Extreme Sea Surface Temperature Research Articles

Trend and interannual variability of summer marine heatwaves in the tropical Indian ocean: Patterns, mixed layer heat budget, and seasonal prediction

Marine heatwaves (MHWs) are extreme sea surface temperature (SST) events in all ocean basins, with far-reaching impacts on marine ecosystems and socio-economy. The leading patterns, trend, and interannual variability of summer MHWs in the tropical Indian Ocean (TIO) are investigated in this study. The first empirical orthogonal function (EOF) mode of frequency of MHWs exhibits a monopole pattern over the entire basin. This mode is highly associated with the concurrent Indian Ocean Basin warming, indicating a remarkable trend over the past four decades. The linear trend in MHW properties largely relates to increased summer Indian Ocean mean SST. The second EOF mode exhibits a zonal dipole with the MHW numbers increasing in the west and decreasing in the east. On the interannual timescale, the first two EOF modes are remotely affected by antecedent and concurrent El Niño events, respectively. The ocean mixed layer budget is utilized for examining the formation of different summer MHW patterns. During the preceding spring, the surface heat flux is important for the development of MHWs, while the ocean advections play a secondary role in the South Indian Ocean for the MHW monopole. Once the SST anomaly rises in summer, the ocean advections play a dominant role in maintaining the SST. Last, we assess the prediction skill of summer TIO MHWs by performing a bilinear seasonal statistical prediction model. Our results suggest the frequency of summer MHWs in the TIO could be predicted one season in advance. This study has great implications for understanding and predicting ocean extreme events in the TIO.

Future Changes in the Intensity and Duration of Marine Heat and Cold Waves: Insights from Coupled Model Initial-Condition Large Ensembles

Abstract The future evolution of sea surface temperature (SST) extremes is of great concern, not only for the health of marine ecosystems and sustainability of commercial fisheries, but also for precipitation extremes fueled by moisture evaporated from the ocean. This study examines the projected influence of anthropogenic climate change on the intensity and duration of monthly SST extremes, hereafter termed marine heat waves (MHWs) and marine cold waves (MCWs), based on initial-condition large ensembles with seven Earth system models. The large number of simulations (30–100) with each model allows for robust quantification of future changes in both the mean state and variability in each model. In general, models indicate that future changes in variability will cause MHW and MCW events to intensify in the northern extratropics and weaken in the tropics and Southern Ocean, and to shorten in duration in many areas. These changes are generally symmetric between MHWs and MCWs, except for the longitude of duration change in the tropical Pacific and sign of duration change in the Arctic. Projected changes in ENSO account for a large fraction of the variability-induced changes in MHW and MCW characteristics in each model and are responsible for much of the intermodel spread as a result of differences in future ENSO behavior. The variability-related changes in MHW and MCW characteristics noted above are superimposed upon large mean-state changes. Indeed, their contribution to the total change in SST during MHW and MCW events is generally <10% except in polar regions where they contribute upward of 50%.

A Glimpse into the Future: The 2023 Ocean Temperature and Sea Ice Extremes in the Context of Longer-Term Climate Change

Abstract In the year 2023, we have seen extraordinary extrema in high sea surface temperature (SST) in the North Atlantic and in low sea ice extent in the Southern Ocean, outside the 4σ envelope of the 1982–2011 daily time series. Earth’s net global energy imbalance (12 months up to September 2023) amounts to +1.9 W m−2 as part of a remarkably large upward trend, ensuring further heating of the ocean. However, the regional radiation budget over the North Atlantic does not show signs of a suggested significant step increase from less negative aerosol forcing since 2020. While the temperature in the top 100 m of the global ocean has been rising in all basins since about 1980, specifically the Atlantic basin has continued to further heat up since 2016, potentially contributing to the extreme SST. Similarly, salinity in the top 100 m of the ocean has increased in recent years specifically in the Atlantic basin, and in addition in about 2015 a substantial negative trend for sea ice extent in the Southern Ocean began. Analyzing climate and Earth system model simulations of the future, we find that the extreme SST in the North Atlantic and the extreme in Southern Ocean sea ice extent in 2023 lie at the fringe of the expected mean climate change for a global surface-air temperature warming level (GWL) of 1.5°C, and closer to the average at a 3.0°C GWL. Understanding the regional and global drivers of these extremes is indispensable for assessing frequency and impacts of similar events in the coming years.

