestimate of the

model in the lowermost stratosphere, but in other cases the model is too high in this region, even if the

model tropopause is 2 km too high (e.g., AER at 40°-60°N in spring). In most cases, the model tropopause is within 2 km of that defined by the observed ozone gradient.

Vertical profiles are shown as mixing ratio in Figure 4.9. The disagreements at 16 km are hidden in

the lower left, while the middle and upper stratosphere are highlighted. The two most obvious features

of these figures are the small, but consistent, underestimate of ozone above about 35 kin, and the fact

that the position of the mixing ratio maximum is too low in altitude at middle and high latitudes, by

4-10 km in the winter half of the year and by 2-4 km in the summer half of the year. The maximum is at

about the correct altitude for 30 N to 50 S.

Annual Cycle in Ozone

The annual cycle of ozone is compared to observations in Figure 4.10. Most models have the correct

phase of the annual cycle at high latitudes, with a spring maximum below 30 kin; most capture the

change of phase at middle latitudes, from a spring maximum below about 24 kin, to a flat distribution,

then a summer maximum by about 30 km, and maxima in spring and fall near 40 km. Some models

311
haveasummermaximumin ozone at mid-latitudes at lower altitudes than in the observations. Ozone

has almost no annual cycle below about 26 km in the tropics, and the models generally reproduce this

feature. At high southern latitudes, there is a winter minimum in ozone at 26 km which most models do

not reproduce. At lower altitudes, ozone is lowest in spring (the ozone hole), and there is a large range

in model behavior, partly because not all models include PSC chemistry.

4.4 Conclusions

Agreement between models and ozone observations (and among models themselves) is best above

25 kin, where chemistry plays the largest role in controlling the ozone distribution. The models start to

diverge below 25 km, and have the largest differences with observations primarily below 20 km at middle and high latitudes. Tests of model chemistry (e.g. Chapter 5.1) indicate that most models have similar gas-phase chemistry. The simplest interpretation is that these differences are due in large part to

differences in model transport, although differences in chemistry may also contribute, particularly in the

treatment of heterogeneous processes. Other studies in MM II (age of air, CO2, SF6, source gases) also

revealed large differences in model transport.

Acknowledgements

We thank Gordon Labow his assistance with the ozone climatology and Karen Sage for generating

the plots.

References

World Meteorological Association, Assessment of Trends in the Vertical Distribution of Ozone, SPARC Report

No. 1,WMO-Ozone Research and Monitoring Project Report No. 43, 1998.

312
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13.

Edmonton 53 N, 88-96; Goose_Bay 53 N, 88-96; Legionowo 53 N, 88-96; and Lindenberg 52 N, 88-96

10

2O

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............. | I ' . ...... ' ; "

I

i|l|l| •

sEP

'D

0 40 80 120 160 200 0 40 80 120 160 200 0 40 80 120 160 200

Ozone (nbars)

Figure 4.2. Vertical profile of ozone at Edmonton and Goose Bay in Canada, and at Lindenberg and Legionowo in

Europe. All stations are near 53°N.

317
318
(IN)I) ePn;!;IV (IN'A)epn;!;IV

319
_20_

.-- 0

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(b) % difference (climatology- TOMS)

0:() 0 .

x t I I I I I I I I t

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Figure 4.5. Ozone column for 1988-1996 derived from TOMS measurements on Nimbus-7, Meteor 3, and Earth

Probe (top panel). The lower panel shows the difference in the ozone column derived from the TOMS data and

that derived by integrating the sonde/SAGE II profile climatology.

320
_" 30

I..U

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B1. Percent Difference

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-- -5--. " '_:-' -..............

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TIME (MONTH)

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0 1 2 3 4 5 6 7 8 9 10 11 12

TIME (MONTH)

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TIME (MONTH)

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0 1 2 3 4 5 6 7 8 9 10 11 12

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Figure 4.6. Percentage differences between models results and the ozone column shown in Figure 4.4.

321
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0 1 2 3 4 5 6 7 8 9 10 11

TIME (MONTH)

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Figure 4.6. Continued.

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Figure 4.6. Concluded.

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B1. OZONE 70-80N

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3

2

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1.2

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J F M A M J J A S O N D J

Time

Figure 4.10. Annual cycle of ozone at various altitudes.

• CLIMATOLOGY

[] ECHAM3/CHEM

v GSFC-3D

o LARC-3D

o SLIMCAT

359
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Time

Figure 4.10. Continued.

• CLIMATOLOGY

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360
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8 i

7

6

5

4

3

12

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J F M A M J J A S O N D J

Time

Figure 4.10. Continued.

• CLIMATOLOGY

v GSFC-2D

o HARVARD

<> LLNL

A NOCAR

[] SUNY-SPB

361
9

8

7

6

5

4

B1. OZONE 60-70S

40 km

i

F

32 km

26 km

_ s

3

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0

20 km

4"01 1

3.5

3.0

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15

11o

• CLIMATOLOGY

v AER

o CSIRO

<> NCAR-2D

UNIVAQ-2D

2.0

1.5

1.0

0.5

0.0

16 km

12 km

1"0i

0.8

0.6

0.4

0.2

0.0

J F M A M J J A S O N D J

Time

Figure 4.10. Concluded.

362
5. Special Diagnostics

5.1 Introduction

This section contains a number of special diagnostics that are designed to examine certain mechanisms. Section 5.2 reports on the method used to test the photochemical partitioning in the models. Sections 5.3 and 5.4 represent efforts to examine the model calculated production and removal rates for

ozone and how the values are combined with transport rates in the models to produce the simulated

ozone distributions. Sections 5.5 and 5.6 concentrate on polar processes including the dynamics aspect

of vortex confinement and the chemical aspects of chlorine activation.

363
5.2 Partitioning of the Radical Species

Malcolm K. W. Ko I and Ross J. Salawitch 2

lAtmospheric and Environmental Research, Inc.

2jet Propulsion Laboratory

Assessment models are primarily designed to simulate ozone and its responses to perturbations.

Although the assessment models also compute radical species concentrations needed to calculate the

production and removal rates of the long-lived species, the models are not routinely used to simulate

radical species concentrations for comparison with observations. It is possible to compare model results

discussed in Chapter 3 with some longer time average radical measurements (several days to a month),

however, it is usually inappropriate to compare model results with observations on shorter time scales

(less than a few days). Models generally have difficulties simulating the short-term variabilities of the

long-lived species that would affect the radical species concentrations. Since it is desirable to find a way

to test whether the models are calculating the radical species correctly so that there is confidence in the

model calculated production and removal rates, one methodology of model comparison is described in

this section.

The results in chapter 3 clearly show that there are significant differences in the concentrations of

the radical species simulated by the different models. The model calculated concentrations of the radical

species at a particular location are influenced by both transport and photochemistry. For the purpose of

testing models, it is necessary to separate the transport effect from the photochemical effect. Since the

time scales for photochemical conversions among the radical species are shorter than the transport time

scales, one can make use of the notion of the family approach, i.e., the radical species are transported

together as a family while they are partitioned among themselves depending on changes in the local

photochemical environment. The effects from transport and chemistry can then be partially separated by

looking at ratios such as [NOz]/[NOy], and [CIO]/[CIy]. However, such ratios still depend on the local

ozone concentration and the concentrations of NOy and Cly that are calculated by the models and are

affected by transport.

One method used in previous studies to isolate the effect of the photochemical calculation was to

isolate the photochemical code in the assessment model. The photochemical code of the more comprehensive model was employed as a box model to simulate the phtotostationery state of an air parcel that

was held at the fixed latitude, longitude and altitude. The temperature and the concentrations of the

tong-lived species such as ozone, H20, CH 4, HNO 3, NOy and Cly were kept fixed in these calculations,

while the solar insolation was allowed to follow the diurnal variation. The models using these constraints computed the partitioning of the radical species. These calculations were performed for situations where there was no Polar Stratospheric Cloud (PSC). Since the model used the same reaction rate

constants, any differences were attributed to approximations inherent to the model code. A drawback to

such approach was that the box model was off-line and may not be identical to the code used in the full

model.

An alternative method is the use of the Photo-Stationary-State (PSS) model [Salawitch, 1994]

which was developed originally to compute concentrations of the radical species using observed temperature and local concentrations of the long-lived species. The assumption was that the photochemical

conversion times of the radical species were sufficiently short that their concentrations were in photochemical equilibrium. The model has been used to compute radical concentrations from long-lived species concentrations measured by the ER-2. The results of these studies suggest that for most situations

where heterogeneous chemistry is relatively slow and metrics are largely zonal, the calculated radical

concentrations were in good agreement with the measured concentrations. In preparing this report, the

364
PSS model was used to calculate the radical species concentrations using long-lived and reservoir

species from the individual models' output. The model calculated radical species' concentrations were

compared to the concentrations calculated by the constrained PSS model. To the extent that the PSS

model has been used to analyze data from the ER-2 and found to perform in a satisfactory manner under

many conditions, verification of the model results by the PSS model provides an indirect test of the

photochemistry codes in the models.

At the same time, the results must be interpreted with care. Under certain conditions, (e.g. low sun

and polar regions) the radical concentrations in the atmosphere may not be in photostationary state.

Models that take this into account would disagree with the results from the PSS model. Some models

use an average sun condition to compute averaged radical concentrations, which were then used with

adjustment factors to compute the production and removal rates for the reservoir species. In those cases,

disagreement with the PSS model at a given time does not necessarily mean that the model calculated

production and removal rates were incorrect over the large time scales for which this approach is

designed.

For models that use explicit diurnal time marching to compute the radical species' concentrations,

there is good agreement with the PSS model. It was reasonable to assume in those cases that the radical

concentrations were correctly calculated. We tried to concentrate on low latitudes and away from

regions where PSCs were present to avoid regions where the comparisons may not be appropriate. Thus,

the effect of PSC chemistry on radical partitioning has not been thoroughly tested.

365
5.3 Ozone Production and Loss from B-1 Runs

Charles H. Jackman

NASA Goddard Space Flight Center

Stratospheric ozone amounts are determined through the interaction of dynamical, radiative, and

photochemical processes. We focus on the photochemical processes that influence the ozone amounts in

this section. Although other photochemical sources of stratospheric ozone are important in various

regions of the stratosphere, the primary production of stratospheric ozone is through the photolysis of

molecular oxygen. Ozone is lost through a large number of reactions which are generally grouped

according to families. The five families contributing to ozone loss are nitrogen [L(NOx)], hydrogen

[L(HOx)], chlorine [L(CIOx)], bromine [L_rOx)], and oxygen [L(Ox)].

The losses due to the various families have been analyzed before in models [see, e.g., Jackman et al.

1986; Stolarski et al. 1995] and measurements [Wennberg et al. 1994]. The model used in Jackman

et al. [1986] included only gas-phase reactions, thus computed a huge dominance of L(NOx) in the middle to lower stratosphere. A snapshot of the losses in the various families was presented for March from

the LLNL model in Stolarski et al. [1995]. The computations in Stolarski et al. [1995] were for a relatively clean atmosphere with a tow background sulfate aerosol condition. The Wennberg et al. [1994]

results showed computations of the ozone losses from measurements during May 1993, a moderately

elevated sulfate aerosol condition.

Modellers were supposed to simulate year 1992 in model experiment B-1, which is a very enhanced

sulfate aerosol condition because of the huge volcanic eruption of Mt. Pinatubo in June 1991. The ozone

loss terms for B-1 are not easily compared with any others discussed in the literature due to large differences in the model or measurement conditions. Therefore, in this analysis we only compare the modelled production and loss terms for 03 from B-1 with other models participating in these exercises.

Seven models (AER, GSFC-2D, HARVARD, LARC-3D, LLNL, SUNY_SPB, and UNIVAQ-2D)

submitted model output for run B-1. Since some models submitted "daytime average" values and others

submitted "24-hour average" results, it was not really productive to compare absolute numbers. The

percentage losses in the various families were compared for run B-1 in Figure 5.3.1(a-g) for March

1992 and in Figure 5.3.2(a-g) for June 1992. In general, there were many similarities among the models

even though the actual methodology for including the losses in each of the families differ from model to

model, especially for L(CIOx), L(BrOx), and L(HOx).

The L(HOx) dominates in the lower stratosphere (<20 km) and upper stratosphere (>45 kin). The

L(NOx) dominates in the middle stratosphere (20-40 kin). The L(C1Ox) can be quite large (>30%) in

the middle and upper stratosphere (35-50 kin) whereas L(BrOx) can be large (>30%) in the lower polar

stratosphere.

There are some significant differences between the percentage ozone loss rates for the two months

compared. Values for the losses in the various families for latitudes poleward of about 67S in June

should be ignored as the ozone catalytic loss cycles do not destroy ozone in the polar night. The percentage of ozone loss rate by the halogen families [L(C1Ox) and L(BrOx)] and hydrogen family [L(HOx)] is

larger at polar northern latitudes in March than in June, whereas the percentage of ozone loss rate by the

nitrogen family [L(NOx)] is larger at polar northern latitudes in June than in March.

366
The ratio of total ozone loss [L(tot)] to total ozone production [P(tot)] was also investigated. These

L(tot)/P(tot) values are given in Figure 5.3.3 for March 1992 and Figure 5.3.4 for June 1992. Again,

there are many similarities among the models. All models showed the tropical lower to middle stratosphere region (tropopause to 30 km) with P(tot) greater than L(tot). The middle to upper stratosphere

showed that P(tot) was about equal to L(tot). L(tot)/P(tot) should be near I in the upper stratosphere as

the photochemical lifetime for ozone is less than a day and ozone should be approximately in photochemical equilibrium.

L(tot) was greater than P(tot) in the middle to polar latitudes and lower to middle stratosphere.

L(tot) depends very critically on the amount of atomic oxygen, O, which is produced by photolysis of

O 3 for wavelengths up to about 1140 nm. P(tot), on the other hand, depends on the very shortest of

wavelengths (< 242 nm). These very high energy photons are absorbed quite efficiently at the very large

column densities of 0 2 and O 3 at low altitudes and high latitudes. Thus atomic oxygen is created at low

altitudes and high latitudes resulting in ozone loss whereas the ozone production is being curtailed in the

same region. For further discussion of L(tot)/P(tot) see Jackman et al. [1986].

References

Jackman, C. H., R. S. Stolarski, and J. A. Kaye, Two-dimensional monthly average ozone balance from limb

infrared monitor of the stratosphere and stratospheric and mesospheric sounder data, J. Geophys. Res., 91,

1103-1116, 1986.

Stolarski, R. S., et al., 1995 Scientific Assessment of the Atmospheric Effects of Stratospheric Aircraft, NASA

Reference Publication 138 I, November 1995.

Wennberg, P. O., et al., Removal of stratospheric 03 by radicals: In situ measurements of OH, HO2, NO, NO2,

CIO, and BrO, Science, 266, 398-404, 1994.

367
-90 -60 -30 0 30 60 90

LATITUDE (DEG)

B1 AER Percentage Ozone Loss Rates

NOx March HOx March

oo

50 50

40 _ 40

N N

20 - 20

10 1_

0 I--C"--_ , , , f _ _,--_z_

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

CLOx March

6o___, , :

50

I0

OL .....

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

40

30

20

10

0

-90

BROx March

I I I t_ I

-60 -30 0 30 60

LATITUDE (BEG)

9O

Ox March

50

N

01 , , , ' '

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3. l a Ozone losses (in percent) due to the five families, L(NOx), L(HOx), L(CIOx), L(BrOx), and L(Ox)

computed for March 1992 from the AER, GSFC-2D, HARVARD, LARC-3D, LLNL, SUNY_SPB, and

UNIVAQ-2D models.

368
6O

5O

4O

3O

20

10

0 •

-90

60l

5O

4O

30

20

10

0

-90

NOx March

B1 GSFC-2D Percentage Ozone Loss Rates

HOx March

-60 -30 0 30 60 90

LATITUDE (DEG)

CLOx March

- _ --_--------L_" ' '_L/.-

6°1_ ' ' ' ..1

40

t 0 _ I I I I I

-60 -30 0 30

LATITUDE (DEG)

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

BROx March

01 .....

60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

3o

N

20

10

Ox March

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3. Ib Continued.

369
60--

5O

10

0 J

-90 -60

4O

30

2O

B1 HARVARD Percentage Ozone Loss Rates

NOx March

I i | I

-30 0 30 6O 90

LATITUDE (DEG)

HOx March

6°__o _' 2-iI

_

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

CLOx March

50_ _¢-

Ot , , , , ,

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

°°

5O t

40

30

20

10

0

-90

BROx March

-60 -30 0 30 60

LATITUDE (DEG)

J

f

F.

f

4

)

Ox March

6o1___C.2_ '-

_ 40 i

_ 30

20 _"

10

0 I I I I t

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.1c Continued.

9O

370
60

5O

40

30

2O

B1 LARC-3D Percentage Ozone Loss Rates

NOx March HOx March

10

60 _------___ " '-.-- 90-_.f

50 _-

4o_o Ii

N

20

o

-90 -60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

CLOx March

60 ' ' ' _____j10

0 I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

3O

20

10

0

-90

BROx March

-60 -30 0 30 60 90

LATITUDE (DEG)

,_Ox March

6o_ ....

so_

r_-_ _ _-

30

'°I

0 t I I I J

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3. ld Continued.

371
60

50

40

_o

N

20

10

B1 LLNL Percentage Ozone Loss Rates

NOx March

0,. , , , , '

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

30

N

20

10

HOx March

o

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

4O

_v 30

20

10

CLOx March

0 i i i | t

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

BROx March

6Ool .....

._o ---_"i

10 !.

0

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Ox March

50 <_

30

N

2O

10

0 i i i i I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.1 e Continued.

372
B1 SUNY-SPB Percentage Ozone Loss Rates

NOx March HOx March

60 ..... 60 _--------_ _' ' cJo_, _

50 - - 50

40 40

N U

20 20

10 1_

0 ,

-90 -60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

CLOx March

60 , 2_-- , ,

_ 40

20

10

0 I I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6°t

5O

4O

30

20,

10

0

-90

BROx March

I I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

Ox March

....

2_

S

1

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3. ! g Continued.

373
B1 UNIVAQ-2D Percentage Ozone Loss Rates

NOx March HOx March

60 _ i 0 i 4

so _ _ - -

0 L ,

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

5 4o_

N _'_

20

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

40

30

2O

10

0

-90

,_CLOx March

-60 -30 0 30 60 90

LATITUDE (DEG)

BROx March

405060 l .....

30 -

2O

10

0

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Ox March

L

40L_ _'_

F ao

20 -._

10I_

Ot , , , , ,

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3. lg Concluded.

374
B1 AER Percentage Ozone Loss Rates

NOx June

60 ....

50 :_

40

U

10 =

0

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

50-

40_ } 3o

20

t0-_

-90

HOx June

__,_ -80 --I

-60 -30 0 30 60 90

LATITUDE (DEG)

60

50

40

3O

20

10

0

-90

CLOx June

t I t I I

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

4oi

" 30

N 20

10

0

-90

BROx June

1 I I I = I

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_. 30

20

10

0

-90

Ox June

I I ,,,1 I I

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.2a Ozone losses (in percent) due to the five families, L(NOx), L(HOx), L(CIOx), L(BrOx), and L(Ox)

computed for June 1992 from the AER, GSFC-2D, HARVARD, LARC-3D, LLNL, SUNY_SPB, and UNIVAQ2D models.

375
60

5O

40

30

20

10

0

-90

60 m

5O

4O

30

20

B1 GSFC-2D Percentage Ozone Loss Rates

NOx June HOx June

_t 60 --

5O

40

10

-60 -30 0 30 60 90

LATITUDE (DEG)

i _ i i i

0 I I I i I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

10

0

-90

CLOx June

-60 -30 0 30 60 90

LATITUDE (DEG)

BROx June

60 i r i

40

3o

20

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

3O

20

10

0

-90

Ox June

i I I I i

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.2b Continued.

376
6O

50

40

30

20

10

0

-90

60

5O

4O

3O

20

10

0

-90

B1 HARVARD Percentage Ozone Loss Rates

NOx June

I I I I t

-60 -30 0 30 60 90

LATITUDE (DEG)

CLOx June

__l I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

4O

30

2O

10

0

-90

HOx June

I I I i _L--

-60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

40

3O

20

10

0

-90

BROx June

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

40

z_

_. 30

20

10

0

-90

Ox June

"__ ' ,

4

C

|,

-60

I I I I

-30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.2c Continued.

