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The scientific discourse on climate change throughout the US has primarily revolved around changes in mean climate and/or climate extremes. However, little is known about the impacts of climate change on mild weather conditions despite its significant relevance to quality of life. Here, we adopt the concept of "outdoor days" defined as those relatively pleasant days when most people may enjoy outdoor activities (Choi et al., 2024). We project how climate change reshapes seasonality of US outdoor days: relatively large drops in summer, late spring, and early fall; and a significant increase in winter. However, annual outdoor days are projected to change slightly, with notable exceptions. We project relatively large drops in southeast (−23%), south (−19%), and Ohio Valley (−19%), and a significant increase in northwest (14%) toward the end of the century. Our findings have implications for quality of life in different regions, and for nation‐wide travel and tourism. Plain Language Summary: Here, we contribute to the understanding of how climate change will influence quality of life in the US by applying the concept of outdoor days—thermal comfort conditions allowing for outdoor activities, such as walking, jogging, and cycling by most people. We project using state‐of‐the‐art global climate models that climate change will shift the seasonality of outdoor days, resulting in less frequent outdoor days in summer and more frequent outdoor days in the other seasons across the country. Our results highlight specific regional hotspots in the US where annual outdoor days could significantly decrease or increase with important implication for quality of life in different climate regions of the US. Key Points: We project how climate change reshapes seasonality of US outdoor daysFuture climate change will likely result in a northwest‐southeast disparity in the projected change of annual outdoor days in the USWe provide new evidence of the impact of global warming on the quality of human life, travel, and tourism in the US [ABSTRACT FROM AUTHOR]

Global climate projections show that humid heat extremes will expand toward the higher latitudes, making the midlatitudes hotspots for these extremes. Therefore, a thorough explanation of their regional characteristics becomes crucial, given that the changes in these extremes can potentially render a large proportion of the global population at risk. Here, we perform the first analysis of historical and projected changes in the intensity and frequency of humid heat extremes and quantify the population exposure to these extremes in Turkey, using long‐term simulations from the non‐hydrostatic mesoscale model of Consortium for Small‐scale Modeling (COSMO‐CLM) under the RCP8.5 emission scenario. We portray not only the nationwide changes in the humid heat extremes and population exposure but also their regional aspects by exploiting the K‐means clustering algorithm. Our results suggest significant future increases in the intensity and frequency of these extremes over a wide geographical area, which includes the surroundings of Adana, Antalya, Izmir, Sakarya, Ordu and Diyarbakir, most of which are coastal locations. Over most of these regions, severe humid heat stress is expected to last nearly a month every year, with almost 56% of the land area is projected to experience local historical upper tail heat stress conditions for at least an additional 10 consecutive hours. Further, we explicate a significant rise in the number of people exposed to severe humid heat stress, concentrated along most coastal regions, by as much as 1.6 million person‐days. More than 20% of Turkey's population may confront severe humid heat stress for at least 1 h, with that percentage falling to 4.15% for at least five consecutive hours, which indicates that people will not only endure more intense humid heat stress but also be exposed to these conditions consecutively over a period of many hours. [ABSTRACT FROM AUTHOR]

This study conducted and evaluated 44 experiments using the non-hydrostatic version of the regional climate model RegCM4 (RegCM4-NH) and an additional three experiments with RegCM version 5 (RegCM5) over Southeast Asia for the period 2010–2015. The initiative was part of the coordinated regional climate downscaling experiment—Southeast Asia (CORDEX-SEA) project, in preparation for downscaling the latest coupled model intercomparison project Phase 6 (CMIP6) global climate models (GCMs). The RegCM4-NH experiments, forced by the ERA5 reanalysis, were configured using combinations of four cumulus, three planetary boundary layer (PBL), and three explicit moisture schemes. The spatiotemporal variability of simulated 2 m-temperature and rainfall for 2010–2015 was evaluated against observational datasets. The best experiments demonstrated reasonable reproduction of observed annual cycles and spatial distribution, while many exhibited unrealistic biases. A score ranking system was implemented to objectively compare the performance of experiments, enabling the identification of top-ranked experiments for Southeast Asia. The ensemble mean of the 44 RegCM4-NH experiments exhibited commendable performance, ranking 11th overall. Furthermore, the three additional RegCM5 experiments did not yield improved results compared to RegCM4-NH under the same physical configuration, suggesting that opting for RegCM4-NH would be a prudent choice for the CORDEX-SEA community in the forthcoming CMIP6 downscaling cycle for Southeast Asia. [ABSTRACT FROM AUTHOR]

A global phenomenon identified 200 years ago as Urban Heat Island (UHI) effect gained popularity as the sheer contributor to the precipitous temperature gradient between rural and urban interface, instigating excess heat gain and associated ill effects on the urban dwellers. UHI is a function of many interrelated geographical, ecological, and economic parameters that require differential treatment in determining the antecedent impacts. This transdisciplinary review assessed the passive strategies (vegetation, cool roofs, cool pavements, and green roofs) from 83 studies that employed a numerical simulation approach to combat UHI. On average, vegetation and cool/green roofs can reduce ambient temperature by 3–5 ​°C, while cool pavements help to reduce surface temperature by 5 ​°C. All passive strategies also reveal it can reduce buildings' energy demand by 4–10%. However, the current methodological framework for evaluating UHI is quite fragmented, using multiple software and estimates only Surface Urban Heat Island (SUHI), ignoring Canopy Urban Heat Island (CUHI), Boundary Urban Heat Island (BUHI), and the nexus of ‘UHI-Climatology,’ which is linked to regional and global climate change, failing to model UHI and its complex connection to climate change accurately. The review found that the efficacy of passive strategies is a function of factors ranging from location, cloud cover, and soil type to simulation accuracy; hence, while these passive strategies alleviate outdoor temperature in one place, they can cause counterproductive impacts in another region. Therefore, as a postlude, the paper explores an alternative methodological framework for evaluating the nexus of UHI-Climatology using digital twin technology, thus espousing better mitigation strategies.

The cool season (November–March) of 2022–2023 was exceptional in the western United States (US), with the highest precipitation totals in ≥128 years in some areas. Recent precipitation extremes and expectations based on thermodynamics motivate us to evaluate the evidence for an anthropogenic intensification of western US cool‐season precipitation to date. Over cool seasons 1951–2023, trends in precipitation totals on the wettest cool‐season days were neutral or negative across the western US, and significantly negative in northern California and parts of the Pacific Northwest, counter to the expected net intensification effect from anthropogenic forcing. Multiple reanalysis data sets indicate a corresponding lack of increase in moisture transports into the western US, suggesting that atmospheric circulation trends over the North Pacific have counteracted the increases in atmospheric moisture expected from warming alone. The lack of precipitation intensification to date is generally consistent with climate model simulations. A large ensemble of 648 simulations from 35 climate models suggests it is too soon to detect anthropogenic intensification of precipitation across much of the western US. In California, the 35‐model median time of emergence for intensification of the wettest days is 2080 under a mid‐level emissions scenario. On the other hand, observed reductions of precipitation extremes in California and the Pacific Northwest are near the lower edge of the large ensemble of simulated trends, calling into question model representation of western US precipitation variability. Plain Language Summary: After warming, one of the best understood climate responses to greenhouse gasses is increased atmospheric moisture (from more evaporation) and resultant storm intensification. Given that hydrological infrastructure and policies are probably more attuned to past climate than future climate, intensified precipitation has important implications for management of flood hazards and water resources. In the western US, the very wet winter of 2023 and the extensive flooding that resulted indicated that this region may be highly vulnerable to increases in precipitation extremes. Are precipitation extremes already intensifying in the western US? We find no evidence for intensified western US winter precipitation thus far. In fact, precipitation intensity actually decreased in northern California and the Pacific Northwest over 1951–2023. In California, most climate models do not project intensified precipitation to become detectable from that region's wide range of natural variability until the 2060s even under heavy greenhouse‐gas emissions. By the end of this century, however, most climate models consistently project the strongest storms to be stronger than they were historically across most or all of the western US. Thus, the lack of precipitation intensification thus far should not dissuade planning for greater precipitation intensity and flood risks. Key Points: Over 1951–2023, precipitation on the wettest cool‐season days did not increase across most of the western US, and even declined in areasIn California, most climate models do not project human‐caused precipitation intensification to become detectable before the 2060sClimate models rarely simulate precipitation intensity to decline as much as it did in northern California and the Pacific Northwest [ABSTRACT FROM AUTHOR]

Abstract The scientific discourse on climate change throughout the US has primarily revolved around changes in mean climate and/or climate extremes. However, little is known about the impacts of climate change on mild weather conditions despite its significant relevance to quality of life. Here, we adopt the concept of “outdoor days” defined as those relatively pleasant days when most people may enjoy outdoor activities (Choi et al., 2024). We project how climate change reshapes seasonality of US outdoor days: relatively large drops in summer, late spring, and early fall; and a significant increase in winter. However, annual outdoor days are projected to change slightly, with notable exceptions. We project relatively large drops in southeast (−23%), south (−19%), and Ohio Valley (−19%), and a significant increase in northwest (14%) toward the end of the century. Our findings have implications for quality of life in different regions, and for nation‐wide travel and tourism.

