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Emanuele Di Lorenzo PICES, October, 2019 ALASKA MARINE HEATWAVE 2019 HOT OFF THE PRESS Dillon Amaya Tongtong Xu
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(a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

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Page 1: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

SPM

Summary for Policymakers

22

Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean surface temperature change, (b) average percent change in annual mean precipitation, (c) Northern Hemisphere September sea ice extent, and (d) change in ocean surface pH. Changes in panels (a), (b) and (d) are shown relative to 1986–2005. The number of CMIP5 models used to calculate the multi-model mean is indicated in the upper right corner of each panel. For panels (a) and (b), hatching indicates regions where the multi-model mean is small compared to natural internal variability (i.e., less than one standard deviation of natural internal variability in 20-year means). Stippling indicates regions where the multi-model mean is large compared to natural internal variability (i.e., greater than two standard deviations of natural internal variability in 20-year means) and where at least 90% of models agree on the sign of change (see Box 12.1). In panel (c), the lines are the modelled means for 1986−2005; the filled areas are for the end of the century. The CMIP5 multi-model mean is given in white colour, the projected mean sea ice extent of a subset of models (number of models given in brackets) that most closely reproduce the climatological mean state and 1979 to 2012 trend of the Arctic sea ice extent is given in light blue colour. For further technical details see the Technical Summary Supplementary Material. {Figures 6.28, 12.11, 12.22, and 12.29; Figures TS.15, TS.16, TS.17, and TS.20}

−0.55 −0.5−0.6 −0.4 −0.35−0.45 −0.25 −0.2−0.3 −0.1 −0.05−0.15(pH unit)

109

−20 −10−30−50 −40 0 10 20 30 40 50

(b)

(c)

RCP 2.6 RCP 8.5

Change in average precipitation (1986 −2005 to 2081−2100)

Northern Hemisphere September sea ice extent (average 2081−2100)29 (3) 37 (5)

3932

(d) Change in ocean surface pH (1986 −2005 to 2081−2100)

(%)

(a) Change in average surface temperature (1986 −2005 to 2081−2100)3932

(°C)−0.5−1−2 −1.5 0 1 1.5 2 3 4 5 7 9 110.5

CMIP5 multi-model average 2081−2100

CMIP5 multi-modelaverage 1986 −2005

CMIP5 subset average 2081−2100

CMIP5 subsetaverage 1986 −2005

Emanuele Di Lorenzo PICES, October, 2019

ALASKA MARINE HEATWAVE 2019 HOT OFF THE PRESS

Dillon AmayaTongtong Xu

Page 2: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

SPM

Summary for Policymakers

22

Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean surface temperature change, (b) average percent change in annual mean precipitation, (c) Northern Hemisphere September sea ice extent, and (d) change in ocean surface pH. Changes in panels (a), (b) and (d) are shown relative to 1986–2005. The number of CMIP5 models used to calculate the multi-model mean is indicated in the upper right corner of each panel. For panels (a) and (b), hatching indicates regions where the multi-model mean is small compared to natural internal variability (i.e., less than one standard deviation of natural internal variability in 20-year means). Stippling indicates regions where the multi-model mean is large compared to natural internal variability (i.e., greater than two standard deviations of natural internal variability in 20-year means) and where at least 90% of models agree on the sign of change (see Box 12.1). In panel (c), the lines are the modelled means for 1986−2005; the filled areas are for the end of the century. The CMIP5 multi-model mean is given in white colour, the projected mean sea ice extent of a subset of models (number of models given in brackets) that most closely reproduce the climatological mean state and 1979 to 2012 trend of the Arctic sea ice extent is given in light blue colour. For further technical details see the Technical Summary Supplementary Material. {Figures 6.28, 12.11, 12.22, and 12.29; Figures TS.15, TS.16, TS.17, and TS.20}

−0.55 −0.5−0.6 −0.4 −0.35−0.45 −0.25 −0.2−0.3 −0.1 −0.05−0.15(pH unit)

109

−20 −10−30−50 −40 0 10 20 30 40 50

(b)

(c)

RCP 2.6 RCP 8.5

Change in average precipitation (1986 −2005 to 2081−2100)

Northern Hemisphere September sea ice extent (average 2081−2100)29 (3) 37 (5)

3932

(d) Change in ocean surface pH (1986 −2005 to 2081−2100)

