Gunson-Pawson-Potter (2009) Project Profile   (updated 27-Jan-2014)
Project Title:NASA CMS Pilot Projects: Surface Carbon Fluxes

Project Leader(s):

Michael (Mike) Gunson, JPL
Kenneth (Ken) Jucks, NASA Headquarters
Steven Pawson, NASA GSFC GMAO
Christopher Potter, NASA ARC

Project Duration: 2009 - 2011
Solicitation:NASA: Directed Funding (2009)
Successor Projects: Bowman (CMS 2011)   Bowman (CMS 2014)   Pawson (CMS 2011)   Ott (CMS 2014)  
Abstract: There are no direct global-scale observations of carbon fluxes between the land and oceans and the over lying atmosphere. Understanding the carbon cycle requires estimates of these fluxes, which can be computed indirectly using models constrained with global space-based observations that provide information about the physical and biological state of the land, atmosphere or ocean. This pilot study will generate CO2 flux maps for one year (July 2009-June 2010) using observational constraints in NASA's state-of-the-art models. Bottom-up surface flux estimates will be computed using data-constrained land (two variants of CASA) and ocean (ECCO2 and NOBM) models; comparison of the different techniques will provide some knowledge of uncertainty in these estimates. Ensembles of atmospheric carbon distributions will be computed using an atmospheric general circulation model (GEOS-5), with perturbations to the surface fluxes and to transport. Top-down flux estimates will be computed from observed atmospheric CO2 distributions (ACOS/GOSAT retrievals) along side the forward-model fields, in conjunction with an inverse approach based on the CO2 adjoint of GEOS-Chem. The forward model ensembles will be used to build understanding of relationships among surface flux perturbations, transport uncertainty and atmospheric carbon concentration. This will help construct uncertainty estimates and information on the true spatial resolution of the top-down flux calculations. The agreement of the top-down and bottom-up flux distributions will be documented.
Project Associations:
  • CMS
CMS Science Theme(s):
  • Land-Atmosphere Flux
  • Ocean-Atmosphere Flux
  • Global Surface-Atmosphere Flux

Participants:

Kevin Bowman, JPL
Holger Brix, UCLA
George (Jim) Collatz, NASA GSFC - retired
Stephanie Dutkiewicz, MIT
Annmarie Eldering, Jet Propulsion Laboratory / Caltech
Martha Farfan, JPL
Joshua Fisher, Chapman University
Michael (Mick) Follows, MIT
Watson Gregg, NASA GSFC
Michael (Mike) Gunson, JPL
Christopher (Chris) Hill, MIT
Kenneth (Ken) Jucks, NASA Headquarters
Stephan (Randy) Kawa, NASA GSFC
Steven Klooster, NASA Ames Research Center - Cal State Univ Monterey Bay
Meemong Lee, JPL
Junjie Liu, JPL
Erica McGrath-Spangler, NASA GSFC / USRA
Dimitris Menemenlis, Jet Propulsion Laboratory
Lesley Ott, NASA GSFC GMAO
Steven Pawson, NASA GSFC GMAO
Christopher Potter, NASA ARC
Cecile Rousseaux, NASA GSFC
John Worden, JPL
Fanwei Zeng, NASA GSFC / SSAI
Zhengxin Zhu, NASA GSFC / SSAI

Contact Support to request an email list of project participants.

Project URL(s): None provided.
 
Data
Products:
Product Title:  CMS- Flux Pilot Project FLUXNET Evaluation
Description:  An independent, globally gridded dataset of land surface CO2 fluxes (i.e., net ecosystem exchange, NEE), called MPI-BGC (Jung et al. 2009, 2010, 2011; Beer et al. 2010), is used to evaluate the corresponding CMS land surface CO2 fluxes. MPI-BGC is a statistical/empirical model trained by associating explanatory variables such as meteorology (precipitation, air temperature, humidity), vegetation type, and fraction of absorbed photosynthetically active radiation (fAPAR) with direct measurements of NEE from the eddy covariance technique using data from 253 FLUXNET sites located globally (Baldocchi et al. 2001; Baldocchi 2008).

New MPI-BGC output was generated for CMS using MERRA meteorology for the GOSAT period; MERIS was used for fAPAR as the model was originally calibrated for MERIS fAPAR. MPI-BGC employs a decision-tree approach, and ultimately generates the mean of an ensemble of varying trees, coined Model Tree Ensemble (MTE).
The FLUXNET sites have a significant bias in global representation:
1. they are mostly located in N. America and Europe (tropics and savannas are underrepresented);
2. the vegetation is typically in an active growing successional stage (i.e., not at equilibrium);
the sites are flat (necessary to satisfy assumptions in the eddy covariance technique); and,
3. disturbance/management history is not well-represented.
For these reasons, the mean annual flux is representative more for these conditions (large carbon sink) and cannot be used for comparison in the CMS activity, and should not be used to evaluate fossil fuel emissions. However, a number of comparative metrics are suitable: a) identification of hotspots; b) seasonal variation; c) spatial distribution; d) interannual variability; e) timing of minimum and maximum NEE; and, f) seasonal amplitude. We follow these guidelines, and provide monthly maps, map differences, scatterplots, and a suite of comparative statistics.

We partition the analysis be sub-global units, including biome type (following the classifications of the International Geosphere-Biosphere Programme), hemisphere, latitudinal band, TRANSCOM region, and by country (Fisher et al. 2011).
Status:  Preliminary
CMS Science Theme(s):  Atmospheric Transport; Global Surface-Atmosphere Flux; Land-Atmosphere Flux
Keywords:  
Spatial Extent:  mostly North America and Europe
Spatial Resolution:  
Temporal Frequency:  
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  Scientists interested in carbon cycling and efforts to estimate carbon budgets at various temporal and spatial scales
Stakeholders:  
Current Application Readiness Level:  
Start Application Readiness Level:  
Target Application Readiness Level:  
Future Developments:  
Limitations:  
Date When Product Available:  2012-03-01
Metadata URL(s):

http://cmsflux.jpl.nasa.gov
Data Server URL(s):

http://cmsflux.jpl.nasa.gov
Archived Data Citation:  

Product Title:  CMS- Flux Pilot Project Land Biosphere Fluxes 2009- 2010 CASA Ames Model
Description:  Using the Ames CASA model we calculated NPP and Rh at a 1.25 by 1 degree resolution. These data were used to calculate NEE at a 3-hourly and at the same spatial resolution. In addition cropland harvest emission data were calculated on an annual timestep and again the same spatial resolution. All data are reported in units of kgC per meter squared per second.


