Climate, Volume 11, Issue 10 (October 2023) – 16 articles

Spatiotemporal climate variability is a leading environmental constraint to the rain-fed agricultural productivity and food security of communities in the Abbay basin and elsewhere in Ethiopia. The previous one-size-fits-all approach to soil and water management technology targeting did not effectively address climate-induced risks to rain-fed agriculture. This study, therefore, delineates homogenous climatic regions and identifies climate-induced risks to rain-fed agriculture that are important to guide decisions and the selection of site-specific technologies for green water management in the Abbay basin. The k-means spatial clustering method was employed to identify homogenous climatic regions in the study area, while the Elbow method was used to determine an optimal number of climate clusters. The k-means clustering used the Enhancing National Climate Services (ENACTS) daily rainfall, minimum and maximum temperatures, and other derived climate variables that include daily rainfall amount, length of growing period (LGP), rainfall onset and cessation dates, rainfall intensity, temperature, potential evapotranspiration (PET), soil moisture, and AsterDEM to define climate regions. Accordingly, 12 climate clusters or regions were identified and mapped for the basin. Clustering a given geographic region into homogenous climate classes is useful to accurately identify and target locally relevant green water management technologies to effectively address local-scale climate-induced risks. This study also provided a methodological framework that can be used in the other river basins of Ethiopia and, indeed, elsewhere. Full article

Agriculture is the world’s main economic activity. According to the Intergovernmental Panel on Climate Change, this activity is expected to be impacted by drought. In the Northeast region of Brazil (NEB), most agricultural activity is carried out by small rural communities. Local socio-economic data were analyzed using multivariate statistical techniques in this study to determine agricultural sensitivity to drought events (SeA) and agricultural vulnerability to drought extremes (VaED). The climate data used to develop the risk factor (R_drought) were the drought indicator with the Standard Precipitation Index (SPI) and the average number of drought disasters from 1991 to 2012. Conditional probability theory was applied to determine agricultural vulnerability to drought extremes (VaED). Characterization of the risk of agricultural drought using the proposed methodology showed that the rainy season presents high risk values in the central region, covering areas of the states of Ceará, Piauí, Pernambuco and Rio Grande do Norte, as well as all areas of the semi-arid region. The risk ranged from high to medium. The results also indicated that part of the south of Bahia and the west of Pernambuco have areas of extreme agro-climatic sensitivity. Consequently, these states have an extreme degree of climate vulnerability during the region’s rainy season. Full article

Changes in climatic conditions increase risks associated with crop production in certain regions. Early detection of these changes enables the implementation of suitable adaptation measures in the local area, thereby stabilising agricultural production. Our analysis shows a significant shift in climatic conditions in Czechia between 1961 and 2020. We examined the changes in observed temperature conditions, precipitation distribution, drought occurrences, and frost incidents at a high resolution (0.5 × 0.5 km). The outputs show a significant increase in air temperatures and drought occurrence. Temperature totals above 5 °C in 1991–2020 were 15% higher than in 1961–1990. Furthermore, the relative change in totals above 10 °C was 26% after 1991. Over the last 30 years, drought incidence was four times more frequent than in 1961–1990, particularly in spring. In contrast, no significant changes in the distribution of precipitation occurred, and there was a slight decrease in the probability of frost during the growing season. Ongoing climate change brings warmer and drier conditions to higher-altitude regions in Czechia. Assessing climatic conditions on a global scale is less precise for relatively small and topographically diverse countries like Czechia due to coarse resolution. Therefore, a high-resolution analysis is more appropriate for these countries. Full article

The Gálvez–Davison Index (GDI) is an atmospheric stability index recently developed to improve the prediction of thunderstorms and shallower types of moist convection in the tropics. Because of its novelty, its use for tropical regions remains largely unexplored. Cuba is a region that suffers extreme weather events, such as tropical storms and hurricanes, some of them worsened by climate change. This research analyzes the effectiveness of the GDI in detecting the potential for convective cloud development, using forecast data from the Weather Research and Forecasting (WRF) model for Western Cuba. To accomplish this, here, we evaluated the performance of the GDI in ten study cases from the dry and wet seasons. As part of our study, we researched how GDI correlates with brightness temperatures (BTs) measured using GOES-16. In addition, the GDI results with the WRF model are compared with results using the Global Forecast System (GFS). Our results show a high correlation between the GDI and BT, concluding that the GDI is a robust tool for forecasting both synoptic and mesoscale convective phenomena over the region studied. In addition, the GDI is able to adequately forecast stability conditions. Finally, the GDI values computed from the WRF model perform much better than those from the GFS, probably because of the greater horizontal resolution in the WRF model. Full article

