We summarize historic New York City (NYC) climate change trends and provide the latest scientific analyses on projected future changes based on a range of global greenhouse gas emissions scenarios. Building on previous NPCC assessment reports, we describe new methods used to develop the projections of record for sea level rise, temperature, and precipitation for NYC, across multiple emissions pathways and analyze the issue of the \"hot models\" associated with the 6th phase of the Coupled Model Intercomparison Project (CMIP6) and their potential impact on NYC\'s climate projections. We describe the state of the science on temperature variability within NYC and explain both the large-scale and regional dynamics that lead to extreme heat events, as well as the local physical drivers that lead to inequitable distributions of exposure to extreme heat. We identify three areas of tail risk and potential for its mischaracterization, including the physical processes of extreme events and the effects of a changing climate. Finally, we review opportunities for future research, with a focus on the hot model problem and the intersection of spatial resolution of projections with gaps in knowledge in the impacts of the climate signal on intraurban heat and heat exposure.

Marine heatwaves (MHWs), increasing in duration and intensity because of climate change, are now a major threat to marine life and can have lasting effects on the structure and function of ecosystems. However, the responses of marine taxa and ecosystems to MHWs can be highly variable, making predicting and interpreting biological outcomes a challenge. Here, we review how biological responses to MHWs, from individuals to ecosystems, are mediated by fine-scale spatial variability in the coastal marine environment (hereafter, local gradients). Viewing observed responses through a lens of ecological theory, we present a simple framework of three \'resilience processes\' (RPs) by which local gradients can influence the responses of marine taxa to MHWs. Local gradients (1) influence the amount of stress directly experienced by individuals, (2) facilitate local adaptation and acclimatization of individuals and populations, and (3) shape community composition which then influences responses to MHWs. We then synthesize known examples of fine-scale gradients that have affected responses of benthic foundation species to MHWs, including kelp forests, coral reefs, and seagrass meadows and link these varying responses to the RPs. We present a series of case studies from various marine ecosystems to illustrate the differential impacts of MHWs mediated by gradients in both temperature and other co-occurring drivers. In many cases, these gradients had large effect sizes with several examples of local gradients causing a 10-fold difference in impacts or more (e.g., survival, coverage). This review highlights the need for high-resolution environmental data to accurately predict and manage the consequences of MHWs in the context of ongoing climate change. While current tools may capture some of these gradients already, we advocate for enhanced monitoring and finer scale integration of local environmental heterogeneity into climate models. This will be essential for developing effective conservation strategies and mitigating future marine biodiversity loss.

This study focuses on understanding how aerosols are transported over long distances, especially during extreme events. Leveraging the integrated vapour transport (IVT) based atmospheric river (AR) algorithm to integrated aerosol transport (IAT) to detect the aerosol atmospheric rivers (AARs) for key aerosol species such as black carbon (BC), organic carbon (OC), dust (DU), sea salt (SS), and sulphate (SU). The present study also assesses the occurrence, intensity, and societal impacts of AARs globally during 2015-2022 on a spatiotemporal resolution of 1.5° × 1.5° and 6 h, respectively. The detection algorithm found a total number of 128,261 AARs found globally for key aerosol species. However, the availability of BC, OC, and SU AARs is most common and intense in densely populated areas like the Indus-Brahmaputra-Ganga (IBG) plains (~ 15-20 AAR days/year), Eastern China (~ 25-40 AAR days/year), and Japan (~ 20-30 AAR days/year), where human activities including agriculture burning contribute to their formation. DU AARs, on the other hand, are more prevalent in Northern Africa (~ 15 AAR days/year), the Gulf (~ 5-10 AAR days/year), the USA, and the Amazon rainforests. SS AARs share similar characteristics with atmospheric rivers and are more intense in higher latitudes and over the oceans (~ 30-40 AAR days/year). The study also validates its findings by analysing recent extreme events involving BC and DU worldwide. The potential applications of specific AARs could assist us in identifying the causes of snow darkening, reducing snow cover area, and accelerating melting rate. Moreover, AARs could aid in quantifying the health risks associated with severe air pollution.

