1. Introduction
Marine heatwaves (MHWs) are characterized by a prolonged, discrete positive temperature anomaly in the upper ocean (Hobday et al. 2016). MHWs are defined by sea surface temperature (SST) exceeding a high percentile threshold (e.g., 90%) for at least five consecutive days (Hobday et al. 2016). In recent years, MHWs frequently occurred worldwide: in the northeast (NE) Pacific (Bond et al. 2015; Di Lorenzo and Mantua 2016; Zhi et al. 2019; Amaya et al. 2020; Chen et al. 2021b; Liu et al. 2022; Shi et al. 2022), the northwest Atlantic (Mills et al. 2013; Chen et al. 2015; Scannell et al. 2016), the western tropical Pacific (Hu et al. 2021; Han et al. 2022), the Bay of Bengal (Gao et al. 2022), off the west coast of Australia (Feng et al. 2013; Pearce and Feng 2013; Xu et al. 2018), and in the Tasman Sea (Oliver et al. 2017; Behrens et al. 2019). These events have attracted a lot of attention. Moreover, Oliver et al. (2018) pointed out that the frequency, intensity, and duration of MHW have significantly increased since the early twentieth century. These trends are primarily the result of long-term ocean temperature rising and are expected to continue under global warming.
Although MHWs have occurred throughout the global oceans, the MHWs in the NE Pacific have attracted considerable scientific and public attention recently, due to the emergence of extremely persistent and large-scale events in the past decade. For instance, from the boreal fall of 2013 to early 2016, an intense MHW event appeared in the NE Pacific, with a pronounced positive SST anomaly (SSTA) spreading in a large area of the Gulf of Alaska (GOA) and then affecting the North America coastline, which is termed as the “the blob” (Bond et al. 2015; Hartmann 2015; Di Lorenzo and Mantua 2016; Gentemann et al. 2017; Hu et al. 2017; Hobday et al. 2018; Myers et al. 2018; Schmeisser et al. 2019). From the boreal summer of 2019 to the end of 2020, another exceptionally strong warming was observed in the upper ocean near the GOA (Amaya et al. 2020; Scannell et al. 2020; Chen et al. 2021b). These intense MHW events that took place in the NE Pacific have caused severe ecological and economic consequences, including mass migration and death of marine species (Cavole et al. 2016; Jones et al. 2018), and a coastwide outbreak of harmful algae that caused closures of many lucrative fisheries (Cavole et al. 2016; McCabe et al. 2016; Ryan et al. 2017). Some studies documented that the anomalously warm SSTA in the NE Pacific can play a role in causing an anomalous cold or warm winter in North America (Hartmann 2015; Walsh et al. 2017) and the severe drought in California (Seager et al. 2015), which implies widespread climatic impacts due to the MHW events in the NE Pacific. Hence, a better understanding of MHWs is of great significance for promoting our ability to forecast the magnitude and persistence of each event, which may help policy-makers develop adaptation strategies and avoid potentially devastating impacts.
A number of studies explored the onset mechanism for MHW events. Bond et al. (2015) suggested that the generation of significant warming in the NE Pacific in winter 2014 was primarily attributed to a combination of reduced cold horizontal advection in the upper ocean and a relatively weak sea surface heat loss. They attributed both anomalous oceanic advection and surface heat flux to the reduction of wind speed, which was induced by a pronounced positive sea level pressure anomaly (SLPA) over the NE Pacific and its associated easterly anomalies. Amaya et al. (2020) investigated the onset mechanism for the summer 2019 MHW in the NE Pacific. They found that positive shortwave radiation heat flux dominated the generation of warm SSTA, and the latent heat flux made a smaller but important contribution. Moreover, the essential role of mixed layer depth variation was stressed, as the heating effect from the surface heat flux anomalies can be dramatically amplified when the mixed layer became thinner than normal. They suggested that such thinner-than-normal mixed layer depth was caused by the slowdown of surface wind, which was traced back to the weakening of the North Pacific high pressure system. Their results, along with some other studies (Hartmann 2015; Di Lorenzo and Mantua 2016; Gentemann et al. 2017; Hu et al. 2017; Chen et al. 2021a), confirmed that the atmospheric circulation anomalies over the North Pacific played an active role in the onset of the MHWs in the NE Pacific. Meanwhile, various factors have been proposed to explain the generation of the anomalous SLPA field associated with the ensuing MHW, including remote teleconnection driven by SSTAs in the tropical Pacific (Wang et al. 2014; Hartmann 2015; Lee et al. 2015; Seager et al. 2015; Hu et al. 2017; Amaya et al. 2020), Arctic sea ice loss with associated thermal effects (Sewall and Sloan 2004; Kug et al. 2015; Lee et al. 2015), and internal atmospheric variability (Seager et al. 2015; Hu et al. 2017).
In terms of the maintenance of MHWs, some studies proposed a positive SST–cloud feedback mechanism (Norris et al. 1998; Bellomo et al. 2014; Myers et al. 2018; Schmeisser et al. 2019; Amaya et al. 2020). Once warm SSTA occurs in the NE Pacific, it decreases cloud fraction, which can increase incoming solar radiation and further contribute to warm SSTA. Di Lorenzo and Mantua (2016) used coupling between the North Pacific Gyre Oscillation (NPGO; Di Lorenzo et al. 2008) and Pacific decadal oscillation (PDO; Mantua et al. 1997) to explain the multiyear persistence of the 2014/15 NE Pacific MHW. Specifically, the atmospheric circulation anomalies that induced the GOA (NPGO-like) warming in early 2014, and the initiated positive SSTA in the NE Pacific further propagated southwestward via the positive wind–evaporation–SST feedback (Xie and Philander 1994). When the positive SSTA arrived at the central equatorial Pacific, an El Niño was established in fall 2014. The established El Niño, through its teleconnection with the extratropics, induced a strong Aleutian low in fall 2015, which finally caused the reoccurrence of NE Pacific warming resembling the expression of the PDO.
Accurately predicting MHWs is another challenging issue, although tremendous efforts have been devoted to exploring the onset and evolution mechanisms for the anomalously warm SSTs in the NE Pacific. Hu et al. (2017) evaluated the performance of a climate model in predicting the seasonal evolution of the 2014–16 NE Pacific MHW. They pointed out that the initiation of these extreme warm anomalies was almost unpredictable because the SSTA in this region was to a great extent controlled by stochastic internal atmospheric variability. The forecast skill for this MHW was also evaluated in Jacox et al. (2019) using eight North American Multimodel Ensemble (NMME) models. They found that although the ensemble mean forecast outperformed a simple damped persistence forecast, it failed to catch the warming peaks even at a lead time of two month. Nevertheless, the above-mentioned results are based on the single MHW in 2014–16 and focus on one specific aspect related to this one case. Investigation of other MHWs is much needed to understand the current prediction skill of NE Pacific MHWs, especially the determining factor(s) for such MHW occurrence.
Throughout all of 2020, intense warm SSTAs spread in a large area of the NE Pacific, with three peaks in April, July, and November. Chen et al. (2021b) reported this MHW event, and suggested that surface heat flux anomaly and potentially ocean entrainment induced the April peak, whereas positive downward latent heat flux anomaly induced its November peak. Although the leading role of less-than-normal latent heat loss in causing this intense MHW was mentioned, there is a lack of quantitative diagnosis regarding the atmospheric and oceanic factors determining the latent heat flux’s heating effect, as its change involves wind speed, sea–air humidity difference and mixed layer depth. Moreover, the prediction skill regarding this extreme MHW event in the state-of-the-art coupled models is unclear.
The aims of this study are to understand the dynamic and thermodynamic processes responsible for the 2020 MHW and to investigate MHW predictability with a specific focus on the 2020 event. To achieve these goals, we structure the rest of this paper as follows. In section 2, we introduce the reanalysis data, model data, and methods. In section 3, we present the characteristics of the observed 2020 MHW in the NE Pacific. We examine the physical cause of the pronounced warm SSTA by diagnosing the ocean mixed layer heat budget in section 4. Further assessment of the key atmospheric processes forcing the warming is given in section 5. In section 6, we assess the possibility of predicting such extreme warm events using the state-of-the-art coupled general circulation models. Finally, discussion and conclusions are given in section 7.
