MIT Joint Program on the

Science and Policy of Global Change

Estimated PDFs of Climate System Properties

Including Natural and Anthropogenic Forcings

Chris E. Forest, Peter H. Stone and Andrei P. Sokolov

Report No. 126

September 2005
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Printed on recycled paper
Estimated PDFs of Climate System Properties Including Natural and

Anthropogenic Forcings

Chris E. Forest, Peter H. Stone and Andrei P. Sokolov

Abstract

We present revised probability density functions (PDF) for climate system properties

(climate sensitivity, rate of deep-ocean heat uptake, and the net aerosol forcing strength)

that include the effect on 20th century temperature changes of natural as well as

anthropogenic forcings. The additional natural forcings, primarily the cooling by volcanic

eruptions, affect the PDF by requiring a higher climate sensitivity and a lower rate of deepocean heat uptake to reproduce the observed temperature changes. The estimated 90%

range of climate sensitivity is 2.4 to 9.2 K. The net aerosol forcing strength for the 1980s

decade shifted towards positive values to compensate for the now included volcanic forcing

with 90% bounds of –0.7 to –0.16 W/m2

. The rate of deep-ocean heat uptake is also reduced

with the effective diffusivity, Kv, ranging from 0.25 to 7.3 cm2

/s. This upper bound implies

that many coupled atmosphere-ocean GCMs mix heat into the deep ocean (below the mixed

layer) too efficiently.

Contents

1. Introduction ......................................................................................................................................... 1

2. Methodology ....................................................................................................................................... 2

2.1 Experimental Design ................................................................................................................... 2

3. Results ................................................................................................................................................. 3

4. Discussion and Conclusions ............................................................................................................... 4

5. References ........................................................................................................................................... 6

Supplement

1. Methods............................................................................................................................................S-1

1.1 Summary of PDF Estimation Algorithm.................................................................................S-1

1.2 Description of MIT 2D Climate Model...................................................................................S-2

1.3 Temperature Change Diagnostics............................................................................................S-3

1.4 Summary of Applied Climate Forcings...................................................................................S-4

2. Sensitivity Tests ...............................................................................................................................S-5

2.1 Effects of Noise Model Truncation .........................................................................................S-5

2.2 2D Model Simulation of Ocean Heat Uptake .........................................................................S-6

2.3 Sensitivity to Latitude Dependence of Ocean Heat Uptake....................................................S-7

2.4 Sensitivity to Volcanic Forcing Uncertainty...........................................................................S-8

References ............................................................................................................................................S-9
1 Introduction

Forest et al. (2002) presented an estimate of the joint probability density function (pdf)

for uncertain climate system properties. Other groups (Andronova & Schlesinger, 2001;

Gregory et al., 2002; Knutti et al., 2003) have estimated similar pdfs although each uses

different methods and data. However all are based on estimating the degree to which a

climate model can reproduce the historical climate record. Parameters within each model

are perturbed to alter the response to climate forcings and a statistical comparison is used

to reject combinations of model parameters.

We use an optimal fingerprint detection technique for comparing model and observational data. This technique consists of running a climate model under a set of prescribed

forcings and using climate change detection diagnostics to determine whether the simulated

climate change is observed in the climate record and is distinguishable from unforced variability of the climate system (see Mitchell et al. (2001) or International ad hoc Detection

and Attribution Group (2005) and references therein). As is well known, it is not possible

to estimate the true climate system variability on longer time scales from observations and

therefore, climate models are run with fixed boundary conditions for thousands of years to

obtain an estimate of the climate variability.

Forest et al. (2000, 2001, 2002) developed a method using the MIT 2D model to analyze

uncertainty in climate sensitivity (S), the rate of heat uptake by the deep ocean (Kv), and

the net aerosol forcing (Faer). These factors (θ = S,Kv,Faer) were jointly constrained by

using three different diagnostics to estimate the probability of rejection for combinations

of model parameters that lead to simulations of the 20th century which are inconsistent

with the observed records of climate change. The individual probability density functions

(pdfs) can be combined to provide stronger constraints for the uncertain properties via

an application of Bayes’ Theorem. A 2D model is required because 3D models are

computationally too inefficient.

Our former analysis only took into account anthropogenic forcings by greenhouse

gases, stratospheric ozone, and aerosols. We now also take into account the change in

radiative forcing by solar irradiance fluctuations, by stratospheric aerosols due to volcanic

eruptions, and by land-use and land-cover vegetation changes. The major uncertain forcing

is that due to sulfate aerosols, so in our earlier study, the sulfate aerosol pattern had a

specified dependence on latitude and surface type, but the amplitude was taken to be one

of the uncertain parameters to be constrained. There are additional uncertainties associated

with the new forcings that we now include, but they are generally believed to be smaller

than the uncertainties associated with sulfate aerosols (Hansen et al., 2002). Thus, in our

new analysis, we retain the amplitude of the sulfate aerosol forcing as the only uncertain

1
parameter describing the forcing. However, we do include in this paper some tests of

whether our new results are sensitive to uncertainties in the new forcings.

2 Methodology

To quantify uncertainty in climate model properties, the basic method (Forest et al., 2001,

2002) can be summarized as consisting of two parts: simulations of the 20th century climate

record and the comparison of the simulations with observations using optimal fingerprint

diagnostics. First, we require a large sample of simulated records of climate change in

which climate parameters have been systematically varied. This requires a computationally

efficient model with variable parameters as provided by the MIT 2D statistical-dynamical

climate model (Sokolov & Stone, 1998). A brief description of the MIT 2D climate model

and its recent modifications are given in supplemental material. Second, we employ a

method of comparing model data to observations that appropriately filters “noise” from

the pattern of climate change. The variant of optimal fingerprinting proposed by Allen &

Tett (1999) provides this tool and yields detection diagnostics that are objective estimates

of model-data goodness-of-fit. These goodness of fit statistics are then used to estimate

the likely value of uncertain parameters (via a likelihood function, L(θ)) for multiple

diagnostics of climate change. Individual L(θ) are then combined to estimate the posterior

distribution, p(θ|ΔTi, CN ), where ΔTi represent the three temperature change patterns and

CN is the noise covariance matrix required to estimate the goodness-of-fit statistics.

In this work, the noise covariance matrix has been estimated from multiple control

runs of AOGCMs. (The noise estimate for deep ocean heat uptake also includes analysis

error estimates from Levitus et al. (2000).) The matrix represents the natural variability in

any predicted pattern which is determined by the variability in the corresponding pattern

amplitude in successive segments of “pseudo-observations” extracted from the control run

for the climate model. The variability of the MIT 2D climate model is somewhat lower

than that of AOGCMs (Sokolov & Stone, 1998), but the model does exhibit changes in

variability which are dependent on S and Kv and are similar to results from Wigley &

Raper (1990).

2.1 Experimental Design

The description of the climate model experiments, the ensemble design, and the algorithm

for estimating the joint PDFs are provided in full in Forest et al. (2001, 2002). The major

change in the new experiments is the inclusion of three additional 20th century forcings

during the period 1860-1995. The set of forcings is now: greenhouse gas concentrations,

2
sulfate aerosol loadings, tropospheric and stratospheric ozone concentrations, land-use

vegetation changes (Ramankutty & Foley, 1999), solar irradiance changes (Lean, 2000),

and stratospheric aerosols from volcanic eruptions (Sato et al., 1993). We refer to these

forcings as GSOLSV with the first three, GSO, being those used in Forest et al. (2002).

