Seasonal Climate Prediction

1. Seasonal Climate Prediction Youmin Tang Environmental Science and Engineering, University of Northern British Columbia

2. Contents • Introduction • Basic theory and methods for the dynamical climate prediction system • International activities on the seasonal climate prediction • Seasonal prediction in Canada • Climate prediction in UNBC • Future Challenges

3. Three types of predictions • Weather Prediction • Prediction of long-term climate change • Seasonal Prediction

4. Prognostic & diagnostic Eq. u, v, T, S w, p,

6. Differences • NWP: time evolution of the exact state of the atmosphere • Long-term prediction: gross features of a changed climate averaged over many years • Seasonal Predictions: describe statistic aspects of atmospheric anomalies over 1-3 months

7. Weather prediction • Goal: to forecast the exact state of the atmosphere from initial conditions, at high time resolution over several days. • Combination of statistical technique, experience, and intuition, in the final stage of most forecast. • Mid-latitude, the limit predictability is usually considered to be 10-14 days (Lorenz, 1982).

8. Weather prediction • Predictability arises solely from internal atmospheric dynamics • Accurate atmospheric initial conditions • Smaller errors in the initial state can grow rapidly and lead to a poor forecast even with a perfect model • Slightly different initial conditions are used for ensemble forecast • Slowly evolving lower boundary conditions are often assumed to be constant

9. Prediction of long-term climate change • Goal: to characterize changes in the long-term mean atmospheric and oceanic circulation and especially to characterize mean changes at the earth’s surface • Tools: a coupled atmosphere-ocean-land-ice model --- Climate system model • Concern with gross features of a changed climate averaged over many years

10. Seasonal Prediction • Focus fairly qualitatively on a few key climate variables • Surface temperature • Precipitation • Distinct from both NWP and Climate Simulation in three aspects: • In Purpose • In Approaches • In timescale

11. The New Challenge: Linking Climate to Weather Climate is traditionally viewed as the integration of discrete weather events and variables over time and space

12. tourism agriculture Why Seasonal Prediction •Growing demand for reliable seasonal forecasts energy

13. Venezuela Germany India

16. Use of better Drought forecasts to Improved Dam and hydropower management for hydroelectric power generation has enabled the domestic and manufacturing industries in Kenya and Tanzania to run at optimum capacity

17. Economic Loss caused by 1982/1983 El Nino Event

18. Predictability of seasonal prediction Obs. Internal chaotic Process Theoretical limit Method (model) Quality of IC & BC Potential predictability Actual predictability In climate prediction, Potential predictability is usually regarded as the predictability with full information of future boundary condition (e.g., SST). Thus, predictability is varied with similarity between the response of real atmosphere and prediction method to the same BC. From Prof. In-Sik kang

19. Why --- limit of Predictability • The limitation of predictability arises from • The imperfections of the forecast model • The nonlinearity of the climate system • The predictive skill depends on: • The field considered • The model used for forecast • The initial state of the system

20. Limitation of Predictability • Any statement about the predictive skill should include the word “for this model” • Predictive skill should be calculated for a large number of situations in order to make the most general statement possible

21. Methods for seasonal prediction • Statistical Method • Dynamical Method • Hybrid Method

22. Statistical Methods for seasonal prediction • Since the beginning of the twentieth century (e.g., Quayle, 1929) • Based on historical data and employ a mathematical relationship between predicted and predictor variables. • SST anomalies (especially over the tropical Pacific Ocean) are the sole predictor for the statistical forecasts of seasonal climate anomalies (e.g., Folland et al., 1991; Ward and Folland, 1991; Barnston, 1994)

23. Statistical Methods for seasonal prediction • Regression approaches • e.g., Knaff and Landsea, 1997 • Canonical correlation analysis (CCA) • e.g., Barnston and Ropelewski, 1992 • Neural network models • e.g., Sahai et al., 2000; Tanggang et al, 1998, Tang et al. 2001; 2002; 2003. ……

24. Statistical Methods for seasonal prediction Limitations： • require a long and accurate data of the earth’s climate • require an understanding of the physically based relationships between predicted and predictor variables • Unstable relationship (e.g. Krishna Kumar, 1999 ) • No physics

25. Dynamical Methods for seasonal prediction • Since late 80’s last century • Based on mathematical representation of physical laws governing the behavior of the atmosphere or the coupled atmosphere-ocean system. • Be able to estimate uncertainty of prediction through Ensemble prediction. • High potential to improve skill in the future

