NASA/TM–2014-104606 / Vol. 33

$XJXVW 2014

Cécile S. Rousseaux and Watson W. Gregg

National Aeronautics and 

Space Administration 

Goddard Space Flight Center

Greenbelt, Maryland 20771

Technical Report Series on Global Modeling and Data Assimilation, 

Volume 33

Randal D. Koster, Editor 

The Effects of Chlorophyll Assimilation on Carbon 

Fluxes in a Global Biogeochemical Model 
Since its founding, NASA has been dedicated to the 

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National Aeronautics and 

Space Administration 

Goddard Space Flight Center

Greenbelt, Maryland 20771

NASA/TM–2014-104606 / Vol. 33

$XJXVW 2014

Cécile S. Rousseaux 

Universities Space Research Association (GESTAR), Greenbelt, Maryland

Watson W. Gregg 

NASA’s Goddard Space Flight Center, Greenbelt, Maryland 

Technical Report Series on Global Modeling and Data Assimilation, 

Volume 33

Randal D. Koster, Editor

The Effects of Chlorophyll Assimilation on Carbon Fluxes in a 

Global Biogeochemical Model
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Abstract

In this paper, we investigated whether the assimilation of remotely-sensed chlorophyll data 

can improve the estimates of air-sea carbon dioxide fluxes (FCO2). Using a global, established 

biogeochemical model (NASA Ocean Biogeochemical Model, NOBM) for the period 2003-

2010, we found that the global FCO2 values produced in the free-run and after assimilation were

within -0.6 mol C m-2 y

-1 of the observations. The effect of satellite chlorophyll assimilation was 

assessed in 12 major oceanographic regions. The region with the highest bias was the North 

Atlantic. Here the model underestimated the fluxes by 1.4 mol C m-2 y

-1 whereas all the other 

regions were within 1 mol C m-2 y

-1 of the data. The FCO2 values were not strongly impacted by 

the assimilation, and the uncertainty in FCO2 was not decreased, despite the decrease in the 

uncertainty in chlorophyll concentration. Chlorophyll concentrations were within ~25% of the 

database in 7 out of the 12 regions, and the assimilation improved the chlorophyll concentration 

in the regions with the highest bias by 10-20%. These results suggest that the assimilation of 

chlorophyll data does not considerably improve FCO2 estimates and that other components of the 

carbon cycle play a role that could further improve our FCO2 estimates. 

1

Table of Contents 

List of Figures ........................................................................................................................ 4

Introduction .......................................................................................................................... 5

2. Methods ............................................................................................................................ 7

3. Results............................................................................................................................... 8

3.1 Chlorophyll Comparisons with Data ................................................................................ 8

3.2 FCO2 Comparisons with Data ......................................................................................... 12

4. Discussion ....................................................................................................................... 15

References .......................................................................................................................... 18

Appendix A: Acknowledgements ......................................................................................... 19


List of Figures

Figure 1: Delineation of the 12 major oceanographic basins. ........................................................ 7

Figure 2: Ocean Basin Correlation between the satellite-based estimates of chlorophyll and both 

the free-run and the assimilation values over the different ocean basins. (*) indicates statistical 

significance (p < 0.05), NS = not significant. ................................................................................. 9

Figure 3: Chlorophyll concentrations for the free-run, the assimilation run, and the satellite 

database, globally and in the twelve major oceanographic regions. ............................................. 10

Figure 4: Map of Chlorophyll concentration (μg chl L-1) in June 2005 for (a) MODIS Aqua, (b) 

Free-run and (c) after assimilation of the MODIS Aqua satellite data. ........................................ 11

Figure 5: Air-Sea fluxes (mol C m-2 y

-1) for the free-run, the assimilation run, and the database 

globally and in the twelve major oceanographic regions. ............................................................. 13

Figure 6: Map of FCO2 (mol C m-2 y

-1) from (a) the LDEO database, (b) the free-run and (c) after 

assimilation of satellite chlorophyll for June 2005. ...................................................................... 14

