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NASA NTRS1998

The Impact of Very Fine Vertical Resolution on the Climate of the GEOS-2 GCM

Typical General Circulation Models (GCM's) that are used for tropospheric climate studies extend vertically with significant resolution only into the lower stratosphere, while GCM's specifically designed for the study of the stratospheric climate do not finely resolve the tropospheric jet or boundary layer regions. The NASA/Goddard Data Assimilation Office (DAO) Goddard Earth Observing System GCM, (GEOS-2 GCM) which is a component of the GEOS-2 Data Assimilation System (DAS), is configured with a sufficient number of vertical levels to provide very fine resolution in the tropospheric and stratospheric regimes. The present study is aimed at understanding the benefits for accurate simulation of the climate seen in the GEOS-2 GCM due to its choice of vertical resolution. Raising the model lid to the lower mesosphere so as to explicitly resolve the stratospheric circulation, in particular the descent over the winter pole, is included in GEOS-2 to address the common GCM deficiency of a stratospheric cold pole bias. Increasing the number of levels is directed at improving the representation of the planetary boundary layer, the troposphere at the altitude of the sub-tropical jet stream, and the stratosphere. Comparisons of the climate of simulations with three different vertical resolution formulations of the GEOS-2 GCM are presented. In addition to the reduction in the climate bias, the impact of the resolution on model variability will be briefly discussed.

Molod, Andrea↗
NASA NTRS1999

1D Variational Assimilation of TOVS/ATOVS Level 1b Data

Recently, it has been shown that the use of observations from satellite-borne microwave and infrared radiometers in data assimilation systems consistently increases forecast skill. Considerable effort has been expended over the past two decades, particularly with the TIROS Operational Vertical Sounder (TOVS), to achieve this result. The positive impact on forecast skill has resulted from improvements in quality control algorithms, systematic error correction schemes, and data assimilation systems. Despite these recent advances, there are still many issues regarding the use of satellite data in data assimilation systems that remain unresolved. For example, at several centers, much of the TOVS data are not assimilated over land as a result of the difficulty in specifying the surface emissivity. In this study, we add the microwave surface emissivity to the state vector so that it is retrieved rather than specified by a model. The LDVAR temperature and humidity information is then assimilated interactively into the Goddard Earth Observing System - Data Assimilation System (GEOS-DAS). Results of 1DVAR assimilations with both TOVS and ATOVS will be shown. We will compare the results obtained with HIRS/MSU/SSU and HIRS/AMSU-A2.

Joiner, J.↗
NASA NTRS1999

Precipitation Recycling in the NASA GEOS Data Assimilation System

Analysis of precipitation recycling can improve the understanding of regional hydrologic anomalies, especially their evolution and maintenance. Diagnostic models of the recycling of precipitation and are applied to 15 years of the NASA Goddard Earth Observing System (GEOS) Data Assimilation System (DAS). Recycled precipitation is defined as the fraction of precipitation within a given region that originated as surface evaporation from the same region. The focus of the present work is on the interannual variability of the central United States hydrologic cycle and precipitation recycling. The extreme years of 1988 (drought) and 1993 (flood) are compared with the 15 year base period mean annual cycle. The results indicate that recycling ratio (the amount of precipitation with a local source relative to the total precipitation) is greater in 1988 than both the base period mean and the 1993 season (with 1993 recycling ratio less than the mean). On the other hand, both the summers of 1988 and 1993 show less total recycled precipitation than the mean. The results also show that precipitation recycling may have been more important in the spring of 1993, when the region was primed for flooding, than the summer, when the sever flooding occurred. The diagnostic approaches to precipitation recycling suffer from some weaknesses. Numerical simulations and assimilation using passive tracers have the potential to provide more accurate calculations of precipitation recycling and the remote sources of water. This ability is being incorporated into the latest GEOS data assimilation system, and some preliminary results will be presented.

