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Bangladesh has been ranked as one of the world's top countries affected by climate change, particularly in terms of agricultural crop sector. The purpose of this study is to identify spatial and temporal changes and trends in long-term climate at local and national scales, as well as their implications for rice yield. In this study, Modified Mann-Kendall and Sen's slope tests were used to detect significant trends and the magnitude of changes in temperature and rainfall. The temperature and rainfall data observed and recorded at 35 meteorological stations in Bangladesh over 65-years in the time span between the years 1949 and 2013 have been used to detect these changes and trends of variation. The results show that mean annual Tmean, Tmin, and Tmax have increased significantly by 0.13°C, 0.13°C, and 0.13°C/decade, respectively. The most significant increasing trend in seasonal temperatures for the respective Tmean, Tmin, and Tmax was 0.18°C per decade (post-monsoon), 0.18°C/decade (winter), and 0.23°C/decade (post-monsoon), respectively. Furthermore, the mean annual and pre-monsoon rainfall showed a significant increasing trend at a rate of 4.20 mm and 1.35 mm/year, respectively. This paper also evaluates climate variability impacts on three major rice crops, Aus, Aman, and Boro during 1970–2013. The results suggest that crop yield variability can be explained by climate variability during Aus, Aman, and Boro seasons by 33, 25, and 16%, respectively. Maximum temperature significantly affected the Aus and Aman crop yield, whereas rainfall significantly affected all rice crops' yield. This study sheds light on sustainable agriculture in the context of climate change, which all relevant authorities should investigate in order to examine climate-resilient, high-yield crop cultivation. [ABSTRACT FROM AUTHOR]

Separating existing historical precipitation data into solid and liquid precipitation remains a challenge in the study of climate change, extreme precipitation, and hydrological modelling. Based on historical daily air temperature and precipitation data, as well as visual observations of precipitation phase (weather phenomena records) in China mainland, this study proposed a snow‐day direct definition method (SDDM) to determine the threshold air temperature (TAT) of rainfall and snowfall, and analysed the spatial pattern and its influential factors. The main findings include: (1) the TAT based on the SDDM varied from −1.2 to 6.3°C, with a mean value of 2.8°C for the entire study region; (2) TAT was generally higher and more variable in the low‐latitude areas, and the Qinghai‐Tibet Plateau was characterized by an abnormally high average TAT of 5.2°C, almost twice as large as that of the eastern monsoon region; (3) TAT exhibited a significant positive correlation with altitude and negative correlation with precipitation and relative humidity. The results presented in this paper have potential application for studies of large‐scale snowfall climatology and climate change, weather forecasting techniques, and hydrological model parameterization in areas with complex and diverse geographical and climatic conditions. [ABSTRACT FROM AUTHOR]

The 2014-2018 drought over South Africa's winter rainfall zone (WRZ) created a critical water crisis which highlighted the region's drought and climate change vulnerability. Consequently, it is imperative to better understand the climatic characteristics of the drought in order to inform regional adaptation to projected climate change. In this paper we investigate the spatio-temporal patterns of drought intensity and the recent rainfall trends, focusing on assessing the consistency of the prevailing conceptual model of drought drivers with observed patterns. For this we use the new spatial subdivision for the region encompassing the WRZ introduced in our companion paper (Conradie et al., 2022). Compared to previous droughts since 1979, the 2014-2018 drought in the WRZ core was characterised by a markedly lower frequency of very wet days (exceeding the climatological 99.5th percentile daily rainfall -- including dry days) and of wet months (SPI1 > 0:5), a sub-seasonal attribute not previously reported. There was considerable variability in the spatial footprint of the drought. Short-term drought began in the south-western core WRZ in spring 2014. The peak intensity gradually spread north-eastward, although a spatially near-uniform peak is seen during mid-2017. The overall drought intensity for the 2015--2017 period transitions radially from most severe in the WRZ core to least severe in the surroundings. During 2014 and 2015, the drought was most severe at those stations receiving the largest proportion of their rainfall from westerly and north-westerly winds; by 2018, those stations receiving the most rain from the south and south-east were most severely impacted. This indicates an evolving set of dynamic drivers associated with distinct rain-bearing synoptic flows. No evidence is found to support the suggestion that the drought was more severe in the mountain catchments of Cape Town's major supply reservoirs than elsewhere in the core nor that rain day frequency trends since 1979 are more negative in this subdomain. Rainfall and rain day trend rates also exhibit some connections to the spatial seasonality structure of the WRZ, although this is weaker than for drought intensity. Caution should be applied in assessing South African rain day trends given their high sensitivity to observed data shortcomings. Our findings suggest an important role for zonally asymmetric dynamics in the region's drought evolution. This analysis demonstrates the utility of the spatial subdivisions proposed in the companion paper by highlighting spatial structure in drought intensity evolution linked to rainfall dynamics. [ABSTRACT FROM AUTHOR]

Background: Agriculture is important for economic growth and development in many countries in Sub-Saharan Africa, including Tanzania. However, agricultural production and productivity remain relatively low, with significant yield gaps attributed to factors such as limited access to and low adoption of appropriate agricultural technologies, and climate-related risks resulting from climate variability and change. This paper explores the drivers of adoption of climate-smart agricultural (CSA) technologies and practices, taking into account the complementarity among agricultural technologies and heterogeneity of the farm households, using data from Lushoto in Tanzania. Methods: We use a Multivariate Probit analysis of cross-sectional data collected from 264 smallholder farmers in Lushoto—a climate hotspot in Tanzania—to understand the drivers of household decisions to adopt CSA technologies and practices. The technologies included diversification of multiple stress (drought, floods, pests, diseases)-tolerant crop varieties, use of fertilizers, and application of herbicides and pesticides. The Multivariate Probit model was preferred as it takes into account the inter-relationships of the technologies as well as heterogeneity of the smallholder farmers for more robust estimates. The independent variables used in the analysis included household socio-economic factors such as the relative importance of crop and livestock enterprises, household land size, social capital, access to agricultural credit and weather information, previous experience with fertilizer use and household characteristics (age, education and gender of household head, and household size). Results: About 63% of the households diversified their crop enterprises, shifting to improved resilient crops and crop varieties. Another 37% adopted fertilizers, while 38% applied pesticides and herbicides. Conditional on the unobservable heterogeneity effects, the results show that household adoption decisions on diversification of multiple stress-tolerant crops and crop varieties, fertilizer, and pesticides and herbicides are complementary. In addition, the results confirm existence of unobserved heterogeneity effects leading to varying impact of the explanatory variables on adoption decisions among farmers with similar observable characteristics. Conclusions: The findings indicate that any effective CSA technology adoption and diffusion strategies and policies should take into account the complementarity of the technologies and heterogeneity of the smallholder farmers. Therefore, inter-related technologies should be promoted as a package or bundled while taking into consideration household and farm-level constraints to adoption. [ABSTRACT FROM AUTHOR]

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country's economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes. [ABSTRACT FROM AUTHOR]

