Search

IntroductionWarming caused by greenhouse gas (GHG) emissions has become a global environmental issue of widespread concern, and China, as a responsible power, has the pressing task of reducing carbon emissions. China is one of the world’s major beef producers and consumers, and at the same time, beef cattle, as a large livestock, is the largest source of GHG emissions in the livestock industry.MethodsThis study considered the panel data of 31 provinces in China from 2008 to 2022. The kernel density estimation and Dagum Gini coefficient were used to analyze the spatiotemporal dynamic evolution patterns and influencing factors of carbon emissions from China’s beef cattle farming industry.Results(1) The carbon emission trajectory of beef cattle production follows a distinctive “ascend-descend-ascend” three-phase pattern. By 2022, the sector’s cumulative carbon emissions had burgeoned by 37.62% relative to 2008, reflecting an average annual escalation of 2.31%. Despite the overall upward trend in carbon emissions, significant regional differences were observed. The Central Plains region has witnessed a consistent decline, in stark contrast to the Southwest and Northeast regions, which have emerged as hotspots for heightened carbon emissions and intensified emission densities within China’s beef cattle production landscape, underscoring the intensifying significance of carbon mitigation measures. (2) The kernel density curve shows an overall rightward shift with a specific gradient effect on carbon emissions. In addition, the range of the right drag of the curve in 2022 was significantly reduced, which laterally reflects the narrowing of the difference between the provinces with the highest and lowest carbon emissions from beef cattle farming. The principal source of variance in the overall carbon emissions from beef cattle production is the disparities between regions, which accounts for an average annual contribution rate of 52.52%. Conversely, the within-region contribution rates have remained relatively stable, while those for the intensity of transvariation have witnessed a substantial rise, with annual averages of 18.31 and 28.96%, respectively. (3) Regarding the factors influencing carbon emissions reduction, environmental regulations and production efficiency significantly drive carbon emissions reduction in beef cattle farming.DiscussionRelevant government departments should actively guide farmers toward green production, establish perfect policies and regulations for low-carbon beef cattle farming, and promote low-carbon beef cattle farming models based on local conditions.

Total solar eclipses (TSEs) are impressive astronomical events that have attracted people's curiosity since ancient times. Their abrupt alterations to the radiation balance have stimulated studies on "eclipse meteorology," most of them documenting events in the Northern Hemisphere while only one TSE (23 November 2003) has been described over Antarctica. On 4 December 2021--just a few days before the austral summer solstice--the moon blocked the sun over the austral high latitudes, with the path of totality arching from the Weddell Sea to the Amundsen Sea, thus producing a ~2-min central TSE. In this work we present high-resolution meteorological observations from Union Glacier Camp (80°S, 83°W), the only location with a working station under totality, and South Pole station. These observations were complemented with meteorological records from 37 surface stations across Antarctica. Notably, the largest cooling (~5°C) was observed over the East Antarctic dome, where obscurity was ~85% while many sectors experienced insignificant temperature changes. This heterogenous cooling distribution, at odds with the seemingly homogeneous land surface of Antarctica, is partially captured by a simple radiative model. To further diagnose the effect of the eclipse on the surface meteorology, we ran multiple pairs of simulations (eclipse enabled and disabled) using the Weather Research and Forecasting (WRF) Model. The overall pattern and magnitude of the simulated cooling agree well with the observations and reveal that, in addition to the solar radiation deficit and cloud cover, low-level winds and the height of the planetary boundary layer are key determinants of the temperature changes and their spatial variability. [ABSTRACT FROM AUTHOR]

Doppler-lidar wind-profile measurements at three sites were used to evaluate NWP model errors from two versions of NOAA's 3-km-grid HRRR model, to see whether updates in the latest version 4 reduced errors when compared against the original version 1. Nested (750-m grid) versions of each were also tested to see how grid spacing affected forecast skill. The measurements were part of the field phase of the Second Wind Forecasting Improvement Project (WFIP2), an 18-month deployment into central Oregon–Washington, a major wind-energy-producing region. This study focuses on errors in simulating marine intrusions, a summertime, 600–800-m-deep, regional sea-breeze flow found to generate large errors. HRRR errors proved to be complex and site dependent. The most prominent error resulted from a premature drop in modeled marine-intrusion wind speeds after local midnight, when lidar-measured winds of greater than 8 m s−1 persisted through the next morning. These large negative errors were offset at low levels by positive errors due to excessive mixing, complicating the interpretation of model "improvement," such that the updates to the full-scale versions produced mixed results, sometimes enhancing but sometimes degrading model skill. Nesting consistently improved model performance, with version 1's nest producing the smallest errors overall. HRRR's ability to represent the stages of sea-breeze forcing was evaluated using radiation budget, surface-energy balance, and near-surface temperature measurements available during WFIP2. The significant site-to-site differences in model error and the complex nature of these errors mean that field-measurement campaigns having dense arrays of profiling sensors are necessary to properly diagnose and characterize model errors, as part of a systematic approach to NWP model improvement. Significance Statement: Dramatic increases in NWP model skill will be required over the coming decades. This paper describes the role of major deployments of accurate profiling sensors in achieving that goal and presents an example from the Second Wind Forecast Improvement Program (WFIP2). Wind-profile data from scanning Doppler lidars were used to evaluate two versions of HRRR, the original and an updated version, and nested versions of each. This study focuses on the ability of updated HRRR versions to improve upon predicting a regional sea-breeze flow, which was found to generate large errors by the original HRRR. Updates to the full-scale HRRR versions produced mixed results, but the finer-mesh versions consistently reduced model errors. [ABSTRACT FROM AUTHOR]

This paper explains the circumstances where economic growth increases the likelihood of interstate war. Optimism created by high and sustained economic expansion permeates a state, increasing elite and mass optimism for the use of deadly force. Without relief, such unbridled sanguinity can lead states to war. However, other forces reduce the probability of war. Regional democracy, bilateral trade, and trade openness slow down the process where states go to war. This paper hypothesizes that key factors raise the temperature of disputes, increasing the likelihood of a political dispute combusting to war, while other attributes inhibit the process to war. Economic growth catalyzes such reactions, while regional and joint democracy impede the probability that a war sparks. This paper produces monadic and dyadic results demonstrating that economic growth increases the likelihood of war, while other factors such as interstate trade openness, bilateral trade, dyadic democracy, and regional democracy slow down the process of war, making war less likely. [ABSTRACT FROM AUTHOR]