2. Data and methods
a. Data
1) Reanalysis data
In this study, the daily SST field is from the National Oceanic and Atmospheric Administration (NOAA) optimum interpolation SST version 2 (OISST.v2; Reynolds et al. 2007). The monthly SST datasets are from the NOAA Extended Reconstructed SST version 5 (ERSST.v5; Huang et al. 2017), Met Office Hadley Centre Sea Ice and Sea Surface Temperature data (HadISST; Rayner et al. 2003), and COBE (Ishii et al. 2005). The monthly and pentad three-dimensional ocean temperature, current, and mixed-layer depth are from the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS; Saha et al. 2006). The GODAS reanalysis has 1/3° in the meridional direction and 1° in the zonal direction, and has 40 levels with 10-m resolution in the upper 200 m. The monthly and daily surface heat fluxes, wind, pressure, precipitation, and specific humidity are from the National Centers for Environmental Prediction version 2 (NCEP2; Kanamitsu et al. 2002). The NCEP2 product has the resolution of ∼2° in the meridional direction and 1.875° in the zonal direction. All datasets used cover the period 1980–2021 and their corresponding trends have been removed prior to analysis. It is worth mentioning that the main conclusions remain valid whether or not linear trends are removed.
2) Model outputs
The NMME project is composed of coupled models from the modeling centers of the United States and Canada (Kirtman et al. 2014). Nine NMME Phase-1 models whose SST or SLP forecast results are publicly available for a long forecast period covering 2020 are adopted in this study, including CanCM4i, CanSIPSv2, COLA-RSMAS-CCSM4, GEM-NEMO, GFDL-CM2p1-aer04, GFDL-CM2p5-FLOR-A06, GFDL-CM2p5-FLOR-B01, GFDL-SPEAR, and NASA-GEOSS2S. Generally, the retrospective forecast results encompass an ensemble of at least 10 members, and the maximum lead time ranges from 9 to 12 months at each initialization time. Particularly, the “one-month lead” in the present study means the forecast conducted from the first day of the initial month to cover the current month; hence, the “two-month lead” denotes the forecast from the initial month to the following (second) month. More model specifications, including the variables, forecast period, lead time, and ensemble size, are summarized in Table 1.
Description of nine NMME models used in this study.
b. Atmospheric general circulation model
The atmospheric general circulation model (AGCM) employed in this study is ECHAM4.6, which is developed by the Max Planck Institute of Meteorology (MPI-M). ECHAM4.6 has 19 vertical levels using a hybrid pressure-sigma coordinate extending from surface to 10 hPa. The cumulus convection parameterization scheme used is based on Tiedtke (1989), with several modifications (Nordeng 1994). More details on the dynamic framework and physical processes of this AGCM are available in Roeckner et al. (1996).
c. Methods
1) Mixed layer heat budget
2) Moisture equation
3. Characteristics of the observed 2020 NE Pacific MHW
Throughout the whole of 2020, a large area in the NE Pacific was remarkably warm (Fig. 1a). Specifically, from January to April, the NE Pacific warmed up continuedly and reached its first peak SSTA in April 2020 (Fig. 1e). The NE Pacific positive SSTA started to decrease in May, but was rapidly reintensified into a second peak in July (Fig. 1h). Afterward, the warm SSTA was weakened again, whereas it spread to the east and along the western North American coastline. In November 2020 (Fig. 1l), the NE Pacific warmed up for the third time, resembling the warm SSTA patterns in April and July. Note that the most significant warm SSTAs in the NE Pacific were always spread over a broad region near the GOA throughout all of 2020; thus, the region of 170°–140°W, 32°–47°N (the black box in Fig. 1) is chosen as the main domain in this study for further analysis.
(a) Horizontal pattern of sea surface temperature anomaly (SSTA; °C) averaged from April to November 2020. (b)–(m) Horizontal patterns of monthly SSTA from January to December 2020. The black box denotes the study region.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
Here the SST data for plotting Fig. 1 are derived from the GODAS dataset, specifically, the oceanic temperature at 5 m in GODAS. In fact, we have examined five datasets (including GODAS, OISSTv2, HadISST, ERSSTv5, and COBE) in total, and found that all these datasets show similar SSTA evolution feature and SSTA pattern (see Fig. S1 in the online supplemental material for details).
Following Hobday et al. (2016), MHW is identified by SSTA exceeding the 90th percentile based on a 42-yr baseline period of 1980–2021. The time series of SSTA from two datasets are shown in Fig. 2. Note that the SSTA exceeding the 90th and 95th percentiles are indicated by orange and purple dots at the top of each panel in Figs. 2a and 2b, respectively. From the historical perspective (Fig. 2a), MHWs in the NE Pacific have frequently occurred since 2010; for example, the MHWs in 2013/14 and 2019 are well-known cases that have received a lot of attention (e.g., Bond et al. 2015; Amaya et al. 2020; Chen et al. 2021b). With a few months’ interval after the MHW in 2019, the NE Pacific warmed up again in 2020. As shown in Fig. 2b, the MHW in the NE Pacific during 2020 exhibits a persistence feature. Specifically, the period when the NE Pacific SSTA exceeds the 90th percentile can last 9 consecutive months with a short gap in October, and in most of the months SSTA was above the 95th percentile (purple dots). Moreover, the 2020 MHW had an exceptionally large intensity. The NE Pacific mean SSTA in July 2020 reached 1.5°C (2.7 standard deviations), yielding a new record for the MHW in our key analysis region during the last 42 years (Fig. 2a). Meanwhile, the SSTA peaks in April and November 2020 are also notable throughout the analysis period from 1980 to 2021, although these two peaks showed slightly weaker amplitude compared to that in July 2020. Corresponding to its large intensity and persistence, the temporal mean SSTA from April to November 2020 broke the record since 1980 (Fig. 2c). Note that remarkable sea surface warming also appeared during July–November 2019 (Fig. 2b); this MHW in 2019 is not considered part of the 2020 event, because there was a gap of 3 months between the two events. Some recent studies made efforts to understand the NE Pacific warming during the latter half of 2019 (Scannell et al. 2020; Chen et al. 2021b). Therefore, we only focus on the MHW in 2020.
(a),(b) Time series of monthly SSTA (°C) averaged in the study region derived from GODAS (black line) and OISSTv2 (blue line) datasets. Orange and purple dots identify months when the SSTA exceeds the 90th and 95th percentile thresholds based on a 42-yr baseline period of 1980–2021. The orange, brown, and red dashed lines in (a) denote the SSTA’s value in April, July, and November 2020, respectively. (c) Time series of temporal mean SSTA (April–November); the red bar denotes the 2020 MHW.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
In short, the 2020 MHW exhibited great complexity in terms of its extremely high intensity and multiple peaks. Next, we will investigate the dynamic and thermodynamic processes responsible for each peak, so that we can have an in-depth understanding about the evolution of the MHW in 2020.
