Irrigation and urban greening can mitigate extreme temperatures and reduce adverse health impacts from heat. However, some recent studies suggest these interventions could actually exacerbate heat stress by increasing humidity. These studies use different heat stress indices (HSIs), hindering intercomparisons of the relative roles of temperature and humidity. Our method uses calculus of variations to compare the sensitivity of HSIs to temperature and humidity, independent of HSI units. We explain the properties of different HSIs and identify conditions under which they disagree. We highlight recent studies where the use of different HSIs could have led to opposite conclusions. Our findings have significant implications for the evaluation of irrigation and urban greening as adaptive responses to overheating and climate adaptation measures in general. We urge researchers to be critical in their choice of HSIs, especially in relation to health outcomes; our method provides a useful tool for making informed comparisons.

Heatwaves are one of the leading causes of climate-induced mortality. Using the examples of recent heatwaves in Europe, the United States and Asia, we illustrate how the communication of dangerous conditions based on temperature maps alone can lead to insufficient societal perception of health risks. Comparison of maximum daily values of temperature with physiological heat stress indices accounting for impacts of both temperature and humidity, illustrates substantial differences in geographical extent and timing of their respective peak values during these recent events. This signals the need to revisit how meteorological heatwaves and their expected impacts are communicated. Close collaboration between climate and medical communities is needed to select the best heat stress indicators, establish them operationally, and introduce them to the public. npj Climate and Atmospheric Science (2023) 6:33.

Previous analyses of the possibility of global breadbasket failures have extrapolated risks based on historical relationships between climate and yields. However, climate change is causing unprecedented events globally, which could exceed critical thresholds and reduce yields, even if there is no historical precedent. This means that we are likely underestimating climate risks to our food system. In the case of wheat, parts of the USA and China show little historical relationship between yields and temperature, but extreme temperatures are now possible that exceed critical physiological thresholds in wheat plants. UNprecedented Simulated Extreme ENsemble (UNSEEN) approaches use large ensembles to generate plausible unprecedented events, which can inform our assessment of the risk to crops. We use the UNSEEN approach with a large ensemble of archived seasonal forecasts to generate thousands of plausible events over the last 40 years and compare the results with historically observed extreme temperature and precipitation. In the US midwest, extreme temperatures that would have happened approximately 1-in-100-years in 1981 now have a return period of 1-in-6 years, while in China, the current return period is on the order of 1-in-16 years. This means that in the US midwest, extreme temperatures that used to have a 1% chance to occur in 1981 now have a 17% chance to occur in any given year, while in China, the chance increased from 1% to 6%. Record-breaking years exceeding critical thresholds for enzymes in the wheat plant are now more likely than in the past, and these record-breaking hot years are associated with extremely dry conditions in both locations. Using geopotential height and wind anomalies from the UNSEEN ensemble, we demonstrate that strong winds over land pull dry air towards the regions these during extremely hot and dry unseen events. We characterize plausible extremes from the UNSEEN ensemble that can be used to help imagine otherwise unforeseen events, including a compound event in which high impacts co-occur in both regions, informing adaptation planning in these regions. Recent temperature extremes, especially in the US midwest, are unlikely to be a good proxy for what to expect in the next few years of today's climate, and local stakeholders might perceive their risk to be lower than it really is. We find that there is a high potential for surprise in these regions if people base risk analyses solely on historical datasets.

Mounting interest in modeling outdoor diffusion and transmission of bioaerosols due to the prevalence of COVID-19 in the urban environment has led to better knowledge of the issues concerning exposure risk and evacuation planning. In this study, the dispersion and deposition dynamics of bioaerosols around a vaccine factory were numerically investigated under various thermal conditions and leakage rates. To assess infection risk at the pedestrian level, the improved Wells-Riley equation was used. To predict the evacuation path, Dijkstra's algorithm, a derived greedy algorithm based on the improved Wells-Riley equation, was applied. The results show that, driven by buoyancy force, the deposition of bioaerosols can reach 80 m on the windward sidewall of high-rise buildings. Compared with stable thermal stratification, the infection risk of unstable thermal stratification in the upstream portion of the study area can increase by 5.53% and 9.92% under a low and high leakage rate, respectively. A greater leakage rate leads to higher infection risk but a similar distribution of high-risk regions. The present work provides a promising approach for infection risk assessment and evacuation planning for the emergency response to urban bioaerosol leakage.