This paper presents a synthesis of the main characteristics of precipitation in the State of Rio de Janeiro (Brazil) based on extreme rainfall indicators. Daily precipitation data are derived from 56 rainfall stations during the second half of the twentieth century and the 2000s. Eight indices related to extreme precipitation were analyzed. The Mann–Kendall nonparametric test and the Sen's Curvature were employed to evaluate the significance and magnitude of trends. The primary climatological aspects and identified trends throughout the last decades are discussed, besides the hydrometeorological impacts associated with them. Lower values of annual total precipitation are recorded in northern Rio de Janeiro (around 800 mm) and higher in the southern State (up to 2,200 mm). The Serra do Mar affects the frequency of heavy precipitation, and the areas near the sea and high relief present the highest values of consecutive days with expressive rainfall (more than 150 mm in 5 days). These areas also showed a high concentration of flood and landslides events. Most of Rio de Janeiro exhibits precipitation intensity of about 13 mm/day. The maximum number of consecutive dry days shows a gradient from the coast (about 30 days) to the State's interior (around 50 days). Regarding trends, there is a growth of accumulated extreme precipitation in various stations near the ocean. The extreme rainfall in 24 h displays an increase in most Rio de Janeiro (+ 1 to + 5 mm/decade). The consecutive dry and rainy days present similar signs of decreasing trends, suggesting irregularly distributed precipitation in the State. This study is especially relevant for decision-makers who need detailed information in the short and long term to prevent natural hazards like floods and landslides and the related impacts in the environmental and socioeconomic sectors of the Rio de Janeiro. [ABSTRACT FROM AUTHOR]

The present paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC (Intergovernmental Panels on Climate Change) in Brazil. We used temperature and precipitation data from 4942 cities collected on the NASA/POWER platform (National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources) from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041–2060 and 2061–2080 associated with four IPCC climate change scenarios. The spatialization of the data occurred according to the bioclimatic classes designed to meet the thermal and water needs of the crop. In the current scenario, class B5 has a predominance of 37.07% of the country, characterizing the Midwest, Southeast, and Northeast regions, as well as the state of Paraná, as suitable for the cultivation of pitaya. Projections of temperature increase and reduction of accumulated rainfall were found throughout Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction of areas suitable for the cultivation of pitaya with a greater predominance of classes B8 and B9. In the South and Southeast regions, class B5 makes up a large part of the areas that remained suitable for the production of this fruit until 2080. The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas suitable for economic production are restricted. [ABSTRACT FROM AUTHOR]

The purpose of the paper is to analyze the spatial variability of precipitation in Poland in the years 1981–2010. The average annual rainfall was 607 mm. Precipitation in Poland is characterized by high spatial and temporal variability. The lowest annual precipitation was recorded in the central part of the country, where they equaled 500 mm. The highest annual precipitation totals were determined in the south, equaling 970 mm. The average precipitation in the summer half-year is 382 mm (63% of the annual total). On the basis of data from 53 climate stations, maps were made of the spatial distribution of precipitation for the period of the year and winter and summer half-year. The kriging method was used to map rainfall distribution in Poland. In the case study, cross-validation was used to compare the prediction performances of three periods. Kriging, with exponential type of semivariogram, gave the best performance in the statistical sense. Their application is justices especially in areas where landform is very complex. In accordance with the assumptions, the mean prediction error (ME), mean standardized prediction error (MSE), and root mean-square standardized prediction error (RMSSE) values are approximately zero, and root-mean-square prediction error (RMSE) and average standard error (ASE) reach values well below 100. [ABSTRACT FROM AUTHOR]

I apply a novel machine-learning based "weather index" method to daily store-level sales data for a national apparel and sporting goods brand to examine short-run responses to weather and long-run adaptation to climate. I find that even when considering potentially offsetting shifts of sales between outdoor and indoor stores, to the firm's website, or over time, weather has significant persistent effects on sales. This suggests that weather may increase sales volatility as more severe weather shocks become more frequent under climate change. Consistent with adaptation to climate, I find that sensitivity of sales to weather decreases with historical experience for precipitation, snow, and cold weather events, but—surprisingly—not for extreme heat events. This suggests that adaptation may moderate some but not all the adverse impacts of climate change on sales. Retailers can respond by adjusting their staffing, inventory, promotion events, compensation, and financial reporting. This paper was accepted by Rajesh Chandy, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the National Science Foundation [Grant 0903551]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4799. [ABSTRACT FROM AUTHOR]

Unchecked climate change will cause precipitation volatility to increase around the world, leading to economic damages in the face of adjustment costs. We estimate these damages for construction—an economically important, climate exposed industry. Empirically, employment falls in response to forecasted rainfall and more so as the forecast horizon increases. This pattern allows for identification of labor adjustment costs via a multisector model of local labor markets calibrated to our estimates. When rainfall is anticipatable one month ahead, construction firms pay 10% of monthly profit to adjust. They pay less than 1% for rainfall anticipatable six months ahead. Without further adaptation or forecast improvements, increased rainfall volatility due to climate change is projected to lead to more costly adjustment. This paper was accepted by Elke Weber, Special Section of Management Science on Business and Climate Change. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4697. [ABSTRACT FROM AUTHOR]

I investigate the intraday effects of rain on the demand for indoor leisure. To do so, I use sub-daily museum attendance and weather data to reveal a dynamic response to precipitation that would be obscured using day-level data. I find that the magnitudes and signs of the effects of rainfall vary significantly throughout the day. In some hours, the predicted increase in visitors is nearly three times larger than would be expected from estimates using daily measures. Many individuals appear to actively adjust their plans throughout the day in response to rain, while others’ attendance depends upon prior weather forecasts of rain. Further analysis reveals that visit duration also increases during rainy periods, and visitors are more likely to attend pay-to-enter special exhibits. International visitors make up a greater share of total visitors during periods of observed precipitation. More broadly, this paper establishes the viability and value of working with widely available sub-daily rainfall data to uncover these dynamics. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental questions on climate change and variability: to what extent has climate changed and/or varied over years in two districts of different agro-ecological regions or zones and how do any changes differ between the zones or districts? Given the rural-rural migration pattern observed between the districts, understanding climate change risk to rural livelihoods cannot be overemphasised. To assess change and variability, we utilise rainfall data-records over a 36-year period from 1980 to 2016. Results show that there are wide variations and differences within and between the districts. Evidence suggests a general reduction in both annual rainfall and wet days. There is also ground to suggest that the rainy season duration is becoming shorter, given that rainfall onset is increasingly starting late, while cessation is increasingly coming early. Dry spells frequency and duration trends within rainy season show an increase over the period examined. We conclude that local climate in both areas has changed over the period investigated. However, while Livingstone seems to have experienced more droughts and unreliable rainfall, Kabwe experienced a bigger change in both rainfall and rainy season duration. We further conclude that migrants into Kabwe and other inhabitants are not any safer from climate change risk. [ABSTRACT FROM AUTHOR]

