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We evaluate tropical cyclones (TCs) in a set of thermodynamic global warming (TGW) simulations over the continental United States (CONUS). A 12 km simulation forced by ERA5 provides a 40-year historical (1980–2019) control. Four complimentary future scenarios are generated using thermodynamic deltas applied to lateral boundary, interior, and surface forcing. We curate a data set of 4,498 6-hourly TC snapshots in the control and find a corresponding “twin” in each counterfactual, permitting a paired comparison. Warming results in an increase in mean dynamical TC intensity and moisture-related quantities, with the latter being more pronounced. TC inner cores contract slightly but outer storm size remains unchanged. The frequency with which TCs become more intense is only moderately consistent, with snapshots having increased hazards ranging from 50% to 80% depending on warming level. The fractions of TCs undergoing rapid intensification and weakening both increase across all warming simulations, suggesting elevated short-term intensity variability.

Abstract: Precipitation and temperature extremes from daily data indexed using the CLIMDEX methodology were calculated over the Central American region. The data comprises the coarsened versions of the Climate Hazards and Infrared Precipitation with stations (CHIRPs) and the corresponding data set for temperature (CHIRTs) from the year 1981 to 2020 and 1983 to 2016, respectively. The objective is to detect trend patterns in extremes in recent periods, use novel statistical techniques for assessing the trend significance and study the monthly and annual trends for each of the indices. Trends of extreme temperature indices show more consistent, robust and widespread significant results according with the observed warming of the region. Significant extreme precipitation indices trends are more localized, and therefore harder to analyse, but it seems that one robust result from several indices is the trend toward more intense extreme precipitation events in Costa Rica. The findings of this work suggest possible impacts in human and environmental systems across the region.

In the mid‐latitudes extreme precipitation events are strongly associated with cold fronts. By exploring drivers across different scales and relating them to precipitation, we aim to improve our understanding of processes influencing cold frontal extremes. Using hourly ERA5 data over Europe and the North Atlantic, cold fronts are detected and the associated conditions are identified. Quantile regression models are employed to find drivers of frontal precipitation and to quantify these relations. Additionally, we use composites to study the synoptic conditions and meso‐scale structure of extreme events. We find that humidity close to the detected fronts, convergence and the low level jet speed contribute most to the formation of extreme precipitation. Synoptic conditions favoring the formation of extreme events are also identified. These results improve our understanding of cold frontal processes leading to precipitation. Additionally, they provide the foundation for a process‐based evaluation of frontal dynamics in climate models. Plain Language Summary: Extreme rain, snow and hail events in the mid‐latitudes are often caused by atmospheric cold fronts. To understand what makes certain events extreme, we need to study the processes and conditions influencing these fronts. In this study, we analyze the characteristics of cold fronts and their connection to extreme rain. First, we identify fronts in the ERA5 climate reanalysis data by finding strong changes in temperature and humidity over Europe and the North Atlantic. Next, we examine the conditions near these fronts. Using statistical models, we link these conditions to rain intensities. Additionally, the large‐scale weather patterns are studied by looking at the average conditions. We find that atmospheric moisture content and the strength of certain frontal circulation patterns play the biggest role in creating extreme weather. These results help us to better understand cold fronts and provide the basis for evaluating frontal conditions in climate models. Key Points: Cold fronts are responsible for up to 70% of hourly extreme precipitation over EuropeERA5 captures the key structural and circulation features of cold fronts that are expected from theoryMoisture content close to the front, low‐level jet speed and convergence are identified as the main drivers of frontal precipitation [ABSTRACT FROM AUTHOR]

In this study, we examined how the nonlinearity  α  of the Langevin equation influences the behavior of extremes in a generated time series. The extremes, defined according to run theory, result in two types of series, run lengths and surplus magnitudes, whose complex structure was investigated using the visibility graph (VG) method. For both types of series, the information measures of the Shannon entropy measure and Fisher Information Measure were utilized for illustrating the influence of the nonlinearity  α  on the distribution of the degree, which is the main topological parameter describing the graph constructed by the VG method. The main finding of our study was that the Shannon entropy of the degree of the run lengths and the surplus magnitudes of the extremes is mostly influenced by the nonlinearity, which decreases with with an increase in  α. This result suggests that the run lengths and surplus magnitudes of extremes are characterized by a sort of order that increases with increases in nonlinearity. [ABSTRACT FROM AUTHOR]

Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low‐cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low‐cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio‐temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity. Plain Language Summary: Ideally, to study how damaging phenomena like heatwaves, droughts and downpours will change in the future under global warming, we would want a large number of climate model runs producing many realizations of climate futures that we can analyze and from which the new characteristics of climate extremes can be quantified. Currently, emulators can rapidly generate simulations of future climate, but often to relatively low frequencies, as decadal, annual or monthly output at best in most cases, which is insufficient for studying extreme events that occur on a daily timescale. We show how it is possible to train a machine learning model to produce daily series of temperature or precipitation from monthly averages, thus facilitating a more robust investigation into how extreme events will change in the future. Key Points: Earth system models (ESMs) are key devices for understanding how human actions will affect the future global climateComputational demands prevent us from running them for more than a handful of scenarios. ESM emulators are most commonly limited to the monthly frequencyWe present DiffESM as a data‐driven emulator of ESMs that closely matches the spatiotemporal distributions of ESMs at daily frequency [ABSTRACT FROM AUTHOR]

Three ~12‐km reanalysis‐driven regional climate models (RCMs) are evaluated in terms of capturing climatologies and extremes of precipitation, temperature and surface wind over Aotearoa/New Zealand (NZ). NZ provides an excellent case study for evaluating high‐resolution RCMs due to its coastal and complex terrain and isolated geographical position in the midlatitudes, exposed to both tropical and polar influences. Overall, we find that the RCMs faithfully reproduce the observed climate, with precipitation and temperature climatologies particularly well‐captured. However, the RCMs display significant differences from observations in capturing the surface wind climatology, highlighting a remaining key challenge. The excess "drizzle problem" is apparent to varying degrees, leading to a weaker representation in the length of meteorological drought in some regions. The RCMs also diverge in reproducing diurnal temperature range which appears partly related to cloud cover. Finally, we discuss the important role of observational uncertainty in the context of model evaluation. [ABSTRACT FROM AUTHOR]

