Influence of "realistic" land surface wetness on predictability of seasonal precipitation in boreal summer

Shinjiro Kanae, Yukiko Hirabayashi, Tomihito Yamada, Taikan Oki

研究成果: Article

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Outputs from two ensembles of atmospheric model simulations for 1951-98 define the influence of "realistic: land surface wetness on seasonal precipitation predictability in boreal summer. The ensembles consist of one forced with observed sea surface temperatures (SSTs) and the other forced with realistic land surface wetness as well as SSTs. Predictability was determined from correlations between the time series of simulated and observed precipitation. The ratio of forced variance to total variance determined potential predictability. Predictability occurred over some land areas adjacent to tropical oceans without land wetness forcing. On the other hand, because of the chaotic nature of the atmosphere, considerable parts of the land areas of the globe did not even show potential predictability with both land wetness and SST forcings. The use of land wetness forcing enhanced predictability over semiarid regions. Such semiarid regions are generally characterized by a negative correlation between fluxes of latent heat and sensible heat from the land surface, and are "water-regulating" areas where soil moisture plays a governing role in land-atmosphere interactions. Actual seasonal prediction may be possible in these regions if slowly varying surface conditions can be estimated in advance. In contrast, some land regions (e.g., south of the Sahel, the Amazon, and Indochina) showed little predictability despite high potential predictability. These regions are mostly characterized by a positive correlation between the surface fluxes, and are "radiation-regulating" areas where the atmosphere plays a leading role. Improvements in predictability for these regions may require further improvements in model physics.

元の言語English
ページ(範囲)1450-1460
ページ数11
ジャーナルJournal of Climate
19
発行部数8
DOI
出版物ステータスPublished - 2006 4 15

ASJC Scopus subject areas

  • Atmospheric Science

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