Ames CASA Model documentation for the NASA Carbon Monitoring System Flux Pilot

Ames CASA is a simulation model that depends on satellite observations of vegetation cover from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) as time-series inputs to estimate monthly carbon fluxes from terrestrial ecosystems worldwide. All current model algorithms, parameter settings, and land cover data sets used in Ames CASA for CMS flux computations have been documented thoroughly in the paper by Potter et al. (2007).

Specifically for the CMS Flux Pilot Project computations of net biosphere fluxes of carbon to the atmosphere, the following additions or modifications to the Ames CASA version documented in Potter et al. (2007) have been made:

-- Global 0.5o (latitude/longitude) Enhanced Vegetation Index (EVI) input data (for the years 2000-2010) were generated by aggregating monthly 0.05o (~6 km) values (MOD13C2 version 005) from the USGS LP DAAC. The EVI layer was selected from each MOD13C2 spatial composite file and surface water values were converted to “NoData”. To aggregate from a 0.05o cell size to 0.5o resolution, the EVI values for each pixel block were averaged. Each monthly layer was then multiplied by 0.0001 to scale the EVI data to the standard CASA-input value range.

-- In cropland areas, 40% of annual NPP carbon is removed each year from the litter decomposition flux pathways and diverted into harvested food products. This is provided as a separate dataset, assumed to be re-emitted as a consistent monthly flux (1/12 of the annual cropland harvest carbon total) with a weighted spatial distribution corresponding to the maps of cropland harvest CO2 emissions developed by Ciais et al. (2007).
References

Potter, C., S. Klooster, A. Huete, and V. Genovese, 2007, Terrestrial carbon sinks for the United States predicted from MODIS satellite data and ecosystem modeling, Earth Interactions, 11: 1-21.

Ciais, P., P. Bousquet, A. Freibauer, and T. Naegler, 2007, Horizontal displacement of carbon associated with agriculture and its impacts on atmospheric CO2, Global Biogeochemical Cycles, doi:10.1029/2006GB002741.
Point of Contract

Christopher Potter, chris.potter@nasa.gov Tel. 650-604-6164
url: http://geo.arc.nasa.gov/sge/casa/


Data product information:

The following data are included:

1. NASA-CASA 3-hourly NEE data AMES.casa.3hr.1x1.25.2009.nc and AMES.casa.3hr.1x1.25.2010.nc.
The resolution is 1.25x1 degrees. Unit is kg C/m^2/s. The data format is netCDF.

2. NASA-CASA cropland harvest emission data casa_dist.1x1.25.2009.nc and casa_dist.1x1.25.2010.nc.
The resolution is 1.25x1 degrees. The data format is netCDF. It does not have seasonal variation.
Unit is kg C/m^2/s.

3. NPP.0.5x0.5.monthly.200901-201012.bin, the NASA-CASA monthly NPP data.
The data format is direct access unformatted binary data. A Fortran program read.NPP.f is provided to read the data. Resolution is 0.5x0.5 degrees.
Unit is kg C/m^2/s.

4. Rh.0.5x0.5.monthly.200901-201012.bin, the NASA-CASA monthly respiration data.
The data format is direct access unformatted binary data. A Fortran program read.Rh.f is provided to read the data. Resolution is 0.5x0.5 degrees.
Unit is kg C/m^2/s.

5. casa_dist.0.5x0.5.2009-2010.bin, the NASA-CASA crop harvest emission data for 2009 and 2010.
The data format is direct access unformatted binary data. A Fortran program read.dist.f is provided to read the data. Resolution is 0.5x0.5 degrees.
Unit is kg C/m^2/s.

The data format is netCDF. It can be read, display or write out with IDL or grads. The grids, unit, and timing of the data can be found by using 'ncdump' command.

The original NPP and Rh of the NASA-CASA are monthly data and in 0.5x0.5 resolution.
The data are interpolated to a 1.25x1 resolution, and the unit is changed from g C/m^2/month to kg C/m^2/s. We produced the 3-hourly NEE data from the monthly NPP and Rh data using the method of Olsen and Randerson (2004, JGR). The 3-hourly surface air temperature (T2m) and short wave radiation reaching the surface of the MERRA analysis data are used for producing the 3-hrly NEE.



Contact Zhengxin.Zhu-1@nasa.gov if you have any questions about how to read the data.
Status:  Preliminary
CMS Science Theme(s):  
Keywords:  
Spatial Extent:  
Spatial Resolution:  
Temporal Frequency:  
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  Scientists interested in carbon cycling and efforts to estimate carbon budgets at various temporal and spatial scales
Stakeholders:  
Current Application Readiness Level:  
Start Application Readiness Level:  
Target Application Readiness Level:  
Future Developments:  
Limitations:  
Date When Product Available:  2012-02-01
Metadata URL(s):

ftp://gmaoftp.gsfc.nasa.gov/pub/data/Zhengzin/NASA-CASA
Data Server URL(s):

ftp://gmaoftp.gsfc.nasa.gov/pub/data/Zhengzin/NASA-CASA
Archived Data Citation:  

Product Title:  CMS- Flux Pilot Project Land Biosphere Fluxes 2009- 2010 CASA GFED Model
Description:  Using the CASA GFED model we calculated monthly NPP, Rh and fPAR at a 0.5 by 0.5 degree resolution. These data were used to calculate NEE at a 3-hourly timestep and at a 1 by 1.25 degree resolution. In addition biomass burning emission data were calculated on both daily and monthly timesteps. All data are reported in units of kgC per meter squared per second.

The Goddard Space Flight Center CASA-GFED3 Terrestrial Carbon Cycle Model

The Carnegie-Ames-Stanford-Approach – Global Fire Emissions Database version 3 (CASA-GFED3) derives from Potter et al. (1993), diverging in development since Randerson et al, (1996).