(Details on all forcings are in the supplement.)

Additionally, we elected to run each perturbation of the 4 member ensemble starting

with different initial conditions in 1860 rather than perturbing the climate system in 1940

as done previously. This provides data for each ensemble member for the entire simulation

as we will be using surface temperatures beginning in 1906. The initial conditions for each

ensemble member were taken every ten years from an equilibrium control simulation. This

results in 4x136 = 544 simulation years for each choice of model parameters.

3 Results

In the simulated climate change, the additional forcings have two major effects that can

be illustrated by examining the simulated response to the GSOLSV and GSO forcings and

comparing with the observed records, directly (Fig. 1). For the “best fit” parameters in each

case, the new results require higher S and slightly weaker Faer. This shift in the “best fit”

parameters results in appropriate shifts in the distributions and are summarized as follows.

The inclusion of the volcanic aerosol forcing provides a net surface cooling during the

latter 20th century (Fig. 1). This requires changes in uncertain model parameters to remain

consistent with the historical climate record (Figs. 2 and 3 ) which can be achieved by

reducing Kv or Faer, increasing S, or combinations of all three. The median net aerosol

forcing is partially reduced from -0.6 to -0.4 W/m2 but there is little change in the width

of the distribution with the 5-95%-ile range being 0.6 W/m2. The reduction in the median

is partially because the net aerosol forcing no longer includes the volcanic term. However,

the net aerosol forcing remains a cooling effect. The medians for S, Kv, and Faer are 3.1

K, 1.4 cm2/s, and -0.35 W/m2, respectively, for the distributions using an expert prior on S

as used in Forest et al. (2002).

The new distributions are compared with that of Forest et al. (2002) in Fig. 2 and two

key comparisons are made. In one, we compare the distributions with identical treatments

of the climate change diagnostics by keeping the number of retained EOFs (κ) in the

decomposition of C −1

N (κ) fixed. Thus, for the surface temperature diagnostic, we use

κsfc = 14 in both the GSO and GSOLSV pdfs and the marginal posterior distributions

for S, Faer, and Kv are altered. In the second comparison, we allow κsfc to vary. In Forest

et al. (2002) for the surface data, we found that we could reject κsfc > 14 based on the

Allen & Tett (1999) criterion. With the additional forcings, we are no longer able to reject

3
the higher EOFs and find that the distributions are insensitive for 15 < κsfc < 19. In a

separate work on Bayesian selection criteria, Curry et al. (2005) using our data find that a

break occurs at κsfc = 16 and thus we select this as an appropriate cutoff. This inclusion of

higher order EOFs is equivalent to stating that smaller spatial and temporal scale patterns

(in five decadal means and four equal-area zonal averages) found in the GSOLSV response

are significant, unlike the GSO case. This also implies that the observations show this

behavior. As a final issue, this shift from κsfc = 14 to 15 further limits the higher Kv

values. We note that the range of effective ocean diffusivities for the existing AOGCMs is

4-25 cm2/s (Sokolov et al., 2003) and these values appear to be highly unlikely according

to our new results. (We note that a more recent analysis of the deep ocean temperatures

(Levitus et al., 2005) has a 20% weaker trend during the period used in our analysis. This

would require even lower acceptable Kv values.)

Several sensitivity tests were performed to assess the robustness of the estimated

distributions. Specifics are found in the supplemental information but we briefly discuss

two here. A first test was designed to determine whether the location of the deep-ocean

heat uptake influenced the spatio-temporal patterns of temperature change. The latitude

dependence of Kv, which was based on observations of Tritium mixing (Sokolov & Stone,

1998), was changed to reflect the pattern identified in the ocean data (Levitus et al., 2000).

Although local surface temperatures were changed, the large-scale averages (four equalarea zonal bands) as used in our diagnostics were not affected. A second test explored

the sensitivity of the results to reducing the strength of the volcanic forcing by 25%. This

requires slightly higher Faer and lower S values to bring the temperature response down to

match the observations but the changes are relatively small. These results suggest that the

PDFs are robust to such changes.

As discussed earlier, the estimated distributions depend on the choice of the truncation

for the eigen-decomposition of CN for the surface temperature diagnostic. We also tested

the choice of AOGCM for estimating CN . In Forest et al. (2002), we used the natural

variability as estimated from the HadCM2 and GFDL R30 AOGCMs. In our new results,

we have used diagnostics based on the natural variability from control runs by the HadCM2,

HadCM3, GFDL R30, and PCM models. The resulting pdfs have not differed qualitatively

(shown in supplemental material). Although the results are not sensitive to the choice of

AOGCM, observations do not exist to test the quality of such estimates.

4 Discussion and Conclusions

We present a revised estimate of the pdfs for climate system properties that now includes

the response to both natural and anthropogenic forcings. With additional new forcings, a

4
larger climate sensitivity and a reduced rate of ocean heat uptake below the mixed layer are

required to match the observed climate record in the 20th century. The primary factor

leading to this change is the strong cooling forcing by volcanic eruptions through the

stratospheric aerosols. Similarly, there is a small change in the aerosol forcing which

tends to offset the volcanic cooling. When using uniform priors on all parameters, these

new results are summarized by the 90% confidence bounds of 2.4 to 9.2 K for climate

sensitivity, 0.25 to 7.3 cm2/s for Kv, and -0.7 to -0.16 W/m2 for the net aerosol forcing

strength. When an expert prior for S is used, the 90% confidence intervals are 2.2 to 5.2 K,

0.1 to 5.5 cm2/s, and -0.62 to -0.05 W/m2 for S, Kv, and Faer, respectively. Our new results

for Kv imply that most AOGCMs are mixing heat into the deep ocean too efficiently, as

shown in Fig. 3.

From the two sensitivity tests regarding the strength of the volcanic forcing and the

location of the ocean heat uptake, we find that our results appear robust. We also explored

the sensitivity to the estimated C −1

N (κ) and find that although the specific AOGCM is not

very important, the method for truncating the number of retained eigenvectors (i.e., patterns

of unforced variability) is critical. For the surface temperature diagnostic, critical changes

in the joint PDF occur when κsfc changes from 14 to 15 and from 19 to 20 and based on

Allen & Tett (1999), we cannot reject these higher modes of variability. Marginal likelihood

results are promising (Curry et al., 2005, from) yet do not appear to be definitive. Based

on κsfc = 14, 15, or 20, the robust result is that the lower bound on S is higher and failure

to reject S > 5 K remains. Additionally, for all three choices, high Kv values are rejected

as producing too much ocean heat uptake and the net aerosol forcing uncertainty remains

stable. Given these considerations, the best choice appears to be κsfc=15.

Finally, the use of the expert prior on S remains a key factor in limiting the possibility

of high values of S. Despite their uncertainties, the paleoclimate results provide data not

directly included in the present framework and this supports using a prior influenced by

such results. The implications of these results are that the climate system response will be

stronger (specifically, a higher lower bound) for a given forcing scenario than previously

estimated via the uncertainty propagation techniques in Webster et al. (2003).

