1. CIAT’sexperience in climatemodeling; Scenarios of futureclimatechange Carlos Navarro, Julián Ramírez, Andy Jarvis

2. Intro Climate Data Why do youneed? Whoneedsthem? Disadvantages? Problems Limitedknowledge Complexity of theclimatesystem Unsuitablemodelresolutions Data provide fine-scalefutureclimate Uncertainties

3. 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.

5. When,where and whattype of change requiere toadapt?

7. Predictingimpacts of climatechange Scenariosfrompopulation, energy, economicsmodels Emissions Concentrations Carboncycle and chemistrymodels Global ClimateChange Global Climatemodels Regional Detail Regional climatemodels Impacts Impactmodels

8. Emissionscenarios Economic PESSIMIST Regional Global Almost Unreal OPTIMIST Environmental Emissionsscenarios are plausible representations of futureemissions of substancesthat are radiatively active (Jones 2004)

9. PredictionModelsGCMs They are calibrate front the past (using time series CRU-UEA), and proyected future GCMs are theonlymeanswehavetopredictfutureclimates…

11. 18 and 56 vertical levelsGlobal scale Regional or local scale

12. GCMs y Resolutions Uncertainties!

13. Dificulties First, they differ on resolution

14. Dificulties Second: they differ in availability (via IPCC)

15. Dificulties Third: limited ability to represent present climates

17. To increase resolution, uniformise, provide high resolution and contextualised data

18. Different methods exist… from interpolation to neural networks and RCMs

19. DELTA (empirical-statistical)

20. DELTA-VAR (empirical-statistical)

21. DELTA-STATION (empirical-statistical)

22. RCMs (dynamical)

24. Climate baseline: WorldClim

25. Used in the majority of studies

26. Takes original GCM timeseries

27. Calculates averages over a baseline and future periods (i.e. 2020s, 2050s)

28. Compute anomalies

29. Spline interpolation of anomalies

32. Climate baseline: weather stations

33. Calculate anomalies over specific periods (i.e. 2020s, 2050s) in coarse GCM cells

34. “Update” weather station values using GCM cell anomalies within a neighborhood (400 km)

35. Inverse distance weighted

37. Climate baseline: CRU

38. Provided by Tyndall Centre (UK)

39. Use captured variability in GCMs (MAGICC) and anomalies

40. Run a new GCM pattern at a higher resolution (CLIMGEN)

42. Climate baseline: CRU, WorldClim

43. Calculate anomalies over periods in GCM cells

45. Climate baseline: GCM boundary conditions

46. Develop complex numerical models to simulate climate behaviour

47. “Nest” the RCM into a coarse resolution model (GCM) and apply equations to re-model processes in a limited geographic domain

48. Resolution varies between 25-50km

49. Takes several months to process

51. CIAT Experience

52. 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

53. Allwillbe at our portal (soon) http://ccafs-climate.org

54. Reachingusersglobally http://ccafs-climate.org

55. 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)

56. 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%

58. Resolution 50 km

60. In process..

61. What´snext? ValidationGCMs Ethiopia TEMP. (JJA) RAINFALL (JJA)

62. 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

63. What’snext? Seiler 2009

64. 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

65. What’snext? MRI Validation

66. MRI What’snext?

67. 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

68. 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

69. Gracias! j.r.villegas@cgiar.org c.e.navarro@cgiar.org a.jarvis@cgiar.org