Figure 7: Correlation between the satellite database of FCO2 and the FCO2 values produced in 

the free-run and after assimilation. (*) indicates statistical significance (p < 0.05), NS = not 

significant. ..................................................................................................................................... 15

4
Introduction 

Every year, approximately 90 gigatons of carbon are exchanged between the atmosphere and 

the ocean (Falkowski et al., 2000). Ocean sinks account for ~half of the anthropogenic CO2

emissions (Sabine et al., 2004). On longer time scales, deep-ocean export of carbon together 

with land-atmosphere exchanges plays an important role and is expected to be the dominant 

sink of anthropogenic carbon. In the subtropical ocean (~40°N and S), water loses heat to the 

atmosphere and consequently takes up CO2 from the atmosphere (Takahashi et al., 2009).

Inversely to the effect of temperature on pCO2, the phytoplankton community takes up CO2

and thrives in nutrient-rich (e.g. recently upwelled) water, thereby counteracting and in some 

cases overwhelming the effects of recently cold, CO2-rich, upwelled water. The air-sea CO2

fluxes in high latitude oceans are governed primarily by deep convection in winter and 

biological uptake during spring and summer. At lower latitudes the warm water increases 

pCO2 in the ocean and leads to a loss of CO2 to the atmosphere. Process-based understanding 

of ocean carbon cycle is needed to predict future variations in the efficacy of the oceanic 

carbon sink.

Takahashi et al. (2002) amassed ~1 million surface pCO2 observations and used these to 

derive air-sea CO2 fluxes for a nominal year, namely, 1995 (Takahashi et al., 2002).

Uncertainties about these fluxes are directly linked to the spatial and temporal coverage of 

the measurements as well as the relationship between wind speeds on air-sea CO2 gas 

transfer velocity. Others have used biogeochemical models of different level of complexity, 

whether local or global, to estimate the source and sinks of CO2. Gregg et al. (2013) used 

MERRA reanalysis data to force an ocean biogeochemical model (NASA Ocean 

Biogeochemical Model, or NOBM) and compared the fluxes obtained with those from public 

archives. The model produced global mean differences of 0.02% (~0.3 μM) for Dissolved 

Organic Carbon (DIC), -0.3% (-1.2 μatm) for pCO2 and -2.3% (-0.03 mol C m-2

y

-1) for 

FCO2 compared to in situ estimates. Basin-scale distributions of all three variables were

significantly correlated with observations. They found that the regions with the largest 

differences between the carbon dioxide fluxes from the model and the observations were in 

the South Atlantic, South Pacific, North Indian, Equatorial Atlantic and North Pacific. 

5
Despite some considerable basin-scale and local departures, they found that all basins were 

represented as sources to the atmosphere or sinks in agreement with in situ estimates.

Data assimilation techniques can be used to constrain the model to track observation time 

series and to optimize certain variables. The assimilation of satellite chlorophyll is known to 

reduce spatial and temporal biases of satellite data. The assimilation of satellite chlorophyll 

produces estimates within 0.1% bias and 33.4% uncertainty as compared to in situ data 

(Gregg, 2008) thereby decreasing the bias of satellite data (-1.3%) while keeping the same 

level of uncertainty (32.7%, Gregg, 2008). Using twin experiments, Tjiputra et al. (2007)

assimilated SeaWiFS chlorophyll in an adjoint model and found that the model-data misfit 

was reduced and that the posteriori run produced realistic carbon flux prediction. Similarly, 

using 1-D twin experiments for two contrasting locations in the North Atlantic, Hemmings 

et al. (2008) found that the assimilation of ocean color improved surface pCO2. Here we 

investigate whether the assimilation of satellite-derived chlorophyll a data can improve 

carbon dioxide fluxes estimates using a global biogeochemical model that assimilates 

satellite-derived chlorophyll. We assess the effects of chlorophyll data assimilation on carbon 

dioxide fluxes globally and in 12 major oceanographic regions (Figure 1) for the period from 

2003 until 2010.

