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Abstract Planners managing coastal flood risk under a constrained computational budget face a tradeoff. Simulating many time periods or scenarios limits how many storm simulations can be run on each landscape. In this analysis, we present a deep learning model to predict storm surge as a function of storm parameters but also landscape features and boundary conditions (e.g., sea level). It is trained on peak surge elevations from Advanced Circulation (ADCIRC) hydrodynamic simulations of coastal Louisiana in a 2020 baseline and decadal periods from 2030 to 2070 under two morphological and climate scenarios. Leave-one-landscape-out cross-validation yielded a 0.086-m RMSE and 0.050-m MAE over 90 storms per landscape and 94,013 geospatial locations. A two-sided Kolmogorov-Smirnov test comparing annual exceedance probability (AEP) estimates from the model predictions to ADCIRC simulations rejected the null hypothesis that the predicted and ADCIRC AEP values were drawn from the same distribution only 1.1% of the time.

Abstract Different tropospheric precursor anomalies leading to sudden stratospheric warmings (SSWs) may result in different circulation evolution. This study finds that there are distinct differences in tropospheric circulation evolutions during SSWs following anomalously strong- (SUR-SSWs) and weak- (WUR-SSWs) Ural ridge. SUR-SSWs exhibit enhanced East Asian trough in the following week, while enhanced Greenland ridge and negative tropospheric annular mode anomalies can persist for 1 month. In contrast, WUR-SSWs exhibit surface cooling over northern Eurasia without notable tropospheric annular mode anomalies. During SUR-SSWs, waves induced by the enhanced Ural wave source tend to propagate below the tropopause, amplifying the East Asian trough. Additionally, due to decreased wave phase speed, the preexisting Ural ridge anomalies migrate westward and amplify the Greenland ridge. Before WUR-SSWs, preexisting cooling over Northeast Asia migrates westward and amplifies northern Eurasia cooling. Thus, the Ural ridge anomalies prior to SSWs significantly influence post-SSW tropospheric circulation.

Abstract To address the challenge of limited training samples, this study employs the model-agnostic meta-learning (MAML) algorithm along with domain-knowledge-based data augmentation to predict winter temperatures on the Korean Peninsula. While data augmentation has been achieved by using global climate model simulations, the proposed augmentation is purely based on the observed data by defining the labels using large-scale climate variabilities associated with the Korean winter temperatures. The MAML-applied convolutional neural network (CNN) (referred to as the MAML model) demonstrates superior correlation skills for Korean temperature anomalies compared to a reference model (i.e., the CNN without MAML) and state-of-the-art dynamical forecast models across all target lead months during the boreal winter seasons. Sensitivity experiments show that the domain-knowledge-based data augmentation enhances the forecast skill of the MAML model. Moreover, occlusion sensitivity results reveal that the MAML model better captures the physical precursors that influence Korean winter temperatures, resulting in more accurate predictions.

Abstract The warming of mountains has become evident in recent years, with a mean global warming rate of 1.19 °C from 1979 to 2022. However, unveiling the global divergent decline of persistent mountain snow cover in the face of climate shifts remains unexplored. However, the global decline of persistent mountain snow cover due to climate change is not well understood. This study uses reanalysis and satellite data to examine changes in snow cover lasting over six months across our global mountain regions. We reveal a significant global mean decline of 7.79% in persistent snow cover over the past 44 years. The regional snow cover trends exhibit a heterogeneous and non-linear response to its regional warming rate. Our findings highlight the interplay between global warming and snow cover, emphasizing the need for sustainable development strategies to address the potential impacts of diminishing mountain snow.

Exceptional rainfall hit western central Africa in October 2019. To understand its underlying mechanisms, we examined the regional moisture and moist static energy (MSE) budgets, intending to highlight the importance of the dynamic and thermodynamic effects associated with this historic event. Analysis of the moisture budget reveals that the precipitation anomalies in October were mainly controlled by dynamic effects. Horizontal moisture advection induced by horizontal wind anomalies controls extreme precipitation north of western central Africa, while vertical moisture advection induced by vertical velocity anomalies controls extreme precipitation south of western central Africa. Changes in the thermodynamic effect, although not the key factor responsible for the events of October 2019, contribute up to 35 % of the total effect on the northern part and 15 % on the southern part of the domain. The residual term on the northern part is important and provides a caveat when estimating dynamic and thermodynamic processes. Diagnosis of the MSE balance averaged over the northern part of western central Africa shows that the anomalous vertical motion is dominated by the dynamic effect, i.e., the wet enthalpy advection induced by the horizontal wind anomalies. This is confirmed by the high spatial correlation (r=0.6) between the two terms compared to the other terms, whereas to the west of the Congo Basin, the increase in the net energy balance dominated the changes in vertical motion (r=0.51). The horizontal advection of the MSE induced by the anomalies of the wet enthalpy and the vertical advection of the MSE induced by the anomalies of the MSE seem less important (r=0.29 and −0.19 to the north and −0.17 and 0.03 to the south). The strong anomalies in the MSE balance in the north are linked to its meridional component, in particular the meridional wind anomalies in the dynamic effect and the meridional anomalies in latent heat in the thermodynamic effect. Our results suggest that dynamic and thermodynamic effects should be jointly considered for adequately anticipating this kind of extreme event. Understanding the associated mechanisms could help us improve our forecasts and projections and increase the region's population resilience to these extreme weather events.

Abstract South America has experienced severe compound drought-heatwaves (CDHW), exacerbating fires. Recently, the unprecedented Pantanal 2020 fire season (P20F), burning a third of the biome, resulted in well-reported local impacts on the ecosystem, economy, and health. Nevertheless, the long-range ripple effects of this event remain unknown. We investigated the P20F-related cascading hazards, integrating models, observational and satellite-based data. P20F-related smoke elevated PM2.5 levels in the SA’s most populated area, exceeding WHO guidelines by up to 600%. Smoke-induced air pollution episodes coincided with widespread heatwaves, amplifying health risks. The mortality burden attributable to this multi-hazard short-term (14 days) exposure was estimated to be 2150 premature deaths (21% increase above expected levels). Our findings highlight that the impacts of CDHW-fires in SA are beyond the local level, implying growing challenges for risk management and public health and the need for governance based on telecoupled flows, linking different systems over multiple scales.

Abstract Risk-informed decisions that promote infrastructure resilience (or the ability to withstand, recover from, and adapt to stressors like natural hazards) require confident predictions of system performance now and into the future. We propose a perspective shift–one capable of handling uncertain and dynamic conditions, leveraging emerging observations from smart systems, and guided by demands for social equity. This shift requires collective efforts, but our future cities demand and deserve it.

Abstract Earthquakes in the Taiwan region have caused significant economic losses. We develop a model to assess seismic economic losses in the Taiwan region using the records of economic losses caused by historical earthquakes. Unlike existing models, we introduce Gross National Income Per Capita (GNIPC) as a parameter that responds to the influence of socio-economic development. The results show that our model can accurately estimate the earthquake economic losses in Taiwan region. The difference between the results of this model and those of existing models is evident, indicating differences between the Taiwan region and mainland China regarding geological background, seismic tectonics, and social resistance to earthquakes. The prediction results also imply that the society significantly underestimates the seismic economic losses in the Taiwan region. Our model can help the Taiwan region in disaster prevention and preparedness, contingency planning, allocation of relief resources, and post-disaster socio-economic recovery.