(%)

(a) Change in average surface temperature (1986 −2005 to 2081−2100)3932

(°C)−0.5−1−2 −1.5 0 1 1.5 2 3 4 5 7 9 110.5

CMIP5 multi-model average 2081−2100

CMIP5 multi-modelaverage 1986 −2005

CMIP5 subset average 2081−2100

CMIP5 subsetaverage 1986 −2005

Emanuele Di Lorenzo PICES, October, 2019

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ALASKA MARINE HEATWAVE 2019 HOT OFF THE PRESS

July-Aug-Sept 2019

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Sea Surface Temperature Anomalies

Page 7: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

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20192014/151957

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MHW Index

Sea Surface Temperature Anomalies

What about the Atmospheric Circulation?

Page 8: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

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Sea Surface Pressure AnomaliesRegression of

MHW Index on SLPa

mlbar

Page 9: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

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SLPI Index

Page 10: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

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dSSTa(t)dt = a ⋅SLPI (t)− SSTa(t)

tdissipation

AR-1 Model

Page 11: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

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NPGO-like

Page 15: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

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CMarine HeatWave Index

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SSTa Anomalies 2013-2019

NPGO-like

Page 16: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

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SSTa Anomalies 2013-2019

NPGO-likeThe dynamics of Marine HeatWave are not

independent of the North Pacific climate modes

ARTICLESPUBLISHED ONLINE: 11 JULY 2016 | DOI: 10.1038/NCLIMATE3082

Multi-year persistence of the 2014/15

North Pacific marine heatwave

Emanuele Di Lorenzo1* and Nathan Mantua2

Between the winters of2013/14 and 2014/15 during the strong North American drought, th

e northeastPacific exp

erienced

the largest marine heatwaveever record

ed. Here we combine observations with an ensemble of climate model simulations

to show that teleconnections

between the North Pacific and the weak 2014/2015El Niño linked the atmospheric forcing

patterns of this even

t. These teleconnection dynamics from the extratropic

s to the tropics during winter 201

3/14, andthen

back to the extratropics during winter 201

4/15, are a key source of multi-year persistence

of the North Pacific atmosphere.

The corresponding ocean anomalies map onto known patterns of North Pacific decadal va

riability, specifically

the North

Pacific Gyre Oscillation(NPGO) in 2014 and the Pacific Decadal O

scillation (PDO) in 2015. A large ensemble of climate

model simulations predicts that

the wintervariance of

the NPGO-and PDO-like p

atterns increases und

er greenhouse forcing

,

consistentwith other stud

ies suggesting an increase in

the atmospheric extremes that lea

d to drought over North America.

During the fall of 2013a large warm temperature anomaly

developedin the upper ocea

n along the axis of theNorth

Pacific Current. As th

e anomaly spreadover a broa

d region

of the Gulf of Alaska (GOA) during the winter of 2013/14, it

reached a record-breaking amplitude wit

h sea surfacetemperature

anomalies (SSTa) exceedingthree standard deviations

(⇠3 �C)

(Fig. 1a and Supplementary Fig. 1, seeMethods for

a description

of the datasets and definitionof the SSTa indices). T

he onset

and growth of this unusual water

mass anomaly is attributed to

forcing associatedwith a persistent

atmospheric ridge over the

northeast Pacific

1 (Fig. 1b) that is conne

cted to the NorthPacific

Oscillation(NPO), a leading pattern of atmospheric variability

2 .

Extreme amplitude and persistencein the NPO pattern is also

implicated inthe record

drought conditions th

at a�ectedCalifornia

in the winterof 2013/14

3–5 and its expression is a known

precursor

of El Niñoconditions

6,7 . By the summer and fall of 2014

, the warm

anomalies reached the Pacific

coastal boundary of N

orth America,

and although the amplitude in t

he GOA and the northern Calif

ornia

Current System (CCS) were

reduced, record-high SSTa were

found

in the regions of south

ern and Baja California

(Fig. 1c). Inthe winter

of 2014/15,the SSTa ov

er the entire northeast

Pacific re-intensified,

exceedingagain the 3 �C threshold (Fig. 1e and Supplementary

Fig. 1). The record-br

eaking high-temperature and the multi-year

persistenceof this warm anomaly, here referred to as a marine

heatwave8 , have had unpreceden

ted impacts on multiple trophic

levels of the marine ecosy

stem and socio-economically important

fisheries. Associatedecosystem

impacts included low primary

productivity9 , 11 new

warm-water copepod species

to the northern

CaliforniaCurrent sh

elf/slope region

10 , a massive influx of dead or

starving Cassin’s aukle

ts (sea birds) onto Pac

ific Northwest beaches

from October through December 201411 , a large whale unusual

mortality event in the western GOA in 201512 , and a California

sea lion unusual mortality event in California

from 2013–201513 .