Background: Numerous reviews of the epidemiology and risk factors for breast cancer have been published previously which heighted different directions of breast cancer. Aim: The present review examined the likelihood that incidence, prevalence, and particular risk factors might vary by geographic region and possibly by food and cultural practices as well. Methods: A systematic review (2017-2022) was conducted following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, reporting on epidemiological and risk factor reports from different world regions. Medical Subject Heading (MeSH) terms: "Breast neoplasm" "AND" country terms such as "Pakistan/epidemiology", "India/epidemiology", "North America/epidemiology", "South Africa/epidemiology" were used to retrieve 2068 articles from PubMed. After applying inclusion and exclusion terms, 49 papers were selected for systematic review. Results: Results of selected articles were summarized based on risk factors, world regions and study type. Risk factors were classified into five categories: demographic, genetic and lifestyle risk factors varied among countries. This review article covers a variety of topics, including regions, main findings, and associated risk factors such as genetic factors, and lifestyle. Several studies revealed that lifestyle choices including diet and exercise could affect a person's chance of developing breast cancer. Breast cancer risk has also been linked to genetic variables, including DNA repair gene polymorphisms and mutations in the breast cancer gene (BRCA). It has been found that most of the genetic variability links to the population of Asia while the cause of breast cancer due to lifestyle modifications has been found in American and British people, indicating that demographic, genetic, and, lifestyle risk factors varied among countries. Conclusion: There are many risk factors for breast cancer, which vary in their importance depending on the world region. However, further investigation is required to better comprehend the particular causes of breast cancer in these areas as well as to create efficient prevention and treatment plans that cater to the local population. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is a vital and vulnerable water tower that supports the livelihoods of billions of people. The use of a data-driven recharge–storage–runoff perspective enables a more comprehensive estimation of multiple aspects of the water cycle. Through an analysis of the diagnostic net water flux from ERA5, water storage changes (dS/dt) from GRACE, runoff estimations (R) from the land–atmosphere water balance, and river discharge measurements (Rd), the annual cycle of recharge–storage–runoff has been studied over the TP and its basins. The net water flux determines a recharge of 326 mm yr−1 over the TP. Recharge in coupled storages, leading to an increase in water mass (dS/dt > 0) and runoff (R > 0) during the wet season, is considered the fast response and is measured using the ratio of runoff to net water flux (r1). Conversely, the slow response determined by the water storage release (dS/dt < 0) during the dry season is quantified by the ratio of storage release to runoff (r2). The ratios of r1 and r2 are influenced by climatic and terrain drivers, indicating specific characteristics of recharge–storage–runoff at the basin scale. Small r1 values and large r2 values suggest high buffer capacity, while the basin of Amu Darya (Salween) is characterized by the highest (lowest) buffer capacity over the TP. However, measurements of river discharge at Amu Darya suggest an uncoupled recharge–storage–runoff. The imbalance between river discharge and runoff estimation was most severe in the first decade of the twenty-first century but has been mitigated since 2012. River discharge at Amu Darya is likely constrained by energy during summer. [ABSTRACT FROM AUTHOR]

Gridded precipitation products from satellite-based systems provide continuous and seamless data that can overcome the limitations of ground-based precipitation data. Remote sensing (RS) products can provide efficient precipitation data in the desert rangelands and the Rocky Mountains of the western United States, where ground-based rain gauges are sparse. In this study, we evaluated the quality of precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infrared Precipitation with Station (CHIRPS), and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) in the Upper Colorado River basin (UCRB) for the period 2000–20. The reliability of daily precipitation data from these products was tested against ground-based observations from the National Oceanic and Atmospheric Administration (NOAA) using two continuous and four categorical statistical evaluation metrics. We investigated the effects of topographical conditions on the quality of precipitation estimates. Results show that all three products have 3–4 mm day−1 differences in daily precipitation rates compared to ground observations. In addition, the difference in monthly precipitation rates was more prominent in the wet season (November–April) than in the dry season (May–October). The margin of errors varied with the type of RS system and by location. A categorical evaluation suggests a moderate ability to detect precipitation occurrence with 50%–60% detection ability. The reliability of precipitation estimates is mainly limited by elevation and different ecoregions and climate features. [ABSTRACT FROM AUTHOR]

The COVID-19 crisis increased unemployment all over the World, with significant regional heterogeneity. This paper intends to analyze this territorial heterogeneity for the Portuguese case and investigate which regional factors complement personal and job characteristics in explaining individual vulnerability to COVID-19 unemployment. By considering personal, job and regional dimensions, we extended the literature and provided a more comprehensive understanding of this new phenomenon in the immediate and medium-term. Furthermore, this knowledge is essential to support policy suggestions for quick and effective action in preventing job losses in the current and future crises. Detailed information on all individuals that lost their jobs in Portugal 1 year after (and before) the COVID-19 outset was used to estimate three logit models that compare the odds of losing a job after and during the pandemic. Significant territorial heterogeneity of the COVID-19 impact on unemployment is obtained. Along with personal and job characteristics, we conclude that regional characteristics are essential for explaining individual vulnerabilities. In particular, workers are more prompted to lose their jobs if they live in regions with higher population densities, lower pre-crisis unemployment, and more dependable international flow. Conversely, individual and regional human capital investment contributes to protecting employment, revealing the existence of external effects. [ABSTRACT FROM AUTHOR]

BackgroundNumerous reviews of the epidemiology and risk factors for breast cancer have been published previously which heighted different directions of breast cancer.AimThe present review examined the likelihood that incidence, prevalence, and particular risk factors might vary by geographic region and possibly by food and cultural practices as well.MethodsA systematic review (2017-2022) was conducted following Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, reporting on epidemiological and risk factor reports from different world regions. Medical Subject Heading (MeSH) terms: “Breast neoplasm” “AND” country terms such as “Pakistan/epidemiology”, “India/epidemiology”, “North America/epidemiology”, “South Africa/epidemiology” were used to retrieve 2068 articles from PubMed. After applying inclusion and exclusion terms, 49 papers were selected for systematic review.ResultsResults of selected articles were summarized based on risk factors, world regions and study type. Risk factors were classified into five categories: demographic, genetic and lifestyle risk factors varied among countries. This review article covers a variety of topics, including regions, main findings, and associated risk factors such as genetic factors, and lifestyle. Several studies revealed that lifestyle choices including diet and exercise could affect a person’s chance of developing breast cancer. Breast cancer risk has also been linked to genetic variables, including DNA repair gene polymorphisms and mutations in the breast cancer gene (BRCA). It has been found that most of the genetic variability links to the population of Asia while the cause of breast cancer due to lifestyle modifications has been found in American and British people, indicating that demographic, genetic, and, lifestyle risk factors varied among countries.ConclusionThere are many risk factors for breast cancer, which vary in their importance depending on the world region. However, further investigation is required to better comprehend the particular causes of breast cancer in these areas as well as to create efficient prevention and treatment plans that cater to the local population.

This study demonstrates the synoptic-scale relationship between the atmospheric heat source over the Tibetan Plateau (TP) (TP) and summer rainfall in eastern China. The results show that the summer TP has three key areas [eastern, western, and northeastern TP (TPE, TPW, TPNE, respectively)] and 3–8-day dominant oscillation periods on the synoptic time scale for each subregion. Analogously, on the synoptic scale, the downstream rainfall of the TP in China has six sensitive regions (zone I to zone VI), each with 3–8-day dominant periods. The TPE, TPW, and TPNE lead rainfall by 4 days with a southeastward propagation pathway, by 5 days with both southeastward and northeastward propagation routes, and by 4 days with an eastward route, respectively. Mechanistically, when TP lies in the positive-to-negative transition phase, the rainfall moves off the TP and reaches the maximum in its most inactive phase; the jet cores embedded in the subtropical westerly jet band and the trough from the region of the TP lead the eastward movement of the rainfall by the propagation of the divergence center and generalized moist potential vorticity. Besides, the southwest vortex and northwest vortex generated at the southeastern and northeastern flank of the TP are the main cause for the southeastward and northeastward or eastward movement of the precipitation. Further internal atmospheric dynamic structures indicate that both the horizontal moisture advection and generalized moist potential vorticity advection act on the entire propagation processes of the synoptic-scale signals for each TP key area. In conclusion, the study has obvious theoretical significance and application value for the development of plateau synoptics. [ABSTRACT FROM AUTHOR]

