Intensifying urban imprint on land surface warming: Insights from local to global scale.

Increasing urbanization exacerbates surface energy balance perturbations and the health risks of climate warming; however, it has not been determined whether urban-induced warming and attributions vary from local, regional, to global scale. Here, the local surface urban heat island (SUHI) is evidenced to manifest with an annual daily mean intensity of 0.99°C-1.10°C during 2003-2018 using satellite observations over 536 cities worldwide. Spatiotemporal patterns and mechanisms of SUHI tightly link with climate-vegetation conditions, with regional warming effect reaching up to 0.015°C-0.138°C (annual average) due to surface energy alterations. Globally, the SUHI footprint of 1,860 cities approximates to 1% of the terrestrial lands, about 1.8-2.9 times far beyond the urban impervious areas, suggesting the enlargements of the imprint of urban warming from local to global scales. With continuous development of urbanization, the implications for SUHI-added warming and scaling effects are considerably important on accelerating global warming.

Long-term remote sensing-based methods for monitoring air pollution and cloud cover in the Balkan countries.

The use of remote sensing and GIS methodology has accelerated the processing of data on pollution, but has also raised a question about the accuracy of the same. The research focuses on four main air pollutants (CO, NO, SO2, O3), the data on which were obtained from satellite images of Landsat 8 and Landsat 9, for the period 2000-2020. The data on relative cloudiness were obtained from the database CHELSA (Climatologies at high resolution for the earth's land surface areas) for the period 1980-2010. All the data were further processed and analyzed through the procedures of numerical GIS analysis, multi-criteria analysis, supervised and unsupervised satellite classification, and pixel analysis. The results of the analysis of cloud cover in the Balkan region showed that the month with the highest cloud cover in this period was February, with the maximum of (93.18%), whereas the lowest cloud cover was in July (0.19%). The analyzed period (2000-2010) was in the middle range for the pollutants NO and SO2 and in the lower range for CO; O3. In the period 2010-2020, there were high concentrations of NO, SO2, and CO and low concentrations of O3. The most polluted cities in the last twenty years are Ordu (Turkey), Sarajevo (Bosnia and Herzegovina), and Bor (Serbia). Finally, two most extreme air pollutants in the territory of Balkan countries were SO2 and NO (2000-2020).

Triple dip La-Nina, unorthodox circulation and unusual spin in air quality of India.

The recent La-Nina phase of the El Nino Southern Oscillation (ENSO) phenomenon unusually lasted for third consecutive year, has disturbed global weather and linked to Indian monsoon. However, our understanding on the linkages of such changes to regional air quality is poor. We hereby provide a mechanism that beyond just influencing the meteorology, the interactions between the ocean and the atmosphere during the retreating phase of the La-Niña produced secondary results that significantly influenced the normal distribution of air quality over India through disturbed large-scale wind patterns. The winter of 2022-23 that coincided with retreating phase of the unprecedented triple dip La-Niña, was marred by a mysterious trend in air quality in different climatological regions of India, not observed in recent decades. The unusually worst air quality over South-Western India, whereas relatively cleaner air over the highly polluted North India, where levels of most toxic pollutant (PM2.5) deviating up to about ±30 % from earlier years. The dominance of higher northerly wind in the transport level forces influx and relatively slower winds near the surface, trapping pollutants in peninsular India, thereby notably increasing PM2.5 concentration. In contrast, too feeble western disturbances, and unique wind patterns with the absence of rain and clouds and faster ventilation led to a significant improvement in air quality in the North. The observed findings are validated by the chemical-transport model when forced with the climatology of the previous year. The novelty of present research is that it provides an association of air quality with climate change. We demonstrate that the modulated large-scale wind patterns linked to climatic changes may have far-reaching consequences even at a local scale leading to unusual changes in the distribution of air pollutants, suggesting ever-stringent emission control actions.

Different explanations for surface and canopy urban heat island effects in relation to background climate.

The background climatic conditions and urban morphology greatly influence urban heat island effects (UHIs), but one-size-fits-all solutions are frequently employed to mitigate UHIs. Here, attribution models for surface UHIs (SUHIs) and canopy UHIs (CUHIs) were developed to describe UHI formation. The contribution of factors to SUHIs and CUHIs shows similar dependencies on background climate and urban morphology. Furthermore, the factors that mainly contributed to CUHIs were more complex, and anthropogenic heat was the more critical factor. Influence from urban morphology also highlights that there is no one-size-fit-all solution for heat mitigation at the neighborhood. In particular, maintaining a low building density should be prioritized, especially mitigating CUHIs. Moreover, it is more effective to prioritize urban irrigation maintenance over increasing green cover in arid regions but the opposite in humid regions. The work can provide scientific evidence to support developing general and regional guidelines for urban heat mitigation.

