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A comprehensive analysis of coherent rainfall patterns in China and potential drivers. Part II: intraseasonal variability

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The causes of subseasonal precipitation variability in China are investigated using observations and reanalysis data for extended winter (November–April) and summer (May–October) seasons from 1982 to 2007. For each season,… Click to show full abstract

The causes of subseasonal precipitation variability in China are investigated using observations and reanalysis data for extended winter (November–April) and summer (May–October) seasons from 1982 to 2007. For each season, the three dominant regions of coherent intraseasonal variability are identified with Empirical Orthogonal Teleconnection (EOT) analysis. While previous studies have focused on particular causes for precipitation variability or on specific regions, here a comprehensive analysis is carried out with an objective method. Furthermore, the associated rainfall anomaly timeseries are tied to specific locations in China, which facilitates their interpretation. To understand the underlying processes associated with spatially coherent patterns of rainfall variability, fields from observations and reanalysis are regressed onto EOT timeseries. The three dominant patterns in winter together explain 43% of the total space–time variance and have their origins in midlatitude disturbances that appear two pentads in advance. Winter precipitation variability along the Yangtze River is associated with wave trains originating over the Atlantic and northern Europe, while precipitation variability in southeast China is connected to the Mediterranean storm track. In summer, all patterns have a strong relationship with the Boreal Summer Intraseasonal Oscillation and are modulated by the seasonal cycle of the East Asian summer monsoon. The wet and dry phases of the regional patterns can substantially modulate the frequency of daily rainfall across China. The discovered links between weather patterns, precursors, and effects on local and remote precipitation may provide a valuable basis for hydrological risk assessments and the evaluation of numerical weather prediction models.

Keywords: precipitation; comprehensive analysis; variability; intraseasonal variability; precipitation variability

Journal Title: Climate Dynamics
Year Published: 2017

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