This paper analyzes climatological characteristics and temporal variation of the start and end dates of the rainy season over East Asia using a daily precipitation dataset for the time period 1951-2009. The rainy season is defined by a 5-day rainfall standard, and the regional average time series is constructed by applying the weighted-average method. Results show that the rainy season starts later, ends earlier and lasts shorter from southeast to northwest. In spring, the rainy belt slowly moves northward from 30 to 33°N in China and from 33 to 36°N on the Korean Peninsula and Japanese Islands. From 1951 to 2009, the rainy season generally began earlier in China but later in Korea and Japan, and it ended earlier at latitudes north of 35°N and later south of 35°N. The region-averaged start and end dates of the rainy season in the study region insignificantly advanced, and the duration of the rainy season insignificantly increased from 1951 to 2009. The rainy season duration slightly decreased in the Russian Far East and in northern and western China, and significantly decreased on the Korean Peninsula and the southern Hokkaido Islands, though it obviously increased in the Yangtze--Huaihe River Basins. [ABSTRACT FROM AUTHOR]

Climate change continues to be a global concern with impacts varying from region to region. Studies and observations establishing the evidence of climate change have often focused on global, regional, and national levels to the neglect of agro-ecological differences at local scale. Using trajectory and time series analysis of temperature anomalies and rainfall distributions for the agro-ecological zones of Ghana, this paper establishes a strong evidence of local climate change across the country in diverse agro-ecological zones. The evidence is based on the spatiotemporal analysis of the varying temperature and rainfall data observed from 1981 to 2009. The analysis shows local increases in temperatures for the period ranging from 0.5 to 1.0 °C with varying inter-annual rainfall distributions. The results explained the spatial differences in the agro-ecological zones in Ghana as a result of climate variability. The study provides supporting evidence for climate change using locally observed in-country data to compliment studies at global, regional, and national levels. [ABSTRACT FROM AUTHOR]

Extreme precipitation can have significant adverse impacts on infrastructure and property, human health, and local economies. This paper examines recent changes in extreme precipitation in the northeast United States. Daily station data from 58 stations missing less than 5% of days for the years 1979–2014 from the U.S. Historical Climatology Network were used to analyze extreme precipitation, defined as the top 1% of days with precipitation. A statistically significant (95% confidence level) increasing trend of the threshold for the top 1% of extreme precipitation events was found (0.3 mm yr−1). This increasing trend was due to both an increase in the frequency of extreme events and the magnitude of extreme events. Rainfall events ≥ 150 mm (24-h accumulation) increased in frequency from 6 events between 1979 and 1996 to 25 events between 1997 and 2014, a 317% increase. The annual daily maximum precipitation, or the highest recorded precipitation amount in a given year, increased by an average of 1.6 mm yr−1, a total increase of 58.0 mm. Decreasing trends in extreme precipitation were observed east of Lake Erie during the warm season. Increasing trends in extreme precipitation were most robust during the fall months of September, October, and November, and particularly at locations further inland. The analysis showed that increases in events that were tropical in nature, or associated with tropical moisture, led to the observed increase in extreme precipitation during the fall months. [ABSTRACT FROM AUTHOR]

This paper describes the assumptions, equations, and procedures of the RheaG weather generator algorithm (WGA). RheaG was conceived for the generation of robust daily meteorological time series, whether in static or transient climate conditions. Here we analyze its performance in four Iberian locations—Bilbao, Barcelona, Madrid, and Sevilla—with differentiated climate characteristics. To validate the RheaG WGA, we compared observed and generated meteorological time series' statistical properties of precipitation, maximum temperature, and minimum temperature for all four locations. We also compared observed and simulated rain events spell length probabilities in all four locations. Finally, RheaG includes two weather generation procedures: one in which monthly mean values for meteorological variables are unconstrained and one in which they are constrained according to a predefined baseline climate variability. Here, we compare the two weather generation procedures included in RheaG using the observed data from Barcelona. Our results present a high agreement in the statistical properties and the rain spell length probabilities between observed and generated meteorological time series. Our results show that RheaG accurately reproduces seasonal patterns of the observed meteorological time series for all four locations, and it is even able to differentiate two climatic seasons in Bilbao that are also present in the observed data. We find a trade-off between generation procedures in which the unconstrained procedure better reproduces the variability of monthly and yearly precipitation than the constrained one, but the constrained procedure is able to keep the same climatic signal across meteorological time series. Thus, the first procedure is more accurate, but the latter is able to maintain spatial autocorrelation among generated meteorological time series. [ABSTRACT FROM AUTHOR]

This paper examines farm household-level impacts of climate change by examining the relationship between climatic variables and Vietnamese agricultural output. The Ricardian technique is applied with panel data which accounts for both adaptation strategies and household characteristics. This study proposes a two-stage Hsiao model to correct for collinearity between climatic variables and individual effects. The results show that in the dry season, increases in temperatures are beneficial to all farms in the warmer southern regions, while increases in precipitation will damage only irrigated farms in the Central and South regions. The impact of higher temperature in the wet season is similar, except that it will negatively affect net revenue of irrigated farms in the long run. More rainfall in the wet season will increase net revenue in the North region only. Finally, this study combines the estimated results with future climate scenarios to predict how future changes in climate will affect farmers on aggregate. [ABSTRACT FROM AUTHOR]

Abstract: Information on the response of vegetation to different environmental drivers, including rainfall, forms a critical input to ecosystem models. Currently, such models are run based on parameters that, in some cases, are either assumed or lack supporting evidence (e.g., that vegetation growth across Africa is rainfall‐driven). A limited number of studies have reported that the onset of rain across Africa does not fully explain the onset of vegetation growth, for example, drawing on the observation of prerain flush effects in some parts of Africa. The spatial extent of this prerain green‐up effect, however, remains unknown, leaving a large gap in our understanding that may bias ecosystem modelling. This paper provides the most comprehensive spatial assessment to‐date of the magnitude and frequency of the different patterns of phenology response to rainfall across Africa and for different vegetation types. To define the relations between phenology and rainfall, we investigated the spatial variation in the difference, in number of days, between the start of rainy season (SRS) and start of vegetation growing season (SOS); and between the end of rainy season (ERS) and end of vegetation growing season (EOS). We reveal a much more extensive spread of prerain green‐up over Africa than previously reported, with prerain green‐up being the norm rather than the exception. We also show the relative sparsity of postrain green‐up, confined largely to the Sudano‐Sahel region. While the prerain green‐up phenomenon is well documented, its large spatial extent was not anticipated. Our results, thus, contrast with the widely held view that rainfall drives the onset and end of the vegetation growing season across Africa. Our findings point to a much more nuanced role of rainfall in Africa's vegetation growth cycle than previously thought, specifically as one of a set of several drivers, with important implications for ecosystem modelling. [ABSTRACT FROM AUTHOR]

Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS. [ABSTRACT FROM AUTHOR]

El Niño Modoki events can be further classified into El Niño Modoki I and II in terms of their opposite impacts on southern China rainfall (Wang and Wang, J Clim 26:1322-1338, 2013) and the Indian Ocean dipole mode (Wang and Wang, Clim Dyn 42:991-1005, 2014). The present paper develops an index to identify the types of El Niño events. The El Niño Modoki II (MII) index is defined as the leading principle component of multivariate empirical orthogonal function analysis of the normalized El Niño Modoki index, Niño4 index and 850 hPa relative vorticity anomalies averaged near the Philippine Sea during autumn. The MII index exhibits dominant variations on interannual (2-3 and 4-5 years) and decadal (10-20 years) timescales. El Niño Modoki II events can be well identified by using the MII index value being larger than 1 standard deviation. Further analyses and numerical model experiments confirm that the MII index can portray the major oceanic and atmospheric features of El Niño Modoki II events. The constructed MII index along with previous ENSO indices can be used for classifying and identifying all types of El Niño events. Because of distinct impacts induced by different types of El Niño events, the implication of the present study is that climate prediction and future climate projection under global warming can be improved by using the MII index and other indices to identify the types of El Niño events. [ABSTRACT FROM AUTHOR]