Social-economic transitions and vulnerability to extreme temperature events from 1960 to 2020 in Chinese cities.

Climate change leads to more frequent and intense extreme temperature events, causing a significant number of excess deaths. Using an epidemiological approach, we analyze all-cause deaths related to heatwaves and cold spells in 2,852 Chinese counties from 1960 to 2020. Economic losses associated with these events are determined through the value of statistical life. Findings reveal that cold-related cumulative excess deaths (1,133 thousand) are approximately 2.5 times higher than heat-related deaths, despite an increase in heat-related fatalities in recent decades. Monetized mortality due to heat-related events is estimated at 1,284 billion CNY, while cold-related economic loss is 1,510 billion CNY. Notably, cities located in colder regions experience more heat-related excess deaths, and vice versa. Economic development does not significantly reduce mortality risks to heatwaves across China. This study provides insights into the spatial-temporal heterogeneity of heatwaves and cold spells mortality, essential for policymakers ensuring long-term climate adaptation and sustainability.

Modulation of PM2.5 in tropical South America by the upper-level atmospheric circulation and El Niño-Southern Oscillation phases.

In the Amazon basin, biomass burning has been identified as a major cause of poor regional air quality and the dominant source of particulate matter (PM). In this study, we analyse the impact of the upper-level jet on PM2.5 (PM with an aerodynamic diameter ≤ 2.5 µm) concentrations in tropical South America (SA) from December to February during the period 2003-2022. Furthermore, we investigate the response of air pollutants to the joint modulation by the upper-level jet and El Niño-Southern Oscillation (ENSO). We find that PM2.5 concentrations in north-eastern Brazil are reduced on days when the subtropical jet (STJ) is absent due to enhanced convection and precipitation over the region. This improvement in air quality is independent of the ENSO phase. Conversely, a prominent STJ inhibits convection and contributes to dry conditions that favour increased biomass burning and elevated pollutant concentrations. Furthermore, the co-occurrence of a prominent STJ with an El Niño phase acts synergistically to increase pollutant concentrations, as both reduce precipitation in north-eastern Brazil. In combination with La Niña, this upper-level pattern does not exert any modulation of the PM2.5 concentrations, as the wet conditions favoured by this ENSO phase prevail to reduce biomass burning.

Significant early end of the growing season of forest vegetation inside China's protected areas.

The land surface phenology (LSP) indicators (i.e., start, end, and length of the growing season: SOS, EOS, LOS) are important to reflect the growth of forest and its response to environmental changes. However, the spatiotemporal variation and its mechanism of forest phenology under different human disturbance' levels are still unclear. Here, we compare the LSP indicators inside and outside China's 257 protected areas (PAs) and explore the influencing factors of phenological differences (ΔSOS, ΔEOS, ΔLOS). We find that in general, EOS inside PAs (mean ± s.e.m: 312.6 ± 1.2days) is significantly earlier than outside (314.6 ± 1.2days), and LOS inside PAs (218.9 ± 2.0days) are significantly shorter than outside (220.6 ± 2.0days). ΔSOS and ΔEOS are controlled by nighttime and daytime temperature differences, respectively, and both factors affect ΔLOS. This evidence provides a new understanding about the functions of PAs and its influence on forest vegetation growth.

Characterizing the spatio-temporal distribution, detection, and prediction of aerosol atmospheric rivers on a global scale.

Aerosol Atmospheric Rivers (AARs) are elongated and narrow regions that carry high concentrations of aerosols (tiny particles suspended in the atmosphere) across large distances, exerting effects on both air quality and human health (Chakraborty et al., 2021, 2022). Monitoring and modeling these aerosols present distinct challenges due to their dynamic nature and complex interactions within the atmosphere. In this context, the present study detects and predicts the AARs using MERRA-2 reanalysis datasets with their seasonal climatology of key aerosol species, including Black Carbon (BC), Dust (DU), Organic Carbon (OC), Sea Salt (SS), and Sulphates (SU). The study employs an innovative Integrated Aerosol Transport (IAT) based AAR algorithm from 2015 to 2022. A total count of 44,020 BC AARs, 13,280 DU AARs, 21,599 OC AARs, 17,925 SS AARs, and 31,437 SU AARs were detected globally. The seasonal climatology of BC and OC AARs intensifies in areas such as the Amazon rainforest and Congo during AMJJAS (April-September) due to forest fires. Similarly, DU AARs are more frequent in regions near the Saharan desert, primarily around the equator during AMJJAS. SS AARs tend to predominate over the oceans, while SU AARs are predominantly found in the northern hemisphere, primarily due to higher anthropogenic emissions. Furthermore, convolutional autoencoder-based models were developed for key aerosol species, strengthening predictive accuracy by effectively capturing complex data relationships and delivering precise predictions for the last 5-time frames. During validation, the model evaluation parameters for image prediction such as the Structural Similarity Index ranged from 0.86 to 0.94, Peak Signal-to-Noise Ratio fluctuated between 1.14 and 42.25 dB, Root Mean Square Error varied from 2.39 to 296.4 mg/(m-sec), and Mean Square Error fell within the range of 1.55-17.22 mg/(m-sec). These collectively reflect image closeness, quality, dissimilarity, and accuracy in AAR prediction. This study demonstrates the effectiveness of advanced machine and deep learning models in predicting AARs, offering the potential for advanced forecasting and enhancing resilience in high-aerosol concentration regions.

