Seasonal Forecasts in the Pacific Region using POAMA 1.5b Australia - - PowerPoint PPT Presentation

Pac ific Adaptatio n Strategy Assistanc e Pro gram

Seasonal Forecasts in the Pacific Region using POAMA 1.5b

Andrew Cottrill1, Eun‐Pa Lim1 and Harry Hendon1

1Bureau of Meteorology (CAWCR), Melbourne, Victoria.

Australia Greenhouse Conference 4‐8th April (2011) ‐ Cairns, Queensland, Australia.

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Outline of Presentation

• Location Map of the Fifteen Pacific Island Nations involved in

the PASAP project;

• Show the Seasonal Rainfall Patterns across the Tropical Pacific;
• Show some typical ENSO Patterns in Rainfall Composites at

three stations;

• Briefly describe the POAMA Seasonal Prediction System and

its Correlation to Tropical SSTs with various lead times;

• Show patterns of Hit Rates of Above Median Rainfall between

CMAP and POAMA over the equatorial Pacific;

• Show Calibration of Seasonal Rainfall at Tarawa as a technique

to improve seasonal forecast outlooks and

• Short Summary

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Pacific Island Countries and PASAP – Fifteen Partner Countries

Cook Islands East Timor Federated States of Micronesia Fiji Kiribati Niue Palau Papua New Guinea Republic of Marshall Islands Republic of Nauru Samoa Solomon Islands Tonga Tuvalu Vanuatu

Map: Yuri Kuleshov - BoM

PASAP – Pacific Adaptation Strategy Assistance Program. The PASAP project has been developed under the International Climate Change Adaptation Initiative to help Pacific Island Countries (PICs) prepare for climate change in coming decades. The PASAP project aims to deliver seasonal climate outlooks to PICs based on POAMA.

More information on PASAP can be found at the Department of Climate Change and Energy Efficiency (DCCEE).

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Summer Rainfall Autumn Rainfall Winter Rainfall Spring Rainfall

ITCZ ITCZ SPCZ SPCZ

CMAP =CPC Merged Analysis of Precipitation.

Units =mm/day

Shows the Mean State

• f

Seasonal Rainfall along the Equator (ITCZ) and the SPCZ in the southwest Pacific. The ITCZ migrates north and south with the change of seasons and the SPCZ migrates northeast and southwest depending

• n the state of ENSO.

Patterns of Seasonal Rainfall Across the Tropical Pacific Using Data from CMAP

CMAP Data: 1979-2006

SE Trades NE Trades

Indian and East Asian Monsoon Australian Monsoon

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ENSO Composites for Three PICs: Tarawa, Port Vila and Nadi Airport (1980‐2006)

Note: Composite Years (El Niño): = 1982, 1986, 1987, 1991, 1994, 1997, 2002 and 2004; and La Niña: 1984, 1988, 1998 and 1999. El Niño – High Rainfall All Seasons La Niña – Low Rainfall All Seasons El Niño – Lower Rainfall Spr, Sum, Aut La Niña – Higher Rainfall Spr, Sum, Aut El Niño – Low Rainfall All Seasons La Niña – High Rainfall All Seasons

Tarawa Port Vila Nadi Airport

La Niña (x4): El Niño (x8):

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Australia Community Ocean Model version 2 Ocean Atmosphere Sea Ice Soil (OASIS) coupler BAM3.0d

Spectral transform (T47L17)

• Consists
• f

the

• cean

model (ACOM2) and the atmospheric model BAM3

• It also has a new

Atmosphere Land Initialisation scheme

• r ALI
• The

Forecasts are based on 10 member ensemble hindcasts run

• ver

27 years (1980-2006)

The POAMA 1.5b Seasonal Prediction System

• Initially developed by the BMRC and the CSIRO

for the prediction of SST anomalies associated with ENSO over the Pacific.

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LT= 5 and 8 months LT= 0 and 2 months

POAMA Forecast of Tropical Pacific SST

Correlation

From Maggie Zhao - BoM

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Hit Rates of Above Median Rainfall using POAMA

• ver the Tropical Pacific Region

Observed:

Yes No Yes No

Forecast

Hits (A) False Alarms (B) Misses (C) Correct Rejection (D)

Hit Rate = A/(A+C)

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Hit Rates of Median Rainfall from POAMA and Observations (CMAP) for DJF

LT=0 LT=2 LT=4 LT=6

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Calibration of Seasonal Rainfall in Ensemble Forecasts using POAMA 1.5b

• The calibration method used here is described in detail in the

paper by Johnson and Bowler (2009) in Monthly Weather Review;

• It is known as the “variance inflation method” and is based on

two conditions;

• The technique adjusts the forecasts so the climatological

variance of the forecasts is the same as the observations and

• The correlation of observations with the unadjusted ensemble

mean is the same as the correlation of the adjusted ensemble members with the unadjusted ensemble mean.

