Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China
Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China
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DOI：
10.1007/s00382-013-1927-1
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发表时间：
2013-09
期刊：
影响因子：
4.6
通讯作者：
L. Luo;Wei Tang;Zhaohui Lin;E. Wood
中科院分区：
文献类型：
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作者：
L. Luo;Wei Tang;Zhaohui Lin;E. Wood
国家环境预测中心最近将其业务季节预测系统升级为完全耦合的气候模拟系统，称为CFSv2。在投入使用之前，CFSv2已被用于回顾1982至2009年间的季节性气候预报。在这项研究中，我们利用1月1日至5月26日期间的120次9个月再预报来评估模式对中国夏季气温和降水的预报能力。这120次再预报运行被评估为使用确定性和概率指标的集合预报。夏季气温总体预报技巧较高，而夏季降水预报技巧较低。集合平均再预报降低了气候学的空间变异性。对于温度，再预报偏差与提前时间相关，即再预报JJA温度在提前时间较短时变暖。超前时间相关的偏差表明，温度的初始条件以某种方式偏向较温暖的条件。CFSv2对中国夏季气温异常有较好的预报能力，但无论是观测还是再预报都有明显的上升趋势。用CFSv2等动力模式在季节尺度和集水区尺度上预报夏季降水仍然是一个挑战，因此有必要改进模式物理和参数，以便更好地预报亚洲季风降水。气温和降水的概率技能相当有限。只有CFSv2的东北夏季平均气温等空间平均量具有较高的预报技巧，对3种分类预报具有区分事件和非事件的能力。潜在的预报技巧表明，高于正常事件和低于正常事件可以比正常事件更好地预报。虽然预测提前期越短，确定性预测技能就越高，但概率预测技能并不会随着提前期的减少而增加。集合大小对总体概率预报技能的影响并不显著，尽管增加更多的成员会略微提高概率预报技能。
National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.