Marine heatwaves and cold spells in the Brazil Overshoot show distinct sea surface temperature patterns depending on the forcing

The Brazil Current Overshoot regions feature the strongest sea surface temperature variations over the global ocean but forecast skill for these extreme variations is limited. Here we investigate the drivers of marine heatwaves and cold spells in this region and find that ocean flows and sea surface heat fluxes are the primary drivers. We also find that sea surface temperature extremes driven by air-sea heat fluxes produce consistent changes in sea surface temperature and related atmospheric variables over a wide horizontal area, whereas ocean flow-driven sea surface temperature extremes exhibit low consistency in air variables and limited consistency in the spatial extent of sea variables across most sea surface temperature extreme events. These differing air-sea characteristics associated with extremes driven by different drivers offer potential avenues for improving prediction skills and advancing the understanding of three-dimensional air-sea impacts associated with extreme events.

Typhoon-Induced Extreme Sea Surface Temperature Drops in the Western North Pacific and the Impact of Extra Cooling Due to Precipitation

Sea surface temperature (SST) responses have been perceived as crucial to consequential tropical cyclone (TC) intensity development. In addition to regular cooling responses, a few TCs could cause extreme SST drops (ESSTDs) (e.g., SST drops more than 6 °C) during their passage. Given the extreme temperature differences and the consequentially marked air–sea flux modulations, ESSTDs are intuitively supposed to play a serious role in modifying TC intensities. Nevertheless, the relationship between ESSTDs and consequential storm intensity changes remains unclear. In this study, satellite-observed microwave SST drops and the International Best Track Archive for Climate Stewardship TC data from 2001 to 2021 were used to elucidate the relationship between ESSTDs and the consequential TC intensity changes in the Western North Pacific typhoon season (July–October). Subsequently, the distributed characteristics of ESSTDs were systematically examined based on statistical analyses. Among them, Typhoon Kilo (2015) triggered an unexpected ESSTD behind its passage, according to existing theories. Numerical experiments based on the Regional Ocean Modeling System were carried out to explore the possible mechanisms that resulted in the ESSTD due to Kilo. The results indicate that heavy rainfall leads to additional SST cooling through the enhanced sensible heat flux leaving the surface layer in addition to the cooling from momentum-driven vertical mixing. This process enhanced the sensible heat flux leaving the sea surface since the temperature of the raindrops could be much colder than the SST in the tropical ocean, specifically under heavy rainfall and relatively less momentum entering the upper ocean during Kilo.

Karadeniz'de 2000-2022 Yılları Arasında Yüzey Sıcaklığının Mekansal ve Zamansal Değişimi

This study presents a comprehensive assessment of the spatio-temporal variability of Sea Surface Temperature (SST) in the Black Sea at monthly to interannual scales, with a focus on understanding its connection to major large-scale atmospheric forcing during the period 2000-2022. Monthly variations of SST in the Black Sea reveal distinct seasonal patterns. The study evaluates the potential impacts of large-scale atmospheric patterns on interannual SST variations using climate indices such as the North Atlantic Oscillation (NAO), East Atlantic/West Russia (EA-WR) and El Niño-Southern Oscillation (ENSO) during the winter months. The results indicated that these large-scale atmospheric oscillations played a substantial role in influencing SST anomalies, with the NAO and EA-WR indices particularly affecting the Black Sea's SST anomalies. The NAO index exhibited negative values during warm winters and positive values during cold winters, with extreme cold and warm winters corresponding to specific years, as observed in 2003, 2006, 2012, 2017 (cold) and 2018, 2020, 2021 (warm). Notably, the relationship between NAO and SST anomalies was not as dominant during 2000-2022. This difference might be explained by the combined influence of NAO and ENSO, which is beyond the scope of this study. The EA-WR pattern was identified as another significant large-scale atmospheric dynamic affecting the Black Sea's SST. Although it explains the cold SST anomalies in certain years, it cannot account for extreme warm SST years. While the influence of ENSO remains somewhat inconclusive for the extreme warm period, the SST pattern between 2016-2022 aligns closely with El Niño events, particularly in 2018 and 2021 when positive SOI index values coincide with warm SST years in the Black Sea.

