1. Introduction
Marine heatwaves (MHWs) are prolonged extreme warm ocean events and induce destructive damage to ecosystems, fishing, and coastal society (e.g., Hobday et al. 2016; Cavole et al. 2016; Holbrook et al. 2020). Recently, MHWs frequently occurred in the global midlatitude region, such as the Mediterranean Sea heatwave event in 2003 (Garrabou et al. 2009), the extreme warm event off western Australia in 2010/11 summer (Pearce and Feng 2013), the Tasman Sea during 2015–16 (e.g., Oliver et al. 2017; Li et al. 2020), the persistent 2013–15 extreme warm anomalies off the west coast of North America (called the Blob; Bond et al. 2015), the Blob-like sea surface temperature (SST) anomalies over the northeast Pacific in the 2019 summer (called the Blob 2.0; Amaya et al. 2020; Scannell et al. 2020), summertime high SST repeated every year from 2010 to 2016 in the Oyashio region (Miyama et al. 2021), a coastal region south of Japan during the Kuroshio large meander path since the 2017 summer (Sugimoto et al. 2020; Sugimoto 2024, manuscript submitted to J. Oceanogr.), and the record-breaking warming south of Japan in the 2020 and 2021 summer (Hayashi et al. 2021; Kuroda and Setou 2021).
MHWs in midlatitude regions are predominantly formed by atmospheric forcings, such as an increase in solar radiation at the sea surface due to a decrease in cloud cover and a weakening in surface winds, which are associated with strengthening in the atmospheric high pressure system (e.g., Holbrook et al. 2019). In summer, the variation of the North Pacific subtropical high (NPSH) (Fig. 1a) is one of the triggers for MHW formation in the midlatitude North Pacific (e.g., Sen Gupta et al. 2020; Athanase et al. 2024). Strengthened NPSH is important for the MHW formation, but it has also been reported that weakening can sometimes be a favorable condition for the MHW formation. For example, Blob 2.0 in the 2019 summer resulted from a prolonged weakening of the NPSH: A decrease in evaporative cooling and a weakening in surface winds induced a shallowing of the surface ocean mixed layer (ML), resulting in the formation of Blob 2.0 (Amaya et al. 2020). For MHW formation over wintertime midlatitude North Pacific, an influence of the Aleutian low has been pointed out (Bond et al. 2015). The original Blob that occurred in October 2013, with peak values near +2.5°C, was generated by the remote forcing from the equatorial Pacific, which weakens the Aleutian low and its related surface wind forcing (e.g., Bond et al. 2015; Di Lorenzo and Mantua 2016).
(a) Climatological map (30-yr mean of 1991–2020) of SLP in September (contours with an interval of 3 hPa) from ERA5. Shading indicates the relative frequency of the low potential vorticity layer (<1.9 × 10−10 m−1 s−1) on the isopycnal surface of σθ = 25.8 kg m−3, consistent with the CMW property, in September 2004–20 from monthly data of the RG gridded Argo data. (b) SST anomalies in September 2021 from OISST, which is different from the climatology for the 30-yr period of 1991–2020. Dots represent regions where MHW did not occur in September 2021 (see details of the MHW definition in text). The black rectangle indicates the CNP region (180°–160°W, 30°–40°N).
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
For the 2021 summer, a long-persisting SST increase was observed in the central North Pacific (Fig. 1b), where the SST anomaly exceeds +3°C. In the central North Pacific, a thick water mass characterized by a pycnostad in the lower ventilated pycnocline, which is called the Central Mode Water (CMW), is distributed typically in depths of 100–500 m (Nakamura 1996; Suga et al. 1997; Oka et al. 2007) (see Fig. 1a). Recent studies suggested that a huge water mass in depths of 100–400 m over the northwestern corner of the North Pacific subtropical gyre, i.e., North Pacific Subtropical Mode Water (STMW; Masuzawa 1969), has an impact on the surface ocean temperature during a warming season by changing its thickness (Kobashi et al. 2021, 2023; Oka et al. 2023): Ocean reanalysis data and observational data based on Argo profiling floats indicated a relationship where surface ocean temperature increases when the STMW thickness decreases, and the seasonal thermocline depth tends to deepen during this period. This is a new hypothesis that discusses the impact of the subsurface ocean on the surface ocean through mode water distribution. We hypothesize that the CMW thickness also has some impact on the surface ocean temperature because both the vertical structure and top depths of the CMW are similar to those of the STMW.
