Temperature Anomalies Research Articles

Equatorial Western–Central Pacific SST Responsible for the North Pacific Oscillation–ENSO Sequence

Abstract El Niño–Southern Oscillation (ENSO), the dominant mode of interannual variability in the tropical Pacific, is well known to affect the extratropical climate via atmospheric teleconnections. Extratropical atmospheric variability may in turn influence the occurrence of ENSO events. The winter North Pacific Oscillation (NPO), as the secondary dominant mode of atmospheric variability over the North Pacific, has been recognized as a potential precursor for ENSO development. This study demonstrates that the preexisting winter NPO signal is primarily excited by sea surface temperature (SST) anomalies in the equatorial western–central Pacific. During ENSO years with a preceding winter NPO signal, which accounts for approximately 60% of ENSO events observed in 1979–2021, significant SST anomalies emerge in the equatorial western–central Pacific in the preceding autumn and winter. The concurrent presence of local convection anomalies can act as a catalyst for NPO-like atmospheric circulation anomalies. In contrast, during other ENSO years, significant SST anomalies are not observed in the equatorial western–central Pacific during the preceding winter, and correspondingly, the NPO signal is absent. Ensemble simulations using an atmospheric general circulation model driven by observed SST anomalies in the tropical western–central Pacific can well reproduce the interannual variability of observed NPO. Therefore, an alternative explanation for the observed NPO–ENSO relationship is that the preceding winter NPO is a companion to ENSO development, driven by the precursory SST signal in the equatorial western–central Pacific. Our results suggest that the lagged relationship between ENSO and the NPO involves a tropical–extratropical two-way coupling rather than a purely stochastic forcing of the extratropical atmosphere on ENSO.

The enduring nature of cranberry production in a changing climate: The interplay of extreme weather, knowledge networks, and adaptation

Anthropogenic climate change is causing critical issues in agriculture, including cranberry production; however, in a previous study the majority of Massachusetts cranberry growers were less likely to see global warming as a threat than the general US population, and more still reported to be little worried about such warming. This research aims to determine the influence of weather and professional information disseminated to cranberry growers on their climate change adaptation. The authors used a mixed-methods, interdisciplinary approach, including content analysis of around 300 issues of UMass Extension’s Cranberry Station monthly newsletter—a widely trusted source of information in the cranberry grower “network of knowledge”—along with historical weather data from 1974 to 2020, and interview and survey data. Despite infrequent usage of direct communication on climate change and adaptation, UMass Extension’s communication on weather challenges in general showed a small but significant increase when monthly temperature anomalies increased. Meanwhile, anomalous monthly precipitation was negatively associated with total chemical mentions (linked to chemical use behavior). Climate impacts such as increased weeds, water issues (e.g., heavy rainfall, water scarcity), and heat waves ruining harvests were important to growers, leading to the adoption of both conventional (e.g., increased strategic flooding, barge sanding due to lack of ice) and emerging (e.g., smart irrigation, solar panel installations for added income) adaptive strategies. Growers demonstrate climate and technological optimism, believing that other growers are hit worse by climate change than themselves and that cranberry plant resilience, better weather forecasting, and improved irrigation technology will allow them to handle future weather challenges. The prioritization of immediate needs over the more abstract, long-term challenge of climate change by growers and the supporting system underscores the imperative to explore the socio-environmental dynamics that shape their climate responses in cranberry production.

Coherency and time lag analyses between MODIS vegetation indices and climate across forests and grasslands in the European temperate zone