Water temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non‐extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro‐meteorological drivers differ from those of non‐extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co‐occurrence analysis to examine the relationship between water temperature extremes and hydro‐climatic extremes. Our results show that mean water temperature has increased by +0.38±0.14 ${+}0.38\pm 0.14$°C per decade, leading to more extreme events at high elevations in spring and summer. While non‐extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes. Key Points: Mountain rivers in Europe have warmed with a rate of +0.38 ± 0.14 °C per decade over the past 30 yearsThe number of extreme water temperature events has increased, especially in high‐elevation catchments and in spring and autumnNon‐extreme water temperature is mainly driven by air temperature, but extremes are also impacted by soil moisture, baseflow and meltwater [ABSTRACT FROM AUTHOR]

Streamflow exhibits persistent decadal variability; however, it is unclear if the magnitude and spatial extent of these variabilities are symmetric for droughts and floods. Here, we examine drought‐rich and flood‐rich periods in 319 streamflow gauges in Brazil from 1940 to 2020. Drought‐ and flood‐rich periods are detected by computing annual streamflow minima and maxima time series and using scan statistics to verify if events exceeding a threshold follow a Bernoulli process. We contrast streamflow time clustering with rainfall, evaporation, water abstraction, the Atlantic Multidecadal Oscillation (AMO), and the Pacific Decadal Oscillation (PDO). We detected a higher spatial frequency of drought‐ than flood‐rich periods. For 5‐year return period thresholds, drought‐rich periods are observed in 81% of the basins, 16.7 times the false positive rate (4.8%) and 4.7 times flood‐rich periods (17%). This asymmetry is linked with sharp increases in water abstractions since the 1990s and a higher prevalence of rainfall‐poor periods (41% of gauges) compared to rainfall‐rich (22% of gauges), which we interpret as being further amplified into drought‐rich periods due to an interannual persistence of water storage deficits. Brazil experienced a dry period until the 1960s, extensive flooding in the 1980s, and record low flows from the 2000s onward. Drought and flood‐rich periods are well aligned with rainfall clustering, water abstractions, the AMO and PDO. Droughts‐rich periods are more frequent in shorter time scales (several years to one decade) and flood‐rich periods in longer time scales (a few decades). Our findings highlight the nonlinearity and asymmetry of drought and flood change at decadal scales. Key Points: We identify periods when streamflow anomalously often falls below/exceeds 5‐year return period thresholds for 319 basins in BrazilDrought‐rich periods were detected in 81% of the basins, while flood‐rich periods were detected in 17% of the basins in 1940–2020The high drought frequency is caused by increased water abstractions and long‐term rainfall deficits associated with climate modes [ABSTRACT FROM AUTHOR]

We offer an alternative proof, using the Stein-Chen method, of Bollobás' theorem concerning the distribution of the extreme degrees of a random graph. Our proof also provides a rate of convergence of the extreme degree to its asymptotic distribution. The same method also applies in a more general setting where the probability of every pair of vertices being connected by edges depends on the number of vertices. [ABSTRACT FROM AUTHOR]

Agriculture is the most vulnerable sector to climate change and weather anomalies. In rainfed ecosystems, rainfall amount and distribution are the deciding factors for crop planning. Rainfall variability and trend analysis help to minimize weather related risk and enhance the productivity by proper crop planning. The study on rainfall distribution and its trend was assessed in a rainfed area of Ramanathapuram district of Tamil Nadu, India by using Simple descriptive statistics along with non-parametric analysis of Men-Kendall analysis, Sen's slope estimator; innovative trend analysis and sequential Men-Kendall test were carried out to understand the quantity of rainfall and rainy-day variability for the period of 1982 to 2022. The results indicated that the mean annual rainfall is 716 mm and spread over 56 rainy days with 21 percent of coefficient of variation. Seasonal analysis indicated that, there was no significant trend was observed among the seasons, but NEM showed a significant increase in trend (p-value = 0.048) with a positive magnitude of 3.2 mm/year of rainfall. An increasing trend in heavy rainfall events during the NEM season is evident with 92 percent. [ABSTRACT FROM AUTHOR]

Bias in regional climate model (RCM) data makes bias correction (BC) a necessary pre-processing step in climate change impact studies. Among a variety of different BC methods, quantile mapping (QM) is a popular and powerful BC method. Studies have shown that QM may be vulnerable to reductions in calibration sample size. The question is whether this also affects the climate change signal (CCS) of the RCM data. We applied four different QM methods without subsampling and with three different subsampling timescales to an ensemble of seven climate projections. BC generally improved the RCM data relative to observations. However, the CCS was significantly modified by the BC for certain combinations of QM method and subsampling timescale. In conclusion, QM improves the RCM data that are fundamental for climate change impact studies, but the optimal subsampling timescale strongly depends on the chosen QM method. [ABSTRACT FROM AUTHOR]

The 1997 New Year's flood event was the most costly in California's history. This compound extreme event was driven by a category 5 atmospheric river that led to widespread snowmelt. Extreme precipitation, snowmelt, and saturated soils produced heavy runoff causing widespread inundation in the Sacramento Valley. This study recreates the 1997 flood using the Regionally Refined Mesh capabilities of the Energy Exascale Earth System Model (RRM-E3SM) under prescribed ocean conditions. Understanding the processes causing extreme events informs practical efforts to anticipate and prepare for such events in the future, and also provides a rich context to evaluate model skill in representing extremes. Three California-focused RRM grids, with horizontal resolution refinement of 14 km down to 3.5 km, and six forecast lead times, 28 December 1996 at 00Z through 30 December 1996 at 12Z, are assessed for their ability to recreate the 1997 flood. Planetary to synoptic scale atmospheric circulations and integrated vapor transport are weakly influenced by horizontal resolution refinement over California. Topography and mesoscale circulations, such as the Sierra barrier jet, are better represented at finer horizontal resolutions resulting in better estimates of storm total precipitation and storm duration snowpack changes. Traditional time-series and causal analysis frameworks are used to examine runoff sensitivities state-wide and above major reservoirs. These frameworks show that horizontal resolution plays a more prominent role in shaping reservoir inflows, namely the magnitude and time-series shape, than forecast lead time, 2-to-4 days prior to the 1997 flood onset.

For { B H (t) = (B H , 1 (t) , ... , B H , d (t)) ⊤ , t ≥ 0 } , where { B H , i (t) , t ≥ 0 } , 1 ≤ i ≤ d are mutually independent fractional Brownian motions, we obtain the exact asymptotics of P (∃ t ≥ 0 : A B H (t) - μ t > ν u) , u → ∞ , where A is a non-singular d × d matrix and μ = (μ 1 , ... , μ d) ⊤ ∈ R d , ν = (ν 1 , ... , ν d) ⊤ ∈ R d are such that there exists some 1 ≤ i ≤ d such that μ i > 0 , ν i > 0. [ABSTRACT FROM AUTHOR]

We propose that in pictures both the extended limbs of actors and the ground are involved in gestalts for movement. Limbs extend to suggest more motion literally when a dancer is in air and refer to a canonical pose when on the ground. A running pony’s curled limbs off the ground depict fast action literally. A horse’s flying-gallop off the ground suggests speedy motion metaphorically. Cast shadows indicate the actor’s location with respect to the ground. We consider extended and curled limbs, on-ground and off, in literal and metaphoric pictures.