In this study, we mainly focus on the warming event in the 2021 summer (Fig. 1b) and investigate its formation process from the viewpoint of atmospheric forcing associated with the NPSH and oceanic influence related to the CMW thickness by using observational and reanalysis data. This study will provide new insights into the oceanic role in the formation of MHW. The rest of this paper is composed of the following contents. Section 2 describes the data and methods used in this study. Section 3 gives the overview of MHW over the central North Pacific in 2021 summer and explores atmosphere and ocean effects on the MHW formation. Section 4 discusses the MHW and the CMW from the viewpoint of winter ML and mentions MHWs that occurred in other years. Finally, we summarize this paper in section 5.
2. Data and methods
We use three high-spatial-resolution SST datasets and one long-term SST dataset: 1) the daily Optimum Interpolation Sea Surface Temperature (OISST), version 2.1 (Huang et al. 2021), with a 0.25° (longitude) × 0.25° (latitude) grid from 1982; 2) Merged Satellite and In Situ Data Global Daily Sea Surface Temperature (MGDSST; Kurihara et al. 2006), with a 0.25° × 0.25° grid from 1982; and 3) fifth major global reanalysis produced by European Centre for Medium-Range Weather Forecasts (ERA5) SST dataset, with a 0.25° × 0.25° grid, based on the Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadISST), version 2 (Titchner and Rayner 2014), from 1940 to August 2007, and the Operational Sea Surface Temperature and Ice Analysis (OSTIA) product (Donlon et al. 2012) from September 2007; and 4) the monthly Centennial In Situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables (COBE-SST) product version 2 (Hirahara et al. 2014), with a 1° × 1° grid from 1900. For the detection of MHW, we adopt the method proposed by Hobday et al. (2016); an MHW is defined where SST exceeds a seasonally varying threshold of the 90th percentile of a 30-yr climatological period of 1991–2020 for at least five consecutive days.
To conduct the ML heat budget analysis, we use monthly temperature, zonal and meridional current velocity, and net surface heat flux (NHF) data (the sum of net surface shortwave radiation, net surface longwave radiation, latent heat flux, and sensible heat flux) from the Ocean Reanalysis System 5 (ORAS5) and its backward extension (Zuo et al. 2019), with a 0.25° × 0.25° grid. We additionally use four atmospheric reanalysis datasets of NHF because surface heat fluxes include uncertainty among the preparation methods (Tomita et al. 2019); 1) ERA5 (Hersbach et al. 2020); 2) the Japanese Meteorological Agency Japanese 55-yr Reanalysis (JRA-55) product (Kobayashi et al. 2015), with a 1.25° × 1.25° grid; 3) the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA2; Gelaro et al. 2017), with a 0.625° × 0.5° grid; and 4) the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR)/the Climate Forecast System, version 2 (CFSv2; Saha et al. 2014), with a 0.5° × 0.5° grid. In the following section, we show only the results using the ORAS5 because almost identical results were obtained from the other four datasets. We use monthly sea level pressure (SLP) and surface winds from ERA5 with a 0.25° × 0.25° grid to investigate the NPSH features. ERA5 has been available since 1940.
3. Results
We investigate the temporal behavior of the warm event over the central North Pacific in 2021. Figure 2a displays the daily time series of SST averaged over the central North Pacific (CNP) region (180°–160°W, 30°–40°N; black rectangle in Fig. 1b). The SST exceeds the 90th percentile in mid-August and then takes a maximum value of 26.3°C with an amplitude of +2.7°C on 15 September. The positive anomalies persist till the following spring. This prolonged-warm SST event sufficiently satisfies the definition of MHW. We call this event MHWCNP21. MHWCNP21 is equivalent to category 2, regarded as a strong event (Hobday et al. 2018). Spatially large-scale MHW events often occur off the western coast of North America, which are summarized as Blobs. On the other hand, the MHWCNP21 has a zonal-elongated structure in the central North Pacific, which is different from traditional Blobs.
