Understanding CMIP6 biases in the representation of the Greater Horn of Africa long and short rains
Understanding CMIP6 biases in the representation of the Greater Horn of Africa long and short rains
复制标题
了解 CMIP6 在大非洲之角长雨和短雨表示中的偏差
DOI：
10.1007/s00382-022-06622-5
复制
发表时间：
2022
期刊：
影响因子：
4.6
通讯作者：
K. Marvel
中科院分区：
文献类型：
--
作者：
K. Schwarzwald;L. Goddard;R. Seager;M. Ting;K. Marvel
大非洲之角（GHA）的社会容易受到两个不同雨季的变化，3月至5月的“长”雨和12月至12月的“短”雨。最近的趋势，在两个雨季，可能与模式的低频变率，增加了兴趣，未来的气候预测，从大气环流模式（GCM）。然而，前几代GCM历史上对区域水文气候的模拟很差。本研究对最新一代的大气环流模式CMIP6中的长雨和短雨模拟进行了基于过程的评估。CMIP5中的关键偏差仍然存在或恶化，包括太短和太弱的长降雨和太长和太强的短降雨。模式偏差是由一组复杂的相关海洋和大气因素驱动的，包括对步行者环流的模拟。模型中的偏湿短雨与印度洋纬向海表面温度（SST）梯度有关，西半球的SST梯度太暖，对流太深。模型将赤道非洲风与短时雨的强度联系起来，尽管在观测中，主要在长降雨中发现了一种强有力的联系。模型平均状态偏差在西印度洋SST季节性周期的时间与某些降雨时间偏差，虽然这两种偏差可能是由于一个共同的来源。由历史SST驱动的模拟（AMIP运行）通常比完全耦合运行具有更大的偏差。使用偏见，以更好地了解GHA降雨预测的不确定性的路径建议。
The societies of the Greater Horn of Africa (GHA) are vulnerable to variability in two distinct rainy seasons, the March–May ‘long’ rains and the October–December ‘short’ rains. Recent trends in both rainy seasons, possibly related to patterns of low-frequency variability, have increased interest in future climate projections from General Circulation Models (GCMs). However, previous generations of GCMs historically have poorly simulated the regional hydroclimate. This study conducts a process-based evaluation of simulations of the long and short rains in CMIP6, the latest generation of GCMs. Key biases in CMIP5 remain or are worsened, including long rains that are too short and weak and short rains that are too long and strong. Model biases are driven by a complex set of related oceanic and atmospheric factors, including simulations of the Walker Circulation. Biased wet short rains in models are connected with Indian Ocean zonal sea surface temperature (SST) gradients that are too warm in the west and convection that is too deep. Models connect equatorial African winds with the strength of the short rains, though in observations a robust connection is primarily found in the long rains. Model mean state biases in the timing of the western Indian Ocean SST seasonal cycle are associated with certain rainfall timing biases, though both biases may be due to a common source. Simulations driven by historical SSTs (AMIP runs) often have larger biases than fully coupled runs. A path towards using biases to better understand uncertainty in projections of GHA rainfall is suggested.
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DOI：
10.1002/joc.6023
发表时间：
2019-06-15
期刊：
INTERNATIONAL JOURNAL OF CLIMATOLOGY
影响因子：
--
作者：
Diem, Jeremy E.;Konecky, Bronwen L.;Hartter, Joel
通讯作者：
Hartter, Joel
影响因子：
4.6
作者：
E. Vizy;K. Cook
通讯作者：
E. Vizy;K. Cook
影响因子：
4.6
作者：
Liu, Weiran;Cook, Kerry H.;Vizy, Edward K.
通讯作者：
Vizy, Edward K.
影响因子：
5.2
作者：
A. Giannini;B. Lyon;R. Seager;N. Vigaud
通讯作者：
A. Giannini;B. Lyon;R. Seager;N. Vigaud
影响因子：
4.9
作者：
L. Hirons;A. Turner
通讯作者：
L. Hirons;A. Turner