Evidence of human influence on Northern Hemisphere snow loss.

Documenting the rate, magnitude and causes of snow loss is essential to benchmark the pace of climate change and to manage the differential water security risks of snowpack declines1-4. So far, however, observational uncertainties in snow mass5,6 have made the detection and attribution of human-forced snow losses elusive, undermining societal preparedness. Here we show that human-caused warming has caused declines in Northern Hemisphere-scale March snowpack over the 1981-2020 period. Using an ensemble of snowpack reconstructions, we identify robust snow trends in 82 out of 169 major Northern Hemisphere river basins, 31 of which we can confidently attribute to human influence. Most crucially, we show a generalizable and highly nonlinear temperature sensitivity of snowpack, in which snow becomes marginally more sensitive to one degree Celsius of warming as climatological winter temperatures exceed minus eight degrees Celsius. Such nonlinearity explains the lack of widespread snow loss so far and augurs much sharper declines and water security risks in the most populous basins. Together, our results emphasize that human-forced snow losses and their water consequences are attributable-even absent their clear detection in individual snow products-and will accelerate and homogenize with near-term warming, posing risks to water resources in the absence of substantial climate mitigation.

Assessing effective radiative forcing from aerosol-cloud interactions over the global ocean.

How clouds respond to anthropogenic sulfate aerosols is one of the largest sources of uncertainty in the radiative forcing of climate over the industrial era. This uncertainty limits our ability to predict equilibrium climate sensitivity (ECS)-the equilibrium global warming following a doubling of atmospheric CO2. Here, we use satellite observations to quantify relationships between sulfate aerosols and low-level clouds while carefully controlling for meteorology. We then combine the relationships with estimates of the change in sulfate concentration since about 1850 to constrain the associated radiative forcing. We estimate that the cloud-mediated radiative forcing from anthropogenic sulfate aerosols is [Formula: see text] W m-2 over the global ocean (95% confidence). This constraint implies that ECS is likely between 2.9 and 4.5 K (66% confidence). Our results indicate that aerosol forcing is less uncertain and ECS is probably larger than the ranges proposed by recent climate assessments.

Flash drought early warning based on the trajectory of solar-induced chlorophyll fluorescence.

Flash drought often leads to devastating effects in multiple sectors and presents a unique challenge for drought early warning due to its sudden onset and rapid intensification. Existing drought monitoring and early warning systems are based on various hydrometeorological variables reaching thresholds of unusually low water content. Here, we propose a flash drought early warning approach based on spaceborne measurements of solar-induced chlorophyll fluorescence (SIF), a proxy of photosynthesis that captures plant response to multiple environmental stressors. Instead of negative SIF anomalies, we focus on the subseasonal trajectory of SIF and consider slower-than-usual increase or faster-than-usual decrease of SIF as an early warning for flash drought onset. To quantify the deviation of SIF trajectory from the climatological norm, we adopt existing formulas for a rapid change index (RCI) and apply the RCI analysis to spatially downscaled 8-d SIF data from GOME-2 during 2007-2018. Using two well-known flash drought events identified by the operational US Drought Monitor (in 2012 and 2017), we show that SIF RCI can produce strong predictive signals of flash drought onset with a lead time of 2 wk to 2 mo and can also predict drought recovery with several weeks of lead time. While SIF RCI shows great early warning potential, its magnitude diminishes after drought onset and therefore cannot reflect the current drought intensity. With its long lead time and direct relevance for agriculture, SIF RCI can support a global early warning system for flash drought and is especially useful over regions with sparse hydrometeorological data.

Urbanization exacerbated the rainfall and flooding caused by hurricane Harvey in Houston.

Category 4 landfalling hurricane Harvey poured more than a metre of rainfall across the heavily populated Houston area, leading to unprecedented flooding and damage. Although studies have focused on the contribution of anthropogenic climate change to this extreme rainfall event1-3, limited attention has been paid to the potential effects of urbanization on the hydrometeorology associated with hurricane Harvey. Here we find that urbanization exacerbated not only the flood response but also the storm total rainfall. Using the Weather Research and Forecast model-a numerical model for simulating weather and climate at regional scales-and statistical models, we quantify the contribution of urbanization to rainfall and flooding. Overall, we find that the probability of such extreme flood events across the studied basins increased on average by about 21 times in the period 25-30 August 2017 because of urbanization. The effect of urbanization on storm-induced extreme precipitation and flooding should be more explicitly included in global climate models, and this study highlights its importance when assessing the future risk of such extreme events in highly urbanized coastal areas.