Bosilovich, Michael G.↗
NASA NTRS1999

Choosing Meteorological Input for the Global Modeling Initiative Assessment of High Speed Aircraft

The Global Modeling Initiative (GMI) science team is developing a three dimensional chemistry and transport model (CTM) to be used in assessment of the atmospheric effects of aviation. Requirements are that this model be documented be validated against observations, use a realistic atmospheric circulation, and contain numerical transport and photochemical modules representing atmospheric processes. The model must also retain computational efficiency to be tractable to use for multiple scenarios and sensitivity studies. To meet these requirements, a facility model concept was developed in which the different components of the CTM are evaluated separately. The first use of the GMI model will be to evaluate the impact of the exhaust of supersonic aircraft on the stratosphere. The assessment calculations will depend strongly on the wind and temperature fields used by the CTM. Three meteorological data sets for the stratosphere are available to GMI: the National Center for Atmospheric Research Community Climate Model (CCM2), the Goddard Earth Observing System Data Assimilation System (GEOS DAS), and the Goddard Institute for Space Studies general circulation model (GISS). Objective criteria were established by the GMI team to identify the data set which provides the best representation of the stratosphere. Simulations of gases with simple chemical control were chosen to test various aspects of model transport. The three meteorological data sets were evaluated and graded based on their ability to simulate these aspects of stratospheric measurements. This paper describes the criteria used in grading the meteorological fields. The meteorological data set which has the highest score and therefore was selected for GMI is CCM2. This type of objective model evaluation establishes a physical basis for interpretation of differences between models and observations. Further, the method provides a quantitative basis for defining model errors, for discriminating between different models, and for ready re-evaluation of improved models. These in turn will lead to a higher level of confidence in assessment calculations.

Douglass, A. R.↗
NASA NTRS1999

An Efficient Objective Analysis System for Parallel Computers

A new objective analysis system designed for parallel computers will be described. The system can produce a global analysis (on a 2 x 2.5 lat-lon grid with 20 levels of heights and winds and 10 levels of moisture) using 120,000 observations in less than 3 minutes on 32 CPUs (SGI Origin 2000). No special parallel code is needed (e.g. MPI or multitasking) and the 32 CPUs do not have to be on the same platform. The system Ls totally portable and can run on -several different architectures at once. In addition, the system can easily scale up to 100 or more CPUS. This will allow for much higher resolution and significant increases in input data. The system scales linearly as the number of observations and the number of grid points. The cost overhead in going from I to 32 CPus is 18%. in addition, the analysis results are identical regardless of the number of processors used. T'his system has all the characteristics of optimal interpolation, combining detailed instrument and first guess error statistics to produce the best estimate of the atmospheric state. It also includes a new quality control (buddy check) system. Static tests with the system showed it's analysis increments are comparable to the latest NASA operational system including maintenance of mass-wind balance. Results from a 2-month cycling test in the Goddard EOS Data Assimilation System (GEOS DAS) show this new analysis retains the same level of agreement between the first guess and observations (0-F statistics) throughout the entire two months.

Stobie, James G.↗
NASA NTRS1999

Measurements of Tc (Q,P): Depression of the Superfluid Transition Temperature by a Heat Current Along the Lambda Line

We report experimental measurements of Tc (Q,P) for heat currents (Q) between I1and 100 micro W/sq cm and pressure (P) between SVP and 15 bar. The measurements were performed in a normal gravity environment, using the low-gravity simulator facility at JPL without the magnet being energized. The sample pressure was controlled to 0.1 micro bar using a hot volume, and a Straty-Adams capacitive pressure gauge. The total volume of helium in the sample cell and the hot volume was held constant using a pneumatic low temperature valve. A melting curve thermometer (MCT) measured the transition temperature (Tc) with a resolution of about 10 nK through a sidewall probe of the thermal conductivity sample cell. We employed the same measurement technique and procedure described by DAS. Preliminary results indicate that Tc (Q,P) depends very little on the pressure in the pressure range between SVP and 15 bar with a variation in the amplitude of Tc(Q,P) of less than about 5% observable in this pressure range. According to the Renormalization-group theory calculation by Haussmann and Dohm, the amplitude of Tc (Q,P) has a leading pressure-dependence term proportional to xi(sub 0) (sup (1/nu)), where xi(sub 0) is the correlation-length amplitude and nu is the correlation-length exponent. Thus, a small pressure dependence of the amplitude of Tc (Q,P) is expected since xi(sub 0) is very weakly dependent on pressure between SVP and 15 bar, consistent with our measurements.