The objective of this work was to study climate variability and its impacts on the temperature of Sobradinho Lake in Northeast Brazil. Surface weather station data and lake measurements were used in this study. The model applied in this work is FLake, which is a one-dimensional model used to simulate the vertical temperature profile of freshwater lakes. First, the climate variability around Sobradinho Lake was analyzed. Observations showed a reduction in precipitation during 1991–2020 compared to 1981–2010. To study climate variability impacts on Sobradinho Lake, the years 2013, 2015, and 2020 were selected to characterize normal, dry, and rainy years, respectively. In addition, the months of January, April, July, and October were analyzed for rainy months, rainy–dry transitions, dry months, and dry–rainy transitions. Dry years showed higher incoming solar radiation at the surface and, consequently, higher 2 m air temperatures. A characteristic of the normal years was more intense surface winds. October presented the highest incoming solar radiation, the highest air temperature, and the most intense winds at the surface. The lowest incoming solar radiation at the surface was observed in January, and the lightest wind was observed in April. To assess the effects of these atmospheric conditions on the thermodynamics of Sobradinho Lake, the FLake model was forced using station observation data. The thermal amplitude of the lake surface temperature (LST) varied by less than 1 °C during the four months. This result was validated against surface lake observations. FLake was able to accurately reproduce the diurnal cycle variation in sensible heat fluxes (H), latent heat fluxes, and momentum fluxes. The sensible heat flux depends directly on the difference between the LST and the air temperature. During daytime, however, Flake simulated negative values of H, and during nighttime, positive values. The highest values of latent heat flux were simulated during the day, with the maximum value was simulated at 12:00 noon. The momentum flux simulated a similar pattern, with the maximum values simulated during the day and the minimum values during the night. The FLake model also simulated the deepest mixing layer in the months of July and October. However, our results have limitations due to the lack of observed data to validate the simulations. Full article

Surface winds over California can compound fire risk during autumn, yet their long-term trends in the face of decadal warming are less clear compared to other climate variables like temperature, drought, and snowmelt. To determine where and how surface winds are changing most, this article uses multiple reanalyses and Remote Automated Weather Stations (RAWS) to calculate autumn 10 m wind speed trends during 1979–2020. Reanalysis trends show statistically significant increases in autumn night-time easterlies on the western slopes of the Sierra Nevada. Although downslope windstorms are frequent to this region, trends instead appear to result from elevated gradients in warming between California and the interior continent. The result is a sharper horizontal temperature gradient over the Sierra crest and adjacent free atmosphere above the foothills, strengthening the climatological nocturnal katabatic wind. While RAWS records show broad agreement, their trend is likely influenced by year-to-year changes in the number of observations. Full article

Forest ecosystems in the mid-latitudes of the Northern Hemisphere are significantly affected by frequent extreme weather events. How different forest ecosystems respond to these changes is a major challenge. This study aims to assess differences in the response of daily net ecosystem exchange (NEE) of CO₂ and latent heat flux (LE) between different boreal and temperate ecosystems and the atmosphere to extreme weather events (e.g., anomalous temperature and precipitation). In order to achieve the main objective of our study, we used available reanalysis data and existing information on turbulent atmospheric fluxes and meteorological parameters from the global and regional FLUXNET databases. The analysis of NEE and LE responses to high/low temperature and precipitation revealed a large diversity of flux responses in temperate and boreal forests, mainly related to forest type, geographic location, regional climate conditions, and plant species composition. During the warm and cold seasons, the extremely high temperatures usually lead to increased CO₂ release in all forest types, with the largest response in coniferous forests. The decreasing air temperatures that occur during the warm season mostly lead to higher CO₂ uptake, indicating more favorable conditions for photosynthesis at relatively low summer temperatures. The extremely low temperatures in the cold season are not accompanied by significant NEE anomalies. The response of LE to temperature variations does not change significantly throughout the year, with higher temperatures leading to LE increases and lower temperatures leading to LE reductions. The immediate response to heavy precipitation is an increase in CO₂ release and a decrease in evaporation. The cumulative effect of heavy precipitations is opposite to the immediate effect in the warm season and results in increased CO₂ uptake due to intensified photosynthesis in living plants under sufficient soil moisture conditions. Full article

