Navarro C - Results Climate Projection Development (CIAT-IDB Project)
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Presentation of the results of Climate Modeling component on the CIAT-IDB project "Climate Change Vulnerability in the Agricultural Sector in Latinm America and the Caribbean"
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Navarro C - Results Climate Projection Development (CIAT-IDB Project)
- 1. Carlos Navarro S. Gourdji, S. Prager, J. Tapasco, D. Obando, J. Mesa, J. Ramirez, A. Jarvis
- 3. Economic Environmental Global Regional Pessimistic “Bussiness as usual” OptimisticPerfect World Intermediate P E P E P E P E Emission Scenarios
- 4. IPCC CMIP5 Transition to a new scenarios…. • Representative Concentration Pathways (RCPs) • More and more robust models (i.e. higher resolution). R. Knutti, J. Sedlácek, 2012 Global temperature change and uncertainty (mean and one standard deviation as shading) relative to 1986–2005
- 5. GCMs are one of the few tools we can predict the future climate. Simulate all relevant parts and processes of the climate system. GCM “Global Climate Model” Model Historical RCP 2.6 RCP 4.5 RCP 6.0 RCP 8.5 BCC-CSM1.1 O O O O O BCC-CSM1.1(m) O O O O O BNU-ESM O O O X O CCCMA-CanESM2 O O O X O CESM1-BGC O X O X O CESM1-CAM5 O O O O O CSIRO-ACCESS1.0 O X O X O CSIRO-ACCESS1.3 O X O X O CSIRO-Mk3.6.0 O O O O O CNRM-CM5 O O O X O EC-EARTH O X X X O FIO-ESM O O O O O GFDL-CM3 O O O O O GFLD-ESM2G O O O O O GFLD-ESM2M O O O O O GISS-E2H O O X O O GISS-E2HCC O X O X X GISS-E2R O O O O O GISS-E2RCC O X O X X INM-CM4 O X O X O IPSL-CM5A-LR O O O O O IPSL-CM5A-MR O O O X O IPSL-CM5B-LR O X X X O LASG-FGOALS-G2 O O O X O MIROC-ESM O O O O O MIROC-ESM-CHEM O O O O O MIROC-MIROC5 O O O O O MOHC-HadGEM2-CC O X O X O MOHC-HadGEM2-ES O O O O O MPI-ESM-LR O O O X O MPI-ESM-MR O O X X O MRI-CGCM3 O O O O O NCAR-CCSM4 O O O O O NCC-NorESM1-M O O O O O NIMR-HADGEM2-AO O O O O O Total 35 26 31 19 33
- 6. • Research and dissemination, aims to contribute to a better understanding of the general effect of climate change on production and productivity of key crops in the region to guide decision makers and the Bank in the identification, design and execution of alternatives to adapt to these impacts and minimize vulnerability. • Specifically this project will use climate change projections to analyze impacts on key crops, perform field experiments to determine the effect of soil temperature on crop production and disseminate findings as well as the methodology employed.
- 7. Model Country BCC-CSM1.1 China BNU-ESM China CCCMA-CanESM2 Canada GFLD-ESM2G United States INM-CM4 Russia IPSL-CM5A-LR France MIROC-MIROC5 Japan MPI-ESM-MR Germany NCC-NorESM1-M Norway • Growth in food demand is expected to be faster before 2050 than after because of decelerating global population growth after 2050 • Uncertainties in emission scenarios beyond the 2050s are far greater than those of the next few decades. • The food security impacts of climate change in the next few decades may be severe in some locations.
- 8. Hawkins, 2012 Global scale Regional or local scale Resolutions • Horizontal resolution 100 to 300 km • 18 and 56 vertical levels Related Biases It’s important to make bias corrections and downscale the raw climate model outputs in order to produce more realistic predictions
- 9. Water and Global Change (WATCH) Forcing Data (0.5 deg) Future GCM daily outputs (raw resolution) Historical GCM daily outputs (resampled 0.5 deg) Historical GCM daily outputs (raw resolution) Future GCM daily outputs (resampled 0.5 deg) Historical GCM daily outputs (calibrated 0.5 deg) Future GCM daily outputs (calibrated 0.5 deg) Water and Global Change (WATCH) Forcing Data (0.5 deg) Resampling Calibration 1971 to 2000
- 10. OREF TREF TBC TRAW Δ correction 𝑇𝐵𝐶 𝑡 = 𝑂 𝑅𝐸𝐹 + 𝜎 𝑜,𝑅𝐸𝐹 𝜎 𝑇,𝑅𝐸𝐹 𝑇𝑅𝐴𝑊 𝑡 − 𝑇𝑅𝐸𝐹 Oref = observations in the historical reference period Tref = GCM output from the historical reference period Traw = raw GCM output for the historical or future period TBC = bias-corrected GCM output) Temperature – Simple Bias Correction Precipitation – Solar Radiation Quantile Mapping Bias Correction Hawkins et al. (2012) Qmap 1950–2000 training period Applied to 1971–2000 (current period) 2020-2049 (future period) BC uses raw model output for the future period, and corrects it using the differences (Δ) between historical reference data from the model and observations. Correct the “drizzle problem” Remove the systematic bias in the GCM simulations
- 11. • We use the Köppen climate classification in order to see differences between regions with particular climate (Rubel and Kottek, 2010) • 18 climate classifications within South America Type Description Af Equatorial rainforest and fully humid Am Equatorial monsoon As Equatorial savannah with dry summer Aw Equatorial savannah with dry winter BWk Desert climate cold steppe/desert BWh Desert climate hot steppe/desert BSh Steppe climate hot steppe/desert Cfa Warm temperate climate with fully humid and hot summer Cfb Warm temperate climate with fully humid and warm summer Cfc Warm temperate climate with fully humid, cool summer and cold winter Csa Warm temperate climate with dry and hot summer Csb Warm temperate climate with dry and warm summer Csc Warm temperate climate with dry and cool summer, and cold winter Cwa Warm temperate climate with dry winter and hot summer Cwb Warm temperate climate with dry winter and warm summer Cwc Warm temperate climate with dry and cold winter, and cool summer Et Tundra climate
- 12. Monthly time series (1980-1989)
- 13. Rainfall frequency (in mm/ month) by month for precipitation, with monthly values averaged across 1971-2000.
