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

1. Spatialdownscaling of futureclimatepredictionsforAgriculture JuliánRamírez Andy Jarvis Carlos Navarro

2. Contents Background: climate and agriculture Future climate and GCMs Downscaling methods Disaggregation CCAFS-T1 / CIAT-DAPA data inventory CCAFS climate data strategy

3. Climate and agriculture Information on climate is critical for agriculture, because: 1. Agriculture is a niche-based activity 2. Abiotic factors (i.e. climate, soils) and their interactions are main drivers Location Performance Adaptive responses Management practices 3. Weather and climate predictability is fairly limited 4. Each system is an specific case, so is its future…

4. Climate and 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

5. Climate and agriculture Due to that, modelling approaches are constrained by input data © CCAFS

6. Despite some improvements in data availability Early 20th century © Global Historical Climatology Network (GHCN) http://www.ncdc.noaa.gov/ghcnm/v2.php Optimal (mid) 20th century

7. And methods

8. GCMs: How do we predict the future? GCMs are the only means we have to predict future climates… ~24 exist up to now All different… so we can expect issues

9. IPCC 4th AR GCMs

10. Issues in GCMs First, they differ on resolution

11. Issues in GCMs Second: they differ in availability

12. Issues in GCMs Third: limited ability to represent present climates

13. Issues in GCMs Finally, they involve uncertainty Averages: do they mislead?

14. BCCR-BCM2.0 CCCMA-CGCM3.1-T47 CNRM-CM3 Research areas: Available and usable climate data CSIRO-MK3.0 CSIRO-MK3.5 GFDL-CM2.0 GFDL-CM2.1 INGV-ECHAM4 INM-CM3.0 IPSL-CM4 MIROC3.2-MEDRES MIUB-ECHO-G MPI-ECHAM5 MRI-CGCM2.3.2A NCAR-CCSM3.0 NCAR-PCM1 UKMO-HADCM3 UKMO-HADGEM1

15. +++ UNCERTAINTY

16. So, what do we use currently? Input climate data used for climate change impact on agriculture assessments? No researchers use GCM data “as is” © CCAFS

17. Key messages… Futureclimatepredictionsneedto be improved (IPCC 5th AR) GCMs are stillnotusefulforagriculturalresearchers (CCAFS + partners)

18. So we need downscaling Even the most precise GCM is too coarse (~100km) To increase resolution, uniformise, provide high resolution and contextualised data Different methods exist… from interpolation to neural networks and RCMs DELTA (empirical-statistical) DELTA-VAR (empirical-statistical) DELTA-STATION (empirical-statistical) RCMs (dynamical) …

19. Why do we need higher resolution data? Temperature Ethiopia Rainfall

20. The delta methodHay et al. 2007 Use anomalies and discard baselines in GCMs Climate baseline: WorldClim Used in the majority of studies Takes original GCM timeseries Calculates averages over a baseline and future periods (i.e. 2020s, 2050s) Compute anomalies Spline interpolation of anomalies Sum anomalies to WorldClim

21. The delta method Downscaling

22. Delta-VARMitchell et al. 2005 AKA pattern scaling Climate baseline: CRU Provided by Tyndall Centre (UK) Use captured variability in GCMs (MAGICC)and anomalies Run a new GCM pattern at a higher resolution (CLIMGEN) Calculate averages over specific periods using the GCM scaled time-series

23. Delta-StationSaenz-Romero et al. 2009 Most similar to original methods in WorldClim Climate baseline: weather stations Calculate anomalies over specific periods (i.e. 2020s, 2050s) in coarse GCM cells “Update” weather station values using GCM cell anomalies within a neighborhood (400 km) Inverse distance weighted Use thin plate smoothing splines with LAT,LON,ALT as covariates for interpolation

24. RCMs: PRECISGiorgi 1990 RCMs (Giorgi 1990) Climate baseline: GCM boundary conditions Develop complex numerical models to simulate climate behaviour “Nest” the RCM into a coarse resolution model (GCM) and apply equations to re-model processes in a limited geographic domain Resolution varies between 25-50km Takes several months to process Requires a new validation (on top of the GCM validation)

25. Disaggregation Similar to the delta method, but does not use interpolation Climate baseline: CRU, WorldClim Calculate anomalies over periods in GCM cells Sum anomalies to climate baseline

26. Which one is best?

27. But, can we downscale (statistically)? Temperature MIROC3.2-HIRES Rainfall

28. Ourdatabases Empiricallydownscaled, disaggregatedforthewholeglobe at 1km to 20km Dinamicallydownscaled (PRECIS) for South America Allwill be at our portal (soon) http://gisweb.ciat.cgiar.org/GCMPage.html

30. Reaching users globallyhttp://gisweb.ciat.cgiar.org/GCMPage

31. Downscaled GCMs 7 periods for 63 scenarios (≈ 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 scenarios (≈ 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%

32. PRECIS runs

33. A quickcomparison 1 PRECIS run (10 year) = 2 weeks 1 interpolation (37 steps) x 15 periods = 1 week x 1 GCM x 7 periods x 1 scenario x 20 GCMs 30 weeks x 3 scenarios ÷ 2 processes 210 weeks ÷ 3 servers ÷ 4 processes = 5 weeks ÷ 4 servers x 20 GCM s Hypothetically.. = 26 weeks x 3 scenarios = 6 months!! = 300 weeks = 6 years!!

34. Capabilities and limitations Our in-house capacity: Four 8-core processing servers in a blade array under Windows (empirical downscaling) Three 16-core processing servers in a blade array under Linux (PRECIS) ~80TB storage Publishing data is a lengthy process and requires massive storage and network capacity (esp. 1km global datasets)

35. What’s next: validation of GCM simulations Ethiopia TEMP. (JJA) RAINFALL (JJA)

36. What’s next? Contextualising / validating GCM and RCM predictions

37. RCM PRECIS BaselineAverage 1961 – 1990 Total Precipitation (mm/yr) ECHAM5 HadCM3Q0 HadCM3Q16 Máx: 4151.01 Mín: 3.454 Máx: 4724.028 Mín: 1.1344 Máx: 4796.844 Mín: 1.1839 BaselineAverageAnnual Mean Temperature (°C) ECHAM5 HadCM3Q0 HadCM3Q16 Máx: 28.8573 Mín: -8.3415 Máx: 28.99 Mín: -9.22 Máx: 30.541 Mín: -7.413

38. What’snext? Seiler 2009

39. What’s nextCCAFS climate data strategy Improve baseline data and metadata (incl. uncertainties) Gather and process AR5 projections Downscale with desired methods Evaluate (against weather stations) and assess uncertainties Publish all datasets (original and downscaled) and results Use the AMKN platform to link climate data, and modelling outputs

40. 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