GEWEX Water and Energy Budget Comparisons
J. Roads1, E. Bainto1, K. Masuda2, M. Rodell3, W. Rossow4
1Experimental Climate Prediction Center, Scripps, UCSD, US
2
Frontier Research Center for Global Change, Japan
3
NASA Goddard Space Flight Center, US
4
CREST at The City College of New York, US
Correspondence: jroads@ucsd.edu
1. Introduction
Closing atmosphere and surface water and energy budgets was one of the goals of the previous
Global Energy and Water-cycle Experiment (GEWEX) Hydrometeorology Panel (GHP; see
Lawford et al. 2004), which was comprised of representatives from the regions formerly known as
the GEWEX Continental-Scale Experiments and now known as the GEWEX Regional
Hydroclimate Projects (RHPs). Towards this goal, a number of RHP Water and Energy Budget
Studies (WEBS) were launched (e.g. Roads et al. 2003; Szeto et al. 2008) to bring together needed
regional data sets and model simulations. Since those initial GHP pilot projects, there have been anumber of important new global observational estimates, atmospheric reanalyses, and land data
assimilation data sets that have become widely available and have provided some impetus for doing
another WEBS for not only individual RHPs but also for more global regions.
The regional studies of GHP are complemented by GEWEX Radiation Panel (GRP) efforts to obtaina complete global description of the water and energy cycle. Observation-based GRP global data
sets now include the: National Aeronautics and Space Administration (NASA) water VAPor
Product (NVAP; Randel et al. 1996); International Satellite Cloud Climatology Project (ISCCP)
cloud products (Rossow and Schiffer 1999), which also include water vapor and radiative fluxes(Zhang et al. 1995, 2004), and the Global Aerosol Climatology Project (GACP; Mishchenko et al.
2007) uncertainty; independent radiative fluxes from the Surface Radiation Budget (SRB;Stackhouse et al. 2000) project; and Global Precipitation Climatology Project (GPCP, Adler et al.
2003) precipitation. Also now available are two runoff based global data products developed by theUniv. of New Hampshire in cooperation with the Global Runoff Data Center (GRDC; Fekete et al.
1999, 2002), as well as the Climate Prediction Center’s (CPC’s) Merged Analysis of Precipitation(CMAP, Xie et al. 1997) precipitation and temperature data sets and Climate Research Unit (CRU;see Brohan et al. 2006) and CPC surface air temperature global data sets. Having more than one
independent set of global observation-based data sets potentially allows some assessment of theassociated uncertainty.
Information about our current ability to simulate and predict these processes is obtained bycomparison of these observational based data sets to more model based output/data sets such asatmospheric reanalyses, which now include: two global reanalyses from the National Centers forEnvironmental Prediction / National Center for Atmospheric Research (NCEP/NCAR R1, Kalnay etal. 1996) and NCEP / Dept. of Energy (NCEP/DOE R2, Kanamitsu et al. 2002), the European
Centre for Medium Range Weather Forecasts (ERA40; Uppala et al. 2005), Japanese 25 year
Reanalysis (JRA25; Onogi et al. 2007) as well as output from the NASA Global Land Data

Assimilation System (GLDAS; Rodell et al. 2004) - a project that contributed to the GEWEXModeling and Prediction Panel (GMPP) Global Soil Wetness Project (GSWP; Dirmeyer et al. 2006).
Since GSWP, GLDAS has now developed 3 new upgraded and unique LDAS simulations from theNASA Mosaic, NCEP Noah, and NCAR Common Land Model (CLM) land surface models (LSMs).
All GLDAS simulations used the same observation-constrained meteorological forcing datasets.
The focus of this study employs global data sets to go beyond the earlier RHP-based studies toexamine more global-scale features. We have limited the present WEBS to the bulk-integrated(atmosphere and surface) water and energy budget processes, including: precipitation, verticallyintegrated atmospheric moisture convergence, total evaporation (including transpiration), totalrunoff, vertically integrated atmospheric energy convergence, latent heat of condensation,
atmospheric radiative cooling, surface radiative heating, sensible and latent heat transfer from thesurface to the atmosphere, and the associated radiation fluxes. Some combination error and tendency
terms are also defined, which include the ground (and ocean - vertical and horizontal) energy fluxes.
