CIAT’sexperience in climatemodeling; Scenarios of futureclimatechange Carlos Navarro, Julián Ramírez, Andy Jarvis
Intro Climate Data Why do youneed? Whoneedsthem? Disadvantages? Problems Limitedknowledge Complexity of theclimatesystem Unsuitablemodelresolutions Data provide fine-scalefutureclimate Uncertainties
Weknow.. Any agroecosystem respond to changes of: Anthropogenic factors (socials), biotics (pest, diseases) abiotics (weather, soilss) Weather and climate predictability is fairly limited. The climate will change. Each system is an specific case.
Wedon’tknow What are theconditions in 30, 50, 100 años? >> UNCERTAINTIES
When,where and whattype of change requiere toadapt?
Whoshould plan? Whoshould leads theprocess? Who should run?
Climate & Agriculture Agriculture demands: Multiple variables Very high spatial resolution Mid-high temporal (i.e. monthly, daily) resolution Accurate weather forecasts and climate projections High certainty Both for present and future
Predictingimpacts of climatechange Scenariosfrompopulation, energy, economicsmodels Emissions Concentrations Carboncycle and chemistrymodels Global ClimateChange Global Climatemodels Regional Detail Regional climatemodels Impacts Impactmodels
Emissionscenarios Economic PESSIMIST Regional Global Almost Unreal OPTIMIST Environmental Emissionsscenarios are plausible representations of futureemissions of substancesthat are radiatively active (Jones 2004)
PredictionModelsGCMs They are calibrate front the past (using time series CRU-UEA), and proyected future GCMs are theonlymeanswehavetopredictfutureclimates…
OurDatabases Empiricallydownscaled, disaggregatedforthewholeglobe at 1km to 20km Dinamicallydownscaled (PRECIS) for South America 20 GCM for 2050, 9 for 2020 (Stanford data) downscaled a 20km, 5km, 1km 7 GCMswithinformationTyndall
Allwillbe at our portal (soon) http://ccafs-climate.org
Capabilities and limitations Our in-house capacity: Four 8-core processing servers in a blade array under Windows (empirical downscaling) Two 24-core and 1-8core processing servers in a blade array under Linux (PRECIS) ~100TB storage Compresing and publishing data is a lengthy process and requires massive storage and network capacity (esp. 1km global datasets)
In process.. Downscaling Downscaled GCMs 7 periods for 63 models (≈ 20 GCMs x 3 scenarios) Downscaled 30 seg= 100% Resample 2.5min, 5min, 10min = 100% Convert to ascii and compress 30 seg = 30 % (19/63) Convert to ascii and compress resampled = 100% Compress grids resampled = 100% Publising compressed asciis and grids = 0% Dissagregated GCMs 7 periods for 63 models (≈ 20 GCMs x 3 scenarios) Downscaled 30 seg= 100% Resample 2.5min, 5min, 10min = 100% Convert to ascii and compress 30 seg = 33 % (21/63) Convert to ascii and compress resampled = 100% Compress grids Resamples = 100% Publising compressed asciis and grids = 0%
What´snext? ValidationGCMs GCM vs Stations Post-processing Formatconversion HistogramsR2, RMSQ, RMSE, slope R2 Vs. Lat / Alt Comparisonwithin situ data Averages/ sums, monthly/ annual RMSQ vs. Lat / Alt RMSQ vs. Lat / Alt Cell maps
What’snext? CCAFS Climate data strategy Improve baseline data and metadata Gather and process AR5 projections Downscale with desired methods Evaluate and assess uncertainties Publish all datasets and results Use the AMKN platform to link climate data, and modelling outputs
In summary CIAT and CCAFS data to be one single product (other datasets are being added) Downscaling is inevitable, so we are aiming to report caveats on the methods Continuous improvements are being done Strong focus on uncertainty analysis and improvement of baseline data Reports and publications to be pursued… grounding with climate science