On the suitability of current atmospheric reanalyses for regional warming studies over China

Abstract. 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  ∼  2200 meteorological stations in China from 1979 to 2010. The results show that  ∼  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 theT_a trends in the reanalyses diverge spatially (standard deviation  =  0.15–0.30 °C decade⁻¹ using 1°  ×  1° 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 °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 °C decade⁻¹, respectively. The biases in the trends in T_a over northern China (on the order of −0.12 °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 °C decade⁻¹).