377
60

5O

40

_. 30

2O

10

0

-90

6O

5O

40

3O

t_

20

B1 LARC-3D Percentage Ozone Loss Rates

10

NOx June

-60 -30 0 3o 6o 90

LATITUDE (DEG)

CLOx June

0 i i i i l

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

HOx June

601 :.,'_ , ,

40 / _ __

1,,'I_!6_'

-90 -60 30 0 30 60 90

LATITUDE (DEG)

6O

5O

4oi

_. 3O

2O

10

0

-90

BROx June

-60 -30 0 30 60 90

LATITUDE (DEG)

60

50

4O

30

2O

10

0

-90

Ox June

i 1 i i i

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5,3.2d Continued.

z

_--

E

=

378
NOx June

60 ..... I 60 +°1 +o

20 20

10 10

o1___._ , , _ o

-9O

B1 LLNL Percentage Ozone Loss Rates

HOx June

__' ' ,. '.

-60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

CLOx June

/

50

_ _o

N

20

10

I I I I I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

60

5O

4O

30

20

BROx June

10

0

-90

I I I J I

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

30

2O

10

0

-90

Ox June

I [ _ | I I

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.2e Continued.

379
60

50- ,

40- !

20-

10- ,'

oi_i

-90

B1 SUNY-SPB Percentage Ozone Loss Rates

NOx June

_/J/[ ( .... -

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

N

20

10

0

-9o

HOx June

_I I I I I

-60 -30 0 30 60 90

LATITUDE (DEG)

,,CLOx June BROx June

50 • 50

40- _ 40

20 • 20

1_ • 10

t 0 I I

-90 -60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

6O

5O

4O

3o

N

2O

10

0

-90

J I T I I

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.2f Continued.

380
B1 UNIVAQ-2D Percentage Ozone Loss Rates

NOx June HOx June

6oI , , I oo_

_ot _ ; _ 40,of 

f

N N

20 20

_o_._/_----____I _o

-90 -60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

LATITUDE (DEG) LATITUDE (DEG)

qLOx June

60 _ ' L '- 60

4O

N

20 ,,o20

I t I

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

0

-90

BROx June

L L I I

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

4O

_v 30

2O

10

0

-90

Oxo June

1 I I 1 I

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.2g Concluded.

381
60

45

_- 30

15

0

-90

(Total Ozone Loss)/(Total Ozone Production)

B1 AER March

N

6O

45

_, 30

15

-60 -30 0 30 60

LATITUDE (DEG)

9O

9O

B1 HARVARD March

60 ....

15

-90 -60 -30 0 30 60

LATITUDE (DEG)

B1 LLNL March

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

B1 UNIVAQ-2D March

60 lJl

N

1

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

45

3O

15

0

-90 -60

B1 GSFC-2D March

-30 0 30 60

LATITUDE (DEG)

90

6O

45

_,_ 30

15

0

B1 LARC-3D March

"=-1.0 .-. _ ,J

_.,.._ ._.o_

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

45

3O

15

0

-90

B1 SUNY-SPB March

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.3. The ratio of total ozone loss [LOot)] to total ozone production [P(tot)] computed for March 1992

from the AER, GSFC-2D, HARVARD, LARC-3D, LLNL, SUNY_SPB, and UNIVAQ-2D models.

Z

=

E

=

382
6O

45

3O

15

0

-90

(Total Ozone Loss)/(Total Ozone Production)

B1 AER June B1 GSFC-2D June

-60 -30 o 30 60 90

LATITUDE (DEG)

6O

45

30

I5

0

-90

_ 1.0.;f-

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

45

_- 30

15

0

-90

B1 HARVARD June

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

45

30

15

0

-90

B1 LARC-3D June

0 .-_.

i % i i i i

-60 -30 0 30 60 90

LATITUDE (DEG)

60f

45

3o

N

15

0

-90

B1 LLNL June

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

45

_._ 30

15

0

-90

B1 SUNY-SPB June

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

45

30

15

0

-90

B1 UNIVAQ-2D June

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 5.3.4. The ratio of total ozone loss [L(tot)] to total ozone production [P(tot)] computed for June 1992 from

the AER, GSFC-2D, HARVARD, LARC-3D, LLNL, SUN__SPB, and UNIVAQ-2D models.

383
5.4 Fixed Ozone Production and Loss

Charles H. Jackman, ! Eric L. Fleming, l and Malcolm K. W. Ko 2

1. NASA Goddard Space Flight Center

2. Atmospheric and Environmental Research, Inc.

The total photochemical production and loss terms for ozone were provided to the modellers for B-3

from the AER model. The only output for this model experiment was ozone. Since the chemistry for

ozone was prescribed, this experiment highlighted the transport differences among the models. Seven

models (AER, GSFC-2D, HARVARD, LLNL, SUNY_SPB, UNIVAQ-2D, UNIVAQ-3D) submitted

model output for run B-3.

If each model had the same winds, diffusion, and numerical transport scheme as AER, then each

model's ozone prediction would be the same. It was no surprise that there were large differences in predictions of total ozone among the models for this experiment. The total ozone predicted for runs B-3

and B-1 are presented in Figure 5.4.1 (a-c) for all models. The TOMS (1988-92) average values are also

shown for comparison.

The total ozone from each model for run B-3 was generally more similar to that particular model's

total ozone prediction for run B-I than the AER total ozone prediction for run B-3. For example, the

LLNL total ozone predictions for runs B-1 and B-3 (see Figure 5.4.1b) were more similar than the

LLNL and AER total ozone predictions for run B-3 (see Figures 5.4.1a and 5.4.1b). There were, however, differences between the total ozone predicted in runs B-1 and B-3 for a particular model, especially in the southern polar latitudes. As an example, the LLNL predicts much more total ozone in the

southern polar latitudes for the spring period in run B-3 than in run B-I (about 340 DU versus 160 DU).

The LLNL photochemical ozone loss in this time period must be much larger than that predicted from

the AER model. A similar difference is noted for the GSFC-2D model.

The total ozone from the various models for March and June is presented in Figure 5.4.2(a-b) for

runs B-3 and B-1. The amount of ozone predicted in the tropics for run B-3 is related to the speed of

transport through the lower stratosphere. The HARVARD model predicts a relatively large amount of

ozone in the tropics, the SUNY_SPB and GSFC-2D models predict a lesser amount of tropical ozone

than the HARVARD model, and the other models (AER, LLNL, UNIVAQ-2D, and UNIVAQ-3D) predict even less ozone in the tropics. The HARVARD model most likely has a much slower tropical

upward advection than the other models. The SUNY_SPB and GSFC-2D models likely have a slower

advection than all the other models except the HARVARD model.

A check on the conclusion about the HARVARD model having the slowest tropical upward advection of all the models can be made by looking at Figure 5.3.3. This figure shows the L/P values for

March from a full chemistry run. The production of ozone (P) is probably very similar among all models as it depends only on the photodissociation of O 2 (as defined for the C-4 analysis). The primary difference among the models for L_ should be related to the total loss of ozone (L). A very slow tropical

circulation would promote a larger ozone loss rate in the lower stratosphere as the radicals prominent in

ozone loss would build up to larger amounts in this region than in a model with a stronger ciculation.

Thus tropical lower stratosphere L/P values for the HARVARD model should be smaller than L/P values in other models and they generally are (see Figure 5.3.3).

The changes in total ozone from one month to the next also point out certain details of model differences. Figure 5.4.3(a-c) shows model changes for three of these periods (March-February; July-June;

384
andSeptember-August) forrunsB-3andB-l. TheGSFC-2Dmodelshowsalargenegativedifference

inthesouthpolarregionforrunB-3(March-February) thatisnotpresentforrunB-1.Suchastructure

in B-3probablymeansthata)theAERmodelprovidedozonelossislargercomparedwiththeproductionthanintheGSFC-2Dmodel,and]orb)theGSFC-2Dmodelhassomeozonelossinthatregiondue

to transport.

TheAERmodelshowsalargernegativeozonechangeinthenorthernmiddlelatitudesforbothruns

B-3andB-! (July-June)comparedto allothermodels.TheUNIVAQ-2Dmodelshowsthelargestnegativeozonechangein thenorthernpolarlatitudesfor bothrunsB-3andB-1.TheSUNY_SPBmodel

indicatesanegativechangein ozoneinthesouthernpolarregionforthistimeperiodinbothrunsB-3

andB-l, whereasall theothermodels(exceptLLNL overthe60Sto 70Sregion)showpositivechanges

in ozone.

Thedifferencesin thespringsouthernpolarlatitudesfor theLLNL andGSFC-2Dmodelsnoted

abovein Figure5.4.1(a-c)areveryapparentin thisFigure5.4.3(a-c).Bothmodelsshowonlymodest

negativechangesinrunB-3(September-August), whereastheyshowhugenegativechangesinrunB-1.

SincetheAERmodelshowsaboutthelargestnegativechangesin runB-3, it appearsthattheAER

modeltransportisamajorcauseof thisozonedecrease. TheLLNL modelshowsaninterestinglatitudinalstructurein thenorthernmiddleto polarlatitudes,beinganegativeozonechangebetweenabout

30Nand60Nandbeingapositiveozonechangefromabout60NtothepoleinbothrunsB-3andB-1.

Figure5.4.4(a-c)showsa comparisonof themodelsfor theirozonecolumnratesof change(in

DobsonUnitsperday)for all latitudesoverthemonthsof theyear.The TOMS (1988-92) average values are also shown for comparison. The models, at least for run B-1, should show a similar structure to

that observed in the TOMS measurements. The AER model does a reasonable job in the northern hemisphere, however, the recovery of ozone after the "Antarctic ozone hole" goes on longer than measured.

The HARVARD model has problems in representing the depth of the "Antarctic ozone hole," both in

runs B-3 and B-1. The HARVARD model does not have polar stratospheric cloud chemistry, thus is not

expected to predict an "Antarctic ozone hole" in run B-I. The LLNL model for run B-1 shows more

structure in the negative and postive ozone "rate of change" fields than indicated in the measurements.

The LLNL, SUNY_SPB, and UNIVAQ-2D models (like the AER model) all show the recovery of

ozone after the "Antarctic ozone hole" going on longer than measured. The SUNY_SPB, UNIVAQ-2D,

and UNIVAQ-3D models show differences compared to the measurements in the tropical latitudes.

385
Ozone Column

AER B3

90 / .... U .... v ' '(

_- _28o_-!

9ol,-T-T_,,D _ % /,/,"_,_ /

" I I [ I I /

1 2 3 4 5 6 7 8 9 10 11 1213

TIME (MONTH)

GSFC-2D B3

_ 3

/ --280

-3o_-__32o_ t

1 2 3 4 5 6 7 8 9 10 11 1213

TIME (MONTH)

9O

6O

(5

3O

0

i-

-30

<

J

-60

-9O

HARVARD B3

360 I

20 320 --

320

__ _-_,_, _,, _t

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

90

6O

(5

30

ILl

,-, 0

"m

,,_ -30

-60

-9oi

AER B1

I I i I I 1/

2 3 4 5 6 7 8 9 10 11 12 3

TIME (MONTH)

90

6O

(5

_3 30

UJ

o 0

::3

I-

-30

J

-60

-9O

GSFC-2D B1

2 3 4 5 6 7 8 9 10 11 1213

TIME (MONTH)

HARVARD B1

t

oo

No

_ 30

-60

-9O

2 3 4 5 6 7 8 9 10 tl 12 13

TIME (MONTH)

Figure 5.4.1a Ozone column (in Dobson Units) for runs B-3 and B-1 as a function of latitude and time for year

1992 from the AER, GSFC-2D, HARVARD, LLNL, SUNY_SPB, UNIVAQ-2D, and UNIVAQ-3D models. The

TOMS (1988-92) average values are also shown for comparison.

386
LLNL B3

II- -30

<

.J

-60

-9O

2 3 4 5 6 7 8 9 10 11 1213

TIME (MONTH)

9O

60

(5

w 30

a

w

o 0

I-

-30

5

-60

-90

Ozone Column

LLNL B1

90 ....... _ ' kl

_ 3o t

."0 t _ -30

-60

-90

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

9O

60

(.9

w 30

tm

w

,-', 0

=3

1-

-30

5

-60

-9O

SUNY-SPB B3

2 3 4 5 6 7 8 9 10 11 1213

TIME (MONTH)

UNIVAQ-2D B3

2 3 4 5 6 7 8 9 1011 12

TiME (MONTH)

9O

60

30

w

0

z3

I--

_ -30

_J

-60

-90

SUNY-SPB B1

i i

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

UNIVAQ-2D B1 90f 60

N 3o

o 0

_ -30 _

_oo -90 , \

2 3 4 5 6 7 8 9 10 11 12 3

TIME (MONTH)

Figure 5.4. lb Continued.

387
Ozone Column

UNIVAQ-3D B3

/ , ,\,_80

60

(.5

30

i.u

5

2 3 4 5 6 7 8 9 10111213

TIME (MONTH)

TOMS 1988-92 AVG.

oo, _ .... __

L.U

U.I

a

_- -30 ___ 280

1 2 3 4 5 6 7 8 9 1011 1213

TIME (MONTH)

Figure 5.4. lc Concluded.

388
5OO

450

400

35O

E 300

250

Or)

"_ 200

D

150

100

5O

0

-90

Ozone Column

B3 MARCH

, , , , , 500

450

400

35O

_3

-_ 300

250

(/)

•+ AER _ 200

GSFC-2D 150

<> HARVARD

-x LLNL

_'7 TOMS

B1 MARCH

I I I I

100

5O

0

-90

+ AER

GSFC-2D

<> HARVARD

-x LLNL

._ TOMS

I I I i ____ l I I I I --

-60 -30 0 30 60 90 -60 -30 0 30 60 90

Latitude Latitude

5OO

450

400

35O

O3

¢-. 300

c 250

0

o 200

tm

150

100

5O

0

-90

B3 MARCH

I I I I I

+ AER

A SUNY-SPB

D UNIVAQ-2D

D UNIVAQ-3D

._7 TOMS

5OO

450

400

35O

E 300

250

O3

_o 200

£3

150

100

5O

0

B1 MARCH

I I I I I

+ AER

z_ SUNY-SPB

-D UNIVAQ-2D

-v TOMS

I I I I I

| I I I I

-60 -30 0 30 60 90 -90 -60 -30 0 30 60 90

Latitude Latitude

Figure 5.4.2a Ozone column (in Dobson Units) for runs B-3 and B-I for March and June of 1992 as a function of

latitude from the AER, GSFC-2D, HARVARD, LLNL, SUNY_SPB, UNIVAQ-2D, and UNIVAQ-3D models.

The TOMS (1988-92) average values are also shown for comparison.

389
500

450

400

350

03

_ 300

g 250

o 200

tm

150

100

5O

0 I .... 1

-90 -60 -30

Ozone Column

B3 JUNE

i i i I i

+ AER

GSFC-2D

-o HARVARD

-x LLNL

.v TOMS

1 t I

0 3O 6O 90

Latitude

B1 JUNE

500 , , , , ,

450

400

350

_9

_ 300

250

o)

_ 200-+ AER

150 _ GSFC-2D

-o HARVARD

100 x LLNL

50 -_ TOMS

0 I I I ] l

-90 -60 -30 0 30 60 90

Latitude

5O0

450

B3 JUNE

350

300

g 250

o 200

a

i i i t i

4oo 

+ AER

150i _ SUNY-SPB UNIVAQ-2D

100I° UNIVAQ-3D

5:I_7 TOMS

I I I l I

-90 -60 -30 0 30 60 90

Latitude

B1 JUNE

500 , , , ,

450

400

350

300

250

"_ 200 + AER

a

150 _A SUNY-SPB

[] UNIVAQ-2D

100 -_7 TOMS

50

0 I I I I I

-90 -60 -30 0 30 60 90

Latitude

Figure 5.4.2b Concluded.

39O
50t

4O

3O

2O

"E 10

c 0

o

..o

o -I0

£3

-2O

-30

-40

-50

-90

B3. Difference in Total Ozone

90

B3 March - February

I I I I I

+ AER

GSFC-2D

<> HARVARD

x LLNL j_ "_..-_..

_. TOMS _

Z

I 1 I ! . I

-60 -30 0 30 60

Latitude

50

40

30

20

o

.8

mo -10

B1 March - February

t I I I I I

+ AER -]

GSFC-2D J

o HARVARD I

x LLNL .._,.J

_2o -30 -40 I t

-50 , , , l i

-90 -60 -30 0 30 60 90

Latitude

5O

40

30

20

o

.8

o -10

E3

-20

-30

-40

-50

-90

B3 March - February

, , , , , _ 50 + AER 40

A SUNY-SPB _ J

[] UNIVAQ-2D j j 30

D UNIVAQ-3D 'f' _ 20

§ o

-10

I I I I I

-60 -30 0 30 60 90

Latitude

B1 March - February

I I I I I /I + AER

z_ SUNY-SPB _ j

[] UNIVAQ-2D J

-20 I

-30

-40

-50 i i , L L

-90 -60 -30 0 30 60

Latitude

90

Figure 5.4.3a Difference in total ozone (in Dobson Units) for runs B-3 and B- 1 for three periods (March-February;

July-June; and September-August) in 1992 as a function of latitude from the AER, GSFC-2D, HARVARD,

LLNL, SUNY_SPB, UNIVAQ-2D, and UNIVAQ-3D models. The TOMS (1988-92) average values are also

shown for comparison.

391
3O

2O

10

0

(t)

:_ -10

g -20

.8

o -30

a

-40

-50

Difference in Total Ozone

B3 July - June

I t I I I

o HARVARD % '_

x LLNL _ -

_7 TOMS _.

-60

-70 i i i i L

-90 -60 -30 0 30 60 90

Latitude

30

20

10

0

-10

_) -20

.8

o -30

-40

-so

-60

-70

B1 July - June

I | I t I

o HARVARD '7

x LLNL

_7 TOMS

I ! I I I

-90 -60 -30 0 30 60 90

Latitude

30

20

10

0

-10

g -20

.8

-30

-40

-50

-60

-70i

-9( -60 -30 0 30 60 90

Latitude

B3 July - June B1 July - June

3°/ ' ' ' ' ' 1

_ -10

+ ._ -20

t_ -30

-40

-50

-60

-70

-90 -60 -30 0 30 60 90

Latitude

Figure 5.4.3b Continued.

392
Difference in Total Ozone

B3 September- August

30| , ' ' '

g -10 _ ....

g -20

.8 + AER

-301- / _ GSFC-2D

I t o HARVARD

-40t- _/ x LLNL

-50 _# V TOMS

t

-60_- -70 I , , J

-90 -60 -30 0 30

Latitude

I

I

60 90

30

20

10

0

-10

8 -20

-30

-40

-50

-60

-70

-90 -60 -30

B1 September- August

I I I I I

GSFC-2D

<> HARVARD

x LLNL

v TOMS

I I I I

0 3O

Latitude

60 90

B3 September - August

30 , ' ' ' '

2O

0

g -20

_o + AER -30 z_ SUNY-SPB

o ,_ [] UNIVAQ-2D

-40 I/ []_7 UNIVAQ-3D TOMS

-50

-60

-70 I , i

-90 -60 -30 0

Latitude

30 60 90

30

20

10

0

-10 I

g -20

g -30

a

-40

-50

-60

-70__4__ i

-90 -60 -30

B1 September- August

I I I I I I

I

+z_ AER SUNY-SPB

[] UNIVAQ-2D

_7 TOMS

I I

0 30 60 90

Latitude

Figure 5.4.3c Concluded.

393
Ozone Column Time Rate of Change (DU/day)

AER B_

_ -30 .... " 2' ..

"O

d

J F M A M J J A S O N D J

Months

GSFC_.___DB3

_60 -90 : ":.' _ , ::::

J F AMJ JASONDJ

MOnths

HARVARD B3

90 2' ,". -' "........

60 L.....O0 ""-., ....'" / ""_

<:>o0,,,:.: oo-- 

-30 iii:."

-60

d FMAMJ J ASOND J

Months

3O

o

-J

=3

,--1

Q)

-J

._=

J

9O

6O

30

0

-30

-60

-90

90

60

30

0

-301 -60

-90

90

6O

30

0

-30

-60

-90

J FMAMJ JASONDJ

Months

GSFC_.,_B1

J FMAMJ JASONDJ

Months

HARVARD B1

J F M A M J J A S O N D J

Months

Figure 5.4.4a Column ozone time rate of change (in DobSon Units/day) for runs B-3 and B- 1 for 1992 as a function of latitude and time from the AER, GSFC-2D, HARVARD, LLNL, SUNY_SPB, UNIVAQ-2D, and

UNIVAQ-3D models. The TOMS (1988-92) average values are also shown for comparison.

