High-Resolution Climate Projections for a Densely Populated Mediterranean Region
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Observation Gridded Data
2.3. Global Climate Models
3. Methodology
- Regrid the GCMs’ outputs to 0.1° for rainfall and 0.05° for temperatures—the same as the resolution of the observation datasets—using the bilinear interpolation technique.
- Correct the bias in regridded GCM temperature/rainfall data for the historical period (1983–2005 for rainfall and 1981–2005 for temperatures) using CNE and ARC2 as reference datasets for temperatures and rainfall, respectively.
- Evaluate the performance of different bias correction methods for rainfall and temperatures, separately, and select the best methods based on different statistical metrics.
- Employ the best bias correction method in regridded projections of future rainfall and temperatures for the generation of high-resolution climate projections for the period 2020–2099.
3.1. Model Output Statistics Downscaling
3.1.1. Linear Scaling
3.1.2. Empirical Quantile Mapping
3.1.3. Power Transformation
3.1.4. Variance Scaling
3.2. Evaluation Metric
4. Results
4.1. Evaluation of Bias Correction Methods
4.2. Projected Change in Spatial Patterns of the Climate Variables
4.3. Projected Regional Changes
4.4. Projection Change in the Climate Variables Distribution
5. Discussion
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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GCM | Developing Centre | Raw Spatial Resolution |
---|---|---|
FGOALS-g2 | Institute of Atmospheric Physics, Chinese Academy of Sciences, China | 2.8° × 2.8° |
GFDL-CM3 | Geophysical Fluid Dynamics Laboratory, USA | 2.5° × 2.0° |
GFDL-ESM2G | Geophysical Fluid Dynamics Laboratory, USA | 2.5° × 2.0° |
MPI-ESM-MR | Max Planck Institute for Meteorology, Germany | 1.9° × 1.9° |
MRI-CGCM3 | Meteorological Research Institute, Japan | 1.1° × 1.1° |
Bias Correction for Rainfall | Bias Correction for Temperatures |
---|---|
Linear Scaling (LS), Power Transformation (PT), and Empirical Quantile Mapping (EQM) | Linear Scaling (LS), Variance Scaling (Var), and Empirical Quantile Mapping (EQM) |
GCM | Rainfall | Tmx | Tmn |
---|---|---|---|
FGOALS-g2 | 0.94 | 0.99 | 0.99 |
GFDL-CM3 | 0.92 | 0.99 | 0.99 |
GFDL-ESM2G | 0.92 | 0.98 | 0.99 |
MPI-ESM-MR | 0.91 | 0.99 | 0.99 |
MRI-CGCM3 | 0.94 | 0.99 | 0.99 |
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Nashwan, M.S.; Shahid, S.; Chung, E.-S. High-Resolution Climate Projections for a Densely Populated Mediterranean Region. Sustainability 2020, 12, 3684. https://doi.org/10.3390/su12093684
Nashwan MS, Shahid S, Chung E-S. High-Resolution Climate Projections for a Densely Populated Mediterranean Region. Sustainability. 2020; 12(9):3684. https://doi.org/10.3390/su12093684
Chicago/Turabian StyleNashwan, Mohamed Salem, Shamsuddin Shahid, and Eun-Sung Chung. 2020. "High-Resolution Climate Projections for a Densely Populated Mediterranean Region" Sustainability 12, no. 9: 3684. https://doi.org/10.3390/su12093684
APA StyleNashwan, M. S., Shahid, S., & Chung, E. -S. (2020). High-Resolution Climate Projections for a Densely Populated Mediterranean Region. Sustainability, 12(9), 3684. https://doi.org/10.3390/su12093684