Why have changes in air-sea heat flux in the South Atlantic increased in recent years and its effects have become more visible in the Northern Hemisphere and have affected global warming? What are the current and future climate scenarios? Is the world's climate future better or worse? What solutions are there for humans?

{Regiane Moura. Ronald Buss de Souza. Fernanda Casagrande. Douglas da Silva Lindemann}

This study investigates the variations in air–sea heat fluxes and temperatures in two ocean front regions, the Southwestern Atlantic Ocean (SWA) and the Drake Passage, widely recognized as hotspot areas with significant influences on South America weather and climate. We analyse means and trends of latent and sensible heat fluxes (LHF and SHF) and their associated air–sea temperatures (SAT and SST), based on monthly ERA5 (1985–2014) and eight CMIP6 models, for historical and long-term simulations (2015–2099). The ERA5 trend for all parameters was positive over SWA, contrasting with the negative values observed in the Drake Passage, indicating surface warming and cooling, respectively. In the SWA, the ERA5 and CMIP6 multi-model ensemble (MME) align in SST and SAT values, with a historical trend of 0.1Cdecade−1 and a significantly increasing trend in warming estimated by 0.4Cdecade−1 up to 2099. The ERA5 LHF and SHF trends were 4 and 0.6 Wm−2decade−1, respectively. The MME shows historical (SSP5-8.5) trends of 0.2 (1.3) Wm−2decade−1 for LHF and −0.2 (−0.1) Wm−2decade−1 for SHF. In the Drake Passage, the models accurately reproduced the air–sea mean temperatures; however, they failed to simulate negative trends observed in SAT and SST. Under the high emissions scenario, all CMIP6 models predict an increasing warming trends of 0.1–0.4Cdecade−1 and ocean heat gain of −0.2 to −1.2 (−1 to −2) Wm−2decade−1 for LHF (SHF). For both regions, spatial analyses of SST and SAT highlight the coupling between the ocean and the atmosphere, alongside changes in air–sea heat fluxes. Our findings reveal inhomogeneous patterns of SST warming trends by the end of the 21st century, which are approximately 2–4 times greater than historical trends. The results suggest the persistence and enhancement of these regions as hotspots with significant potential to influence oceanic and atmospheric dynamics.

KEYWORDS

air–sea heat fluxes, CMIP6 climate model, ocean warming, Southern Atlantic Ocean, SST front

The anthropogenic CO2 forcing has continuously warmed the world ocean in recent decades, impacting air–sea heat fluxes, ocean heat storage and transport (Fox-Kemper et al., 2021; Pörtner et al., 2019). The global mean sea surface temperature (SST) has increased by approximately 0.6C from 1980 to 2020 and by 0.88C from 2011 to 2020, related to 1850–1900 pre-industrial times (Fox-Kemper et al., 2021). Over the past 50 years, the ocean has absorbed more than 90% of the additional heat gained by the Earth's system (Dong et al., 2020). The areas where SST has changed most rapidly during these years, with a tendency to persist in the coming years, are called “hotspots” and are considered prime locations for assessing climate change impacts (Diffenbaugh et al., 2008; Frusher et al., 2013; Giorgi, 2006; Hobday & Pecl, 2014).

Most surface heat resulting from global warming is transported to the interior of the global ocean, manifesting itself as ocean warming (Cheng et al., 2021; Lyu et al., 2021; Roemmich et al., 2015; Su et al., 2023). According to Su et al. (2023), between 1993 and 2021, the ocean heat absorption was mainly concentrated in the surface and upper layers. Subsequently, this heat was gradually transferred to the subsurface and deeper layers, resulting in varying degrees of warming at different depths. The authors demonstrated that the warming rate at 0–2000 m (0–300 m) increased from 0.88 (0.38) Wm−2 during 1993–2021 to 0.93 (0.40) Wm−2 between 2005 and 2021 and reached 1.11 (0.52) Wm−2 during 2011–2021 (Su et al., 2023). It is noteworthy that global warming rates are unevenly distributed spatially, with slight cooling observed in some regions (Bulgin et al., 2020; Collins et al., 2019; Fox-Kemper et al., 2021). The emerging impact of ongoing climate change on the ocean is expected to influence regions of lateral SST gradients, such as oceanographic fronts, in particular, and remains a subject of debate (IPCC, 2021; Laffoley & Baxter, 2016; Reid et al., 2016; Rhein et al., 2013).

