pred briefing CPC 9 14 2010

Assessment of
Intraseasonal to Interannual
Climate Prediction and Predictability

Ben Ki r t man, Uni v. of Mi ami
Randy Kost er , NASA
Eugeni a Kal nay, Uni v. of Mar yl and
Li sa Goddar d, Col umbi a Uni v.
Duane Wal i ser , Jet Pr opul si on Lab, Cal Tech

Sept ember 14, 2010
Cl i mat e Pr edi ct i on Cent er

The National Academies
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advice t o t he Nat ion on science, engineering, and
medicine.

ƒ Nat ional Academy of Sciences (NAS) chart ered in 1863;

The Nat ional Research Council (NRC) is t he operat ing arm

of t he NAS, NAE, and IOM.

ƒ NRC convenes ad hoc commit t ees of expert s who serve
pro bono, and who are caref ully chosen f or expert ise,
balance, and obj ect ivit y

ƒ All report s go t hrough st ringent peer-review and must be
approved by bot h t he st udy commit t ee and t he
inst it ut ion.

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Committee Membership
ROBERT A. WELLER (Chair), Woods Hole Oceanographic Inst it ut ion
ALBERTO ARRIBAS, Met Of f ice, Hadley Cent re
JEFFREY L. ANDERSON, Nat ional Cent er f or At mospheric Research
ROBERT E. DICKINSON, Universit y of Texas
LISA GODDARD, Columbia Universit y
EUGENIA KALNAY, Universit y of Maryland
BENJAMIN KIRTMAN, Universit y of Miami

RANDAL D. KOSTER, NASA
MICHAEL B. RICHMAN, Universit y of Oklahoma
R. SARAVANAN, Texas A&M Universit y
DUANE WALISER, Jet Propulsion Laborat ory, Calif ornia Inst it ut e of Technology
BIN WANG, Universit y of Hawaii
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Charge to the Committee
The st udy commit t ee will:
1. Review current underst anding of climat e predict abilit y on
int raseasonal t o int erannual t ime scales;
2. Describe how improvement s in modeling, observat ional capabilit ies,
and ot her t echnological improvement s have led t o changes in our
underst anding of predict abilit y;
3. Ident if y key def iciencies and gaps remaining in our underst anding of
climat e predict abilit y and recommend research priorit ies t o address
t hese gaps;
4. Assess t he perf ormance of current predict ion syst ems;
5. Recommend st rat egies and best pract ices t hat could be used t o assess
improvement s in predict ion skill over t ime.

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Outline
1) Mot ivat ion and Commit t ee Approach
ƒ
ƒ
ƒ

Why Int raseasonal t o Int erannual (ISI) Timescales?
What is “ Predict abilit y?”
Framework f or report

2) Recommendat ions
ƒ
ƒ
ƒ

Research Goals
Improvements to Building Blocks
Best Practices

3) Case St udies
4) Summary
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Why Intraseasonal to Interannual
(ISI) Timescales?
ƒ “ ISI” - t imescales ranging f rom a couple of weeks
t o a f ew years.

ƒ Errors in ISI predict ions are of t en
relat ed t o errors in longer t erm
climat e proj ect ions

ƒ Usef ul f or a variet y of

resource management
decisions

ƒMany realizat ions/ verif icat ions possible.

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What is “ Predictability?”
ƒ “ The ext ent t o which a process cont ribut es t o
predict ion qualit y. ”

ƒ Lit erat ure provides variet y of int erpret at ions;
commit t ee agreed on qualit at ive approach.
Key aspect s of commit t ee approach
ƒ Quant it at ive est imat es of a upper l i mi t of predict abilit y
f or t he real climat e syst em are not possible.
ƒ Verif icat ion of f orecast s provide a l ower bound f or
predict abilit y.
ƒ Tradit ional predict abilit y st udies (e. g. , t win model
st udies) are qualit at ively usef ul.
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Framework for Analyzing
ISI Forecasting
Perf ormance of ISI f orecast ing syst ems is based upon:

1) Knowledge of Sources of Predictability
How well do we underst and a climat e process/ phenomenon?
2) Building Blocks of Forecasting Systems
To what ext ent do observat ions, dat a assimilat ion syst ems,
and models represent import ant climat e processes?
3) Procedures of Operational Forecasting Centers
How do t hese cent ers make, document , and disseminat e
f orecast s?