Climate change is currently one of the most important challenges faced by mankind. The purpose of this article is to give a coherent account of the scope and nature of these changes and their impact on universal security threats. The research process consisted largely of a review of the literature on the subject and an analysis of specialized, available documents and reports prepared by international environmental organizations. Climate change includes increases in temperature, changes in precipitation, sea level rise, extreme weather events and many other impacts that have enormous consequences for people, ecosystems and economies. Barriers are described that could prove to be an obstacle to implementing more sustainable development that could directly contribute to increased preventive and adaptive capacity, reduced emissions and vulnerability. The importance of adaptive capacity, which is a complex function of wealth, technology, information, skills, infrastructure, institutions and equity, was emphasized.

Background The intensity and frequency of climate extremes are exacerbating agricultural droughts, particularly impacting smallholder farming. This study assessing daily precipitation and temperature extremes in the context of climate change is crucial for local-scale climate change adaptation. Spatial changes of climate indices from 1981 to 2018 in three different agroecologies in the Hamassa watershed, Ethiopia, were examined based on the World Meteorological Organization’s (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Methods We obtained Daily temperature and precipitation data from the Ethiopia National Meteorological Agency (NMA). Additionally, I used data from focus group discussions (FGDs) and key informant interviews (KIIs) to corroborate the findings. We conducted the Mann- Kendall test and Sen’s slope estimation to assess the significance and magnitude of rainfall and extreme temperature changes in the watershed between 1981 and 2018. Furthermore, I examined the correlation between crop and standardized precipitation-evapotranspiration index (SPEI). Results The temperature data indicated that the warming condition is increasing overall agroecologies. However, the extreme indices from rainfall data indicate insignificant positive and negative trends in all agroecological zones (AEZs). The warmest day (Txx) is significantly increasing overall AEZs having magnitude values close to each other, 0.0420c, 0.03960c, and 0.03850c in the highland, midland, and lowland, respectively. The coldest day (Txn) also showed an increasing insignificant and significant trend in the highland, midland, and lowland, respectively. Results of cool days (TX10p) indicate a significant decreasing trend over all three AEZs. The magnitude of the decreasing trend is about -0.040c, -0.0450c, and -0.0360c in highland, midland, and lowland, respectively. Furthermore, the correlation result indicated a strong and significant relationship between crop production and climate variables (SPEI-), which varied degrees across Conclusions Results differ in different agroecologies demanding technical, institutional, and policy responses respective of Agroecologies.

Dynamical downscaling provides physics-based high-resolution climate change projections across regional and local scales. This is particularly important for island nations characterized by complex terrain, where the coarse resolution of global climate model (GCM) output often prohibits direct use. One of the main motivations for dynamical downscaling is to reduce biases relative to the host GCM at the local scale, which can be quantified through assessing 'added value'. However, added value from downscaling is not guaranteed; quantifying this can help users make informed decisions about how best to use available climate projection data. Here we describe the experiment design of the updated national climate projections for New Zealand based on dynamical downscaling. The global non-hydrostatic Conformal Cubic Atmospheric Model (CCAM) is primarily used for downscaling, with a global stretched grid targeting high resolution over New Zealand (12-km) and the wider South Pacific region (12–35-km). Focusing on the historical simulations, we assess added value for a range of metrics, climatological fields, extreme indices, and tropical cyclones. The main strengths of the downscaling include generally large improvements relative to the host GCM for temperature and orographic precipitation. Inter-annual variability in temperature is well captured across New Zealand, and several temperature and precipitation-based extreme indices show large improvements. The representation of tropical cyclones reaching at least category 2 intensity is generally improved relative to the large consistent under-representation in the host GCMs. The remaining biases are explored and discussed forming the basis for ongoing bias-correction work. [ABSTRACT FROM AUTHOR]

The 2021 heatwave over Western North America (WNA) led to record‐breaking air temperatures and human‐perceived heat stress (humidex) values. The event was accompanied by drier conditions driven by prolonged atmospheric blocking. During the heatwave, the maximum 6‐day means of humidex and temperature (HX‐6 and TX‐6) exhibited larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C), relative to 1981–2021 extended summer (June‐September) averages. Extreme indices of humidex show faster and larger increases than those of temperature, reflecting the nonlinear positive relationship between humidex and temperature. Future projections from a multi‐model ensemble of 19 Coupled Model Intercomparison Project Phase six (CMIP6) Global Climate Models (GCMs) clearly show an increase in humidex and temperature extremes, especially under intermediate and high emissions scenarios. Humidex indices (HX‐6 and HX95) show faster and larger increases than temperature indices (TX‐6 and TX95) for the same future years and global warming levels. Controlling for differences in GCM climate sensitivity to greenhouse gas forcing yields robust projections at various global warming levels, reducing the ranges of projected changes from the multi‐model ensemble. At 3.0°C global warming from pre‐industrial, the multi‐model ensemble projects occurrences of HX‐6, TX‐6, HX95, and TX95 over WNA that exceed 2021 levels to occur every 3.9, 1.7, 1.4, and 2.2 years, respectively, increasing to almost annually at 4.0°C. Plain Language Summary: In 2021 summer, Western North America (WNA) experienced an unprecedented heatwave, breaking air temperature and human‐perceived heat stress (humidex) records despite lower humidity due to prolonged atmospheric blocking. During the heatwave, consecutive 6‐day means of heat stress (HX‐6) and temperature (TX‐6) showed larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C) over the region, relative to their averages over the 1981–2010 summer period (June‐September). Heat stress increased faster and more significantly than air temperature due to their nonlinear relationship. Future projections from 19 climate models reveal clear increases in heat stress and temperature, especially under intermediate and high emission scenarios. Heat stress indices show larger and faster increases than temperature indices at the same future years and global warming levels. Accounting for uncertainty related to climate model sensitivity enhances projection reliability. At 3.0°C global warming from pre‐industrial levels, 2021‐level events over WNA are projected to occur every few years, becoming an almost annual occurrence at 4.0°C. The study underscores the critical need for robust projections using multi‐model ensembles. Key Points: Unprecedented 2021 summer heatwave over Western North America resulted in record‐breaking humidex and air temperature extremesLarger increases in humidex than temperature, exceeding their 2021 levels, under intermediate and high emissions scenariosRobust projections of multi‐model ensemble, mitigating climate sensitivity uncertainty of GCMs response to greenhouse gas emissions [ABSTRACT FROM AUTHOR]

