Challenges of Seasonal Forecasting: El Niño, La Niña, and La Nada Mike Halpert NOAA-NWS-Climate Prediction Center December 2016

How Does CPC Make Operational Seasonal Climate Outlooks? 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 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 often provides the starting point for the outlook map Model forecasts (specifically the NMME) now play a large role A forecaster subjectively adjusts the forecast A team of seasonal forecasters reviews the forecasts with input from across NOAA and other agencies A conference call on Tuesday prior to the release date reviews the current climate state, previous forecasts, and 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 (2nd Thursday) + Please provide a brief overview of current missions, models, products, etc.

Where does seasonal predictability come from? 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 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 Niño and La Niña: anomalous climate states whose development, persistence and evolution are somewhat understood Potentially 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: Climate Change Anomalies in the large scale ocean circulation can vary over decadal timescales e.g. Atlantic Meridional Overturning (AMOC) + Please provide a brief overview of current missions, models, products, etc.

Optimal Climate Normal (OCN) OCN, as it is used as a tool at CPC is, quite simply, a measure of the trend. For a given station and season, the OCN forecast is the difference between the seasonal mean (median) temperature (precipitation) during the last 15 years and the 30 year climatology.

G H C N - A M S F 2 8

DJF 15 year Temperature Trend

DJF 15 year Precipitation Trend

El Niño / Southern Oscillation

Pacific Niño 3.4 SST Outlook About half of the multi-model averages indicate weak La Niña conditions through the Northern Hemisphere early winter 2016-17. Figure provided by the International Research Institute (IRI) for Climate and Society (updated 15 November 2016).

North American Multi-Model Ensemble (NMME) Niño 3.4 SST Model Outlook CPC/IRI forecasters favor “weak” (-0.5ºC < ONI < -1.0ºC) amplitude during the early winter.

December-February Precipitation Anomalies associated with La Niña http://www.cpc.ncep.noaa.gov/products/precip/CWlink/ENSO/composites/

December-February Temperature Anomalies associated with La Niña http://www.cpc.ncep.noaa.gov/products/precip/CWlink/ENSO/composites/

December-February Temperature Anomalies associated with La Niña + Trends http://www.cpc.ncep.noaa.gov/products/precip/CWlink/ENSO/composites/

What is the NMME? NMME (North American Multi-Model Ensemble) is an unprecedented MME system intended to improve intra-seasonal to interannual (ISI) operational predictions based on the leading US and Canada climate models. Models are imperfect: biases and poor estimations of their own skill Performance of multi-model ensembles is better than single models; skill increase comes from error cancellation and non-linearity of diagnostics Ensembles allow for characterization of uncertainty Users require predictions with minimal uncertainty accompanied by reliable estimates of that uncertainty NCEP was recommended by the National Research Council to implement an NMME system to improve ISI forecasting

Individual NMME Model Forecasts DJF 2016-17

Individual NMME Model Forecasts DJF 2016-17

Statistical Tools Canonical Correlation Analysis (CCA) Constructed Analog (CA) Screening Multiple Linear Regression (SMLR) Consolidation (CON)

Canonical Correlation Analysis (CCA) CCA is a statistical technique relating tropical Pacific Ocean sea-surface temperatures (SSTs), 700 hPa heights, (the predictors) and U.S. surface temperatures (T) and precipitation (P) (the predictands) When CCA is developed, relationships are found between observed U.S. T and P for a given season, say, January-February-March (JFM) and the predictors for the prior four non-overlapping seasons, in this case, OND, JAS, AMJ and JFM of the prior year.

Screening Multiple Linear Regression (SMLR) SMLR is a statistical technique relating global sea-surface temperatures (SSTs), 700-hPa NH heights, and station values of mean temperature and total precipitation and U.S. surface temperatures (T) and precipitation (P) (the predictands) Data used are from the 3-month period prior to the forecast initial time, with regression relationships derived from data for the 1955-95 period.

Constructed Analog (CA) Because natural analogues are highly unlikely to occur in high degree-of-freedom processes, we benefit from constructing an analogue having greater similarity than the best natural analogue. The construction is a linear combination of past observed anomaly patterns in the predictor fields such that the combination is as close as desired to the initial state (or 'base'). The predictors (the analogue selection criterion) are the first 5 EOFs of the global SST field at four consecutive 3-month periods prior to forecast time.

Questions

Verification Temperature

Verification Precipitation