Spatialdownscaling of futureclimatepredictionsforAgriculture<br />JuliánRamírez<br />Andy Jarvis<br />Carlos Navarro<br />
Contents<br />Background: climate and agriculture<br />Future climate and GCMs<br />Downscaling methods<br />Disaggregatio...
Climate and agriculture<br />Information on climate is critical for agriculture, because:<br />1. Agriculture is a niche-b...
Climate and agriculture<br />Agriculture demands:<br />Multiple variables<br />Very high spatial resolution<br />Mid-high ...
Climate and agriculture<br />Due to that, modelling approaches are constrained by input data<br />© CCAFS<br />
Despite some improvements in data availability<br />Early<br />20th  century<br />© Global Historical Climatology Network ...
And methods<br />
GCMs: How do we predict the future?<br />GCMs are the only means we have to predict future climates…<br />~24 exist up to ...
IPCC 4th AR GCMs<br />
Issues in GCMs<br />First, they differ on resolution<br />
Issues in GCMs<br />Second: they differ in availability<br />
Issues in GCMs<br />Third: limited ability to represent present climates<br />
Issues in GCMs<br />Finally, they involve uncertainty<br />Averages: do they mislead?<br />
BCCR-BCM2.0<br />CCCMA-CGCM3.1-T47<br />CNRM-CM3<br />Research areas: Available and usable climate data<br />CSIRO-MK3.0<b...
+++ UNCERTAINTY<br />
So, what do we use currently?<br />Input climate data used for climate change impact on agriculture assessments?<br />No r...
Key messages…<br />Futureclimatepredictionsneedto be improved (IPCC 5th AR)<br />GCMs are stillnotusefulforagriculturalres...
So we need downscaling<br />Even the most precise GCM is too coarse (~100km)<br />To increase resolution, uniformise, prov...
Why do we need higher resolution data?<br />Temperature<br />Ethiopia<br />Rainfall<br />
The delta methodHay et al. 2007<br />Use anomalies and discard baselines in GCMs<br />Climate baseline: WorldClim<br />Use...
The delta method<br />Downscaling<br />
Delta-VARMitchell et al. 2005<br />AKA pattern scaling<br />Climate baseline: CRU<br />Provided by Tyndall Centre (UK)<br ...
Delta-StationSaenz-Romero et al. 2009<br />Most similar to original methods in WorldClim<br />Climate baseline: weather st...
RCMs: PRECISGiorgi 1990<br />RCMs (Giorgi 1990)<br />Climate baseline: GCM boundary conditions<br />Develop complex numeri...
Disaggregation<br />Similar to the delta method, but does not use interpolation<br />Climate baseline: CRU, WorldClim<br /...
Which one is best?<br />
But, can we downscale (statistically)?<br />Temperature<br />MIROC3.2-HIRES<br />Rainfall<br />
Ourdatabases<br />Empiricallydownscaled, disaggregatedforthewholeglobe at 1km to 20km<br />Dinamicallydownscaled (PRECIS) ...
Reaching users globallyhttp://gisweb.ciat.cgiar.org/GCMPage<br />
Downscaled GCMs <br />7 periods for 63 scenarios (≈ 20 GCMs x 3 scenarios) <br />Downscaled 30 seg=  100% <br />Resample 2...
PRECIS<br />runs<br />
A quickcomparison<br />1 PRECIS run (10 year)	<br />=   2 weeks<br />1 interpolation (37 steps) 	<br />x 15 periods<br />=...
Capabilities and limitations<br />Our in-house capacity:<br />Four 8-core processing servers in a blade array under Window...
What’s next: validation of GCM simulations<br />Ethiopia<br />TEMP. (JJA)<br />RAINFALL (JJA)<br />
What’s next?<br />Contextualising / validating GCM and RCM predictions<br />
RCM PRECIS	<br />BaselineAverage 1961 – 1990 Total Precipitation (mm/yr)<br />ECHAM5		                   HadCM3Q0	        ...
What’snext?<br />Seiler 2009<br />
What’s nextCCAFS climate data strategy<br />Improve baseline data and metadata (incl. uncertainties)<br />Gather and proce...
In summary	<br />CIAT and CCAFS data to be one single product (other datasets are being added)<br />Downscaling is inevita...
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Julian R - Spatial downscaling of future climate predictions for agriculture CIP Lima March 2011

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Climate data downscaling workshop held in CIP headquarters in Lima, as part of a project funded by CCAFS Theme 4.