394
Ozone Column Time Rate of Change

"-'I .i,-,

-J

LLNL B3

90 ,, /'Ut_.::' ' :"::[l_L:..J--

3o i

._ 0

w -30 i

-60

-90

J FMAM J J A SON D J

Months

SUNY-SPB B3

90t/,_::,..,'. ' ''.'i '.... v' '_

o \

J FMAMJ JASON D J

Months

UNIVAQ-2D B3

-30

-60

-90 :

J FMAMJ JASONDJ

Months

LLNL B1

90 ,,L:,I,:., _:,..:,_.._':.,' ' ' :! ' '

80 >_k_L_',:::._.:....,.,. ,.,:

I o

-30

-60

-90

J FMAM J J A SON D J

Months

SUNY-SPB B1

90 "4 :::,' ' ' :', .... .7 V"

...A

-30

-60

-90

J FMAMJ JASONDJ

Months

UNIVAQ-2D B1

':- "-:' ': _1

-60

J FMAMJ J ASOND J

Months

-_= o

.-I

-30

Figure 5.4.4b Continued.

395
O

-,i

.-J

90

60

30

0

-30

-60

-90

Ozone Column Time Rate of Chang,

UNIVAQ-3D B3

"_i._!,__.."._/,:,.-.->.t_/_3 _

J FMAMJ JASONDJ

Months

TOMS 1988-92 AVG.

":ii t

J F M A M J J A S O N D J

Months

Figure 5.4.4c Concluded.

396
5.5 Signatures of High Latitude Dynamical Processes and

Denitrification

David B. Considine

University of Maryland

5.5.1 Introduction

In order to have confidence in a model prediction of the response of atmospheric 0 3 concentrations

to a perturbation it is necessary but not sufficient that the model reproduce reasonably well observed 03

concentrations. If the model happens to get 03 concentrations about right for the wrong reasons it is

unlikely that its response to a perturbation will resemble that of the atmosphere. It is thus important to

also determine that the processes regulating model 03 concentrations are similar to those in the

atmosphere.

Polar dynamical processes are difficult to represent in both 2D and 3D models. Wobbling of the

polar winter vortex smooths zonal mean gradients of long-lived tracers without any transport across the

vortex edge. A 2D model may therefore overestimate the cross-vortex transport of material because vortex wobbling is represented in the model by diffusion, which produces irreversible mixing. In 3D models the amount of wintertime descent and the polar temperatures depend sensitively on planetary and

gravity wave dissipation, which can be too weak or in the wrong location.

In this section we primarily examine the degree to which model representations of polar dynamical

processes result in adequate simulations of observed atmospheric constituents. Because a direct intermodel comparison of these processes would be very difficult due to differences in model formulation,

we look instead for the signature of the processes of interest in the model constituents, emphasizing

N20 distributions. This enables comparison with observations and allows us to ignore the model formulation details. In addition to N20 distributions we intercompare model Cly with values inferred from

atmospheric observations. Without an adequate simulation of Cly a model has no hope of correctly simulating atmospheric 03 concentrations or its response to a perturbation. We also evaluate the models'

ability to reproduce observed HNO 3 concentrations. HNO 3 strongly influences the rate at which 03

recovers from PSC-induced depletion, so its correct simulation in a model is critical. It is controlled

both by dynamical processes and high latitude denitrification from PSCs.

Below we compare the run B-1 model simulations from 8 2D models and 4 3D models. The

2D models submitting B-1 run results are those from the Goddard Space Flight Center (GSFC-2D), the

Lawrence Livermore National Laboratory (LLNL), the National Oceanographic and Atmospheric

Administration - National Center for Atmospheric Research (NOCAR), the Universita degli Studi,

L'Aquila (UNIVAQ-2D), the State University of New York (SUNY-SPB), Atmospheric and Environmental Research, Inc. (AER), Harvard University (HARVARD), and CSIRO. The 3D models submitting B-1 run results are those from GSFC (GSFC-3D), the University of Cambridge (SLIMCAT), and

the NASA Langley Research Center (LARC-3D). It should be noted that neither the HARVARD nor

the CSIRO models include polar heterogeneous chemical reactions so only their representation of polar

dynamics will be considered. We also present N20 results from the Goddard Institute for Space Studies

(GISS8xl0) model, which were made for run A-5.

The B-I run is described in Chapter 1. Briefly, the run was each modeling group's best simulation

of 1992. The year was chosen so that observations made by the CLAES instrument on the upper

397
atmosphereresearchsatellitecouldbeused.ObservedzonallyaveragedtemperaturesfromMLS and

surfaceareadensitydistributionsderivedfromSAGEII satelliteobservationswereprovidedto each

modelinggroup,buttherewasnorequirementthatthegroupsusethesedata.Eachmodelinggroup

includedtheirownparameterizationf polarheterogeneous processes.

5.5.2 Vortex and Tropical Boundaries

The wintertime stratosphere is characterized by strong planetary wave dissipation at midlatitudes

driving a residual circulation consisting of ascent in the tropics, transport toward the winter pole at high

altitudes above the planetary wave dissipation region, and descent at high latitudes over the winter pole.

[e.g., Eluszkiewicz et al., 1996; Rosenlof, 1995]. The isopleths of long-lived species such as N20 reflect

this transport pattern [e.g., Bacmeister et al., 1995]. Tropical, midlatitude, and polar regions with relatively weak internal gradients are separated from each other by stronger gradients. This occurs because

the time scales for mixing between the regions are longer than the timescales of the processes which

establish the tracer values typical for each region. Strong tracer gradients can therefore be used to identify the boundaries of these dynamically distinct regions [Nakamura and Ma, 1997].

The absolute value of the first derivative of a long-lived tracer distribution such as CLAES N20

[Roche et al., 1996] will have peaks where the gradients are the strongest. It should be easy to identify

the tropical and polar boundaries in the atmosphere or a model by plotting the first derivative of a longlived tracer. Figure 5.5.1 is a six-panel plot of the absolute value of the first derivative with respect to

latitude of 1992 CLAES version 8 N20 at 21.5, 27, and 32 km (46.4, 21.5, and 10.0 mbars, respectively). The left-hand side of the figure shows plots of the northern hemisphere N20 gradients and the

right-hand side shows plots of the southern hemisphere gradients. The figure was obtained by zonally

averaging all of the available CLAES data for each yaw cycle in 1992, taking the absolute value of the

derivative, and then using the north-looking yaws to construct the northern hemisphere plots and the

south-looking yaws to construct the southern hemisphere plots, Averaging over entire yaw cycles

results in a smooth and fairly low resolution contour plot. AIthough low resolution, the figure provides a

good picture of the seasonal evolution of the tropical and vortex barriers throughout the lower

stratosphere.

Figure 5.5.1 shows that at 21.5 km the northern hemisphere vortex sets up in December at about

70 degrees North. Peak gradients in N20 move south through January and February to about 60 degrees

North. The vortex structure weakens in March and April. There is a weaker but definite tropical barrier

centered at about 22 degrees North Which is Strongest in January_and February. Between the Vortex and

tropical boundaries there is a region of relatively weak N20 gradients which has been identified as the

"surf zone," where planetary wave breaking, dissipation, and mixing is sffongest [Mclntyre and Palmer,

1984]. The tropical barrier moves north in March, and by the end 6f April the vortex and trop{ca! barri':

ers have merged. The summer is thus characterized only by a high latitude arid a 10W latitude ?egion,

with one transport barrier between them. This single structure persists until the end of October, at which

time distinct tropical and vortex edges begin to reform.

The two remaining northern hemisphere plots in Figure 5.5. ! show the evolution of the N20 gradients at 27 and 32 km. At 27 km there is a more pronounced northern hemisphere vortex than at 21.5 kin,

but at 32 km the plot shows a considerably weakened vortex. The gradients defining the boundary

between the tropics and midlatitudes increase in both the 27 km and the 32 km plots.

The right-hand side of Figure 5.5.1 shows the time evolution of the N20 gradients in the southern

hemisphere. At 21.5 km the boundary between the tropics and midlatitudes is more persistent than in the

northern hemisphere. The gradients defining the boundary between the southern hemisphere polar

398
vortexandmidlatitudesarealsosomewhatdifferentfromthenorthernhemisphere. At higheraltitudes

thegradientsarestrongerearlierintheyear.Also,atboth27kmand32kmthepeakvaluesof theN20

gradientsaresmallerthanat21.5km.

Becausethepotentialvorticitybutnotthelatitudeof anairparcelis conservedin theabsenceof

mixingprocesses, a parcel'spotentialvorticityis abetterindicatorof itstropical,midlatitude,orpolar

identitythanits latitude.Sinceonly thelatitudeis consideredwhenit is includedin a zonalmean,

parcelswithtracerconcentrationscharacteristicof dynamicallydifferentregionscanbeaveragedover.

Thezonalmeanthereforeartificially"mixes"theseregions,resultingin weakenedmeridionaltracer

gradients.

A meridional coordinate which explicitly accounts for potential vorticity conservation would therefore be more appropriate than latitude for separating these regions and quantifying the actual rate of

mixing between them. Equivalent latitude is such a meridional coordinate. The equivalent latitude characterizing a parcel of air can be defined as the latitude bounding a circle of area A centered over the

pole, where A is the area enclosed by the potential vorticity contour intersecting the parcel in question

[Nash et al., 1996]. Averaging tracer observations along lines of constant equivalent latitude produces a

2 dimensional picture of the atmosphere similar to the zonal mean but with meridional gradients that are

more characteristic of the actual amount of mixing between air masses.

Figure 5.5.2 shows CLAES N20 gradients as a function of equivalent latitude in the northern and

southern hemispheres at 21.5 km, 27 km, and 32 km. Note that the contouring of this plot is more

widely spaced than in Figure 5.5.1 to avoid too tight contours. The equivalent latitude N20 gradients

characterizing the northern hemisphere boundary between the winter polar vortex and the midlatitudes

are the most different from the zonal mean gradients shown in Figure 5.5.1. Peak gradients have approximately doubled and the location of the gradient varies less in equivalent latitude than in latitude. The

gradients defining the edge of the tropics are somewhat weaker than the zonal mean N20 gradients at

this location. The southern hemisphere polar vortex boundary is very similar in the latitude and equivalent latitude coordinates.

Figure 5.5.2 is probably most appropriate for comparison to atmospheric models. The 2D models

participating in this intercomparison are technically zonal mean modeIs, suggesting that Figure 5.5.1 is

the correct comparison. However, a zonal mean model reproduces atmospheric zonal mean gradients by

irreversible mixing. During times when the atmosphere is zonally asymmetric, zonal means can average

over two distinct unmixed regions. This leads to zonal mean gradients which are weaker than the actual

gradients separating the two regions. The result is that if a 2D model reproduces the atmospheric zonal

mean gradients, the mixing between the two regions will be overestimated. To correctly model atmospheric fluxes under zonally asymmetric conditions it may be necessary to maintain gradients in the 2D

models which resemble those in Figure 5.5.2. A zonal mean of a 3D model tracer distribution could

average over dynamically distinct regions as is the case with the atmosphere, provided that it reproduces

the observed zonal asymmetry. In this situation Figure 5.5.1 would be the more appropriate comparison

for 3D models. However, if the 3D model is essentially zonally symmetric, then reproducing the gradients in Figure 5.5.1 would imply an overestimate of the mixing between the two regions similar to the

2D models.

Figures 5.5.1 and 5.5.2 can be compared with N20 gradients at 22 km, 28 km, and 32 km from the

8 2D and 4 3D models shown in Figure 5.5.3, 5.5.4 and 5.5.5, respectively. The left hand plots in the

figures show the northern hemisphere gradients and the right hand plots show the southern hemisphere

gradients for each of the models. There is a wide variation in nature of the vortex and tropical boundaries shown in these plots. The LLNL and NOCAR models reproduce the observed N20 gradients most

399
convincingly.Theybothshowverystronggradientswhichsometimesexceedthoseof theobservations,

andtropicalandvortexboundarieswhichcanbeidentifiedfairlyeasily.Thesetwomodelstendtoplace

theirvortexboundariesequatorwardandtheirtropicalboundariespolewardof theirobservedlocation.

In bothhemispheresthemodelpolarvorticessetupanddecayearlierathigheraltitudes,asobserved.

The GSFC-2D, CSIRO, and LARC-3D models show the development of relatively well-defined and

well-placed vortex and tropical boundaries at most altitudes which are generally weaker than is

observed. The remainder of the models reproduce the observed tracer gradients less consistently than

the above five models. For instance, the SLIMCAT model produces good southern hemisphere vortex

gradients which resemble the observations at all altitudes. However, the model northern hemisphere

vortex boundary is weak and poleward of its observed location, and it has no well-defined tropical

boundaries. The HARVARD model appears to have no northern or southern hemisphere vortices at any

altitude. The 3D models tend to have less well-defined tropical boundaries than the 2D models.

Comparing the 3D model output with the CLAES N20 gradients in Figures 5.5.1 and 5.5.2 may be

unfair. The smoothness of the CLAES contour plots results from significant averaging in time. Short

time scale dynamical features in the observations have been eliminated in this process. The model

outputs to which these data are compared have not been time averaged. This does not much affect the

2D models because they are not designed to reproduce dynamical features on sub seasonal time scales.

The 3D models are however, and as a result show more small-scale structure than the smoothed

observations.

5.5.3 High Latitude Seasonal Cycle of N20

The high latitude seasonal behavior of long lived tracers is strongly affected by the annual formation and breakup of the polar winter vortices, which isolate columns of rapidly descending air from their

surroundings. Mixing from lower latitudes also affects the high latitude tracer isopleths, however, and

sometimes this effect can be significant even when a well-defined vortex is present. For instance,

Strahan et al. [1996] note that CLAES CH 4 isopleths descend more rapidly than N20 isopleths during

times of strong horizontal mixing. They attribute this difference to weaker horizontal CH 4 gradients.

The net flux of CH 4 into high latitudes from horizontal mixing is smaller than that for N20, resulting in

faster isopleth descent.

The seasonal cycle of modeled and observed long-lived tracers will reflect the combined effects of

vertical descent and mixing, so comparison of the seasonal cycles can reveal the extent to which models

capture the interplay between these high latitude transport processes.

The left-hand plots in Figure 5.5.6 show the seasonal cycle of CLAES N20 in 1992 at high northern

latitudes, at 21.5 km, 27 km, and 32 km in altitude. The right-hand plots show the corresponding cycles

in the southern hemisphere. The crosses show averages over the UARS data in approximately 12 day

time periods averaged in latitude between 75 degrees and 80 degrees. The asterisks show averages

between 75 degrees and 80 degrees of equivalent latitude. The error bars included on the equivalent latitude averages indicate the geophysical variability of the observations included in the averages.

In the northern hemisphere at 21.5 km, N20 reaches peak values of about 200 ppbv in December.

The equivalent latitude averages show decreasing values through January and February, reaching a

minimum in March of about 75 ppbv. There is almost a factor of 2 difference between the equivalent

latitude and zonal mean averages at this time, indicating a significant zonal asymmetry in the atmosphere. The equivalent latitude averages show an annual amplitude of about 125 ppbv while the zonal

mean values have an amplitude of less than half this amount. At higher altitudes the peak values

decrease to about 110 and 40 ppbv at 27 and 32 kin, respectively. At 27 km there is a factor of 3 to 4

400
difference in February and March between the zonal mean and equivalent latitude averages, resulting in

an equivalent latitude annual amplitude of about 100 ppbv. At 32 km the difference between the zonal

mean and equivalent latitude averages is still important, and the annual amplitude is a much smaller

30 ppbv at 32 kin. The timing of the mixing ratio peak at 27 and 32 km moves earlier in the year, to near

the end of May in both cases.

Figure 5.5.7 shows the northern hemisphere seasonal cycles in N20 for 8 2D and 4 3D models at

80 degrees, at 22 km, 28 km, and 32 kin. At all levels there is a wide variation amongst the models in

the amount of N20, the magnitude of the seasonal cycle, and the timing of the extrema. The models

typically underestimate the amplitude of the seasonal cycle. There is a tendency for the minima to

occur in the late winter or early spring which is consistent with the observations. The maxima appear to

occur earlier in the year than is indicated by the observations at 22 kin, but later in the year at 28 and

32 km than the observed seasonal cycle suggests. Overall, the LARC-3D model compares best with the

observations, but in general the models do not convincingly simulate the northern hemisphere high latitude seasonal cycle of N20.

The right-hand plots in Figure 5.5.6 show the southern hemisphere high latitude seasonal cycle

observed by CLAES at 21.5, 27, and 32 km. The amplitude of the cycle at all altitudes is significantly

larger than its northern hemisphere counterpartl This illustrates the point that although wintertime

descent is larger in the northern compared to the southern hemisphere [Rosenfield et al., 1994;

Rosenlof, 1995], the relative lack of meridional mixing in the southern hemisphere results in larger

changes in tracer isopleths ]Bacmeister et al., t995]. There is also much less difference between the

equivalent latitude and zonal mean averages during the winter, indicating a more zonally symmetric

southern hemisphere vortex.

The CLAES N20 data for the June/July and August/September yaw cycles are problematic due to a

low temperature bias in the CLAES temperature retrieval and frequent occurrence of thick polar stratospheric clouds. The low temperature bias causes higher N20 values to be retrieved. The bias is about

1-2 Kelvin at typical stratospheric temperatures and is as much as 3 Kelvin inside the southern hemisphere winter vortex. Schoeberl et al. [1996] compared N20 from CLAES with values inferred from

HALOE CH 4 observations on August 17, 1992, and found HALOE to be about 35% lower than the

CLAES values. Because the low temperature bias is itself a function of temperature, the N20 values in

the southern hemisphere at high latitudes may be about 10% higher during the colder winter and spring

yaw cycles than during other times of year. Polar stratospheric cloud interference tends to reduce N;O

and is probably the cause of the large error bars seen at 21.5 km and 27 km from June to September

[A. Roche, personal communication].

CLAES N20 mixing ratios should be unaffected by PSC formation and changing temperature

biases through the end of April. Thus it appears that N20 mixing ratios increase through at least April at

21.5 km and are relatively constant through this time at 27 km. This occurs even though descent over

the southern hemisphere pole is strong from March through September between 20 and 30 km

[Rosenfield et al., 1994; Rosenlof, 1995]. Because the N20 decreases strongly with altitude during the

entire time period, the N20 isopleths must be substantially affected by meridional mixing during the

fall. As noted in Strahan et al., [1996], this means that polar descent cannot be reliably estimated at

these altitudes from changes in isopleth altitudes.

Figure 5.5.8 shows the seasonal cycles of N20 in the southern hemisphere from the models. As in

the northern hemisphere there is a significant intermodel variability in the N20 seasonal cycle at all altitudes, and no model represents the seasonal cycle of N20 particularly well at all altitudes. The amplitude of the seasonal cycle is typically underestimated. There is an interesting but weak anticorrelation

401
between correctly representing the overall magnitude of the seasonal cycle and its amplitude. For

instance, the AER and SUNY-SPB models compare well with annually averaged N20 mixing ratios but

have very weak seasonal amplitudes. The NOCAR model has one of the largest amplitudes, but N20

mixing ratios are much too low for most of the year. The model meridional N20 gradients seen in Figures 5.5.3, 5.5.4, and 5.5.5 in comparison with the observed gradients in Figures 5.5.1 and 5.5.2 show

that the SUNY-SPB and AER models have very weak meridional gradients throughout the year suggesting large mixing of N20 at all times. Consequently a fair amount of N20 is transported to high latitudes but the amplitude is weak. The NOCAR model shows a strong N20 gradient throughout the year,

even during the summer when the observed N20 gradients are weak. Since there is never a time when

meridional mixing of N20 to high latitudes is large, the overall amount of high latitude N20 is low. In

order to correctly capture the seasonal cycle in N20 at high latitudes it is necessary to represent well the

seasonal variability in meridional mixing in addition to the correct magnitude of the wintertime polar

boundary.

At 22 km the LARC-3D model seasonal amplitude is largest and is about 65% of the observed

amplitude. Note that if the CLAES N20 observations are indeed too high due to retrieval errors, the seasonal amplitude seen by the LARC-3D model at 22 km is very good. At 32 km, the N20 amplitudes in

the SLIMCAT and LARC-3D models compare well with the observations. There is a lot of variability

in the timing of the peak, but there is a tendency for the peak values to occur earlier at higher altitudes,

as is observed in the data. There is also a tendency for the model minima to occur in the late spring as

observed, but there is considerable variability in the timing of the minima. At higher altitudes the minima typically occur earlier in the year, as is seen in the observations.