Abstract Volcanic eruptions are typically preceded by unrest, marked by increased seismicity, ground deformation, and gas emissions. Unrest can last from decades to minutes. Accurate eruption forecasting relies on real-time monitoring and understanding the volcano’s past behavior. Long-term hazard assessments, combined with real-time data, help identify probable eruptive scenarios (short-term hazard assessment), improving forecasting during volcanic crises.

Abstract The continued increase in demand for food is such that flood-irrigation-based agricultural techniques are not expected to change in the future. Consequently, in cultivated areas dominated by river terraces that experience neotectonic movements, catastrophes caused by earthquakes that trigger material liquefaction may continue to occur. Among such catastrophes, low-angle, ultra-long-distance landslides induced by synergistic effects of tectonic and human activities are events that are not fully understood but can potentially be mitigated. A landslide-mudflow that occurred on December 18, 2023, during an Ms 6.2 earthquake in Jishishan, China, on the tertiary terrace of the Yellow River. We reveal the causal mechanisms by which geomorphology, tectonics, and human activities controlled the landslide-mudflow, and outline a new, potentially widespread, dual liquefaction layer-dominated failure mode for earthquake-induced landslides on river terraces. Our findings serve as a warning that urgent landslide-potential assessments should be conducted across river terrace irrigation areas in tectonically active regions.

Abstract The uncertainty regarding the correlation between sea spray aerosol (SSA) formation and sea surface temperature (SST) hinders the accurate estimation of SSA’s impact on global climate. Here, we developed a temperature-controlled plunging SSA simulation tank to investigate the impact of SST on SSA formation from two perspectives: SSA particle size distribution and organic enrichment. Our findings show that SSA particle size decreases with decreasing SST, as exhibited by an increase in SSA within Aitken mode and a decrease in SSA within accumulation and coarse modes. SST can significantly enhance organic enrichment in SSA particles, while the multiplicative increases vary from 2 to 10 times depending on the organic matter species and the SSA particle size. Based on our experimental results, it is predicted that SST reduction may lead to a significantly higher contribution of Aitken modal SSA-derived CCN in cold waters (0 °C) than in warm waters (30 °C). Additionally, we incorporate SST for the first time in estimating the global flux of dissolved organic carbon (DOC) emitted via SSA, yielding a value ranging from 23.45 to 55.78 Tg C yr−1. Compared to previous works, our study reveals the crucial role of SST in influencing both cloud formation and the atmospheric organic burden of SSA.

Abstract The night-time temperature-related injury risks and burdens were unclear. Using 11,512,467 non-fatal injury cases in 243 surveillance hospitals across China from 2006-2021, we estimated the associations between daytime or night-time temperature and injury by a time-stratified case-crossover study, and compared their injury burden during 1990s–2010s. We found the excess risk (ER) for per 1°C rise in night-time temperature (ER = 1.21%, 95%CI:1.03%,1.39%) was greater than that in daytime (ER = 0.86%, 95%CI:0.72%,1.00%). Compared with the 1980s, the attributable fractions (AFs) for daytime and night-time temperature change during the 1990s–2010s were 0.59% (95%eCI:0.54%,0.67%) and 0.73% (95%eCI:0.69%,0.77%), respectively. Spatially, the higher AFs of night-time temperature were more widely distributed than daytime temperature. The non-fatal injury risk attributed to night-time temperature was stronger than daytime temperature, and increased night-time temperatures posed a heavier injury burden compared with daytime temperature in China. Our findings indicate that high night-time temperature is an important injury risk in the context of climate change.

Abstract The study proposes an approach to elucidate spatiotemporal mesoscale variations of seawater Dimethylsulfide (DMS) concentrations, the largest natural source of atmospheric sulfur aerosol, based on the Gaussian Process Regression (GPR) machine learning model. Presently, the GPR was trained and evaluated by nested cross-validation across the warm-oligotrophic Mediterranean Sea, a climate hot spot region, leveraging the high-resolution satellite measurements and Mediterranean physical reanalysis together with in-situ DMS observations. The end product is daily gridded fields with a spatial resolution of 0.083° × 0.083° (~9 km) that spans 23 years (1998–2020). Extensive observations of atmospheric methanesulfonic acid (MSA), a typical biogenic secondary aerosol component from DMS oxidation, are consistent with the parameterized high-resolution estimates of sea-to-air DMS flux (FDMS). This represents substantial progress over existing coarse-resolution DMS global maps which do not accurately depict the seasonal patterns of MSA in the Mediterranean atmospheric boundary layer.

Abstract In this study, available large ensemble datasets in the Coupled Model Intercomparison Phase 6 (CMIP6) archive were used to provide the first multi-variate overview of the evolution of extreme seasons over Antarctica and the Southern Ocean during the 20th and 21st centuries following medium-to-high radiative forcing scenarios. The results show significant differences between simulated changes in background mean climate and changes in low (10th percentile) and high (90th percentile) extreme seasons. Regional winter warming is most pronounced for cold extremes. In summer, there are more pronounced increases in high extremes in precipitation and westerly wind during the ozone hole formation period (late 20th century), affecting coastal regions and, in particular, the Antarctic Peninsula. At midlatitudes, there is a reduction in the range of summer season wind extremes. Suggested mechanisms for these differences are provided relating to sea ice retreat and westerly jet position.

Abstract Atmospheric rivers (ARs) are characterized by intense lower tropospheric plumes of moisture transport that are frequently responsible for midlatitude wind and precipitation extremes. The prediction of ARs at subseasonal-to-seasonal (S2S) timescales is currently at a low level of skill, reflecting a need to improve our understanding of their underlying sources of predictability. Based on 20 year hindcast experiments from the Geophysical Fluid Dynamics Laboratory’s SPEAR S2S forecast system, we evaluate the S2S prediction skill of AR activities in the northern winter. Higher forecast skill is detected for high-frequency AR activities (3–7 days/week) compared to low-frequency AR activities (1–2 days/week), even though the occurrence rate of high-frequency ARs exceeds that of low-frequency ARs. For the first time, we have applied the Average Predictability Time technique to the SPEAR system to identify the three most predictable modes of AR in the North Pacific sector. These modes can be attributed to the influences of the El Niño–Southern Oscillation, the Pacific North American pattern, and the Arctic Oscillation. S2S AR forecast skill in western United States is modulated by various phases of large-scale variability. This study highlights potential windows of opportunity for operational S2S AR forecasting.

Abstract Internal variability arising from the inherently chaotic nature of the climate system has amplified or obscured human-caused changes, especially at regional scales in the extratropics, where its contribution to climate variability is the largest. It is virtually certain that this will continue in the near-term. We here focus on the Northern Europe region, whose variability is largely controlled by the North Atlantic Oscillation (NAO) and the Atlantic Meridional Overturning Circulation (AMOC) through remote dynamical and thermodynamic processes, and introduce the concept of internal variability storylines (IVS) to explore, understand, and quantify the role of the two combined drivers of internal variability in the modulation of the anthropogenic warming by 2040 in winter. Based on a large ensemble of historical-scenario simulations, we show that the high-impact IVS, characterised by weak AMOC decline and a decadal shift of the NAO toward dominant positive phase, leads faster to warmer-wetter conditions independently of actual and future greenhouse gases emissions. By contrast, amplified AMOC reduction and more recurrent negative NAO can considerably damp both warming and wettening at near-term. In the latter IVS, we provide evidence that winter-severe conditions similar to those in 2010, that had been responsible for widespread socio-economic disruptions, remain almost as likely to occur by 2040. Reframing the uncertain climate outcomes into the physical science space in a conditional form through the prism of IVS makes climate information relevant for accurate risk assessments and adaptation planning.