Severe, negative socio-econ

omic impacts resulted from the 2015

harmful algal bloom that extend

ed from southern Californiato

southeast Alaska, the la

rgest ever recorded

14 . Toxins produced by th

e

extreme harmful algal bloom contaminated shell

fish inWashington,

Oregon and California,prompting prolo

nged closures for valuable

shellfish fisheries. Although the socio-econ

omic consequences of

this climate event need to be fur

ther evaluated, it is po

ssible that the

northeast Pacific warm

anomaly of 2014–15 is the most ecologic

ally

and economically signif

icant marine heatwave on record.

Althoughprevious studies

1,3,15–17 have documented the onset

and nature of the atmospheric variabilitythat forced

the winter

2013/14 SSTa, thedynamics underlying

the persistenceand re-

intensification of the anom

aly in 2015 are still unclear.

The relative

role of ocean internal dy

namics versus direct atmospheric fo

rcing

in driving theexpression

of the 2015SSTa has n

ot been examined.

It is also unclear if the January

–February–March (JFM) 2014 and

JFM 2015 SSTa patterns (Fig. 1a and e) are dynamically linked,

and if they are, how?There is good evidence that atmospheric

teleconnections of tro

pical originplayed a key role in the winter

2013/14 sea-level pressure anomalies (SLPa)

4,15–17 (Fig. 1b),and

that the variance of this anomaly pattern may intensify under

greenhouseforcing

3,4 , hence leading to more extremes in ocean

temperature and western US precipit

ation. Thisraises the q

uestion

of whethertropical/ex

tratropicalteleconnec

tions werealso impor-

tant in driving theexceptiona

l SSTa in the winterof 2014/15.

Atmospheric forcing of the marine heatwave

To understand the rol

e of atmospheric forcing in driving the

strong

North Pacific warm anomalies, we begin by inspectingmaps of

the seasonal evolution of SSTa and SLPa between JFM 2014 and

JFM 2015 (Fig. 1). The patterns of the peak SSTa in JFM 2014

and 2015 show important spatial di�eren

ces. Whereas in 2014 the

core SSTa are centred

in the GOA (Fig. 1a) and exhibit a N

PGO-

like expression18 or Victoria Pattern

19 , in 2015 the largest warm

anomalies are further to the

east and extend along

the entire Pacific

North American coastal boundary, rese

mbling the expressionof

the PDO20 , also referred to as the ‘ARC’ patte

rn (Fig. 1e). These

di�erencesin SSTa patter

ns are mirrored by a change inthe SLPa

patterns, which exhibit a st

rong dipolesystem in JFM 2014, typic

al

of the NPO2 (Fig. 1b), a

nd a more pronounced single SLPa low

in 2015, resembling the express

ion of a deeperand southeastw

ard

extended Aleutian Low (Fig. 1f). To measure the strength of the

2014 and 2015 anomaly patterns wecompute the av

erage SSTain

© ƐƎƏƖɥMacmillan Publishers LimitedƦɥ/�13ɥ.$ɥ�/1(-%#1ɥ��

341#. All rights reservedƥ1 School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, Georgia 30332, USA. 2 Southwest Fisheries Science Center, National

Marine Fisheries Service, National Oceanographic and Atmospheric Administration, 110 Sha�er Road, Santa Cruz, California 95060, USA.