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the "signal-tonoise paradox" and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media. [ABSTRACT FROM AUTHOR]

Deforestation in the Brazilian Amazon (BA) for cattle and soybean production has significant consequences for the various aspects of the climate system. Land surface modifications due to deforestation directly influence surface energy and moisture availability, hence impacting rainfall patterns, air temperature and the onset of the agricultural rainy season. Here, we assess the forest loss‐related climate risks for the first and second crop seasons of the soy‐maize double cropping system in the BA. We utilized long‐term, daily, remote sensed climate data and annual land‐use maps as input for a machine learning algorithm to isolate the signal of forest loss on the climate. Our findings indicate that forest loss in the BA intensifies the risks of climate change from the local to the regional geographical scale, with the impact being more pronounced at the regional scale. Between 1999 and 2019, largely deforested regions exhibited a delay of approximately 76 days in the onset of the agricultural rainy season. These regions also experienced a 360 mm decrease in rainfall and an increase in maximum air temperature of 2.5°C. In view of these results, there are collective advantages of halting deforestation. Conservation of the Amazon Forest is vital for maintaining the early onset of the agricultural rainy season, favourable temperatures and adequate rainfall volume needed for attaining high yields in the soy‐maize double cropping system. [ABSTRACT FROM AUTHOR]

Changes in tropical cyclones due to greenhouse‐gas forcing in the Shanghai area have been studied in a double‐nesting regional model experiment using the Met Office convection‐permitting model HadREM3‐RA1T at 4 km resolution and the regional model HadREM3‐GA7.05 at 12 km for the intermediate nest. Boundary conditions for the experiment have been constructed from HadGEM2‐ES, a General Circulation Model (GCM) from the 5th Coupled Model Intercomparison Project (CMIP5), directly using high‐frequency data for the atmosphere (6‐hourly) and the ocean (daily), for the historical period (1981–2000) and under the Representative Concentration Pathway 8.5 (2080–2099). These choices identify one of the warmest climate scenarios available from CMIP5. Given the direct use of GCM data for the baseline, large scale conditions relevant for tropical cyclones have been analyzed, demonstrating a realistic representation of environmental conditions off the coast of eastern China. GCM large scale changes show a reduction in wind shear in addition to the expected strong increase in sea‐surface temperature. Tropical cyclones from the 4 km historical simulation have a negative bias in intensity, not exceeding Category 4, and a wet bias in the rainfall associated with these cyclones. However, there is a clear improvement in cyclone intensity and rainfall at 4 km in comparison with the 12 km simulation. Climate change responses in the 4 km simulation include an extension of the tropical cyclone season, and strong increases in frequency of the most intense cyclones (approximately by a factor of 10) and associated rainfall. These are consistent with the results from the 12 km simulation. Plain Language Summary: Since global climate models do not have the spatial resolution to simulate tropical cyclones, higher resolution limited area models are commonly used to improve the representation of these phenomena. In this study, where changes in the Shanghai area have been estimated, this was done in two steps, using a regional model at 12 km resolution as the intermediate step to finally reach a 4 km resolution for the region around Shanghai with a model which can explicitly represent convection processes. This was done by using one of the warmest climate scenarios available from the climate projections run under realistic increases of greenhouse‐gas concentrations, to study climate change at the end of this century. The global climate model reproduces large scale conditions relevant for tropical cyclones which compare well with observations in the historical period and projects changes in the atmosphere and the ocean which are relevant for tropical cyclones. The downscaled model at 4 km reproduces the observation with some biases in intensity, not sufficient to reach Category 4, and in the cyclone precipitation, through marked improvements with respect to the 12 km model. The climate change response shows a strong increase in intense tropical cyclone frequency and rainfall, common to both models. Key Points: Double‐nesting experiment with a 4 km convective scale model driven by a high end Coupled Model Intercomparison Project general circulation model to study changes in tropical storms near ShanghaiImprovement at 4 km over the intermediate 12 km nest on rainfall and intensity of tropical storms, but not sufficient to reach category 4Future changes at 4 km show a marked increase in frequency of intense tropical cyclones and in rainfall, well matched by the 12 km model [ABSTRACT FROM AUTHOR]

Wild edible plants (WEPs) can provide diverse and nutrient-rich food sources that contribute to the health and well-being of communities worldwide. In northwestern Kenya, WEPs are vital dietary components for nomadic pastoral communities with limited access to diverse cultivated food crops. However, the increasing impact of climate change poses a threat to these valuable food resources, and their sustainable utilization remains precarious. Here, we assessed the potentially suitable habitats and richness of 23 selected WEPs in the region using a species distribution modeling (SDM) approach. We used species occurrence points from global databases, a national herbarium, and field surveys and made predictions spanning two future time intervals, 2041–2070 and 2071–2100, across three shared socioeconomic pathways (126, 370, and 585) using bioclimatic variables from five global circulation models. We also included soil and topographic variables in our models. We calibrated maximum entropy models using individually tuned parameters. Our future predictions showed a predominant decline in habitat suitability for half the studied WEPs. The richness of the selected WEPs are predicted to remain rather stable under projected future climates concentrating in southern parts of Turkana County. Conservation and management measures need to consider the changing availability of these valuable resources in order to underpin the dietary diversification of local communities. [ABSTRACT FROM AUTHOR]

This study aimed to analyze data from meteorological stations in central Ukraine that have the longest observation period and to search for patterns in the dynamics of temperature indicators over the past 140-200 years. Data and methods. To characterize the climate of central Ukraine, we analyzed the average monthly and average annual temperatures of Uman, Kropyvnytskyi, and Poltava, which have the longest continuous or almost continuous periods of observation. Based on these data, we have constructed graphs of changes in the average annual and average monthly temperatures for the winter and spring seasons. To analyze the dynamics of temperature indicators, we built linear and 11-year moving averages. The results. The analysis of meteorological data from weather stations in central Ukraine over the entire period of observation showed the following: average annual temperatures increased from 1.4 degrees in Kropyvnytskyi, 1.6 degrees in Uman to 2.5 degrees (since 1886 - 2.2 degrees) in Poltava. The highest average annual air temperatures at all weather stations were recorded in 2020 and 2021. The largest temperature increase occurred in the winter months. Over the entire observation period, the average monthly temperature in December increased by 2.0 degrees in Kropyvnytskyi, 2.3 degrees in Uman, and 3.6 degrees in Poltava (3.3 degrees since 1886). The average monthly temperature in January increased from 2.4 degrees in Uman and Kropyvnytskyi to 4.9 degrees (3.5 degrees since 1886) in Poltava. The average monthly temperature in February increased from 2.2 degrees in Kropyvnytskyi, 3.4 degrees in Uman to 4.1 degrees (since 1886 - 2.9 degrees) in Poltava. All three weather stations have common periods of rising and falling temperatures, with an increase in the average monthly temperature in the winter months from 1987-1989 to 2022. The air temperature in the spring months also increased significantly. Over the entire observation period, the average monthly temperature in March increased from 2.3 degrees in Kropyvnytskyi, 3.0 degrees in Uman to 3.6 degrees (since 1886 - 3.5 degrees) in Poltava. The average monthly temperature in April increased from 2.1 degrees in Kropyvnytskyi, 2.4 degrees in Uman to 4.2 degrees (since 1886 - 3.4 degrees) in Poltava. The average monthly temperature in May increased from 0.5 degrees in Uman and Kropyvnytskyi to 2.9 degrees (since 1886 - 1.3 degrees) in Poltava. All three weather stations have common periods of rising and falling temperatures, with a slight increase in average monthly spring temperatures from 1988-1990 to 2022. Analyzing the graphs of 11-year moving averages, one can notice the presence of periods of increase and decrease in average monthly temperatures lasting about 33 years or doubled periods lasting about 66 years. Scientific novelty. For the first time, the data of weather stations in central Ukraine for the entire period of observation (138 years – Uman, 148 years – Kropyvnytskyi, 198 years – Poltava) were analyzed and regularities in the dynamic of temperature indicators were determined. The practical significance lies in the possibility of using the researchers results to predict future climate change.

