资源环境科技发展态势分析平台

Reanalyses are widely used because they add value to routine observations by generating physically or dynamically consistent and spatiotemporally complete atmospheric fields. Existing studies include extensive discussions of the temporal suitability of reanalyses in studies of global change. This study adds to this existing work by investigating the suitability of reanalyses in studies of regional climate change, in which land-atmosphere interactions play a comparatively important role. In this study, surface air temperatures (T-a) from 12 current reanalysis products are investigated; in particular, the spatial patterns of trends in T-a are examined using homogenized measurements of T-a made at similar to 2200 meteorological stations in China from 1979 to 2010. The results show that similar to 80% of the mean differences in T-a between the reanalyses and the in situ observations can be attributed to the differences in elevation between the stations and the model grids. Thus, the T-a climatologies display good skill, and these findings rebut previous reports of biases in T-a. However, the biases in the T-a trends in the reanalyses diverge spatially (standard deviation = 0.15-0.30 degrees C decade(-1) using 1 degrees x 1 degrees grid cells). The simulated biases in the trends in T-a correlate well with those of precipitation frequency, surface incident solar radiation (R-s) and atmospheric downward longwave radiation (L-d) among the reanalyses (r = -0.83, 0.80 and 0.77; p < 0.1) when the spatial patterns of these variables are considered. The biases in the trends in T-a over southern China (on the order of -0.07 degrees C decade(-1)) are caused by biases in the trends in R-s, L-d and precipitation frequency on the order of 0.10, -0.08 and -0.06 degrees C decade(-1), respectively. The biases in the trends in T-a over northern China (on the order of 0.12 degrees C decade(-1)) result jointly from those in L-d and precipitation frequency. Therefore, improving the simulation of precipitation frequency and R-s helps to maximize the signal component corresponding to regional climate. In addition, the analysis of T-a observations helps represent regional warming in ERA-Interim and JRA-55. Incorporating vegetation dynamics in reanalyses and the use of accurate aerosol information, as in the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), would lead to improvements in the modelling of regional warming. The use of the ensemble technique adopted in the twentieth-century atmospheric model ensemble ERA-20CM significantly narrows the uncertainties associated with regional warming in reanalyses (standard deviation = 0.15 degrees C decade(-1)).