Water and energy state variables include: precipitable water, terrestrial soil moisture, snow
equivalent water, atmospheric sensible heat, surface air and skin temperature.
It needs to be stressed here that the chosen observational estimates, atmospheric reanalyses, andGLDAS simulations are in some cases, only representative rather than fully inclusive samples ofavailable research water and energy data sets that can be used to assess not only regional but also
global water and energy budget means and uncertainties. In that regard, GSWP developed a morecomprehensive set of community LDAS simulations of the surface budgets and there are a numberof more comprehensive assessments for radiation, precipitation, clouds, and aerosols currentlyunderway. What is perhaps unique here is our attempt to combine a relatively diverse collection ofobservational estimates, atmospheric reanalyses, and LDAS simulations for a global to regional
assessment of our current ability to characterize, close and simulate surface and atmosphere waterand energy budgets and to characterize the current uncertainty. We hope this effort provides somecontext for these individual assessments as well as for future assessments within individual RHP
regions. We also note that there have been many previous attempts to provide syntheses of water andenergy budgets, but these syntheses have not really focused on the uncertainty, which is more of afocus here.
3. Preliminary results
Fig. 1a shows how the annual mean 2-meter temperature, T2m, was used to order the RHP regions
from the cold Lena and MacKenzie (MAGS) river basins to the hot African region of AMMA. Thereis not too much disagreement between the observations, reanalyses, and GLDAS simulations,although a few regions do stand out. For example, the atmospheric reanalyses’ Tibetan plateau 2meter temperatures tend to be relatively lower than the observational estimates and GLDAS. Fig. 1bshows how the atmospheric precipitable water is exponentially related to T2m, although there are
obviously exceptionally dry regions like the MDB and AMMA region and an exceptionally moist
HUBEX region. The reanalyses and observations are in fairly good agreement, with perhaps themajor exception again being the AMMA region, where the available atmospheric observations arerelatively scarce and the atmospheric structure is seasonally complex in that the region since it istouched in different ways by the Atlantic Intertropical Convergence zone, especially during theBoreal summer.

Fig. 1a Annual mean 2 m temperature for each of the identified regions. The two observational estimates are shown by
the black and gray squares. These observational estimates straddle their mean plus or minus one standard deviation of
10-year means, shown by the hatched lines. Individual reanalyses are shown by the small circles and their mean is shownby the large yellow circle. Individual GLDAS simulations are shown by the small squares and their mean is shown by
the large yellow square. Fig. 1b Annual mean precipitable water. Fig. 1c Annual mean T2m-Ts. Fig. 1d Annual meansoil moisture.

Fig. 1c shows the difference between the 2-meter temperature, T2m, and the surface skintemperature, Ts, which only comes from the reanalysis models. It should be noted here that the two
observational estimates provided here (Rmean and Gmean) are based entirely on models and show
the difference between using a reanalysis versus a forced GLDAS simulation. It is perhaps a little
surprising that the uncertainty in this surface skin-air temperature difference is relatively large in not
only the reanalyses models but also the GLDAS simulations, which all have the same T2m, although
this is obviously due to the myriad ways in which Ts can be computed for a large scale region
comprised of bare soil, water bodies, vegetation, etc. The AMMA region has perhaps the largest
differences. Further examination of the geographic maps for the annual mean, as well as DJF andJJA differences (not shown), suggests that the uncoupled GLDAS surface temperature differences
are much larger than coupled reanalyses models. Fig. 1d shows the total surface water 2m variations
also vary greatly among models. The reanalysis models are perhaps closer than the GLDAS modelvariations. In general there tends to be somewhat higher values in the colder regions than in the drysubtropical areas, then large amounts in the tropical LBA and GAME-T regions and smaller
amounts in the relatively dry AMMA region. Again, there is fairly large disagreement for the landmeans between the reanalyses and GLDAS simulations, which also have fairly large disagreements
among themselves. This was previously discussed by GSWP, who suggested that seasonal variations
would have greater agreement than annual means.
Additional variables and budgets are shown in Roads et al. (2008) and are also posted at
http://ecpc.ucsd.edu/projects/ghp/WEBS/.
