Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of the Study Area
2.2. Data and Sources
2.2.1. Gridded Dataset
2.2.2. Coupled Model Inter-comparison Project Phase 5 (CMIP5) GCM Datasets
3. Methodology
- Extracting and re-gridding of the selected 26 GCM datasets and CRU gridded datasets to a spatial resolution of 0.5° × 0.5° was carried out.
- SU was then applied to evaluate and assess the association between the 26 GCMs and the CRU gridded observations (prcp, Tmax, and Tmin) at each of the 22 grid points of 0.5° × 0.5° resolution covering the study area (Figure 1), over the reference period 1980–2005.
- The GCMs were then ranked based on the computed SU weight obtained at each grid points using the SU weighting technique, where a higher rank was given to GCMs with more weight in most of the grid points. A separate list of rank is prepared for each climatic variable (prcp, Tmax, and Tmin) and each gridded dataset (Table 2).
- The overall GCM ranks were then derived (Equation (4)) considering all their ranks and the weights obtained at all 22 grids over the entire study area.
- The final ranks of all three datasets were determined based on the frequency of occurrence of each GCM to combine the overall ranks in order to obtain a single rank for each GCM valid for the entire study.
- For simplicity, the easiest and the most common method of bias correction was carried out for correction of the biases in the best-selected future GCM ensemble against the CRU gridded observations. The additive correction method was used for temperature bias correction while the multiplicative correction method was used to correct the biases in prcp for GCM simulations under the two RCP scenarios for the period 2010–2099.
- The ensemble of the best four performing GCMs was then used for the prediction of spatial, temporal, and seasonal changes in rainfall for three future periods (2010–2039, 2040–2069, and 2070–2099) against the historical period (1980–2005).
3.1. Model Selection Using Symmetrical Uncertainty
3.2. Ranking of GCMs Using the Weighting Method
3.3. Bias Correction
3.4. Performance Assessment
4. Results and Discussion
4.1. Ranking of the GCMs
4.2. Spatial Distribution of Top-Ranked GCMs
4.3. Selection of GCM Ensemble
4.4. Ensemble Model Validation
4.5. Spatial Changes in Mean Annual Prcp, Tmax, and Tmin
5. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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GCM No | GCM Name | Institute | Resolution |
---|---|---|---|
1 | ACCESS1.3 | Commonwealth Scientific and Industrial Research Organization–Bureau of Meteorology, Australia | 1.9 × 1.2 |
2 | CanCM4 | Canadian Center for Climate Modelling and Analysis, Canada | 2.8 × 2.8 |
3 | CanESM2 | ||
4 | CCSM4 | National Centre for Atmospheric Research, USA | 0.94 × 1.25 |
5 | CMCC.CESM | Centro Euro-Mediterraneo sui Cambiamenti Climatici, Italy | 0.7 × 0.7 |
6 | CMCC.CMS | 1.9 × 1.9 | |
7 | CNRM.CM5 | Centre National de Recherches Météorologiques, Centre, France | 1.4 × 1.4 |
8 | CSIRO.Mk3.6.0 | Commonwealth Scientific and Industrial Research Organization, Australia | 1.9 × 1.9 |
9 | CSIRO.Mk3L.1.2 | ||
10 | GFDL.CM3 | Geophysical Fluid Dynamics Laboratory, USA | 2.5 × 2.0 |
11 | GFDL.ESM2M | ||
12 | GISS.E2.H | NASA/GISS (Goddard Institute for Space Studies), USA | 2.5 × 2.0 |
13 | HadCM3 | Met Office Hadley Centre, UK | 1.9 × 1.2 |
14 | HadGEM2.AO | ||
15 | HadGEM2.CC | ||
16 | HadGEM2.ES | ||
17 | INMCM4 | Institute of Numerical Mathematics, Russia | 2.0 × 1.5 |
18 | IPSL.CM45A.LR | Institut Pierre Simon Laplace, France | 2.5 × 1.3 |
19 | IPSL.CM5A.MR | 3.7 × 1.9 | |
20 | MIROC.ESM | The University of Tokyo, National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology, Japan | 2.8 × 2.8 |
21 | MIROC.ESM.CHM | ||
