the role of initial and boundary conditions for sub-seasonal atmospheric predictability thomas...

The Role of Initial and Boundary Conditions for Sub-Seasonal Atmospheric Predictability

Thomas Reichler

Scripps Institution of Oceanography

University of California San Diego

La Jolla, CA

(now at: NOAA-GFDL / Princeton University, Princeton NJ)

Outline

1. Motivation and Goal

2. Methodology

3. Predictability

•temporal evolution

•horizontal distribution

•vertical structure

4. The initial condition effect and the Antarctic oscillation

5. Summary

Elements of predictability

Initial conditions (ICs)

Boundary conditions (BCs)

Physical model

MEtBCICftS );,();0,(),( xxx

Goal of this study Sub-seasonal (2 weeks to 2 months) predictability of the atmosphere

= IC (weather) + BC (climate) prediction problem

ICs initially very strong, but rapid decrease in timeclassical predictability range: ~ 2 weeksbeyond that: weak or zero IC influence!?

persistent features (e.g. blocking, major modes, stratosphere) periodic features (e.g. MJO)

BCs effects are weak, require long time averagingrecent studies: mostly seasonal and longer, impacts of ENSOsub-seasonal range: relatively short averaging period

ocean & land tropics & extratropics

Outline

1. Motivation and Background

2. Methodology

3. Predictability

• temporal evolution

• spatial distribution

• vertical structure

4. The initial condition effect and the Antarctic oscillation

5. Summary

Experimental Design

AGCM with prescribed SSTs Different “qualities” of ICs and BCs, find out how important they are

Base runs • observed (2x) or climatological SST• continuously over many years• to produce ICs for subsequent experiments

Experiments• branching off from base runs• 107 days: DJFM and JJAS (start on the 15th)• 10-20 members, from perturbed ICs (breeding) • 22 years (1979-2000)• different combinations of ICs and BCs

Experiments

•

BC IC BC’ IC’ i ME

ICBC

iBC

BC 0

IC 0

CC 0 0

ICBC-r

rean 0 0

• experiments

MECIICCBBCf

MEICtBCftS

i ;;

);0,();,(),(

xxx

(IC’=0: initial conditions from base run with BC’=0)

Verification Strategy

verification 10-member ensemble-mean of experiment against 1 member of “observation”

“observation” a. one realization of ICBC (perfect model skill) repeat 20 times and average

no model errors > upper limit of predictability

(this is what I mostly show)

b. NCEP reanalysis (real world skill)

measure of skill correlation of geopotential

spatial or temporal (year-to-year)

The Model

• NCEP seasonal forecasting model (e.g. Kanamitsu et al. 2002)

• originates from MRF, similar to reanalysis-2 model

• T42 (300km) L28

• RAS Convection: Moorthi and Suarez (1992)

• SW: Chow (1992)

• LW: Chow & Suarez (1994)

• Clouds: Slingo (1987)

• Gravity wave drag: Alpert et al. (1988)

• 2-layer soil model: Pan & Mahrt (1987)

• Orography: smoothed

• Ozone: zonal mean climatology

10

18

extratropical tropopause

Outline

1. Motivation and Background

2. Methodology

3. Predictability

• temporal evolution

• spatial distribution

• vertical structure

4. The initial condition effect and the Antarctic oscillation

5. Summary

Classical predictabilityevolution of spatial AC for global Z500 during DJFM

CC vs. CC (IC’=0, BC’=0)co

rre

latio

n

lead time (days) lead time (days)

corr

ela

tion

Effects of IC’

initial condition effect has very long time scale anomalous initial conditions (IC’) lead to prolonged predictability possible reason: excitation of low-frequency modes by BC’

lead time (days)

corr

ela

tion

30 day averages

IC vs. IC

CC vs. CC

evolution of spatial AC for global Z500 during DJFM

Effects of IC’ and BC’

