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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