Summer temperatures are often above freezing along the Antarctic coastline, which makes ice shelves and coastal snowpacks vulnerable to warming events . Here, we project changes in the frequency, duration and amplitude of summertime warming events expected until end of century according to two emission scenarios. By using both global and regional climate models, we found that these events are expected to be more frequent and last longer, continent-wide. By end of century, the number of warming events is projected to double in most of West Antarctica and to triple in the vast interior of East Antarctica, even under a moderate-emission scenario. We also found that the expected rise of warming events in coastal areas surrounding the continent will likely lead to enhanced surface melt, which may pose a risk for the future stability of several Antarctic ice shelves. While snow rarely melts in most parts of the continent, meltwater production is significant in coastal areas and on ice-shelves in the Antarctic Peninsula .The maximum air temperature (TX) in Antarctica is well below the freezing point in its vast interior but is much closer to 0 °C in summer (December–January–February (DJF)) on ice-shelves across the Antarctic ice sheets Fig. a, especi4. A combination of meteorological conditions led to this warming event. A ridge of high pressure was centered over Cape Horn at the beginning of the month, which allowed temperatures to rise. The Southern Hemisphere westerlies, that typically shield the Peninsula from warm air masses, were in a weakened state allowing extra-tropical warm air to cross the Southern Ocean5. Attributable to this single warming event, snowpack on Eagle Island (at the northern tip of the Antarctic Peninsula) melted 106 mm from February 6 to February 115. About 20% of seasonal snow accumulation melted in this single event on Eagle Island5. While the conditions that lead to HWs are different elsewhere in Antarctica, these events are expected to become more frequent as temperatures rise.Temperatures associated with summertime warming events at high latitudes may be inconsequential elsewhere, but they can lead to widespread melt intensification along the Antarctic coastline. An intense and long HW affected the northern Antarctic Peninsula from February 5 to February 13, 2020. On February 6, 2020, thermometers at the Esperanza Base on reached 18.3 °C (the highest ever measured in the Antarctic continent)6. The South Pole7 and the central West Antarctica8 have recorded since the late 1950s some of the largest increases in air temperature in the Southern hemisphere. The largest increases in air temperature in the continent have been recorded in Antarctic Peninsula10, resulting in strongly positive and statistically significant trends in the duration of melting conditions12. Yet, meltwater runoff contributions to the surface mass balance have been outpaced by the rise in snow accumulation over ice sheet interiors.Thirteen of the 17 stations  show a positive trend in their annual mean near-surface air temperature over the full length of their record14, glacier discharge in Antarctica is accelerating due to the weakening of ice shelves that have seen reduced their ability to restrain grounded ice-sheet flow17. In the Amundsen and Bellingshausen regions, ice shelf thinning arises mostly from basal melt attributable to intrusions of circumpolar deep water on the continental shelf20. However, in the Antarctic Peninsula, atmospheric warming and the resulting meltwater ponding also appears to be contributing to ice shelf weakening and accelerated glacier discharge into the ocean22.Although the surface mass balance for the total Antarctic Ice Sheet (including ice shelves) remains positive due to the increased snowfallHere we assess the progression and expected changes in summertime HWs understood as a period of at least 3 consecutive summer days with “very high” maximum temperatures (TX). We consider a TX value to be “very high” if it falls above the 90th percentile of the daily base climatology  from the Coupled Model Inter-comparison Project Phase 5 (CMIP5)24 24 Table . Althoug24 Table , we alsoOur analysis suggests that across Antarctica, HWs will become more frequent and last longer, regardless of the emission scenario Fig. . HW metrChanges in the number of HW per season (HWN) exhibit a similar regional pattern. Under the RCP4.5 scenario, the number of HWs is projected to double (from 1 in 1961–1990 to about 3 in 2070–2099) in most of the West Antarctica, while they may triple at locations in the vast interior of East Antarctica  are projected to rise from about 5 days in 1961–1990 to about 20 days in 2070–2099 (RCP4.5) in East Antarctica  is projected to surge in the vast interior of East Antarctica from less than 5 days in 1961–1990 to about 9 days in 2070–2099 (RCP4.5) . The RACMO2 model has been broadly evaluated across Antarctica. Both its simulations of the air temperatureves Fig. .Projections of “very warm” DJF days (TX90) until end of century show significant inter-decadal increments for several Antarctic ice shelves, especially beyond the Antarctic Peninsula Fig. . The gre32.Surface melt is strongly responsive to increases in air temperature. Responding to very high temperatures the daily surface melt intensity on Antarctic ice shelves can often double the seasonal mean. Changes in the DJF surface melt simulations are consistent with the changes in DJF air temperatures, but the response of the surface melt to increases in air temperature is nonlinear Figs. , S4. For33. Snow properties change in case of melting much more than in case of meltwater refreezing. However, surface melt does alter the surface radiative budget by changing the surface albedo. The refreezing of meltwater leads to an increase in snow grain size, which in turn decreases the albedo34. Moreover, periods of consecutive very warm days  can lead to persistent meltwater ponds. For example, in January 2020 (at the peak of the 2019–2020 melt season), meltponds spanned a vast area of the George VI ice shelf spanning a length of about 140 km on the shelf. The impressive extent of the melt suggests that it resulted from multiple days of melting35. Melt ponds are darker than surrounding ice and therefore have a lower albedo, thus absorbing more shortwave radiation in a feedback loop that can lead to further and rapid melting36. On an ice shelf, ponded water can fill and magnify ice crevasses, destabilizing and eventually breaking the ice shelf up38. This process, often referred to as hydrofracturing, was the leading mechanism in the collapse of the Larsen A and Larsen B ice shelves41.The short-term surface melt events associated with HWs on ice shelves may not significantly contribute to the annual Antarctic mass losses (volume change) because in Antarctica most of the annual meltwater from snowmelt refreezes−1 in December 1994 and further increased to 19.6 mm w.e. day−1 in January 1995. The resulting meltwater production reached a record of about 1000 mm w.e. (12.7 × 31 days + 19.6 × 31 days) over that short period (December 1994–January 1995).The extreme surface melt intensities on the Larsen A ice shelf prior its collapse led to record levels of meltwater production, which probably contributed to the disintegration of the ice shelf via meltwater ponding and hydrofracture. As shown in Fig. The record pre-collapse meltwater production (1000 mm w.e.) on Larsen A has not been observed on other ice shelves elsewhere in Antarctica. However, the rise in frequency of HWs and the resulting surface melt may produce such extreme values of meltwater production on several major ice shelves in the Antarctic Peninsula before end of century. Although we cannot project how specific ice shelves will actually respond, levels of meltwater production as those observed on Larsen A before its collapse (1000 mm w.e.) pose a risk for the stability of any ice shelf. Several paths can lead an ice shelf to reaching the record 1000 mm w.e. meltwater production; for example, an intense warming event , or a long warm summer , or a combination of both .The more frequent “very warm” days will enhance meltwater production per season (SML). As in the case of the “very warm” DJF days Fig. , a surgeAlthough decadal averages of the DJF meltwater production will likely remain moderate on most of the Antarctic ice shelves, Fig. Antarctic HWs are expected to be more frequent and last longer, continent-wide. By end of century, the number of warming events is projected to double in West Antarctica (including the Antarctic Peninsula) and to triple in the vast interior of East Antarctica, even under a moderate-emission scenario. The length in days of the longest event during a season is projected to double in West Antarctica and the Antarctica Peninsula up to about 6 days by end of century. Projections under a high-emission scenario are even more striking.32. Antarctica is therefore particularly sensitive to rapid increases in melting and mass loss in response to relatively small increases in air temperature.The increases in warming events in coastal areas surrounding the continent  are particularly concerning since they can lead to enhanced surface melt. Surface melt responds to increases in air temperature through a nonlinear relationship. Nonlinearity arises from the melt-albedo feedback and from the effect of persistent warm days In response to rapidly warming air temperatures, surface melt is projected to significantly increase in upcoming decades regardless of the emission scenario. Sustained high temperatures, significantly above freezing, will become more frequent and surface melt intensities are expected to approach those currently observed in the Antarctic Peninsula elsewhere in Antarctica. Nevertheless, the impact of warming events and the associated enhanced surface melt is not expected to become large in terms of long-term ice-sheet mass balance (volume change) because they are constrained mostly to coastal areas and because of refreezing.Most of the meltwater from snowmelt currently refreezes in Antarctica. However, the projected surge in the number of consecutive very warm days  will lead to lasting and larger meltwater ponds. On an ice shelf, ponded water can fill and magnify ice crevasses, destabilizing and eventually breaking the ice shelf up via hydrofracturing. Surface meltwater ponding may drive the collapse of Larsen A and Larsen B ice shelves as well as the speedup of tributary glaciers in the northeast Antarctic Peninsula. Potential ice-shelf disintegration and subsequent increases in ice sheet mass loss highlight the importance of ice shelf stability.Attributable to the projected rise in warming events and very warm days, we found that several Antarctic ice shelves have an increasing probability of reaching the record meltwater production that the Larsen A and Larsen B ice shelves exhibited before their collapse. For some ice shelves , the risk of surpassing the risky 1000 mm w.e. level is considerable well before end of century. Although, we do not aim to project how specific ice shelves will actually respond, levels of meltwater production as those observed on Larsen A before its collapse (1000 mm w.e.) likely pose a risk for the stability of any ice shelf. The projected warming events and the associated enhanced surface melt, especially after mid-century under the RCP8.5 scenario, raise concerns about the stability of several ice shelves, which may have important consequences in terms of ice shelf buttressing and sea level rise.Lastly, we found substantial differences between warming figures under different emission scenarios (RCP8.5 and RCP4.5), especially in the second half of this century. Therefore, curbing greenhouse gas emissions and limiting global warming will make a significant difference in the number of extreme warming events that the Antarctic ice shelves will have to endure.43. For each DJF day and at each location (or grid point of the climate model) and, we used a 15-day rolling window of the daily estimates of the maximum air temperature (TX) over a base period of 30 years (1961–1990), in order to form datasets of 450 values (15 days × 30 years). Then, we used these datasets of 450 values in order to compute the mean (that defined the daily base climatology) and the daily 90th percentile threshold. For each summer season and at each location (or grid point of the climate model), we took the number of “very warm” DJF days (TX90) as equal to the number of DJF days when the maximum temperature (TX) exceeded the 90th percentile threshold.We applied a widely used methodology for assessing changes in the occurrence probability of extreme eventsHWAthe HW amplitude ;HWDthe HW duration ;HWFthe HW frequency ; and theHWNthe number of HWs per season.Here, we considered a HW as period of at least 3 consecutive “very warm” DJF days. According to this definition, for each summer (DJF) season and at each location (or grid point of a climate model), we computed the following HW metrics:23 from 15 CMIP5 GCMs (see Table Simulations for TX90 estimates and HW metrics were conducted under two representative concentration pathways (RCP4.5 and RCP8.5)25: the Regional Atmospheric Climate Model RACMO, Version 2.1 (48-km grid resolution)26, here referred to as RACMO2. RACMO2 has been broadly evaluated across Antarctica. Its simulations of the air temperature28 and meltwater production31 have been extensively tested and are considered to be reliable.Projections of HW metrics (and meltwater production) were also computed by using simulations from a Regional Climate Model (RCM) from the Coordinated Regional Climate Downscaling Experiment (CORDEX)32 as a reference for testing CMIP5 GCMs.Figure The higher resolution of RACMO2, enabled us to focus on points of interest along the Antarctic coastline Fig. . AntarctFor the grid points shown in Fig. Supplementary Information.