Protist dynamics in the eastern Tsugaru Strait, Japan from 2010 to 2018: Implications for the relationship between decadal climatology and aquaculture production.

Environmental changes such as seasonality, decadal oscillation, and anthropogenic forcing may shape the dynamics of lower trophic-level organisms. In this study, 9-years (2010-2018) of monitoring data on microscopic protists such as diatoms and dinoflagellates, and environmental variables were analyzed to clarify the relationships between plankton and local/synoptic environmental changes. We found that time-series temperature increased in May, whereas it decreased in August and November. Nutrients (e.g., phosphate) decreased in May, remained unchanged in August, and increased in November from 2010 to 2018. The partial pressure of CO2 increased in May, August, and November over time. It is notable that the change in seawater temperature (-0.54 to 0.32 °C per year) and CO2 levels (3.6-5.7 µatm CO2 per year) in the latest decade in the eastern Tsugaru Strait were highly dynamic than the projected anthropogenic climate change. Protist abundance generally increased or stayed unchanged during the examined period. In August and November, when cooling and decreases in pH occurred, diatoms such as Chaetoceros subgenus Hyalochaete spp. and Rhizosoleniaceae temporally increased from 2010 to 2018. During the study period, we found that locally aquacultured scallops elevated soft tissue mass relative to the total weight as diatom abundance increased, and the relative scallop soft tissue mass was positively related to the Pacific Decadal Oscillation index. These results indicate that decadal climatic forcing in the ocean modifies the local physical and chemical environment, which strongly affects phytoplankton dynamics rather than the effect of anthropogenic climate change in the eastern Tsugaru Strait.

Validation of Citizen Science Meteorological Data: Can They Be Considered a Valid Help in Weather Understanding and Community Engagement?

Citizen science has emerged as a potent approach for environmental monitoring, leveraging the collective efforts of volunteers to gather data at unprecedented scales. Within the framework of the I-CHANGE project, MeteoTracker, a citizen science initiative, was employed to collect meteorological measurements. Through MeteoTracker, volunteers contributed to a comprehensive dataset, enabling insights into local weather patterns and trends. This paper presents the analysis and the results of the validation of such observations against the official Italian civil protection in situ weather network, demonstrating the effectiveness of citizen science in generating valuable environmental data. The work discusses the methodology employed, including data collection and statistical analysis techniques, i.e., time-series analysis, spatial and temporal interpolation, and correlation analysis. The overall analysis highlights the high quality and reliability of citizen-generated data as well as the strengths of the MeteoTracker platform. Furthermore, our findings underscore the potential of citizen science to augment traditional monitoring efforts, inform decision-making processes in environmental research and management, and improve the social awareness about environmental and climate issues.

Improving the prediction of wildfire susceptibility on Hawai'i Island, Hawai'i, using explainable hybrid machine learning models.

This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on Hawai'i Island, Hawai'i. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms - Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) - were employed to map areas susceptible to wildfire. To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWO-XGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics - sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision-Recall Curves (PRCs) - were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on Hawai'i Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWO-XGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOA-XGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors. The wildfire susceptibility maps generated in this study can be used by local authorities for wildfire management and fire suppression activity.

A unified deep learning framework for water quality prediction based on time-frequency feature extraction and data feature enhancement.

Deep learning methods exhibited significant advantages in mapping highly nonlinear relationships with acceptable computational speed, and have been widely used to predict water quality. However, various model selection and construction methods resulted in differences in prediction accuracy and performance. Hence, a unified deep learning framework for water quality prediction was established in the paper, including data processing module, feature enhancement module, and data prediction module. In the established model, the data processing module based on wavelet transform method was applied to decomposing complex nonlinear meteorology, hydrology, and water quality data into multiple frequency domain signals for extracting self characteristics of data cyclic and fluctuations. The feature enhancement module based on Informer Encoder was used to enhance feature encoding of time series data in different frequency domains to discover global time dependent features of variables. Finally, the data prediction module based on the stacked bidirectional long and short term memory network (SBiLSTM) method was employed to strengthen the local correlation of feature sequences and predict the water quality. The established model framework was applied in Lijiang River in Guilin, China. The maximum relative errors between the predicted and observed values for dissolved oxygen (DO), chemical oxygen demand (CODMn) were 12.4% and 20.7%, suggesting a satisfactory prediction performance of the established model. The validation results showed that the established model was superior to all other models in terms of prediction accuracy with RMSE values 0.329, 0.121, MAE values 0.217, 0.057, SMAPE values 0.022, 0.063 for DO and CODMn, respectively. Ablation tests confirmed the necessity and rationality of each module for the established model framework. The established method provided a unified deep learning framework for water quality prediction.