Vecchia Likelihood Approximation for Accurate and Fast Inference with Intractable Spatial Max-Stable Models

Max-stable processes are the most popular models for high-impact spatial extreme events, as they arise as the only possible limits of spatially-indexed block maxima. However, likelihood inference for such models suffers severely from the curse of dimensionality, since the likelihood function involves a combinatorially exploding number of terms. In this article, we propose using the Vecchia approximation, which conveniently decomposes the full joint density into a linear number of low-dimensional conditional density terms based on well-chosen conditioning sets designed to improve and accelerate inference in high dimensions. Theoretical asymptotic relative efficiencies in the Gaussian setting and simulation experiments in the max-stable setting show significant efficiency gains and computational savings using the Vecchia likelihood approximation method compared to traditional composite likelihoods. Our application to extreme sea surface temperature data at more than a thousand sites across the entire Red Sea further demonstrates the superiority of the Vecchia likelihood approximation for fitting complex models with intractable likelihoods, delivering significantly better results than traditional composite likelihoods, and accurately capturing the extremal dependence structure at lower computational cost. Supplementary materials for this article are available online.

Likelihood-Free Parameter Estimation with Neural Bayes Estimators

Neural Bayes estimators are neural networks that approximate Bayes estimators. They are fast, likelihood-free, and amenable to rapid bootstrap-based uncertainty quantification. In this article, we aim to increase the awareness of statisticians to this relatively new inferential tool, and to facilitate its adoption by providing user-friendly open-source software. We also give attention to the ubiquitous problem of estimating parameters from replicated data, which we address using permutation-invariant neural networks. Through extensive simulation studies we demonstrate that neural Bayes estimators can be used to quickly estimate parameters in weakly identified and highly parameterized models with relative ease. We illustrate their applicability through an analysis of extreme sea-surface temperature in the Red Sea where, after training, we obtain parameter estimates and bootstrap-based confidence intervals from hundreds of spatial fields in a fraction of a second.

Comparison of the opposite behaviours of Korean heatwaves with extreme hot sea surface temperatures in August 2016 and 2022

AbstractIn South Korea, an unprecedented heatwave occurred in August 2016. That year, sea surface temperature (SST) in the Korean marginal seas (KMS) was remarkably higher than that in climatology, and upper‐level anticyclonic circulations favoured this heatwave. More recently, similar conditions that can expect the frequent occurrence of heatwaves prevailed in August 2022. However, the opposite extreme phenomenon, heatwave days below climatology, occurred with the emergence of a stationary rainband in South Korea. This study aims to understand the two different behaviours of heatwaves in Korea under the same hot SST conditions in the KMS. The most pronounced difference between these two extreme cases is the existence of moisture flux related to the western North Pacific summer monsoon (WNPSM). Although the WNPSM is not generally highly correlated with temperature and precipitation in Korea, their relationship is particularly strong when hot SST conditions develop in the KMS. This SST condition, combined with the WNPSM, plays a role in determining this anomalous moisture flux in Korea. Enough tropical moisture flux occurred to build a stationary front with declining heatwave days during August 2022. In contrast, extremely dry and hot weather was more likely in 2016 due to inactive moisture fluxes from the western North Pacific.

Analysis of Extreme Sea Surface Temperature along the Western Coastal area of Chungnam: Current Status and Future Projections

Analysis of Extreme Sea Surface Temperature along the Western Coastal area of Chungnam: Current Status and Future Projections

Summer heatwaves affect coastal Antarctic plankton metabolism and community structure

In the austral summer of 2020, record high temperatures were registered in the Western Antarctic Peninsula. This offered a unique opportunity to evaluate the effect of extreme sea surface temperature and natural heatwaves on the metabolic balance (i.e. the balance between production and respiration), lipid damage and the possible change in lipid composition of coastal Antarctic (Potter Cove, King George/25 de Mayo Island, South Shetlands) microbial communities. Two marine heatwaves, one in January and a second one in February 2020 showed mean temperatures of 1.8 °C above the 20-year climatology values. During the first heatwave, a rapid and strong (60%) decrease in microbial community biomass was observed, Criptophyceae were replaced by unidentified nanophytoflagellates and other heterotrophic groups. The community experienced an increase in heterotrophic metabolism via an increase in community respiration (CR, 12.79 mmolO2 m−3d−1) and a negative net community production (NCP, −14.9 ± 0.31 mmolO2 m−3d−1), leading to a production:respiration (P:R) rate < 1. Additionally, the most representative fatty acids (FAs: 14:0, 16:0, 18:0, 16:1ω9, 18:1ω9, 18:2ω6 and 18:3ω3) decreased, except for the monounsaturated FAs (MUFA) 16:1ω9 and 18:1 ω9, which increased during this first heatwave. In the second marine heatwave, total biomass dropped to the minimum values reported during the entire study. Here, CR was at its maximum (25.09 mmolO2 m−3d−1), but NCP was also positive (1.96 mmolO2 m−3d−1) and P:R > 1, associated with an active autotrophic community. Again, significant lipid damage, a decrease in saturated FAs and ⍵6 polyunsaturated FAs, and an increase in MUFAs occurred. This field study validates previous experimental results on changes in natural plankton composition and physiology under global warming scenarios.

