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The COVID-19 lockdowns drastically reduced human activity, emulating a controlled experiment on human–land–atmosphere coupling. Here, using a fusion of satellite and reanalysis products, we examine this coupling through changes in the surface energy budget during the lockdown (1 April to 15 May 2020) in the Indo-Gangetic Basin, one of the world’s most populated and polluted regions. During the lockdown, the reduction (>10%) in columnar air pollution compared to a five year baseline, expected to increase incoming solar radiation, was counteracted by a ∼30% enhancement in cloud cover, causing little change in available energy at the surface. More importantly, the delay in winter crop harvesting during the lockdown increased surface vegetation cover, causing almost half the regional cooling via evapotranspiration. Since this cooling was higher for rural areas, the daytime surface urban heat island (SUHI) intensity increased (by 0.20–0.41 K) during a period of reduced human activity. Our study provides strong observational evidence of the influence of agricultural activity on rural climate in this region and its indirect impact on the SUHI intensity.

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts. There has been a surge in global datasets of urban land recently. This paper shows large discrepancies in urban area across scales among multiple such datasets, which can influence the magnitude and direction of estimated urban climate impacts.

Accurately capturing the impact of urban trees on temperature can help optimize urban heat mitigation strategies. Recently, there has been widespread use of remotely sensed land surface temperature ( T s ) to quantify the cooling efficiency (CE) of urban trees. However, remotely sensed T s reflects emitted radiation from the surface of an object seen from the point of view of the thermal sensor, which is not a good proxy for the air temperature ( T a ) perceived by humans. The extent to which the CEs derived from T s reflect the true experiences of urban residents is debatable. Therefore, this study systematically compared the T s -based CE (CE T s ) with the T a -based CE (CE T a ) in 392 European urban clusters. CE T s and CE T a were defined as the reductions in T s and T a , respectively, for every 1% increase in fractional tree cover (FTC). The results show that the increase in FTC has a substantial impact on reducing T s and T a in most cities during daytime. However, at night, the response of T s and T a to increased FTC appears to be much weaker and ambiguous. On average, for European cities, daytime CE T s reaches 0.075 °C % −1 , which is significantly higher (by an order of magnitude) than the corresponding CE T a of 0.006 °C % −1 . In contrast, the average nighttime CE T s and CE T a for European cities are similar, both approximating zero. Overall, urban trees can lower daytime temperatures, but the magnitude of their cooling effect is notably amplified when using remotely sensed T s estimates compared to in situ T a measurements, which is important to consider for accurately constraining public health benefits. Our findings provide critical insights into the realistic efficiencies of alleviating urban heat through tree planting.

India faces significant air quality challenges, with one of the highest air pollution levels of any country in the world. Here, we examine two decades (2001–2019) of both particulate matter (PM2.5) concentration and aerosol optical depth (AOD) over the country. Increases are seen between the two decadal averages, for 2001–2010 and 2011–2019, in western India, particularly in the Indo-Gangetic Plain (IGP). IGP region, including Bihar, West Bengal, Jharkhand, and Uttar Pradesh, shows the highest increases in AOD (+0.03, 13%) and PM2.5, s (+8 µg m−3). Seasonal AOD patterns fluctuate, with the IGP experiencing the highest wintertime increase, especially in Bihar (+0.07). In summer, there are increases in AOD along the southern and eastern coastal areas. Monsoons cause a slight rise in AOD, except in Rajasthan. In the post-monsoon season, the IGP experiences a notable increase in AOD (+0.057, 25%), potentially driven by biomass burning in Bihar (+0.11) and Uttar Pradesh (+0.075). Dividing our study area into urban and peri-urban clusters (n = 2791), AOD is found to be similar, possibly due to advective mixing. However, the differences between urban and rural areas become more noticeable, especially in the second decade. Correlations between AOD and PM2.5, g vary across locations, with the highest found in Kanpur (R2 = 0.61) and weaker in Delhi (R2 = 0.42), highlighting the need for more ground monitoring. However, it suggests that satellite-derived AOD can generally be used to examine trends in PM2.5 over longer time frames.