CASA is a light use efficiency type model: net primary production (NPP) is expressed as the product of photosynthetically active solar radiation, a light use efficiency parameter, scalars that capture temperature and moisture limitations, and fractional absorption of solar radiation by the vegetation canopy (FPAR). This latter variable is derived from satellite data.

Fire parameterization was incorporated into the model by van der Werf et al. (2004) producing CASA-GFED and the model has undergone revisions (van der Werf et al, 2006, 2010) leading to its most recent version CASA-GFED3. Input data sets include air temperature, precipitation, incident solar radiation, a soil classification map, and a number of satellite derived products (MODIS MOD12Q1 vegetation classification, MODIS MOD44B vegetation continuous fields, MODIS MOD09GA/MYD09GA based burned area, and AVHRR FPAR).

CASA-GFED3 is run at monthly time steps with 0.5 degree spatial resolution. For this project it uses MERRA meteorology and FPAR derived from AVHRR NDVI (Tucker et al., 2005) according to the procedure of Los et al. (2000). The original 8-km, biweekly AVHRR NDVI was aggregated up to the monthly, 0.5 degree×0.5 degree grid by averaging.

The model output includes NPP, heterotrophic respiration (Rh), and fire emissions (from forest, savanna, deforestation, peat, and agriculture). For this project, fire emissions were disaggregated from monthly to quasi-daily using the eight-day MODIS MYD14A2 Active Fire Product.

Updated files covering the period 2003 through 2011 are available through the NACP Kawa-01 project (http://nacp-files.nacarbon.org/nacp-kawa-01/)

Contact
Jim Collatz (George.J.Collatz@nasa.gov)

References

Los SO, Collatz GJ, Sellers PJ, Malmstrom CM, Pllack NH, DeFries RS, Bounoua L, Parris MT, Tucker CJ, Dazlich DA, 2000, A global 9-yr biophysical land surface dataset from NOAA AVHRR data. Journal of Hydrometeorology 1, 183-199.

Potter, C. S., Randerson, J. T., Field, C. B., Matson, P. A., Vitousek, P. M., Mooney, H. A., and Klooster, S. A.: Terrestrial ecosystem production – A process model based on global satellite and surface data, Global Biogeochem. Cycles, 7, 811–841, 1993.


Randerson JT, Thompson MV, Malmstrom CM, 1996,Substrate limitations for heterotrophs: Implications for models that estimate the seasonal cycle of atmospheric CO2. Global Biogeochemical Cycles 10, 585-602.

Tucker CJ, Pinzon JE, Brown ME, Slayback DA, Pak EW, Mahoney R, Vermote EF, El Saleous N, 2005, International Journal of Remote Sensing 26, 4485-4498.

van der Werf GR, Randerson JT, Collatz GJ, Giglio L, Kasibhatla PS, Arellano AF, Olsen SC, Kasischke ES, 2004, Continental-scale partitioning of fire emissions during the 1997 to 2001 El Nino/La Nina period. Science 303, 73-76.

van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Kasibhatla PS, Arellano Jr AF, 2006, Interannual variability of global biomass burning emissions from 1997 to 2004. Atmospheric Chemistry and Physics 6, 3423-3441

van der Werf GR, Randerson JT, Giglio L, Collatz GJ, Mu M, Kasibhatla PS, Morton DC, DeFries RS, Jin Y, van Leeuwen TT, 2010, Global fire emissions and the contribution of deforestation, agriculture, and peat fires (1997-2009). Atmospheric Chemistry and Physics 10, 11707-11735


Information about the data files:

The following data are included:

1. CASA-GFED3.3hrly.NEE.1x1.25.20090701-20100630.nc, the CASA-GFED3 3-hourly NEE data.
The resolution is 1.25x1 degrees. Unit is kg C/m^2/s. The data format is netCDF.

2. BB_em.daily.1x1.25.20090701-20100630.nc, the CASA-GFED3 daily biomass burning
CO2 emission data. Fuel wood burning emission is also included.
The resolution is 1.25x1 degrees. Unit is kg C/m^2/s. The data format is netCDF.

3. NPP.0.5x0.5.monthly.200907-201006.bin, the CASA-GFED3 monthly NPP data.
The data format is direct access unformatted binary data. A Fortran program
read.NPP.f is provided to read the data. Resolution is 0.5x0.5 degrees.
Unit is kg C/m^2/s.

4. Rh.0.5x0.5.monthly.200907-201006.bin, the CASA-GFED3 monthly respiration data.
The data format is direct access unformatted binary data. A Fortran program
read.Rh.f is provided to read the data. Resolution is 0.5x0.5 degrees.
Unit is kg C/m^2/s.

5. FPAR.0.5x0.5.monthly.200907-201006.bin, the CASA-GFED3 monthly FPAR data.
The data format is direct access unformatted binary data. A Fortran program
read.FPAR.f is provided to read the data. Resolution is 0.5x0.5 degrees.
Unit is fraction.

6. BB_em.monthly.0.5x0.5.200901-201012.bin,the monthly biomass burning + fuel wood
emission data. The data format is direct access unformatted binary data.
A Fortran program read.BB_em.f is provided to read the data. Resolution is 0.5x0.5 degrees.
Unit is kg C/m^2/s.
Status:  Preliminary
CMS Science Theme(s):  
Keywords:  
Spatial Extent:  
Spatial Resolution:  
Temporal Frequency:  
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  Scientists interested in carbon cycling and efforts to estimate carbon budgets at various temporal and spatial scales
Stakeholders:  
Current Application Readiness Level:  
Start Application Readiness Level:  
Target Application Readiness Level:  
Future Developments:  
Limitations:  
Date When Product Available:  2012-02-01
Metadata URL(s):

ftp://gmaoftp.gsfc.nasa.gov/pub/data/Zhengzin/CASA-GFED

http://nacp-files.nacarbon.org/nacp-kawa-01/
Data Server URL(s):

ftp://gmaoftp.gsfc.nasa.gov/pub/data/Zhengzin/CASA-GFED

http://nacp-files.nacarbon.org/nacp-kawa-01/
Archived Data Citation:  

Product Title:  CMS- Flux Pilot Project Ocean-Atmosphere Fluxes 2003- 2012 NOBM Model
Description:  Using the NASA Ocean Biogeochemical Model model we calculated daily and monthly CO2 flux from the oceans to the atmosphere at a 1 by 1.25 degree resolution. All data are reported in units of kgC per meter squared per second.