Since the availability of study opportunities is unequal across regions, entering the phase of post-secondary education is often accompanied by leaving (the parental) home. In these life-course transitions, social background plays a crucial role in the form of resources, e.g., to afford living independently while studying. We use a unique set of geospatial data by aggregating information on the municipality level flexibly within travel-time radii and link the data to the German National Educational Panel Study (NEPS) to examine whether socioeconomic and university infrastructure in the region can compensate for a lack of parental resources (i) in transitions to university and (ii) in the likelihood of staying in or leaving the home region to study. We analyse this across cohorts between 1986 and 2015. We find that the region makes a difference: a wide availability of universities in the region offsets social inequality in the transition to university. Yet, the increasing availability of alternative educational routes over time via vocational training and universities of applied sciences causes this moderating influence to decrease across cohorts. Our findings call for gearing the attention of policymakers towards the varying relevance of regional conditions over time and across social groups for individuals' life-course transitions. [ABSTRACT FROM AUTHOR]

This paper examines the trends in hot summer days for the Pacific Northwest in observations and a regional climate model ensemble. Hot days are identified by the temperature threshold for several percentile values computed over 10-yr intervals (85th, 90th, and 95th percentiles and absolute maximum) to differentiate heat events of different intensities and are compared to the median temperature (50th percentile). For the stations analyzed, the observed rate of warming during hot days is not statistically different from the warming rate of median days since the 1950s. However, for projections to 2100, hot days show a statistically significant increase in the warming rate of the hottest days compared to the warming rate for median days. Depending on location, the 95th-percentile daily maximum temperature shows a warming rate of up to 0.2°C decade−1 above the median warming rate. The divergence in the trends of median and extreme temperature shows substantial regional variation depending on local terrain and coastlines. The warming trend during hot days is related to the unique circulation patterns during heat events, which respond to different feedbacks and amplifying effects in the land–atmosphere system from those that prevail during typical days. The regional climate model simulations are taken from an ensemble of the Weather Research and Forecasting (WRF) Model simulations forced by 12 global climate model simulations from phase 5 of the Climate Model Intercomparison Project (CMIP5) using the RCP8.5 emissions scenario and 12-km grid spacing. Significance Statement: The heatwave of June 2021 had substantial societal and ecological impacts, illustrating vulnerability to record-shattering events. This paper addresses whether climate change can cause heat events in the Pacific Northwest to warm at a more rapid rate than typical days. Based on observations, in the recent past, the hottest days warmed at about the same rate as typical days in the recent past. However, results from a high-resolution climate model show a higher rate of warming during the warmest days relative to median days under the most aggressive future emissions scenario. This effect could cause heatwaves to become more intense relative to typical days by 2100, making adaptation to extreme events more difficult. [ABSTRACT FROM AUTHOR]

Convection-permitting (CP) models promise much in response to the demand for increased localization of future climate information: greater resolution of influential land surface characteristics, improved representation of convective storms, and unprecedented resolution of user-relevant data. In practice, however, it is contended that the benefits of enhanced resolution cannot be fully realized due to the gap between models' computational and effective resolution. Nevertheless, where surface forcing is strongly heterogeneous, one can argue that usable information may persist close to the grid scale. Here we analyze a 4.5-km resolution CP projection for Africa, asking whether and where fine-scale projection detail is robust at sub-25-km scales, focusing on geolocated rainfall features (rather than Lagrangian motion). Statistically significant detail for seasonal means and daily extremes is most frequent in regions of high topographic variability, most prominently in East Africa throughout the annual cycle, West Africa in the monsoon season, and to a lesser extent over Southern Africa. Lake coastal features have smaller but significant impacts on projection detail, whereas ocean coastlines and urban conurbations have little or no detectable impact. The amplitude of this sub-25-km projection detail can be similar to that of the local climatology in mountainous regions (or around a third near East Africa's lake shores), so potentially beneficial for improved localization of future climate information. In flatter regions distant from coasts (the majority of Africa), spatial heterogeneity can be explained by chaotic weather variability. Here, the robustness of local climate projection information can be substantially enhanced by spatial aggregation to approximately 25-km scales, especially for daily extremes and equatorial regions. Significance Statement: Recent substantial increases in the horizontal resolution of climate models bring the potential for both more reliable and more local future climate information. However, the best spatial scale on which to analyze such data for impacts assessments remains unclear. We examine a 4.5-km resolution climate projection for Africa, focusing on seasonal and daily rainfall. Spatially fixed fine-scale projection detail is found to be statistically robust at sub-25-km scales in the most mountainous regions and to a lesser extent along lake coastlines. Elsewhere, the model data may be better aggregated to at least 25-km scales to reduce sampling uncertainties. Such evolving guidance on the circumstances and extent of high-resolution data aggregation will help users gain greater benefit from climate model projections. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between daily carbon emissions for California's savanna and forest wildfires and regional meteorology over the past 18 years. For each fuel type, the associated weather [daily maximum wind, daily vapor pressure deficit (VPD), and 30-day-prior VPD] is determined for all fire days, the first day of each fire, and the day of maximum emissions of each fire at each fire location. Carbon emissions, used as a marker of wildfire existence and growth, for both savanna and forest wildfires are found to vary greatly with regional meteorology, with the relationship between emissions and meteorology varying with the amount of emissions, fire location, and fuel type. Weak emissions are associated with climatologically typical dryness and wind. For moderate emissions, increasing emissions are associated with higher VPD from increased warming and only display a weak relationship with wind speed. High emissions, which encompass ∼85% of the total emissions but only ∼4% of the fire days, are associated with strong winds and large VPDs. Using spatial meteorological composites for California subregions, we find that weak-to-moderate emissions are associated with modestly warmer-than-normal temperatures and light winds across the domain. In contrast, high emissions are associated with strong winds and substantial temperature anomalies, with colder-than-normal temperatures east of the Sierra Nevada and warmer-than-normal conditions over the coastal zone and the interior of California. Significance Statement: The purpose of this work is to better understand the influence of spatially and temporally variable meteorology and spatially variable surface fuels on California's fires. This is important because much research has focused on large climatic scales that may dilute the true influence of weather (here, high winds and dryness) on fire growth. We use a satellite-recorded fire emissions dataset to quantify daily wildfire existence and growth and to determine the relationship between regional meteorology and wildfires across varying emissions in varying fuels. The result is a novel view of the relationship between California wildfires and rapidly variable, regional meteorology. [ABSTRACT FROM AUTHOR]

The factors controlling the present-day pattern of temperature variance are poorly understood. In particular, it is unclear why the variance of wintertime near-surface temperatures on daily and synoptic time scales is roughly twice as high over North America as over Eurasia. In this study, continental geometry's role in shaping regional wintertime temperature variance is investigated using idealized climate model simulations run with midlatitude continents of different shapes. An isolated, rectangular midlatitude continent suggests that in the absence of other geographic features, the highest temperature variance will be located in the northwest of the continent, roughly collocated with the region of largest meridional temperature gradients, and just north of the maximum near-surface wind speeds. Simulations with other geometries, mimicking key features of North America and Eurasia, investigate the impacts of continental length and width, sloping coastlines, and inland bodies of water on regional temperature variance. The largest effect comes from tapering the northwest corner of the continent, similar to Eurasia, which substantially reduces the maximum temperature variance. Narrower continents have smaller temperature variance in isolation, implying that the high variances over North America must be due to the nonlocal influence of stationary waves. Support for this hypothesis is provided by simulations with two midlatitude continents, which show how continental geometry and stationary waves can combine to shape regional temperature variance. Significance Statement: Wintertime temperature variance over North America is roughly twice as high as over Eurasia, but the reasons for this are unknown. Here we use idealized climate model simulations to investigate how continental geometry shapes regional temperature variance. We find that the smaller variance over Eurasia is largely due to the tapering of its northwest coast, which weakens temperature gradients in the continental interior. Our simulations also suggest that in isolation a narrow continent, like North America, should have weak temperature variance, implying that stationary waves are responsible for the high variance over North America. Understanding the controls on regional temperature variance is important for interpreting present-day winter climates and how these will change in the future. [ABSTRACT FROM AUTHOR]

