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Statistical Projection of Global Climate Change Scenarios onto Hawaiian Rainfall

Statistical Projection of Global Climate Change Scenarios onto Hawaiian Rainfall. Oliver Timm, International Pacific Research Center, SOEST, University of Hawai'i at Manoa Henry Diaz, NOAA/ESRL/CIRES, Boulder, Colorado. Climate Change in the News.

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Statistical Projection of Global Climate Change Scenarios onto Hawaiian Rainfall

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  1. Statistical Projection of Global Climate Change Scenarios onto Hawaiian Rainfall Oliver Timm, International Pacific Research Center, SOEST, University of Hawai'i at Manoa Henry Diaz, NOAA/ESRL/CIRES, Boulder, Colorado

  2. Climate Change in the News • Hawaii researchers to look at effect of global warming on the islands,USA TODAY, Aug, 14, 2006 • UH to study how global warming affects isles,Star*Bulletin, Aug, 13, 2006 • Floods, hotter climate in Isles likely by 2090, Honolulu Advertiser, Feb., 25, 2007

  3. Presentation overview • Introduction • What is the present knowledge of Hawaii's rainfall changes during the 21st century? • Uncertainty in future climate change projections • The idea behind statistical downscaling • Results from the statistical downscaling • Connection between large-scale circulation changesand regional precipitation • Discussion & Outlook

  4. Introduction: What is the scientific information behind our present understanding of rainfall changes over Hawaii?

  5. CO2 emission 2000-2100 CO2 during the last 1000 years Introduction: Changes in atmospheric Greenhouse gas concentrations Uncertainty in the scenarios

  6. Introduction: Uncertainty in the anthropogenic climate forcing Changes in atmospheric Greenhouse gas concentrations CO2 concentrations 2000-2100

  7. CO2 concentrations 2000-2100 A1B Scenario: 2-4.5 deg C warming (3.6-8F) Introduction: Uncertainty in the global temperature increase Changes in atmospheric Greenhouse gas concentrations

  8. Greenhouse gas emission Dynamical or statistical downscaling methods Introduction: Uncertainties in regional projections of climate change

  9. IPCC's Fourth Assessment Report, 2007: (more than 20 climate models took part) • precipitation change: likely to decrease • but for Hawaii, no robust signals Introduction: Models show a drier climate No significant change Models show a wetter climate Most models: drier climate Models results inconsistent Most models: wetter climate

  10. Differences among climate models Greenhouse gas emission Dynamical or statistical downscaling methods Introduction: Uncertainties in regional projections of climate change

  11. Sampling (statistical) error Differences among climate models Greenhouse gas emission Dynamical or statistical downscaling methods Introduction: Uncertainties in regional projections of climate change

  12. Downscaling uncertainty Sampling (statistical) error Differences among climate models Greenhouse gas emission Dynamical or statistical downscaling methods Introduction: Introduction: Linkage between large-scaleand regional climate changes

  13. Ad hoc (unguided) downscaling uncertainty Introduction: Goal of downscaling procedure: Reducing the uncertainties of projected regional climate change Statistical/dynamical/expert information downscaling uncertainty

  14. Statistical, dynamical, and elaborated experts' estimates + Introduction: What is the scientific information behind our present understanding of rainfall changes over Hawaii?

  15. Regional downscaling projects: The Prediction of Regional scenarios and Uncertainties for Defining Euorpean Climate change risks and Effects (PRUDENCE) Their goal: Provide a dynamically downscaled scenario for Europe Huge project > 20 research groups!

  16. Key steps in downscaling procedure: • Investigating the physical links between Hawaiian rainfall andlarge-scale climate variability (diagnostic analysis) • Building a statistical transfer-model • Analysing the IPCC models (model analysis) • Comparison models' 20th century simulations with observations • Identification of circulation changes around Hawaii • Robustness of the projected changes • Application of the statistical transfer-model to the IPCC scenarios(Statistical downscaling)

  17. Mean surface pressure pattern during the wet season (Nov-Apr), 1970-2000 Results: ERA-40 Prevailing NE trade winds with showers on the windward sites H L Data ERA-40 data avaiable at IPRC's Asia-Pacific Data-Research Center http://apdrc.soest.hawaii.edu/

