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

  1. 1. CIAT’sexperience in climatemodeling; <br />Scenarios of futureclimatechange<br />Carlos Navarro, Julián Ramírez, Andy Jarvis<br />
  2. 2. Intro<br />Climate Data<br />Why do youneed?<br />Whoneedsthem?<br />Disadvantages?<br />Problems<br />Limitedknowledge<br />Complexity of theclimatesystem<br />Unsuitablemodelresolutions<br />Data provide fine-scalefutureclimate<br />Uncertainties<br />
  3. 3. Weknow.. <br />Any agroecosystem respond to changes of:<br /> Anthropogenic factors (socials), <br />biotics (pest, diseases)<br />abiotics (weather, soilss)<br />Weather and climate predictability is fairly limited. <br />The climate will change.<br />Each system is an specific case. <br />
  4. 4. Wedon’tknow<br />What are theconditions in 30, 50, 100 años?<br />>> UNCERTAINTIES<br /><ul><li>Howoursystemrespondtotheseconditions?
  5. 5. When,where and whattype of change requiere toadapt?
  6. 6. Whoshould plan? Whoshould leads theprocess? Who should run?</li></li></ul><li>Climate & 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 />
  7. 7. Predictingimpacts of climatechange<br />Scenariosfrompopulation, energy, economicsmodels<br />Emissions<br />Concentrations<br />Carboncycle and chemistrymodels<br />Global ClimateChange<br />Global Climatemodels<br />Regional Detail<br />Regional climatemodels<br />Impacts<br />Impactmodels<br />
  8. 8. Emissionscenarios<br />Economic<br />PESSIMIST<br />Regional<br />Global<br />Almost Unreal<br />OPTIMIST<br />Environmental<br />Emissionsscenarios are plausible representations of futureemissions of substancesthat are radiatively active (Jones 2004)<br />
  9. 9. PredictionModelsGCMs<br />They are calibrate front the past (using time series CRU-UEA), and proyected future<br />GCMs are theonlymeanswehavetopredictfutureclimates…<br />
  10. 10. GCMs y Resoluciones<br />Mainfeatures<br /><ul><li> Horizontal resolution 100 to 300 km
  11. 11. 18 and 56 vertical levels</li></ul>Global scale<br />Regional or local scale <br />
  12. 12. GCMs y Resolutions<br />Uncertainties!<br />
  13. 13. Dificulties<br />First, they differ on resolution<br />
  14. 14. Dificulties<br />Second: they differ in availability (via IPCC)<br />
  15. 15. Dificulties<br />Third: limited ability to represent present climates<br />
  16. 16. Options<br />Downscaling<br /><ul><li>Even the most precise GCM is too coarse (~100km)
  17. 17. To increase resolution, uniformise, provide high resolution and contextualised data
  18. 18. Different methods exist… from interpolation to neural networks and RCMs
  19. 19. DELTA (empirical-statistical)
  20. 20. DELTA-VAR (empirical-statistical)
  21. 21. DELTA-STATION (empirical-statistical)
  22. 22. RCMs (dynamical)
  23. 23. …</li></li></ul><li>StatisticalDownscaling<br />The delta method<br /><ul><li>Use anomalies and discard baselines in GCMs
  24. 24. Climate baseline: WorldClim
  25. 25. Used in the majority of studies
  26. 26. Takes original GCM timeseries
  27. 27. Calculates averages over a baseline and future periods (i.e. 2020s, 2050s)
  28. 28. Compute anomalies
  29. 29. Spline interpolation of anomalies
  30. 30. Sum anomalies to WorldClim</li></li></ul><li>The delta method<br />
  31. 31. StatisticalDownscaling<br />Delta- Station<br /><ul><li>Most similar to original methods in WorldClim(Saenz-Romero et al. 2009)
  32. 32. Climate baseline: weather stations
  33. 33. Calculate anomalies over specific periods (i.e. 2020s, 2050s) in coarse GCM cells
  34. 34. “Update” weather station values using GCM cell anomalies within a neighborhood (400 km)
  35. 35. Inverse distance weighted
