GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface

Dirmeyer, Paul A.; Gao, Xiang; Zhao, Ming; Guo, Zhichang; Oki, Taikan; Hanasaki, Naota

doi:10.1175/bams-87-10-1381

Cited by 723 publications

(602 citation statements)

References 28 publications

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“…For example, the mean AET in China estimated by Boreal Ecosystem Productivity Simulator (BEPS) model (369.8 mm yr -1 ) (Liu et al, 2013a) was approximately 26% smaller than the value estimated by Remote Sensing-Penman Monteith model (500 mm yr -1 ) (Li et al, 2014). The global mean AET differed even more largely among 15 simulations modeled in the Global Soil Wetness Project-2, ranging from 272 to 441 mm yr -1 (Dirmeyer et al, 2006). These uncertainties may result from the complexity in modeling ET and the lack of its direct observations as well.…”

Section: Introductionmentioning

confidence: 92%

Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: Results from eddy covariance measurements

Zheng

Wang

et al. 2016

J. Geogr. Sci.

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Abstract:Understanding the spatial variation in annual actual evapotranspiration (AET) and its influencing factors is crucial for a better understanding of hydrological processes and water resources management. By synthesizing ecosystem-level observations of eddy-covariance flux sites in China (a total of 61 sites), we constructed the most complete AET dataset in China up to now. Based on this dataset, we quantified the statistic characteristics of AET and water budgets (defined as the ratio of AET to annual mean precipitation (MAP), AET/MAP) of terrestrial ecosystems in China. Results showed that AET differed significantly among both different vegetation types and climate types in China, with overall mean AET of 534.7±232.8 mm yr -1 . AET/MAP also differed significantly among different climate types, but there were no distinct differences in AET/MAP values across vegetation types, with mean AET/MAP of 0.82±0.28 for non-irrigated ecosystems. We further investigated how the main climatic factors and vegetation attributes control the spatial variation in AET. Our findings revealed that the spatial variation of AET in China was closely correlated with the geographical patterns of climate and vegetation, in which the effects of total annual net radiation (R n ), MAP and mean annual air temperature (MAT) were dominant. Thus, we proposed an empirical equation to describe the spatial patterns of AET in China, which could explain about 84% of the spatial variation in AET of terrestrial ecosystems in China. Based on the constructed dataset, we also evaluated the uncertainties of five published global evapotranspiration products in simulating 1392 Journal of Geographical Sciences site-specific AET in China. Results showed that large biases in site-specific AET values existed for all five global evapotranspiration products, which indicated that it is necessary to involve more observation data of China in their parameterization or validation, while our AET dataset would provide a data source for it.

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Section: Introductionmentioning

confidence: 92%

Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: Results from eddy covariance measurements

Zheng

Wang

et al. 2016

J. Geogr. Sci.

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“…These differences in the parameterizations can give rise to large variability in the outputs depending upon the variables of interest. The multi-model analysis carried out under the Global Soil Wetness Project-2 (GSWP-2) (Dirmeyer et al, 2006) illustrated that LSM variables, especially those associated with snow processes (i.e., snow water equivalent) and soil water (i.e., soil mois- ture in the lower layers), have a large spread. The same is true for groundwater recharge (Xia et al, 2012a).…”

Section: Potential Reasons Of Differences Among Modelsmentioning

confidence: 99%

“…The partitioning of saturation excess into surface runoff and drainage and how they vary in space are also quite different from one LSM to another (Lohmann et al, 1998(Lohmann et al, , 2004Boone et al, 2004). Nevertheless, LSMs provide spatially and temporally continuous estimates of hydrological variables that would be impossible to obtain using observations alone, and often the results are surprisingly good considering their limitations (Dirmeyer et al, 2006;Syed et al, 2008;Jimenez et al, 2011;Wang et al, 2011;Li et al, 2015).…”

Section: Limitations Of Lsmsmentioning

confidence: 99%

Comparing potential recharge estimates from three Land Surface Models across the western US

Niraula

Meixner

Ajami

et al. 2017

Journal of Hydrology

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a b s t r a c tGroundwater is a major source of water in the western US. However, there are limited recharge estimates in this region due to the complexity of recharge processes and the challenge of direct observations. Land surface Models (LSMs) could be a valuable tool for estimating current recharge and projecting changes due to future climate change. In this study, simulations of three LSMs (Noah, Mosaic and VIC) obtained from the North American Land Data Assimilation System (NLDAS-2) are used to estimate potential recharge in the western US. Modeled recharge was compared with published recharge estimates for several aquifers in the region. Annual recharge to precipitation ratios across the study basins varied from 0.01% to 15% for Mosaic, 3.2% to 42% for Noah, and 6.7% to 31.8% for VIC simulations. Mosaic consistently underestimates recharge across all basins. Noah captures recharge reasonably well in wetter basins, but overestimates it in drier basins. VIC slightly overestimates recharge in drier basins and slightly underestimates it for wetter basins. While the average annual recharge values vary among the models, the models were consistent in identifying high and low recharge areas in the region. Models agree in seasonality of recharge occurring dominantly during the spring across the region. Overall, our results highlight that LSMs have the potential to capture the spatial and temporal patterns as well as seasonality of recharge at large scales. Therefore, LSMs (specifically VIC and Noah) can be used as a tool for estimating future recharge in data limited regions.

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“…It has 28 vertical sigma levels, and the horizontal is at T62 spectral resolution (~1.9°×1.9°). Three LSMs are coupled to the AGCM in this study: the simplified simple biosphere model (SSiB) (based on Xue et al (1991); Dirmeyer and Zeng (1999)), the Community Land Model (CLM) version 3.5 (Oleson et al 2008), and version 2.7 of the Noah land model (Ek et al 2003). These LSMs have been widely used in weather and climate research.…”

Section: Model and Experimentsmentioning

confidence: 99%

“…Land surface models (LSMs) have evolved from the first-generation buck-type schemes in the 1960s, the secondgeneration schemes with biophysics in the 1980s, to the thirdgeneration schemes with carbon cycle and dynamic vegetation (Sellers et al 1997;Pitman 2003). However, due to the complexity of the land surface processes and the scarcity of large-scale land surface observations, current LSMs still have large uncertainties in simulating the surface processes (e.g., Dirmeyer et al 2006a;Dirmeyer et al 2006b;Jimenez et al 2011). When driven by the same atmospheric forcing, different LSMs may give significantly different surface fluxes (Henderson-Sellers et al 1995;Henderson-Sellers et al 1996;Pitman et al 1999).…”

Section: Introductionmentioning

confidence: 99%

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

Wei

Dirmeyer

Yang

et al. 2017

Theor Appl Climatol

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Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.

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GSWP-2: Multimodel Analysis and Implications for Our Perception of the Land Surface

Abstract: By combining the simulations of more than a dozen different global land surface models, an unprecedented analysis of terrestrial water and energy budgets is realized.

Cited by 723 publications

References 28 publications

Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: Results from eddy covariance measurements

Spatial variation in annual actual evapotranspiration of terrestrial ecosystems in China: Results from eddy covariance measurements

Comparing potential recharge estimates from three Land Surface Models across the western US

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

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