Climate-sensitive Regions Research Articles

Changing Rainfall Patterns in the Northeastern South Kivu Region, Democratic Republic of the Congo: A Detailed Analysis Using CHIRPS Rainfall Data (1981–2023)

AbstractUnderstanding changes in rainfall patterns is vital for effective water resource management and agricultural planning in climate-sensitive regions. In Northeastern South Kivu, Democratic Republic of the Congo, limited knowledge of rainfall dynamics poses challenges for local climate assessments. This study analyzed rainfall patterns using the Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) dataset from 1981 to 2023 to fill this knowledge gap. The findings revealed significant spatial and temporal changes in rainfall across the regions of Kabare, Bukavu, Kalehe, and Idjwi. Mean annual rainfall ranged from 1136.92 mm to 2048.85 mm, with coefficients of variation between 8.23% and 8.45%. Monthly rainfall fluctuations were substantial, ranging from 16 to 56.35%. Standardized rainfall anomalies indicated a shift from predominantly wet conditions in the 1980s to drier conditions in later decades, with over half the years from 1990 to 2023 recording below-average rainfall. Mann-Kendall trend analysis, supported by Innovative Trend Analysis (ITA), confirmed significant decreases in annual rainfall, with rates between − 3.53 to -5.72 mm/year. Additionally, rainfall intensity indices highlighted a decline in total annual precipitation, suggested fewer extreme events. Spatial variability was observed, with regions like Kalehe and Bukavu showed higher values for indices such as R95P and RX1day compared to Kabare and Idjwi. These results underscored the urgent need for adaptive strategies to mitigate water scarcity and manage changing rainfall patterns in the region.

Modeling Heat Transfer for Assessing the Convection Length in Ventilated Caves

AbstractThe present study focuses on heat transfer in ventilated caves for which the airflow is driven by the temperature contrast between the cave and the external atmosphere. We use a numerical model that couples the convective heat transfer due to the airflow in a single karst conduit with the conductive heat transfer in the rock mass. Assuming dry air and a simplified geometry, we investigate the propagation of thermal perturbations inside the karst massif. We perform a parametric study to identify general trends regarding the effect of the air flowrate and conduit size on the amplitude and spatial extent of thermal perturbations. Numerical results support the partition of a cave into three regions: (a) a short (few meters) diffusive region, where heat mainly propagates from the external atmosphere by conduction in the rock mass; (b) a convective region where heat is mainly transported by the air flow; (c) a deep karst region characterized by quasi‐constant temperatures throughout the year. Numerical simulations show that the length of the convective region is approximately proportional to the amplitude of the flowrate annual fluctuations divided by the square root of the cave radius. This result is tested against field data from a mine tunnel and two caves. Our study provides first estimates to identify climate sensitive regions for speleothem science and/or ecosystemic studies.

A Beacon in the Dark: Grey Literature Data Mining and Machine Learning Enlightening Historical Plankton Seasonality Dynamics in the Ligurian Sea

The Mediterranean Sea, as one of the world’s most climate-sensitive regions, faces significant environmental changes due to rising temperatures. Zooplankton communities, particularly copepods, play a vital role in marine ecosystems, yet their distribution dynamics remain poorly understood, especially in the Ligurian Sea. Leveraging open-source software and environmental data, this study adapted a methodology to model copepod distributions from 1985 to 1986 in the Portofino Promontory ecosystem using the Random Forest machine learning algorithm to produce the first abundance and distribution maps of the area. Five copepod genera were studied across different trophic guilds, revealing habitat preferences and ecological fluctuations throughout the seasons. The assessment of model accuracy through symmetric mean absolute percentage error (sMAPE) highlighted the variability in copepod dynamics influenced by environmental factors. While certain genera exhibited higher predictive accuracy during specific seasons, others posed challenges due to ecological complexities. This study underscores the importance of species-specific responses and environmental variability in predictive modeling. Moreover, this study represents the first attempt to model copepod distribution in the Ligurian Sea, shedding light on their ecological niches and historical spatial dynamics. The study adhered to FAIR principles, repurposing historical data to generate three-dimensional predictive maps, enhancing our understanding of copepod biodiversity. Future studies will focus on developing abundance distribution models using machine learning and artificial intelligence to predict copepod standing crop in the Ligurian Sea with greater precision. This integrated approach advances knowledge of copepod ecology in the Mediterranean and sets a precedent for integrating historical data with contemporary methodologies to elucidate marine ecosystem dynamics.