Water mass transformation in the Nordic and Barents Seas, triggered by air-sea heat fluxes, is an integral component of the Atlantic Meridional Overturning Circulation (AMOC). These regions are undergoing rapid warming, associated with a retreat in ice cover. Here we present an analysis covering 1950−2020 of the spatiotemporal variability of the air-sea heat fluxes along the region’s boundary currents, where water mass transformation impacts are large. We find there is an increase in the air-sea heat fluxes along these currents that is a function of the currents’ orientation relative to the axis of sea-ice change suggesting enhanced water mass transformation is occurring. Previous work has shown a reduction in heat fluxes in the interior of the Nordic Seas. As a result, a reorganization seems to be underway in where water mass transformation occurs, that needs to be considered when ascertaining how the AMOC will respond to a warming climate. Water mass transformation in the Nordic and Barents Seas is important for the Atlantic Meridional Overturning Circulation (AMOC). Here, the authors show increases in air-sea heat fluxes linked to sea ice retreat along the boundary currents of the Nordic and Barents Seas that could influence how the AMOC reacts to climate change. The wintertime densification of this Atlantic Water, via the transfer of heat and moisture to the atmosphere as it passes through the Nordic  and Barents Seas, is an important contributor to the deep southward return flow of the AMOC5. This water mass modification produces Atlantic-origin overflow water along the rim current system encircling the Nordic Seas4. Atlantic Water also enters the Arctic Ocean through the east side of Fram Strait and the Barents Sea7 where it is further transformed8, impacting the thermohaline structure of the Arctic Ocean, as well as the distribution of sea ice9. Ultimately this modified Atlantic Water reenters the Nordic Seas through the west side of Fram Strait where it contributes to overflow waters crossing the Greenland-Scotland Ridge12. In addition, colder and fresher Arctic-origin overflow water is formed within the interior basins of the western Nordic Seas14. The regional air−sea interaction that is necessary for these water mass changes also impacts the atmosphere, through a warming and moistening of the atmospheric boundary layer15, as well as marine ecosystems16.The northward transport of warm and salty water within the North Atlantic Ocean, emanating from the Gulf Stream system, plays a fundamental role in the Earth’s climate18; the Norwegian Atlantic Current that flows northward through Fram Strait into the Nansen Basin as the Svalbard Branch (SB)8; and the Barents Sea Branch (BSB) that progresses from the Norwegian Sea through the Barents Sea towards Novaya Zemlya20. These latter two branches merge in the Arctic Ocean to form the circumpolar Atlantic Water Boundary Current. Water mass modification also occurs throughout the central Nordic Seas within the Norwegian Sea’s Lofoten Basin21, as well as within the Iceland and Greenland Seas13. Both the EGC and SB flow along the shelfbreak and we used this characteristic to define the orientation of the domains for these currents .Within the Nordic and Barents Seas, there exist three major boundary currents where water mass modification occurs Fig. : the Eas23 that includes a significant expansion off East Greenland during the late 1960s and early 1970s, the so-called ice years24. More recently, there has been a sustained retreat of sea ice that has resulted in the disappearance of the Greenland Sea’s Odden Ice Tongue25, as well as reductions in ice cover across the northern and eastern parts of the Barents Sea26 and to the north of Svalbard27.In the Nordic and Barents Seas, there is evidence of multi-decadal variability in winter ice cover28. At the same time, this sea-ice retreat is exposing part of the EGC to the atmosphere, leading to enhanced air−sea interaction in that region29. Within the Barents Sea, the retreat of winter sea ice has resulted in profound changes in the climate—an Atlantification of the region30.This retreat appears to be resulting in a reduction in air−sea interaction over the central Iceland and Greenland Seas that may be lessening the production of dense overflow waters there32. Please see the “Methods” section for additional details. Here we show that there is an increase in the air−sea heat fluxes along these currents that is a function of the currents’ orientation relative to the axis of sea-ice change suggesting enhanced water mass transformation is occurringIn this paper, we focus on the surface turbulent heat flux, which is the sum of the surface sensible and latent heat fluxes, with the convention being that fluxes out of the ocean are positive. This flux plays a dominant role in the high-latitude water mass transformation33. There are also minima in the Iceland and Greenland Seas that are the result of the competing influences of the two climatological low-pressure systems, the Icelandic and Lofoten Lows, that are prevalent in the region during winter33.Figure 17. Over both the Barents Sea and the region north of Svalbard, the retreat of sea ice has resulted in changes in the turbulent heat fluxes along the SB and the BSB currents.The variability in ice cover clearly modulates the winter-mean turbulent heat flux along the EGC, the SB, and the BSB. For example, in the Greenland Sea, an expansion of ice cover during the 1960s and its subsequent retreat over the following decades corresponds with a movement of the region of enhanced heat fluxes relative to the EGC, which is dynamically tied to the shelfbreak and upper slope along east GreenlandTo quantify these changes, decadal means of the along-current turbulent heat flux and sea ice concentration were constructed Fig. . Over th28.The character of the turbulent heat flux along the SB Fig.  and BSB Sea-ice retreat is instrumental to the changes in surface heat fluxes along all three currents. However, it is clear that the orientation of the current relative to the direction of sea-ice retreat is crucial in how the changes are manifested. For the EGC, the orientation of the current is approximately perpendicular to the direction of sea-ice retreat leading to changes in heat fluxes along the length of the current, while for the SB and BSB the currents are aligned with the direction of sea-ice retreat leading to changes in heat fluxes in focused regions.Although the decadal means shown in Figs. The results presented so far indicate that there is complexity in the distribution of the heat fluxes along the three current systems Fig. . As note28.The along-current integrated heat flux, which quantifies the heat relinquished to the atmosphere, is a critical factor in the transformation of the water masses advected by the currents. Figure 2, half the 52 W/m2 of the preceding 10-year period (1955−1964) and only a third of the 75 W/m2 of the following 10-year period (1974−1984). The changes across each of these periods are statistically significant at the 95th percentile confidence level. The maxima in heat fluxes during the mid 1980s and 1990s stand out as well. The moving window trend for the EGC . The ERA5 data have a spatial resolution of ~30 km and cover the period 1950-onwards, which includes the recently released early-period extension. The sea surface temperature in ERA5 is a prescribed field that is a blend of a number of products from the UK Met Office and others40. In ice-covered regions, where observations are limited, the sea surface temperature is a regressed function of the sea ice concentration42.The documented variability in ice cover motivates an examination of the spatiotemporal variability of air−sea heat fluxes along these boundary currents. To accomplish this, the 5th generation reanalysis from the European Centre for Medium-Range Forecasts (ECMWF) known as ERA5 was used44 resulting in only small changes to the surface density45. Freshwater fluxes from terrestrial sources are also neglected. While this could have relevance for the EGC due to its proximity to Greenland’s melting ice sheet46, it has been suggested29 that there is substantial Ekman transport associated with the strong northerly barrier flow during the fall and winter along the East Greenland coast that keeps this freshwater on the shelf, thereby limiting its impact on the EGC. A recent modelling study confirmed this scenario47. We also neglect a number of oceanographic and cryospheric processes that can have impacts on water mass modification along these currents48.It should be noted that the present study neglects the impact of precipitation and evaporation on the surface salinity . This is because a number of studies have indicated that evaporation and precipitation over the region of interest are, on seasonal timescales, in balance51. The complex spatial heterogeneity of the marginal ice zone leads to mesoscale variability52 that is not fully captured in reanalysis datasets that blend the open ocean and ice-covered fluxes in these regions53. To assess the impact of this uncertainty, a number of sensitivity tests were performed where the ERA5 air−sea heat fluxes in nearby ice-covered regions were merged with those from the COARE bulk flux parameterization to represent the heat flux over the open ocean regions of the marginal ice zone as a function of ice concentration. These tests provided sufficient confidence in the ERA5 air−sea heat fluxes within the marginal ice zone for this study.A comparison with in situ observations indicates that IFS-based reanalyses are able to represent the air−sea fluxes in these subpolar seas with a good degree of fidelitySupplementary Information


