Mechanisms of Pacific Ocean Climate & Ecosystem Variability BY E M ANU ELE D I LO R ENZO, VI N CENT COM B E S , JU LI E KEIST ER , T ED ST RUB , AN D R EW T H OM A S , PE T ER J .S . FR AN KS , M AR K O HM AN , AN NALIS A B R ACCO,

JA S O N FURTAD O, ST EVEN B O G R AD, WI LLIA M PE T ER S O N , FR AN K S CHWI N G , S ANAE CH I BA , BU NM EI TAGUCH I , S A MU EL H O R M AZABAL AN D C ARO LI NA PAR ADA

Oceanography | Vol. xx, No.xx, x No. x 1 Oceanography | Vol.

TABLE OF CONTENT

INTRODUCTION

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L ARGE-SCALE MECHANICS OF PACIFIC CLIMATE & ECOSYSTE M

4

Eastern Pacific ENSO and the Pacific Decadal Oscillation

4

Central Pacific ENSO and the North Pacific Gyre Oscillation

5

A synthesis of Pacific climate variability

6

REGIONAL-SCALE TR ANSPORT PROCESSES AND MARINE ECOSYSTE M RESPONSE Large-scale climate controls coastal upwelling and primary productivity

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Change in ocean transport explain observed zooplankton variability

8

Mesoscale eddies control cross-shelf exchanges and impact fish habitats DISCUSSION AND FUTURE CHALLENGES

10 11

A double-integration hypothesis to explain the ecosystem response to climate11 Climate change impacts on marine ecosystems REFERENCES

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e POBEX project ABSTR ACT e goal of the Pacific Ocean Boundary Ecosystem Climate Study (POBEX) was to diagnose the large-scale climate controls on regional transport dynamics and lower-trophic marine ecosystem variability in Pacific Ocean boundary systems. An international team of collaborators shared observational and eddy-resolving modeling datasets in the Northeast Pacific (NEP) including the Gulf of Alaska (GOA) and California Current System (CCS), the Humboldt or Peru-Chile Current System (PCCS), and the Kuroshio-Oyashio Extension (KOE) region. POBEX found that a dominant fraction of decadal variability in basin and regional-scale salinity, nutrients, chlorophyll, and zooplankton taxa is explained by a newly discovered pattern of ocean-climate variability dubbed the North Pacific Gyre Oscillation (NPGO), and the Pacific Decadal Oscillation (PDO). NPGO dynamics originate in the tropics and reflect the decadal expression of central Pacific El Niños (CP-Niño), much as the PDO captures the low-frequency expression of eastern Pacific El Niños (EP-Niño). By combining hindcasts of eddy-resolving ocean models over the period 1950-2008 with model passive tracers and long-term observations (e.g., CalCOFI, Line-P, Newport and Odate Collection), POBEX showed that the PDO and NPGO combine to control low-frequency upwelling and alongshore transport dynamics in the North Pacific section, while the EP-Niño dominates in the South Pacific. Although different climate modes have different regional expressions, changes in vertical transport (e.g. upwelling) were found to explain the dominant nutrient and phytoplankton variability in the CCS, GOA and PCCS, while changes in alongshore transport forced much of the observed long-term changes in zooplankton species composition in the KOE, the northern and southern CCS. In contrast, cross-shelf transport dynamics were linked to mesoscale eddy activity and driven by regional-scale dynamics that are largely decoupled from variations associated with the large-scale climate modes. Indeed, preliminary findings suggested that mesoscale circulation plays a key role in offshore transport of zooplankton and the life cycle of higher trophic levels (e.g., fish) in the CCS, PCCS and GOA. Looking forward, POBEX results may guide the development of new modeling and observational strategies to establish the mechanistic links between climate forcing, mesoscale circulation, and marine population dynamics.

INTRODUCTION e POBEX project (www.pobex.org, 2008-2012) brought together researchers from North America, Japan and South America to investigate the mechanisms of climate and ecosystem variability in three Pacific boundary regions: the Northeast Pacific (NEP) including the Gulf of Alaska (GOA) and California Current System (CCS), the Humboldt or Peru-Chile Current System (PCCS), and the Kuroshio-Oyashio Extension (KOE) region (Fig. 1).  e main objectives of POBEX were to: (1) understand and quantify how large-scale climate variability drives regional-scale physical variability that is coherent along the Pacific boundary and (2)

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use the regional-scale dynamics in combination with existing long-term ecological observations to interpret marine ecosystem processes. Specifically, POBEX quantified how changes in regional ocean processes (e.g., upwelling, transport dynamics, mixing and mesoscale structure) in each Pacific Ocean boundary region control phytoplankton and zooplankton dynamics, and the extent to which these regional ocean dynamics are driven by large-scale climate modes such as the Pacific Decadal Oscillation (PDO; Mantua et al., 1997), the El Niño Southern Oscillation (ENSO), and the recently discovered North Pacific Gyre Oscillation (NPGO; Di Lorenzo et al., 2008). e underlying hypothesis of

