Near-term prediction of impact-relevant extreme temperature indices
Near-term prediction of impact-relevant extreme temperature indices
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DOI：
10.1007/s10584-014-1191-3
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发表时间：
2015-09-01
期刊：
影响因子：
4.8
通讯作者：
Smith, D. M.
中科院分区：
文献类型：
--
作者：
Hanlon, H. M.;Hegerl, G. C.;Smith, D. M.
先前对欧洲温度指数可预测性的研究表明，根据欧洲大部分地区，特别是地中海地区的每日最高和最低温度，预测夏季平均和最高5天平均温度的5/10年平均指数具有显著的技巧。在这里，这项工作被扩展到研究与高温有关的对欧洲能源使用、人类健康和玉米产量影响的相关指数。这些指数的预测能力是通过对夏季日最高气温、平均气温和最低气温高于临界阈值的日数的十年预测来评估的。将这些预测与观测条件进行比较后发现，欧洲部分地区的年代际预测超过了利用观测到的气候学和持续当前条件进行的预测。地中海地区在近期预测中显示出最高的技能，而北欧/中欧地区的技能则较少。甚至在小尺度上也有一些技巧的证据。该系统被认为不适合预测英国的指数，因为该模型明显高估了这些指数的趋势。进一步的试验研究了用观测初始化年代际预报的效果。包括外部强迫（如温室气体增加）在内的模拟在预测热事件频率变化方面比不包括在内的模拟表现出更好的能力，而本文使用的模式初始化预测并没有提高这种能力。
A previous study of predictability of European temperature indices revealed significant skill in predictions of 5/10-year average indices of summer mean and maximum 5-day average temperatures based on daily maximum and minimum temperatures for a large area of Europe, particularly in the Mediterranean. Here, this work is extended to study indices relevant to high heat-related impacts on energy use, human health and maize yields in Europe. The skill of predictions of these indices is assessed using decadal predictions of the number of days above critical thresholds of daily maximum, mean and minimum Summer temperatures. Following comparison of these predictions with observed conditions, there is skill found in parts of Europe where the decadal predictions exceed that of using observed climatology and persisting present conditions. Areas in the Mediterranean show the most skill in near-term predictions, while skill is small in Northern/Central Europe. There is even some evidence of skill on small scales. This system is determined to be not appropriate for predicting indices in the UK as the model significantly overestimates the trend in these indices. A further test studies the effect of initialising the decadal forecasts with observations. Simulations that include external forcing, such as greenhouse gas increases, show better skill in predicting changes in the frequency of hot events than those that do not, and the initialisation of forecasts with the model used here does not improve this skill.