*e-mail: [email protected]

1042

NATURE CLIMATE CHANGE | VOL 6 | NOVEMBER 2016 | www.nature.com/natureclimatechange

Increasing Coupling Between NPGO and PDO Leads

to Prolonged Marine Heatwaves

in the Northeast PacificYoungji Joh1

and Emanuele Di Lorenzo1

1School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA

Abstract The marine heatwave of 2014/2015 in the Northeast Pacific caused significant impacts on

marine ecosystems and fisheries. While several studies suggest that land and marine heatwaves may

intensify under climate change, less is known about the prolonged multiyear nature (~2 years) of the

Northeast Pacific events. Examination of reanalysis products and a 30-member climate model ensemble

confirms that prolonged multiyear marine heatwaves are linked to the dynamics of the two dominant modes

of winter sea surface temperature variability in the North Pacific, the Pacific Decadal Oscillation (PDO), and

the North Pacific Gyre Oscillation (NPGO). Specifically, we find a significant correlation between winter warm

NPGO anomalies and the following winter PDO arising from extratropical/tropical teleconnections. In the

model projections for 2100 under the RCP8.5 scenario, this NPGO/PDO 1 year lag correlation exhibits a

significant positive trend (~35%) that favors more prolonged multiyear warm events (>1°C) with larger

spatial coverage (~18%) and higher maximum amplitude (~0.5°C for events>2°C) over the Northeast Pacific.

Plain Language Summary Between the winters of 2014 and 2015 the Northeast Pacific

experienced the largest and longest marine heatwave ever recorded in the instrumental record. A

distinguishing feature of this event is themultiyear persistence of the ocean warm anomalies from one winter

to the other. By analyzing and comparing different reanalysis products and an ensemble of climate model

projections for 2100, we find that the observational trend for stronger winter to winter persistence of

anomalies in the Northeast Pacific is consistent with climate model projections under the RCP8.5 radiative

forcing scenario. We link this trend to an increase coupling between the two dominant modes of North

Pacific decadal variability.1. IntroductionThe 2013/2015 marine heatwave of the Northeast Pacific was characterized by the strongest ocean tempera-

ture extremes ever recorded in the North Pacific (Anderson et al., 2016; Baxter & Nigam, 2015; Bond et al.,

2015; Hartmann, 2015; Hobday et al., 2016; Peterson et al., 2016; Wang et al., 2014) and by an unusual persis-

tence that spanned the winters of 2013/2014 and 2014/2015 (Di Lorenzo &Mantua, 2016), culminating in one

of the strongest El Niño events of the twentieth century in the fall/winter of 2015/2016. The progression of

the event followed distinct spatial and temporal winter patterns in the ocean and atmosphere that

closely resemble the two dominant modes of variability of sea surface temperature and sea level pressure

anomalies (SSTa and SLPa). Specifically, the spatial structures of the January-February-March (JFM) SSTa in

2013/2014 and 2014/2015 are captured by the 2nd and 1st principal components of the North Pacific SSTa

(Di Lorenzo & Mantua, 2016) (Figure S1 in the supporting information). In the Northeast Pacific, these modes

are commonly referred to as the North Pacific Gyre Oscillation (NPGO) (Di Lorenzo et al., 2008) and the Pacific

Decadal Oscillation (PDO) (Mantua et al., 1997) (Figure S1). The similarity between the marine heatwave pat-

terns and the mode of Pacific decadal variability suggests that the statistics and persistence of these ocean

extremes are linked to the dynamics underlying the North Pacific modes.

Using historical reanalysis products and a climate model ensemble, this study provides a diagnostic of ocean

extremes statistics in past observations and in future model projections under the radiative forcing scenario

RCP8.5. The goal of this study is to (1) confirm the hypothesis that prolonged ocean extremes events follow

recurrent patterns with a transition from a winter NPGO-like pattern to PDO-like pattern in the following win-

ter and (2) examine how the coupling between these modes via tropical/extratropical teleconnections is

changing under a warmer climate favoring more prolonged winter to winter warm events.

JOH AND DI LORENZO

MARINE HEATWAVES IN NORTHEAST PACIFIC

11,663

PUBLICATIONSGeophysical Research Letters

RESEARCH LETTER10.1002/2017GL075930Special Section:

Midlatitude Marine Heatwaves:

Forcing and ImpactsKey Points:• Multiyear SST warm events in the

Northeast Pacific typically emerge as a

winter NPGO-like warm pattern and

transition to a PDO-like pattern in the

following winter• The coupling between winter NPGO

and the following winter PDO is a

robust climate teleconnection in both

observations and the CESM-LENS over

the period 1920-2100• A stronger NPGO-PDO coupling is

predicted under anthropogenic

forcing in the CESM-LENS and leads

to more prolonged and larger area

multiyear marine heatwavesSupporting Information:

• Supporting Information S1Correspondence to:Y. Joh,[email protected]

Citation:Joh, Y., & Di Lorenzo, E. (2017).