4. Mixed layer heat budget
a. Overview of the diagnosed results
As the 2020 MHW event in the NE Pacific had three peaks, we employed the datasets with higher temporal resolution to uncover the key factors driving each peak. Using the pentad SST dataset from GODAS and daily SST dataset from OISST, we obtain detailed evolution of the 2020 MHW (Fig. 3). Three peaks can be clearly identified on 20 April, 24 July, and 16 November, respectively, which are consistent with the months derived from the monthly dataset (see Figs. 2a and 2b). The rapid development periods for the three SSTA peaks are discerned (Fig. 3), including 4 February–20 April (period I), 30 May–24 July (period II), and 7 October–16 November (period III). We then carry out the mixed layer heat budget diagnosis for the SSTA averaged in the analysis region (170°–140°W, 32°–47°N) during each period. Note that the diagnosis results below are not sensitive to slight alteration in the length of each period (e.g., the results still hold if period I is from 9 February to 20 April instead). Figure 4 shows the contributions from individual dynamic and thermodynamic terms for each period. Clearly, the positive mixed layer temperature anomaly (MLTA) tendency in the NE Pacific for each period is dominated by the net surface heat flux, while the ocean dynamic terms are relatively minor, except that the ocean dynamic term of
Time series of SSTA (°C) averaged in the study region from 1 Jan to 31 Dec 2020 derived from GODAS pentad (black line) and OISSTv2 daily (blue line) datasets. The orange, brown, and red shadings denote three periods for SSTA growth.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
Diagnosis of the mixed layer temperature anomaly over the study region for (a) period I, (b) period II and (c) period III. Based on Eq. (2), terms along the x axis from left to right are, respectively,
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
Since the positive net surface heat flux played a decisive role in the formation of each of the three SSTA peaks in the 2020 MHW event, we present the detailed evolution of the four associated heat flux terms to distinguish the relative contributions from different thermodynamic processes. As illustrated in Fig. 5a, the evolution of the latent heat flux (LH; red solid line) shows a good consistency with that of the MLTA tendency (black solid line) in periods I and III. This indicates that the LH was the dominant factor responsible for the April and November peaks. However, during period II the leading contribution of LH to the MLTA development sometimes became blunt due to the perturbed contributions from the sensible heat flux (SH), shortwave radiation heat flux (SW), and longwave radiation heat flux (LW).
(a) Time series of heat flux terms (°C pentad−1) of mixed-layer heat budget from 5 Jan to 31 Dec 2020. Budget terms include latent heat flux (LH; red solid line), sensible heat flux (SH; green solid line), longwave heat flux (LW; blue solid line), shortwave heat flux (SW, orange solid line), sum of four flux terms (black dashed line), and temperature anomaly tendency (black solid line). The orange, brown, and red shadings denote three periods for SSTA growth, respectively. All terms are averaged over the study region. The budget terms are averaged for (b) period I, (c) period II, and (d) period III. Red and blue bars represent positive and negative values, respectively.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
To illustrate the integrated contributions of the surface heat fluxes to each development period of warm SSTA, we present the heat flux terms averaged for each period (Figs. 5b–d). Obviously, the positive LH resulted in the development of warm SSTA during periods I and III and the corresponding peaks in April and November. As for period II, the positive LH also played a dominant role in the development of warm SSTA and the corresponding peak in July, while the positive SH played a secondary role. In contrast, the overall contributions from LW and SW were negligible. Although the overall contribution of SW is close to zero during period II, the SW showed a positive contribution to the warming during the second half of period II (4–24 July), during which a positive feedback between SSTA and low cloud may play a role in amplifying the warm SSTA. During summertime the marine stratus clouds are pervasive over the NE Pacific (Fig. S4; Ronca and Battisti 1997; Norris et al. 1998), and previous studies (Schmeisser et al. 2019; Amaya et al. 2020) have suggested that, once the NE Pacific SSTA reaches a relatively high magnitude (e.g., 1°C), an SSTA–low cloud feedback may be triggered and contribute to the maintenance of positive SSTA in the NE Pacific. Some specific observational details are given in Text S1 in the online supplemental material. Nonetheless, the positive LHA played an irreplaceable role in triggering this SSTA warming over the whole of period II, with the overall contribution ranking the first place. Therefore, we still consider the positive LHA as the dominant contributor during period II.
In short, the positive LH term was the leading contributor to the three development periods and the corresponding three peaks during the evolution of 2020 MHW event in the NE Pacific. Our diagnosed results emphasize the importance of surface heat flux, especially the LH term, in the MHW evolution. This finding is generally consistent with the studies that focused on the past MHW events in the NE Pacific (Hartmann 2015; Gentemann et al. 2017; Schmeisser et al. 2019; Amaya et al. 2020; Chen et al. 2021b).
b. Role of MLD in the 2020 MHW evolution
A recent study (Amaya et al. 2020) highlighted the essential role of MLD variation in the formation of the MHW in summer 2019. Specifically, MLD became thinner than normal during the development of MHW, and such thinner MLD can amplify heat flux’s heating effects. It is worth noting that the climatological MLD in the NE Pacific is quite thin during the summertime (about 10–30 m). This means even slight shoaling of MLD in summer may spike the thermodynamic heating effect, which is conducive to the rapid increase of SSTA (Amaya et al. 2020, 2021; Shi et al. 2022). Therefore, the potential effects of the anomalous MLD on the formations of the April, July, and November peaks in the 2020 MHW event deserve in-depth investigation.
Figures 6a and 6b show the evolution of the actual and climatological MLD (solid and dashed line, respectively) in the NE Pacific during 2020. Note that the MLD exhibited a shoaling in period I but barely changed in both period II and period III. Figures 6c–e further illustrate the relative contributions from the LHA and MLDA to the anomalous heating effect of LH for each period based on Eq. (4). Notably, the MLDA had a slight negative contribution to the development of warm SSTA during period I and the resultant peak in April, and the MLDA hardly made any difference in the formation of the following July and November peaks. Meanwhile, the positive LHA played a dominant role in causing the development of warm MLTA during all three periods of the 2020 MHW event. In sharp contrast, the MLD in summer 2019 shoaled almost 40% compared to the climatology (Fig. 6b). The exceptionally thinner than normal MLD played the decisive role in amplifying the SW’s heating effect, leading to significant SSTA warming in the NE Pacific (Amaya et al. 2020).
(a) Time series of the actual (black solid line) and climatological (black dashed line) MLD (m) averaged in the study region from 5 Jan to 31 Dec 2020 derived from the GODAS pentad dataset. The orange, brown, and red shadings denote three periods for SSTA growth, respectively. (b) Actual (black bar) and climatological (shaded bar) MLD (m) and MLD shoaling (blue bar; %) for periods I, II, and III during the 2020 MHW, and JJA during 2019 MHW (averaged in the region of 147°–128°W, 34°–47°N; Amaya et al. 2020). Contributions from latent heat flux anomaly (LHA) and MLDA to the LH term (°C pentad−1) for (c) period I, (d) period II, and (e) period III are shown. Red and blue bars represent positive and negative values, respectively.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
One may wonder why the similar thinner MLD in period I of 2020 had a cooling effect (blue bar in Fig. 6c). In fact, this difference arises from the opposite signs of the climatological LH and SW in the NE Pacific: there is a cooling effect from LH but a heating effect from SW. Specifically, as shown by RHS-II in Eq. (4) that measures the contribution of MLDA [i.e.,
Conclusively, the mixed layer heat budget diagnosis reveals that the remarkable warming peaks in April, July, and November 2020 were primarily the results of the stronger than normal heating effect of LH. By isolating the effects of LHA and MLDA, we further verify the dominant role of LHA in the evolution of the 2020 MHW and the formation of all three warming peaks, while the MLDA during the evolution of the 2020 MHW event did not play a role. Next, we attempt to trace the atmospheric processes that were responsible for the positive LHA during the evolution of the 2020 MHW event in the NE Pacific.
5. Atmospheric processes
a. Factors determining positive LHA
The atmospheric anomaly fields associated with the LHA are shown in Fig. 7. For each period, the atmosphere exhibited pronounced humidity in the key region in the NE Pacific (Figs. 7a–c). The positive surface specific humidity anomaly (
Horizontal patterns of (a)–(c) surface specific humidity anomaly (i.e.,
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
The relative contributions from different atmospheric anomaly fields to LHA for each period are shown in Figs. 7j–l. The diagnosed results reveal that the anomalously positive surface specific humidity played a decisive role in forming the positive LHA for all three periods. Meanwhile, the weakened WS made a minor contribution. In particular, the WSA exerted a minimal effect during period II, which is consistent with the weakest zonal wind anomaly found during period II. In contrast, the sea surface saturated specific humidity anomaly made a negative contribution to the positive LHA in all three periods. This is not surprising because the warm SSTA increased the sea surface saturated specific humidity following the Clausius–Clapeyron equation, which was conducive to more evaporation and negative LHA. Interestingly, similar impacts of sea–air humidity difference anomaly on SST variations can be found in the formation of intraseasonal variability in the midlatitude (Wang et al. 2012).
