Toward a regional-scale seasonal climate prediction system over the Mediterranean basin: evaluation and comparison of RegCM- and WRF-based dynamical downscaling approaches

Seasonal Climate Predictions (SCPs) represent a challenging intermediate field where aspects typical of the short-term weather forecasts and long-term climate projections interact. Skillful SCPs represent an essential tool to reduce societal vulnerabilities to the inter-annual climate fluctuation through short-term (i.e., next season) climate impact mitigation measures. This is especially true over areas characterized by large climate inter-annual variability as the Mediterranean basin, which is also traditionally characterized by a poor seasonal predictability.

The primary research question of present study is to assess the capability of two dynamical downscaling approaches to improve the seasonal inter-annual variability signal coming from the global-scale driving SCP system on the Mediterranean basin.

In this work the Weather Research and Forecasting model (WRF3.9.1.1) and the Regional Climatic Model (RegCM4.1) were nested into NCEP’s operational seasonal forecast model Climate Forecast System version 2 (CFSv2) to dynamically downscale seasonal predictions over Mediterranean basin.

Using the initial and boundary conditions of an ensemble of the CFSv2 we compare the capability of the two downscaling approaches on improving the large scale CFSv2 prediction of a climatological period of 22-cold seasons (December–February) during 1982–2002.

The SCP systems (WRF- and RegCM-based) consist on a double dynamical downscaling where a height-member lagged ensemble of 3-month CFSv2 climate predictions represent the common driving fields. Both the nested models dynamically downscales CFSv2 climate prediction from the original 100 km resolution to 60 km over a domain covering the Mediterranean basin and Central Europe. The first downscaling feeds a second downscaling performed over a domain centered over Central Italy with a resolution of 12 km.

Climate variables considered are: 2 m temperature, precipitation, geopotential height at different pressure levels and mean sea level pressure. Results will be discussed by means of mean bias spatial distribution, inter-annual anomaly variability reproduction and probabilistic hit-rate of anomalous seasons, through tercile plots and reliability diagrams of the above mentioned variables.

Preliminary results, considering the RegCM, identify temperature variability reproduction benefiting from the downscaling. At the same time, precipitation shows an improved spatial distribution patterns but not improved inter-annual variability representation if compared to the driving CFSv2 reference period climate predictions.