The Southern Ocean (SO), broadly limited south of 30S, is a dynamically complex region, characterized by several frontal regions, different current systems with distinct hydrographical properties, intense mesoscale activity and a significant impact on the global climate system (Beadling et al., 2020; Dufour et al., 2015; Shi et al., 2021; Wang et al., 2021). In regions with strong ocean fronts, intense momentum and heat fluxes can be generated at the air–sea interface. The energy input provided by the heat fluxes modifies the vertical mixing and can modulate the atmosphere, playing an important role in atmospheric processes at the meteorological synoptic scale, including frontogenesis, cloud generation, precipitation bands and intensification of frontal systems at midlatitudes (Kilpatrick et al., 2014; Leyba et al., 2017; O'Neill et al., 2010; Parfitt & Seo, 2018; Small et al., 2008, 2014; Tokinaga et al., 2005). In particular, between 35S and 65S there is a preferential range for extratropical cyclones, called storm tracks, which persist throughout the year and vary seasonally (Chapman et al., 2015; de Jesus et al., 2022, 2021; Hoskins & Hodges, 2005;

Priestley et al., 2020). According to Hoskins and Hodges (2005), the SST gradients on storm track locations support air–sea heat fluxes intensifications and the occurrence of extratropical cyclones.

A better understanding of the air–sea heat fluxes is essential to advance our knowledge of how the atmosphere and the ocean interact with each other and to validate or improve numerical model simulations (Garzoli & Matano, 2011; Leyba et al., 2019; Wainer et al., 2003; Yang et al., 2016a, 2016b). Global ocean–atmosphere coupled models give us an opportunity for a comprehensive analysis of coupled ocean–atmosphere processes and to offer projections of future climate change (Bracegirdle et al., 2020; Fox-Kemper et al., 2019; Fox-Kemper et al., 2021; Sung et al., 2021). The Coupled Model Intercomparison Project Phase 6 (CMIP6) enables an integrated assessment of these coupled processes. Moreover, model ensembles based on the CMIP improve our knowledge of the models' uncertainties on different time scales (Eyring et al., 2016; O'Neill et al., 2016).

Here, we investigate the trends in latent (LHF) and sensible (SHF) air–sea heat fluxes and their associated variables, SST and surface air temperature (SAT), in two hotspot regions in the Southern Atlantic Ocean (SAO): the Southwestern Atlantic Ocean (SWA) and the Drake Passage, using historical and future CMIP6 climate model simulations and reanalysis. The paper is organized as follows: In section 2, we described the study area, dataset and methods. In section 3, we analyse our results in terms of spatiotemporal variability and discuss the climate models' ability to represent the variables. Finally, in section 4, we draw our conclusions and lay out ideas for future work.

1 | METHODOLOGY

1.1 | Study area

We investigated two SAO hotspot regions, the SWA, which extends from 32S to 50S and from 44W to 60W, and the Drake Passage, between 55S and 60S and 57W and 70W (Figure 1). These regions are characterized by intense SST gradients and strong air–sea fluxes, related to both the oceanic and atmospheric

FIGURE 1 ERA5 30-year mean horizontal SST gradient (shaded). Black boxes indicate the sectored study areas for Southwestern Atlantic Ocean

(top) and Drake Passage (bottom). Black lines illustrate the schematic diagram of ocean circulation (Brazil, Malvinas, South Atlantic and Antarctic Circumpolar currents) and oceanographic fronts (Brazil-

Malvinas, Subtropical,

Subantarctic and Polar fronts).

Adapted from Talley (2011) and

Ruiz-Etcheverry and Saraceno (2020). [Colour figure can be viewed at wileyonlinelibrary.com]

large-scale circulation and to other drivers that influence South America's weather and climate (Abello et al., 2021; Bulgin et al., 2020; da Silveira & Pezzi, 2014; Hobday & Pecl, 2014).