(a) Daily time series of SST averaged over the CNP region from OISST data (dashed line). The black line represents the climatological SST of 1991–2020. Multiples of the 90th-percentile difference (twice, three times, etc.) from the climatology value (once, twice, three times, etc.) define each of the categories: category 1 (yellow), category 2 (orange), and category 3 (red). (b) Time series of September mean SST anomaly averaged over the CNP region, from COBE-SST2 (gray line), OISST (red line), MGDSST (blue line), and ERA5 (yellow line), with the climatological SST of 1991–2020. The vertical red dashed line indicates the year 2021.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
To emphasize the magnitude of MHWCNP21, we investigate the SST anomalies over the past 122 years. Here, we focus on September, when the MHWCNP21 took the highest SST. The result shows that the MHWCNP21 recorded the highest temperature in observation history, which is remarkable compared to past years (Fig. 2b).
a. Small influence of the NPSH on MHWCNP21 formation
The NPSH dominates the lower troposphere over the central–eastern North Pacific in summer. We investigate the influence of the atmospheric field in September on the MHWCNP21 formation, focusing on the NPSH. Over the CNP region, positive SLP anomalies are present in September 2021 (Fig. 3a), which indicates a westward extension of the NPSH. Interestingly, the SLP over the CNP region also shows the highest positive anomaly in September during the last 80 years (Fig. 3b). This is expected to be a favorable condition for the MHWCNP21. We check the NHF in September 2021, but the result in Fig. 4a shows the NHF and its four components are within one standard deviation and do not have pronounced amplitude corresponding to record breaking. This is probably because the CNP region was originally covered by the NPSH (Fig. 3a), and even if the NPSH strengthens, its contribution does not change significantly. Therefore, we focus on ocean effects as a cause of MHWCNP21 formation.
(a) SLP anomalies in September 2021 from ERA5 (color), with the climatological SLP values of 1991–2020. The black rectangle indicates the CNP region. The gray contours represent the SLP climatological values with an interval of 3 hPa. (b) Time series of SLP anomaly in September averaged over the CNP region from ERA5. The vertical red dashed line indicates the year 2021.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
(a) Heat fluxes averaged over the CNP region in September 2021 (shaded bars) from ORAS5: NHF (black), net shortwave radiation flux (blue), net longwave radiation flux (orange), sensible heat flux (green), and latent heat flux (red). Positive values indicate heat input from the atmosphere to the ocean. White bars represent the climatological value in September for 1991–2020, and error bars indicate one standard deviation. (b) As in (a), but for MLD: ORAS5 (blue) and RG Argo Climatology (red), except for the climatological periods of 1991–2020 and 2004–20, respectively.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
Amaya et al. (2020) pointed out the influence of the MLD decreasing on the occurrence of Blob 2.0. First, we check MLD averaged in the CNP region in September: 24.8 m in ocean reanalysis and 19.7 m in observation (Fig. 4), which are almost comparable to 22.9 ± 2.6 m in ocean reanalysis and 21.5 ± 2.1 m of 2004–20 in observation in long-term mean. For the MHWCNP21 formation, we thus conclude that the MLD cannot be the main factor.
