Predictability from Strat-Trop Coupling in Reforecasts and the...

Short-Term Climate Predictability

Associated With Stratospheric Influences

in Operational Forecast Systems

Thomas Reichler, Junsu Kim (Univ. Utah)

and

Arun Kumar (NOAA/NCEP/CPC)

Predictability from Strat-Trop Coupling in

Reforecasts and the GFDL Climate Model

Thomas Reichler1, Junsu Kim1, and Arun Kumar2

(1 Univ. of Utah, 2 NOAA)

Supported by the Center for High Performance Computing, Univ. of Utah

AGU Chapman Conference on The Role of the Stratosphere in Climate and Climate Change

Santorini, Greece, 24-28 September 2007

Questions & Method

• To what extent do current prediction systems

exploit stratospheric signals?

• How much practical skill is associated with it?

• Does an improved stratospheric component

increase skill?

2

I. Retrospective forecasts

II. Climate model simulations

GFDL AM2NCEP MRF

L28

p (

hP

a)

he

igh

t (k

m)

“high-top”“low-top”

Models: Vertical Structure

NCEP/GFS GFDL AM3

L28 LOW HIGH

“low-top”

7 3 18

3

2.5 hPa 3 hPa

0.02 hPa

Part I

CDC Reforecasts

• Fixed 1998 version of NCEP/GFS, T62 L28

• 1979 - present

• Daily forecasts out to day 15

• 15-member ensemble

• Hamill et al. (2006, BAMS)

Daily forecasts, capture each SSW event

Only out to day 15, miss some of the interesting

action at later times

No stratospheric data

NOAA/CDC Reforecasts

5

Annual mean RMS error in SLP over Northern

Extratropics between reforecasts and reanalysis

Reforecast Climatology Drift

Day 1

4

13

JFM AMJ JAS OND

SLP errors

6

• Ensemble means

• Focus: Northern Annular Mode (NAM)

• Sea level pressure (SLP), and Z850, 700,

500, 250, and 150

• Reference: NCEP/NCAR reanalysis, 1979-

2007

Validation Strategy

7

A. Downward

Propagating Features

Case Study: Winter 2004

JAN1 FEB1 MAR1

Day 8.5 NAMSLPDay 14.5 NAMSLP

Day 0.5 NAMSLP

9

NAMSLP

NCEP/NCAR reanalysis

NAM index

by level and time

Strong Events

• NCEP/NCAR reanalysis:

1979-2007

• NAM10<-3 → 16 events

• Discard 6 events which

exhibit no downward

propagation

• Remain with 10 strong

downward propagating

events

10

Day 15 Forecasts (EM)Reanalysis

Days after event

SSW Composite

10 strong events

NCEP/NCAR Reanalysis

11

Day 15

Day 0

Day 10

Day 5

SSW

Composite

12

CDC reforecasts

Corr

ela

tion

Lagged Correlation NAMSLP

NAM10/SLP

Evolution

NAM10 Reanalyses

Ind

ex

Days after SSW event

Reforecasts day 15

Composite NAMSLP Index

61 days centered

at day 19

Reforecast signal

reaches surface

delayed by ca. 10

days

Reanalyses

B. NAMSLP Predictability

Basic PredictabilityACCSLP vs. NAMSLP

ACCSLP

NAMSLPWinter:NAMSLP

15

NAMSLP Predictability

Da

y o

f ye

ar

Lead time (day)

r

16

• 1979-2007, all days

• Unsmoothed

(shading)

• 31 day running

means (contours)

• Sharp decline in

spring; maybe

related to breakdown

of polar vortex

• Gradual increase in

fall

Correlation: NCEP/NCAR reanalysis vs. CDC reforecast

r=0.6

Correlation AnomaliesCorrelations

Correlation between

reanalysis and

reforecasts

31 day unsmoothed

NAMSLP index

Composite of 10 SSW

events (NAM10 < -3)

Anomalies: February

climatology removed.

NAMSLP

Predictability

During SSWs

0

t [days]

AO index

-10

-20

-30

10

20

30

17

SSW

0

Conclusion I

Despite low vertical stratospheric resolution (7

levels), reforecasts exhibit clear downward

propagating signals.

SSW signals arrive too late at surface; delay

increases with lead time.