5.5.4 Meridional N20 Gradients at 20 km from ER-2 Observations

Observations of a number of important atmospheric constituents were made from the ER-2 aircraft

during several missions to the northern and southern hemisphere polar regions. The AASE II mission

took data in the 1992 northern hemisphere winter and spring [Anderson and Toon, 1993]. We can compare these observations to the model simulations, which were designed to simulate 1992. The AAOE

mission measured constituents in the southern hemisphere in the winter of 1987 [Tuck et al., 1989].

Although this is not the year simulated by the models, interannual variability in the southern hemisphere

is smaller than in the northern hemisphere. Useful comparisons between these observations and the

models should be possible. The aircraft measurements are made at altitudes in the range of 18 kilometers, several kilometers lower than the CLAES observations described above. The ER-2 data thus provide information about an important part of the atmosphere somewhat below the range of the satellite

observations.

Figure 5.5.9 compares January northern hemisphere model N20 mixing ratios as a function of latitude with the N20 distribution obtained by averaging all of the data from January 1992 ER-2 flights into

5 degree wide latitude bins. Figures 5.5.9a - 5.5.9c show 20 km model results overplotting the ER-2

data, which is the same in each plot. The crosses show the ER-2 data. The error bars show the standard

deviation of all the observations in each bin, which indicates the geophysical variability of the data.

Figure 5.5.9d shows only the ER-2 data, this time averaged as a function of equivalent latitude. This

average shows the expected sharper gradients compared to the latitude averages at both the tropical and

vortex edges. Because the data were taken on different days and at similar longitudes, The ER-2 latitude

averaged N20 is not technically a zonal mean.

The ER-2 observations indicate that N20 values of about 100 ppbv are typical within the 1992

January northern hemisphere polar vortex. The model values vary from about 75 ppbv to about

220 ppbv. Most models err on the high side, due either to too rapid mixing or too weak descent within

402

m

r
the vortex. The NOCAR and the LLNL models have vortex edge gradients that are similar to those seen

in the data, although the LLNL edge is about 7 degrees south of its placement in the ER-2 data. The

other models show weak edges or none at all. No consistent difference between the 2D and the 3D models is seen in this comparison.

Figure 5.5.10 is similar to Figure 5.5.9 except that it compares model N20 mixing ratios at 20 km

with AAOE observations for the month of September, 1987. The observations also show that N20 concentrations around 100 ppbv characterize this time of year and altitude. Model values range between

about 60 and 250 ppbv. The ER-2 observations suggest a vortex edge at about -55 degrees - -60 degrees,

which is consistent with the location of the edge as defined by gradients in the 1992 CLAES N20 observations at 21.5 km. The most convincing model vortex edges are those seen in the 2D LLNL model and

the 3D LARC model. Models which do not resolve the vortex edge well typically have too large high

latitude N20 mixing ratios in comparison with the observations.

5.5.5 Meridional Cly Gradients Inferred from ER-2 N20 Observations

Woodbridge et al. [1995] provide a method for inferring the amount of Cly that exists at some location from a colocated N20 observation. Figure 5.5.11 shows the northern hemisphere Cly mixing ratios

inferred by this method from the AASE II N20 data presented in Figure 5.5.9. The Cly mixing ratios

calculated by the models at 20 km are also plotted in Figure 5.5.11. The GSFC-2D, LLNL, NOCAR,

HARVARD, and AER models all have reasonable amounts of high latitude Cly in comparison with

inferred values, while the rest of the models have about 60% of the N20-inferred Cly. The AER and

HARVARD models do not represent the meridional structure of the Cly distribution well, showing very

little evidence of a midlatitude surf zone at this altitude.

Figure 5.5.12 compares ER-2 inferred Cly from AAOE N20 data in the southern hemisphere with

model calculations. ER-2 estimates of Cly in the southern hemisphere are similar to northern hemisphere values. Model Cly mixing ratios range from only 1 ppbv in the UNIVAQ-2D model to over

3 ppbv in the HARVARD model. High latitude Cly concentrations in the GSFC-2D, LLNL, and

NOCAR models are within 15% of the ER-2 inferred values, and most of the models fall within 35% of

the inferred concentrations.

5.5.6 High Latitude Seasonal Cycle of HNO 3

High latitude gas phase HNO 3 concentrations respond to the amount of descent within the wintertime polar vortex, the amount of mixing from lower latitudes, the sequestration of NOy into condensed

phases, and the amount of irreversible denitrification which occurs due to particle sedimentation over

the course of the winter. Comparison of model HNO 3 concentrations with observations will not test the

individual adequacy of a model's representation of these processes but will show their overall effect.

Figure 5.5.13 compares observed high latitude northern hemisphere HNO 3 with 6 2D and 3 3D time

series. The HARVARD and CSIRO models are not included in this comparison because they do not

include denitrification in their models. The crosses represent CLAES HNO 3 observations between

75 degrees and 80 degrees North averaged over 10 to 12 day intervals for the year of 1992. The asterisks

which lie nearly on top of the crosses represent CLAES HNO 3 observations between 75 degrees and

80 degrees in equivalent latitude, averaged over the same time intervals. The plus/minus 1 sigma error

bars associated with the equivalent latitude averages do not account for instrumental error and indicate

the geophysical variability of the observations used to construct the average values. There is little difference between equivalent latitude and latitude averages even in February and March when there are large

403
differencebetweenzonalmeanandequivalentlatitudeaveragedN20.Thisisprobablyduetotherelativelyweakhighlatitudemeridionalandverticalgradientsin HNO3atthesealtitudes.

Peakvaluesof about12ppbvof HNO3occurinJanuaryinthenorthernhemisphereanddecreaseto

minimumvaluesof about8ppbvinlateJulyandearlyAugust.ThemodelsplottedinFigure5.5.12 generally do a good job of representing the overall magnitude of HNO 3, the amplitude of the seasonal

cycle, and the tinting of the extrema. A notable exception is the NOCAR model, which overestimates

both the magnitude and the amplitude.

Figure 5.5.14 shows a time series of CLAES HNO 3 at about 21.5 km at high southern latitudes,

compared with 6 2D and 3 3D models. The figures show that CLAES observed HNO 3 increases from

about 7 ppbv to around 11 ppbv during the southern hemisphere summer, peaking in the early fall. At

this time HNO 3 mixing ratios begin to decrease rapidly. Note that this is probably not due to dynamical

processes because N20 at this altitude continues to increase throughout the winter. HNO 3 mixing ratios

decline throughout the fall and winter, reaching minimum values between 1 and 2 ppbv toward the end

of the winter. A recovery to values of about 4 ppbv occurs by the end of spring.

The 9 models included in this comparison show behavior similar to the observations. There is a

fairly significant variability in the timing of peak HNO 3 concentrations, with most models late by 1 to

several months. The model fall peak mixing ratios are generally within about 50% of the CLAES observations, with a general tendency to be low. The NOCAR and the GSFC-2D models significantly overestimate HNO 3 mixing ratios in the late southern hemisphere spring, with peak values 3 to 5 times higher

than the observations. This may be due to descent of high HNO 3 mixing ratios from above in these two

models, which would be consistent with the decreasing N20 mixing ratios observed in these two models

at this time and altitude. It may also be due to weak irreversible denitrification resulting in the return of

gas phase HNO 3 from the condensed phase when HNO3-rich PSCs evaporate.

Most of the model HNO 3 time series are similar to the observed HNO 3 time series, with the exception of the NOCAR model. The agreement between modeled and observed HNO 3 is significantly better

than the agreement between modeled and observed N20. Peak modeled HNO 3 mixing ratios tend to

occur later in the year than the observations indicate, with the model minima similarly phase-shifted.

The amplitude of the model HNO 3 mixing ratios is generally weaker than that seen in the observations.

5.5.7 Summary and Conclusions

Atmospheric dynamical processes establish the background environment in which polar chemical

processes occur and determine how the polar chemistry influences the rest of the globe. It is thus important for the processes to be well-represented in models hoping to accurately predict the response of the

atmosphere to perturbations. We have used observations of several atmospheric constituents sensitive to

polar processes to evaluate how well the modeled polar processes reproduce the features seen in the

observations. We have not attempted to explicitly evaluate the adequacy of any particular polar process.

The meridional first derivative of CLAES N20 mixing ratios clearly highlights vortex and tropical

edges, and is therefore a useful diagnostic of model performance. Significant differences between zonal

mean and equivalent latitude averages show that vortex wobble significantly affects northern hemisphere high latitude zonal means during winter. There is not as much of an effect in the southern hemisphere high latitudes or in the tropics of either hemisphere.

The observed structure of long-lived atmospheric tracers are not consistently well-simulated in

either 2D or 3D models. There is a significant amount of intermodel variability, with some models

404
doing much better than others. There is a tendency to underestimate the strength of the gradients in both

2D and 3D models, but the strong gradients seen in the 2D NOCAR and LLNL models indicate that 2D

models do not necessarily underestimate the observed gradients. It is probably desirable in a 2D model

to reproduce the N20 gradients seen in the CLAES equivalent latitude averages. Doing so may produce

more accurate fluxes between dynamically distinct atmospheric regions. Correctly representing the

fluxes is probably more important to predicting how the atmosphere will respond to a perturbation than

the correct zonal mean gradients. These conclusions need to be explicitly demonstrated in modeling

studies, however.

The seasonal cycle of N20 is not well-simulated in most models. The seasonal amplitude of the

cycle is underestimated by the models at 21.5 and 27 kin, while at 32 km there is better agreement. Peak

N20 values generally occur too early in the year compared to the observations. The timing of the minimum varies considerably from model to model. The models generally do succeed in placing higher altitude extrema earlier in the year than at lower altitudes, consistent with observations.

Estimates of Cly inferred from ER-2 observations suggest values between 2.5 and 3 ppbv characterize the polar latitudes in both the northern and southern hemispheres. There is more variability in the

model northern hemisphere Cly than in the southern hemisphere. Some models significantly underestimate Cly in both the northern and southern hemispheres, but the GSFC-2D, LLNL, NOCAR, HARVARD, and AER models agree reasonably well with Cly in both hemispheres.

Model simulations of the seasonal behavior of HNO 3 are in reasonable agreement with CLAES

HNO 3 mixing ratios at 21.5 km in both the northern and southern hemispheres. The weak meridional

gradients in HNO 3 at high latitudes apparently make it easier to simulate well HNO 3 concentrations.

There is no evidence from the data of extensive sequestration of HNO 3 at high northern latitudes in

1992.

Observed HNO 3 in the southern hemisphere decreases when N20 concentrations are still increasing, suggesting that the decreases are due to condensation rather than dynamical processes. The models

differ significantly in the timing of the onset of decreasing HNO 3 values, typically being late by up to

several months. The GSFC-2D and NOCAR models recover after the annual HNO 3 minimum in the

southern hemisphere to values which are 3 to 5 times observed values. This may be due to too rapid

descent or to too weak irreversible denitrification. Other models show a slower recovery of HNO 3 in

better agreement with the observations.

References

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20, 2499 - 2502, 1993.

Bacmeister, J. T., M. R. Schoeberl, M. E. Summers, J. R. Rosenfield, and X. Zhu, Descent of long-lived trace

gases in the winter polar vortex, J. Geophys. Res., 100, 11,669 - 11,684, 1995.

Eluszkiewicz, J., D. Crisp, R. Zurek, L. Elson, E. Fishbein, L. Froidevaux, J. Waters, R. G. Grainger, A. Lambert,

R. Harwood, and G. Peckham, Residual circulation in the stratosphere and lower mesosphere as diagnosed

from Microwave Limb Sounder data, J. Atmos. Sci., 53,217 - 240, 1996.

McIntyre, M. E., and T. N. Palmer, The "surf zone" in the stratosphere, J. Atmos. Terr. Phys., 46, 825 - 849, 1984.

Nakamura, N., and J. Ma, Modified Lagrangian -mean diagnostics of the stratospheric polar vortices 2. Nitrous

oxide and seasonal barrier migration in the cryogenic limb array etalon spectrometer and SKYHI general

circulation model, J. Geophys. Res., 102, 25,72! - 25,735, 1997.

405
Nash, E. R., P. A. Newman, J. E. Rosenfield, and M. R. Schoeberl, An objective determination of the polar vortex

using Ertel's potential vorticity, J. Geophys. Res., 9471-9478, 1996.

Roche, A. E., et al., Validation of CH4 and N20 measurements by the cryogenic limb array etalon spectrometer

instrument on the upper atmosphere research Satellite, J. Geophys. Res., 101,9679 - 9710, 1996.

Rosenfield, J. E., P. A. Newman, and M. R. Schoeberl, Computations of diabatic descent in the stratospheric polar

vortex, J. Geophys. Res., 99, 16,677 - i6,689, i994.

Rosenlof, K. H., Seasonal cycle of the residual mean meridional circulation in the stratosphere, J. Geophys. Res.,

100, 5173 - 5191, 1995.

Schoeberl, M. R., A. R. Douglass, S. R. Kawa, A. E. Dessler, P. A. Newman, R. S. Stolarski, A. E. Roche, J. W.

Waters, and J. M. Russell, III, Development of the Antarctic ozone hole, J. Geophys. Res., 101, 20,909 -

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Geophys. Res., I01, 26,615 - 26,629, 1996.

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1989. :

Woodbridge, E. L., J. W. Elkins, D. W. Fahey, L. E. Heidt, S. Solomon, T. J. Baring, T. M. Gilpin, W. H. Pollock,

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406
CLAES NH d[NzO]/dLAT, 21.5 KM, PPBV/DEG

so___o.o '- .....

20_oo --

0 60 120 180 240 300 360

DAY OF YEAR

NORTH LOOKING YAWS ONLY

CLAES NH d[N20]/dLAT, 27 KM, PPBV/DEG

j i i i

80 o ol

60 120 180 240 .300 360

DAY OF YEAR

NORTH LOOKING YAWS ONLY

CLAES NH d[N20]/dLAT, 32 KM, PPBV/DEG

60

4o

L " '- '-

0 60 t20 180 240 300 360

DAY OF YEAR

NORTH LOOKING YAWS ONLY

CLAES SH d[NzO]/dLAT,

O i t i

-20

-40

21.5 KM, PPBV

-60

-BO

/DEG

_ -40

2

i--

5

-60

i _ , i 0.01 _i

0 60 120 1BO 240 300 360

DAY OF YEAR

SOUTH LOOKING YAWS ONLY

CLAES SH d[N_O]/dLAT, 27 KM, PPBV/DEG

o

-BO

o

-20

_ -40

-60

-80

"_ 0.01 k

t l I |

60 120 180 240 300 360

DAY OF YEAR

SOUTH LOOKING YAWS ONLY

CLAES SH d[N20]/dLAT, 32 KM, PPBV/DEG

_j,_

60 120 t80 240 300 360

DAY OF YEAR

SOUTH LOOKING YAWS ONLY

Figure 5.5.1. Absolute value of first derivative of CLAES version 8 N20 mixing ratios with respect to latitude.

The six plots show the seasonal changes in the derivative in the northern hemisphere (left side) and the southern

hemisphere (right side) at 21.5 km (top), 27 km (middle), and 32 km (bottom). Contours are 1, 2, 3, 4, 5, 6, 7, 8, 9,

and 10 ppbv/degree latitude. The plots were made by first calculating zonal mean averages of N20 for each yaw

cycle with 5 degree latitude resolution, and then taking the first derivative of the resulting distributions. The 5

north-looking yaw cycles in 1992 were used to construct the northern hemisphere plots, while the 5 south-looking

yaw cycles were used for the southern hemisphere.

407
CLAES NH d[N20]/dEQLAT ,

60 1.oo

w

o

__6o

_, 40

0

uJ 20

21.5 KM, PPBV/DEG CLAE',

o

0 60 120 180 240 300 360

DAY OF YEAR

NORTH LOOKING YAWS ONLY

CLAES NH d[N20]/dEQLAT , 27 KM, PPBV/DEG

i i _ i i i

g

i

,#_ _\ \-

0 60 120 180 240 300 360

DAY OF YEAR

NORTH LOOKING YAWS ONLY

CLAES NH d[N20]/dEQLAT , 32 KM, PPBV/DEG

80- ' ' ' v ' ' __

i

0 60 t 20 180 240 300 360

DAY OF YEAR

NORTH LOOKING YAWS ONLY

-2O w

I--

7-

:_-4o

5 -60

-80

SH d[N20]/dEQLAT , 21.5 KM, PPBV/DEG

l.O0 _

_ 2.00

I

0 60 120 180 240 300 .360

DAY OF YEAR

SOUTH LOOKING YAws ONLY

CLAES SH d[N20]/dEQLAT , 27 KM, PPBV/DEG

o _._. ,_

0 60 120 180 240 300 360

DAY OF YEAR

SOUTH LOOKING YAWS ONLY

CLAES SH d[NzO]/dEQLAT , 32 KM, PPBV /DEG

- _._-_" )'d_d_L_ _.__

 ,ooJ5 %%_.<v>

I,OOC

- 0

1 I f I I

0 60 120 180 240 300 360

DAY OF YEAR

SOUTH LOOKING YAWS ONLY

-20

-._ -40

,<

),

-60

o

-20

:5 -,to

_ -60

-80

Figure 5.5.2. Absolute value of first derivative of CLAES version 8 N20 mixing ratios with respect to equivalent

latitude. The six plots show the seasonal changes m the derivative in the northern hemisphere (left side) and the

southern hemisphere (right side) at 21.5 km (top), 27 km (-middle), and 32 km (bottom). Contours are l, 2, 3, 4, 6,

8, 10, and 12 ppbv/degree latitude. The plots were made by first calculating equivalent latitude averages of N20

for each yaw cycle with 5 degree latitude resolution, and then taking the first derivative of the resulting distributions. The 5 north-looking yaw cycles in 1992 were used to construct the northern hemisphere plots, while the 5

south-looking yaw cycles were used for the southern hemisphere.

408
GS2

9o

75

6o

_- 45

NH d[N20]/dLAT, 22

I

KM, PPBV/DEG

I--- •

.J .; ---%o.-

0 2 4 6 8 10 2

MONTH OF YEAR

LLNL NH d[N20]/dLAT, 22 KM, PPBV/DEG

9O ' ' ' ",,...// ' |

.30 --__9_ _

15_

1.00

0 _ 2100_I ' '

0 2 4 6 8 10 12

MONTH OF YEAR

NOCR NH d[N20]/dLAT, 22 KM, PPBV/DEG

9 o _Z.do_ _ , _ .i.00_ '

75 --_ "

,-_ 45--j

5

lsl- -=- ) _ _'-i

'.oo-/

O|_'l i i t _ /

0 2 4 6 8 10 12

MONTH OF YEAR

w

P,

:3,

,- -45

I--

GS2 SH d[NzO]/dLAT, 22 KM, PPBV/DEG

eL,....

.oO_

-15 _-- a.oo _

.--..-3.00

_ %°(

-75 - %o\/7/

-90 _ , , ,

0 2 4 6 8 10 12

MONTH OF YEAR

LLNL SH d[N20]/dLAT, 22 KM, PPBV/DEG

; 0-- l i i

°1_"°__ . _1

_ -45

-60 -

-90 , , , ' '

0 2 4 6 8 10 2

MONTH OF YEAR

NOCR SH d[N20]/dLAm, 22 KM, PPBV/DEG

-45/_

-60

-75 -

r_-_ o,o!

0 2 4 6 8 10 2

MONTH OF YEAR

Figure 5.5.3. Absolute value of the first derivative of model N20 distributions with respect to latitude at 22 km in

the northern hemisphere (left side) and southern hemisphere (right side) for the 8 2D and 4 3D models mentioned

in the chapter text. Units are ppbv/degree and contours are the same as Figure 5.5.1. The abscissae tick marks indicate the end of the month.

409
UAQ2 NH d[N20]/dLAT, 22 KM, PPBV/DEG

oo| u .... b-

\ j -

so -_ _ _/-

0 2 4 6 8 10 12

MONTH OF YEAR

6O

LIJ

_- 45

i.-5

NYSP NH d[N20]/dLAT , 22 KM, PPBV/DEG

75 -) '

__ 1.oo__

I I i i i

15

0

0 2 4 6 8 10 t2

MONTH OF YEAR

AER NH d[N20]/dLAT , 22 KM, PPBV/DEG

_ _.oo

o / , , _ _.oo_/

0 2 4 6 8 10 12

MONTH OF YEAR

-30

I.lJ

C3

m- -45

I--

w

p5

-3O

Q

_ -45

p-5

UAQ2 SH d[N20]/dLAT, 22 KM, PPBV/DEG

0 r _ r i p

-,,-_,.oo_J

-_

-60 __ _'--

, J , L ,I 1 -90

0 2 4 6 8 10 12

MONTH OF YEAR

NYSP SH d[N20]/dLAT, 22 KM, PPBV/DEG

0 ; 4 i l i l

-15 --"---- l.O0 _

3.00

-60 --_ -- -75 -- '°° --_

-90 , , J , i

0 2 4 6 8 10 12

MONTH OF YEAR

AER SH d[N20]/dLAT, 22 KM, PPBV/DEG

°....