Abstract. Identifying the climate-induced variability in the condition of vegetation is particularly important in the context of recent climate change and plants' impact on the mitigation of climate change. In this paper, we present the coherence and time lags in the spectral response of three individual vegetation types in the European temperate zone to the influencing meteorological factors in the period 2002–2022. Vegetation condition in broadleaved forest, coniferous forest and pastures was measured with monthly anomalies of two spectral indices – normalised difference vegetation index (NDVI) and enhanced vegetation index (EVI). As meteorological elements we used monthly anomalies of temperature (T), precipitation (P), vapour pressure deficit (VPD), evapotranspiration (ETo), and the teleconnection indices North Atlantic Oscillation (NAO) and North Sea Caspian Pattern (NCP). Periodicity in the time series was assessed using the wavelet transform, but no significant intra- or interannual cycles were detected in both vegetation (NDVI and EVI) and meteorological variables. In turn, coherence between NDVI and EVI and meteorological elements was described using the methods of wavelet coherence and Pearson's linear correlation with time lag. In the European temperate zone analysed in this study, NAO produces strong coherence mostly for forests in a circa 1-year band and a weaker coherence in a circa 3-year band. For pastures these interannual patterns are hardly recognisable. The strongest relationships occur between conditions of the vegetation and T and ETo – they show high coherence in both forests and pastures. There is a significant cohesion with the 8–16-month (ca. 1-year) and 20–32-month (ca. 2-year) bands. More time-lagged significant correlations between vegetation indices and T occur for forests than for pastures, suggesting a significant lag in the forests' response to the changes in T.

Decadal preference of seasonal ENSO transition through a southern hemisphere climate mode

El Niño Southern Oscillation (ENSO) is the leading interannual coupled climate mode in the tropical Pacific. The seasonal transition of ENSO from boreal winter to the following summer can significantly affect the global climate. One of the major hurdles in understanding the seasonal transition of ENSO is the spring predictability barrier. Here, we show that ENSO’s seasonal transition is modulated by a multidecadal climate mode of boreal spring sea-level pressure (SLP) in the extratropical Southern Hemisphere. This ENSO transition mode (ETM), when characterised by a decrease in SLP and associated clockwise circulation of the surface winds centred over the southeastern subtropical Pacific Ocean, produces westerly anomalies at the equator. These wind anomalies in the equatorial eastern Pacific aid the seasonal warming of Niño3.4 sea surface temperature anomalies (N34SST) from boreal winter to the following summer. The ETM time series shows prominent multidecadal variations at around 50 years. This creates a conducive environment for alternate cold and warm seasonal transitions leading to multidecadal variations in boreal summer N34SST. Thus, ETM provides a physical insight into the seasonal transition of ENSO and leads to a new paradigm for ENSO evolution beyond its peak. This has implications for seasonal ENSO forecasts and decadal climate predictions.

Regional divergent evolution of vegetation greenness and climatic drivers in the Sahel-Sudan-Guinea region: nonlinearity and explainable machine learning

IntroductionThe vegetation dynamics of the Sahel-Sudan-Guinea region in Africa, one of the largest transition zones between arid and humid zones, is of great significance for understanding regional ecosystem changes. However, a time-unvarying trend based on linear assumption challenges the overall understanding of vegetation greenness evolution and of tracking a complex ecosystem response to climate in the Sahel-Sudan-Guinea region.MethodsThis study first applied the ensemble empirical mode decomposition (EEMD) method to detect the time-varying trends in vegetation greenness based on normalized difference vegetation index (NDVI) data in the region during 2001–2020, and then identified the dominant climatic drivers of NDVI trends by employing explainable machine learning framework.ResultsThe study revealed an overall vegetation greening but a significant nonlinear spatio-temporal evolution characteristic over the region. Trend reversals, i.e., browning-to-greening and greening-to-browning, were dominant in approximately 60% of the study area. The browning-to-greening reversal was primarily observed in the southern Sahel, Congo Basin north of the Equator, and East Africa, with a breakpoint around 2008, while the greening-to-browning reversal was mainly observed in West Africa, with a breakpoint around 2011. The sustained greening primarily took place in northern Sahel, Central African Republic and South Sudan; while sustained browning clustered in central West Africa and Uganda, mainly in agricultural lands. Furthermore, the combination of Random Forest (RF) algorithm and the SHapley Additive exPlanations (SHAP) method could robustly model and reveal the relationships between the observed trends in NDVI and in climatic variables, also detected by applying EEMD. The results suggested that air temperature and precipitation were the most important climatic drivers controlling the NDVI trends across the Sahel-Sudan-Guinea region. The NDVI trends were more likely to have negative correlations with solar radiation and vapor pressure deficit in arid areas, while they could have positive correlations in humid areas. The study also found that large-scale climate changes induced by sea surface temperature (SST) anomalies had strong relationships with trend reversals in vegetation greenness at a sub-continental scale. These findings advanced the understanding of the impacts of climatic drivers on vegetation greenness evolution in the Sahel-Sudan-Guinea region.

