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