Land carbon fluxes play a critical role in ecosystems, and acquiring a comprehensive global database of carbon fluxes is essential for understanding the Earth’s carbon cycle. The primary methods of obtaining the spatial distribution of land carbon fluxes include utilizing machine learning models based on in situ measurements, estimating through satellite remote sensing, and simulating ecosystem models. Recently, an innovative machine learning product known as the Global Carbon Flux Dataset (GCFD) has been released. In this study, we assessed the reliability of the GCFD by comparing it with existing data products, including two machine learning products (FLUXCOM and NIES (National Institute for Environmental Studies)), two ecosystem model products (TRENDY and EC-LUE (eddy covariance–light use efficiency model)), and one remote sensing product (Global Land Surface Satellite), on both site and global scales. Our findings indicate that, in terms of average absolute difference, the spatial distribution of the GCFD is most similar to the NIES product, albeit with slightly larger discrepancies compared to the other two types of products. When using site observations as the benchmark, gross primary production (GPP), respiration of ecosystem (RECO), and net ecosystem exchange of machine learning products exhibit higher R² (ranging from 0.57 to 0.85, 0.53–0.79, and 0.31–0.70, respectively) compared to model products and remote sensing products. Furthermore, we analyzed the spatial and temporal distribution characteristics of carbon fluxes in various regions. The results demonstrate an upward trend in both GPP and RECO over the past two decades, while NEE exhibits an opposite trend. This trend is particularly pronounced in tropical regions, where higher GPP is observed in tropical, subtropical, and oceanic climate zones. Additionally, two remote sensing variables that influence changes in carbon fluxes, i.e., fraction absorbed photosynthetically active radiation and leaf area index, exhibit relatively consistent spatial and temporal characteristics. Overall, our study can provide valuable insights into different types of carbon flux products and contribute to understanding the general features of global carbon fluxes. Full article

Agriculture, entwined with climatic conditions, plays a pivotal role in Thailand’s sustenance and economy. This study aimed to examine the trends of climate variability and its correlation with crop yields and social and farm factors affecting farm net income in Chiang Mai province, Thailand. Time series climate data (2002–2020) on temperature and rainfall and yields were analyzed using the Mann–Kendall trend test and Sen’s slope estimation to investigate the trends and their changes. The Pearson correlation was used to assess the association between climate variability and cultivated crop yields, and multiple linear regression was used to detect the factors influencing the farm net income. The findings show that the total annual rainfall showed an unchanged trend, but the annual temperature increased over time. Higher temperature negatively impacted longan yield but positively affected maize, with no significant impact on rice yield. The rainfall trend had no effect on crop yields. Despite declining trends in some cultivated crops’ yield, farm net income was unaffected by individual crop types. Farm income relied on cumulative output and geographic location. This research emphasizes the need for integrating climate data and forecasting models considering agronomic and socio-economic factors and crop suitability assessments for specific regions into adaptation policies and practice. Full article

The European Green Deal comprises various policy initiatives with the goal of reaching carbon neutrality by 2050. The “Fit for 55 packages” include the Social Climate Fund, which aims to help, among others, vulnerable households and transport users meet the costs of the green energy transition. Thus, analyzing households’ expenditures and the associated carbon emissions is crucial to achieving a net-zero society. In the present study, we combine scenarios of households’ expenditures according to the Classification of Individual Consumption According to Purpose with economic decoupling scenarios to assess, for the first time, the European carbon budget allocation on a consumption basis. Expenditure projections based on socioeconomic scenarios were calculated using the Bayesian structural time series, and the associated emissions were estimated through the greenhouse gas intensity of the Gross Domestic Product. The model can be used to report the carbon budget of households and monitor the effectiveness of the measures funded by the Social Climate Fund. However, the emissions burden obtained by means of averaged greenhouse gas intensity of Gross Domestic Product results in a rough approximation of outcomes, and more accurate indicators should be developed across the member states. Full article