evolution of spatial AC for NH Z500 during DJFMverified against ICBC

instantaneous 30 days 90 days

4 weeks

ICs dominate for first 4 weeks (3 weeks during ENSO, 5 weeks during neutral)

lead time (days) lead time (days) lead time (days)

corr

ela

tion

Southern Hemisphere

7 weeks

evolution of spatial AC for SH Z500 during DJFMverified against ICBC

instantaneous 30 days 90 days

Tropics

3 weeks

evolution of spatial AC of tropical Z200 during DJFMverified against ICBC

instantaneous 30 days 90 days

DJFM

JJAS

0

5

10

15

20

25

30

35

40

45

50tim

e (

da

ys) NH

PNA

SH

TROP

Summary: Effects of IC’ and BC’

Time scale for: IC = BC

Effect of model uncertainty

evolution of spatial AC of NH Z500 during DJFMICBC/ICBC vs. ICBC-r/reanalysis

90 days averages

= model error

Outline

1. Motivation and Background

2. Methodology

3. Predictability

• temporal evolution

• horizontal distribution

• vertical structure

4. The initial condition effect and the Antarctic oscillation

5. Summary

Horizontal structure I

ICBC

January monthly mean (week 3-6), Z500, temporal correlation

temporal correlation

Pacific South American region (PSA)

Pacific North American region (PNA)

Antarctica

Tropics

longitude

lati

tude

Horizontal structure II

ICBC iBC BC IC

January monthly mean (week 3-6), Z500, temporal correlation

Effects of persistencepersistence Z500 (Jan)

ICBC

IC

predictability Z500 (Jan)

ICBC

IC

persistent boundary forcing

atmospheric persistence

ICBC

major modes Z500 (JFM)

AAO

SO

NAO

PNA

NA

Outline

1. Motivation and Background

2. Methodology

3. Predictability

• temporal evolution

• horizontal structure

• vertical structure

4. The initial condition effect and the Antarctic oscillation

5. Summary

Vertical structure I

Jan Feb Mar

ICBC: temporal correlations of monthly and zonal mean geopotential

temporal correlation

latitude latitudelatitude

he

igh

t

Vertical structure II

Jan Jan Feb Feb MarMar

ICBC

IC-ICBC

BC-ICBC

Vertical structure III: neutral ENSO

Jan Jan Feb Feb MarMar

ICBC

IC-ICBC

BC-ICBC

Outline

1. Motivation and Background

2. Methodology

3. Predictability

• temporal evolution

• spatial distribution

• vertical structure

4. The initial condition effect and the Antarctic oscillation

5. Summary

Antarctic Oscillation (AAO)

ICBC-B (0.81)

EOF1 (59%)

ICBC-A

IC (0.80)

BC (0.10)

January, Z500

AAO index (Jan 1) and forecast skill (Jan)

AAO index (Jan 1)

El Nino

La Nina

ICBC (0.53)

iBC (0.05) BC (-0.15)

spatial AC for SH Z500 during January, verified against ICBC

AAO index (Jan 1)

IC (0.75)

Outline

1. Motivation and Background

2. Methodology

3. Predictability

• temporal evolution

• spatial distribution

• vertical structure

4. The initial condition effect and the Antarctic oscillation

5. Summary

Summary

The effects of ICs on forecast skill• were detectable for ca. 8 week,• were more important than BCs for ca. 4 weeks,• were particularly important over Antarctica, the Tropics, and the lower stratosphere.

Regions of large skill coincided with regions of major modes.

Total skill (ICBC) can be understood as the sum of IC and BC produced skill (ICBC=BC+IC).

IC produced skill came mostly from atmospheric persistence in relationship with major modes.

Conclusion: Do not underestimate the importance of ICs for seasonal to sub-seasonal forecasts.

