Mike Halpert NOAA-NWS-Climate Prediction Center October, 2011 Climate Prediction Center Outlooks (ERF/LRF): Basis, Tools, Verification Mike Halpert NOAA-NWS-Climate Prediction Center October, 2011

Climate is Constructed from Weather Wildly oscillating curve = daily “weather” Smooth curve = 30 year mean (climatology) Weather and climate are different parts of the same thing and are dependent upon one another.

Objectives Understand the science behind CPC extended-range and long-range forecasts Understand the proper interpretation and limitations of CPC’s operational forecasts Obtain the ability to conduct basic interviews about CPC’s forecasts, including interpretation, science behind, and verification (skill) Weather and climate are different parts of the same thing and are dependent upon one another.

Outline Part I – Long-Range Forecasts Sources of LRF predictability Meaning (interpretation) of the forecasts Operational forecast tools and example

Sources of S-I Predictability ENSO – Walker and Bliss (1932), Bjerknes (1969), Rasmussen and Carpenter (1982)……. Trend (Huang (1996), van den Dool (2006)….. Ocean-Atmosphere-Land (van den Dool (2006)….. Statistically-derived signals of unknown origin (Barnston, 1994) Dynamical model-derived signals (Saha et al, 2006)

Outline Part I – Long-Range Forecasts Sources of LRF predictability Meaning (interpretation) of the forecasts Operational forecast tools and example

U. S. Seasonal Outlooks March 2011 - May 2011 Temperature Precipitation What is the interpretation of these forecasts? What is the probability of above-average temperatures in central Texas; near-average?; below-average? a) 50,30,20 b) 50,33,17 c) 50,25,25 d) 50,50, 0

U. S. Seasonal Outlooks March 2011 - May 2011 Temperature Precipitation N. Georgia Below: 29% Near: 33% Above: 38% Minnesota Below: 33% Near: 33% Above: 33% What is the interpretation of these forecasts? What is the probability of above-average temperatures in central Texas; near-average?; below-average? a) 50,30,20 b) 50,33,17 c) 50,25,25 d) 50,50, 0

Outline Part I – Long-Range Forecasts Sources of LRF predictability Meaning (interpretation) of the forecasts Operational forecast tools and example (DJF 2011-12)

Seasonal Forecasts Which of the following factors influence the seasonal forecast (select all that apply): A) Trends – 91% B) Soil Moisture – 59% C) El Niño/Southern Oscillation – 95% D) Atmospheric Noise – 18%

FACTORS INFLUENCING A CLIMATE FORECAST Climate Change - trends Natural Climate Variability – “organizes” weather El Niño-Southern Oscillation (ENSO) Mid-latitude Oscillation modes (NAO, AO, PNA, …) Land Surface Processes (Soil moisture, Snow cover, …) Atmospheric Noise - unpredictable “climate” signals produced by chance through cumulative effects of weather. This is large in middle latitudes, small in the Tropics. Major cause of “uncertainty” in forecasts.

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 10 (15) years and the 30 year climatology. Much of CPC’s skill has historically been derived from OCN.

30 year WMO normals: 1961-1990; 1971-2000; 1981-2010 etc OCN = Optimal Climate Normals: Last K year average. All seasons/locations pooled: K=10 is optimal (for US T). Forecast for Jan 2012 = (Jan02+Jan03+... Jan11)/10. – WMO-normal plus a skill evaluation for some 50+ years. Why does OCN work? 1) climate is not constant (K would be infinity for constant climate) 2) recent averages are better 3) somewhat shorter averages are better (for T) see Huang et al 1996. J.Climate. 9, 809-817.

G H C N - A M S F 2 8

OCN DJF 2011-12 Data through 2010 Data through 2011

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.

CCA DJF 2011-12 Temperature Precipitation

CFS Niño 3.4 Forecast PDF-corrected CFSv1 CFS21

CPC Official SST Forecast

CFS skill 1982-2003

Pacific Niño 3.4 SST Outlook An increasing number of ENSO models predict the continuation of La Niña into the Northern Hemisphere winter (Niño-3.4 SST anomalies less than -0.5°C). Figure provided by the International Research Institute (IRI) for Climate and Society (updated 19 October 2011).