Since agricultural productivity is weather and climate-related and fundamentally depends on climate stability, climate change poses many diverse challenges to agricultural activities. The objective of this study is to review adaptation strategies and interventions in countries around the world proposed for implementation to reduce the impact of climate change on agricultural development and production at various spatial scales. A literature search was conducted in June–August 2023 using electronic databases Google Scholar and Scientific Electronic Library eLibrary.RU, seeking the key words “climate”, “climate change”, and “agriculture adaptation”. Sixty-five studies were identified and selected for the review. The negative impacts of climate change are expressed in terms of reduced crop yields and crop area, impacts on biotic and abiotic factors, economic losses, increased labor, and equipment costs. Strategies and actions for agricultural adaptation that can be emphasized at local and regional levels are: crop varieties and management, including land use change and innovative breeding techniques; water and soil management, including agronomic practices; farmer training and knowledge transfer; at regional and national levels: financial schemes, insurance, migration, and culture; agricultural and meteorological services; and R&D, including the development of early warning systems. Adaptation strategies depend on the local context, region, or country; limiting the discussion of options and measures to only one type of approach—"top-down” or “bottom-up”—may lead to unsatisfactory solutions for those areas most affected by climate change but with few resources to adapt to it. Biodiversity-based, or “ecologically intensive” agriculture, and climate-smart agriculture are low-impact strategies with strong ecological modernization of agriculture, aiming to sustainably increase agricultural productivity and incomes while addressing the interrelated challenges of climate change and food security. Some adaptation measures taken in response to climate change may not be sufficient and may even increase vulnerability to climate change. Future research should focus on adaptation options to explore the readiness of farmers and society to adopt new adaptation strategies and the constraints they face, as well as the main factors affecting them, in order to detect maladaptation before it occurs. Full article

Study region: The Três Marias 396 MW power plant located on the São Francisco River in Brazil. Study focus: Hydropower generation is directly and indirectly affected by climate change. It is also a relevant source of energy for electricity generation in many countries. Thus, methodologies need to be developed to assess the impacts of future climate scenarios. This is essential for effective planning in the energy sector. Energy generation at the Três Marias power plant was estimated using the water balance of the reservoir and the future stream flow projections to the power plant, for three analysis periods: FUT1 (2011–2040); FUT2 (2041–2070); and FUT3 (2071–2100). The MGB-IPH hydrological model was used to assimilate precipitation and other climatic variables from the regional Eta climatic model, via global models HadGEM2-ES and MIROC5 for scenarios RCP4.5 and RCP8.5. New hydrological insights for the region: The results show considerable reductions in stream flows and consequently, energy generation simulations for the hydropower plant were also reduced. The average power variations for the Eta-MIROC5 model were the mildest, around 7% and 20%, while minimum variations for the Eta-HadGEM2-ES model were approximately 35%, and almost 65% in the worst-case scenario. These results reinforce the urgent need to consider climate change in strategic Brazilian energy planning. Full article

This study considers the problem of approximating the temporal dynamics of the urban-rural temperature difference (ΔT) in Moscow megacity using machine learning (ML) models and predictors characterizing large-scale weather conditions. We compare several ML models, including random forests, gradient boosting, support vectors, and multi-layer perceptrons. These models, trained on a 21-year (2001–2021) dataset, successfully capture the diurnal, synoptic-scale, and seasonal variations of the observed ΔT based on predictors derived from rural weather observations or ERA5 reanalysis. Evaluation scores are further improved when using both sources of predictors simultaneously and involving additional features characterizing their temporal dynamics (tendencies and moving averages). Boosting models and support vectors demonstrate the best quality, with RMSE of 0.7 K and R² > 0.8 on average over 21 years. For three selected summer and winter months, the best ML models forced only by reanalysis outperform the comprehensive hydrodynamic mesoscale model COSMO, supplied by an urban canopy scheme with detailed city-descriptive parameters and forced by the same reanalysis. However, for a longer period (1977–2023), the ML models are not able to fully reproduce the observed trend of ΔT increase, confirming that this trend is largely (by 60–70%) driven by megacity growth. Feature importance assessment indicates the atmospheric boundary layer height as the most important control factor for the ΔT and highlights the relevance of temperature tendencies as additional predictors. Full article