This study focuses on a new compounding concern, the sudden turn from drought to flood (STDF), that is becoming increasingly prominent. This study investigates the long‐term trends and variability of STDFs in China during 1961–2020. The findings indicate that STDFs are prevalent in north and northeast China, and the Yangtze River Delta (YRD). The probability of a drought being followed by a severe flood is approaching 35% in northern and northeastern China. Since 1961, the number of STDFs in China has increased at a rate of average 2.8 events per decade. This increase mainly has occurred in late spring and early summer. The likelihood that a drought being followed by a significant pluvial is also increasing. The increasing STDF in northern China is mainly attributable to the increasing flood frequency and volatility of precipitation. Changes in the STDF trend is more dependent on the drought frequency in the YRD. Plain Language Summary: Droughts usually end due to increased precipitation, but if excessive rainfall occurs, it can lead to secondary impacts on already barren land, increasing the likelihood of landslides and making farmland flooding significantly costlier than it would have been if only flooding had occurred. Therefore, we must pay more attention to compound disasters that increase the vulnerability of populations and ecosystems. Most studies on rapid drought‐to‐flood transitions have analyzed individual cases, whereas few have studied the STDF characteristics in China or even globally or the long‐term changes in the STDF trend. In this study, we selected an STDF screening method that is accurate on a daily scale. The results show that STDFs have been increasing more frequently in China at a rate of average 2.8 events per decade. The most significant increases occurred in May and June, resulting in an advance of 1 month for the STDF peak. The STDF hotspots are concentrated in north and northeast China and YRD. Nearly 35% of droughts in northern and northeast China have been immediately followed by a flood rather than a gradual drought mitigation or a drought alone. STDFs have become more prevalent in northern China as a result of increased flood frequency and precipitation volatility, while in southern China, the increase in STDF frequency is primarily due to an increase in drought frequency. Key Points: Sudden turn from drought to flood (STDF) hotspots are concentrated in north and northeast China, Yangtze River Delta (YRD), with a significant increase trendThe likelihood that a drought being followed by a significant pluvial rather than gradual mitigation has increasedIncreasing STDF is contributed mainly by increasing flood in north and is more dependent on drought frequency in YRD [ABSTRACT FROM AUTHOR]

Climate change significantly impacts forest ecosystems in arid and semi-arid regions. However, spatiotemporal patterns of climate-sensitive changes in individual tree growth under increased climate warming and precipitation in north–west China is unclear. The dendrochronological method was used to study climate response sensitivity of radial growth of Picea schrenkiana from 158 trees at six sites during 1990–2020. The results show that climate warming and increased precipitation significantly promoted the growth of trees. The response to temperature first increased, then decreased. However, the response to increased precipitation and the self-calibrating Palmer Drought Severity Index (scPDSI) increased significantly. In most areas of the Tianshan Mountains, the proportion of trees under increased precipitation and scPDSI positive response was relatively high. Over time, small-diameter trees were strongly affected by drought stress. It is predicted that under continuous warming and increased precipitation, trees in most areas of the Tianshan Mountains, especially those with small diameters, will be more affected by precipitation. [ABSTRACT FROM AUTHOR]

Climate change has emerged across many regions. Some observed regional climate changes, such as amplified Arctic warming and land-sea warming contrasts have been predicted by climate models. However, many other observed regional changes, such as changes in tropical sea surface temperature and monsoon rainfall are not well simulated by climate model ensembles even when taking into account natural internal variability and structural uncertainties in the response of models to anthropogenic radiative forcing. This suggests climate model predictions may not fully reflect what our future will look like. The discrepancies between models and observations are not well understood due to several real and apparent puzzles and limitations such as the “signal-to-noise paradox” and real-world record-shattering extremes falling outside of the possible range predicted by models. Addressing these discrepancies, puzzles and limitations is essential, because understanding and reliably predicting regional climate change is necessary in order to communicate effectively about the underlying drivers of change, provide reliable information to stakeholders, enable societies to adapt, and increase resilience and reduce vulnerability. The challenges of achieving this are greater in the Global South, especially because of the lack of observational data over long time periods and a lack of scientific focus on Global South climate change. To address discrepancies between observations and models, it is important to prioritize resources for understanding regional climate predictions and analyzing where and why models and observations disagree via testing hypotheses of drivers of biases using observations and models. Gaps in understanding can be discovered and filled by exploiting new tools, such as artificial intelligence/machine learning, high-resolution models, new modeling experiments in the model hierarchy, better quantification of forcing, and new observations. Conscious efforts are needed toward creating opportunities that allow regional experts, particularly those from the Global South, to take the lead in regional climate research. This includes co-learning in technical aspects of analyzing simulations and in the physics and dynamics of regional climate change. Finally, improved methods of regional climate communication are needed, which account for the underlying uncertainties, in order to provide reliable and actionable information to stakeholders and the media.

Transmission line towers are highly exposed to weather hazards. In southern Brazil, several damages to transmission lines have been reported due to severe weather events. Climate change projections indicate that the frequency and magnitude of extreme events may increase, making planning and adaptation of the transmission system exposure even more critical. In this work, we propose a severe weather index (SWI) as a proxy for detecting severe storms that can potentially result in extreme weather events. The SWI combines thresholds of instability indices based on observed severe events around the transmission tower-line systems. These indices were calculated using the downscaling by the Eta model at 20 km resolution of three global climate model projections and at 5 km of one global climate model for the historical period and the near future. The results show that the SWI captures the severe storms observed in the region. All downscaling projections agree with the increase of extreme weather events in the near future and the expansion of vulnerable areas. The constructed index can be employed to assess the risks and to plan the power transmission system better.

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Impact of climate change on railway transport manifests in a variety of consequences, such as rail buckling, rail flooding, expansion of swing bridges, overheating of electrical equipment and its damage, bridge scour, failure of earthworks, ground settlement, pavement deterioration, damage to sea walls, coastal erosion of tracks and earthworks, and an increased number of railway accidents in general. Such impacts can cause considerable disruption of railway operations and lead to substantial financial expenses for repair of the railway infrastructure. Therefore, it is crucial to include adaptation strategies already in the design phase of the railway construction to ensure stability and integrity of the railway operations. This paper provides a literature review of adaptation considerations in Canada, China and Sweden and discusses climate change challenges that these countries face in their railway systems. In conclusion, the authors provide recommendations for adaptation approaches based on the reviewed international experience which can be useful for policymakers and managers of railway companies.

Irrigated crops have experienced a significant global expansion. The biophysical response of climate change to irrigated crop expansion in different regions, particularly in terms of monitoring the influence mechanism of nighttime land surface temperature (LST) change, however, remains insufficiently explored. Taking the three northeastern provinces of China as our study area, we apply window analysis, partial correlation analysis, and geographical detector to quantitatively characterize the spatial and temporal distribution pattern of daytime and nighttime LST (diurnal LST) and biophysical parameters, and the main driving mechanism of diurnal LST change. The results showed that irrigated crop expansion led to asymmetric changes in daytime (−2.11 ± 0.2°C, 97.4%) and nighttime (0.64 ± 0.2°C, 79.9%) LST. ΔLSTDT had a negative correlation with ΔLE (63%), but a positive correlation with ΔSSR and ΔH (91% and 77%). This revealed that the cooling effect caused by the superposition of the output latent heat flux and the absorbed solar shortwave radiation was greater than its heating effect. ΔLSTNT and ΔLE had a positive connection across 69% of the region. ΔLSTNT demonstrated a negative correlation with ΔSSR and ΔH in 82% and 75% of the regions, respectively. At this time, the superposition of latent heat flux and heating potential term produces a greater heating effect. The explanatory power of the single factor (the mean of q0.50, p

The amount, frequency and intensity of extreme precipitation over Xinjiang have increased dramatically under the wetting trend in Northwest China, but long‐term trends in the precipitation‐recycling process remain largely unexplored. Based on dynamic recycling model and MERRA2 reanalysis, we revealed a mean recycling ratio for extreme precipitation in Xinjiang of 42.3% with a growth rate of 2.3% decade−1 during 1982–2019. The increasing trend of extreme precipitation was almost equally attributed to increased recycling precipitation (49%) and external precipitation (51%). The extreme precipitation in Xinjiang exhibited two peak centers, the Tianshan Mountains region (TS) and Kunlun Mountains region (KL), highlighting variations in the water cycle. Specifically, the external cycle predominated the increased extreme precipitation in TS (61%), while the recycling process mainly influenced the increase in KL (67%) due to markedly enhanced evapotranspiration. Moisture source attribution further proved the crucial role of evapotranspiration from Xinjiang and its vicinity in extreme precipitation. Plain Language Summary: Local evapotranspiration is essential for precipitation over arid and semiarid regions, which can be evaluated via the precipitation recycling process. To date, studies investigating the precipitation‐recycling process over Xinjiang have primarily focused on general precipitation, but long‐term trends in the precipitation‐recycling process during extreme precipitation events remain largely unexplored. In this study, we used a dynamic recycling model and revealed that the recycling process accounted for 42.3% of extreme precipitation in climatology and increased by 2.3% decade−1 during 1982–2019. The ascending trend of extreme precipitation over Xinjiang was almost equally attributed to increased recycling precipitation and external precipitation. However, there are regional differences in the dominant factor. Specifically, the Tianshan Mountains region was predominated by the external water cycle, while Kunlun Mountains region was mainly determined by the recycling process. Considering precipitation and evapotranspiration along the moisture trajectory, we further showed that the majority of moisture responsible for extreme precipitation in Xinjiang came from this locality and surrounding area. These findings indicate that the recycling process plays a crucial role in the formation of extreme precipitation in Xinjiang, and a deeper understanding of its changing mechanisms can help reduce the flood risks caused by extreme precipitation. Key Points: The mean recycling ratio of extreme precipitation in Xinjiang was 42.3%, with an increasing trend of 2.3% decade−1Recycling and external precipitation contributed 49% and 51% to the growing trend of extreme precipitation in Xinjiang, respectivelyThe recycling process (external process) dominated the increased extreme precipitation in the Kunlun (Tianshan) Mountains [ABSTRACT FROM AUTHOR]