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Julian R - Spatial downscaling of future climate predictions for agriculture CIP Lima March 2011

  1. 1. Spatialdownscaling of futureclimatepredictionsforAgriculture<br />JuliánRamírez<br />Andy Jarvis<br />Carlos Navarro<br />
  2. 2. Contents<br />Background: climate and agriculture<br />Future climate and GCMs<br />Downscaling methods<br />Disaggregation<br />CCAFS-T1 / CIAT-DAPA data inventory<br />CCAFS climate data strategy<br />
  3. 3. Climate and agriculture<br />Information on climate is critical for agriculture, because:<br />1. Agriculture is a niche-based activity<br />2. Abiotic factors (i.e. climate, soils) and their interactions are main drivers<br />Location<br />Performance<br />Adaptive responses<br />Management practices<br />3. Weather and climate predictability is fairly limited<br />4. Each system is an specific case, so is its future…<br />
  4. 4. Climate and agriculture<br />Agriculture demands:<br />Multiple variables<br />Very high spatial resolution<br />Mid-high temporal (i.e. monthly, daily) resolution<br />Accurate weather forecasts and climate projections<br />High certainty<br />Both for present and future<br />
  5. 5. Climate and agriculture<br />Due to that, modelling approaches are constrained by input data<br />© CCAFS<br />
  6. 6. Despite some improvements in data availability<br />Early<br />20th century<br />© Global Historical Climatology Network (GHCN)<br />http://www.ncdc.noaa.gov/ghcnm/v2.php<br />Optimal (mid)<br />20th century<br />
  7. 7. And methods<br />
  8. 8. GCMs: How do we predict the future?<br />GCMs are the only means we have to predict future climates…<br />~24 exist up to now<br />All different… so we can<br />expect issues<br />
  9. 9. IPCC 4th AR GCMs<br />
  10. 10. Issues in GCMs<br />First, they differ on resolution<br />
  11. 11. Issues in GCMs<br />Second: they differ in availability<br />
  12. 12. Issues in GCMs<br />Third: limited ability to represent present climates<br />
  13. 13. Issues in GCMs<br />Finally, they involve uncertainty<br />Averages: do they mislead?<br />
  14. 14. BCCR-BCM2.0<br />CCCMA-CGCM3.1-T47<br />CNRM-CM3<br />Research areas: Available and usable climate data<br />CSIRO-MK3.0<br />CSIRO-MK3.5<br />GFDL-CM2.0<br />GFDL-CM2.1<br />INGV-ECHAM4<br />INM-CM3.0<br />IPSL-CM4<br />MIROC3.2-MEDRES<br />MIUB-ECHO-G<br />MPI-ECHAM5<br />MRI-CGCM2.3.2A<br />NCAR-CCSM3.0<br />NCAR-PCM1<br />UKMO-HADCM3<br />UKMO-HADGEM1<br />
  15. 15. +++ UNCERTAINTY<br />
  16. 16. So, what do we use currently?<br />Input climate data used for climate change impact on agriculture assessments?<br />No researchers use GCM data “as is”<br />© CCAFS<br />
  17. 17. Key messages…<br />Futureclimatepredictionsneedto be improved (IPCC 5th AR)<br />GCMs are stillnotusefulforagriculturalresearchers (CCAFS + partners)<br />
  18. 18. So we need downscaling<br />Even the most precise GCM is too coarse (~100km)<br />To increase resolution, uniformise, provide high resolution and contextualised data<br />Different methods exist… from interpolation to neural networks and RCMs<br />DELTA (empirical-statistical)<br />DELTA-VAR (empirical-statistical)<br />DELTA-STATION (empirical-statistical)<br />RCMs (dynamical)<br />…<br />
  19. 19. Why do we need higher resolution data?<br />Temperature<br />Ethiopia<br />Rainfall<br />
  20. 20. The delta methodHay et al. 2007<br />Use anomalies and discard baselines in GCMs<br />Climate baseline: WorldClim<br />Used in the majority of studies<br />Takes original GCM timeseries<br />Calculates averages over a baseline and future periods (i.e. 2020s, 2050s)<br />Compute anomalies<br />Spline interpolation of anomalies<br />Sum anomalies to WorldClim<br />
  21. 21. The delta method<br />Downscaling<br />
  22. 22. Delta-VARMitchell et al. 2005<br />AKA pattern scaling<br />Climate baseline: CRU<br />Provided by Tyndall Centre (UK)<br />Use captured variability in GCMs (MAGICC)and anomalies<br />Run a new GCM pattern at a higher resolution (CLIMGEN)<br />Calculate averages over specific periods using the GCM scaled time-series<br />
  23. 23. Delta-StationSaenz-Romero et al. 2009<br />Most similar to original methods in WorldClim<br />Climate baseline: weather stations<br />Calculate anomalies over specific periods (i.e. 2020s, 2050s) in coarse GCM cells<br />“Update” weather station values using GCM cell anomalies within a neighborhood (400 km)<br />Inverse distance weighted<br />Use thin plate smoothing splines with LAT,LON,ALT as covariates for interpolation<br />
  24. 24. RCMs: PRECISGiorgi 1990<br />RCMs (Giorgi 1990)<br />Climate baseline: GCM boundary conditions<br />Develop complex numerical models to simulate climate behaviour<br />“Nest” the RCM into a coarse resolution model (GCM) and apply equations to re-model processes in a limited geographic domain<br />Resolution varies between 25-50km<br />Takes several months to process<br />Requires a new validation (on top of the GCM validation)<br />
  25. 25. Disaggregation<br />Similar to the delta method, but does not use interpolation<br />Climate baseline: CRU, WorldClim<br />Calculate anomalies over periods in GCM cells<br />Sum anomalies to climate baseline<br />
  26. 26. Which one is best?<br />
  27. 27. But, can we downscale (statistically)?<br />Temperature<br />MIROC3.2-HIRES<br />Rainfall<br />
  28. 28. Ourdatabases<br />Empiricallydownscaled, disaggregatedforthewholeglobe at 1km to 20km<br />Dinamicallydownscaled (PRECIS) for South America<br />Allwill be at our portal (soon) http://gisweb.ciat.cgiar.org/GCMPage.html<br />
  29. 29.