Model information:
Global ocean carbon dynamics are simulated by the NASA Ocean Biogeochemical Model (NOBM).It is a three-dimensional representation of coupled circulation/biogeochemical/ radiative processes in the global oceans (Gregg et al., 2003; Gregg and Casey, 2007). It spans the domain from –84 degrees to 72 degrees latitude in increments of 1.25 degrees longitude by 2/3 degree latitude, including only open ocean areas, where bottom depth>200m. Surface spectral irradiance is derived from the Ocean-Atmosphere Spectral Irradiance Model (OASIM; Gregg and Casey, 2009). NOBM underwent spin-up for 200 years under climatological forcing. Initial conditions for DIC were derived from the Global Data Analysis Project (GLODAP; Key et al., 2004). We averaged DIC over oceanographic basins and depth and used these mean values for initial conditions. DOC initial conditions were set to 0 microM. Other initial conditions are described in Gregg and Casey (2007). For the forcing data sets, monthly climatologies were used in all cases. All except soil dust (iron), ozone, clouds, and atmospheric CO2 were obtained from MERRA products. Ozone was from the Total Ozone Mapping Spectrometer, and soil dust deposition was from Ginoux et al. (2001). Cloud data (cover and liquid water path) were obtained from the International Satellite Cloud Climatology Project. Atmospheric CO2 was taken from the NOAA/ESRL data set for the year 2000.
NOBM was forced with time synchronized MERRA data and atmospheric CO2 concentrations appropriate for the period from NOAA/ESRL. MODIS-Aqua chlorophyll data were assimilated with the model using data from the 2010 re-processing.

Contact

Cecile Rousseaux (Cecile.S.Rousseaux@nasa.gov)
Watson Gregg (Watson.Gregg@nasa.gov)

References:
Ginoux, P., M. Chin, I. Tegen, J.M. Prospero, B. Holben, O. Dubovik, and S.-J. Lin, 2001. Sources and distributions of dust aerosols simulated with the GOCART model. Journal of Geophysical Research 106, 20255-20273.

Gregg, W.W., P. Ginoux, P.S. Schopf, and N.W. Casey, 2003. Phytoplankton and Iron: Validation of a global three-dimensional ocean biogeochemical model. Deep-Sea Research II 50: 3143-3169.

Gregg, W.W. and Casey, N.W., 2007. Modeling coccolithophores in the global oceans. Deep-Sea Research II 54: 447-477.

Gregg, W.W. and N.W. Casey, 2009. Skill assessment of a spectral ocean-atmosphere radiative model. Journal of Marine Systems 76: 49-63.

Key, R.M., A. Kozyr, C.L. Sabin, K. Lee, R. Wanninkhof, J.L. Bullister, R.A. Feely, F.J. Millero, C. Mordy, and T.-H. Peng, 2004. A global ocean carbon climatology: Results from Global Data Analysis Project (GLODAP). Global Biogeochemical Cycles 18: 10.1029/2004GB002247.


Data file information:
Daily oceanic CO2 flux data : NOBM.1x1.25.daily.YYYY.nc
The data format is netCDF for the period YYYY=2003 until 2012. It can be read, display or write out with IDL or grads. The grids, unit, and timing of the data can be found by using 'ncdump' command.

Gregg W.W, N.W. Casey and C.S. Rousseaux, 2012. Global Surface Ocean Carbon Estimates in a Model Forced by MERRA. NASA Technical Report Series on Global Modeling and Data Assimilation, NASA TM-2013-104606, Vol. 31, 39 pp.
Status:  Preliminary
CMS Science Theme(s):  
Keywords:  
Spatial Extent:  
Spatial Resolution:  
Temporal Frequency:  
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  Scientists interested in carbon cycling and efforts to estimate carbon budgets at various temporal and spatial scales
Stakeholders:  
Current Application Readiness Level:  
Start Application Readiness Level:  
Target Application Readiness Level:  
Future Developments:  
Limitations:  
Date When Product Available:  2012-02-01
Metadata URL(s):

ftp://gmaoftp.gsfc.nasa.gov/pub/data/NOBM/FCO2/
Data Server URL(s):

ftp://gmaoftp.gsfc.nasa.gov/pub/data/NOBM/FCO2/
Archived Data Citation:  

Product Title:  CMS- Flux Pilot Project Ocean-Atmosphere Fluxes 2009- 2010 ECCO2 Model
Description:  Using the ECCO2-Darwin Ocean Carbon Cycle Model we calculated daily and monthly CO2 flux from the oceans to the atmosphere at a 1 by 1.25 degree resolution. All data are reported in units of kgC per meter squared per second.

NOTE A revised version (Vers 2.1) of the ECCO2-Darwin Model outputs has also been made available as a research contribution

ECCO2-Darwin Ocean Carbon Cycle Model

The ECCO2-Darwin Ocean Carbon Cycle Model is based on a global, eddying, ocean and sea ice configuration of the Massachusetts Institute of Technology general circulation model (MITgcm; Marshall et al., 1997a, 1997b) and on results from two separately funded projects: the Estimating the Circulation and Climate of the Ocean, Phase II (ECCO2) Project, which provides a data-constrained estimate of the time-evolving physical ocean state, and the Darwin Project, which provides time-evolving ocean ecosystem variables. Together, ECCO2 and Darwin provide a time-evolving physical and biological environment for carbon biogeochemistry, which is used to compute surface fluxes of carbon at high spatial and temporal resolution.