This study aimed to analyze data from meteorological stations in central Ukraine that have the longest observation period and to search for patterns in the dynamics of temperature indicators over the past 140-200 years. Data and methods. To characterize the climate of central Ukraine, we analyzed the average monthly and average annual temperatures of Uman, Kropyvnytskyi, and Poltava, which have the longest continuous or almost continuous periods of observation. Based on these data, we have constructed graphs of changes in the average annual and average monthly temperatures for the winter and spring seasons. To analyze the dynamics of temperature indicators, we built linear and 11-year moving averages. The results. The analysis of meteorological data from weather stations in central Ukraine over the entire period of observation showed the following: average annual temperatures increased from 1.4 degrees in Kropyvnytskyi, 1.6 degrees in Uman to 2.5 degrees (since 1886 - 2.2 degrees) in Poltava. The highest average annual air temperatures at all weather stations were recorded in 2020 and 2021. The largest temperature increase occurred in the winter months. Over the entire observation period, the average monthly temperature in December increased by 2.0 degrees in Kropyvnytskyi, 2.3 degrees in Uman, and 3.6 degrees in Poltava (3.3 degrees since 1886). The average monthly temperature in January increased from 2.4 degrees in Uman and Kropyvnytskyi to 4.9 degrees (3.5 degrees since 1886) in Poltava. The average monthly temperature in February increased from 2.2 degrees in Kropyvnytskyi, 3.4 degrees in Uman to 4.1 degrees (since 1886 - 2.9 degrees) in Poltava. All three weather stations have common periods of rising and falling temperatures, with an increase in the average monthly temperature in the winter months from 1987-1989 to 2022. The air temperature in the spring months also increased significantly. Over the entire observation period, the average monthly temperature in March increased from 2.3 degrees in Kropyvnytskyi, 3.0 degrees in Uman to 3.6 degrees (since 1886 - 3.5 degrees) in Poltava. The average monthly temperature in April increased from 2.1 degrees in Kropyvnytskyi, 2.4 degrees in Uman to 4.2 degrees (since 1886 - 3.4 degrees) in Poltava. The average monthly temperature in May increased from 0.5 degrees in Uman and Kropyvnytskyi to 2.9 degrees (since 1886 - 1.3 degrees) in Poltava. All three weather stations have common periods of rising and falling temperatures, with a slight increase in average monthly spring temperatures from 1988-1990 to 2022. Analyzing the graphs of 11-year moving averages, one can notice the presence of periods of increase and decrease in average monthly temperatures lasting about 33 years or doubled periods lasting about 66 years. Scientific novelty. For the first time, the data of weather stations in central Ukraine for the entire period of observation (138 years – Uman, 148 years – Kropyvnytskyi, 198 years – Poltava) were analyzed and regularities in the dynamic of temperature indicators were determined. The practical significance lies in the possibility of using the researchers results to predict future climate change. [ABSTRACT FROM AUTHOR]

The issue of the added value (AV) of high resolution regional climate models is complex and still strongly debated. Here, we approach AV in a perfect model framework within a 16-member single model initial condition ensemble with the regional climate model RACMO2 embedded in the global climate model EC-Earth2.3. In addition, we also used an ensemble produced by a pseudo global warming (PGW) approach. Results for winter temperature and precipitation are investigated from two different perspectives: (1) a signal-to-noise perspective analysing the systematic response to changing emission forcings versus internal climate variability, and (2) a prediction perspective aimed at predicting a 30-year future climate state. Systematic changes in winter temperature and precipitation contain fine-scale response patterns, but in particular for precipitation these patterns are small compared to internal variability. Therefore, single members of the ensemble provide only limited information on these systematic patterns. However, they can be estimated more reliably from PGW members because of the stronger constraints on internal variability. From the prediction perspective, we analysed AV of fine-scale information by comparing three prediction pairs. This analysis shows that there is AV in the fine-scale information for temperature, yet for precipitation adding fine-scale changes generally deteriorates the predictions. Using only the large-scale change (without fine scales) from a single ensemble member as a delta change on top of the present-day climate state, already provides a robust estimate of the future climate state and therefore can be used as a simple benchmark to measure added value. [ABSTRACT FROM AUTHOR]

Abstract The amount, frequency and intensity of extreme precipitation over Xinjiang have increased dramatically under the wetting trend in Northwest China, but long‐term trends in the precipitation‐recycling process remain largely unexplored. Based on dynamic recycling model and MERRA2 reanalysis, we revealed a mean recycling ratio for extreme precipitation in Xinjiang of 42.3% with a growth rate of 2.3% decade−1 during 1982–2019. The increasing trend of extreme precipitation was almost equally attributed to increased recycling precipitation (49%) and external precipitation (51%). The extreme precipitation in Xinjiang exhibited two peak centers, the Tianshan Mountains region (TS) and Kunlun Mountains region (KL), highlighting variations in the water cycle. Specifically, the external cycle predominated the increased extreme precipitation in TS (61%), while the recycling process mainly influenced the increase in KL (67%) due to markedly enhanced evapotranspiration. Moisture source attribution further proved the crucial role of evapotranspiration from Xinjiang and its vicinity in extreme precipitation.

Formulation of the problem. Today, in changing climate conditions, it is very relevant to study the impact of regional climate change on the regime of hydrological indicators and ecological status of the Siversky Donets river basin within Kharkiv region. Analysis of recent research and publications. Hydrometeorological studies are complex and large-scale. In the late 20th - early 21st centuries, a number of articles studied annual runoff of the rivers of Ukraine under the influence of atmospheric processes. Present-day changes in temperature and humidity of the territory affect the hydrological conditions of the rivers. The aim of the work is to assess the relationship between climatic and hydrological indicators (environmental dynamics) of the Siversky Donets basin against the background of regional climate change. In these conditions, monitoring of climate, hydrological and environmental indicators, allows us to make further management decisions on water resources management. Research methods are presented by statistical and cartographic analysis. The source data are a number of climatological, hydrological and environmental observations within the state network of the Ukrainian Hydrometeorological Center of the SES of Ukraine. Problems of further research. Modern changes in climatic conditions in Ukraine are characterized by locality and rapidity. Considering the volume of water use from the river Siversky Donets, the question arises about the water supply of the region, optimization of its use and further rational management. Presentation of the main research material. The dates of the ice cover have changed in recent years, and often ice phenomena may not occur at all. We can see significant warming in winter on the example of January air temperatures. Summer temperatures are growing the fastest (by 0.37°C every 10 years), autumn temperatures are in the second place in terms of growth rate. which means that stable ice cover on most rivers of the Donets basin has been absent in recent decades, which clearly indicates a warming trend. Practical value. Based on the main provisions of the national environmental policy of Ukraine on the use of water resources the study of changes in hydrological regime of rivers is of practical importance for sustainable management. Calculation of the IWP has revealed that most rivers belong to the third and fourth categories - "moderately polluted" and "polluted", but there are also absolutely catastrophic cases. Research results. Heavy economic burden on the waterway will increase its over-regulation. In future, comprehensive assessment of climate change impact on the hydrological conditions of the rivers will determine the degree of change in the ecological state of the waterways, their rational use and protect.

Winter roads, or zimnik, serve as major connections between communities across the global Arctic, including Sakha (Yakutia) Republic. Although accessible to general public, winter roads in remote regions are primarily used by indigenous communities. Sustainability of winter roads is reduced by climate change effects, via shorter and milder winters, extended shoulder seasons, delayed freeze up and advanced ice break up on rivers used as ice roads. We review the observed and projected change in mean monthly air temperatures, MMAT, °C, during cold season in six localities of the Sakha (Yakutia) Republic, important residence areas of the indigenous peoples of the North. In observed MMAT records, only North-Western Yakutia hasn’t experienced significant warming. In other localities, a significant step-shift change is observed in months from March to June, and at several stations, also in October and November. Under future climate, assessed with a regional ensemble of global climate models, projected change is expected in core winter months, November to February. In the near future, 2021–2050 period, increase in MMAT is expected mostly in December and January, with only minor increase in shoulder seasons, except in southern Yakutia. In the far future, 2071–2100, only under optimistic SSP 1–2.6 scenario the MMAT change is contained within +3.5 °C, and even in this case, April MMAT increases above −2°C at stations in southern Yakutia. Under SSP 5–8.5 scenario, highest MMAT increase, up to over +12 °C, is projected in the Yakutian Arctic from December to February. In southern Yakutia, both October and April MMAT around or above 0 °C are projected. Winter Road Sustainability Index is assessed based on observed and projected climate. Over northern Yakutia, higher MMAT in core winter months suggest reduced ice thickness on rivers, but overall climate severity allows sustainable winter road operations throughout the season even under high emission scenarios. In the near future, only winter road operations around Tyanya, in the Evenki residence area, become moderately affected under most SSP scenarios. In the far future, winter road operations around Tyanya, Ust-Maya and Neryungri, also Evenki residence area, become highly vulnerable under most scenarios. Practical implications include institutional response, transport system adaptation, adjustment of road maintenance protocols, and reconsidering local production.