In the last several decades, extended dry periods have affected the Mediterranean area with dramatic impacts on water resources. Climate models are predicting further warming, with negative effects on water availability. The authors analyze the hydroclimatic tendencies of a typical Mediterranean basin, the Flumendosa basin located in Sardinia, an island in the center of the Mediterranean Sea, where in the last 30 years a sequence of dry periods has seriously impacted the water management system. Interestingly, in the historic record the annual runoff reductions have been more pronounced than the annual precipitation reductions. This paper performs an analysis that links this runoff decrease to changes in the total annual precipitation and its seasonal structure. The seasonality is a key determinant of the surface runoff process, as it reflects the degree to which rainfall is concentrated during the winter. The observed reductions in winter precipitation are shown here to be well correlated (Pearson correlation coefficient of 20.5) with the North Atlantic Oscillation (NAO) index. Considering the predictability of the winter NAO, there is by extension an opportunity to predict future winter precipitation and runoff tendencies. The recent hydroclimatic trends are shown to impact hydrologic design criteria for water resources planning. The authors demonstrate that there is a dangerous increase of the drought severity viewed from the perspective of water resources planning. [ABSTRACT FROM AUTHOR]

Climate change harms the households of coastal areas in Bangladesh because of the geographical position and changes in the climatic elements that are primarily at play. This study looks at the pattern of two prominent climatic elements: temperature and precipitation over thirty years, from 1992 to 2021, focusing on how those factors have changed. The findings revealed an upward increase in all elements, i.e., the maximum temperature, the lowest temperature, and the amount of precipitation. Using the Livelihood Vulnerability Index, this research looked at how vulnerable households are to the effects of climate change in the southwestern coastal area, namely in Amadi Union and Koyra Upazila in the Khulna district of Bangladesh. The original data came from a survey of 276 households and secondary data on rainfall and temperature for the period beginning in 1992 and continuing through 2021. The research conducted a comparative analysis to determine the household's vulnerability to climate change. The empirical research found that families in the Amadi union were moderately exposed to the effects of climate change and the weather variability regarding their strategies for making a living, their social networks, the food they have, and their level of health. By considering the socio-demographic elements, social networks, livelihoods, natural disasters, and climate variability, households of the Amadi union were equally vulnerable regarding their adaptation potential and exposure. These findings have implications for the governments, donor agencies, and other relevant agencies in Amadi Union, Koyra Upazila, and Khulna, initiating and implementing initiatives to adapt to climate change and increase household resilience. [ABSTRACT FROM AUTHOR]

The magnitude and frequency of hydrological events are expected to increase in coming years due to climate change in megacities of Asia. Intensity-Duration-Frequency (IDF) curves represent essential means to study effects on the performance of drainage systems. Therefore, the need for updating IDF curves comes from the necessity to gain better understanding of climate change effects. The present paper explores an approach based on spatial downscaling-temporal disaggregation method (DDM) to develop future IDFs using stochastic weather generator, Long Ashton Research Station Weather Generator (LARS-WG) and the rainfall disaggregation tool, Hyetos. The work was carried out for the case of Bangkok, Thailand. The application of LARS-WG to project extreme rainfalls showed promising results and nine global climate models (GCMs) were used to estimate changes in IDF characteristics for future time periods of 2011-2030 and 2046-2065 under climate change scenarios. The IDFs derived from this approach were corrected using higher order equation to mitigate biases. IDFs from all GCMs showed increasing intensities in the future for all return periods. The work presented demonstrates the potential of this approach in projecting future climate scenarios for urban catchment where long term hourly rainfall data are not readily available. [ABSTRACT FROM AUTHOR]

In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice. [ABSTRACT FROM AUTHOR]

This paper presents results of a study of long term trends in the characteristics of the within-season temporal profile of southwest monsoon rainfall over western India during the last five decades in relation to global warming induced regional climate change. In contrast to recent climate change analyses and projections, no significant long-term trends have been observed in this study. Slow decadal scale variations observed are analysed in relation to Pacific Decadal Oscillations (PDO). Daily variations in rainfall anomaly show opposite characteristics during negative and positive phases of PDO. The above-normal rainfall (>25%) is found during the starting phase of monsoon in negative PDO. Over the last decade, i.e., during 2000-2007, the seasonal rainfall amount, as well as seasonal span of southwest monsoon over western India is indicative of a gradual increase. [ABSTRACT FROM AUTHOR]

Few if any high-resolution (annually resolved) paleoclimate records are available for the Hawaiian Islands prior to ~1850 CE, after which some instrumental records start to become available. This paper shows how atmospheric teleconnection patterns between North America and the northeastern North Pacific (NNP) allow for reconstruction of Hawaiian Islands rainfall using remote proxy information from North America. Based on a newly available precipitation dataset for the state of Hawaii and observed and reconstructed December-February (DJF) sea level pressures (SLPs) in the North Pacific Ocean, the authors make use of a strong relationship between winter SLP variability in the northeast Pacific and corresponding DJF Hawaii rainfall variations to reconstruct and evaluate that season's rainfall over the period 1500-2012 CE. A general drying trend, though with substantial decadal and longer-term variability, is evident, particularly during the last ~160 years. Hawaiian Islands rainfall exhibits strong modulation by El Niño-Southern Oscillation (ENSO), as well as in relation to Pacific decadal oscillation (PDO)-like variability. For significant periods of time, the reconstructed large-scale changes in the North Pacific SLP field described here and by construction the long-term decline in Hawaiian winter rainfall are broadly consistent with long-term changes in tropical Pacific sea surface temperature (SST) based on ENSO reconstructions documented in several other studies, particularly over the last two centuries. Also noted are some rather large multidecadal fluctuations in rainfall (and hence in NNP SLP) in the eighteenth century of undetermined provenance. [ABSTRACT FROM AUTHOR]

The present study investigates the temporal variability in monthly, seasonal and annual rainfall over a drought prone district in India. Long period rainfall data, from 1950 to 2012, were investigated over the district by analysis of anomalies and application of both parametric and non-parametric statistical tests. Decline in annual rainfall is evident in the study area with highest number of below mean rainfall years observed in the recent decade (2000–2009). Only 25% of the annual rainy days span across pre-monsoon, post-monsoon and winter season, reinforcing the high dependence of local communities on monsoonal rainfall for effective crop growth. Against this, a decreasing trend in monsoonal rainfall is noticed over the years. In conclusion, the present paper argues for district level rainfall studies which will provide local stakeholders with the necessary information for better planning and management of rainfall dependent development sectors, thus resulting in reduced overall socio-economic upheaval of rural communities. [ABSTRACT FROM PUBLISHER]