The SAO western region comprises three important oceanographic fronts, from north to south: the Subtropical Front (STF), the Subantarctic Front (SAF) and the Polar Front (PF). The STF and SAF fronts are characteristic systems of the SWA, while the SAF and PF are associated with the Antarctic Circumpolar Current (ACC) in the Drake Passage (Belkin & Gordon, 1996; de Boer et al., 2013; Dong et al., 2006; Graham & De Boer, 2013; Moore et al., 1999; Orsi et al., 1995; Shao et al., 2015).

The major feature of the SWA is the Brazil-Malvinas Confluence (BMC), located at an annual mean position of 38S and shifting seasonally by 3 (Jullion et al., 2010; Orúe-Echevarría et al., 2019; Piola et al., 2008). The BMC is marked by the convergence of the southward Brazil Current, carrying warmer and more saline Subtropical Water, with the northward Malvinas Current, transporting colder and relatively fresh Subantarctic Water (Peterson & Stramma, 1991; Saraceno et al., 2003; Stramma & England, 1999). Another important feature of the SWA dynamics is the strong anticyclonic ocean circulation around the Zapiola Rise, stretching zonally along 45S between 36W and 51W (Jullion et al., 2010; Saraceno et al., 2009; Volkov & Fu, 2008; Weijer et al., 2020).

In the SAO, the Drake Passage is recognised as an important contributor to the local coupled ocean– atmosphere processes. The region represents the main point narrowing of the ACC (Auger et al., 2021; Cunningham et al., 2003; Piola et al., 2008). Part of the ACC surface water, when crossing the Drake Passage, is directed southwards, towards the Antarctic continent, and in this way exposes the dense and deep layers of the ocean to interactions with the atmosphere and cryosphere (Rintoul, 2018; Wu et al., 2019). The ACC is the strongest current in the global ocean, connecting the three major ocean basins, and is considered a fundamental component of the global overturning circulation. On the surface, the ACC regulates the air–sea heat fluxes and the exchange of mass and freshwater between the Indian, Atlantic and Pacific oceans and the way they respond to recent climate change (Abello et al., 2021; Carter et al., 2008; Rintoul & Garabato, 2013).

1.2 | Data sets

The monthly LHF, SHF, SST and SAT datasets were obtained from the European Center for Medium-Range Weather Forecasts Reanalysis 5 (ECMWF ERA5; Hersbach et al., 2019) and numerical simulations from eight state-of-the-art CMIP6 global climate models (Eyring et al., 2016; O'Neill et al., 2016), displayed in Table 1.

ERA5 provides atmospheric, land and ocean climate data from 1979 to the present, offering both hourly and monthly data. This dataset features a native resolution of

TABLE 1 List of the eight CMIP6 coupled climate models with their respective atmospheric, oceanography and sea-ice components.

The CMIP6 numerical experiment used here includes present climate (historical experiment) and future climate projections (SSP5-8.5), following the CMIP protocol described in Eyring et al. (2016) and O'Neill et al. (2016). The historical experiment is designed to capture the historical changes in various climate variables, driven by observed external forcing such as greenhouse gas concentrations, solar radiation and aerosols. The period covered is typically from 1850 to 2014, including the industrial era, during which significant human-induced changes in CO2 and land use occurred. This work analysed the historical period from 1985 to 2014 in relation to the availability of the model dataset and compared with ERA5.

The CMIP6 future scenarios are defined by five Shared Socioeconomic Pathways (SSP) and categorized by low and high socioeconomic challenges to mitigation and adaptation (Kriegler et al., 2017; O'Neill et al., 2017). Here, we used the SSP5-8.5, characterized by a radiative forcing value of 8.5 Wm−2 (equivalent to 1200 ppm) in 2100 relative to pre-industrial times. This scenario exhibits very high levels of fossil fuel use, representing the highest emissions of a no-policy baseline scenario developed in the SSP protocol (Kriegler et al., 2017; O'Neill et al., 2016).

Data from eight CMIP6 models was processed using the Climate Data Operator (CDO, 2023) tool to obtain multi-model ensemble (MME) average and trend temporal and spatial values. All datasets were regridded through bilinear interpolation onto 1.0 × 1.0 lat/lon grids, in order to allow intercomparison and consistency between CMIP6 and ERA5, and to estimate average and linear trend values. Moreover, a landmask was applied that considered only the parameters obtained over the ocean.