Due to the vital impact of anomalously positive specific humidity on LHA, we conduct a moisture budget at the air–sea interface (1000 hPa) based on Eq. (3) to reveal the underlying physical processes responsible for the increase of near-surface moisture. Note that the diagnosed results remain the same when the moisture budget is performed for the entire atmospheric boundary layer, as shown in Fig. S5. The diagnosed results (Fig. 8) show that the anomalously humid near-surface atmosphere over the study region during all the three periods primarily stemmed from the term of
Diagnosed results of moisture equation at 1000 hPa, terms along the x axis from left to right:
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
b. Origin of southerly wind anomalies
The above analysis shows that the surface southerly wind anomalies were the key factor contributing to the positive LHA and the evolution of the 2020 MHW. Additionally, the southerly wind anomalies could drive the northward ocean current anomaly, which advected warmer water from lower latitudes to the key region, contributing to SSTA warming there (figure not shown). Therefore, the sea level pressure anomaly (SLPA) pattern over the NE Pacific, which is closely related to regional wind anomaly, is worth investigation. During period I, a meridional dipole of SLPA occurred over the North Pacific, with a prominent positive SLPA located near the GOA and negative SLPA south of 30°N (Fig. 9a). Note that the pattern of SLPA looks like the expression of the North Pacific Oscillation (NPO; Rogers 1981) to some extent. The region of interest is located to the southeast of the positive SLPA, and hence was dominated by the positive SLPA-related southeasterly wind anomaly (Fig. 9a). Afterward, the significant positive SLPA rapidly dissipated, and then a low pressure anomaly appeared in the North Pacific with a center at 40°N, 175°W. The low pressure anomaly was accompanied by anomalous southerlies over the study region (Fig. 9b). During period III, the NPO-like SLPA pattern staged a comeback, and the southeasterly wind anomaly dominated over the key study region (Fig. 9c).
Horizontal patterns of sea level pressure anomaly (shading; hPa) and surface wind anomaly (vectors; m s−1) for (a) period I, (b) period II, and (c) period III.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
c. Possible linkage with NPO and NPGO
It is worth mentioning that the NPO-like SLPA pattern was considered as the main cause of the prominent 2013/14 and 2019 MHW events that occurred in the NE Pacific (e.g., Bond et al. 2015; Di Lorenzo and Mantua 2016; Hu et al. 2017; Amaya et al. 2020; Chen et al. 2021a). Indeed, previous studies documented that the NPO-like SLPA can influence the variation of SSTA in the NE Pacific. Specifically, the second dominant empirical orthogonal function (EOF) mode of North Pacific SSTA is termed as the Victoria mode (VM; Bond et al. 2003) and the second EOF mode of the North Pacific sea surface height anomaly (SSHA) is termed as the NPGO (Di Lorenzo et al. 2008). Both the VM and NPGO can be considered as oceanic responses to the NPO variability, as they share great similarity in spatial distribution, principal component (PC) variability, and climatic impacts (Ding et al. 2015).
To verify whether or not the evolution of the 2020 MHW is associated with the typical NPO-forced NPGO (or VM), we first present the spatial distribution of NPGO-related SSTA and the NPGO index (available online at http://www.o3d.org/npgo). As illustrated in Figs. 10a and 10c, the NPGO-related SSTAs for the peak time of periods I and II (i.e., April and July) do not resemble the horizontal distribution of the observed warm SSTA at the same time, which show the same sign over the key study region. Additionally, both the correlation coefficients between the April NPGO index (black curve) and the box-averaged SSTA in April (red curve; Fig. 10b) and that between the July NPGO index and the box-averaged SSTA in July (Fig. 10d) are not significant. This indicates that the NPGO mode may not be responsible for the warm SSTA peaks in April and July 2020. For the peak time of period III (i.e., November), the region of interest (black box) covered the majority of the northeast pole of the NPGO pattern (Fig. 10e), and the correlation coefficient between the November NPGO index and the box-averaged SSTA in November reaches 0.59 (Fig. 10f), which is significant at the 95% level. This implies that the warm peak in November may be linked to the NPGO mode.
(a) The April NPGO-related SSTA pattern and (b) time series of NPGO index (black solid line) and box-averaged SSTA in April. (c),(d) As in (a) and (b) but for July. (e),(f) As in (a) and (b), but for November. Stippling indicates exceeding the 95% confidence level.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
Next, we present the typical NPO pattern that corresponds to a positive NPGO through regressing the SLPA and wind anomaly onto the NPGO index. For periods I and III, the so-called typical NPO exhibited a clear meridional dipole (contour in Figs. 11a,e) with strong easterly anomalies (blue vectors in Figs. 11a,e) dominating over the NE Pacific. It is conceivable that such easterly wind anomaly related to the NPO-like SLPA would slow down WS, because the climatological westerly wind prevails over the NE Pacific. This caused the shoaling of MLD and reduced surface evaporation, collectively leading to a spike of warm SSTA in the NE Pacific (e.g., Alexander 2010; Bond et al. 2015; Ding et al. 2015; Amaya et al. 2020, 2021; Chen et al. 2021a,b; Shi et al. 2022). However, the observational wind anomalies in 2020 differed from the typical NPO-related wind anomalies. Specifically, for both period I and period III, the positive SLPA in the northern lobe of the NPO stretched farther south (contours in Figs. 11b,f), leading to the emergence of southerly wind anomalies over the study region (red vectors in Figs. 11b,f). The diagnosed results demonstrate that the northward advection of humid air associated with the southerly wind anomalies was the main cause for the reduced LH loss. For period II, the typical NPO pattern (see the contour in Fig. 11c) remained, but it became weaker compared with those in the other two periods. It is not surprising since the NPO variability is the weakest in summer (Ding et al. 2015). As indicated by Fig. 11d, the southerly wind anomaly during period II arose from the negative SLPA with a low pressure anomaly center at 40°N, 175°W; however, such an observational SLPA pattern during period II does not satisfy the NPO expression. Hence, it is inferred that the warm SSTA’s evolution during period II may be related to atmospheric noise rather than to the NPO.
Regression maps of SLPA (contours; interval: 0.5 hPa) and wind anomaly (vectors) on the NPGO index for (a) period I, (c) period II, and (e) period III. Vector and stippling indicate the wind anomaly and SLPA exceeding the 95% confidence level, respectively. Also shown are observed SLPA (contours; interval: 2 hPa) and wind anomaly (vectors) in 2020 MHW for (b) period I, (d) period II, and (f) period III. Red and blue vectors indicate southerly and northerly wind anomalies, respectively.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
Overall, the physical mechanisms causing the three SSTA peaks in the 2020 MHW differ from those of the past MHWs in the NE Pacific. The diagnosed results based on observational datasets reveal that the southerly wind anomalies and the associated northward advection of humid air were essential. It is possible that a small perturbation in the atmosphere (e.g., anomalous SLP) may affect the magnitude and location of the NE Pacific warming, indicating the great difficulty in predicting MHW.
d. Origin of SLPA over North Pacific: SST forcing versus internal variability
As SLPA was essential for the evolution of the 2020 MHW, we will discuss the possible factors determining the SLPA pattern in each period. Previous studies (Wang et al. 2014; Hartmann 2015; Lee et al. 2015; Seager et al. 2015; Hu et al. 2017) suggested that SLPA in the northeast Pacific may stem from the internal atmospheric variability, a combination of local and remote SSTA forcing, or sea ice variation. To decipher the role of SST forcing and internal atmospheric variability in the formation of the positive SLPA, we carry out a suite of observed SST-forced AGCM experiments, as in some previous studies (Hu et al. 2017; Amaya et al. 2020). Specifically, we conduct three sets of AGCM experiments, which are forced by different prescribed SSTs from January to December. For each set of the experiment, the AGCM is integrated with different initial conditions, so that we obtain 60 members for each set of the experiment. The first set is called the GlobalSST experiment, in which the prescribed SSTs are derived from the global SST from January to December 2020 (Fig. 12a). The second and third sets are, respectively, called the NorthPacSST and TropicalSST experiments, in which the prescribed SSTs in the North Pacific (110°E–90°W, 15°–67°N; Fig. 12e) and tropical (0°–360°, 10°S–10°N; Fig. 12i) region are the observational SSTs from January to December 2020, and the SSTs outside are the climatological annual cycle in the observation. For more details about AGCM experiment design, refer to Text S2.
