Dust storm 1935Dust storm 1935

Ed O’LenicEd O’Lenic

National Weather ServiceNational Weather Service

NWS NWS Medium-Range and Medium-Range and Long-Range ForecastsLong-Range Forecasts

Summaryo The Sun is the ultimate source of all weather and climate variability.o Climate is the average of weather over weeks or longer.o Climate variability (CV) is driven by the tropical oceans, which have a

long “memory” and by complicated interactions among ocean, atmosphere and land.

o Dynamical models are our best hope for predicting weather and climate.

o The climate system is noisy. Only a small portion of CV is predictable – mainly ENSO, some lesser tropical disturbances, and a number of known mid-latitude disturbances.

o Prediction in 2004 is increasingly invested in dynamical models.o Humans assess uncertainty in the models using large numbers of

forecasts called ensembles and construct the forecasts subjectively.o Quantitative assessments of forecast performance relative to some

standard (skill) are used to condition the use of the forecast tools, to assign probabilities and to inform users.

o Automated objective forecasts, a possibility in the future, heighten security risks, since they are more reproducible than human ones.

The Weather-Climate Link

•Smooth curve = 30 year mean (climatology)

•Wildly oscillating curve = daily “weather”

Subtracting the

climatology and

performing a 31-day

running mean reveals

the low-frequency

signal or short-term

climate variations we are

trying to predict.

Examples of time series

Time series

TIME

VA

LU

E ->

0 10 20 30 40 50 60 70 80 90 100

Standard deviation ofwinter-time (DJF) 500 hPa height forDAILY, WEEKLY, MONTHLY and SEASONALtime scales.

Standard deviation ofwinter-time (DJF) 500 hPa height forDAILY, WEEKLY, MONTHLY and SEASONALtime scales.

D M

W S

Time Behavior: Frequency SpectraTime Behavior: Frequency Spectra88..White noiseWhite noise is a time series that has the same is a time series that has the same

variance in every frequency, i.e. a flat spectrum:variance in every frequency, i.e. a flat spectrum:

Frequency

Pow

er

Time Behavior: Time Behavior: AutocorrelationAutocorrelation

T i m e B e h a v i o r : A u t o c o r r e l a t i o n1 . A s i m p l e d e f i n i t i o n o f a u t o c o r r e l a t i o n a t t i m e l a g τ i s

t h a t v a l u e s o f a t i m e s e r i e s s e p a r a t e d b y τ a r e c o r r e l a t e d w i t h e a c h o t h e r ; m a t h e m a t i c a l l y t h e a u t o c o r r e l a t i o n f u n c t i o n i s

2 . S e r i a l c o r r e l a t i o n i s a n o t h e r n a m e f o r a u t o c o r r e l a t i o n .

3 . P e r s i s t e n c e i s p e r f e c t l a g 1 a u t o c o r r e l a t i o n .

x xx t x x t x s ( ) ( / 2

- 2 5

- 2 0

- 1 5

- 1 0

- 5

0

5

1 0

1 5

2 0

2 5

1 9 7 0 1 9 7 1 1 9 7 2 1 9 7 3 1 9 7 4

7-da

y M

A T

max

anom

alie

s (°

F)

Time Behavior: AutocorrelationTime Behavior: Autocorrelation

4.4. A special form of autocorrelation occurs when a A special form of autocorrelation occurs when a time series can be represented bytime series can be represented by

The prime represents a departure from the The prime represents a departure from the time series mean, often called an time series mean, often called an anomalyanomaly, and , and epsilon white noise.epsilon white noise.