- 14. Interannual variability (i.e. the standard deviation) of monthly values
- 15. Monthly time series (1980-1989)
- 16. Hot days frequency (in days/ month where a hot day is defined as having ≥ 30 °C maximum temperature).
- 17. Monthly time series (1980-1989)
- 18. Tropical wet (Am) WFD OBSERVATIONS RAW HISTORICALBC HISTORICAL ABS(RPearson)
- 19. Dry and semiarid (BWk) WFD OBSERVATIONS RAW HISTORICALBC HISTORICAL ABS(RPearson)
- 20. Tropical wet (Am) Dry and semiarid (BWk) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 prec : rsds prec : tmax prec : tmin rsds : tmax rsds : tmin ABS(RPearson) WFD OBS BC_HIST RAW_HIST 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 prec : rsds prec : tmax prec : tmin rsds : tmax rsds : tmin ABS(RPearson)
- 21. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 ABS(RPearson) Dry and semiarid (BWk) prec : rsds 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 ABS(RPearson) Tropical wet (Am) prec : rsds
- 22. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 ABS(RPearson) Dry and semiarid (BWk) rsds : tmax Tropical wet (Am) rsds : tmax 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 ABS(RPearson)
- 23. Changes in accumulated rainfall (mm) Changes in solar radiation (%)
- 24. Changes in maximum temperature (˚C) Changes in minimum temperature (˚C)
Editor's Notes
- - Apart of del CC change we have to put the face to the growing population - We moved 1 7 billions! + 2-3 billions more 2050 (~9000 millones) Producir 60 a 70% Harder agriculture Misma tierra, + producción How we could preparr for the future?
- These represent our capacity of answer (mitigation) and technical development It is not predict the climate, is more like understand the uncertainties and alternative futures. Growing population and energy consumption.
- Emision scenarios give conditions to the Global Climate Models. They divide the world in grids and seek the relations between atmosphere, ocean and land. A lot of set of these models..
- In terms of variables and timesteps are oriented to that goal
- Many features visible in the observations are not seen in the AOGCMs, e.g. the cooler temperatures over the Alps. AOGCMs show a wide range of temperatures for the same location, differing by more than 6 ◦C in some places, All have larger grid cells than the observational data available.
- GCM : 20 models RCP 4.5 WFD : based on the 40-year ECMWF Re-Analysis (ERA-40)
- Temperatures… Not only the mean values, also the inteannual variability. QMAP: technique remove the systematic bias in the GCM simulations and has the benefit of accounting for GCM biases in all statistical moments
- tropical wet (Am), dry and semiarid (BWk), subtropical (Csb)
- GCM-Raw overstimate the amount of precipitation in dry climates and understimate in wet climates. Sometimes, When the difference is not significative between GCM-Raw and observations not represent very well the frecuency It’s necessary not only correct the intensity of precipitation is also important correct rain frequency.
- Quantile mapping effectively improved the magnitude and the frequency of the precipitation for all zones. GCM-BC better significatively in most months and places of low and medium precipitation amount. Solves the “drizzle problem”.
- GCM-Raw como GCM-BC could not represent very well the interannual variation paterns In wet zones like in Am climate, variability is higher than in the observations But, the performance improve in climate with lower precipitation
- BC more realistic GCM-Raw, Dispersion between GCM is lower in hot climates and medium in tropical zones. Temperatura has lower uncertatinties than precipitation In medium-hotter climates maximum temperature are underestimate by GCM raw Both effects has solution in the bias correction method chose.
- Hot days were not well represented by the GCM-Raw GCM-BC produce enough hot days on average in all zones and are confident and realistic.
- It’s not an easy variable for modeling. But also the bias correction method chose solve this problem The skill of this model is better in tropical climates.
- Projected change in rainfall are varied and complex, ranging from -30 to +20%, High decreases in the East at the second part of the year Increases in Pacific coast of South America along the year Precipitation is likely to become heavier in magnitude and frequency. Over Mexico and the Caribbean region are projected reductions in rainfall at the peak of the rainy season (MAM and JJA). The greatest increases in maximum temperature and decreases in precipitation are shown in JJA and SON which probably will cause droughts, the possibility of delays in the start of the rainy season and desertification, mainly in the Caribbean, Venezuela, Northeast Brazil and the most of the part of the Amazonia. Changes in radiation solar are directly correlated with the increases of the maximum and minimum temperature. Important decreases of solar radiation are evident to the Southern Brazil, Uruguay and Argentina which can severely affect negatively the productivity of these regions.
- Overall, seasonal maximum temperatures are predicted to increase by 1–4°C and minimum temperatures by 1-3°C At mid-to-high latitudes the projections shows warmer temperatures than the high latitudes. Hablar un poco acerca de las limitaciones, pero los alcances logrados.