North Atlantic midlatitude cyclones are among the most severe weather systems, causing enormous economic damages and threatening human lives. The cyclone is typically characterized by cyclonic convergent surface winds, strong updrafts, and precipitation. However, extreme surface winds are often observed within the cyclone where downdrafts develop. The present study investigates the dynamical and thermodynamical characteristics of the horizontal winds impinging on the cold frontal surface and the associated downdrafts. It is shown that the cyclonic winds into the cold frontal surface are mainly responsible for the downdrafts that transport the high‐altitude horizontal momentum to the surface and cause intense surface winds. About half of the North Atlantic midlatitude cyclones are accompanied by the downdrafts especially in the southern and western parts of the cyclone center. Plain Language Summary: In the midlatitudes, air temperature decreases toward the poles, and cyclone systems typically travel eastward along regions characterized by strong meridional temperature gradients. Over the cyclone‐influencing area, the cyclonic, counterclockwise rotating, wind induces southward cold advection on the western side of the cyclone center. Therefore, during the early phase of the North Atlantic midlatitude cyclone, the generation of the cold front, which has a steep gradient of temperature to the west of the cyclone center, and that is a common feature. Even after the formation of the cold front, the horizontal winds keep impinging on the frontal surface. Then the horizontal winds blocked by the frontal surface and downdrafts are induced. The downdrafts transport the upper‐level intense wind speed to the lower level strengthening the surface winds. Key Points: Surface winds within North Atlantic midlatitude cyclones are strengthened by strong downdrafts and downward momentum transportsDowndrafts can be generated by the horizontal winds impinging on the cold frontal surfaceCyclones tend to accompany the downdrafts more to the south and west of the cyclone center [ABSTRACT FROM AUTHOR]

More than 1000 years, the Meiyu–Baiu have shaped the uniqueness of natural resources, civilization and culture in the Yangtze River Basin of China and the main islands of Japan. In recent decades, frequent rainstorms and droughts have seemingly diminished the misty features of traditional Meiyu–Baiu rainfall. However, there is still no consensus on whether their traditional nature is suspended. In this study, we quantitatively demonstrate that the Meiyu–Baiu almost completely lost their traditional features during 1961–2023, ∼80% of which can be attributed to anthropogenic warming. Furthermore, in a warmer future, the traditional Meiyu–Baiu will be more unlikely to appear. This study underscores the urgency in adapting to climate shift because destructive extremes are measurably taking the place of mild and maternal rains. [ABSTRACT FROM AUTHOR]

Antarctic coastal surface winds affect ice‐sheet stability, sea ice, and local ecosystems. The strongest coastal winds are especially important due to the nonlinear relationship between wind speed and wind stress. We investigate the dynamics of extreme coastal winds using a simplified momentum budget calculated across the period 2010–2020 from the ERA5 reanalysis. The pressure‐gradient forcing term in the budget is decomposed into a large‐scale component and one associated with the temperature deficit layer. The role of budget terms across the coastal sector is compared for weak and strong winds. We then calculate composites of the top 100 easterly wind events across six east Antarctic coastal sectors, identifying terms responsible for the evolution of coastal extremes. A simple balance of terms exists offshore, dominated by large‐scale forcing, contrasting with the complex balance in the onshore sector where katabatic forcing is large. Large‐scale forcing explains 57% of offshore coastal wind‐speed variance overall, improving to 81% when budget terms associated with the temperature deficit layer and horizontal advection are included, with significant regional variation. The residual term plays an increasingly active role as wind speed increases. Extremes in all coastal sectors are associated with a synoptic‐scale transient dipole of pressure anomalies driving warm‐air advection. Although katabatic forcing is a very large term in magnitude, it is found to play a passive role, declining as wind speeds increase during extreme conditions. In some regions, an anomalous southerly component develops during extremes, which we attribute to an ageostrophic barrier wind. This research underscores the major role for large‐scale forcing in Antarctica's coastal winds, but also reveals a significant regional locally driven component. The results have implications for improving numerical model simulations of coastal easterlies and for studying their impacts on ocean circulation, sea ice, and ice‐shelf basal melt. [ABSTRACT FROM AUTHOR]

Accurate modeling of snow depth (SD) processes is critical for understanding global energy balance changes, affecting climate change mitigation strategies. This study evaluates the Coupled Model Intercomparison Project Phase 6 (CMIP6) model performance in simulating daily SD across Canada. We assess CMIP6 outputs against observed data, focusing on daily SD averages, snow cover durations, and rates of accumulation and depletion, alongside annual SD peaks for 11 major Canadian catchments. Our findings reveal that CMIP6 simulations generally overestimate daily SD by 57.7% and extend snow cover duration by 30.5 days on average. While three models (CESM2, UKESM1‐0‐LL and MIROC6) notably align with observed annual SD peaks, simulation biases suggest the need for enhanced model parameterization to accurately capture snow physics, particularly in regions with permanent snow cover and complex terrains. This analysis underscores the necessity of refining CMIP6 simulations and incorporating detailed geographical data for better SD predictions. Plain Language Summary: Precise simulations of snow depth (SD) are crucial for understanding changes in the Earth's energy balance and helping efforts to combat climate change. In this research, we evaluate how well the latest phase of the Coupled Model Intercomparison Project (CMIP6) can simulate the daily SD across Canada. By comparing these simulations to various SD data sets, we looked at average daily SD, how long snow remains on the ground, and how quickly it accumulates or melts away, focusing on 11 major catchments in Canada. Our study found that the CMIP6 models tend to simulate more SD, by about 57.7% on average, and suggest that snow cover remains around 30.5 days longer than observed durations. Although three specific models excel in matching the annual peak of daily SD, we noticed biases that point to a need for improving how these models represent various land covers. Our findings highlight the importance of making these models more accurate by using more detailed information about land cover properties, which helps better predict SD and understand its role in climate change. Key Points: Coupled model intercomparison project phase 6 (CMIP6) simulations mostly overestimate the average daily SD values by a median of 57.7% (6.9 cm)The average snow cover duration is overestimated by 30.5 days compared to station data, confirmed by the CMC data setCMIP6 simulations relatively capture extremes well, with a median bias of 12.73% [ABSTRACT FROM AUTHOR]

