Extended and Long Range Outlooks - SoMASxs1.somas.stonybrook.edu/~na-thorpex/meeting_files... ·...
Transcript of Extended and Long Range Outlooks - SoMASxs1.somas.stonybrook.edu/~na-thorpex/meeting_files... ·...
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Behind the Climate Prediction Center’s
Extended and Long Range Outlooks
Mike Halpert, Deputy Director
Climate Prediction Center / NCEP
September 2012
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• Mission
• Extended Range Outlooks (6-10/8-14)
• Long Range Outlooks (Monthly/Seasonal)
• Societal Needs
Outline
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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
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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
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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
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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)
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E. Alaska
Below: 4% Near: 33% Above: 63%
E. Montana
Below: 32% Near: 36% Above: 32%
E. Nebraska
Below: 42% Near: 33% Above: 25%
Extended Range Outlooks (6-10 Day)
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Basis for Forecasts
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(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
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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.
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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
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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)
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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
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Temp/Prec Outlooks
• NAEFS (T/P)
• ESRL (CDC) Reforecasts (T/P)
• Calibrated Model Output (T/P)
• Klein Equations (T)
• Analogs (T/P)
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S. Florida
Below: 53% Near: 33% Above: 12%
Long Range Outlooks (Seasonal)
C. Texas
Below: 22% Near: 33% Above: 45%
N. Minnesota
Below: 43% Near: 33% Above: 24%
C. California
Below: 33% Near: 33% Above: 33%
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Where does seasonal predictability come from?
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• 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)
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What changes the climate state?
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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
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How Does CPC Make Operational Seasonal Climate Outlooks?
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• 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
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Trends
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OCN DJF 2012-13
Data through
DJF 2011-12
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CPC Official SST Forecast
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Pacific Niño 3.4 SST Outlook
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El Niño Composites DJF El Niño Temperature DJF El Niño Precipitation
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CFS DJF 2012-13 Outlook
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
°C mm/day
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MME DJF 2012-13 Outlook - Temp
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MME DJF 2012-13 Outlook - Prec
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Consolidation – Temp.
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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)
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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
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
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