In order to quantify the level of agreement between CMIP6 historical simulations and ERA5 we used the Taylor diagram. The diagram displays the standard deviation (SD), the root-mean-square error (RMSE) and the Pearson correlation coefficient (r), graphically summarizing how closely one dataset is related to the other (Fan et al., 2020; Lyu et al., 2020; Taylor, 2001). To reduce the intra-annual variability, a 12-month running mean filter was applied (de Souza et al., 2019), for the temporal series, considering 95% confidence level. By convention, we choose positive (negative) value of heat flux to indicate heat transfer from the ocean to the atmosphere, or ocean heat loss (gain).

2 | RESULTS AND DISCUSSION

2.1 | Climate model skill assessments

In order to assess the spread between the monthly means of observed and simulated air–sea sensible and latent heat fluxes (LHF and SHF, respectively), SST and SAT, the Taylor diagram was used with ERA5 reanalysis as a reference during the historical period between 1985 and 2014 (Figure 2). In terms of RMSE and r values, the SWA showed a better statistical representation of the SST and SAT (Figure 2a), whereas in Drake Passage the LHF was more accurately represented (Figure 2b). Most models showed a SD higher and more dispersed in SWA (0.7 < SD < 1.3) than in the Drake Passage region (0.8 < SD < 1.2).

According to the Taylor diagram, the models were capable of reliably reproducing variables with moderate to strong correlation strengths (r > 0.85), except for SHF (Figure 2). The ability of CMIP6 models to represent SHF varies significantly in both study regions. In the SWA, the SHF correlation coefficients ranged from 0.39 (MIROC6) to 0.74 (GFDL-ESM4), with standard deviations between 0.64 (MIROC6) and 1.45 (MPI-ESM1.2HR). In the Drake Passage, the SHF correlation coefficients varied from 0.36 (MIROC6) to 0.5 (UKESM1.0 and MME), and the SD varied from 0.7 (MIROC6) to 1.3 (CanESM5).

The CMIP6 model's ability to reproduce the annual means and trends of air–sea temperatures and heat fluxes indicates better agreement for SST and SAT than for air– sea heat fluxes, particularly in the SWA region. Our findings suggest that the significant differences in simulated SST, SAT and heat fluxes are therefore expected to affect the proper location and climatology of cyclogenic events, as well as the ability to predict precipitation and extreme events in South America. According to Wild (2020), deviations in climatic models' LHF outputs are related to global evaporation and precipitation differences, consequently impacting the models' ability to represent the intensity of the global water cycle.

2.2 | Time series analysis

Here, we examine the annual variations in air–sea temperatures and heat fluxes for the historical period of 1985– 2014 and long-term future projections from 2015 to 2099 (Figures 3 and 4). The changes in air–sea temperatures and heat fluxes have been considerable over the last decades, as observed by the reanalysis data. The SWA shows positive trends for both air–sea heat fluxes and the associated SST and SAT variables (Figure 3 and Table 2). In contrast, the Drake Passage shows negative values for these parameters (Figure 4 and Table 3). The mean LHF (SHF) values observed in ERA5 increased from about 75 (10) Wm−2 in 1985 to more than 85 (15) Wm−2 in 2020 in SWA. Conversely, in the Drake Passage, the mean LHF (SHF) values observed in ERA5 decreased from about 43 (7) Wm−2 in 1985 to less than 30 (−2) Wm−2 in 2020.

The MME and ERA5 reanalysis showed similar values for the annual SST and SAT (Figure 3a,b) for the SWA, with a historical trend value of 0.1Cdecade−1 and a substantial projected increase in trend estimated at 0.4Cdecade−1 up to 2099 (Table 2). The relevance of long-term ocean warming is a crucial indicator of past

FIGURE 2 Taylor diagrams of monthly SST, SAT, LHF and SHF for eight CMIP6 models over (a) SWA and (b) Drake Passage regions.