Acknowledgments. This work was supported in part by the NOAA Climate Change

Data and Detection Program with support from DOE. We thank many scientists who have

encouraged this work including Myles Allen and Jim Hansen (MIT) and the support of

the Joint Program on the Science and Policy of Global Change at MIT. This research was

supported in part by the Office of Science (BER), U.S. Department of Energy, Grant No.

DE-FG02-93ER61677. The views, opinions, and findings contained in this report are those

of the authors.

5
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Density Function for Climate Sensitivity. J. Geophys. Res., 106(D19), 22,605–22,612.

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prep.

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in climate models using climate change detection methods. Geophys. Res. Let., 27(4),

569–572.

Forest, C. E., Allen, M. R., Sokolov, A. P., & Stone, P. H. 2001. Constraining Climate

Model Properties Using Optimal Fingerprint Detection Methods. Clim. Dynamics, 18,

277–295.

Forest, C. E., Stone, P. H., Sokolov, A. P., Allen, M. R., & Webster, M. D. 2002.

Quantifying uncertainties in climate system properties with the use of recent climate

observations. Science, 295, 113–117.

Gregory, J.M., Stouffer, R.J., Raper, S.C.B., Stott, P.A., & Rayner, N.A. 2002. An

Observationally Based Estimate of the Climate Sensitivity. J. Climate, 15(22), 3117–

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T., Shindell, D., Santer, B., Stone, P., Novakov, T., Thomason, L., Wang, R., Wang,

Y., Jacob, D., Hollandsworth, S., Bishop, L., Logan, J., Thompson, A., Stolarski, R.,

Lean, J., Willson, R., Levitus, S., Antonov, J., Rayner, N., Parker, D., & Christy, J.

2002. Climate Forcings in GISS SI2000 Simulations. J. Geophys. Res., 107, DOI

10.1029/2001JD001143.

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F. Zwiers, G. Hegerl, M. Allen, T. Crowley (coordinator), N. Gillett, K. Hasselmann, P.

Jones, B. Santer, R. Schnur, P. Stott, K. Taylor, S. Tett]. 2005. Detecting and Attributing

External Influences on the Climate System: A Review of Recent Advances. J. Climate,

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Anglia, Climate Research Unit.

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projections using neural networks. Clim. Dyn., 21, 257–272.

Lean, J. 2000. Evolution of the Sun’s Spectral Irradiance Since the Maunder Minimum.

Geophys. Res. Lett., 27, 2421–2424.

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Levitus, S., Antonov, J., Boyer, T. P., & Stephens, C. 2000. Warming of the World Ocean.

Science, 287, 2225–2229.

Levitus, S., Antonov, J., & Boyer, T. P. 2005. Warming of the World Ocean, 1955–2003.

Geophys. Res. Let., 32(L02604), doi:10.1029/2004GL021592.

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J. 2001. Detection of Climate Change and Attribution of Causes. Pages 695–738

of: Houghton, J. T., Ding, Y., Griggs, D. J., Noguer, M., van der Linden, P. J., Dai,

X., Maskell, K., & Johnson, C. A. (eds), Climate Change 2001: The Scientific Basis.

Cambridge University Press, Cambridge, UK and New York, NY, USA.

Ramankutty, N., & Foley, J. A. 1999. Estimating historical changes in global land cover:

croplands from 1700 to 1992. Global Biogeochemical Cycles, 13(4), 997–1027.

Sato, M., Hansen, J. E., McCormick, M. P., & Pollack, J. B. 1993. Stratospheric aerosol

optical depths. J. Geophys. Res., 98, 22987–22994.

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assessments. Clim. Dyn., 14, 291–303.

Sokolov, A. P., Forest, C. E., & Stone, P. H. 2003. Comparing Oceanic Heat Uptake in

AOGCM Transient Climate Change Experiments. J. Climate, 16, 1573–1582.

Webster, M., Forest, C., Reilly, J., Babiker, M., Mayer, M., Prinn, R., Sarofim, M., Sokolov,

A., Stone, P., & Wang, C. 2003. Uncertainty Analysis of Climate Change and Policy

Response. Climatic Change, 295–320.

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detection of the greenhouse effect. Nature, 344, 324–327.

7
8

(a) Surface Temperature

-0.20

-0.10

0.00

0.10

0.20

0.30

0.40

0.50

T (oC )

G S OLS V : B est fit (S =3.5 Kv=4. F aer=-0.50)

G S OLS (S =3.5 Kv=4. F aer=-0.50)

G S O: B est fit (S =2.5 Kv=7.5 F aer=-0.75)

Observations

(b) Deep-ocean Temperature

1950 1960 1970 1980 1990

Year

-0.04

-0.02

0.00

0.02

0.04

Te mperature (oC )

G S OLS V : B est fit (S =3.5 Kv=4. F aer=-0.50)

G S OLS (S =3.5 Kv=4. F aer=-0.50)

G S O: B est fit (S =2.5 Kv=7.5 F aer=-0.75)

Observations

Figure 1: Representative MIT 2D model simulations with the GSOLSV, GSOLS, and GSO forcings for

(a) global-mean annual-mean surface temperature change, and (b) 0 to 3 km global mean

annual-mean ocean temperature change. We show cases near the distributions’ modes for

GSOLSV (black) and GSOLS (blue) with S = 3.5 K, Kv = 4 cm2

/s, and Faer = –0.5 W/m2 and for GSO

(green) with S = 2.5 K, Kv = 7.5 cm2

/s, and Faer = –0.75 W/m2. Observations (red) from Jones (2000)

(surface) and Levitus et al. (2000) (deep-ocean). Surface temperatures have been smoothed with

a three-point moving average.
9

p(Kv): marginal posteriors

02468

SQRT( Effective Oceanic Diffusion ) (Sqrt(cm2

/s))

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Density

p(S): marginal posteriors

0 2 4 6 8 10 12

Climate Sensitivity (K)

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Density

GSOLSV - Expert S prior (ksfc=16)

GSOLSV - Uniform (ksfc=14)

GSO - Uniform (k =14)

GSOLSV - Uniform (ksfc=16)

-1.5 -1.0 -0.5 0.0 0.5

Net Aerosol Forcing (W/m2

)

0.00

0.05

0.10

0.15

0.20

0.25

0.30

Density

p(F ): marginal posteriors aer

sfc

Figure 2: The marginal posterior probability density function for the three climate system properties

for four cases. In each panel, the marginal pdfs are shown for the GSOLSV forcings with sfc = 16

(black) and 14 (green) and for GSO case (blue) with sfc = 14 from Forest et al. (2002). A fourth

case (red) includes an expert prior on S and uniform priors elsewhere with sfc = 16. Marginal

distributions are estimated by integrating the density function over the remaining two

parameters and renormalizing. The whisker plots indicate boundaries for the percentiles 2.5 to

97.5 (dots), 5 to 95 (vertical bar at ends), 25 to 75 (box ends), and 50 (vertical bar in box). The

mean is indicated with the diamond and the mode is the peak in the distribution.
10

p(S,Kv): Expert Prior on S

02468

Rate of Ocean Heat Uptake [Sqrt(Kv), (Sqrt(cm2/s))]

0

2

4

6

8

10

Climate Sensitivity (K)