The ECCO2 model configuration is a cube-sphere grid (Adcroft et al. 2004) with 18-km horizontal grid spacing and 50 vertical levels (Menemenlis et al., 2005a, 2008). The ECCO2 model configuration includes a dynamic/thermodynamic sea ice model (Losch et al., 2010; Heimbach et al., 2010). In a first step, the ECCO2 model configuration was adjusted using a low-dimensional (Green’s functions) estimation approach (Menemenlis et al., 2005b). In a second step, the method of Lagrange multipliers (adjoint method) was used to adjust initial and time-evolving surface boundary conditions (Wunsch and Heimbach, 2007). Data constrains include sea level anomaly from Jason-1 and OSTM, sea surface temperature from AMSR-E, and temperature and salinity profiles from Argo. This adjoint-based ECCO2 solution is used to drive the Darwin ecosystem model, described next.

The Darwin Project is an initiative to advance the development and application of novel models of marine microbial communities, identifying the relationships of individuals and communities to their environment, connecting cellular-scale processes to global microbial community structure (Follows et al., 2007, 2011; Dutkiewicz et al., 2009). The particular configuration used for the CMS Flux Pilot Project includes five phytoplankton functional types (choices based on results from previous versions of the model) and two zooplankton types. The carbon cycle is explicitly included in this configuration, along with those of nitrogen, phosphorus, iron, silica, oxygen, and alkalinity. The carbonate chemistry follows the simplified model proposed by Follows et al. (2006) and air-sea CO2 exchange is parameterized according to Wanninkhof (1992).

Contacts:
Dimitris Menemenlis, JPL (menemenlis@jpl.nasa.gov)
Holger Brix, UCLA (hbrix@ucla.edu)

References

Adcroft, A., J. Campin, C. Hill, and J. Marshall, 2004: Implementation of an atmosphere-ocean general circulation model on the expanded spherical cube. Mon. Weather Rev., 132, 2845–2863.

Dutkiewicz, S., M. Follows, and J. Bragg, 2009: Modeling the coupling of ocean ecology and biogeochemistry. Global Biogeochem. Cycles, 23, GB4017.

Follows, M., T. Ito, and S. Dutkiewicz, 2006: On the solution of the carbonate chemistry system in ocean biogeochemistry models. Ocean Modelling, 12, 290–301.

Follows, M., S. Dutkiewicz, S. Grant, and S. Chisholm, 2007: Emergent biogeography of microbial communities in a model ocean. Science, 315, 1843–1846.

Follows, M. and S. Dutkiewicz, 2011:
Modeling diverse communities of marine microbes.
Annu. Rev. Marine Science, 3, 427–451.

Heimbach, P., D. Menemenlis, M. Losch, J. Campin, and C. Hill, 2010: On the formulation of sea-ice models. Part 2: Lessons from multi-year adjoint sea ice export sensitivities through the Canadian Arctic Archipelago. Ocean Modelling, 33, 145–158.

Losch, M., D. Menemenlis, P. Heimbach, J. Campin, and C. Hill, 2010: On the formulation of sea-ice models. Part 1: Effects of different solver implementations and parameterizations. Ocean Modelling, 33, 129–144.

Marshall, J., A. Adcroft, C. Hill, L. Perelman, and C. Heisey, 1997a: A finite-volume, incompressible Navier-Stokes model for studies of the ocean on parallel computers. J. Geophys. Res., 102, 5753–5766.

Marshall, J., C. Hill, L. Perelman, and A. Adcroft, 1997b: Hydrostatic, quasi-hydrostatic and non-hydrostatic ocean modeling. J. Geophys. Res., 102, 5733–5752.

Menemenlis, D., C. Hill, A. Adcroft, J. Campin, B. Cheng, B. Ciotti, I. Fukumori, P. Heimbach, C. Henze, A. Koehl, T. Lee, D. Stammer, J. Taft, and J. Zhang, 2005a: NASA supercomputer improves prospects for ocean climate research. Eos Trans. AGU, 86, 89, 95–96.

Menemenlis, D., I. Fukumori, and T. Lee, 2005: Using Green's functions to calibrate an ocean general circulation model. Mon. Weather Rev., 133, 1224–1240.

Menemenlis, D., J. Campin, P. Heimbach, C. Hill, T. Lee, A. Nguyen, M. Schodlock, and H. Zhang, 2008: ECCO2: High resolution global ocean and sea ice data synthesis. Mercator Ocean Quarterly Newsletter, 31, 13–21.

Wanninkhof, R. (1992), Relationship between wind speed and gas exchange
over the ocean. J. Geophys. Res., 97(C5), 7373–7382.

Wunsch, C. and P. Heimbach, 2007: Practical global ocean state estimation. Physica D, 230, 197–208.


The following data are included:

1. ECCO2.1x1.25.3hrly.2009.nc and ECCO2.1x1.25.3hrly.2010.nc, the 3-hourly ECCO2 ocean CO2 flux data.
The resolution is 1 x 1.25 degrees. The unit is Kg C/m^2/s.
The data format is netCDF. It can be read, display or write out with IDL or grads. The grids,
unit, and timing of the data can be found by using 'ncdump' command.

2. ECCO2.1x1.25.monthly.200901-201012.bin, the monthly mean data from Jan 2009 to Dec 2010.
The data formate is direct access unformatted binary data. A Fortran program read.ECCO2.monthly.f is provided to read it.

Contact Zhengxin.Zhu-1@nasa.gov if you have any questions about how to read the data.
Status:  Preliminary
CMS Science Theme(s):  
Keywords:  
Spatial Extent:  
Spatial Resolution:  
Temporal Frequency:  
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  Scientists interested in carbon cycling and efforts to estimate carbon budgets at various temporal and spatial scales
Stakeholders:  
Current Application Readiness Level:  
Start Application Readiness Level:  
Target Application Readiness Level:  
Future Developments:  
Limitations:  
Date When Product Available:  2012-02-01
Metadata URL(s):

ftp://gmaoftp.gsfc.nasa.gov/pub/data/Zhengzin/ECCO2/

http://cmsflux.jpl.nasa.gov
Data Server URL(s):

ftp://gmaoftp.gsfc.nasa.gov/pub/data/Zhengzin/ECCO2/

http://cmsflux.jpl.nasa.gov
Archived Data Citation:  

Product Title:  CMS- Flux Pilot Project Ocean-Atmosphere Fluxes 2009- 2010 ECCO2 Model Vers 2.1
Description:  This solution has been updated from version 2 ( see the description for research contribution #4 above) and is based on the same model run and results as version 2. The interpolation from the native cube sphere to the rectangular (1.25x1 degree) grid had caused differences in the globally integrated flux values from up to 0.2 PgC/yr between the native and the lat-lon grid for both versions 1 and 2. This has been corrected by using a different interpolation method for the data published as version 2.1.