Many cities are experiencing increases in extreme heat because of global temperature rise combined with the urban heat island effect. The heterogeneity of urban morphology also leads to fine-scale variability in potential for heat exposure. Yet, how this rise in temperature and local variability together impacts urban residents differently at exposure-relevant scales is still not clear. Here we map the Universal Thermal Climate Index, a more complete indicator of human heat stress at an unprecedentedly fine spatial resolution (1 m), for 14 major cities in the United States using urban microclimate modeling. We examined the different heat exposure levels across different socioeconomic and racial/ethnic groups in these cities, finding that income level is most consistently associated with heat stress. We further conducted scenario simulations for a hypothetical 1 °C increase of air temperature in all cities. Results show that a 1 °C increase would have a substantial impact on human heat stress, with impacts that differ across cities. The results of this study can help us better evaluate the impact of extreme heat on urban residents at decision-relevant scales.

Satellite‐based thermal infrared (TIR) land surface temperature (LST) is hindered by cloud cover and is applicable solely under clear‐sky conditions for estimating surface urban heat island intensity (SUHII). Clear‐sky SUHII may not accurately represent all‐sky conditions, potentially introducing quantitative biases in assessing urban heat islands. However, the differences between clear‐sky and all‐sky SUHIIs and their spatiotemporal variations are still poorly understood. Our analysis of over 600 global cities demonstrates that clear‐sky SUHII is mostly higher than all‐sky SUHII, particularly in summer, daytime, and precipitation‐rich regions. Besides, clear‐sky SUHII typically exhibits stronger seasonal and diurnal contrasts than all‐sky SUHII, especially for cities located in humid regions. These discrepancies can be attributed mainly to the increased missing LST data caused by cloud enhancement in urban areas. Our findings highlight the tendency for clear‐sky observations to overestimate SUHII, providing valuable insights for standardizing the quantification of surface urban heat islands. Plain Language Summary Surface urban heat island intensity (SUHII) and its spatial and temporal variations are important for describing the urban thermal environment. SUHII is usually estimated from remotely sensed land surface temperature (LST), which is only available under clear‐sky conditions. The SUHII derived from clear‐sky observations may differ from the SUHII under all‐sky conditions. However, there is currently a lack of large‐scale quantitative assessments addressing the differences between clear‐sky and all‐sky SUHIIs. This study fills this research gap and indicates a substantial overestimation of SUHII in humid regions when using clear‐sky LST. This overestimation can be explained by the increased occurrence of missing LST data caused by the enhanced presence of clouds in urban areas. Our findings show the importance of utilizing all‐sky LST data in the examination of urban surface thermal environments, especially for cities situated in humid regions. Key Points Clear‐sky surface urban heat island intensity (SUHII) shows higher values and stronger spatiotemporal variations than all‐sky SUHII, notably in summer, daytime, and humid areas The annual daytime SUHII for tropical cities is, on average, overestimated by 30% when relying on clear‐sky land surface temperature (LST) observations Differences in clear‐sky and all‐sky SUHIIs can be explained by more missing LST data caused by increased clouds in urban areas

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed Is and Ic trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean Is trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six‐times the corresponding Ic trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime Is trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime Ic trend. At night, both global Is and Ic trends are mainly controlled by background climate. Plain Language Summary Surface and canopy urban heat islands (surface and canopy UHIs, termed Is and Ic) are two major UHI types. These two counterparts are both related to urban population heat exposure and have long been a focus of urban climate research. However, the differences in the trends and major determinants of Is and Ic over global cities remain largely unclear. Based on spatially continuous land surface temperature and surface air temperature observations from 2003 to 2020, we find that the global mean Is trends are about 6.3 times and 2 times the Ic trends during the day and at night, respectively. During the day, the global Is trend is more regulated by surface property than by background climate, and vice versa for global Ic trend. At night, both the global Is and Ic trends are mainly regulated by background climate. These findings are important for better understanding global urban climate change and informing heat mitigation strategies. Key Points The global Is trend is six‐fold and twofold larger than the Ic trend during the day and at night, respectively During the day, global Is trend is slightly more controlled by surface property, yet background climate plays a dominant role in Ic trend At night, both global Is and Ic trends are more regulated by background climate