The eastern United States is vulnerable to flooding from tropical cyclone rainfall. Understanding how both the frequency and intensity of this rainfall will change in the future climate is a major challenge. One promising approach is the dynamical downscaling of relatively coarse general circulation model results using higher-resolution regional climate models (RCMs). In this paper, we examine the frequency of landfalling tropical cyclones and associated rainfall properties over the eastern United States using Zetac, an 18-km resolution RCM designed for modeling Atlantic tropical cyclone activity. Simulations of 1980-2006 tropical cyclone frequency and rainfall intensity for the months of August-October are compared against results from previous studies and observation-based datasets. The 1980-2006 control simulations are then compared against results from three future climate scenarios: CMIP3/A1B (late twenty-first century) and CMIP5/RCP4.5 (early and late twenty-first century). In CMIP5 early and late twenty-first century projections, the frequency of occurrence of post-landfall tropical cyclones shows little net change over much of the eastern U.S. despite a decrease in frequency over the ocean. This reflects a greater landfalling fraction in CMIP5 projections, which is not seen in CMIP3-based projections. Average tropical cyclone rain rates over land within 500 km of the storm center increase by 8-17 % in the future climate projections relative to control. This is at least as much as expected from the Clausius-Clapeyron relation, which links a warmer atmosphere to greater atmospheric water vapor content. Over land, the percent enhancement of area-averaged rain rates from a given tropical cyclone in the warmer climate is greater for larger averaging radius (300-500 km) than near the storm, particularly for the CMIP3 projections. Although this study does not focus on attribution, the findings are broadly consistent with historical tropical cyclone rainfall changes documented in a recent observational study. The results may have important implications for future flood risks from tropical cyclones. [ABSTRACT FROM AUTHOR]

Changes in rainfall patterns due to climate change are expected to have negative impact on urban drainage systems, causing increase in flow volumes entering the system. In this paper, two emission scenarios for greenhouse concentration have been used, the high (A1FI) and the low (B1). Each scenario was selected for purpose of assessing the impacts on the drainage system. An artificial neural network downscaling technique was used to obtain local-scale future rainfall from three coarse-scale GCMs. An impact assessment was then carried out using the projected local rainfall and a risk assessment methodology to understand and quantify the potential hazard from surface flooding. The case study is a selected urban drainage catchment in northwestern England. The results show that there will be potential increase in the spilling volume from manholes and surcharge in sewers, which would cause a significant number of properties to be affected by flooding. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper revisits the strength and stationarity of linear statistical relationships between indices of monsoon rainfall derived from rain gauge data for three regions in tropical West Africa [West Sahel (WS), Central Sahel (CS), and Guinea Coast (GC)] and various indices describing remote variations in the ocean-atmosphere climate systems from 1921 to 2009. The results reveal that both the Atlantic Multi-decadal Oscillation ( AMO) and the Atlantic Meridional Mode ( AMM) show a positive linear correlation to Sahel rainfall. The percent variance explained ( PVE) ranges from 10 to 25%. The correlation stems from periods longer than 8 years for AMO, but AMM does show a correlation on interannual time scales for WS that was absent in the 1970s and 1980s. The PVE by El Niño Southern Oscillation ( ENSO) indices is, though statistically significant, on the order of 10% and found mainly on the interannual time scale. A strong and stable correlation with PVEs larger than 50% is found between the sea surface temperatures (SSTs) in the Atlantic 3 region ( ATL3, 0°-20°W, 3°S-3°N) and the GC rainfall. On the other hand, the correlation between ATL3 and WS rainfall changed from significantly negative to significantly positive after the 1970s. The Eastern Mediterranean SSTs are found to be significantly related to CS rainfall, especially in recent years with PVEs between 36 and 47%. Multi-linear regression analyses reveal that the relative importance of the Indian Ocean is 42% in the optimal regression model. For the CS, this value is 37% for the Eastern Mediterranean SSTs and 70% in case of the GC using the ATL3 index. However, except for the GC, non-stationarities in the correlation between the climate state indexes and West African monsoon ( WAM) rainfall suggest the need of the application of different regression models depending on the active 'teleconnection regime'. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ambiente e Água is the property of Revista Ambiente e Agua and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

High‐resolution Taiwan Climate Change Projection Information and Adaptation Knowledge Platform (TCCIP) gridded precipitation data are used to characterize days in the Mei‐yu season with the most extreme precipitation (EP). These "EP days" are grouped into weather types based on the presence of features such as tropical cyclones (TCs) and atmospheric rivers (ARs), then analyzed from the perspective of weather type frequency and synoptic changes. During the 1979–2019 period, EP days associated with ARs were associated with significant increasing trends in season‐total precipitation. These AR‐related precipitation increases are due to four events in 2005, 2006, 2012, and 2017 which had long duration and unusually intense precipitation, and which were anomalous even within the longer 1960–2019 time period. Meanwhile, TC‐related EP days contribute less precipitation than they did in the 1980s due to decreased frequency of TCs on EP days and in the Mei‐yu season climatology. Over the 1979–2019 period, the AR‐related and TC‐related trends combine to produce EP increases in western Taiwan and decreases in eastern Taiwan. Mei‐yu season southwesterly integrated vapor transport (IVT), wind speed, and specific humidity have all increased in association with these extreme events. Low‐level winds appear to the primary factor influencing the IVT increase, with increased moisture also contributing. The wind trends are consistent with climatological pressure increases south of Taiwan and decreases over the East Asian landmass, which facilitate a strengthened circulation in a corridor extending from the southern China coastline over Taiwan during this season. [ABSTRACT FROM AUTHOR]

This paper analyses the daily precipitation data recorded for the last 75-80 years in SE Spain. Prior to statistical analysis, homogeneity tests were performed to determine the usefulness of the data (recorded in six pluviometric stations). If the data were homogeneous, the Mann–Kendall test was performed to detect monotonic trends for variables such as total precipitation, number of days with precipitation greater than 1, 20 or 40 mm, number of consecutive dry days and number of consecutive wet days. Moreover, the linear tendency was calculated and accepted (or rejected) by performing a Student's t-test of the calculated residual values. The results showed, on average, a clear change in precipitation patterns for the last 20 years. Annual rainfall had decreased, with values ranging from 0 to 15%, dry periods had increased in length, and the number of rainy days had decreased (especially when high daily precipitation was considered). This latter notion did not exactly concur with the conclusions of the Intergovernmental Panel on Climate Change (IPCC) 2014 Synthesis Report related to the study area. [ABSTRACT FROM AUTHOR]

The Las Vegas metropolitan area in Nevada has experienced extensive urban growth since 1950 coincident with regional and local climate change. This study explores the nonstationary flood history of the Las Vegas Wash (LVW) watershed by deconstructing it into its constituent physical drivers. Observations and reanalysis products are used to examine the hydroclimatology, hydrometeorology, and hydrology of flash flooding in the watershed. Annual peak flows have increased nonlinearly over the past seven decades, with an abrupt changepoint detected in the mid-1990s, which is attributed to the implementation of flood conveyance systems rather than changes in land use. The LVW watershed exhibits two pronounced flood seasons, associated with distinct synoptic atmospheric circulations: winter floods linked to inland-penetrating atmospheric rivers and summer floods linked to the North American monsoon. El Niño–Southern Oscillation also plays a role in modulating extreme rainfall and the resultant floods because annual maximum daily rainfall totals positively correlate with El Niño, with Spearman's correlation coefficient of 0.36 (p value < 0.05). Winter maximum daily rainfall totals have increased since 1950, whereas summer daily rainfall maxima have decreased. The trends in hydrometeorological drivers interact with urbanization to shift flood seasonality toward more frequent winter floods in the LVW watershed. A process-based understanding of the flood hydrology of the watershed also provides insights into flood frequency analysis and flood forecasting. [ABSTRACT FROM AUTHOR]

