在多项再分析中发现的南半球陆地上空随季节变化的次周温度变化的增加

Patrick Martineau, S. Behera, Masami Nonaka, Hisashi Nakamura, Yu Kosaka
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引用次数: 0

摘要

摘要。评估了 12 个全球大气再分析数据集之间南半球中纬度陆块近地面(850 hPa)次周温度变化趋势的数据集间一致性。对气候学温度变异和变异的主要源和汇进行比较后发现,除 NCEP-NCAR (R1)和 NCEP-DOE (R2)外,其他数据集在卫星时代(1980-2022 年)的变异幅度和空间分布方面都有较好的一致性,这表明副周变异的主要特征得到了充分体现。在卫星时代发现的影响南非 9 月-10 月-11 月(SON)和南美洲 12 月-1 月-2 月(DJF)的副周变率的正向趋势也有很好的一致性。虽然大多数再分析对 SON 期间影响澳大利亚的积极趋势达成了一致,但在仅考虑卫星时代时,这一趋势尚未从与年际变率相关的噪声中显现出来。然而,当时间段延长(1954-2022 年)或仅限于最近几十年(1990-2022 年)时,这一趋势则非常明显。这种趋势的主要原因是水平温度平流更有效地产生了副周温度变异。这种变化也是数据集偏差的来源之一。即使是那些没有同化卫星数据(JRA-55C)或只同化地表观测数据(ERA-20C、20CRv2c 和 20CRv3)的再分析,也能再现这些趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Seasonally dependent increases in subweekly temperature variability over Southern Hemisphere landmasses detected in multiple reanalyses
Abstract. The inter-dataset agreement of trends in subweekly near-surface (850 hPa) temperature variability over Southern Hemisphere midlatitude land masses is assessed among 12 global atmospheric reanalysis datasets. A comparison of the climatological temperature variance and dominant sources and sinks of the variance reveals that, except for NCEP-NCAR (R1) and NCEP-DOE (R2), there is a relatively good agreement for their magnitudes and spatial distributions during the satellite era (1980–2022), which indicates that the key features of subweekly variability are sufficiently well represented. A good agreement is noted for the positive trends found in subweekly variability over the satellite era affecting South Africa in September–October–November (SON) and South America in December–January–February (DJF). Although there is agreement in most of the reanalyses concerning the positive trend affecting Australia in SON, this has not yet emerged from the noise associated with interannual variability when considering only the satellite era. It is significant, however, when the period is extended (1954–2022) or limited to the most recent decades (1990–2022). The trends are explained primarily by a more efficient generation of subweekly temperature variance by horizontal temperature advection. This generation is also identified as a source of biases among the datasets. The trends are found to be reproduced even in those reanalyses that do not assimilate satellite data (JRA-55C) or that assimilate surface observations only (ERA-20C, 20CRv2c, and 20CRv3).
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