High-quality climate information at appropriate spatial and temporal resolution is essential to develop and provide tailored climate services for Africa. A common method to produce regional climate change data is to dynamically downscale global climate projections by means of regional climate models (RCMs). Deficiencies in the representation of the sea surface temperatures (SSTs) in earth system models (ESMs) and missing atmosphere–ocean interactions in RCMs contribute to the precipitation bias. This study analyzes the influence of the regional atmosphere–ocean coupling on simulated precipitation and its characteristics over Africa, and identifies those regions providing an added value using the regionally coupled atmosphere–ocean model ROM. For the analysis, the MPI-ESM-LR historical simulation and emission scenario RCP8.5 were dynamically downscaled with ROM at a spatial resolution of 0.22° × 0.22° for the whole African continent, including the tropical Atlantic and the Southwest Indian Ocean. The results show that reduced SST warm biases in both oceans lead to more realistic simulated precipitation over most coastal regions of Sub-Saharan Africa and over southern Africa to varying degrees depending on the season. In particular, the annual precipitation cycles over the coastal regions of the Atlantic Ocean are closer to observations. Moreover, total precipitation and extreme precipitation indices in the coupled historical simulation are significantly lower and more realistic compared to observations over the majority of the analyzed sub-regions. Finally, atmosphere–ocean coupling can amplify or attenuate climate change signals from precipitation indices or even change their sign in a regional climate projection. [ABSTRACT FROM AUTHOR]

Ground‐level ozone is a major air pollutant harmful to human health. In the scope of climate change, it is essential to provide high‐quality local‐scale assessments of the anticipated changes for public health and policy interventions. Assessments and projections of ground‐level ozone usually rely on numerical modeling, but statistical approaches are also available. The present study enhances the validity of statistical downscaling by taking climate change as well as air pollution changes into account. Besides considering meteorological predictors such as air temperature, short‐wave radiation, humidity, and wind, ozone trends from changes in precursor emissions were included in the statistical models. Meteorological and ozone predictor information extracted from reanalysis data for the observational period and output of seven Earth System Models (ESMs) for the projection periods were used, with three of them having interactive chemical modeling, while the other four used prescribed ozone changes. Ground‐level ozone, more precisely daily maximum 8‐hr running means (MDA8) as well as daily maximum 1‐hr values (MDA1), at 798 measurement stations across the European area in the "ozone season" from April to September were assessed. Results depended strongly on whether only meteorological information or additional information about emission changes were considered. As a general picture under the consideration of climate and emission changes, decreasing ground‐level ozone concentrations were projected under the moderate SSP2‐4.5 scenario, while for the more pessimistic scenario SSP3‐7.0 increasing ozone concentrations over Europe, especially at the end of the 21st century, were assessed. Plain Language Summary: Ground‐level ozone is a gaseous air pollutant that is formed under sunlight from other air pollutants. Ground‐level ozone is harmful to human health. There are concerns that under climate change, ground‐level ozone will increase. To assess changes on local‐scale various methods are available, which can roughly be divided into numerical models and statistical approaches. The present study developed statistical models further, to give a more realistic picture of future changes in ground‐level ozone in the European area. Information about meteorological changes such as air temperature, atmospheric humidity, and wind as well as changes in air pollutants like nitrogen oxides was considered. During the 21st century decreasing ground‐level ozone concentrations were projected under a moderate climate change scenario, while for a more pessimistic scenario increasing ozone concentrations over Europe, especially at the end of the century, were assessed. This result highlights the necessity to further reduce greenhouse gases and air pollution, bringing forth better protection of human health. Key Points: Precursor emission changes are represented by the inclusion of ozone trend in the statistical downscaling modelsStatistical projections results are sensitive to the predictor types and Earth System Model output usedClimate change scenarios have a major impact on future ozone, and greenhouse gas and air pollution reductions can mitigate health risks [ABSTRACT FROM AUTHOR]

Abstract Ground‐level ozone is a major air pollutant harmful to human health. In the scope of climate change, it is essential to provide high‐quality local‐scale assessments of the anticipated changes for public health and policy interventions. Assessments and projections of ground‐level ozone usually rely on numerical modeling, but statistical approaches are also available. The present study enhances the validity of statistical downscaling by taking climate change as well as air pollution changes into account. Besides considering meteorological predictors such as air temperature, short‐wave radiation, humidity, and wind, ozone trends from changes in precursor emissions were included in the statistical models. Meteorological and ozone predictor information extracted from reanalysis data for the observational period and output of seven Earth System Models (ESMs) for the projection periods were used, with three of them having interactive chemical modeling, while the other four used prescribed ozone changes. Ground‐level ozone, more precisely daily maximum 8‐hr running means (MDA8) as well as daily maximum 1‐hr values (MDA1), at 798 measurement stations across the European area in the “ozone season” from April to September were assessed. Results depended strongly on whether only meteorological information or additional information about emission changes were considered. As a general picture under the consideration of climate and emission changes, decreasing ground‐level ozone concentrations were projected under the moderate SSP2‐4.5 scenario, while for the more pessimistic scenario SSP3‐7.0 increasing ozone concentrations over Europe, especially at the end of the 21st century, were assessed.

Climate model simulations' outputs are prone to biases compared to observations; furthermore, climate projections can be very different in modelling future temperature characteristics. One possible solution for reducing uncertainty and eliminating possible systematic errors within climate projections is the bias‐adjustment of the raw climate model data. We used the quantile mapping method for bias‐adjustment of a mini ensemble consisting of 8‐member high‐resolution (0.11°) regional climate model simulations provided by the CORDEX community. As the method requires a reliable observational dataset serving as reference data, we used the quality controlled and homogenized observational dataset: CARPATCLIM. Quantile mapping bias‐adjustment technique was applied on the following variables: daily mean, minimum and maximum temperature. We analysed changes in mean temperature characteristics and of climate indices for future periods of 2021–2050 and 2070–2099 with respect to the reference period 1976–2005 for the Carpathian Region. The selected climate indices are based on minimum (frost days, FD) and maximum daily temperature data (summer days, SU). Our bias‐adjusted RCM results suggest a similar degree of mean temperature change as the raw RCMs' data. Bias‐adjusted and raw RCM data project a remarkable annual temperature increase on average under the RCP8.5 scenario (1.4°C and 3.9°C by 2021–2050 and 2070–2099, respectively). The highest temperature increase is likely to occur in summer: it is 4.3°C on average by the end of the 21st century. More pronounced differences were found for the projected changes of the number of summer and frost days based on the bias‐adjusted and the raw RCM data. Our results draw attention to the fact that bias‐adjusted RCM data are crucial for the provision of regional climate change impact and adaptation studies for the Carpathian Region. [ABSTRACT FROM AUTHOR]

Projected changes in the Northern Hemisphere stratospheric polar vortex are analyzed using Climate Model Intercomparison Project Phase 6 experiments. Previous studies showed that projections of the wintertime zonally averaged polar vortex strength diverge widely between climate models with no agreement on the sign of change, and that this uncertainty contributes to the regional climate change uncertainty. Here, we show that there remains large uncertainty in the projected strength of the polar vortex in experiments with global warming levels ranging from moderate (SSP245 runs) to large (Abrupt‐4xCO2 runs), and that the uncertainty maximizes in winter. Partitioning of the uncertainty in wintertime polar vortex strength projections reveals that, by the end of the 21st century, model uncertainty contributes half of the total uncertainty, with scenario uncertainty contributing only 10%. Regression analysis shows that up to 20% of the intermodel spread in projected precipitation over the Iberian Peninsula and northwestern US, and 20%–30% in near‐surface temperature over western US and northern Eurasian, can be associated with the spread in vortex strength projections after accounting for global warming. While changes in the magnitude and sign of the zonally averaged vortex strength are uncertain, most models (>95%) predict an eastward shift of the vortex by 8°–20° degrees in longitude relative to its historical location with the magnitude of the shift increasing for larger global warming levels. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the zonally averaged vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift. Plain Language Summary: Previous studies showed that changes in the strength of the winds in the Northern Hemisphere wintertime stratosphere, the so‐called polar vortex, can affect near‐surface winds and precipitation on various timescales. However, climate models do not agree on whether the polar vortex will weaken or strengthen during the 21st century. Here, we use Climate Model Intercomparison Project Phase 6 experiments to better understand how the polar vortex will respond to future greenhouse gas emissions. We show that half of the uncertainty in the vortex strength projections by the end of the 21st century is due to climate model errors (model uncertainty). We show that the uncertainty in the vortex strength projections is linked to the uncertainty in projected precipitation over the Iberian Peninsula and northwestern US and projected near‐surface temperature over the western US and northern Eurasia. Most models predict an eastward shift of the vortex relative to its historical location but we do not detect any influence of the vortex longitudinal shift on surface precipitation and temperatures. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift of the vortex. Key Points: Model uncertainty contributes half of the total uncertainty in the projected strength of the Northern winter stratospheric polar vortexUncertainty in the projected polar vortex strength is linked to uncertainty in projected regional surface temperature and precipitationMost climate models project an eastward shift of the Northern winter stratospheric polar vortex [ABSTRACT FROM AUTHOR]

Western U.S. (WUS) rainfall and snowpack vary greatly on interannual and decadal timescales. This combined with their importance to water resources makes future projections of these variables highly societally relevant. Previous studies have shown that precipitation events in the WUS are influenced by the timing, positioning, and duration of extreme integrated water vapor transport (IVT) events (e.g., atmospheric rivers) along the coast. We investigate end-of-21st-century projections of WUS precipitation and IVT in a collection of regional climate models (RCMs) from the North American Coordinated Regional Downscaling Experiment (NA-CORDEX). Several of the NA-CORDEX RCMs project a decrease in cool season precipitation at high elevation (e.g., across the Sierra Nevada) with a corresponding increase in the Great Basin of the U.S. We explore the larger-scale controls on this terrain-related precipitation change in a subset of the NA-CORDEX RCMs through an examination of IVT-events. Projected changes in frequency and duration of IVT-events depend on the event's extremity: by the end of the century extreme IVT-events increase in frequency whereas moderate IVT-events decrease in frequency. Furthermore, in the future, total precipitation across the WUS generally increases during extreme IVT-events, whereas total precipitation from moderate IVT-events decreases across higher elevations. Thus, we argue that the mean cool season precipitation decreases at high elevations and increases in the Great Basin are largely determined by changes in moderate IVT-events which are projected to be less frequent and bring less high-elevation precipitation. [ABSTRACT FROM AUTHOR]