In this Pseudo‐global Warming study, potential future changes in the Southeast Asia tropical cyclone (TC) exposure climatology are quantified. One hundred and seventeen landfalling TCs in the last 20 years are simulated with their current climate conditions and also with the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble perturbed conditions under the SSP2‐4.5 and SSP3‐7.0 climate change scenarios. Our simulations suggest that landfalling TCs are projected to be 8% more intense at landfall, 2.8% faster and have smaller sizes by the end of the 21st century under the SSP3‐7.0 scenario. In addition, TC landfall locations shift northward with tracks extending further inland toward Laos and Thailand. In particular, TC exposures, wind and rainfall impacts significantly increase in the northern Philippines, Taiwan, southwestern coast of China, and northern Vietnam; and significantly decrease in the southern areas of Southeast Asia and the southeastern coast of China. Key Points: Tropical cyclones are projected to be more intense, smaller in size, and to move fasterLandfalling locations shift poleward and further west by the end of the 21st centuryWind and rain impacts increase in the northern Southeast Asia, extend further inland [ABSTRACT FROM AUTHOR]

This study evaluated the rainfall historical simulations of 15 Global Climate Models (GCMs) of the Coupled Model Intercomparison Project phase 6 (CMIP6) in replicating annual and seasonal rainfall climatology, their temporal variability and trends in Bangladesh for the period 1979–2014, considering ERA5 (ECMWF Reanalysis 5th Generation) reanalysis as the reference dataset. Shannon's Entropy decision‐analysis was employed for GCMs' rating based on eight statistical indicators and a comprehensive rating metric for the final grading of the GCMs. The majority of the CMIP6 GCMs accurately reproduced the spatial feature of ERA5 rainfall. However, the GCMs underestimated annual rainfall by an average of 190.5 mm, with the highest underestimation in monsoon (131.76 mm) and least in winter (3.52 mm) seasons. Most GCMs also underestimated rainfall variability for all seasons except winter. Besides, the GCMs showed an increasing trend in pre‐monsoon and a decreasing trend in post‐monsoon rainfall like ERA5, but an opposite (negative) to ERA5 trend (positive) in monsoon season rainfall. The ensemble mean of the GCMs showed higher skill in reconstructing rainfall climatology, temporal variability and trends than the individual GCMs. The study identified MPI‐ESM1‐2‐LR, MPI‐ESM1‐2‐HR, and GFDL‐ESM4 as the most effective GCMs in reproducing precipitation over Bangladesh. The selected models' simulation can be used for climate change impact assessment in Bangladesh after bias minimization. [ABSTRACT FROM AUTHOR]

This study, for the first time, investigates the physical association between Predecessor Rain Events (PREs) and peak runoff generation in seven catchments over the Upper Mahanadi River basin (UMRB), India. A statistical–dynamical framework is developed to assess the compounding impact of PREs (as preconditioned events) versus riverine floods during both retrospective and projected climate. Based on models' fidelity to capture historical climatology, we select four out of nine Global Climate Models (GCMs) during historical (1980–2005), and the three projected time windows, that is, near future (2010–2039), mid‐century (2040–2069), and the far future (2070–2099) planning horizons with RCP8.5 emission scenario. We assess changes in compound flood hazards in historical versus projected periods using a newly proposed Bivariate Hazard Ratio (BHR) index, which represents the ratio between bivariate return periods during the projected versus the historical time windows. Assessing bivariate return periods (characterized by 'AND' operator) of rainfall‐driven compound floods shows decreased flood hazard in the mid‐century and far‐future planning horizons. Accounting ranges of uncertainty from climate model simulations and the propagation of uncertainty across the numerical model chain, overall we show considering PRE as the covariate, floods in larger catchments show an increase in compound flood hazard in the projected period. [ABSTRACT FROM AUTHOR]

A renewed focus on southern Africa's winter rainfall zone (WRZ) following the Day Zero drought and water crisis has not shed much light on the spatial patterns of its rainfall variability and climatological seasonality. However, such understanding remains essential in studying past and potential future climate changes. Using a dense station network covering the region encompassing the WRZ, we study spatial heterogeneity in rainfall seasonality and temporal variability. These spatial patterns are compared to those of rainfall occurring under each ERA5 synoptic-scale wind direction sector. A well-defined "true" WRZ is identified with strong spatial coherence between temporal variability and seasonality not previously reported. The true WRZ is composed of a core and periphery beyond which lies a transition zone to the surrounding year-round rainfall zone (YRZ) and late summer rainfall zone. In places, this transition is highly complex, including where the YRZ extends much further westward along the southern mountains than has previously been reported. The core receives around 80% of its rainfall with westerly or north-westerly flow compared to only 30% in the south-western YRZ incursion, where below-average rainfall occurs on days with (usually pre-frontal) north-westerly winds. This spatial pattern corresponds closely to those of rainfall seasonality and temporal variability. Rainfall time series of the core and surroundings are very weakly correlated (R² < 0:1), also in the winter half-year, implying that the YRZ is not simply the superposition of summer and winter rainfall zones. In addition to rain-bearing winds, latitude and annual rain day climatology appear to influence the spatial structure of rainfall variability but have little effect on seasonality. Mean annual rainfall in the true WRZ exhibits little association with the identified patterns of seasonality and rainfall variability despite the driest core WRZ stations being an order of magnitude drier than the wettest stations. This is consistent with the general pattern of near homogeneity within the true WRZ, in contrast to steep and complex spatial change outside it. [ABSTRACT FROM AUTHOR]