Air Current Circulation as a Possible Cause of Preseismic Anomalies in the Surface Electric Field

Ground-based measurements of the vertical atmospheric electric field of the Earth sometimes show bay-like anomalies that precede certain earthquakes. In some cases, these anomalies have even been accompanied by a change in the sign of the field under fair weather conditions. Possible causes of this phenomenon are typically attributed to anomalous changes in electrical conductivity in the surface air and increased radon emission from the soil. This paper proposes another mechanism of atmospheric electrical anomalies that involves the entrainment of charged aerosols and light and heavy ions by air flows. Such flows can be produced by small temperature anomalies observed before some seismic events. Theoretical analysis shows that anomalously strong electrical variations may occur even in the presence weak air flows provided that they persist for a long time and there is a specific structure of the velocity field with vertical air circulation and particle exchange between different atmospheric layers. For this type of flows, an analytical solution is derived and spatial distributions of atmospheric electric perturbations are obtained. The results of calculations and estimates confirm that the proposed mechanism can explain the observational data.

Why Does Extreme Precipitation Occur in Borneo During Both El Niño and La Niña Winters?

AbstractExtreme precipitation over the Maritime Continent (MC) is not only vital for local livelihoods, but also influences atmospheric circulation in tropical and extra‐tropical regions. The warm (cold) phase of El Niño‐Southern Oscillation usually leads to dry (wet) condition around the MC in boreal winter. This study unravels that extreme precipitation occurs in Borneo during both El Niño and La Niña winters. Extreme precipitation in Borneo is associated with tripole and dipole anomalous vertical motion pattern, respectively, over the MC‐tropical Pacific in El Niño and La Niña years. A parallel analysis reveals that weak precipitation in Borneo in El Niño and La Niña years occurs with dipole and tripole anomalous vertical motion pattern, respectively, over the MC‐tropical Pacific. The amplitude of the equatorial central‐eastern Pacific sea surface temperature (SST) anomalies may be a factor in the change of anomalous vertical motion pattern over the MC‐tropical Pacific. Further analysis identifies a tendency of extreme precipitation in late December to early−mid January and weak precipitation in February in Borneo. This suggests a critical role of seasonal change in the eastern equatorial Pacific SST in the occurrence of extreme precipitation in Borneo.

Can seasonal prediction models capture the Arctic mid‐latitude teleconnection on monthly time scales?

AbstractThis study explores Arctic warming's effect on Eurasia's temperature variability, notably the warm Arctic–cold Eurasia (WACE) pattern, and assesses seasonal prediction models' accuracy in capturing this phenomenon and its monthly variation. Arctic warming events are categorized into deep Arctic warming (DAW), shallow Arctic warming (SAW), warming aloft (WA), and no Arctic warming (NOAW), based on the temperatures at 2 m and 500 hPa in the Barents–Kara Sea. It is revealed that DAW events are significantly correlated with monthly cold temperature anomalies in East Asia, predominantly occurring in January–February, excluding December. This study evaluates two primary capabilities of seasonal prediction models: their proficiency in forecasting these Arctic warming events, particularly DAW, and their ability to replicate the spatial patterns associated with DAW. Some models demonstrated notable predictive skill for DAW events, with enhanced performance in January and February. Regarding spatial pattern reproduction, models showed limited alignment with the reference dataset over the Northern Hemisphere (above 25° N) in December, whereas a higher degree of concordance was observed in January–February. This indicates their capability in capturing the atmospheric circulation patterns associated with DAW, pointing to areas where model performance can be enhanced.

