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Indian summer monsoon variability forecasts in the North American multimodel ensemble. | LitMetric

Indian summer monsoon variability forecasts in the North American multimodel ensemble.

Clim Dyn

2Center for Ocean-Land-Atmosphere Studies, George Mason University, Fairfax, VA 22031 USA.

Published: April 2018

AI Article Synopsis

  • The study evaluates how well nine global ocean-atmosphere models predict the Indian summer monsoon rainfall (ISMR) from 1982-2009, comparing results from two phases of the North American Multimodal Ensemble (NMME).
  • The multi-model ensemble shows modest skill in forecasting ISMR but exhibits systematic biases, with no significant improvement in forecast skill from phase 1 to phase 2.
  • While the models successfully predict seasonal mean sea surface temperatures and some teleconnections with rainfall, they struggle with extremes of rainfall and display incorrect Indian Ocean-monsoon teleconnections.

Article Abstract

The representation of the seasonal mean and interannual variability of the Indian summer monsoon rainfall (ISMR) in nine global ocean-atmosphere coupled models that participated in the North American Multimodal Ensemble (NMME) phase 1 (NMME:1), and in nine global ocean-atmosphere coupled models participating in the NMME phase 2 (NMME:2) from 1982-2009, is evaluated over the Indo-Pacific domain with May initial conditions. The multi-model ensemble (MME) represents the Indian monsoon rainfall with modest skill and systematic biases. There is no significant improvement in the seasonal forecast skill or interannual variability of ISMR in NMME:2 as compared to NMME:1. The NMME skillfully predicts seasonal mean sea surface temperature (SST) and some of the teleconnections with seasonal mean rainfall. However, the SST-rainfall teleconnections are stronger in the NMME than observed. The NMME is not able to capture the extremes of seasonal mean rainfall and the simulated Indian Ocean-monsoon teleconnections are opposite to what are observed.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6934244PMC
http://dx.doi.org/10.1007/s00382-018-4203-6DOI Listing

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