This is called a This is called a first-order Markov processfirst-order Markov process, , damped persistencedamped persistence, and , and red noisered noise. They all . They all mean the same thing.mean the same thing.

x t x t tx1 1

Time Behavior: AutocorrelationTime Behavior: Autocorrelation

5.5. The remarkable thing The remarkable thing about red noise is about red noise is that its spectrum is that its spectrum is characterized by characterized by increasing variance increasing variance towards lower towards lower frequencies:frequencies:

Thus time series of Thus time series of red noise exhibit red noise exhibit trends and swings trends and swings that can be that can be misinterpreted as misinterpreted as deterministic or deterministic or systematic, when systematic, when they are just part of a they are just part of a ‘drunkard’s walk.’‘drunkard’s walk.’ 0 10 20 30 40 50

Index

-3-2

-10

12

day

ano

mal

ies

frequency

Spe

ctra

po

wer

0.0 0.1 0.2 0.3 0.4 0.5redspec$freq

-10

-50

51

0

Idealized spectrum of extra-tropical Idealized spectrum of extra-tropical height variabilityheight variability

0

0.2

0.4

0.6

0.8

1

1.2

0.001 0.01 0.1 1 10

20 days

( = 2 / r )

200020000 200 2

~ 10 dayseasonal~ 6 yr~ 60 yr daily

anthropogenic forcing ?

ENSOeffect

synoptic broadening

red noisebackground

Idealized spectrum of extratropical height variability

P

log

Time Averages

Periods

DefinitionsDefinitions

- Weather – A snapshot of the atmosphere. - Climate – A “time exposure” of the weather over

weeks - years.- Climate Forecast – Probability of unusual warm, cold,

wet, dry, stormy, calm, conditions over weeks, seasons, …

- Natural Variability – Range of possible values of climate.

- Skill – Accuracy of a forecast compared to a standard.- Dynamical Model – Allows physically-based calculation

of likely future values of weather/climate variables.- Statistical Model – Uses empirical relationships to

calculate likely future values of weather/climate variables.

- ENSO – El Nino/Southern Oscillation, comprised of El Nino, Neutral, La Nina

Forecast Process SchematicForecast Process Schematic

Dynamical model forecasts/multi-

model ensembles

Recent observations

Historical observations..

Verifications/Statistical tools Downscaling, Analogs, Composites

WEB PAGES/AUTOMATED DATABASES

Peer-reviews of the forecast tools and of the penultimate forecast via web/telephone conference with partners and through local discussions (map

discussions,sanity check, conference calls, etc…)

Forecaster-created or automated products

Dissemination to public

Long-Range Temperature Forecast Process

RECENT AND HISTORICAL OBSERVATIONS - Mean and typical variability (climatology)

- Most recent status of the atmosphere- Developmental data for statistical models

- Starting values for dynamical models- Verification data for models and forecasts

ASSESSMENT OF UNCERTAINTY- Ensembles of forecasts/inter-compare models

- Status of ENSO- Known skill of models

- Bias removal

HUMAN PREDICTION- Community assessment of forecast models

- Remove uncorrected biases- Make categorical forecasts

- Assign probabilities subjectively

DISSEMINATE

Climatology – The “what” we compare things to, Climatology – The “what” we compare things to, is just the 30-year average of something, like is just the 30-year average of something, like ocean temperature. ocean temperature.

The El Nino/Southern Oscillation (ENSO) is a major factor in global climate variability.

ENSO SST and ENSO SST and SSTaSSTa

Locations For M ajor Storm s (Days Per W inter Season)

La Nina 1998-1999~Above AverageStorm s, H urricane

Force W indsNov 98 - Feb 99

Active HurricaneSeason

Aug-Nov 98

TornadoesJan 1999

No M ajor “Northeasters”

Below Norm alStorm iness

Dec 98-Feb 99

M ajor Ice StormJan 99

M ajorSnow Storm

Jan 99

Extensive IceStorm

Dec 98

Exam ples of C limate Control Over Weather Patterns.The Basis of Linking Climate and W eather

Extensive Storm sDec 97-M ar 98

M assiveIce Storm

Jan 98

Strong “Northeaster”

Storm sJan-Feb 98

Tornado O utbreakFeb 98

Frequent Storm sNov 97-M ar 98

El Nino 1997-1998~

DroughtSpring 1999

Dim inishedHurricane

Threat

Very strong ENSO events produce reliable impacts on short-term U.S. climate, unfortunately, few ENSO events are.