Beetaloo gas, hydrogen and geothermal resources – insights from 3D basin modelling

The Beetaloo Sub-basin hosts a large proven shale gas resource, and this study demonstrates the opportunities for other low-emission energy sources including geothermal energy and natural hydrogen. CSIRO has developed a 3D basin model that predicts reservoir rock properties, pore pressure, composition of hydrocarbons and gas capacity of the Velkerri and Kyalla shales. Kinetic modelling also demonstrates that where Velkerri shale is overmature for hydrocarbons it potentially generated significant amounts of free molecular hydrogen. However, the proportion of this hydrogen currently preserved in the basin is unknown. The model also reveals considerable temperature anomalies around specific structural domains with heat flow up to 100 mW/m−2, and these are likely associated with radiogenic basement lithologies. These thermal domains are targets to further investigate geothermal and natural hydrogen resource potential in the Beetaloo Sub-basin.

Long-term trend and variability in surface temperatures over Emilia-Romagna from 1962 to 2022

AbstractScientific interest is increasingly drawn towards regional meteorological extremes, given their impacts on populations, infrastructure, and ecosystems. These extremes are shaped by complex interactions between internal climate variability and long-term trends. The aim of the present work is to evaluate changes in high-frequency variability and the influence of long-term trends on the frequency of occurrences of extremes, with a focus on surface temperatures over the period from 1962 to 2022 in Emilia-Romagna, a region of Northern Italy. Daily data of 2 m air temperatures averaged over the region are retrieved from ERACLITO, a high-resolution climate analysis. The distributions of daily temperature anomalies show a general broadening in 1992–2022 with respect to 1962–1991. This is true for maximum, minimum, and mean daily surface temperatures, especially during the summer and spring seasons. A significant warming trend of 0.37 °C/decade is detected in annual mean surface temperatures over the period considered. The study is completed with a comparison between the observed frequency of record-breaking annual temperature events, a hypothetical stationary climate distribution and a theoretical derivation that accounts for changes in trends and variability. During the last decade, the theoretical count of extreme events is 1.26, which yields a likelihood of 86% that this is owed to the trend rather than interannual variability. Idealized experiments demonstrate that the expected occurrences of record-breaking events in future decades depend on the warming rate rather than the warming level. Finally, an analysis performed at seasonal level shows that the majority (minority) of record events are occurring in the summer (spring) seasons.

A Gaussian Function Model of Mesoscale Eddy Temperature Anomalies and Research of Spatial Distribution Characteristics

Mesoscale eddies are ubiquitous oceanic phenomena and play an important role in ocean circulation, ocean dynamics, and the transport of material energy. Temperature anomalies are a crucial parameter that reflects the state of mesoscale eddies. This study proposes a Gaussian function model to fit the vertical temperature anomaly (TA) profile to facilitate the analysis of variations, and the principle of the model is based on the fact that each TA profile tends to fluctuate around one or more peaks. The model is extracted and validated using Argo profiles within cyclonic and anticyclonic eddies in the Northwest Pacific Ocean spanning over the period from 2002 to 2021. The validation demonstrates that the model can accurately recover the vertical TA profiles with a limited number of parameters. This makes it suitable for analysing the spatial distribution patterns that require a large sample count. The analysis indicates that eddies with different TA profiles have a spatial aggregation effect in geographic distribution. Eddies with lower extreme temperature anomalies, at depths of 200–300 m, are mainly distributed along two bands on the north side of the Kuroshio Extension (KE) and the North Equatorial Current. Eddies with extreme TAs at the deepest depth (500–600 m) are distributed along the KE.