Wintertime meteorological conditions are usually unfavorable for ozone (O) formation due to weak solar irradiation and low temperature. Here, we observed a prominent wintertime O pollution event in Shijiazhuang (SJZ) during the Chinese New Year (CNY) in 2021. Meteorological results found that the sudden change in the air pressure field, leading to the wind changing from northwest before CNY to southwest during CNY, promotes the accumulation of air pollutants from southwest neighbor areas of SJZ and greatly inhibits the diffusion and dilution of local pollutants. The photochemical regime of O formation is limited by volatile organic compounds (VOCs), suggesting that VOCs play an important role in O formation. With the developed O formation path tracing (OFPT) approach for O source apportionment, it has been found that highly reactive species, such as ethene, propene, toluene, and xylene, are key contributors to O production, resulting in the mean O production rate (P) during CNY being 3.7 times higher than that before and after CNY. Industrial combustion has been identified as the largest source of the P (2.6 ± 2.2 ppbv h), with the biggest increment (4.8 times) during CNY compared to the periods before and after CNY. Strict control measures in the industry should be implemented for O pollution control in SJZ. Our results also demonstrate that the OFPT approach, which accounts for the dynamic variations of atmospheric composition and meteorological conditions, is effective for O source apportionment and can also well capture the O production capacity of different sources compared with the maximum incremental reactivity (MIR) method.

There is debate about the use of wetlands as natural climate solutions due to their ability to act as a "double-edged sword" with respect to climate impacts by both sequestering CO while emitting CH. Here, we used a process-based greenhouse gas (GHG) perturbation model to simulate wetland radiative forcing and temperature change associated with wetland state conversion over 500 years based on empirical carbon flux measurements, and CO-equivalent (CO-e.q.) metrics to assess the net flux of GHGs from wetlands on a comparable basis. Three CO-e.q. metrics were used to describe the relative radiative impact of CO and CH-the conventional global warming potential (GWP) that looks at pulse GHG emissions over a fixed timeframe, the sustained-flux GWP (SGWP) that looks at sustained GHG emissions over a fixed timeframe, and GWP* that explicitly accounts for changes in the radiative forcing of CH over time (initially more potent but then diminishing after about a decade)-against model-derived mean temperature profiles. GWP* most closely estimated the mean temperature profiles associated with net wetland GHG emissions. Using the GWP*, intact wetlands serve as net CO-e.q. carbon sinks and deliver net cooling effects on the climate. Prioritizing the conservation of intact wetlands is a cost-effective approach with immediate climate benefits that align with the Paris Agreement and the Intergovernmental Panel on Climate Change timeline of net-zero GHG emissions by 2050. Restoration of wetlands also has immediate climate benefits (reduced warming), but with the majority of climate benefits (cooling) occurring over longer timescales, making it an effective short and long-term natural climate solution with additional co-benefits.

Early career (EC) Earth system scientists in the Latin America and the Caribbean region (LAC) have been facing several issues, such as limited funding opportunities, substandard scientific facilities, lack of security of tenure, and unrepresented groups equality issues. On top of this, the worsening regional environmental and climatic crises call for the need for this new generation of scientists to help to tackle these crises by increasing public awareness and research. Realizing the need to converge and step up in making a collective action to be a part of the solution, the Latin America Early Career Earth System Scientist Network (LAECESS) was created in 2016. LAECESS's primary goals are to promote regional networking, foster integrated and interdisciplinary science, organize soft skills courses and workshops, and empower Latin American EC researchers. This article is an initial step towards letting the global science community grasp the current situation and hear the early career LAC science community's perspectives. The paper also presents a series of future steps needed for better scientific and social development in the LAC region.

Global crop yields are highly dependent on climate variability, with the largest agricultural failures frequently occurring during extremely dry and hot years. Land-atmosphere feedbacks are thought to play a crucial role in agricultural productivity during such events: precipitation deficits cause soil desiccation, which reduces evaporation and enhances sensible heating from the land surface; the amplified local temperatures and moisture deficits can be detrimental to crop yield. While this impact of local land-atmosphere feedbacks on agricultural productivity has recently been reported, the dependency of crop yields on upwind regions remains understudied. Here, we determine the spatio-temporal origins of moisture and heat over the world's largest 75 rainfed breadbaskets, and illustrate the crop yield dependency on upwind regions. Further, we disentangle the role of local and upwind land-atmosphere interactions on anomalous moisture and heat transport during low-yield years. Our results indicate that crop failure increases on average by around 40% when both upwind and local land-atmosphere feedbacks cause anomalously low moisture and high heat transport into the breadbaskets. The impact of upwind land-atmosphere feedbacks on productivity deficits is the largest in water-limited regions, which show an increased dependency on moisture supply from upwind land areas. Better understanding these upwind-downwind dependencies in agricultural regions can help develop adaptation strategies to prevent food shortage in a changing climate.