GFDL-R15

HadCM2 ECHAM3/LSG

NCAR CSM

CGCM1 CSIRO

MRI1

HadCM3

PCM

GISS-HYCOM

GISS-GR

Figure 3: The marginal posterior probability density function for GSOLSV results with expert prior on

S for the S-Kv parameter space. The blue shading denotes rejection regions for a given

significance level: 10% (light) and 1% (dark). The positions of AOGCMs (from Sokolov et al., 2003)

represent the parameters in the MIT 2D model that match the transient response in surface

temperature and thermal expansion component of sea-level rise under a common forcing

scenario. Lower Kv values imply less deep-ocean heat uptake and hence, a smaller effective heat

capacity of the ocean.
Supplement to: Estimated PDFs of climate system

properties including natural and anthropogenic

forcings

Chris E. Forest and Peter H. Stone and Andrei P. Sokolov 

This supplement provides additional information for Forest et al. (2005). It presents

details on the methods used (Sect. 1) and a set of sensitivity tests for the estimated PDFs

(Sect. 2). Three tests provide details on sensitivies to the noise model truncation, to the latitude dependence of the deep-ocean heat uptake, and to uncertainty in the volcanic forcing.

There is also a section on the ability of the 2D climate model to reproduce the ocean heat

uptake in response to the 20th century forcings.

1 Methods

1.1 Summary of PDF Estimation Algorithm

The following steps provide further details of the method for estimating the probability

density functions (PDFs):

1. Simulate 20th century climate using anthropogenic and natural forcings while systematically varying the choices of climate system properties, θ = {S, Kv, Faer}, as

set in the MIT 2D climate model, where S is the climate sensitivity (defined as the

equilibrium change in surface air temperature when the CO2 concentration doubles),

Kv is an effective vertical diffusivity controlling the rate at which heat anomalies

penetrate into the deep ocean (below the mixed layer), and Faer is the net aerosol

forcing and represents the uncertainty in the net historical forcing.

S-1
2. Compare the spatio-temporal patterns of climate change in each model temperature

response, T(θ), against observed patterns, Tobs, as in an optimal fingerprint detection

algorithm using unforced variability estimated from AOGCMs to obtain an estimate,

CN (κ), of the covariance matrix and only the first κ modes of variability are retained

from the eigenvalue decomposition. The best choice of κ is discussed later. The

observed temperature change diagnostics that we use are described in Appendix A2.

3. Use the goodness-of-fit statistics, r2(θ, Tobs)=(T(θ) − Tobs)TC−1

N (κ)(T(θ) − Tobs),

to obtain the likelihood function, L(θ) = p(Tobs|θ, CN ) for each T(θ) diagnostics.

This likelihood is based on the result that r2 must differ by mFm,ν , for a given significance level, to be considered different from the minimum r2 value (Forest et al.,

2001). The minimum r2 is estimated as where the model obtains its best-fit with

observations for each diagnostic and also has uncertainties.

4. Use Bayes theorem to estimate the joint distribution p(θ|Tobs, CN ) that results from

combining the likelihood functions for each diagnostic.

We note that this algorithm has similar features to those of both Andronova & Schlesinger

(2001) and Gregory et al. (2002) with differences arising in different choices of climate

models and observational data. Only the Forest et al. (2002) approach uses spatio-temporal

patterns of climate change that are integral to the optimal fingerprint detection algorithm

(e.g., Allen & Tett, 1999). The resulting posterior pdf then determines the regions of the

parameter space, θ, that can be rejected as being inconsistent with the multiple observational data sets.

1.2 Description of MIT 2D Climate Model

The MIT 2D climate model consists of a zonally averaged atmospheric model coupled to

a mixed-layer Q-flux ocean model, with heat anomalies diffused below the mixed-layer.

The model details can be found in Sokolov & Stone (1998). The atmospheric model is a

zonally averaged version of the Goddard Institute for Space Studies (GISS) Model II general circulation model (Hansen et al., 1983) with parameterizations of the eddy transports

of momentum, heat, and moisture by baroclinic eddies (Stone & Yao, 1987, 1990). The

model version we use has 46 latitude bands ( Δφ = 4o ) and 11 vertical layers with 4 layers

above the tropopause. The 8◦latitudinal resolution used in our earlier study was improved

to 4◦mainly to allow for smoother transitions of melting sea-ice in high latitudes.

S-2
The model also employs a 2.5D Q-flux ocean mixed layer model with 4◦x 5◦latitudelongitude grid cells and diffusion of heat anomalies into the deep-ocean below the climatological mixed layer. Allowing for changing sea-ice in multiple grid cells, this provides smoother melt transitions than before. This ocean component model is described by

Hansen et al. (1983) and only increased computations by a few percent. The model uses

the GISS radiative transfer code which contains all radiatively important trace gases as well

as aerosols and their effect on radiative transfer. The surface area of each latitude band is

divided into a percentage of land, ocean, land-ice, and sea-ice with the surface fluxes computed separately for each surface type. This allows for appropriate treatment of radiative

forcings dependent on underlying surface type such as anthropogenic aerosols. The atmospheric component of the model, therefore, provides most important nonlinear interactions

between components of the atmospheric system.

The MIT model has two parameters that determine the timescale and magnitude of the

decadal to century timescale response to an external forcing. These are the equilibrium

climate sensitivity (S) to a doubling of CO2 concentrations and the global-mean vertical

thermal diffusivity (Kv) for the mixing of thermal anomalies into the deep ocean. Sokolov

& Stone (1998) have shown that the large-scale response of a given 3D AOGCM can be duplicated by the MIT 2D model with an appropriate choice of these two parameters for any

forcing (see supplemental material, section 2.2). Published values of 3D AOGCM model

sensitivities range from 2.0◦to 5.1◦C (Cubasch et al., 2001). Comparisons between timeseries of transient climate changes calculated with the MIT model and with 3D AOGCMs

show that the GCM’s equivalent vertical diffusivities range from 4.0 to 25.0 cm2/s (see

Figure 3 in the main text). The model’s flexibility to duplicate AOGCM responses, along

with its computational efficiency, provides the tool needed for exploring questions which

would be impractical to explore with 3D AOGCMs.

1.3 Temperature Change Diagnostics

We have elected to use the same climate change diagnostics as used in Forest et al. (2002).

This allows us to isolate the effect of the additional forcings on the posterior distributions.

The climate change diagnostics used in Forest et al. (2002) were:

• Surface temperatures: 4 equal-area latitude averages for each of five decades from

1946–1995 referenced to 1905-1995 climatology. Source: Jones (2000)

• Deep-ocean temperatures: trend in global-mean 0–3km deep layer of pentadal avS-3
Table 1: Comparison of applied forcings for GSO and GSOLSV scenarios.

GSO GSOLSV

(Forest et al., 2001, 2002) (This study.)

