General Circulation Model Regional Predictions Using MOS techniques: Seasonal Forecasting

Ocean and atmospheric Coupled Global Climate Models (CGCMs) have been widely used to provide more accurate and coherent seasonal forecasts. However, they still show some limitations. Model Output Statistics (MOS) approaches may improve performance if observed and forecast values are available for a long record. This study investigates the skills of a MOS approach on ECHAM4p5 in simulating rainfall and temperature on a seasonal time scale over the South West (SW) Ontario region. ECHAM4p5 model has 20 ensemble members and those 20 members along with their mean are compared with real time observational data collected locally by Environment and Climate Change Canada (ECCC) weather stations. Presently, the ECHAM4p5 model is run by the Foundation Cearense for Meteorology and Water Management (FUNCEME), Brazil. The model is run at the beginning of every month based on persisted Sea Surface Temperature (SST) from 0000 of 1st day of that month. An ensemble average of 20 realizations is used for the forecasts. The model uses for eight-month weather predictions ahead of the start date. Historical model data were available from International Research Institute for Climate and Society (IRI), Columbia University and used together with Global Precipitation Climatology Centre (GPCC) rainfall data and average daily temperature obtained from the Climatic Research Unit (CRU) at University of East Anglia. Ten years of daily forecasts for SW Ontario from the ECHAM4p5 model are used to develop Regional Correction Factors (RCF) to help in improving the model seasonal forecast confidence level. The basic (bias correction based) MOS technique is applied for seasonal and regional bias corrections. Our focus has been on the first three months of the forecast and comparisons are made against Meteorological Terminal Air Report (METAR) and other data for SW Ontario. The comparison of tuned data and observations has been made over SW Ontario. The motivation of taking this domain is that our industrial partner is mainly working with the farmers in SW Ontario. The approach used had given encouraging results (based on personal communication) in larger geographic areas and improved seasonal predictions in Pakistan. The results so far in the much smaller SW Ontario domain, with a somewhat different climatology, have not been as successful but have provided ideas for future research. We have worked on both monthly and daily precipitation and temperature, but in particular we have focused to investigate day to day differences between different ensemble members to see what information might be gained from them. Our results show that there is huge variation among 20 ensemble members and those variations are canceled out while taking their mean for ensemble mean forecasting technique. Furthermore, while comparing individual ensemble members with observation data, we also get the idea that few ensemble members are following observation data closely. We also compare the same method for larger domain to improve our forecasting results.