Indian Ocean temperature anomalies predict long-term global dengue trends

Despite identifying El Niño events as a factor in dengue dynamics, predicting the oscillation of global dengue epidemics remains challenging. Here, we investigate climate indicators and worldwide dengue incidence from 1990 to 2019 using climate-driven mechanistic models. We identify a distinct indicator, the Indian Ocean basin-wide (IOBW) index, as representing the regional average of sea surface temperature anomalies in the tropical Indian Ocean. IOBW is closely associated with dengue epidemics for both the Northern and Southern hemispheres. The ability of IOBW to predict dengue incidence likely arises as a result of its effect on local temperature anomalies through teleconnections. These findings indicate that the IOBW index can potentially enhance the lead time for dengue forecasts, leading to better-planned and more impactful outbreak responses.

Influence of Spring Precipitation over Maritime Continent and Western North Pacific on the Evolution and Prediction of El Niño–Southern Oscillation

Previous studies suggested that spring precipitation over the tropical western Pacific Ocean can influence the development of El Niño–Southern Oscillation (ENSO). To identify crucial precipitation patterns for post-spring ENSO evolution, a singular value decomposition (SVD) method was applied to spring precipitation and sea surface temperature (SST) anomalies, and three precipitation and ENSO types were obtained with each highlighting precipitation over the Maritime Continent (MC) or western north Pacific (WNP). High MC spring precipitation corresponds to the slow decay of a multi-year La Niña event. Low MC spring precipitation is associated with a rapid El Niño-to-La Niña transition. High WNP spring precipitation is related to positive north Pacific meridional mode and induces the El Niño initiation. Among the three ENSO types, ocean current and heat content behave differently. Based on these spring precipitation and oceanic factors, a statistical model was established aimed at predicting winter ENSO state. Compared to a full dynamical model, this model exhibits higher prediction skills in the winter ENSO phase and amplitude for the period of 1980–2022. The explained total variance of the winter Niño-3.4 index increases from 43% to 75%, while the root-mean-squared error decreases from 0.82 °C to 0.53 °C. The practical utility and limitations of this model are also discussed.

Factors influencing subseasonal predictability of northern Eurasian cold spells

AbstractCold‐air outbreaks have significant impacts on human health, energy consumption, agriculture, and overall well‐being. This study aims to evaluate the effectiveness of Subseasonal‐to‐Seasonal (S2S) models in predicting cold conditions over northern Eurasia, defined here as the lower tercile of weekly mean 2‐metre temperature anomalies. To assess the predictability of these events we use ensemble hindcasts from five prediction systems from the S2S database. Our analysis focuses on identifying the conditions under which the models confidently predict cold temperatures with a high (>0.5) probability 3–4 weeks ahead, which potentially can represent windows of forecast opportunity. We compare the group of forecasts that correctly predicted the events to the group that forecasted events that did not occur in practice (false alarms). Most of the confident forecasts of cold spells, both correct and false alarms, have cold anomalies already in the initial conditions, often in conjunction with either a negative phase of the North Atlantic Oscillation, or Scandinavian Blocking. We find that S2S models tend to overpredict cold temperatures, with false alarms occurring more likely when the forecasts are initialized during a weak polar vortex. Furthermore, most of the confident false alarms receive the signal from the stratosphere rather than following internal tropospheric dynamics. False alarms initialized during the weak polar vortex conditions are more common when the vortex is in a recovery stage and, subsequently, the downward ‐propagating signal is short‐lived in the troposphere. The analysis of forecasts during different Madden–Julian Oscillation (MJO) phases shows that nearly half of all confident correct cold‐temperature forecasts are initialized during an active MJO in phases 6–8. On the other hand, most false alarms occur during phase 3, which we suggest is due to the presence of the Scandinavian Blocking regime in the initial conditions for this phase.