Quantifying the risk from extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Climate models capturing different scenarios are often the starting point for physical risk. However, accurate risk assessment for mitigation and adaptation often demands a level of detail they typically cannot resolve. Here, we develop a dynamic data‐driven downscaling (super‐resolution) method that incorporates physics and statistics in a generative framework to learn the fine‐scale spatial details of rainfall. Our approach transforms coarse‐resolution (0.25°) climate model outputs into high‐resolution (0.01°) rainfall fields while efficaciously quantifying the hazard and its uncertainty. The downscaled rainfall fields closely match observed spatial fields and their distributions. Contrary to conventional thinking, our results suggest that coupling simple statistics and physics to learning improves the efficacy of downscaling midlatitude rainfall extremes from climate models. Plain Language Summary: Accurate risk assessment of extreme weather events in a changing climate requires high‐resolution data, which is expensive for climate models to produce. Thus, there arises a need for downscaling (super‐resolution) methods. We show that by using simplified physics and statistics to prime generative adversarial learning, one can reconstruct observed high‐resolution mid‐latitude extreme rainfall fields from low‐resolution climate models with high fidelity. As a result, uncertainty quantifying downscaling (25:1 ratio) has become feasible, overcoming limitations of extant dynamical and statistical downscaling methods. Key Points: We leverage data, physics, and machine learning to develop an approach to downscale rainfallPriming a two‐step generative adversarial learning with simplified physics and statistics and applying optimal estimation‐based bias correction overcomes common limitations of conventional downscaling strategiesThe proposed downscaling method enables the rapid and robust estimation of extreme rainfall hazards [ABSTRACT FROM AUTHOR]

We consider the clustering of extremes for stationary regularly varying random fields over arbitrary growing index sets. We study sufficient assumptions on the index set such that the limit of the point processes of the exceedances above a high threshold exists. Under the so-called anti-clustering condition, the extremal dependence is only local. Thus the index set can have a general form compared to previous literature (Basrak and Planinić in Bernoulli 27(2):1371–1408, 2021; Stehr and Rønn-Nielsen in Extremes 24(4):753–795, 2021). However, we cannot describe the clustering of extreme values in terms of the usual spectral tail measure (Wu and Samorodnitsky in Stochastic Process Appl 130(7):4470–4492, 2020) except for hyperrectangles or index sets in the lattice case. Using the recent extension of the spectral measure for star-shaped equipped space (Segers et al. in Extremes 20:539–566, 2017), the Υ -spectral tail measure provides a natural extension that describes the clustering effect in full generality. [ABSTRACT FROM AUTHOR]

Graphical models with heavy-tailed factors can be used to model extremal dependence or causality between extreme events. In a Bayesian network, variables are recursively defined in terms of their parents according to a directed acyclic graph (DAG). We focus on max-linear graphical models with respect to a special type of graph, which we call a tree of transitive tournaments. The latter is a block graph combining in a tree-like structure a finite number of transitive tournaments, each of which is a DAG in which every two nodes are connected. We study the limit of the joint tails of the max-linear model conditionally on the event that a given variable exceeds a high threshold. Under a suitable condition, the limiting distribution involves the factorization into independent increments along the shortest trail between two variables, thereby imitating the behaviour of a Markov random field. We are also interested in the identifiability of the model parameters in the case when some variables are latent and only a subvector is observed. It turns out that the parameters are identifiable under a criterion on the nodes carrying the latent variables which is easy and quick to check. [ABSTRACT FROM AUTHOR]

Abstract In the mid‐latitudes extreme precipitation events are strongly associated with cold fronts. By exploring drivers across different scales and relating them to precipitation, we aim to improve our understanding of processes influencing cold frontal extremes. Using hourly ERA5 data over Europe and the North Atlantic, cold fronts are detected and the associated conditions are identified. Quantile regression models are employed to find drivers of frontal precipitation and to quantify these relations. Additionally, we use composites to study the synoptic conditions and meso‐scale structure of extreme events. We find that humidity close to the detected fronts, convergence and the low level jet speed contribute most to the formation of extreme precipitation. Synoptic conditions favoring the formation of extreme events are also identified. These results improve our understanding of cold frontal processes leading to precipitation. Additionally, they provide the foundation for a process‐based evaluation of frontal dynamics in climate models.

Abstract Earth system models (ESMs) are essential for understanding the interaction between human activities and the Earth's climate. However, the computational demands of ESMs often limit the number of simulations that can be run, hindering the robust analysis of risks associated with extreme weather events. While low‐cost climate emulators have emerged as an alternative to emulate ESMs and enable rapid analysis of future climate, many of these emulators only provide output on at most a monthly frequency. This temporal resolution is insufficient for analyzing events that require daily characterization, such as heat waves or heavy precipitation. We propose using diffusion models, a class of generative deep learning models, to effectively downscale ESM output from a monthly to a daily frequency. Trained on a handful of ESM realizations, reflecting a wide range of radiative forcings, our DiffESM model takes monthly mean precipitation or temperature as input, and is capable of producing daily values with statistical characteristics close to ESM output. Combined with a low‐cost emulator providing monthly means, this approach requires only a small fraction of the computational resources needed to run a large ensemble. We evaluate model behavior using a number of extreme metrics, showing that DiffESM closely matches the spatio‐temporal behavior of the ESM output it emulates in terms of the frequency and spatial characteristics of phenomena such as heat waves, dry spells, or rainfall intensity.

Australia is one of the regions strongly affected by the El Niño‐Southern Oscillation (ENSO). The recent 2020–2023 La Niña event was marked by record‐breaking rainfall and flooding across eastern Australia. The continuous wet conditions during the triple La Niña motivated us to explore the impacts of single‐year and multi‐year ENSO events on Australian rainfall using observational data sets. We find that, while there is no difference in the rainfall impacts during single or double El Niño events, Australian rainfall tends to increase in the third year of triple La Niña events compared to the first and second years. The enhanced rainfall impact during the third La Niña year occurs despite no strengthening of La Niña in the tropical Pacific, suggesting that other processes such as local rainfall‐soil moisture feedback may play a role in prolonging the effects of multi‐year La Niña events in Australia. Plain Language Summary: Australia is strongly affected by the El Niño‐Southern Oscillation (ENSO), with rainfall more likely to increase during La Niña and below‐average rainfall more common during El Niño. The recent 2020–2023 multi‐year La Niña was marked by continuous wet conditions across eastern Australia, leading to record‐breaking rainfall and flooding. Multi‐year La Niña events, where La Niña occurs in two or three consecutive austral summers, happened in about 50% of all La Niña events, including five triple La Niña events since 1900. We explored the impacts of multi‐year ENSO events on Australian rainfall and found that, while there is no difference in the rainfall impacts during single or double El Niño events, rainfall tends to increase in the third year of triple La Niña events compared to the first and second years. This rainfall increase occurs despite no strengthening of La Niña in the tropical Pacific Ocean, suggesting that local processes such as feedback between high/saturated soil moisture and rainfall may play a role in prolonging the effects of multi‐year La Niña events in Australia. Key Points: Eastern Australia tends to experience record‐breaking rainfall and flooding during La Niña eventsRainfall impact of multi‐year El Niño‐Southern Oscillation (ENSO) persists during double and triple events, despite no strengthening of ENSOAustralian rainfall increases in the third year of triple La Niña likely due to soil moisture‐rainfall feedback [ABSTRACT FROM AUTHOR]