The budget analysis (Fig. 5) shows that the surface ocean warms gradually until June 2021, but that most of the warming is canceled in July 2021, followed by a large warming in September 2021. This means that the MHWCNP21 was not formed by a persistent influence, but rather began suddenly in September 2021. During the year 2021, both the air–sea heat exchange and Ekman advection terms are of small amplitude, and the geostrophic advection term constantly acts to cool the ocean. On the other hand, the residual term has a large amplitude, and its behavior at the onset of MHWCNP21 is consistent with the rate of change of the temperature term. As mentioned above, the residual term includes effects such as the vertical entrainment process and eddy heat flux. Here, we examine eddy heat flux: The eddy heat flux is given by υ′T′, where υ′ and T′ are high-passed anomalies in meridional velocity and temperature at the sea surface with time scales shorter than 300 days, respectively. In September 2021, the value of eddy heat flux over the CNP region is not remarkable (Fig. 6): 7.2 × 10−4 m s−1 °C in September 2021, which is within one standard deviation for a period of 1991–2020. The zonal component was also examined, but its value was as large as the climatological value (not shown). This implies that the warming effect by ocean eddy is inefficient for MHWCNP21 and that entrainment could be dominant in the residual term. In general, the entrainment term is expressed as the product of the temporal change rate of MLD and the temperature difference between the water in the ML and the water below the ML [see Eq. (1) of Yasuda et al. 2000]. Calculating the temporal change rate of MLD from August to September, the 2021 values are roughly comparable to the climatological value (Fig. 5b), within one standard deviation. Therefore, it is pointed out that the entrainment process in September 2021 is dominated by the influence of water below the ML. We attempt to obtain an indirect interpretation of the entrainment process by investigating ocean temperature structure, in the following subsection.
(a) Anomaly in September 2021 in heat budget analysis over the CNP region: the rate of change of temperature term (black), air–sea heat exchange term (orange), Ekman advection term (light blue), geostrophic advection term (purple), and residual term (red). Positive values represent a heat input to the ocean ML. Here, the anomaly is different from the monthly climatology for the period of 1991–2020. Time labels indicate the beginning of the month. (b) Temporal change in MLD averaged over the CNP region from August to September 2021 (black bar), from ORAS5. White bar represents the climatological value for the 30-yr period of 1991–2020, and the error bar indicates one standard deviation.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
(a) Eddy heat flux in September 2021, from ORAS5. The black rectangle represents the CNP region. (b) As in Fig. 4a, but for eddy heat flux.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
b. Impact of CMW loss on MHWCNP21 formation
Vertical temperature profile over the CNP region in September 2021 indicates positive anomalies in the summertime shallow ML (Fig. 7a). Interestingly, positive anomalies also existed below the ML, with more than +1°C (red line in Fig. 7a). Over the CNP region, the CMW is usually distributed in depths of 100–500 m (Nakamura 1996; Oka et al. 2007) (see Fig. 1 and dashed line in Fig. 7b). However, the CMW is hardly identified in September 2021 (solid line in Fig. 7b).
(a) Vertical profile of temperature over the CNP region, from RG Argo Climatology: September 2021 (black solid line) and long-term mean of 2004–20 in September (black dashed line). The red line represents a temperature anomaly in September 2021 as the difference from the monthly climatology for the period of 2004–20. The horizontal blue line represents MLD in September 2021. (b) As in (a), but for Q over the CNP region. The gray vertical dashed line represents Q = 1.9 × 10−10 m−1 s−1. The horizontal gray band represents the low-Q layer < 1.9 × 10−10 m−1 s−1 in September 2021.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
We explore the temporal behavior of temperature and CMW thickness from 2004 to 2021 in the CNP region (Fig. 8a). In the summer of 2021, it was found that the warming was remarkable not only in the ML but also below ML, and the CMW extremely decreased compared to other periods. The upper boundary of CMW has been gradually deepening since 2017. After 2019, when the CMW sufficiently decreases, positive temperature anomalies are observed in the surface and subsurface ocean. On the other hand, the depth of the lower boundary has remained unchanged. During the analysis period, temperature from the sea surface to the upper boundary of CMW tends to increase when CMW becomes thin and vice versa (Fig. 8a). Recently, Kobashi et al. (2021) reported that over the northwestern corner of the North Pacific subtropical gyre, surface ocean temperature is linked to the vertical displacement of the isopycnal surfaces associated with the STMW thickness. We examine whether or not a similar process works in CMW.