Characteristics of Dry and Wet Changes and Future Trends in the Tarim River Basin Based on the Standardized Precipitation Evapotranspiration Index

Global changes in drought and wetness and their future trends in arid regions have recently become a major focus of research attention. The Tarim River Basin (TRB) in Xinjiang, China, is among the most climate-sensitive regions in the world. This study uses data from the past 60 years (1962–2021) to analyze the spatial and temporal features of drought and wetness conditions in the TRB, calculating the Standardized Precipitation Evapotranspiration Index (SPEI). Trend detection for SPEI is performed using the BEAST mutation test, identification of drought events using the theory of operations, and spatial and temporal analyses of dry and wet changes using Empirical Orthogonal Function (EOF) decomposition. Additionally, the CMIP6 dataset is used to estimate future changes. The study results indicate the following: (1) From 1962 to 1998, the TRB exhibited a “warm and wet” trend that suddenly shifted from “wet-to-dry” in 1998 and subsequently transitioned to a pronounced “warm and dry” trend. (2) After the “wet-to-dry” shift, the frequency of drought events noticeably increased. The northern section of the basin witnessed more frequent drought events, albeit with lower severity, while the southern part had fewer occurrences but with higher severity. The spatial distribution of drought event frequency and severity is inconsistent. (3) The EOF decomposition results for SPEI-variable fields at 1-, 3-, and 6-month time scales show that the cumulative variance contribution rate of the first three principal spatial modal feature vectors exceeds 70%. The spatial distribution of the modes includes a consistent pattern across the entire basin, a north–south opposite pattern, and an east–west opposite pattern. (4) The future trend of drought in the TRB is expected to intensify, manifesting a spatial pattern characterized by dryness in the middle of the basin and wetness around the periphery. These research findings can provide support for decisions addressing regional drought risks.

WASH interventions and child diarrhea at the interface of climate and socioeconomic position in Bangladesh

Many diarrhea-causing pathogens are climate-sensitive, and populations with the lowest socioeconomic position (SEP) are often most vulnerable to climate-related transmission. Household Water, Sanitation, and Handwashing (WASH) interventions constitute one potential effective strategy to reduce child diarrhea, especially among low-income households. Capitalizing on a cluster randomized trial population (360 clusters, 4941 children with 8440 measurements) in rural Bangladesh, one of the world’s most climate-sensitive regions, we show that improved WASH substantially reduces diarrhea risk with largest benefits among children with lowest SEP and during the monsoon season. We extrapolated trial results to rural Bangladesh regions using high-resolution geospatial layers to identify areas most likely to benefit. Scaling up a similar intervention could prevent an estimated 734 (95% CI 385, 1085) cases per 1000 children per month during the seasonal monsoon, with marked regional heterogeneities. Here, we show how to extend large-scale trials to inform WASH strategies among climate-sensitive and low-income populations.

Seed quality as a proxy of climate-ready orphan legumes: the need for a multidisciplinary and multi-actor vision.

In developing countries, orphan legumes stand at the forefront in the struggle against climate change. Their high nutrient value is crucial in malnutrition and chronic diseases prevention. However, as the 'orphan' definition suggests, their seed systems are still underestimated and seed production is scanty. Seed priming is an effective, sustainable strategy to boost seed quality in orphan legumes for which up-to-date guidelines are required to guarantee reliable and reproducible results. How far are we along this path? What do we expect from seed priming? This brings to other relevant questions. What is the socio-economic relevance of orphan legumes in the Mediterranean Basin? How to potentiate a broader cultivation in specific regions? The case study of the BENEFIT-Med (Boosting technologies of orphan legumes towards resilient farming systems) project, developed by multidisciplinary research networks, envisions a roadmap for producing new knowledge and innovative technologies to improve seed productivity through priming, with the long-term objective of promoting sustainability and food security for/in the climate-sensitive regions. This review highlights the existing drawbacks that must be overcome before orphan legumes could reach the state of 'climate-ready crops'. Only by the integration of knowledge in seed biology, technology and agronomy, the barrier existing between research bench and local agricultural fields may be overcome, generating high-impact technical innovations for orphan legumes. We intend to provide a powerful message to encourage future research in line with the United Nations Agenda 2030 for Sustainable Development.