6
Figure 1: Delineation of the 12 major oceanographic basins.

2. Methods 

Global ocean carbon dynamics were simulated using the NASA Ocean Biogeochemical 

Model (NOBM) for the period 2003-2010. NOBM is a three-dimensional representation of 

circulation/biogeochemical/radiative processes in a domain that spans from -84° to 72° at a 1.25°

resolution in water deeper than 200 m. NOBM is coupled with the Poseidon ocean general 

circulation model, which is driven by wind stress, sea surface temperature, and shortwave 

radiation (Gregg and Casey, 2007). The model includes 3 detrital pools (silica, N/C and iron 

detritus), 4 phytoplankton groups (diatoms, coccolithophores, chlorophytes and cyanobacteria) 

and 4 nutrients (ammonium, nitrate, iron and silicate). The phytoplankton groups differ in their 

maximum growth rates, sinking rates, nutrient requirements, and optical properties. 

Carbon cycling involves dissolved organic carbon (DOC) and dissolved inorganic carbon 

(DIC). DOC has sources from phytoplankton, herbivores, and carbon detritus, and a sink to DIC. 

DIC has sources from phytoplankton, herbivores, carbon detritus, and DOC, and it can be 

exchanged with the atmosphere, which serves as either a source or a sink. The ecosystem sink 

7
for DIC is phytoplankton, through photosynthesis. A complete description of the model can be 

found in Gregg et al. (2013). The main output of interest in this effort is the flux of CO2 (FCO2 ,

notation following Arrigo et al., 2010; Doney et al., 2009), representing the exchange of carbon

between the atmosphere and ocean. Positive is defined upward, indicating a source to the 

atmosphere. The fluxes from the model are compared to a data set obtained from the LamontDoherty Earth Observatory (LDEO)

(http://cdiac.ornl.gov/oceans/LDEO_Underway_Database/index.html; Takahashi et al., 2009).

These in situ LDEO data use pCO2 and estimate fluxes from wind speed reanalysis data and 

climatological nutrients from the National Oceanographic Data Center (Takahashi et al., 2009).

The model is initialized with DIC from GLODAP (GLobal Ocean Data Analysis Project) and

is spun up with climatological forcing data from MERRA and atmospheric pCO2 data from 

NOAA ESRL for the year 2000 (368.6 μatm). The model is spun up for 200 years to remove 

residual trends from the model and produce a steady state (Gregg et al., 2013). It is then run 

from 2003 to 2010 using time-varying forcing data from MERRA and atmospheric pCO2 for 

each year from NOAA ESRL. A concurrent run utilizes MODIS Aqua chlorophyll data in 

assimilation mode (Gregg, 2008). Multi-variate nutrient adjustments corresponding to the 

chlorophyll assimilation (Rousseaux and Gregg, 2012) are also included in this latter run. The 

fluxes derived from a free-running simulation (hereafter referred to as free-run) model are 

compared to those from the run with MODIS Aqua chlorophyll data assimilation and to the 

estimates from Takahashi et al. (2009). The chlorophyll concentrations from both the free-run 

and after assimilation are compared to the Level 3 MODIS Aqua chlorophyll data.

3. Results 

3.1 Chlorophyll Comparisons with Data 

The correlation coefficients between satellite-derived chlorophyll and chlorophyll from the 

model increased considerably after assimilation. The correlation coefficients for the free-run 

ranged over the years from 0.54 to 0.69, whereas those for the assimilation run ranged from 0.84 

to 0.96 (Figure 2). The correlation between satellite chlorophyll concentration and chlorophyll 

concentration from the model (from either run) was significant for all years between 2003 and 

2010.

8
Chlorophyll concentrations were within ~25% of the satellite chlorophyll in seven of the 12 

regions (Figure 3). The regions with the highest biases (>30%, both in free-run and after

assimilation) were the North Indian, North Pacific and North Atlantic. In those regions, where 

the bias was high, the assimilation marginally improved the chlorophyll concentration (by 10-

20%, Figure 4). Note that these regions also correspond to where the highest chlorophyll 

concentration was encountered (~0.55 μg chl L-1).

