Spatial ACC Z500 - University of Utah

Transcript Spatial ACC Z500 - University of Utah

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

Physical model Initial conditions (ICs) Boundary conditions (BCs)

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Goal of this study

 Sub-seasonal (2 weeks to 2 months) predictability of the atmosphere

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

initially very strong, but rapid decrease in time classical predictability range: ~ 2 weeks beyond that: weak or zero IC influence!?

  persistent features (e.g. blocking, major modes, stratosphere) periodic features (e.g. MJO) effects are weak, require long time averaging recent studies: mostly seasonal and longer, impacts of ENSO sub-seasonal range: relatively short averaging period   ocean & land tropics & extratropics

Outline

1. Motivation and Background 2. Methodology 3. Predictability

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temporal evolution

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spatial distribution

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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 15 th ) • 10-20 members, from perturbed ICs (breeding) • 22 years (1979-2000) • different combinations of ICs and BCs

Experiments

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ICBC iBC BC IC CC ICBC-r rean

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(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

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

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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

 extratropical tropopause

Outline

1. Motivation and Background 2. Methodology 3. Predictability

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temporal evolution

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spatial distribution

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vertical structure 4. The initial condition effect and the Antarctic oscillation 5. Summary

Classical predictability

evolution of spatial AC for global Z500 during DJFM CC vs. CC (IC’=0, BC’=0) lead time (days) lead time (days)

Effects of IC’

evolution of spatial AC for global Z500 during DJFM 30 day averages

IC vs. IC CC vs. CC

 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’

Effects of IC’ and BC’

evolution of spatial AC for NH Z500 during DJFM verified against ICBC instantaneous 30 days 90 days lead time (days) lead time (days) lead time (days)  4 weeks ICs dominate for first 4 weeks (3 weeks during ENSO, 5 weeks during neutral)

Southern Hemisphere

evolution of spatial AC for SH Z500 during DJFM verified against ICBC instantaneous 30 days 90 days  7 weeks

Tropics

evolution of spatial AC of tropical Z200 during DJFM verified against ICBC instantaneous 30 days 90 days  3 weeks

Summary: Effects of IC’ and BC’

Time scale for: IC = BC 50 45 40 35 30 25 20 15 10 5 0 NH PNA SH TROP DJFM JJAS

Effect of model uncertainty

evolution of spatial AC of NH Z500 during DJFM ICBC/ICBC vs. ICBC-r/reanalysis 90 days averages

= model error

Outline

1. Motivation and Background 2. Methodology 3. Predictability

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temporal evolution

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horizontal distribution

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vertical structure 4. The initial condition effect and the Antarctic oscillation 5. Summary

Horizontal structure I

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

Pacific North American region (PNA) ICBC Tropics Antarctica longitude Pacific South American region (PSA)

Horizontal structure II

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

ICBC iBC BC IC

Effects of persistence

persistence Z500 (Jan) ICBC NA PNA persistent boundary forcing SO NAO AAO IC IC atmospheric persistence

Outline

1. Motivation and Background 2. Methodology 3. Predictability

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temporal evolution

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horizontal structure

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vertical structure 4. The initial condition effect and the Antarctic oscillation 5. Summary

Vertical structure I

ICBC: temporal correlations of monthly and zonal mean geopotential

Jan Feb Mar

latitude latitude latitude

ICBC IC ICBC BC ICBC

Vertical structure II

Jan Feb Mar

Vertical structure III: neutral ENSO

Jan Feb Mar ICBC IC ICBC BC ICBC

Outline

1. Motivation and Background 2. Methodology 3. Predictability

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temporal evolution

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spatial distribution vertical structure 4. The initial condition effect and the Antarctic oscillation 5. Summary

Antarctic Oscillation (AAO)

January, Z500 ICBC-A EOF1 (59%) ICBC-B (0.81) IC (0.80) BC (0.10)

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

spatial AC for SH Z500 during January, verified against ICBC ICBC (0.53) IC (0.75) El Nino La Nina iBC (0.05) BC (-0.15)

AAO index (Jan 1) AAO index (Jan 1)

Outline

1. Motivation and Background 2. Methodology 3. Predictability

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temporal evolution

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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

Saturation of spectral error energy globally, Z500, DJFM 4-10 10-20 20-40 40-100 Maximum gain from ICBC n (total) m (zonal)

ICBC IC ICBC BC ICBC

Perfect ENSO JFM Z

JAN FEB MAR

ICBC BC ICBC JAN

Real world JFM Z

FEB MAR

ICBC IC ICBC BC ICBC JUL

Perfect JAS Z

AUG SEP

ICBC

Vertical structure II

Jan Feb Mar IC iBC ICBC BC ICBC

latitude latitude latitude

Predictability of MJO

30-70 day filtered 200 hPa velocity potential

~ 4 weeks lead time (days)

• initial conditions are crucial • boundary conditions are important

Real world, Z500, DJFM

verified against NCEP/NCAR reanalysis 30 days 90 days = model error SH

significant IC influence

Temporal correlation: Z500

JAN (week 3-6) FEB (week 7-10) MAR (week 11-14)

Perfect world: JFM

Zonal mean temporal correlation: Z500

JAN FEB MAR

BC ICBC ICBC IC

Perfect world: JAS

Zonal mean temporal correlation: Z500

JUL AUG SEP

IC ICBC ICBC BC

Real world: JFM

Zonal mean temporal correlation: Z200

JAN FEB MAR

BC ICBC

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JAN FEB MAR ICBC IC BC BC1

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JAN FEB MAR ICBC IC BC BC1

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JUL AUG SEP ICBC IC BC BC1

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JAN FEB MAR ICBC IC BC BC1

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JAN FEB MAR ICBC IC BC BC1

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JUL AUG SEP ICBC IC BC BC1

Outline

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

time (d) 107

U850 (10N-10S)

temporal correlation

ICBC IC BC-ICBC 0 Atl Ind W Pac Atl Atl Ind W Pac Atl Atl Ind W Pac Atl