the dynamics and predictability of sudden stratospheric

Domeisen | ECMWF | November 2019

The dynamics and predictabilityof sudden stratospheric warmings

and their surface impacts

Daniela DomeisenAtmospheric Predictability, ETH Zurich

with contributions fromBernat Jiménez-Esteve, Alexander Wollert, Hilla Gerstman-Afargan,

Amy Butler, Andrew Charlton-Perez, Peter Hitchcock, Christian Grams, Lukas Papritz


[m2

K / s

/ kg

]

video: A. Wollert

THE SUDDEN STRATOSPHERIC WARMING EVENT ON FEBRUARY 12, 2018

Feb 8 Feb 10 Feb 12

Feb 14 Feb 16 Feb 18

Figures: potential vorticity at 10hPa by Alexander Wollert


video: A. Wollert

PART I: WHY DO WE CARE ABOUT THE STRATOSPHERE?

Part I. The diversity of sudden stratospheric warming events

Part II. The diversity of the tropospheric impact

The central date of a sudden stratospheric warming (SSW) event is not predictable beyond ~2 weeks.

perc

enta

ge o

f ens

embl

e m

embe

rs [%

] tha

t are

abl

e to

pre

dict

the

SSW

eve

nt


manuscript submitted to JGR: Atmospheres

Figure 5. The average across all events of the percentage of ensemble members as a func-

tion of lead time [days] that detect the event within ± 3 days of the observed event for (a) early

stratospheric warming events, (b) strong polar vortex events, (c) SSW events, (d) negative heat

flux events, and (e) final warming events. The black line shows the multi-model mean based

on 5 prediction systems (CMA, ECCC, ECMWF, JMA, and UKMO). Dotted lines show where

25% and 75% of ensemble members detect the event. ’⇥’ marks the high-top models in the leg-

end. Where a prediction system was not used for the analysis or where there were not enough

available ensemble members (at least 10 members were required for a given lead time range) is

marked by an ⇥ in the color of the prediction system. Patterned black bars give the “false alarm

rate” (events that were predicted but not detected at the given lead times).

–13–












–13–












–13–

false alarm rate

S2S prediction systems (Vitart et al 2017):multi-model mean

Figure: M. Taguchi & A. Butler. From: Domeisen et al., 2019, JGR special issue on S2S prediction

WHAT IS THE PREDICTABILITY LIMITFOR SUDDEN STRATOSPHERIC WARMINGS? Part I – Part II

11 SSW events (1996 – 2010)

x = high-top models:

In the stratosphere (10hPa): Sudden Stratospheric Warming (SSW) events can be characterized as…

Jan 24, 2009Jan 5, 2004

displacement eventwave-1

split eventwave-2

DIFFERENT TYPES OFSUDDEN STRATOSPHERIC WARMINGS


Part I – Part II

THE PREDICTABILITY DIFFERSFOR DIFFERENT TYPES OF SSW EVENTS

Split SSW events tend to be less predictable than displacement events(note the small sample size!)



Figure 6. Same as Fig. 5 but for SSW events separated into (a) displacement and (b) split

events. The black line corresponds to the multi-model mean from Figure 5c, the blue / red lines

indicate the multi-model mean for the displayed events only. A student t-test of the di↵erences

between the detection of splits and displacements gives the following p-values for lead times from

left to right: [0.6948,0.0279,0.7550,0.357,0.0925,0.3740]. The false alarm rates shown by the black

patterned bars are for all SSW events, as in Fig. 5c.

removing the model climatology (leaving the year to be corrected out) and then adding365

back ERA-interim climatology. The bias-correction had the strongest influence on the366

detection of strong vortex and negative heat flux events at long-leads (not shown). In367

particular, after bias-correction, a smaller percentage of members across prediction sys-368

tems detected strong vortex events at long lead times (suggesting an overestimation of369

these events in the model climatology), and a greater percentage of detected negative370

heat flux events at long lead times (suggesting an underestimation of these events in model371

climatology, in agreement with results from the the Coupled Model Intercomparison Project372