Abstract Climate change remains a polarizing issue in the United States, with conservatives expressing greater skepticism regarding its risks. We investigate how fear shapes climate attitudes across political orientations. Utilizing data from a 2023 online survey, results support hypotheses that conservatives demonstrate lower climate concern and that fear of natural and environmental disasters increases climate concern. Interaction results show that fear of anthropogenic environmental disasters elicits greater climate concern amongst conservatives. At high levels of ecological fear, the political divisions diminish as all orientations converge on higher levels of acknowledging climate risks and causes. Practically, this relationship highlights messaging opportunities by focusing on relevant environmental threats that aid in influencing conservatives to build collective support across political divides. Theoretically, these findings challenge expectations of dominant paradigms related to threat perception and political orientation, contributing to the ongoing development of new models that more thoroughly represent the relationship between these multifaceted constructs.

Abstract Climate action under the Paris Agreement (PA) requires a strategic approach that addresses the climate action needs of the participating countries. The most relevant documents for identifying such needs are the nationally determined contributions (NDCs) submitted by the parties to the PA. Academia has paid insufficient attention to this dataset and has attempted to clarify climate action pathways by utilizing various established theories from different fields. As a result, a strategic framework for global climate action is lacking. This article examines the NDCs of 19 lower-middle-income countries (LMICs) to consolidate their climate action needs into a cohesive framework. A grounded theory approach with process coding techniques is employed, and the data are analyzed through three coding cycles: initial process coding, focused process coding, and conceptual process coding. The results suggest a 4 A cyclic process: assess, arrange, access, and action, which forms an integrated global response to climate change.

Abstract In recent years, new social movements advocating climate action have emerged, varying in demographics and tactics. A notable parent-led movement, driven by the moral imperative to care for future generations, offers a unique perspective on climate action. Despite its growing presence in local and regional debates, it remains under-explored. This Brief Communication highlights the movement’s potential to reshape climate action narratives and introduces an open-access database mapping its global reach, suggesting avenues for future research.

Medicane Ianos in September 2020 was one of the strongest medicanes observed in the last 25 years. It was, like other medicanes, a very intense cyclone evolving from a baroclinic mid-latitude low into a tropical-like cyclone with an axisymmetric warm core. The dynamical elements necessary to improve the predictability of Ianos are explored with the use of simulations with the Met Office Unified Model (MetUM) at 2.2 km grid spacing for five different initialisation times, from 4 to 2 d before Ianos's landfall. Simulations are also performed with the sea surface temperature (SST) uniformly increased and decreased by 2 K from analysis to explore the impact of enhanced and reduced sea surface fluxes on Ianos's evolution. All the simulations with +2 K SST are able to simulate Medicane Ianos, albeit too intensely. The simulations with control SST initialised at the two earliest times fail to capture intense preceding precipitation events at the right locations and the subsequent development of Ianos. Amongst the simulations with −2 K SST, only the one initialised at the latest time develops the medicane. Links between sea surface fluxes and upper-level baroclinic processes are investigated. We find three elements that are important for Ianos's development. First, an area of low-valued potential vorticity (PV), termed a “low-PV bubble”, formed within a trough above where Ianos developed; diabatic heating associated with a preceding precipitation event triggered a balanced divergent flow in the upper levels, which contributed to the creation and maintenance of this low-PV bubble as shown by results from a semi-geostrophic inversion tool. Second, a quasi-geostrophic ascent was forced by middle and upper levels during Ianos's cyclogenesis. It is partially associated with the geostrophic vorticity advection, which is enhanced by the growth and advection of the low-PV bubble. Third, diabatic heating dominated by deep convection formed a vertical PV tower during Ianos's intensification and continued to produce diabatically induced divergent outflow aloft, thus sustaining Ianos’s development. Simulations missing any of these three elements do not develop Medicane Ianos. Our results imply the novel finding that preceding convection was essential for the subsequent development of Ianos, highlighting the importance of the interactions between near-surface small-scale diabatic processes and the upper-level quasi-geostrophic flow. A warmer SST strengthens the processes and thus enables Ianos to be predicted in simulations initiated at the earlier times.

Abstract Summer 2022 was exceptionally hot and dry in Europe and especially in Southwest France, where the most important wildfires since 1949 had serious environmental and socio-economic impacts. Here we conduct an impact-oriented climate change attribution study by first investigating which climate indices are the most correlated with the burnt area between 2003 and 2022. We find that an index combining soil moisture integrated over 6 months and temperature and vapour pressure deficit integrated over 3 months is correlated with large burnt areas. Using the index developed, we estimate that anthropogenic climate change made climate conditions propitious for wildfire development, such as the ones of July 2022, two times more likely, with a return period of 13 years in the current climate. Our study raises the question of the sustainability of the Landes Forest and stresses the urgent need to mitigate greenhouse gas emissions and adapt to climate change.

Abstract Under a warmer climate, the enhancement of dry-wet abrupt alternation (DWAA) risk poses a great challenge for sustainable development. Here, we introduce a novel framework for DWAA detection based on our proposed soil moisture concentration index. By the end of this century, over humid southern China, the shift of soil moisture time series from anomalously wet to anomalously dry pattern, or the other way around, will be more abrupt. In addition, the proposed framework driven by Coupled Model Intercomparison Project Phase 6 simulations projects more widespread DWAA-affected areas over southwestern China, coastal regions of southeastern China, and the lower reaches of the Yangtze River, especially under a high emission scenario. The framework proposed in this study provides an efficient system for DWAA detection and prediction, and the findings of this study provide a reference for upgrading hydraulic infrastructure and mitigating future DWAA events.

Abstract Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

Abstract Ice core oxygen isotope (δ18O) records from low-latitude regions preserve high-resolution climate records in the past, yet the interpretation of these ice core δ18O records is still facing difficulty due to the uncertainty of ice core dating. Here we present a new established δ18O time series from Qiangtang (QT) No. 1 ice core retrieved from the central Tibetan Plateau. Due to the vague seasonal signals in the QT ice core, we investigated the spectral properties of δ18O record with depth and discussed the implications of significant spectral power peaks in the QT ice core. We employed a variational mode decomposition (VMD) analysis for the upper part of the QT ice core to decompose the δ18O depth series in order to separate the El Niño Southern Oscillation (ENSO) mode, a signal strongly preserved in the QT ice core δ18O record. With this approach, we established a time series of 335 years (1677–2011 CE) for the upper 50 m of the QT ice core. Subsequently, we examined the frequency of the new established δ18O time series and detected strong signals of the bidecadal and multidecadal modes of Pacific Decadal Oscillation (PDO). The PDO consists of two modes with periods of approximately 25–35 years and 50–70 years, and we found that the 50–70 years periodicity has persisted since 1700 CE, succeeded by dominance of the 25–75 years periodicity after 1900 CE. Additionally, we analyzed the δ18O series of the QT ice core during the past century and determined that the increasing frequency of El Niño events is an important factor contributing to the increase in recent ice core δ18O.