Assessing the Impact of Renewable Energy in Mitigating Climate Change: A Comprehensive Study on Effectiveness and Adaptation Support

The objective of this study is to evaluate the effectiveness of renewable energy in combating climate change and supporting adaptation efforts. By applying the ARDL model to data from 58 countries spanning the period from 1990 to 2022, this study sheds light on the opportunities and challenges associated with this involved relationship. The results indicate that renewable energy plays a significant role in reducing greenhouse gas emissions and mitigating temperature anomalies. Specifically, wind energy, biomass, geothermal energy, solar energy, and waste utilization demonstrate a positive correlation with greenhouse gas emission reduction. Moreover, wind energy, solar energy, waste utilization, and the proportion of renewable energy in primary energy consumption are also linked to a decrease in temperature anomalies. These findings emphasize the importance of implementing concrete measures such as promoting sustainable transportation, ensuring sustainable management of natural resources, raising awareness and enhancing education, increasing subsidies and incentives for renewable energy and strengthening regulations on greenhouse gas emissions.

Hyporheic exchange flows in a mountainous river catchment identified by distributed temperature sensing

AbstractElevated stream temperatures under low‐flows, exacerbated by global warming, are a stressor that affects aquatic species directly or in combination with other stressors. Stream temperatures are influenced by energy fluxes across the air–water interface as well as by hydrological exchange processes occurring at the water–riverbed interface. Small‐scale stream temperature dynamics influenced by exchange flows are still underrepresented in stream temperature research. To investigate high‐resolution temperature dynamics and hydrological exchange processes at the sediment–water interface we applied fiber‐optic distributed temperature sensing (FO‐DTS) at two sites in the mountainous Kinzig catchment combined with mapping and measurement of additional environmental conditions. Two types of temperature anomalies could be observed at one site under conditions of low flow and high air temperature. Dampening effects coincided with riverine features such as pools, vegetation roots, fine sediment, and signs of streambank seepage which indicated hyporheic exchange flows. Increased heating of the substrate during the day was identified in shallow sections where sediment was exposed to the air and shading from riparian vegetation was patchy. At another site, at which the cable could not be buried because of the sediment composition, temperature anomalies in the overlying water indicated diffuse groundwater exfiltration. The results show that small‐scale processes in the hyporheic zone, low water tables, and riparian shading influence stream temperature in mountainous streams and can be identified with FO‐DTS under suitable conditions. The results improve our understanding of stream temperatures (in the hyporheic zone) and provide important information on how to improve hydrological modeling.

Winter “warm Arctic-cold Eurasia” pattern and its statistical linkages to oceanic precursors during the era of satellite observations

AbstractA striking recurrent feature of winter climate variability is the “warm Arctic-cold Eurasia” (WACE) pattern of opposite sign anomalies of surface air temperature (SAT) in the Barents Sea region and midlatitude Eurasia. Its origins and mechanisms are hotly debated, and its predictability remains unknown. This study investigates statistical relationships of the winter WACE dipole with concurrent anomalies of atmospheric circulation and oceanic precursors during the era of satellite observations. The results reveal a high potential for seasonal predictability of not only the WACE dipole but also several related indicators of winter climate variability, including the Arctic and Eurasian SAT anomalies. During subperiods of extreme covariability between the Arctic and Eurasian SATs around the early 1980s and late 2000s, most of the WACE variability is explained by ocean temperature and surface turbulent heat flux anomalies in the Barents Sea region during the preceding months. Anomalies in summer Atlantic water temperature (AWT) and autumnal sea surface temperature (SST) in this region explain about 70–80% of the variance of the following winter WACE variability during all events of strong Arctic-Eurasian SAT covariability. Analysis of SST variability in the Arctic-North Atlantic region suggests that the winter WACE link to the summer AWT anomalies reflects an atmospheric response to a large-scale surface reemergence of ocean temperature anomalies. However, this linkage had been robust only until the early 2000s. Since then, the winter WACE variability has been strongly related to autumnal SST anomalies in the Barents Sea region and the North Pacific.

