Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Comparision of Climate Change Projections for Ethiopia under CMIP5 GCMs Jemal Seid1 , Andualem Shemelis2
[email protected],
[email protected] 1 Haramaya University, Department of Physics, Dire Dawa, Ethiopia 2 Ethiopian Institutes of Agricalture, Addis Ababa, Ethiopia
National Workshop on CCA in Ethiopian Agriculture, EIAR, Addis Ababa, Ethiopia 19 September 2013 Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Outline of the Talk 1
Introduction
2
Objectives:
3
Data & Methods
4
Result I
5
Result II
6
Conclusions
7
Recommendations
8
References
9
Acknowledgment
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 Global climate model representations
Introduction IPCC published the Special Report on Emissions Scenarios (SRES) in the year 2000 based on different socio-economic development assumptions (emission pathways, A1, B1, A2 & B2). Now, the scientific community has developed a set of new emission scenarios termed as Representative Concentration Pathways (RCPs). RCPs represent pathways of radiative forcing, not detailed socio-economic narratives or scenarios. Four RCP scenarios are available: RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Changes in atmospheric composition lead to regional and global changes in temperature, precipitation and other climate variables.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 Global climate model representations
Coupled Model Intercomparison Project #5 Taylor et al. (2012) Coordinated GCM experiments to address key issues in climate science: Paleoclimate, response to CO2 , aerosols, volcanoes, high-resolution, decadal predictability, earth-system modeling, geoengineering... Around 20 centers worldwide Institutions are participated. Each centers have made data publicly available. Follows on to CMIP3 (mid-2000s), CMIP2(late 1990s), CMIP (early 1990s. Some results will be assessed in IPCC-AR5.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 Global climate model representations
Mathematical representations of process of processes controlling ocean, atmosphere, land and ice system (and interactions)
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Objective of the study Validate the new CMIP5-based climate projections (temperature and rainfall) over Ethiopia. Assess CMIP5 based short (2030s), medium-(2060s) and long-term (2080s) climate change projections (temperature and rainfall) for Ethiopia. Investigating the performance of GCMs in capturing spatial and temporal trends of June-September (JJAS) mean climate in comparison with observation over Ethiopia.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Objective of the study Validate the new CMIP5-based climate projections (temperature and rainfall) over Ethiopia. Assess CMIP5 based short (2030s), medium-(2060s) and long-term (2080s) climate change projections (temperature and rainfall) for Ethiopia. Investigating the performance of GCMs in capturing spatial and temporal trends of June-September (JJAS) mean climate in comparison with observation over Ethiopia.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Objective of the study Validate the new CMIP5-based climate projections (temperature and rainfall) over Ethiopia. Assess CMIP5 based short (2030s), medium-(2060s) and long-term (2080s) climate change projections (temperature and rainfall) for Ethiopia. Investigating the performance of GCMs in capturing spatial and temporal trends of June-September (JJAS) mean climate in comparison with observation over Ethiopia.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Data Fifteen CMIP5 GCM’s from ESGF gateways. CRU-TS data sets are used as reference for assessing CMIP5 model performance. Satellite-Gauge combined monthly precipitation, GPCP v2.2 from NOAA, monthly mean wind for 17 pressure level from NCEP has been used to validate CMIP GCMs. Data from the experiments are binned into 25-year climatologies that span the 21st century. We measure variability of temperature and precipitation and also the spread of the simulated climate change anomalies for Ethiopia.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Data Fifteen CMIP5 GCM’s from ESGF gateways. CRU-TS data sets are used as reference for assessing CMIP5 model performance. Satellite-Gauge combined monthly precipitation, GPCP v2.2 from NOAA, monthly mean wind for 17 pressure level from NCEP has been used to validate CMIP GCMs. Data from the experiments are binned into 25-year climatologies that span the 21st century. We measure variability of temperature and precipitation and also the spread of the simulated climate change anomalies for Ethiopia.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Data Fifteen CMIP5 GCM’s from ESGF gateways. CRU-TS data sets are used as reference for assessing CMIP5 model performance. Satellite-Gauge combined monthly precipitation, GPCP v2.2 from NOAA, monthly mean wind for 17 pressure level from NCEP has been used to validate CMIP GCMs. Data from the experiments are binned into 25-year climatologies that span the 21st century. We measure variability of temperature and precipitation and also the spread of the simulated climate change anomalies for Ethiopia.