Recent years have shown that secondary ice production (SIP) is ubiquitous, affecting all clouds from polar to tropical regions. SIP is not described well in models and may explain biases in warm mixed-phase cloud ice content and structure. Through modeling constrained by in-situ observations and its synergy with radar we show that SIP in orographic clouds exert a profound impact on the vertical distribution of hydrometeors and precipitation, especially in seeder-feeder cloud configurations. The mesoscale model simulations coupled with a radar simulator strongly support that enhanced aggregation and SIP through ice-ice collisions contribute to observed spectral bimodalities, skewing the Doppler spectra toward the slower-falling side at temperatures within the dendritic growth layer, ranging from -20 °C to -10 °C. This unique signature provides an opportunity to infer long-term SIP occurrences from the global cloud radar data archive, particularly for this underexplored temperature regime.

Organic compounds released from wildfires and residential biomass burning play a crucial role in shaping the composition of the atmosphere. The solubility and subsequent reactions of these compounds in the aqueous phase of clouds and fog remain poorly understood. Nevertheless, these compounds have the potential to become an important source of secondary organic aerosol (SOA). In this study, we simulated the aqueous SOA (aqSOA) from residential wood burning emissions under atmospherically relevant conditions of gas-liquid phase partitioning, using a wetted-wall flow reactor (WFR). We analyzed and quantified the specific compounds present in these emissions at a molecular level and determined their solubility in clouds. Our findings reveal that while 1% of organic compounds are fully water-soluble, 19% exhibit moderate solubility and can partition into the aqueous phase in a thick cloud. Furthermore, it is found that the aqSOA generated in our laboratory experiments has a substantial fraction being attributed to the formation of oligomers in the aqueous phase. We also determined an aqSOA yield of 20% from residential wood combustion, which surpasses current estimates based on gas-phase oxidation. These results indicate that in-cloud chemistry of organic gases emitted from wood burning can serve as an efficient pathway to produce organic aerosols, thus potentially influencing climate and air quality.

Deposition of wildfire smoke on snow contributes to its darkening and accelerated snowmelt. Recent field studies have identified dark brown carbon (d-BrC) to contribute 50-75% of shortwave absorption in wildfire smoke. d-BrC is a distinct class of water-insoluble, light-absorbing organic carbon that co-exists in abundance with black carbon (BC) in snow across the world. However, the importance of d-BrC as a snow warming agent relative to BC remains unexplored. We address this gap using aerosol-snow radiative transfer calculations on datasets from laboratory and field measurement. We show d-BrC increases the annual mean snow radiative forcing between 0.6 and 17.9 W m , corresponding to different wildfire smoke deposition scenarios. This is a 1.6 to 2.1-fold enhancement when compared with BC-only deposition on snow. This study suggests d-BrC is an important contributor to snowmelt in midlatitude glaciers, where ~40% of the world's glacier surface area resides.

Oxidized Organic Aerosol (OOA), a major component of fine atmospheric particles, impacts climate and human health. Previous experiments and atmospheric models emphasize the importance of nocturnal OOA formation from NO· oxidation of biogenic VOCs. This seasonal study extends the understanding by showing that nocturnal oxidation of biomass-burning emissions can account for up to half of total OOA production in fall and winter. It is the first to distinguish nocturnal OOA characteristics from daytime OOA across all seasons using bulk aerosol measurements. Summer observations of nocturnal OOA align well with regional chemistry transport model predictions, but discrepancies in other seasons reveal a common model deficiency in representing biomass-burning emissions and their nocturnal oxidation. This study underscores the significance of near-ground nocturnal OOA production, proposes a method to differentiate it using bulk aerosol measurements, and suggests model optimization strategies. These findings enhance the understanding and prediction of nighttime OOA formation.