The severity of dry and hot climate extremes and their related impacts on vegetation in Madagascar.

Madagascar is a low-income country, highly vulnerable to natural disasters affecting the small-scale subsistence farming system. Recently, climate change and environmental degradation have contributed to an intensification of food insecurity. We aim to monitor the link between dry and hot extremes on vegetation conditions, separated or concurrently, using satellite data, such as LST, ET, ET0, and FAPAR products from SEVIRI/MSG disseminated by LSASAF-EUMETSAT. The analysis was made for a long record from 2004 to 2021, focusing on the extreme seasons of 2020 and 2021. Results highlight the higher impact of combined dry and hot events when compared with isolated events, with a strong response of vegetation in the southern part of Madagascar. Results point to the added value of using the recent data records from geostationary satellites with high temporal resolution and updated in near real-time, to early detect, monitor, and characterize the impact of climate extremes on vegetation dynamics.

Prediction of ctenophore Beroe ovata Bruguiere, 1789 habitat expansion and control of invading Mnemiopsis leidyi Agassiz, 1865 in the Ponto-Caspian basin associated with climate change.

The seas of Ponto-Caspian basin (Black, Azov and Caspian) are exposed to species invasions, including harmful ctenophore Mnemiopsis leidyi A. Agassiz, 1865 and its predator Beroe ovata Bruguière, 1789. Current environmental conditions of invasive ctenophores M. leidyi and B. ovata occurrence, development and variability in the Ponto-Caspian basin have been compiled, based on own field observations and published data. Analysis of climatological data on basic environmental parameters (water temperature, salinity, chlorophyll concentration) and modeling approach were used to predict favourable for both ctenophores conditions, and changes of those conditions, associated with the climate variations. The role of B. ovata as a bio-controller of M. leidyi population has been assessed. Several climate change scenarios have been considered in this study i.e., uniform increase/decrease of SST throughout the year by 2 °C and only spring temperatures increase/decrease by 2 °C. The most sensitive to increase in SST in terms of M. leidyi reproduction duration are the western coast of the Black Sea and the Southern and Middle Caspian. On the other hand, B. ovata reproduction duration is expected to increase in several areas, including the northern coast of the Black Sea and the Southern and Middle Caspian. The coastal areas of the Black Sea and the transitional regions between the Southern and Middle Caspian are exposed to an earlier start of M. leidyi reproduction during warmer springs. Regarding B. ovata, the whole Black Sea is vulnerable to spring SST changes, but in the Caspian Sea B. ovata extends its reproduction duration only in the Middle Caspian during warmer springs. Since B. ovata consumes mostly M. leidyi, it is an important biocontrolling agent of M. leidyi, harmless for the ecosystem.

Adaptation cannot keep pace with projected temperature increase.

An emerging argument is that since humans can readily adapt to changing climatic conditions, there is less need to pursue aggressive emissions mitigation strategies. As temperature adaptation is a function of repeated exposure over time, we need empirical approaches that can depict individuals' temperature history to rigorously examine this claim. Using a longitudinal dataset representative of China, we construct lifetime temperature exposure unique to each individual based on their birth-dates, birth-locations, and movement history. We show that a 1°C increase in individualized temperature anomalies cause a 2% decrease in 1 standard deviation (S.D.) of well-being, where most of the impacts are driven by "hotter-than-expected" weather. In turn, while the adverse impacts of future temperature changes wane after accommodating for adaptation, acclimatization is unlikely to keep pace with future temperature increases except in the net-zero emissions scenario, indicating that stringent greenhouse gas (GHG) emissions cuts are still needed even in this less-pessimistic scenario.

Drought as a trigger of the rapid rise of professional skateboarding in 1970s Southern California.