Understanding the impacts of extreme weather events on freshwater ecosystems is imperative during a time when a multitude of challenges compromises these environments\' health. Exploring how such events affect macroinvertebrate communities in rivers sheds light on the resilience of freshwater ecosystems, which is essential for human well-being and biodiversity conservation. In this study, long-term time series of benthic macroinvertebrate communities from four sites along three freshwater streams within the Rhine-Main-Observatory Long-Term Ecological Research site in Germany were analyzed. Each of them was sampled annually over a span of ~20 years to assess the impacts of extreme weather events (floods, droughts, and extreme heat) on macroinvertebrate communities. The findings reveal that the effects of extreme events are site-specific, suggesting that the impacts of an extreme event can vary based on several potential factors, including the life history traits of the organisms within the community and, among others, the hydrography of the site. Moreover, the analysis highlights that the cumulative impact of these events over time is more significant than the impact of a single event\'s magnitude, while following distinct temporal dynamics. This underscores the importance of considering both the temporal dynamics and the biological characteristics of communities when evaluating the consequences of extreme weather events on biodiversity, illustrating that the resilience of freshwater ecosystems and their biodiversity under such conditions depends on a complex interplay of factors rather than the severity of individual events.

Wildfire intensity and severity have been increasing in the Iberian Peninsula in recent years, particularly in the Galicia region, due to rising temperatures and accumulating drier combustible vegetation in unmanaged lands. This leads to substantial emissions of air pollutants, notably fine particles (PM2.5), posing a risk to public health. This study aims to assess the impact of local and regional wildfires on PM2.5 levels in Galicia\'s main cities and their implications for air quality and public health. Over a decade (2013-2022), PM2.5 data during wildfire seasons were analyzed using statistical methods and Lagrangian tracking to monitor smoke plume evolution. The results reveal a notable increase in PM2.5 concentration during the wildfire season (June-November) in Galicia, surpassing health guidelines during extreme events and posing a significant health risk to the population. Regional wildfire analyses indicate that smoke plumes from Northern Portugal contribute to pollution in Galician cities, influencing the seasonality of heightened PM2.5 levels. During extensive wildfires, elevated PM2.5 concentration values persisted for several days, potentially exacerbating health concerns in Galicia. These findings underscore the urgency of implementing air pollution prevention and management measures in the region, including developing effective alerts for large-scale events and improved wildfire management strategies to mitigate their impact on air quality in Galician cities.

Episodes of high near-surface ozone concentrations tend to cover large areas for several days. They are strongly dependent on meteorology, precursor emissions, and the ambient photochemical conditions. This study introduces a new pseudo-Lagrangian algorithm that identifies the spatiotemporal patterns of episodes, allowing for a good characterization of their areal extent and an assessment of their drivers. The algorithm has been used to identify ozone episodes in Europe from April to September over the last twenty years (2003-2022) in the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis as well as in the historical simulation (1950-2014) and four shared socio-economic pathways (SSPs, spanning 2015-2100) of three Earth system models (UKESM1-0-LL, EC-Earth3-AerChem and GFDL-ESM4). While the total number of episodes has increased in recent years, the frequency of large episodes has decreased following European precursor emission reductions. The analysis of the 100 largest episodes shows that they tend to occur in Northern Europe during spring and in the center and south of the continent from June onwards. Most of the top 10 episodes occurred in the first years of the century and were associated with high temperatures, enhanced solar radiation, and anticyclonic conditions. Despite the decrease in large episodes in recent years, there is uncertainty regarding future European episodes. Episodes of reduced size are found for SSPs with weak greenhouse forcing and low precursor emissions, whereas episode sizes increase in scenarios with high methane concentrations and enhanced radiative forcing, even exceeding the maximum historical size. However, the three models project episodes of different sizes for any given scenario, probably associated with their differing warming trends and the varying level of complexity in the implementation of processes. These results point to the need to implement both effective climate and air quality policies to address the ozone air pollution problem in Europe in a warming climate.

Freak or rogue waves are a danger to ships, offshore infrastructure, and other maritime equipment. Reliable rogue wave forecasts could mitigate this risk for operations at sea. While the occurrence of oceanic rogue waves at sea is generally acknowledged, reliable rogue wave forecasts are unavailable. In this paper, the authors seek to overcome this shortcoming by demonstrating how rogue waves can be predicted from field measurements. An extensive buoy data set consisting of billions of waves is utilized to parameterize neural networks. This network is trained to distinguish waves prior to an extreme wave from waves which are not followed by an extreme wave. With this approach, three out of four rogue waves are correctly predicted 1 min ahead of time. When the advance warning time is extended to 5 min, it is found that the ratio of accurate predictions is reduced to seven out of ten rogue waves. Another strength of the trained neural networks is their capabilities to extrapolate. This aspect is verified by obtaining forecasts for a buoy location that is not included in the networks\' training set. Furthermore, the performance of the trained neural network carries over to realistic scenarios where rogue waves are extremely rare.