CFS DJF 2011-12 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

NMME DJF 2011-12 Outlook National Multi-Model Ensemble – Average of 7 models (CFSv1, CFSv2, NCAR, GFDL, NASA, IRI and (ECHAMA, ECHAMF) from October 2011.

Soil Moisture Largest impacts during summer season Largest potential when extremes exist (flooding/drought). Dry conditions have positive impact on T; negative impact on P Wet Conditions have negative impact on T; positive impact on T

SMLR CCA OCN LAN CFSV1 LFQ ECP IRI ECA CON La Nina Composites:

Seasonal Forecasts Which of the following factors influence the seasonal forecast (select all that apply): A) Trends B) Soil Moisture C) El Niño/Southern Oscillation D) Atmospheric Noise

Questions

Outline Part II – Extended-Range Forecasts Meaning of the forecasts Scientific basis of ERF forecasts Forecast Tools / Operational forecast example

Extended Range Forecasts E. Montana Below: 32% Near: 36% Above: 32% E. Nebraska Below: 42% Near: 33% Above: 25% E. Alaska Below: 4% Near: 33% Above: 63%

Outline Part II – Extended-Range Forecasts Meaning of the forecasts Scientific basis of ERF forecasts Forecast Tools / Operational forecast example Verification/Skill

Basis for Forecasts 31

Forecast Process Schematic Recent observations Dynamical model forecasts/multi- model ensembles Historical observations. Verifications/Statistical tools Downscaling, Analogs, Composites Subjective weighted average 500-hPa height and anomaly forecast (BLEND). Downscale to create surface temperature and precipitation tools using BLEND input. Subjective formulation of the probability of temperature and precipitation tercile categories. Write the forecast bulletin, FEUS40. Generic forecast process. Basic elements of ANY forecast system. Dissemination to public between 3-4 PM Eastern Time

Outline Part II – Extended-Range Forecasts Meaning of the forecasts Scientific basis of ERF forecasts Forecast Tools / Operational forecast example

Question Which of the following tools is not used in the preparation of the extended-range forecasts? Analogs – 5% Trends – 5% Regression – 14% Calibration – 72%

Forecast tools DYNAMICAL MODELS STATISTICAL TOOLS (Downscaling) 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.

500-hPa Heights Forecast made: 1/31 Valid: 2/6-2/10

500-hPa Height Anomalies Forecast made: 1/31 Valid: 2/6-2/10

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

Downscaled Temp/Prec Probabilities Analogs (T/P) Klein Equations (T) NAEFS Calibrated Model Output (T/P) ESRL (CDC) Reforecasts (T/P)

500-hPa Analog to the GFS Z500 Superensemble Mean 500-hPa Height/Anomaly centered on 02/27/2009 0.87 500-hPa Analog to the GFS Z500 Superensemble Mean Forecast made: 1/31 Valid: 2/6-2/10

Temperature Analog based on previous analog Forecast made: 1/31 Valid: 2/6-2/10

Precipitation Analog based on previous analog Forecast made: 1/31 Valid: 2/6-2/10

Stepwise Forward Screening Regression Given a set of inputs, xj, and an output, y, Start with all coefficients, bj= 0 Find the predictor, xi (700-hPa height) most correlated with y (Surface temperature). Include this predictor in the model. Find residuals Continue in this manner, at each stage, adding the predictor most correlated with r to the model. Stop when a threshold minimum correlation with r is reached (typically 4 or less terms).

Klein T 0Z GFS Superensemble Mean Forecast made: 1/31 Valid: 2/6-2/10

Klein Ensemble Percentages GFS ECMWF Forecast made: 1/31 Valid: 2/6-2/10

2m Temperature – GFS ENS Mean Uncalibrated Calibrated Forecast made: 1/31 Valid: 2/6-2/10

Precipitation - GFS Calibrated ENS Mean Percentages Forecast made: 1/31 Valid: 2/6-2/10 Forecast made: 2/19 Valid: 2/25-3/1

North American Ensemble Forecast System Weather modeling system run by NWS and CMC Multi-model ensemble that combines the global forecast model ensemble and the Canadian global forecast model ensemble. Bias corrected using forecasts and observations over the Past 120 days using a decaying average mean error.