Tailored Forecasts Can Predict Extreme Climate Informing Proactive Interventions in East Africa

AbstractThis commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision‐making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring &gt;$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution‐based insights can be combined with the latest dynamical models to predict droughts at 8‐month lead‐times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro‐pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization “Early Warning for All” Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African‐led EWS, and building better links between EWS and agricultural development efforts can support long‐term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross‐silo collaborations.

Signs of local adaptation by genetic selection and isolation promoted by extreme temperature and salinity in the Mediterranean seagrass Posidonia oceanica.

Adaptation to local conditions is known to occur in seagrasses; however, knowledge of the genetic basis underlying this phenomenon remains scarce. Here, we analysed Posidonia oceanica from six sites within and around the Stagnone di Marsala, a semi-enclosed coastal lagoon where salinity and temperature exceed the generally described tolerance thresholds of the species. Sea surface temperatures (SSTs) were measured and plant samples were collected for the assessment of morphology, flowering rate and for screening genome-wide polymorphisms using double digest restriction-site-associated DNA sequencing. Results demonstrated more extreme SSTs and salinity levels inside the lagoon than the outer lagoon regions. Morphological results showed significantly fewer and shorter leaves and reduced rhizome growth of P. oceanica from the inner lagoon and past flowering events were recorded only for a meadow farthest away from the lagoon. Using an array of 51,329 single nucleotide polymorphisms, we revealed a clear genetic structure among the study sites and confirmed the genetic isolation and high clonality of the innermost site. In all, 14 outlier loci were identified and annotated with several proteins including those relate to plant stress response, protein transport and regulators of plant-specific developmental events. Especially, five outlier loci showed maximum allele frequency at the innermost site, likely reflecting adaptation to the extreme temperature and salinity regimes, possibly due to the selection of more resistant genotypes and the progressive restriction of gene flow. Overall, this study helps us to disentangle the genetic basis of seagrass adaptation to local environmental conditions and may support future works on assisted evolution in seagrasses.

Extreme cooling of 12.5 °C triggered by Typhoon Fungwong (2008)

Typhoon Fungwong, a category-2 but deadly typhoon in the 2008 Pacific typhoon season, made landfall first on Taiwan and then on Southeast China. During its approach toward Taiwan, it triggered an extreme sea surface temperature (SST) drop of 12.5 °C, which was the strongest SST drop recorded by Longdong buoy northeast of Taiwan coast from 1998 to 2017. In this study, including moored buoy temperature measurements, Argo float temperature profiles, satellite-observed SSTs, and a suite of numerical experiments performed using the Regional Ocean Modeling System were used to unveil the detailed processes of how Fungwong triggered such an extreme cooling. Subsequently, the source of the cold waters feeding the extreme cooling, possible mechanisms triggering the cooling, and consequential effects of cooling on the ambient ocean environment were systematically investigated. Results show that the extreme cooling was triggered mainly by a process of uplift of subsurface cold water tied to shore-ward Kuroshio intrusion driven by easterly/northeasterly winds and consequential entrainment mixing, while coastal upwelling driven by persistent longshore (southerly) winds plays a minor role. Nevertheless, the southerly winds still help the enhancement of entrainment mixing and thus the sea surface cooling. Finally, modeled float trajectories with temperature tracers identified where the cold water goes and indicate that the temperature drop might extend all the way toward the south end of Japan (Kyushu) along the flowing path of Kuroshio.

