Abstract

Abstract. Precipitation and shortwave radiation play important roles in climatic, hydrological and biogeochemical cycles. Several global and regional forcing data sets currently provide historical estimates of these two variables over China, including the Global Land Data Assimilation System (GLDAS), the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the China Meteorological Forcing Dataset (CMFD). The CN05.1 precipitation data set, a gridded analysis based on CMA gauge observations, also provides high-resolution historical precipitation data for China. In this study, we present an intercomparison of precipitation and shortwave radiation data from CN05.1, CMFD, CLDAS and GLDAS during 2008–2014. We also validate all four data sets against independent ground station observations. All four forcing data sets capture the spatial distribution of precipitation over major land areas of China, although CLDAS indicates smaller annual-mean precipitation amounts than CN05.1, CMFD or GLDAS. Time series of precipitation anomalies are largely consistent among the data sets, except for a sudden decrease in CMFD after August 2014. All forcing data indicate greater temporal variations relative to the mean in dry regions than in wet regions. Validation against independent precipitation observations provided by the Ministry of Water Resources (MWR) in the middle and lower reaches of the Yangtze River indicates that CLDAS provides the most realistic estimates of spatiotemporal variability in precipitation in this region. CMFD also performs well with respect to annual mean precipitation, while GLDAS fails to accurately capture much of the spatiotemporal variability and CN05.1 contains significant high biases relative to the MWR observations. Estimates of shortwave radiation from CMFD are largely consistent with station observations, while CLDAS and GLDAS greatly overestimate shortwave radiation. All three forcing data sets capture the key features of the spatial distribution, but estimates from CLDAS and GLDAS are systematically higher than those from CMFD over most of mainland China. Based on our evaluation metrics, CLDAS slightly outperforms GLDAS. CLDAS is also closer than GLDAS to CMFD with respect to temporal variations in shortwave radiation anomalies, with substantial differences among the time series. Differences in temporal variations are especially pronounced south of 34° N. Our findings provide valuable guidance for a variety of stakeholders, including land-surface modelers and data providers.

Highlights

  • Precipitation and shortwave radiation are the fundamental sources of water and energy for land-surface biological, physical and chemical processes

  • The distributions of precipitation based on China Meteorological Forcing Dataset (CMFD) and CN05.1 are generally similar, these two data sets still have some mutual discrepancies in western China

  • The area for which annual mean precipitation exceeds 1500 mm is smaller in CLDAS and Global Land Data Assimilation System (GLDAS) than in CN05.1 and CMFD, and precipitation over northern China is considerably smaller in CLDAS than in the other three data sets

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Summary

Introduction

Precipitation and shortwave radiation are the fundamental sources of water and energy for land-surface biological, physical and chemical processes The resulting forcing data sets, which typically include precipitation, shortwave radiation, temperature, specific humidity, wind speed, surface pressure and other meteorological data, are derived by assimilating numerical weather forecast information, ground observation data and remote sensing data into an analysis product (Xie et al, 2011; Zhao et al, 2010; Pan et al, 2010). Separate efforts have produced new gridded analyses of station-based measurements, including the CN05.1 interpolation of CMA rain gauge data released by the National Climate Center (Wu and Gao, 2013). These forcing data sets are widely used because they have high spatial resolution, cover a large area over a long period and are convenient to obtain and process. The CN05.1 data set has been used in many fields, such as simulating climate change over China (Gao et al, 2013) and studying shifts in the western Pacific subtropical high (Huang et al, 2015)

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