Assessing how increasing greenhouse gas concentrations may modify regional climates is an ongoing challenge. Relatively little work has examined how climate change may influence processes related to regional thunderstorm activity. This is important to consider in areas where thunderstorms are important for maintaining regional hydroclimates. This study focuses on three convection‐allowing climate simulations—namely, a retrospective simulation (1990–2005) and two possible climate change scenarios (2085–2100)—with domains encompassing the conterminous United States. Regional and seasonal variability is noted in the response of thunderstorm activity as measured by thresholds in simulated radar reflectivity factor in the two climate change scenarios. A decrease in thunderstorm activity is projected for the Southern Plains, whereas the Southeast and Midwest experience an increase in thunderstorm activity. An examination of environmental parameters related to thunderstorm activity reveals an overall increase in convective instability but spatially varying changes in convective inhibition. Plain Language Summary: More favorable conditions may exist for thunderstorm activity throughout the United States in the late 21st century. However, certain factors prevent thunderstorms from taking advantage of these conditions by suppressing their formation. This leads to a varying response to possible climate change scenarios, with less thunderstorm activity in the Great Plains, and more in the Midwest and Southeast. Key Points: A regional climate model is used to simulate two climate change scenariosFewer Southern Plains thunderstorm days, more in the Midwest and SoutheastSome increases in conditional instability are offset by convective inhibition [ABSTRACT FROM AUTHOR]

Changes in vegetation dynamics play a critical role in terrestrial ecosystems and environments. Remote sensing products and dynamic global vegetation models (DGVMs) are useful for studying vegetation dynamics. In this study, we revised the Community Land Surface Biogeochemical Dynamic Vegetation Model (referred to as the BGCDV_CTL experiment) and validated it for the Tibetan Plateau (TP) by comparing vegetation distribution and carbon flux simulations against observations. Then, seasonal–deciduous phenology parameterization was adopted according to the observed parameters (referred to as the BGCDV_NEW experiment). Compared to the observed parameters, monthly variations in gross primary productivity (GPP) showed that the BGCDV_NEW experiment had the best performance against the in situ observations on the TP. The climatology from the remote sensing and simulated GPPs showed similar patterns, with GPP increasing from northwest to southeast, although the BGCDV_NEW experiment overestimated GPP in the semi-arid and arid regions of the TP. The results show that temperature warming was the dominant factor resulting in the increase in GPP based on the remote sensing products, while precipitation enhancement was the reason for the GPP increase in the model simulation. [ABSTRACT FROM AUTHOR]

This article considers various aspects of the impact of climate change on the railway infrastructure and operations. A brief international overview and the importance of this issue for Russia are given. Temperature effects, permafrost thawing, strong winds, floods and sea level rise, long-term effects, and adaptation measures are discussed. In conclusion, the authors give several recommendations on further research in this area, and highlight that special attention should be given to the areas in the Russian Federation which already face or might soon experience damage from storm events or flooding and sea level rise, namely Kaliningrad Region on the Baltic Sea, the area between Tuapse and Adler in Krasnodar Region on the Black Sea, and on Sakhalin Island from the side of the Sea of Japan.

This study aims to investigate the effects of urban and forest areas measured in three dimensions on seasonal temperature over forty years in South Korean cities. We measure the urban and forest areas at the city, neighborhood, and spatially clustered levels in four periods every ten years. Using Hot Spot Analysis (Getis-Ord Gi*), this study detects the spatially clustered urban and forest areas. We establish a multilevel regression model to explore the relationship between urban and forest areas measured in three dimensions, as well as seasonal temperatures. The study shows that while spatially clustered urban and forest areas have consistent associations with the four seasonal temperatures, urban and forest areas at the city scale have different associations with the seasonal temperature, depending on the season. When spatially clustered urban areas increase by 10 km2, four seasonal temperatures increase by about 0.0016–0.0067 Celsius degree (°C); on the other hand, when spatially clustered forest areas increase by 10 km2, four seasonal temperatures decrease by about 0.0001–0.0016 °C. At the neighborhood level, urban and forest areas are negatively associated with the four seasonal temperatures. The results of this study can be utilized by urban planners and policymakers to establish land use planning or policy by providing evidence of whether land use plans should be established and at what scales to manage regional thermal environments. To alleviate seasonal warming, we recommend increasing forest areas at the neighborhood and spatially clustered levels and controlling the size of spatially clustered urban areas. • Spatially clustered urban areas are positively associated with seasonal temperatures. • Spatially clustered forest areas are negatively associated with seasonal temperatures. • To alleviate regional warming, interconnected forested areas are recommended. • Regulating the expansion of urban areas is also recommended. [ABSTRACT FROM AUTHOR]

South of Minas Gerais state, in Southeast Brazil, is known for the coffee crop production (more than 30% of country's production) and hydroelectricity generation (1216 MW installed power). Droughts are natural climate phenomena that may strongly affect a region during a certain period. In this study, the severity of the droughts that hit southern Minas Gerais state was analyzed in the period from 1970 to 2020 and was projected up to 2098/2099 using four global circulation models (HadGEM2-ES, MIROC5, BESM, CanESM2), downscaled by Eta model to 20-km resolution, under two Representative Concentration Pathways (RCP4.5 and RCP8.5). To access the severity of the droughts, the Standard Precipitation Index considering the hydrological year (SPI12) was investigated over time and space. The results demonstrated that the 2013–2014 hydrological year was the dryest in southern Minas Gerais, followed by 2014/2015, which led to water shortage, reduction of the hydroelectricity and reduction of coffee crop production. Future projections indicate that extreme droughts will continue occurring, but with similar rarity. However, the RCM downscaling pointed out the possible occurrence of several dry consecutive years, which can collapse the hydrology and put at risk the economy of the region. Except from the Eta-MIROC under RCP 8.5, that simulated most of the droughts in middle to the end of XXI century, the other RCMs projected recurrent droughts for the next two decades, supporting the detection drought anomalies and helping in adoption actions to anticipate and mitigate drought effects in the future. [ABSTRACT FROM AUTHOR]

Education is of great importance in the context of climate change, as it can promote pro-environmental behaviour. However, climate change education is accompanied by didactic and pedagogical challenges because, among other reasons, climate change is a complex phenomenon and many people have a psychological distance to the topic. A promising approach to face these challenges is inquiry-based learning (IBL), as several studies show. To date, however, there are barely any empirically tested instructional designs, especially for close-to-science IBL, focusing on climate change. The study presented here therefore addresses the question of how a science propaedeutic seminar for upper secondary schools on the regional implications of climate change should be designed to ensure successful learning processes. Based on the design-based research approach, qualitative research methods (focus group discussions, semi-standardised written teacher surveys, and participant observations) were used to identify target-oriented design guidelines and implementation principles for such seminars. In the seminars, 769 students have so far researched different aspects of climate change in their own regions. The identified design guidelines and implementation principles were further operationalised for teaching practice, so that the research generated both a contribution to theory building and an applicable concept for schools. [ABSTRACT FROM AUTHOR]

We evaluate a 12-member perturbed parameter ensemble of regional climate simulations over Europe at 12 km resolution, carried out as part of the UK Climate Projections (UKCP) project. This ensemble is formed by varying uncertain parameters within the model physics, allowing uncertainty in future projections due to climate modelling uncertainty to be explored in a systematic way. We focus on present day performance both compared to observations, and consistency with the driving global ensemble. Daily and seasonal temperature and precipitation are evaluated as two variables commonly used in impacts assessments. For precipitation we find that downscaling, even whilst within the convection-parameterised regime, generally improves daily precipitation, but not everywhere. In summer, the underestimation of dry day frequency is worse in the regional ensemble than in the driving simulations. For temperature we find that the regional ensemble inherits a large wintertime cold bias from the global model, however downscaling reduces this bias. The largest bias reduction is in daily winter cold temperature extremes. In summer the regional ensemble is cooler and wetter than the driving global models, and we examine cloud and radiation diagnostics to understand the causes of the differences. We also use a low-resolution regional simulation to determine whether the differences are a consequence of resolution, or due to other configuration differences, with the predominant configuration difference being the treatment of aerosols. We find that use of the EasyAerosol scheme in the regional model, which aims to approximate the aerosol effects in the driving model, causes reduced temperatures by around 0.5 K over Eastern Europe in Summer, and warming of a similar magnitude over France and Germany in Winter, relative to the impact of interactive aerosol in the global runs. Precipitation is also increased in these regions. Overall, we find that the regional model is consistent with the global model, but with a typically better representation of daily extremes and consequently we have higher confidence in its projections of their future change. [ABSTRACT FROM AUTHOR]

