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Carlos N - CIAT Experience In Climate Modeling; Scenarios of future climate change
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Carlos N - CIAT Experience In Climate Modeling; Scenarios of future climate change

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  • 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.
  • 4. Wedon’tknow
    What are theconditions in 30, 50, 100 años?
    >> UNCERTAINTIES
    • Howoursystemrespondtotheseconditions?
    • 5. When,where and whattype of change requiere toadapt?
    • 6. 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
  • 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…
  • 10. GCMs y Resoluciones
    Mainfeatures
    • Horizontal resolution 100 to 300 km
    • 11. 18 and 56 vertical levels
    Global 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
  • 16. Options
    Downscaling
    • Even the most precise GCM is too coarse (~100km)
    • 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)
    • 23.
  • StatisticalDownscaling
    The delta method
    • Use anomalies and discard baselines in GCMs
    • 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
    • 30. Sum anomalies to WorldClim
  • The delta method
  • 31. StatisticalDownscaling
    Delta- Station
    • Most similar to original methods in WorldClim(Saenz-Romero et al. 2009)
    • 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
    • 36. Use thin plate smoothing splines with LAT,LON,ALT as covariates for interpolation
  • StatisticalDownscaling
    Delta- Var
    Delta-VAR (Mitchell et al. 2005
    • AKA pattern scaling
    • 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)
    • 41. Calculate averages over specific periods using the GCM scaled time-series
  • StatisticalDownscaling
    Disaggregation
    • Similar to the delta method, but does not use interpolation
    • 42. Climate baseline: CRU, WorldClim
    • 43. Calculate anomalies over periods in GCM cells
    • 44. Sum anomalies to climate baseline
  • DynamicalDownscaling
    RCMs PRECIS
    • RCMs (Giorgi 1990)
    • 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
    • 50. Requires a new validation (on top of the GCM validation)
  • Whichoneisthebest?
  • 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%
  • 57. In process..
    PRECIS
    • Region: Andes
    • 58. Resolution 50 km
    • 59. Grid : 151 x 153
  • In process..
    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!!
  • 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