Changes in Intraseasonal to Interannual Variability of the Pan American Monsoons Under a Warmer Climate and Their Impacts on Extreme Events Assessed by the CMIP5 Models and Observations

We propose to characterize the changes of intraseasonal, seasonal and interannual variability (ISI) and their impact on extreme events over the Pan America monsoon region as simulated and projected by the Coupled Model Inter-comparison Phase Five (CMIP5) and National Oceanic and Atmospheric Administration (NOAA) Climate Forecast System (CFS) models. Our analysis will first focus on observations of changes in rainfall and temperature characteristics and extreme weather events in the recent past and their underlying mechanisms. The observational results will be used to assess the skills of the CMIP5 class models in reproducing the observed ISI variability and their changes, in focusing on the mechanisms that control ISI variability in the Pan American monsoon regions and their links to sea surface temperature (SST) changes over the adjacent oceans, the local land surface process and the extratropical synoptic weather systems, as well as bias estimations. The results of this model evaluation will be used to filter-out “unrealistic” models from the climate projections and also for model bias corrections.

The proposal aims to address the following questions:

a) Will the spatial patterns, ISI variabilities and statistical distributions of temperature and rainfall shift significantly in a warming climate in the Pan American monsoon region? How would such changes impact the intensity and frequency of the droughts, floods and heat waves?

b) What external forcings are responsible for these changes and how would local land surface feedbacks contribute to these changes? What processes are key in determining the influence of these forcings? How are changes in the North American and the South American monsoon connected?

c) How realistically can global climate models simulate the key process that control changes of ISI in the Pan American monsoon region? How can such model evaluation be used to reduce random error and biases in climate projections? We will extensively use observations and reanalysis products including in situ observations from surface and upper air meteorological networks, remote sensing datasets, the North American Regional Reanalysis (NARR), the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Reanalysis (CDAS), the North-American Land Data Assimilation System (NLDAS) and the CFS reanalysis (CFSR) when it becomes available. We will also analyze daily and monthly outputs from ensemble simulations of the CMIP5 class models for the pre-industrial scenario, the 20th century simulations forced by estimated increase of the anthropogenic forcing, and the 21st century under..