Water and disaster risk management require accurate information about hydrometeorological extremes. However, estimation of rare events using extreme value analysis is hampered by short observational records, with large resulting uncertainties. Here, we present a surrogate world setup that makes use of data samples from meteorological and hydrological seasonal re‐forecasts to explore extremes for long return periods. The surrogate timeseries allow us to pool the re‐forecasts into 1000‐year‐long timeseries. We can then calculate return values of extremes and explore how they are affected by the size of sub‐samples as method for estimating the uncertainty. The approach relies on the fact that probabilistic seasonal re‐forecasts, initialized with perturbed initial conditions, have limited predictive skill with increasing lead time. At long lead times re‐forecasts will diverge into independent samples. The meteorological seasonal re‐forecasts are taken from the SEAS5 system, and hydrological re‐forecasts are generated with the E‐HYPE process‐based model for the pan‐European domain. Extreme value analysis is applied to annual maxima of precipitation and streamflow for return periods of 100 years. The analysis clearly demonstrates the large uncertainty in long return period estimates with typical available samples of only few decades. The uncertainty is somewhat reduced for 100‐year samples, but several 100 years seem to be necessary to have robust estimates. The bootstrap with replacement approach is applied to shorter timeseries, and is shown to well reproduce the uncertainty range of the longer samples. However, the main estimate of the return value can be significantly offset. Although the method is model based, with the associated uncertainties and bias compared to the real world, the surrogate approach is likely useful to explore rare and compounding extremes. [ABSTRACT FROM AUTHOR]

We present a variable‐resolution global chemistry‐climate model (AM4VR) developed at NOAA's Geophysical Fluid Dynamics Laboratory (GFDL) for research at the nexus of US climate and air quality extremes. AM4VR has a horizontal resolution of 13 km over the US, allowing it to resolve urban‐to‐rural chemical regimes, mesoscale convective systems, and land‐surface heterogeneity. With the resolution gradually reducing to 100 km over the Indian Ocean, we achieve multi‐decadal simulations driven by observed sea surface temperatures at 50% of the computational cost for a 25‐km uniform‐resolution grid. In contrast with GFDL's AM4.1 contributing to the sixth Coupled Model Intercomparison Project at 100 km resolution, AM4VR features much improved US climate mean patterns and variability. In particular, AM4VR shows improved representation of: precipitation seasonal‐to‐diurnal cycles and extremes, notably reducing the central US dry‐and‐warm bias; western US snowpack and summer drought, with implications for wildfires; and the North American monsoon, affecting dust storms. AM4VR exhibits excellent representation of winter precipitation, summer drought, and air pollution meteorology in California with complex terrain, enabling skillful prediction of both extreme summer ozone pollution and winter haze events in the Central Valley. AM4VR also provides vast improvements in the process‐level representations of biogenic volatile organic compound emissions, interactive dust emissions from land, and removal of air pollutants by terrestrial ecosystems. We highlight the value of increased model resolution in representing climate–air quality interactions through land‐biosphere feedbacks. AM4VR offers a novel opportunity to study global dimensions to US air quality, especially the role of Earth system feedbacks in a changing climate. Plain Language Summary: NOAA's Geophysical Fluid Dynamics Laboratory has developed a new variable‐resolution global chemistry‐climate model for research at the nexus of US climate and air quality extremes. In contrast with the global models contributing to the latest Intergovernmental Panel on Climate Change Report, this model features more than 10 times finer spatial resolution over the contiguous US, allowing it to better resolve cities, mountain valleys, thunderstorms, and urban‐to‐rural air quality variations. This model features much improved representation of regional rainfall extremes, drought, and severe air pollution events in diverse US air basins, including California. Notably, this model reduces the central US dry‐and‐warm bias that has persisted in many generations of climate models. As global climate change leads to more hot and dry weather, the resulting droughts are creating dust‐prone bare lands or stressing plants, making them less able to remove ozone pollution from the air. These effects are included in this model, with particular focus on integrating physical, chemical, and biological components at high spatial resolution to understand Earth system feedbacks to US air quality extremes in a changing climate. Key Points: A new variable‐resolution global chemistry‐climate model has been developed for research at the nexus of US climate and air quality extremesThis model unifies component advances in physics, chemistry and land‐atmosphere interactions within a seamless variable‐resolution frameworkThis model features much improved US regional precipitation, drought, and air quality extremes compared to previous models [ABSTRACT FROM AUTHOR]

Copyright of Journal of Modern Science is the property of Alcide De Gasperi University of Euroregional Economy and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Thirteen Coupled Model Intercomparison Project phase 6 (CMIP6) models were employed to simulate mean, maximum, and minimum temperature across 7 homogenous temperature regions of India for both annual and summer season (June, July, and August (JJA)). The model fidelity was assessed by comparing them with observed Climate Research Unit temperature dataset. The JJA multi‐model ensemble for the present (1981–2014) suggests large warm biases in the temperature. Although the models could simulate the spatial variability of the mean and maximum temperature over most of the homogeneous regions, they do not compare well for representing the temporal variability. We also found, that although different individual models have strengths and weaknesses in representing spatial and temporal temperature characteristics over India, a few of the models perform better than the others. For example, CNRM‐CM6 could better represent the spatial temperature patterns however they struggle in capturing the temporal variability. HadGEM3‐GC31‐LL, KACE‐1‐0G, and UKESM1‐0‐LL are comparably the best‐performing models for temporal temperature features over India. The annual maximum temperature during far future period is projected to increase by 1.5°C, 2.3°C, and 4.1°C for Socioeconomic Pathways (SSPs) SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 respectively. At regional scales, JJA mean temperature for SSP5‐8.5 revealed significant increases in Interior Peninsula (3.8°C), Western Himalaya (5.6°C), Northwest (3.9°C), West Coast (3.6°C), East Coast (3.6°C), Northeast (3.6°C), and North Central (3.8°C), highlighting the Western Himalaya's heightened sensitivity. Further, heat wave frequency is projected to rise, with the northern territories (NW, NC, NE, and part of IP) most affected, anticipating week‐long heat waves affecting around 50% of India's population under stronger SSPs. Such unprecedented impacts seem to be less profound in case of abatement scenarios such as the SSP1‐2.6. Our findings support the urgent need for more ambitious mitigation and adaptation strategies to alleviate the public health impacts of climate change. [ABSTRACT FROM AUTHOR]

