Environmental Research Communications (Jan 2024)

A statistic of the subseasonal forecast skill windows of 2-meter air temperature

  • Xiaolei Liu,
  • Jingzhi Su,
  • Yihao Peng,
  • Xinli Liu

DOI
https://doi.org/10.1088/2515-7620/ad6667
Journal volume & issue
Vol. 6, no. 8
p. 085002

Abstract

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The forecast skill on subseasonal time scale is currently limited, inhibiting its effective application broadly. Under some special conditions, the subseasonal-seasonal (S2S) forecast skill would be increased intermittently, namely, the forecast opportunity window. The identification of such forecast windows can increase the credibility of subseasonal prediction results, providing final users the opportunity to make the appropriate decision. To identify the subseasonal forecast window of 2-m air temperature (T2m), based on the S2S multi-model prediction results, this study evaluated the pattern correlation coefficient between the third-week T2m forecasted by S2S models and the observation. When the forecast skills of the majority of models reach the prescribed threshold, this period is defined as a forecast skill window. In this way, the subseasonal forecast skill windows of T2m over different regions of the global lands are identified. From the perspective of seasonal distribution, the forecast skill windows over almost all the continents appear more in boreal winter, while the forecast skill windows of the Australian continent mainly appear in boreal summer. Significant differences can be found in the occurrence frequency of forecast windows during different phases of ENSO. During El Niño events, forecast windows appear more frequently over North America, Asia, and South America, especially during winter-spring from January to April. From the T2m spatial pattern during the window periods, the forecast skill windows have some relevance among several continents, and the windows over the whole global land mainly correspond to those over Asia, Europe, and North America. Deep investigation of the physical mechanism behind the forecast skill windows helps understand the sources of predictability and improves the skill of subseasonal prediction.

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