(a),(e),(i) The region where SST forcing is added into the climatological SST for AGCM sensitivity experiments. As displayed in (a), (e), and (i) by gray shading, specific SST forcing is added over global, North Pacific, and tropical regions, respectively. Ensemble mean SLPA (shading; hPa) from all 60 members for (b)–(d) GlobalSST, (f)–(h) NorthPacSST, and (j)–(l) TropicalSST AGCM experiments averaged over (b),(f),(j) period I (February–April), (c),(g),(k) period II (June and July), and (d),(h),(l) period III (October and November). Contours indicate observed SLPA (interval: 1 hPa).
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
The pattern correlation coefficients (PCCs) of the SLPA over 150°E–120°W, 30°–60°N between the AGCM runs and observations are indicated by the dots in the box-and-whiskers plot (Fig. 13). Note that the PCC results are not sensitive to slight alteration in the region; for example, the main results can still hold if the key region (170°–140°W, 32°–47°N) is used to calculate the PCCs (Fig. S7). For all three sets of experiments, the PCCs of simulated SLPA from 20% of the members can reach approximately 0.5. Overall, the intermember spread of the pattern similarity is considerably large (Fig. 13). This indicates that the internal atmospheric variability may play an important role in determining the observed North Pacific atmospheric circulation anomalies in 2020.
Pattern correlations of SLPA between observation and 60 members for global tropical SST-forced (pink dots and box and whiskers), North Pacific-SST forced (blue dots and box and whiskers), and global SST-forced (black dots and box and whiskers) AGCM experiments over the region of 150°E–120°W, 30°–60°N for periods I, II, and III. Dots denote individual members. Box and whiskers show the spread among different members.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
The model experiment results from the ensemble mean of whole 60 members suggest that the associated SLPA and atmospheric circulation anomalies can be partly constrained by both the tropical SST forcing and the local air–sea feedback processes in North Pacific (Fig. 12). The overall discrepancy between the ensemble mean SLPA derived from the model experiments and the observed SLPA may be partly attributed to the cancellations among the individual ensemble members, which is due to the atmospheric internal variability (“noise”). On the other hand, despite the large intermember spread across the members, some individual members can still accurately capture the observed anomalies (Fig. 13), indicating that the internal atmospheric variability is also essential for the formation of the associated atmospheric circulation anomalies and the MHW in 2020.
The above assessment suggests that the SLPA over the North Pacific in 2020 partly stemmed from internal atmospheric variability, but it can be partly constrained by both the tropical SST forcing and the local air–sea feedback processes in the North Pacific, which may provide some predictability for the 2020 MHW event. This requires further examination of the prediction skill of the 2020 MHW event in the current dynamic models (i.e., the NMME models), which will be presented in the following section.
6. Prediction skills of 2020 MHW in NMMES models
Figure 14 shows the evolution of SSTA averaged in the study region from January to December 2020, derived from the observation and predictions made by nine NMME models. In general, the individual models and their multimodel ensemble (MME) can predict the SSTA evolution well in one-month-lead forecasts (Fig. 14a), partly due to the persistence of SSTA itself. However, the observed three warming peaks in April, July, and November cannot be predicted by any models with the increase of the lead time (Figs. 14b,c). For the eight-month-lead forecast (Fig. 14d), the MME forecast only exhibits a slow warming tendency throughout 2020, which is distinguished from the observed SSTA evolution involved with multiple rapid growth and decay processes.
Time series of SSTA averaged in the study region from January to December 2020 derived from the observation (black solid line) and (a) 1-month-lead, (b) 2-month-lead, (c) 4-month-lead, and (d) 8-month-lead forecasts made by nine NMME models (colored dashed lines). The red solid line denotes the MME results of the nine models.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
As shown in Fig. 15, specific prediction skills at different lead times are measured by the anomaly correlation coefficient (ACC) and root-mean-square error (RMSE) analysis between the observed and predicted SSTAs during 2020. Specifically, the MHW in 2020 can be successfully predicted in the one-month-lead forecast, and the overall forecast skills drop with increasing lead time. Specifically, the ACC in the two- and three-month-lead forecasts are below 0.6, and the overall prediction biases become larger with the increase of lead time. Note that the two-month-lead MME forecast presents a dramatically low ACC of 0.2 (or high RMSE of 0.45°C), compared with the forecasts at longer leads (Fig. 15), which may be caused by the fact that the prediction source mainly comes from the SSTA’s persistence and the associated SSTA during the 2020 MHW usually grows or decays one month later. The limitation of the prediction skill is consistent with the understanding that NE Pacific SSTA is mostly controlled by atmospheric anomalies, which may be significantly influenced by stochastic internal atmospheric variability and partly constrained by local and remote SST forcing.
(a) Correlation and (b) root-mean-square error (RMSE; °C) of SSTA averaged in our key analysis region (170°–140°W, 32°–47°N) between the observation and NMME forecasts at the lead times from one month to eight months. The colored circles denote individual models, and the black solid line denotes the average of the nine models.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
To verify the role of SLPA in the prediction of the 2020 MHW, we show the SLPA pattern for each period from the observation and MME forecast (contours vs shadings; Fig. 16). Due to the availability issue, only predicted SLP fields from three NMME models (CanCM4i, CanSIPSv2, and GEM-NEMO) are used in the following analysis. For period I, the one- and two-month-lead predictions generally capture the observed high pressure anomaly near the GOA (Figs. 16a,d). With the increase of lead time, the positive SLPA from the MME forecast shows obvious biases in terms of both distribution and magnitude (Figs. 16g,j). For the other two periods, the MME forecast exhibits no skill in predicting SLPA over the North Pacific (Fig. 16). The success of these three models in predicting positive SLPA during period I matches well with their good performances in predicting the SSTA in the April peak (refer to brown, orange, and light green dashed curves in Fig. 14b); and the failure in predicting the SLPAs for periods II and III coincides with their poor performances in predicting the SSTA evolution during periods II and III. This further indicates the important role of accurately predicting SLPA in improving SSTA prediction in the NE Pacific.
Horizontal patterns of SLPA averaged over (left) period I, (center) period II, and (right) period III derived from the observation (contours; interval: 1 hPa) and (a)–(c) 1-month-lead, (d)–(f) 2-month-lead, (g)–(i) 4-month-lead, and (j)–(l) 8-month-lead forecasts made by the ensemble mean of the three NMME models (shading).
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
Conclusively, the current dynamic predictions made by the NMME models show limited skills in predicting the SSTA and SLPA evolution during the 2020 MHW, indicating that it is challenging to skillfully predict MHW and its associated atmospheric anomalies in the middle and high latitudes (Wen et al. 2012; Hu et al. 2017; Jacox et al. 2019).
7. Discussion and conclusions
a. Discussion
It is interesting to note that MHWs have occurred frequently over the last decade (Fig. 17), while the characteristics and formation mechanisms of the MHW events in the NE Pacific differ from each other. For one thing, the specific regions of the warm blobs are slightly different among the MHW events. As summarized in Fig. 17a, the colored boxes indicate the study regions for the persistent MHW event during 2014–16 (Bond et al. 2015; Di Lorenzo and Mantua 2016; Hu et al. 2017; Zhi et al. 2019; Schmeisser et al. 2019; Scannell et al. 2020), the MHW in June–August 2019 and November 2019 (Amaya et al. 2020; Scannell et al. 2020; Chen et al. 2021b), and the recently occurred persistent MHW during 2020 (Chen et al. 2021b). Therefore, the individual cases are worthy of specific investigation.