Phase 5 (CMIP5) models (Shaw et al., 2014, Fig. 5)).373

Figure 5 shows the percentage of ensemble members for each prediction system that374

detects the observed event within ± 3 days of its actual date, for lead times averaged375

over 5-day periods prior to the event, which occurs on day 0. The bin length is chosen376

as a balance between having su�cient hindcasts in each bin for each event while resolv-377

ing the lead times before each event. The “false alarm rate” is the percentage of mem-378

bers that predict an event to occur within a 1-30 day lead time when no event was ob-379

served. The comparison of the hit rate with the false alarm rate in Fig. 5 provides a mea-380

sure of the predictive skill.381

Below, we describe the di↵erences in the predictability between the di↵erent types382

of polar vortex events. The results should be prefaced by a number of caveats: 1) not383

all prediction systems produce a hindcast in each time bin for each event; 2) the num-384

ber of ensemble members varies across prediction systems; 3) the number of events is gen-385

erally small, due to the short period covered by the hindcasts; 4) hindcast data from dif-386

ferent model versions of a given model are sometimes used; 5) the ± 3-day window is an387

arbitrary choice which could matter for the accuracy in the detection of the events shown388

here; 6) the false alarm rates are used as a baseline for skill but the prediction systems389

could over- or underestimate these events, even after bias-correction; and 7) the percent-390

age of ensemble members forecasting an event is only one metric for the assessment of391

–14–

multi-model mean

perc

enta

ge o

f ens

embl

e m

embe

rs [%

] tha

t are

abl

e to

pre

dict

the

SSW

eve

nt

Can the difference in predictability of these events tell us anything about the different dynamics of these events?

Part I – Part II

Figure: M. Taguchi & A. Butler. From: Domeisen et al., 2019, JGR special issue on S2S prediction

6 displacement SSWs

5 split SSWs

SSW = sudden stratospheric warming

Data: S2S prediction systems (Vitart et al 2017)

x = high-top models:

THE DYNAMICS: SSWS ARE CAUSED BY UPWARD PROPAGATING WAVES

following Charney & Drazin (1961)

Upward propagation of planetary Rossby waves is limited to a range of background wind speeds whose limits depend on the wave characteristics


zonal wavenumber

background zonal wind

critical velocity

zonal phase speed of the wave

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The majority of waves are stationary (c = 0), but c ≠ 0 can have important consequences:

For c > 0, the propagation window opens towards higher wind speeds u.

For c < 0, the propagation window closes for high wind speeds u.

Part I – Part II

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westward eastwardwestward eastward

~ 5.5.km above ground ~ 35 km above ground


Figures: Domeisen, Martius, Jiménez-Esteve, 2018, GRL

troposphere stratosphere

phase speed c [m/s]

HOW DO THESE CONSTRAINTS MANIFEST IN THE ATMOSPHERE?

Peak moves towards eastward propagating waves with heightzo

nal w

ave

num

ber

stat

iona

ry (c

=0)

If eastward propagating waves are favored for upward propagation, are these waves prominent before SSW events?

Increased eastward phase speed for model simulation with SSW events

Figure: Domeisen & Plumb, 2012, GRL

Figure 5. Comparison of the growth rates s [s!1] as a function of latitude (equation (1)), proportional to the Eady growthrate for the most unstable mode.

Figure 4. Phase speed spectra computed from geopotential height for zonal wave-2 at 189 hPa and 60 "S for (a) the controlrun, (b) the trunc12 run, and (c) the trunc2 run.

DOMEISEN AND PLUMB: ORIGIN OF TRAVELING PLANETARY WAVES L20817L20817

4 of 5

Figure 5. Comparison of the growth rates s [s!1] as a function of latitude (equation (1)), proportional to the Eady growthrate for the most unstable mode.