Here, we investigated if neflamapimod-mediated acute inhibition of p38 MAPKα could induce vasodilation in resistance-size rat mesenteric arteries. Our pressure myography data demonstrated that neflamapimod produced a dose-dependent vasodilation in mesenteric arteries. Our Western blotting data revealed that acute neflamapimod treatment significantly reduced the phosphorylation of p38 MAPKα and its downstream target heat-shock protein 27 (Hsp27) involved in cytoskeletal reorganization and smooth muscle contraction. Likewise, non-selective inhibition of p38 MAPK by SB203580 attenuated p38 MAPKα and Hsp27 phosphorylation, and induced vasodilation. Endothelium denudation or pharmacological inhibition of endothelium-derived vasodilators such as nitric oxide (NO) and prostacyclin (PGI2) had no effect on such vasodilation. Neflamapimod-evoked vasorelaxation remained unaltered by the inhibition of smooth muscle cell K+ channels. Altogether, our data for the first time demonstrates that in resistance mesenteric arteries, neflamapimod inhibits p38 MAPKα and phosphorylation of its downstream actin-associated protein Hsp27, leading to vasodilation. This novel finding may be clinically significant and is likely to improve systemic blood pressure and cognitive deficits in AD and DLB patients for which neflamapimod is being investigated.Neflamapimod, a selective inhibitor of p38 mitogen activated protein kinase alpha  Among the four isoforms of p38 MAPK, namely α, β, γ and δ6, p38 MAPKα is present in most cell types and is best characterized for its role in chronic inflammatory processes linked to degenerative diseases of the nervous system, musculoskeletal system, and cardiovascular system7. In microglia, p38 MAPKα activation is associated with the release of pro-inflammatory cytokines. Stimulation of p38 MAPKα signaling in neurons regulates tau localization and neuronal plasticity10. In addition, p38 MAPKα regulates the function of a Ras-related protein Rab5, which is a key regulator of early endosomal trafficking involved in a wide variety of cell signaling and cellular processes. Due to the involvement of p38 MAPKα in different neuronal pathologies, p38 MAPKα inhibitors have recently attracted considerable attention as potential therapeutics for inflammatory and neurodegenerative diseases, such as Alzheimer’ Disease (AD), Dementia with Lewy Bodies (DLB) and Huntington’ Disease (HD)12. Neflamapimod, a selective inhibitor of p38 MAPKα, is under clinical investigation for its efficacy in improving cognitive deficits in AD and DLB. Preclinical and recent phase II clinical trial data show that neflamapimod treatment is effective in reversing cognitive decline in DLB13, however, its efficacy in reversing AD-related cognitive deficiencies remains somewhat unclear. Although the exact mechanism is not fully known, it is established that hypertension and vascular dysfunction drive neurodegeneration and cognitive dysfunction14. Hypertension and altered vascular tone are implicated in vascular cognitive impairment that encompasses all sorts of cognitive deficits arising from vascular dysfunction17. Since vascular tone is a key regulator of peripheral resistance and systemic blood pressure, drugs that relax small resistance arteries are expected to lower systemic blood pressure and enhance cognitive function.p38, a member of the mitogen-activated protein kinase (MAPK) family, is traditionally known to be a stress sensor that is activated by physical and chemical stressors, inflammatory mediators, vasoactive compounds, and growth factors18, high-glucose or diabetes activates p38 MAPK in rat aortic smooth muscle cells. As p38 MAPK activation often reflects gene expression changes leading to cell growth or apoptosis21, glucose- or diabetes-associated activation of p38 MAPK was suggested to regulate vascular inflammation, thrombosis and other pathological changes22. Subsequent studies reported that p38 MAPK activation also regulates the contractility of resistance arteries. Vasoconstrictors such as angiotensin II and norepinephrine were reported to cause p38 MAPK activation leading to smooth muscle contraction24. This raises the possibility that acute inhibition of p38 MAPK could stimulate vasodilation, which could reduce vascular resistance and systemic blood pressure. Non-selective p38 MAPK inhibition by SB203580 relaxed high K+-contracted renal arteries and enhanced acetylcholine-induced vasodilation4. It has been suggested that p38 MAPK activation may influence the function of the cardiovascular system by regulating systemic and renal hemodynamics. To our knowledge, no previous studies have investigated what effect selective p38 MAPKα inhibition by neflamapimod may have on the contractility of resistance mesenteric arteries. Here, we examined if neflamapimod could relax resistance-size rat mesenteric arteries and its mechanism of action. Our data demonstrates that acute neflamapimod treatment stimulates dose-dependent vasodilation in mesenteric arteries, in a manner consistent with p38 MAPKα inhibition and reduced Hsp27 phosphorylation, and independent of endothelial signaling and smooth muscle cell K+ channels. Such a novel vasodilatory action of neflamapimod may be clinically translatable and is likely to lower systemic blood pressure and improve cognitive deficits in AD and DLB patients.Although p38 MAPKα has been extensively studied in the context of neuroinflammation and neurodegeneration, little is known about its role in vascular contractility. According to a previous study2, 5% CO2, and 74% N2. Intravascular pressure was gradually increased to 80 mmHg to stimulate the development of myogenic tone. At 80 mmHg, mesenteric arteries developed ~ 24% myogenic tone, after which a cumulative concentration response (0.0001–10 µM) to neflamapimod was performed and diameter changes recorded. Our myography data shows that, within 2–3 min of application, neflamapimod produces a fully reversible and concentration-dependent vasodilation , continuously perfused with 37 °C PSS, gassed with a mixture of 21% OAs neflamapimod is a selective inhibitor of p38 MAPKα, we asked if neflamapimod could inhibit p38 MAPKα phosphorylation and its kinase activity, in mesenteric arteries to stimulate vasodilation. To do so, we incubated mesenteric arteries for 30 min with either neflamapimod or SB203580, a non-selective inhibitor of p38 MAPK. Our Western blotting data reveals that mesenteric arteries have a basal p38 MAPKα phosphorylation, as detected by phospho-p38 MAPKα antibody . Application of 1 µM ACh fully reversed PE constriction in endothelium-intact arteries. In contrast, endothelium-denuded vessel showed only 5% reversal, which is 95% less than that shown by endothelium-denuded arteries -mediated dilation as described previously Fig. a25–28. Bies Fig. a,b. Howeies Fig. b. 60K-inies Fig. c. Since ies Fig. d,e. On ties Fig. d,e. This2, another important endothelium-derived vasodilator  with L-NNA and the production of the most important physiological vasodilator molecule NO do not reduce neflamapimod-evoked vasodilation Fig. a,b, prector Fig. a,b. Thes+ channels and K+ efflux induce smooth muscle cell hyperpolarization, leading to vasodilation. Arterial smooth muscle cells express a variety of K+ channels including voltage-gated K+ channels (KV), large-conductance Ca2+-activated K+ channels (BKCa), and ATP-sensitive K+ channels (KATP). We therefore assessed the role of the major K+ channels in neflamapimod-induced mesenteric artery vasodilation. Application of 4-aminopyridine (4AP), an inhibitor of KV channels, produced a mean dilation of ~ 37 µm, which is 90% of that produced by neflamapimod  activity in arterial contractility surfaced from earlier reports of angiotensin II- and norepinephrine-induced phosphorylation of p38 MAPK and Hsp27, leading to vasoconstriction4. However, whether selective inhibition of the α isoform of p38 MAPK could relax resistance arteries of the systemic circulation remained unknown. Our study provides pharmacological and biochemical evidence that selective inhibition of p38 MAPKα and downstream Hsp27 phosphorylation underlie neflamapimod-induced vasodilation in mesenteric arteries.Our finding is in agreement with previous studies in that selective p38 MAPKα inhibition, like non-selective p38 MAPK inhibition, reduces the phosphorylation of Hsp27. Reduced phosphorylation of Hsp27 decreases its actin capping ability, leading to smooth muscle relaxation. Komers et al. demonstrated that non-selective inhibition of α and β isoforms of p38 MAPK by SB203580 relaxed renal arteries and enhanced ACh-induced vasodilationNeflamapimod at 10 µM concentration induced ~ 17% relaxation of mesenteric artery diameter from baseline. Therefore, such vasodilation upon acute neflamapimod application may lead to a significant drop in arteriolar resistance and systemic blood pressure in vivo. Mesenteric arteries are part of the splanchnic circulation that receive approximately 25% of cardiac output. The splanchnic circulation regulates the distribution of cardiac output and acts as a blood reservoir. Thus, vasodilation in small mesenteric arteries is likely to lower peripheral vascular resistance and systemic blood pressure. However, future studies will be required to investigate these possibilities.Earlier studies suggest that non-selective inhibition of p38 MAPK improves endothelial dysfunction, enhances NO bioavailability and improves ACh-induced vasodilation, presumably by reducing vascular inflammation. Based on the findings in our study and previous reports, it is possible that neflamapimod treatment may improve long-term blood pressure