Despite the accelerated global warming, the central‐eastern U.S. experienced summer daytime cooling during the 20th century. A number of studies have attributed various factors responsible for this abnormal regional cooling, termed the "warming hole (WH)". This study demonstrates that another WH also existed in the south‐central China that shares similar temporal trend and geographical location to the U.S. WH, including the cooling reversal around 1995. Generalizing the two WHs unravels that they both occurred (a) to the east of major mountain ranges, (b) in heavily cultivated agricultural regions, and (c) near the terminus of the low‐level jets. This study further finds that the WH patterns are the leading mode of surface maximum temperature variability in summer of their respective regions. The WH cooling was consistent with reduction in cloud‐forced solar radiation supported by moisture convergence through weakening of low‐level southerly flow downstream of China WH and strengthening of the southerly flow upstream of U.S. WH. The long‐term trends of WH temperature are highly correlated to the Atlantic Multidecadal Oscillation (AMO) and WH cooling turned into warming around 1995 when the AMO shifted to the positive phase, which combined with overall regional warming, may explain why the summer WH phenomenon has been in diminution for past two decades or so. Given the AMO's association with global climate trend, the WH phenomenon could also be viewed as a local thermal response to the global warming via AMO modulation of regional dynamic and moisture circulation. Plain Language Summary: Despite the accelerated global warming, the central‐eastern U.S. experienced summer daytime cooling during the 20th century. This abnormal regional cooling was termed the "warming hole (WH)". This study demonstrates that another WH also existed in the south‐central China that shares similar temporal trend and geographical location to the U.S. WH, including the cooling reversal around 1995. Generalizing the two WHs unravels that they both occurred (a) to the east of major mountain ranges, (b) in heavily cultivated agricultural regions, and (c) near the terminus of the low‐level jets. The WH cooling was consistent with reduction in cloud‐forced solar radiation supported by moisture convergence through low‐level southerly flow that is in turn modulated by Atlantic Multidecadal Oscillation. Key Points: Defying global warming, south‐central China and central eastern U.S. has experienced cooling in 2nd half of 20th centuryThe cooling regions are located to the east of major mountain ranges, in heavy agricultural areas, and downstream of low‐level jetsThe cooling is highly correlated with Atlantic Multidecadal Oscillation that modulates low‐level jet and thus clouds/radiation/precipitation [ABSTRACT FROM AUTHOR]

Formulation of the problem. Today, in changing climate conditions, it is very relevant to study the impact of regional climate change on the regime of hydrological indicators and ecological status of the Siversky Donets river basin within Kharkiv region. Analysis of recent research and publications. Hydrometeorological studies are complex and large-scale. In the late 20th - early 21st centuries, a number of articles studied annual runoff of the rivers of Ukraine under the influence of atmospheric processes. Presentday changes in temperature and humidity of the territory affect the hydrological conditions of the rivers. The aim of the work is to assess the relationship between climatic and hydrological indicators (environmental dynamics) of the Siversky Donets basin against the background of regional climate change. In these conditions, monitoring of climate, hydrological and environmental indicators, allows us to make further management decisions on water resources management. Research methods are presented by statistical and cartographic analysis. The source data are a number of climatological, hydrological and environmental observations within the state network of the Ukrainian Hydrometeorological Center of the SES of Ukraine. Problems of further research. Modern changes in climatic conditions in Ukraine are characterized by locality and rapidity. Considering the volume of water use from the river Siversky Donets, the question arises about the water supply of the region, optimization of its use and further rational management. Presentation of the main research material. The dates of the ice cover have changed in recent years, and often ice phenomena may not occur at all. We can see significant warming in winter on the example of January air temperatures. Summer temperatures are growing the fastest (by 0.37°C every 10 years), autumn temperatures are in the second place in terms of growth rate. which means that stable ice cover on most rivers of the Donets basin has been absent in recent decades, which clearly indicates a warming trend. Practical value. Based on the main provisions of the national environmental policy of Ukraine on the use of water resources the study of changes in hydrological regime of rivers is of practical importance for sustainable management. Calculation of the IWP has revealed that most rivers belong to the third and fourth categories - "moderately polluted" and "polluted", but there are also absolutely catastrophic cases. Research results. Heavy economic burden on the waterway will increase its over-regulation. In future, comprehensive assessment of climate change impact on the hydrological conditions of the rivers will determine the degree of change in the ecological state of the waterways, their rational use and protect. [ABSTRACT FROM AUTHOR]

Changes in snow cover associated with the warming of the French Alps greatly influence social-ecological systems through their impact on water resources, mountain ecosystems, economic activities, and glacier mass balance. In this study, we investigated trends in snow cover and temperature over the twentieth century using climate model and reanalysis data. The evolution of temperature, precipitation and snow cover in the European Alps has been simulated with the Modèle Atmospherique Régional (MAR) applied with a 7-km horizontal resolution and driven by ERA-20C (1902-2010) and ERA5 (1981–2018) reanalyses data. Snow cover duration and snow water equivalent (SWE) simulated with MAR are compared to the SAFRAN - SURFEX-ISBA-Crocus - MEPRA meteorological and snow cover reanalysis (S2M) data across the French Alps (1958–2018) and in situ glacier mass balance measurements. MAR outputs provide a realistic distribution of SWE and snow cover duration as a function of elevation in the French Alps. Large disagreements are found between the datasets in terms of absolute warming trends over the second part of the twentieth century. MAR and S2M trends are in relatively good agreement for the decrease in snow cover duration, with higher decreases at low elevation (∼ 5–10%/decade). Consistent with other studies, the highest warming rates in MAR occur at low elevations (< 1000 m a.s.l) in winter, whereas they are found at high elevations (> 2000 m a.s.l) in summer. In spring, warming trends show a maximum at intermediate elevations (1500 to 1800 m). Our results suggest that higher warming at these elevations is mostly linked to the snow-albedo feedback in spring and summer caused by the disappearance of snow cover at higher elevation during these seasons. This work has evidenced that depending on the season and the period considered, enhanced warming at higher elevations may or may not be found. Additional analysis in a physically comprehensive way and more high-quality dataset, especially at high elevations, are still required to better constrain and quantify climate change impacts in the Alps and its relation to elevation. [ABSTRACT FROM AUTHOR]

Attribution—the explanation of an observed change in terms of multiple causal factors—is the cornerstone of climate-change science. For anthropogenic climate change (ACC), the central causal factor is evidently ACC itself, and one of the primary tools used to reveal ACC is aggregation, or grouping together, of data, e.g. global mean surface temperature. Whilst this approach has served climate-change science well, the landscape is changing rapidly. First, there is an increasing focus on regional or local aspects of climate change, and on singular or unprecedented events, which require varying degrees of disaggregation. Relatedly, climate change is increasingly apparent in observations at the local scale, which is challenging the primacy of climate model simulations. Finally, the explosion of climate data is leading to more phenomena-laden methodologies such as machine learning. All this demands a re-think of how attribution is performed and causal explanations are constructed. Here we use Lloyd’s ‘Logic of Research Questions’ framework to show how the way in which the attribution question is framed can strongly constrain its possible and responsive answers. To address the Research Question ‘What was the effect of ACC on X?’ (RQ1), scientists generally consider the question ‘What were the causal factors leading to X, and was ACC among them?’. If the causal factors include only external forcing and internal variability (RQ2), then answering RQ2 also answers RQ1. However, this unconditional attribution is not always possible. In such cases, allowing the causal factors to include elements of the climate system itself (RQ3)—the conditional, storyline approach—is shown to allow for a wider range of possible and responsive answers than RQ2, including that of singular causation. This flexibility is important when uncertainties are high. As a result, the conditional RQ3 mitigates against the sort of epistemic injustice that can arise from the unconditional RQ2.

To study the nature of climate change in the hydrometeorological parameters of the Black and Azov Seas—surface air temperature (SAT), sea surface temperature (SST), ice cover, and sea level—under conditions of ongoing global warming, we used reanalyses and remote sensing data, as well as information from known publications of recent years. It was found that against an increase in SAT over the Black–Azov Sea region (+0.053°C/year in 1980–2020) and SST of the Black Sea (+0.052°C/year in 1982–2020), the values of these parameters in the 2000s differ significantly from those in the 1980s–1990s: the maximum average monthly summer and minimum average monthly winter temperatures have increased, as well as the number of mild winters. The average annual SST of the Black Sea, which practically did not exceed 15°C in the 1980s–early 1990s, has exceeded 16°C in most cases since 2010 (maximum 16.71°C in 2018). In the 2010s, the average monthly winter minima, with the exception of the winters of 2011/2012 and 2016/2017, did not fall below 8°С. A consequence of the increase in winter temperatures was a decrease in the ice concentration in the Sea of Azov (the trend of the mean monthly concentration is –1.2%/10 years). From about 2004–2010 in the Black Sea and since 2004 in the Sea of Azov, the tendency towards increase in their levels (on average) has been replaced by a slight decrease, so that the average positive trends for the period 1993–2020 (+0.32 ± 0.16 cm/year in the Black Sea and +0.21 ± 0.05 cm/year in the Sea of Azov) were approximately 2.5 times less than in 1993–2012. The reason for this decrease in levels (on average) in the last 10–15 years was apparently a decrease in the incoming part of the freshwater balance of both seas, which is indirectly confirmed by the observed increase in salinity of their waters. [ABSTRACT FROM AUTHOR]