Assessing Predictability of Marine Heatwaves With Random Forests

AbstractMarine heatwaves (MHWs) have increased in frequency and duration over the last century and are expected to intensify in the future. Such events have become an increasing threat for marine ecosystems and subsequently the economies and populations that rely on them. Here we apply random forests to assess skill in forecasting MHWs onset and severity at multiple prediction lead times. Random forests models are trained on a range of atmospheric and oceanic conditions to identify precursors of MHWs. The best performing random forest model accurately captures (76%) MHW presence/absence in the northeast Pacific and is capable of forecasting realistic extreme sea surface temperature patterns at weekly lead times. However, the total accuracy drops to 38% when forecasting MHW severity. Machine learning algorithms affirm further exploration as forecasting tools and have the potential to accelerate our predictive ability and preparedness against upcoming extreme climate changes.

Probabilistic extreme SST and marine heatwave forecasts in Chesapeake Bay: A forecast model, skill assessment, and potential value

We test whether skillful 35-day probabilistic forecasts of estuarine sea surface temperature (SST) are possible and whether these forecasts could potentially be used to reduce the economic damages associated with extreme SST events. Using an ensemble of 35-day retrospective forecasts of atmospheric temperature and a simple model that predicts daily mean SST from past SST and forecast atmospheric temperature, we create an equivalent ensemble of retrospective SST forecasts. We compare these SST forecasts with reference forecasts of climatology and damped persistence and find that the SST forecasts are skillful for up to two weeks in the summer. Then, we post-process the forecasts using nonhomogeneous Gaussian regression and assess whether the resulting calibrated probabilistic forecasts are more accurate than the probability implied by the raw model ensemble. Finally, we use an idealized framework to assess whether these probabilistic forecasts can valuably inform decisions to take protective action to mitigate the effects of extreme temperatures and heatwaves. We find that the probabilistic forecasts provide value relative to a naive climatological forecast for 1-2 weeks of lead time, and the value is particularly high in cases where the cost of protection is small relative to the preventable losses suffered when a heatwave occurs. In most cases, the calibrated probabilistic forecasts are also more valuable than deterministic forecasts based on the ensemble mean and naive probabilistic forecasts based on damped persistence. Probabilistic SST forecasts could provide substantial value if applied to adaptively manage the rapid impacts of extreme SSTs, including managing the risks of catch-and-release mortality in fish and Vibrio bacteria in oysters.

Responses of Satellite Chlorophyll-a to the Extreme Sea Surface Temperatures over the Arabian and Omani Gulf

Extreme events such as Marine Heat Waves (MHWs) and Low Chlorophyll-a (LChl-a) in the ocean have devastating impacts on the marine environment, particularly when they occur simultaneously (i.e., the compound of MHWs and LChl-a events). In this study, we investigate the spatiotemporal variability of MHWs and LChl-a events in the Arabian and Omani Gulf. For this purpose, we used satellite-based high-resolution observations of SST (0.05° × 0.05°; from 1982 to 2020) and chlorophyll-a concentration data (0.04° × 0.04°; from 1998 to 2020). Hourly air temperature, wind, and heat flux components from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5) were used to explain the link between these extreme events and atmospheric forcings. Moreover, our results revealed that the annual frequency of MHW and LChl-a is related to the El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). The results revealed an average SST warming trend of about 0.44 ± 0.06 °C/decade and 0.32 ± 0.04 °C/decade for the Arabian Gulf (AG) and the Gulf of Oman (OG), respectively. This warming rate was accompanied by MHW frequency and duration trends of 0.97 events/decade and 2.3 days/decade, respectively, for the entire study region from 1982 to 2020. The highest annual MHW frequencies were recorded in 2010 (6 events) and 2020 (5 events) associated with LChl-a frequency values of 4 and 2, respectively. La Niña events in 1999, 2010, 2011, and 2020 were associated with higher frequencies of MHW and LChl-a. The positive phase of IOD coincides with high MHW frequency in 2018 and 2019. The longest compound MHW and LChl-a event with a duration of 42 days was recorded in 2020 at OG. This extreme compound event was associated with wind stress reduction. Our results provide initial insights into the spatiotemporal variability of the compound MHW and LChl-a events that occurred in the AG and OG.

Transgenerational exposure to marine heatwaves ameliorates the lethal effect on tropical copepods regardless of predation stress.