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Data Fifteen CMIP5 GCM’s from ESGF gateways. CRU-TS data sets are used as reference for assessing CMIP5 model performance. Satellite-Gauge combined monthly precipitation, GPCP v2.2 from NOAA, monthly mean wind for 17 pressure level from NCEP has been used to validate CMIP GCMs. Data from the experiments are binned into 25-year climatologies that span the 21st century. We measure variability of temperature and precipitation and also the spread of the simulated climate change anomalies for Ethiopia.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Data Fifteen CMIP5 GCM’s from ESGF gateways. CRU-TS data sets are used as reference for assessing CMIP5 model performance. Satellite-Gauge combined monthly precipitation, GPCP v2.2 from NOAA, monthly mean wind for 17 pressure level from NCEP has been used to validate CMIP GCMs. Data from the experiments are binned into 25-year climatologies that span the 21st century. We measure variability of temperature and precipitation and also the spread of the simulated climate change anomalies for Ethiopia.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
In this study the following models are used: Model
Institution
CCSM4 GISS-E2-R HadGEM2-ES IPSL-CM5A-LR CESM1(CAM5) FIO-ESM GFDL-CM3 GFDL-ESM2G GFDL-ESM2M HadGEM2-AO NorESM1-ME
National Center for Atmospheric Research, USA NASA Goddard Institute for Space Studies, USA Met Office Hadley Centre, UK Institut Pierre-Simon Laplace, France Community Earth System Model Contributors The First Institute of Oceanography, SOA, China NOAA Geophysical Fluid Dynamics Laboratory NOAA Geophysical Fluid Dynamics Laboratory NOAA Geophysical Fluid Dynamics Laboratory Met Office, Hadley Centre, UK Norwegian Climate Centre
Table: List of climate models used in this study,and research groups responsible for their development Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Cont... The performance of CMIP5 Models are evaluated quantitatively by analyzing: Bias: The annual or monthly bias is the difference between the model control (present) simulation and the observations (calculated as control - observation). RMSE: The annual RMSE is a measure of how well the simulated seasonal cycle matches that of the observations. Correlation statistics Change Mean
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Cont... The performance of CMIP5 Models are evaluated quantitatively by analyzing: Bias: The annual or monthly bias is the difference between the model control (present) simulation and the observations (calculated as control - observation). RMSE: The annual RMSE is a measure of how well the simulated seasonal cycle matches that of the observations. Correlation statistics Change Mean
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Cont... The performance of CMIP5 Models are evaluated quantitatively by analyzing: Bias: The annual or monthly bias is the difference between the model control (present) simulation and the observations (calculated as control - observation). RMSE: The annual RMSE is a measure of how well the simulated seasonal cycle matches that of the observations. Correlation statistics Change Mean
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Cont... The performance of CMIP5 Models are evaluated quantitatively by analyzing: Bias: The annual or monthly bias is the difference between the model control (present) simulation and the observations (calculated as control - observation). RMSE: The annual RMSE is a measure of how well the simulated seasonal cycle matches that of the observations. Correlation statistics Change Mean
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
CMIP5 models Methods
Cont... The performance of CMIP5 Models are evaluated quantitatively by analyzing: Bias: The annual or monthly bias is the difference between the model control (present) simulation and the observations (calculated as control - observation). RMSE: The annual RMSE is a measure of how well the simulated seasonal cycle matches that of the observations. Correlation statistics Change Mean
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Change in annual mean rainfall Change in annual mean temperature spatial Change in annual mean temperature Time series plots for temperature and rainfall Model performance comparison for temperature Model performance comparison for rainfall
Result: Rainfall & temperature change Rainfall: Mean Model
Change
Short-Term Meduim-Term Long-Term
0.15 0.29 0.43
Mean Model
Change
Short-Term Meduim-Term Long-Term
1.46 2.68 3.80
Temperature:
Table: Quantitative Information about the Mean Model simulations of rainfall (mm/day) & temperature (0 C) Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Change in annual mean rainfall Change in annual mean temperature spatial Change in annual mean temperature Time series plots for temperature and rainfall Model performance comparison for temperature Model performance comparison for rainfall