Figure 4. Phase speed spectra computed from geopotential height for zonal wave-2 at 189 hPa and 60 "S for (a) the controlrun, (b) the trunc12 run, and (c) the trunc2 run.

DOMEISEN AND PLUMB: ORIGIN OF TRAVELING PLANETARY WAVES L20817L20817

4 of 5

model run WITHOUT sudden stratospheric warming events

model run WITH (split) sudden stratospheric warming events

wave-2

wave-2

189hPa / 60⚬ lat

CAN SIMPLE MODELS REPRODUCE THIS?

Model: GFDL dry dynamical core (Held-Suarez)


Part I – Part II

ARE EASTWARD PHASE SPEEDS FAVORED BEFORE SSWS IN REANALYSIS?

Increased eastward phase speed before split SSW events

Split SSW events are likely helped along by increased zonal phase speeds

Confidential manuscript submitted to Geophysical Research Letters

-20 -15 -10 -5 0 5 10 15 20

wave-1

0

0.05

0.1

0.15median SSW (days -19 to -6) = -1.03 median clim = -0.68KStest: not signif.

phase speed (100hPa)

-20 -15 -10 -5 0 5 10 15 20

ae

eeve

0

0.05

0.1

0.15median SSW (days -19 to -6) = -0.68 median clim = 0.04KStest: 99%

-20 -15 -10 -5 0 5 10 15 20

eve

0

0.05

0.1

0.15median SSW (days -19 to -6) = 1.67 median clim = -0.68KStest: 99%

zonal phase speed [m/s]-20 -15 -10 -5 0 5 10 15 20

wave-2

0

0.05

0.1

0.15= 1.03median SSW (days -19 to -6)

median clim = 0.04KStest: 99%

0 100 200 300 400 500 600 700

×10-3

0

1

2

3

4

5

6

7median SSW (days -19 to -6) = 296.91


amplitude (100hPa)

0 100 200 300 400 500 600 700

×10-3

0

1

2

3

4

5

6


median clim = 184.39KStest: not signif.

0 100 200 300 400 500 600 700

×10-3

0

1

2

3

4

5

6



wave amplitude [gpm]0 100 200 300 400 500 600 700

×10-3

0

1

2

3

4

5

6


median clim = 184.39KStest: not signif.

wave-2

wave-1

Figure 3. Phase speed [ms�1] (left) and amplitude [gpm] (right) at 100hPa for daily average values for

days -19 to -6 before a SSW event (gray bars) and for the NDJFM climatology (black bars). Additional hori-

zontal bars indicate the climatology where the gray bars are taller than the black bars. The median values for

the respective distributions and significant di�erences between the distributions according to a two-sample

Kolmogorov-Smirnov test are indicated. Values of 90%, 95%, and 99% significance are tested. Significance

values below 90% are indicated as ’not significant’. All distributions are normalized for comparison.

170

171

172

173

174

175

–16–

Figure: Domeisen, Martius, Jiménez-Esteve, 2018, GRL

eastwardwestward

westward eastward


100hPa

100hPa

climatological phase speed distribution

phase speed distribution before a SSW event

Part I – Part II

Phase speed does NOT play a major role before displacementSSW events

climatological phase speed distribution

phase speed distribution before a SSW event

THIS IS NOT THE CASE FOR DISPLACEMENT SSWS


eastwardwestward

westward eastward

100hPa

100hPa

Part I – Part II

Displacement events instead exhibit a large increase in amplitude of wave-1.

wave-2 is not helping with displacement events


Do split and displacement SSW events exhibit different predictability because they are driven by a different mechanism?