-- 1.00 -------.__

-is --_ ° __

-- .0_

I I I I I

2 4 6 8 10 12

MONTH OF YEAR

-90

0

Figure 5.5.3. Continued.

410
60

:D

_- 45

HARV NH d[N20]/dLAT, 22 KM, PPBV/DEG

90 , i

75--

-- 1.00

-- 2.00

3.00

-- 4.00 -----.......

30

15

90

75

6O

::)

45

I--

/-

/

/--

3O

15

0

/

'

0 I I I I 1

2 4 6 8 10 2

MONTH OF YEAR

CSI NH d[NzO]/dLAT, 22 KM, PPBV/DEG

/ _" / /1.oo----_

- -

2 4 6 8 10 2

MONTH OF YEAR

HARV SH d[N20]/dLAT, 22 KM, PPBV/DEG

-15

-30

I.iJ

i.- -45

i-

-60

i t i i i

-- 1.00 -------_

-- 2.00 _ /

_4.00 ------_ "-"----- i/

-- 5.00 ------_ 1

11 5,00

t.oo

-90 , i , l l

0 2 4 6 8 10 12

MONTH OF YEAR

CSI SH d[N20]/dLAT, 22 KM, PPBV/DEG

'

-90 , _ l _

0 2 10 12

-30

0

P" -45

i-

-60

4 6 8

MONTH OF YEAR

Figure 5.5.3. Continued.

411
60

ILl

a

__ 45

LARC NH d[NzO]/dLAT, 22 KM, PPBV/DEG

",_ ---'liLy's° 0

75 ,-{

_'_ .

. ©o

15 p.

0

0 2 4 6 8 10 i2

MONTH OF YEAR

GISS NH d[N20]/dLAT, 22 KM, PPBV /DEG

60

r-,

45

I-.

5

0 I 1 t: I I

0 2 4 6 8 10 12

MONTH OF YEAR

SLIM d[N20]/dLAT, 22 KM, PPBV/DEGREE

0 i J I z i I'00 _1

0 2 4 6 8 10 12

MONTH OF YEAR

2

F5

-30

w

Cl

I.- -45

i..-

-60

LARC SH d[NzO]/dLAT, 22 KM, PPBV/DEG

°----'L'°°] ' ' ' ' _ '

-45 (>

0 2 4 6 8 10 12

MONTH OF YEAR

GISS SH d[N20]/dLAT, 22 KM, PPBV/DEG

0 ' ' ' ' ' /

1

-15 --

0 2 4 6 8 10 12

MONTH OF YEAR

SLIM d[NzO]/dLAT, 22 KM, PPBV/DEGREE

0 _ 1 i i 1

-15 -- ' o_

- (>"_ _

_

I,

-30

-45

5

-60

-75

-90

0

(

I I I I

2 4 6 8 10 12

MONTH OF YEAR

Figure 5.5.3. Continued.

412
GS:

90

75

6O

_- 45

I--

3O

15

0

0

NH d[N20]/dLAT , 22 KM, PPBV/DEGREE

O0

I I I I

2 4 6 8 10 12

MONTH OF YEAR

-3O

I.J

g

-45

i5

GS3 SH d[N20]/dLAT , 22 KM, PPBV/DEGREE

-15 --

__,._ _

-60 -

-75 -

-90

o 2

I I I I

4 6 8 10

MONTH OF YEAR

12

Figure 5.5.3. Concluded.

413
GS2 NH d[N20]/dLAT, 28 KM, PPBV/DEG

I I I I I

so |

75 _-t-Oo

0 ' "_-- '

0 2 4 6 8 10 12

MONTH OF YEAR

LLNL NH d[N20]/dLAT, 28 KM, PPBV/DEG

90[ ' "%-°,/ _---..'I '

l_j .... °'>:

15 _.oo

oI_ _-___, ",..,

0 2 4 6 8 10 12

MONTH OF YEAR

NOCRNHd[N20]/dLA'r,28 KM,PPeV/DEG

90 .....

4.oo _"_

15

0 __ ' "---i'--_ ".00I 4/

0 2 4 6 8 10 2

MONTH OF yEAR

GS2 SH d[N20]/dLAT, 28 KM, PPBV/DEG

or-- , , , , -,..___.__

i%;-oo 

2 -

_ -45 _ .

3 S

-75 -- "

-90 , i i i

0 2 4 6 8 10 12

MONTH OF YEAR

LLNL SH d[N_O]/dLAT, 28 KM, PPBV/DEG

I------4:_>"/\IIL _. __<>" I

-30L-s'°°-'%'_'//._--- -)_-I

-75

-90

0 2 4 6 8 10 12

MONTH OF YEAR

NOCR SH d[N20]/dLAT, 28 KM, PPBV/DEG

-15 -"_.oo _'_/_'___--

- 4.00

o %o\

-45

5

-60

-75

-9o _ , [------,o.o_

2 4 6 8 10 2

MONTH OF YEAR

Figure 5.5.4. Absolute value of the first derivative of model N20 distributions at 28 km in the northern hemisphere

(left side) and southern hemisphere (right side) for the 8 2D and 4 3D models mentioned in the chapter text. Units

are ppbv/degree and contours are the same as Figure 5.5.1. The abscissae tick marks indicate the end of the month.

414
UAQ2 NH d[N20]/dLAT, 28 KM, PPBV/DEG

,j , ooj ' I'

75 __ ,V.¢ A 7"00\--

,., / O

0 "

0 2 4 6 8 10 12

MONTH OF YEAR

NYSP NH d[N20]/dLAT, 28 KM, PPBV/DEG

1.00

75

_ .o

0 _ 1 I l |.J

0 2 4 6 8 10 12

MONTH OF YEAR

AER NH d[N20]/dLAT, 28 KM, PPBV/OEG

90 ' ' ' ' ' I//

-4 75--

_ 1.00 _

60-- .

-<°12

15 -_---_-_ _ s._ ---

o , , ""<' ,"_'.-_--_-

0 2 4 6 8 10 2

MONTH OF YEAR

UAQ2 SH d{NzO]/dLAT, 28 KM, PPBV

0

-15

-30

hi

:3

i- -45

I--

-6O

-75

-90

IDEG

0 2 4 6 8 10 12

MONTH OF YEAR

NYSP SH d[N20]/dLAT, 28 KM, PPBV/DEG

Oi i i i i l

_15____/ i.O0

-- -- _--__ ! ,00

-90 _ _ _ _ _

0 2 4 6 8 10

MONTH OF YEAR

-15

-30

hi

(:3

:3i- -45

-60

-75

-90

0

AER SH d[N20]/dLAT, 28 KM, PPBV/DEG

0 _l.ull- _' _ O0 _2.oo _ _'"

3.00 --4.00

_- 4.oo--

/

_5,0 O t

L I I I I

2 4 6 8 10 12

MONTH OF YEAR

Figure 5.5.4. Continued.

415
HARV NH d[NzO]/dLAT , 28 KM, PPBV/DEG HARV SH d[N20]/dLAT , 28 KM, PPBV/DEG

,orJ ..... | o,_-- 1.00 ..... f, /--t

8oF-2oo--___. -__ -3or- __

2 45 4 oo

-45 6.00_ -

30- - :

15 _ ..-----4.oo _75___i.oo.... -

3.00 _--

_.00

0 t I _ _.oo _ -90 L __ i i , i

0 2 4 6 8 10 12 0 2 4 6 8 10 12

MONTH OF YEAR MONTH OF YEAR

CSI NH d[N20]/dLAT , 28 KM, PPBV/DEG CSI SH d[N20]/dLAT , 28 KM, PPBV/DEG

90_ , ' , ' ' ) _2. _'/ _ o _-1 .oo'_ -- _.oo-.----_I I--2.oo

,o : ,o :

0 "-'--'----w- _.nr__ , -:-------_.9 -- -90 l _ _ j _ i

0 2 4 6 8 10 12 0 2 4 6 8 10 12

MONTH OF" YEAR MONTH OF YEAR

Figure 5..5.4. Continued.

416
6O _

_ 45

I--

LARC NH d[N20]/dLAT, 28 KM, PPBV/DEG

9 o './_._ ' _- _'.oo_' ' /.

0 ,

0 2 4 6 8 10 1

MONTH OF YEAR

GISS NH d[N20]/dLAT, 28 KM, PPBV/DEG

90 _ _ _'_---_oo i -,.: -_

,5L-o C)#- v >LJ60 _.oo _--

2 4s

_or_.o_ __/-_ \-

0 2 4 6 B 10 12

MONTH OF YEAR

SLIM d[N20]/dLAT, 28 KM, PPBV/DEGREE

90

_- 45

, ,L._,.oo

i-.-

5

3O

15

0

0 2 4 6 8 10 12

MONTH OF YEAR

2

r--

5

-3O

___.-45

i-.

5

0 _ I .(}0 -t- ' '

0 2 4 6 B 10

MONTH OF YEAR

LARC SH d[N20]/dLAT, 28 KM, PPBV/DEG

-6o

-75

-9o

-3(

I.i.I

tm

-4_

I--

-60

-75

-90

0

12

GISS SH d[N20]/dLAT, 28 KM, PPBV/DEG

;IO

0 2 4 6 8 10 2

MONTH OF YEAR

SLIM d[N20]/dLAT, 28 KM, PPBV/DEGREE

O i i i i

_._Ooo_

I I I I I

2 4 6 8 10 12

MONTH OF YEAR

Figure 5.5.4. Continued.

417
LzJ

Cl

i- 45

I--

GS3 NH d[N20]/dLAT, 28 KM, PPBV/DEGREE

9oi-_;o-,."....

75 --

60 --

-) _ ?-____

30-_ '_/_ ?.o/

is ___j- ,.oo\

O' I I I I I

0 2 4 6 8 10 12

MONTH OF YEAR

GS3 SH d[N20]/dLAT, 28 KM, PPBV/DEGREE

OF--- , , , , ,

_1 i_ I .oo 

-_oI--"°°---_ (

_.----

U

-75 _'_..-r_

-go _ _ ,

0 2 4 6 8 10 12

MONTH OF YEAR

Figure 5.5.4. Concluded.

418
60

hi

CI

:3i.- 45

iI

GS2 NH d[N20]/dLAT, 32 KM, PPBV/DEG

15 °.Oo_-

_ _

0 2 4 6 8 10 2

MONTH OF YEAR

15/_ " -_-_,._',. \_

0 2 4 6 8 10 12

MONTH OF YEAR

NOCR NH d[NzO]/dLAT, 32 KM, PPBV/DEG

90 ' .... I

I

0

-15

-30

2 -4s

-60

-75

-90

0

GS2 SH d[N20]/dLAT, 32 KM, PPBV/DEG

=__.oo .9 --

2 4 6 8 10 12

MONTH OF YEAR

LLNL NH d[N20]/dLAT, 32 KM, PPBV/DEG LLNL SH d[N20]/dLAT, 32 KM, PPBV/DEG

,o ' '_ o.o, ' ' ' °i,/_.oo-_

75 - 15 [-j 3.00 y

-45 --

h-

-75 -- "

/_-o.o, --.x

-90 , , /, ,

0 2 4 6 8 10 12

MONTH OF YEAR

NOCR SH d[N20]/dLAT, 32 KM, PPBV/DEG

75-z'_\ -4 -15- / 4Z'v__.-_.% -_--

- F- - ./- i:o- ,:_

o/----_ _--_---_,._ ..... -90 ---1,, , ,I ,= ,

0 2 4 6 8 10 12 0 2 4 6 8 10 2

MONTH OF YEAR MONTH OF YEAR

Figure 5.5.5. Absolute value of the first derivative of model N20 distributions at 32 km in the northern hemisphere

(left side) and southern hemisphere (right side) for the 8 2D and 4 3D models mentioned in the chapter text. Units

are ppbv/degree and contours are the same as Figure 5.5. I. The abscissae tick marks indicate the end of the month.

419
5

UAQ2 NH d[N20]/dLAT, 52 KM, PPBV/DEG

,o_j,j LL

3ol-_';?o°o _-------_ _ _ f-1

0 2 4 6 8 10 12

MONTH OF YEAR

NYSP NH d[N20]/dLAT, 32 KM, PPBV

90

75

6O

I,M

Q

45

I.-

30

2

UAQ2 SH d[NzO]/dLAT, 32 KM, PPBV/DEG

o _,oo_ ' _'

-30 - . _2.oo _-

-45 -_ _'.oo --

,o. , , ,..

0 2 4 6 8 10 12

MONTH OF YEAR

15

2

IDEG

, _ v, //'°o,/ '/F

' _

7, /,...... /

2 4 6 8 10 12

MONTH OF YEAR

-30

W

Q

-45

r-

-60

NYSP SH d[N20]/dLAT, 32 KM, PPBV/DEG

o L'.oo/, )'

-90

0 2 4 6 8 10 12

MONTH OF YEAR

AER NH d[N20]/dLAT, 32 KM, PPBV/DEG

90 ' ' ' ' ' /

75-

1.00

30 _j'¢-O0 .,=

I _._

15 _C_" -_ _ _--

0 2 4 6 8 10 12

MONTH OF YEAR

-15

-30

i,=

Q

_- -45

-60

-75

-90 J I l I

0 2 4 6 8

MONTH OF YEAR

AER SH d[N20]/dLAT, 32 KM, PPBV/DEG

o _-_#__,_

--. ,__.I_<_

__,._o_

,._

I

10 12

Figure 5.5.5. Continued.

420
6O

g

_- 45

rHARV NH d[N20]/dLAT, 52 KM, PPBV/DEG

90 , , , , ,

75-

_ 1.00 _

_ 2.00

--_"-" 3.00

=

_-" 4.00 -

- .30 - _ <oo

15 _ _ 3.oo--

__ 2.00 --

_""--_----'-'-_ 1.00

0 i 1 J J ,L

0 2 4 6 8 10 12

MONTH OF YEAR

CSI NH d[N20]/dLAT, 32 KM, PPBV/DEG

90 ' ' ' ' '

/ ',

75 - 8 l-oo --_

15

0

0 2 4 6 8 10 12

MONTH OF YEAR

6O

g

_- 45

-75

-90

0 2 4 6 8 10 2

MONTH OF YEAR

CSI SH d[NzO]/dLAT, 32 KM, PPBV/DEG

._-_!!I |2_ J ,.oo___@.__\,%__ "'_

-901 , , I i i

0 2 4 6 8 10 12

MONTH OF YEAR

Figure 5.5.5. Continued.

421
==

LARC NH d[N20]/dLAT, 32 KM, PPBV/DEG

' ' ' 1

60 .oo

b,.]

O

o _-,_W ,-__ , ,"</

0 2 4 6 8 10 12

MONTH OF YEAR

GISS NH d[NzO]/dLAT, 32 KM, PPBV/DEG

,sl-O0 ;_-1

0 2 4 6 8 10 2

MONTH OF YEAR

SLIM d[N20]/dLAT, 32 KM, PPBV/DEGREE

90 _ ) '

_ 4s

3O

,\ .... 0 !

0 2 4 6 8 10 12

MONTH OF YEAR

-3O

hi

r',

:3

_ -45

LARC SH d[N20]/dLAT, 32 KM, PPBV/DEG

o -_ _._

- 15 --_ 1.oo/.._ f _ _-.._

_.oo f _ x__=___

W /_ .\\_ ao I

-90

0 2 4 6 8 10 12

MONTH OF YEAR

GISS SH d[N20]/dLAT, 32 KM, PPBV/DEG

O__o_) C;.oo"

-15 - _----L_@

-I.oo _i.oo _--_"_ -_.oo_-'>--, C o _ ./

_'_" r--

0 2 4 6 8 10 12

MONTH OF YEAR

SLIM d[N20]/dLAT, 32 KM, PPBV/DEGREE

0 - -- ' ""-----L_.O0 "J ' - ) )

_'_= f.O0

-9oL _ , , ,

0 2 10 t2

-30

L=J

_- -45

I--

5

-60

-30

LIJ

E3

i- -45

i-

-60

4 6 8

MONTH OF YEAR

Figure 5.5.5. Continued.

422
GS3 NH d[N20]/dLAT, 32 KM, PPBV/DEGREE

_oI--,-°o_....

75t_ <_

oo_-_<

45--

1-

30 --

15 -

0 _i I I I I

2 4 6 8 10 2

MONTH OF YEAR

GS3 SH d[NzO]/dLAT, 32 KM, PPBV/DEGREE

-t5 --

_2

__o_,y_ -

-_t- /:,._o_

_ |_,.oo/

-75_ --

I ._..2po.. l "o0 l

-90 _. _ _ ,

0 2 4 6 8 10 12

MONTH OF YEAR

Figure 5.5.5. Concluded.

423
300

250

2OO

g

150

1 O0

5O

0

0

200

160

120

80

4O

0

0

150

125

100

g

%

z

5O

25

0

0

CLAES NzO 75°N - BooN, 21.5 KM

i _ i = i

I I I I I

2 4 6 8 10

MONTH OF YEAR

CLAES N20 75°N - BooN, 27 KM

t2

+

l I I I I

2 4 6 8 10

MONTH OF YEAR

CLAES NzO 75°N - BooN, 32 KM

7

300

250

2OO

_150

10O

5

0

200

160

120

%z 80

4O

0

12 0

150

125

CLAES N20 75°S - 80°S

IO0

>

a. 75

o

£

2

0

21.5 KM

I

/

I I I I I

2 4 6 8 10

MONTH OF YEAR

C LAlcS N20 7505 - 800S, 27 Kk4

r i _ i T

I I I J!

2 4 6 8 10

MONTH OF YEAR

CLAES N20 75°5 - 80°S, 32 KM

i i i _ i

2 4 6 8 10 12 2 4 6 8 10

MONTH OF YEAR MONTH OF YEAR

12

12

12

Figure 5.5.6. Seasonal cycle of CLAES version 8 N20 in 1992 for the northern hemisphere (left side) and southern

hemisphere (right side) at 21.5 km (top), 27 km (middle), and 32 km (bottom). Each of the points represent an

average using approximately 12 days of data. Crosses indicate zonal mean averages between 75 degrees and

80 degrees. Asterisks represent equivalent latitude averages between 75 degrees and 80 degrees equivalent latitude. Error bars are plotted for the equivalent latitude points and indicate plus/minus 1 standard deviation. No

instrumental error has been considered so the error bars indicate the statistical (and perhaps geophysical) variability of the data. The abscissae tick marks indicate the end of the month.

424
3OO

250

200

150

o

Z

100

I ,

"- -., ,/ /

50 F --. J

0 1 z

0 2 4

200

MODEL NzO, 80°N, 22 KM

160

120

z" 8o

4O

_-._ j_ __

\

150

125

100

.=

_. 75

o

Z

I I I I

6 8 10 12

MONTH OF YEAR

MODEL NaO , 80°N, 28 KM

\ J

_-.1-" /

0 I I I I I

0 2 4 6 8 10 12

MONTH OF YEAR

MODEL N20, 80°N, 32 KM

' ' ' GS2 '

LLNL

..... NOCR

UAQ2

NYSP

-_--_-AER

\ /

O/ -- -- r i l i .... _'_ -/

0 2 10 12

z"

200

MODEL N20 , 80°N, 22 KM

300 _ , , ,

250

200

0 2 4 6 8 10 12

MONTH OF YEAR

MODEL N20, 80°N, 28 KM

I I I I I

160

m_"12°

o

z 80

4O

150

125

1 O0

Q- 75

z

50

0

0

25

0

2 4 6 8 10

MONTH OF YEAR

MODEL NzO, 8O°N, 32 KM

4 6 8 4 6 8

MONTH OF YEAR MONTH OF YEAR

I _ l I I

A-----_LARC

_----DSLIM

x----><GS3

, c-----oGISS 1

0 2 10 12

Figure 5.5.7. Seasonal cycle of N20 at 80 degrees North for 8 2D and 4 3D models (see chapter text to determine

which model is 2D and which is 3D). The left hand plots show the behavior of 6 models, and the right hand plots

show the behavior of the remainder at 22 km (top), 28 km (middle) and 32 km (bottom). The abscissae tick marks

indicate the end of the month.