  30. 30. Reaching users globallyhttp://gisweb.ciat.cgiar.org/GCMPage<br />
  31. 31. Downscaled GCMs <br />7 periods for 63 scenarios (≈ 20 GCMs x 3 scenarios) <br />Downscaled 30 seg= 100% <br />Resample 2.5min, 5min, 10min = 100%<br />Convert to ascii and compress 30 seg = 30 % (19/63)<br />Convert to ascii and compress resampled = 100%<br />Compress grids resampled = 100%<br />Publising compressed asciis and grids = 0% <br />Dissagregated GCMs <br />7 periods for 63 scenarios (≈ 20 GCMs x 3 scenarios) <br />Downscaled 30 seg= 100% <br />Resample 2.5min, 5min, 10min = 100%<br />Convert to ascii and compress 30 seg = 33 % (21/63)<br />Convert to ascii and compress resampled = 100%<br />Compress grids Resamples = 100%<br />Publising compressed asciis and grids = 0% <br />
  32. 32. PRECIS<br />runs<br />
  33. 33. A quickcomparison<br />1 PRECIS run (10 year) <br />= 2 weeks<br />1 interpolation (37 steps) <br />x 15 periods<br />= 1 week<br />x 1 GCM <br />x 7 periods <br />x 1 scenario<br />x 20 GCMs <br /> 30 weeks<br />x 3 scenarios<br />÷ 2 processes<br /> 210 weeks<br />÷ 3 servers<br />÷ 4 processes<br />= 5 weeks<br />÷ 4 servers<br />x 20 GCM s<br />Hypothetically..<br />= 26 weeks<br />x 3 scenarios<br />= 6 months!!<br />= 300 weeks<br />= 6 years!!<br />
  34. 34. Capabilities and limitations<br />Our in-house capacity:<br />Four 8-core processing servers in a blade array under Windows (empirical downscaling)<br />Three 16-core processing servers in a blade array under Linux (PRECIS)<br />~80TB storage<br />Publishing data is a lengthy process and requires massive storage and network capacity (esp. 1km global datasets)<br />
  35. 35. What’s next: validation of GCM simulations<br />Ethiopia<br />TEMP. (JJA)<br />RAINFALL (JJA)<br />
  36. 36. What’s next?<br />Contextualising / validating GCM and RCM predictions<br />
  37. 37. RCM PRECIS <br />BaselineAverage 1961 – 1990 Total Precipitation (mm/yr)<br />ECHAM5 HadCM3Q0 HadCM3Q16<br />Máx: 4151.01<br />Mín: 3.454<br />Máx: 4724.028<br />Mín: 1.1344<br />Máx: 4796.844<br />Mín: 1.1839<br />BaselineAverageAnnual Mean Temperature (°C)<br />ECHAM5 HadCM3Q0 HadCM3Q16<br />Máx: 28.8573<br />Mín: -8.3415<br />Máx: 28.99<br />Mín: -9.22<br />Máx: 30.541<br />Mín: -7.413<br />
  38. 38. What’snext?<br />Seiler 2009<br />
  39. 39. What’s nextCCAFS climate data strategy<br />Improve baseline data and metadata (incl. uncertainties)<br />Gather and process AR5 projections<br />Downscale with desired methods<br />Evaluate (against weather stations) and assess uncertainties<br />Publish all datasets (original and downscaled) and results<br />Use the AMKN platform to link climate data, and modelling outputs<br />
  40. 40. In summary <br />CIAT and CCAFS data to be one single product (other datasets are being added)<br />Downscaling is inevitable, so we are aiming to report caveats on the methods<br />Continuous improvements are being done<br />Strong focus on uncertainty analysis and improvement of baseline data<br />Reports and publications to be pursued… grounding with climate science<br />

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