Dots represent individual models, and numbers indicate the parameters. [Colour figure can be viewed at wileyonlinelibrary.com] FIGURE 3 ERA5 (solid red) and CMIP6 (coloured lines) time series of (a) SST, (b) SAT, (c) LHF and (d) SHF in the SWA (at 95%

confidence interval). The solid black line shows the MME and the grey envelopes represent the models' amplitude range. Dashed lines represent the trends for ERA5 (red) and MME (black). [Colour figure can be viewed at wileyonlinelibrary.com]

and present climate dynamics, particularly in hotspot regions, as emphasized by the persistent influence of decadal variability and secular trends. These findings are consistent with previous research (e.g., Cheng et al., 2021; Hobday & Pecl, 2014; Rhein et al., 2013; Trenberth et al., 2016).

FIGURE 4 ERA5 (solid red) and CMIP6 (coloured lines) time series of (a) SST, (b) SAT, (c) LHF and (d) SHF in the Drake Passage (at 95% confidence interval). The solid black line shows the MME and the grey envelopes represent the models' amplitude range. Dashed lines represent the trends for ERA5 (red) and MME (black). [Colour figure can be viewed at wileyonlinelibrary.com]

The ERA5 air–sea heat flux showed positive trends, indicating ocean heat loss, with values of 4 and

0.6 Wm−2decade−1 for LHF and SHF, respectively (Figure 3c,d and Table 2). The individual models displayed a large spread in both LHF and SHF trends (related to ERA5), with most models underestimating the

LHF trend (Figure 3c,d). Notably, the MME exhibited distinct results between latent and sensible air–sea heat fluxes, reporting historical (SSP5-8.5) trends of 0.2 (1.3)

Wm−2decade−1 for LHF, and −0.2 (−0.1)

Wm−2decade−1 for SHF (Table 2). Although future scenarios simulate an increase in air–sea heat fluxes, especially in LHF, compared to historical data, the projected LHF trends until the end of the century are unlikely to reach the observed values shown by ERA5. Our results agree with Dufour et al. (2015), L'ecuyer et al. (2015) and

Yang et al. (2016a, 2016b) in terms of trends and variability.

In Drake Passage, reanalysis SST and SAT trends were negative, suggesting surface cooling (−0.2 and −0.1Cdecade−1, respectively), while the MME exhibited positive trends, indicating surface warming (0.1Cdecde−1 for both SST and SAT). The MME SST is slightly higher than the MME SAT annual mean values, with both overestimating by approximately 1C compared to ERA5 data (Figure 4a,b and Table 3). Despite their similar annual mean temperatures, the models failed to accurately simulate the trend values (Table 3). Among the individual models, MIROC6 exhibited the highest average values, with differences exceeding 3C for both air and sea temperatures (Table 3). For the annual trends, MPI-ESM1.2-HR was the model closest to the reanalysis, showing negative temperature values (−0.1Cdecade−1). For future climate trends, all CMIP6 models predict an increase in warming trends ranging from 0.1 to 0.4Cdecade−1 until 2099.

The observed ERA5 LHF (−2 Wm−2decade−1) and SHF (−1.5 Wm−2decade−1) values also presented pronounced negative trend values. Although the MME follows negative trends, it consistently underestimates them (Figure 4c,d and Table 3). For the long-term, MME simulations predict negative air–sea heat fluxes trend, suggesting ocean heat gain, with greater variation in SHF annual trends (−0.2 to −1 Wm−2decade−1) than in LHF trends (−0.4 to −0.5 Wm−2decade−1) towards the end of the century compared to the historical period, implying an increased atmosphere-to-ocean surface heat flux.

2.3 | Spatial variability

This section analyses the spatial trend patterns of air–sea temperatures and heat fluxes (Figure 5) for the historical period (1985–2014) and future scenarios (2015–2099). This spatial approach helps us avoid the compensation error that can occur when using average measures in time series analysis, particularly along the ocean fronts.

As mentioned earlier, the ocean and atmosphere interact through surface fluxes, which are primarily driven by thermal imbalances between air and sea temperatures. This exchange plays a crucial role in regulating the climate system's mean state and variability (Bishop et al., 2017).