Human activities in terms of greenhouse gas (GHG) emissions and aerosols resulting from the combustion of fossil fuels have been shown to have affected the temperature of the Earth on global and continental scales. The surface air temperature (TAS) over India has also been observed to be increasing over the last 100 years. Understanding the underlying causes of regional climate change over India can help in developing appropriate mitigation and adaptation strategies. Differentiating signals of externally forced climate changes from the noise of natural internal variability generally becomes more difficult as spatial scale reduces. Therefore detecting and attributing the influence of external forcings such as GHGs and aerosols is harder at local and regional scales. In this study, we applied a detection and attribution (D&A) method to study annual and seasonal mean TAS over the Indian region. We found that the observed warming over India from 1906 to 2005 cannot be explained by natural climate variability alone. We found that the warming is largely driven by the increase in GHGs, and partially offset by regional anthropogenic emissions of aerosols. These results were confirmed for the shorter 1956–2005 period, but results were sensitive to the choice of observational data set. The changes cannot be explained by internal climate variability or natural external forcings alone, but are compatible with the responses to combined anthropogenic GHG and aerosol forcings. Plain Language Summary: Understanding the nature and causes of near‐surface temperature change at regional scales can help policymakers at the regional levels in making informed adaptation and mitigation policies. Surface air temperature over India has been observed to be increasing over the last 100 years. Here, we use a pattern‐based fingerprint technique to look for time‐increasing spatial correspondence between the observed changes in surface temperature over continental India over the 1906–2005 period (as well as the 1956–2005 period for which multiple observational datasets were available). We examine both the annual mean changes, as well as the changes in individual seasons, and find, in all cases, a detectable human influence in the observed temperature changes over India. Key Points: The observed variations in temperature are inconsistent with unforced variability at the 1% significance levelThe increase in temperature is mainly due to increases in greenhouse gases but partially offset by anthropogenic aerosols [ABSTRACT FROM AUTHOR]

Global models are an essential tool for climate projections, but conventional coarse‐resolution atmospheric general circulation models suffer from errors both in their parameterized cloud physics and in their representation of climatically important circulation features. A notable recent study by Terai et al. (2020, https://doi.org/10.1029/2020ms002274) documents a global model capable of reproducing the regime‐based effect of aerosols on cloud liquid water path expected from observational evidence. This may represent a significant advance in cloud process fidelity in global models. Such models can be expected to give a better estimate of the effective radiative forcing of the climate. If this advance in cloud process representation can be matched by advances in the representation of circulation features such as monsoons, then such models may also be able to navigate the complex tangle between spatially heterogeneous aerosol–cloud interactions and regional circulation patterns. This tight link between aerosol and circulation results in anthropogenic perturbations of climate variables of societal importance, such as regional rainfall distributions. Upcoming global models with km‐scale resolution may improve the regional circulation and be able to take advantage of the Terai et al. (2020, https://doi.org/10.1029/2020ms002274) improvement in cloud physics. If so, an era of significantly improved regional climate projection capabilities may soon dawn. If not, then the improvement in cloud physics might spur intensified efforts on problems in model dynamics. Either way, based on the rapid changes in aerosol emissions in the near future, learning to make reliable projections based on biased models is a skill that will not go out of style. Plain Language Summary: Human activity releases particles into the atmosphere. Some of these particles are small enough to remain suspended in air, forming an "aerosol." Aerosols interact with clouds to make them brighter or dimmer. Clouds are very good sunlight reflectors, so changing their properties even slightly results in large changes to how much sunlight is absorbed by the Earth. Over time, this changes the rate at which the climate warms. It also affects the regional circulation, that is, recurring weather patterns at the regional scale. This is especially important to society when it affects regions that experience intense rainfall, such as the summer monsoon. If circulation patterns change, locations that used to experience intense rain may receive far less rain than they are used to, and vice versa. To predict how human activity will change regional rainfall in the future requires using models. Unfortunately, the models that are currently available struggle with both the interactions between aerosols and clouds and with some regional circulation features, so their regional predictions are not as reliable as society needs them to be. A recent study describes a model that appears to be much better at aerosol–cloud interactions than previous global models, potentially solving half the problem. Many groups are currently at work on a new generation of models that may be better at regional circulation, which would solve the other half, although it may be good to temper any optimism until this has actually been demonstrated. If these two advances can be combined within the same model, then that model will be able to provide a much more reliable estimate of future regional climate. But model development is hard and slow work, while aerosol emissions are changing rapidly, so it is also important to find ways to extract useful information from imperfect models. Key Points: Global models are deficient in how they represent the aerosol influence on clouds and regional circulation patternsNevertheless, global models are essential for reliable regional climate projectionsRecent advances make it more likely that future global models will be greatly improved [ABSTRACT FROM AUTHOR]

The new Coordinated Output for Regional Evaluations (CORDEX-CORE) ensemble provides high-resolution, consistent regional climate change projections for the major inhabited areas of the world. It serves as a solid scientific basis for further research related to vulnerability, impact, adaptation and climate services in addition to existing CORDEX simulations. The aim of this study is to investigate and document the climate change information provided by the CORDEX-CORE simulation ensemble, as a part of the World Climate Research Programme (WCRP) CORDEX community. An overview of the annual and monthly mean climate change information in selected regions in different CORDEX domains is presented for temperature and precipitation, providing the foundation for detailed follow-up studies and applications. Initially, two regional climate models (RCMs), REMO and RegCM were used to downscale global climate model output. The driving simulations by AR5 global climate models (AR5-GCMs) were selected to cover the spread of high, medium, and low equilibrium climate sensitivity at a global scale. The CORDEX-CORE ensemble has doubled the spatial resolution compared to the previously existing CORDEX simulations in most of the regions (25 km (0.22 ∘ ) versus 50 km (0.44 ∘ )) leading to a potentially improved representation of, e.g., physical processes in the RCMs. The analysis focuses on changes in the IPCC physical climate reference regions. The results show a general reasonable representation of the spread of the temperature and precipitation climate change signals of the AR5-GCMs by the CORDEX-CORE simulations in the investigated regions in all CORDEX domains by mostly covering the AR5 interquartile range of climate change signals. The simulated CORDEX-CORE monthly climate change signals mostly follow the AR5-GCMs, although for specific regions they show a different change in the course of the year compared to the AR5-GCMs, especially for RCP8.5, which needs to be investigated further in region specific process studies. [ABSTRACT FROM AUTHOR]

Regional climate change is one of the key factors that should be taken into account when planning the development of the coastal tourism, including investments and construction of tourismrelated infrastructure. A case study for the Black Sea coast of Russia shows a series of potential negative hydrological, meteorological, and biological factors that accompany regional warming of the Black Sea Region, that can impede the development of coastal tourism and devalue billions of dollars in investments by the State, private companies, and individuals. We discuss such natural phenomena as air and sea warming, extreme weather events, coastal upwelling, heavy rains, river plumes, wind and waves, tornado, rip currents, sea-level rise, algal bloom, introduced species, and other features characteristic for the region that seriously impact coastal tourism today, and may intensify in the nearest future. Sporadic occurrence of extreme weather events, unpleasant and sometimes dangerous sea and atmosphere phenomena during the summer tourist season, and from year to year can be of critical psychological importance when choosing your next vacation and tourism destination. The research does not include anthropogenic factors, geopolitical, and socio-economic processes, and the COVID-19 pandemic that play an important role in the sustainable development of coastal tourism as well. [ABSTRACT FROM AUTHOR]

Many regions of the world are affected by a major land cover change resulting from the encroachment of woody plants and the conversion of grasslands into shrublands. In the southwestern United States, such a change in vegetation cover has been found to increase the winter nighttime temperature, thereby contributing to a positive feedback between shrub encroachment and microclimate in areas encroached by cold-sensitive shrubs. Temperature measurements show that winter minimum temperatures are on average ~2K higher in shrubland than in adjacent grassland sites. It is unclear how the nighttime warming induced by shrub encroachment compares with regional climate trends. We address this question by analysing both the historical and future regional temperature trends in central New Mexico. The estimated regional increase in minimum winter temperature ranges from 1 to 4K per century using observations and climate models. Thus, the warming resulting from shrub encroachment is equivalent to a change in regional climate over a time period of century scale, which suggests that shrub encroachment has an overall important effect on the regional climate.

Abstract Global models are an essential tool for climate projections, but conventional coarse‐resolution atmospheric general circulation models suffer from errors both in their parameterized cloud physics and in their representation of climatically important circulation features. A notable recent study by Terai et al. (2020, https://doi.org/10.1029/2020ms002274) documents a global model capable of reproducing the regime‐based effect of aerosols on cloud liquid water path expected from observational evidence. This may represent a significant advance in cloud process fidelity in global models. Such models can be expected to give a better estimate of the effective radiative forcing of the climate. If this advance in cloud process representation can be matched by advances in the representation of circulation features such as monsoons, then such models may also be able to navigate the complex tangle between spatially heterogeneous aerosol–cloud interactions and regional circulation patterns. This tight link between aerosol and circulation results in anthropogenic perturbations of climate variables of societal importance, such as regional rainfall distributions. Upcoming global models with km‐scale resolution may improve the regional circulation and be able to take advantage of the Terai et al. (2020, https://doi.org/10.1029/2020ms002274) improvement in cloud physics. If so, an era of significantly improved regional climate projection capabilities may soon dawn. If not, then the improvement in cloud physics might spur intensified efforts on problems in model dynamics. Either way, based on the rapid changes in aerosol emissions in the near future, learning to make reliable projections based on biased models is a skill that will not go out of style.