Verification of the First 11 Years of IRI's Seasonal Climate Forecasts
Verification of the First 11 Years of IRI's Seasonal Climate Forecasts
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DOI：
10.1175/2009jamc2325.1
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发表时间：
2010-03-01
影响因子：
3
通讯作者：
Gong, Xiaofeng
中科院分区：
文献类型：
--
作者：
Barnston, Anthony G.;Li, Shuhua;Gong, Xiaofeng
本文研究了国际气候与社会研究所（I RI）从1997年末到2008年发布的近全球温度和降水的季节概率预报的质量，主要使用了一个双层多模式动力预测系统。技能水平虽然在全球平均水平上不高，但明显取决于季节和地点，热带地区的平均水平高于温带地区。首先，有用的季节和区域对应于已知的直接影响以及来自太平洋（例如，与ENSO有关）和其他热带盆地的热带海面温度异常的远程遥相关。这一结果与IRI和其他机构之前的技能评估一致，并表明了有利于知情客户为特定应用做出气候风险管理决策的技能水平。与降水技能相比，温度技能水平一般较高，对季节和区域的依赖程度较低，部分原因是对与变暖趋势相关的高于正常温度的可能性提高的正确预测。然而，对高于正常温度的低预报表明，可以通过纳入时变温室气体浓度来改进动态预报系统。作为最终预报员修正后发布预报的主要依据，客观多模式概率预报的技能与最终预报的技能相当，但其概率可靠性略弱。多模型输出的自动重新校准应该允许提高它们的可靠性，允许它们按原样发布。IRI目前正在开发单层预测组件。
This paper examines the quality of seasonal probabilistic forecasts of near-global temperature and precipitation issued by the International Research Institute for Climate and Society (I RI) from late 1997 through 2008, using mainly a two-tiered multimodel dynamical prediction system. Skill levels, while modest when globally averaged, depend markedly on season and location and average higher in the tropics than extratropics. To first order, seasons and regions of useful skill correspond to known direct effects as well as remote teleconnections from anomalies of tropical sea surface temperature in the Pacific Ocean (e.g., ENSO related) and in other tropical basins. This result is consistent with previous skill assessments by IRI and others and suggests skill levels beneficial to informed clients making climate risk management decisions for specific applications. Skill levels for temperature are generally higher, and less seasonally and regionally dependent, than those for precipitation, partly because of correct forecasts of enhanced probabilities for above-normal temperatures associated with warming trends. However, underforecasting of above-normal temperatures suggests that the dynamical forecast system could be improved through inclusion of time-varying greenhouse gas concentrations. Skills of the objective multimodel probability forecasts, used as the primary basis for the final forecaster-modified issued forecasts, are comparable to those of the final forecasts, but their probabilistic reliability is somewhat weaker. Automated recalibration of the multimodel output should permit improvements to their reliability, allowing them to be issued as is. IRI is currently developing single-tier prediction components.