Figure 2: Ocean Basin Correlation between the satellite-based estimates of chlorophyll and both the free-run and the 

assimilation values over the different ocean basins. (*) indicates statistical significance (p < 0.05), NS = not 

significant.

9
Figure 3: Chlorophyll concentrations for the free-run, the assimilation run, and the satellite database, globally and in 

the twelve major oceanographic regions.

10
Figure 4: Map of Chlorophyll concentration (μg chl L-1) in June 2005 for (a) MODIS Aqua, (b) Free-run and (c) 

after assimilation of the MODIS Aqua satellite data.

11
3.2 FCO2 Comparisons with Data 

Globally and regionally, we found that the fluxes were not greatly impacted by the 

assimilation of satellite chlorophyll (Figure 5 and Figure 6). In nine out of the twelve regions, the 

difference between the fluxes from the free-run and those after assimilation was less than 0.1 mol 

C m-2

y

-1. The three regions where these differences were greater than 0.1 mol C m-2

y

-1 were the 

North Central Atlantic, South Pacific and South Indian. In the southern basins the assimilation 

run produced the more biased flux estimates, though the increase in bias was small (Figure 5 and 

Figure 6). 

Globally the fluxes produced by the free-run and after assimilation were -0.25 mol C m-2

y

-1,

which represents an underestimate of FCO2 by ~0.6 mol C m-2

y

-1 (Figure 5). The region with the 

greatest difference between the data and modeled FCO2, for both runs, was the North Atlantic,

where the model underestimated the fluxes by 1.4 mol C m-2

y

-1. All the other regions were 

within ~1 mol C m-2

y

-1of the data. In the North Atlantic, the assimilation improved the FCO2 by 

a marginal 0.04 mol C m-2

y

-1. In general the fluxes produced by the model were weaker than 

the in situ estimates (Figure 5). This was especially true in the regions north and south of 10°.

The regions that served as the highest sinks and sources of FCO2 were the same before and 

after assimilation. For both runs, the region with the highest FCO2 sink was the North Atlantic 

(~-0.7 mol C m-2

y

-1) followed by the South Atlantic (~-0.6 mol C m-2

y

-1). Of the twelve major 

regions, only two (Equatorial Indian and Equatorial Pacific) were a source of CO2 (FCO2 of 0.05 

and 0.26 mol C m-2y

-1). The Equatorial Pacific also became a source of CO2 after assimilation 

(0.24 mol C m-2

y

-1), but the FCO2 in the Equatorial Indian became slightly negative after the 

assimilation (-0.003 mol C m-2

y

-1). The Equatorial Atlantic and North Indian were the only two 

regions for which the fluxes from the database were positive but the FCO2 produced by the 

model in both the free-run and assimilation were negative.

The assimilation of chlorophyll did not decrease the uncertainty in FCO2 (Figure 7). There 

was a significant correlation between modeled FCO2 and the fluxes from the database for six out 

of the eight years analyzed, namely, 2003-2008. Correlation coefficients for these six years were 

a minimum of 0.72 for the free-run and a minimum of 0.76 for the assimilation run. The 

correlation between the data and the model at regional levels was highly dependent on the 

number of observations available. In 2009 and 2010, when the number of months of available 

12
data dropped to 7 and 4, the correlation was no longer significant. Out of the six years for which

significant correlations were found, the correlation coefficients for the assimilation run were only 

slightly improved over those in the free-run for three years. 

Figure 5: Air-Sea fluxes (mol C m-2 y-1) for the free-run, the assimilation run, and the database globally and in the 

twelve major oceanographic regions.

13
Figure 6: Map of FCO2 (mol C m-2 y-1) from (a) the LDEO database, (b) the free-run and (c) after assimilation of 

satellite chlorophyll for June 2005. 

14
Figure 7: Correlation between the satellite database of FCO2 and the FCO2 values produced in the free-run and after 

assimilation. (*) indicates statistical significance (p < 0.05), NS = not significant.