POBEX was that large-scale Pacific climate forcing drives changes in transport dynamics that exert a dominant and coherent bottom-up control on coastal ocean ecosystems. To explore how interannualto-decadal variations in upwelling and horizontal transport affect the lower trophic levels of the Pacific boundary marine ecosystems, POBEX combined a series of historical (1950present) eddy-resolving (10 km spatial resolution) ocean simulations at both global and regional scales with model passive tracers to generate temporal indices for transport processes such as upwelling and horizontal advection. e transport indices were then used to test the extent to which observed changes in

SYNTHESIS OF PACIFIC CLIMATE DYNAMICS & CONNECTIONS

Synthesis of Pacific Climate Dynamics & Connections AL

NPO

EXTRA-TROPICS

Aleutian Low Trenberth and Hurrell 1995

North Pacific Oscillation Walker and Bliss, 1932 Rogers, 1981

atmosphere

(winter)

KOE Meridional Mode Axis of KOE Taguchi et al. 2007

atmosphere

PDO ocean

(winter)

ocean

KOE Zonal Mode Strength of KOE Taguchi et al. 2007

Di Lorenzo et al. 2010 Furtado et al. 2012

Alexander, 1992; 2002 Newman et al., 2003 Vimont et al. 2005

TROPICS

EP-Niño

Central Pacific non-Canonical ENSO

Eastern Pacific Canonical ENSO (mature)

s et

Ceballo

North Pacific Gyre Oscillation Di Lorenzo et al. 2008 ocean al., 2009

(winter)

ocean

CP-Niño Atmospheric Bridge

NPGO

(winter)

Pacific Decadal Oscillation Mantua et al. 1997 ., 2007 Qiu et al

Chhak et al., 2009

(mature)

SFM Vimont et al. 2003 Anderson et al., 2003

CPW

high frequency

Central Tropical Pacific Warming (onset) Alexander et al. 2010 Vimont et al., 2010

phytoplankton are1:connected to climate dynamics improve our understanding of the Figure Synthesis of Pacific and teleconnections Figure 2. Schematic synthesizing climate dynamics and the connections between thedecadal different modes. modeled changes in thePacific strength, mechanisms driving

structure and timing of upwelling, and to explore specific hypotheses concerning links between changes in modeled horizontal transport and changes in zooplankton abundance and species diversity. By exploring regional-scale dynamics, POBEX identified the important role of recently identified patterns of climate variability (e.g., central Pacific El Niños and NPGO) and clarified their large-scale and regional-scale dynamics. is has given us an improved understanding of the mechanisms of large-scale Pacific climate variability and their regionalscale impacts on the coastal ocean and marine ecosystems. Here we report on some of the major accomplishments of the POBEX program in the context of a synthesis of the mechanics of Pacific climate and ecosystem lower-trophic variability.

L ARGE-SCALE MECHANICS OF PACIFIC CLIMATE & ECOSYSTEM One of the most significant contributions of the POBEX program from a physical standpoint was to

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variability in the North Pacific. In particular the models revealed the ocean-atmosphere connections driving the PDO and NPGO: these decadal oceanic responses originate from the interannual ENSO fluctuations [Zhang et al., 1997; Di Lorenzo et al., 2010]. ENSO fluctuations propagate through the atmosphere; their effects are ultimately integrated by the ocean into the observed responses. Two main pathways are apparent, driven by different types of ENSO, and culminating in the PDO and NPGO (Fig. 1). In one, an Eastern Pacific ENSO (EP-ENSO) influences the Aleutian Low (AL) atmospheric system to drive the PDO [Newman et al., 2003; Vimont, 2005]. In the other, a Central Pacific ENSO (CP-ENSO) forces variability in the North Pacific Oscillation (NPO) – the 2nd dominant mode of atmospheric variability in the North Pacific [Walker and Bliss, 1932; Rogers, 1981], resulting in the NPGO [Di Lorenzo et al., 2010; Furtado et al., 2012]. As we show below, these two different pathways drive distinct physical and biological responses, and

their relative dominance is changing with time. Interestingly, in the Southern Pacific, the extremely tight coupling of the oceanic response with ENSO precludes the strong decadal variability seen north of the equator. Additionally, POBEX researchers showed that the AL/PDO and NPO/ NPGO systems drive decadal variations in the north Pacific western boundary (e.g. KOE) that are coherent with those in the eastern boundary. is occurs through Rossby waves that propagate westward from the central and eastern North Pacific and modulate the KOE upon arrival. AL/PDO Rossby waves drive changes in the axis of the KOE [Miller and Schneider, 2000; Qiu et al., 2007; Taguchi et al., 2007], while NPO/NPGO Rossby waves modulate the speed and strength of the KOE [Ceballos et al., 2009]. A similar Rossby wave connection has been also isolated in the South Pacific [Holbrook et al., 2011].