The complex topography of East Africa poses challenges for accurate modeling of regional climate. The Turkana Channel in northwestern Kenya is an important feature because of a persistent low‐level jet (LLJ) that blows through it, which has complex interactions with local and regional rainfall. We establish the annual cycle and interannual variability of the LLJ in the ERA5, MERRA‐2, and JRA‐55 reanalyses. The jet is strongest during wet seasons in the surrounding region. Results suggest a statistically significant weakening of the LLJ over the last 30–40 years in two out of the three reanalyses. We propose an explanation based on the jet's relationship with regional warming patterns and zonal surface pressure gradients, which link the jet to larger‐scale climate dynamics including the Walker Circulation. If these changes continue in the future, there may be significant implications for rainfall including increases in northwest Kenya and decreases further inland. However, the global models used to produce climate projections vary in their simulations of the LLJ in part because they represented topography. Consequently, it is not possible to assess how future LLJ changes will affect regional climate using CMIP5 models alone. Differences between the reanalyses preclude their direct use for model evaluation. Improving the processes by which topographical observations are mapped onto model grids could lead to improvements in the simulation of the East African climate. A field campaign to measure the LLJ directly could resolve uncertainties in the literature, help constrain reanalyses, and determine which models have the most realistic LLJ representation. Plain Language Summary: The Turkana Low‐Level Jet is an important physical feature of the East African climate. It is present throughout the year and is both a factor in the aridity of the channel through which it blows and a source of moisture for rainfall in the wider region. We investigate how the jet varies throughout the year and across longer timescales, and suggest that it may have been getting weaker over the last 30–40 years. We suggest that this is due to connections between the jet and large‐scale climate dynamics, such as the Walker Circulation. However, because the jet is represented differently in different datasets, and climate models struggle to simulate it, we can't be sure of the impact these changes might have in the future. One potential way forward is to measure the jet directly in a field campaign. Key Points: The Turkana Low‐Level Jet may be slowing down on decadal timescales. However, reanalysis simulations of the trend and annual cycles differThe jet slowdown is a plausible consequence of changes to zonal surface pressure gradients, linking the jet to the Walker CirculationCoupled Model Intercomparison Project 5 representations of the jet are resolution‐dependent [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the ability of a set of large ensembles (LE) of Global Climate Model (GCM) simulations from the Multi‐Model Large Ensemble Archive, Coupled Model Intercomparison Project Phase 6 (CMIP6) and database for Policy Decision making for Future climate change in reproducing the variability and change of the austral summer precipitation observed in South America along the second part of the 20th century and beginning of the 21st. LE show similar biases in the mean austral summer rainfall and interannual variability than those detected in previous model sets, such as the Coupled Model Intercomparison Project Phase 5 (CMIP5). The positive trends in south‐eastern South America (SESA) and negative ones in the southern Andes (SAn), that are the most significant observed regional features, are identified to some extent in LE simulations. The negative trend in SAn is a feature consistently shown among different models. For all models, mean trend is negative in that particular region and larger than the inter‐member dispersion for each large ensemble. Trend magnitudes in the SESA region show a larger dispersion between models than the SAn region. While the mean trend among models is consistent with the observations, models underestimate the observed trend. The multi‐model ensemble of selected models that best reproduce both mean and interannual variability features of rainfall in South America shows an 18% larger positive mean trend in SESA rainfall than those that have the worst performance. However, internal variability uncertainty is still higher than the mean trend for both sets of multi‐model ensembles [5% (18%) for selected (non‐selected) models]. Improvements in the representation of the main features of mean rainfall and variability are needed in order to better describe and reduce the uncertainties associated with the main climate change related signals in the continent. [ABSTRACT FROM AUTHOR]

Climate impact studies often require climate data at a higher space–time resolution than is available from global and regional climate models. Weather generator (WG) models, generally designed for mesoscale applications (e.g., 101–105 km2), are popular and widely used tools to downscale climate data to finer resolution. One advantage of using WGs is their ability to generate the necessary climate variables for impact studies in data sparse regions. In this study, we evaluate the ability of a previously established state of the art WG (the AWE‐GEN‐2d model) to perform in data sparse regions that are beyond the mesoscale, using the Zambezi River basin (106 km2) in southeast Africa as a case study. The AWE‐GEN‐2d model was calibrated using data from satellite retrievals and climate re‐analysis products in place of the absent observational data. An 8‐km climate ensemble at hourly resolution, covering the period of 1976–2099 (present climate and RCP4.5 emission scenario from 2020), was then simulated. Using the simulated 30‐member ensemble, climate indices for both present and future climates were computed. The high‐resolution climate indices allow detailed analysis of the effects of climate change on different areas within the basin. For example, the southwestern area of the basin is predicted to experience the greatest change due to increased temperature, while the southeastern area was found to be already so hot that is less affected (e.g., the number of 'very hot days' per year increase by 18 and 9 days, respectively). Rainfall intensities are found to increase most in the eastern areas of the basin (1 mm·d−1) in comparison to the western region (0.3 mm·d−1). As demonstrated in this study, AWE‐GEN‐2d can be calibrated successfully using data from climate reanalysis products in the absence of ground station data and can be applied at larger scales than the mesoscale. [ABSTRACT FROM AUTHOR]

Climate change will likely impact rainfall characteristics in particular locations; the amount, seasonality, variability and spatial patterns. In developing countries, this presents challenges for rural smallholder farmers as their livelihoods are largely based on rain-fed practices. Changes in climate patterns could increase farmers' vulnerability and the need for intervention. In this paper, we develop new metrics of analysis motivated by qualitative research with smallholder farmers. Previous research found that farmers' understanding of historical rainfall change is accurate, yet diverge from some research studies. We analyze meteorological station rainfall data using metrics that are familiar to smallholders. Farmers' perceptions of rainfall in southern Ethiopia were explored through interviews conducted in three communities. Our findings identified some forms of convergence, as well as divergence, in farmers' perception of rainfall trends and meteorological station data results. In asking the question 'Why do data based on farmer experiences of rainfall variability differ from meteorological station data?', we show that using existing data and applying farmer-influenced metrics can improve the information shared with farmers. We argue that, under further climate change, it will be increasingly important to convey meteorological information to farmers in ways that are relevant to them and their agricultural livelihoods. • Smallholder farmer experiences and climate science often diverge. • We develop new metrics motivated by research with smallholders. • New metrics and analyses can bridge some divergences. • Methods developed are relevant for communicating climate science with farmers. [ABSTRACT FROM AUTHOR]

This study assessed the climate change in Pauri district, Uttarakhand, India, a region highly vulnerable to climate change with potentially high loss of livelihoods and lives. The scale of change in the district's climate was analyzed using meteorological station data (1901–2000) and grid data (1985–2015). Perceptions of climate change among forest-dependent communities in three altitude zones (< 1200 m asl (zone A); 1200–1800 m asl (zone B), and > 1800 m asl (zone C)) in the study region were surveyed with respect to 14 climate-specific indicators. Annual mean, maximum, and minimum temperature of seasonal data indicated increasing trends except monsoon. Percentage cloud cover showed an increase, of approximately 3%, while diurnal temperature displayed decreasing trends. Rainfall in the district showed a decreasing trend, with more than 50% of years 1985–2015 receiving less rainfall than the annual average. More than 90% of respondents in zones A and B, and around 65–70% respondents in zone C, reported changes in climate parameters. These findings confirm the long-term observable changes in climate in the region and demonstrate the utility of station data, grid data, and surveys of local communities' perceptions when analyzing climate change. The analysis provided important clues about the nature of climate changes in the district. The results can be used to reduce the gap between bottom-up understanding and top-down policies and to formulate precautionary and ongoing site-specific adaptation practices for communities in different altitude zones in the study region, leading to effective and efficient mitigation of climate change impacts. [ABSTRACT FROM AUTHOR]