Scale variations

0-4 d0-4 d0-4 d 4-104-10 10-2010-20 20-4020-40 40-10040-100

ICBCICBC

Saturation of spectral error energy globally, Z500, DJFM

Maximum gain from ICBC

ICIC

BCBC

m (zonal)

n (total)

Perfect ENSO JFM ZPerfect ENSO JFM ZJANJAN FEBFEB MARMAR

ICBCICBC

IC-IC-ICBCICBC

BC-BC-ICBCICBC

Real world JFM ZReal world JFM Z

JANJAN FEBFEB MARMAR

ICBCICBC

BC-BC-ICBCICBC

Perfect JAS ZPerfect JAS ZJULJUL AUGAUG SEPSEP

ICBCICBC

IC-IC-ICBCICBC

BC-BC-ICBCICBC

Vertical structure II

ICBC

IC

iBC-ICBC

BC-ICBC

Jan Feb Mar

latitude latitude latitude

Predictability of MJOPredictability of MJO30-70 day filtered 200 hPa velocity potential

lead time (days)

corr

ela

tion

• initial conditions are crucial

•boundary conditions are important

~ 4 weeks

Real world, Z500, DJFMReal world, Z500, DJFM

30 days30 days 90 days90 days

NHNH

SHSH

= model error

verified against NCEP/NCAR reanalysis

BCBC

ICIC

Temporal correlation: Z500JAN JAN

(week 3-6)(week 3-6)FEB FEB

(week 7-10)(week 7-10)MAR MAR

(week 11-14)(week 11-14)

significant IC influence

ICBCICBC

BC

ICBC

IC

ICBC

Perfect world: JFM

JAN JAN FEB FEB MARMAR

Zonal mean temporal correlation: Z500

BCBC

ICIC

ICBCICBC

BC

ICBC

IC

ICBC

Perfect world: JAS

JULJUL AUGAUG SEPSEP

Zonal mean temporal correlation: Z500

ICIC

BCBC

ICBCICBC

BC

Real world: JFM

JAN JAN FEB FEB MAR MAR

ICBC

Zonal mean temporal correlation: Z200

BCBC

ICBCICBC

JANFEB

MAR

-0.2

0

0.2

0.4

0.6

0.8

corr

elat

ion

AAO JFM H500 pr9 EM

ICBC

IC

BC

BC1

AAO, JFM, perfectAAO, JFM, perfect

JANFEB

MAR

-0.2

0

0.2

0.4

0.6

0.8co

rrel

atio

n

AAO JFM H500 rean2 EM

ICBC

IC

BC

BC1

AAO, JFM, realAAO, JFM, real

JULAUG

SEP

-0.2

0

0.2

0.4

0.6

0.8co

rrel

atio

n

AAO JAS H500 pr33 EM

ICBC

IC

BC

BC1

AAO, JAS, perfectAAO, JAS, perfect

JANFEB

MAR

-0.2

0

0.2

0.4

0.6

0.8co

rrel

atio

n

AO JFM H1000 pr9 EM

ICBC

IC

BC

BC1

AO, JFM, perfectAO, JFM, perfect

JANFEB

MAR

-0.2

0

0.2

0.4

0.6

0.8

corr

elat

ion

AO JFM H1000 rean2 EM

ICBC

IC

BC

BC1

AO, JFM, realAO, JFM, real

JULAUG

SEP

-0.2

0

0.2

0.4

0.6

0.8co

rrel

atio

n

AO JAS H1000 pr33 EM

ICBC

IC

BC

BC1

AO, JAS, perfectAO, JAS, perfect

OutlineOutline

I. Introduction

II. Experimental Design

III. Results

a. Time evolution of skill and scale variations

b. Regional variations and vertical structure

c. Antarctic oscillation

d. Tropical predictability

IV. Summary

U850 (10N-10S)U850 (10N-10S)

time (d)

0

107

Atl Ind W Pac Atl Atl Ind W Pac Atl Atl Ind W Pac Atl

temporal correlation

ICBCICBC ICIC BC-ICBCBC-ICBC