2015
DOI: 10.1002/2014jd023040
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Evaporation variability under climate warming in five reanalyses and its association with pan evaporation over China

Abstract: With the motivation to identify actual evapotranspiration (AE) variability under climate warming over China, an assessment is made from five sets of reanalysis data sets [National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR), NCEP-Department of Energy (NCEP-DOE), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Interim Reanalysis, and Japanese 55-year Reanalysis (JRA-55)]. Based on comparison with AE estimates calculated using the Budyko equa… Show more

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Cited by 49 publications
(49 citation statements)
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“…The variability of evaporation in JRA55 has been evaluated by Su et al . []. The evaporation in JRA55 has a high performance in capturing the observed variability in arid and semiarid China (especially at the interdecadal timescale), although it, as well as Japanese 25 year Reanalysis, has some inaccuracies over eastern China when focusing on the interannual variation [ Liu et al ., ; Su et al ., ].…”
Section: Resultsmentioning
confidence: 99%
“…The variability of evaporation in JRA55 has been evaluated by Su et al . []. The evaporation in JRA55 has a high performance in capturing the observed variability in arid and semiarid China (especially at the interdecadal timescale), although it, as well as Japanese 25 year Reanalysis, has some inaccuracies over eastern China when focusing on the interannual variation [ Liu et al ., ; Su et al ., ].…”
Section: Resultsmentioning
confidence: 99%
“…ERA-Interim shows the highest fidelity among reanalyses in terms of reproducing the EA monsoon precipitation climatology and its interannual variability (Lin et al 2014). When comparing the evaporation in reanalyses to observations over China (Su et al 2015), ERA-Interim produced a reasonable estimate in both the spatial pattern and interannual variations.…”
Section: Datamentioning
confidence: 89%
“…In this paper, AE_Budyko is mainly used as the reference to evaluate the performances of the six reanalysis data sets in reproducing the interannual variation and long‐term trend in AE. To maintain the simplicity of the model, we use the recommended default value for n (i.e., 1.8) across all of China (Choudhury, ; Su, Feng, & Feng, ).…”
Section: Methodsmentioning
confidence: 99%
“…Substantial attention has also been paid to AE values derived from reanalysis data sets. Su, Feng, and Feng (), Su, Feng, Zhou, et al () used reanalyses to examine AE variability over China with respect to climate change and found that the relationship between AE and pan evaporation was complementary with water control and proportional to energy control. Mao and Wang () compared three evapotranspiration data sets for China based on surface water balance values, a modified Penman‐Monteith (MPM) model, and reanalysis data and concluded that reanalysis evapotranspiration variables are consistent with MPM estimates in terms of spatial patterns, interannual variability, and temporal trends at the station scale.…”
Section: Introductionmentioning
confidence: 99%