In 1977 California, authorities responded to an extreme drought with an unprecedented state order to drastically reduce domestic water usage and leave countless newly built swimming pools empty. These curved pools became "playgrounds" for inspired surfers to develop professional vertical skateboarding in the Los Angeles area. Industrial production of polyurethane, and the advent of digital photography, laser printing, and high gloss mass media further contributed to the explosive popularization of skateboarding, creating a global subculture and multibillion-dollar industry that still impacts music, fashion, and lifestyle worldwide. Our interdisciplinary investigation demonstrates that neither the timing nor the location of the origin of professional skateboarding was random. This modern case study highlights how environmental changes can affect human behavior, transform culture, and engender technical innovation in the Anthropocene.

Positive feedback to regional climate enhances African wildfires.

Regional climate strongly regulates the occurrence of wildfires partly because drying of fuel load increases fires. The large amounts of aerosols released by wildfires can also strongly affect regional climate. Here we show positive feedback (a seasonal burned area enhancement of 7-17%) due to wildfire aerosol forcing in Africa found in the simulations using the interactive REgion-Specific ecosystem feedback Fire (RESFire) model in the Community Earth System Model (CESM). The positive feedback results partly from the transport of fire aerosols from burning (dry) to wet regions, reducing precipitation and drying fuel load to enhance fires toward the non-burning (wet) region. This internally self-enhanced burning is an important mechanism for the regulation of African ecosystems and for understanding African fire behaviors in a changing climate. A similar mechanism may also help sustain wildfires in other tropical regions.

Seasonal weather impacts wine quality in Bordeaux.

Critics judge quality based upon subjective characteristics of wine. These judgments are converted by critics into quantitative scores, which allow for comparison of vintages. This paper uses high resolution discrete and continuous time-based weather estimates at both a local and regional level to determine the role of weather conditions on producing high quality Bordeaux vintages, as determined by critics scores. By using discrete-time weather variables across local AOCs, this study reveals climate-quality relationships across the whole year, including previously ignored season effects. By using continuous time weather variables, we reinforce the evidence for these local effects by finding higher quality wine is made in years with higher rainfall, warmer temperatures; and earlier, shorter seasons. We propose management impacts of our results and suggest that as the climate continues to change, the quality of Bordeaux wines may continue to improve.

Frequency dominates intensity of future heat waves over India.

Future changes in heat wave characteristics over India have been analyzed using Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SA) regional climate model simulations for mid-term (2041-2060) and long-term (2081-2099) future under the representative concentration pathway (RCP) 4.5 and RCP 8.5 emission scenarios, respectively. SMHI_CSIRO-MK3.6 was found to be the best model in simulating heat wave trend over India for historical period. Future projections show a four-to-seven-fold increase in heat wave frequency for mid-term and long-term future under RCP 4.5 scenario, and five-to-ten-fold increase under RCP 8.5 scenario with increase in frequency dominating intensity in both the scenarios. Northwestern, Central, and South-central India emerged as future heat wave hotspots with largest increase in the south-central region. This high-resolution regional future projection of heat wave occurrence will serve as a baseline for developing transformational heat-resilient policies and adaptation measures to reduce potential impact on human health, agriculture, and infrastructure.

Coupling relationships between urban block spatial morphology and microclimate in severe cold regions.

This study investigates the relationship between urban block spatial morphology and microclimate in severe cold regions, using Shenyang, China as a case study. We employed computational fluid dynamics theory-based numerical simulation software and a controlled variable approach to analyze the microclimate effects of four key aspects: street conforming line ratio, street interface density, street aspect ratio, and building roof forms. The primary findings are as follows: Decreasing conforming line ratios initially increase average wind speed and temperature. Lower interface densities reduce average wind speed but raise temperature. Higher aspect ratios correspond to increased wind speed and decreased temperature. Additionally, upward sloping roofs correlate with higher average wind speed and temperature. This research provides a perspective for evaluating urban microclimates, considering human perception of urban block space. It also suggests spatial layout design strategies for different types of streets in severe cold regions, considering the climate environment.

An Australian convective wind gust climatology using Bayesian hierarchical modelling.