control by reducing both vascular resistance and vascular inflammation.32, investigated the in vivo efficacy of a related p38 MAPKα inhibitor, losmapimod in hypercholesterolemic patients and found that a 7.5 mg twice daily treatment with losmapimod for 28 days improved endothelium-dependent vasodilation and forearm blood flow32. However, losmapimod treatment did not alter baseline vascular responses, which contrasts with the findings of our study. As hypercholesterolemia triggers a complex series of changes in the signaling landscape due to oxidative stress, it is likely that the aberrant signaling promotes both endothelial and smooth muscle dysfunction35. Upon exposure to oxidative stress and inflammation, smooth muscle cells undergo phenotypic switch from a contractile to a non-contractile, synthetic phenotype. While endothelial dysfunction could be reversed by treatment with a p38 MAPK inhibitor, this approach may not reverse altered smooth muscle contractility, especially after a short-term treatment period of 4 weeks. Since vascular contractility is regulated differently between different species, vessel types as well as in health and disease, the vascular effects of losmapimod on basal vascular reactivity remains unclear and will require future investigation.Cherian et al.17. In addition, elevated systemic blood pressure negatively impacts cerebral perfusion, which alters brain structure and function by promoting ischemic injury, oxidative stress and endothelial dysfunction14. Although hypertension and increased vascular tone are known drivers of cognitive dysfunction17, it remains unknown if neflamapimod treatment-associated improvement in cognitive function in DLB and AD could be due to a reduction in peripheral vascular resistance as a result of direct vasorelaxation of the systemic resistance vessels such as the resistance-size mesenteric arteries. Our finding of a direct vasodilatory effect of neflamapimod suggests that the observed clinical benefit with neflamapimod pharmacotherapy may result from direct vasodilation and reduction of vascular resistance, as well as the reduction of inflammation of the cardiovascular and nervous systems as proposed by others earlier.Due to the involvement of p38 MAPKα in inflammation, there is great enthusiasm for developing selective p38 MAPKα inhibitors for the treatment of inflammatory diseases. As a selective inhibitor of p38 MAPKα, neflamapimod is currently being investigated for its efficacy in reversing neuroinflammation, neurodegeneration and cognitive deficits in DLB and AD. In clinical trials, neflamapimod treatment appears to be promising in reversing cognitive deficits in DLB and likely AD. Hypertension, through a variety of mechanisms, may lead to cognitive dysfunction in these patients. Proposed mechanisms by which hypertension leads to dementia include microvascular damage and dysfunction that results in white matter disease, microinfarcts, microhemorrhages, poor clearance of amyloid-β peptide among othersThis study laid the foundation for future studies that acute inhibition of p38 MAPKα could induce vasodilation, which in turn, could enhance regional organ blood flow. Our future study will examine if neflamapimod directly relaxes resistance cerebral arteries that control brain perfusion and cognitive function.+ channels. As elevated blood pressure and altered vascular tone contribute to cognitive dysfunction, vasodilatory effect of this drug is likely to have positive impact on the cognitive performance. We propose that the vasodilatory action of neflamapimod may be clinically translatable and is likely to improve systemic blood pressure and cognitive deficits in AD and DLB patients for which neflamapimod is currently being investigated.In conclusion, our study demonstrates that acute neflamapimod application relaxes resistance mesenteric arteries via selective inhibition of smooth muscle cell p38 MAPKα and the phosphorylation of its downstream target Hsp27. Inhibition of Hsp27 phosphorylation decreases its actin capping ability, resulting in smooth muscle relaxation and vasodilation. Neflamapimod-evoked vasodilation appears to independent of endothelial signaling and smooth muscle cell Kl-arginine , sodium nitroprusside , indomethacin  and acetylcholine ) were purchased from Tocris . 4-AP, SNP, indo and PE were dissolved in distilled water. Nefla, gliben, paxilline, SB203580, L-NNA, and ACh were dissolved in dimethyl sulfoxide (DMSO). The maximum concentration of DMSO in the myograph chamber was < 0.1%, which had no effect on arterial contractility.Neflamapimod (Nefla) was purchased from MedChemExpress  and used in the concentration range of 0.001–10 µM. SB203580 was purchased from MedChemExpress and used at a final concentration of 10 µM. Phenylephrine (PE), and 4-aminopyridine (4-AP) purchased from Sigma-Aldrich  were used at a final concentration of 1 µM and 1 mM, respectively. Glibenclamide (10 µM), paxilline (10 µM), nitro-36.Animal protocols were reviewed and approved (Ref. No. A2107011) by the Mercer University Institutional Animal Care and Use Committee (IACUC), and animal experiments were performed according to the guidelines set by the United States National Institutes of Health (NIH). We used male Sprague Dawley (SD) rats between 7 and 10 weeks of age. Rats were purchased from Charles River Laboratories  and acclimatized for 1 week at the Mercer University College of Pharmacy Vivarium prior to experimentation. Rats were individually caged in a temperature-regulated room  and had access to standard chow diet and drinking water ad libitum. Animals were randomized for all experments2 inhalation was used as per the ‘2020 AVMA Guidelines for the Euthanasia of Animals’ to euthanize rats followed by decapitation. Entire mesenteric artery bed was dissected out and placed into ice-cold PSS containing (in mM) KCl 6.0, NaCl 112, NaHCO3 1.18, MgSO4 1.18, KH2PO4 1.18, CaCl2 1.8, and glucose 10. Third to fifth order branches of mesenteric arteries (150–250 µm) were dissected cleaned of adventitial tissue in ice-cold PSS. Cleaned arterial branches were cut into 1–2 mm long segments that were kept in ice-cold PSS until cannulated for pressure myography28. PSS for vessel dissection, isolation and preparation contained (in mM) KCl 6.0, NaCl 112, NaHCO3 1.18, MgSO4 1.18, KH2PO4 1.18, CaCl2 1.8, and glucose 10. pH of the PSS was adjusted to and maintained at 7.4 by gassing with a mixture of 21% O2, 5% CO2, and 74% N2. 60 mM K+-PSS (60 K) was prepared by equimolar replacement of NaCl with KCl in the PSS36.CO2, 5% CO2, and 74% N2 to maintain pH 7.4. 60 mM K+-PSS (60 K) was prepared by equimolar replacement of NaCl with KCl in the PSS and used for checking the viability of the cannulated arteries at 10 mmHg. Using a pressure servo controller with peristaltic pump  intraluminal pressure was gradually increased to 80 mmHg with increments of 20 mmHg at each step. Mounted arteries were maintained for at least 10 min at each pressure step so that the arteries reach a stable diameter. Mesenteric arterial segments developed a stable myogenic tone at 80 mmHg, after which a cumulative concentration response to neflamapimod (0.0001–10 µM) was performed36. Vessel diameter was read at 1 Hz using a charge-coupled device (CCD) camera connected to a Nikon Ts2 microscope and the automatic edge-detection function of IonWizard software . Myogenic tone (%) was calculated as: 100 × (1 – Dactive/Dpassive), where Dactive is active arterial diameter in the presence of Ca2+ and Dpassive is the arterial diameter determined in Ca2+-free PSS supplemented with 5 mM EGTA28. Endothelium denudation was performed by slowly passing air bubbles through the vessel lumen for 1–2 min. Arteries exhibiting at least 80% reduction of ACh-induced vasodilation were considered endothelium denuded27. Endothelium denudation selectively abolishes ACh-induced vasodilation without altering artery responses to sodium nitroprusside (SNP) and 60K36.Arterial segments were cannulated in a temperature-controlled perfusion chamber , continuously perfused with 37 °C PSS, and gassed with mixture of 21% O2HPO4, pH 7.4) containing protease and phosphatase inhibitor cocktails (Roche). Arterial lysates were centrifuged at 12,000 rpm for 10 min at 4 °C and supernatants collected in clean eppendorf tubes. After measuring protein concentrations using BCA method, 40 µg protein for each sample was boiled with 2× SDS-sample buffer (Bio-Rad) for 5 min and resolved by SDS-PAGE. Proteins were transferred onto PVDF membranes using BioRad semidry transfer apparatus37. PVDF membranes containing transferred proteins were blocked with 5% milk solution in TBST  and incubated with the following primary antibodies overnight: phospho-p38 MAPKα , p38 MAPK , Hsp27 , and phospho-Hsp27 . Due to molecular weight overlap, phospho-p38 MAPKα/Hsp27 and total p38 MAPK/Hsp27 were probed in parallel using the same samples. For loading control, we used GAPDH antibody . Following primary antibody incubation, blots were washed three times, 5 min per wash to remove unbound primary antibodies, and then incubated with HRP-conjugated secondary antibodies  for 1 h at room temperature. PVDF membranes were washed three times and developed using ECL (Pierce). Protein bands were detected using Gel Doc XR + System (Bio-Rad) and quantified by densitometry using ImageJ software  was used for statistical analysis. Values were expressed as mean ± standard error of mean (SEM). One-way analysis of variance (ANOVA) along with Newman-Keuls post-hoc test was used for multiple group comparison. p < 0.05 was considered statistically significant38.OriginLab software version 9.55 (2018b, URL: Supplementary Figures.