425
300

250

200

15o

o

Z

IDO

200

5O

0

0

t 60

120

>

(:O

0

ff 8o

4o

150

125

1oo

==

75

o

Z

50

25

MODEL N20, 80°S, 22 KM

I I I I J

I I I I I

2 4 6 8 t0

MONTH OF YEAR

MODEL N20, 80°S, 28 KM

I t I I I

12

/ \

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432
5.6 Chlorine Partitioning and Ozone in the Polar Vortex

Anne R. Douglass

NASA Goddard Space Flight Center

5.6.1 Introduction

A model calculation of the impact on ozone of polar processes during winter depends on the amount

of inorganic chlorine, the extent to which the chlorine is activated, and the partitioning of the chlorine

species. In this section, model ozone and C10 in the high latitude winter lower stratosphere are compared with MLS observations of ozone (Froidevaux et al., 1996) and CIO (Waters et ai., 1996). The CIO

depends on model descent, which affects total inorganic chlorine, the model scheme for release of reactive chlorine from the reservoirs HC1 and C1ONO 2 in the presences of PSC's or cold sulfate, and on the

partitioning between C10 and the dimer C1202. The ozone depends upon descent as well as chemical

loss, which takes place largely through the dimer cycle.

Here, comparisons are made for the northern and southern winters for 6 two-dimensional models

(AER, GSFC-2D, SUNY-SPB, UNIVAQ-2D, NOCAR, LLNL) and 3 three-dimensional models

(LARC-3D, SLIMCAT, and GSFC-3D (northern winter only)).

5.6.2 Southern Hemisphere - Chlorine

Comparison of Cly

In 5.5 (Dynamical Signatures in Long-Lived Tracers) the model Cly at 20 km is compared with Cly

as derived by Woodbridge et al. [1995] from observations of N20. The pair of model profiles of Cly for

September and November are shown for the altitude range 14-26 km at 75S in Figure 5.6.1. The models

are quite different in their Cly profiles. Although several of the models ( GSFC-2D, LLNL, NOCAR)

have reasonable amounts of Cly at 20 km 75S (> 2.5 ppbv) comparable to the inferred values as discussed above, the profile shapes are quite different. Except for UNIVAQ-2D and SUNY-SPB, all of the

models show CIy between 2 and 3 ppbv at 20 km. At lower altitudes, the models diverge. NOCAR only

falls to 2 ppbv by 14 kin; the other models which are similar to the observations at 20 km are between

0.5 ppbv and 1.5 ppbv at 14 km. All of the models converge at higher altitude. The difference reflects

the difference in descent and the amount of horizontal mixing in the models, which is also seen in the

comparisons with N20. Several of the models (NOCAR, LLNL, GSFC-2D, LARC-3D) show continued

descent between September and November, which is identified by an increase of 0.15 ppbv or more at

18 km.

Chlorine Activation

The models differ in the extent to which chlorine is released from its reservoirs HCI and C1ONO 2

and found in CIO and its dimer. The ratio (C10 + 2"C1202)/C1y for September 15 at 75S is given in Figure 5.6.2. The models differ in both the maximum value of the activation and in the vertical extent over

which polar processes are important. NOCAR, SLIMCAT, GSFC-2D and UNIVAQ-2D show a ratio as

high as 0.8 somewhere in the profile. For UNIVAQ-2D, this is found between 22 and 24 km. For

NOCAR and SLIMCAT, the region where activation is important is broad; the ratio is between 0.8 and

433
0.9 for the range 14 and 22 km. For GSFC-2D, the region of high activation is narrow, 16 - 18 km. For

the other models, the activation is significantly lower, and in the case of the AER model, it is confined

in altitude as well.

Model Comparisons with MLS CIO

As expected given the Cly profile differences and the differences in the activation, the model C10

profiles show significant variance. The daytime average C10 profiles on Sept. 15 at 65S and 75S are

compared with MLS daily zonal averages at 46 hPa for a 10 degree latitude band centered at 65S and

75 S for Sept. 5-21, 1992 in Figure 5.6.3. The daily zonal averages are shown on the figure for both latitudes; the CIO average decreases nearly monotonically with time. From MLS observations [Waters

et al., 1996], we expect that CIO is less than a few tenths ppbv at 100 hPa (-16 km), and 22 hPa

(-27 kin) (not shown on this figure). At 75S, the models tend to underpredict C10, with the exception of

the NOCAR model; however, only NOCAR shows CIO greater than 1 ppbv as low as 15 km. At 65S,

the models are more consistent with the observations, partly because there is much less active chlorine

(observed or modeled). Again, NOCAR has the highest C10. This is consistent with the high Cly (and

low N20) found at high latitudes, and also consistent with the high chlorine activation.

Partitioning of CIO/Cly and Cl202/CIO

The models also show variance in the partitioning between C1202 and C10, which is shown for September 15 at 75S in Figure 5.6.4. The amount of variance is surprising, given that these two constituents

are simply related through the formation reaction for C120 2 which should be balanced by its thermal

decomposition and photolysis. The models also show variance in C10/Cly (Figure 5.6.5). The NOCAR

model has the highest ratio over the largest range. LARC-3D, AER, SLIMCAT and GSFC-2D all show

CIO/CIy to be about 0.4 between 18 and 20 km; the models diverge from one another at higher and

lower altitude.

The bottom line for this section is that there are several key areas which are different among the

models and which affect C10. The total Cly, the fraction of activation, and the partitioning among chlorine species all differ significantly. In comparing with the MLS CIO, at 75S only the NOCAR and the

LARC-3D model have a peak value close to the observations. Knowledge (observations) of the profile

of total inorganic chlorine and the efficiency of activation would provide a key discriminator among the

models.

5.6.3 Southern Hemisphere - Ozone

The daytime zonal average ozone is given as a function of time at I00, 46 and 22 hPa in

Figure 5.6.6 for 65S and Figure 5.6.7 at 75S for 8 models and for MLS observations. The model ozone

differs from observations for all models in significant ways. The seasonal change at the highest MLS

level shown (22 hPa) is generally underestimated - only the SLIMCAT model shows comparable low

values in June and a strong buildup in December. MLS shows low ozone at 46 hPa in early September

which decrease steadily. None of the models show as strong temporal decrease of ozone as observed by

MLS at 20-22 km. The model ozone difference is generally smaller than observed by MLS. At 100 hPa,

several of the models show a strong decrease in ozone, and agree fairly well with MLS at the end of the

year. Only the NOCAR model approximates the decrease in ozone between day 250 and day 300, but

this model also greatly exceeds the observed ozone around day 180.

434
The model change in ozone is somewhat related to the model C1202 and CIO, but there is significant

variance among the models. The maximum change in ozone between October and September for each

model is plotted as a function of the September C10 at that level in Figure 5.6.8(a) and as a function of

the September C1202 at that level in Figure 5.6.8(b). This is a simplistic view of the ozone change, but

the average Cly changes in the lower stratosphere over the month (Figure 5.6.1) are small, suggesting

limited influence of descent and mixing. The model change is directly related to the magnitude of the

CIO and C1202, but the broad dependence shown in Figure 5.6.8 reflects the differences in the models

that also affect the partitioning. The differences in the models and the modeled ozone change are a result

of differences in the total cly, in the polar processes though which chlorine is activated, and the partitioning between C10 and C1202.

5.6.4 Northern Hemisphere - Chlorine

Comparison of Cly

The Cly profiles at February at 75N are shown in Figure 5.6.9. The models show a wide range for

Cly, and a somewhat different profile shape. As in the southern hemisphere, this is indicative of the difference in the model descent and horizontal mixing. As in the southern hemisphere, the model calculated ozone toss will depend sensitively on Cly, the degree of activation, and the partitioning between

C10 and C120 2.

Chlorine Activation

All of the models show less chlorine activation in the winter northern hemisphere than in the southern hemisphere. There is still significant variance among the models. At 20 km, the percent of chlorine

in C10 and the dimer ranges from a low of about 10% (GSFC-2D) to a high near 40% (UNIVAQ-2D).

This is shown in Figure 5.6.10.

Model Comparisons with MLS CIO

It is more difficult to compare these models with MLS CIO in the northern hemisphere than in the

southern, since the MLS shows much more variability. Figure 5.6.11 shows the model C10 profiles at

65N and at 75 N, and the daily vortex average MLS C10 for each of 4 years. During every year except

1992, the MLS CIO was much higher than is calculated in these models. GSFC-3D and SLIMCAT use

winds and temperatures appropriate for 1992, and compare well with each other and with the MLS

observations. All of the other models should be producing a climatological value for CIO. One problem

is that in February, the vortex average CIO is elevated partly because the vortex is pushed off the pole to

lower latitude. In the model, there are never psc's at latitudes lower than 60. In the models, the greatest

chance for psc's is in January and February, where there is little sun at high latitudes.

5.6.5 Northern Hemisphere - Ozone

In the northern winter lower stratosphere, ozone can be affected by both transport and photochemistry. Downward motion tends to increase the ozone, and chlorine processes produce ozone loss. In several winters, Manney et al. (1994, 1995) have compared ozone for a conservative calculation with

observations from MLS to derive the chemical ozone change.

435
The MLS zonal average ozone is compared with the model zonal average at 75N for DecemberApril in Figure 5.6.12. For MLS, the vortex average is nearly equal to the zonal average at 75N throughout the time period. The comparisons between MLS and the models at 65N are qualitatively similar.

The vortex average from MLS shows very low values at the end of 1996 and 1997 winters, consistent

with the high CIO found those years. Since the model chlorine activationis more consistent with 1992

than the later years, it is not surprising -that the model ozone does not show such low mixing ratios. It is

perhaps more significant that most of the models show a somewhat different seasonal cycle for 1992

when the ozone loss derived by Manney et al. (1994) is not large. MLS ozone increases in March, and in

most of the models the ozone mixing ratio peaks in February or by March 1. Exceptions are the two of

the 3D models ( SLIMCAT model and GSFC-3D), the NOCAR model and the CSIRO model. The differences among the model seasonal change in 0 3 are reflected in the differences in the among model

seasonal changes in N20; it is important to account for effects of both descent and horizontal mixing as

discussed above.

These northern hemisphere comparisons also illustrate the difficulty in calculating the effects of

polar processes in the northern hemisphere in an assessment model. The effects show=|arge interannual

variability, which is not simulated in climatological models. Models using assimilated data for inputs

(SLIMCAT and GSFC-3D) may reproduce the interannual variability for observed years, but unless an

assessment included meteorological data from many different years for input, these models would not

simulate the interannual variability either. If the response of the model to a perturbation is sensitive to

the model base calculation for the effects of psc's, then an assessment calculation would need to encompass a wide range of base perturbations to cover observed variability and make a reasonable evaluation

of the perturbation sensitivity.

References

Froidevaux, L., W. G. Read, T. A. Lungu, R. E. Cofield, E. F. Fishbein, D. A. Flower, R. F. Jamot, B. P.

Ridenoure, Z. Shippony, J. W. Waters, J. J. Margitan, I. S. McDermid, R. A. Stachnik, G. E. Peckman,

G. Braathen, T. Deshler, J. Fishman, D. J. Hofmann, and S. J. Oltmans, Validation of UARS Microwave Limb

Sounder ozone measurements, J. Geophys. Res., 101, 10017-10060, 1996.

Manney, G. L., L. Froidevaux, J. W. Waters, R. W. Zurek, W. G. Read, L. S. Elson, J. B. Kumer, J. L.

Mergenthaler, A. E. Roche, A. O'Neill, R. S. Harwood, I. MacKenzie, and R. Swinbank, Chemical depletion

of ozone in the Arctic lower stratosphere during winter 1992-93, Nature, 370, 429-434, 1994.

Manney, G. L., R. W. Zurek, L. Froidevaux, and J. W. Waters, Evidence for arctic ozone depletion in late

February and early March 1994,' Geophys. Res. Lett., 22, 2941-2944, 1995.

Waters, J. W., W. G. Read, T. A. Lungu, L. Froidevaux, V. S. Perun, R. A. Stachnik, R. F. Jarnot, R. E. Cofield,

E. F. Fishbein, D. A. Flower, J. R. Burke, J. C. Hardy, L. L. Nakamura, B. P. Ridenoure, Z. Shippony, R. P.

Thurstans, L. M. Avallone, D. W. Toohey, R. L. deZafra, and D. T. Shindell, Validation of UARS MLS CIO

measurements, J. Geophys. Res., in press, 1996.

Woodbridge, E. L., et al., Estimates of total organic and inorganic chlorine in the lower stratosphere from in situ

and flask measurements during AASE II, J. Geophys. Res., 100, 3057-3064, 1995.

436
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Figure 5.6.1 I. The daytime zonal average model C10 at 65N and 75N is compared with vortex averaged daytime

MLS CIO. The northern hemisphere CIO is highly variable from year to year. The models are in overall fair agreement with MLS for 1992 (which was a year with relatively little chemical ozone depletion (Manney et al., 1994)).

None of the models approach the high values seen in 1996 and 1997.

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448
6. Future Atmospheric Perturbation from NOx Injection in the

Lower Stratosphere

Donald Wuebbles

University of IIlinois

The High Speed Civil Transport (HSCT) aircraft emissions studies provide a means to evaluate how

the models being compared respond to a perturbation. The standard scenario of 500 HSCTS (Baughcum

and Henderson, 1998) is used, but with a limited representation of the emissions so as to simplify the

interpretation of the model results for the intercomparison. Emissions of nitrogen oxides corresponding

to an Emissions Index of 10 are used in addition to two different assumptions about the effects of sulfur

emissions. The first case assumes the background level of sulfuric aerosols (SA0) is left unchanged. The

second case (SA1) assumes that the distribution of sulfuric aerosols have been modified by the aircraft

sulfur emissions, with the distribution of aerosol surface area based on the AER model (Weisenstein

et al., 1997). In this case, 50 % of the SO 2 emitted by the aircraft is assumed to be immediately convened to sulfate particles in the plume. Neither case assumes any H20 aircraft emission. The zonal

average of the HSCT NOx emissions used are shown in Figure 6.1, while Figure 6.2 shows the ratio of

the aerosol surface area density for SA1 relative to SA0. All HSCT calculations were done relative to a

"2015" background atmosphere assuming SA0 sulfuric aerosol surface area.

Nine models participated in the HSCT runs, including seven 2-D models and two 3-D models. The

3-D ECHAM3/CHEM model was only used for the SA1 case while the other eight models ran both

cases.

6.1 Change in NOy

Contour plots of the absolute (ppbv) change in NOy for the HSCT perturbation relative to the background atmosphere are shown in Figure 6.3 for the SAI case. The percentage changes in NOy are

shown in Figure 6.4. Only the SA1 results are shown as the changes in NOy for the SA0 and the SAI

cases are essentially the same. The models are similar in having the peak absolute change in NOy at

mid- to high latitudes at altitudes near the peak HSCT emissions. The GSFC-2D and Harvard twodimensional models have the largest changes in NOy with peak mixing ratio changes greater than 2 and

3 ppbv, respectively. These models, along with the three- dimensional ECHAM3/CHEM model, have

the largest mixing ratio changes extending into the southern hemisphere. LLNL has a much smaller

area of 2 ppbv change in NOy, while SUNY-SPB and UNIVAQ-3D peak at a change of about 1 ppbv.

As seen in Figure 6.4, most models have a peak percent change in NOy on the order of 30 %, while the

Harvard, UNIVAQ-2D, and ECHAM3]CHEM models having peaks greater than 50 %.

Table 6.1 shows that the HARVARD and GSFC-2D two-dimensional models also have the largest

changes in total column NOy while UNIVAQ-3D has the smallest change. Figure 6.5 further amplifies

the differences in the aircraft-induced changes in total NOy from the models.

Figures 6.6a, 6.6b, and 6.6c shows the annually-averaged changes in NOy as a function of latitude

at altitudes of 14, 20, and 24 km, respectively. Figure 6.7 shows the change in NOy as a function of alitude at 40 N latitude. All of these analyses support the findings above. In addition, there are several

other interesting findings. UNIVAQ-2D is only model to have non-zero changes in NOy in the tropics

at 14 km, which should be in the troposphere. At 20 km, all of the models have peak changes in NOy at

449
40-50 N. At latitudes greater than about 40 N (see Figure 6.7), the UNIVAQ-3D model actually determines a decrease in NOy for the HSCT perturbation at altitudes greater than 25 km.

The distributions of the absolute changes in NOy shown in Figures 6.3, 6.6 and 6.7 are very similar

to the shape and magnitude of the changes in the HSCT NOx tracer study, run A3. As discussed in the

Transport Experiments chapter, there are strong connections between the HSCT NOx tracer experiment

(run A3) and the analyses of age of air and other transport indicators determined from the distributions

of observable stratospheric tracers. Given these relationships, the derived changes in NOy from the B4

HSCT perturbation runs will be subject to the effects from transport differences discussed in the Transport Experiments chapter. The strong correlation between the change in nitrogen oxides in the A3 and

the B4 runs leads to the conclusion that uncertainties in transport processes are having an important

impact on the abilities of current stratospheric models to evaluate the effects of HSCTs on stratospheric

chemistry.

6.2 Change in NOx and 0 3

Figure 6.8 shows contours of the absolute change in NOx in ppbv for each model. Figure 6.9 shows

the percentage change in NOx relative to the background atmosphere. Figures 6.10 and 6.11 show the

corresponding contour plots for the SA1 case. In examining Figure 6.8, the peak change in NOx is not

as isolated to the region of the aircraft emissions as was found with NOy. GSFC-2D has a peak change

in NOx of greater than 0.5 ppbv between 30 and 40 km in the equatorial region, whereas peak change in

NOy is in the 18-20 km region at mid- to high latitudes (see Figure 6.3). HARVARD has a slightly

smaller peak in the 30 km region in the tropics. ECHAM3/CHEM also has large changes, greater than

0.5 ppbv, at these altitudes, near the upper boundary for this model. The other models all have peak

changes in NOx that are much smaller, with values of 0.15 to 0.3 ppbv.

For this SA0 case, many of the models have surprisingly similar changes in the percentage change

in NOx. Figure 6.9 shows similar 15 % change contours for the AER, GSFC-2D, LLNL, SUNY-SPB,

and CSIRO models. These models have peak changes of about 30-40 %. In contrast, the UNIVAQ

models, - 2D and -3D, have much larger changes, of over 110 % in the tropical region near 20 km in

UNIVAQ-2D (also see Figure 6.12).

Figures 6.8 and 6.10 show that the changes in NOx for the SA0 and SA1 cases have similar contours, but the magnitude of the changes in the lower stratosphere are different in the two cases. This is

expected as a result of the interactions of the additional aerosol surface area density leading to additional heterogeneous reactivity in the SA1 case. However, the effects of the two aerosol density cases

are substantially different in the various models. A comparison of Figures 6.12 and 6.13, giving the

annually-averaged changes in NOx at 20 km for the two cases, shows the AER and LLNL have a

decrease in the percentage change in NOx in going from the SA0 case to the SAI case. In contrast, the

GSFC-2D and UNIVAQ-2D models show an increase in the percentage change in NOx such contrasting changes are also shown in Figures 6.14 and 6.15, which show the annually-averaged changes in

NOx as a function of altitude at 20 N and 40 N, respectively, for the two aerosol cases. The AER model

actually shows a decrease in NOx at many stratospheric altitudes outside the emissions area for the SA !

case. UNIVAQ-3D also shows a decrease but only above 50 N and is the only other model to show this

behavior for the SA 1 case.

Figures 6.16 and 6.17 show the change in the annually-averaged total NOx for the two aerosol

cases. The models generally demonstrate the behavior discussed above. AER is the only model to show

_i globally-averaged decrease in the change in NOx. However, UNIVAQ-3D had a total column

450
decrease in NOx at all latitudes greater than 40 N for the SA0 case (and then showed an overall increase

for the SAI case). HARVARD shows the largest annually-averaged globally-averaged increase in NOx

for both the SA0 and the SAI cases (decreasing from 11.0 % for the SA0 case to 8.0 % for the SA1

case).

The ratio of the change in globally-averaged total NOx to the change in NOy is shown in Table 6.2

for both the SA0 and SA1 cases. For the SA0 case, AER has the smallest ratio (0.039)and SUNY-SPB

the largest (0.197) For the SAI case, UNIVAQ-2D and UNIVAQ-3D have the largest ratios, 0.218 and

0.219 respectively, showing significant increases in the fraction of NOx change relative to total NOy

change compared to other models.

