Evaluation of Relative Model Skill and Intraseasonal to Interannual Variability of the Indian Monsoon in the NMME
Abstract
ABSTRACT The Phase-1 North American Multimodel Ensemble (NMME) data set is most likely the most extensive archive of seasonal predictions made using active seasonal forecast models currently available (Kirtman et al. 2013). The NMME Phase-2 will include a data from an updated suite of models, as well as collecting output at higher spatial and temporal resolution. Despite the wealth of data available, most analysis of the NMME data thus far has focused on basic aspects of ISI predictions and there is a pressing need for more in-depth analysis of this unique and extensive data set. The variations of the seasonal rainfall associated with the south Asian monsoon are enormously important for millions of lives on the Indian subcontinent and beyond. The high and low extremes of the seasonal mean ISMR produce floods and droughts that have major economic and societal impacts. The intra-seasonal variability (ISV) of the monsoon, comparable in magnitude to its annual cycle, is a major factor in these highimpact climate extremes. Likewise, better understanding of variations in temperature and precipitation, particularly snowfall, over the neighboring mountainous regions (e.g. Pakistan, Afghanistan and surrounding countries) would be of significant scientific and practical interest given the geopolitical significance of that area. The prediction of precipitation and temperature at both inter-annual and intraseasonal time scales in these regions is a challenging task for the present generation of climate models for numerous reasons, among them the impact of complex orographically-influenced structure, the interaction between convection and the largescale atmospheric circulation, wave propagation in both the zonal and meridional directions, and air-sea interaction. In addition to intra-seasonal variations in precipitation, inter-annual variations also have a large impact on the lives and livelihoods of people throughout southern Asia. One issue that has received significant attention in recent years is the apparent change in the strength of the relationship between the monsoon and the El Niño – Southern Oscillation (ENSO). However, it remains unclear how much of the observed change can be attributed to sampling and internal variability of the climate system, and how much is due to the changing climate. In the proposed work we will investigate the ability of the NMME to represent and predict temperature and precipitation both point-by-point globally and for regional averages in both the Phase-1 and Phase-2 data. We will also investigate the relative skill of the models in predicting tropical cyclone counts in the Atlantic and Pacific basins. We will employ novel statistical techniques such as the Sign test to rigorously determine the relative skill of individual models in comparison to each other and to the NMME mean, and we will use the Multi-Channel Singular Spectrum Analysis (MSSA) technique to identify intra-seasonal propagating modes of variability. We will also leverage the extensive suite of existing, independent hindcasts generated by COLA researchers and collaborators to investigate the potential gains from expanding the NMME to include additional member models. In particular we will investigate the impact of including integrations made using the European Centre for Medium-Range Weather Forecasting (ECMWF) seasonal forecast system as part of Project Minerva. This model, which is not generally available to the research community, is used in the EuroSIP ensemble whose mean values are used in US operational climate predictions (referred to as the International Multi-Model Ensemble or IMME). Analysis of the ENSO-monsoon relationship is ongoing in the Minerva data, and we will extend that analysis to the multimodel data available via the NMME. We will also investigate the impact of including runs made using models with super-parameterization in place of standard convection schemes.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2016
- Source ID
- N000141512745
Entities
People
- Benjamin Cash
Organizations
- George Mason University
- Office of Naval Research
- United States Navy