From reanalysis, the SWA region exhibited enhanced ocean warming from −0.01Cdecade−1 to an increase exceeding 0.5Cdecade−1 (Figure 5.1), with significant spatial variation (marked by intense changes over mesoscale eddies), which results in intense air–sea heat fluxes. The increase in LHF (SHF) trend values range from approximately 0.1 to 7 Wm−2decade−1 (0.1 to 2 Wm−2decade−1) (Figure 5.5, 5.7). ERA5 maps show evidence of a strong spatial fit between SST and SAT, even with the warming trend of SAT being slightly lower than SST, accompanied by its air–sea heat fluxes (Figure 5). Most models were able to represent the positive trends over BMC and STF for historical periods; however, most of them failed to capture the spatial trend patterns, underestimating the magnitude when compared to ERA5, particularly the air–sea heat fluxes.

The MME trend for SST and SAT reflects a faster warming under SSP5-8.5, approximately 2–4 times greater than historical trends (Figure 5.1a–4a). The SST trends in CanESM5 and UKESM1.0 (MIROC6) simulated the highest (lowest) values, mainly between 40S and 45S and nearest the coast region, with a historical mean value of about 0.3 (−0.05)Cdecade−1, reaching up to 0.6 (0.2)Cdecade−1 for the long-term period (Figure 5 and Table 2). Air–sea heat fluxes are projected to increase significantly in the SSP5-8.5 scenario in response to CO2 forcing, notably between 40S and 45S (Figure 5.6a, 5.8a). Here, it is important to note that, considering the underestimation observed in Figure 5.5a and 5.7a for the historical period in SWA, these simulations may tend towards unrealistic estimates of the air–sea heat fluxes in this region.

Yang et al. (2016a, 2016b) associated the increased SST trend (between 1958 and 2013) with an increase in surface heat loss to the atmosphere, indicating that the climate trend is partly ocean-driven. Bishop et al. (2017) showed that surface heat flux variations (and their trend) have a large positive correlation with SST (and SST trend) along the ACC, as well as, in the ocean's western boundaries, also indicating that ocean-driven SST variability is important in these regions. The authors also point out that the internal ocean processes, which include mesoscale eddies, drive variability in SST and surface heat flux over several time scales.

Figure 5 shows that the highest trend variations were particularly notable in the BMC and Zapiola Rise regions around the STF. Some studies have shown that the ocean

FIGURE 5 Spatial trend distributions of SST, SAT, LHF and SHF for the historical (1985–2014) and SSP5-8.5 (2015–2099) periods.

[Colour figure can be viewed at wileyonlinelibrary.com]

warming trend occurs in this region due to the poleward shift of the subtropical gyre and/or the intensification of western boundary currents, resulting in SST increase and leading to a positive air–sea heat flux trend (Leyba et al., 2019; Wu et al., 2012; Yang et al., 2016a, 2016b). These changes are also associated with a systematic westward shift of the semi-permanent atmospheric South Atlantic Subtropical High (SASH) (Cherchi et al., 2018; Vizy et al., 2018).

A poleward shift of the westerlies, associated with positive phase of the Southern Annular Mode (SAM), induces variations in barotropic and baroclinic instabilities, contributing to the generation of oceanic mesoscale eddies in the western boundary current extensions and, thus, leading to increased ocean warming in colder regions where warm eddies propagate, transferring more heat into their extensions (Beech et al., 2022; Li et al., 2022a, 2022b; Yang, 2022). Additionally, ocean eddies and atmospheric synoptic systems form a dynamically coupled system in which, during winter, storms enhance the vertical heat transport to the atmosphere from mesoscale eddies (Jing et al., 2020).

Vasconcellos et al. (2023) attributed the SWA warming to the (i) wave-train from different Pacific regions caused by a warm anomaly in the subtropical South Pacific, (ii) an omega blocking formation in the South Atlantic, (iii) a positive phase of the SAM and (iv) enhanced Hadley cell activity in response to warmer SST in the tropical North Atlantic. These patterns induce an amplification of the SASH and the western boundary current. Furthermore, over warmer oceans, transient synoptic systems tend to be stationary in this region

(Vasconcellos et al., 2023).