Importantly,the under-ice seawater (0.5 m depth) displayed some of the highestconcentrations notably for the long-chain PFAA , which were ≈3-fold higher thanthose of deeper water (5 m depth) and ≈2-fold higher than thoserecently measured in surface waters of the North Sea infuenced byindustrial inputs of PFAAs. The evidence provided here suggests thatmeltwater arising early in the melt season from snow and other surfaceice floe components drives the higher PFAA concentrations observedin under-ice seawater, which could in turn influence the timing andextent of PFAA exposure for organisms at the base of the marine foodweb.Poly- and perfluoroalkylsubstances are synthetic chemicals thatare widely present in the global environment including the Arctic.However, little is known about how these chemicals  enter the Arctic marine system and cycle between seawaterand sea ice compartments. To evaluate this, we analyzed sea ice, snow,melt ponds, and near-surface seawater at two ice-covered stationslocated north of the Barents Sea (81 °N) with the aim of investigatingPFAA dynamics in the late-season ice pack. Sea ice showed high concentrationsof PFAA particularly at the surface with snow-ice (the uppermost seaice layer strongly influenced by snow) comprising 26–62% ofthe total PFAA burden. Low salinities (<2.5 ppt) and low δ Sea ice accumulates perfluoroalkyl acids(PFAAs) from seawaterand from atmospheric deposition in snow. Subsequent melting of surfaceice layers through seasonal changes later results in high PFAA concentrationsin the under-ice seawater. Moreover,long-chain PFAAs (long chain = PFCA with eight carbons and greaterand PFSA with six carbons and greater2)are bioaccumulative and display a range of adverse toxic effects inhumans and biota. Understanding the fate and behavior of PFAAs inthe environment is therefore important particularly in relation toremote ecosystems such as the Arctic that are reported to be currentlyexperiencing other environmental stressors.3Poly- and perfluoroalkyl substances (PFASs) consist of a largegroup of synthetic chemicals that are used in a wide variety of industrialand consumer applications.4 and Chukchi/Beaufort Sea regions of the westernArctic5 with their presence having beenlinked to transport through ocean currents originating from industrialregions. PFAAs are also transported to remote environments such asthe Arctic indirectly through photochemical oxidation of volatileprecursors in the atmosphere followed by deposition, with their occurrencein the central Arctic Basin snowpack5 andon the Devon Island ice cap8 as evidence of atmospheric deposition.However, little is known about the relative importance of these twopathways with even less information about the fate and behavior ofPFAS in sea ice and their subsequent fate during seasonal thaw.PFAAs have been observed in the remote Greenland Sea14 but only recent mechanistic investigations have revealed that brinein sea ice plays an important role in distributing these chemicalsduring sea ice growth.15 Sea ice brine has also been shownto contain contaminant concentrations at levels significantly greaterthan those observed in the underlying seawater.15 In the warming Arctic Ocean dominated by brine-rich single-seasonice, this has important implications for contaminant exposure to themany organisms situated at the base of the pelagic food web, whichare abundant in sea ice. Sympagic organisms, such as ice algae andassociated heterotrophic protists and metazoans, inhabit a networkof brine inclusions and brine channels at the base of the ice andmay be particularly vulnerable to brine which is enriched in contaminants.13The observation of PFAAs in sea ice along with other persistentorganic pollutants is limited to a handful of field studies,16 or transferred in meltwaterto deeper layers of the sea ice system14 or even pass directly into the under-ice seawater. Melt pondsare also common features on late-season ice floes ,100 and both snowfall/precipitationand gas exchange with the overlying atmosphere have been shown toincrease some contaminant levels in the ponds.17 Due to their high water solubilities and low volatility,PFAAs are likely to be transferred from thawing snow and sea ice toseawater. The aim of this study was to determine the concentrationsand distribution of PFAAs in the various compartments of the late-seasonsea ice system and investigate the fate of PFAAs during the thawingprocess. Physical and chemical properties of snow, sea ice, and seawaterincluding density, salinity, and stable isotope analysis were measuredto evaluate the quantity of entrained meteoric water in ice/meltwatercomponents and the role of melt/freshwater in PFAA fate.The late-season ice pack is a dynamicsystem, whereby organic contaminantsthat have accumulated in the winter snowpack are revolatilized tothe atmosphere22.1Kronprins Haakon on 26–28 August 2019. Samples were taken at two study sitessituated on the Barents Sea shelf break and Nansen Basin , whichwe refer hereafter as P6 and P7, respectively. Both sites were selectedon separate large (≈1 km2) undeformed ice floesthat were fully covered with a thin layer of snow (≈0.05 m)except in areas where melt ponds were present. Seawater samples  were collected at two depths (0.5 and 5.0m) using a Niskin bottle via an access hole in the sea ice. Sea iceand snow were collected in close proximity (<20 m) to the seawateraccess hole.Environmental sampleswere collected in the European High Arctic see 1 during n = 3). Snow samples were collected above each core site(approximately 0.25 m2). Sea ice cores (n = 4) were drilled  and then immediatelycut into horizontal sections (n = 7–10) thatwere between 0.1 and 0.2 m in length. Ice core sections were bulkedwith adjacent ice core samples  to obtain sufficient meltwater for PFAS analysis (≈1 L)and placed into polyethylene bags before melting at ambient room temperature(≈20 °C). All equipment and sampling bottles (polypropylene)for PFAS analysis were pre-cleaned following set protocols, and anyclothing worn by samplers that was suspected to contain residues offluoropolymers was avoided to prevent possible contamination. Formore information on sampling protocols, equipment, and ancillary measurements see Tables S1–S5 and Figure S2.Melt ponds were covered by a thin layer of ice, which was removedbefore sampling water  were taken to assess possiblecontamination. Briefly, an internal standard (IS) mix (13C mass-labelled standards) was added to each sample before beingloaded onto pre-conditioned OASIS WAX cartridges . Cartridges were then dried under vacuum and stored at −20°C before further analysis at Helmholtz-Zentrum Hereon, Germany.Instrumental analysis of PFASs was performed by high-performanceliquid chromatography tandem mass spectrometry, using an HP 1100 LCsystem  coupled to an API 4000 triple quadrupolemass spectrometer . It was equipped with a Turbo Vion source , operating in the negative electrosprayionization mode. For chromatographic separation, a polar-embeddedreversed-phase C18 separation column  was combined with a reversed-phaseguard column . As solvents for the gradient elution,2 mM ammonium acetate aqueous solution (A) and 0.05% acetic acid inmethanol (B) were used. The injection volume was 10 μL for samplesand standards, both dissolved in 80:20 (% v/v) methanol/water. Targetcompounds included 11 PFCAs (C4–C14)and 5 PFSAs . See Table S6 for moreinformation on analytical standards.Solid-phaseextraction (SPE) of PFASs took place onboard the ship, and severalprocedural blanks . Sample δ18O analyses wereundertaken in the pyrolysis mode using sample injection of 0.4 μLover glassy carbon chips at 1450 °C. δ18O valueswere corrected against laboratory calibration standards  relative to Vienna Standard Mean Ocean Water  and Greenland Ice Sheet Precipitation . Laboratorystandards containing NaCl in similar proportion to the field sampleswere also used to check for any effects of salinity on the true samplevalue. Results confirmed no adverse effect of salinity on analyticalprecision or accuracy. Within-run standard replication for δ18O was <0.2‰ (1.s.d.).Aliquots of each sample(0.05 L) were also taken for salinity and stable oxygen isotope analysisthat were stored at 4 °C in airtight bottles with minimal headspacefor <14 days before analysis was undertaken at Lancaster University,UK. Salinity was measured  using a calibrated conductivity probe (Hach HQd40 loggerwith the CDC401 probe) and reported as parts per thousand (ppt org/L). Sample 2.3Table S7). Analytical recovery (%) and analytical precision(%) are displayed in Figures S3 and S4,respectively. Method detection limits were determined for each PFAA using procedural blanks (n = 7) that comprised laboratory  and field blanks .Concentrations of PFAA in samples that were below method detectionlimits were considered as non-detects, and sample values were thensubject to blank subtraction using an average of all procedural blanks(n = 7). All non-detects were taken as zero, andall blank-corrected values (see Table S8–S12) were included in further calculations (see eqs S1–S13) and statistical testing. Statistical analyseswere performed using concentration data (pg L–1)in RStudio  using a significancelevel of α = 0.05. Normality was tested using the Shapiro–Wilkstest, before further statistical comparisons. Significant differencesin PFAA concentrations were determined using Wilcoxin’s signed-ranktest. Spearman’s correlation analysis was used to investigaterelationships between PFAA and other physical properties in sea ice.To fully evaluate the origin of water in sea ice and assess the contributionof meltwater to the surrounding sea ice system, volumetrically weightedconcentrations were calculated (see eqs S4–S7).All reported PFAA concentrationswere recovery-corrected using amass-labelled analogue unless stated otherwise , particularly during late summer when additional ice massmay also have been gained through the incorporation of fresh snow.Furthermore, sea ice may have undergone surface “flushing”of fresh meltwater, a process that occurs frequently during the Arcticsummer21 and also serves to remove saltsfrom sea ice. While the sea ice was thinner at P6 (1.2 ± 0.1m) compared to that in P7 (1.4 ± 0.1 m), both showed a vertical“S”-shape salinity profile, where layers at the snow–iceinterface were significantly fresher, indicating the influence ofsnow water on the distribution and likely dilution of salt in theupper ice layers  through precipitation such as snow. e layers 2.18OH20 values, whichrevealed δ18OH20 values in sea ice (δ18Osea ice = 0.5 ± 1.2‰) that weresignificantly more positive than those in pure snow (δ18Osnow = −15.2 ± 1.1‰) and seawater(δ18Oseawater = −0.1 ± 0.4‰).During freezing of ocean water to form sea ice, equilibrium fractionationof oxygen isotopes in water enriches δ18Osea ice by approximately 2.6‰.22 Thisgenerates isotopic signatures in sea ice that are enriched in 18O compared to precursor seawater values. The measurementof δ18Osea ice values which are frequentlyisotopically lighter than seawater values indicates the entrainmentof meteoric water, which is expected to have occurred during the agingof sea ice at both sites. One potential other source of meteoric waterto sea ice is from glacial run-off entering into fjord environments/coastalfringes of the Barents Sea23 during thesummer season. However, given the sampling locations in this studyand the volume of glacial meltwater required to