(a) Depth–time section of temperature anomaly during 2004–21, averaged over the CNP region from RG Argo Climatology. The thick black line indicates the MLD. The gray line represents the CMW distribution, as low-Q layer with CMW properties. (b) Correlation coefficients between CMW thickness and depth of isopycnal surfaces in September averaged over the CNP region. The black circles represent values exceeding a 5% significance level. The depth of the potential density shown in the panel is <150-m depth. (c) Vertical gradient of density at isopycnal surfaces: September 2021 (solid) and climatology in September (dashed). (d) Time series of CMW thickness (black line) and OHC (red line) in September averaged over the CNP region from RG Argo Climatology.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
Here, we focus on the vertical displacement of isopycnal surfaces of σθ = 23.5–25.8 kg m−3, corresponding to the depth from the sea surface to around the upper boundary of CMW (about 150-m depth). Over the CNP region, the seasonal pycnocline characterized by a large vertical gradient of density lies at σθ = 24.0–24.5 kg m−3 surfaces in both September 2021 and climatological view (Fig. 8c). The displacement of isopycnal surfaces, including the seasonal pycnocline, is negatively correlated with the CMW thickness (Fig. 8b), and this indicates the deepening of isopycnal surfaces associated with the decrease in CMW thickness. Interestingly, the strength of the seasonal pycnocline in 2021 weakens, and a close look at Fig. 8b shows that the heavier isopycnal surfaces are more sensitive to the CMW thickness; in 2021, when there is very little CMW, the interval between the isopycnal surfaces is wider, which is consistent with the weakening of seasonal pycnocline seen in Fig. 8c. We expect that the vertical displacement of isopycnal surfaces affects the temperature distribution. To understand the relationship between the CMW thickness and temperature, we investigate ocean heat content (OHC), here defined as a vertically averaged temperature from the sea surface to 100-dbar depth. In Fig. 8d, CMW thickness and OHC are out of phase, and correlation and regression coefficients between the two time series are −0.73° and −0.33°C (100 dbar)−1, values of which exceed the 1% significance level, respectively. Since the CMW thickness in September 2021 is 33 dbar (less than 20% of that of the long-term mean), the increase in OHC is estimated as +0.46°C. The OHC anomaly in September 2021 is 0.88°C, and this means that the CMW-related OHC can explain 52% of the anomaly. Therefore, CMW significantly influences the surface ocean structure.
We propose the following scenario for the formation process of MHWCNP21; when the CMW thickness decreases, the isopycnal surfaces from the sea surface to the upper boundary of CMW deepen. The heavier isopycnal surfaces are deeper, which leads to a weakening of the seasonal pycnocline, which in turn leads to the weakening of cooling heat flux associated with the entrainment of subsurface waters into the mixed layer, resulting in surface warming.
4. Discussion
In this study, we demonstrated that the MHWCNP21 formation results from the drastic decrease in CMW. The CMW is formed in the wintertime deep ML near the northern boundary of the subtropical gyre, is subducted into the subsurface ocean since spring, is advected eastward, and then is distributed in the CNP region (Oka et al. 2011). The decrease in CMW observed in 2021 summer might reflect an absence of deep ML in the previous winter. We examine the distribution of MLD in 2021 winter by using Argo profiling floats. Here, we regard an ML with a depth > 150 dbar as a deep ML, following Oka et al. (2011). In 2021 (Fig. 9a), the winter ML in the central North Pacific is not as deep as the long-term mean, and the deep MLs with the CMW property are detectable in the region west of the international date line. Kawakami et al. (2016) indicated that the CMW formed in the region east of the international date line subducted efficiently, while the CMW formed west of the international date line, especially west of 155°E, was hardly detected in the subsurface ocean. The situation where the deep winter ML is relatively distributed west of the international date line and the summer CMW is very reduced is consistent with the result in Kawakami et al. (2016). Since 2017, the CMW thickness in the CNP region has decreased gradually (Fig. 8a), and the deep winter ML is relatively distributed west of the international date line (Fig. 9b), as in the case of the 2021 winter. On the other hand, the deep winter ML is present in the CNP region during the CMW thickness increasing years of 2013–16. The drastic decrease in CMW in 2021 was likely caused by a westward shift in the deep winter ML distribution since 2017. Kawakami et al. (2016) also pointed out that the winter ML, with CMW property, west of the international date line tends to deepen when the Kuroshio Extension path becomes more convoluted. However, since 2017, the Kuroshio Extension (KE) path has become a straight path (Qiu et al. 2020, 2023), associated with the Kuroshio large meaner path south of Honshu, Japan (Sugimoto and Hanawa 2012). This indicates that factors other than the KE path states form the deep winter ML west of the international date line, and this is a point that should be addressed in future work.
