A Look at Climate Prediction Center’s Products and

ServicesEd O’Lenic

NOAA-NWS-Climate Prediction Center

Camp Springs, Marylanded.olenic@noaa.gov

301-763-8000, ext 7528Eastern Region Climate Services Workshop

Raliegh, North CarolinaSeptember 17, 2003

 

Objectives

– CPC products and terms– CPC’s web site– MJO & tropical convection - hope– Outlooks– Verification

Climate Versus WeatherTop Graph: Observed daily average Temperature (T), May 2000-April 2001 (jagged curve, an example of “weather”), and 30-year average (1971-2000) of daily average T (also called the “normal”, (smooth curve) the standard definition of “climate”) at Albany, New York. Note the large day-to-day variability indicated by the red (above-normal) and blue (below normal) daily T events.

Bottom Graph: Result of subtracting the normal from the daily average T in the top graph and then performing a 31- day running average. Note the expanded scale on the lower graph. The extended periods of above and below normal 31-day average T are, examples of short-term climate variability. Green line is the average of the departures overMay 2000-April 2001.

CPC Data-to-Product Process Schematic

DYNAMICAL MODEL

ENSEMBLES

REAL-TIME OBS HISTORICAL OBSERV-ATIONS

MONITORING - PREDICTION TOOLS, WEB PAGES

FORECASTS, EXPERT ASSESSMENTS, MONITORING PRODUCTS

AWIPS, PRINTED PUBLICATIONS, WEB PAGES/AUTOMATED PRODUCTS AND

DATABASES

CLIMO

STATISTICAL

MODELS

USER FEEDBACK

VIA CSD

Some Definitions• Climate: Average of weather over days, weeks, months, years• Climate Prediction: Concerned with averages and variability, rather than weather.• Climatology: Average over a long time relative to features being studied (BORING)• 3-class system: Divide the climatology into highest, lowest and middle thirds.• Lead time: Time between the issuance of a forecast and when it becomes valid.• 6-10 day forecast: 5-day mean forecast at lead 5 days.• 8-14 day forecast: 7-day mean forecast at lead 7 days.• 0.5 month outlook: ½ month lead forecast of seasonal mean T, P• Probability = P(A) = (# possible events (H)) / (total # possible outcomes (H+T))• Probability anomaly: probability of an event – climatological probability of an event.• Total probability: probability anomaly + climatological probability • Probability distribution: Graph where the area under the curve=prob. of an event.• Extreme event: An event in the upper-, or lower-most part of a probability dist.• Southern Oscillation: A back and forth pressure variation with opposite sign

between the eastern (Tahiti) and western (Darwin) portions of the tropical Pacific.• ENSO: El Nino/Southern Oscillation, made up of El Nino (warm) and La Nina (cold) • ENSO-Neutral: Usually refers to years which are neither El Nino nor La Nina• Trend: most recent 10(15) year means of observed T(P) minus the 30-yr climatology• Maritime Continent: Indonesia and surrounding region, a focus of tropical

convection• MJO: Madden-Julian Oscillation, a wave #1 tropical disturbance with dry and wet

phases which moves from west-to-east around the global tropics in 30-60 days.

Total SST

7-day mean centered on 03 September, 2003

SSTa

nino 3.4 Cold Tongue

Warm Pool

CPC Home Page

Climate HighlightsUS Hazards Assessment The U.S. Hazards

Assessment pageis a comprehensive source of up-to-date information on the status of the Global climate. The left columnis intended to letyou self-brief,and is used by Hazards briefersto prepare and brief the product.

US Seas Drought Outlook

Climate Highlights – ENSO Diagnostic Discussionhttp://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_advisory/index.html

Outlook Products link

Outlooks – Products http://www.cpc.ncep.noaa.gov/products/predictions

6-10 day T

6-10 day P

Monthly Outlook

S/N is larger forSeasonal thanMonthly.

0.5 mo lead seasonal T, P outlook

Outlooks combine long-term trends, soil-moisture effects, model forecasts with typical ENSO cycle impacts, when appropriate.

SST forecast

Diurnal Cycle of Convection Can Lead to Large Time and Space

Scale Phenomena

Why the MJO is important

• Intimately related to active/break cycles of the Australian and Asian Monsoons

• Offers potential to provide extended predictability up to 15-20 days in tropics

• Affects weather (predictably?) over the western US and elsewhere

• Associated westerly wind events generate Tropical Pacific ocean Kelvin waves which may significantly modify the evolution and amplitude of El Nino (e.g. 1997, 1991)

• Large inter-annual variability in the activity of the MJO has implications for the predictability of the coupled ocean-atmosphere system

MJO Activity and Inter-annual SST

• West Pacific SST in Fall versus MJO Activity in Winter

• Foundation for link with ENSO?