Abstract Ice nucleating particles (INPs) affect the cloud radiative budget in the rapidly warming Arctic by changing the cloud liquid/ice phase balance. Dust emitted in the Arctic (Arctic dust) has been suggested to be a major contributor to INPs in the Arctic lower troposphere. However, how Arctic dust and its impacts on ice nucleation change with Arctic warming has not been explored. Here we find that the simulated dust emission flux in the Arctic (>60°N) in global model simulations increases by 20% from 1981–1990 to 2011–2020. This increase weakens the sensitivity of ice nucleation in Arctic lower tropospheric clouds to warming by 40% compared to the case without considering Arctic dust emission increases. Our results demonstrate a better understanding of the counterbalancing feedbacks of Arctic dust (i.e., increasing emissions and decreasing ice nucleation efficiency) is needed for more accurate estimates of changes in ice nucleation in the rapidly changing Arctic climate.

Abstract Constructed with used plastic bottles, the eco-cooler has been widely adopted in resource-poor communities in Bangladesh and other countries. We tested the eco-cooler under controlled conditions using a scientific wind tunnel in a climatic chamber. In our tests, we used seven eco-cooler designs in 27 climate conditions typical of Bangladesh (temperatures of 40 °C, 35 °C, and 30 °C; humidity levels of 70%, 60%, and 40%; and wind speeds of 4.0 m s−1, 2.0 m s−1, and 0.2 m s−1) in 92 experiments (N = 7686 measurements in 87 short experiments and N = 23,428 measurements in five long experiments). We found no significant temperature reductions with eco-cooler use, except at low wind speeds, where temperature reduced by up to 0.2 °C. In theoretical calculations extending our empirical findings, the greatest temperature drop was 0.85 °C at 4.0 m s−1 with a 40 °C static air inflow temperature. However, this temperature drop did not extend beyond the nozzles of the bottles in the eco-cooler. The eco-cooler did not work effectively as an indoor air cooler.

Abstract Societal decarbonization likely requires changes to building standards encouraging electrification, partly through restricting connections to legacy utilities such as natural gas. Yet while some municipalities have taken action, an important parallel shift undermines it: more than half of U.S. states (covering 47% of the population) have, since 2020, passed state-level laws preempting municipalities from restricting utilities. We investigate the timing, content, and partisan support of these bills, examining similarity in text use across them using a plagiarism-detection tool. States passing preemption were not only more Republican but more ideologically conservative, typically featuring less professionalized state legislatures. We also examine qualitative evidence of the natural gas industry’s lobbying, showing that industry groups claimed influence over key bills (supported largely by Republican legislators). We consider the broader implications of these findings for supply-side decarbonization in a context of climate federalism under significant influence by fossil fuel industries and allied policymakers.

Abstract Fossil fuel subsidy removal may hinder access to clean fuels like LPG. Our analysis of urban Senegal shows that LPG use fell sharply after subsidies ended in 2009, despite later price drops. Households switched to charcoal, and the new availability of energy-efficient charcoal stoves made a return to LPG less appealing. This highlights how energy transitions among the poor are price sensitive, with implications for subsidy and carbon-tax policies.

Abstract Limiting global warming requires the effective implementation of energy mitigation measures by individual countries. However, the consequences of the timing of these efforts on the technical feasibility of adhering to cumulative carbon budgets—which determines future global warming—are underexplored. Moreover, existing national studies on carbon budgets either overlook integrated sectoral interactions, path dependencies, or comprehensive demand-side strategies. To address this, we analyse Ireland’s mitigation pathways under equal per-capita carbon budgets using an energy systems optimisation model. Our findings reveal that delayed mitigation brings forward the need for a net-zero target by five years, risks carbon lock-in and stranded assets, increase reliance on carbon dioxide removal technologies and leads to higher long-term mitigation costs. To keep the Paris Agreement targets, countries must set and meet accelerated mid-term mitigation goals and address energy demand.

Abstract We estimated the environmental impact and financial cost of two exemplar in-person academic events organised by the European academic society, the European Academy of Nursing Science, identified the main sources of these emissions, and then mapped them against the COM-B behaviour change framework of capability, opportunity, motivation to identify strategies that could be applied by organisers and participants to reduce this impact. These events contributed 41 tonnes and 99 tonnes of CO2e emissions per event, a per-participant mean of either 0.324 (SD 0.173) or 0.724, (SD 0.263) tonnes, representing 2 to 5.5 times the daily per-person European average. Distance from home was the largest contributor to emissions. Costs were similar for both events. Our multi-component behavioural change programme includes environmental change, enablement, education, incentivisation and persuasion, by which organisers provide participants with the opportunity for less-polluting behaviour, and enhance participants capabilities and motivation to act on the opportunities provided.

The optimal interpolation method is widely used in meteorological applications worldwide. However, due to the different precipitation climate characteristics and the spatial distribution of gauges, the implementation of the optimal interpolation method involves many empirical models and key parameters. There is still some uncertainty on how to derive a set of optimised parameters for the application of the optimal interpolation method in a local region. This study analyses and optimises the key parameters of the optimal interpolation method for the calibration of weather radar quantitative precipitation estimation using radar and gauge observations from May to September 2020 in Qingdao, Shandong. The search radius of gauges from a grid point is determined by analysing the spatial distribution of all gauges relative to the analysis point. The optimal number of gauges used to calibrate the precipitation estimation of a grid point is determined by analysing the change of relative analysis error with respect to the number of gauges. Eight groups of sensitivity experiments with different correlation functions are compared by random sampling and cross-validation to find the best set of parameters for the Qingdao radar. The verification of calibration results produced by the best parameters shows that the calibration significantly improves the accuracy of the quantitative precipitation estimation. The median values of MAE, RMAE, and BIAS are 1.5 mm, 1.0, and 0.03 mm respectively, and the CORR is higher than 0.9. Comparing the quantitative precipitation estimation at different levels of precipitation before and after calibration produced by the best parameters shows that the MAE and RMAE of light rain are reduced by 90%, and the CORR is about 0.87. The MAE and RMAE in moderate to heavy rain are decreased by 89%, and the CORR is higher than 0.9. The MAE and RMAE of rainstorm are decreased by more than 83.9%, and the CORR is about 0.77. Based on the verification of a widespread rainfall case on 26 August 2020, the intensity and spatial distribution of the quantitative precipitation estimation after calibration are closer to the gauge observations. The original quantitative precipitation estimation is relatively smooth and lacks small-scale variations. The calibrated results can reflect the characteristics of the local change that is consistent with the observation of gauges. The results of this paper suggest that the optimal interpolation method with optimised local parameters can significantly improve the accuracy of the quantitative precipitation estimation, which has important application value for rainstorm warning and flood disaster prevention.