4. Discussion 

The assimilation of satellite chlorophyll in NOBM was shown to decrease the uncertainties in 

chlorophyll concentration but to have little impact on simulated FCO2. The regions where the 

highest biases in FCO2 were found (North Indian, North Pacific and North Atlantic) were not 

improved by the assimilation. Note that these three regions also show the greatest bias in 

chlorophyll concentration. Monthly maps of chlorophyll concentration (not shown) in these three 

regions highlight the fact that the satellite-based estimates there are particularly high during 

specific times of the year. For example, in the North Pacific and North Atlantic, the satellite 

chlorophyll is considerably higher than the model during the spring and summer. Clouds are one 

of the largest sources of sampling error, especially in the North Pacific (Gregg and Casey, 2007).

Here, even in the middle of the growing season, there can be <5 days of observations per month. 

The presence of these clouds can lead to overestimate in chlorophyll concentration that are 

driven by period of high growth that occur during limiting periods of clear skies suitable for 

15
remote sensing. Gregg and Casey (2007) also reported biases due to clouds in the North 

Atlantic, although they noted that the overestimates tend to be smaller than in the North Pacific. 

The third region where bias is considerable is the North Indian. In this region, aerosols are 

known to contribute to sampling error (Gregg and Casey 2007). Aerosol obscurations increase 

before and during the Monsoon season, at the same time as does chlorophyll. In this study, we 

found that the satellite chlorophyll values were considerably higher than those produced by the 

model in either the free-run and assimilation only during the northeast monsoon (between 

November and February). Other sources of bias include sun glint, inter-orbit gap, and sensor tilt 

changes, all of which occur primarily in mid-latitudes (Gregg & Casey 2007).

The correlations between the FCO2 values estimated from in situ measurements and those 

produced by the model in either the free-run model/assimilation were significant for the first six 

years of the study (2003-2008). The high correlation coefficients indicate a reasonably low 

uncertainty in our model-derived FCO2. The correlation for FCO2 was again not significant for 

2009 and 2010 due to a lack of data in those years. No clear improvements in either correlation 

or bias resulted from the assimilation of satellite chlorophyll.

The North Atlantic was the region with the greatest difference between the observations-based

data and the modeled FCO2 (from either run). Although the biased sampling in satellite 

chlorophyll is likely to have played a role here, the limited effect the assimilation had on FCO2 in 

other regions indicates that other processes may be responsible. Chlorophyll is only one of the 

several components in the ocean carbon system, and therefore modest changes from the 

assimilation are not unrealistic. Deficiencies in carbon cycling processes in the model are not 

ameliorated by the improved chlorophyll abundances and distributions associated with the 

satellite chlorophyll assimilation. In fact, the simulated fluxes can actually get worse through 

assimilation (e.g., in the southern basins, as shown in Figure 5). This is likely the result of 

compensating deficiencies in the free-run model, where flaws in the simulated biological 

components compensate flaws in the carbon components. The chlorophyll compensation 

corrects the estimates of chlorophyll but not the underlying biological processes, exposing the 

carbon cycling issues. It is not clear whether this issue is unique to our model formulation or 

whether it is, in fact, general. It is reassuring that the correlations between data estimates and 

models are significant when sufficient in situ data are available, but it is also disconcerting that 

the biases are large. 

16
In conclusion, these results suggest that ocean carbon and biological systems are complex and 

that work is needed to improve their representations in models, independently and 

simultaneously. Data assimilation of chlorophyll is not a panacea for model deficiencies. The 

number of products that can be derived from satellite ocean color is increasing, and the 

assimilation of products other than chlorophyll may further improve the fluxes estimates. Finally,

the regions with the highest biases are complex regions which further reinforce our need to 

improve, through combined satellite and modeling efforts, our understanding of the complex 

interactions underlying the global carbon cycle. Further sensitivity analyses are needed to detect 

the components of the ocean carbon system that, if modeled and quantified more accurately, can

lead to improved FCO2 estimates both regionally and globally.

