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data submission


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Type: Resource
Storage: The size of this resource is 3.2 MB
Created: Dec 25, 2020 at 12:20 p.m.
Last updated: Dec 25, 2020 at 1:05 p.m.
DOI: 10.4211/hs.48b3fba7ff3f4e379b7fe9a2dfd2cf55
Citation: See how to cite this resource
Content types: Multidimensional Content 
Sharing Status: Published
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Abstract

Using a weakly coupled data assimilation (WCDA) system to constrain the soil moisture and soil temperature in a coupled climate model with a global land data assimilation product, this study demonstrates significant improvements in simulating the interannual variations of EASM rainfall, capturing the notable shift to a "wetter-South-drier-North" rainfall pattern in China in the early 1990s.
Improvements in predicting the EASM rainfall are attributed to the strong land-atmosphere coupling in large areas over China, which allows improved predictions of soil moisture to influence precipitation through soil moisture-precipitation feedback, and the effects of land anomalies on the EASM circulation. This study highlights the significant contribution of land to the interannual predictability of EASM rainfall, with a great potential to advance skillful interannual predictions of benefit to the large populations influenced by the annual whiplash of the summer monsoon rain.

Subject Keywords

Coverage

Spatial

Coordinate System/Geographic Projection:
WGS 84 EPSG:4326
Coordinate Units:
Decimal degrees
North Latitude
37.6731°
East Longitude
118.1250°
South Latitude
23.7202°
West Longitude
112.5000°

Temporal

Start Date:
End Date:

Content

Data Services

The following web services are available for data contained in this resource. Geospatial Feature and Raster data are made available via Open Geospatial Consortium Web Services. The provided links can be copied and pasted into GIS software to access these data. Multidimensional NetCDF data are made available via a THREDDS Data Server using remote data access protocols such as OPeNDAP. Other data services may be made available in the future to support additional data types.

How to Cite

Shi, P. (2020). data submission, HydroShare, https://doi.org/10.4211/hs.48b3fba7ff3f4e379b7fe9a2dfd2cf55

This resource is shared under the Creative Commons Attribution CC BY.

http://creativecommons.org/licenses/by/4.0/
CC-BY

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