Summer mean (June, July, and August) surface air temperature (SSAT) in East Asia during the period from 1958 to 2001 has shown a warming. However, the relative contributions of external forcing (EF) and internal climate variability (ICV) to the SSAT changes over East Asia remain unclear. In this study, a new approach is applied to estimate the changes in the SSAT determined by the effects of EF and ICV over East Asia during the period from 1958 to 2001. Reanalysis data as well as simulated results from both global atmosphere–ocean coupled model outputs and a regional climate model (RCM) are used for this approach. The observed SSATs over East Asia have undergone a decreasing trend from 1958 to 1972 (20.148C decade-1, p<0.01) and an increasing trend after 1972 (0.248C decade-1, p<0.01). While these features are not captured by the reanalysis studied here, they are reproduced when the reanalysis output is downscaled using an RCM. The effects of the EF and the ICV on the SSAT can be separated based on the RCM downscaling simulation. The results show that the SSAT with EF displayed significant warming over most regions of East Asia, whereas the SSAT with ICV mainly exhibited cooling over East Asia. Furthermore, EF mainly influenced the decadal changes of the SSAT, whereas the ICV mainly influenced the interannual changes in the SSAT over East Asia. The interannual changes of the SSAT over East Asia that were influenced by the ICV are mainly manifested as the combined effects of the large-scale ocean–atmosphere circulations, which expressed 79% explanatory power on the SSAT changes. [ABSTRACT FROM AUTHOR]

Since the availability of study opportunities is unequal across regions, entering the phase of post-secondary education is often accompanied by leaving (the parental) home. In these life-course transitions, social background plays a crucial role in the form of resources, e.g., to afford living independently while studying. We use a unique set of geospatial data by aggregating information on the municipality level flexibly within travel-time radii and link the data to the German National Educational Panel Study (NEPS) to examine whether socioeconomic and university infrastructure in the region can compensate for a lack of parental resources (i) in transitions to university and (ii) in the likelihood of staying in or leaving the home region to study. We analyse this across cohorts between 1986 and 2015. We find that the region makes a difference: a wide availability of universities in the region offsets social inequality in the transition to university. Yet, the increasing availability of alternative educational routes over time via vocational training and universities of applied sciences causes this moderating influence to decrease across cohorts. Our findings call for gearing the attention of policymakers towards the varying relevance of regional conditions over time and across social groups for individuals’ life-course transitions.

Knowledge about precipitation generation remains limited in the tropical Andes due to the lack of water stable isotope (WSI) data. Therefore, we investigated the key factors controlling the isotopic composition of precipitation in the P'aramo highlands of southern Ecuador using event-based (high frequency) WSI data collected between November 2017 and October 2018. Our results show that air masses reach the study site preferentially from the eastern flank of the Andes through the Amazon basin (73.2%), the Orinoco plains (11.2%), and the Mato Grosso Massif (2.7%), whereas only a small proportion stems from the Pacific Ocean (12.9%). A combination of local and regional factors influences the δ118O isotopic composition of precipitation. Regional atmospheric features (Atlantic moisture, evapotranspiration over the Amazon rainforest, continental rain-out, and altitudinal lapse rates) are what largely control the meteoric δ118O composition. Local precipitation, temperature, and the fraction of precipitation corresponding to moderate to heavy rainfalls are also key features influencing isotopic ratios, highlighting the importance of localized convective precipitation at the study site. Contrary to δ118O, d-excess values showed little temporal variation and could not be statistically linked to regional or local hydrometeorological features. The latter reveals that large amounts of recycled moisture from the Amazon basin contribute to local precipitation regardless of season and predominant trajectories from the east. Our findings will help to improve isotope-based climatic models and enhance paleoclimate reconstructions in the southern Ecuador highlands. [ABSTRACT FROM AUTHOR]

This study examines the influences of state and local political affiliation and local exposure to weather-related impacts on local government climate change adaptation efforts in 88 U.S. cities. Although climate adaptation takes place when cities replace critical infrastructure damaged by severe weather events, little is known about the influence of political affiliation and severe weather events on climate adaptation in a broader sense. Using multiple linear regression models, this study analyzes variations in local government climate adaptation efforts as a function of local gross domestic product (as a control variable), historical weather-related factors [i.e., number of extreme weather events, weather-related economic impact due to property damage, and weather-related human impact (injuries and fatalities)], and state and local political affiliation. The findings of this study indicate that local political affiliation significantly influences local government climate adaptation efforts; however, state political affiliation does not. Further, local weather-related impacts do not appear to affect the likelihood of local government to engage in climate adaptation efforts, even when accounting for potential interactions with local political affiliation. These results support the hypothesis that local political affiliation is a strong and robust predictor of local climate adaptation in U.S. cities. This study contributes to literature aimed at addressing the widely acknowledged need for understanding key barriers to U.S. climate adaptation, as well as the role of politics in moderating climate action. [ABSTRACT FROM AUTHOR]

Flood-induced fatalities are among the more poorly reported effects of flood disasters in many developing countries because of poor data inventory and management. Specific objectives of this study are to assess the spatial and temporal variations in flood fatalities in Nigeria, the most populous country in Africa. The study explored available datasets from the National (Nigerian) Meteorological and Emergency Management Agencies as well as those from the Dartmouth Flood Observatory (DFO) at the University of Colorado Boulder and complemented those with scattered reports from Nigerian newspapers to achieve the stated objectives. Using a mix of statistical and geographical information analysis approaches, the study showed that most of Nigeria is vulnerable to flood, given the nature of the dominant climate that often results in “medium” to “high” rainfall intensity (i.e., rainfall 5 38.1–50.1 mm or . 50.1 mm in 24 h, respectively), inadequate settlement planning/land-use and land-cover management, and dam failure. Analysis of the frequency of the flood–fatality relationship indicates an increase in flood fatalities by 4.7% relative to flood cases between 1985 and 2017. The study complemented the results with information from newspapers and some other non-peer-reviewed documents (especially reports from relevant agencies) and revealed the need for a better flood information management system in the country, especially since the national database and DFO records were not the same. The study concluded that flood fatalities are on the increase but are poorly reported. It thus recommends improved information systems for flood and other disasters and their fatalities in the country. [ABSTRACT FROM AUTHOR]

Sahel summertime precipitation declined from the 1950s to 1970s and recovered from the 1970s to 2000s. Anthropogenic aerosol contributions to this evolution are typically attributed to interhemispheric gradient changes of Atlantic Ocean sea surface temperature (SST). However recent work by Hirasawa et al. indicates a more complex picture, with the response being a combination of "fast" direct atmospheric (DA) processes and "slow" ocean-mediated (OM) processes. Here, we extend this understanding using the Community Atmosphere Model 5 to determine the role of regional ocean-basin perturbations and regional aerosol emission changes in the overall aerosol-driven OM and DA responses, respectively. From the 1950s to 1970s, there was an OM Sahel wetting response due to Pacific Ocean cooling that was offset by drying due to Atlantic cooling. By contrast, from the 1970s to 2000s, Atlantic trends reversed and amplified the Pacific cooling-induced wetting. This wetting was partially offset by drying driven by Indian Ocean cooling. Thus, the OM Sahel precipitation response to aerosol crucially depends on the balance of responses to Atlantic, Pacific, and Indian Ocean SST anomalies. From the 1950s to 1970s, there is DA Sahel drying that was principally due to North American aerosol emissions, with negligible effect from European emissions. DA drying from the 1970s to 2000s was mainly due to African aerosol emissions. Thus, the shifting roles of regional OM and DA effects reveal a complex interplay of direct driving and remote teleconnections in determining the time evolution of Sahel precipitation due to aerosol forcing in the late twentieth century. Significance Statement: Studies of global climate models consistently indicate that anthropogenic aerosol emissions were a significant contributor to a severe drought that occurred in the Sahel region of Africa in the late twentieth century. The drying influence of aerosol forcing is the combined result of rapid atmospheric responses directly due to the forcing and slower responses due to forced ocean temperature changes. Using a set of simulations targeted at determining the influences from different ocean basins and different emission regions for two periods in the late twentieth century, we find there is a surprising range of mechanisms through which aerosol emissions affect the Sahel. This results in a complex interplay of at times competing and at times complementary regional influences. [ABSTRACT FROM AUTHOR]