G Radiative forcing by greenhouse All greenhouse gas concentrations

gases prescribed as equivalent specified explicitly

CO2 concentrations

S Sulfate aerosol loading scaled Updated historical sulfur emissions

by historical sulfur emissions to Smith et al. (2003)

(Hameed & Dignon, 1992)

O Stratospheric and tropospheric Stratospheric and tropospheric ozone

ozone concentrations specifed specified from 1860-2001

from 1979-1995 (Hansen et al., 2002)

L N/A Land-use and land-cover change

(Ramankutty & Foley, 1999)

S N/A Solar irradiance change including

secular change. (Lean, 2000)

V N/A Volcanic forcing specified as

stratospheric aerosol optical depth

Sato et al. (1993) updated to 2001.

erages from 1952–1995. Source: Levitus et al. (2000)

• Upper-air temperatures: Difference between 1986–1995 and 1961–1980 averages

at eight standard pressure levels from 850-50 hPa on 5 degree grid. GSO: Years

1963-4 and 1992 were removed. GSOLSV: all years used. Source: Parker et al.

(1997)

1.4 Summary of Applied Climate Forcings

The current set of simulations has an updated set of historical climate forcings during the

period 1860-1995. The set of forcings is now: greenhouse gas concentrations, sulfate

aerosol loadings, tropospheric and stratospheric ozone concentrations, land-use vegetation changes, solar irradiance changes, and stratospheric aerosols from volcanic eruptions.

GSOLSV is the shorthand notation for this set of forcings (summarized in Table 1.)

Previously, greenhouse gas concentrations were prescribed as equivalent CO2 concenS-4
trations such that the radiative forcing by all gases was converted into a change in the CO2

concentration alone with all other gases remaining fixed. Now, we specify the concentration of each gas separately. The climate model calculates the historical aerosol forcing

prescribed by a change in the surface albedo which depends on the sulfate loadings as a

function of latitude. This loading pattern remains fixed but is scaled by the time series

of SO2 emissions to obtain the time varying forcing. In the new simulations, we have

updated the SO2 emissions after 1990 with Smith et al. (2003). Previously, the stratospheric and tropospheric ozone concentrations were held constant prior to 1979 and then

specified from 1979-1995. Now, we include a historical time series (Hansen et al., 2002)

based on GISS estimates of tropospheric ozone in 1890 (Wang & Jacob, 1998). Prior

to 1890, the concentrations are held fixed. From 1890 to 1979, a linear interpolation of

concentrations were used and after 1979, observed estimates of concentrations were specified. Stratospheric ozone concentrations were held constant prior to 1970, but with a

QBO and solar cycle included, and a trend is prescribed for 1970-1979 that is half that

in 1979-1996, and observed concentrations from 1979-2001. The stratospheric aerosols

from volcanic eruptions are specified as a change in its optical depth for the stratospheric

model layers. The solar irradiance changes are from Lean (2000) with the secular trend

included. (The ozone, stratospheric aerosol, and solar irradiance forcings are described at

http://www.giss.nasa.gov/data/simodel/ and described in Hansen et al. (2002).) The landuse vegetation changes (Ramankutty & Foley, 1999) are specifed for 1860-1992 and held

fixed after 1992.

2 Sensitivity Tests

2.1 Effects of Noise Model Truncation

The treatment of the noise model is a key element of the detection problem as it represents

the noise component in the goodness of fit statistic: r 2 = ΔT TC−1

N (κ)ΔT where ΔT

is the difference between the model and observed temperature change pattern. C −1

N (κ)

is the pseudo-inverse (Mardia et al., 1979) in which the eigendecomposition of the noise

covariance matrix is used: CN = UΛUT with U = matrix of eigenvectors and Λ is the

diagonal matrix of eigenvalues, λi. This allows us to write: C −1

N (κ) = UTΛ−1U where the

diagonal elements of Λ−1 are 1/λi and the other elements are zero. The truncation must

be chosen to retain only the first κ values of Λ−1 and thereby eliminating the projection of

the temperature change pattern onto those patterns of variability with the smallest variance

S-5
in the control run data. Depending on the dimension of the diagnostic, these small λi are

considered underconstrained and treated as poorly estimated.

We find that the posterior distributions are sensitive to the choice of κsfc but not for

κupper−air and therefore, the selection criteria for κsfc must be considered. The marginal

posteriors (Fig. 1) indicate changes when κsfc change from 14 to 15 but weak sensitivity

to subsequent changes. Based on Allen & Tett (1999), κsfc = 20 cannot be rejected, yet

a better selection method does not appear available. Using our data, Curry et al. (2005)

explored an alternative method for choosing κsfc based on Bayesian methods and find a

strong cutoff when κsfc changes from 18 to 19 and a break in the posteriors when κsfc

changes from 16 to 17. Multiple selection criteria were tested and from the Marginal Likelihood method (Chib & Jeliazhov, 2001), κsfc = 16 appears to be an appropriate truncation

but both methods give similar results.

We test the sensitivity to the truncation for the surface CN estimate with κsfc = 14,15,16

(Fig. 1a) and κsfc = 16,18,20 (Fig. 1b). The PDFs change from 14 to 15, and from 19 to

20, with little change from 15 to 19.

2.2 2D Model Simulation of Ocean Heat Uptake

Sokolov & Stone (1998) compared the performance of the MIT 2D climate model in transient global warming scenarios with the performance of AOGCMs, and showed that the

net heat uptake simulated by any given AOGCM could be modeled accuratedly by the 2D

model with a choice of S and Kv unique to each AOGCM. In particular, the unique choice

worked for different forcing scenarios. However, the scenarios they examined were mostly

ones with forcing stronger than that experienced in the 20th century, e.g., CO2 concentrations increasing 1% per year, or the IPCC IS92a scenario, and none of these included

volcanic forcing. Thus, to determine whether the values of S and Kv determined from such

scenarios would still simulate accurately the heat uptake by an OGCM in a 20th century

scenario, we carried out several simulations in which the 2D ocean model in the MIT 2D

model was replaced by a 3D OGCM.

The OGCM used was the MIT OGCM (Marshall et al., 1997) at coarse resolution (4◦)

with conventional subgrid-scale parameterizations. The climate model formed by coupling

the standard version of the MIT 2D atmospheric model with this 3D OGCM is documented

at “http://web.mit.edu/globalchange/www/MITJPSPGC Rpt122.pdf”. The 2D/3D coupled

model was then run in a scenario in which CO2 increased by 1% per year for 100 years,

and the 2D model’s S and Kv were picked so that the surface temperature and deep ocean

S-6
heat uptake of the 2D model matched the results with the 3D ocean. Then the coupled

2D/3D model and the matching version of the MIT 2D model were both integrated from

1860 to 2100 with identical forcings as further discussed in Sokolov et al. (2005). From

1860-1990, all forcings are the same as used in this paper with the net aerosol forcing set to

-0.35 W/m2. From 1991-2100, the model is forced by a reference emission scenario from

the MIT EPPA4 emissions model and yields a ≈7 W/m2 net forcing in 2100 with respect to

1990 due to anthropogenic and natural greenhouse gas emissions (for complete details, see

Sokolov et al., 2005). The results for the global mean surface temperature and the thermal

expansion component of sea-level rise from 1860-2100 are shown in Fig. 2 and indicate

that the 2D model closely matches the changes simulated by the coupled 2D/3D model.

2.3 Sensitivity to latitude dependence of ocean heat uptake

In the MIT 2D climate model, heat anomalies in the mixed layer are diffused into the deepocean (below the climatological mixed layer) based on a latitude dependent profile, Kv(φ).