Percentage changes in ozone as a function of latitude and altitude are shown in Figures 6.18 and

6.19 for the two aerosol cases. The models all show regions of increase in ozone in the troposphere and

lower stratosphere, with decreases in ozone above. The altitude of the transition from increase to

decrease changes with latitude and with each model. The models generally have larger regions of

decrease in the SAI case, extending to lower altitudes at high latitudes in most of the models. The

HARVARD two-dimensional model gives local decreases in ozone approaching 5-6 % in the lower

stratosphere at high latitudes in the SA1 case. The other models all have substantially smaller changes

in ozone. The AER two-dimensional model has a decrease in ozone of about 2 % at high latitudes while

other models give 1% or less.

As with NOx, the changes in ozone are substantially different between the SA0 and SA1 cases. Figure 6.20 a and b gives the annually-averaged change in ozone as a function of latitude at 20 km for the

SA0 and SAI cases. Figures 6.21,6.22, and 6.23 give the annually-averaged change in ozone with altitude for 20 N, 40 N, and 60 N for the two cases. The models generally are give changes in ozone that are

less positive or more negative, particularly at high latitudes for the SA1 case. The AER and UNIVAQ2D models give the largest changes in going from SA0 to SA1.

Figure 6.24 and Figure 6.25 give the changes in total ozone for the SA0 and SA1 cases, respectively. For SA0, the models have substantially different patterns in their changes in total ozone. The

Harvard model has much larger decreases than the other models, reaching -3 % at high latitudes in September. UNIVAQ-2D has large increases (-1.5 %) in total ozone at the same period. The other models

range from small increases to small decreases in total ozone at northern latitudes.The SAI results in

Figure 6.25, as expected, are more negative than the SA0 results. Table 6.3(a-b) gives the globallyaveraged changes in total ozone for the two aerosol cases.

The relationship between the changes in NOx and changes in ozone are substantially different in the

various models. Figure 6.26 gives representative examples of the change in NOx plotted relative to the

change in ozone. Shown in Figure 6.26 are the AER, SUNY-SPB, LLNL, and Harvard models for the

SA0 case and AER for the SA 1 case. Some models, like SUNY-SPB and UNIVAQ-2D have very compact relationships. Others like LLNL and AER are more intermediate in their spread. HARVARD and

GSFC-2D have relationships that are much more spread out. Only one SA1 case is shown because the

AER model is indicative of the general conclusion that all of the models tend to maintain very similar

changes in NOx relative to changes in ozone relationships for both the SA0 and SA1 cases. This suggests that the primary differences in this relationship between the models is determined by differences

in transport processes.

451
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5O

8

2O

S_

10

0

-9O

EINOx = 10 *** 500 HSCTs

, , , ,_ [rr-, , , i , , , _ , i , , , , , i , , , , , i , , , , ,

-60 -50 0 50 60 90

Lotitude, deg

Figure 6.1. Aircraft NOx rate - molecules cm -3 sec -l.

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x

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Latitude, deg

Figure 6.2.

90

453
60

50

40  ̧

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N 2O

10--

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5O

40

U 20

10

6O

5O

0

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10

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AER

50

40

3o

N 20

-60 -30 0 30 60 90

LATITUDE (DEG)

GSFC-2D

-_ .

10

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

LLNL

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-2D

0

-90

. ,....-.--_--._

-60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

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30

20

10

0

-90

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50

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10

0

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-60 -30 0 30 60

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HARVARD

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5O

4O

3o

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10

0

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60

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40

30

20

10

0

-90

-60 -30 0 30 60

LATITUDE (DEG)

SUNY-SPB

9O

-60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-3D

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 6.3. NOy DIFF (ppbv); Annual Avg; Run B4 SAI vs Run B4 Base.

454
AER

60 .....

_ 40 "

N 20

10

01 .__-- _:.,_ 2.__ ,.u.u --

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

GSFC-2D

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50 _ _-

40-_

30-

;4 20

10 _- 0

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

LLNL

6o ___- __

50 l- 0.__ -- -

40_ 30

20_.

0

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-2D

60L

50 L_ 0.3 ---" __--_

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

40

30

_4 20

10

0

CSlRO

60

5040- - 0.___ _-

3°i i 20

10

0

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

HARVARD

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

SUNY-SPB

°°Oo - 1

40

3o

N 20

1

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-3D

- _- , _,.<., ...,_ ..._._

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

6O

5O

40

30

t_ 20

10

Figure 6.4. NOy PCT DIFF; Annual Avg; Run B4 SAI vs Run B4 Base.

455
10

2

z_ SUNY-SPB

x LLNL

+ AER

GSFC-2D

o HARVARD

[] UNIVAQ-2D

o UNIVAQ-3D

< CSIRO

Annual Average

I I I I I I I I t I I I I

0 I 1 I t t I I t 1 t I I I 1._ I

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

LATITUDE

Figure 6.5 Total NOy; Run B4; Year 2015 (SAI).

456
3 I I I I I I

Annual Average

I I I I I I I I I I I

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o UNIVAQ-3D

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0 I 1 I I 2___

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

LATITUDE

Figure 6.6a. NOy DIFF; Run B4 SAI; Year 2015; Alt 14 km.

457
41

3

1 I I I 1

z_ SUNY-SPB

x LLNL

+ AER

GSFC-2D

o HARVARD

[] UNIVAQ-2D

[] UNIVAQ-3D

< CSIRO

Annual Average

I 1 I I t t I I t I 1

0 I I I I L I t I I I 1 1 1___

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

LATITUDE

Figure 6.6b. NOy DIFF; Run B4 SAI; Year 2015; Alt 20 kin.

458
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Q.

v

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t-

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3

Annual Average

t I I I I I I 1 I I I I I I

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[] UNIVAQ-3D

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LATITUDE

Figure 6.6c. NOy DIFF; Run B4 SAI; Year 2015; Alt 24 km.

459
6O

55-

50-

45-

40--

35-

E

30-

__=

25-

20-

15-

10-

5-

Annual Average

I 1

z_ SUNY-SPB

x LLNL

+ AER

GSFC-2D

o HARVARD

[] UNIVAQ-2D

u UNIVAQ-3D

< CSIRO

t I _ I I

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Change in NOy (ppbv)

Figure 6.7. NOy DfFF; Run B4 SAI; Year 2015; Lal 40N.

1 t

3 4 5

460
6O

5O

40

30

20

10

6O

5O

40

30

20

10

6O

5O

40

30

20

10

0

-90

6O

5O

40

30

t,,4 20

10

0

-90

AER

0 ' _vo

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

,..r..r_3SFC-2 D

__°

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

LLNL

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LATITUDE (DEG)

UNIVAQ-2D

-60 -30 0 30 60

LATITUDE (DEG)

90

60

5O

40

30

20

10

0 --

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40

30

20

10

0

CSIRO

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6O

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40

30

20

10

0

-90

60

50

_- 40

30

20

10

0

-90

-60 -30 0 30 60

LATITUDE (DEG)

HARVARD

-60 -30 0 30 60

LATITUDE (DEG)

SUNY-SPB

, r------',q--,-,-,_, _

-60 -30 0 30 60

LATITUDE (DEG)

UNIVAQ-3D

,

-60 -30 0 30 60

LATITUDE (DEG)

Figure 6.8. NO + NO 2 DIFF (ppbv); Annual Avg; Run B4 SA0 vs Run B4 Base.

90

9O

9O

90

461
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5O

4o

30

N 20

10

0

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AER

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LATITUDE (DEG)

GSFC-2D

601 ....

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-90 -60 -3O 0 3O 6O 90

LATITUDE (DEG)

LLNL

5O

40

_v 30

20

10

0

-90

60--

5O

40

30

2O

10

0

-90

-60 -30 0 30

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LATITUDE (DEG)

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Figure 6.9. NO + NO 2 PCT DIFF; Annual Avg; Run B4 SA0 vs Run 134Base.

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0

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20

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LATITUDE (DEG)

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5O

40

30

20

10

0

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6O

5O

4O

3O

2O

10

0

-90 -60 -30 0 30 60 90 -90 -60

LATITUDE (DEG)

-60 -30 0 30 60

LATITUDE (DEG)

SUNY-SPB

-60 -30 0 30 60

LATITUDE (DEG)

UNIVAQ-3D

9O

9O

-30 0 30 60 90

LATITUDE (DEG)

Figure 6.10. NO + NO 2 D1FF (ppbv); Annual Avg; Run B4 SAI vs Run B4 Base.

463
60

5O

40

30

20

10

0

-90

6O

5O

40

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20

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-60 -30 0 30 60 90

LATITUDE (DEG)

GSFC-2D

-90 -60 -30 0 30 60 90

6O

5O

40

30

20

10

0

-90 -60 -30 0 30 60

LATITUDE (DEG)

UNIVAQ-2D

LATITUDE (DEG)

LLNL

9O

60

5O

40

3O

N 20

10

0

-90

J

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-60 -30 0 3O 6O

LATITUDE (DEG)

90

6O

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_- 40

30

20

10

CSIRO

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

HARVARD

6O

50

40

3O

2O

10

0

-90

60

5O

40

30

20

10

0

-90

-60 -30 0 30 60

LATITUDE (DEG)

SUNY-SPB

I- 0.6 f.1

6O

50

40

3O

2O

10

o

-90

-60 -30 0 30 60

LATITUDE (DEG)

UNIVAQ-3D

-60 -30 0 30 60

LATITUDE (BEG)

Figure 6.1 l. NO + NO 2 PCT DIFF; Annual Avg; Run B4 SA 1 vs Run B4 Base.

9O

9O

9O

464
0.50

0.25

..O

O_

Q.

v

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o

cO O.OC

(n

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0

Z

+

0

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-0.25

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Annual Average

I t [-- I I I I 1 ; 1 I/e__ _ I I I l I

A SUNY-SPB /

GSFC-2D / \

o HARVARD _

[] UNIVAQ-2D ,_ _,_,., k_ -

- a UNIVAQ-3D _'_ _'%_ "_

cso

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-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

LATITUDE

Figure 6.12. NO + NO 2 DIFF; Run B4 SAO; Year 2015; Ah 20 km.

465
0.25

(31.

0

C

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0

z

+

0

z

Annual Average

0.501 I I I I I I I I I I I I

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zx SUNY-SPB

x LLNL

+ AER /

GSFC-2D _ _ 7

o HARVARD /' _/\

[] uN,vAQ-2o _ /

- u UNIVAQ-3D Z /_

-0.25

-0.50i _ 1 I 1 1 l I I 1

1 I 1 1 t

I 1 t.__L [ J I L___

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

LATITUDE

Figure 6.13. NO + NO 2 DIFF; Run B4 SA 1; Year 2015; Alt 20 km.

!

=

i

[

E

466
Annual Average

A SUNY-SPB

x LLNL

+ AER

GSFC-2D

o HARVARD

[] UNIVAQ-2D

[] UNIVAQ-3D

< CSIRO

I I L 1 I I

10 20 30 40 50 60 70

Pct Diff in NO+NO2

] I

80 90

Figure 6.14a. NO + NO2; Run B4; Year 2015; Lat 20N (SA0).

100

467
6O

55-

50-

45-

4

q

4

40 - _J

I!

[

35-

E

v

30- "- 'o

< ÷

25-

"k

11

20-

15-

10- !

m

5-

I

|

O__J

-10 0

I I

Annual Average

t I I 1 I I

A SUNY-SPB

x LLNL

+ AER

GSFC-2D

<> HARVARD -

El., UNIVAQ-2D

UNIVAQ-3D

< CSIRO

I I I I I t

30 40 50 60 70 80

Pct Diff in NO+NO2

Figure 6.14b. NO + NO2; Run B4; Year 2015; Lal 20N (SAI).

9O

J

100

|

i

_=

=

=

468
40

60 --

55 -

[

[

50 -

45-

35

E

v

30

25

20

Annual Average

I I t I l I

I I I

Z_ SUNY-SPB

x LLNL

+ AER

GSFC-2D

o HARVARD

D UNIVAQ-2D

UNIVAQ-3D

< CSIRO

15

10

5

0

-10

I I t I I I

10 20 30 40 50 60 70 80

Pct Diff in NO+NO2

Figure 6.15a. NO + NO2; Run B4; Year 2015; Lat 40N (SA0).

I

90 100

469
2O

15

10

0

-10

Annual Average

I t 1 1 t I 1 I [

A SUNY-SPB

x LLNL

+ AER

GSFC-2D

O HARVARD

[] UNIVAQ-2D

a UNIVAQ-3D

< CSIRO

I I I I I I

10 20 30 40 50 60 70 80

Pct Diff in NO+NO2

Figure 6.15b. NO + NO2; Run B4; Year 2015; La! 40N (SAI).

1

9O 100

i

E

g

_=

470
12 I I I I I I

Annual Average

I I 1 I I

I 1 t I

10

,x SUNY-SPB

x LLNL

+ AER

GSFC-2D

<> HARVARD

[] UNIVAQ-2D

UNIVAQ-3D

< CSIRO

o

-2 t I I I I I I I I 1 1 t L I I I

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

LATITUDE

Figure 6.16. Total NOx; Run B4; Year 2015 (SA0).

471
I t I t

_. SUNY-SPB

x LLNL

+ AER

GSFC-2D

o HARVARD

[] UNIVAQ-2D

o UNIVAQ-3D

< CSIRO

Annual Average

1 I I I I I I I t I I I I

!

Z

i

i

z

_-=

-2L 1 I t I I I I I I

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

LATITUDE

Figure 6.17. Total NOx; Run B4; Year 2015 (SA1).

472
AER

60 . .: -,

50'-" ............. -- "......

_;;_ ...........

::..:.:::::::: :::::....0.4.....

40

_. 30_

20

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

GSFC-2D

.....

_ ::::::::::::::::::::::::::::::::::::ZU 30 [_I_N ' '-"'-:::::: :: ::::: .... . .... _::---:" :::-:-.-.--:. ----

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

LLNL

60

50

40

3O

2O

10

0

-90

60

_3

....=========================================== ...

-60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-2D

50

40

30

_4 20

10

0

-90

0'.__j

°.

° ............ . ...... .-, ..

-:::1:::--:---: ..... ,--.-:-:-:-:............. 0.4.-.

-60 -30 0 30 60 90

LATITUDE (DEG)

CSIRO

6°I ' t

50

.or l

60"

50

40

30

_4 20

10

0

-90

-90 -60 -30 0 30 60

LATITUDE (DEG)

HARVARD

-60 -30 0 30 60

LATITUDE (DEG)

SUNY-SPB

5060 p______ , /--7_-__<>_

_ 20

10 -0.8_ (

0 _0.8

-90 -60 -30 0 30 60

LATITUDE (DEG)

UNIVAQ-3D

6oL" . ._

5oF_ f__

N 20 "

-90 -60 -30 0 30

LATITUDE (DEG)

90

Figure 6.18.03 PCT DIFF; Annual Avg; Run B4 SA0 vs Run B4 Base.

90

90

6O 9O

473
AER

60 ...... ' ..; .: ..

50 -............ "--. ............. ".".i;:-.-._._..-..-..-_.'.-.-=0"4 --

_. 40 /-_6.4:.:::_J. :: .-:...

20 ........_.

10o ,

-90 -60 -30 0 30 60: 90

LATITUDE (DEG)

GSFC-2D

.......

50 ....................

...... -----:":-:--::- ::6-4-::--::i

40 ...... i :-.:-""""i'i.--_'i.-i --: :0._-.-_ _"- ___

30 : ----"- : ' _......,-,-;:-;::,:,:.:,[_:.._. "......

N :::::-"-).___

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

LLNL

6O

5O

20 _-'["'"'.::2:.., _'L_''__'/:':'[:::":'_-::':::::

10 -_.,__i i'-_ 0 ,_.------':--'_, 0.,0 _ ,

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-2D

60 ' ' _ I

5O

_._"3040i;::!:-i:Oi4\! "--: :::: :..-.::::::::::.-'"-:-:--:-:.:- 0.4-..!

20-. ..... ,0" :::.:.:..:_;.:.:.:.:.

0

-9( -60 -30 0 30 60 90

LATITUDE (DEG)

CSIRO

6°f

50 -

.......... -.... ,: ....... o.a-::::

201 --0._.!!!::' : - -.:::.--'i':':: : :-

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

HARVARD

60 f>

5O

_- 40

30

20

10

6O

5O

40

30

20

10

0

-90

6O

5O

_- 4o

_ 3o

_ 20

10

0

-90

. ...:.:.:.:-.:-.:-- --:--:---:-;-:--- - -

0 - 0.0' _ I,-"::-:--:--:--::

-90 -60 -30 0 30 60 90

LATITUDE (DEG)

SUNY-SPB

_.°-°-*, .....

........... :::::::::::::::::::::::::::::

: ::-0-,4"- :.:_..--:-:-:-:--:- "

-60 -30 0 30 60 90

LATITUDE (DEG)

UNIVAQ-3D

-60 -30 0 30 60 90

LATITUDE (DEG)

Figure 6.19.0 3 PCT DIFF; Annual Avg; Run B4 SAI vs Run B4 Base.

474
,O

tE)

"5

13,.

('O

O

Annual Average

10 I I I I I I I I I l I I -I I I I [ --

9- ,", SUNY-SPB

x LLNL

8--

+ AER

7- _ GSFC-2D

6- <> HARVARD

E- UNIVAQ-2D

5 --

UNIVAQ-3D _...e__

4

32

]

0

-1 --

-2 --

-90 -80 -70 -60 -50 -40 -30 -20 -10 0 10 20 30 40 50 60 70 80 90

LATITUDE

Figure 6.20a. 03 Pet DIFF; Run B4; Year 2015; Alt 20 km (SA0).

475
10

9

8

I 1- 1

- A SUNY-SPB

x LLNL

+ AER

- • GSFC-2D

o HARVARD

13 UNIVAQ-2D

-- r'l UNIVAQ-3D

< CSIRO

Annual Average

t t i i i i i I I i _ t i i

0

__ l__ I I 1 I 1 L I I I 1 _J_ t I t 1 I

-90-80-70-60-50-40-30-20-10 0 10 20 30 40 50 60 70 80 90

LATITUDE

Figure 6.20b. 03; Pct DIFF; Run B4; Year 2015; Alt 20 km (SA1).

476
E

v

6O

55-

50-

45-

40-

35-

30-

25-

20-

15-

10-

5-

0

-5

I I I

Annual Average

I 1

•'_ SUNY-SPB

x LLNL

+ AER

GSFC-2D

HARVARD

UNIVAQ-2D

o UNIVAQ-3D

< CSIRO

\

I

-4

1 I 1 I

-3 -2 -1 0 1 2 3

Pct Diff in 03

Figure 6.21 a. 03; Run B4; Year 2015; Lat 20N (SA0).

477
60 I

55-

50-

45-

40-

35-

E

v

-_ 30-

<

25-

20-

15-

10-

5-

0 I

-5 -4

Annual Average

I t I

t I I t

-3 -2 -1 0 1 2

Pct Diff in 03

Figure 6.21b. 03; Run B4; Year 2015; Lat 20N (SA1).

1 t

z_ SUNY-SPB

x LLNL

+ AER

GSFC-2D

<> HARVARD

[] UNIVAQ-2D

D UNIVAQ-3D

< CSIRO

I 1

3 4 5

=

_=

478
E

v

<

6O

55

50

45

4O

35

30-

25-

20-

15-

10-

5-

0

-5

Annual Average

I I I

A SUNY-SPB

x LLNL

+ AER

GSFC-2D

o HARVARD

[] UNIVAQ-2D

o UNIVAQ-3D

< CSIRO

0

Pct Diff in 03

I ] I

-2 -1 1 2 3 4 5

Figure 6.22a. 03; Run B4; Year 2015; Lat 40N (SA0).

479
6O

55

50

45

4O

35-

E

v

30-

<

25-

20-

15-

10-

5-

0

-5 -4 -3 -2

Annual Average

I

I

-1 0 1

Pct Diff in 03

t I I

SUNY-SPB

x LLNL

+ AER

GSFC-2D

o HARVARD

,z UNIVAQ-2D

[] UNIVAQ-3D

< CSIRO

2 3 4 5

Figure 6.22b. 03; Run B4; Year 2015; Eat 40N (SA1).

480
E

v

<

6O

55-

50-

45-

40-

35-

30-

25-

20-

15-

10-

5-

0

-5

Annual Average

I I I I I

I I I I

-4 -3 -2 -1 0 1 2 3

Pct Diff in 03

I 1

A SUNY-SPB

x LLNL

+ AER

GSFC-2D

<> HARVARD

[] UN1VAQ-2D

[] UNIVAQ-3D

< CSIRO

Figure 6.23a. 03; Run B4; Year 2015; Lat 60N (SA0).