Eastern Pacific ENSO and the Pacific Decadal Oscillation In its positive phase the “traditional” El Niño is characterized by a pronounced warming of the tropical eastern Pacific (e.g. EPENSO), a weakening of the trade winds, and positive (negative) atmospherically forced sea-level pressure anomalies (SLPa) over the western (eastern) tropical Pacific (Fig. 2a). ese changes in the tropical atmospheric circulation modify the large-scale Hadley Cell and drive and important fraction of the extratropical variability of the AL through the ENSO atmospheric teleconnection pattern (Fig.2a)

re 1

(a) Eastern Pacific El Niño (EP-ENSO) (b) Central Pacific El Niño (CP-ENSO) [Alexander, 1992; 60 60 SLPa pattern SLPa pattern Alexander et al., KOE KOE NEP NEP 40 40 2002]. is 20 20 teleconnection EP-ENSO Teleconnection CP-ENSO Teleconnection 0 0 between EP-ENSO −20 −20 and the AL is clearly PCCS PCCS −40 −40 evident in the −300 −250 −200 −150 −100 −300 −250 −200 −150 −100 significant correlation 60 60 SSTa pattern SSTa pattern (R=0.8) that exists KOE KOE NEP NEP 40 40 between an index of 20 20 60 the EP-ENSO imprint 0 0 40 on North Pacific SLPa −20 −20 20 (EP-ENSO projection PCCS PCCS −40 −40 0 index) and the AL −300 −250 −200 −150 −100 −300 −250 −200 −150 −100 −20 index defined as the Correlation ENSO Atmospheric Projections POBEX Regions −240 −220 −200 −180 −160 −140 −120 −100 −80 first principal −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 KOE: Kuroshio-Oyashio Extension Regions of North Pacific SLPa component of North NEP: Northeast Pacific driven by ENSO teleconnection PCCS: Peru-Chile Current System during the EP and CP El Niños. Pacific SLPa (Fig. 3a). e ocean integrates these changes in leads to skillful reconstruction of the the PDO. Using a set of historical atmospheric forcing, acting as a filter PDO index (Fig. 3b). ocean simulations with coupled that enhances the decadal energyFigure of 2: The flavors of ENSO and their teleconnections. Sea surface temperature (SST) physical-biological models, POBEX and sea level pressure (SLP) anomalies during the eastern Pacific or canonical El Niño the atmospheric forcing and of the (positive phase of ENSO) (panels a) and the central Pacific El Niño (panel b). The back identified a new pattern of North Central Pacific ENSO and the ENSO teleconnection. e EP-ENSOrectangles show the atmospheric projections of a positive ENSO onto the North Pacific Pacific decadal variability associated North Pacific Gyre Oscillation atmosphere, also referred to as the ENSO teleconnections. The purple rectangle show the derived variability of the AL drives with changes in strength of the subgeographical domains targeted by POBEX. While many ecosystem changes in the ocean circulation and tropical and sub-polar gyre – the fluctuations in the Pacific can be temperature that are captured in the North Pacific Gyre Oscillation explained within the physical PDO pattern (Fig. 3b). is (NPGO) [Di Lorenzo et al. 2008] (Fig. framework of ENSO and PDO dynamical links allow the PDO to be 4). Defined as the second dominant variability [Mantua et al., 1997; Hare modeled as a simple integrator of the mode of SSHa variability in the and Mantua, 2000; and others], longEP-ENSO teleconnection [Newman et Northeast Pacific [180°–110°W; term observational timeseries from al., 2003; Schneider et al. 2005; 25°N–62°N], the NPGO explains the the California Cooperative Oceanic Vimont et al. 2005]. A simple dominant decadal fluctuations of Fishery Investigation (CalCOFI) and integration of the EP-ENSO salinity, nutrient upwelling and the Line-P program in the Gulf of projection index with an autochlorophyll-a (CHL-a) in the NEP Alaska show decadal-scale regressive model of order-1 (AR1) region (Fig. 4) [Di Lorenzo et al. fluctuations that are not connected to 2008; 2009] as well as important state transitions in marine ecosystems (e.g., fish, Sydeman and ompson, 2010; THE NORTH PACIFIC GYRE OSCILLATION CONTROLS Cloern et al., 2010]). e NPGO PREVIOUSLY UNEXPLAINED VARIATIONS OF SALINITY, signature in SSTa tracks the 2nd NUTRIENT UPWELLING AND CHL-A RECORDED IN THE dominant mode of North Pacific SSTa NORTHEAST PACIFIC, ITS VARIANCE HAS INCREASED [Bond et al., 2003].

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BETWEEN 1980-2012.