We note that this diffusive process represents all mixing processes and not just a diffusion

process in the interior deep-ocean. In the original Q-flux model (Hansen et al., 1983),

Kv(φ) was based on observations of tritium mixing into the deep ocean. As presented by

Sun & Hansen (2003), the changes in ocean heat content with depth for the 1951-1998

period differ from the mixing implied by the tritium distribution. The ocean heat content

changes show stronger heat uptake in the tropical and mid-latitude regions as compared to

high latitude regions.

We test whether the deep-ocean heat uptake distribution affects the climate change diagnostics by estimating an empirical latitude dependent profile, K

v(φ), to reflect the observed changes in ocean heat content (see Fig. 3). This provides a pattern of deep-ocean

heat uptake that mimics the observed pattern (Fig. 4).

The effect of using K

v(φ) based on the observed ocean heat content changes results

in almost no change in the global mean surface temperatures (differing by at most ±0.05

K). There are very minor differences in the response and they have very little effect on the

large-scale averages that are used in the optimal fingerprint analyses to estimate the PDF of

the climate system properties. The conditional probability distribution, p(θ|Tobs, CN , Faer

= -0.5 W/m2) (Figure 5) is virtually unchanged with the new Kv distribution.

We conclude that the spatial distribution of heat-uptake is not a critical component for

estimating the p(θ|Tobs, CN ) for climate system properties. Because large-scale averages

are used in the analysis, the small regional differences are not affecting the diagnostics.

S-7
The ability for AOGCMs to match the observed behavior on smaller scales remains an

open research question.

2.4 Sensitivity to Volcanic Forcing Uncertainty

A key difference between the responses to the GSO and GSOLSV forcing scenarios is the

strong decrease in global temperature following the volcanic eruptions. The response to the

volcanic aerosol distribution has a significant impact on the temperature time-series for a

given choice of θ (main text Fig. 1a). The difference in the 10-year running mean is larger

than the observational errors for global mean surface temperature and thus, will lead to a

shift in the p(θ|Tobs, CN ) that cannot be attributed to unforced variability alone.

As discussed in the main text, the uncertainty in the climate forcings has been treated by

changing the amplitude of the sulfate aerosol forcing and maintaining all other forcings at

their specified strengths. Given the impact of the volcanic forcing, we test whether the uncertainty in the volcanic forcing has a significant impact on the resulting pdfs. The forcing

uncertainty (2σ) has been subjectively assessed as 30%, 20%, and 15% for the Mt. Agung

(1964), El Chichon (1982), and Mt. Pinatubo (1992) eruptions (Hansen et al., 2002). We

chose to run a cross-section experiment (S-Faer) with constant Kv = 4. cm2/s and volcanic

forcing reduced by 25% to assess whether the uncertainty would have a significant impact

on the estimated pdfs. The posteriors (Fig. 6) for GSOLSV and GSOLSV-75 indicate that

slightly stronger Faer and lower S are required to match the observations under reduced

volcanic forcing. The changes in these two parameters are, however, relatively small.

S-8
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S-12

0 2 4 6 8 10 12

0.0

0.1

0.2

0.3

0.4

02468

0.0

0.1

0.2

0.3

0.4

0.5

0.6

-1.5 -1.0 -0.5 0.0 0.5

0.00

0.05

0.10

0.15

0.20

0.25

0.30

κsfc = 16

κsfc = 15

κsfc = 14

p(CS): GSOLSV

p(Kv): GSOLSV

p(Faer): GSOLSV

Climate Sensitivity (K)

SQRT (Effective Oceanic Diffusion) (Sqrt (cm2/s))

Net Aerosol Forcing (W/m2)

Density Density Density

Figure S1: The marginal posterior probability density function for the three climate system

properties for uniform priors while varying sfc = 16, 18, 20 (a) and 14, 15, 16 (b) cases. This

marginal distribution is estimated by integrating the density function over the remaining two

parameters and renormalizing. The whisker plots indicate percentile boundaries for the 2.5 to

97.5 (dots), 5 to 95 (vertical bar ends), 25 to 75 (box ends), and 50 (vertical bar in box). The mean

is indicated with the diamond and the mode is the peak in the distribution. The whisker plots

correspond to the density function curves depicted the corresponding color.
S-13

1900 1950 2000 2050 2100

 Years 

0.0

10.0

20.0

30.0

40.0

 Sea level (cm)

0.0

1.0

2.0

3.0

4.0

Temperature (C)

(b) Sea level rise

IGSM2.2 

IGSM2.3 

(a) Surface Air Temperature (C) 

IGSM2.2 

IGSM2.3 

Figure S2: Changes in global-mean (a) annual-mean surface temperatures, and (b) sea-level rise due

to thermal expansion from MIT 2D climate model with 2.5D Q-flux mixed layer model (IGSM2.2)

as used in this study and with 3D ocean model (IGSM2.3) in response to 20th century GSOLSV

forcings (1860-1990) and to MIT EPPA4 reference emissions scenario after 1990. (Sokolov et al.

(2005) provides further details.)
S-14

-90 -60 -30 0 30 60 90

Ocean heat content based

Tritium based

Latitude

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Effective Diffusivity (cm2/s)

Figure S3: Latitude dependence of Kv() for standard distribution based on mixing of tritium and

new distribution based on observed changes in ocean heat content (Levitus et al., 2000).

-20

-10

0

10

20

30

40

50

-1 -0.5 0 0.5 1

OBS

Tritium based

OHC based

Ocean Heat Content

W-year/m2

Sin(latitude)

Figure S4: Changes in ocean heat content for observed (after Sun & Hansen, 2003) and GSOLSV

simulations with old (tritium based) and new (ocean heat content based) Kv() distributions. The

total heat uptake in the simulations is larger than observed because the combination of S, Kv,

and Faer result in too much deep ocean heat uptake. Global-mean Kv are identical in both cases.
S-15

02468

0

1

2

3

4

5

6

G FDL-R 15

G FDL-R 15

HadC M2

E C HAM3/LS G

NCAR CS M

CG C M1

CS IR O

CS IR O

MR I1

HadC M3

PCM

GIS S -HYC OM

G IS S -G R

(b)

(a)

02468

0

1

2

3

4

5

6

HadC M2

E C HAM3/LS G

NCAR CS M

CG C M1

MR I1

HadC M 3

PCM

GIS S -HYC OM

G IS S -G R

Climate Sensitvity (K) Climate Sensitvity (K)

Rate of Ocean Heat Uptake [Sqrt(Kv)]

Rate of Ocean Heat Uptake [Sqrt(Kv)]

Figure S5: Conditional probability distributions, p(|Tobs, CN, Faer = –0.5 W/m2), based on GSOLSV

simulations with (a) tritium based and (b) ocean heat content based Kv() distributions. Blue

shading indicates rejection regions of 10% (light) and 1% (dark) significance levels. The modes

of the distributions are S = 2.8 and 2.6 K and Kv = 2.0 and 1.7 cm2/s for (a) and (b), respectively.

Uniform priors for all parameters were used in both distributions.
S-16

-1.5 -1.0 -0.5 0.0 0.5

0

1

2

3

4

5

6

-1.5 -1.0 -0.5 0.0 0.5

0

1

2

3

4

5

6

Climate Sensitvity (K) Climate Sensitvity (K)

p(S,Faer) with standard volcano forcing

p(S,Faer) with reduced volcano forcing

Net Aerosol Focring in 1980s (W/m2)

Net Aerosol Focring in 1980s (W/m2)

(b)

(a)

Figure S6: Conditional probability distributions, p(|Tobs, CN, Kv = 4 cm2/s), based on GSOLSV

simulations with (a) standard volcanic forcing and (b) with volcanic forcing reduced by 25%.

