CESM1-LE 模拟的北极和波罗的海地区之间的大气远程联系

E. Jakobson, L. Jakobson
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摘要

摘要本文研究了北极和波罗的海地区之间的远缘联系,并基于共同体地球系统模式第一版大集合(CESM-LE)气候模式模拟的两种情况:工业化前辐射强迫的静止情况和 RCP8.5 辐射强迫的气候变化情况。静止控制模拟的 1800 年长时间序列用于静止远缘联系,而正在进行的气候变化期间的远缘联系则使用 1920-2100 年期间的 40 个成员集合。我们分析了 2 米水平的季节温度、海平面气压、海冰浓度、降水、位势高度和 10 米水平风速。北极地区被划分为七个区域。波罗的海地区的气候与北极气候有很强的远距离联系;与斯瓦尔巴群岛和格陵兰岛地区的联系最强。远缘联系具有很强的季节性,冬季相关性最强,夏季相关性最低,因为此时当地气象因素较强。北大西洋涛动(NAO)和北极涛动(AO)气候指数可以解释冬季和春季的大部分远缘联系。在持续的气候变化过程中,到 21 世纪末,远缘连接模式并未出现显著变化。波罗的海地区温度和海冰浓度之间的模式变化较小。我们计算了参数之间的相关性及其脊回归估计,以估算不同北极地区与波罗的海地区的集体统计联系。冬季的季节判定系数 R2 最高:T2 米,R2=0.64;海平面气压(SLP),R2=0.44;降水量(PREC),R2=0.35。如果对北极地区各季前一个月的数值进行同样的计算,其关系则要弱得多,其中春季气温的 R2=0.09 最高。因此,北极气候数据对波罗的海地区的预报能力较弱。虽然北极和波罗的海地区之间在统计上存在显著的远程联系,但北极的影响是区域性的,主要与气候指数有关。没有简单的因果关系。到 21 世纪末,北极冰的浓度明显下降。不过,到 21 世纪末,北极和波罗的海地区之间的总体远距离联系模式不会发生重大变化。
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Atmospheric teleconnections between the Arctic and the Baltic Sea region as simulated by CESM1-LE
Abstract. This paper examines teleconnections between the Arctic and the Baltic Sea region and is based on two cases of Community Earth System Model version 1 large ensemble (CESM-LE) climate model simulations: the stationary case with pre-industrial radiative forcing and the climate change case with RCP8.5 radiative forcing. The stationary control simulation's 1800-year long time series were used for stationary teleconnection and a 40-member ensemble from the period 1920–2100 is used for teleconnections during ongoing climate change. We analyzed seasonal temperature at a 2 m level, sea-level pressure, sea ice concentration, precipitation, geopotential height, and 10 m level wind speed. The Arctic was divided into seven areas. The Baltic Sea region climate has strong teleconnections with the Arctic climate; the strongest connections are with Svalbard and Greenland region. There is high seasonality in the teleconnections, with the strongest correlations in winter and the lowest correlations in summer, when the local meteorological factors are stronger. North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) climate indices can explain most teleconnections in winter and spring. During ongoing climate change, the teleconnection patterns did not show remarkable changes by the end of the 21st century. Minor pattern changes are between the Baltic Sea region temperature and the sea ice concentration. We calculated the correlation between the parameter and its ridge regression estimation to estimate different Arctic regions' collective statistical connections with the Baltic Sea region. The seasonal coefficient of determination, R2, was highest for winter: for T2 m, R2=0.64; for sea level pressure (SLP), R2=0.44; and for precipitation (PREC), R2=0.35. When doing the same for the seasons' previous month values in the Arctic, the relations are considerably weaker, with the highest R2=0.09 being for temperature in the spring. Hence, Arctic climate data forecasting capacity for the Baltic Sea region is weak. Although there are statistically significant teleconnections between the Arctic and Baltic Sea region, the Arctic impacts are regional and mostly connected with climate indexes. There are no simple cause-and-effect pathways. By the end of the 21st century, the Arctic ice concentration has significantly decreased. Still, the general teleconnection patterns between the Arctic and the Baltic Sea region will not change considerably by the end of the 21st century.
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