Global warming leads to growth increase in Pinus sylvestris in the Kazakh steppe

Forest community structure and function in climate-sensitive regions exhibit uncertain changes under the influence of global warming. Especially in northern Central Asia, the response of pine growth to climate change remains unclear. Here, we identified changes in growth trends and climate response in a total of 646 trees for the abundant species Scots pine (Pinus sylvestris) at 30 sites in the Kazakh steppe. We used basal area increment, regional curve standardization and negative exponential curve detrended chronologies to comprehensively analyze trends in pine growth, and linear mixed effects and random forest models were calculated to explore growth-climate relationships. An increase in the overall growth of Scots pine under the influence of global warming, expressed in this region by increasing temperatures and precipitation, was observed during the period 1950–2018. Spatial differences in climate response between the sub-regions were identified showing that pine growth is more influenced by summer precipitation in the eastern compared to the western and central Kazakh steppe. Warm spring temperatures and more precipitation are important common climatic factors affecting pine growth in northern Central Asia. Moreover, increased drought intensity and longer drought duration negatively impact pine growth in this region. The Pacific Decadal Oscillation and North Atlantic Oscillation were identified to be the main atmospheric circulation patterns that influence the spatial differentiation in pine growth in this steppe. Albeit pine growth in northern Central Asia shows an increasing trend currently but also in future climate scenarios, the risks and challenges posed by drought should not be underestimated.

Trends of High and Low Values of Annual and Seasonal Precipitation in Turkey

The exploration of precipitation data trends using innovative analysis methods is anticipated to offer significant contributions to sustainability across various domains, including water resources management, agricultural planning, climate change understanding, environmental protection, risk assessment, and preparedness. This study employs an improved visualization of the innovative trend analysis (IV-ITA) method for detecting annual and seasonal trends in precipitation variables across 194 stations in Turkey over the period 1969–2020. The outcomes derived from the IV-ITA method were further juxtaposed with the results obtained from the classical Mann–Kendall (MK) test. Using the MK test, statistically significant increasing trends were detected in the annual pattern along the northern coasts of Turkey. Based on the results obtained from the IV-ITA, substantial decreasing trends were identified in the basins within the Southeastern Anatolia Region of Turkey. Conversely, an increasing trend was observed in the basins situated in the northwest. It was also established that an increasing trend was observed throughout Turkey, except for some stations in the low-data group. The most significant increasing trend was noted in the basins along the southern coastline, characterized by the prevalence of the Mediterranean climate. In the high-data group, an increase of up to 50% was noted along the southwest coast. Upon comparing the outcomes derived from the IV-ITA method with those obtained using the MK test, it becomes apparent that the IV-ITA yields significantly more detailed results than the MK test. Additionally, applying the IV-ITA method in climate-sensitive regions and basins is envisioned to enhance the accuracy of assessments, foster a sustainable environment, and improve the effective management of water resources.

Soil microbial responses to simulated climate change across polar ecosystems

The polar regions are among the most biologically constrained in the world, characterized by cold temperatures and reduced liquid water. These limitations make them among the most climate-sensitive regions on Earth. Despite the overwhelming constraints from low temperatures and resource availability, many polar ecosystems, including polar deserts and tundras across the Arctic and Antarctic host uniquely diverse microbial communities. Polar regions have warmed more rapidly than the global average, with continued warming predicted for the future, which will reduce constraints on soil microbial activity. This could alter polar carbon (C) cycles, increasing CO2 emissions into the atmosphere. The objective of this study was to determine how increased temperature and moisture availability impacts microbial respiration in polar regions, by focusing on a diversity of ecosystem types (polar desert vs. tundra) that are geographically distant across Antarctica and the Arctic. We found that polar desert soil microbes were co-limited by temperature and moisture, though C and nitrogen (N) mineralization were only stimulated at the coldest and driest of the two polar deserts. Only bacterial biomass was impacted at the less harsh of the polar deserts, suggesting microbial activity is limited by factors other than temperature and moisture. Of the tundra sites, only the Antarctic tundra was climate-sensitive, where increased temperature decreased C and N mineralization while water availability stimulated it. The greater availability of soil resources and vegetative biomass at the Arctic tundra site might lead to its lack of climate-sensitivity. Notably, while C and N dynamics were climate-sensitive at some of our polar sites, P availability was not impacted at any of them. Our results demonstrate that soil microbial processes in some polar ecosystems are more sensitive to changes in temperature and moisture than others, with implications for soil C and N storage that are not uniformly predictable across polar regions.

Exploring the intensity, distribution and evolution of teleconnections using climate network analysis.

Teleconnections refer to long-range climate system linkages occurring over typically thousands of kilometers. Generally speaking, most teleconnections are attributed to the transmission of energy and propagation of waves although the physical complexity and characteristics behind these waves are not fully understood. To address this knowledge gap, we develop a climate network-based approach to reveal their directions and distribution patterns, evaluate the intensity of teleconnections, and identify sensitive regions using global daily surface air temperature data. Our results reveal a stable average intensity distribution pattern for teleconnections across a substantial spatiotemporal scale from 1948 to 2021, with the extent and intensity of teleconnection impacts increasing more prominently in the Southern Hemisphere over the past 37 years. Furthermore, we pinpoint climate-sensitive regions, such as southeastern Australia, which are likely to face increasing impacts due to global warming. Our proposed method offers new insights into the dynamics of global climate patterns and can inform strategies to address climate change and extreme events.

The control of particulate matter and hydrogeochemistry on uranium–thorium partition coefficients in two representative lakes, Qaidam Basin, China

Radionuclides (including those of uranium and thorium) are commonly used to quantify complex geochemical processes in aqueous environments. The estimation of mass transport fluxes for uranium (U) and thorium (Th) isotopes is plagued, however, by uncertainties predominantly driven by the inability to determine solute–particulate partition coefficients (Kd). Such uncertainties are especially pronounced for lakes in rarely studied, environmentally sensitive areas. This study examines the factors controlling the Kd values of U and Th isotopes in both freshwater (Keluke Lake) and saline water (Tuosu Lake) lakes within the Qaidam Basin, China, a basin sensitive to the impacts of climatic change. The analysis found a negative correlation of both Kd(Th) and Kd(U) with the concentration of suspended particulate matter (SPM), suggesting that particle concentrations affect Kd values in the sandy Keluke Lake. In contrast, there was no significant correlation between Kd values and SPM in the muddy (fine-grained) Tuosu Lake. Compositionally, three types of particles were found in both lakes and were derived from the same sediment sources. The Kd values were primarily controlled by lake evaporation and/or secondary water–rock interactions that were influenced by hydrogeochemical ecosystem differences between the lakes. In addition to differences in particle composition and concentrations, redox potential, ionic strength, organic matter concentration and pH of the lake water were likely to influence the Kd(Th) and Kd(U) values. These results are not only of significance to quantitative studies of U and Th isotopes in water (e.g., reducing transport flux uncertainties and improving the precision of U–Th isotope dating), but also provide an important reference for the application of radionuclides in other climate sensitive regions of the world.

The key role of ecological resilience in radial growth processes of conifers under drought stress in the subalpine zone of marginal deserts

Global climate change is exacerbating drought pressure on forests. However, the response patterns and physiological mechanisms of conifer species to drought, specifically in terms of radial growth, ecological resilience and soil water utilization, are not clearly understood. This study aims to quantify the effects of resilience on radial growth and identify the role of soil moisture utilization strategies in the resilience of species under drought intensities. We focus on two conifer species, Picea crassifolia (spruce) and Pinus tabuliformis (pine), located on the southern edge of the Tengger Desert in northwestern China. The dynamics of radial growth and ecological resilience were identified, and the seasonal growth rates of species based on soil water were simulated using the VS-oscilloscope model under varying drought stress. The results showed that spruce growth and recovery contributed by soil water were suppressed with frequent severe droughts, leading to a decline in growth (−0.5 cm2 year−1/10a, p < 0.05), despite its greater resistance to mild and moderate drought (−4.63 %). However, pine exhibited a stronger recovery (+40.25 %, p < 0.05) and higher variation in growth (−0.3 cm2 year−1/10a, p < 0.05) under soil moisture stress, despite its weaker resistance to drought (−23.53 %, p < 0.05). These findings provide insights into the growth, resilience, and water adaptation mechanisms of species under drought events, and theoretical support for the conservation and management of conifer diversity and forest ecosystem stability in climate-sensitive regions.

Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0

Abstract. The climate impact of non-CO2 emissions, which are responsible for two-thirds of aviation radiative forcing, highly depends on the atmospheric chemistry and weather conditions. Hence, by planning aircraft trajectories to reroute areas where the non-CO2 climate impacts are strongly enhanced, called climate-sensitive regions, there is a potential to reduce aviation-induced non-CO2 climate effects. Weather forecast is inevitably uncertain, which can lead to unreliable determination of climate-sensitive regions and aircraft dynamical behavior and, consequently, inefficient trajectories. In this study, we propose robust climate-optimal aircraft trajectory planning within the currently structured airspace considering uncertainties in standard weather forecasts. The ensemble prediction system is employed to characterize uncertainty in the weather forecast, and climate-sensitive regions are quantified using the prototype algorithmic climate change functions. As the optimization problem is constrained by the structure of airspace, it is associated with hybrid decision spaces. To account for discrete and continuous decision variables in an integrated and more efficient manner, the optimization is conducted on the space of probability distributions defined over flight plans instead of directly searching for the optimal profile. A heuristic algorithm based on the augmented random search is employed and implemented on graphics processing units to solve the proposed stochastic optimization computationally fast. An open-source Python library called ROOST (V1.0) is developed based on the aircraft trajectory optimization technique. The effectiveness of our proposed strategy to plan robust climate-optimal trajectories within the structured airspace is analyzed through two scenarios: a scenario with a large contrail climate impact and a scenario with no formation of persistent contrails. It is shown that, for a nighttime flight from Frankfurt to Kyiv, a 55 % reduction in climate impact can be achieved at the expense of a 4 % increase in the operating cost.

Southern Himalayas rainfall as a key driver of interannual variation of pre-monsoon aerosols over the Tibetan Plateau

The Tibetan Plateau (TP) is one of the most climate-sensitive regions around the world. Aerosols imported from adjacent regions reach their peak during the pre-monsoon season and play a vital role in the TP environment. However, the strong interannual variation in aerosols transported to the TP has not been fully understood. Here, we show that the interannual variability of pre-monsoon aerosols transported to the TP is influenced more by rainfall over the southern Himalayas than near-surface wind. Rainfall modulates fire events and biomass burning emissions and reduces aerosols over the TP by wet scavenging. Contrary to the role of wind in increasing aerosol transport, the positive correlation between wind and aerosols in the TP reported in previous studies is contributed by the negative interannual correlations between wind and rainfall and between rainfall and fire events over the southern Himalayas. This study highlights the co-variability of wind and rainfall and their confounding impacts on aerosols in the southern Himalayas and over the TP. With pre-monsoon rainfall projected to increase in adjacent regions of southern TP, aerosol transport to the TP may be mitigated in the future.

Tracing the origin of elevated springtime atmospheric sulfate on the southern Himalayan-Tibetan plateau

The Himalayan Tibetan Plateau (HTP) is one of the world's most climate-sensitive regions outside the polar regions.

Techno-economic analysis of micro-grid system design through climate region clustering

Micro-grid systems utilizing renewable energy sources (RES) have emerged as a viable technology for addressing the present global climate crisis. Nonetheless, the considerable uncertainty of RES and the climatic variability make it challenging to build a suitable system. To understand the implications of climate on the optimal micro-grid design, we present a techno-economic analysis of 13,488 regions. On the basis of climatic similarities, the locations were divided into nine groups, and the correlations between climates and optimal designs were analyzed. Based on their climate sensitivity and the change trend of two climates, we discovered that they fell into four separate categories. In climate-sensitive places where PV module size increases 6.79 ± 0.13% and wind turbine size increases at the same rate, the overall cost increases 7.6%, which is 2–4 times that of other types. On the other side, climate-insensitive locations with same/opposite tendencies experience the least change in overall micro-grid system size due to climate change. Although total system sizes are most sensitive in climate-sensitive regions with opposing tendencies, their total costs vary the least due to a trade-off between PV module and wind turbine size variations. The analyses reveal the effects of climate on the sizes and costs of micro-grid systems and emphasize the importance of considering climatic fluctuations when designing micro-grid systems.