(a) Distribution of the deep ML (>150 dbar) with the CMW property in winter (February–April) of 2021 (color dots). Gray dots indicate observation points where deep ML with the CMW property was absent (Argo profiles from GDAC). (b),(c) As in (a), but for CMW thickness increasing period (2017–21) and decreasing period (2013–16).
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
MHWCNP21 has a zonal-elongated structure in the central North Pacific (Fig. 1b). We examine whether or not the MHWCNP21 in the CNP region is a phenomenon associated with warming over the eastern North Pacific. In the eastern North Pacific (black rectangle in Fig. 10a), MHW occurred in spring, had a mature phase at the end of August, and was damped until October (Fig. 10b), while the mature phase in September persisted till the following spring in the CNP region (Fig. 2a). The heat budget analysis for the eastern North Pacific indicated ocean heating through air–sea heat exchange in May and no remarkable signal in the summer season (Fig. 10c). This implies that a warmer thermocline might play a role for sustaining the long MHW in the eastern North Pacific. We concluded that the MHWCNP21 of our study is different from that in the eastern North Pacific in terms of the beginning time, mature time, end time, and the cause of formation. Because CMW is not distributed in the eastern North Pacific, this result would be one of evidence for the contribution of CMW to the MHW formation.
(a) SST anomalies in August 2021 from OISST. The black rectangle indicates the eastern North Pacific region (35°–45°N, 160°–130°W). The black contours represent the SLP climatological values with an interval of 4 hPa. (b) Daily time series of SST averaged over the east North Pacific (dashed line). The black line represents the climatological SST of 1991–2020, and yellow, orange, and red shadings indicate MHW in categories 1, 2, and 3, respectively. (c) Anomaly in heat budget analysis over the eastern North Pacific region. Positive values represent a heat input to the ocean ML.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
Since 2019, CMW thickness has become thin (Fig. 8a). In addition to the 2021 summer, MHW occurred over the CNP region in the summers of 2019 and 2020 (Fig. 11). The 2019 MHW was a short-lived event, lasting only about a month, with a peak in September. The 2020 MHW lasted less than 2 months. In these two events, the NHF is of a magnitude similar to the long-term mean value (bottom panels of Fig. 11) and does not have enough impact to form MHW. This would also provide evidence of the importance of the ocean structure, i.e., CMW distribution, to the MHW formation. The CMW was also extremely low in 2012 (Fig. 8a), and MHW occurred around the CNP region in early summer (Figs. 12a,b). The downward NHF decreases compared to the long-term mean value (bottom panel of Fig. 12) unlike 2019–21, which could act to cool the surface ocean. The results indicate the importance of both effects of atmospheric heating and ocean structure for the intense MHW formation over the central North Pacific.
(a),(d) Daily time series of SST averaged over the CNP region from OISST data (red line) for 2019 and 2020. Gray shading represents the 90th percentile of 1991–2020 SST. (b),(e) As in Fig. 1b, but for September 2019 and August 2020, when the MHWs took the mature phase. Dots represent regions where MHW is not detected. (c),(f) As in Fig. 4a, but for September 2019 and August 2020.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
As in Fig. 11, but for July 2012.
Citation: Journal of Physical Oceanography 54, 11; 10.1175/JPO-D-24-0021.1
The relationship between mode water thickness and surface ocean temperature has been studied only in the North Pacific (Kobashi et al. 2021, 2023; Oka et al. 2023; our study). However, mode waters are distributed in the world’s oceans (Feucher et al. 2019). These mode waters would also affect surface ocean temperatures and could even influence the MHW formation as pointed out by our study. It will be useful to use mode waters to understand the surface ocean structure.
