Statistical Projection of Global

Climate Change Scenarios onto

Hawaiian RainfallOliver 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 21st 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

CO2 during the last 1000 years

CO2 emission 2000-2100

Uncertainty in the scenariosUncertainty in the scenarios

Introduction:

Changes in atmospheric

Greenhouse gas concentrations

Uncertainty in the anthropogenic

climate forcing

Uncertainty in the anthropogenic

climate forcing

CO2 concentrations 2000-2100

CO2 concentrations 2000-2100

A1B Scenario:

2-4.5 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 methodsGreenhouse

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 signalsModels show a drier

climate

Models results inconsistentMost models: drier climateMost models: wetter

climate

No significant changeModels show a wetter

climate

Introduction:

Uncertainties in regional projections of climate change

Introduction:

Dynamical or statistical

downscaling methodsGreenhouse

gas emission

Differences among

climate models

Uncertainties in regional projections of climate change

Introduction:

Dynamical or statistical

downscaling methodsGreenhouse

gas emission

Differences among

climate models

Sampling

(statistical) error

Linkage between large-scale

and regional climate changes

Introduction:

Dynamical or statistical

downscaling methodsGreenhouse

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' 20th 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), 1970-2000

Data ERA-40 data avaiable at IPRC's

Asia-Pacific Data-Research Center

http://apdrc.soest.hawaii.edu/

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, 18,4796- 4813, 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, 1970-2000

Results:

High Preciptation Low Preciptation

2) Developing a statistical transfer model:

Hawaiian Rainfall as a function of large-

scale circulation changes

Results:

Results:

Linear regression of surface wind field onto regional rainfall

[ERA-40, 1000 hPa winds, Nov-Apr, 1970-2000, 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

< SLP(t) , E > 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, 1970-2000, region 10S-40N/180W-120W]

[ERA-40 reanalysis 1970-2000]

Control simulation model #18 Control 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 (1970-2000, winter seasons)

Anomalies (with respect to a climatological mean)

Results:

Model #16

Dominant pattern of observed sea level pressure

variability (control simulation, 1970-2000, wet season)

Results:

Model #18

Dominant pattern of observed sea level pressure

variability (control simulation, 1970-2000, 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 simulationEO

F p

atte

rn 1

-10

in o

bse

rva

tion

Model #18 Model #22

Results:

Model #1 Model #28 Model #30

Model #38 Model #40 Model #53

Changes in the mean sea level pressure

2061-2099 – Control simulation

4) Application of the transfer model

downscaled projection of rainfall changes

Results:

Results: Statistical transfer-model projects circulation

changes onto the 'template'

=> rainfall projection index

Sea level pressure

anomaly (SLPA) 2061-2099

< SLPA , E > 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.