Atmospheric Transport of Polycyclic Aromatic Hydrocarbons into Three Alpine Valleys: Influence of Local-Scale Wind Patterns and Chemical Partitioning.

Current understanding of atmospheric transport of polycyclic aromatic hydrocarbons (PAHs) is limited in alpine areas due to complex meteorology and topography. To better understand atmospheric transport in these areas, we measured 16 PAHs in lichens, biomonitors of atmospheric PAHs, along three transects extending from a highway into otherwise remote alpine valleys. While the valleys neighbored one another and were morphologically similar, they differed in their orientation relative to regional winds. In the valley characterized by regional winds oriented up-valley, PAH concentrations in lichens remained consistent across the transect. In the other two valleys, where regional winds were oriented down or across the valley, 3-6 ring PAHs declined rapidly with increasing distance from the highway, and PAH concentrations in the lichens declined more rapidly for higher molecular weight PAHs than lower molecular weight PAHs. We hypothesize that this trend was driven by differences in gas-particle partitioning and vegetative scavenging between PAH congeners. These results illustrate the importance of both physical transport and chemical partitioning in alpine areas where small differences in topography can lead to significant differences in chemical transport.

Assessing Detection Efficiencies for Continuous Methane Emission Monitoring Systems at Oil and Gas Production Sites.

Continuous monitoring systems, consisting of multiple fixed sensors, are increasingly being deployed at oil and gas production sites to detect methane emissions. While these monitoring systems operate continuously, their efficiency in detecting emissions will depend on meteorological conditions, sensor detection limits, the number of sensors deployed, and sensor placement strategies. This work demonstrates an approach to assess the effectiveness of continuous sensor networks in detecting infinite-duration and fixed-duration emission events. The case studies examine a single idealized source and a group of nine different sources at varying heights and locations on a single pad. Using site-specific meteorological data and dispersion modeling, the emission detection performance is characterized. For these case studies, infinite-duration emission events are detected within 1 h to multiple days, depending on the number of sensors deployed. The percentage of fixed-duration emission events that are detected ranged from less than 10% to more than 90%, depending on the number of sources, emission release height, emission event duration, and the number of sensors deployed. While these results are specific to these case studies, the analysis framework described in this work can be broadly applied in the evaluation of continuous emission monitoring network designs.

Climatology and landscape determinants of AOD, SO2 and NO2 over Indo-Gangetic Plain.

Indo-Gangetic Plains (IGP) experiences high loading of particulate and gaseous pollutants all year around and is considered to be the most polluted regions of India. Understanding the effect of landscape determinants on air pollution in IGP regions is crucial to make its environment sustainable. We examined satellite retrievals of OMI NO2 and SO2, and MODIS AOD to analyse the long-term trend, spatio-seasonal pattern and dynamics of aerosols, NO2 and SO2 over three IGP regions, namely Upper Indo-Gangetic plain (UIGP), Middle Indo-Gangetic plain (MIGP) and Lower Indo-Gangetic plain (LIGP) over the period 2005-2019. IGP experienced an overall increment in AOD (R2 = 0.63) and SO2 (R2 = 0.67) values, with LIGP (AOD, R2 = 0.8 & SO2, R2 = 0.8) experiencing the largest rate of enhancement. The levels of NO2 (R2 = 0.2) experienced a decrement after 2012 (owing to implementation of vehicle emission policy) except in MIGP, with UIGP (R2 = 0.23) exhibiting the largest rate of decrement. Seasonal heterogeneity in the nature of sources was observed over IGP regions. AOD (0.61 ± 0.1) and NO2 value (3.82 ± 0.98 × 1015 molecules/cm2) were found highest during post-monsoon in UIGP owing to crop residue burning activity. The value of NO2 (3.8 ± 1.4 × 1015 molecules/cm2) in MIGP was found highest during pre-monsoon due to high consumption of coal in power plants for summer cooling demand. The highest SO2 level (0.09 ± 0.06 DU) was observed during post-monsoon in UIGP, as a large number of brick kilns are fired during this period. Correlations among landscape determinants and pollutants revealed that topography is the dominant variable that affect the spatial pattern of AOD compared to vegetation and land use. Lower elevation tends to have high AOD values compared to higher elevation. Vegetation-AOD relationship showed an inverse association in IGP regions and is influenced by factors such as seasonal meteorology and size of the airborne particles. Vegetation possesses positive relationship with SO2 and NO2, implying no pollution abatement effect on SO2 and NO2 pollutants. Built-up change has deteriorating effect as well as quenching effect on pollutants. Increase in built terrain have deteriorated the air quality in UIGP whereas it favored in suppressing the aerosol level in LIGP.