The SWA is considered a key hotspot region, exerting a substantial influence on various meteorological and oceanographic phenomena, including storms, atmospheric jet streams, sea level rise, ocean carbon uptake by releasing large heat and moisture quantities into the atmosphere (Jing et al., 2020; Ruiz-Etcheverry & Saraceno, 2020; Wu et al., 2012; Wu et al., 2019; Yang et al., 2016a, 2016b). In addition, it has a significant impact on South America climate and economy (Cheng et al., 2022; Vasconcellos et al., 2023; Yang et al., 2016a, 2016b). For instance, faster warming in this area intensifies the air–sea heat fluxes (Figure 5.5 and 5.7), which could lead to changes in South America climate (IPCC, 2021). According to Vasconcellos et al. (2023), a positive SST trend over the SWA influences large-scale circulation, resulting in heating and drying in eastern South America, particularly over southeastern Brazil, northern Argentina and western Paraguay. The authors argue that precipitation and cloudiness decrease is associated with changes in the South Atlantic Convergence Zone, atmospheric blocking configuration and is consistent with strengthening of SASH. On the other hand, cyclone-related precipitation between southern Brazil, Uruguay and Argentina is projected to increase by 30% until 2099, also associated with SST and air–sea heat fluxes variations (Reboita et al., 2019, 2021).

The Drake Passage exhibited surface temperatures decrease, with a cooling rate of −0.4 and −0.2Cdecade−1 for SST and SAT, respectively (Figure 5.1, 5.3 and Table 3). These cooling trends are accompanied by an increased ocean heat uptake, as indicated by negative heat flux trends varying from approximately −0.5 to −3 Wm−2decade−1 (Figures 5.5, 5.7). Most models are not able to represent the air–sea temperature trends, with significant positive values (except GISS-E2.1G and MPI-ESM1.2HR). This includes the MME, which had historical SST and SAT trends of 0.1Cdecade−1, showing a notable discrepancy compared to the reanalysis (Figure 5.1a, 5.3a). The individual model that was closest to the reanalysis, MPIESM1.2HR, showed historical air–sea temperature trends of approximately −0.1Cdecade−1. In contrast, UKESM1.0 overestimated temperature trends by up to 4 times more than reanalysis (Figure 5.1i, 5.3i).

With respect to air–sea heat fluxes trends, the models exhibited large widespread. CESM2, MIROC6 and UKESM1.0 were the most consistent with the reanalysis, including the same trend flux direction as ERA5 over the Drake Passage. This alignment is particularly significant because the flux direction plays a critical role in the analysis and prediction of various regional and global ocean–atmosphere processes. While air–sea surface temperatures in Drake Passage are projected to increase in response to atmospheric CO2 forcing (Figure 5.2a–i, 5.4a–i), there is no corresponding increase in air–sea heat fluxes (Figure 5.6a–i, 5.8a–i). In fact, the projections show a negative trend, with simulated values ranging from −0.2 to −1.2 Wm−2decade−1 (−0.6 to −1.7 Wm−2decade−1) for LHF (SHF) until 2099. Furthermore, the projected LHF variation as a response to anthropogenic CO2 forcing is higher in SWA than in Drake Passage, whereas the SHF variation is higher in Drake Passage than in SWA (Figures 3–5 and Tables 2 and 3).

The negative heat flux trends may be related to multiple mechanisms associated with changes in Antarctic sea-ice concentration, Antarctic ice-sheet and Antarctic meltwater (Bulgin et al., 2020; Casagrande et al., 2023; Fan et al., 2020; Meredith & Brandon, 2017; Rye et al., 2020), as well as cloud feedback (Morrison et al., 2022; Rose & Rayborn, 2016; Wang et al., 2022), and natural variability, for example, SAM (Song & Yu, 2012; Yu et al., 2012).

Cai et al. (2023) have highlighted the connection between Antarctic stratospheric ozone depletion and the warming observed, along with radiative heat and freshwater fluxes. Our research reveals projected air–sea warming in future climate scenarios in the Drake Passage, which contrasts with the cooling trend observed up to the early 21st century (Figures 4a,b and 5.1, 5.3). In addition, the SO circulation and water mass transformation, driven by the increase in momentum and buoyancy fluxes, also induce variations in heat uptake. However, it remains unclear how this absorbed heat is distributed across water masses, although it carries significant implications for ongoing ocean warming, sea level rise and climate impacts (Li et al., 2022a, 2022b). As the region continues to warm in high emission scenarios, changes in air–sea heat fluxes are expected, contributing to changes in sea ice formation, ice shelf and sheet melting, precipitation patterns, poleward intensification of westerly winds, ocean mixing layer depth and global climate patterns, thereby influencing changes in extreme events.