lower the isotopiccomposition in sea ice by the required amount, the incorporation ofatmospheric snowfall is the most likely source of meteoric water.The influence of snow on sea ice properties was also supportedby measurements of δ18OH2O values at the surface of sea ice at both P6 and P7 were lower comparedto those in deeper ice layers, which indicates a strong influenceof atmospheric precipitation (probably mostly snow) on the compositionof sea ice at both sites. However, a comparison of δ18Osea ice profiles at P6 and P7 also revealed somemarked differences between sea ice at either sites. A notable featureat P7 was low δ18Osea ice values(δ18Osea ice < −2‰)in the deepest ice situated at the seawater–ice interface.One explanation for this observation is the percolation and subsequentrefreezing of meltwater derived from surface precipitation, formingan ice layer known as superimposed ice. This is also supported bysalinity measurements, which show that the bottom layers of sea iceat P7 were notably fresher. Superimposed ice forms when the surroundingice matrix is colder than the freezing temperature of freshwater24 and is a common feature in Arctic and Antarcticglaciers. Superimposed ice has also been reported in sea ice,26 and its presence suggests that the ice at P7 was second-year ice(SYI) or MYI.Similar to salinity profiles, δ18OH20 values : (i) “Snow-ice”that retains a strong meteoric signal; (ii) “bulk ice”predominantly influenced by sea water and less by snow meltwater;and (iii) “superimposed ice” ,present at deeper layers and influenced by snow meltwater . Using the cumulativeice sample length (m) and measured ice densities (kg m–3), the mass or load of water (kg m–2 ice column)was calculated for each of the respective ice-types 21 wereused to investigate this further by calculating the relative amount(% meteoric water mass/newly formed sea ice mass) of meteoric waterincorporated within the ice floes at P6 and P7, based on an initialisotopic fractionation in newly formed sea ice of δ18Onewly formed sea ice = 2.6‰.22 This indicated that sea ice at P6 and P7 wascomposed of 8 ± 1 and 13 ± 0 % meteoric water, respectively.These estimations of entrained meteoric water in the respective icefloes correspond well with those given by Granskog et al., (2017)for first-year sea ice  and SYI (12.7–16.3%)sampled in a comparable region (adjacent to the Barents Sea) duringwinter. This suggests that sea ice at P6 and P7 is FYI and SYI (orolder), respectively, and supports the assertion that P7 had receiveda higher snow load (probably over successive seasons) compared toP6. While our values of meteoric water content for P6 ice are slightlyhigher than those reported by Granskog et al., our samples of seaice were collected during late summer and hence may have entrainedmore precipitation  or been further influencedby freeze–thaw cycles and/or drainage of surface snow-derivedmeltwater during summer.In order to differentiate between compartmentsof sea ice thatwere influenced by differing quantities of water of meteoric origin,sea ice was classified into three broad ice-types based on δ3.2Supporting Information).Notable differences in concentrations were observed between the short- and long-chain PFAAs, and therefore,results for each group are reported accordingly. In snow samples,most target PFAAs were detected, with the exception of PFBS (C4) and PFOS (C8) and showed similar levels acrossboth sites (n = 6). Consequently, concentrationsin snow (meltwater equiv.) were averaged, and values hereafter referto the mean concentrations from both sites. The presence of PFAAsin the snowpack implies a meteoric source, signifying atmospherictransport as a major pathway to the Arctic marine environment, althougha fraction of PFAAs in ice-rafted snow may also have arisen from marineaerosol generated from sea spray.29 Concentrationsof short-chain PFAAs in snow in this study were significantly higher than thoseof long-chain PFAAs. Similar findings were reported in snow , which probably reflects thehigher industrial consumption of chemical precursors resulting inits formation in recent years. Concentrations of long-chain PFAAsin snow, however, were similar to those measured in previous studies,7 which shows that atmospheric deposition, presumably through thephoto-oxidation of volatile precursors ,is still ongoing at the same rate over the last 10 years or so.snow see 1 sampledAverageconcentrations (mean ± s.d.) use all data from P6and P7 sampling stations (∼81 °N). Surface seawater inthe North Sea (∼58–62 °N) and Greenland Sea (∼68–79°N) was taken along a latitudinal transect and sampled at a depthof 11 m. Surface seawater (∼66–70 °N) and sea ice(∼77–87 °N) samples analyzed by Cai et al. (2012)were collected in different areas of the Western Arctic Ocean, andthe seawater sampling depth was not explicitly stated. Concentrationslisted in sea ice in this study are volumetrically weighted. n/a =no available data. Note: ΣPFAA concentrations listed in thisstudy may differ slightly from those stated in the original referencedue to the selection of PFAAs to match the same target PFAAs as inthis study.Figure S4). Whilethe presence of relatively high levels of PFBA (C4) isprobably linked to chlorofluorocarbon-replacement compounds,6 other important precursors include perfluoroalkanesulfonyl fluorides and fluorotelomer-based compounds (FT-based).30 The presence of the long-chain PFCAs such asPFDA (C10) and PFUnA (C11)  is most likely through the atmospheric photochemicaltransformation of fluorotelomer chemicals including olefins,31 acrylates,32 andiodides33 and alcohols.35 Interestingly, PFBS (C4) and PFOS (C8) werethe only PFSAs detected in this study, which shows they are stillthe main perfluoroalkyl sulfonates present in the Arctic environment,36 but they were below detection limits in oursnow samples. While low levels of PFOS in snow may be related to industryinitiatives aimed to lower environmental emissions of PFOS and relatedcompounds , remote Arctic snow is stillexpected to contain levels of PFBS (C4) due to the increasinguse of analogous short-chain products as replacements to sustain global demand of high performing surfaceactive agents.37 This shows that our currentunderstanding of the sources and environmental processes that governthe transport and cycling of some PFAAs in the Arctic environmentis incomplete and warrants further research. Nevertheless, given thatPFOS have recently been detected in most snow samples from the CanadianArctic,7 the absence of PFSAs in our snowsamples shows that concentrations of some compounds vary considerably.This could be related to to seasonal changes in photochemical activity38 or even seasonal variations in the atmospherictransport of specific volatile precursor compounds to the Arctic.39 However, it is also likely that PFOS and PFBS(and probably other PFAAs) have undergone early elution from the snowpackin meltwater during snow ageing/thawing episodes,42 as indicatedby high levels of PFBS in sea ice layers directly beneath the snow. 5 indicating similar Arctic-wideprocesses affecting PFAA accumulation in sea ice. Elevated concentrationsof PFAAs in the uppermost ice layers of sea ice may be a result ofentrainment of chemicals present in seawater during initial sea iceformation.43 However, snow is also likelyto play an important role in the delivery of PFAAs44 to sea ice with the amount and timing of snowfall depositionlikely to affect accumulation dynamics.The mean concentration of bothshort- and long-chain PFAAs in seaice was comparable to that of snow, with short-chain PFAAs also showingsimilarly higher levels than long-chain PFAAs  between sites is unknown but could be dueto greater inflow of warmer Atlantic water at P6 compared to P747 and/or related to the amount of snowfall  at P6, and this demonstratesthe different ice ages and “weathering” processes  between these two sites. The short-chainPFAAs are more water-soluble than the long-chain PFAAs and likelyto be more mobile when liquid water is present in the ice system.fall see 3.3 servi43 and even hydrophobic organicpollutants15 during winter. This featuredevelops through a process known as gravity drainage, whereby chemicalconstituents present in freezing seawater are excluded from the pureice crystal matrix and rejected into adjacent brine channels.20 The stronger resemblance of this c-shape concentrationprofile for long-chain PFAAs at P6 compared to P7 therefore supportsthe previous assertion that sea ice at P6 and P7 comprises FYI andSYI (or older), respectively, as discussed in Long-chain PFAAs also showed a gradual decrease inconcentrationsfrom the surface ice layers of sea ice but with an increase in concentrationsin lower layers (notably at P6) to form a concentration profile thatresembled a “C” shape. A c-shape concentration profilein sea ice is typical for salt (NaCl) and other dissolved solutessuch as PFASs50 Concentrations of PFAAs in the seawater directlybelow the ice (0.5 m depth) also support this by revealing significantlyhigher  valuesthan at the greater depth of 5 m at both sites (see –1) with recent measurements made in ice-freeparts of the Greenland Sea (ΣPFAA = ≈0.4 ng L–1) showed that our concentrations are comparable to previous observations.In contrast, seawater concentrations at 0.5 m in this study (ΣPFAA= ≈2.0 ng L–1) were ≈5-fold more andwere even higher than those measured in surface seawater (ΣPFAA= ≈0.7 ng L–1)4 in the North Sea affected by industrial inputs of PFAAs. Given theremoteness of the sampling sites in this study, the lack of localsources,51 and low blank values , the high concentrations ofPFAAs in seawater in close proximity to sea ice must be driven bythe overlying ice pack through release from meltwater drainage giventhe time of year when sampling occurred. This is supported by meteorologicaldata (Tables S4 and S5) and measurementsmade on the sea ice cores  thatshow that temperatures were high enough to cause some melting. Differencesin concentrations between the two depths (0.5 and 5 m) are unlikelyto be caused by sampling different stratified water masses, as thepolar mixed layer extends from the surface to ∼10 m depth inthe Barents Sea under summer ice-covered conditions52 and is characterized by slightly lower salinity comparedto deeper waters. These findings are analogous to a fresh water columnin a lake located in the Canadian High Arctic, whereby elevated concentrationsof PFAAs were found in surface waters following the onset of melt.53 Although higher concentrations of short-chainPFAAs in under-ice seawater were not as marked relative as those oflong-chain PFAAs, this probably reflects the earlier elution of theseparticular chemicals from the ice pack with subsequent dispersal.It is also noteworthy that the analytical recovery of some short-chainPFAAs in seawater samples was low . Giventhat PFBA contributed >90% of the sum of short-chain PFAAs in snow/seaice samples , which suggests that PFAAs originate from the overlying meltingice pack. Although the PFAA profile (% ΣPFAAlong-chain) in seawater was not significantly different between sites , concentration ratios for individualPFAAs P6_seawater_0.5 m/c(PFAA)P7_seawater_0.5 m) revealedthat PFAA concentrations at P6 were on average 4-fold higher thanat P7, and this is likely to be related to the different thawing or“weathering” history of the overlying ice pack at thetwo study sites. To investigate this further using a mass-apportionmentapproach, Tables