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Recommendations Regarding
Sources of Predictability
Many sources of predictability remain to
be fully exploited by ISI forecast systems.
Cr i t er i a f or i dent i f yi ng hi gh-pr i or i t y sour ces:
1) Physical principles indicat e t hat t he source has an
impact on ISI variabilit y and predict abilit y.
2) Empirical or modeling evidence support s (1).

3) Ident if iable gaps in knowledge/ represent at ion in
f orecast ing syst ems.
4) Pot ent ial social value.
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Six Research Goals for
Sources of Predictability
1 ) Madden-Julian Oscillation (MJO)
Develop model diagnost ics and f orecast met rics. Expand
process knowledge regarding ocean-at mosphere coupling,
mult i-scale organizat ion of t ropical convect ion, and cloud
processes.

2) Stratosphere-troposphere interactions
Improve underst anding of link bet ween st rat ospheric
processes and ISI variabilit y. Successf ully simulat e/ predict
sudden warming event s and subsequent impact s.

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Six Research Goals for
Sources of Predictability
3) Ocean-atmosphere
coupling
Underst anding of sub-grid scale
processes should be improved.

4) Land-atmosphere feedbacks
Invest igat e coupling st rengt h bet ween land and
at mosphere. Cont inue t o improve init ializat ion of
import ant surf ace propert ies (e. g. , soil moist ure).
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Six Research Goals for
Sources of Predictability
5) High impact events
(volcanic eruptions, nuclear
exchange)
Develop f orecast s f ollowing rapid, large
changes in aerosols/ t race gases.

6) Non-stationarity
Long-t erm t rends af f ect ing
component s of climat e syst em
(e. g. , greenhouse gases, land use
change) can af f ect predict abilit y
and verif icat ion t echniques.
Changes in variabilit y may also be
12import ant .

Building Blocks of ISI Forecasting Systems
Data Assimilation
Systems
Statistical/
Dynamical
Models

Observational
Networks

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Forecast Improvements involve each
of the Building Blocks
Past improvement s t o ISI
f orecast ing syst ems have
occurred synergist ically.
(e. g. , wit h new
observat ions comes t he
need f or model
improvement and
expansion of DA syst em)

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Improvements to Building Blocks
1) Errors in dynamical models should be
identified and corrected. Sustained
observations and process studies are
needed.

Observat ions (t op) and Model (bot t om)

ƒ Examples:

* doubl e i nt er t r opi cal conver gence zone
* poor r epr esent at i on of cl oud pr ocesses
ƒ Climate Process Teams serve as a usef ul
model f or bringing t oget her modelers
and observat ionlist s
ƒ Ot her programmat ic mechanisms should
be explored
(e. g. f acilit at ing t est ing of increased
model resolut ion)
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SST (shading); precipit at ion (cont ours)

Improvements to Building Blocks
Continue to develop and employ statistical
techniques, especially nonlinear methods.
2)

St at ist ical met hods are usef ul in making predict ions, assessing
f orecast perf ormance, and ident if ying errors in dynamical models.
Cut t ing-edge nonlinear met hods provide t he opport unit y t o augment
t hese st at ist ical t ools.

Statistical methods and dynamical models are
complementary and should be pursued.
3)

Using mult iple predict ion t ools leads t o improved f orecast s.
Examples of complement ary t ools:
ƒ Model Out put St at i st i cs
ƒ St ochast i c Physi cs
ƒ Downscal i ng t echni ques
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Improvements to Building Blocks
4) Multi-model ensemble forecast
strategies should be pursued, but
standards and metrics should be
developed.
MME mean (in red)
out perf orms individual
models (ot her colors).
Black is persist ence (baseline
f orecast ).

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Improvements to Building Blocks
5) For operational forecast systems, state-of-the-art
data assimilation systems should be used (e. g. 4-D
Var, Ensemble Kalman Filters, or hybrids).
Operational data assimilation systems should be
expanded to include more data, beginning with ocean
observations.