Liu, Yuan-Ming↗
NASA NTRS2000

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

A method is developed to retrieve surface ground temperature (T(sub g)) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing longwave radiation (CLR) is sensitive to upper level moisture (q(sub l)) over wet regions and (T(sub g)) over dry regions The clear sky window flux from 800 to 1200/cm (RadWn) is sensitive to low level moisture (q(sub t)) and T(sub g). Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub l) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in T(sub g), q(sub h) and q(sub l). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing Delta(T(sub g)) and broad layer Delta(q(sub l) from .500 hPa to surface and Delta(q(sub h)) from 200 to .300 hPa provides a good approximation to the full radiative transfer calculations. typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters. Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for Delta(T(sub g)) to about 20% for Delta(q(sub l)). Our initial application of the methodology employed an early CERES-TRMM data set (CLR and Radwn) to assess the quality of the GEOS2 data. The results showed that over the tropical and subtropical oceans GEOS2 is, in general, too wet in the upper troposphere (mean bias of 0.99 mm) and too dry in the lower troposphere (mean bias of -4.7 min). We note that these errors, as well as a cold bias in the T(sub g). have largely been corrected in the current version of GEOS-2 with the introduction of a land surface model, a moist turbulence scheme and the assimilation of SSM/I total precipitable water.

Wu, Man Li C.↗
NASA NTRS2000

Preliminary Results from an Assimilation of TOMS Aerosol Observations Into the GOCART Model

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions that compare well with TOMS satellite observations. Surface, mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from.08-10 microns and only simulates Saharan dust. TOMS radiance observations in the ultra violet provide information on the mineral and carbonaceous aerosol fields. We use two main observables in this study: the TOMS aerosol index (AI) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance. These are sensitive to the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer. The Goddard Aerosol Assimilation System (GAAS) uses the Data Assimilation Office's Physical-space Statistical Analysis System (PSAS) to combine TOMS observations and GOCART model first guess fields. At this initial phase we only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols dominant and carbonaceous aerosols are minimal, Our preliminary results during summer show that the assimilation with TOMS data modifies both the aerosol mass loading and the single scattering albedo. Assimilated aerosol fields will be compared with assimilated aerosol fields from GOCART and AERONET observations over Cape Verde.

daSilva, Arlindo↗
NASA NTRS2000

Estimates of Ground Temperature and Atmospheric Moisture from CERES Observations

A method is developed to retrieve surface ground temperature (Tg) and atmospheric moisture using clear sky fluxes (CSF) from CERES-TRMM observations. In general, the clear sky outgoing long-wave radiation (CLR) is sensitive to upper level moisture (q(sub h)) over wet regions and Tg over dry regions The clear sky window flux from 800 to 1200 /cm (RadWn) is sensitive to low level moisture (q(sub j)) and Tg. Combining these two measurements (CLR and RadWn), Tg and q(sub h) can be estimated over land, while q(sub h) and q(sub t) can be estimated over the oceans. The approach capitalizes on the availability of satellite estimates of CLR and RadWn and other auxiliary satellite data. The basic methodology employs off-line forward radiative transfer calculations to generate synthetic CSF data from two different global 4-dimensional data assimilation products. Simple linear regression is used to relate discrepancies in CSF to discrepancies in Tg, q(sub h) and q(sub t). The slopes of the regression lines define sensitivity parameters that can be exploited to help interpret mismatches between satellite observations and model-based estimates of CSF. For illustration, we analyze the discrepancies in the CSF between an early implementation of the Goddard Earth Observing System Data Assimilation System (GEOS-DAS) and a recent operational version of the European Center for Medium-Range Weather Prediction data assimilation system. In particular, our analysis of synthetic total and window region SCF differences (computed from two different assimilated data sets) shows that simple linear regression employing (Delta)Tg and broad layer (Delta)q(sub l) from 500 hPa to surface and (Delta)q(sub h) from 200 to 500 hPa provides a good approximation to the full radiative transfer calculations, typically explaining more than 90% of the 6-hourly variance in the flux differences. These simple regression relations can be inverted to "retrieve" the errors in the geophysical parameters. Uncertainties (normalized by standard deviation) in the monthly mean retrieved parameters range from 7% for (Delta)T to about 20% for (Delta)q(sub t). Our initial application of the methodology employed an early CERES-TRMM data set (CLR and Radwn) to assess the quality of the GEOS2 data. The results showed that over the tropical and subtropical oceans GEOS2 is, in general, too wet in the upper troposphere (mean bias of 0.99 mm) and too dry in the lower troposphere (mean bias of -4.7 mm). We note that these errors, as well as a cold bias in the Tg, have largely been corrected in the current version of GEOS-2 with the introduction of a land surface model, a moist turbulence scheme and the assimilation of SSTM/I total precipitable water.