Compared to inland areas, meteorological stations in the island regions appear scarce and unevenly distributed, which leads to noteworthy uncertainty in detailed characterisation of various meteorological elements. For the Zhoushan Islands, located in Southeast China, there exist many islands and islets, and the local terrain is quite complex. Therefore, different interpolation strategies usually generate diverse gridded results, which largely influence the reliability and accuracy of operational climate monitoring and diagnosing. Under the background of climate change, the Zhoushan region is frequently invaded by cold waves in recent years, so how to scientifically choose an interpolation scheme to reasonably represent spatial distribution characteristics of temperature becomes an important issue in local climate operations. To solve this problem, based on the index of root mean square error (RMSE), the interpolation effect of Ordinary Kriging (OK), Inverse Distance Weighting (IDW), and ANUSPLIN (ANU) are comparatively analysed for 8 cold wave processes influencing Zhoushan during 2014-2021. Two subdivided indices, i.e., temporal RMSE (TRMSE) and spatial RMSE (SRMSE) are further designed to evaluate the interpolation results on temporal and spatial dimensions respectively. Eleven stations are randomly selected from the total 53 meteorological observational stations to test the interpolation results of OK, IDW, and ANU for the minimum temperature, reduction of daily minimum temperature and daily-mean temperature in the 8 processes. It can be found that the bias in the ANU case is higher than that in the OK and IDW cases. To explain such a phenomenon, 3 interpolation experiments with dense surrounding stations, sparse surrounding stations, and specific distribution of examining stations (all the examining stations are not distributed in the main island of Zhoushan) are further designed. The results demonstrate that the performance of the ANU strategy is closely linked to the spread situation of peripheral stations. When the surrounding stations are concentrated, the interpolation bias of ANU is usually smaller than that of OK and IDW. However, if the surrounding stations appear sparse, the bias of ANU exhibits much larger. In the scenario of dense peripheral stations, regardless of the examining sites distributed over the main island or not, the ANU solution can always get the optimal interpolation results, which implies that the impact of topography on the performance of ANU in temperature interpolation is of less importance. Also, the influence of horizontal resolution for interpolation seems secondary. When the horizontal resolution for three interpolation schemes falls down to 1 km×1 km from 30 m×30 m, the change of RMSE is generally less than 0.1 ℃ for most circumstances, so the impact of interpolation resolution can be neglected.

Effective data cleaning methods can improve the quality of wind turbine measurement data. The quality of wind turbine data plays a very important role in wind resource assessment, wind power accurate prediction, and performance diagnosis of wind turbines. There are many uncertainties in the data collection and monitoring systems of different wind turbines for fault diagnosis, which result in uneven quality of wind measurement data for wind turbines. This paper proposes a new method for identifying the probability interval of wind power. This method uses the characteristic changes between wind speed and power to clean and correct the effective data of wind turbine measurement data. It can effectively improve the utilisation rate of wind turbine data. This paper selects wind turbine data from a wind farm in the northern part of Ulanqab, Inner Mongolia Autonomous Region from 2020 to 2022. By sequentially subjecting the data to rationality and validity tests, wind power interval checks, and finally, cleaning and correcting abnormal data, which are carried out by utilising the correlation of the turbine. The final results indicate that: by using the wind power interval method, it is difficult to distinguish abnormal wind speeds. This method can improve data quality and enhance the accuracy of wind speed and power. According to statistics, the data integrity has been significantly improved from 68.7%-92.5% to 90.1%-92.7%. Above all, the data integrity has been significantly improved. This method achieves mutual calibration between wind speed and power through the wind power probability interval recognition method. It provides fundamental support data for predicting and regulating the power generation of wind farms. It provides guidance and a basis for more refined meteorological service products for power and other related sectors.

Prolonged and widespread heavy rainfall events can significantly impact hydrological conditions, leading to devastating floods. This study focuses on individual cases of flood-waterlogging rainfall events in the middle and lower reaches of the Yangtze River since the 1960s. We employed a combination of SAN (simulated annealing and diversified randomization) clustering method and perturbed ensemble analog method to investigate the key circulation patterns associated with these flood-waterlogging rainfall events and quantify their contributions to heavy rainfall. Results indicate that these flood-waterlogging rainfall events in the middle and lower reaches of the Yangtze River typically occur during the Meiyu season. Averaged over all events, the daily peak precipitation intensity reaches the level of a rainstorm. The 500 hPa circulation patterns associated with extreme rainfall were categorized into four classes: East Asian dipole mode (Class 1), East Asian Sandwich mode (Class 2), South Branch Trough (Class 3), and High-Latitude Double Block (Class 4). The key circulation features of each class are distributed in the Western Pacific Subtropical High, South Branch Trough, mid-latitude westerly trough, and high-latitude blocking activity areas, contributing to 40% to 70% of flood-waterlogging rainfall events. The Western Pacific Subtropical High and South Branch Trough contribute relatively consistently in all four categories, accounting for approximately 30% and 15%, respectively. However, the influence of mid-high latitude systems is less stable, with the Northeast Asian circulation anomaly contributing to an average of nearly 20% to Classes 1, 3, and 4, and the Baikal Lake blocking anomaly making a weak contribution to Class 2. The mid-latitude westerly trough anomaly contributes to approximately 20% of Class 4 events. The significant anomaly regions of low-frequency circulation at 10-30 days and 30-60 days in the four classes are generally consistent with the observed circulation anomaly key regions. Low-frequency circulation in the Western Pacific Subtropical High region has a positive contribution to all events, ranging from about 20% to 70%. Among these, the 10-30 day low-frequency activity also has a notable impact on Class 1 and 2 events. The 30-60 day low-frequency circulation in the South Branch Trough region contributes 27% and 16% to Classes 3 and 4 events, respectively. The key high-latitude low-frequency circulation relating to the flood-waterlogging rainfall events located in Lake Baikal and the Okhotsk Sea (Class 1), the Ural Mountains and the westerly trough region (Class 2), the cold vortex region in Northeast China (Class 3), with their contribution varying among different classes (12%-31%). The findings of this study on key circulation patterns and their quantitative contributions provide valuable insights for a deeper understanding of the formation and prediction of flood-waterlogging rainfall events.

Based on conventional meteorological observation data, ERA5 reanalysis data and numerical model data, the mesoscale characteristics, circulation background, physical quantity environment and predictability in different stages of typhoon Chaba rainstorm at northern Hubei are analyzed. The results indicate that: (1) This process occurred under the circulation background of the typhoon moving northward and the combination of the westerly trough. The 700-925 hPa strong southerly jet on the east side of typhoon Chaba, combined with the upper-level typhoon circulation, provided the dynamic lifting conditions and water vapour supply conditions for the heavy precipitation. According to the influence of the system, this process could be divided into two precipitation stages: spiral cloud belt and low pressure main body. Both stages had obvious characteristics of convective warm cloud precipitation, and the hourly rain was strong. However, the precipitation in the spiral cloud belt was short in duration and dispersed in scope, while the precipitation in the low pressure main body was long in duration, wide in scope and large in cumulative rainfall. (2) The primary factor contributing to the disparity in rain mass evolution between the two stages lay in the fact that the spiral cloud belt stage exhibited robust convective instability and potential energy, while vertical motion and water vapour convergence at mid-to-low levels were relatively weak. Consequently, convective triggering was more dispersed and short-lived, primarily driven by cold outflows generated by ambient wind fields and cold precipitation areas. Conversely, during the main stage of the low pressure main body, there was also strong convective instability. The ascending movement, water vapour transport and convergence formed by the convergence of the lower level and the divergence of the upper level were significantly enhanced compared with the spiral cloud belt stage. At the same time, the cold air from the north invaded into the energy front, providing favourable thermal, dynamic and water vapour conditions for a large range of strong convection for a long time. Additionally, topographic blocking effects induced backward propagation of mesoscale convective systems (MCS), which together with synoptic-scale systems gave rise to a “train effect”. (3) In this process, the short-term predictability of heavy rain was high, but the predictability of precipitation extremes and strong central falling areas was low. In comparison, the global model had more advantages for the precipitation forecast of the low pressure main body stage, among which ECMWF yielded the best results, while the mesoscale model had more advantages for the precipitation forecast of the spiral cloud belt stage.