Robust estimates of climate sensitivity are important for decision-making on mitigation of climate change. However, climate sensitivity and its governing processes are still subject to large uncertainty. Recently it has been established that climate sensitivity changes over time in numerical climate model experiments with abrupt quadrupling of the CO2 concentration. Here we conduct an analysis of such experiments from a range of climate models from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP). Climate feedbacks associated with clouds, lapse rate, Planck radiation, surface albedo, and water vapor and their changes over time are diagnosed based on a radiative kernel method. We find two clearly distinct model groups, one with weak and one with strong lapse-rate feedback change. The Arctic is the region showing the largest differences between these two model groups, with respect to both warming change and individual feedback changes. We retrace this change to the development over time of the Arctic sea ice, which impacts both the surface-albedo and lapse-rate feedbacks. Generally, models that warm quickly, both globally and in the Arctic, also quickly lose their Arctic sea ice and change their total global-mean climate feedback only little, and vice versa. However, it remains unclear if the Arctic changes are a cause or rather a by-product of the total global-mean feedback change. Finally, we find support for the results of previous studies finding that the relative warming in the tropical Indo-Pacific region may control the change of total climate feedback over time. [ABSTRACT FROM AUTHOR]

The aim of the study was to quantify total extractive contents and lipophilic compounds, stilbenes, and lignans in Scots pine stem wood, stem bark, branch biomass, and sawmill residues in four climatic regions of Finland to evaluate the most optimal sources of extractives for bio-based chemical biorefining and bioenergy products. Data were derived from 78 chip samples from the before-mentioned raw materials, the samples being pooled by tree height position from the sample trees of 42 experimental forest stands, and sawdust lots from 10 log stands. Accelerated solvent extraction (ASE) was employed to determine total extractive contents, followed by gas chromatography with flame ionization detection (GC–FID) to quantify extractive groups and gas chromatography-mass spectrometry (GC–MS) to analyse individual extractive compounds. Resin acids and triglycerides followed by fatty acids were the dominant extractive groups. Resin acids were most abundant in stem wood from final fellings and in sawdust, fatty acids in bark and branch biomass, and triglycerides also in stem wood from thinnings and the top parts of trees. Of the minor extractive groups, stilbenes were the most abundant in stem wood from final fellings and in sawdust, and steryl esters, sterols, and lignans in bark and branch biomass, the two last groups almost missing from other biomass components. Regional differences in the contents of extractive groups were generally small, 1.0−1.5 percentage points at the maximum, but factor analysis distinguished northern and southern regions into their own groups. Bark was the most potential source of fatty acids and sterols in southern Finland, and triglycerides and steryl esters in northern Finland. In stem wood, steryl esters, triglycerides, and lignans decreased and stilbenes increased from north to south. Certain fatty acids and resin acids were more frequent in the north. The results highlighted the importance of focused procurement and efficient sorting of raw materials, purity, unique properties, and feasible isolation techniques for competitive ability as well as large raw material volumes or well-defined value-added products. [ABSTRACT FROM AUTHOR]

Anomalous sea levels along the mid-Atlantic and South Atlantic coasts of the United States are often linked to atmosphere–ocean dynamics, remote- and local-scale forcing, and other factors linked to cyclone passage, winds, waves, and storm surge. Herein, we examine sea level variability along the U.S. Atlantic coast through satellite altimeter and coastal tide gauge data within the context of synoptic-scale weather pattern forcing. Altimetry data, derived from sea level anomaly (SLA) data between 1993 and 2019, were compared with self-organizing map (SOM)-based atmospheric circulation and surface wind field categorizations to reveal spatiotemporal patterns and their interrelationships with high-water-level conditions at tide gauges. Regional elevated sea level patterns and variability were strongly associated with synergistic patterns of atmospheric circulation and wind. Recurring atmospheric patterns associated with high-tide flooding events and flood risk were identified, as were specific regional oceanographic variability patterns of SLA response. The incorporation of combined metrics of wind and circulation patterns further isolate atmospheric drivers of high-tide flood events and may have particular significance for predicting future flood events over multiple spatial and temporal scales. Significance Statement: Mean sea level and minor to moderate coastal flood events, also called blue-sky or high-tide floods, are increasing along many U.S. coastlines. While the drivers of such events are numerous, here we identified key contributing weather patterns and environmental factors linked to increased risk of regional and local high-water conditions along the Atlantic coast. Our results indicate that the predictability of elevated sea levels and high-tide floods is highly dependent upon atmospheric drivers including wind and circulation patterns and, if applied in a tested modeling framework, may prove useful for predicting future floods at various time scales. [ABSTRACT FROM AUTHOR]

We describe the first effort within the Coordinated Regional Climate Downscaling Experiment–Coordinated Output for Regional Evaluation, or CORDEX-CORE EXP-I. It consists of a set of twenty-first-century projections with two regional climate models (RCMs) downscaling three global climate model (GCM) simulations from the CMIP5 program, for two greenhouse gas concentration pathways (RCP8.5 and RCP2.6), over nine CORDEX domains at ∼25-km grid spacing. Illustrative examples from the initial analysis of this ensemble are presented, covering a wide range of topics, such as added value of RCM nesting, extreme indices, tropical and extratropical storms, monsoons, ENSO, severe storm environments, emergence of change signals, and energy production. They show that the CORDEX-CORE EXP-I ensemble can provide downscaled information of unprecedented comprehensiveness to increase understanding of processes relevant for regional climate change and impacts, and to assess the added value of RCMs. The CORDEX-CORE EXP-I dataset, which will be incrementally augmented with new simulations, is intended to be a public resource available to the scientific and end-user communities for application to process studies, impacts on different socioeconomic sectors, and climate service activities. The future of the CORDEX-CORE initiative is also discussed. [ABSTRACT FROM AUTHOR]

A key goal of the 2015 Paris Climate Agreement is to keep global mean temperature change at 2°C and if possible under 1.5°C by the end of the century. To investigate the likelihood of achieving this target, we calculate the year of exceedance of a given global warming threshold (GWT) temperature across 32 CMIP6 models for Shared Socioeconomic Pathway (SSP) and radiative forcing combinations included in the Tier 1 ScenarioMIP simulations. Threshold exceedance year calculations reveal that a majority of CMIP6 models project warming beyond 2°C by the end of the century under every scenario or pathway apart from the lowest emission scenarios considered, SSP1–1.9 and SSP1–2.6, which is largely a function of the ScenarioMIP experiment design. The U.K. Earth System Model (UKESM1) ScenarioMIP projections are analyzed in detail to assess the regional and seasonal variations in climate at different warming levels. The warming signal emerging by midcentury is identified as significant and distinct from internal climate variability in all scenarios considered and includes warming summers in the Mediterranean, drying in the Amazon, and heavier Indian monsoons. Arctic sea ice depletion results in prominent amplification of warming and tropical warming patterns emerge that are distinct from interannual variability. Climate changes projected for a 2°C warmer world are in almost all cases exacerbated with further global warming (e.g., to a 4°C warmer world). Significance Statement: In this study, we look into changes occurring in climate due to global warming by analyzing data from the latest global climate models to see when the Earth will warm by 2° or 4°C, compared to preindustrial temperatures. We then use the UKESM1 climate model to identify regions on Earth where significant climate change is simulated in the future and discuss possible reasons for these changes. Simulations from this model also show that regions such as the Mediterranean, Amazon forests, and tropical countries are likely to see significant changes in climate impacting human lives. Future work to study the regional changes in greater detail will help us improve climate policies to protect society. [ABSTRACT FROM AUTHOR]