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(a) Aleutian Low (AL)

Modeling studies conducted within POBEX [Di Lorenzo et. al., 2008; Chaak et al., 2009] revealed that the NPGO is the oceanic response to the North Pacific Oscillation (NPO) (Fig. 3b), a well-known pattern of atmospheric variability. e NPO affects weather patterns, particularly storm tracks, temperatures, and precipitation, over Eurasia and North America [Seager et al., 2005; Linkin and Nigam, 2008; and references therein]. Following previous work on the connection between EP-ENSO and the AL/PDO system, POBEX explored whether the decadal variability of the NPO/NPGO had a tropical, ENSO-like source. is link was found in the recently discovered central Pacific ENSO (CP-ENSO) [Ashok et al., 2007; Ashok and Yamagata, 2009]. In this ENSO type, or flavor, the El Niño phase (also referred to as the dateline El Niño [Larkin and Harrison, 2005], El Niño Modoki [Ashok and Yamagata, 2009] or warm pool El Niño [Kug et al., 2009; Kao et al., 2009] is characterized by peak SST anomalies in the central Pacific capable of modifying the largescale atmospheric circulation. However, the CP-ENSO signature is different from the EP-ENSO (see SLPa patterns in Fig. 2a,b) in that the center of maximum convection is displaced westward with respect to the EP-ENSO. Consequently, the CPENSO induces a different pattern of atmospheric teleconnections to the extra-tropics [Weng et al., 2009]. Using an ensemble of coupled oceanatmosphere simulations POBEX showed that CP-ENSO teleconnections drive low-frequency

Figure 2

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(c) North Pacific Oscillation (NPO)

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Figure 3: The of forcing of the two3c, dominant modes of North Pacific The first (panel variability the NPO (Fig. decades most of thevariability. physical and a, AL) and second (panel c, NPO) principal components of North Pacific SLPa are compared to compare NPO Index with CP-ENSO biological variability of the North indices that track the ENSO atmospheric projections (black line) onto the North Pacific. The projection index) [Di principal Lorenzo components et al., Pacific driven by the NPGO spatial correlation of the with SLPahas arebeen shown in panel a and c). The AL al., 2012]. is rather [Cummins and2010; NPOFurtado drive theetoceanic PDO andCPNPGO (panel b,d than show the the PDO spatial correlationand patterns in theENSO/NPO SSTa). The timeseries of the PDO andisNPGO (panel b,d)2007; can be modeled with2013]. a simple autoatmospheric variability Freeland, Kilduff et al., regressive model of order-1 forced by the ENSO’s projection indices, showing that a large then integrated to yield the oceanic fraction of the low-frequency variance of the modes originates from the tropical Pacific and is NPGO pattern al. injected through the[Di AL Lorenzo and NPOetsystems.

2010] (Fig. 3d). CP-El Niños have become stronger and more frequent in the late 20th century, possibly a result of climate change [Yeh et al., 2009]. Indeed, the variance of the NPGO has increased over time (Sydeman et al. 2013) and there is evidence that over the last two

A synthesis of Pacific climate variability  e mechanistic relationships between EP-ENSO/AL/ PDO and CP-ENSO/NPO/NPGO highlight the strong dynamical linkages between tropical and extratropical modes of climate variability

REGIONAL-SCALE TR ANSPORT PROCESSES AND MARINE ECOSYSTEM RESPONSE

Vertical and Horizontal Transport dynamics along the eastern boundary diagnosed with eddy-resolving models and passive tracers (a) Eastern Gulf of Alaska Downwelling System upwelling | down.

4 2

I

0

-2

downwelling upwelling

downwelling

down.

upwelling

-4

upwelling

in the Pacific basin, and the important role played by ENSO in synchronizing them (Fig.1 Figure schematic). e discovery of this 5 new dynamical link between the CP-ENSO→NPO→NPGO redefines our physical understanding of how the tropical Pacific climate is coupled to the extra-tropics and provides the basis for a potential positive feedback between tropics and extra-tropics (Fig. 1). e existence of such a feedback is supported by past studies on the Seasonal Footprinting Mechanism (SFM; [Vimont et al., 2003; Anderson, 2003]) whereby boreal winter-time variability in the positive phase of the NPO drives warm SST anomalies in the North Pacific that in turn propagate into the central tropical Pacific by end of spring/summer. ose warm anomalies in the central Pacific weaken the Walker Cell and may trigger a positive ENSO response in the tropics that peaks in the following winter [Alexander et al., 2010]. e response can be either an EP-El Niño or CP-El Niño. A CP-El Niño response implies a positive feedback whereby NPO(winter)→CP-El Niño (next winter)→NPO(next winter). is feedback may provide a longer year-to-year persistence of the central Pacific warming in the tropics, which could explain why the CP-ENSO has stronger decadal energy than the EPENSO [e.g. Nurhati et al. 2011].