(a) Here the boxes denote the Northeast Pacific MHW areas. The colored boxes indicate the regions where the strong NE Pacific MHWs occurred in the last decade, as documented by previous studies (see figure legend); and the black box indicates the “broad area” covering the large region where the strong NE Pacific MHWs occurred. (b) Time series of monthly SSTA (°C) averaged in the broad area [black box in (a)] derived from GODAS (black line), OISSTv2 (blue line), and HadISST (green line) datasets. The brown dot and dashed line denote the SSTA’s value in July 2020.
Citation: Journal of Climate 36, 23; 10.1175/JCLI-D-22-0947.1
Here we utilize the “broad box” (thick black box) to simply describe the performance of recent MHWs over the NE Pacific. As shown in Fig. 17b, in addition to the persistent NE warming during 2014–16 (Bond et al. 2015), the NE Pacific experienced anomalous warming from late 2018 to 2021, with the major warming period occurring in 2019 and 2020. It should be noted that the formation mechanisms for the MHWs in 2019 differ from those in 2020. For the 2019 MHW case, recent studies have identified two warming peaks, one in June–August 2019 (Amaya et al. 2020) and another one in November 2019 (Scannell et al. 2020; Chen et al. 2021b). Amaya et al. (2020) reported that the warm SSTA in summer 2019 arose from the positive shortwave radiation heat flux and the thinner-than-normal MLD. Chen et al. (2021b) suggested that the warming peak in November 2019 was caused by the less-than-normal latent heat loss due to the reduced surface wind speed. For the MHW in 2020, although we also found that the warming in 2020 is primarily caused by the LHA, the key factors behind the LHA are distinct. We found that the source of the LHA in the 2020 MHW case is primarily traced back to the reduced sea–air humidity difference, rather than the change in MLD and wind speed. In this sense, we suggest that it is necessary to discuss the formation mechanism of the MHW in 2020 separately, in the context of the continuous warming over the NE Pacific in 2018–21. The finding about the role of sea–air humidity difference in influencing the LHA provides a new insight into the formation mechanism of MHW.
b. Conclusions
This study first presents an overview of the characteristics of the 2020 MHW event in the NE Pacific, which exhibited an extraordinarily strong intensity and long persistence. Then this study investigates the contributing factors for the SSTA evolution through analyzing dynamic and thermodynamic processes, and further evaluates the prediction skill of the 2020 MHW using the forecast results derived from the NMME models. The main findings are summarized as follows.
-
The MHW event that took place in the NE Pacific during 2020 is the strongest on record in our key analysis region since 1980. Moreover, it exhibited a persistence feature with three SSTA peaks in April, July, and November 2020. Specifically, the July peak exhibited the strongest intensity with 2.7 standard deviations above the climatology. The April and November peaks were slightly weaker than the July peak but were also prominent throughout the analysis period of 1980–2021.
-
Through investigating the dynamic and thermodynamic processes responsible for each positive SSTA peak, we reveal that the stronger-than-normal heating effect of LH was the leading contributor to the three development periods and corresponding peaks. By isolating the effects of LHA and MLDA, we verify the dominant role of positive LHA in the 2020 MHW’s generation, whereas MLDA did not play a role. Furthermore, we point out that the positive LHA in the 2020 case was primarily caused by the reduced sea–air humidity difference (i.e., the reduced difference between the saturated specific humidity at sea surface and actual surface specific humidity), rather than the typical “wrecker” (i.e., wind speed change). Last, the moisture budget unveiled that the positive surface specific humidity anomaly was mainly due to the southerly wind anomalies, which transported the moisture from the lower latitudes to the key study region. The anomalous southerlies that induced the April and November peaks were closely associated with the NPO-like SLPA, and the counterparts inducing the July peak were linked to a low pressure anomaly in the North Pacific (175°W, 40°N).
-
As the diagnosed results show the importance of the North Pacific SLPA in the evolution of the 2020 MHW, we investigate the origins of these atmospheric circulation anomalies. Previous studies suggested that the local and remote SSTA forcing, or internal atmospheric variability may play a role in the formation of the North Pacific SLPA. We conduct three sets of AGCM experiments, which are forced by global SST, North Pacific SST, and tropical SST, respectively. By analyzing the ensemble mean of the whole 60 members for each set of the experiment, we demonstrate that the SLPA may be partly constrained by both the tropical SST forcing and the local air–sea feedback processes in North Pacific. On the other hand, the fact that some individual members can still accurately capture the observed anomalies indicates that the internal atmospheric variability is also essential for the formation of the associated atmospheric circulation anomalies and the MHW in 2020.
-
With the aid of forecast results derived from nine NMME models, we evaluate their prediction skills of the evolution of the 2020 MHW event. The results show that the NMME models’ skills in predicting SSTA are generally limited to a short lead time of one month. Moreover, the assessment of the SLPA forecasts made by three NMME models shows that the models’ poor performances in predicting SSTA are consistent with their failures in predicting the SLPA evolution. It indicates that a skillful prediction of the ocean–atmosphere modes (e.g., NPGO and NPO) that largely control the SLPA in the North Pacific may be helpful to improve the SSTA prediction in the NE Pacific.
Acknowledgments.
We thank three anonymous reviewers for insightful suggestions and comments. This work was jointly supported by the National Natural Science Foundation of China (42088101, 42005020), the Natural Science Foundation of Jiangsu Province (Grant BK20230061), the Laoshan Laboratory (Grant LSKJ202202403) and the Postgraduate Research and Practice Innovation Program of Jiangsu Province (Grant KYCX21_0964). We acknowledge the High Performance Computing Center of Nanjing University of Information Science & Technology for their support of this work.
Data availability statement.
The daily dataset of the National Oceanic and Atmospheric Administration (NOAA) optimum interpolation sea surface temperature version 2 (OISST.v2) is available at https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html. The monthly dataset of the NOAA Extended Reconstructed SST version 5 (ERSST.v5) is available at https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html. The HadISST data are available at https://www.metoffice.gov.uk, and the COBE data are available at https://psl.noaa.gov/data/gridded/data.cobe.html. The monthly and pentad datasets of the National Center for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) are available at https://www.cpc.ncep.noaa.gov/products/GODAS/. The NCEP2 monthly and daily datasets are available at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis2.html. The North American Multi-model Ensemble project (NMME) datasets are available at http://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/.
REFERENCES
Alexander, M., 2010: Extratropical air–sea interaction, sea surface temperature variability, and the Pacific decadal oscillation. Climate Dynamics: Why Does Climate Vary? Geophys. Monogr., Vol. 189, 216 pp., https://doi.org/10.1029/2008GM000794.
Alexander, M., and C. Penland, 1996: Variability in a mixed layer ocean model driven by stochastic atmospheric forcing. J. Climate, 9, 2424–2442, https://doi.org/10.1175/1520-0442(1996)009<2424:VIAMLO>2.0.CO;2.
Amaya, D. J., A. J. Miller, S.-P. Xie, and Y. Kosaka, 2020: Physical drivers of the summer 2019 North Pacific marine heatwave. Nat. Commun., 11, 1903, https://doi.org/10.1038/s41467-020-15820-w.
Amaya, D. J., M. A. Alexander, A. Capotondi, C. Deser, K. B. Karnauskas, A. J. Miller, and N. J. Mantua, 2021: Are long-term changes in mixed layer depth influencing North Pacific marine heatwaves? Bull. Amer. Meteor. Soc., 102 (1), S59–S66, https://doi.org/10.1175/BAMS-D-20-0144.1.
Behrens, E., D. Fernandez, and P. Sutton, 2019: Meridional oceanic heat transport influences marine heatwaves in the Tasman Sea on interannual to decadal timescales. Front. Mar. Sci., 6, 228, https://doi.org/10.3389/fmars.2019.00228.
