Estimating climate variability over the next 1-25 years

Dr Scott PowerDr Scott Power

IOCI, August 2005IOCI, August 2005

Using history as a guide (for 2006-2024)

Probability Density Function of Perth Inflow (Glt)

0

0.2

0.4

0.6

0 100 200 300 400 500 600 700 800 900 1000

Inflow (Glt)

Rel

ativ

e F

req

1911-2001

1975-2001

1911-1974

1975-2001

Data: courtesy WA Water Corp

Can we use climate models to provide better PDFs?

Anomalies of averaged OZ rainfall and Nino4 SST for CPA later 40 years

-1.5

-1

-0.5

0

0.5

1

1.5

time (year)

OZ rain anom

Nino4 SSTa

Australian rainfall v. NINO4 SST in BMRC Climate ModelAustralian rainfall v. NINO4 SST in BMRC Climate Model

Models + data provide climate predictions for 6-12 months ahead. They exhibit some skill in predicting some things.

Models + data provide climate predictions for 6-12 months ahead. They exhibit some skill in predicting some things.

Using initial data can change PDFs (Probability Density Functions) if there is predictability

% Years, Apia Wet Season (NDJFM) Rainfall vs. JJASO SOI < -5, JJASO SOI > +5

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Rainfall

% Y

ears

Data: Courtesy Samoa Meteorology Division

Data: Courtesy Samoa Meteorology Division

A prediction as a change in

a PDF

Can we predict beyond 2006 years? BMRC CGCM (BMRC CGCM (Power et al. 1998)Power et al. 1998)

MOM OGCM - MOM OGCM - Pacanowski et al. 1991Pacanowski et al. 1991 L25, 2 deg by (0.5, 6 deg)L25, 2 deg by (0.5, 6 deg) hybrid mixing (ml, Ri); see Power et al. hybrid mixing (ml, Ri); see Power et al.

19951995 thermodynamic sea-icethermodynamic sea-ice R21 L17 “unified” AGCM - R21 L17 “unified” AGCM - Colman Colman

(2000)(2000) Colman 2000Colman 2000 spectral, Rotstayn (1999) prognostic clouds; spectral, Rotstayn (1999) prognostic clouds;

Tiedtke (1989) convection; GW drag (Palmer et Tiedtke (1989) convection; GW drag (Palmer et al. 1986); McAvaney & Hess (1996) BL schemeal. 1986); McAvaney & Hess (1996) BL scheme

Q, Sf flux adjustedQ, Sf flux adjusted

Climate models suggest that ENSO predictability is very limited beyond 1-2 years

Chaos limits predictabilityChaos limits predictability

Sensitivity of NINO4 index to small initial nudges Sensitivity of NINO4 index to small initial nudges

NINO4NINO4

Time (Years 1 to 4))Time (Years 1 to 4))

BMRC CGCM (Power et al. 1998)BMRC CGCM (Power et al. 1998)

Predictability beyond 2 years is present, e.g. off-equatorial, deep (310m) Pacific Ocean

<……………. 100 years ………….….><……………. 100 years ………….….> <……………. 100 years ………….….><……………. 100 years ………….….>

Deep Ocean Temperature

Deep Ocean Temperature

Off-Equatorial, Deep Pacific Ocean - highly predictable

<…………………….. 13 years …………...……….><…………………….. 13 years …………...……….> <…………………….. 13 years …………...……….><…………………….. 13 years …………...……….>

Exhibits predictability Exhibits predictability

Thermohaline Circulation

Power et al. (2005, in press)

Kick-starting forecasts with data

Sea-level Sea-level from from

satellitesatellite

Subsurface Ocean Subsurface Ocean TemperatureTemperature

Winds from Winds from satellitesatellite

Courtesy Neville Smith, BMRCCourtesy Neville Smith, BMRC

XBTs & moored XBTs & moored instrumentsinstruments

swWA Rainfall Anomaly (mm), June-JulyIPCC runs (C20C, A2 scenario), 11-yr ra

-120

-90

-60

-30

0

30

1900 1950 2000 2050 2100

Year

Rai

nfa

ll (m

m)

Relative Frequency of swWA Rainfall Anomalies, IPCC models, 1901-2000, June-July, A2 Scenario

0

5

10

15

20

25

30

35

-150 -100 -50 0 50 100 150

Rainfall Anomaly (mm)

Rel

Fre

qu

ency

(%

)1901-1974

1975-2000

2001-2025

IPCC model output courtesy Pandora Hope, BMRC

IPCC model output courtesy Pandora Hope, BMRC

A big step forward, but approach neglects

information about initial state of climate system

A big step forward, but approach neglects

information about initial state of climate system

Estimating future PDFs• Approach will borrow from Approach will borrow from

1)1) seasonal prediction e.g. initialisation, ensembles seasonal prediction e.g. initialisation, ensembles 2)2) climate change projections e.g. scenarios for climate change projections e.g. scenarios for

future CO2 emissions future CO2 emissions 3)3) strategic research on decadal predictabilitystrategic research on decadal predictability