Sudden stratospheric warming event on January 24, 2009

Confidential manuscript submitted to Geophysical Research Letters

-25 -20 -15 -10 -5 0 5phase speed [m/s]

10 15 20 25

ampl

itude

[gpm

]100

200

300

400

500

600

700

Jan 4

Jan 7Jan 10Jan 13Jan 16

Jan 19

-25 -20 -15 -10 -5 0 5phase speed [m/s]

10 15 20 25

ampl

itude

[gpm

]

100

200

300

400

500

600

700

Jan 4Jan 7

Jan 10

Jan 13Jan 16

Jan 19

wave-1

wave-2

Figure 4. Daily zonal phase speed [ms�1] vs wave amplitude [gpm] for November - March 1958-2013

(gray circles) and for days -20 to -5 before the 2009 SSW event (red circles) for wave-1 (top) and wave-2

(bottom) at 100 hPa. Every circle represents a daily average value and every third day before the 2009 SSW

event is indicated.

206

207

208

209

–17–

CASE STUDY: THE 2009 SPLIT SSW EVENT


The acceleration and subsequent deceleration of the phase speed hints at a resonant effect responsible for the 2009 SSW event.see also: Matthewman & Esler, 2011; Plumb 2010


days -20 to -5 before 2009 SSW event

Nov – Mar 1958-2013

Daily zonal phase speed vs wave amplitude for

Part I – Part II

SUMMARY – PART I


Stratospheric eventPredictability: days to weeks

1. Eastward zonal phase speeds increase wave propagation into strong winds and can thereby help to efficiently weaken the vortex before SSW events.

2. Strong changes in phase speed can hint at resonant behavior.

3. The very unpredictable 2009 event shows strong signs of resonance, which might be responsible for the low predictability.

Domeisen, Martius, Jiménez-Esteve, 2019;see also: Matthewman & Esler, 2011

Predictability likely depends on the mechanism causing the event.

Part I – Part II

PART II: THE SURFACE IMPACT OF THE STRATOSPHERE



Tropospheric impactPredictability: weeks to months

What is the surface impact of SSWs and what does it depend on?

WHY DO WE CARE ABOUT SUDDEN STRATOSPHERIC WARMING EVENTS?

Figure: modified from cpc.ncep.noaa.gov

positiveNAO

negative NAO

OBSERVATIONS

Negative NAO tendency after a sudden stratospheric warming event changes the weather over Europe

SSW event


Part I – Part II


warm

cold, snow

cold, dry

warm, rain

warm, rain

dry

cold

warmjet streamL

H

L

H

Sudden stratospheric warming events lead to a wavy jet stream with cold weather in northern and central Europe.

Negative NAO tendency after a sudden stratospheric warming event changes the weather over Europe

Positive NAO:early Dec 2017 – mid-February 2018

Negative NAO: After SSW event:Late February 2018 – end of March 2018

Part I – Part II

WHY DO WE CARE ABOUT SUDDEN STRATOSPHERIC WARMING EVENTS?

WHAT IS THE“DOWNWARD IMPACT” OF SSW EVENTS?


all SSW (24)SSW not followed by persistent NAO or switch to negative NAO (8)

SSW followed by persistent NAO (14)

Figure: Domeisen, 2019, JGR-Atmospheres 500hPa geopotential height [m]

days

8 to

52

afte

r SSW

eve

nt

A negative NAO / NAM event is generally seen as the response to SSW events. see e.g. Baldwin & Dunkerton, 2001; Charlton-Perez et al, 2018; Karpechko et al 2017

NAO = North Atlantic OscillationNAM = Northern Annular Mode

Part I – Part II

Shading: values significant at p < 0.05 level.

NOT ALL SSW EVENTS EXHIBITA “DOWNWARD IMPACT”


all SSW (24)

500hPa geopotential height [m]Figure: Domeisen, 2019, JGR-Atmospheres


This signal is dominated by the ~ two thirds of SSW events that exhibit a negative NAO signal. Domeisen, 2019.see also: Charlton-Perez et al, 2018; Karpechko et al 2017

days

8 to

52

afte

r SSW

eve

nt

NAO = North Atlantic Oscillation

Part I – Part II


NOT ALL SSW EVENTS EXHIBITA “DOWNWARD IMPACT”


all SSW (24)

500hPa geopotential height [m]Figure: Domeisen, 2019, JGR-Atmospheres

SSW not followed by persistent NAO or switch to negative NAO (8)


days

8 to

52

afte

r SSW

eve

nt

Note: in turn, only 25% of persistent negative NAO events are preceded by a SSW event.