This study investigates whether extreme heat episodes (heat waves) have contributed to the development of air conditioning (AC) technology in the United States. To this end, we use weather data to identify days at which heat and relative humidity were above levels comfortable to the human body, and match these with patent data at the county level for nearly a hundred years. We find that in the 2 years after a county has experienced extreme heat AC patents increase. Overall, average extreme heat exposure results in an increase of 7.5% greater innovation. We find no similar increase in the frequency of non-AC-related patent filings, and therefore conclude that heat waves result in innovation targeting their mitigation. [ABSTRACT FROM AUTHOR]

Future projections of precipitation change over tropical land are often enhanced by vegetation responses to CO2 forcing in Earth system models. Projected decreases in rainfall over the Amazon basin and increases over the Maritime Continent are both stronger when plant physiological changes are modeled than if these changes are neglected, but the reasons for this amplification remain unclear. The responses of vegetation to increasing CO2 levels are complex and uncertain, including possible decreases in stomatal conductance and increases in leaf area index due to CO2 fertilization. Our results from an idealized atmospheric general circulation model show that the amplification of rainfall changes occurs even when we use a simplified vegetation parameterization based solely on CO2-driven decreases in stomatal conductance, indicating that this mechanism plays a key role in complex model projections. Based on simulations with rectangular continents we find that reducing terrestrial evaporation to zero with increasing CO2 notably leads to enhanced rainfall over a narrow island. Strong heating and ascent over the island trigger moisture advection from the surrounding ocean. In contrast, over larger continents rainfall depends on continental evaporation. Simulations with two rectangular continents representing South America and Africa reveal that the stronger decrease in rainfall over the Amazon basin seen in Earth system models is due to a combination of local and remote effects, which are fundamentally connected to South America's size and its location with respect to Africa. The response of tropical rainfall to changes in evapotranspiration is thus connected to size and configuration of the continents. [ABSTRACT FROM AUTHOR]

Teleconnections are sources of predictability for regional weather and climate, but the relative contributions of different teleconnections to regional anomalies are usually not understood. While physical knowledge about the involved mechanisms is often available, how to quantify a particular causal pathway from data are usually unclear. Here, we argue for adopting a causal inference-based framework in the statistical analysis of teleconnections to overcome this challenge. A causal approach requires explicitly including expert knowledge in the statistical analysis, which allows one to draw quantitative conclusions. We illustrate some of the key concepts of this theory with concrete examples of well-known atmospheric teleconnections. We further discuss the particular challenges and advantages these imply for climate science and argue that a systematic causal approach to statistical inference should become standard practice in the study of teleconnections. [ABSTRACT FROM AUTHOR]

Administrative divisions are the important foundation of national governance and social development, and the adjustment of administrative divisions is a critical way to promote regional coordinated development. Under the background of globalization and regionalization, timely adjustment of administrative divisions is a major step to reconstruct the regional development pattern. In the perspective of regional coordinated development, this paper constructs an Administrative Rank Potential Energy (ARPE) model to explore the mechanisms of administrative division adjustment (ADA) under coordinated regional development based on the theory of regional interaction, spatial field energy model and power exponential function. The results show that: (1) The development potential and influence of an administrative region are closely related to the administrative jurisdiction, administrative resources, and the potential of regional coordinated development. (2) The growth rate of ARPE in the study area from 2010 to 2015 was 20.7% compared to the period 2005 to 2010, and the potential for coordinated development increased to 3.05% from 0.21% before the "cancelling" the prefecture-level city of Chaohu. (3) The measurement results of the usefulness of ADA can not only distinguish the complex impacts brought about by social development, but also accord with real social and economic development conditions. The ARPE focuses on the both regional integrated development and individual development, thereby serving as a reference for explaining and evaluating adjustments to administrative divisions at the macro- and micro-scales. [ABSTRACT FROM AUTHOR]

Soil moisture is an important variable of the climate system and is used to measure dry–wet change in hydroclimate. The warming trend has slowed in China over the past 20 years since 1998, and how the soil moisture changes in this period deserves our attention. With North China as a research region, this study uses the Global Land Data Assimilation System and ground observations to investigate the causes of changes in soil moisture during 1998–2017 versus 1961–97. The results show that 1) annual mean soil moisture experienced an almost continued decrease from the 1960s to 2010s, and no pause in the decrease of soil moisture over the regional warming slowdown of the past 20 years could be detected; 2) with the stabilization or even increase in solar radiation and wind speed as well as the continuous increase of land surface air temperature, the impact of potential evapotranspiration on soil moisture gradually became prominent, and the impact of precipitation decreased, since 1998; 3) the percent contribution of annual potential evapotranspiration to soil moisture variation increased by 26% during 1998–2017 relative to that in 1961–97, and the percent contribution of summer potential evapotranspiration even increased by 45%. Our results will provide insight into the land surface water budget and mechanism involved in drought development in North China. [ABSTRACT FROM AUTHOR]

In the atmospheric boundary layer, phenomena exist with challenging properties such as spatial heterogeneity, particularly during stable weak wind situations. Studying spatially heterogeneous features requires spatially distributed measurements on fine spatial and temporal scales. Fiber-optic distributed sensing (FODS) can provide spatially distributed measurements, simultaneously offering a spatial resolution on the order of decimeters and a temporal resolution on the order of seconds. While FODS has already been deployed to study various variables, FODS wind direction sensing has only been demonstrated in idealized wind tunnel experiments. We present the first distributed observations of FODS wind directions from field data. The wind direction sensing is accomplished by using pairs of actively heated fiber-optic cables with cone-shaped microstructures attached to them. Here we present three different methods of calculating wind directions from the FODS measurements, two based on using combined wind speed and direction information and one deriving wind direction independently from FODS wind speed. For each approach, the effective temporal and spatial resolution is quantified using spectral coherence. With each method of calculating wind directions, temporal resolutions on the order of tens of seconds can be achieved. The accuracy of FODS wind directions was evaluated against a sonic anemometer, showing deviations of less than 15° most of the time. The applicability of FODS for wind direction measurements in different environmental conditions is tested by analyzing the dependence of FODS wind direction accuracy and observable scales on environmental factors. Finally, we demonstrate the potential of this technique by presenting a period that displays spatial and temporal structures in the wind direction. Significance Statement: We developed a way to measure wind direction in a spatially and temporally distributed way by distributed temperature sensing, providing an unprecedented concurrent spatial and temporal resolution. Temporally and spatially distributed measurements are necessary in order to be able to better understand spatially heterogeneous processes in the atmosphere. By using optical fibers with attached cone-shaped microstructures, horizontal wind directions can be measured during different environmental conditions. The quality of the measured wind direction is quantified by comparison with sonic anemometer measurements, with deviations between the instruments less than 15° most of the time. Structures in the wind direction are resolvable down to a time scale of tens of seconds and a spatial scale of meters. [ABSTRACT FROM AUTHOR]