Blue shading indicates rejection regions of 10% (light) and 1% (dark) significance levels. Uniform

priors for all parameters were used in both distributions.
REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change

Contact the Joint Program Office to request a copy. The Report Series is distributed at no charge.

1. Uncertainty in Climate Change Policy Analysis Jacoby &

Prinn December 1994

2. Description and Validation of the MIT Version of the

GISS 2D Model Sokolov & Stone June 1995

3. Responses of Primary Production and Carbon Storage

to Changes in Climate and Atmospheric CO2

Concentration Xiao et al. October 1995

4. Application of the Probabilistic Collocation Method

for an Uncertainty Analysis Webster et al. January 1996

5. World Energy Consumption and CO2 Emissions:

1950-2050 Schmalensee et al. April 1996

6. The MIT Emission Prediction and Policy Analysis

(EPPA) Model Yang et al. May 1996

7. Integrated Global System Model for Climate Policy

Analysis Prinn et al. June 1996 (superseded by No. 36)

8. Relative Roles of Changes in CO2 and Climate to

Equilibrium Responses of Net Primary Production

and Carbon Storage Xiao et al. June 1996

9. CO2 Emissions Limits: Economic Adjustments and the

Distribution of Burdens Jacoby et al. July 1997

10. Modeling the Emissions of N2O & CH4 from the

Terrestrial Biosphere to the Atmosphere Liu August

1996

11. Global Warming Projections: Sensitivity to Deep

Ocean Mixing Sokolov & Stone September 1996

12. Net Primary Production of Ecosystems in China and

its Equilibrium Responses to Climate Changes Xiao et

al. November 1996

13. Greenhouse Policy Architectures and Institutions

Schmalensee November 1996

14. What Does Stabilizing Greenhouse Gas

Concentrations Mean? Jacoby et al. November 1996

15. Economic Assessment of CO2 Capture and Disposal

Eckaus et al. December 1996

16. What Drives Deforestation in the Brazilian Amazon?

Pfaff December 1996

17. A Flexible Climate Model For Use In Integrated

Assessments Sokolov & Stone March 1997

18. Transient Climate Change and Potential Croplands of

the World in the 21st Century Xiao et al. May 1997

19. Joint Implementation: Lessons from Title IV’s

Voluntary Compliance Programs Atkeson June 1997

20. Parameterization of Urban Sub-grid Scale Processes

in Global Atmospheric Chemistry Models Calbo et al.

July 1997

21. Needed: A Realistic Strategy for Global Warming

Jacoby, Prinn & Schmalensee August 1997

22. Same Science, Differing Policies; The Saga of Global

Climate Change Skolnikoff August 1997

23. Uncertainty in the Oceanic Heat and Carbon Uptake

and their Impact on Climate Projections Sokolov et al.

Sept 1997

24. A Global Interactive Chemistry and Climate Model

Wang, Prinn & Sokolov September 1997

25. Interactions Among Emissions, Atmospheric

Chemistry and Climate Change Wang & Prinn

September 1997

26. Necessary Conditions for Stabilization Agreements

Yang & Jacoby October 1997

27. Annex I Differentiation Proposals: Implications for

Welfare, Equity and Policy Reiner & Jacoby October 1997

28. Transient Climate Change and Net Ecosystem

Production of the Terrestrial Biosphere Xiao et al.

November 1997

29. Analysis of CO2 Emissions from Fossil Fuel in Korea:

19611994 Choi November 1997

30. Uncertainty in Future Carbon Emissions: A

Preliminary Exploration Webster November 1997

31. Beyond Emissions Paths: Rethinking the Climate

Impacts of Emissions Protocols Webster & Reiner

November 1997

32. Kyoto’s Unfinished Business Jacoby et al. June 1998

33. Economic Development and the Structure of the

Demand for Commercial Energy Judson et al. April

1998

34. Combined Effects of Anthropogenic Emissions &

Resultant Climatic Changes on Atmospheric OH

Wang & Prinn April 1998

35. Impact of Emissions, Chemistry, and Climate on

Atmospheric Carbon Monoxide Wang & Prinn April

1998

36. Integrated Global System Model for Climate Policy

Assessment: Feedbacks and Sensitivity Studies Prinn et

al. June 98

37. Quantifying the Uncertainty in Climate Predictions

Webster & Sokolov July 1998

38. Sequential Climate Decisions Under Uncertainty: An

Integrated Framework Valverde et al. September 1998

39. Uncertainty in Atmospheric CO2 (Ocean Carbon Cycle

Model Analysis) Holian Oct. 1998 ( superseded by No. 80)

40. Analysis of Post-Kyoto CO2 Emissions Trading Using

Marginal Abatement Curves Ellerman & Decaux

October 1998

41. The Effects on Developing Countries of the Kyoto

Protocol and CO2 Emissions Trading Ellerman et al.

November 1998

42. Obstacles to Global CO2 Trading: A Familiar Problem

Ellerman November 1998

43. The Uses and Misuses of Technology Development as

a Component of Climate Policy Jacoby November 1998

44. Primary Aluminum Production: Climate Policy,

Emissions and Costs Harnisch et al. December 1998

45. Multi-Gas Assessment of the Kyoto Protocol Reilly et

al. January 1999
REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change

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46. From Science to Policy: The Science-Related Politics of

Climate Change Policy in the U.S. Skolnikoff January

1999

47. Constraining Uncertainties in Climate Models Using

Climate Change Detection Techniques Forest et al.

April 1999

48. Adjusting to Policy Expectations in Climate Change

Modeling Shackley et al. May 1999

49. Toward a Useful Architecture for Climate Change

Negotiations Jacoby et al. May 1999

50. A Study of the Effects of Natural Fertility, Weather

and Productive Inputs in Chinese Agriculture Eckaus

& Tso July 1999

51. Japanese Nuclear Power and the Kyoto Agreement

Babiker, Reilly & Ellerman August 1999

52. Interactive Chemistry and Climate Models in Global

Change Studies Wang & Prinn September 1999

53. Developing Country Effects of Kyoto-Type Emissions

Restrictions Babiker & Jacoby October 1999

54. Model Estimates of the Mass Balance of the

Greenland and Antarctic Ice Sheets Bugnion October

1999

55. Changes in Sea-Level Associated with Modifications

of Ice Sheets over 21st Century Bugnion October 1999

56. The Kyoto Protocol & Developing Countries Babiker et

al. October 1999

57. Can EPA Regulate Greenhouse Gases Before the

Senate Ratifies the Kyoto Protocol? Bugnion & Reiner

November 1999

58. Multiple Gas Control Under the Kyoto Agreement

Reilly, Mayer & Harnisch March 2000

59. Supplementarity: An Invitation for Monopsony?

Ellerman & Sue Wing April 2000

60. A Coupled Atmosphere-Ocean Model of Intermediate

Complexity Kamenkovich et al. May 2000

61. Effects of Differentiating Climate Policy by Sector: A

U.S. Example Babiker et al. May 2000

62. Constraining Climate Model Properties Using

Optimal Fingerprint Detection Methods Forest et al.