3 | CONCLUSION

Here, we examined present and future variabilities (mean and trend) in air–sea latent and sensible heat fluxes, SST and SAT, in two oceanic front regions widely recognized as hotspots: the SWA and the Drake Passage. The ERA5 temperature trends across the SAO revealed distinct patterns of warming and cooling, accompanied by changes in air–sea heat fluxes, highlighting the complex dynamics of climate variability. The SWA showed positive trends for both air–sea heat fluxes and the associated SST and SAT variables, while in the Drake Passage these parameters were negative (Figures 3–5 and Tables 2 and 3). The SST and SAT spatial trends underscore the close coupling between the ocean and the atmosphere in this area, alongside changes in air–sea heat fluxes (Figure 5). The SST trend varies faster than the SAT, suggesting an ocean-driven climate trend. These findings align with previous studies (e.g., Bulgin et al., 2020; Franco et al., 2020, 2022; Hobday & Pecl, 2014; Leyba et al., 2019; Risaro et al., 2022).

All individual models outperformed in representing the fundamental variables SST and SAT compared to the parameterized air–sea heat fluxes. Our findings show that, quantitatively, most models tend to underestimate LHF in SWA and overestimate it in the Drake Passage compared to ERA5 reanalysis (Figures 2–5 and Tables 2 and 3). The spatial trend analysis highlights that some models fail to accurately capture the directions of the SHF trend, simulating positive values instead of the negative trend values as observed in ERA5. The disparities and uncertainties between models and reanalysis data could be associated with spatial resolutions, model parameterizations, unresolved sub-grid scales, and the representation of coupling ocean–atmosphere processes, including biogeochemicals (Hewitt et al., 2020; Souza et al., 2021; Wang et al., 2022). Additionally, the discrepancies exhibited in the spatial patterns reinforce the high dynamic ocean activity response in SAT and air–sea heat fluxes changes, especially in the SWA region (mainly due to the mesoscale activity).

Yu (2019) attributes differences in air–sea heat flux outputs in climate models to their distinct parameterization schemes, which cause imbalances in the global-ocean budgets. Additionally, Boisvert et al. (2022) outlined several factors contributing to the discrepancies between modelled and observed turbulent fluxes, including the ability of climate models to represent the SAT and humidity gradients, variations in seaice properties, temporal and spatial resolution, and the parameterizations and assumptions used in bulk formulas. According to Bourassa et al. (2013), climate models often use midlatitude boundary layer parameterizations, with the caveat that the boundary layer over sea-ice is more stable than the nocturnal boundary layer over land, leading to significant heat flux errors.

In the future, all models, even the most conservative ones, project a pronounced warming trend for the longterm scenarios, accompanied by changes in heat fluxes trends. The MME trend for both SST and SAT reflects a faster warming under SSP5-8.5, approximately 2–4 times greater than historical trends, suggesting the persistence of these regions as hotspots, particularly in the context of global warming.

Changes in air–sea temperatures and heat fluxes influence the heat content of the upper ocean and the moisture in the MABL with significant impact on South America climate (Leyba et al., 2017, 2019; Souza et al., 2021). How changes in air–sea heat fluxes in the SO will affect the climate of South America remains a prominent topic for the scientific community, mainly due to the complexities associated with the coupled multiscale processes and the scarcity of in situ collected data. Based on findings proposed by Vasconcellos et al. (2023) and Bernardino et al. (2018), we argue that faster warming in this area and enhanced air–sea heat fluxes projected (Figure 5.5 and 5.7) may affect the large scale atmospheric circulation pattern, resulting in changes in climate in South America. This SWA warming is associated with heating and drying in eastern South America, particularly over southeastern Brazil (Vasconcellos et al., 2023).

Despite their inherent limitations and uncertainties, CMIP climate models remain a valuable tool for simulating changes in SO hotspot regions, since the in situ measurements are extremely difficult to obtain due to the remoteness and high associated costs. These climate models allow us to simulate and understand the responses of these regions to anthropogenic forcing and future scenarios.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request

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