To facilitate usage of the data they are now available in a single NetCDF file per month and temporal resolution, i.e., for each month there are three NetCDF files containing 3-hourly, daily, and monthly means.

Contact person: Dr Holger Brix
Status:  Preliminary
CMS Science Theme(s):  Ocean-Atmosphere Flux
Keywords:  
Spatial Extent:  
Spatial Resolution:  
Temporal Frequency:  
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  Scientists interested in carbon cycling and efforts to estimate carbon budgets at various temporal and spatial scales
Stakeholders:  
Current Application Readiness Level:  
Start Application Readiness Level:  
Target Application Readiness Level:  
Future Developments:  
Limitations:  
Date When Product Available:  2012-03-01
Metadata URL(s):

http://ecco2.jpl.nasa.gov/data1/cube/darwin/ECCO2-Darwin/CarbFlux_v2.1/
Data Server URL(s):

http://ecco2.jpl.nasa.gov/data1/cube/darwin/ECCO2-Darwin/CarbFlux_v2.1/
Archived Data Citation:  

Product Title:  CMS- Flux Pilot Project Surface and Column-averaged CO2 mixing ratios 2009- 2010 GEOS-5 Atmospheric General Circulation Model
Description:  Using the GEOS-5 model we calculated three-hour average surface and column-averaged CO2 mixing ratios for four different combinations of land-surface and ocean-surface fluxes

The GEOS-5 Atmospheric General Circulation Model

The GEOS-5 Atmospheric General Circulation Model (AGCM) has been developed as a flexible tool to represent the atmosphere on a variety of temporal and spatial scales. It is a central component of the GEOS-5 atmospheric data assimilation system (Rienecker et al., 2008), where it is used with half-degree spatial resolution for meteorological analysis and forecasting (Zhu and Gelaro, 2008) including the Modern Era Retrospective-Analysis for Research and Applications (MERRA) which covers the period from 1979 to present (Rienecker et al., 2011). It has also been developed as a tool for studying atmospheric composition and climate (e.g., Ott et al., 2011). The AGCM combines the finite volume dynamical core described in Lin (2004) with the GEOS-5 column physics package, summarized in Rienecker et al. (2008). The model domain extends from the surface to 0.01hPa and uses 72 hybrid layers that transition from terrain-following near the surface to pure pressure levels above 180 hPa. In this study, the horizontal resolution is 1 degree×1.25degree (latitude by longitude) and the time step is 30 minutes for physical computations, with more frequent computations of resolved-scale transport in the dynamical core. Trace gases are transported on-line in GEOS-5 using the Lin (2004) dynamical core for resolved scales; turbulent mixing of CO is performed in the same way as for moisture (using the Lock et al. (2000) boundary-layer turbulence module); and using the Relaxed-Arakawa Schubert convective scheme (Moorthi and Suarez, 1992) to represent convective transport. In the present simulations, transport is constrained with MERRA reanalysis fields to ensure consistency with observed meteorological features.

Land biosphere, biomass burning, fossil fuel, and ocean CO2 fluxes are prescribed in GEOS-5. For the CMS project, GEOS-5 has been configured to simulate the emission, uptake and transport of several different CO2 tracers representing differing combinations of land and ocean fluxes described in Table 1. All fluxes were disaggregated from their native grids to the 1degree × 1.25degree GEOS-5 grid for these computations. Following Olsen and Randerson (2004), the net primary productivity (NPP) and heterotrophic respiration (Rh) fields from both versions of CASA were converted to Gross Primary Production and Ecosystem Respiration, then disaggregated temporally to three-hour time steps.

In addition to the land biosphere and ocean CO2 fluxes computed in the CMS flux pilot project, 2008 CO2 emissions from fossil fuels are taken from the DOE’s Carbon Dioxide Information Analysis Center (Boden et al., 2011). Prior to the target 2009-2010 CMS period, CO2 tracers were spun up from 2000-2008 using a uniform initial condition of 350 ppmv to ensure realistic atmospheric distributions. During this period, land biosphere and biomass burning fluxes from an earlier version of the CASA/GFED model were used in combination with ocean and fossil fuel fluxes from the TransCom Continuous Experiment (Law et al., 2008). Simulated CO2 mixing ratios for December 2008 were calculated at the locations of NOAA Earth Science Research Laboratory (ESRL) remote surface stations (Novelli et al., 1992) and compared with observations; on the basis of this comparison a uniform global offset was added to the simulated CO2 fields to ensure that global surface CO2 concentrations were representative of atmospheric conditions at the beginning of the CMS period.

GEOS-5 model output is comprised of three-hour average surface and column-averaged CO2 mixing ratios for each of the following flux combinations: CASA/GFED-3 (land) with NOBM (ocean), NASA-CASA (land) with NOBM (ocean), CASA/GFED-3 (land) with ECCO-2-Darwin (ocean) and NASA-CASA (land) with ECCO-2-Darwin (ocean). Filenames denote the midpoint of the three-hour averaging period. Note that because GEOS-5 model calculations begin at 3Z, no file is available for July 1, 2009 at 0130Z.

Contact

Lesley Ott (Lesley.E.Ott@nasa.gov)
References

Boden, T.A., G. Marland, and R.J. Andres. 2011. Global, Regional, and National Fossil-Fuel CO2 Emissions. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A. doi 10.3334/CDIAC/00001_V2011.

Law, R. M., et al. (2008), TransCom model simulations of hourly atmospheric CO2: Experimental overview and diurnal cycle results for 2002, Global Biogeochem. Cycles, 22, GB3009, doi:10.1029/2007GB003050.

Lin, S.-J. (2004), A “vertically Lagrangian” finite-volume dynamical core for global models, Mon. Wea. Rev., 132(10):2293-2307.