  36. 36. Use thin plate smoothing splines with LAT,LON,ALT as covariates for interpolation</li></li></ul><li>StatisticalDownscaling<br />Delta- Var<br />Delta-VAR (Mitchell et al. 2005<br /><ul><li>AKA pattern scaling
  37. 37. Climate baseline: CRU
  38. 38. Provided by Tyndall Centre (UK)
  39. 39. Use captured variability in GCMs (MAGICC) and anomalies
  40. 40. Run a new GCM pattern at a higher resolution (CLIMGEN)
  41. 41. Calculate averages over specific periods using the GCM scaled time-series</li></li></ul><li>StatisticalDownscaling<br />Disaggregation<br /><ul><li>Similar to the delta method, but does not use interpolation
  42. 42. Climate baseline: CRU, WorldClim
  43. 43. Calculate anomalies over periods in GCM cells
  44. 44. Sum anomalies to climate baseline</li></li></ul><li>DynamicalDownscaling<br />RCMs PRECIS<br /><ul><li>RCMs (Giorgi 1990)
  45. 45. Climate baseline: GCM boundary conditions
  46. 46. Develop complex numerical models to simulate climate behaviour
  47. 47. “Nest” the RCM into a coarse resolution model (GCM) and apply equations to re-model processes in a limited geographic domain
  48. 48. Resolution varies between 25-50km
  49. 49. Takes several months to process
  50. 50. Requires a new validation (on top of the GCM validation)</li></li></ul><li>Whichoneisthebest?<br />
  51. 51. CIAT Experience<br />
  52. 52. OurDatabases<br />Empiricallydownscaled, disaggregatedforthewholeglobe at 1km to 20km<br />Dinamicallydownscaled (PRECIS) for South America<br />20 GCM for 2050, 9 for 2020 (Stanford data) downscaled a 20km, 5km, 1km<br />7 GCMswithinformationTyndall<br />
  53. 53. Allwillbe at our portal (soon) http://ccafs-climate.org<br />
  54. 54. Reachingusersglobally<br /> http://ccafs-climate.org <br />
  55. 55. Capabilities and limitations<br />Our in-house capacity:<br />Four 8-core processing servers in a blade array under Windows (empirical downscaling)<br />Two 24-core and 1-8core processing servers in a blade array under Linux (PRECIS)<br />~100TB storage<br />Compresing and publishing data is a lengthy process and requires massive storage and network capacity (esp. 1km global datasets)<br />
  56. 56. In process..<br />Downscaling<br />Downscaled GCMs <br />7 periods for 63 models (≈ 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 models (≈ 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 />
  57. 57. In process..<br />PRECIS<br /><ul><li> Region: Andes
  58. 58. Resolution 50 km
  59. 59. Grid : 151 x 153</li></li></ul><li>In process..<br />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 />
  60. 60. In process..<br />
  61. 61. What´snext? ValidationGCMs<br />Ethiopia<br />TEMP. (JJA)<br />RAINFALL (JJA)<br />
  62. 62. What´snext? ValidationGCMs<br />GCM vs Stations<br />Post-processing<br />Formatconversion<br />HistogramsR2, RMSQ, RMSE, slope<br />R2 Vs. <br />Lat / Alt<br />Comparisonwithin situ data<br />Averages/<br />sums, monthly/<br />annual<br />RMSQ vs. Lat / Alt<br />RMSQ vs. <br />Lat / Alt<br />Cell maps<br />
  63. 63. What’snext?<br />Seiler 2009<br />
  64. 64. 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 />
  65. 65. What’snext?<br />MRI Validation<br />
  66. 66. MRI<br />What’snext?<br />
  67. 67. What’snext?<br />CCAFS Climate data strategy<br />Improve baseline data and metadata<br />Gather and process AR5 projections<br />Downscale with desired methods<br />Evaluate and assess uncertainties<br />Publish all datasets and results<br />Use the AMKN platform to link climate data, and modelling outputs<br />
  68. 68. 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 />
  69. 69. Gracias!<br />j.r.villegas@cgiar.org<br />c.e.navarro@cgiar.org<br />a.jarvis@cgiar.org<br />

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