Wu, Man Li C.↗
NASA NTRS2000

The Distribution of Carbon Monoxide in the GOCART Model

Carbon monoxide (CO) is an important trace gas because it is a significant source of tropospheric Ozone (O3) as well as a major sink for atmospheric hydroxyl radical (OH). The distribution of CO is set by a balance between the emissions, transport, and chemical processes in the atmosphere. The Georgia Tech/Goddard Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) model is used to simulate the atmospheric distribution of CO. The GOCART model is driven by the assimilated meteorological data from the Goddard Earth Observing System Data Assimilation System (GEOS DAS) in an off-line mode. We study the distribution of CO on three time scales: (1) day to day fluctuation produced by the synoptic waves; (2) seasonal changes due to the annual cycle of CO sources and sinks; and (3) interannual variability induced by dynamics. Comparison of model results with ground based and remote sensing measurements will also be presented.

Fan, Xiaobiao↗
NASA NTRS2001

Regional Climate Studies with Variable-Resolution Stretched-Grid GCMs

A variable resolution GCM using a global stretched grid with fine resolution over the area(s) of interest, is a viable new approach to regional and subregional climate studies and applications. It is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step in regional climate modeling. The first version of the SG-GCM based on the GEOS (Goddard Earth Observing System) GCM using a finite-difference approximation, has been developed and thoroughly tested during the last few years. Successful simulations have been performed with the SG-GCM for the anomalous regional climate events of the U.S. 1988 summer drought and 1993 summer flood. They have shown the practical feasibility of the SG-approach for regional climate modeling. The GEOS SG-DAS (Data Assimilation System) incorporating the SG-GCM has also been developed and tested. The assimilated regional fields and diagnostics are used for validating the SG-GCM regional simulations. Two new SG-GCMs are being developed. The first is the SG-version of the new NASANCAR FV-GCM (with the finite-volume (FV) dynamics), and the second is the SG-version of the new GCM with spectral-element dynamics. Both GCMs use the WAR CCM4 physics. Using these advanced numerics will provide increased computational efficiency for the new the SG-GCMs, and will allow us to employ more flexible stretching strategies beneficial for the efficient regional down-scaling. The major current developments are focused on: simulating the 1997-1999 (and beyond) ENSO cycle and related monsoonal circulations, with enhanced regional resolution; studying intraseasonal and interannual regional climate variability for the extended multiyear (AMIP-type) SG-GCM simulations; and studying the impact of ensemble integrations.

Fox-Rabinovitz, Michael↗
NASA NTRS2000

Preliminary Results from an Assimilation of Saharan Dust Using TOMS Radiances and the GOCART Model

At NASA Goddard we are developing a global aerosol data assimilation system that combines advances in remote sensing and modeling of atmospheric aerosols. The goal is to provide high resolution, 3-D aerosol distributions to the research community. Our first step is to develop a simple assimilation system for Saharan mineral aerosol. The Goddard Chemistry and Aerosol Radiation model (GOCART) provides accurate 3-D mineral aerosol size distributions. Surface mobilization, wet and dry deposition, convective and long-range transport are all driven by assimilated fields from the Goddard Earth Observing System Data Assimilation System, GEOS-DAS. Our version of GOCART transports sizes from .08-10 microns and only simulates Saharan dust. We draw the assimilation to two observables in this study: the TOMS aerosol index (Al) which is directly related to the ratio of the 340 and 380 radiances and the 380 radiance alone. The forward model that simulates the observables requires the aerosol optical thickness, the single scattering albedo and the height of the aerosol layer from the GOCART fields. The forward model also requires a refractive index for the dust. We test three index values to see which best fits the TOMS observables. These are 1) for Saharan dust reported by Patterson, 2) for a mixture of Saharan dust and a highly reflective material (sea salt or sulfate) and 3) for pure illite. The assimilation works best assuming either pure illite or the dust mixture. Our assimilation cycle first determines values of the aerosol index (Al) and the radiance at 380 nm based on the GOCART aerosol fields. Differences between the observed and GOCART model calculated Al and 380 nm radiance are first analyzed horizontally using the Physical-space Statistical Analysis System (PSAS). A quasi-Newton iteration is then performed to produce analyzed 3D aerosol fields according to parameterized background and observation error covariances. We only assimilate observations into the the GOCART model over regions of Africa and the Atlantic where mineral aerosols are dominant and carbonaceous aerosols are minimal.