Marine heatwaves (MHWs) are emerging as a severe stressor in marine ecosystems. Extreme warm sea surface temperatures during MHWs often exceed the optimal thermal range for more than one generation of tropical coastal zooplankton. However, it is relatively unknown whether transgenerational plasticity (TGP) to MHWs may shape the offspring's fitness, particularly in an ecologically relevant context with biotic interactions such as predation stress. We addressed these novel research questions by determining the survival, reproductive success, and grazing rate of the copepod Pseudodiaptomus incisus exposed to MHW and fish predator cues (FPC) for two generations (F1 and F2). The experiment was designed in a full orthogonal manner with 4 treatments in F1 and 16 treatments in F2 generation. In both generations, MHW reduced P. incisus survival, reproductive parameters, and grazing by 10%–62% in MHW, but these parameters increased by 2%–15% with exposure to FPC, particularly at control temperature. F2 reproductive success and grazing rate as indicated by cumulative fecal pellets were reduced by 20%–30% in F1‐MHW, but increased by ~2% in F1‐FPC. Strikingly, MHW exposure reduced 17%–18% survival, but transgenerational exposure to MHWs fully ameliorated its lethal effect and this transgenerational effect was independent of FPC. Increased survival came with a cost of reduced reproductive success, constrained by reduced grazing. The rapid transgenerational MHW acclimation and its associated costs are likely widespread and crucial mechanisms underlying the resilience of coastal tropical zooplankton to MHWs in tropical coastal marine ecosystems.

Role of Sea Surface Physical Processes in Mixed-Layer Temperature Changes During Summer Marine Heat Waves in the Chile-Peru Current System.

We identified anomalously warm sea surface temperature (SST) events during 1980–2019 near the major upwelling center at Punta Lavapié in the central Chile‐Peru Current System, using the European Centre for Medium‐Range Weather Forecasts reanalysis and focusing on time scales of 10 days to 6 months. Extreme warm SST anomalies on these time scales mostly occurred in the austral summer, December through February, and had spatial scales of 1000s of km. By compositing over the 37 most extreme warm events, we estimated terms in a heat budget for the ocean surface mixed layer at the times of strongest warming preceding the events. The net surface heat flux anomaly is too small to explain the anomalous warming, even when allowing for uncertainty in mixed‐layer depth. The composite mean anomaly of wind stress, from satellite ocean vector wind swath data, during the 37 anomalous warming periods has a spatial pattern similar to the resulting warm SST anomalies, analogous to previous studies in the California Current System. The weakened surface wind stress suggests reduced entrainment of cold water from below the mixed layer. Within 100–200 km of the coast, the typical upwelling‐favorable wind stress curl decreases, suggesting reduced upwelling of cold water. In a 1000‐km area of anomalous warming offshore, the typical downwelling‐favorable wind stress curl also decreases, implying reduced downward Ekman pumping, which would allow mixed‐layer shoaling and amplify the effect of the positive climatological summertime net surface heat flux.

The roles of global warming and Arctic Oscillation in the winter 2020 extremes in East Asia

The 2019/20 winter was extremely warm globally and in the Northern Hemisphere extratropics. The main cause of climate extremes particularly in East Asia, was the extreme positive Arctic Oscillation (AO) event superimposed on steady global warming. The negligible trend in the AO over the preceding 41 years makes it possible to distinguish the roles of AO and global warming in the observed extremes. We estimate and compare contributions to January–March 2020 climate extremes by the AO and global warming represented by local temperature trends using the ERA5 reanalysis data. Based on results from a preliminary study, we estimate the contribution by global warming using linear regression while that by the AO using cubic regression, which is more restrained for the high AO index values than linear. The results show that the temperature extremes were mainly caused by the extreme positive AO event which accounts for approximately 3/4 of the observed temperature anomalies in northern East Asia and 2/3 in eastern East Asia. In southern East Asia, the AO contributes negligibly and positive temperature anomalies are related to global warming and local and regional impacts, particularly extreme sea surface temperature, enhance south-westerlies and local radiative forcing. General conclusion is that the observed strong positive temperature anomalies including extreme anomalies over East Asia could have been achieved only as a combined effect of the extreme positive AO event and global warming. Quantification of the roles of the AO and global warming in climate extremes helps to estimate future anomalies caused by extreme AO events as well as assess uncertainties in climate model projections.