May 2000

63. Linking Local Air Pollution to Global Chemistry and

Climate Mayer et al. June 2000

64. The Effects of Changing Consumption Patterns on

the Costs of Emission Restrictions Lahiri et al. August

2000

65. Rethinking the Kyoto Emissions Targets Babiker &

Eckaus August 2000

66. Fair Trade and Harmonization of Climate Change

Policies in Europe Viguier September 2000

67. The Curious Role of “Learning” in Climate Policy:

Should We Wait for More Data? Webster October 2000

68. How to Think About Human Influence on Climate

Forest, Stone & Jacoby October 2000

69. Tradable Permits for Greenhouse Gas Emissions: A

primer with reference to Europe Ellerman November

2000

70. Carbon Emissions and The Kyoto Commitment in the

European Union Viguier et al. February 2001

71. The MIT Emissions Prediction and Policy Analysis

Model: Revisions, Sensitivities and Results Babiker et al.

February 2001

72. Cap and Trade Policies in the Presence of Monopoly

and Distortionary Taxation Fullerton & Metcalf March

2001

73. Uncertainty Analysis of Global Climate Change

Projections Webster et al. March 2001 ( superseded by

No.95 )

74. The Welfare Costs of Hybrid Carbon Policies in the

European Union Babiker et al. June 2001

75. Feedbacks Affecting the Response of the

Thermohaline Circulation to Increasing CO2

Kamenkovich et al. July 2001

76. CO2 Abatement by Multi-fueled Electric Utilities: An

Analysis Based on Japanese Data Ellerman & Tsukada

July 2001

77. Comparing Greenhouse Gases Reilly et al. July 2001

78. Quantifying Uncertainties in Climate System

Properties using Recent Climate Observations Forest

et al. July 2001

79. Uncertainty in Emissions Projections for Climate

Models Webster et al. August 2001

80. Uncertainty in Atmospheric CO2 Predictions from a

Global Ocean Carbon Cycle Model Holian et al.

September 2001

81. A Comparison of the Behavior of AO GCMs in

Transient Climate Change Experiments Sokolov et al.

December 2001

82. The Evolution of a Climate Regime: Kyoto to

Marrakech Babiker, Jacoby & Reiner February 2002

83. The “Safety Valve” and Climate Policy Jacoby &

Ellerman February 2002

84. A Modeling Study on the Climate Impacts of Black

Carbon Aerosols Wang March 2002

85. Tax Distortions & Global Climate Policy Babiker et al.

May 2002

86. Incentive-based Approaches for Mitigating

Greenhouse Gas Emissions: Issues and Prospects for

India Gupta June 2002

87. Deep-Ocean Heat Uptake in an Ocean GCM with

Idealized Geometry Huang, Stone & Hill September

2002

88. The Deep-Ocean Heat Uptake in Transient Climate

Change Huang et al. September 2002
REPORT SERIES of the MIT Joint Program on the Science and Policy of Global Change

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89. Representing Energy Technologies in Top-down

Economic Models using Bottom-up Info McFarland et

al. October 2002

90. Ozone Effects on Net Primary Production and Carbon

Sequestration in the U.S. Using a Biogeochemistry

Model Felzer et al. November 2002

91. Exclusionary Manipulation of Carbon Permit

Markets: A Laboratory Test Carlén November 2002

92. An Issue of Permanence: Assessing the Effectiveness

of Temporary Carbon Storage Herzog et al. December

2002

93. Is International Emissions Trading Always Beneficial?

Babiker et al. December 2002

94. Modeling Non-CO2 Greenhouse Gas Abatement

Hyman et al. December 2002

95. Uncertainty Analysis of Climate Change and Policy

Response Webster et al. December 2002

96. Market Power in International Carbon Emissions

Trading: A Laboratory Test Carlén January 2003

97. Emissions Trading to Reduce Greenhouse Gas

Emissions in the U.S.: The McCain-Lieberman Proposal

Paltsev et al. June 2003

98. Russia’s Role in the Kyoto Protocol Bernard et al. June

2003

99. Thermohaline Circulation Stability: A Box Model

Study Lucarini & Stone June 2003

100. Absolute vs. Intensity-Based Emissions Caps

Ellerman & Sue Wing July 2003

101. Technology Detail in a Multi-Sector CGE Model:

Transport Under Climate PolicySchafer & Jacoby July

2003

102. Induced Technical Change and the Cost of Climate

Policy Sue Wing September 2003

103. Past and Future Effects of Ozone on Net Primary

Production and Carbon Sequestration Using a Global

Biogeochemical Model Felzer et al. (revised) January

2004

104. A Modeling Analysis of Methane Exchanges

Between Alaskan Ecosystems & the Atmosphere

Zhuang et al. November 2003

105. Analysis of Strategies of Companies under Carbon

Constraint Hashimoto January 2004

106. Climate Prediction: The Limits of Ocean Models Stone

February 2004

107. Informing Climate Policy Given Incommensurable

Benefits Estimates Jacoby February 2004

108. Methane Fluxes Between Ecosystems & Atmosphere

at High Latitudes During the Past Century Zhuang et

al. March 2004

109. Sensitivity of Climate to Diapycnal Diffusivity in the

Ocean Dalan et al. May 2004

110. Stabilization and Global Climate Policy Sarofim et al.

July 2004

111. Technology and Technical Change in the MIT EPPA

Model Jacoby et al. July 2004

112. The Cost of Kyoto Protocol Targets: The Case of

Japan Paltsev et al. July 2004

113. Economic Benefits of Air Pollution Regulation in the

USA: An Integrated Approach Yang et al. (revised) January

2005

114. The Role of Non-CO2 Greenhouse Gases in Climate

Policy: Analysis Using the MIT IGSM Reilly et al. August

2004

115. Future United States Energy Security Concerns

Deutch September 2004

116. Explaining Long-Run Changes in the Energy

Intensity of the U.S. Economy Sue Wing September

2004

117. Modeling the Transport Sector: The Role of Existing

Fuel Taxes in Climate Policy Paltsev et al. November

2004

118. Effects of Air Pollution Control on Climate Prinn et al.

January 2005

119. Does Model Sensitivity to Changes in CO2 Provide a

Measure of Sensitivity to the Forcing of Different

Nature? Sokolov March 2005

120. What Should the Government Do To Encourage

Technical Change in the Energy Sector? Deutch May

2005

121. Climate Change Taxes and Energy Efficiency in

Japan Kasahara et al. May 2005

122. A 3D Ocean-Seaice-Carbon Cycle Model and its

Coupling to a 2D Atmospheric Model: Uses in Climate

Change Studies Dutkiewicz et al. May 2005

123. Simulating the Spatial Distribution of Population

and Emissions to 2100 Asadoorian May 2005

124. MIT Integrated Global System Model (IGSM)

Version2: Model Description and Baseline Evaluation

Sokolov et al. July 2005

125. The MIT Emissions Prediction and Policy Analysis

(EPPA) Model: Version4 Paltsev et al. August 2005

126. Estimated PDFs of Climate System Properties

Including Natural and Anthropogenic Forcings

Forestet al. September 2005