(a)

PDO Index Model Upwelling Tracer 1970

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(b) Central California Upwelling System Model Upwelling Tracer

NPGO Index 1970

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Gulf of Alaska Downwelling System

(b)

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(c) Chile Upwelling System Model Upwelling Tracer

Chile-Peru Upwelling System

Peru

EP-ENSO Index

(d)

1970 1975 1980 1985 1990 1995 2000

(d) Peru Upwelling System Model Upwelling Tracer

Eddies

EP-ENSO Index 1970 1975 1980 1985 1990 1995 2000

year

Filaments Combes et al., 2009; 2013a; 2013b

To examine how the large-scale 2013b; Keister et al., 2011; Chiba et al. Figure 5: The Regional Ocean Modeling System (ROMS) passive tracer transport climate forcing impacts regional 2013]. experiments. Map showing a February 1998 snapshot of passive tracers released form transport dynamics and ecosystem the coast in the nested eddy-resolving ROMS hindcast from 1950-2008. The passive tracers were to study ocean in eachclimate of the POBEX regions. processes the used POBEX teamtheran the transport dynamics Large-scale controls The timeseries panels show how indices of upwelling defined from the passive tracers regional ocean modeling system coastal and primary are linked to different Pacific climate indices in the differentupwelling POBEX regions. (ROMS) for the regions of interest, productivity using the output of the global eddyFor each region we generated indices resolving OFES model historical of coastal upwelling by releasing hindcast from 1950-2008 [Masumoto passive tracers in the model et al., 2004; Sasaki et al., 2008] as subsurface along the coastline. We boundary conditions. e ROMS found that most of the low-frequency model historical simulations were variability in upwelling along the combined with passive tracer Pacific eastern boundary current experiments (Fig. 5) that allowed us system is driven by the large-scale to (1) diagnose circulation dynamics climate patterns. However, the relative such as upwelling, alongshore and importance and structure of the local cross-shelf transport, and (2) explore forcing are different in different how changes in transport are linked regions. In the NEP region south of to ecosystem dynamics [e.g., Chhak et 38N the teleconnections of the CPal., 2009; Combes et al., 2009; 2013a; ENSO on NPO dominate the lowfrequency modulation of the

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(c)

Correlation CHL-a with Pacific modes

Reconstructing CHL-a with Pacific modes

alongshore winds (Figure 2b) and the (a)60 EP-ENSO (e) Linear Model CHL-a decadal upwelling variability mainly 40 CHL-a(x,t) = a(x) ∙ EP-ENSO(t) + tracks the NPGO (-CP-ENSO→20 b (x) ∙ CP-ENSO (t) + NPO→+NPGO→upwelling) [Di 0 c (x) ∙ PDO (t) + Lorenzo et al., 2008; Combes et al. d (x) ∙ NPGO (t) −20 2013a] (Fig.5b). Some fraction of −40 interannual variability results from −60 (f ) Total CHL-a Variance Explained coastally trapped waves excited by −200 −150 −100 60 (b)60 CP-ENSO EP-ENSO (+waves → downwelling). 40 40 North of 38N, along Northern 20 20 50 California and the Gulf of Alaska, the 0 0 interannual signal of ENSO 0 −20 −20 associated with coastally trapped −40 −40 waves is weaker; most of the −50 −60 −60 upwelling variance is controlled by −150 −100 −200 −150 −100 −200 −150 −200 −100 60 (c) PDO the PDO (+CP-ENSO→+AL→ −60 −40 −20 00 20 60 30% 40 60% 40 +PDO→downwelling) (Fig. 5a) (g) Satellite CHL-a 20 [Chhak and Di Lorenzo, 2007; Linear Model CHL-a 0 Combes at al., 2013a]. 3 −20 In contrast, in the PCCS the local 2 PC1 (26%) 1 −40 PC1 (90% PDO) atmospheric forcing is generally 0 −60 weak, and the upwelling variability −200 −150 −100 −1 60 R=0.85 (d) NPGO inferred from passive tracers −2 40 −3 [Combes et al., 2013b] is mostly at 3 −4 20 2 interannual scales and controlled by 50 2002 2005 2007 2010 1 0 the coastally trapped waves generated 0 −20 0 by EP-ENSO (+waves → −1 −40 −2 downwelling) (Fig. 5c,d). e CPR=0.82 −3 PC2 (13%) −50 −60 ENSO atmospheric teleconnection −200 −150 −100 PC2 ( 65% CP-ENSO + 35% NPGO) −200 −150 −100 −4 (Fig. 2) has no projection along the 2002 2005 2007 2010 −60 −40 −20 20 40 60 -0.7 R0 0.7 Thomas et al., 2012 PCCS and therefore the NPGOequivalent variability is absent. Figure 6:CHL-a Large-scale climate forcing Correlation maps (Fig. 6e): CHL-a(x,t) = of Chlorophyll-a. CHL-a distributions reflectbetween regional climate indi  e large-scale climate and satellite Chlorophyll-a (panels a,b,c,d). A simple linear reconstruction of the sate a(x)∙EP-ENSO(t) + b(x)∙CP-ENSO(t) expressions of both the large-scale control of coastal upwelling is also cholorophyll-a with the climate models (panel e) is able to capture the largest fraction of + c(x)∙PDO(t) + d(x)∙NPGO (t). is climate modes and local forcing, and evident in analyses of satellite CHL-a temporal variance at each spatial location (panel f). The principal components of the linear mo linear model reconstruction CHL-a further investigation. images conducted during POBEX reconstruction also capture the firstoftwo dominant need modes of satellite chlorophyll-a vairbaiity (pa of the localthe total g), whichexplains captureup in to the70% observation largest fraction of variance (~35%) that is spatially coher (Fig. 6a-d). 13 years of satellite ocean across the Pacific. All data has been smoothed a 1-yearinrunning prior to the analyses. satellite CHL-a variance (Fig. 6). Two withChange color data (1999-2011) allow oceanmean transport principal modes of CHL-a appear in visualization of regional ecosystem explain observed zooplankton the linear model (Fig. 6e): the first responses in the CCS, the PCCS and variability mode is 90% PDO (decadal, as KOE (Fig. 6) [omas et al., 2012; e large-scale climate forcing was documented by Martinez et al., 2009), Correa-Ramirez et al., 2007; Kahru also found to affect the regional-scale while the second mode is 65% ENSO and Mitchell, 2000]. e Pacific horizontal circulation with impacts (interannual) and 35% NPGO climate modes can be linearly on lower trophic levels, especially (decadal) (Fig. 6e). Higher modes in combined to reconstruct the satellite zooplankton species distribution. In