Summary of the first Brazilian Symposium on Human Biometeorology.

This brief background highlights Brazil as a 'climate-health hotspot', i.e. a country where climate affects local populations negatively through multiple pathways (Di Napoli et al. BMC Public Health 22(1):1-8, 2022). Knowledge gaps still need to be filled concerning the various climaterelated dimensions of tourism, vector-borne diseases, mortality and morbidity in urban centers in the country (Krüger et al. Int J Biometeorol 66(7):1297-1315, 2022). Motivated by this, the first Brazilian Symposium on Human Biometeorology (Simpósio Brasileiro de Biometeorologia Humana 2022) was organized and held at the Federal University of Rio Grande do Norte (UFRN) in Natal, northeastern Brazil, between July 4 and 8, 2022. The symposium was organized as a hybrid event by a committee composed of researchers acting in different regions of the country, and who had an ongoing research collaboration on matters related to human biometeorology. The event was partly sponsored by the ISB and partly self-supported by the organizers and institutions involved. The symposium aimed to promote the development of the research area on human biometeorology in Brazil in facing challenges imposed by a globally and locally changing climate. To achieve this, the symposium focused on five main topics of discussion: a) climate-driven diseases; b) thermal comfort, urban and architectural biometeorology; c) atmospheric pollution and health; d) climate change; e) climate, health and climate change. This summary highlights the main findings, future research directions, and policy implications in each topic from the presentations and panel discussions.

Climate and human health: a review of publication trends in the International Journal of Biometeorology.

The climate-health nexus is well documented in the field of biometeorology. Since its inception, Biometeorology has in many ways become the umbrella under which much of this collaborative research has been conducted. Whilst a range of review papers have considered the development of biometeorological research and its coverage in this journal, and a few have reviewed the literature on specific diseases, none have focused on the sub-field of climate and health as a whole. Since its first issue in 1957, the International Journal of Biometeorology has published a total of 2183 papers that broadly consider human health and its relationship with climate. In this review, we identify a total of 180 (8.3%, n = 2183) of these papers that specifically focus on the intersection between meteorological variables and specific, named diagnosable diseases, and explore the publication trends thereof. The number of publications on climate and health in the journal increases considerably since 2011. The largest number of publications on the topic was in 2017 (18) followed by 2021 (17). Of the 180 studies conducted, respiratory diseases accounted for 37.2% of the publications, cardiovascular disease 17%, and cerebrovascular disease 11.1%. The literature on climate and health in the journal is dominated by studies from the global North, with a particular focus on Asia and Europe. Only 2.2% and 8.3% of these studies explore empirical evidence from the African continent and South America respectively. These findings highlight the importance of continued research on climate and human health, especially in low- and lower-middle-income countries, the populations of which are more vulnerable to climate-sensitive illnesses.

Application of microcontroller-based systems in human biometeorology studies: a bibliometric analysis.

Urban development creates several inadvertent impacts related to urban climate and human biometeorology. Monitoring systems based on microcontrollers are slowly emerging as an alternative to conventional devices for monitoring outdoor thermal comfort (OTC), thus overcoming limitations imposed by the high costs of commercially available equipment. This review was conducted using the Scopus database, searching for articles and conference papers according to a pre-defined search string, which included the terms "microcontrollers" and "human thermal comfort" up to 2022. From a total sample of 113 articles, 52 papers met the desired criteria (written in English, published in peer-reviewed journals, and within the given time frame). Results show a growing, yet timid trend of published material on low-cost, open-source technologies for diverse applications in human biometeorology.

Biometeorological conditions during cold spells in south-east Poland and west Ukraine.

