Statistical Downscaling of Global Climate Projections along the Egyptian Mediterranean coast.

The climatic parameters (surface air temperature, surface relative humidity, surface wind regime, and mean sea level pressure) are known to be important inaddressing adaptation/mitigation to the climatic changes. In particular, recent, and future of these climatic parameters along the Egyptian Mediterranean Coast (EMC) were analyzed based on hourly real observed data (2007—2020), and hourly reanalysis (ERA5) database (1979—2020) together with daily GFDL (Global climate model) mini-ensemble mean (2006—2100). Recent climatic studies along the study area have not given enough attention to the downscaling approach, underscoring the need to set up a statistical downscaling technique for a better understanding of the forces that govern climatic change. Here we analyze the current climatic and future scenarios for the studied parameters calls for three steps. The first step is to study the short-term (14 years) current weather variabilities using the real observed data. The second step is to describe the long-term (42 years) current weather variabilities using reanalysis ERA5 database after bias removal by comparing with the observations. The third step is to statistically downscale the GFDL mini-ensemble means to describe the future projection along the study area up to 2100. The used statistical downscale technique is built on developed a bias correction statistical model by matching cumulative distribution functions (CDF) of the mini-ensemble mean and corrected ERA5 during the overlapped period (2006— 2020). The results show that ERA5 describes efficiency the weather characteristic of the five studied stations. This data along the Egyptian Mediterranean Coast (EMC), 2006—2020, displays a significant positive trend for surface air temperature, and significant negative trends for surface wind speed, relative humidity, and sea level pressure. The GFDL mini-ensemble mean projection, up to 2100, has a significant bias with the studied weather parameters. This is partly due to GFDL coarse resolution (2˚x2˚). After removing the bias, the statistically downscaled simulations from the GFDL mini-ensemble mean show that the study area’s climate will experience a significant change especially surface air temperature and relative humidity with a great range of uncertainties according to the scenario used and regional variations. Our results are the cornerstone for better understanding and developing statistical downscaling to project the future climatic studies over EMC