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Exploring the Possibility to Forecast Annual Mean Temperature with IPCC and AMIP Runs

Exploring the Possibility to Forecast Annual Mean Temperature with IPCC and AMIP Runs. Peitao Peng Arun Kumar CPC/NCEP/NWS/NOAA. Acknowledgements: Bhaskar Jha and Yun Fan. Background: Warming Temperatures. Annual Mean/Global Mean Land Temperature (Hoerling et al, 2008).

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Exploring the Possibility to Forecast Annual Mean Temperature with IPCC and AMIP Runs

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  1. Exploring the Possibility to Forecast Annual Mean Temperature with IPCC and AMIP Runs Peitao Peng Arun Kumar CPC/NCEP/NWS/NOAA Acknowledgements: Bhaskar Jha and Yun Fan

  2. Background: Warming Temperatures Annual Mean/Global Mean Land Temperature (Hoerling et al, 2008)

  3. Background: Warming Temperatures • GISS • GHCN (NCDC) • Reynolds merged land/sea • Reanalysis • CRU Hoerling et al., 2008: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. CCSP SAP1.3

  4. Background: Warming Temperatures

  5. Background: How is Warming Trend Affecting S/IPredictions? OBS Forecast Contingency Table for a performance evaluation of CPC 1-month lead seasonal temp forecast ; 168 seasons over 1995-2008 period and 232 grid points over CONUS give 38976 cases. About 50% are above normal in OBS, 60% of them are correctly forecasted About 70% skill are from warm cases.

  6. Background: How is Warming Trend Affecting S/I Predictions? Cold Bias due to the underestimated GHG Fixed GHG content of 1988 Cai et al. 2009: The role of long-term trend in seasonal predictions: Implications of global warming in the NCEP CFS. Weather and Forecasting

  7. Objectives • Use available model simulations to understand the predictability and prediction skill of land temperatures; • Examine the predictability and prediction skill of trends in land temperature;

  8. Procedures of the Study • Examine the performance of IPCC and AMIP data in simulating land temperature variations, in particular the warming trend; • Make empirical hindcasts with IPCC and AMIP data; • Compare the model data based empirical hindcasts with that based on analysis data.

  9. Data Sets • AR4 CMIP3 • Climate of 20th Century (with the observed evolution of external forcing; 1880-1999) • Special Emission Scenario (SRES) A1B (2000-2008) • 48 simulations from 22 different models • AMIP • Forced with SSTs • Ensemble of simulations • Three AGCMs (1950-2008) • OBS • CPC Merged GHCN-CAMS analysis (Fan and Huug) (1949-current) Data of their common period (1950-2008) are used for our analysis

  10. Empirical Methods in Hindcast: • Persistent: one year persistent extension of bias corrected model data; • Sloped Extension: make a linear regression over the latest n year model data, then do a sloped extension to the target year. • Flat Extension (OCN): take the latest n (n>1) year average as the forecast for the target year Note: • Flat extension is only applied to analysis data and the result will be used as a bench mark to evaluate other methods applied to model data. • Optimal n corresponds to the highest forecast skill over the verification period 1970-2008

  11. Annual and Global Mean Land Temperatures: OBS vs Models Total Quantities Anomalies w.r.t. 50-69 climate

  12. Simulation Skill of CIMP3 and AMIP for Land Temp Indices ACC RMS AMIP is superior to CMIP3 in simulation

  13. Annual and Global Mean Land Temperature Forecasts with CMIP3 CMIP3 based forecast has the almost the same skill as the analysis based OCN

  14. Annual and Global Mean Land Temperature Forecast with AMIP The skill of AMIP based forecast is lower than the OCN

  15. Forecast Skill Comparison ACC RMS

  16. Simulation Skill: CIMP vs AMIP CMIP3 AMIP

  17. Forecast Skill: CMIP3 Persistent vs OCN CMIP3 Persistent OCN

  18. Forecast Skill: Sloped Extension of CMIP3 vs OCN Sloped Ext of CMIP3 OCN

  19. Summary and Future Work • Warming trend is an important source of CPC S/I forecast skill in last couple of decades; • CMIP3 well catches the warming trend of land temperature in observations; • Empirical extension of CMIP3 data has a potential to provide compatible or better forecast than the flat extension of observational data (OCN) for annual mean temperature. • AMIP runs are generally better than CMIP3 runs in simulation, but their empirical extension is not as good as that of CMIP3 in forecast due to bigger “noise”. • The analysis will be extended to seasonal mean and longer lead forecast, and a further study will be toward a statistical-dynamical tool to project warming trend and other LF components onto S/I forecast.

  20. Optimal Window length

  21. Background: How is Warming Trend Affecting SI Predictions? Official CPC Sfc. Temp Forecasts

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