In this paper, the characteristics of a squall line that occurred in central and northern Zhejiang on 21 July 2022 are analyzed by using S-band dual-polarization radar, X-band phased array radar data, wind retrieval, lightning location and ERA5 reanalysis data. The results show that: The squall line affected the area for up to 6 hours, causing large-scale winds of 8-10 levels along the way. The pressure surge ahead of the strong winds and precipitation at the extremely strong wind station served as a precursor to extreme winds. The squall line occurred under the control of the subtropical high. The dry and cold air at the bottom of the high-level cold vortex invaded, with the high-energy and high humidity in the low-level forming an unstable layer structure, which provided favourable environmental conditions for the generation and maintenance of the squall line. The observation of the S-band dual polarisation radar showed that the evolution of squall lines went through processes such as initiation, merging, growth, maturity, and disappearance. The initial convection was formed in the mesoscale convergence lines in the northwest and east of Hangzhou. Strong storms in the northwest of Hangzhou moved faster than those in the east, leading to the rapid merging and development of the two into squall lines. After the squall line entered the south bank of the Hangzhou Bay, the storm outflow overlapped with the mesoscale convergence centre on the south bank of the Hangzhou Bay, leading to the rapid development of the squall line to its mature stage. In addition, the growth of vertical wind shear was also conducive to the rapid development of the squall line. Strong winds often occurred in areas with dense cloud flashes. The X-band phased array radar could finely detect the vertical structure of strong cells before and after extreme winds occurred. The development of ZDR and KDP columns in mature strong cells represented storm intensification, and the rapid decline of the echo centroid could indicate surface winds. The falling and dragging of precipitation particles intensified the sinking motion, which further enhanced the strong winds. The configuration of inclined upward and downward airflow in strong squall lines ensured that the falling of precipitation particles did not affect the upward airflow, which was beneficial for the development and maintenance of storms. The oblique sinking airflow met the near-surface environmental wind, triggering the development of frontal convection, which was conducive to the propagation of squall lines downstream.

This study uses litchi from Conghua and Zengcheng regions in Guangzhou as insurance targets, and combines the meteorological disasters throughout the entire growth period of litchi to design litchi meteorological index insurance products and verify insurance payout rates. Based on the litchi yield data and historical meteorological data, litchi yield reduction rate models are structured. Based on the correlation between meteorological factors and yield reduction rate, key meteorological indicators that affect litchi yield are screened. Further, the distribution of litchi meteorological index is fitted, the insurance pure premium rate and the insurance amount per unit area for different meteorological index grades which trigger compensation are determined. The meteorological index is set to 2 to 4 levels; different weather index levels of the amount of compensation are determined; and the rationality of compensation rates at different time scales is verified. In this study, the time scale of litchi yield and planting area data is from 2001 to 2020, while the time scale of meteorological data is from 1959 to 2020. The results show that the precipitation index at the flowering stage of litchi in Conghua district, the low-temperature index at the heading stage, and the precipitation index at the ripening stage of litchi in Zengcheng district are the key meteorological indicators that lead to the reduction of litchi yield in the two regions mentioned above, respectively. When the cumulative precipitation in Conghua district reaches 350 mm at the flowering stage of litchi, the minimum temperature is 3.3 ℃ at the heading stage of litchi, and the cumulative daily rainfall of ≥100 mm at the ripening stage of litchi lasts 2 days in Zengcheng district, a claim is triggered. The premium amounts corresponding to different levels of three meteorological indices are between 448-2582 yuan/hm2, 522-2567 yuan/hm2, 1403-3284 yuan/hm2, respectively. The compensation ratios for different meteorological indices and levels are between 7%-10%, 7%-10%, and 10%-12%, respectively. The average payout rates of Conghua district and Zengcheng district at three times in 10, 20 and 30 years are 77.0%, 69.6% and 63.7% respectively, which is consistent with the requirement of 65%-75% of the insurance company’s claim rate. The designed insurance product of Litchi Meteorological Index can provide a reference for a new round of policy agricultural insurance in Guangzhou, and provide technical support for litchi farmers to disperse and transfer the risk of meteorological disasters.

Based on the observation data from the coastal island stations in Beibu Gulf and the fifth generation ECMWF reanalysis (ERA5) data, the probability of occurrence of mixed-type (that is, high-pressure rear type to southwest inverted trough type) sea fog processes in Beibu Gulf is analyzed. Furthermore, the typical mixed-type sea fog process from 15 March to 23 March 2016 is selected for in-depth analysis. The results show the following: (1) Compared with other types of sea fog processes, the probability of occurrence of the mixed-type sea fog processes was the highest during 2015-2020, with a cumulative number of 15 times (58 days), accounting for 34.9% (39.5%). (2) The typical mixed-type sea fog process from 15 March to 23 March 2016 was characterised by long duration and wide range. In the early stage, the Beibu Gulf was influenced by the easterly flow behind the high pressure entering the sea. In the later period, the plateau trough developed and moved eastward, which guided the warm and humid south-western airflow to expand inland, and the easterly airflow gradually turned to the southerly airflow at the edge of the south-west inverted trough in Beibu Gulf. (3) In terms of thermal conditions, the strong warm and humid transport of the low-level jet stream increased the specific humidity over the Beibu Gulf, and then the water vapour saturated layer extended from the near-surface layer to 800 hPa on the one hand. It also contributed to the establishment of the temperature inversion layer on the other hand. And the inversion layer cooperated with the high humidity region below to reach a stable state. As for the dynamical conditions, the wind shear in the vertical direction maintained a certain intensity, which was favourable to the turbulent mixing in the boundary layer. The cooperation of the wind shear with the baroclinic atmosphere was the configuration that favoured the maintenance of a stable layer in the low-layer. In addition, the weak upward motion below 800 hPa was conducive to water vapour uplift and condensation in the low-layer, which promoted the development and maintenance of the sea fog. (4) During the occurrence of the typical mixed-type sea fog, the air temperature was mostly higher than the sea surface temperature in each sea area. And the difference between the air temperature and sea surface temperature in the early stage (late stage) was between 0 and 1.5 ℃ (1.5 and 2.5 ℃).

There are many ships and ports in Shanghai coastal zones, where disastrous weather occurs frequently. Meteorological disasters often threaten the safety of people’s lives and properties along the coast and in the ports. In the past, the meteorological warnings for Shanghai coastal zones are mainly based on those issued for Yangshan Port by Shanghai Marine Meteorological Centre (SMMC), which are called “unified warnings”. However, there are obvious differences in the time and intensity of meteorological disasters in each region, and the unified warnings cannot meet the needs of the production and operation of the shipping and ports. In 2020, Shanghai coastal zones were divided into five sub-zones, where the meteorological forecast and warnings were carried out separately from July 2020. Based on hourly observational data of the representative stations in Shanghai coastal zones and warning signal data from 2016 to 2022, the gale events are selected to analyse the statistical characteristics of temporal and spatial distribution and evaluate the forecast quality and the economic benefits of the zonal warnings. The results show that: (1) The farther away from the coastline, the more gale days, the higher the wind speed and the longer the duration; the higher the wind speed during the process, the more obvious the difference of wind scale. In particular, the wind scale caused by typhoons can range up to 5 levels. (2) Compared with the “unified warning”, the missing alarm rate (MAR) of gale warnings in each sea area has been reduced significantly, by up to 5%, the false alarm rate (FAR) for the western part of Yangtze River estuary is reduced by more than 8% and the TS score is significantly improved by more than 10%. (3) The advance time of gale warnings has been reduced by more than 3 hours, the maintaining duration has been shortened by more than 16 hours at most, which can reduce the loss of nearly 18 million RMB and improve the production efficiency of the coastal zones of Shanghai greatly. The results of this paper show that refined marine meteorological forecasts and early warnings provide a safety guarantee for marine transportation and port production operations, resulting in significant social and economic benefits and the enhancement of the comprehensive guarantee level of marine meteorological services in Shanghai. In the next step, we will continue to research and develop more refined objective forecast methods adapted to this business, then build a regional shared operational system platform and extend it to the Yangtze River Delta region, so as to promote the high-quality development of shipping meteorological integration in the Yangtze River Delta region.