Understanding trends in extreme precipitation events in the context of global warming is critical for assessing climate change impacts. This study employs a novel methodology developed by Giorgi and Ciarlo (2022) to analyze record-breaking daily precipitation events from 1980 to 2020, utilizing three reanalysis products (ERA5, MERRA-2, and JRA-55) and one global observation dataset (MSWEP). Our results indicate a consistent and statistically significant increase in record-breaking precipitation events globally, with variations across different latitude bands and between land and ocean areas. This trend is evident in all datasets, with the most substantial increases observed over oceans in ERA5 and over land in JRA and MERRA. Notably, the Southern Hemisphere shows mixed results, with some regions displaying negative trends. This study highlights the increasing frequency of extreme precipitation events, supporting the hypothesis of intensified hydrological cycles under a warming climate. Our findings enhance understanding of precipitation extremes and underscore the importance of regional analyses in climate impact studies. Future work could extend these findings to formal attribution studies linking observed trends directly to anthropogenic climate change.
In recent decades, observations have shown changes in how often and how intensely it rains, which can be linked to global warming. Our study analyses record-breaking rainfall events, i.e. days when rainfall reaches unprecedented highs, in different observational and reanalysis records for the last 40 years. We use a new method to compare daily rainfall records with the values that would be expected in stable climate conditions, i.e. without warming. Our findings show that extreme rainfall events have become more frequent around the world. This trend is predominant across various latitudinal regions and over oceans and land, though there are some differences depending on the location. Notably, the increase in record rainfall events is more consistent across the oceans than the continental regions, with some of the latter showing negative trends in the southern hemisphere. This conclusion has important implications for how we prepare for and manage flooding and other related natural disasters in the future.

Satellite-based precipitation estimates are a critical source of information for understanding and predicting hydrological processes at regional or global scales. Given the potential variability in the accuracy and reliability of these estimates, comprehensive performance assessments are essential before their application in specific hydrological contexts. In this study, six satellite-based precipitation products (SPPs), namely, CHIRPS, CMORPH, GSMaP, IMERG, MSWEP, and PERSIANN, were evaluated for their utility in hydrological modeling, specifically in simulating streamflow using the Variable Infiltration Capacity (VIC) model. The performance of the VIC model under varying flow conditions and timescales was assessed using statistical indicators, viz., R2, KGE, PBias, RMSE, and RSR. The findings of the study demonstrate the effectiveness of VIC model in simulating hydrological components and its applicability in evaluating the accuracy and reliability of SPPs. The SPPs were shown to be valuable for streamflow simulation at monthly and daily timescales, as confirmed by various performance measures. Moreover, the performance of SPPs for simulating extreme flow events (streamflow above 75%, 90%, and 95%) using the VIC model was assessed and a significant decrease in the performance was observed for high-flow events. Comparative analysis revealed the superiority of IMERG and CMORPH for streamflow simulation at daily timescale and high-flow conditions. In contrast, the performances of CHIRPS and PERSIANN were found to be poor. This study highlights the importance of thoroughly assessing the SPPs in modeling diverse flow conditions.

Levels of dissolved oxygen in open ocean and coastal waters are decreasing (ocean deoxygenation), with poorly understood effects on marine megafauna. All of the more than 1000 species of elasmobranchs (sharks, skates, and rays) are obligate water breathers, with a variety of life-history strategies and oxygen requirements. This review demonstrates that although many elasmobranchs typically avoid hypoxic water, they also appear capable of withstanding mild to moderate hypoxia with changes in activity, ventilatory responses, alterations to circulatory and hematological parameters, and morphological alterations to gill structures. However, such strategies may be insufficient to withstand severe, progressive, or prolonged hypoxia or anoxia where anaerobic metabolic pathways may be used for limited periods. As water temperatures increase with climate warming, ectothermic elasmobranchs will exhibit elevated metabolic rates and are likely to be less able to tolerate the effects of even mild hypoxia associated with deoxygenation. As a result, sustained hypoxic conditions in warmer coastal or surface-pelagic waters are likely to lead to shifts in elasmobranch distributions. Mass mortalities of elasmobranchs linked directly to deoxygenation have only rarely been observed but are likely underreported. One key concern is how reductions in habitat volume as a result of expanding hypoxia resulting from deoxygenation will influence interactions between elasmobranchs and industrial fisheries. Catch per unit of effort of threatened pelagic sharks by longline fisheries, for instance, has been shown to be higher above oxygen minimum zones compared to adjacent, normoxic regions, and attributed to vertical habitat compression of sharks overlapping with increased fishing effort. How a compound stressor such as marine heatwaves alters vulnerability to deoxygenation remains an open question. With over a third of elasmobranch species listed as endangered, a priority for conservation and management now lies in understanding and mitigating ocean deoxygenation effects in addition to population declines already occurring from overfishing.