The aim of this research is to analyze the biometeorological conditions, based on the Physiologically Equivalent Temperature (PET) thermal index, during cold spells (CSs) in south-east Poland and west Ukraine during the years 1966-2021. The research shows a high variability of the occurrence of CSs in the study period and a clear increase in the frequency and total duration of CSs in the east of the study area. The number of CSs in the analyzed years varies from 6 cases in the west (in Katowice) to 34 in the east of the study area (in Shepetivka). The total duration of CSs varied from 26 days (in Raciborz and Katowice) to 166 days (in Rivne). At the majority of stations, CSs occurred most frequently in the first two decades (1966/1967-1975/1976, 1976/1977-1985/986) and in the last full decade (2006/2007-2015/2016). The average PET values at 12:00 UTC during CSs decreased eastwards throughout the study domain and were generally lower than -20.0 °C in the west of Ukraine, while in south-east Poland varied between -18.1 and -20.0 °C. At 40% of stations across the study domain, the lowest average PET values were recorded during a cold spell in January 1987, with PET values varying from -28.0 °C in Chernivtsi to -12.7 °C in Yaremche. The longest or one of the longest spells in most stations (in 77% of stations across the study domain) was the cold spell of 2012 and characterized by mean PET values ranging from -25.4 °C in Rivne to -19.5 °C in Zakopane.

Forecasting daily emergency ambulance service demand using biometeorological indexes.

This study aims to study the effectiveness of using biometeorological indexes in the development of a daily emergency ambulance service demand forecast system for Taipei City, Taiwan, compared to typical weather factors. Around 370,000 emergency ambulance service patient records were aggregated into a daily emergency ambulance service demand time series as the study's dependent variable. To assess the effectiveness of biometeorological indexes in making a 1 to 7-day forecast of daily emergency ambulance service demand, five forecast models were developed to make the comparison. The model with average temperature as the only predictor performed the best consistently from 1 to 7-day forecasts. The models with net effective temperature and apparent temperature as their only predictors ranked second and third, respectively. It is surprising that the model with both average temperature and relative humidity as predictors only ranked fourth. The unexpected outperformance of average temperature over net effective temperature and apparent temperature in forecasting daily emergency ambulance service demand suggested the need to develop updated locational-specific biometeorological indexes so that the benefit of the indexes can be fully utilized. Although adopting popular biometeorological indexes that are already available would be cheap and convenient, the benefit from these general indexes may not be guaranteed.

NASA POWER satellite meteorological system is a good tool for obtaining estimates of the temperature-humidity index under Brazilian conditions compared to INMET weather stations data.

Heat stress negatively affects livestock, with undesirable effects on animals' production and reproduction. Temperature and humidity index (THI) is a climatic variable used worldwide to study the effect of heat stress on farm animals. Temperature and humidity data can be obtained in Brazil through the National Institute of Meteorology (INMET), but complete data may not be available due to temporary failures on weather stations. An alternative to obtaining meteorological data is the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) satellite-based weather system. We aimed to compare THI estimates obtained from INMET weather stations and NASA POWER meteorological information sources using Pearson correlation and linear regression. After quality check, data from 489 INMET weather stations were used. The hourly, average daily and maximum daily THI were evaluated. We found greater correlations and better regression evaluation metrics when average daily THI values were considered, followed by maximum daily THI, and hourly THI. NASA POWER satellite-based weather system is a suitable tool for obtaining the average and maximum THI values using information collected from Brazil, showing high correlations with THI estimates from INMET and good regression evaluation metrics, and can assist studies that aim to analyze the impact of heat stress on livestock production in Brazil, providing additional data to complement the existing information available in the INMET database.

LSTMAtU-Net: A Precipitation Nowcasting Model Based on ECSA Module.

Precipitation nowcasting refers to the use of specific meteorological elements to predict precipitation in the next 0-2 h. Existing methods use radar echo maps and the Z-R relationship to directly predict future rainfall rates through deep learning methods, which are not physically constrained, but suffer from severe loss of predicted image details. This paper proposes a new model framework to effectively solve this problem, namely LSTMAtU-Net. It is based on the U-Net architecture, equipped with a Convolutional LSTM (ConvLSTM) unit with the vertical flow direction and depthwise-separable convolution, and we propose a new component, the Efficient Channel and Space Attention (ECSA) module. The ConvLSTM unit with the vertical flow direction memorizes temporal changes by extracting features from different levels of the convolutional layers, while the ECSA module innovatively integrates different structural information of each layer of U-Net into the channelwise attention mechanism to learn channel and spatial information, thereby enhancing attention to the details of precipitation images. The experimental results showed that the performance of the model on the test dataset was better than other examined models and improved the accuracy of medium- and high-intensity precipitation nowcasting.