The diurnal temperature range (DTR) is a significant indicator of climate change, and a previous study has shown its impact on human health. However, research investigating the influence of DTR on road traffic accidents is scarce. Thus, this study aims to evaluate the impact of changes in DTR on road traffic accidents. The present study employs two methods to address the complexities of road accidents. Firstly, panel data from 20 cities and counties in Taiwan are utilized, and the autoregressive distributed lag (ARDL) model is employed for estimation. Secondly, distributed lag non-linear models (DLNMs) are used with quasi-Poisson regression analysis to assess the DTR’s lagged and non-linear relationships with road accidents using time series data from six Taiwanese metropolitan cities. The study results indicate that a decrease of 1 °C in DTR raises long-term road traffic accidents by 17.1%. In the short term, the impact of declining DTR on road accidents is around 4%. Moreover, the effect of low DTR values differs in each city in Taiwan. Three cities had high levels of road accidents, as evidenced by an increase in the relative risk value; two cities had moderate responses; and one city had a relatively lower response compared to high DTR values. Finally, based on the cumulative relative risk estimations, the study found that a low diurnal temperature range is linked to a high road traffic accident rate, especially during the lag-specific 0–5 months. The findings of this study offer fresh evidence of the negative impact of climate factor on road traffic accidents. Full article

The impact of climate change on river systems is a multifaceted threat to the environment, affecting various aspects of ecosystems. The Upper Benue River Basin (UBRB) in Nigeria is an area of concern, as river flow and water levels are crucial for irrigation and transportation. In this study, we investigate the impact of climate change on the hydrology of the UBRB using data on rainfall, temperature, relative humidity, wind speed, river discharge, and water level. Trend, correlation, and stepwise regression analyses were conducted using Excel and SPSS 20 to analyze the data. The results indicate that the UBRB is experiencing climate change, as evidenced by annual decreases in rainfall and relative humidity and increases in maximum and minimum temperatures. Specifically, mean annual rainfall and relative humidity exhibit a negative trend, while the maximum and minimum temperature exhibit a positive trend. Furthermore, we found that rainfall and relative humidity have a significant positive relationship with river discharge and level (p < 0.01), whereas maximum temperature and wind speed have a significant negative relationship with water discharge and level. We also identified wind speed and rainfall as the critical climatic indices influencing river discharge, accounting for 21.7% of the variation in river discharge within the basin (R² = 21.7). Based on these findings, we conclude that increases in rainfall and relative humidity will lead to significant increases in river discharge and level, while increases in wind speed and maximum temperature will decrease river discharge and level. Moreover, wind speed and rainfall are the critical climatic indices influencing river discharge, whereas relative humidity, wind speed, and rainfall are the critical climatic indices influencing water level. Thus, we recommend constructing more reservoirs (dams) to mitigate the negative trend in rainfall and encourage climate change control, such as afforestation among the population of the region. These findings have important implications for understanding the impact of climate change on river systems and developing effective strategies to mitigate its effects. Full article

Significant riverine archaeological sites around the world are vulnerable to flooding associated with climate change. However, identifying sites most at risk is not straightforward. We critically review the parameters used in 22 published analyses of risk to riverine archaeology from climate change (ARRACC). Covering 17 countries globally, the ARRACC’s risk parameters are highly variable. Proximity to rivers and projected changes to extreme flood frequency are the most commonly employed. However, to be robust, future ARRACC should select from a wider range of hazard parameters, including channel mobility/type, erosion/sedimentation patterns, land use and engineering works, as well as parameters for site sensitivity to flooding and heritage significance. To assist in this, we propose a basic field survey for ARRACC, to be treated primarily as a conceptual checklist or as a starting point for a bespoke ARRACC method adapted for a particular river and the objectives of local stakeholders. The framework proposes a pathway to optimal prioritisation of sites most in need of adaptation so that scarce management resources can be targeted. Full article