extended and long range outlooks - somasxs1.somas.stonybrook.edu/~na-thorpex/meeting_files... ·...

Behind the Climate Prediction Center’s

Extended and Long Range Outlooks

Mike Halpert, Deputy Director

Climate Prediction Center / NCEP

September 2012

• Mission

• Extended Range Outlooks (6-10/8-14)

• Long Range Outlooks (Monthly/Seasonal)

• Societal Needs

Outline

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

3

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

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

4

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)

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)

Basis for Forecasts

(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

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.

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

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)

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

Temp/Prec Outlooks

• NAEFS (T/P)

• ESRL (CDC) Reforecasts (T/P)

• Calibrated Model Output (T/P)

• Klein Equations (T)

• Analogs (T/P)

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%

Where does seasonal predictability come from?

14

• 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)

What changes the climate state?

15

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

How Does CPC Make Operational Seasonal Climate Outlooks?

16

• 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

17

Trends

OCN DJF 2012-13

Data through

DJF 2011-12

CPC Official SST Forecast

Pacific Niño 3.4 SST Outlook

El Niño Composites DJF El Niño Temperature DJF El Niño Precipitation

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

MME DJF 2012-13 Outlook - Temp

MME DJF 2012-13 Outlook - Prec

Consolidation – Temp.

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)

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

27