Downscaling the ocean response to the Madden–Julian Oscillation in the Northwest Atlantic and adjacent shelf seas

AbstractSubseasonal-to-seasonal (S2S) prediction is a global effort to forecast the state of the atmosphere and ocean with lead times between two weeks and a season. This study explores the feasibility of S2S prediction of the ocean using a variety of tools including statistical analysis, a statistical-dynamical mixed layer model, and a regional, high-resolution ocean circulation model based on physical principles. Ocean predictability on S2S timescales is analyzed by compositing winter sea surface temperature (SST) anomalies in the North Atlantic with respect to the state of the Madden–Julian Oscillation (MJO). It is found that statistically significant, large-scale SST changes, particularly along the eastern seaboard of North America, can be related to the MJO. This signal is shown to be driven by anomalous air–sea heat fluxes caused by atmospheric perturbations in response to the MJO. The high-resolution model of the Gulf of Maine and Scotian Shelf is used to downscale the mean ocean response to the MJO. The model is able to capture the observed relationship between the MJO and SST in the northwest Atlantic. It is also shown that the anomalous atmospheric circulation in response to the MJO leads to anomalous upwelling on the Scotian Shelf. Overall, this study demonstrates that it is feasible, and of value, to use regional ocean models for S2S prediction.

A comparative study on multidimensional signal evolution during thermal runaway of lithium-ion batteries with various cathode materials

Cathode materials significantly affect the failure behavior of lithium-ion batteries, but there is a lack of comparative research on the multidimensional signal evolution for batteries with various cathode materials when subjected to thermal runaway (TR). This study investigates the effect of cathode materials on battery safety properties by conducting overheating and overcharging abuse experiments on LiNixCoyMn(1-x-y)O2 (NCM 523, 622, and 811) as well as LiFePO4 (LFP) batteries. The multidimensional signals, including expansion force, gas concentrations, temperature, and voltage, are comprehensively and quantitatively analyzed. The results demonstrate that expansion force shows the earliest anomalies regardless of the cathode material in both abuse conditions. Gas can be detected after venting, followed by significant anomalies in voltage and temperature. Additionally, under overheating conditions, LFP cells exhibit the longest TR warning time of 677 s. However, the force anomaly-based warning method is more suitable for low-nickel NCM cells under overcharging scenarios. Specifically, NCM 523 cells achieve an overcharging warning time of 992 s at temperatures as low as 42.9 °C, and hold the highest venting force of 9859 N and venting temperature at 114.1 °C. On this basis, a hierarchical warning strategy based on multidimensional signal fusion is proposed. This study provides valuable insights into TR early warning and safety management in LIBs with different cathode materials.

Atmospheric blocking and temperatures in the Antarctic Peninsula

The Antarctic Peninsula (AP) has displayed a propensity for persistent blocking ridges and anticyclonic conditions, particularly during recent summertime extreme weather events. This study investigates atmospheric blocking patterns over the AP through historical (1981–2010) and future (2071–2100, SSP5–8.5) periods using ERA5 reanalysis and six CMIP6 models, including multi-member realizations from two models totaling ten simulations. We focus particularly on 500 hPa geopotential height (Z500) and near-surface air temperature (T2m) anomalies. The historical analysis highlights significant differences between the CMIP6 models and ERA5 reanalysis, especially in the austral winter, with EC-Earth3 and INM-CM4 models matching closest with the ERA5. Future projections show that while the northern AP and the Drake Passage largely do not exhibit a clear trend towards increased blocking, there are exceptions. The EC-Earth3 model predicts more blocking–like conditions northwest of the AP in summer and a pronounced ridge over the Bellingshausen Sea in winter, indicating a potential increase in blocking events. The INM-CM4 model projects a minor increase in summer Z500 heights off the western and southern AP, without clear blocking patterns over the AP, and negligible winter changes. Localized intensification is noted in the northern parts of the blocking domain and southern AP during extreme blocking conditions. These variations are mirrored in T2m anomalies, suggesting warming in the northern and southern sections of AP but little change elsewhere. The results of this study underscore the need to more accurately capture complex blocking mechanisms and their impacts on regional climate patterns around the AP. We also suggest employing refined blocking definitions and incorporating a broader range of climate models to enhance our understanding of blocking patterns and their impacts in a changing climate.