The climatology plot (left) compares monthly averages and standard deviations (vertical bars), which are a measure of variability. The histogram (right) displays gives a sense of the range and distribution of climate change simulated by the models. 2030s (upper), 2060s (middle) & 2080s (lower) Figure: Annual mean rainfall (mm/day) comparison for CMIP5 GCMs model mean with CRU 1 2
Jemal Seid ,
Andualem Shemelis
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Change in annual mean rainfall Change in annual mean temperature spatial Change in annual mean temperature Time series plots for temperature and rainfall Model performance comparison for temperature Model performance comparison for rainfall
The climatology plot (left) compares monthly averages and standard deviations (vertical bars), which are a measure of variability. The histogram (right) displays gives a sense of the range and distribution of climate change simulated by the models. 2030s(upper), 2060s (middle) & 2080s (lower) Figure: Annual mean temperature (0 C) comparison for CMIP5 GCMs model mean with CRU Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Change in annual mean rainfall Change in annual mean temperature spatial Change in annual mean temperature Time series plots for temperature and rainfall Model performance comparison for temperature Model performance comparison for rainfall
Figure: Spatial Change in annual mean temperature (0 C) Short-Term (left), Meduim-Term (center), Long-Term (right)
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Change in annual mean rainfall Change in annual mean temperature spatial Change in annual mean temperature Time series plots for temperature and rainfall Model performance comparison for temperature Model performance comparison for rainfall
Figure: CMIP5 model-based time series of rainfall(mm/day)[upper] & temperature(0 C) [lower] (historical and projections) 1980-2100 Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Change in annual mean rainfall Change in annual mean temperature spatial Change in annual mean temperature Time series plots for temperature and rainfall Model performance comparison for temperature Model performance comparison for rainfall
Figure: All CMIP5 models RMSE, bias, change & mean of temperature
MPI-ESM-MR, CanESM2, bcc-csm1-1 models are better performed for temperature over Ethiopia with small RMSE and Bias. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Change in annual mean rainfall Change in annual mean temperature spatial Change in annual mean temperature Time series plots for temperature and rainfall Model performance comparison for temperature Model performance comparison for rainfall
Figure: All CMIP5 models RMSE, bias, change & mean of rainfall
MPI-ESM-MR,GFDL-ESM2G, HadGEM2-ES, bcc-csm1-1 models are better performed over Ethiopia with small RMSE and Bias. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Wind climatology Temperature climatology Rainfall climatology
GCM’s model comparison In this part we will see the performance of GCMs over Ethiopia in capturing for the purpose of downscaling using ICTP version Regional Climate Model (RegCM4.2). Wind climatology Temperature climatology and Rainfall climatology This study was done with the collaboration between EIAR and Department of Physics, Addis Ababa University
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Wind climatology Temperature climatology Rainfall climatology
TEJ is reasonably captured by both GCMs, the structure in HadGEM2 differs slightly on the southern part relative to NCEP reanalysis. LLJ is over estimated in CanESM2.
Figure: vertical cross section of zonal wind profile during JJAS, CanESM2 (upper left panel), HadGEM2 (upper right panel) and NCEP-reanalysis 1 , Andualem Shemelis2
[email protected],
[email protected] (lowerSeid panel) Jemal Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Wind climatology Temperature climatology Rainfall climatology
CanESM2 model around 20 C warm bias over east & north, but this model has about -40 C cold bias over the border. HadGEM2 model depicted about cold bias over NE part Rift valley region and it also shows a positive bias over the remaining part of the region. Figure: Mean JJAS Temperature Climatology, Bias(Lower) over Ethiopian Domain; CRU (upper), CanESM2 (Middle left), HadGEM2 (Middle center), and CCSM3 (Middle right) Jemal Seid1 ,
Andualem Shemelis2
CCSM3 model about warm bias 20 C over NW to SW .
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Wind climatology Temperature climatology Rainfall climatology
Figure: Mean JJAS Precipitation Climatology, GPCP compared with CanESM2 (left panel), HadGEM2 (middle panel), and CCSM3 (right panel).) Jemal Seid1 ,
Andualem Shemelis2
The climatology plot (left) compares monthly averages and standard deviations (vertical bars), which are a measure of variability. The histogram (right) displays gives a sense of the range and distribution of climate change simulated by the models
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Wind climatology Temperature climatology Rainfall climatology
Spatial correlation
Figure: Spatial correlation of GPCP JJAS all Ethiopia seasonal mean precipitation with CanESM2 (left panel), HadGM2 (middle panel), and CCSM3 (right panel).