Bellomo, K., A. C. Clement, J. R. Norris, and B. J. Soden, 2014: Observational and model estimates of cloud amount feedback over the Indian and Pacific Oceans. J. Climate, 27, 925–940, https://doi.org/10.1175/JCLI-D-13-00165.1.
Bond, N. A., J. E. Overland, M. Spillane, and P. Stabeno, 2003: Recent shifts in the state of the North Pacific. Geophys. Res. Lett., 30, 2183, https://doi.org/10.1029/2003GL018597.
Bond, N. A., M. F. Cronin, H. Freeland, and N. Mantua, 2015: Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett., 42, 3414–3420, https://doi.org/10.1002/2015GL063306.
Cavole, L. M., and Coauthors, 2016: Biological impacts of the 2013–2015 warm-water anomaly in the northeast Pacific: Winners, losers, and the future. Oceanography, 29, 273–285, https://doi.org/10.5670/oceanog.2016.32.
Chen, K., G. Gawarkiewicz, Y.-O. Kwon, and W. G. Zhang, 2015: The role of atmospheric forcing versus ocean advection during the extreme warming of the Northeast U.S. continental shelf in 2012. J. Geophys. Res. Oceans, 120, 4324–4339, https://doi.org/10.1002/2014JC010547.
Chen, L., T. Li, Y. Yu, and S. K. Behera, 2017: A possible explanation for the divergent projection of ENSO amplitude change under global warming. Climate Dyn., 49, 3799–3811, https://doi.org/10.1007/s00382-017-3544-x.
Chen, Z., J. Shi, and C. Li, 2021a: Two types of warm blobs in the Northeast Pacific and their potential effect on the El Niño. Int. J. Climatol., 41, 2810–2827, https://doi.org/10.1002/joc.6991.
Chen, Z., J. Shi, Q. Liu, H. Chen, and C. Li, 2021b: A persistent and intense marine heatwave in the Northeast Pacific during 2019–2020. Geophys. Res. Lett., 48, e2021GL093239, https://doi.org/10.1029/2021GL093239.
Cronin, M. F., N. A. Pelland, S. R. Emerson, and W. R. Crawford, 2015: Estimating diffusivity from the mixed layer heat and salt balances in the North Pacific. J. Geophys. Res. Oceans, 120, 7346–7362, https://doi.org/10.1002/2015JC011010.
Di Lorenzo, E., and N. Mantua, 2016: Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Climate Change, 6, 1042–1047, https://doi.org/10.1038/nclimate3082.
Di Lorenzo, E., and Coauthors, 2008: North Pacific gyre oscillation links ocean climate and ecosystem change. Geophys. Res. Lett., 35, L08607, https://doi.org/10.1029/2007GL032838.
Ding, R., J. Li, Y.-h. Tseng, C. Sun, and Y. Guo, 2015: The Victoria mode in the North Pacific linking extratropical sea level pressure variations to ENSO. J. Geophys. Res. Atmos., 120, 27–45, https://doi.org/10.1002/2014JD022221.
Feng, M., M. J. McPhaden, S.-P. Xie, and J. Hafner, 2013: La Nina forces unprecedented Leeuwin Current warming in 2011. Sci. Rep., 3, 1277, https://doi.org/10.1038/srep01277.
Gao, X., G. Li, J. Liu, and S.-M. Long, 2022: The trend and interannual variability of marine heatwaves over the Bay of Bengal. Atmosphere, 13, 469, https://doi.org/10.3390/atmos13030469.
Gentemann, C. L., M. R. Fewings, and M. García-Reyes, 2017: Satellite sea surface temperatures along the West Coast of the United States during the 2014–2016 northeast Pacific marine heat wave. Geophys. Res. Lett., 44, 312–319, https://doi.org/10.1002/2016GL071039.
Han, W., and Coauthors, 2022: Sea level extremes and compounding marine heatwaves in coastal Indonesia. Nat. Commun., 13, 6410, https://doi.org/10.1038/s41467-022-34003-3.
Hartmann, D. L., 2015: Pacific sea surface temperature and the winter of 2014. Geophys. Res. Lett., 42, 1894–1902, https://doi.org/10.1002/2015GL063083.
Hobday, A. J., and Coauthors, 2016: A hierarchical approach to defining marine heatwaves. Prog. Oceanogr., 141, 227–238, https://doi.org/10.1016/j.pocean.2015.12.014.
Hobday, A. J., and Coauthors, 2018: Categorizing and naming marine heatwaves. Oceanography, 31, 162–173, https://doi.org/10.5670/oceanog.2018.205.
Hsu, P.-c., and T. Li, 2012: Role of the boundary layer moisture asymmetry in causing the eastward propagation of the Madden–Julian oscillation. J. Climate, 25, 4914–4931, https://doi.org/10.1175/JCLI-D-11-00310.1.
Hu, S., and Coauthors, 2021: Observed strong subsurface marine heatwaves in the tropical western Pacific Ocean. Environ. Res. Lett., 16, 104024, https://doi.org/10.1088/1748-9326/ac26f2.
Hu, Z.-Z., A. Kumar, B. Jha, J. Zhu, and B. Huang, 2017: Persistence and predictions of the remarkable warm anomaly in the northeastern Pacific Ocean during 2014–16. J. Climate, 30, 689–702, https://doi.org/10.1175/JCLI-D-16-0348.1.
Huang, B., and Coauthors, 2017: Extended Reconstructed Sea Surface Temperature, version 5 (ERSSTv5): Upgrades, validations, and intercomparisons. J. Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1.
Ishii, M., A. Shouji, S. Sugimoto, and T. Matsumoto, 2005: Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection. Int. J. Climatol., 25, 865–879. https://doi.org/10.1002/joc.1169.
Jacox, M. G., D. Tommasi, M. A. Alexander, G. Hervieux, and C. A. Stock, 2019: Predicting the evolution of the 2014–2016 California Current system marine heatwave from an ensemble of coupled global climate forecasts. Front. Mar. Sci., 6, 497, https://doi.org/10.3389/fmars.2019.00497.
Jones, T., and Coauthors, 2018: Massive mortality of a planktivorous seabird in response to a marine heatwave. Geophys. Res. Lett., 45, 3193–3202, https://doi.org/10.1002/2017GL076164.
Kanamitsu, M., W. Ebisuzaki, J. Woollen, S.-K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter, 2002: NCEP–DOE AMIP-II Reanalysis (R-2). Bull. Amer. Meteor. Soc., 83, 1631–1644, https://doi.org/10.1175/BAMS-83-11-1631.
Kataoka, T., T. Tozuka, and T. Yamagata, 2017: Generation and decay mechanisms of Ningaloo Niño/Niña. J. Geophys. Res. Oceans, 122, 8913–8932, https://doi.org/10.1002/2017JC012966.
Kirtman, B. P., and Coauthors, 2014: The North American multimodel ensemble: Phase-1 seasonal-to-interannual prediction; Phase-2 toward developing intraseasonal prediction. Bull. Amer. Meteor. Soc., 95, 585–601, https://doi.org/10.1175/BAMS-D-12-00050.1.
Kug, J.-S., J.-H. Jeong, Y.-S. Jang, B.-M. Kim, C. K. Folland, S.-K. Min, and S.-W. Son, 2015: Two distinct influences of Arctic warming on cold winters over North America and East Asia. Nat. Geosci., 8, 759–762, https://doi.org/10.1038/ngeo2517.
Lee, M.-Y., C.-C. Hong, and H.-H. Hsu, 2015: Compounding effects of warm sea surface temperature and reduced sea ice on the extreme circulation over the extratropical North Pacific and North America during the 2013–2014 boreal winter. Geophys. Res. Lett., 42, 1612–1618, https://doi.org/10.1002/2014GL062956.
Li, T., Y. Zhang, E. Lu, and D. Wang, 2002: Relative role of dynamic and thermodynamic processes in the development of the Indian Ocean dipole: An OGCM diagnosis. Geophys. Res. Lett., 29, 2110, https://doi.org/10.1029/2002GL015789.
