The Long Journey of Medium-Range Climate Prediction Ed O’Lenic, NOAA-NWS-Climate Prediction Center

Outline: Medium-range climate prediction is a work-in-progress… History Introduction to medium range forecasts Motivation Current MR climate forecast methods Model-observations calibration Future

Historical Outline MIT, USWB - Rossby ca –Long wave theory –Slow changes in the position of the Aleutian/Icelandic centers of action –Zonal index –1-5 day forecasting – MIT, USWB (Namias). First NWP models – LPE, 3LPE, 7LPE + barotropic extension – First 6-10-day average T, P forecast, Dec Global Spectral Model – August, Simmons, Wallace, Branstator, teleconnections ~ excited by internal dynamics and boundary forcing. Hughes – quantitative verification – Operational ensemble forecasts available – Grease pencils-to-work-stations, verifications, new tools Probabilistic, daily 6-10-, 8-14-day forecasts – Begin use of CDC calibration of MRF model.

Medium range forecasts of 5-day average 500 mb heights and anomalies and categorical surface temperature (T) and precipitation (P), at a lead of 5 days (6-10 day forecasts) have been prepared operationally since late Medium range 7-day mean forecasts at a lead of 7 days (8-14-day forecasts) have been produced operationally at CPC since Both forecasts are deterministic for 500 mb and probabilistic for T, P. 500 hPa height and anomaly 3-class T probabilities

History of skill day forecasts - Skill of 6-10 day forecast has been rising steadily since 1980, when the global spectral model was implemented. - Large/small-scale spatial patterns lead to large/small skill variability in T/P. - Large inter-annual T skill variability. - Skill leveled-off beginning in 1990 for P and 1991 for T. - Drop in P skill in due to the loss of all P forecast tools in the Cray fire of Sep 27, 1999 and, in Feb 2000 to a change in forecast format from categorical to probabilistic. CRAY FIRE SEP 27, 1999

Temporal standard deviation of winter- time 500 hPa height computed from daily time series over 20 years. The lion’s share of 500 hPa height variability found for time scales of weekly averages. Daily – weekly ave data Weekly - Mo ave data Monthly – Seas. ave data Seasonal ave data Blackmon and Wallace

6-10-Day Forecast Process in 1979: (2) DOWNSCALE: Klein equations. Composite 500 hPa hgt barotropic + 3LPE extended to 240hrs from7LPE 84hr forecast Forecast tools (1) PREDICT 500 HPA HEIGHTS AND ANOMALIES (3) FINAL FORECAST Categorical 5-class T, 3-class P, CONUS only

6-10-, 8-14-Day Forecast Process in 2004: (2)DOWNSCALE T & MODEL OUTPUT P: Klein equations, Natural analogs CDC-Calibrated MRF T, P Neural Network for each GFS ENS member + CAN, ECMWF, BAR, ENS 500 hPa height multi- model composite GFS ENS, CAN ENS, ECMWF ENS, Barotropic, Z GFS Forecast tool maps ~30/variable (1) PREDICT 500 HPA HEIGHTS AND ANOMALIES (3) ESTIMATE UNCERTAINTY (4) FINAL FORECAST Obs/Fcst freq/probs calib???

A comprehensive skill evaluation system continually informs forecasters

Calibrate the observed frequency of the above and below categories with probabilities from 15-member daily ensembles over 23 yrs using the 1998 MRF, initialized from reanalysis. This simple technique improves week 2 RPSS from zero to and improves forecasts of extremes. Runs every 5-days for 20 years would use equivalent of 4 years of daily forecasts. Considering the pay-off and user requirements for accurate estimates of uncertainty, shouldn’t this should be a routine part of model-change-implementation? Hamill, T.M., J. S. Whitaker, and X. Wei, 2004: Ensemble re-forecasting: improving medium-range forecast skill using retrospective forecasts. J. Climate, June, 2004.

R. Wayne Higgins – CPC Composite Madden-Julian Oscillation and tropical storm origination points MJO is related to heavy precipitation events in the Northwestern United States

Summary Rossby, Namias – NWP, Charney, Phillips - Ensembles Most 500 hPa variability is in weekly average data. Skill of MR forecast closely related to NWP and computing ability. Value and protection of life and property – why we do 6-10-day and week-2 forecasts – most popular CPC products. Current MR forecast methods are ensemble-model-based. Calibration greatly improves ENS MR probabilistic forecasts. Ensembles of ensembles from different models (MM ensembles) are better than single-model ensembles. Better modeling of global tropics -> MJO, diurnal cycle of convection -> better week 2 & week 3, 4 forecasts. Better prediction of odds of extreme events. Prediction of a wide array of parameters beyond T, P.

Medium range climate prediction is a work in progress. It has come a long way. Thanks to Rossby’s vision and NWP, It is alive, well and has a promising future.