Near-term climate prediction: new opportunities and challenges

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1 Near-term climate prediction: new opportunities and challenges Noel Keenlyside Geophysical Institute, University of Bergen Jin Ba, Jennifer Mecking, and Nour-Eddine Omrani

2 Atlantic multi-decadal variability in SST drives shifts in African and Indian summer monsoons Precipitation: regression onto AMV index EASM may be similarly influenced by SST in the Atlantic and Pacific Zhang & Delworth (2006)

3 What is the potential to predict the decadal shifts in the shifts global in SST? monsoons? Near-term prediction initial/boundary value problem Some promising results from CMIP5 Challenges Summary

4 Multidecadal temperature fluctuations: Internal versus externally driven?

5 Internal variability large at decadal/regional scales External dominates on centennial Surface temperature trends ( C/10 yr) ( C/30 yr) Observed PDV AMV IO warming IPCC AR4 (CMIP3) Keenlyside & Ba, 2010

6 Models simulate similar decadal variability independent of external forces Kiel Climate Model, preindustrial control simulation Atlantic multi-decadal variability index Regression pattern Courtesy Jin Ba 1000 Model year Similarly for PDV, and various other modes

7 Externally driven climate projections Prediction uncertainty: scenario, model, and internal Surface temperature projections from 15 climate models Near-term Model scenario Internal Hawkins and Sutton, BAMS, 2009

8 Near-term surface temperature prediction: model and initial condition uncertainty large Relative importance computed from CMIP3 models Hawkins and Sutton, BAMS, 2009 First decade, scenario uncertainty ~10% at global scale Internal variability contributes more on regional scales Hawkins and Sutton, BAMS, 2009

9 Near-term prediction is an initial/boundary value problem How much skill is there? Focus here is on SST

10 IPCC AR5 near-term predictions

11 CMIP5 initialised predictions of global mean surface temperature (Annual mean) Anomaly initialised Full field Kim et al. 2012

12 Skill of CMIP5 initialized near-term forecasts Indian Ocean and western Pacific North Atlantic Guemas et al. 2012

13 CMIP5: Indian Ocean skill is due to external forcing driving a long-term trend Correlation skill in predicting SST Initialised forecasts Radiative forced only Years 2-5 Years 6-9 Guemas et al. 2012

14 AMV predictable up to 5 years in advance Kim et al. 2012

15 North Atlantic subpolar gyre very predictable Bias corrected Raw CCSM4 CCSM4 predictions predictions of SPG of SPG heat heat content content anomalies anomalies 10 year long; 10 members Budget analysis shows predictability comes from ocean circulation changes Yeager et al. 2012

16 Prediction of Pacific Decadal Variability possible a few years in advance CCCma predictions of PDO index Every five years from 1961, 10 year long; 10 members Anomaly correlation Fabian Leinert PhD thesis Lead-time (years, running average)

17 Not much multi-year predictability for PDO Kim et al. 2012

18 Challenges to near-term climate prediction: an illustration from the Atlantic Model uncertainty Ocean initial conditions uncertain

19 AMV: ocean circulation, not turbulent heat-flux Models indicate Atlantic MOC drives AMV CMIP3 pre-industrial control simulations Meridional overturning circulation (MOC) Courtesy: Jin Ba

20 AMV may also be driven by external forcing Booth et al. 2012

21 Large uncertainty model projections of AMOC AMOC at 30N, CMIP3 models, 20C/A1B Schmittner et al. (2005)

22 Uncertainties in internal variability Spectra of AMOC 30N CMIP3 Pre-industrial Runs BCM IPSL ECEARTH MPIOM MPI MIUB CCSM3 GFDL MIROC CSIRO INMCM

23 Uncertainties in initial conditions Keenlyside & Ba (2010)

24 Ocean-atmosphere coupling: Potential role of stratosphere Observed SST anomaly NCEP/NCAR 1000hPa GPH anomaly Observed anomalies Lower stratosphere resolving Entire stratosphere resolving Simulated (ECHAM5)resp onse to Atlantic SST hPa GPH Omrani et al., submitted

25 What is the potential to predict the decadal shifts in the climate system? Some initial success especially in the N. Atlantic internal variability Indian/Western Pacific external forcing Major challenges exist: Model and initial conditions uncertain large We are in a highly experimental stage

26 Decadal Forecast Exchange Doug Smith and Adam Scaife, UKMO-Hadley Uni. Tokyo Kimoto Masahide MRI Masayoshi Ishii SMHI Klaus Wyser,Colin Jones KNMI Wilco Hazeleger, Bert Wouters IC3 Francisco Doblas-Reyes, Virginie Guemas MPI Daniela Matei, Wolfgang Muller RSMAS Ben Kirtman CCCMA George Boer, Bill Merryfield UKMO-Hadley Doug Smith, Adam Scaife NRL Judith Lean, David Rind NOAA Arun Kumar Crown copyright Met Office

27 We are exchanging very basic quantities: Please Global contact: Annual Mean Temperature One to file contribute for each your year, forecasts each member Exchanged once per year around November Equal ownership Example diagnostics: Crown copyright Met Office

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29 Ocean driven by stochastic atmospheric variability Ocean model simulation driven by stochastic NAO forcing Wavelet spectrum: AMV : North Atlantic average SST Period (years) Time (years) Courtesy: Jenny Mecking

30 Quantifying uncertainties in decadal AMOC change Relative uncertainty (CMIP3) Atlantic Meridional Overturning Circulation at 30N Courtesy Annika Reintges