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Wind climatology Temperature climatology Rainfall climatology
Seasonal variability
Figure: Mean JJAS seasonal precipitation (mm/day)(1979 - 1999), averaged over (2 - 16 N, 32 - 49 E) domain for GPCP (blue line), HadGM2 (cyanide line), CCSM3 (black line), and CanESM2 (red line) Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Wind climatology Temperature climatology Rainfall climatology
Interannual variability
Figure: Interannual rainfall variability for a period of (1979 - 1999), averaged over (2 - 16 N, 32 - 49 E) domain, GPCP (blue line), HadGM2 (cyanide line), CCSM3 (black line), and CanESM2 (red line)
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Conclusions CMIP5 ensemble mean climate is closer to observed climate than any individual model. Under RCP8.5 scenario, mean warming in Ethiopia is likely to be in the range 1.46-2.00 C by 2030s and 2.68 - 3.800 C by 2080s relative to the historical period. While rainfall projections are generally less reliable than temperature projections, Model agreement in rainfall projections increases from short-to long-term projections, indicating that long-term rainfall projections are generally more robust than their short-term counterparts. The main synoptic wind system responsible for Ethiopian summer rainfall are well represented by HadGEM2 & CanESM2s. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Conclusions CMIP5 ensemble mean climate is closer to observed climate than any individual model. Under RCP8.5 scenario, mean warming in Ethiopia is likely to be in the range 1.46-2.00 C by 2030s and 2.68 - 3.800 C by 2080s relative to the historical period. While rainfall projections are generally less reliable than temperature projections, Model agreement in rainfall projections increases from short-to long-term projections, indicating that long-term rainfall projections are generally more robust than their short-term counterparts. The main synoptic wind system responsible for Ethiopian summer rainfall are well represented by HadGEM2 & CanESM2s. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Conclusions CMIP5 ensemble mean climate is closer to observed climate than any individual model. Under RCP8.5 scenario, mean warming in Ethiopia is likely to be in the range 1.46-2.00 C by 2030s and 2.68 - 3.800 C by 2080s relative to the historical period. While rainfall projections are generally less reliable than temperature projections, Model agreement in rainfall projections increases from short-to long-term projections, indicating that long-term rainfall projections are generally more robust than their short-term counterparts. The main synoptic wind system responsible for Ethiopian summer rainfall are well represented by HadGEM2 & CanESM2s. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Conclusions CMIP5 ensemble mean climate is closer to observed climate than any individual model. Under RCP8.5 scenario, mean warming in Ethiopia is likely to be in the range 1.46-2.00 C by 2030s and 2.68 - 3.800 C by 2080s relative to the historical period. While rainfall projections are generally less reliable than temperature projections, Model agreement in rainfall projections increases from short-to long-term projections, indicating that long-term rainfall projections are generally more robust than their short-term counterparts. The main synoptic wind system responsible for Ethiopian summer rainfall are well represented by HadGEM2 & CanESM2s. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Conclusions CMIP5 ensemble mean climate is closer to observed climate than any individual model. Under RCP8.5 scenario, mean warming in Ethiopia is likely to be in the range 1.46-2.00 C by 2030s and 2.68 - 3.800 C by 2080s relative to the historical period. While rainfall projections are generally less reliable than temperature projections, Model agreement in rainfall projections increases from short-to long-term projections, indicating that long-term rainfall projections are generally more robust than their short-term counterparts. The main synoptic wind system responsible for Ethiopian summer rainfall are well represented by HadGEM2 & CanESM2s. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Recommendations These new climate projections should be used in future assessment of impact of climate change and adaptation planning. There is need to consider not just the mean climate projections, but also the more important extreme projections in impact studies and as well in adaptation planning. It’s possible to use for further study (i.e downscaling using RegCM4.2) to study the regional effect of climate variability for those performed GCMs. It’s possible to use for impact studies(Crop modeling, hydrological models, ....) to improve agricultural productivity.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Recommendations These new climate projections should be used in future assessment of impact of climate change and adaptation planning. There is need to consider not just the mean climate projections, but also the more important extreme projections in impact studies and as well in adaptation planning. It’s possible to use for further study (i.e downscaling using RegCM4.2) to study the regional effect of climate variability for those performed GCMs. It’s possible to use for impact studies(Crop modeling, hydrological models, ....) to improve agricultural productivity.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Recommendations These new climate projections should be used in future assessment of impact of climate change and adaptation planning. There is need to consider not just the mean climate projections, but also the more important extreme projections in impact studies and as well in adaptation planning. It’s possible to use for further study (i.e downscaling using RegCM4.2) to study the regional effect of climate variability for those performed GCMs. It’s possible to use for impact studies(Crop modeling, hydrological models, ....) to improve agricultural productivity.