• Challenging, strategic, resource intensiveChallenging, strategic, resource intensive• Improve models, secure obs networks Improve models, secure obs networks

• Requires closer collaboration between Requires closer collaboration between CSIRO, BureauCSIRO, Bureau

• ACCESS timely (& exciting possibility)ACCESS timely (& exciting possibility)

Seamless prediction

““Increasingly, decade- and Increasingly, decade- and century-long climate projection century-long climate projection will become an initial-value will become an initial-value problem requiring knowledge problem requiring knowledge of the current observed state of of the current observed state of the atmosphere, the oceans, the atmosphere, the oceans, cryosphere, and land surface to cryosphere, and land surface to produce the best climate produce the best climate projections as well as state-of-projections as well as state-of-the-art decadal and interannual the-art decadal and interannual predictions” (WCRP, 2005) predictions” (WCRP, 2005)

ACCESS Australian Climate Community Earth Australian Climate Community Earth

System SimulatorSystem Simulator New initiative in planning stagesNew initiative in planning stages Bureau, CSIRO, AGOBureau, CSIRO, AGO Universities, other agencies (federal and Universities, other agencies (federal and

state)state)

Thermohaline Circulation

Variability in model’s conveyor beltVariability in model’s conveyor belt

Variability in model’s Southern Ocean Temperature

Variability in model’s Southern Ocean Temperature

Using initial data can change PDFs (Probability Density Functions) if there is predictability

% Years, Apia Wet Season (NDJFM) Rainfall vs. JJASO SOI < -5, JJASO SOI > +5

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

Rainfall

% Y

ears

Data Courtesy Samoa Meteorology Division

Data Courtesy Samoa Meteorology Division

A prediction as a change in the PDF

Apia Wet Season (NDJFM) Rainfall (< 300mm, 300 - 400mm, > 400mm)

after JJASO SOI < -5

39%

46%

15%

Apia Wet Season (NDJFM) Rainfall(< 300mm, 300 - 400mm, > 400mm)

after JJASO SOI > +5

14%

39%

47%

Decadal changes in southern Indian Ocean linked with Africa

Decadal changes in Southern Indian Ocean linked with Australia (in Model)

Research Only!Research Only! Research Only!Research Only!

Courtesy: J Arblaster (NCAR/BMRC)Courtesy: J Arblaster (NCAR/BMRC)

Argo floats supply temperature, salinity, pressure, velocity information - a revolution in data acquisition

Courtesy Howard Freeland, Institute of Ocean Sciences, CANADA

Courtesy Howard Freeland, Institute of Ocean Sciences, CANADA

Caveat:

Decadal predictability arising from Initial Conditions might be substantial in some things (e.g. deep ocean) but low in variables of more significance to humans (e.g. rainfall over land)

Strategic research in this area continues

Provide realistic local information for Impact Studiesusing coarse information from Global Climate Models

Statistical Downscaling Techniques:

From BoM booklet: “The From BoM booklet: “The greenhouse effect and climate greenhouse effect and climate change”, 2004.change”, 2004.

Courtesy Bertrand Timbal, BMRCCourtesy Bertrand Timbal, BMRC

Coordinated Observation and Prediction of the Earth System, COPES

Aim:Aim:

To facilitate analysis and prediction of To facilitate analysis and prediction of Earth system variability and change for use Earth system variability and change for use in an increasing range of practical in an increasing range of practical applications of direct relevance, benefit and applications of direct relevance, benefit and value to societyvalue to society

Conveyor belt variability appears to precede (by 4 years) SST & possibly some Africa/Australia variability in BMRC CGCM

Courtesy CSIROCourtesy CSIRO

Climate Change

Projections can help

Climate Change

Projections can help

Estimating future• Approach will borrow fromApproach will borrow from

seasonal prediction (e.g. using data, ensembles)seasonal prediction (e.g. using data, ensembles)climate change projections (e.g. scenarios for future CO2 climate change projections (e.g. scenarios for future CO2

emissions)emissions)strategic research on decadal predictabilitystrategic research on decadal predictability

• Challenging, strategic, resource intensive Challenging, strategic, resource intensive

• Requires closer collaboration between CSIRO, Bureau & beyond Requires closer collaboration between CSIRO, Bureau & beyond – ACCESS– ACCESS

• Intermediate steps will be used, e.g. Intermediate steps will be used, e.g. selective/nudged climatologiesselective/nudged climatologiesuse existing climate change projectionsuse existing climate change projectionsstrategic research on decadal prediction strategic research on decadal prediction