Nevertheless, SSW events improve AO

predictability 0-15 days after the events and at

leads of 8-15 days.

Beside the 10 SSW events, additional skill from

stratospheric influences may be realized during

other warm events or during cold events.

Part II

AGCM Experiments

Basic simulation features and sensitivities to forcings

• Very similar to IPCC-AR4 version

• Non-orographic gravity wave drag (Alexander and

Dunkerton 1999)

• RAS deep convection

• Resolution• Horizontal: N45 (144x90)

• Vertical: L24 and L48 (LOW and HIGH)

• LOW and HIGH have identical physics! The only

difference is number and location of vertical levels

20

GFDL AM2/3 Nalanda

A. Mean Climate

and Variability

AMIP-type runs forced with observed boundary

conditions and atmospheric composition (1983-2003)

1983-2001L24NO / L48NO

Mean Temperatures

LOW HIGH

Strato -0.7 / 7.8 2.3 / 3.2

Tropo -0.9 / 2.1 -0.5 / 2.3

L24 L48

Strato -0.7 / 9.8 2.8 / 4.5

Tropo -0.6/ 1.9 -0.2 / 1.8

(mean bias / rms error) [K]

JJA

DJF

ERA LOW-ERA HIGH-ERA

22

1983-2001L24NO / L48NO

Mean U-Winds

(mean bias / rms error) [m/s]

LOW HIGH

Strato -1.2 / 13 -1.3 / 5.7

Tropo -0.2 / 1.5 0.0 / 1.7

LOW HIGH

Strato -4.7/ 20 -4.5 / 14

Tropo -0.0/ 1.7 0.3 / 2.1

ERA LOW–ERA HIGH–ERA

JJA

DJF

23

1983-2001L24NO / L48NO

Interannual Variability U-Wind

JJA

DJF

ERA LOW HIGH

(mean bias / rms error) [m/s]

LOW HIGH

Strato -1.8 / 2.7 -1.1 / 2.3

Tropo 0.0 / 0.3 0.1 / 0.5

LOW HIGH

Strato -2.2/ 4.4 -0.9 / 4.3

Tropo 0.0/ 0.2 0.2 / 0.3

24

LOW HIGH

• [u] and

EOF [u]

• 1983-

1999

• L24/48

NO

ERA

MAM

DJF

JJA

SON

25

SAMu

NAMu

MAM

DJF

JJA

SON

LOW HIGHERA40

26

NAM/SAM Timescales

Reanalysis

LOW

HIGH

NAM SAM

MonthP

ressu

re

NAM10 Lagged Correlations

• DJFM, 1983-2003

• 61 day time series

• 21 day averages

• L24/48NO

HIGH

LOW

Reanalysis

28

Baldwin & Dunkerton CompositesReanalysis LOW

HIGH• 1983-2003

• Reference EOFs from

reanalysis

• Composite on NAM10 < -3

• Reanalysis: 12 events

• Low: 9 events

• High: 12 events

B. Equilibrium Climate

Simulations

Experimental Setup

31

• 20-40 year long AMIP type simulations, const. forcing

• Change only one forcing at a time:

• Varying SSTs: Primarily test stratosphere’s response

to tropospheric climate change

• Frozen SSTs: Primarily test troposphere’s response

to stratospheric climate change

O3 CO2 SST Aerosol Sun Levels

1950 280 1950s 1950 1950 LOW

2000 380 2000s HIGH

720 A1B

1120 4xCO2

=CTRL

Sensitivty to O3 (SON)

Δu

90S 60S 30S Eq 30N 60N 90N 60S 30S Eq 30N 60N 90N

60S 30S Eq 30N 60N 90N

LOW HIGH

[m/s]

[K]

32

ΔT

O3

2000 minus1950

90S 60S 30S Eq 30N 60N 90N

-2.8 ppm

90S 60S 30S Eq 30N 60N 90N

Sensitivity to CO2 (JJA)

LOW HIGH

1120 ppm minus CTRL720 ppm minus CTRL380 ppm minus CTRL

ΔT

Δu

-90 -60 -30 Eq 30 60 90

33

[m/s]

[K]

-90 -60 -30 Eq 30 60 90 -90 -60 -30 Eq 30 60 90 -90 -60 -30 Eq 30 60 90 -90 -60 -30 Eq 30 60 90 -90 -60 -30 Eq 30 60 90

