Behind the Climate Prediction Center s Extended and Long Range Outlooks Mike Halpert, Deputy Director Climate Prediction Center / NCEP September 2012
Outline Mission Extended Range Outlooks (6-10/8-14) Long Range Outlooks (Monthly/Seasonal) Societal Needs
N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N CPC Mission We deliver climate prediction, monitoring, and diagnostic products for timescales from weeks to years to the Nation and the global community for the protection of life and property and the enhancement of the economy. Operational Requirements: Deliver national outlook products: temperature, precipitation, drought, hurricanes,.. Span weeks, months, seasons, year(s) Embrace collaborative forecasting with other NCEP Service Centers, NOAA line offices, other agencies Ensure real-time, on-time, all the time (since 79) Enable NGSP Societal Challenges: Water and Extremes Temperature Outlook 3
4 N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N CPC Climate Prediction and Monitoring Products Official Outlooks focused on week-2, monthly, seasonal Precipitation & Temperature Outlooks Hazards Outlooks (US, Global Tropics) Seasonal Drought Outlook Seasonal Hurricane Outlooks (Atlantic and Eastern Pacific) El Nino / La Nina Prediction Real-time and historic monitoring of atmosphere, ocean, land surface conditions Daily and monthly data, time series, and spatial maps Primary modes of climate variability (ENSO, MJO, NAO, PNA, AO,...) Storm Tracks and Blocking Monsoons Precipitation and Surface Temperature Drought (US, North America; NIDIS)
Extended Range Outlooks (6-10 Day) E. Alaska Below: 4% Near: 33% Above: 63% E. Montana Below: 32% Near: 36% Above: 32% E. Nebraska Below: 42% Near: 33% Above: 25%
Basis for Forecasts
(Historical) Strategy Leverage model skill in forecasting longwave pattern by creating 500-hPa height map Downscale to get T/P using statistical tools such as regression and analogs Limited use of model output other than heights
Forecast tools DYNAMICAL MODELS Global Forecast System (GFS) and ensembles European Centre for Medium-range Weather Forecasts (ECMWF) ensembles Canadian ensembles STATISTICAL TOOLS (Downscaling) Klein T screening regression ESRL calibrated T, P calibrates recent model frequencies with atmos. NAEFS Bias-corrected ensemble forecasts T, P GFS P, T Dynamical model output calibrated P, T Analog composites Average T, P for the 10 best 500-hPa analogs Tele-connections Simultaneous, significant temporal correlations for two or more widely separated locations.
Blended 500-hpa Height/Anomalies ECMWF ENS MEAN 10% Canadian ENS MEAN 10% GFS Superensemble 40% 0Z GFS ENS MEAN 10% 6Z GFS ENS MEAN 10% 0Z Operational 10% 6Z Operational 10% Forecast made: 1/31 Valid: 2/6-2/10
Temp/Prec Outlooks Klein Equations (T) Analogs (T/P) Neural Network (T/P) Calibrated Model Output (T/P) ESRL (CDC) Reforecasts (T/P) NAEFS (T/P)
Evolving Strategy Improving model skill allows for increased use of direct model output of T/P Forecaster continues to produce 500-hPa height map Downscaling often takes a back seat to corrected model output
Temp/Prec Outlooks NAEFS (T/P) ESRL (CDC) Reforecasts (T/P) Calibrated Model Output (T/P) Klein Equations (T) Analogs (T/P)
Long Range Outlooks (Seasonal) N. Minnesota Below: 43% Near: 33% Above: 24% C. Texas Below: 22% Near: 33% Above: 45% C. California Below: 33% Near: 33% Above: 33% S. Florida Below: 53% Near: 33% Above: 12%
Where does seasonal predictability come from? Persistent or recurring atmospheric circulation patterns associated with anomalies in the initial state of the climate system, or boundary conditions El Nino and La Nina: anomalous climate states whose development, persistence and evolution are somewhat understood Persistent or recurring atmospheric circulation patterns that are less well understood: AO, NAO, PNA Unidentified persistent atmospheric patterns may arise from the initial state of the climate system or from boundary forcing Decadal variability or trends: 1. Climate Change 2. Anomalies in the large scale ocean circulation can vary over decadal timescales e.g. Atlantic Meridional Overturning (AMOC) 14
What changes the climate state? Forcing any anomalous boundary condition that changes the state of the atmosphere or interaction with the surface What forces or alters the climate state on seasonal timescales or longer? Sea surface temperature anomalies force the atmosphere The atmosphere adjusts to changing SST in about a month Snow and ice anomalies Annual sea-ice cycle; decadal to millennial for land ice Large soil moisture anomalies or vegetation anomalies May persist for months and drive feedbacks with regional climate Subsurface ocean temperature and salinity: seasonal timescales Changes in the atmospheric composition of greenhouse gases Decadal to millennial Large scale atmospheric anomalies can persist for weeks, through feedbacks to other climate system components, oceans and sea-ice 15
How Does CPC Make Operational Seasonal Climate Outlooks? Seasonal temperature and precipitation forecasts are based on a combination of statistical and dynamical forecasts An objective consolidation of forecast information provides a basis for a single outlook map A forecaster subjectively adjusts the forecast A team of seasonal forecasters reviews the forecasts with input from across NOAA and other agencies First conference call on Friday before release date to review the current climate state and previous forecasts Second call on Tuesday before release date to review the forecaster s preliminary maps Release date every third Thursday of the month Monthly ENSO forecast is always updated prior to the start of the seasonal forecast process 16
Trends 17
OCN DJF 2012-13 Data through DJF 2011-12
CPC Official SST Forecast
Pacific Niño 3.4 SST Outlook
El Niño Composites DJF El Niño Temperature DJF El Niño Precipitation
CFS DJF 2012-13 Outlook C mm/day Climate Forecast System version 2 Ensemble average of 40 members from October 2011. Base period for climo is 1999-2010. Forecast skill in gray areas is less than 0.3
MME DJF 2012-13 Outlook - Temp
MME DJF 2012-13 Outlook - Prec
Consolidation Temp.
Societal Needs Understandable forecasts that meet user needs for decision making Bridging the gap in the NWS seamless suite (weeks 3-4) Information on extremes throughout the period (month/season)
N A T I O N A L O C E A N I C A N D A T M O S P H E R I C A D M I N I S T R A T I O N Framing Performance Outcomes for Seasonal Predictions Key areas where the CPC will continue to work with partners to frame performance outcomes for ISI predictions Improve evaluation: Verification techniques Performance metrics Explain scientific basis: Identify Sources of Predictability and Prediction Skill Communicate Confidence / Uncertainties Communicate probabilistic nature of forecasts Engage in problem focused assessments: Provide context on what is occurring and why for events (e.g. extremes) Provide advice on research directions to improve predictions Address challenges with credibility, communication, education and buy-in 27