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Predictability of summertime North American precipitation

Predictability of summertime North American precipitation. Gilbert P. Compo and Prashant D. Sardeshmukh NOAA-CIRES Climate Diagnostics Center. To what extent is the actual precipitation skill smaller than the expected skill?. Data for June-August.

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Predictability of summertime North American precipitation

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  1. Predictability of summertime North American precipitation Gilbert P. Compo and Prashant D. Sardeshmukh NOAA-CIRES Climate Diagnostics Center To what extent is the actualprecipitation skill smaller than the expected skill?

  2. Data for June-August 200 mb and 500 mb height , 500 mb vertical velocity, and precipitation NCEP MRF9: T40 JJA integrations: Climatological SSTs 135 members 1987 Global SSTs 90 members 1988 Global SSTs 90 members 1950-94 integrations: Global SSTs 13 members 30oN-30oS Pacific SSTs 9 members NCAR CCM3:T42 1950-99 integrations: Global SSTs 12 members Tropical SSTs 11 members ECHAM 4.5:T42 1950-2002 integrations: Global SSTs 24 members NCEP-NCAR reanalysis dataset: smoothed to T42 1950-2002, 500 mb vertical velocity is “chi-corrected” GPCP Precipitation: smoothed to T42 1979-2002

  3. -1 0.7 0.5 0.3 10-1Pa/s -0.15 0.0 0.15 Local correlation of summertimesummer mean rainfall and 500 mb w anomalies X10 Correlations are lower in regions of descent Long-term summer mean 500 mb w Observed (1979-2002) Simulated by NCEP AGCM (1950-1994)

  4. 0.7 0.5 0.3 Causal Chain: Tropical SSTs NH vertical motion anomalies NH precipitation anomalies w=7 EOFs P=7 EOFs 500 mb omega to predict precipitation w=7 EOFs T=14 EOFs Tropical Pacific SST to predict 500 mb omega P=7 EOFs T=14 EOFs Tropical Pacific SST to predict precipitation

  5. 1988 Drought Will predictability in specific cases be substantially different from average skill suggested? Some evidence in literature: Bates et al. 2001, Hong and Kalnay 2002 Construct 90 member ensembles using NCEP MRF9 AGCM with specified monthly SSTs for JJA 1987 and 1989. Assess predictability using Signal to Noise ratio: ratio of ensemble mean anomaly (88-87) to ensemble spread.

  6. Ensemble mean anomaly Ensemble spread S = 0.6 0.4 0.2 -0.2 -0.4 -0.6 Predictability of 1988 summer using the signal to noise ratio Predictability of 200 mb and 500 mb height does not accurately reflect precipitation Predictability of precipitation closely tied to 500 mb omega

  7. Verification of 1988 summer Where S is large, GCM verifies Small-scale height gradients that lead to vertical motions not captured

  8. Forecast skill as a function of the signal to noise ratio(mean shift to standard deviation) from an ensemble of forecasts. Valid for any forecasting situation at any lead time. Perfect model Imperfect model Applies to any multivariate forecast distribution. A large ensemble of forecasts can improve skill. Sardeshmukh, Compo, and Penland 2000; Kumar and Hoerling 2000; Rowell 1998, van den Dool and Toth 1990 Model systematic error can negate this improvement. Sardeshmukh, Compo, and Penland 2000

  9. Expected skill of 12-member ensemble with time-varying systematic error Se Perfect model Model with systematic error

  10. Case- and GCM-sensitivity of summertime rainfall predictability Compute signal as RMS ensemble-mean anomaly over domain from 12-member ensembles 1979-1999 (CCM3.0) and 1979-2003 (ECHAM4.5). Noise from independent 135 member ensemble from NCEP MRF9 forced with climatological SSTs. Pattern correlation of ensemble-mean anomaly with verification. Bin correlations by the S-values. North America western USA

  11. Actual skill for CCM3.0 (1979-1999) Skill is larger for larger values of S. A time-varying systematic error is present, particularly in precipitation.

  12. Actual skill for ECHAM4.5 (1979-2002) Skill is larger for larger values of S. Newer model still has time-varying systematic errors.

  13. Actual skill and expected skill r12 for combinedCCM3 and ECHAM4.5 predicting JJA anomalies North America western USA

  14. Conclusions • Predictability of precipation is largely determined by the predictability of 500 mb vertical motions, with some enhancements by local land-atmosphere feedbacks. • Case-to-case variations of skill over North America are consistent with model-predicted precipitation signal-to-noise ratio S. • 3. A substantial systematic is present in the CCM3.0 and ECHAM4.5, preventing actual precipitation skill from reaching the expected skill. • 4. Larger ensembles (>128) are needed to estimate case-to-case variations of S reliably.

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