To explore the influence of meteorological variables on the growth of Korean pine (Pinus koraiensis Sieb. et Zucc.) plantations and provide a scientific reference for the production and management of Korean pine, three approaches to interpolate meteorological variables during the growing season (i.e., May–September) were compared in Heilongjiang Province, China. Optimized meteorological variable interpolation results were then combined with stand and individual tree variables, based on data from 56 sample plots and 2886 sample trees from Korean pine plantations in two regions of the province to develop an individual-tree diameter growth model (Model I) and an individual-tree diameter growth model with meteorological variables (Model II) using a stepwise regression method. Moreover, an individual-tree diameter growth model with regional effects (Model III) was developed using dummy variables in the regression, and the significance of introducing these dummy variables was verified with an F-test statistical analysis. The models were validated using an independent data set, and the predictive performance of the three models was assessed via the adjusted coefficient of determination ( R a 2 ) and root mean square error (RMSE). The results suggest that the growth increment in tree diameter of Korean pine plantations was significantly correlated with the natural logarithm of initial diameter (ln D), stand basal area (BAS), logarithmic deformation of the stand density index (ln SDI), ratio of basal area of trees larger than the subject tree to their initial diameter at breast height (DBH) (BAL/D), and the maximum growing-season precipitation (Pgmax). The individual-tree diameter growth models of Korean pine plantations developed in this study will provide a good basis for estimating and predicting growth increments of Korean pine forests over larger areas. [ABSTRACT FROM AUTHOR]

The aim of the study was to quantify total extractive contents and lipophilic compounds, stilbenes, and lignans in Scots pine stem wood, stem bark, branch biomass, and sawmill residues in four climatic regions of Finland to evaluate the most optimal sources of extractives for bio-based chemical biorefining and bioenergy products. Data were derived from 78 chip samples from the before-mentioned raw materials, the samples being pooled by tree height position from the sample trees of 42 experimental forest stands, and sawdust lots from 10 log stands. Accelerated solvent extraction (ASE) was employed to determine total extractive contents, followed by gas chromatography with flame ionization detection (GC–FID) to quantify extractive groups and gas chromatography-mass spectrometry (GC–MS) to analyse individual extractive compounds. Resin acids and triglycerides followed by fatty acids were the dominant extractive groups. Resin acids were most abundant in stem wood from final fellings and in sawdust, fatty acids in bark and branch biomass, and triglycerides also in stem wood from thinnings and the top parts of trees. Of the minor extractive groups, stilbenes were the most abundant in stem wood from final fellings and in sawdust, and steryl esters, sterols, and lignans in bark and branch biomass, the two last groups almost missing from other biomass components. Regional differences in the contents of extractive groups were generally small, 1.0−1.5 percentage points at the maximum, but factor analysis distinguished northern and southern regions into their own groups. Bark was the most potential source of fatty acids and sterols in southern Finland, and triglycerides and steryl esters in northern Finland. In stem wood, steryl esters, triglycerides, and lignans decreased and stilbenes increased from north to south. Certain fatty acids and resin acids were more frequent in the north. The results highlighted the importance of focused procurement and efficient sorting of raw materials, purity, unique properties, and feasible isolation techniques for competitive ability as well as large raw material volumes or well-defined value-added products.

This is the second part of a three-part paper on North American climate in phase 5 of the Coupled Model Intercomparison Project (CMIP5) that evaluates the twentieth-century simulations of intraseasonal to multidecadal variability and teleconnections with North American climate. Overall, the multimodel ensemble does reasonably well at reproducing observed variability in several aspects, but it does less well at capturing observed teleconnections, with implications for future projections examined in part three of this paper. In terms of intraseasonal variability, almost half of the models examined can reproduce observed variability in the eastern Pacific and most models capture the midsummer drought over Central America. The multimodel mean replicates the density of traveling tropical synoptic-scale disturbances but with large spread among the models. On the other hand, the coarse resolution of the models means that tropical cyclone frequencies are underpredicted in the Atlantic and eastern North Pacific. The frequency and mean amplitude of ENSO are generally well reproduced, although teleconnections with North American climate are widely varying among models and only a few models can reproduce the east and central Pacific types of ENSO and connections with U.S. winter temperatures. The models capture the spatial pattern of Pacific decadal oscillation (PDO) variability and its influence on continental temperature and West Coast precipitation but less well for the wintertime precipitation. The spatial representation of the Atlantic multidecadal oscillation (AMO) is reasonable, but the magnitude of SST anomalies and teleconnections are poorly reproduced. Multidecadal trends such as the warming hole over the central-southeastern United States and precipitation increases are not replicated by the models, suggesting that observed changes are linked to natural variability. © 2013 American Meteorological Society.

Monetary policy is an important means for a country to regulate macroeconomic operations and achieve established economic goals. Moreover, a reasonable monetary policy improves the efficiency of financial operations on a global scale and effectively resolves the financial crisis. At present, scholars from various countries have begun to pay attention to the issue of differentiated formulation of monetary policy among regions. This paper combines machine learning to construct a monetary policy differentiation effect analysis model based on the GVAR model. Moreover, this paper uses the gray correlation analysis method to obtain the gray correlation matrix between industries, and then introduces the industry's own characteristics, industry relevance and macroeconomic factors into the macro stress test of credit risk. In addition, this paper constructs a conduction model based on the industry GVAR model, and uses the first-order difference sequence of GDP growth rate, CPI growth rate and M2 growth rate of each economic region to construct a GVAR model to test the impulse response function. The results of the test show that the monetary policy shocks of various economic regions are significantly different. All in all, the research results show that the performance of the model constructed in this paper is good. [ABSTRACT FROM AUTHOR]

There are now a plethora of data, models, and approaches available to produce re- gional and local climate information intended to inform adaptation to a changing climate. There is, however, no framework to assess the quality of these data, models, and approaches that takes into account the issues that arise when this information is produced. An evaluation of the quality of regional climate information is a fundamental requirement for its appropriate application in societal decision-making. Here, an analytical framework is constructed for the quality assessment of science-based statements and estimates about future climate. This framework targets statements that project local and regional climate at decadal and longer time scales. After identifying the main issues with evaluating and presenting regional climate information, it is argued that it is helpful to consider the quality of statements about future climate in terms of 1) the type of evidence and 2) the relationship between the evidence and the statement. This distinction not only provides a more targeted framework for quality, but also shows how certain evidential standards can change as a function of the statement under consideration. The key dimensions to assess regional climate information quality are diversity, completeness, theory, adequacy for purpose, and transparency. This framework is exemplified using two research papers that provide regional climate information and the implications of the framework are explored. [ABSTRACT FROM AUTHOR]

This study presents the application of k-means clustering to satellite-based solar irradiation in different regions of Vietnam. The solar irradiation products derived from the Himawari-8 satellite, named AMATERASS by the solar radiation consortium under the Japan Science and Technology Agency (JST), are validated with observations recorded at five stations in the period from October 2017 to September 2018 before their use for clustering. High correlations among them enable the use of satellite-based daily global horizontal irradiation for spatial variability analysis and regionalization. With respect to the climate regime in Vietnam, the defined 6-cluster groups demonstrate better agreement with the conventionally classified seven climatic zones rather than the four climatic zones of the Köppen classification. The spatial distribution and seasonal variation in the regionalized solar irradiation reflect interchangeable influences of large-scale atmospheric circulation in terms of the East Asian winter monsoon and the South Asian summer monsoon as well as the effect of local topography. Higher daily averaged solar radiation and its weaker seasonal variation were found in two clusters in the southern region where the South Asian summer monsoon dominates in the rainy season. Pronounced seasonal variability in solar irradiation in four clusters in the northern region is associated with the influence of the East Asian monsoon, resulting in its clear reduction during the winter months. [ABSTRACT FROM AUTHOR]