Number of
sat ellit e
observat ions
assimilat ed int o
ECMWF
f orecast s.

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Relationship between
Research and Operations
Collaborat ion has expanded knowledge of ISI
processes and improved perf ormance of ISI
f orecast s.

Collaboration is necessary BOTH:
ƒ bet ween r esear ch and oper at i onal sci ent i st s
ƒ among r esear ch sci ent i st s; l i nki ng obser vat i ons,
model devel opment , dat a assi mi l at i on, and
oper at i onal f or ecast i ng.

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20

Examples of
Collaborative Programs

Making Forecasts More Useful

Value of ISI f orecast s f or bot h
researchers and decision makers can
be t ied t o:

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ƒAccess
ƒTr anspar ency
ƒKnowl edge of f or ecast per f or mance
ƒAvai l abi l i t y of t ai l or ed pr oduct s

Best Practices
1) Improve the synergy between research
and operational communities.
ƒ Workshops t arget ing specif ic f orecast syst em
improvement s, held at least annually

ƒ Short -t erm appoint ment s t o visit ing
researchers

ƒ More rapid sharing of dat a, dat a assimilat ion
syst ems, and models

ƒ Dialog regarding new observat ional syst ems
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Best Practices
2) Establish publicly-available
archives of information
associated with forecasts
ƒ
ƒ
ƒ

Includes observat ions, model code, hindcast s,
f orecast s, and verif icat ions.
Will allow f or quant if icat ion and t racking of
f orecast improvement .
Bridge t he gap bet ween operat ional cent ers
and f orecast users involved in making
climat e-relat ed management decisions or
conduct ing societ ally-relevant research.

3) Minimize t he subj ective components of
operational ISI forecasts.
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Best Practices
4) Broaden and make available forecast metrics.
ƒ Mult iple met rics should be used;

No perf ect met ric exist s.
ƒ Assessment of probabilist ic inf ormat ion is import ant .
ƒ Met rics t hat include inf ormat ion on t he dist ribut ion of skill
in space and t ime are also usef ul.

Examples of
probabilit y densit y f unct ions
represent ing f orecast s f or ENSO

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Case Studies
El Niño–Southern Oscillation (ENSO)
Madden-Julian Oscillation (MJO)
Soil Moisture

Case st udies illust rat e how improvements of
building blocks of ISI f orecast ing syst em led t o an
improved representation of a source of
predictability.
Also illust rat e collaboration among researchers
and operat ional f orecast ing cent ers.
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ENSO: Progress to Date
ƒ Observat ions by
TAO/ TRITON have been
crit ical t o progress in
underst anding and
simulat ion.

ƒ Dynamical models have
improved and are
compet it ive wit h st at ist ical
models.

ƒ MME mean out perf orms
individual models.
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Errors in Nino3. 4 Predict ions since 1962

ENSO: Gaps in Understanding

ƒ How does int raseasonal variabilit y (e. g. , MJO,

west erly wind burst s) af f ect ENSO event init iat ion and
evolut ion?

ƒ Chronic biases (e. g. , double ITCZ) in climat e models
af f ect ENSO simulat ion.

ƒ Gaps st ill remain in init ializing f orecast s.

Ef f ort s should f use improvement s in
underst anding ocean-at mosphere coupling t o t he
upgrading of predict ion t ools (t arget ed process
st udies, simulat ion of sub-grid scale processes,
expanded dat a assimilat ion, et c. )
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MJO: A key source of
intraseasonal
predictability
ƒ Dominant f orm of int raseasonal
at mospheric variabilit y, af f ect ing
precipit at ion and convect ion
t hroughout t he t ropics

ƒ Can int eract wit h Indian
monsoon and ext rat ropical
circulat ion

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MJO: Gaps in
Understanding
ƒ Evaluat ing
available
predict ion t ools is
crit ical

ƒ Target ed

invest igat ions of
cloud processes,
vert ical st ruct ure
of diabat ic heat ing
are necessary
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Observat ions

ƒ Forecast ing of MJO is
relat ively new; many
dynamical models st ill
represent MJO poorly
Models

Soil Moisture:
Predictability for Temperature,
Precipitation, and Hydrology

Soil moist ure can af f ect
regional t emperat ure
and precipit at ion. It
also has implicat ions f or
st reamf low and ot her
hydrological variables.