Increasing coupling between NPGO

and PDO leads to prolonged marine

heatwaves in the Northeast Pacific.

Geophysical Research Letters, 44,

11,663–11,671. https://doi.org/10.1002/

2017GL075930Received 4 OCT 2017Accepted 8 NOV 2017Accepted article online 13 NOV 2017

Published online 30 NOV 2017

©2017. American Geophysical Union.

All Rights Reserved.

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2

3

4

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

C

Correlation of MHW Index with SSTa

CMarine HeatWave Index

MHW Index

NPGO Index R=0.49 PDO Index R=0.36

-2 -1.5 -1 -0.5 0 0.5 1 1.5 20

1

2

3

4

5

MHW Index PDF -NPGO > 1 STD

MHW Index PDF -NPGO < -1 STD

SST Anomaly

Occ

urre

nces

Amplitude of SST Trend

Page 18: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

C

Correlation of MHW Index with SSTa

CMarine HeatWave Index

MHW Index

NPGO Index R=0.49 PDO Index R=0.36

-2 -1.5 -1 -0.5 0 0.5 1 1.5 20

1

2

3

4

5

MHW Index PDF -NPGO > 1 STD

MHW Index PDF -NPGO < -1 STD

SST Anomaly

Occ

urre

nces

Amplitude of SST Trend

Problem: small size statistics

Observational Record

Page 19: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

C

Correlation of MHW Index with SSTa

CMarine HeatWave Index

MHW Index

NPGO Index R=0.49 PDO Index R=0.36

-2 -1.5 -1 -0.5 0 0.5 1 1.5 20

1

2

3

4

5

MHW Index PDF -NPGO > 1 STD

MHW Index PDF -NPGO < -1 STD

SST Anomaly

Occ

urre

nces

Amplitude of SST Trend

MPI-Grand Ensemble: 100 ensemble members

1850-2005 with historical radiative forcing

2006-2100 with RCP8.5

Observational Record

Problem: small size statistics

Page 20: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

0 1 2 3-2 10

0.3

0.6

SST Anomaly (MHW Index)

Probability Distribution Function

Pre-Industrial 1850-1879

MPI Model 100 Member Ensemble

Page 21: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

0 1 2 3-2 10

0.3

0.6

Present Day 1980-2019

SST Anomaly (MHW Index)

Probability Distribution Function

Pre-Industrial 1850-1879

Average Shift

MPI Model 100 Member Ensemble

Page 22: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

0 1 2 3-2 10

0.3

0.6

-NPGO > 1 STD

-NPGO < -1 STD

SST Anomaly (MHW Index)

Probability Distribution Function

Average Shift

MPI Model 100 Member Ensemble

Page 23: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

0 1 2 3-2 10

0.3

0.6

-NPGO > 1 STD

-NPGO < -1 STD

SST Anomaly (MHW Index)

Probability Distribution Function

Average Shift

MPI Model 100 Member Ensemble

BlobIndex

Annual

∆95th0.130.260.42

∆99th0.050.130.21

+NPGOPresentDay

-NPGO

Page 24: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

0 1 2 3-2 10

0.3

0.6

-NPGO > 1 STD

-NPGO < -1 STD

SST Anomaly (MHW Index)

Probability Distribution Function

Average Shift

MPI Model 100 Member Ensemble

BlobIndex

Annual

∆95th0.130.260.42

∆99th0.050.130.21

+NPGOPresentDay

-NPGO

Changes in Extremes associated with trend is comparable to that of the phases of the decadal modes

Page 25: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature Anomalies

Question: Is the Blob going to continue this winter?

Page 26: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature Anomalies

Question: Is the Blob going to continue this winter?

Empirical Dynamical Model Prediction

dxdt

= Lx + ξ Linear Inverse Model

Page 27: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature Anomalies

Question: Is the Blob going to continue this winter?