Aim: Climate variability threatens to destabilize production in many ecosystems. Asynchronous species dynamics may buffer against such variability when a decrease in performance by some species is offset by an increase in performance of others. However, high climatic variability can eliminate species through stochastic extinctions or cause similar stress responses among species that reduce buffering. Local conditions, such as soil nutrients, can also alter production stability directly or by influencing asynchrony. We test these hypotheses using a globally distributed sampling experiment. Location: Grasslands in North America, Europe and Australia. Time period: Annual surveys over 5 year intervals occurring between 2007 and 2014. Major taxa studied: Herbaceous plants. Methods: We sampled annually the per species cover and aboveground community biomass [net primary productivity (NPP)], plus soil chemical properties, in 29 grasslands. We tested how soil conditions, combined with variability in precipitation and temperature, affect species richness, asynchrony and temporal stability of primary productivity. We used bivariate relationships and structural equation modelling to examine proximate and ultimate relationships. Results: Climate variability strongly predicted asynchrony, whereas NPP stability was more related to soil conditions. Species richness was structured by both climate variability and soils and, in turn, increased asynchrony. Variability in temperature and precipitation caused a unimodal asynchrony response, with asynchrony being lowest at low and high climate variability. Climate impacted stability indirectly, through its effect on asynchrony, with stability increasing at higher asynchrony owing to lower inter‐annual variability in NPP. Soil conditions had no detectable effect on asynchrony but increased stability by increasing the mean NPP, especially when soil organic matter was high. Main conclusions: We found globally consistent evidence that climate modulates species asynchrony but that the direct effect on stability is low relative to local soil conditions. Nonetheless, our observed unimodal responses to variability in temperature and precipitation suggest asynchrony thresholds, beyond which there are detectable destabilizing impacts of climate on primary productivity. [ABSTRACT FROM AUTHOR]

Convection‐permitting models (CPMs) provide a better representation of sub‐daily precipitation statistics and convective processes on both climate and weather forecasting timescales. This study aims to establish what horizontal resolution and physical process settings are suitable for the first ensemble of convection‐permitting climate projections for the United Kingdom. For this purpose, 12‐year‐long simulations were run using three different convection‐permitting resolutions (grid spacing of 4 km, 2.2 km and 1.5 km) each with several configurations. The focus is on the ability to represent sub‐daily precipitation due to its potential for high impacts on society through flooding. The analysis shows that the use of a finer grid spacing within the convection‐permitting regime improves the representation of sub‐daily precipitation but the added value decreases as the grid becomes increasingly finer, while the computational costs substantially increase. Changes in the representation of physical processes can have as much impact as the grid spacing and can lead to similar differences in the diurnal cycle as between convection‐parametrized models and CPMs. Although the 4km model shows some key deficiencies that make it unreliable for climate projection, the 2.2km model performs as well as the 1.5km model for most of the metrics examined with considerable benefits in terms of computer cost. The optimal configuration and resolution identified here is being used for an ensemble of simulations run within the UK Climate Projections 2018 (UKCP18) project, which allows, for the first time, an estimate of uncertainty in future changes at convection‐permitting scale and providing more reliable climate change projections at local and hourly scales. [ABSTRACT FROM AUTHOR]

There is a growing appreciation of the uncertainties in the estimation of snow‐melt and glacier‐melt as a result of climate change in high elevation catchments. Through a detailed examination of three hydrological models in two catchments, and interpretation of results from previous studies, we observed that many variations in estimated streamflow could be explained by the selection of a best parameter set from the possible good model parameters. The importance of understanding changing glacial dynamics is critically important for our study areas in the Upper Indus Basin where Pakistan's policymakers are planning infrastructure to meet the future energy and water needs of hundreds of millions of people downstream. Yet, the effect of climate on glacial runoff and climate on snowmelt runoff is poorly understood. With the HBV model, for example, we estimated glacial melt as between 56% and 89% for the Hunza catchment. When rainfall was a scaled parameter, the models estimated glacial melt as between 20% and 100% of streamflow. These parameter sets produced wildly different projections of future climate for RCP8.5 scenarios in 2046–2075 compared to 1976–2005. Assuming no glacial shrinkage, for one climate projection, we found that the choice among good parameter sets resulted in projected values of future streamflow across a range from +54% to +125%. Parameter selection was the most significant source of uncertainty in the glaciated catchment and amplified climate model uncertainty, whereas climate model choice was more important in the rainfall dominated catchment. Although the study focuses on Pakistan, the overall conclusions are instructive for other similar regions in the world. We suggest that modellers of glaciated catchments should present results from at least the book‐ends: models with low sensitivity to ice‐melt and models with high sensitivity to ice‐melt. This would reduce confusion among decision makers when they are faced with similar contrasting results. [ABSTRACT FROM AUTHOR]

Understanding spatiotemporal patterns of climate extremes has gained considerable relevance in the context of ongoing climate change. With enhanced computational capacity, data driven methods such as functional climate networks have been proposed and have already contributed to significant advances in understanding and predicting extreme events, as well as identifying interrelations between the occurrences of various climatic phenomena. While the (in its basic setting) parameter free event synchronization (ES) method has been widely applied to construct functional climate networks from extreme event series, its original definition has been realized to exhibit problems in handling events occurring at subsequent time steps, which need to be accounted for. Along with the study of this conceptual limitation of the original ES approach, event coincidence analysis (ECA) has been suggested as an alternative approach that incorporates an additional parameter for selecting certain time scales of event synchrony. In this work, we compare selected features of functional climate network representations of South American heavy precipitation events obtained using ES and ECA without and with the correction for temporal event clustering. We find that both measures exhibit different types of biases, which have profound impacts on the resulting network structures. By combining the complementary information captured by ES and ECA, we revisit the spatiotemporal organization of extreme events during the South American Monsoon season. While the corrected version of ES captures multiple time scales of heavy rainfall cascades at once, ECA allows disentangling those scales and thereby tracing the spatiotemporal propagation more explicitly. [ABSTRACT FROM AUTHOR]

Copyright of Geominas is the property of FUNDA-GEOMINAS. (Fundacion de Egresados d Amigos de la Escuela de Ingenieria Geologica y de Minas) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While long‐term rainfall trends and related atmospheric dynamics have been researched over the past several decades across equatorial Africa, little is known about rainfall in western Uganda, a transition zone in the middle of the continent. Using satellite‐derived rainfall and reanalysis data from 1983 to 2017, this study examines atmospheric characteristics of seasons and multidecadal trends in rainfall. Most of the region has a biannual rainfall regime (i.e., the first rains within March–May and the second rains during August–November). Ascending (descending) air and increased (decreased) specific humidity are observed over western Uganda during the rainy (dry) seasons. Southeasterly air‐parcel back trajectories are common throughout western Uganda at all times except for the first dry season (i.e., December–February). For all seasons, wet days in western Uganda are characterized by increases in ascending air and specific humidity in addition to westerly flow anomalies. Wet days in most seasons also have a disproportionately high frequency of westerly back trajectories extending over the Congo Basin. These Congo westerlies are associated with more vertical ascent and a more humid middle troposphere compared to the other trajectories. Rainy seasons, especially the first rains, have gotten longer and wetter throughout western Uganda. The duration of the first rains increased by about 1 month over the 35 years; in turn, the rainfall total increased by approximately 70%. Rainfall also has increased for climatological seasons, with the exception being December–February. Increases in middle‐troposphere specific humidity and vertical ascent over time provide support for the wetting trends derived from the satellite‐derived rainfall data. Key Points: The two rainy seasons have gotten longer and wetter, with significant increases in duration and rainfall total for the first rainsRainfall increased in all seasons, except DJF, throughout the region, and southwestern Uganda had the largest increase in annual rainfallCongo westerlies contribute substantially to rainfall, especially during JJA and SON, but were not responsible for the wetting trends [ABSTRACT FROM AUTHOR]