Influence of SST biases on future climate change projections
Influence of SST biases on future climate change projections
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DOI：
10.1007/s00382-010-0875-2
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发表时间：
2011-04-01
期刊：
影响因子：
4.6
通讯作者：
Diffenbaugh, Noah S.
中科院分区：
文献类型：
--
作者：
Ashfaq, Moetasim;Skinner, Christopher B.;Diffenbaugh, Noah S.
我们使用基于分位数的偏差校正技术和NCAR CCSM 3（CAM 3）模拟的大气成分的多成员集合来研究海表温度（SST）偏差对未来气候变化预测的影响。模拟涵盖历史时期的1977-1999年和未来（A1 B）时期的2077-2099年，使用CCSM 3生成的SST作为规定的边界条件。偏置校正应用于SST的月时间序列，以保持SST平均值和变率的模拟变化。我们的比较有和没有SST校正的CAM 3模拟表明，SST偏差影响降水分布在许多地区的CAM 3通过引入大气水分含量和高层（低层）发散（收敛）的错误。此外，偏差校正导致显着不同的降水和地表温度的变化在许多海洋和陆地区域（主要是在热带），以响应未来的人为增加温室效应强迫。SST偏差订正对降水响应的差异表现在平均值和变化率上，与海气耦合无关。这些差异中有许多与CMIP 3集合中未来降水变化的分布相当或更大。这种偏差可以通过水文循环和海洋盐度的变化影响耦合气候模式集成中模拟的陆地反馈和温盐环流。此外，CCSM 3生成的SST的偏差一般类似于CMIP 3集合平均SST的偏差，这表明其他GCM可能显示出类似的预测气候变化对SST误差的敏感性。这些结果有助于量化气候模型偏差对模拟气候变化的影响，因此应告知进一步开发可靠的气候变化预测方法的努力。
We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977-1999 in the historical period and 2077-2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean-atmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.