Part I – Part II


THE DIVERSITY IN THE SURFACE IMPACT OF SSWS MANIFESTS IN THE STORM TRACK RESPONSE


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–19–

equatorward Atlantic jet (16)

poleward Atlantic jet (8)

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from: Afargan-Gerstman & Domeisen, in rev.

all SSW events (24)

Figure: tropospheric circulation averaged over 30 days following SSW events

response to SSWs:equatorward shifted jet in Atlantic

response to SSWs:weaker storm track in Atlantic

Part I – Part II


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tth

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JFcl

imat

olog

yof

(b,e

,h)

300-

hPa

zona

lwin

d(c

onto

ur

inte

rval

10m

s-1,s

tart

ing

from

10m

s-1),

and

(c,f,

i)E

KE

(con

tour

inte

rval

0.1

MJ

m-2

,sta

rtin

g

from

0.3

MJ

m-2

).A

nom

alie

ssi

gnifi

cant

atth

e95

%le

vel(

usin

ga

Stud

ent’

st-

test

)ar

een

clos

ed

bygr

ayco

ntou

rs.

The

red

box

in(b

)in

dica

tes

the

area

wit

hth

est

rong

est

SSW

influ

ence

on

zona

lwin

din

the

mid

lati

tude

Nor

thA

tlan

tic.

The

brac

kets

inth

eti

tle

ofea

chpa

neli

ndic

ate

the

num

ber

ofSS

Ws

inea

chco

mpo

site

.

355

356

357

358

359

360

361

362

363

364

365

366

–19–

equatorward Atlantic jet (16)


zona

l win

d an

omal

y at

30

0hPa

eddy

kin

etic

ene

rgy

anom

aly

inte

grat

ed

from

100

0 to

200

hPa


all SSW events (24)


negative NAO response

negative NAO response

Part I – Part II

THE DIVERSITY IN THE SURFACE IMPACT OF SSWS MANIFESTS IN THE STORM TRACK RESPONSE

IS THE SURFACE IMPACT IN THE NORTH ATLANTIC MODULATED BY THE PACIFIC?


man

uscr

ipt

subm

itte

dto

Geo

phys

ical

Res

earc

hLe

tter

s

Fig

ure

1.C

ompo

site

sof

the

trop

osph

eric

circ

ulat

ion

aver

aged

over

the

30da

ysfo

llow

ing

(top

row

)al

l24

hist

oric

alSS

Wev

ents

,and

SSW

even

tsas

soci

ated

wit

h(m

iddl

e)eq

uato

rwar

dan

d

(bot

tom

)po

lew

ard

Atl

anti

cje

tsh

ifts

(16

and

8ev

ents

,res

pect

ivel

y)in

the

ER

A-I

nter

imre

anal

y-

sis

(197

9–20

14).

Col

orsh

adin

gre

pres

ents

the

(a,d

,g)

MSL

Pan

omal

ies

(hPa)

,(b,

e,h)

zona

lwin

d

anom

alie

s(m

s-1)

at30

0hP

a,an

d(c

,f,i)

stor

mtr

ack

inte

nsity

,exp

ress

edus

ing

EK

E(M

Jm

-2),

vert

ical

lyin

tegr

ated

from

1000

to20

0hP

a.A

nom

alie

sar

eco

mpu

ted

wit

hre

spec

tto

the

daily

cli-

mat

olog

y.B

lack

cont

ours

repr

esen

tth

eD

JFcl

imat

olog

yof

(b,e

,h)

300-

hPa

zona

lwin

d(c

onto

ur

inte

rval

10m

s-1,s

tart

ing

from

10m

s-1),

and

(c,f,

i)E

KE

(con

tour

inte

rval

0.1

MJ

m-2

,sta

rtin

g

from

0.3

MJ

m-2

).A

nom

alie

ssi

gnifi

cant

atth

e95

%le

vel(

usin

ga

Stud

ent’

st-

test

)ar

een

clos

ed

bygr

ayco

ntou

rs.