Weaver, C. J.↗
NASA NTRS2001

Results from Global Land-Surface Data Assimilation Methods

Realistic representation of the land surface is crucial in global climate modeling (GCM). Recently, the Mosaic land-surface Model (LSM) has been driven off-line using GEOS DAS (Goddard Earth Observing System Data Assimilation System) atmospheric forcing, forming the Off-line Land-surface Global Assimilation (OLGA) system. This system provides a computationally efficient test bed for land surface data assimilation. Here, we validate the OLGA simulation of surface processes and the assimilation of ISCCP surface temperatures. Another component of this study as the incorporation of the Physical-space Statistical Analysis System (PSAS) into OLGA, in order to assimilate surface temperature observations from the International Satellite Cloud Climatology Project (ISCCP). To counteract the subsequent forcing of the analyzed skin temperature back to the initial state following the analysis. incremental bias correction (IBC) was included in the assimilation. The IBC scheme effectively removed the time mean bias, but did not remove him in the mean diurnal cycle. Therefore, a diurnal him correction (DBC) scheme was developed, where the time-dependent bias was modeled with a sine wave parameterization. In addition, quality control of the ISCCP data and anisotropic temperature correction were implemented in PSAS. Preliminary results showed a substantial impact from the inclusion of PSAS and DBC that was visible in the surface meteorology fields and energy budget. Also, the monthly mean diurnal cycle from the experiment closely matched the diurnal cycle from the observations.

Radakovich, Jon D.↗
NASA NTRS2001

Temperature Humidity and Sea Level Pressure Increments Induced by 1DVAR Analysis of GPS Refractivity

The Global Positioning System (GPS) transmitted signals are affected by the atmosphere. Using the radio occultation technique, where a receiver is placed on a low-Earth-orbiting platform. it is possible to perform soundings. by measuring the bending angles of the rays. The information can be converted into atmospheric refractivity. We have developed a one dimensional variational (1DVAR) analysis that uses GPS/MET 1995 refractivity and 6-hour FVDAS (Finite Volume Data Assimilation System) forecasts as background information to constrain the retrievals. The analysis increments are defined as 1DVAR minus background temperature, humidity and sea level pressure. Before assimilating the 1DVAR profiles into the FVDAS. the increments need to be understood. First, some bias could be induced in the retrievals when confronted with actual biased data: second. bias in the back-round could create undesired bias in the retrievals. Anv bias in the analyses will ultimately change the climatology of the model the retrievals will be assimilated into. We relate the increments to the reduction of the difference between observed minus computed refractivity profiles. We also point out the difference in the mean increments using backgrounds which have assimilated either NESDIS TIROS Operational Vertical Sounder (TOVS) operational retrievals or Data Assimilation Office (DAO) TOVS interactive retrievals. The climatology of the model in terms of refractivity is significantly different and this impacts the GPS 1DVAR increments. This testifies that changing the basic load of assimilated data has an influence on the impact the GPS data may have in a DAS.

