Quantitative decomposition of radiative and non-radiative contributions to temperature anomalies related to siberian high variability
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In this study, we carried out an attribution analysis that quantitatively assessed relative contributions to the observed temperature anomalies associated with strong and weak Siberian High (SH). Relative contributions of radiative and non-radiative processes to the variation of surface temperature, in terms of both amplitude and spatial pattern, were analyzed. The strong SH activity leads to the continental-scale cold temperature anomalies covering eastern Siberia, Mongolia, East China, and Korea (i.e., SH domain). The decomposition of the observed temperature anomalies associated with the SH variation was achieved with the Coupled atmosphere–surface climate Feedback-Responses Analysis Method, in which the energy balance in the atmosphere–surface column and linearization of radiative energy perturbation are formulated. For the mean amplitude of −3.13 K of cold temperature anomaly over the SH domain, sensible heat flux is tightly connected with a cooling of −1.26 K. Atmospheric dynamics adds another −1.13 K through the large-scale cold advection originated from the high latitudes. The longwave effects of cloud and water vapor account for the remaining cold anomalies of −1.00 and −0.60 K, respectively, while surface dynamics (0.71 K) and latent heat flux (0.26 K) help to mitigate the cold temperature anomalies. Influences of ozone and albedo processes are found to be relatively weak.
KeywordsSiberian High-related temerature change Temperature decomposition CFRAM Radiative and non-radiative processess
The ERA Interim data used in this study were provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). Yi Deng and Tae-Won Park were supported by the DOE’s Office of Science Regional and Global Climate Modeling (RGCM) Program (DE-SC0005596) and by NSF grants AGS-1147601 and AGS-1354402. Jee-Hoon Jeong was supported by the Korea Polar Research Institute (PE14010).
- Cai M, Lu JH (2009) A new framework for isolating individual feedback processes in coupled general circulation climate models. Part II: method demonstrations and comparisons. Clim Dynam 32(6):887–900. doi: 10.1007/s00382-008-0424-4
- Dee DP, Uppala SM, Simmons AJ, Berrisford P, Poli P, Kobayashi S, Andrae U, Balmaseda MA, Balsamo G, Bauer P, Bechtold P, Beljaars ACM, van de Berg L, Bidlot J, Bormann N, Delsol C, Dragani R, Fuentes M, Geer AJ, Haimberger L, Healy SB, Hersbach H, Holm EV, Isaksen L, Kallberg P, Kohler M, Matricardi M, McNally AP, Monge-Sanz BM, Morcrette JJ, Park BK, Peubey C, de Rosnay P, Tavolato C, Thepaut JN, Vitart F (2011) The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q J Roy Meteor Soc 137(656):553–597. doi: 10.1002/Qj.828 CrossRefGoogle Scholar
- Ding YH (1990) Buildup, air-mass transformation and propagation of siberian high and its relations to cold surge in East-Asia. Meteorol Atmos Phys 44(1–4):281–292Google Scholar
- Geleyn J-F, Hollingsworth A (1979) An economical, analytical method for the computation of the interaction between scattering and line absorption of radiation. Contribut Atmos Phys 52:1–16Google Scholar
- Hong CC, Hsu HH, Chia HH, Wu CY (2008) Decadal relationship between the North Atlantic Oscillation and cold surge frequency in Taiwan. Geophys Res Lett 35(24). doi: 10.1029/2008gl034766
- Jeong JH, Ou TH, Linderholm HW, Kim BM, Kim SJ, Kug JS, Chen DL (2011) Recent recovery of the Siberian High intensity. J Geophys Res Atmos 116. doi: 10.1029/2011jd015904
- Lydolf PE (1977) Climates of the Soviet Union. Elsevier, AmsterdamGoogle Scholar
- Park T-W, Deng Y, Cai M, Jeong J-H, Zhou R (2014) A dissection of the surface temperature biases in the Community Earth System Model. Clim Dynam 1–17. doi: 10.1007/s00382-013-2029-9
- Taylor PC, Ellingson RG, Cai M (2011) Geographical distribution of climate feedbacks in the NCAR CCSM3.0. J Climate 24(11):2737–2753. doi: 10.1175/2010JCLI3788.1