To enhance the forecasting capability for daily extreme wind speeds, particularly for winds exceeding force 8, this paper uses the “past 3 h gust” wind speed forecast output from the European Centre for Medium-Range Weather Forecasts (ECMWF) model as the primary input factor. Additionally, the paper addresses the extremely uneven sample distribution in the daily extreme wind speed series (samples with wind force above level 8 constitute a very small proportion of the total sample, while samples with wind force below level 5 constitute the vast majority). Moreover, the ECMWF model’s “past 3 h gust” wind speed forecast tends to overestimate low-level winds and underestimate high-level winds. Therefore, the paper leverages nearly five years of surface observations and ECMWF model “past 3 h gust” forecast data to develop a Tabnet-based daily extreme wind classification correction forecast model. The model’s input design includes previous observations, geographic information of the stations, ECMWF forecast fields, and previous forecast error terms. In the evaluation of an independent sample over one and a half years, the new correction forecast model reduces the mean absolute error (MAE) by 45.2% and the root mean square error (RMSE) by 25.7% compared to the interpolated ECMWF model. Furthermore, for wind force levels 1-5 and above 8-9, the new correction forecast model significantly improves the forecasting accuracy compared to the method using interpolated ECMWF forecast fields, demonstrating the feasibility of this forecasting approach.The model is constructed with a focus on overcoming the inherent limitations of the ECMWF model’s wind speed forecasts. By incorporating comprehensive input factors such as historical observation data, the geographical context of observation stations, and systematic forecast error corrections, the model aims to provide a more accurate prediction of extreme wind events. The primary challenge addressed by the model is the skewed distribution of wind force levels in the dataset, where extreme wind events are underrepresented. The innovative use of the Tabnet algorithm allows for a sophisticated analysis and adjustment of the forecast data, thus ensuring higher accuracy in predicting both low and high wind force levels. The independent validation over an extensive period highlights the robustness of the model. The significant reduction in MAE and RMSE underscores the model’s enhanced performance. Specifically, the accuracy improvements for the critical wind force levels 1-5 and 8-9 plus indicate the model’s practical applicability in real-world scenarios. This advancement is crucial for sectors reliant on precise wind forecasts, such as maritime operations, aviation, and disaster preparedness. The results clearly suggest that integrating historical data and addressing the ECMWF model’s biases can lead to substantial improvements in extreme wind speed forecasting. In conclusion, the development of the Tabnet-based correction forecast model represents a significant step forward in meteorological forecasting. By effectively addressing the biases and limitations of existing models, this new approach offers a more reliable tool for predicting extreme wind events.

The increasing frequency, intensity and scope of extreme heat events due to climate change, which is mainly characterised by significant warming, is one of the current key climate stressors for sustainable development in terms of socio-economics, ecological balance and agricultural production in Jiangxi Province. High-temperature dangerousness evaluation is the basic work of high-temperature disaster risk assessment. However, in Jiangxi Province, the current research on high-temperature hazards mainly focuses on the analysis of trends and spatial distribution patterns, and few studies are conducted to reveal the risk of high-temperature occurrence through disaster risk theory. In this paper, based on the daily maximum temperature data of 79 meteorological stations in Jiangxi Province from 1961 to 2022, the trends of three disaster-inducing factors (the number of high-temperature days, the extreme maximum temperature and the high-temperature intensity) and their values under four return periods (1 in 5 years, 1 in 10 years, 1 in 20 years, and 1 in 50 years, respectively) are analysed using the least square method and the Kernel density estimation method, respectively. Then, through K-mean cluster analysis, the dangerousness distribution of each disaster-causing factor is obtained and a comprehensive high-temperature dangerousness map is produced. Finally, according to the disaster risk theory, the agricultural heat risk is assessed by the product of high-temperature dangerousness, agricultural exposure (quantified by land use cover) and agricultural fragility (quantified by gross domestic product kilometre gridded data). The results show that: (1) The overall trend of the number of high-temperature days, extreme maximum temperature and high-temperature intensity in Jiangxi Province during 1961-2022 shows an increasing trend, but the trend has a phased character, with a decreasing trend before 1997. (2) The dangerousness of each disaster-inducing factor is relatively high, with the proportion of high-risk areas in the province ranging from 41.7% to 61.4%. (3) The comprehensive dangerousness shows a spatial distribution pattern of low in the north and low in the centre, and the high-risk areas are mainly concentrated in the eastern part of Shangrao and most parts of Ji’an. (4) Agricultural medium-high risk zones are consistent with the spatial distribution of the dangerousness map. However, due to the uneven distribution of agricultural fragility, the low-risk zone is more surrounded by cities, and is mainly concentrated in southern Ganzhou, most of Xinyu, north-central Nanchang, and eastern Jiujiang. This paper can provide some reference for the comprehensive risk assessment of meteorological disasters.

NO2 is the common precursor of the secondary conversion of PM2.5 and O3. Understanding its change characteristics and influencing factors is of great significance for the collaborative treatment of PM2.5 and O3. Based on the VLF/LF three-dimensional lightning location monitoring system, SNPP/VIIRS satellite fire point data, NO2 column density in Sentinel-5P NRTI NO2 data products and other data, using various statistical methods, selecting February to April with high NO2 density and June to August with frequent lightning activities, this paper compares and analyses the influence of lightning activities on NO2 density in region A (96.5°-102°E, 20.5°-24°N) with high biomass burning in southwest Yunnan and its surrounding areas and region B (102°-104°E, 24°-26°N) with high human activities in central Yunnan. The results show that: (1) There are obvious differences in the spatial and temporal distribution of the number of lightning and NO2 column density in region A and region B. The NO2 column density outside area A is higher than that in China, but the distribution of lightning times is the opposite. The NO2 column density in region B decreases from Kunming to the surrounding area, and the number of lightning is less and more. In the dry season (November to April of the next year), the concentration of NO2 density is higher, and the rainy season (May to October) is lower, and the number of lightning is opposite. (2) From February to April, the NO2 column density in region A and region B has a significant positive spatial correlation with the number of fire points and anthropogenic CO2 emissions, respectively, but a significant negative correlation with the number of lightning. (3) Lightning activity is mostly accompanied by obvious rainfall (R≥1 mm). When the lightning activity is weak, the wet deposition effect of rainfall on the ground NO2 density is obvious, and the wet deposition effect of rain falling from the stronger lightning activity cannot completely offset the contribution of lightning to the increase of ground NO2 density. (4) The change of surface NO2 density during the first lightning from June to August is more regular than that from February to April, which shows that the ground NO2 density increases hourly in the first 6 hours and decreases slowly hourly in the last 3 hours. (5) The ground NO2 density on the lightning days in the two regions is generally higher than those on the days without lightning. Changes in biomass combustion intensity, rainfall intensity and planetary boundary layer height have a significant impact on the ground NO2 density.