17
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nutrient data into an adjoint three-dimensional ocean carbon cycle model: Sensitivity 

analysis and ecosystem parameter optimization. Global biogeochemical cycles 21.

18
Appendix A: Acknowledgements 

We thank the NASA/MERRA Project, Lamont-Doherty Earth Observatory, NOAA Carbon 

Dioxide Information Analysis Center, and the NOAA National Oceanographic Data Center for 

the data sets and public availability. This work was supported by the NASA MAP and CMS 

Programs.

19
Previous Volumes in This Series

Volume 1

September 

1994

Documentation of the Goddard Earth Observing System (GEOS) general 

circulation model - Version 1 

L.L. Takacs, A. Molod, and T. Wang

Volume 2

October 1994

Direct solution of the implicit formulation of fourth order horizontal 

diffusion for gridpoint models on the sphere

Y. Li, S. Moorthi, and J.R. Bates

Volume 3

December 

1994

An efficient thermal infrared radiation parameterization for use in 

general circulation models 

M.-D. Chou and M.J. Suarez 

Volume 4

January 1995

Documentation of the Goddard Earth Observing System (GEOS) Data 

Assimilation System - Version 1 

James Pfaendtner, Stephen Bloom, David Lamich, Michael 

Seablom, Meta Sienkiewicz, James Stobie, and Arlindo da Silva

Volume 5

April 1995

Documentation of the Aries-GEOS dynamical core: Version 2 

Max J. Suarez and Lawrence L. Takacs

Volume 6

April 1995

A Multiyear Assimilation with the GEOS-1 System: Overview and 

Results 

Siegfried Schubert, Chung-Kyu Park, Chung-Yu Wu, Wayne 

Higgins, Yelena Kondratyeva, Andrea Molod, Lawrence Takacs, 

Michael Seablom, and Richard Rood

Volume 7

September 

1995

Proceedings of the Workshop on the GEOS-1 Five-Year Assimilation 

Siegfried D. Schubert and Richard B. Rood

Volume 8

March 1996

Documentation of the Tangent Linear Model and Its Adjoint of the 

Adiabatic Version of the NASA GEOS-1 C-Grid GCM: Version 5.2 

Weiyu Yang and I. Michael Navon

Volume 9

March 1996

Energy and Water Balance Calculations in the Mosaic LSM 

Randal D. Koster and Max J. Suarez

Volume 10

April 1996

Dynamical Aspects of Climate Simulations Using the GEOS General 

Circulation Model 

Lawrence L. Takacs and Max J. Suarez

Volume 11

May 1997

Documentation of the Tangent Linear and its Adjoint Models of the 

Relaxed Arakawa-Schubert Moisture Parameterization Package of the 

NASA GEOS-1 GCM (Version 5.2) 

Weiyu Yang, I. Michael Navon, and Ricardo Todling

Volume 12

August 1997

Comparison of Satellite Global Rainfall Algorithms 

Alfred T.C. Chang and Long S. Chiu

Volume 13

December 

Interannual Variability and Potential Predictability in Reanalysis 

Products 

20
1997 Wie Ming and Siegfried D. Schubert

Volume 14

August 1998

A Comparison of GEOS Assimilated Data with FIFE Observations 

Michael G. Bosilovich and Siegfried D. Schubert

Volume 15

June 1999

A Solar Radiation Parameterization for Atmospheric Studies 

Ming-Dah Chou and Max J. Suarez

Volume 16

November 

1999

Filtering Techniques on a Stretched Grid General Circulation Model 

Lawrence Takacs, William Sawyer, Max J. Suarez, and Michael S. 