50 years of tropical storm activity

IsabelIsabel

Hurricane Isabel about to make landfall Sep 18, 2003

May 2003 Atlantic Hurricane Season May 2003 Atlantic Hurricane Season OutlookOutlook

Climate Model-derived streamflow Climate Model-derived streamflow forecast for 2003forecast for 2003

CPC’s Seasonal forecasts, a collaborative activity

Each month, on the second Friday, and the following Tuesday,

CPC joins with our main collaborators - NOAA’s ClimateDiagnostics Center (CDC) and the private, non-profit, NOAA-supported International Institute for Climate Prediction (IRI)to discuss the recent status of the climate system, theperformance of our recent forecasts, and the informationavailable in the latest set of forecast tools (Friday) and thefirst draft of the forecast (Tuesday). CPC’s Official Forecastis released on the third Thursday of the month at 8:30 AM.A web page and a telephone conference call are used toexchange information.

Impact on markets is less than 6-10- and 8-14-day forecasts.

CPC forecast system schematic

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

LLF SSTa from 1 Jan 2004LLF SSTa from 1 Jan 2004

LLF for FMA from 1 Jan LLF for FMA from 1 Jan 20042004

FMA 2004 Off TFMA 2004 Off T

CFS skillCFS skill

CFS Obs and FcstCFS Obs and Fcst

OFF T skill

CPC’s Medium-Range Forecasts

- Valid for an average of days 6-10 and 8-14 in the future- Prepared daily. Weekdays by a human. Weekend

automated.- Relies nearly completely on dynamical model forecasts.- A single forecaster prepares the forecast and releases it

at 3PM- Secure computer systems are used to disseminate the

forecasts- Internet and NWS circuits are used to disseminate.- No outside collaboration is used.- Strongly impacts markets.

MRF die off curveMRF die off curveDIE-OFF of model daily forecast skill

6-10 day T6-10 day T

Medium-Range Forecast Process Mirrors that for Long-Range

RECENT AND HISTORICAL OBSERVATIONS - Mean and typical variability (climatology)

- Most recent status of the atmosphere- Developmental data for statistical models

- Starting values for dynamical models- Verification data for models and forecasts

ASSESSMENT OF UNCERTAINTY- Ensembles of forecasts/inter-compare models

- Status of major circulation anomalies- Known skill of models

- Bias removal

HUMAN PREDICTION- Assessment of forecast models

- Remove uncorrected biases- Make categorical forecasts

- Assign probabilities subjectively

DISSEMINATE

Spaghetti diagram

Canadian KL T

T forecast toolsT forecast tools

CDC Pr(upper tercile 850T)

Uncalibrated forecastsUncalibrated forecasts

Calibrated forecastsCalibrated forecasts

6-10 skill

Summaryo The Sun is the ultimate source of all weather and climate variability.o Climate is the average of weather over weeks or longer.o Climate variability (CV) is driven by the tropical oceans, which have a

long “memory” and by complicated interactions among ocean, atmosphere and land.

o Dynamical models are our best hope for predicting weather and climate.

o The climate system is noisy. Only a small portion of CV is predictable – mainly ENSO, some lesser tropical disturbances, and a number of known mid-latitude disturbances.

o Prediction in 2004 is increasingly invested in dynamical models.o Humans assess uncertainty in the models using large numbers of

forecasts called ensembles and construct the forecasts subjectively.o Quantitative assessments of forecast performance relative to some

standard (skill) are used to condition the use of the forecast tools, to assign probabilities and to inform users.

o Automated objective forecasts, a possibility in the future, heighten security risks, since they are more reproducible than human ones.