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

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.

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

    • Spatialdownscaling of futureclimatepredictionsforAgriculture
      JuliánRamírez
      Andy Jarvis
      Carlos Navarro
    • Contents
      Background: climate and agriculture
      Future climate and GCMs
      Downscaling methods
      Disaggregation
      CCAFS-T1 / CIAT-DAPA data inventory
      CCAFS climate data strategy
    • 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…
    • 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
    • Climate and agriculture
      Due to that, modelling approaches are constrained by input data
      © CCAFS
    • 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
    • And methods
    • 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
    • IPCC 4th AR GCMs
    • Issues in GCMs
      First, they differ on resolution
    • Issues in GCMs
      Second: they differ in availability
    • Issues in GCMs
      Third: limited ability to represent present climates
    • Issues in GCMs
      Finally, they involve uncertainty
      Averages: do they mislead?
    • 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
    • +++ UNCERTAINTY
    • 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
    • Key messages…
      Futureclimatepredictionsneedto be improved (IPCC 5th AR)
      GCMs are stillnotusefulforagriculturalresearchers (CCAFS + partners)
    • 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)

    • Why do we need higher resolution data?
      Temperature
      Ethiopia
      Rainfall
    • 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
    • The delta method
      Downscaling
    • 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
    • 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
    • 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)
    • 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
    • Which one is best?
    • But, can we downscale (statistically)?
      Temperature
      MIROC3.2-HIRES
      Rainfall
    • 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
    • Reaching users globallyhttp://gisweb.ciat.cgiar.org/GCMPage
    • 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%
    • PRECIS
      runs
    • 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!!
    • 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)
    • What’s next: validation of GCM simulations
      Ethiopia
      TEMP. (JJA)
      RAINFALL (JJA)
    • What’s next?
      Contextualising / validating GCM and RCM predictions
    • 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
    • What’snext?
      Seiler 2009
    • 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
    • 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