S13–S14), which in turn will lead to significant increases in PFAA concentrationsin the receiving under-ice seawater, particularly when atmospherictemperatures rise through seasonal changes.The concentrations of PFAAs measured in the under-iceseawater (0.5 m) at P6 were the highest in this study  during winter21 gives rise to an estimated annual snow waterequivalent load of 178 ± 49 kg m–2. Mass-apportionmentcalculations utilizing mean δ18Osnow values(assuming constant sea ice mass) show only 81 ± 10 kg m–2 of entrained meteoric water at P6, whereas the entrained meteoricwater mass at P7 is higher at 144 ± 4 kg m–2 and closer to the annual snow water equivalent value mentioned above.This demonstrates that a substantial loss of the annual snowfall occurredover the course of the season at P6, resulting in the lower overallmass of the entrained meteoric water. The loss of meteoric water atP6 is likely to have occurred through thawing of snow and snow-icelayers followed by meltwater percolation through the ice into theunder-ice seawater. In turn, this meteorically influenced meltwaterwill transfer its high PFAA burden either deeper into the bulk iceor into the under-ice seawater. In contrast, the higher mass of meteoricwater at P7, an ice floe likely comprising older ice, demonstratesrelatively less thawing during its recent history and hence lowerrelease of meteorically derived meltwater compared to P6. As a consequence,high concentrations of PFAAs in sea ice are retained in snow-ice andother upper sea ice layers, enabling chemicals to accumulate and concentrationsto exceed those in sea ice at P6.Using an average (mean ± s.d.) snow depth of 0.49 ± 0.13m and snow density of 363 ± 24 kg mlong-chain) ofsea ice and adjacent compartments also indicates that the ice floesat both sites had been influenced by distinct processes, leading todifferent PFAA patterns , with P6 and P7 being only weakly correlated  with each other. Sea ice composition at P6 was more highly correlatedwith snow  than seawater, which impliesthat the ice floe was affected by recent meltwater arising from thawingof moderately fresh snow. Thus, the lower entrainment of meteoricwater at P6 via snowfall, as indicated by isotopic measurements, illustratesthat melting of surface sea ice layers  probablyoccurred earlier in the season with later melt originating from freshsnow. Conversely, sea ice at P7 was more highly correlated with seawater than snow, which suggests less thawing andgreater surface flooding of sea ice with seawater (i.e. snow-ice).The excellent agreement between stable isotope measurements and PFAAobservations made on the same samples in this study provides highconfidence that our account of the evolution history of sea ice atboth sites is accurate.The composition to the under-ice seawater.17 Given thevery different physico–chemical properties of PFAAs comparedwith these other previously studied organic contaminants, investigatingthe significance of melt ponds on their environmental cycling is warranted.By comparing the melt pond composition  with the surrounding endmembers , their meteoric/oceanic origin can be established.Using average values of salinity and δ18OH2O in endmembers (Table S15), the mass fraction(%) that each of these three sources contributed to individual meltponds can be calculated.55 The resultsshown in Table S16 suggest that snow (35± 9%) and sea ice (64 ± 10%) were the major contributorsto the composition of melt ponds, with only a small fraction comingfrom seawater (0 ± 2%). The mass fraction (%) and measured PFAAconcentration (ng L–1) in each respective endmemberwere multiplied and then summed to give “predicted”concentrations of each PFAA in melt pond water (Tables S17 and S18). In general, the measured concentrationsof PFAAs in melt ponds were considerably lower than in the under-iceseawater, and a comparison between the “measured” and“predicted” PFAA concentrations in melt ponds revealedno significant difference . This shows that the melt ponds we sampled were not asignificant source of PFAAs and are unlikely to have played a rolein the high PFAA concentrations measured in seawater. These resultssupport the assertion that the melt ponds had formed during the laterstages of the Arctic summer season after significant thawing had alreadyoccurred. Together, this information strongly suggests that the occurrenceof high PFAA concentrations in under-ice seawater is attributed tothawing of surface sea ice layers early on in the season and showsthat different contaminant classes  do not necessarily follow the same environmental cyclingpatterns in the marine polar environment.We also measuredPFAAs in water samples collected from melt ponds that were presenton the ice floes at P6 and P7. Melt ponds feature on the surface ofpredominantly late-season sea ice in the Arctic and form as snow andsea ice thaw.The concentrationsof short- and long-chain PFAAs in under-iceseawater are comparable to levels observed in coastal seas in temperatelatitudes, but the duration of these high concentrations is probablydependent on the presence of different ice-types. Following completeice breakup and thawing, concentrations in surface seawater are likelyto decline. Nonetheless, the presence of PFAAs in the under-ice environmentat concentrations comparable to those of temperate coastal seas whichare influenced by direct inputs of pollution presents an exposurehazard to ice-associated biota and organisms at the base of the pelagicfood web. Further efforts are now required to investigate the durationand timing of these periods of elevated concentrations and how theylink with biological events such as planktonic blooms. Although morework is needed to ascertain the impact of high concentrations of PFAAson biological communities, the work presented in this study showsthat the thawing of the ice pack may present an efficient pathwayfor these chemicals to enter the base of the Arctic marine food web.


This study investigates the underlying climate processes behind the largest recorded mangrove dieback event along the Gulf of Carpentaria coast in northern Australia in late 2015. Using satellite-derived fractional canopy cover (FCC), variation of the mangrove canopies during recent decades are studied, including a severe dieback during 2015–2016. The relationship between mangrove FCC and climate conditions is examined with a focus on the possible role of the 2015–2016 El Niño in altering favorable conditions sustaining the mangroves. The mangrove FCC is shown to be coherent with the low-frequency component of sea level height (SLH) variation related to the El Niño Southern Oscillation (ENSO) cycle in the equatorial Pacific. The SLH drop associated with the 2015–2016 El Niño is identified to be the crucial factor leading to the dieback event. A stronger SLH drop occurred during austral autumn and winter, when the SLH anomalies were about 12% stronger than the previous very strong El Niño events. The persistent SLH drop occurred in the dry season of the year when SLH was seasonally at its lowest, so potentially exposed the mangroves to unprecedented hostile conditions. The influence of other key climate factors is also discussed, and a multiple linear regression model is developed to understand the combined role of the important climate variables on the mangrove FCC variation. Mangroves are an important part of the local ecosystem, providing habitat for many marine and coastal species, protecting coastlines from extreme weather and erosion, filtering out sediment from river run-off to protect seagrass, as well as sequestering significant levels of carbon in their sediments6. Because of their ecological importance, considerable attention has been paid to this dieback event to understand its extent, cause, and severity9. Mangrove dieback events were also observed during 2015–2016 in Kakadu National Park, Northern Territory10 and in a semi-arid stand near Exmouth, Western Australia11, but those events were not as extensive and severe as the Gulf of Carpentaria mangrove dieback. Preliminary observations have ruled out any direct influence of human activities , pathogens, or any extreme weather event  at this time. Thus, the prevailing climate conditions, driven by the mega-El Niño that occurred during 2015–2016, might be the primary factor for the dieback12, but the precise connection with the climate drivers has not previously been identified.In late 2015, about 8000 hectares of the forested tidal wetland along a 1000 km stretch of the coastline of Australia’s Gulf of Carpentaria , a change in the ecosystem can be triggered with a shift from mangrove forest to drier and more saline saltmarsh, where only a few specialized plants can survive19.Mangroves exhibit wide physiological tolerance and can utilize rainfall and groundwater as well as saline water during tidal inundation20. If the lowering of SLH occurs during the late dry season following persistent below-normal wet season, a significant physiological water stress is likely to the mangroves21. However, mangroves are reasonably heat tolerant22 and the Gulf mangroves cope with the dry season every year. Furthermore, they have apparently remained mostly unaffected during other El Niño events in the recent past, even during previous strong El Niños such as the 1997–1998 event1. This evokes the questions: what was unusual about the 2015–2016 El Niño that resulted in the massive dieback? Was it simply the El Niño’s magnitude, or was the timing and possibly its co-occurrence with other climatic factors crucial? Any effort to address these outstanding questions will help improve our understanding of how such an environmental catastrophe occurred and should facilitate improved capability for monitoring and prediction. This would have a great value in the optimization of risk management for policymakers, rangers, and communities in the future.The Gulf of Carpentaria has a monsoonal climate with about 6 months of the dry season that extends from mid-April to mid-OctoberIn this study, we examine the key climate variables during the 2015–2016 El Niño in the context of conditions during previous El Niño events. We utilize satellite-based mangrove fractional green cover observations to investigate the variation of mangrove canopies associated with changes in SLH, rainfall, surface air temperature, and a measure of vegetation moisture stress formed by comparing potential and actual evapotranspiration. Based on the observed mangrove canopy change during previous El Niño events and any associated droughts and heat extremes, we provide a plausible explanation for the 2015 dieback that points to the seasonal timing and magnitude of the 2015–2016 mega El Niño as the primary driver. We also derive a statistical relationship between mangrove canopy changes and SLH, temperature, and evapotranspiration variations to provide a stress index model which can be used to flag future threatening climate conditions that may lead to mangrove dieback. We believe this to be an essential component of any coastal monitoring system, especially for similar tropical conditions.17. The mangrove stands dominated by Avicennia marina with seaward patches of Rhizophora stylosa trees and under-canopy occurrence of the shrub species Aegialitis annulata. The 2015–2016 dieback was most severe in the fringing mangrove communities with A. marina particularly impacted, while riverine and estuarine stands experienced the lowest levels of