  18. Previous diagnostic climate studies of Hawaiian Rainfall Results: Dry minus wet composite Strong dependence on El Nino-Southern Oscillation and the Pacific Decadal Oscillation (P.-S. Chu and Chen, Journal of Climate, 18,4796- 4813, 2007) El Nino/+PDO minus La Nina/-PDO Models project more La Nina and more El Nino-like tropical Pacific climate G.A. Vecchi, A. Clement, B.J. Sodon, EOS,89(9),81-82,2008

  19. Months with high/low precipitation in Hilo site of Big Island (region #5) [ERA-40 sea level pressure, Nov-Apr, 1970-2000 Results: High Preciptation Low Preciptation H H

  20. Results: 2) Developing a statistical transfer model: Hawaiian Rainfall as a function of large-scale circulation changes

  21. Selection of circulation pattern associated with rainfall variability over the Hawaiian Islands Results: Linear regression of surface wind field onto regional rainfall [ERA-40, 1000 hPa winds, Nov-Apr, 1970-2000, n=186] ‘Trade Wind’ pattern ‘Kona Low’ pattern

  22. Selection of circulation pattern associated with rainfall variability over the Hawaiian Islands Results: Maximum Covariance Analysis of surface wind field and the regional rainfall

  23. Selection of circulation pattern associated with rainfall variability over the Hawaiian Islands Results: Maximum Covariance Analysis of sea level pressure and the regional rainfall For region (#5)

  24. Statistical transfer-model projects circulation anomaly onto the 'template' => rainfall projection index Results: Observed sea level pressure anomaly in year t < SLP(t) , E > y(t)

  25. Results: 2) How well do the IPCC models reproduce the natural variability? - Mean sea level pressure fields - Decompostion of the interannual sea level pressure variability into its dominant modes (Principal Component Analysis) [ERA-40, Nov-Apr, 1970-2000, region 10S-40N/180W-120W]

  26. Blue low pressure Orange high pressure [ERA-40 reanalysis 1970-2000] Control simulation model #18 Control simulation model #15 Analysis of IPCC models Results: Comparison of the observed mean sea level pressure field (wet season) with control simulations of the IPCC models

  27. Results: Dominant pattern of observed sea level pressure variability (1970-2000, winter seasons) ERA-40 Anomalies (with respect to a climatological mean)

  28. Results: Dominant pattern of observed sea level pressure variability (control simulation, 1970-2000, wet season) Model #16

  29. Results: Dominant pattern of observed sea level pressure variability (control simulation, 1970-2000, wet season) Model #18

  30. Results: Dominant pattern of observed sea level pressure variability (control simulation) Model #22

  31. Finding objective criterions to select the ‘most reliable’ models Similarity of the dominant climate variability pattern: Observation vs control simulation. EOF pattern 1-10 in observation EOF pattern 1-10 in simulation EOF pattern 1-10 in simulation Results: Model #18 Model #22 0 correlation 1

  32. Changes in the mean sea level pressure 2061-2099 – Control simulation Results: Model #1 Model #28 Model #30 Model #38 Model #40 Model #53

  33. Results: 4) Application of the transfer modeldownscaled projection of rainfall changes

  34. Statistical transfer-model projects circulation changes onto the 'template' => rainfall projection index Results: Sea level pressure anomaly (SLPA) 2061-2099 Projection template pattern (E) for Hilo area rainfall (wet season) < SLPA , E > y

  35. Preliminary results for the Hilo area on the Big Island • Projected changes in the wet season • (November-April) mean rainfall: • 1 inch/month more rainfall • large spread among models

  36. Summary • rainfall in different Hawaiian regions are connected different large-scale circulation pattern (‘Trade wind’, ’Kona Low’ pattern) • Statistical downscaling of sea level pressure allows first estimates for rainfall changes • On average, small positive rainfall changes are associated with trade wind changes • IPCC model uncertainty for Hawaii region is very large=> downscaled uncertainty is also very large.

  37. Future Research/Improvements • Refining the regional structure of our diagnostic studies • Including other large-scale circulation information to improve the statistical transfer model (e.g. wind field, stratification of the lower atmosphere) • Using model-weighted ensemble averages • Investigating changes in the extreme precipitation (using daily data, instead of monthly /seasonal means) • Developing spatial maps of rainfall changes with confidence intervals.

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