3. Summary
This study made use of observationally based estimates developed by GEWEX and other global
communities as well as products from the current global atmospheric and land reanalyses groups totry to characterize the means and uncertainty in simplified bulk-integrated (atmosphere and surface)water and energy budget variables, including: precipitable water, terrestrial soil moisture, snow
equivalent water, atmospheric sensible heat, surface air and skin temperature, precipitation,
vertically integrated moisture convergence, evaporation, runoff, vertically integrated dry static
energy convergence, latent heat of condensation, atmospheric radiative cooling, surface radiative
heating, and sensible and latent heat transfers from the surface to the atmosphere. In particular, thisWEBS compared NVAP and ISCCP FD water vapor, SRB and ISCCP FD radiation, GPCP andCPC precipitation, GRDC and EOS runoff, CRU and CPC surface temperature, and HOAP3 andGSST2 turbulent fluxes to four recent atmospheric reanalysis data sets NCEP (R1 and R2), ECMWF(ERA40), and JMA (JRA25) and three GLDAS simulations.
It was demonstrated that for the surface terms, the most constrained system, the models from theGLDAS, probably had slightly better analyses over land for many of the surface water and energy
terms, in that the closure was smaller when using these estimates for the observed land surface
fluxes. It is interesting, however, that the improvements are perhaps smaller than we might haveanticipated (Qu and Henderson Sellers, 1998) a priori, that the spread among the reanalyses models
was only slightly larger than the corresponding spread among the GLDAS simulations and that theGLDAS 2 m and surface skin temperature differences were quite different from those in the coupledreanalyses. This indicates that current GLDAS simulations might be improved by moving toward
more coupled systems while the reanalyses should be moving toward more constrained systems (i.e.
by including observed precipitation, as for example was done for the recent North American
Regional Reanalysis. See Mesinger et al. 2006 and Nigam and Ruiz-Barradas 2006). It should also
be noted that some of the observations as well as constrained atmospheric and land based analyses
seemed to have larger errors than we might have anticipated a priori, indicating that more efforts areneeded to observe as well as simulate water and energy budgets.
Despite various errors, it did seem that our anticipated theoretical characterization of the global
water and energy cycle could still be readily discerned from available observation and model based
data/output. On the average, atmospheric precipitation is balanced with surface evaporation; water
vapor convergence over land is balanced by outgoing streamflow to the ocean. On the average, net
radiation at the top of the atmosphere is balanced by net transport of energy; net surface radiative
heating over land is balanced by the net turbulent transport of energy back to the atmosphere;atmospheric radiative cooling is balanced by the latent heat of condensation associated withprecipitation, the sensible heat transport from the surface and the transport of energy from otherregions. In addition, it was clear that the terms in the budgets were quite different depending upon
the RHP. For example, low latitude RHPs with relatively warm annual mean climates are energy
source regions whereas high latitude RHPs with relatively cold climates are energy sink regions.
Despite the observational estimates and more model based atmospheric reanalyses and GLDAS data
sets showing these characteristic features, they do so with a 10-20% closure error for annual meansand even larger closures for individual regions. RMS errors for 10 year means are even larger andpresumably even larger errors occur for shorter (monthly) time scales. Much more work is certainly
needed to continue to accurately develop all of the appropriate WEBS data sets and to further reduce
perceived errors in global and regional atmospheric, ocean, and land water and energy budgets.
Again, further details are provided by Roads et al. (2008)
Acknowledgements
This research was mostly funded by NASA grants to individual investigators working on the NASA
NEWS project. Grants include NASA-NNG05GR40G to J. Roads. We thank the following
individuals for their help with obtaining and explaining the various data and reanalyses: R. Adlerand G. Huffman for GPCP, P. Arkin and P. Xie for CMAP, W. Ebisuzaki for R1 and R2, C.
Vorosmarty, F. Balazcs, and E. Douglas for GRDC and ISLSCP runoff data sets., J. Janowiak for
CPC temperature, A. Ruane for ECPC CEOP analysis, A. Beljaars for ERA40 guidance to ECMWFand NCAR data sets. We also thank all of the other unnamed individuals and institutions who havecontinued to work to provide free and open access to all of the GEWEX and other global data andmodel output used for this WEBS. We also thank the anonymous reviewers for their comments,
which helped to improve the presentation of this work.
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