An Inside Look at CPC’s Medium and Long- Range Forecasts Ed O’Lenic NOAA-NWS-Climate Prediction Center Camp Springs, Maryland , ext 7528

WEATHER vs. CLIMATE Smooth curve = 30 year mean (climatology) Wildly oscillating curve = daily “weather”

Forecast Process Schematic Dynamical model forecasts/multi- model ensembles Recent observations Historical observations.. Verifications/Statistical tools Downscaling, Analogs, Composites WEB PAGES/AUTOMATED DATABASES Peer-reviews of the forecast tools and of the penultimate forecast via web/telephone conference with partners and through local discussions (map discussions,sanity check, conference calls, etc…) Forecaster-created or automated products Dissemination to public

B EC A EC=Equal Chances for the tercile categories, 33-1/3 each. Contours are labeled with the deviation from EC for the indicated category. Generic Seasonal Climate Forecast Map

Part 1. Long-Lead Seasonal Forecasts

Forecast Maps and Bulletins Each month, on the Thursday between the 15 th 21 st, CPC issues a set of 13 seasonal outlooks. There are two maps for each of the 13 leads, one for temperature and one for precipitation for a total of 26 maps. Each outlook covers a 3-month “season”, and each forecast overlaps the next and prior season by 2 months. Bulletins include: the prognostic discussion for the seasonal outlook over North America, and, for Hawaii. The monthly outlook is issued at the same time as the seasonal outlook. It consists of a temperature and precipitation outlook for a single lead, 0.5 months, and the monthly bulletin. All maps are sent to AWIPS, Family of Services and internet.

Statistical Prediction Tools Multiple Linear Regression: - Predicts a single variable from historical and recent observations of two or more predictors. Canonical Correlation Analysis (CCA): –Uses recent and historical observations of Northern Hemisphere circulation (Z), global sea surface temperature (SST), US surface T (Tus) to create a set of 5 or 6 EOFs of predictors and predictands. – Looks at cross-correlations between time series of predictors and predictands. –Predicts temporal and spatial patterns from patterns.

Statistical Prediction Tools Constructed Analogs (CA) –Uses recent observations (base) of a single variable and historical observations, to construct a weighted mean of all prior years which best explains the base data. Assumes the evolution to subsequent seasons is also best explained by the weights used to construct the analog to the base. Optimal Climate Normals (OCN) –Uses the difference between the most recent 10 (15) years of temperature (precipitation) observations and the 30-year climatology (i.e., the trend) for a given season as the prediction for future occurrences of that season.

Detailed operations concept for NCEP’s ocean-atmosphere model An ensemble of 16 ocean SST forecasts are created using a coupled GCM. The average of these is used as the lower boundary for… An AGCM, along with 20 different sets of initial conditions, to create a set of 20 ensemble atmosphere forecasts out to 9 months. A 20-year AMIP run of the AGCM is made each month for use as the climatology to create anomalies/remove model bias. In collaboration with partners (CDC, IRI), forecasters use the NCEP model tools, together with other model tools to subjectively create outlook maps of the probability of monthly and seasonal mean temperature and total precipitation category.

NCEP Two-Tier Climate Modeling System INTEGRATED OCEAN MODEL- DATA ASSIMILATION SYSTEM COUPLED OCEAN- ATMOSPHERE GCM AGCM FORECAST S STATISTICAL TOOLS: CCA, CA STATISTICAL TOOLS SSTTOPEXXBTTAO OCEAN INITIAL CONDITIONS STRESS EVAP- PRECIP FLUX SSTA SSMI/ERS-2 HEAT FLUXES OFFICIAL SST FCST OFFICIAL PROBABILISTIC T,P OUTLOOKS FORECASTERS SURFACE T, P ANOMALIES IRI,CDC CDC