22 | MIROC5 | 1.4 × 1.4 | |
23 | MPI.ESM.LR | Max Planck Institute for Meteorology, Germany | 1.9 × 1.9 |
24 | MPI.ESM.MR | ||
25 | MRI.CGCM3 | Meteorological Research Institute, Japan | 1.1 × 1.1 |
26 | Noer.ESM1.M | Meteorological Institute, Norway | 2.5 × 1.9 |
Ranks | GCMs | Prcp | Tmax | Tmin | ||||||
---|---|---|---|---|---|---|---|---|---|---|
SU | NSE | R2 | SU | NSE | R2 | SU | NSE | R2 | ||
1 | ACCESS1-3 | 0.28 | 0.58 | 0.81 | 0.15 | 0.67 | 0.84 | 0.09 | 0.86 | 0.55 |
2 | MIROC-ESM | 0.14 | 0.63 | 0.84 | 0.10 | 0.87 | 0.44 | 0.12 | 0.62 | 0.49 |
3 | MIROC-ESM-CHM | 0.15 | 0.69 | 0.56 | 0.11 | 0.86 | 0.86 | 0.02 | 0.50 | 0.58 |
4 | Noer-ESM1-M | 0.13 | 0.57 | 0.82 | 0.14 | 0.82 | 0.89 | 0.05 | 0.38 | 0.48 |
5 | MIROC5 | 0.08 | 0.62 | 0.82 | 0.15 | 5.36 | 0.88 | 0.06 | 1.88 | 0.45 |
6 | HadGEM2-ES | 0.19 | 0.59 | 0.81 | 0.03 | 0.88 | 0.91 | 0.02 | 0.54 | 0.53 |
7 | CanCM4 | 0.07 | 0.49 | 0.74 | 0.08 | 0.91 | 0.91 | 0.01 | 0.65 | 0.48 |
8 | MRI-CGCM3 | 0.06 | 0.55 | 0.78 | 0.11 | 0.70 | 0.87 | 0.03 | 0.48 | 0.51 |
9 | MPI-ESM-MR | 0.06 | 0.35 | 0.78 | 0.05 | 0.75 | 0.87 | 0.02 | 0.49 | 0.51 |
10 | CMCC-CMS | 0.08 | 0.55 | 0.78 | 0.11 | 0.63 | 0.79 | 0.04 | 0.31 | 0.45 |
11 | CNRM-CM5 | 0.10 | 0.44 | 0.73 | 0.07 | 0.64 | 0.81 | 0.01 | 0.28 | 0.51 |
12 | CanESM2 | 0.10 | 0.52 | 0.77 | 0.12 | 2.18 | 0.67 | 0.01 | 6.48 | 0.25 |
13 | IPSL-CM45A-LR | 0.15 | 0.63 | 0.85 | 0.03 | 0.81 | 0.91 | 0 | 0.42 | 0.57 |
14 | HadGEM2-CC | 0.21 | 0.58 | 0.82 | 0 | 0.90 | 0.90 | 0 | 0.63 | 0.45 |
15 | HadCM3 | 0.09 | 0.58 | 0.80 | 0 | 0.91 | 0.91 | 0 | 0.67 | 0.51 |
16 | CMCC-CESM | 0.08 | 0.61 | 0.81 | 0.00 | 0.89 | 0.89 | 0 | 0.65 | 0.48 |
17 | IPSL-CM5A-MR | 0.11 | 0.47 | 0.74 | 0 | 0.84 | 0.85 | 0 | 0.64 | 0.46 |
18 | GFDL-ESM2M | 0.11 | 0.49 | 0.74 | 0.04 | 0.79 | 0.85 | 0 | 0.40 | 0.55 |
19 | CSIRO-Mk3L-1-2 | 0.05 | 0.31 | 0.60 | 0 | 0.89 | 0.89 | 0 | 0.68 | 0.53 |
20 | HadGEM2-AO | 0.03 | 0.62 | 0.83 | 0 | 0.84 | 0.81 | 0 | 0.49 | 0.29 |
21 | GISS-E2-H | 0.14 | 0.32 | 0.62 | 0 | 0.74 | 0.76 | 0 | 0.56 | 0.37 |
22 | MPI-ESM-LR | 0.06 | 0.55 | 0.80 | 0 | 0.00 | 0.87 | 0 | 0.63 | 0.56 |
23 | CSIRO-Mk3-6-0 | 0.13 | 0.44 | 0.71 | 0.02 | 0.15 | 0.83 | 0 | 0.06 | 0.57 |
24 | INMCM4 | 0.05 | 0.42 | 0.68 | 0 | 0.60 | 0.69 | 0 | 0.54 | 0.42 |
25 | CCSM4 | 0.09 | 0.51 | 0.76 | 0 | 0.70 | 0.66 | 0 | 0.44 | 0.46 |
26 | GFDL-CM3 | 0.21 | 0.35 | 0.42 | 0 | 0.62 | 0.15 | 0 | 0.37 | 0.20 |
Mean Annual | Observed | GCM Ensemble | ||
---|---|---|---|---|
All | SU | |||
Prcp (mm) | Sum | 2227.95 | 2166.91 | 2255.49 |
Mean | 6.19 | 5.94 | 6.23 | |
RMSE | - | 2.42 | 2.62 | |
NSE | - | 0.58 | 0.62 | |
R2 | - | 0.86 | 0.83 | |
Tmax (°C) | Mean | 31.13 | 31.20 | 31.06 |
RMSE | - | 0.68 | 0.71 | |
NSE | - | 1.00 | 1.00 | |
R2 | - | 0.92 | 0.92 | |
Tmin (°C) | Mean | 22.63 | 23.12 | 22.72 |
RMSE | - | 0.88 | 1.17 | |
NSE | - | 1.00 | 1.00 | |
R2 | - | 0.64 | 0.62 |
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Hassan, I.; Kalin, R.M.; White, C.J.; Aladejana, J.A. Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria. Water 2020, 12, 385. https://doi.org/10.3390/w12020385
Hassan I, Kalin RM, White CJ, Aladejana JA. Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria. Water. 2020; 12(2):385. https://doi.org/10.3390/w12020385
Chicago/Turabian StyleHassan, Ibrahim, Robert M. Kalin, Christopher J. White, and Jamiu A. Aladejana. 2020. "Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria" Water 12, no. 2: 385. https://doi.org/10.3390/w12020385
APA StyleHassan, I., Kalin, R. M., White, C. J., & Aladejana, J. A. (2020). Selection of CMIP5 GCM Ensemble for the Projection of Spatio-Temporal Changes in Precipitation and Temperature over the Niger Delta, Nigeria. Water, 12(2), 385. https://doi.org/10.3390/w12020385