4.6 Article

Does the modern-era retrospective analysis for research and applications-2 aerosol reanalysis introduce an improvement in the simulation of surface solar radiation over China?

Journal

INTERNATIONAL JOURNAL OF CLIMATOLOGY
Volume 39, Issue 3, Pages 1305-1318

Publisher

WILEY
DOI: 10.1002/joc.5881

Keywords

aerosol; cloud; MERRA2; surface incident solar radiation

Funding

  1. National Key Research and Development Program of China [2017YFA0603601]
  2. National Natural Science Foundation of China [41525018]

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Surface incident solar radiation (R-s) is a key parameter of energy and water cycles of the Earth. Reanalyses represent important sources of information on R-s. However, reanalyses R-s may have important bias due to their imperfect parameterizations and input errors of cloud and aerosol. NASA's Global Modelling and Assimilation Office has recently released Version 2 of the Modern-Era Retrospective Analysis for Research and Applications (MERRA2), which incorporates a reanalysis of atmospheric optical depth for the first time. In this study, we evaluate R-s from MERRA2 and its predecessor (MERRA) in China from 1980 to 2014. We first compare three possible reference data sources: (a) observed R-s at 122 stations, (b) satellite retrievals of R-s and (c) R-s values derived from sunshine durations measured at 2,400 weather stations. We find sunshine duration derived R-s is a reliable reference and use it to evaluate MERRA and MERRA2. Our results show that both MERRA and MERRA2 have a high mean bias of 38.63 and 43.86 W/m(2) over China due to their underestimation of cloud fraction, which is greater in southern China. MERRA2 displays improved capability in reproducing monthly and annual variability, and national mean trend of R-s. MERRA overestimates the trend of R-s by 3.23 W/m(2) in eastern China. MERRA2 reduced this trend bias over the North China Plain likely due to its aerosol assimilation. However, MERRA2 show a negative bias in trend of R-s (-3.44 W/m(2)) in the south China likely due to its overestimation of atmospheric aerosols loading and aerosol-cloud interaction. The results provide guidance for future development of reanalysis and its scientific applications for ecological and hydrological models.

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