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

Abstract

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 ).

Explaining Extreme Events of 2017 from a Climate Perspective

“…1b). More (less) partitioning of surface incident solar radiation into sensible (latent) heat flux induced by impervious surfaces in the urban region can largely explain the urbanization-induced warming in summer over the Yangtze River Delta (Wang and Dickinson 2013;Zhou and Wang 2016;K. Wang et al 2017).…”

“…1a) are identified in comparison with urban stations to quantify the urban heat island effect. The west-extending ridge point position (WRPP) index of the WPSH is calculated (Tao and Xu 1962;Ding 1994;Liu et al 2012) with the S98 JANUARY 2019 | 2.5° × 2.5° 500-hPa geopotential height of the NOAA-CIRES Twentieth Century Reanalysis from 1873 to 2014 (Compo et al 2011) and extended to 2017 by that of National Centers for Environmental Prediction Reanalysis (NCEP-R1) Zhou et al 2018a). We define the years with a WRPP index smaller (larger) than the climatological value (e.g., 137.5°E in reanalysis) as strong (weak) WPSH years.…”

Surface temperature response to the major volcanic eruptions in multiple reanalysis data sets

“…Summary descriptions of the forecast model, assimilation scheme, and assimilated observations for each of these reanalyses have been provided by Fujiwara et al (2017) and Zhou et al (2018). The reanalyses ERA-Interim, ERA-40, JRA-55, JRA-20 25, MERRA-2, MERRA, CFSR, and R-1 are "full-input" reanalyses that assimilate surface and upper-air conventional and satellite observational data, with satellite data from around the early 1970s but mainly from 1979 onward (for satellite data, R-1 assimilates only retrievals, while others assimilate both radiances and retrievals).…”

“…Basic intercomparisons of 2-metre temperatures across reanalyses have been provided by Simmons et al (2017), Draper et al 30 (2018), and Zhou et al (2018), as well as in the references listed in Table 1.…”

Attribution of a Record-Breaking Heatwave Event in Summer 2017 over the Yangtze River Delta

“…1a) are identified in comparison with urban stations to quantify the urban heat island effect. The west-extending ridge point position (WRPP) index of the WPSH is calculated (Tao and Xu 1962;Ding 1994;Liu et al 2012) with the 2.5° × 2.5° 500-hPa geopotential height of the NOAA-CIRES Twentieth Century Reanalysis from 1873 to 2014 (Compo et al 2011) and extended to 2017 by that of National Centers for Environmental Prediction Reanalysis (NCEP-R1) (Kalnay et al 1996;Zhou et al 2018a). We define the years with a WRPP index smaller (larger) than the climatological value (e.g., 137.5°E in reanalysis) as strong (weak) WPSH years.…”