Biases in CMIP5 Sea Surface Temperature and the Annual Cycle of East African Rainfall
Biases in CMIP5 Sea Surface Temperature and the Annual Cycle of East African Rainfall
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DOI：
10.1175/jcli-d-20-0092.1
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发表时间：
2020-10
影响因子：
4.9
通讯作者：
B. Lyon
中科院分区：
文献类型：
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作者：
B. Lyon
在东非大部分地区，气候降雨呈双峰分布，其特点是降雨长（3 月至 5 月）和降雨短（10 月至 12 月）。大多数 CMIP5 耦合模型无法正确模拟这一年度周期，通常会颠倒短雨和长雨相对于观测的幅度。本研究调查了 CMIP5 气候海面温度 (SST) 偏差如何影响东非降雨年周期的模拟误差。 CMIP5 气候海表温度 (50°S–50°N) 的每月偏差首先在 31 个模型的历史运行 (1979–2005) 中确定，并检查其一致性。然后，大气环流模型（AGCM）被强制使用观测到的海表温度（1979-2005）生成一组控制运行和观测到的海表温度加上每月的多模型平均海表温度偏差，生成一组同一时期的“偏差”运行。对照运行通常捕获观测到的东非降雨年周期，而偏差运行捕获显着的 CMIP5 年周期偏差，包括长降雨（短降雨）期间降水过少（过多）以及长降雨峰值相对于观测值滞后 1 个月。诊断表明，年度循环偏差与印度洋季节性变化的南北和东西向的海温偏差模式以及区域尺度的大气环流和稳定性变化有关，后者主要与低层湿静态能量的变化有关。总体而言，结果表明 CMIP5 气候海表温度偏差是东非降雨年周期模拟不当的主要驱动因素。讨论了气候变化预测的一些影响。
In much of East Africa, climatological rainfall follows a bimodal distribution characterized by the long rains (March–May) and short rains (October–December). Most CMIP5 coupled models fail to properly simulate this annual cycle, typically reversing the amplitudes of the short and long rains relative to observations. This study investigates how CMIP5 climatological sea surface temperature (SST) biases contribute to simulation errors in the annual cycle of East African rainfall. Monthly biases in CMIP5 climatological SSTs (50°S–50°N) are first identified in historical runs (1979–2005) from 31 models and examined for consistency. An atmospheric general circulation model (AGCM) is then forced with observed SSTs (1979–2005) generating a set of control runs and observed SSTs plus the monthly, multimodel mean SST biases generating a set of “bias” runs for the same period. The control runs generally capture the observed annual cycle of East African rainfall while the bias runs capture prominent CMIP5 annual cycle biases, including too little (much) precipitation during the long rains (short rains) and a 1-month lag in the peak of the long rains relative to observations. Diagnostics reveal the annual cycle biases are associated with seasonally varying north–south- and east–west-oriented SST bias patterns in Indian Ocean and regional-scale atmospheric circulation and stability changes, the latter primarily associated with changes in low-level moist static energy. Overall, the results indicate that CMIP5 climatological SST biases are the primary driver of the improper simulation of the annual cycle of East African rainfall. Some implications for climate change projections are discussed.