5

481
E

v

__=

<

60

55

5O

45

40

35

3O

25

20

15

10

5

0

-5

I 1 1

Annual Average

<

-4 -3

I

-2 -1 0 1 2

Pct Diff in 03

Figure 6.23b. 03 Run B4 Year 2015 Lat 60N (SAI).

I

SUNY-SPB

LLNL

AER

GSFC-2D

HARVARD

UNIVAQ-2D

UNIVAQ-3D

CSIRO

I I

3 4 5

482
9O

(5 60

IJJ

30

u.I

0

D

_- -30

I-

< -60

J

-90

90

(.9 60

LU

30

LIJ

o 0

D

F- -30

I.-

'_ -60

-90

9O

(5 60

IJ_l

3O

0

-30

-60

-90

AER

0.1--

-"----::---0.1----L;:: ..... --0.t

__{J.1

0.4-

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

GSFC-2D

•' ,'; , , , _',,, -_ ,

..... .i i;-:-i?:.-0.4 .._.

"'-"**" * - -.. ...... °

..... 0.4 .......

"ii_:-o.f;:--;;--:---:-:":::--L.:-..-.

_ • .... i i i i i

2 3 4 5 6 7 8 9 10 11 12 3

TIME (MONTH)

LLNL

--0.4_

1 2 3 4 5 6 7 8 9 10 11 12 13

90

(.9 60

uJ

o 30

_ -30

F3 -6o

-90

TIME (MONTH)

UNIVAQ-2D

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

CSIRO

90 .......... -...- ....... :::.-; ........

LU(560 .... ._j:._ _ . .__'-':.'-':: ........

0 30 ': _ -::: .....

uJ

O 0 _ "_-----0.1-------

............ .T..... -0.1 -- -30 .........

. -60 ".......................

-90 i i i !

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

HARVARD

90 .-' ............. -.-._.......-.-.-.. : ::]. _.

(5 60

w ii ii-1[. i --8--"-:--"------::--"--"--"---:-:"-"-:-: : : : : : ::

30 _::.:-.:- -.-:--.-_........ ---:..'-- _ uJ

o 0

I- -30 .----_.'..-.-._

< -60 --"

.......... 0.4 .......... -90 ......... - .... , , , ,

2 3 4 5 6 7 8 9 10 11 12 3

TIME (MONTH)

SUNY-SPB

(-9 60

HJ

30 " c

uJ

o 0

_. -30

-90 ..... -, , ., ,

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

UNIVAQ-3D

(5 60

_ 30

LU

c_ 0

_ -30

I---

< -60

--J

-90

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

Figure 6.24. Total 03; Pct DIFF; Run B4 SA0 vs Run B4 Base.

483
=

90

(.9 60

UJ

30

UJ

o 0

i- -30

-60

-90

90

(.9 60

W

o 30

W

a 0

i-- -30

< -60

J

-90

AER

...... 0.4 ................................

i + - .......... "°'"=':', ...... + " o +.

. ._- ......... .o +-- .-

....... 0.1 .............. ,'....... ,..+. - -.v.'.--

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

GSFC-2D

...+

.... 0.8 ..............

::::: '6)i ---::------:--:\-::- :-_:-.

-.-.----.-.............. ..

90

60

W

30

W

o 0

-30

b-

< -60

J

-90

90

_" 60

W

30

0

-30

< -60

..J

-90

1 2 3 4 5 6 7 8 9 t0 11 12 13

TIME (MONTH)

LLNL

"- -o]8-:i-+-:---: ....

-::.- - [.'.'_-.-', "... ...................

......... o ......... + ....

::::.....................

2 3 4 5 6 7 8 9 10 11 12 3

TIME (MONTH)

UNIVAQ-2D

q

. + ........ + ....... "_. ..... _. °. ° o

2 3 4 5 6 7 8 9 10 1I 12 13

TIME (MONTH)

90 '

60

W

3O

UJ

o 0

D

_- -30

-90

90

C_ 60

W

30

W

¢_ 0

D

}- -30

__ -60

-90

90

(.9 60

UJ

30

UJ

o 0

2 -30

-60

J

-90

CSIRO

..... 0.4 ........ -"'"+""-"": "+; ...........

.............. - .-.;.:_::.:.......... ..,.,

+ .- .+ .......

: ..... 0.1 ...................... "...........

................ 0.1 --

....... - ......... o+-.

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

HARVARD

............. " ......... • " - ...... 0 4

2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

SUNY-SPB

_--_.._-7____: , Jr.. , :., , .,+.

..... 0.1 ......... " :-": ........

-:-0.1 -"

.. ........ .[-;--+.+_ : .." - ..

.--:- -.• ......... :....:...-. +

90

60

W

30

UJ

o 0

I-- -30

i5 -60

-90

1 2 3 4 5 6 7 8 9 10 11 12 13

TIME (MONTH)

UNIVAQ-3D

__,_.,.'_:'.°-:': :: + :-_

._.ETAT\;: ":,: ¢_:) • , , -,:;-. .-- :,"'.

2 3 4 5 6 7 8 9 10 11 12 3

TIME (MONTH)

Figure 6.25. Total 03; Pct DIFF; Run B4 SA I vs Run B4 Base.

484
O3

$

x

OJ

O

z

4-

O

Z

cQ)

O_

ctO

8 I I

AER (SAO)

I I I

I 1

F

6-

5--

4-

3-

2-_

1-

<

0-

X

- I []

-2 --

V

-3

-300

Lat = 30N - 90N

AIt = 16km - 25km

Lat = 30N - 90N

AIt = 10km - 15km

Lat = 30S - 30N

AIt = 26km & above

Lat = 30S - 30N

AIt = 16km - 25km

Lat = 30S - 30N

AIt = 10km - 15km

Lat = 90S - 30S

Alt = 16km - 25km

Lat = 90S - 30S

AIt = 26km & above

I I.

-25O -20O

Lat = 90S - 30S

O

AIt = 10km - 15km

Lat = 30N - 90N

o

AIt = 26km & above

t I

-150 -100 -50

Change in Ozone x le-9

I I

0 5O

Figure 6.26a. 2015 Annual Average; Change in NOx vs Change in Ozone.

100

485
oa

x

od

O

z

4-

O

z

¢-

ttO

8

7-

6-

5-

4-

3-

2-_

1-

<

0-

x

-1 --El

_3

-2 --

v

-3

-300

AER (SA1)

I t I 1 t I t

Lat = 30N - 90N

AIt = 16km - 25km _'_

Lat = 30N - 90N

AIt = 10km - 15km

Lat -- 30S - 30N

AIt = 26km & above

Lat = 30S- 30N I___

AIt = 16km - 25km :

Lat = 30S - 30N __

AIt =10km-15km - _'

Lat = 90S - 30S Lat = 90S - 30S "=-

AIt = 16km- 25km o AIt = 10km - 15km

Lat = 90S - 30S Lat = 30N - 90N

AIt = 26km & above o AIt = 26km & above

I 1 I 1 l I

-250 -200 -150 -100 -50 0 50

Change in Ozone x le-9

Figure 6.26b. 2015 Average Annual; Change in NOx vs Change in Ozone.

100

Z

486
x

O_1

O

z

+

O

z

cz

c"

¢.-

o

8

7 F

6-

5-

4--

3-

2-_

1-

<

0-

X

• _ []

F'

-2 --

v

-3i

-300

I I

SUNY-SPB (SA0)

I 1 t

Lat = 30N - 90N

AIt = 16km - 25km

Lat = 30N - 90N

Alt = 10km - 15km

Lat = 30S - 30N

AIt = 26km & above

Lat = 30S - 30N

AIt = 16km - 25km

Lat = 30S - 30N

AIt =10km-15km

Lat = 90S - 30S

AIt= 16km - 25km

Lat = 90S - 30S

Lat = 90S - 30S

o

AIt = 10km - 15km

Lat = 30N - 90N

O

AIt = 26km & above

I I

-250 -2O0

AIt = 26km & above

I 1 1

-150 -100 -50

Change in Ozone x le-9

I •

0 50

Figure 6.26c. 2015 Annual Average; Change in NOx vs Change in Ozone.

00

487
Cg

x

C_J

O

Z

..F

O

z

tC3_

C

tO

8

7-

6-

5-

4-

3-

LLNL (SA0)

t I I I I I I

2-)K

1-

<

0-

X

-1 -[3

-2 --

_7

-3

-300

Lat = 30N - 90N o

AIt = 16km - 25km <_

Lat = 30N - 90N o o:_

AIt = 10km - 15km

Lat = 30S - 30N

AIt = 26km & above

Lat = 30S - 30N

Alt = 16km - 25km

Lat = 30S - 30N

AIt = 10km - 15km

Lat = 90S - 30S Lat = 90S - 30S

AIt =16km-25km o AIt =10km-15km

Lat = 90S - 30S Lat = 30N - 90N

AIt = 26km & above ° AIt -- 26km & above

I I I I I

-250 -200 -150 -] 00 -50

Change in Ozone x le-9

I

50

Figure 6.26d. 2015 Annual Average; Change in NOx vs Change in Ozone.

100

Ii

i

m

.=

Z

m

-E

z

488
x

OJ

O

z

+

o

Z

f- .l

(D

r"

tO

8

7--

6-

5-

4-

3-

2--_

1--

<

0-

x

• _ []

-2 -

V

-3

-300

I < I

< <<<<<

< < %

<

<

Lat = 30N - 90N

AIt = 16km - 25km

Lat = 30N - 90N

AIt = 10km - 15km

Lat = 30S - 30N

AIt = 26km & above

Lat = 30S - 30N

AIt = 16km - 25km

Lat = 30S - 30N

HARVARD (SA0)

I I I I I

<

<L

<

< <

< <.

. " < _ X

< <

_ o ...-_ o <, :_ <xx -

0 <" 6r,_ _< < X

_> << • < ,

.Ov_

 f,.x

<I_,7_¢_I

0

<

AIt = 10km - 15km

Lat = 90S - 30S Lat = 90S - 30S

o

AIt = 16km - 25km AIt = 10km - 15km

Lat = 90S - 30S Lat = 30N - 90N

AIt = 26km & above o AIt = 26km & above

I I f 1

-250 -200 -150 -100 -50

Change in Ozone x le-9

1 I I

0 50

Figure 6.26e. 2015 Annual Average; Change in NOx vs Change in Ozone.

100

489
Table 6. I. Change in Total NOy 2015 RUN B4 (SA I vs Base)

:

Model Base Atmosphere % Change Absolute Change

AER

CSIRO

GSFC-2D

HARVARD

LLNL

SUNY_SPB

UNIVAQ-2D

UNIVAQ-3D

Units = Molecules/cm 2

1.9662e+I6

2.1013e+16

2.8473e+16

2.3401e+16

2.3376e+16

1.7111e+16

1.7067e+I6

9.39

8.91

11.13

16.64

8.95

7.39

6.64

2.0776e+16 4.05

1.85e+15

1.87e+I5

3.17e+15

3.34e+15

2.0%+15

1.26e+15

1.13e+15

8.0%+14

Table 6.2. Ratio of change in total NOx to change in total NOy for the B4 runs

(HSCT with either SA0 or SAI aerosols)

Model Ratio (SA0) Ratio (SAI)

AER

CSIRO

GSFC-2D

HARVARD

LLNL

SUNY_SPB

UNIVAQ-2D

UNIVAQ-3D

0.039

0.179

0.121

0.152

0.146

0.197

0.145

0.098

-0.029

0.111

0.I83

0.112

0.095

0.143

0.218

0.219

B

|

E

m

Z

E

P

490
Table 6.3a. Total 03 2015 Run B4 (SA0 - Base)

Model SA0 Atmosphere Base Atmosphere Change in 03

AER

GSFC-2D

LLNL

CSIRO

SUNY-SPB

UNIVAQ-2D

UNIVAQ-3D

HARVARD

ECHAM3

3.2139e+02

3.0404e+02

3.3513e+02

3.0942e+02

3.0878e+02

3.1612e+02

2.9931e+02

3.0306e+02

3.3076e+02

3.2074e+02

3.0441e+02

3.3457e+02

3.0964e+02

3.0856e+02

3.1380e+02

2.9856e+02

3.0410e+02

3.3076e+02

6.5219e-01

-3.7628e-01

5.5954e-01

-2.1664e-01

2.1912e-01

2.3214e+00

7.5650e-01

-1.0412e+00

0.0000e+00

Units = Dobson Units

Table 6.3b. Total 03 2015 Run B4 (SAI - Base)

Model SA1 Atmosphere Base Atmosphere Change in 03

AER

GSFC-2D

LLNL

CSIRO

SUNY-SPB

UNIVAQ-2D

UNIVAQ-3D

HARVARD

3.1993e+02

3.0304e+02

3.3381e+02

3.2074e+02

3.0441e+02

3.3457e+02

-8.1198e-01

-1.3695e+00

-7.5836e-01

3.0919e+02

3.0847e+02

3.1408e+02

2.9849e+02

3.0276e+02

3.0964e+02

3.0856e+02

3.1380e+02

2.9856e+02

3.0410e+02

-4.5111e-01

-9.3781e-02

2.8094e-01

-6.3690e-02

-1.3373e+00

Units = Dobson Units

491
iEEEm=
S.L. Baughcum

P.P. Bhatt

K.A. Boering

G.P. Brasseur

D.E. Brown

C. Bruhl

M.P. Chipperfield

P.S. Connell

D.B. Considine

A.R. Douglass

V.L. Dvortsov

R.S. Eckman

J.W. Elkins

E.L. Fleming

R.R. Friedl

R.R. Garcia

W.L. Grose

V. Grewe

T.M. Hall

B. Hannegan

M.H. Hitchman

T.Y. Huang

C.H. Jackman

D.B.A. Jones

I.L. Karol

S.R. Kawa

J.A. Kaye

R. Kendall

R. Khosravi

D.E. Kinnison

M.K.W. Ko

L. Kogan

J. Lerner

G.J. Labow

J.A. Logan

R.D. McPeters

P.W. Mote

N. Andronova

S. Nossal

S.J. Oltmans

J.H. Park

G. Pitari

R.A. Plumb

M.J. Prather

P.N. Purcell

L. K. Randeniya

W.J. Randel

P.J. Rasch

E.E. Remsberg

D.H. Rind

C.P. Rinsland

H.L. Rogcrs

J.M. Rodriguez

R.B. Rood

J.E. Rosenfield

D. Rotman

K.R. Ryan

E.V. Rozanov

K.H. Sage

R.J. Salawitch

M.E. Schlesinger

C.J. Scott

H.R. Schneider

AppendixmList of Contributors

steven.l.baughcum@ boeing.corn

p.p.bhatt@larc.nasa.gov

boering@cchem.berkeley.ed u

brasseur@ ncar.ucar.edu

d.e.brown@larc.nasa.gov

chb@ mpch-mainz.mpg.de

martyn@ lec.leeds.ac.uk

connell2@ llnl.gov

dbc@ welkin.gs fc.nasa.gov

douglass@persephone.gsfc.nasa.gov

vdvor@dombai.uars,sunysb.edu

r.s.eckman@Iarc.nasa.gov

jelkins@cmdl.noaa.gov

fleming@ welkin.gsfc.nasa.gov

rfriedl@jpl.nasa.gov

rgarcia@ucar.edu

grose@haloe 1.larc.nasa.gov

volker.grewe@ dlr.de

thall@haggis.giss.nasa.gov

bjhanneg@ uci.edu

matt@ adams.meteor.wisc.edu

Theresa.Y.Huang@jpl.nasa,gov

jackman@assess.gsfc.nasa.gov

dbj@ io.harvard.edu

karol@ main.mgo.rssi.ru

kawa@ maia.gsfc.nasa.gov

jkaye@hq.nasa.gov

rose@ember.gsfc.nasa.gov

rashid@ ucar.edu

dkin@ acd.ucar.edu

mko@ aer.com

lar@europa.harvard.edu

cdjal@ cleo.giss.nasa.gov

labow@ chapman.gsfc,nasa.gov

jal@io.harvard.edu

mcpctcrs@ qhearts.gsfc.nasa.gov

mote@met.ed.ac.uk

natasha@ almos.uiuc.edu

nossal@ wisp.physics.wisc.edu

soltmans@cmdl.noaa.gov

j.h.park@larc.nasa.gov

pitari@ aquila,infn.il

rap@rossby.mit.edu

mprather@uci.edu

p.n.purcell@ larc.nasa.gov

Lakshman.Randeniya@ tip.csiro.au

randel@ucar.edu

pjr@ chinook.cgd.ucar.edu

e.e.remsberg@larc.nasa.gov

drind@giss.nasa.gov

rinsland@ riscbox.larc.nasa.gov

helen.rogers@alm.ch.cam.ac.uk

jose@aer.com

rood@dao.gsfc.nasa.gov

rose@euterpc.gsfc.nasa.gov

drotman@Ilnl.gov

keith.rTan@tip.csiro.au

rozanov@uiatma.atnaos.uiuc.edu

k.h.sage@larc.nasa.gov

rjs@caesar.jpl.nasa.gov

schlesin@atmos.uiuc.edu

Courtney_Scolt@ aer.com

hrs@europa.harvard.edu

493
Z

i

S.P. Smyshlyaev

R. Soderholm

S. Solomon

B. Stcil

X.X. Tic

P.F. Vohralik

D.W. Waugh

C.J. Weaver

C.F, Wei

D.K. Wcisenstein

K. Wolfe

D.J. Wuebbles

F. Yang

V.A. Yudin

V, Zubov

X. Zhu

sergey@geluaO.uars.sunysb.edu

randy@ember.gsfc.nasa.gov

solomon@ al.noaa.gov

steil@mpch-mainz.mpg.de

xxtie@ncar.ucar.edu

peter.vohralik@tip.csiro.au

waugh@jhu.edu

weaver@ aurora.gsfc.nasa.gov

c-wei@uiuc.edu

dkweis@aer.com

kwolfe@al.noaa.gov

wuebbles@ atmos.uiuc.edu

fangl in@ atmos,ui uc.edu

valery@dombai.uars.sunysb.edu

zubov@main.mgo.rssi.ru

xzhu@uci.edu

494

I

E

|

i

Form Approved

REPORT DOCUMENTATION PAGE OMBNo 07704-0188

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1, AGENCY USE ONLY (Leave blank) 2. REPORT DATE 3. REPORTTYPE AND DATES COVERED

September 1999 Technical Memorandum

4. TITLE AND SUBTITLE

Models and Measurements Intercomparison II

6. AUTHOR(S)

Jae H. Park, Malcohn K. W. Ko, Charles H. Jackman, R. Alan Plumb, Jack A.

Kaye, and Karen H. Sage, Editors

7. PERFORMINGORGANIZATIONNAME(S)ANDADDRESS(ES)

NASA Langley Research Center

Hampton, VA 23681-2199

9. SPONSORING,_IONITORINGAENCYNAME(S)ANDADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

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WU 622-57-72-70

8. PERFORMING ORGANIZATION

REPORT NUMBER

L-17895

10. SPONSORING/MONITORING

AGENCY REPORTNUMBER

NASA/TM-1999-209554

11. SUPPLEMENTARY NOTES

Park: Langley Research Center, Hampton, VA; Ko: Atmospheric and Environmental Research, Inc., Cambridge, MA:

Jackman: Goddard Space Flight Center, Greenbelt, MD; Plumb: Massachusetts Institute of Technology, Cambridge, MA;

Kaye: Headquarters, National Aeronautics and Space Administration, Washington, De; Sage: SAIC, Hampton, VA.

12a. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified Unlimited

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12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

Models and Measurement lntercomparison II (MM II) summarizes the intercomparison of results from model simulations and observations of stratospheric species. Representatives from twenty-three modeling groups using

twenty-nine models participated in these MM II exercises between 1996 and 1999. Twelve of the models were twodimensional zonal-mean models while seventeen were three-dimensional models. This was an international effort

as seven were from outside the United States. Six transport experiments and five chemistry experiments were

designed for various models. Models participating in the transport experiments performed simulations of chemically inert tracers providing diagnostics for transport. The chemistry experiments involved simulating the distributions of chemically active trace gases including ozone. The model run conditions for dynamics and chemistry were

prescribed in order to minimize the factors that caused differences in the models. The report includes a critical

review of the results by the participants and a discussion of the causes of differences between modeled and

measured results as well as between results from different models. A sizable effort went into preparation of the

database of the observations. This included a new climatology for ozone. The report should help in evaluating the

results from various predictive models for assessing humankind perturbations of the stratosphere.

14. SUBJECT TERMS

Ozone; Stratospheric chemistry and dynamics; Atmospheric modeling and observation; Assessment of humankind perturbation; Atmospheric perturbation by high speed

civil transport aircraft

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Unclassified

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