Lock, A. P., A. R. Brown, M. R. Bush, G. M. Martin, and R. N. B. Smith (2000), A New Boundary Layer Mixing Scheme. Part I: Scheme Description and Single-Column Model Tests, Mon. Wea. Rev., 128, 3187–3199.

Moorthi S., and M. J. Suarez (1992), Relaxed Arakawa–Schubert: A parameterization of moist convection for general circulation models, Mon. Wea. Rev., 120, 978–1002.

Novelli, P.C., L.P. Steele, and P.P. Tans (1992), Mixing ratios of carbon monoxide in the troposphere, J. Geophys. Res., 97, 20,731-20,750.

Olsen, S.C., and J.T. Randerson(2004), Differences between surface and column atmospheric CO2 and implications for carbon cycle research. Journal of Geophysical Research 109, doi:10.1029/2003JD003968.

Ott, L.E., S. Pawson, J.T. Bacmeister, (2011), An Analysis of the Impact of Convective Parameter Uncertainty on Simulated Global Atmospheric CO Distributions. J. Geophys. Res., 116, D21310, doi:10.1029/2011JD016077.

Reinecker, M. M., Suarez, M. J., Todling, R., et al. (2008), The GEOS-5 Data Assimilation System-Documentation of Versions 5.0.1, 5.1.0, and 5.2.0, Tech. Rep. 104606 V27, NASA.

Rienecker, M.M., et al. (2011), MERRA - NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648. doi: 10.1175/JCLI-D-11-00015.1.

Zhu, Y. and R. Gelaro (2008), Observation Sensitivity Calculations Using the Adjoint of the Gridpoint Statistical Interpolation (GSI) Analysis System, Mon. Wea. Rev., 136, 335-351.
Status:  Preliminary
CMS Science Theme(s):  
Keywords:  
Spatial Extent:  
Spatial Resolution:  
Temporal Frequency:  
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  Scientists interested in carbon cycling and efforts to estimate carbon budgets at various temporal and spatial scales
Stakeholders:  
Current Application Readiness Level:  
Start Application Readiness Level:  
Target Application Readiness Level:  
Future Developments:  
Limitations:  
Date When Product Available:  2012-03-01
Metadata URL(s):

http://cmsflux.jpl.nasa.gov
Data Server URL(s):

ftp://gmaoftp.gsfc.nasa.gov/pub/data/lott/CMS_monthly_average/

http://cmsflux.jpl.nasa.gov
Archived Data Citation:  

Product Title:  CMS- Flux Pilot Project Top-down surface flux estimation 2009- 2010
Description:  The Four-Dimensional Variational Inversion Method

A four-dimensional variational (4D-Var) or adjoint approach, based on the GEOS-Chem chemistry transport model, is used for the top-down surface flux estimation. The adjoint relates, in a computationally efficient manner, the sensitivity of an atmospheric CO2 concentration at any time back to a surface flux at any location at an earlier time (see Giering and Kaminski, 1998) via the linearization of the transport model operator. GEOS-Chem uses analyzed meteorological fields from GEOS-5 analyses, mapped from the original resolution of 0.5degrees×0.67degrees to a coarser grid of 2degrees×2.5degrees. Transport in GEOS-Chem and in GEOS-5 is based on the flux-form semi-Lagrangian technique of Lin and Rood (1996), so that inverse computations made with GEOS-Chem will be consistent with the forward model computations using GEOS-5. Suntharalingam et al. (2003, 2004) described and analyzed the first forward CO2 simulations with GEOS-Chem. These were subsequently updated in Nassar et al . (2010), to incorporate additional processes such as direct CO2 chemical production. This version was incorporated in a time-invariant inversion scheme constrained by TES observations to estimate regionally resolved CO2 fluxes [Nassar et al, 2011]. The adjoint of GEOS-Chem was originally developed by Henze et al. (2007) and has been applied to optimize Asian CO sources using MOPITT data (Kopacz et al., 2009) and global CO sources using multi-sensor satellite (AIRS, MOPITT, TES and SCIAMACHY) data (Kopacz et al., 2010). This approach has also been applied to ozone data assimilation [Singh et al, 2011a] and incorporates off-diagonal a-priori background terms from those studies [Singh et al, 2011b]. GOSAT observations processed through the NASA ACOS algorithm v2.9 have been incorporated into the GEOS-Chem carbon flux system. Observation operators for both column and profile CO2 retrievals have been developed and integrated for both nadir and glint modes. Bias corrections to nadir observations have been applied following Wunsch et al. (2011).

Contact

Kevin Bowman (Kevin.P.Bowman@jpl.nasa.gov)
References
Giering, R., and T. Kaminski (1998), Recipes for Adjoint Code Constructions. ACM Transactions on Mathematical Software. 24, 437-474.

Henze, D. K., A. Hakami and J. H. Seinfeld (2007), Development of the adjoint of GEOS-Chem, Atmos. Chem. Phys., 7, 2413-2433.

Kopacz, M., D. J. Jacob, D.K. Henze, C.L. Heald, D.G. Streets, Q. Zhang (2009), Comparison of adjoint and analytical Bayesian inversion methods for constraining Asian sources of carbon monoxide using satellite (MOPITT) measurements of CO columns, J. Geophys. Res., 114, D04305, doi: 10.1029/2007JD009264.
Kopacz, M, D. J. Jacob, J. A. Fisher, J. A. Logan, L. Zhang, I. A. Megretskaia, R. M. Yantosca, K. Singh, D. K. Henze, J. P. Burrows, M. Buchwitz, I. Khlystov, W. W. McMillan, J. C. Gille, D. P. Edwards, A. Eldering, V. Thouret, and P. Nedelec (2010), Global estimates of CO sources with high resolution by adjoint inversion of multiple satellite datasets (MOPITT, AIRS, SCIAMACHY, TES), Atm. Chem. Phys., 10, 855-876.