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Soil Moisture: Gaps in Understanding
ƒ Init ializat ion is a challenge due t o spat ial and

t emporal het erogeneit y in soil moist ure

ƒ Procedures f or measuring land-at mosphere

coupling st rengt h are st ill being developed

ƒ Land Data Assimilation
Systems (LDAS) coupled with
satellite observations could
contribute to initialization

ƒ Further evaluation and

intercomparison of models
are necessary

Forecast skill: r 2 wit h land ICs minus t hat
obt ained w/ o land ICs

Summary of Recommendations
Research Goals
Improve knowledge of sources
of predict abilit y

ƒMJO
ƒOcean-at mosphere
ƒLand-at mosphere
ƒSt rat osphere
ƒNon-st at ionarit y
ƒHigh impact event s

Long-t er m:
year s t o decades;
mai nl y t he r esear ch
communi t y

Improvements to Building
Blocks

ƒ Ident if y and correct model errors by
support ing sust ained observat ions and
process st udies

ƒ Implement nonlinear st at ist ical
met hods

ƒ Use st at ist ical and dynamical
predict ion t ools t oget her

ƒ Cont inue t o pursue mult i-model
ensembles

ƒ Upgrade dat a assimilat ion schemes

Medium-t erm:
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comi ng year s; shar ed r esponsi bi l i t y of
r esear cher s and oper at i onal cent er s

Summary of Recommendations
Best Practices

ƒImproved synergy bet ween
research and operat ions

ƒArchives
ƒMet rics

Shor t -t er m:
r el at ed t o cur r ent and
r out i ne act i vi t i es of
oper at i onal cent er s

ƒMinimize subj ect ive int ervent ion
Adoption of Best Practices:

• requires st able support f or research gains t o be int egrat ed int o operat ions;
• est ablishes an inst it ut ional inf rast ruct ure t hat is commit t ed t o doing so;
• will est ablish “ f eedbacks” t hat guide f ut ure invest ment s in making
observat ions, developing models, and aiding decision-makers
(i. e. , BEYOND “ t radit ional” operat ions);

• represent s
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a cont inuous improvement process.

For more information:
Nat ional Research Council
Joe Casola
202. 334. 3874
j casola@nas. edu
Report is available online at www. nap. edu.

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Image Credits
Sl i de
6 Grand Coolee Dam – Bonneville Power Administ rat ion; Wheat f ield – USDA; Cloud
f ract ion image - M. Wyant , R. Wood, C. Bret hert on, C. R. Mechoso, Pr e-VOCA
11 ENSO - McPhaden (2004), BAMS, 85 , 677–695
12 Volcano – USGS; Keeling curve – Scripps Inst it ut e of Oceanography
13 Buoy – NOAA; Model globe - NOAA
14 SST graph – Balmaseda et al. , Pr oceedi ngs of Oceanobs’ 09, ESA Pub. WPP-306,
(2009)
15 Double-ITCZ - Lin (2007) J. Cl i mat e, 20 , 4497–4525.
17 MME – Jin et al. , Cl i mat e Dynami cs, 31 , 647-664 (2008)
18 Sat ellit e obs – ECMWF
20 Sources are f rom t he respect ive organizat ions
21 Flooding – NRCS; Volcano – NASA; Drought – NESDIS; Moscow sun - BBC
23 Nat ional Archives
24 Pdf ’ s - IRI
26 Line plot – St ockdale et al. , Cl i m. Dyn. (in review, 2010); CFS – adapt ed f rom
Saha et al. , J. Cl i mat e, 19 , 3483-3517 (2006)
28 MJO – Waliser, Pr edi ct abi l i t y of Weat her and Cl i mat e, Cambridge Univ. Press
(2006)
29 MJO Models – Kim et al. , J. Cl i mat e, 22, 6413-6436 (2009)
30 Soil moist ure – Senevirat ne et al. , Ear t h-sci . Revi ews, 99 , 3-4, 125-161 (2010)
31 US Map plot - Kost er et al. , GRL, doi10. 1029/ 2009GL041677, (2010)
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