Conservation agriculture improves agronomic, economic, and soil fertility indicators for a clay soil in a rainfed Mediterranean climate in Morocco

CONTEXTDeclining rainfall with increasing variability, increasing temperature extremes, and declining soil fertility are threatening crop production and ultimately food security in the rainfed Mediterranean environment in Morocco. Conservation agriculture (CA) practices such as reduced tillage, soil cover, and appropriate crop rotation are recognized as a set of adaptive agricultural systems in such climate-sensitive regions. Systematic evaluation of agronomic, economic, and soil fertility indicators with medium-and long-term adoption of CA in different crop rotations in such variable climatic conditions is needed to drive wider adoption of CA in the region. OBJECTIVEThe objective of this study was to systematically evaluate agronomic, economic, and soil fertility indicators under CA and conventional tillage (CT) using field experimentation (medium-term) and simulation modeling (long-term) for a clay soil of a rainfed Mediterranean environment. METHODSMethodologies included the following: 1) Field experimentation for 5 years (2015–2019), comparing CA and CT in four major food crops: wheat, barley, lentil, and chickpea, conducted in Merchouch, Morocco. The objective was to determine the effect of CA on crop productivity, yield stability, profitability, precipitation use efficiency, and soil fertility indicators of individual crops and cropping systems. (2) Dynamic simulation modeling to understand the long-term effect of adopting CA and CT under cereal–legume and cereal–cereal rotation systems. Using 5 years of experimental data, we calibrated and validated a Decision Support System for Agrotechnology Transfer (DSSAT) model for four crops; and ran the model for 36 years for two major rotations. RESULTS AND CONCLUSIONSAcross the five contrasting rainfall years, in comparison to CT, CA had greater yield stability and increased wheat grain yield by 43%, barley by 8%, lentil by 11%, and chickpea by 19%. In 5 years of cereal-legume rotation cycle, CA resulted in increased system yield (by 20%), total benefits (by 40%), precipitation use efficiency (by 13%), and available soil moisture (by14%) with production cost reduced by 14.5%. The CA system had higher soil organic matter (+7%), available phosphorus (+3%), and exchangeable potassium (+15%) than in CT, although all differences were non-significant. Our field experiment and long-term simulation results suggest that CA adoption improves a range of agronomic and economic, and soil fertility indicators compared to CT in the clay soil of a rainfed Mediterranean environment. SIGNIFICANCEThe outcomes of this experimental and simulation study on the multiple benefits of CA provide evidence for extensionists, policymakers, and farmers to drive its wider adoption in Morocco and similar production environments.

Building social resilience in North Korea can mitigate the impacts of climate change on food security.

Adaptation based on social resilience is proposed as an effective measure to mitigate hunger and avoid food shocks caused by climate change. But these have not been investigated comprehensively in climate-sensitive regions. North Korea (NK) and its neighbours, South Korea and China, represent three economic levels that provide us with examples for examining climatic risk and quantifying the contribution of social resilience to rice production. Here our data-driven estimates show that climatic factors determined rice biomass changes in NK from 2000 to 2017, and climate extremes triggered reductions in production in 2000 and 2007. If no action is taken, NK will face a higher climatic risk (with continuous high-temperature heatwaves and precipitation extremes) by the 2080s under a high-emissions scenario, when rice biomass and production are expected to decrease by 20.2% and 14.4%, respectively, thereby potentially increasing hunger in NK. Social resilience (agricultural inputs and population development for South Korea; resource use for China) mitigated climate shocks in the past 20 years (2000-2019), even transforming adverse effects into benefits. However, this effect was not significant in NK. Moreover, the contribution of social resilience to food production in the undeveloped region (15.2%) was far below the contribution observed in the developed and developing regions (83.0% and 86.1%, respectively). These findings highlight the importance of social resilience to mitigate the adverse effects of climate change on food security and human hunger and provide necessary quantitative information.