Nassar, R., D. B. A. Jones, P. Suntharalingam, J. M. Chen, R. J. Andres, K. J. Wecht, R. M. Yantosca, S. S. Kulawik, K. W. Bowman, J. R. Worden, T. Machida, and H. Matsueda (2010), Modeling global atmospheric CO2 with improved emission inventories and CO2 production from the oxidation of other carbon species. Geosci. Model Dev., 3(2):689–716..

Nassar, R., D. B. A. Jones, S. S. Kulawik, J. R. Worden, K. W. Bowman, R. J. Andres, P. Suntharalingam, J. M. Chen, C. A. M. Brenninkmeijer, T. J. Schuck, T. J. Conway, and D. E. Worthy (2011), Inverse modeling of CO2 sources and sinks using satellite observations of CO2 from TES and surface flask measurements. Atmos. Chem. Phys., 11(12):6029–6047.

K. Singh, A. Sandu, K. W. Bowman, M. Parrington, D. B. A. Jones, and M. Lee (2011a), Ozone data assimilation with GEOS-Chem: a comparison between 3-D-Var, 4-D-Var, and suboptimal Kalman filter approaches. Atmos. Chem. Phys. Discuss., 11(8):22247–22300.

K. Singh, M. Jardak, A. Sandu, K. Bowman, M. Lee, and D.B.A. Jones. (2011b), Construction of non-diagonal background error covariance matrices for global chemical data assimilation. Geosci. Model Dev., 4(2):299– 316.

Suntharalingam, P., C. M. Spivakovsky, J. A. Logan, and M. B. McElroy (2003), Estimating the distribution of terrestrial CO2 sources and sinks from atmospheric measurements: Sensitivity to configuration of the observation network. J. Geophys. Res., 108, D15, doi:10.1029/2002JD002207.

Suntharalingam, P., D. J. Jacob, P. I. Palmer, J. A. Logan, R. M. Yantosca, Y. Xiao, M. J. Evans, D. Streets, S. A. Vay and G. Sachse, J. Geophys. Res., 109, D18S18, 2004.

Wunch, D., Wennberg, P. O., Toon, G. C., Connor, B. J., Fisher, B., Osterman, G. B., Frankenberg, C., Mandrake, L., O'Dell, C., Ahonen, P., Biraud, S. C., Castano, R., Cressie, N., Crisp, D., Deutscher, N. M., Eldering, A., Fisher, M. L., Griffith, D. W. T., Gunson, M., Heikkinen, P., Keppel-Aleks, G., Kyrö, E., Lindenmaier, R., Macatangay, R., Mendonca, J., Messerschmidt, J., Miller, C. E., Morino, I., Notholt, J., Oyafuso, F. A., Rettinger, M., Robinson, J., Roehl, C. M., Salawitch, R. J., Sherlock, V., Strong, K., Sussmann, R., Tanaka, T., Thompson, D. R., Uchino, O., Warneke, T., and Wofsy, S. C.: A method for evaluating bias in global measurements of CO2 total columns from space, Atmos. Chem. Phys., 11, 12317-12337, doi:10.5194/acp-11-12317-2011, 2011.
Status:  Preliminary
CMS Science Theme(s):  
Keywords:  
Spatial Extent:  
Spatial Resolution:  
Temporal Frequency:  
Input Data Products:  
Algorithm/Models Used:  
Evaluation:  
Intercomparison Efforts/Gaps:  
Uncertainty Estimates:  
Uncertainty Categories:  
Application Areas:  
Relevant Policies/Programs:  
Potential Users:  Scientists interested in carbon cycling and efforts to estimate carbon budgets at various temporal and spatial scales
Stakeholders:  
Current Application Readiness Level:  
Start Application Readiness Level:  
Target Application Readiness Level:  
Future Developments:  
Limitations:  
Date When Product Available:  2012-03-01
Metadata URL(s):

http://cmsflux.jpl.nasa.gov
Data Server URL(s):

http://cmsflux.jpl.nasa.gov
Archived Data Citation:  

 
Publications: Potter, C., Klooster, S., Genovese, V., Hiatt, C., Boriah, S., Kumar, V., Mithal, V., Garg, A. 2012. Terrestrial Ecosystem Carbon Fluxes Predicted from MODIS Satellite Data and Large-Scale Disturbance Modeling. International Journal of Geosciences. 03(03), 469-479. DOI: 10.4236/ijg.2012.33050

4th NACP All-Investigators Meeting Posters (2013):
  • Evaluating the interannual variability in North American carbon fluxes: the impacts of uncertainties in model parameters and driver data -- (Fanwei Zeng, George James Collatz, Jorge Pinzon, Alvaro Ivanoff) [abstract]   [poster]
Additional
Comments:
AGU Fall Meeting 2012 oral presentations:
  • Fire induced carbon emissions and regrowth uptake in western United States forests: Documenting variation across forest types, fire severity, and climate regions (Invited) Christopher A. Williams; Bardan Ghimire; G. J. Collatz; Jeffery G. Masek
  • Carbon consequences of droughts, fires, bark beetles, and harvests affecting forests of the United States: comparative analysis and synthesis (Invited) Christopher A. Williams; Bardan Ghimire; Christopher Schwalm; G. J. Collatz; Jeffery G. Masek
  • What observations of atmospheric CO2 are needed to constrain processes represented in terrestrial carbon cycle models? George (Jim) J. Collatz; Stephan R. Kawa; Yuping Liu; Alvaro Ivanoff
  • Spatial variability in column CO2 inferred from high resolution GEOS-5 global model simulations: Implications for remote sensing and inversions Lesley Ott; William M. Putman; Steven Pawson; G. J. Collatz; Watson W. Gregg
  • Preliminary estimates of carbon emissions constrained by GOSAT from the NASA Carbon Monitoring System Flux Pilot Project. (Invited) Kevin W. Bowman; Junjie Liu; Meemong Lee; Steven Pawson; Dimitris Menemenlis; Joshua B. Fisher; George J. Collatz; Christopher S. Potter; Watson W. Gregg; Holger Brix; Lesley E. Ott; Zhengxin Zhu; Christopher N. Hill; Stephanie Dutkiewicz; Michael J. Follows; Daven K. Henze; Ray Nassar; Dylan B. Jones; Susan S. Kulawik; Richard J. Weidner; Michael R. Gunson