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
References Delworth, T.L. and coauthors. Simulated climate and climate change in the GFDL CM2.5 high-resolution coupled climate model. J. Climate doi:10.1175/JCLI-D-11-00316.1 (2012; in press). Hawkins, E., & R. Sutton. The potential to narrow uncertainty in regional climate predictions. Bulletin of the American Meteorological Society 90, 1095-1107 (2009). Hawkins, E., & R. Sutton. The potential to narrow uncertainty in projections of regional precipitation change. Climate Dynamics 37, 407-418 (2011). Held, I. M. & Soden, B. J.. Robust responses of the hydrological cycle to global warming. J. Clim. 19, 5686-5699 (2006). Taylor, K.E., R.J. Stouffer, & G.A. Meehl. An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-11-00094.1, (2012; in press). Vecchi, G.A., B.J. Soden, I.M. Held, and coauthors. The response of the hydrological cycle, atmospheric circulation and conditions impacting tropical2 cyclones in the CMIP5 suite.1 (2012, in preparation) 1
Jemal Seid ,
Andualem Shemelis
[email protected],
[email protected] Comparision of Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Acknowledgment Biometrics, GIS and Agrometreology (BGA) Research Directorate, EIAR and staffs. The Rockefeller Foundation for financial support. World Climate Research Programmes Working Group on Coupled Modeling, which is responsible for CMIP, and the climate modeling groups for making available their model outputs. Climate Research Unit for CRU-TS datasets. CNCS, Department of Physics, Haramaya University. Department of Physics, Addis Ababa University for HPC machine. Ethio-Agromet (wwww.ethioagromet.org) web developers. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Acknowledgment Biometrics, GIS and Agrometreology (BGA) Research Directorate, EIAR and staffs. The Rockefeller Foundation for financial support. World Climate Research Programmes Working Group on Coupled Modeling, which is responsible for CMIP, and the climate modeling groups for making available their model outputs. Climate Research Unit for CRU-TS datasets. CNCS, Department of Physics, Haramaya University. Department of Physics, Addis Ababa University for HPC machine. Ethio-Agromet (wwww.ethioagromet.org) web developers. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Acknowledgment Biometrics, GIS and Agrometreology (BGA) Research Directorate, EIAR and staffs. The Rockefeller Foundation for financial support. World Climate Research Programmes Working Group on Coupled Modeling, which is responsible for CMIP, and the climate modeling groups for making available their model outputs. Climate Research Unit for CRU-TS datasets. CNCS, Department of Physics, Haramaya University. Department of Physics, Addis Ababa University for HPC machine. Ethio-Agromet (wwww.ethioagromet.org) web developers. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Acknowledgment Biometrics, GIS and Agrometreology (BGA) Research Directorate, EIAR and staffs. The Rockefeller Foundation for financial support. World Climate Research Programmes Working Group on Coupled Modeling, which is responsible for CMIP, and the climate modeling groups for making available their model outputs. Climate Research Unit for CRU-TS datasets. CNCS, Department of Physics, Haramaya University. Department of Physics, Addis Ababa University for HPC machine. Ethio-Agromet (wwww.ethioagromet.org) web developers. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Acknowledgment Biometrics, GIS and Agrometreology (BGA) Research Directorate, EIAR and staffs. The Rockefeller Foundation for financial support. World Climate Research Programmes Working Group on Coupled Modeling, which is responsible for CMIP, and the climate modeling groups for making available their model outputs. Climate Research Unit for CRU-TS datasets. CNCS, Department of Physics, Haramaya University. Department of Physics, Addis Ababa University for HPC machine. Ethio-Agromet (wwww.ethioagromet.org) web developers. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Acknowledgment Biometrics, GIS and Agrometreology (BGA) Research Directorate, EIAR and staffs. The Rockefeller Foundation for financial support. World Climate Research Programmes Working Group on Coupled Modeling, which is responsible for CMIP, and the climate modeling groups for making available their model outputs. Climate Research Unit for CRU-TS datasets. CNCS, Department of Physics, Haramaya University. Department of Physics, Addis Ababa University for HPC machine. Ethio-Agromet (wwww.ethioagromet.org) web developers. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Acknowledgment Biometrics, GIS and Agrometreology (BGA) Research Directorate, EIAR and staffs. The Rockefeller Foundation for financial support. World Climate Research Programmes Working Group on Coupled Modeling, which is responsible for CMIP, and the climate modeling groups for making available their model outputs. Climate Research Unit for CRU-TS datasets. CNCS, Department of Physics, Haramaya University. Department of Physics, Addis Ababa University for HPC machine. Ethio-Agromet (wwww.ethioagromet.org) web developers. Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
Introduction Objectives: Data & Methods Result I Result II Conclusions Recommendations References Acknowledgment
Thank You!
Jemal Seid1 ,
Andualem Shemelis2
[email protected],
[email protected] Comparision of 1Climate Haramaya Change University, Projections Department for Ethi o
