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 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

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

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

Introduction: Changes in atmospheric Greenhouse gas concentrations CO 2 during the last 1000 years CO 2 emission Uncertainty in the scenarios

Introduction: Changes in atmospheric Greenhouse gas concentrations Uncertainty in the anthropogenic climate forcing Uncertainty in the anthropogenic climate forcing CO2 concentrations

A1B Scenario: deg C warming (3.6-8F) Introduction: Uncertainty in the global temperature increase Uncertainty in the global temperature increase Changes in atmospheric Greenhouse gas concentrations

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

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

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

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

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

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

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

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!

Key steps in downscaling procedure: 1) Investigating the physical links between Hawaiian rainfall and large-scale climate variability (diagnostic analysis) 2) Building a statistical transfer-model 3) Analysing the IPCC models (model analysis) a) Comparison models' 20 th century simulations with observations b) Identification of circulation changes around Hawaii c) Robustness of the projected changes 4) Application of the statistical transfer-model to the IPCC scenarios (Statistical downscaling)

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

Results: Previous diagnostic climate studies of Hawaiian Rainfall Strong dependence on El Nino-Southern Oscillation and the Pacific Decadal Oscillation (P.-S. Chu and Chen, Journal of Climate, 18, , 2007) 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 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 Dry minus wet composite El Nino/+PDO minus La Nina/-PDO

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

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

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

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

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

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

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

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

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

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

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

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

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

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

4) Application of the transfer model downscaled projection of rainfall changes Results:

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

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

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.

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.