LOW HIGH LOW HIGH

Tropical Expansion

• Change in width of Hadley Cell, derived from

mean meridional mass streamfunction

Degre

es latitu

de

LOW

HIGH

O3 CO2 CO2+O3 SST SST+CO2 SST+

O3+CO2

SST+O3

-0.6

-0.4

-0.2

-1E-15

0.2

0.4

0.6

0.8

1

1.2SS

T50

O3_

SST5

0

CO2_

SST5

0

CO2_

O3_

SST5

0

2xCO

2_SS

T50

2xCO

2_O

3_SS

T50

4xCO

2_SS

T50

4xCO

2_O

3_SS

T50

SST2

k

SST2

k_a

O3_

SST2

k

O3_

SST2

k_a

CO2_

SST2

k

CO2_

O3_

SST2

k

L24 SAM DJF

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2SS

T50

O3_

SST5

0

CO2_

SST5

0

CO2_

O3_

SST5

0

2xCO

2_SS

T50

2xCO

2_O

3_SS

T50

4xCO

2_SS

T50

4xCO

2_O

3_SS

T50

SST2

k

SST2

k_a

O3_

SST2

k

O3_

SST2

k_a

CO2_

SST2

k

CO2_

O3_

SST2

k

L48 SAM DJF

SAM Response

SAM DJF

LOW

HIGH

35

Conclusion II

• HIGH shows improved - but not yet satisfying

stratospheric performance; troposphere

generally degrades (tuning problem?)

• Having a well-resolved stratosphere does not

necessarily guarantee a better troposphere

• 20 year simulation length is often not enough to

cleanly separate signal from noise

• This is preliminary analysis, more work is

underway …

• Everybody is invited to look at these and more

simulations

Thank you.

thomas.reichler@utah.edu

Response to SSTs: JJA

L24 L48 L24 L48 L24 L48

x4CO2A1B2000s (obs.)

ΔT

Δu

-90 -60 -30 Eq 30 60 90

38

[m/s]

[K]

-90 -60 -30 Eq 30 60 90 -90 -60 -30 Eq 30 60 90 -90 -60 -30 Eq 30 60 90 -90 -60 -30 Eq 30 60 90 -90 -60 -30 Eq 30 60 90

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

1.2SS

T50

O3

_SS

T50

CO

2_

SST5

0

CO

2_

O3

_SST

50

2xC

O2

_SS

T50

2xC

O2

_O

3_SS

T50

4xC

O2

_SS

T50

4xC

O2

_O

3_SS

T50

SST2

k

SST2

k_a

O3

_SS

T2k

O3

_SS

T2k_

a

CO

2_

SST2

k

CO

2_

O3

_SST

2k

L24 NAM JJA

-0.6

-0.4

-0.2

-1E-15

0.2

0.4

0.6

0.8

1

1.2

SST5

0

O3

_SS

T50

CO

2_

SST5

0

CO

2_

O3

_SST

50

2xC

O2

_SS

T50

2xC

O2

_O

3_SS

T50

4xC

O2

_SS

T50

4xC

O2

_O

3_SS

T50

SST2

k

SST2

k_a

O3

_SS

T2k

O3

_SS

T2k_

a

CO

2_

SST2

k

CO

2_

O3

_SST

2k

L48 NAM JJA

Annular Mode Response

NAM JJA

L24

L48

39

Trend in HC from mmc (400-600)SST50, CO2, SST2kPS_EXP/plt_exp_hc.ps (plt_exp_hc.pro)

40

Trend in HC from mmc (400-600)SST50, CO2, 2xCO2, 4xCO2PS_EXP/plt_exp_hc.ps (plt_exp_hc.pro)

41

Timescales NAM u1000-100

Winter Summer

NAMu

SAMu

NAM10 Lagged Correlations

• Lagged correlation of NAM10 with NAM at any other level

• NCEP/NCAR reanalysis

• 61 day time series, 21 day averages

• Similar to: Baldwin & Dunkerton, (1999)

DJFM 1979-2007

43

Composite of 10 SSW events

NAM10 Lagged Correlations

CDC reforecasts (15 members)

Day 0

• 10 SSW

composites

44

Day 15

Day 8