The

red

box

in(b

)in

dica

tes

the

area

wit

hth

est

rong

est

SSW

influ

ence

on

zona

lwin

din

the

mid

lati

tude

Nor

thA

tlan

tic.

The

brac

kets

inth

eti

tle

ofea

chpa

neli

ndic

ate

the

num

ber

ofSS

Ws

inea

chco

mpo

site

.

355

356

357

358

359

360

361

362

363

364

365

366

–19–

all SSW events (24) equatorward Atlantic jet (16)


zona

l win

d an

omal

y at

30

0hPa

eddy

kin

etic

ene

rgy

anom

aly

inte

grat

ed

from

100

0 to

200

hPaThe opposite

responses in the North Atlantic storm track also exhibit opposite “precursors” in the eastern North Pacific!

The troposphere can have a strong impact on the manifestation of the downward response to SSWs. see also: Garfinkel et al 2013, Chan & Plumb, 2009



Part I – Part II

TO WHAT EXTENT DOES THE SURFACE IMPACT OF SSWSDEPEND ON THE STATE OF THE TROPOSPHERE?


(a) (b)

(c) (d)

[hPa]

[hPa]

[hPa]

[hPa]

[std]

Figure 3. Standardized geopotential height anomalies for the sector -80�E to 40�E / 60�N to 90�N for (a) all SSW events, and (b - d) sub-

divided by the weather regime that is dominant at the onset of the SSW event as indicated in the panel titles. Hatching (stippling) indicates

that the confidence intervals and the random distributions overlap by less than 25% (10%).

is again illustrated by a case study of the SSW events on Feb 12, 2018 (Fig. 4c). This anomaly is not highly robust, but it is

nevertheless significantly different from a random sample at the 25 % level. Several SSWs with a cyclonic regime at the onset

are followed by GL at a longer lag (Fig. 2d), thus, likely causing these anomalies. Still the GL frequencies only reach 25% at

most and also other regimes occur more often albeit with low frequencies around 25%. These findings and the small amplitude

of the anomalies suggest that the variability in the tropospheric response to SSWs is large after a cyclonic regime at lag zero -220

which is also confirmed by the inspection of individual cases (not shown).

5 Impact on Surface Weather

Since each weather regime is associated with characteristic surface weather, the modulation of regime successions in the

aftermath of a SSW by the tropospheric state at the time of a SSW is likely a key contributor to the marked variability in

the surface impact, such as, for example, 2m temperature. Hence, we here consider spatial composites of 2m temperature225

anomalies and anomalies of 500 hPa geopotential height (Z500’) for the three groups of SSW events discussed in the previous

sections (Fig. 5).

During SSWs with GL at the onset, initially strong warm anomalies prevail over Greenland and the Canadian Archipelago,

whereas western Russia and Scandinavia are anomalously cold (Fig. 5a). Consistent with the subsequent progression of weather

regimes - typically towards the cyclonic AT regime or EuBL - mild conditions are established throughout central Europe (from a230

9

Domeisen, Grams, Papritz, in prep.

The events with “downward impact” are dominated by events that show blocking over Europe at the onset of the SSW.

These regimes show a favor for transitioning into Greenland blocking, the “canonical response” to SSWs.

at onset of SSW (day 0) at onset of SSW (day 0)

at onset of SSW (day 0)

Units: Standard deviation of geopotential height anomalies for Atlantic sector.