A maximum temperature of 48.8°C (119.8°F) was purportedly recorded for the automated station in Siracusa (Syracuse) Contrada Monasteri, on the island of Sicilia (Sicily) Italy on August 11, 2021. A World Meteorological Organization (WMO) ad hoc evaluation committee was assembled to assess the possibility that the Sicilia temperature was the highest recorded temperature in WMO Region VI (continental only). After a detailed review of the site considerations and of the regional synoptic weather conditions, combined with detailed sensor testing and calibration by the Istituto Nazionale di Ricerca Metrologica (INRiM), the WMO evaluation committee concluded (and the Rapporteur accepted) that (1) on August 11, 2021 the high temperature recorded for the automated station in Siracusa C. da Monasteri, did reach a maximum value of 48.8°C (119.8°F), (2) that temperature is recommended to be listed as the WMO official "highest recorded temperature in WMO Region VI (continental only)" and (3) although, as the INRiM testing established, the recorded value of 48.8°C is actually an underestimate of the temperature, the committee recommend that the recorded (likely conservative) value of 48.8°C be the value listed in the Archive. An arbitrated archive of current weather and climate extremes is one means of ensuring that we have the best possible data for climate change analysis and public dissemination. [ABSTRACT FROM AUTHOR]

Cold-season windstorms represent an important, and potentially changing, geophysical hazard in the Northeastern United States. Here we employ an integrated research methodology including both a storyline approach, where three intense windstorms from the current climate are subjected to pseudo-global warming (PGW) experiments, and a long-term transient simulation using the Weather Research and Forecasting (WRF) model. An ensemble of WRF simulations is built for each windstorm using different planetary boundary layer and microphysical parameterizations. The fidelity assessment suggests all ensemble members capture the windstorm evolution in contemporary climate. The configuration with highest fidelity is used in the PGW experiments performed with perturbed temperature fields, constant relative humidity, and deiced Great Lakes. These perturbation simulations indicate some evidence for a reduction of sea level pressure and increases in wind speed over and downwind of the Great Lakes and over the Atlantic Ocean plus an increase in precipitation accumulation but a reduction in snow coverage. These changes are spatially inhomogeneous in terms of magnitude and sign but are consistent with changes in potential vorticity. Alberta Clippers and Colorado Lows dominate the cyclones responsible for historical windstorms and thus are sampled in the PGW simulations. However, the transient simulation suggests an increasing role for tropical cyclones that undergo transition to extratropical cyclones. This reinforces the value of combining information from both PGW perturbation experiments within a storyline context and transient simulations when seeking to quantify the future risk associated with cold-season windstorms under changing climate. [ABSTRACT FROM AUTHOR]

The Central Limit Theorem justifies using a normal distribution when looking at sums of many terms. In a parallel way, extreme value distributions arise in the study of maxima of many terms. The goal of this paper is to briefly review the univariate theory of extremes based on the Fisher-Tippet-Gnedenko Theorem. We then state the basics of the multivariate theory, which is significantly more complicated because it requires a measure to define the distribution. Some properties of these laws are explored, including a description of the support, an expression for the density when it exists, and some examples that illustrate possible joint dependence structures. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Water Resources Journal / Revue Canadienne des Ressources Hydriques is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The 2021 heatwave over Western North America (WNA) led to record‐breaking air temperatures and human‐perceived heat stress (humidex) values. The event was accompanied by drier conditions driven by prolonged atmospheric blocking. During the heatwave, the maximum 6‐day means of humidex and temperature (HX‐6 and TX‐6) exhibited larger anomalies (6.70 and 5.57°C) compared to the 95th percentiles (HX95 and TX95) (4.12 and 3.73°C), relative to 1981–2021 extended summer (June‐September) averages. Extreme indices of humidex show faster and larger increases than those of temperature, reflecting the nonlinear positive relationship between humidex and temperature. Future projections from a multi‐model ensemble of 19 Coupled Model Intercomparison Project Phase six (CMIP6) Global Climate Models (GCMs) clearly show an increase in humidex and temperature extremes, especially under intermediate and high emissions scenarios. Humidex indices (HX‐6 and HX95) show faster and larger increases than temperature indices (TX‐6 and TX95) for the same future years and global warming levels. Controlling for differences in GCM climate sensitivity to greenhouse gas forcing yields robust projections at various global warming levels, reducing the ranges of projected changes from the multi‐model ensemble. At 3.0°C global warming from pre‐industrial, the multi‐model ensemble projects occurrences of HX‐6, TX‐6, HX95, and TX95 over WNA that exceed 2021 levels to occur every 3.9, 1.7, 1.4, and 2.2 years, respectively, increasing to almost annually at 4.0°C.

Abstract North Atlantic midlatitude cyclones are among the most severe weather systems, causing enormous economic damages and threatening human lives. The cyclone is typically characterized by cyclonic convergent surface winds, strong updrafts, and precipitation. However, extreme surface winds are often observed within the cyclone where downdrafts develop. The present study investigates the dynamical and thermodynamical characteristics of the horizontal winds impinging on the cold frontal surface and the associated downdrafts. It is shown that the cyclonic winds into the cold frontal surface are mainly responsible for the downdrafts that transport the high‐altitude horizontal momentum to the surface and cause intense surface winds. About half of the North Atlantic midlatitude cyclones are accompanied by the downdrafts especially in the southern and western parts of the cyclone center.

Abstract Quantifying the risk from extreme weather events in a changing climate is essential for developing effective adaptation and mitigation strategies. Climate models capturing different scenarios are often the starting point for physical risk. However, accurate risk assessment for mitigation and adaptation often demands a level of detail they typically cannot resolve. Here, we develop a dynamic data‐driven downscaling (super‐resolution) method that incorporates physics and statistics in a generative framework to learn the fine‐scale spatial details of rainfall. Our approach transforms coarse‐resolution (0.25°) climate model outputs into high‐resolution (0.01°) rainfall fields while efficaciously quantifying the hazard and its uncertainty. The downscaled rainfall fields closely match observed spatial fields and their distributions. Contrary to conventional thinking, our results suggest that coupling simple statistics and physics to learning improves the efficacy of downscaling midlatitude rainfall extremes from climate models.