NAEFS Forecast (T/P) Probabilities Forecast made: 1/31 Valid: 2/6-2/10

Question Which of the following tools is not used in the preparation of the extended-range forecasts? Analogs Trends Regression Calibration

Automated Forecast Forecast made: 1/31 Valid: 2/6-2/10

Official Forecast Forecast made: 1/31 Valid: 2/6-2/10

Observed Temp/Precipitation Official - 64.5 Auto – 61.5 Official - 11.4 Auto – 25.8 Forecast made: 1/31 Valid: 2/6-2/10

Questions

Outline Part III – Verification 1. Modified Heidke Skill Score 2. Long Lead Forecasts 3. Extended Range Forecasts 4. Comparison

Verification Verification of temperature/precipitation outlooks done on 2x2 grid for CONUS. This encompasses 232 valid grid squares. Main statistic used is the modified Heidke Skill Score, although other statistics are also calculated (RPSS, .

Modified Heidke Skill Score: % Improvement over Random Forecasts c = # correct forecasts t = # total forecasts e = # correct randomly (climatology)

2° x 2° Grid 20° - 56°N, 130° - 60°W, 232 valid points

Outline Part III – Verification 1. Modified Heidke Skill Score 2. Long Lead Forecasts 3. Extended Range Forecasts 4. Comparison

Temperature Skill Scores Long term actual Mean = 22.3, Coverage = 50.9%

After the fact….. Source Peitao Peng

Precipitation Skill Scores Long term actual Mean = 10.9, Coverage = 31.4%

Regional Verification DJF/MAM DJF - Temp MAM - Temp DJF - Prec MAM - Prec

Regional Verification JJA/SON JJA - Temp SON - Temp JJA - Prec SON - Prec

Extended Range Temperature Verification Mean = 29.2 High = 86.5 Low = -31.3 Mean = 20.9 High = 85.1 Low = -37.9

Extended Range Precipitation Verification Mean = 13.8 High = 66.2 Low = -27.2 Mean = 8.1 High = 54.5 Low = -31.1

Extended Range Regional Verification - Temperature

Extended Range Regional Verification - Precipitation

Question Which response ranks the skill of CPC temperature forecasts from highest to lowest? Seasonal, 6-10, 8-14, 0.5 monthly – 0% 6-10, 8-14, 0.5 monthly, seasonal – 90% Seasonal, 0.5 monthly, 6-10, 8-14 – 0% 6-10, seasonal, 8-14, 0.5 monthly – 10%

FY07-FY10 Mean Heidke Skill Scores Outlook Period Temperature Precipitation 6-10 Day 29.2 (22.3) 13.8 (13.3) 8-14 Day 20.9 8.1 30 Day (0.5 Mo Lead) 14.8 (non-EC), 06.7 (All) 09.3 (non-EC), 02.7 (All) 30 Day (0.0 Mo Lead) 30.7 (non-EC), 13.9 (All) 24.1 (non-EC), 09.0 (All) 90 Day 22.0 (non-EC), 13.2 (All) 13.2 (non-EC), 04.3 (All)

Percent of “Successful” CPC Forecasts 48 719 1016 296 72 72 796 992 1094 1106 284

Questions

Seasonal Predictions Lab Mike Halpert NOAA/NWS Climate Prediction Center

Scenario You are called by noted New York Times science writer Andrew Revkin, who asks you if he can do a brief interview with you about climate forecasting on time scales from weeks to seasons. He says he wants to focus specifically on the techniques, meaning, and verification of the Climate Prediction Center’s extended range and long range forecasts. He says he wants to focus and CPC’s seasonal forecast for winter 2009-10 and the extended range forecasts that were issued late in December 2009. He has provided you the attached 6 questions: