Are Long-Term Changes
in Mixed Layer Depth
Influencing North Pacific
Marine Heatwaves?
Dillon J. Amaya, Michael A. Alexander, Antonietta Capotondi, Clara Deser,
Kristopher B. Karnauskas, Arthur J. Miller, and Nathan J. Mantua

AFFILIATIONS: Amaya—Cooperative Institutefor Research in Environmental Sciences (CIRES),
University of Colorado Boulder, Boulder, Colorado; Alexander—NOAA Physical Sciences Laboratory, Boulder, Colorado; Capotondi—CIRES,
University of Colorado Boulder, and NOAA
Physical Sciences Laboratory, Boulder, Colorado;
Deser—National Center for Atmospheric Research, Boulder, Colorado; Karnauskas—CIRES
and Department of Atmospheric and Oceanic Sciences, University of Colorado Boulder,
Boulder, Colorado; Miller—Scripps Institutionof Oceanography, University of California San
Diego; Mantua—NOAA Southwest Fisheries
Science Center, Fisheries Ecology Division
CORRESPONDING AUTHOR: Dillon J. Amaya,
dillon.amaya@colorado.edu
DOI:10.1175/BAMS-D-20-0144.1
A supplement to this article is available online(10.1175/BAMS-D-20-0144.2)©2021 American Meteorological Society
For information regarding reuse of this content
and general copyright information, consult the
AMS Copyright Policy.

AMERICAN METEOROLOGICAL SOCIETY

The 20191RUWKZHVW3DFLœFPDULQHKHDWZDYHZDVDPSOLœHGE\QDWXUDOPXOWLGHFDGDOVKRDOLQJRIWKH
ocean mixed layer; anthropogenic mixed layer shoaling will amplify marine heatwaves in the future.

In boreal summer 2019, the northeast Pacific Ocean (NEPac) experienced a resurgence of extremely warm upper
ocean temperatures (Fig. 1a). The strength and patternof the sea surface temperature anomalies (SSTAs) earned
this event the moniker “Blob 2.0” (Amaya et al. 2020; hereafter A2020), a reference to the original warm “Blob” that
initiated a multi-year marine heatwave (MHW) that devastated regional ecosystems over 2014–16 (Bond et al. 2015;
Cavole et al. 2016; Amaya et al. 2016; Piatt et al. 2020). In
particular, the intraseasonal persistence of the 2019 Blob
2.0 generated similar widespread concern among fisheryand wildlife managers for sensitive marine ecosystems
along the west coast of North America (NOAA 2019).
Blob 2.0 primarily resulted from a record minimum
mixed layer depth (MLD; Fig. 1a shading), which formed
due to weaker than normal wind speeds and strong surface heating from reduced cloud cover (A2020). Equation(1) illustrates how shallow mixed layer depths affectmixed layer temperature changes, wTcm/wt, when considerJANUARY 2021

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Fig. 1. (a) JJA 2019 MLDAs (m; shading) and SSTAs (°C; contours, positive solid and negative dashed) from Argo and GODAS,
respectively. Contour interval is 0.5°C starting at 0.5r& VROLGEODFN  E 0/'$VIRUœYHREVHUYDWLRQDODQDO\VHV 7DEOH1) averaged in the black box (i.e., 34°–47°N, 213°–232°W) shown in each map. Circles mark 2019 values. Shading for SODA3 andORAS5 represents the full range (min-to-max) across respective ensembles. (c),(d) Observed JJA MLD trends (m decade–1;
shading) from 1980 to 2015 averaged across two groupings: (c) GODAS, ORAS5, and Argo and (d) ORAS4 and SODA3. (e),(f)Simulated JJA MLD trend (m decade–1; shading) from 1980 to 2015 in ensemble means of CESM1-LE and CMIP5. (g),(h) As in(e),(f), but for the period 2016–99$OOWUHQGVEDVHGRQOLQHDUOHDVWVTXDUHVœW6WLSSOLQJUHSUHVHQWV95VLJQLœFDQFHIRUD
Mann-Kendall test.

ing only local heat sources and sinks (i.e., neglecting advection) and separating eachbudget term into mean and perturbation components:

ρcp∂Tm′ Q′ Qh′  Q′h′ − Q′h′ ≈− 2 −
,
∂t
h
h
h2


I

II

(1)

III

where Q is the net surface heat flux into the ocean, h is the MLD, ρ is seawater density,and cp is the specific heat of seawater. Primes denote time anomalies and overbars

S60 |JANUARY 2021

represent time mean values. For the full derivation see Alexander and Penland (1996).
For Blob 2.0, strong downward Q anomalies (i.e., positive Qc) led to excess heat being
distributed over a thin climatological mixed layer, since h̄ is small in summer (termI). More importantly, anomalous MLD shoaling (i.e., negative hc) contributed to upper
ocean warming through term II (A2020).
As discussed in A2020, the 2019 MLD anomalies (MLDAs) superpose on a MLD
shoaling trend from 1980 to the present, which they suggest may indicate a role foranthropogenic forcing. Upper ocean warming in response to future climate change is
expected to reduce mixing and shoal the mixed layer (Capotondi et al. 2012; Alexander
et al. 2018). A long-term trend in the mean MLD would have significant implicationsfor SSTAs since, according to Eq. (1), decreasing the mean MLD (h̄) results in a stronger temperature response for the same heat flux and MLD anomalies. Therefore, if theobserved MLD shoaling first reported by A2020 is robust across different datasets andconsistent with the projected response to anthropogenic climate change, then Blob 2.0
may have been exacerbated by anthropogenic forcing. Here, we investigate the presence of NEPac MLD trends in a suite of observational analyses. We then compare theseresults to coupled model simulations to assess the potential influence of anthropogenic climate change on NEPac MLD trends, and by extension, on the likelihood andintensity of the 2019 MHW.

Data and methods.

For observed MLD, we use monthly mean data from the NOAA Global Ocean Data Assimilation System (GODAS; Behringer and Xue 2004), ECMWF Ocean Reanalysis System 4 (ORAS4; Balmaseda et al. 2013) and 5 (ORAS5; Zuo et al. 2019), Simple Ocean
Data Assimilation version 3 (SODA3; Carton et al. 2018), and gridded Argo profiles(Hosoda et al. 2008). See Table 1 for more details.
We estimate the externally forced MLD trends using the Community Earth System
Model version 1 Large Ensemble (CESM1-LE; Kay et al. 2015). Additionally, we use 13
models from phase 5 of the Coupled Model Intercomparison Project (CMIP5; Taylor
et al. 2012) with the same radiative forcing protocol. Model details are provided inTable 1 and also Table ES1 in the supplemental material. We use the ensemble mean ofeach model ensemble (CESM1-LE and CMIP5) as two estimates of the forced response.
To compare trends across datasets, we calculate MLD in each observational analysis and coupled model simulation as the interpolated depth at which potential density
first exceeds 0.125 kg m–3 greater than the surface value (Suga et al. 2004). For datasets that do not include potential density, we calculate it from monthly mean potential
temperature and salinity profiles. To compare to Blob 2.0, we only analyze MLD values averaged over boreal summer [June–August (JJA)]. Unless otherwise specified, allanomalies are relative to the period 2001–15, which is the longest overlapping period
for the data used in this study.
Our results are not sensitive to the choice of MLD definition. Additionally, while it ispreferred to calculate long-term MLD trends based on daily mean values, many of thedatasets only provided monthly means (e.g., ORAS4, ORAS5, CESM1-LE, and CMIP5).
However, we do not expect our results or conclusions to be influenced by this choice,since the temperature and density gradients are very strong at the base of the mixed
layer in summer. Finally, we define the term “NEPac” to represent the region boundedby 34°–47°N, 213°–232°W (black box, Fig. 1), the same area used in A2020.

Results.

MLD trends in observations. We begin by assessing MLD trends in observations. Interannual MLD variability in the NEPac is quite consistent across the various observational
analyses (Fig. 1b, Table 1), particularly during the Argo era (2001–present). However,
earlier in the instrumental record, two groupings emerge, with GODAS and ORAS5
exhibiting a more pronounced shoaling trend than ORAS4 and SODA3. While the
AMERICAN METEOROLOGICAL SOCIETYJANUARY 2021

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Table 1. Observational and coupled model data used in this study and their JJA-averaged MLD trends in the
NEPac (black box; Fig. 1a). For ensemble datasets, the mean MLD trend is reported with minimum and maximum ensemble trends in parentheses. For Argo data, the trend is reported for 2001–196LJQLœFDQWWUHQGV
are bolded and are based on a 95% Mann-Kendall test. Datasets marked with an asterisk (*) did not provide
potential density as a variable. Therefore, the potential density used to calculate MLD for these datasets is
EDVHGRQWKHLUUHVSHFWLYHWHPSHUDWXUHDQGVDOLQLW\œHOGV

Datasets

Data availability

Ensemble details

Observations
GODAS*

NEPac MLD trends(m decade –1)
Trend for 1980–2015

1980–2019–1.3
5 members, different initialconditions

ORAS5*

1979–2018

Argo

2001–19–2.2

ORAS4*

1958–2017–0.3

SODA3.X

SODA3.3.2: 1980–2018
SODA3.4.2: 1980–2018
SODA3.11.2: 1980–2015
SODA3.12.2: 1980–2017

4 members, different atmospheric reanalysis forcing–1.2 (–1.2 to –1.2)–0.2 ň0.6 to 0.6)

Obs. avg. (excluding Argo)–0.7

ModelsTrend for 1980–2015/projected trend for 2016–2099

CESM1-LE

CMIP5*

Forced model avg.

1920–2100

40 members, historical forcing until 2005, RCP8.5 after–0.0 (ň0.6 to 0.7)/–0.6(ň0.7 to –0.3)

1900–2099

13 models, one member each(see Table ES1 for model
details), historical forcing
until 2005, RCP8.5 after–0.2 (ň0.6 to 0.5)/–0.4(ň1.0 to –0.1)–0.1/–0.5

magnitude of the observed MLD shoaling varies among datasets, the average NEPac
trend (−0.7 m decade−1) is significant at the 95% confidence level (Table 1). Given thatthe climatological JJA MLD in the NEPac region is ~18 m, such a trend would correspond~15% decrease in the mean MLD from 1980 to 2015.
Creating two groupings of observational analyses (GODAS, ORAS5, and Argo vsORAS4 and SODA3), we produce two observational MLD trend maps from 1980 to 2015.
The average of GODAS, ORAS5, and Argo shows widespread MLD shoaling trends withtwo main centers of action, one around the Aleutian Islands and one off the California
coast (Figs. 1c,d; see also Fig. ES1). While the average of ORAS4 and SODA3 shows
weaker MLD trends overall, the two centers of action are also generally present in thesedata (Fig. ES1). Additionally, the close spatial correspondence of the 2019 MLDAs near
California (Fig. 1a, shading) with some of the observed trends (Fig. 1c and Fig. ES1)
suggests that this extreme event was likely exacerbated by these longer-term features.
MLD trends in climate models. Are these and other North Pacific MLD trends attributable
to anthropogenic forcing? To address this question, we show maps of JJA MLD trends
from 1980 to 2015 for the ensemble means of CESM1-LE and CMIP5 to estimate the forced
component (Figs. 1e,f). There is some spatial correspondence with the observational
analyses (Fig. 1c and Fig. ES1), especially with CMIP5. The spatial similarities between
the historical trends in observations and the forced trends in models are even more
apparent when the latter are extended into the future (2016–99; Figs. 1g,h). For the
NEPac, the JJA MLD time series show significant forced trends in both the CESM1-LE

S62 |JANUARY 2021

(0.6 m decade 1) and CMIP5 (0.4 m decade 1) during 2016–99, which amounts to ~4m MLD shoaling by the end of the twenty-first century (Fig. 2a). However, many of theobserved NEPac trend estimates are larger than the CESM1-LE and CMIP5 ensemble
mean trends from 1980 to 2015 (Table 1), suggesting that the observations contain significant contributions from internal variability.
Climate change impacts on future MLD extremes. How extreme were the 2019 MLDAs
relative to the full range of internal variations in present and future climates? Diagnosing
the 2019 event in this context will help us understand how future MLDAs contributeto future MHWs.
Given that the magnitude of interannual variability of NEPac JJA MLD in CESM1-LE
compares well with observations (standard deviation of 2.1 vs 2.3 m, respectively,
based on detrended data during 1950–2018), this model ensemble can be used to contextualize the observations. Compared to the CESM1-LE probability distribution function (PDF) for a 30-yr period centered on 2019, the observed 2019 NEPac MLDA was anextremely rare event, falling at the far negative tail of the model distribution (Fig. 2b).
When viewed against the projected decrease in the model’s MLD by 2100, the observed
2019 anomalies become less extreme, rising from percentile ranks of 0.06% and 0.08%
to 7.6% and 10.4% for GODAS and Argo, respectively.
Climate change impacts of MLD on future SST extremes. Long-term shoaling of the mean
MLD also has important implications for the magnitude of future SST extremes. We
illustrate this by calculating Eq. (1) terms I and II in CESM1-LE for the NEPac box. In
this region, the CESM1-LE JJA mean net surface heat flux (Q̄) increases by ~10 W m–2 by
the end of the century, while the mean MLD (h̄) decreases by ~4 m (Fig. 3a). As a result,the denominators of terms I and II decrease, while the numerator of term II increases,suggesting that both term I and II will contribute to more mixed layer warming for thesame heat flux anomaly (Qc) and MLDA (hc) in the future.
By fixing Qc and hc at observed JJA 2019 values (Qc = 7.8 W m–2 and hc = −6.2 m
in NEPac using ERA5 and GODAS, respectively) and calculating terms I and II in a
30-yr sliding window, we see that the superposition of increasing Q̄ and decreasingh̄ leads to term II dominating over term I (Fig. 3b). In particular, for the same Qc andhc, term II is projected to generate SSTAs that are ~4.5°C warmer in 2071–2100 thanin 2001–30. Term III, which represents the nonlinear interaction of Qc and hc, is negligible. Therefore, in the absence of compensating damping from processes such as

Fig. 2. (a) JJA MLDAs averaged in NEPac (black box, Fig. 1a) for two observational groupings (red/orange) and ensemble meansof CESM1-LE (black) and CMIP5 (blue). Shading for each time series represents the full range (min-to-max) across respective
ensembles. (b) Probability distributions of JJA ensemble mean CESM1-LE MLDAs averaged in NEPac during the “present”(blue; 2005–34) and “future” (orange; 2070–99). Vertical black lines mark JJA 2019 MLDA values from GODAS (solid) and Argo(dashed) data averaged in same region.
AMERICAN METEOROLOGICAL SOCIETYJANUARY 2021

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Fig. 3. (a) CESM1/(--$PHDQQHWVXUIDFHKHDWŔX[ Q̄ ; left axis; blue) and mean MLD ( h̄ ; right axis; red) in the NEPac (blackbox, Fig. 1). (b) Contributions of Eq. (1) terms I (purple) and II (green) to anomalous mixed layer temperature changes (wTcm).
(DFKWHUPLQ E LVFDOFXODWHGXVLQJWKHWLPHHYROYLQJPHDQVLQ D DQGDœ[HGQHWVXUIDFHKHDWŔX[DQRPDO\ Qc) and MLDA ( hc)
set to observed JJA 2019 values (see text). Terms I and II are calculated in 30-yr sliding windows starting in 2001; years along
the x axis denote the end of the window. For example, term I in 2060UHSUHVHQWVœ[HGQc divided by the projected CESM1-LE h̄
for the period 2031–60. CESM1-LE data are subject to a 10-yr running mean prior to calculating changes.

entrainment and ocean–atmosphere feedbacks, extreme MLDAs like that in 2019 willcontribute to larger and/or more frequent SST extremes in the future.

Discussion.

We have investigated the influence of climate change on North Pacific MLD trends
and, by extension, the likelihood and intensity of the summer 2019 MHW. We showedthat some parts of the NEPac have likely experienced long-term MLD shoaling since1980 (i.e., within the black box in Fig. 1), but significant observational uncertainty
regarding the strength of these trends remains (Fig. ES1 and Table 1). Even so, the 2019
MLDAs, which were an important driver of Blob 2.0 (A2020), were likely exacerbatedby these multidecadal trends. Consequently, the marine ecosystem impacts generated
by this MHW (PFMC 2020; Lambert 2019) may have also been intensified by the shoaling of the ocean mixed layer.
The anthropogenic contribution to these observed MLD trends is less clear. Thereis model uncertainty in the strength and pattern of the estimated forced NEPac trends
from 1980–2015 (Figs. 1e,f, Tables 1 and ES1). Additionally, the fact that the observed
MLD trend greatly exceeds the forced trends estimated by the model ensemble means(Table 1) suggests that internal variability makes a strong contribution to the observed
trend. However, given the close spatial correspondence between Fig. 1c and the forced
trends from 2016 to 2099, it is possible that the models underestimate the strength of
the forced response in recent decades. Regardless, it is clear that large internal climate
variability complicates the detectability of the forced MLD signal in observations.
Our analysis supports previous findings that pronounced changes in mean MLD
may have significant implications for the frequency and strength of MHWs in the future (Alexander et al. 2018). This causal link arises primarily through an enhancedrole for the MLDA effect on SSTA (Fig. 3b, term II), which will be further exacerbated as extreme negative MLDAs become more common in response to climate change(Fig. 2b). This process is likely to be more important in summer when mean MLDs areshallowest (Alexander and Penland 1996; Alexander et al. 2000). A shallower MLD
and associated smaller heat capacity could further intensify SST warming in summer,
contributing to thermal stress on marine organisms. More research is needed intomechanisms that may offset the projected effects of a shallower MLD on future MHWs.
Finally, our results emphasize the importance of focusing on the underlying dynamics

S64 |JANUARY 2021

that modulate the mixed layer heat budget when assessing the influence of climatechange on future MHWs, which complements recent studies focusing primarily on theinfluence of climate change on the SST itself (Frölicher et al. 2018; Jacox et al. 2018;
Walsh et al. 2018).

Acknowledgments. Three referees, Stephanie Herring, and Robin Webb provided
important comments. James Scott provided MLD estimates from the CMIP5 models. The
views expressed herein are those of the authors and do not necessarily reflect the viewsof NOAA or the DOC. NCAR, sponsored by NSF, partly supported this work. Funding from
NSF (OCE1600283, OCE1637632, and OCE2022868) and NOAA (MAPP NA17OAR4310106)
is also appreciated.

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