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Calibration of Seasonal Rainfall at Tarawa – Kiribati Rainfall Anomaly with LT= 0

MAM JJA SON DJF

r=0.71 r=0.89 r=0.81 r=0.71

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Correlation Skill and Lead Times at 14 Pacific Island Stations

Stations: Nadi Airport, Suva, Rarawai, Nabouwalu, Rotuma, Port Vila, Tarawa, Funafuti, Apia, Nuku’alofa, Alofa, Honiara, Port Moresby, Rarotonga

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

RMSE Skill and Lead Times at 14 Pacific Island Stations

Stations: Nadi Airport, Suva, Rarawai, Nabouwalu, Rotuma, Port Vila, Tarawa, Funafuti, Apia, Nuku’alofa, Alofa, Honiara, Port Moresby, Rarotonga

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Summary

• Seasonal Rainfall over the PICs is mostly controlled by the ITCZ

and the SPCZ;

• Strong rainfall changes associated with the different phases of

ENSO over the tropical Pacific region provide coupled models, with the skill to produce seasonal forecasts with up to 6 months

• r more lead time;
• Hit Rates using POAMA1.5b are typically 60‐80% across the

equatorial Pacific and parts of the southwest Pacific;

• Calibration of seasonal rainfall will be used in seasonal

forecasting products, and is planned to compliment “SCOPIC”, which is currently used in many PICs .

• Models, like POAMA, have the ability to produce better season

forecasts than statistical models, as they can account for aspects

• f

climate change and climate variability not represented in the historical record.

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Acknowledgements:

The Bureau of Meteorology would like to thank all the PIC nations involved in the PASAP project for providing valuable rainfall data from a number of stations across the region, and to AUSAID, who provided the funds for this PASAP project. The PASAP website can be found at the following address http://poama.bom.gov.au/experimenttal/pasap Username: pasap Password:pacifica

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Spares

Pac ific Adaptatio n Strate gy Assistanc e Pro gram CMAP POAMA 1.5b

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

ENSO Composites for Three PICs: Tarawa, Port Vila and Nadi Airport (1980‐2006)

Note: Composite Years (El Niño): = 1982, 1986, 1987, 1991, 1994, 1997, 2002 and 2004; and La Niña: 1984, 1988, 1998 and 1999. El Niño – High Rainfall All Seasons La Niña – Low Rainfall All Seasons El Niño – Lower Rainfall Spr, Sum, Aut La Niña – Higher Rainfall Spr, Sum, Aut El Niño – Low Rainfall All Seasons La Niña – High Rainfall All Seasons

Tarawa Port Vila Nadi Airport

La Niña (x4): El Niño (x8):

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Seasonal Correlation between CMAP Rainfall and Reynolds SSTs Across the Tropical Pacific (1982‐2006)

SPCZ ITCZ ITCZ SPCZ SPCZ SPCZ

Correlation

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Typical ENSO SST Patterns – Warm and Cold Events

Modoki ‐ December 2009 ‘Classic’ El Niño ‐ Feb 1998 La Niña ‐ January 1999 La Niña ‐ December 2010

Images from: www.LongPaddock.qld.gov.au

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Seasonal Correlation of CMAP and POAMA Rain and Station Correlation to POAMA Rain

Summer LT=0 Autumn LT=0 Winter LT=0 Spring LT=0

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Hit Rates Plots of POAMA and CMAP (MAM) Above Median Rainfall

LT=0 LT=2 LT=4 LT=6

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Hit Rates Plots of POAMA and CMAP (JJA) Above Median Rainfall

LT=0 LT=2 LT=4 LT=6

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Hit Rates Plots of POAMA and CMAP (SON) Above Median Rainfall

LT=0 LT=2 LT=4 LT=6

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Reliability Diagrams - DJF

LT=0 LT=4 LT=2 LT=6

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Reliability Diagrams

Autumn Winter

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Reliability Diagrams

Spring Summer

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Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b at Nadi Airport-Fiji (LT= 0)

MAM JJA SON DJF

r=0.61 r=0.26 r=0.67 r=0.62

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b at Port Vila - Vanuatu (LT= 0)

MAM JJA SON DJF

r=0.53 r=0.51 r=0.65 r=0.59

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Nabouwalu - Fiji)

MAM JJA SON DJF

r=0.44 r=0.53 r=0.73 r=0.53

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Suva - Fiji)

MAM JJA SON DJF

r=0.26 r=0.40 r=0.37 r=0.06

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Rarawai - Fiji)

MAM JJA SON DJF

r=0.63 r=0.18 r=0.51 r=0.59

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Rotuma - Fiji)

MAM JJA SON DJF

r=0.37 r=0.42 r=0.05 r=0.30

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Alofi -Niue)

MAM JJA SON DJF

r=0.43 r=0.02 r=0.52 r=0.58

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Port Moresby)

MAM JJA SON DJF

r=0.46 r=0.22 r=0.68 r=0.11

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Apia - Samoa)

MAM JJA SON DJF

r=0.31 r=0.45 r=0.21 r=0.46

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Honiara - Solomons)

MAM JJA SON DJF

r=0.38 r=0.32 r=0.21 r=0.49

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Nuku’alofa - Tonga)

MAM JJA SON DJF

r=0.37 r=0.35 r=0.52 r=0.58

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Funafuti - Tuvalu)

MAM JJA SON DJF

r=0.09 r=0.53 r=0.40 r=0.34

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Calibration of Seasonal Rainfall to Pacific Island Stations using POAMA 1.5b (Rarotonga – Cook Is)

MAM JJA SON DJF

r=0.51 r=0.11 r=0.46 r=0.40

Pac ific Adaptatio n Strate gy Assistanc e Pro gram

Results of Calibration

• Seasonal forecasts are less emphatic;
• Reliability has improved at stations with moderate to

high correlation;

• Forecasts are near climatology when the correlation

is low (< ~0.20)

• Assumptions: Assumes the observations represent

normal distributions