During summer, ammonia emissions in Southeast Asia influence air pollution and cloud formation. Convective transport by the South Asian monsoon carries these pollutant air masses into the upper troposphere and lower stratosphere (UTLS), where they accumulate under anticyclonic flow conditions. This air mass accumulation is thought to contribute to particle formation and the development of the Asian Tropopause Aerosol Layer (ATAL). Despite the known influence of ammonia and particulate ammonium on air pollution, a comprehensive understanding of the ATAL is lacking. In this modelling study, the influence of ammonia on particle formation is assessed with emphasis on the ATAL. We use the EMAC chemistry-climate model, incorporating new particle formation parameterisations derived from experiments at the CERN CLOUD chamber. Our diurnal cycle analysis confirms that new particle formation mainly occurs during daylight, with a 10-fold enhancement in rate. This increase is prominent in the South Asian monsoon UTLS, where deep convection introduces high ammonia levels from the boundary layer, compared to a baseline scenario without ammonia. Our model simulations reveal that this ammonia-driven particle formation and growth contributes to an increase of up to 80% in cloud condensation nuclei (CCN) concentrations at cloud-forming heights in the South Asian monsoon region. We find that ammonia profoundly influences the aerosol mass and composition in the ATAL through particle growth, as indicated by an order of magnitude increase in nitrate levels linked to ammonia emissions. However, the effect of ammonia-driven new particle formation on aerosol mass in the ATAL is relatively small. Ammonia emissions enhance the regional aerosol optical depth (AOD) for shortwave solar radiation by up to 70%. We conclude that ammonia has a pronounced effect on the ATAL development, composition, the regional AOD, and CCN concentrations.

Wildfire impacts the global carbon cycle, property, harvestable timber, and public health. Canada saw a record fire season in 2023 with 14.9 Mha burned-over seven times the 1986-2022 average of 2.1 Mha. Here we utilize a new process-based wildfire module that explicitly represents fire weather, fuel type and availability, ignition sources, fire suppression, and vegetation's climate response to project the future of wildfire in Canada. Under rapid climate change (shared socioeconomic pathway [SSP] 370 & 585) simulated annual burned area in the 2090 s reaches 10.2 ± 2.1 to 11.7 ± 2.4 Mha, approaching the 2023 fire season total. However, climate change below a 2 °C global target (SSP126), keeps the 2090 s area burned near modern (2004-2014) norms. The simulated area burned and carbon emissions are most sensitive to climate drivers and lightning but future lightning activity is a key uncertainty.

Understanding the relationship between tropical cyclone (TC) precipitation and sea surface temperature (SST) is essential for both TC hazard forecasting and projecting how these hazards will change in the future due to climate change. This work untangles how global TC precipitation is impacted by present-day SST variability (known as apparent scaling) and by long-term changes in SST caused by climate change (known as climate scaling). A variety of datasets are used including precipitation and SST observations, realistic climate model simulations, and idealized climate model simulations. The apparent scaling rates depend on precipitation metric; examples shown here have ranges of 6.1 to 9.5% per K versus 5.9 to 9.8% per K for two different metrics. The climate scaling is estimated at about 5% per K, which is slightly less than the atmospheric moisture scaling based on thermodynamic principles of about 7% per K (i.e., the Clausius-Clapeyron scaling). The apparent scaling is greater than the climate scaling, which implies that the relationship between TC precipitation and present-day SST variability should not be used to project the long-term response of TC precipitation to climate change.

The U.S. coastlines have experienced rapid increases in occurrences of High Tide Flooding (HTF) during recent decades. While it is generally accepted that relative mean sea level (RMSL) rise is the dominant cause for this, an attribution to individual components is still lacking. Here, we use local sea-level budgets to attribute past changes in HTF days to RMSL and its individual contributions. We find that while RMSL rise generally explains > 84% of long-term increases in HTF days locally, spatial patterns in HTF changes also depend on differences in flooding thresholds and water level characteristics. Vertical land motion dominates long-term increases in HTF, particularly in the northeast, while sterodynamic sea level (SDSL) is most important elsewhere and on shorter temporal scales. We also show that the recent SDSL acceleration in the Gulf of Mexico has led to an increase of 220% in the frequency of HTF events over the last decade.

Various shipping emissions controls have recently been implemented at both local and national scales. However, it is difficult to track the effect of these on PM levels, owing to the non-linear relationship that exists between changes in precursor emissions and PM components. Positive Matrix Factorisation (PMF) identifies that a switch to cleaner fuels since January 2020 results in considerable reductions in shipping-source-related PM, especially sulphate aerosols and metals (V and Ni), not only at a port site but also at an urban background site. CMAQ sensitivity analysis reveals that the reduction of secondary inorganic aerosols (SIA) further extends to inland areas downwind from ports. In addition, mitigation of secondary organic aerosols (SOA) in coastal urban areas can be anticipated either from the results of receptor modelling or from CMAQ simulations. The results in this study show the possibility of obtaining human health benefits in coastal cities through shipping emission controls.