Poli, Paul↗
NASA NTRS2001

Evaluation of Stratospheric Transport in New 3D Models Using the Global Modeling Initiative Grading Criteria

The Global Modeling Initiative (GMI) Team developed objective criteria for model evaluation in order to identify the best representation of the stratosphere. This work created a method to quantitatively and objectively discriminate between different models. In the original GMI study, 3 different meteorological data sets were used to run an offline chemistry and transport model (CTM). Observationally-based grading criteria were derived and applied to these simulations and various aspects of stratospheric transport were evaluated; grades were assigned. Here we report on the application of the GMI evaluation criteria to CTM simulations integrated with a new assimilated wind data set and a new general circulation model (GCM) wind data set. The Finite Volume Community Climate Model (FV-CCM) is a new GCM developed at Goddard which uses the NCAR CCM physics and the Lin and Rood advection scheme. The FV-Data Assimilation System (FV-DAS) is a new data assimilation system which uses the FV-CCM as its core model. One year CTM simulations of 2.5 degrees longitude by 2 degrees latitude resolution were run for each wind data set. We present the evaluation of temperature and annual transport cycles in the lower and middle stratosphere in the two new CTM simulations. We include an evaluation of high latitude transport which was not part of the original GMI criteria. Grades for the new simulations will be compared with those assigned during the original GMT evaluations and areas of improvement will be identified.

Strahan, Susan E.↗
NASA NTRS2002

Assimilation of Cloud- and Land-affected TOVS/ATOVS Level 1b data at DAO

Despite significant advances in the assimilation of TIROS Operational Vertical Sounder/Advanced TIROS Operational Vertical Sounder (TOVS)/(ATOVS) data over the last decade, there are still many unresolved issues. For example, at several centers, cloud-and land-affected TOVS data are not assimilated. In this study, we show positive impact from the use of cloud cleared and land-affected TOVS data in the NASA Data Assimilation Office's (DAO) Finite Volume Data Assimilation System (fv-DAS). We will discuss how treatment of TOVS data affects the stratosphere and tropopause in the fvDAS. We will also describe the use of TOVS data for land-surface analysis and assimilation and other developments regarding the use of TOVS data at the DAO.

Joiner, Joanna↗
NASA NTRS2001

Model Simulations of CO2 Transport Using Assimilated Meteorological Fields

The numerical simulation of CO2 transport (and other tracers such as CO, CH4, and biomass burning tracers) in the atmosphere is required to determine the fate of anthropogenic source gases. Estimation of the CO2 exchange between the ocean surface, the terrestrial biosphere, and the atmosphere is of first-order importance to understanding the global carbon cycle and the processes that are most crucial in determining the atmospheric CO2 concentration. Forward transport simulations have been conducted using two-dimensional, time-dependent grids of average surface fluxes (from TRANSCOM) and three-dimensional wind data from a prototype data assimilation system (FV-DAS) run by the Goddard Data Assimilation Office. The objective is to better understand the contribution of meteorological variability to changes in CO2 and other constituents, By accurately accounting for meteorological variability, through use of assimilated winds, we hope to better characterize the distribution of surface sources and sinks (and chemistry where applicable). With assimilated meteorology such chemistry/transport runs provide the basic framework to analyze existing (and proposed) measurement data on a point-by-point, real-time basis. We compare with measured CO2 concentration gradients on a daily, seasonal, regional, and interhemispheric basis to examine the consistency of sources, sinks, and transport formulation. We will also examine the inter-annual variability of atmospheric CO2 due to atmospheric circulation changes using longer runs with assimilated winds.

Kaea, S. R.↗
NASA NTRS2002

Surface Temperature Assimilation in the Global Land Data Assimilation System (GLDAS)

The Global Land Data Assimilation System (GLDAS) is a global land parameterization that uses prescribed meteorology as forcing in order to determine regular gridded land surface states (temperature and moisture) and other properties (e.g. water and heat fluxes). In the present experiment, the assimilation of surface skin temperature is incorporated into the land parameterizations. The meteorological forcing was derived from the Goddard Earth Observing System (GEOS-3) Data Assimilation System (DAS) for the full year of 1998 GLDAS can use several land parameterizations, but here we use the Mosaic land surface model and the Common Land Model (CLM). TOVS surface temperature observations are assimilated into GLDAS. The TOVS observations are less frequent that observations used in previous experiments (ISCCP). The purpose of this presentation is to evaluate the impact of the TOVS assimilation on both Mosaic and CLM. We will especially consider the impact of coarse temporal observations on the assimilation and bias correction.

Bosilovich, Michael G.↗