Case Study for Testing the Validity of NOx-Ozone Algorithmic Climate Change Functions for Optimising Flight Trajectories

One possibility to reduce the climate impact of aviation is the avoidance of climate-sensitive regions, which is synonymous with climate-optimised flight planning. Those regions can be identified by algorithmic Climate Change Functions (aCCFs) for nitrogen oxides (NOx), water vapour (H2O) as well as contrail cirrus, which provide a measure of climate effects associated with corresponding emissions. In this study, we evaluate the effectiveness of reducing the aviation-induced climate impact via ozone (O3) formation (resulting from NOx emissions), when solely using O3 aCCFs for the aircraft trajectory optimisation strategy. The effectiveness of such a strategy and the associated potential mitigation of climate effects is explored by using the chemistry–climate model EMAC (ECHAM5/MESSy) with various submodels. A summer and winter day, characterised by a large spatial variability of the O3 aCCFs, are selected. A one-day air traffic simulation is performed in the European airspace on those selected days to obtain both cost-optimised and climate-optimised aircraft trajectories, which more specifically minimised a NOx-induced climate effect of O3 (O3 aCCFs). The air traffic is laterally and vertically re-routed separately to enable an evaluation of the influences of the horizontal and vertical pattern of O3 aCCFs. The resulting aviation NOx emissions are then released in an atmospheric chemistry–climate simulation to simulate the contribution of these NOx emissions to atmospheric O3 and the resulting O3 change. Within this study, we use O3-RF as a proxy for climate impact. The results confirm that the climate-optimised flights lead to lower O3-RF compared to the cost-optimised flights, although the aCCFs cannot reproduce all aspects of the significant impact of the synoptic situation on the transport of emitted NOx. Overall, the climate impact is higher for the selected summer day than for the selected winter day. Lateral re-routing shows a greater potential to reduce climate impact compared to vertical re-routing for the chosen flight altitude. We find that while applying the O3 aCCFs in trajectory optimisation can reduce the climate impact, there are certain discrepancies in the prediction of O3 impact from aviation NOx emissions, as seen for the summer day. Although the O3 aCCFs concept is a rough simplification in estimating the climate impact of a local NOx emission, it enables a reasonable first estimate. Further research is required to better describe the O3 aCCFs allowing an improved estimate in the Average Temperature Response (ATR) of O3 from aviation NOx emissions. A general improvement in the scientific understanding of non-CO2 aviation effects could make climate-optimised flight planning practically feasible.

Gendered vulnerability and inequality: understanding drivers of climate-smart agriculture dis- and nonadoption among smallholder farmers in Malawi and Zambia

In this study we explore gender-differentiated drivers of disadoption and nonadoption of climate-smart agriculture (CSA) technologies among smallholder farmers for everyday adaptation and resilience building in the face of the increasing threat of climate risk. We apply theoretical perspectives from mainstream technology adoption and gendered vulnerability to identify underlying vulnerabilities and inequalities that drive disadoption (the decision to discontinue any CSA technology previously practiced) and nonadoption (the decision not to use any form of CSA technology). We used an exploratory-sequential mixed methods design at the local level in Chikwawa, Malawi, and Gwembe, Zambia, to understand gender-differentiated drivers of CSA disadoption and nonadoption. Key interviews were conducted with identified critical informants at the district level, followed by focus group discussions with men and women at the village level to obtain qualitative data. We collected quantitative data through a cross-sectional household survey. Findings show that gender-differentiated drivers of CSA disadoption and nonadoption fall within social, economic, institutional, and environmental categories and underlying gendered vulnerability and inequality shape these drivers. CSA is introduced within preexisting gendered vulnerability and inequality, shaping adoption decisions by diverse groups of female and male smallholder farmers. Consequently, CSA outcomes of improved agricultural productivity, adaptation, and resilience building may not be equally achieved because of gender inequalities and vulnerabilities that demotivate diverse households from adopting CSA. This work contributes to a contemporary gender-transformative paradigm in climate change adaptation and disaster risk reduction by focusing on CSA adoption in climate-sensitive regions.