Liu, Y., C. Sun, and J. Li, 2022: Nonidentical mechanisms behind the North Pacific summer blob events in the satellite era. Climate Dyn., 61, 507–518, https://doi.org/10.1007/s00382-022-06584-8.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Amer. Meteor. Soc., 78, 1069–1080, https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2.
McCabe, R. M., and Coauthors, 2016: An unprecedented coastwide toxic algal bloom linked to anomalous ocean conditions. Geophys. Res. Lett., 43, 10 366–10 376, https://doi.org/10.1002/2016GL070023.
Mills, K. E., and Coauthors, 2013: Fisheries management in a changing climate: Lessons from the 2012 ocean heat wave in the Northwest Atlantic. Oceanography, 26, 191–195, https://doi.org/10.5670/oceanog.2013.27.
Myers, T. A., C. R. Mechoso, G. V. Cesana, M. J. DeFlorio, and D. E. Waliser, 2018: Cloud feedback key to marine heatwave off Baja California. Geophys. Res. Lett., 45, 4345–4352, https://doi.org/10.1029/2018GL078242.
Nordeng, T., 1994: Extended versions of the convective parametrization scheme at ECMWF and their impact on the mean and transient activity of the model in the tropics. ECMWF Tech. Memo. 206, 42 pp., https://doi.org/10.21957/e34xwhysw.
Norris, J. R., Y. Zhang, and J. M. Wallace, 1998: Role of low clouds in summertime atmosphere–ocean interactions over the North Pacific. J. Climate, 11, 2482–2490, https://doi.org/10.1175/1520-0442(1998)011<2482:ROLCIS>2.0.CO;2.
Oliver, E. C. J., J. A. Benthuysen, N. L. Bindoff, A. J. Hobday, N. J. Holbrook, C. N. Mundy, and S. E. Perkins-Kirkpatrick, 2017: The unprecedented 2015/16 Tasman Sea marine heatwave. Nat. Commun., 8, 16101, https://doi.org/10.1038/ncomms16101.
Oliver, E. C. J., and Coauthors, 2018: Longer and more frequent marine heatwaves over the past century. Nat. Commun., 9, 1324, https://doi.org/10.1038/s41467-018-03732-9.
Pearce, A. F., and M. Feng, 2013: The rise and fall of the “marine heat wave” off Western Australia during the summer of 2010/2011. J. Mar. Syst., 111–112, 139–156, https://doi.org/10.1016/j.jmarsys.2012.10.009.
Rayner, N. A., D. E. Parker, E. B. Horton, C. K. Folland, L. V. Alexander, and D. P. Rowell, 2003: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670.
Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 5473–5496, https://doi.org/10.1175/2007JCLI1824.1.
Roeckner, E., and Coauthors, 1996: The atmospheric general circulation model, ECHAM-4: Model description and simulation of present-day climate. Max-Planck-Institut für Meteorologie Rep. 218, 94 pp., https://pure.mpg.de/rest/items/item_1781494/component/file_1786328/content.
Rogers, J. C., 1981: The North Pacific Oscillation. J. Climate, 1, 39–57, https://doi.org/10.1002/joc.3370010106.
Ronca, R. E., and D. S. Battisti, 1997: Anomalous sea surface temperatures and local air–sea energy exchange on intraannual timescales in the northeastern subtropical Pacific. J. Climate, 10, 102–117, https://doi.org/10.1175/1520-0442(1997)010<0102:ASSTAL>2.0.CO;2.
Ryan, J. P., and Coauthors, 2017: Causality of an extreme harmful algal bloom in Monterey Bay, California, during the 2014–2016 northeast Pacific warm anomaly. Geophys. Res. Lett., 44, 5571–5579, https://doi.org/10.1002/2017GL072637.
Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 3483–3517, https://doi.org/10.1175/JCLI3812.1.
Scannell, H. A., A. J. Pershing, M. A. Alexander, A. C. Thomas, and K. E. Mills, 2016: Frequency of marine heatwaves in the North Atlantic and North Pacific since 1950. Geophys. Res. Lett., 43, 2069–2076, https://doi.org/10.1002/2015GL067308.
Scannell, H. A., G. C. Johnson, L. Thompson, J. M. Lyman, and S. C. Riser, 2020: Subsurface evolution and persistence of marine heatwaves in the Northeast Pacific. Geophys. Res. Lett., 47, e2020GL090548, https://doi.org/10.1029/2020GL090548.
Schmeisser, L., N. A. Bond, S. A. Siedlecki, and T. P. Ackerman, 2019: The role of clouds and surface heat fluxes in the maintenance of the 2013–2016 northeast Pacific marine heatwave. J. Geophys. Res. Atmos., 124, 10 772–10 783, https://doi.org/10.1029/2019JD030780.
Seager, R., and Coauthors, 2015: Causes of the 2011–14 California drought. J. Climate, 28, 6997–7024, https://doi.org/10.1175/JCLI-D-14-00860.1.
Sewall, J. O., and L. C. Sloan, 2004: Disappearing Arctic sea ice reduces available water in the American west. Geophys. Res. Lett., 31, L06209, https://doi.org/10.1029/2003GL019133.
Shi, J., C. Tang, Q. Liu, Y. Zhang, H. Yang, and C. Li, 2022: Role of mixed layer depth in the location and development of the northeast Pacific warm blobs. Geophys. Res. Lett., 49, e2022GL098849, https://doi.org/10.1029/2022GL098849.
Tiedtke, M., 1989: A comprehensive mass flux scheme for cumulus parameterization in large-scale models. Mon. Wea. Rev., 117, 1779–1800, https://doi.org/10.1175/1520-0493(1989)117%3C1779:ACMFSF%3E2.0.CO;2.
Walsh, J. E., P. A. Bieniek, B. Brettschneider, E. S. Euskirchen, R. Lader, and R. L. Thoman, 2017: The exceptionally warm winter of 2015/16 in Alaska. J. Climate, 30, 2069–2088, https://doi.org/10.1175/JCLI-D-16-0473.1.
Wang, L., T. Li, and T. Zhou, 2012: Intraseasonal SST variability and air–sea interaction over the Kuroshio Extension region during boreal summer. J. Climate, 25, 1619–1634, https://doi.org/10.1175/JCLI-D-11-00109.1.
Wang, S.-Y., L. Hipps, R. R. Gillies, and J.-H. Yoon, 2014: Probable causes of the abnormal ridge accompanying the 2013–2014 California drought: ENSO precursor and anthropogenic warming footprint. Geophys. Res. Lett., 41, 3220–3226, https://doi.org/10.1002/2014GL059748.
Wen, C., Y. Xue, and A. Kumar, 2012: Seasonal prediction of North Pacific SSTs and PDO in the NCEP CFS hindcasts. J. Climate, 25, 5689–5710, https://doi.org/10.1175/JCLI-D-11-00556.1.
Xie, S.-P., and S. G. H. Philander, 1994: A coupled ocean–atmosphere model of relevance to the ITCZ in the eastern Pacific. Tellus, 46, 340–350, https://doi.org/10.3402/tellusa.v46i4.15484.
Xu, J., R. J. Lowe, G. N. Ivey, N. L. Jones, and Z. Zhang, 2018: Contrasting heat budget dynamics during two La Niña marine heat wave events along northwestern Australia. J. Geophys. Res. Oceans, 123, 1563–1581, https://doi.org/10.1002/2017JC013426.
Yanai, M., S. Esbensen, and J.-H. Chu, 1973: Determination of bulk properties of tropical cloud clusters from large-scale heat and moisture budgets. J. Atmos. Sci., 30, 611–627, https://doi.org/10.1175/1520-0469(1973)030<0611:DOBPOT>2.0.CO;2.
Zhi, H., P. Lin, R.-H. Zhang, F. Chai, and H. Liu, 2019: Salinity effects on the 2014 warm “blob” in the Northeast Pacific. Acta Oceanol. Sin., 38, 24–34, https://doi.org/10.1007/s13131-019-1450-2.