Contexts in which people live such as neighbourhoods, cities or regions influence individuals. However, research analysing effects of different contexts simultaneously is limited. Our paper's focus is to examine the interrelation between regional and neighbourhood conditions and their influence on school‐to‐work transitions. We investigate the process of transitioning from vocational training to employment, with a specific focus on adolescents from non‐Western immigrant origin. We link Dutch survey data on almost 15,000 graduates with spatial information from Statistics Netherlands and compare results across multilevel linear probability models. Overall, we find employment prospects to be reduced in neighbourhoods and regions that are socioeconomically unfavourable. However, neighbourhood influences diminish or even disappear once accounted for the region. Deprived conditions in urban regions are particularly detrimental for adolescents of immigrant descent. However, a high share of coethnics in the neighbourhood reduces the unemployment risk of second‐generation non‐Western minorities in deprived urban regions. This finding stresses the relevance of immigrants' neighbourhood networks for education and employment prospects. [ABSTRACT FROM AUTHOR]

This article shows the current focus of impact studies of regional effects of research infrastructures in German. A literature review clarifies whether these studies concentrate on economic effects or assume a multi-dimensional perspective. Furthermore, it will determine where there is still a need for research in the evaluation of infrastructures and which effects are still of interest to the regional impact of a research institute. Based on this analysis, a first approach to the multidimensional evaluation of research infrastructures is presented. The evaluation investigates not only economic effects of research infrastructures, but also new knowledge effects (e.g. project innovation type), political effects (regionality of projects), and cultural effects (e.g. anchoring in the cultural memory). The general objective is to highlight the positive impact of a research infrastructure on the region (the associated federal state) and to expand the examined aspects of previous studies.

This paper investigates the role of the regional context with regard to the influence of human capital and knowledge spillovers on SMEs' financial soundness. Our empirical setting is based on a multilevel analysis of panel data, which allows superior treatment of hierarchical data. The analysis is applied to SMEs belonging to the manufacturing sector and operating in four European countries over the period 2010–2015. We find that a combination of individual and regional‐level characteristics explains firm soundness better than individual features alone. Furthermore, we find that a high local educational level and knowledge spillovers improve firm soundness and that their effects vary according to the regional level of knowledge. These results are confirmed by several robustness tests. [ABSTRACT FROM AUTHOR]

Eastern Colorado is one of the most active hail regions in the United States, and individual hailstorms routinely surpass millions of dollars in crop loss and physical damage. Fifteen semistructured interviews with eastern Colorado farmers and ranchers were conducted in the summer of 2019 to gauge perceptions of the severity and vulnerability associated with hailstorms, as well as to understand how forecasts and warnings for severe hail are received and acted upon by the agricultural community. Results reveal a correspondence between perceived and observed frequency of hailstorms in eastern Colorado and highlight financial losses from crop destruction as the greatest threat from hailstorms. In contrast to the National Weather Service defining severe hail as at least 1.0 in. (25.4 mm) in diameter, the agricultural community conceptualizes hail severity according to impacts and damage. Small hail in large volumes or driven by a strong wind are the most worrisome scenarios for farmers, because small hail can most easily strip crop heads and stalks. Larger hailstones are perceived to pose less of a threat to crops but can produce significant damage to physical equipment and injure livestock. Eastern Colorado farmers and ranchers are avid weather watchers and associate environmental cues with hailstorms in addition to receiving warning messages, primarily via alerts on mobile telephones. Hailstorms elicit feelings of dejection and anxiety in some respondents, whereas others accept hailstorms as part of the job. Increasing awareness of the agricultural perceptions of hailstorms can help the meteorological community direct hail prediction research efforts and improve risk communication to the agricultural sector. [ABSTRACT FROM AUTHOR]

The unique originality of the tourist exchange is that it is not the product but the tourists who consume it that are subject to relocation from their place of permanent residence to the tourist destination. The tourist as consumer moves to the tourist "product" to take advantage of its integrated components - natural (landscapes, climate), material (services and equipment) and human (societies and civilizations, historical and socio-cultural landmarks). With the development of this process, complex relationships emerge between tourists and local community with reflections on economic and social life. And they require management in an organizational and functional way. The awareness in this regard is in fact, very important for the competitiveness of the tourist offer and has a significant effect on regional sustainability. It is in this direction that the present study aims to explore one of the aspects of the impact that is being discovered in connection with the implementation of cross-border projects funded under the EU Cross-Border Cooperation Program - Interreg A with regional orientation to the Romania-Bulgaria border area. The focus is on identifying the manifestations of sustainability as a consequence of indirect investment in tourism and on outlining and taking concrete action as a result of the implemented projects. It is also defined by the presumption, that effects on regional economies and social characteristics should be high while respecting the sustainability of project results. [ABSTRACT FROM AUTHOR]

Regional-scale precipitation responses over Indonesia to major climate modes in the tropical Indo–Pacific Oceans, namely canonical El Niño, El Niño Modoki, and the Indian Ocean dipole (IOD), and how the responses are related to large-scale moisture convergences are investigated. The precipitation responses, analyzed using a high-spatial-resolution (0.5° × 0.5°) terrestrial precipitation dataset for the period 1960–2007, exhibit differences between the dry (July–September) and wet (November–April) seasons. Canonical El Niño strongly reduces precipitation in central to eastern Indonesia from the dry season to the early wet season and northern Indonesia in the wet season. El Niño Modoki also reduces precipitation in central to eastern Indonesia during the dry season, but conversely increases precipitation in western Indonesia in the wet season. Moisture flux analysis indicates that corresponding to the dry (wet) season precipitation reduction due to the canonical El Niño and El Niño Modoki anomalous divergence occurs around the southern (northern) edge of the convergence zone when one of the two edges is located near the equator (10°S–15°N) associated with their seasonal migration. This largely explains the seasonality and regionality of precipitation responses to canonical El Niño and El Niño Modoki. IOD reduces precipitation in southwestern Indonesia in the dry season, associated with anomalous moisture flux divergence. The seasonality of precipitation response to IOD is likely to be controlled by the seasonality of local sea surface temperature anomalies in the eastern pole of the IOD. [ABSTRACT FROM AUTHOR]

The Bay Area of California and surrounding region receives much of its annual precipitation during the October–March wet season, when atmospheric river events bring periods of heavy rain that challenge water managers and may exceed the capacity of storm sewer systems. The complex terrain of this region further complicates the situation, with terrain interactions that are not currently captured in most operational forecast models and inadequate precipitation measurements to capture the large variability throughout the area. To improve monitoring and prediction of these events at spatial and temporal resolutions of interest to area water managers, the Bay Area Advanced Quantitative Precipitation Information project was developed. To quantify improvements in forecast precipitation, model validation studies require a reference dataset to compare against. In this paper we examine 10 gridded, high-resolution (≤10 km, hourly) precipitation estimates to assess the uncertainty of high-resolution quantitative precipitation estimates (QPE) in areas of complex terrain. The products were linearly interpolated to 3-km grid spacing, which is the resolution of the operational forecast model to be validated. Substantial differences exist between the various products at accumulation periods ranging from hourly to annual, with standard deviations among the products exceeding 100% of the mean. While the products seem to agree fairly well on the timing of precipitation, intensity estimates differ, sometimes by an order of magnitude. The results highlight both the need for additional observations and the need to account for uncertainty in the reference dataset when validating forecasts in this area. [ABSTRACT FROM AUTHOR]