Figure 6

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(a) Pacific Decadal Oscillation (PDO) (positive phase) Ocean

③ Stronger ④ Alternation    of cold water - warm water zooplankton

Ocean

and distribution of cold water zooplankton

③ KOE Axis

PDO 1950 1970

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SSHa Rossby Waves w/2~3 yrs lag

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(b) North Pacific Gyre Oscillation (NPGO) (positive phase)

Zooplankton Di Lorenzo & Ohman, 2013

Ecosystem ④ Abundance

③ Vertical Mixing

Alongshore Transport Index

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② Wind stress

Zooplankton

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Keister et al., 2011

Ocean Circulation changes and impacts on Zooplankton

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retention ④ Less Retention of of warm water water warm zooplankton zooplankton

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② NPGO: SSH NPGO (lead 2.5) R=0.75 1965 1975 1985 1995 Chiba et al., 2013

gth

en E Str ③KO

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the CCS and KOE where long timeseries of zooplankton are available, studies during POBEX discovered that a large fraction of decadal variability in zooplankton species composition is controlled by changes in basin-scale horizontal advection of surface waters rather than through changes in local upwelling, as had been previously supposed. Our initial experiments to test hypotheses about the effects of large-scale forcing on zooplankton were conducted in the northern CCS

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(NCCS, Fig. 7) where zooplankton have been collected approximately biweekly to monthly since 1996 by the NOAA Northwest Fisheries Science Center off Newport, Oregon. ere, the zooplankton communities cycle in the relative dominance of cold-water or warm-water species [e.g., Peterson and Keister, 2003] in correlation with the PDO: cold-water species vastly dominate the community during cold (negative) phases of the PDO whereas warm-water species are more important during warm (positive)

phases. Passive tracer experiments in ROMS showed that surface current variability associated with the PDO is tightly correlated to the observed changes in copepod species composition (Fig. 7, NCCS). Stronger equatorward advection over the shelf (-PDO) contrasted with anomalously strong poleward currents and downwelling conditions (+PDO). When smoothed over multi-year timescales, these transport fluctuations explained nearly all of the variance of the copepod community

(R=-0.96). Similarly, in the southern CCS (SCCS) a simple PDO-transport model captured almost all of the lowfrequency variance in abundances of the euphausiid, Nyctiphanes simplex (R=0.82) (Fig. 7, SCCS) [Di Lorenzo and Ohman 2013]. Interestingly, the PDO imprint on the SCCS zooplankton – like the ocean response to atmospheric fluctuations – was best modeled as an integrated response to the physical forcing (e.g., the transport index in Fig. 7 for the SCCS is obtained by integrating the PDO, see Di Lorenzo and Ohman [2013]). Similarly, changes in the strength of the KOE control variability in the abundance of warmwater copepods in the KuroshioOyashio Transition (KOT) region [Chiba et al. 2013 in review] (Fig. 7 KOE). ese changes are driven by Rossby waves excited in the central North Pacific by the NPO/NPGO system. e waves arrive in the KOE with an approximate lag of 2.5 years following changes in the eastern North Pacific (Fig. 7, KOE). Passive tracer experiments showed that during years of a weak KOE (NPGO), warm-water species are transported farther north and are retained in the KOT region (Figure 7), leading to the observed zooplankton anomalies (Chiba et al. 2013 in review). Together, these studies provided strong evidence that largescale climate changes affect marine ecosystems coherently around ocean boundaries through changes in ocean transport. e different lags inherent in the processes complicated the discovery of the common signals, but can provide a degree of predictability

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CLIMATE-DRIVEN CHANGES IN ALONGSHORE TRANSPORT DRIVE LONG-TERM FLUCTUATIONS OF ZOOPLANKTON OBSERVED IN THE CALIFORNIA AND KUROSHIO CURRENT SYSTEMS.

to ecosystem changes when the mechanisms are sufficiently well understood.

Mesoscale eddies control crossshelf exchanges and impact fish habitats While the large-scale climate modes strongly controlled upwelling and alongshore transport variability, the cross-shelf exchanges diagnosed from passive tracers [Combes et al., 2009; 2013a; 2013b] were not as coherent across the POBEX region; indeed, they were mostly independent of the large-scale climate forcing. e cross-shelf transport variability along the eastern boundary is dominated by mesoscale features (e.g. eddies and filaments) rather than wind-driven Ekman transport [Combes et al., 2009; 2013a; 2013b]. ese structures drive the offshore export of surface waters, and sub-surface waters of the eastern boundary undercurrents [Combes et al., 2013a for the CCS; Hormazabal et al. 2013 for the PCCS]. Although a large fraction of the mesoscale variance is internal to the ocean and is unpredictable, regionalscale forcings were found to control the statistics of eddies in the Gulf of Alaska and CCS, especially the anticyclones, which showed stronger lowfrequency variability than the

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cyclones [Combes et al., 2009; Davis and Di Lorenzo, 2013a].  ere is growing evidence that mesoscale circulation features have strong impacts on ecosystem dynamics (e.g. primary productivity, McGillicuddy et al. 2007). POBEX did not fully explore their influence on the marine ecosystem due to a lack of adequate long-term observations that resolve eddy-scale processes. Still, studies in the NEP and PCCS suggest strong links between mescoscale circulation and the distribution of zooplankton and higher tropic levels (e.g., fish). Upwelling filaments transport significant portions of coastal zooplankton populations offshore [Keister et al. 2008; Keister and Pierce, 2013] resulting in offshore ‘hot-spots’ of upper trophic activity. In the PPCS, a biophysical modeling study highlighted the potential impact of mesoscale eddies on retention of jack mackerel (Trachurus murphyi, Nichols) in the Challenger and East Pacific ridges, more than 3500 km from historically-known coastal nursery grounds and ocean spawning regions [Parada et al., 2013]. Retention for at least 4 months in anticyclonic eddies with their associated environmental conditions, such as sea surface temperature, CHL-a, wind and turbulence levels, suggests strong recruitment in these features. Evidence of jack mackerel juveniles in the region for over 20

(a) White Noise (Spectrum)

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Aleutian Low

Atmospheric Forcing

variance

2 0

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−2 frequency

variance

(b) Red Noise (Spectrum)

red (-2)

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1960 1970 1980 1990 2000 Integrated Aleutian Low

1X INTEGRATION

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−2 frequency

(c) Super Red Noise (Spectrum)

−4 4

R=0.61

PDO Index

1960 1970 1980 1990 2000 Integrated PDO Index

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−2 frequency

2X INTEGRATION

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Zooplankton

Zooplankton Nyctiphanes simplex

1950 1960 1970 1980 1990 2000 2000 2010

years from Russian research vessel Although more observations Figure 8: Schematic of double integration hypothesis. (panels row a) logbooks support the hypothesis and modeling studies are needed to Aleutian Low index. (panels row b) Integrated Aleutian Low index (black) and PDO index (blue). (panels row c) Integrated PDO index (blue) (Parada et al, 2013). understand andand constraint the zooplankton time series of Nyctiphanes simplex in the California Current Similar mechanics underlying the links Systemresults (red). emerged from statistical analysis in the NEP, where between the mesocale dynamics, long-term timeseries of recruitment zooplankton and fish habitats, these data were significantly correlated with results anticipate a new “ocean indices of mesoscale eddies. For mesoscale” phase in the joint example, Sablefish and Arrowtooth observational/modeling studies of flounder recruitment timeseries climate and marine ecosystem where found to co-vary significantly dynamics. with the formation of the largeanticyclones in the Gulf of Alaska DISCUSSION AND FUTURE during strong downwelling events CHALLENGES (Smith et al., 2013; Stachura et al., Investigations of the POBEX pers. comm.). Indices of mesoscale program provide a mechanistic anti-cyclone activity in model understanding of the role of bottomhindcasts were better predictors of up climate forcing on dynamics of fish recruitment data than linear lower trophic levels of the Pacific models based on sea surface boundary current ecosystems. While temperature and wind observations. the impacts of the large-scale climate is finding reinforces the concept forcings differ in the various that regional model hindcasts provide boundary ecosystems (e.g., the NEP, an important tool to further explore KOE, and PCCS), POBEX studies the mesoscale dynamics impacting were able to identify common fish and inform our forecasts of fish dynamics of the ecosystem response data. to climate, in particular for primary

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producers and zooplankton. Specifically, while most of the largescale and regional-scale variability of primary productivity is linked to changes in upwelling dynamics forced by large-scale climate modes (Fig. 6), the low-frequency, bottom-up control of zooplankton does not follow the traditional model where changes in upwelling → changes in productivity → changes in zooplankton. Rather, it was found that low-frequency variations of zooplankton are more sensitive to changes in large-scale horizontal transport. is finding has important consequences for both our understanding of lower-trophic zooplankton dynamics and the fundamental dynamics of how marine populations respond to climate forcing – “the double integration hypothesis” [Di Lorenzo and Ohman, 2013].

A double-integration hypothesis to explain the ecosystem response to climate Research during POBEX has shown that ocean decadal fluctuations like the PDO and NPGO are a forced response of the ocean to atmospheric variability. From a statistical point of view, the ocean response is equivalent to an auto-regressive process of order-1 (AR-1) driven by white noise atmospheric forcing [Hasselmann, 1976]. For example if we take the Aleutian Low index (1st principal component of North Pacific SLPa) (Fig. 8a), and integrate using the AR-1 model formulation, we can reconstruct the PDO index timeseries with high skill (R=0.6, Fig. 8b). is integration by the ocean filters out the high-frequency variability of the

white noise atmospheric forcing and enhances the decadal and lowerfrequency variability (Fig. 8b). Because the zooplankton and other marine populations are sensitive to changes in ocean conditions, the ecosystem will integrate the white noise atmospheric forcing a second time – first through the ocean, and second through the zooplankton life history – to filter away even more of the high-frequency atmospheric variability. is double integration leads to timeseries of marine ecosystem variability that are dominated by low-frequency fluctuations (Fig. 8c) with largeamplitude state transitions that can persist for decades (i.e. a super red power spectrum). Long-term timeseries of zooplankton in the CCS that are sensitive to PDO-related ocean advection were reconstructed with high skill by integrating the PDO index with an AR-1 model (R=0.82), supporting the double-integration hypothesis [Di Lorenzo and Ohman, 2013].  ese results point to the important need to develop proper null hypotheses of the variability expected in marine populations (e.g. fish) that result from cumulative integration of one or more environmental forcings before interpreting time series of biological or physical variables as demonstrating nonlinear ‘regime shis’. is need is particularly relevant for correctly assessing the underlying causes and significance of apparent ‘state transitions’ and climate change signatures in marine ecosystems.

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Climate change impacts on marine ecosystems Understanding and modeling the impacts of climate change on marine ecosystem remains an important challenge. Given the important role that Pacific Ocean decadal modes have in driving a large fraction of the low-frequency variability observed in long-term ecological timeseries [e.g. Hare and Mantua, 2002], it is critical that we understand and constrain the statistics and dynamics of these modes under changing climate conditions. Although IPCC-class climate models provide an important tool for predicting changes in the physical environment, POBEX found

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process-models of the marine ecosystem responses to climate change. ese process models offer an alternative approach for exploring the sensitivity of marine ecosystems to climate change (e.g., POBEX; PICES WG27). In addition to developing proper modeling strategies to address climate change impacts on marine ecosystems, it is also clear how important long-term observations of marine ecosystem variability are. Unfortunately, these long-term observations are rare and oen not widely available to researchers because of the nature of the sampling programs that depend on different regional organizations. One path towards accessing and analyzing these

DEVELOPING A SOCIAL-NETWORK OF INTERNATIONAL COASTAL ECOSYSTEM SCIENTISTS THAT SHARE MODELS AND DATA IS NECESSARY TO ASSESS THE IMPACTS OF CLIMATE ON MARINE ECOSYSTEMS

that these models are still unable to properly capture the statistics of the large-scale Pacific decadal climate modes [Furtado et al., 2011]. From a modeling point of view this finding raises additional challenges and questions of how to properly use IPCC-class models to downscale climate scenarios and examine climate-change impacts on marine ecosystems (e.g., PICES WG20 & WG29; www.pices.int). It also points to the important need of advancing our mechanistic understanding of the links between physical climate and ecosystems (e.g., POBEX) that can lead to hypotheses and reduced-order

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datasets is the development of social networks of Pacific scientists from different countries.  e success of the POBEX project heavily relied on a prompt exchange of data and methods among the members of the international team. is international collaboration was made successful by prolonged student exchanges (3-months to 1year) among universities in Japan, Chile and the US. In this context the activities of international organizations such as PICES were invaluable for building the exchange channels that led and maintained the POBEX activities. POBEX also

leveraged and partnered with the ongoing California Current Ecosystem Long Term Ecological Research (CCE-LTER; http:// cce.lternet.edu). is partnership builds on the process studies and modeling activities carried out by CCE-LTER, together with the 65-year CalCOFI times series program. ese research collaborations between US GLOBEC with intergovernmental organizations like PICES and US funded LTERs provide the necessary infrastructure for addressing the scientific and societal issues of climate change impacts on marine resources. While the US GLOBEC POBEX was a short-term 4-year project, it was through these partnerships that POBEX was able to conduct a broad range of climate and ecosystem interdisciplinary research (e.g. www.pobex.org). As US GLOBEC has ending it will be critical to find new sources of funding to support science networks like POBEX.

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