Forecast tools web page

Global SST

TAO Ocean T Obs

NCEP AGCM Forecasts for DJF , , and SST ForcingGlobal and NOAM T Fcst

MAM 2003 NCEP AGCM T Forecast

CCA 0.5 Mo lead MAM T Outlook

OCN 0.5 Mo lead MAM T Outlook

ENSO Composites given CPC consolidated SST forecast

OFFICIAL MAM 2003 T Outlook

SS1= ((c-e)/(t-e))*100 SS2= ((c+(1/3)*cl - e)/(t-e))*100 Mean ss1 Mean ss2 0 U.S. Temperature Skill

Precipitation Skill

The Forecast Forecast 1-3 years: Week 2 forecasts for the Pacific Region & Caribbean based on MJO, ENSO, Monsoon, improved dynamical & statistical models. 3-5 years: Week 3, 4 forecast based on NAM/AO relationships, MJO, ENSO, improved statistical & dynamical models years: Improved seasonal, monthly, week 2, 3-4 forecasts based on improved dynamical & statistical model prediction of NAM/AO, diurnal cycle of convection, MJO, ENSO, Monsoon, decadal oscillations, ocean-atmosphere coupling, MM ensembles, more & better observations, …

Part 2. Medium-Range Forecasts

6-10 day/week 2 process schematic Multi-model ensemble 9:00 AM Weighted average of model 500 hPa height Downscale: get surface weather from 500 mb height via analogs, regression, neural network. RR Forecaster formulates maps of predicted T, P, PMD bulletin Disseminate via web, AWIPS, FOS 3-4 PM R = Forecaster reconciliation of tools required

Recent Changes to Procedures From 3 times/week to daily in October 2000 Automated weekend forecasts from October 2000 Percent probability format from October 2000 Alaska and week 2 added October 2000 Automated weekend forecasts improved October 2001—neural net tool omitted and consistency with weekday forecasts added Bias-corrected precipitation forecast tool and other improvements added in the fall of 2001

Forecast Maps and Bulletins Each day,between 3 and 4 PM Eastern Time, CPC issues a set of 6-10 day and week 2 outlooks. These are formulated by a forecaster (Monday through Friday) and are automated on weekends. There are two 500 mb height maps, two surface maps and a single bulletin. Sample 6-10 day outlook 500 mb height and anomaly forecast map from CPC web page.

GFS Ensemble upper-air- height forecasts, analog

ECMWF upper-air-height forecasts, analog

Official 6-10 day 500 hPa forecast

Teleconnections (TC) Definition: Composite of those maps, for a calendar month, with largest + (top 10%) or – (bottom 10%) 500 hPa height at a specified space point from (~150 maps). Forecaster computes TC on major anomaly centers (base points) of 500 hPa forecast maps. Strong TC ~ large correlation values at the distant centers ~ frequent/persistent pattern Weak TC ~ the pattern is probably transient and not as likely to be well predicted by the model as would a persistent pattern.

Teleconnection on 500 hPa center at 50N/140W (+) and 55N/90W (-)

Composite of observed T, P anomalies associated with teleconnecion on hPa anomalies at 56N 10W

ENSEMBLE T, P prediction analog maps ECMWF

6-10 Day MRF precipitation bias correction 8-14 Day

Official 6-10 day T forecast

Official 6-10 day P forecast

6-10 day Monthly Average Skill Scores s=((c-e)/(t-e))*100 c = # hits e = # chance hits t = # forecasts

Skill of Official 6-10 day T, P, 500 hPa Ensemble Mean Z

Skill of 6-10 day T tools, 500 hPa Ensemble Mean Z

The Forecast Forecast 1-3 years: Week 2 forecasts for the Pacific Region & Caribbean based on MJO, ENSO, Monsoon, improved dynamical & statistical models. 3-5 years: Week 3, 4 forecast based on NAM/AO relationships, MJO, ENSO, improved statistical & dynamical models years: Improved seasonal, monthly, week 2, 3-4 forecasts based on improved dynamical & statistical model prediction of NAM/AO, diurnal cycle of convection, MJO, ENSO, Monsoon, decadal oscillations, ocean-atmosphere coupling, MM ensembles, more & better observations, …

The End