Higher temperatures in urban areas expose a large fraction of the human population to potentially dangerous heat stress. Green spaces are promoted worldwide as local and city‐scale cooling strategies but the amount, type, and functioning of vegetation in cities lack quantification and their interaction with urban climate in different settings remains a matter of debate. Here we use state‐of‐the‐art remote sensing data from 145 city clusters to disentangle the drivers of surface urban heat islands (SUHI) intensity and quantify urban‐rural differences in vegetation cover, species composition, and evaporative cooling. We show that nighttime SUHIs are affected mostly by abiotic factors, while daytime SUHIs are highly correlated with vegetation characteristics and the wetness of the background climate. Magnitude and seasonality of daytime SUHIs are controlled by urban‐rural differences in plant transpiration and leaf area, which explain the dependence of SUHIs on wetness conditions. Leaf area differences are caused primarily by changes in vegetation type and a loss of in‐city forested areas, highlighting the importance of maintaining “natural reserves” as a sustainable heat mitigation policy. Plain Language Summary More than half of the world's population currently lives in cities. Large cities are exposed to higher temperatures than their surrounding rural areas, a phenomenon known as the Urban Heat Island (UHI). Greening our cities has been proposed as an effective Urban Heat mitigation strategy. However, a detailed global scale quantification of the effect of urban vegetation to urban microclimate remains an open debate. The reason for that has been traditionally been the lack of global scale data needed to describe the urban form, and how plants operate within in. In this study, we used the last generation satellite data to quantify all those factors. Utilizing those data, we found that urban vegetation is the most important factor regulating UHI intensities globally. Most importantly we found that the type of vegetation (urban forest or urban grasslands) play a major role in explaining the development or UHIs. Key Points Urban vegetation explains the largest fraction of the surface urban heat island (UHI) variability Urban vegetation type explains the dependence of surface UHI intensity to background climate Maintaining natural reserves in cities effectively mitigates UHIs

The roots of intra-urban heat disparity in the U.S. often trace back to historical discriminatory practices, such as redlining, which categorized neighborhoods by race or ethnicity. In this study, we compare the relative impacts of historic redlining and current income inequality on thermal disparities in Los Angeles. A key innovation of our work is the use of land surface temperature data from the ECOSTRESS instrument aboard the International Space Station, enabling us to capture diurnal trends in urban thermal disparities. Our findings reveal that present-day income inequality is a stronger predictor of heat burden than the legacy of redlining. Additionally, land surface temperature disparities exhibit a seasonal hysteresis effect, intensifying during extreme heat events by 5−7 °C. Sociodemographic analysis highlights that African-American and Hispanic populations in historically and economically disadvantaged areas are often the most vulnerable. Our findings suggest that while the legacy of redlining may persist, the present-day heat disparities are not necessarily an immutable inheritance, where targeted investments and interventions can pave the way for a more thermally just future for these communities. In many cities, such as Los Angeles, some neighborhoods are much hotter than others. This study finds that it’s not just history-contemporary income inequality plays a bigger role than redlining in driving who bears the brunt of urban heat

Climate models generally predict higher precipitation in a future warmer climate. Whether the precipitation intensification occurred in response to historical warming continues to be a subject of debate. Here, using observations of the ocean surface energy balance as a hydrological constraint, we find that historical warming intensified precipitation at a rate of 0.68 ± 0.51% K −1 , which is slightly higher than the multi-model mean calculation for the historical climate (0.38 ± 1.18% K −1 ). The reduction in ocean surface albedo associated with melting of sea ice is a positive contributor to the precipitation temperature sensitivity. On the other hand, the observed increase in ocean heat storage weakens the historical precipitation. In this surface energy balance framework, the incident shortwave radiation at the ocean surface and the ocean heat storage exert a dominant control on the precipitation temperature sensitivity, explaining 91% of the inter-model spread and the spread across climate scenarios in the Intergovernmental Panel on Climate Change Fifth Assessment Report. There is some disagreement between climate models about how much precipitation changes under global warming. Here, the authors use the ocean surface energy balance to constrain the sensitivity of precipitation to historical warming and find that it is increasing by 0.68 ± 0.51% per degree warming.