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Tropical rainfall subseasonal-to-seasonal predictability types
npj Climate and Atmospheric Science ( IF 9 ) Pub Date : 2020-01-30 , DOI: 10.1038/s41612-020-0107-3
Vincent Moron , Andrew W. Robertson

Tropical rainfall is mostly convective and its subseasonal-to-seasonal (S2S) prediction remains challenging. We show that state-of-art model forecast skill 3 + 4 weeks ahead is systematically lower over land than ocean, which is matched by a similar land-ocean contrast in the spatial scales of observed biweekly rainfall anomalies. Regional differences in predictability are then interpreted using observed characteristics of daily rainfall (wet-patch size, mean intensity as well as the strength of local S2S modes of rainfall variation), and classified into six S2S predictability types. Both forecast skill and spatial scales are reduced over the continents, either because daily rainfall patches are small and poorly organized by S2S modes of variation (as over equatorial and northern tropical Africa), or where the daily mean intensity is very high (as over South and SE Asia). Forecast skill and spatial scales are largest where daily rainfall is synchronized by intraseasonal (such as the Madden-Julian Oscillation) as well as interannual ocean-atmosphere modes of variation (such as El Niño-Southern Oscillation), especially over northern Australia and parts of the Maritime Continent, and over parts of eastern, southern Africa and northeast South America. The oceans exhibit the highest skill and largest spatial scales, especially where interannual (central equatorial Pacific) or intraseasonal (central and eastern Tropical Indian Ocean and Western Pacific) variability is largest. These results provide a relevant regional typology of the potential drivers and controls on S2S predictability of tropical rainfall, informing intrinsic limits and possible improvements toward useful S2S climate prediction at regional scale.



中文翻译:

热带降雨季节到季节的可预测性类型

热带降雨主要是对流性降雨,其次季节到季节(S2S)的预测仍然充满挑战。我们显示,最先进的模型预测技术在陆地上比陆地上提前3 + 4周的时间比海洋上要低,这与观察到的两周一次的降雨异常的空间尺度上的相似的海洋-海洋对比相匹配。然后,使用观察到的每日降雨特征(湿斑大小,平均强度以及局部S2S降雨变化模式的强度)来解释可预测性的区域差异,并将其分为六种S2S可预测性类型。在大陆上,预报技能和空间尺度都降低了,这是因为每天的降雨斑块很小,并且由于S2S的变化模式(如在赤道和非洲北部热带地区)组织不善,或日平均强度很高的地区(如南部和东南亚)。预报技能和空间尺度最大,每日降水与季节内(如Madden-Julian涛动)以及年际海洋-大气变化模式(如厄尔尼诺-南方涛动)同步,尤其是在澳大利亚北部和部分地区海陆,以及东部,南部非洲和南美东北部的部分地区。海洋表现出最高的技能和最大的空间尺度,尤其是在年际(赤道中部太平洋)或季节内(热带印度洋中部和东部以及印度洋西部)变化最大的地方。这些结果提供了有关热带降雨S2S可预测性的潜在驱动因素和控制方法的相关区域类型,

更新日期:2020-01-30
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