Abstract Accurate modeling of snow depth (SD) processes is critical for understanding global energy balance changes, affecting climate change mitigation strategies. This study evaluates the Coupled Model Intercomparison Project Phase 6 (CMIP6) model performance in simulating daily SD across Canada. We assess CMIP6 outputs against observed data, focusing on daily SD averages, snow cover durations, and rates of accumulation and depletion, alongside annual SD peaks for 11 major Canadian catchments. Our findings reveal that CMIP6 simulations generally overestimate daily SD by 57.7% and extend snow cover duration by 30.5 days on average. While three models (CESM2, UKESM1‐0‐LL and MIROC6) notably align with observed annual SD peaks, simulation biases suggest the need for enhanced model parameterization to accurately capture snow physics, particularly in regions with permanent snow cover and complex terrains. This analysis underscores the necessity of refining CMIP6 simulations and incorporating detailed geographical data for better SD predictions.

In 2015, the United Nations General Assembly launched seventeen Sustainable Development Goals (SDGs), aimed at being achieved by 2030. The SDGs 6 (clean water & sanitation) and 13 (climate actions) are two critically important goals concerning water resources that need to be addressed. This study used the Scopus database to explore climate extremes, specifically droughts and floods, in East Asia and highlight the region's efforts and contributions towards achieving SDGs 6 and 13. We found that even before the implementation of SDGs, the topics related to solving the problems of water resources, water quality, and treatment of wastewater using different conceptual models and methodologies were the main concerns in the region. The adoption of SDGs has led to a heightened focus on water and climate sustainability in East Asia, with the considerable surge in climate-related studies after 2019. Under SDG 13, all countries have contributed substantially to climate action research. Keyword analysis indicates that climate change, water management, water treatment, water quality, and adsorption remain prominent. SDGs 6 and 13 have emerged as crucial areas of focus for research and initiatives as the global community grapples with escalating water resources and climate challenges. Under specific keywords search, China has 2nd place in the search with climate and water during the SDGs period, accounting for 21 % of the entire publication from 2015 to 2023. Japan and South Korea account for of 4 % and 3 %, respectively. The research on floods and droughts has garnered significant attention, with half of the ten highly co-cited literature examining the changing pattern of drought, the influence of extreme events on crop yield, and other related topics. Despite the positive contribution of the East Asia region towards SDGs 6 and 13, there is still an urgent need for a more robust framework to improve the complex interconnections between climate actions, clean water, and sanitation for a sustainable soil-water-plant-atmosphere ecosystem.

Storm surges are the most important driver of flooding in many coastal areas. Understanding the spatial extent of storm surge events has important financial and practical implications for flood risk management, reinsurance, infrastructure reliability and emergency response. In this paper, we apply a new tracking algorithm to a high-resolution surge hindcast (CODEC, 1980–2017) to characterize the spatial dependence and temporal evolution of extreme surge events along the coastline of the UK and Ireland. We quantify the severity of each spatial event based on its footprint extremity to select and rank the collection of events. Several surge footprint types are obtained based on the most impacted coastal stretch from each particular event, and these are linked to the driving storm tracks. Using the collection of the extreme surge events, we assess the spatial distribution and interannual variability of the duration, size, severity, and type. We find that the northeast coastline is most impacted by the longest and largest storm surge events, while the English Channel experiences the shortest and smallest storm surge events. The interannual variability indicates that the winter seasons of 1989-90 and 2013–14 were the most serious in terms of the number of events and their severity, based on the return period along the affected coastlines. The most extreme surge event and the highest number of events occurred in the winter season 1989–90, while the proportion of events with larger severities was higher during the winter season 2013–14. This new spatial analysis approach of surge extremes allows us to distinguish several categories of spatial footprints of events around the UK/Ireland coast and link these to distinct storm tracks. The spatial dependence structures detected can improve multivariate statistical methods which are crucial inputs to coastal flooding assessments.

In recognition of the increased risk to national resilience from flooding, we provide an overview of recent and future improvements to flood risk forecasting and communication at the Flood Forecasting Centre (FFC). We draw on the analysis of fluvial and surface water flooding across England and Wales in 2021 to highlight these areas of improvement. Already implemented improvements in both the underpinning science and our long lead-time product are described in the context of high-magnitude, high-impact floods. In addition, we consider more substantial developments from improved modelling of convection to translating this to surface water flood risk and to the essential communication and service provision. Finally, recognising that many of the challenges are shared internationally, we distil our key recommendations for future improvement. These improvements rely on collaboration for them to be successful. HIGHLIGHTS We consider the most noteworthy floods that affected England and Wales in 2021 and suggest developments and further improvements to both our services and the underpinning science.; We describe where service improvements have already been implemented in our longer lead time service, the 30 day Flood Outlook.; We highlight underlying improvements to our science, drawing on the evolution of weather types from multi-model Decider, the Surface Water Flooding Hazard Impact Model (SWFHIM), and improvements from high resolution modelling.; Future recommendations for improvement are then highlighted, illustrating the need for international collaboration, the co-design and co-creation of future services with users.;

Flooding in 2021 has highlighted the increased risk to national resilience. This is against a backdrop of the UK climate projected to become more extreme over the next few decades. This paper considers the notable river and surface water flood events within England and Wales during 2021 and examines the performance of the Flood Forecasting Centre in highlighting the flood risk to our customers. We reflect on team debriefs as well as feedback and surveys from our customers. We distil our learnings and make connections with improvements in our underpinning science, forecasting tools, products and services. Finally, we highlight challenges associated with surface water flooding and suggest how we may collectively start to overcome these. HIGHLIGHTS We provide an overview of the most noteworthy floods that affected England and Wales in 2021 and outline key products and services provided by the Flood Forecasting Centre (FFC).; Our post flood event reviews are summarised and our reflections on these 2021 events are highlighted.; Key findings from our customer reviews are provided.; Future recommendations for improvements are then highlighted, illustrating the need for international collaboration and the co-design and co-creation of future services with users.;

In this study, we examined how the nonlinearity α of the Langevin equation influences the behavior of extremes in a generated time series. The extremes, defined according to run theory, result in two types of series, run lengths and surplus magnitudes, whose complex structure was investigated using the visibility graph (VG) method. For both types of series, the information measures of the Shannon entropy measure and Fisher Information Measure were utilized for illustrating the influence of the nonlinearity α on the distribution of the degree, which is the main topological parameter describing the graph constructed by the VG method. The main finding of our study was that the Shannon entropy of the degree of the run lengths and the surplus magnitudes of the extremes is mostly influenced by the nonlinearity, which decreases with with an increase in α. This result suggests that the run lengths and surplus magnitudes of extremes are characterized by a sort of order that increases with increases in nonlinearity.