Staggered-peak production (SP)-a measure to halt industrial production in the heating season-has been implemented in North China Plain to alleviate air pollution. We compared the variations of PM composition in Beijing during the SP period in the 2016 heating season (SP) with those in the normal production (NP) periods during the 2015 heating season (NP) and 2016 non-heating season (NP) to investigate the effectiveness of SP. The PM mass concentration decreased from 70.0 ± 54.4 μg m in NP to 53.0 ± 56.4 μg m in SP, with prominent reductions in primary emissions. However, the fraction of nitrate during SP (20.2%) was roughly twice that during NP (12.7%) despite a large decrease of NO, suggesting an efficient transformation of NO to nitrate during the SP period. This is consistent with the increase of oxygenated organic aerosol (OOA), which almost doubled from NP (22.5%) to SP (43.0%) in the total organic aerosol (OA) fraction, highlighting efficient secondary formation during SP. The PM loading was similar between SP (53.0 ± 56.4 μg m) and NP (50.7 ± 49.4 μg m), indicating a smaller difference in PM pollution between heating and non-heating seasons after the implementation of the SP measure. In addition, a machine learning technique was used to decouple the impact of meteorology on air pollutants. The deweathered results were comparable with the observed results, indicating that meteorological conditions did not have a large impact on the comparison results. Our study indicates that the SP policy is effective in reducing primary emissions but promotes the formation of secondary species.

Anthropogenic aerosols mask the climate warming caused by greenhouse gases (GHGs). In the absence of observational constraints, large uncertainties plague the estimates of this masking effect. Here we used the abrupt reduction in anthropogenic emissions observed during the COVID-19 societal slow-down to characterize the aerosol masking effect over South Asia. During this period, the aerosol loading decreased substantially and our observations reveal that the magnitude of this aerosol demasking corresponds to nearly three-fourths of the CO-induced radiative forcing over South Asia. Concurrent measurements over the northern Indian Ocean unveiled a ~7% increase in the earth's surface-reaching solar radiation (surface brightening). Aerosol-induced atmospheric solar heating decreased by ~0.4 K d. Our results reveal that under clear sky conditions, anthropogenic emissions over South Asia lead to nearly 1.4 W m heating at the top of the atmosphere during the period March-May. A complete phase-out of today's fossil fuel combustion to zero-emission renewables would result in rapid aerosol demasking, while the GHGs linger on.

The use of air quality monitoring networks to inform urban policies is critical especially where urban populations are exposed to unprecedented levels of air pollution. High costs, however, limit city governments' ability to deploy reference grade air quality monitors at scale; for instance, only 33 reference grade monitors are available for the entire territory of Delhi, India, spanning 1500 sq km with 15 million residents. In this paper, we describe a high-precision spatio-temporal prediction model that can be used to derive fine-grained pollution maps. We utilize two years of data from a low-cost monitoring network of 28 custom-designed low-cost portable air quality sensors covering a dense region of Delhi. The model uses a combination of message-passing recurrent neural networks combined with conventional spatio-temporal geostatistics models to achieve high predictive accuracy in the face of high data variability and intermittent data availability from low-cost sensors (due to sensor faults, network, and power issues). Using data from reference grade monitors for validation, our spatio-temporal pollution model can make predictions within 1-hour time-windows at 9.4, 10.5, and 9.6% Mean Absolute Percentage Error (MAPE) over our low-cost monitors, reference grade monitors, and the combined monitoring network respectively. These accurate fine-grained pollution sensing maps provide a way forward to build citizen-driven low-cost monitoring systems that detect hazardous urban air quality at fine-grained granularities.

Precipitation nowcasting, which is critical for flood emergency and river management, has remained challenging for decades, although recent developments in deep generative modeling (DGM) suggest the possibility of improvements. River management centers, such as the Tennessee Valley Authority, have been using Numerical Weather Prediction (NWP) models for nowcasting, but they have been struggling with missed detections even from best-in-class NWP models. While decades of prior research achieved limited improvements beyond advection and localized evolution, recent attempts have shown progress from so-called physics-free machine learning (ML) methods, and even greater improvements from physics-embedded ML approaches. Developers of DGM for nowcasting have compared their approaches with optical flow (a variant of advection) and meteorologists' judgment, but not with NWP models. Further, they have not conducted independent co-evaluations with water resources and river managers. Here we show that the state-of-the-art physics-embedded deep generative model, specifically NowcastNet, outperforms the High Resolution Rapid Refresh (HRRR) model, which is the latest generation of NWP, along with advection and persistence, especially for heavy precipitation events. Thus, for grid-cell extremes over 16 mm/h, NowcastNet demonstrated a median critical success index (CSI) of 0.30, compared with median CSI of 0.04 for HRRR. However, despite hydrologically-relevant improvements in point-by-point forecasts from NowcastNet, caveats include overestimation of spatially aggregate precipitation over longer lead times. Our co-evaluation with ML developers, hydrologists and river managers suggest the possibility of improved flood emergency response and hydropower management.