The wind profile radar data observed from the western mountainous and eastern plain areas of Chengdu from 1 Jan to 31 Dec 2022 are used to analyse the regional differences in the vertical structure and daily variation trend of the boundary layer wind field. The results indicate that: (1) There are regional differences in the prevailing wind direction of the lower boundary layer in Chengdu, with north-north-east winds prevailing in the western mountains area and northeast winds prevailing in the eastern plains, and without seasonal difference. While there is no regional difference in the prevailing wind direction between the east and west regions in the middle and upper boundary layer, both occur alternately with northeast and southwest winds that are consistent with the direction of the western mountain range. (2) The variation trend of the average wind speed profile in the boundary layer between the eastern and western areas of Chengdu is consistent, and the horizontal wind speed in the eastern plain area of the same detection height layer is greater than that in the western mountains area. (3) The local mountain-valley breeze is significant in the lower boundary layer of the western mountains area; the valley breeze with southeast winds prevails during the day; and the mountain breeze with northwest winds prevails at night. (4) There are no regional differences in the diurnal variation characteristics of horizontal wind speed at the same detection height layer, and it is a “single peak and single valley” type, with a peak during the day and a valley at night. (5) The differences in the wind field characteristics of the lower boundary layer between the east and west regions of Chengdu are mainly caused by the local complex terrain of the western mountains area.

Abstract Our understanding of drought evolution and land-atmosphere interactions under climate mitigation scenarios remains limited. Here, we analyzed future drought under net-zero and net-negative emission scenarios using the Community Earth System Model version 2, particularly focusing on three atmospheric CO2 states: linearly increases, decreases, and a return to the initial state. Interestingly, results revealed that net-zero emissions are more effective for drought mitigation than net-negative targets. Drying trends and drought characteristics — such as the duration, frequency, intensity, and area expansion are prominently increased under net-negative emissions due to higher potential evapotranspiration (PET). This is because the soil moisture and temperature couplings are stronger over drought regions and years, especially under net-negative forcing, with notable impacts in Central Africa and South Asia. Nevertheless, both target scenarios offer regional benefits, such as weakened dryness. This suggests that mitigating CO2 alone may not be sufficient to manage future droughts, highlighting the need for advanced water management strategies.

Abstract The impacts of Arctic sea ice loss on summertime weather in the Northern Hemisphere have garnered considerable attention. Despite the extensive focus on this relationship, the influence of tropical systems on Arctic regions has been relatively underexplored, with only a limited number of existing studies concentrating exclusively on either dynamic or thermodynamic effects. This study aims to address this gap by examining a barotropic anomalous circulation over the Arctic region associated with Indian Summer Monsoon (ISM) rainfall. The observed anomalous circulation exhibits a distinct zonally dipole pattern, characterized by anomalous high pressure over Northern Canada and Asia, coupled with anomalous low pressure located east of Greenland. Verification through model experiments demonstrates that the diabatic heating of ISM rainfall contribute to the formation of the observed ISM-related circulation. The modulation of surface clear sky downwelling longwave radiation ( $${{DLR}}_{{clear\; sky}}$$ DLR clear sky ) by the circulation anomalies over the Arctic modified surface thermal conditions, thereby influencing subsequent variations in sea ice thickness and concentration. Under anomalous high pressure, $${{DLR}}_{{clear\; sky}}$$ DLR clear sky increases, leading to a decline in sea ice thickness, and vice versa. Additionally, from a dynamic standpoint, low-level wind-driven sea ice drift helps shape the spatial distribution and extent of sea ice cover. Besides, the impacts of ISM on Arctic sea ice are largely independent of contemporary ENSO. These findings present fresh perspectives on the role of extrapolar phenomena, such as the ISM, in driving variability in Arctic sea ice during the summer months. This enhanced comprehension holds promise for enhancing predictions of changes in summertime Arctic sea ice extent.

Abstract Coral reconstruction often serves as a major proxy of high-resolution sea surface temperature (SST) variability beyond the instrumental era. However, coral reconstructions are sparse and are usually studied for interannual variability, with few studies on the monthly features. In this study, we reconstruct the monthly SST spatial field by applying the paleoclimate data assimilation method to the coral records of the latest CoralHydro2k data set for the instrument period of 1880–2000. A comparison with observed SST variability shows that our assimilated tropical SST variability performs reasonably well for the seasonal cycle and monthly ENSO characteristics, notably the phase-locking and onset timing, and more realistic spatial fields relative to the model simulations. This study suggests the feasibility of applying paleoclimate data assimilation to reconstruct the monthly SST in the historical period.

Abstract The Southeastern Siberia (SES) region has recently experienced increasingly extensive wildfires in spring, which have threatened its large carbon sequestration capacity from vast forests and peatlands. However, the underlying mechanisms propelling the increased fires and their potential responses to future climate change remain unclear. Here, by using reanalysis data and climate model output together with a deep learning model, we explore the relationship between positive-phase North Atlantic Tripole (NAT) sea-surface temperature anomalies and SES wildfire increases and project the future trend in SES wildfire intensities under climate change. We found that the positive-phase April NAT enhances the Siberian anticyclone, causing increased temperatures and snowmelt via strengthened transport of warm-air advection into the SES region. The latter process heightens the exposure of local high-density peatlands to favorable conditions for fire ignition and leads to more intensive wildfire incidents. We further demonstrate that the projected NAT variations can drive interdecadal changes in future April SES wildfires. With future phase shifting of NAT modes under global warming, the regionally averaged burned area in SES could be increased by 47–62% under different warming scenarios from 1982–2014 to 2015–2100. Our findings reveal the climate-driven escalation of future wildfires in SES in the context of global warming and call for active and urgent fire management strategies to mitigate the fire risk.

Abstract The vertical variations and key drivers of ozone and its precursors, namely NOx and VOCs, in the atmospheric boundary layer, have vital impacts on surface ozone budgets but are poorly understood so far. Using online gradient measurements from a 356 m tower, we obtained continuous vertical profiles of ozone and its precursors, which exhibited strong gradients throughout the day. In the daytime, the vertical gradients of ozone precursors are significantly regulated by reactions with OH radicals. At night, our observations confirmed more intense VOC reactions with NO3 radicals in the residual layer than in the boundary layer. Additionally, we found that residual layer entrainment could contribute to over half of the boundary-layer ozone enhancements in the morning periods. Our results underscore the importance of considering vertical changes of ozone and its precursors in the atmospheric boundary layer when developing future ozone mitigation strategies.

Abstract The Arctic Oscillation (AO) has been observed to undergo distinct decadal structural fluctuations that significantly influence regional weather and climate. Understanding the drivers and mechanisms behind the AO’s spatial nonstationarity is critical for improving climate predictions related to the AO. We present evidence that the Atlantic Multidecadal Oscillation (AMO) plays a pivotal role in modulating AO’s Pacific center in recent decades. The poleward amplified cooling associated with negative AMO enhances the north-south temperature gradient and results the strengthened westerly winds and stratospheric polar vortex (SPV) responses, which reflects more planetary waves from the North Pacific to the North Atlantic. This enhances the atmospheric coupling between these regions and leads to a more pronounced Pacific center within the AO pattern. Numerical simulations from ECHAM5 and 35 CMIP6 models further corroborate the essential role of the AMO. These findings advance our understanding of the mechanisms driving the variability of the AO pattern.

Abstract The record-breaking temperatures in Europe during the 2022 summer were associated with over 60,000 heat-related deaths. By combining epidemiological models with detection and attribution techniques, we attribute half of this mortality burden (~56% [95% CI 39–77%]) to anthropogenic warming. Likewise, this applies to all sexes, ages, and heat-related mortality burdens during previous years (2015–2021). Our results urgently call for increasing ambition in adaptation and mitigation.