a look at climate prediction center’s products and services ed o’lenic noaa-nws-climate...
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A Look at Climate Prediction Center’s Products and
ServicesEd O’Lenic
NOAA-NWS-Climate Prediction Center
Camp Springs, [email protected]
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