Empirical Dynamical Model Prediction

dxdt

= Lx + ξ Linear Inverse Model

By solving the LIM system, we obtain

x̂(t + τ) = exp(Lτ)x(t) = G(τ)x(t)

Page 28: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature AnomaliesEmpirical Dynamical Model Prediction

dxdt

= Lx + ξ Linear Inverse Model

By solving the LIM system, we obtain

x̂(t + τ) = exp(Lτ)x(t) = G(τ)x(t)

As data consist of SSTA and SLPA, our model system is

[ ̂s(t + τ)p̂(t + τ)] = G(τ = 6months)[s(t)

p(t)] SSTASLPA

Page 29: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature AnomaliesEmpirical Dynamical Model Prediction

dxdt

= Lx + ξ Linear Inverse Model

By solving the LIM system, we obtain

x̂(t + τ) = exp(Lτ)x(t) = G(τ)x(t)

[ ̂s(t + τ)p̂(t + τ)] = G(τ = 6months)[s(t)

p(t)]As data consist of SSTA and SLPA, our model system is

SSTASLPA

Initialize September

Forecast March

Jan-Feb-March

6 Months Prediction

Page 30: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature AnomaliesEmpirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

6 Months Prediction

Page 31: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature AnomaliesEmpirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

Marine HeatWave Index

MHW Index

Empirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

6 Months Prediction

Page 32: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature AnomaliesEmpirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

Marine HeatWave Index

MHW Index1950 1960 1970 1980 1990 2000 2010 2020

-3

-2

-1

0

1

2

3

4

2015 Blob

Winter Average Jan-Feb-March

Empirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

6 Months Prediction

Page 33: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature Anomalies

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

Marine HeatWave Index

MHW Index1950 1960 1970 1980 1990 2000 2010 2020

-3

-2

-1

0

1

2

3

4

2015 Blob

2015 Prediction

Empirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

6 Months Prediction

Winter Average Jan-Feb-March

Skill R=0.5

Cross-Validation

Page 34: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature Anomalies

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

Marine HeatWave Index

MHW Index1950 1960 1970 1980 1990 2000 2010 2020

-3

-2

-1

0

1

2

3

4

Empirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

6 Months Prediction

Winter Average Jan-Feb-March

Skill R=0.5

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

Winter 2015 ObservedWinter 2015 Prediction

Sea Surface Temperature Anomalies

2015 Blob

2015 Prediction

Page 35: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature Anomalies

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

Marine HeatWave Index

MHW Index1950 1960 1970 1980 1990 2000 2010 2020

-3

-2

-1

0

1

2

3

4

2015 Blob

2015 Prediction

Empirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

6 Months Prediction

Winter Average Jan-Feb-March

Skill R=0.5 ?

Page 36: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2

C

July-Aug-Sept 2019

Sea Surface Temperature AnomaliesEmpirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

Marine HeatWave Index

MHW Index1950 1960 1970 1980 1990 2000 2010 2020

-3

-2

-1

0

1

2

3

4

2015 Blob

2015 Prediction

Empirical Dynamical Model Prediction

Initialize September

Forecast March

Jan-Feb-March

6 Months Prediction

Winter Average Jan-Feb-March

Skill R=0.52020 Prediction

Page 37: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2degree C

WINTER

2020

Sea Surface Temperature Anomalies

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

Marine HeatWave Index

Observed 6m Prediction

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

2015 Blob

2015 Prediction

Prediction R=0.5

2020 Prediction

PredictionNorth

America

Asia Alaska

C

Page 38: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2degree C

SUMMER

2019

Sea Surface Temperature Anomalies

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

Marine HeatWave Index

Observed 6m Prediction

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

2015 Blob

2015 Prediction

Prediction R=0.5

2020 Prediction

North America

Asia Alaska

C

Page 39: (a) SPM - PICES€¦ · SPM Summary for Policymakers 22 Figure SPM.8 | Maps of CMIP5 multi-model mean results for the scenarios RCP2.6 and RCP8.5 in 2081–2100 of (a) annual mean

-220 -200 -180 -160 -140 -120 -100

0

20

40

60

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2degree C

WINTER

2020

Sea Surface Temperature Anomalies

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

Marine HeatWave Index

Observed 6m Prediction

1950 1960 1970 1980 1990 2000 2010 2020-3

-2

-1

0

1

2

3

4

2015 Blob

2015 Prediction

Prediction R=0.5

2020 Prediction

PredictionNorth

America

Asia Alaska

C