(a) (b)

(c) (d)

[hPa]

[hPa]

[hPa]

[hPa]

[std]

Figure 3. Standardized geopotential height anomalies for the sector -80�E to 40�E / 60�N to 90�N for (a) all SSW events, and (b - d) sub-

divided by the weather regime that is dominant at the onset of the SSW event as indicated in the panel titles. Hatching (stippling) indicates

that the confidence intervals and the random distributions overlap by less than 25% (10%).

is again illustrated by a case study of the SSW events on Feb 12, 2018 (Fig. 4c). This anomaly is not highly robust, but it is

nevertheless significantly different from a random sample at the 25 % level. Several SSWs with a cyclonic regime at the onset

are followed by GL at a longer lag (Fig. 2d), thus, likely causing these anomalies. Still the GL frequencies only reach 25% at

most and also other regimes occur more often albeit with low frequencies around 25%. These findings and the small amplitude

of the anomalies suggest that the variability in the tropospheric response to SSWs is large after a cyclonic regime at lag zero -220

which is also confirmed by the inspection of individual cases (not shown).

5 Impact on Surface Weather

Since each weather regime is associated with characteristic surface weather, the modulation of regime successions in the

aftermath of a SSW by the tropospheric state at the time of a SSW is likely a key contributor to the marked variability in

the surface impact, such as, for example, 2m temperature. Hence, we here consider spatial composites of 2m temperature225

anomalies and anomalies of 500 hPa geopotential height (Z500’) for the three groups of SSW events discussed in the previous

sections (Fig. 5).

During SSWs with GL at the onset, initially strong warm anomalies prevail over Greenland and the Canadian Archipelago,

whereas western Russia and Scandinavia are anomalously cold (Fig. 5a). Consistent with the subsequent progression of weather

regimes - typically towards the cyclonic AT regime or EuBL - mild conditions are established throughout central Europe (from a230

9

Hatching (stippling): confidence intervals and the random distributions overlap by less than 25% (10%).

Cyclonic regimes: zonal regime, Atlantic trough, Scandinavian trough

Part I – Part II

Negative NAO response to sudden stratospheric warming event in reanalysis

500hPa geopotential height anomaly for days 15 – 60 after an SSW event [m]


ERAinterim(32 SSW events)

HOW ABOUT THE ROLE OF THE STRATOSPHERE FOR THE SURFACE IMPACT?

Domeisen, Hitchcock et al, in prep.

MPI-ESM model(894 SSW events)

Part I – Part II

Data: MPI-ESM seasonal prediction model: Baehr et al 2015

Negative NAO response in reanalysis and seasonal forecasting model is considerably stronger for persistent SSW events (Polar Jet Oscillation events)

500hPa geopotential height anomaly for days 15 – 60 after an SSW event [m]


HOW ABOUT THE ROLE OF THE STRATOSPHERE FOR THE SURFACE IMPACT?

Domeisen, Hitchcock et al, in prep.

ERAinterim(13 persistent SSW events)

MPI-ESM model(403 persistent SSW events)

Part I – Part II

Data: MPI-ESM seasonal prediction model: Baehr et al 2015

SUMMARY PART II: THE SURFACE IMPACT OF THE STRATOSPHERE


Swiss Data Science Center

Only two thirds of SSW events are followed by the “canonical” negative NAO event. Domeisen, 2019, Charlton-Perez et al, 2018, Karpechko et al 2017

The storm track weakens for these two thirds of SSW events. The eastern Pacific might influence the storm track response over the North Atlantic.Afargan-Gerstman & Domeisen, in rev.

The “downward response” of SSW events depends on the tropospheric state at the onset of the SSW and the persistence of the signal in the lower stratosphere.Domeisen, Grams, Papritz, in prep..see also: Garfinkel et al 2013, Chan & Plumb, 2009

Eastern Pacific precursor Tropospheric impact

Predictability: weeks to months

Thank you!@Domeisen_DSSW competition:

the dynamics and predictability of sudden stratospheric

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