Download Dr. Scott Power, BMRC - Indian Ocean Climate Initiative

Estimating climate variability
over the next 1-25 years
Dr Scott Power
IOCI, August 2005
Using history as a guide (for 2006-2024)
Probability Density Function of Perth Inflow (Glt)
Relative Freq
0.6
0.4
1911-1974
1911-2001
1975-2001
1975-2001
0.2
0
0
100
200
300
400
500
600
700
800
900
1000
Inflow (Glt)
Data: courtesy WA Water Corp
Can we use climate models to
provide better PDFs?
Australian rainfall v. NINO4 SST in
BMRC Climate Model
Models + data provide
climate predictions for 612 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
35
30
A prediction
as a change in
a PDF
% Years
25
20
15
10
5
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Rainfall
14
15
16
17
18
19
20
21
22
Data: Courtesy Samoa
Meteorology Division
23
Can we predict beyond 2006 years?

BMRC CGCM (Power et al. 1998)

MOM OGCM - Pacanowski et al. 1991


L25, 2 deg by (0.5, 6 deg)
hybrid mixing (ml, Ri); see Power et
al. 1995
thermodynamic sea-ice
 R21 L17 “unified” AGCM - Colman

(2000)
 Colman 2000
 spectral, Rotstayn (1999) prognostic clouds;
Tiedtke (1989) convection; GW drag (Palmer
et al. 1986); McAvaney & Hess (1996) BL
scheme

Q, Sf flux adjusted
Climate models suggest that ENSO
predictability is very limited beyond 1-2
years
Sensitivity of NINO4 index to small initial nudges
NINO4
Chaos limits predictability
Time (Years 1 to 4))
BMRC CGCM (Power et al. 1998)
Predictability beyond 2 years is
present, e.g. off-equatorial, deep
(310m) Pacific Ocean
Deep Ocean
Temperature
<……………. 100 years ………….….>
Off-Equatorial, Deep Pacific
Ocean - highly predictable
Exhibits predictability
<…………………….. 13 years …………...……….>
Thermohaline Circulation
Power et al. (2005, in press)
Kick-starting forecasts with data
Subsurface Ocean
Temperature
Sea-level
from
satellite
Winds from
satellite
Courtesy Neville Smith, BMRC
XBTs & moored
instruments
swWA Rainfall Anomaly (mm), June-July
IPCC runs (C20C, A2 scenario), 11-yr ra
A big step forward, but
approach neglects
information about initial
state of climate system
30
-30
-60
-90
-120
1900
1950
2000
2050
2100
Year
Relative Frequency of swWA Rainfall Anomalies,
IPCC models, 1901-2000, June-July, A2 Scenario
35
30
1901-1974
1975-2000
2001-2025
25
IPCC model output courtesy
Pandora Hope, BMRC
Rel Frequency (%)
Rainfall (mm)
0
20
15
10
5
0
-150
-100
-50
0
Rainfall Anom aly (m m )
50
100
150
Estimating future PDFs
•
Approach will borrow from
1)
2)
3)
seasonal prediction e.g. initialisation, ensembles
climate change projections e.g. scenarios for
future CO2 emissions
strategic research on decadal predictability
•
Challenging, strategic, resource intensive
•
Improve models, secure obs networks
•
Requires closer collaboration between
CSIRO, Bureau
•
ACCESS timely (& exciting possibility)
Seamless prediction
“Increasingly, decade- and
century-long climate
projection will become an
initial-value problem
requiring knowledge of the
current observed state of the
atmosphere, the oceans,
cryosphere, and land
surface to produce the best
climate projections as well
as state-of-the-art decadal
and interannual predictions”
(WCRP, 2005)
ACCESS

Australian Climate Community Earth
System Simulator
 New initiative in planning stages
 Bureau, CSIRO, AGO
 Universities, other agencies (federal and
state)
Thermohaline Circulation
Variability in model’s conveyor belt
Variability in model’s Southern Ocean
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
Apia Wet Season (NDJFM) Rainfall
(< 300mm, 300 - 400mm, > 400mm)
after JJASO SOI > +5
35
A prediction as a
change in the PDF
30
% Years
25
14%
47%
39%
20
Apia Wet Season (NDJFM) Rainfall
(< 300mm, 300 - 400mm, > 400mm)
after JJASO SOI < -5
15
10
15%
39%
5
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Rainfall
14
15
16
17
46%
18
19
20
21
22
Data Courtesy Samoa
Meteorology Division
23
Decadal changes in southern Indian
Ocean linked with Africa
Decadal changes in Southern Indian
Ocean linked with Australia (in
Model)

Research Only!
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
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
Statistical Downscaling Techniques:
Provide realistic local information for Impact Studies
using coarse information from Global Climate Models
From BoM booklet: “The
greenhouse effect and climate
change”, 2004.
Courtesy Bertrand Timbal, BMRC
Coordinated Observation and
Prediction of the Earth System,
COPES
Aim:
To facilitate analysis and prediction of Earth
system variability and change for use in an
increasing range of practical applications of
direct relevance, benefit and value to
society
Conveyor belt variability appears to precede
(by 4 years) SST & possibly some
Africa/Australia variability in BMRC CGCM
mate
ange
ections
n help
Courtesy CSIRO
Estimating future
•
Approach will borrow from
seasonal prediction (e.g. using data, ensembles)
climate change projections (e.g. scenarios for future CO2
emissions)
strategic research on decadal predictability
•
Challenging, strategic, resource intensive
•
Requires closer collaboration between CSIRO, Bureau & beyond
– ACCESS
•
Intermediate steps will be used, e.g.
selective/nudged climatologies
use existing climate change projections
strategic research on decadal prediction