5. Summary and concluding remarks
In the CNP region, the SST in September 2021 was the highest in September since 1900, the warming signal of which was not only detected near the sea surface but also reached a depth of around 300 dbar. The NPSH expanded westward and then covered the CNP region, but downward heat input from the atmosphere to the ocean was comparable to the long-term mean, and the ML heat budget analysis also indicated that the contribution of atmospheric heating to MHWCNP21 formation is small. Ocean reanalysis data and Argo floats revealed that the isopycnal surfaces from the sea surface to the upper boundary of CMW (around 300 m depth) deepen during the period of decrease in CMW thickness. The heavier isopycnal surfaces are deeper, leading to a weakening of the seasonal pycnocline. Then, this causes the weakening of cooling heat flux associated with the entrainment of subsurface waters into the mixed layer, resulting in surface ocean warming, which in turn contributed to form the MHWCNP21. The quantitative assessment of the summertime entrainment process has yet to be done due to the coarse vertical resolution around the seasonal pycnocline in the observational data. To better understand the role of the ocean in the MHW formation process, it is useful to quantify the response of the surface ocean to subsurface ocean structure through numerical experiments.
Okajima et al. (2014) pointed out that, in 2011, the positive SST anomalies in summer/fall near the CNP region induced hot and dry climate over North America in October, through the equivalent barotropic atmospheric response by conducting numerical experiments. It is therefore expected that the record-breaking MHWCNP21 also has considerable impacts on the overlying atmosphere and on the climate over North America in fall and/or winter. Investigating the physical processes linking the SST anomalies in the central North Pacific with the large-scale atmosphere field will be meaningful in predicting the climate over North America.
Recently since 2019, the MHW occurred every summer over the CNP region as can be easily speculated from high SST conditions in Fig. 2b. Most of this period coincides with a multiyear La Niña event (Nishihira and Sugimoto 2022). It implies that the atmospheric and/or oceanic field associated with La Niña events may have been suitable conditions for MHW formation over the CNP region. Recent studies (e.g., Iwakiri et al. 2023) suggest a linkage between El Niño/La Niña and the North Pacific Meridional Mode (NPMM) in the extratropics, characterized by a dipole SST structure, whose poles lie in the extratropical North Pacific and tropical Pacific (e.g., Di Lorenzo and Mantua 2016; Richter et al. 2022). The CNP region overlaps the extratropical pole of NPMM, so MHWs in this study may affect tropical climates in addition to midlatitude climates and even the occurrence of El Niño. It will be crucial to reveal the role of ocean-forced MHW in weather and climate in the future.
Acknowledgments.
We thank B. Qiu, K. Richards, N. Schneider, and T. Suga for valuable comments. GN was supported by the International Joint Graduate Program in Earth and Environmental Sciences, Tohoku University (GP-EES), and JSPS KAKENHI Grant 23KJ0198. SS was supported by JSPS KAKENHI Grants 19H05704, 22K03714, and 24H0221. This work was also supported by the Strategic International Research Cooperative Program, Japan Science and Technology Agency (JST SICORP Grant JPMJSC21E7), and the World Premier International Research Center Initiative (WPI), MEXT, Japan.
Data availability statement.
We used SST datasets: OISST version 2.1 (https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html), MGDSST (https://www.data.jma.go.jp/gmd/goos/data/pub/JMA-product/mgd_sst_glb_D/), ERA5 (https://climate.copernicus.eu/climate-reanalysis), and COBE-SST2 (https://psl.noaa.gov/data/gridded/data.cobe2.html). RG Argo Climatology was available at https://sio-argo.ucsd.edu/RG_Climatology.html. NHF data were obtained from ORAS5 (https://doi.org/10.24381/cds.67e8eeb7), JRA55 (http://search.diasjp.net/en/dataset/JRA55), MERRA2 (https://climatedataguide.ucar.edu/climate-data/nasas-merra2-reanalysis), and CFSR/CFSv2 (https://rda.ucar.edu/datasets/). Argo profiling float data were collected and made freely available by the International Argo Program and the national programs contributing to it (http://www.argo.ucsd.edu, http://argo.jcommops.org). The Argo Program is part of the Global Ocean Observing System. We used Argo profiles from GDAC.
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