dieback3. The satellite-based Landsat Thematic Mapper (TM), enhanced TM and operational land imager provide 25 m spatial resolution observation of mangrove fractional green cover every 16 days (cloud permitting) since the late 1980s23. The vegetation cover is estimated at sampling sites that are randomly located within the maximum historical extent of the mangroves  constrain a randomly generated sampling dataset and (ii) ensure that sampling does not capture any non-target vegetation species. The random points dataset is used to position 42 sampling sites , moderate (60–79%), and low (30–59%) severity based on the relative FCC change calculated between pre-dieback (preceding dry season of 2015) and post-dieback (following wet season of 2015–2016). Outliers are excluded from the time-series using a rolling mean to detect data points that exceeded more than 20% FCC change from the three-month average. All fractional cover products are extracted using DEA’s sandbox platform using notebook and tool repositories developed by the DEA, and the products are accessed from the open data26.The Gulf of Carpentaria is a large, shallow inlet from the Arafura Sea with limited coastal development and industry. Its shoreline consists of coastal plains of very low topographic relief with extensive saltpans, saltmarsh, and a mix of fringing, riverine, estuarine, and basin mangroves that grow along the seaward margin of the intertidal zone27. As the daily mean tidal residual of the SLH are similar across the stations in Gulf28, it is reasonable to use a single tide-gauge location as representative of SLH in the Gulf of Carpentaria. Monthly SLH variations are monitored with observations from the Australian Bureau of Meteorology (BOM) Milner Bay, Groote Eylandt tide-gauge station  Interim reanalysis (ERA-I)31, which are available globally on a 33 is used to examine the evaporative stress along the Gulf’s coast. This index is the normalized ratio of evapotranspiration to potential evapotranspiration, which are derived from the outputs of the AWRA-L landscape water balance model over Australia (version 6) and indicative of surface moisture supply and evaporative demand34. The rainfall, Monthly rainfall and maximum surface air temperature  and include in our composite of the previous strong La Niña events but flip the sign of the anomalies. Based on this index, we identify the austral summers of 1957–1958, 1965–1966, 1972–1973, 1982–1983, 1987–1988, 1991–1992, 1997–1998, 2015–2016 as strong El Niño years and 1973–1974, 1975–1976, 1988–1989, 1998–1999, 1999–2000, 2007–2008, 2010–2011 as strong La Niña years. The Interdecadal Pacific Oscillation (IPO) is monitored using SST-based IPO tripole index (TPI)36.The anomalies are calculated by removing the seasonal cycle  from the variable and the linear trends are removed from the anomalous time-series. The anomalies are standardized by their own standard deviation to obtain the normalized anomalies. We identify previous strong El Niño Southern Oscillation (ENSO) events as when the oceanic Niño3.4 index exceeds 1.5 standard deviations in the November–January season, when El Niño conditions typically peak in central to the eastern equatorial Pacific. This Niño3.4 index is an average of sea surface temperature (SST) over the central Pacific region (1). The seasonal variation of the mangrove FCC is The monthly time-series of the mangrove FCC in the three dieback categories for over 30 years are displayed in Fig. r Figure . During 28.Considering the coincidence of the strong El Niño of 2015–2016 with the 2015 dieback event, we examine the variation of different climate variables during the 2015–2016 El Niño and compare them with previous strong El Niño conditions. This provides a framework for understanding the extreme climate conditions that might have stressed the mangroves during 2015–2016. Figure 18.During the 2015–2016 El Niño, the Niño3.4 SST index was 50% stronger compared to the composite at its peak in December 2015. It started to become positive earlier since the preceding austral winter and lasted later into the following autumn. A strong positive We further explore the historical relationship between SLH and mangrove FCC and their association with El Niño conditions in the equatorial Pacific using cross-spectral analysis between SLH and FCC. This analysis is useful for quantifying the relationship between two variables as a function of frequency and estimating their phase relationship. We compute the coherence squared by Fourier transforming the deseasonalized monthly time-series for 1994–2016, which provides a bandwidth of 1/276 The top panels of Fig. A similar quantification of the relationship between Gulf’s mangrove FCC and coastal Gulf region area-averaged ESI is shown at the bottom panels of Fig. l Figure  and unli37). This sensitivity of FCC to the lower frequency component of ENSO presumably arises for two reasons. First, the lower frequency component of ENSO drives a stronger variation of SLH in the Gulf than it does by the typical interannually varying El Niño event. This is confirmed by examining the coherence of SLH in the Gulf with SLH at every grid point for the dominant frequency band . The three El Niño events shown in Fig. 41. The most dramatically different behavior of the 2015–2016 El Niño is the much stronger negative SLH anomalies during austral autumn and winter, when the anomalies were more than 12% stronger than the previous two very strong El Niño events. The extreme sea level decline in 2015 coincided with the seasonal minimum, so potentially exposed the mangroves to unprecedented low SLH.To gain insight into the SLH variation during El Niños, we look at the timing of the SLH decline during 2015–2016 and compare that with other strong El Niño events. Figure The extremity of the low SLH during 2015 is quantified by computing the cumulative deficit of detrended SLH. It is done for both cumulative anomaly and cumulative anomaly below the climatological minimum (assumed to be the threshold below which the mangroves cannot be sustained). We show this for the 1997–1998  SLH was the key driver of the mangrove dieback event. A persistent drop in SLH associated with El Niño likely established a stressful environment for the mangroves, especially during the dry months of the year . Additionally, the evaporative stress, as quantified by the ESI, appears to be vital for the mangroves as does To understand the combined role of the potential climate variables on the dieback event, we reconstruct the mangrove FCC anomalies for each of the dieback categories using SLH, ESI and mentclass2pt{minim44) and climate model projections, and it will form the focus of future work.The correlations between estimated and observed FCC range between 0.47 and 0.62. Relatively higher correlations are noted for the high dieback, suggesting adequate skill of the regression model in representing the vulnerable mangrove FCC. The reconstructed FCC also shows an unprecedented reduction in FCC during 2015, though the estimated decrease is much weaker than the observed. Nevertheless, the analysis in Fig. We investigate the key climate conditions during the record-breaking 2015 mangrove dieback event along the southern coastline of the Gulf of Carpentaria using available observations and reanalysis datasets. The decline in SLH associated with the positive phase of ENSO is shown to be primarily responsible for the dieback event. The eastward shift of the equatorial Walker circulation during the 2015–2016 El Niño drove a decline in sea level throughout the Gulf of Carpentaria, which potentially exposed the mangroves to hostile conditions. Our analysis over the period 1994–2018 suggests that low-frequency SLH variability (i.e. a period longer than 2 years) associated with ENSO explains about 50% variation of the observed low-frequency mangrove FCC variation. Slowly varying FCC is shown to be more strongly associated with the lower frequency  component of ENSO than its higher frequency interannual components.The phase lag between SLH change and the variation of mangrove FCC (FCC declines after the SLH drop occurs) indicates that FCC reduces about 5–6 months after the persistent drop in SLH. During 2015, a much stronger SLH drop, about 12% stronger than previous strong El Niño events, and much earlier onset of the SLH decline  occurred during June–July 2015 when SLH was seasonally at its lowest, and that potentially exposed the mangroves to unprecedented lower water availability conditions. A prolonged period of low SLH coupled with stronger dry and warmer conditions during the 2015–2016 El Niño resulted in a highly stressful environment, caused the mass dieback of the mangroves.45. This synchronous wider influence reflects the rapid adjustment of SLH along the north and west coasts of Australia in response to El Niño . The below normal SLH that began in 2014 due to weak El Niño conditions in that year were further lowered in the early and middle months of 2015 by the strong westerly wind anomalies around the equatorial western Pacific leading to critically below-normal SLH in the Gulf of Carpentaria during the dry season of 2015. The mechanism behind this remote response of sea level during El Niño in the Gulf , along the Western Australia coast is well established and what set the 2015 El Niño apart from previous events. This event was distinguished by its strong amplitude and its early onset.The drop in SLH in the Gulf in 2015 was also associated with mangrove FCC reduction and dieback across the wider northern coastline of Australia3. There was a detectable impact of the 1997–1998 El Niño on the FCC at a few sites, causing Rhizophora stylosa, sustained extensive dieback in 2015, while other unaffected sites occurred in relatively broad and lush areas next to tidal creeks. The remaining dieback sites were at least a few meters (60–200 m) away from the shoreline and had minimal elevation above the mean SLH. These contrasting patterns of dieback at fine-scales emphasize the importance of the individual hydro-geomorphic settings  and hydroperiod  as important factors in determining the severity of mangrove dieback. Further assessment of the site-scale geomorphic variation is beyond the scope of this present study.The severity of the 2015 mangrove dieback in the Gulf of Carpentaria is found to differ with geographical location, primarily due to distance to the shoreline, local elevation, and distance from catchment drainage channels1 that the 2015 mangrove dieback event was primarily driven by large-scale stressors , however, regional variation of mangrove species, inundation period, distance to drainage channels influenced extent of the dieback. This dieback event has a number of similarities with the 2000 saltmarsh dieback on the coast of Louisiana, USA in that multiple drivers  resulted in severe dieback of saltmarsh vegetation with severity determined by micro-topographical differences47. With the global warming48, and the changing features of ENSO49, mangroves are likely to experience further stress in coming decades18. The future investigation could include an examination of changes in the large-scale climate stressors in climate model projections to determine if there is an increased likelihood of experiencing mangrove stress events similar to 2015 in the Gulf of Carpentaria in a warming climate.It may be inferred from our analyses and preliminary assessment of Duke et al.Supplementary Figures.