To quantify the hazard or risks associated with severe convective wind gusts, it is necessary to have a reliable and spatially complete climatology of these events. The coupling of observational and global reanalysis (ERA-Interim) data over the period 2005-2015 is used here to facilitate the development of a spatially complete convective wind gust climatology for Australia. This is done through the development of Bayesian Hierarchical models that use both weather station-based wind gust observations and seasonally averaged severe weather indices (SWI), calculated using reanalysis data, to estimate seasonal gust frequencies across the country while correcting for observational biases specifically, the sparse observational network to record events. Different SWI combinations were found to explain event counts for different seasons. For example, combinations of Lifted Index and low level wind shear were found to generate the best results for autumn and winter. While for spring and summer, the composite Microburst Index and the combination of most unstable CAPE and 0-1 km wind shear were found to be most successful. Results from these models showed a minimum in event counts during the winter months, with events that do occur mainly doing so along the southwest coast of Western Australia or along the coasts of Tasmania and Victoria. Summer is shown to have the largest event counts across the country, with the largest number of gusts occurring in northern Western Australia extending east into the Northern Territory with another maximum over northeast New South Wales. Similar trends were found with an extended application of the models to the period 1979-2015 when utilizing only reanalysis data as input. This implementation of the models highlights the versatility of the Bayesian hierarchical modelling approach and its ability, when trained, to be used in the absence of observations.

Correcting biases in regional climate model boundary variables for improved simulation of high-impact compound events.

Although climate models have been used to assess compound events, the combination of multiple hazards or drivers poses uncertainties because of the systemic biases present. Here, we investigate multivariate bias correction for correcting systemic bias in the boundaries that form the inputs of regional climate models (RCMs). This improves the representation of physical relationships among variables, essential for accurate characterization of compound events. We address four types of compound events that result from eight different hazards. The results show that while the RCM simulations presented here exhibit similar performance for some event types, the multivariate bias correction broadly improves the RCM representation of compound events compared to no correction or univariate correction, particularly for coincident high temperature and high precipitation. The RCM with uncorrected boundaries tends to produce a negative bias in the return period of these events, suggesting a tendency to over-simulate compound events with respect to observed events.

Using Satellite-Based Terrestrial Water Storage Data: A Review.

Land water storage plays a key role for the Earth's climate, natural ecosystems, and human activities. Since the launch of the first Gravity Recovery and Climate Experiment (GRACE) mission in 2002, spaceborne observations of changes in terrestrial water storage (TWS) have provided a unique, global perspective on natural and human-induced changes in freshwater resources. Even though they have become much used within the broader Earth system science community, space-based TWS datasets still incorporate important and case-specific limitations which may not always be clear to users not familiar with the underlying processing algorithms. Here, we provide an accessible and illustrated overview of the measurement concept, of the main available data products, and of some frequently encountered technical terms and concepts. We summarize concrete recommendations on how to use TWS data in combination with other hydrological or climatological datasets, and guidance on how to avoid possible pitfalls. Finally, we provide an overview of some of the main applications of GRACE TWS data in the fields of hydrology and climate science. This review is written with the intention of supporting future research and facilitating the use of satellite-based terrestrial water storage datasets in interdisciplinary contexts.

Universal thermal climate index in the Arctic in an era of climate change: Alaska and Chukotka as a case study.

The modern unambiguous climate change reveals in a rapid increase of air temperature, which is more distinctly expressed in the Arctic than in any other part of the world, affecting people health and well-being. The main objective of the current research is to explore the inter- and intra-annual changes in thermal stress for people in the Arctic, specifically for two parts of Beringia: Alaska, USA, and Chukotka, Russia, using climatology of the universal thermal climate index (UTCI). Data for 39 locations are taken from the ERA5-HEAT reanalysis for the period 1979-2020. Climatologically, the study area is divided into four subregions in Alaska: North, Interior, West and South, and two in Chukotka: Interior and Coast. The extreme coldest UTCI categories (1 and 2) are most common in coastal locations of northern Alaska and Chukotka, where strong winds exacerbate the low temperatures during winter. The results show that the frequency of category 1 (UTCI<-40°C) varies spatially from a quarter of all hours annually in Alaska North to almost zero in Alaska South. On the other hand, the warmest categories are rarely reached almost everywhere in Alaska and Chukotka, and even categories 7 and 8 (UTCI between +26 and +38°C) are found occasionally only at interior locations. Category 6 with no thermal stress (UTCI between +9 and+26°C) has frequencies up to 3% and 25% in Alaska North and Interior, respectively. The extremely cold thermal stress frequencies have substantially decreased over the 1979-2020 period, especially in Alaska North and Chukotka Coast. At the same time, the number of hours with UTCI in the comfortable category of thermal perception has increased depending on subregion, from 25 to 203 h/year. Overall, a decrease in the UTCI categories of extremely cold stress is coupled with an increase in the comfortable range in both Alaska and Chukotka. The salient conclusion is that, from the point of view of comfort and safety, global warming has a positive impact on the climatology of thermal stress in the Arctic, providing advantages for the development of tourism and recreation.