26. Seasonal-to-interannual prediction • The Basis for all predictions at timescales longer than a month is the hypothesis that, on these timescales, the atmospheric statistics are in equilibrium with the surface boundary conditions • A prediction of Surface Boundary conditions will lead to some statistical knowledge of the atmosphere

27. Seasonal-to-interannual prediction • Slowly varying boundary conditions, impose a slow variation of atmospheric statistics • Sea surface temperature • Soil moisture • Sea ice extent • Surface albedo • ……

28. Boundary conditions • Strong interact with the atmosphere: • Soil moisture --- rainfall & evaporation • Albedo --- Snow and ice extent • SST --- fluxes of heat and momentum from atmosphere • Evolve with their own dynamics A climate system model is needed

33. Model SSTA Climato-logy ENSEMbLE Predict SSTA SST ENSO prediction 合成海 温异常 Ensemble SSTA AGCM Observed SSTA Initial condition 互联网 ECMWF STEP-II STEP-I Operational prediction－Two tiers

34. Ensemble Prediction • The Ensemble prediction simulates possible initial uncertainties by adding, to the original analysis, small perturbations within the limits of uncertainty of the analysis. From these alternative analyses, a number of alternative forecasts are produced

35. Ensemble runs How to optimally perturb system? • The model dimensionality is large, typically 10^6. • We must perturb the system wisely such that we can use affordable perturbation members for ensemble predictions. • We need to find the optimal perturbation patterns singular vectors or breeding vectors of the linearized operator of the original system.

37. The growth (forecast error) of perturbation (or initial error) in the time interval can be expressed in the form:

39. The vector of the small perturbation that maximizes is the first eigenvector of ,i.e, the singular vector of A. where A* is the ad joint operator of A.

40. International Research Activities on the dynamical Seasonal prediction

41. Prediction and predictability

42. International Projects • CLIVAR (Study of CLImate VARiability and Predictability) • SMIP ( Seasonal Prediction Model Intercomparison Project --- Phase I & Phase II ) • NSIPP (NASA Seasonal to Interannual Prediction Project) • PROVOST (PRediction Of Climate Variation On Seasonal to interannual Time scale) • DEMETER（Development of a European Multimodel Ensemble system for Seasonal to interannual prediction) • APCN Multi-model Ensemble Project • ……

43.  Development of a European Multi-Model Ensemble System for Seasonal to Interannual Prediction （http://www.ecmwf.int/research/demeter/general/index.html)

44. •DEMETER system: 6 coupled global circulation models Multi-Model Ensemble System 9 member ensembles ERA-40 initial conditions SST and wind perturbations 4 start dates per year 6 months hindcasts •Hindcast production for: 1987-1998 (1958-2001)

45. APCN Multi-Model Ensemble System

46. Participating Models

47. Research Institutions • International Research Institute for Climate Prediction (IRI) (Initiated in 1994) • Climate Prediction Center(CPC/NCEP) • ECMWF (Initiated in 1995) • UK Met office (Initiated in 1987, Ward and Folland, 1991)) • CCCma (Canada) • RPN (Canada) • BMRC (Australia) • Experimental climate prediction center(ECPC), Scripps Institute of Oceanography • Korea Meteorology Administration (KMA) • Japan Meteorology Administration (JMA) ……

48. Seasonal Prediction in Canada • Since September 1995, the Canadian Meteorological Centre has been producing 0-3 month outlooks for Canada. • The seasonal forecast results from an ensemble of 12 model runs: 6 runs from a Global Environmental Multiscale model (GEM) of RPN, that has a horizontal resolution of 1.875 degrees with 50 vertical levels, and 6 runs from a Climate model (GCM2) of the CCCma.

49. Surface Air Temperature Forecast • an average of the daily temperature as predicted by the models. • The climatologies of the models are then subtracted from the mean forecast seasonal temperatures to derived the forecast anomalies of each model. The anomalies of the two models are then normalized and combined using an arithmetic average. • The anomalies are divided in three categories (above, near and below the normal).

50. Precipitation Forecast • The forecasts are made using the total accumulated water precipitation over the season. The precipitation predicted is the total liquid and includes all types: snow, rain, ice pellets, etc. The climatology of the models is subtracted from the total precipitation forecast to derive the anomalies. The anomalies of the two models are then combined using a simple normalized average. Finally the precipitation anomalies are divided in three categories (above, near and below the normal) as is done for the temperature anomaly forecast.