• 95% level assuming zero correlation and 22 dof is 0.43

1 season lag correlation = 0.65

West Pacific SST

MJOACTIVITY

MJOs and 1991-92 El Nino: Late Onset

1

2

3

1

2

3

Zonal Wind Anoms SST Anoms 20C Depth Anoms

Sep 91

Nov 90

Mar 91

Annual Mean Precipitation Errors in HadAM3:

Sensitivity to Horizontal Resolution

(Neale and Slingo, 2003: J. Clim., 16, 834-838)

HadAM3 Sensitivity Experiments: Impact of removing the islands of the Maritime

Continent (Neale and Slingo, 2003: J. Clim., 16, 834-838)

•Land grid-points removed and replaced by ocean grid-points.•Increased moisture availability from the sea surface leads to enhanced convection and partial correction of the model dry bias.•Note also corrections to model’s wet bias in adjacent areas.

Global Impacts of Improved Maritime Continent Heat Source

DJF: 500hPa height (m) and Surface Temperature (K)

•Potential improvements in the Maritime Continent heat source can have significant remote effects.

•Related to the generation of Rossby waves by the enhanced divergent outflow from the Maritime Continent heat source.

•Substantially reduces model systematic error over the extra-tropics of the winter hemisphere.

•Emphasizes the importance of considering the global context of model systematic error in which biases in the tropics may be a key factor.

Applied Research, Diagnostics and Forecast ToolsCollaborators: EMC, TPC, CDC, GFDL, IRI, Scripps, COLA, U. Wash.

Inter-Annual Variability

- ENSO

Decadal Variability

- PDO- AO/NAO- Global Warming

Intra-seasonal Variability

- Tropical MJO- Blocking- AO/NAO/NPO/PNA

SeasonalExtended Range

Climate Prediction Center Forecast System Schematic

HighFrequency:Interannual

Low-Frequency:

Trend

U.S.Threats Assessment

6-10 Day

Week Two

Monthly

International Threats

Dynamical/statistical models

- Real-Time Diagnostics- Model Simulations- Ensembles- Verification

Weather/climate links

- Composites- Teleconnections- Extreme events- Tropical storms- Drought/Floods- Climate/Weather Monitoring

CPC Forecast System

Long-Lead Seasonal Forecasts

Forecast Maps and Bulletins

•Each month, on the Thursday between the 15th 21st, 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.

Trend (10-yr mean-minus-30-yr mean) Temperature for Alaska and CONUS

Loop shows 3-month mean T anomalies expressed as tenths of

standard deviations.JAS, ASO, SON, …, MJJ, JJA

JAS

ASO

SON

OND

NDJ

DJF

JFM

FMA

MAM

AMJ

MJJ

JJA

http://www.cpc.ncep.noaa.gov/products/predictions/90day/tools/briefing/index.pri.html

2000-01

2001-02

2002-03

NCEP AGCM Forecasts for DJF 2000-01, 2001-02, and 2002-03

SST ForcingGlobal and NOAM T Fcst

CCA, OCN, CMP, OFF T Tools DJF2003

CCA OCN

CMPOFF

Probability of Exceedance (POE) Map

PROBABILITY OF EXCEEDANCE ~ THE SHIFT IN THE CENTER OF THE DISTRIBUTION IMPLIED BY THE FORECAST

Verification

• CPC official forecasts and tools are continuously verified

• Verification statistics are made available to forecasters

• Categorical verifications – blunt instrument, understandable

• Probabilistic verifications – much more informative, highlight need for calibration

Heidke Skill Score

s = ((c-e)/(t-e))*100

c = # stations correct

e = # stations expected correct

t = # stations in total

Categorical Skill Scores of Seasonal Forecasts0.5 Month-Lead Temperature

Categorical Skill Scores of Seasonal Forecasts0.5 Month-Lead Precipitation

Seasonal forecastSkill: CCA EC stations not scored

EC stations scored as Normal

Comparison of CCA coverage DJF, MAM, JJA, SON

CCA

DJF

CCA

MAM

CCA

JJA

CCA

SON

6-10 day VerificationOfficial, Automated, 1st Guess Temperature

8-14-day Precipitation Accumulatio

n7-, 30-Day

Bias &

Bias Correction

THE END