Fox-Rabinowitz

Volume 17

July 2000

Atlas of Seasonal Means Simulated by the NSIPP-1 Atmospheric GCM 

Julio T. Bacmeister, Philip J. Pegion, Siegfried D. Schubert, and 

Max J. Suarez

Volume 18

December 

2000

An Assessment of the Predictability of Northern Winter Seasonal Means 

with the NSIPP1 AGCM 

Philip J. Pegion, Siegfried D. Schubert, and Max J. Suarez

Volume 19

July 2001

A Thermal Infrared Radiation Parameterization for Atmospheric Studies 

Ming-Dah Chou, Max J. Suarez, Xin-Zhong, and Michael M.-H. 

Yan

Volume 20

August 2001

The Climate of the FVCCM-3 Model 

Yehui Chang, Siegfried D. Schubert, Shian-Jiann Lin, Sharon 

Nebuda, and Bo-Wen Shen

Volume 21

September 

2001

Design and Implementation of a Parallel Multivariate Ensemble Kalman 

Filter for the Poseidon Ocean General Circulation Model 

Christian L. Keppenne and Michele M. Rienecker

Volume 22

August 2002

Coupled Ocean-Atmosphere Radiative Model for Global Ocean 

Biogeochemical Models 

Watson W. Gregg

Volume 23

November 

2002

Prospects for Improved Forecasts of Weather and Short-term Climate 

Variability on Subseasonal (2-Week to 2-Month) Time Scales 

Siegfried D. Schubert, Randall Dole, Huang van den Dool, Max J. 

Suarez, and Duane Waliser

Volume 24

July 2003

Temperature Data Assimilation with Salinity Corrections: Validation for 

the NSIPP Ocean Data Assimilation System in the Tropical Pacific Ocean, 

1993–1998 

Alberto Troccoli, Michele M. Rienecker, Christian L. Keppenne, 

and Gregory C. Johnson

Volume 25

December 

2003

Modeling, Simulation, and Forecasting of Subseasonal Variability 

Duane Waliser, Siegfried D. Schubert, Arun Kumar, Klaus 

Weickmann, and Randall Dole

21
Volume 26

April 2005

Documentation and Validation of the Goddard Earth Observing System 

(GEOS) Data Assimilation System – Version 4

Senior Authors: S. Bloom, A. da Silva and D. Dee

Contributing Authors: M. Bosilovich, J-D. Chern, S. Pawson, S. 

Schubert, M. Sienkiewicz, I. Stajner, W-W. Tan, and M-L. Wu

Volume 27

December 

2008

The GEOS-5 Data Assimilation System - Documentation of Versions 

5.0.1, 5.1.0, and 5.2.0.

M.M. Rienecker, M.J. Suarez, R. Todling, J. Bacmeister, L. Takacs, 

H.-C. Liu, W. Gu, M. Sienkiewicz, R.D. Koster, R. Gelaro, I. Stajner, 

and J.E. Nielsen

Volume 28

April 2012

The GEOS-5 Atmospheric General Circulation Model: Mean Climate 

and Development from MERRA to Fortuna

Andrea Molod, Lawrence Takacs, Max Suarez, Julio Bacmeister, 

In-Sun Song, and Andrew Eichmann

Volume 29

May 2012

Atmospheric Reanalyses – Recent Progress and Prospects for the Future.

A Report from a Technical Workshop, April 2010

Michele M. Rienecker, Dick Dee, Jack Woollen, Gilbert P. Compo, 

Kazutoshi Onogi, Ron Gelaro, Michael G. Bosilovich, Arlindo da Silva, 

Steven Pawson, Siegfried Schubert, Max Suarez, Dale Barker, 

Hirotaka Kamahori, Robert Kistler, and Suranjana Saha

Volume 30

September

2012

The GEOS-ODAS, description and evaluation

Guillaume Vernieres, Michele M. Rienecker, Robin Kovach and 

Christian L. Keppenne

Volume 31

March 2013

Global Surface Ocean Carbon Estimates in a Model Forced by 

MERRA

Watson W. Gregg, Nancy W. Casey and Cecile S. Rousseaux

Volume 32

April 2013

The Quick Fire Emissions Dataset (QFED) – Documentation of 

versions 2.1, 2.2 and 2.4

Anton S. Darmenov and Arlindo da Silva

22