High-Resolution Climate Projections for a Densely Populated Mediterranean Region

Shamsuddin Shahid,Mohamed Salem Nashwan,Eun-Sung Chung

doi:10.3390/su12093684

Abstract

The present study projected future climate change for the densely populated Central North region of Egypt (CNE) for two representative concentration pathways (RCPs) and two futures (near future: 2020–2059, and far future: 2060–2099), estimated by a credible subset of five global climate models (GCMs). Different bias correction models have been applied to correct the bias in the five interpolated GCMs’ outputs onto a high-resolution horizontal grid. The 0.05° CNE datasets of maximum and minimum temperatures (Tmx, and Tmn, respectively) and the 0.1° African Rainfall Climatology (ARC2) datasets represented the historical climate. The evaluation of bias correction methodologies revealed the better performance of linear and variance scaling for correcting the rainfall and temperature GCMs’ outputs, respectively. They were used to transfer the correction factor to the projections. The five statistically bias-corrected climate projections presented the uncertainty range in the future change in the climate of CNE. The rainfall is expected to increase in the near future but drastically decrease in the far future. The Tmx and Tmn are projected to increase in both future periods reaching nearly a maximum of 5.50 and 8.50 °C for Tmx and Tmn, respectively. These findings highlighted the severe consequence of climate change on the socio-economic activities in the CNE aiming for better sustainable development.

Highlights

Climate change has drastically affected the Mediterranean human and natural systems, including human health, agriculture, water management, and ecological diversity [1], making it one of the hotspots of climate change [2]
Different bias correction models were developed to correct the bias in rainfall and temperatures data using African Rainfall Climatology v.2 (ARC2) and Central North region of Egypt (CNE), respectively, as historical reference datasets
The results revealed that all the bias correction methods were capable of improving the raw global climate models (GCMs) simulations to varying degrees

Summary

Introduction

Climate change has drastically affected the Mediterranean human and natural systems, including human health, agriculture, water management, and ecological diversity [1], making it one of the hotspots of climate change [2]. Successive efforts have been made to simulate the global climate, and several models have been introduced in the fifth phase of the Coupled Models Intercomparison Project (CMIP5). They have been extensively used to project the future climate at global and regional levels [10,11]. GCMs can simulate the large-scale characteristics of climate, the credibility of their historical outputs of different climatic variables (e.g., temperature and rainfall) varies with the model used and the geographical location [12,13,14,15,16]. To bridge the spatial gap between the coarse-scale GCMs’ outputs and the fine-scale information required, downscaling methods of climate variables have been developed

Methods

Results

Conclusion