Future Climate Change Research Articles

Impacts and State‐Dependence of AMOC Weakening in a Warming Climate

AbstractAll climate models project a weakening of the Atlantic Meridional Overturning Circulation (AMOC) strength in response to greenhouse gas forcing. However, the climate impacts of the AMOC decline alone cannot be isolated from other drivers of climate change using existing Coupled Model Intercomparison Project simulations. To address this issue, we conduct idealized experiments using the EC‐Earth3 climate model. We compare an abrupt 4×CO2 simulation with the same experiment, except we artificially fix the AMOC strength at preindustrial levels. With this design, we can formally attribute differences in climate change impacts between these two experiments to the AMOC decline. In addition, we quantify the state‐dependence of AMOC impacts by comparing the aforementioned experiments with a preindustrial simulation in which we artificially reduce the AMOC strength. Our findings demonstrate that AMOC decline impacts are state‐dependent, thus understanding AMOC impacts on future climate change requires targeted model experiments.

Optimal reliability ensemble averaging approach for robust climate projections over China

AbstractAccurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China.

Range‐wide intraspecific variation reflects past adaptation to climate in a gypsophile Mediterranean shrub

Abstract Phenotypic differences among populations stem from the interaction between neutral and adaptive processes, and phenotypic plasticity. Although clinal trait variation along climatic gradients often evolves in widely distributed species, it is unknown whether substrate specialization, such as that of Mediterranean gypsum plants, has constrained adaptation to climate. Using a common garden experiment with two contrasting watering treatments, we quantified phenotypic plasticity, assessed evidence for footprints of selection using FST – QST comparisons, and evaluated the ecological factors driving genetically based phenotypic differentiation of 11 populations encompassing the full environmental range of the gypsum shrub Lepidium subulatum. We found evidence for genetic differentiation among populations related to climatic differences, with populations from warmer and drier sites showing lower specific leaf area and leaf N, earlier phenology, greater water use efficiency and greater fitness. Multiple lines of evidence suggest that this differentiation was driven by past divergent selection rather than neutral processes. All populations showed high phenotypic plasticity, indicating that plasticity has not been selected against, even in populations from sites with harsher climatic conditions. Synthesis. Our results indicate that despite strong substrate specialization, adaptive differentiation related to climatic gradients occurs in this species. However, we also found that populations from mesic sites may be particularly vulnerable to future climate change given their relatively lower fitness under both wet and dry conditions.

Anthropogenic forcings reverse a simulated multi-century naturally-forced Northern Hemisphere Hadley cell intensification

The Hadley circulation plays a central role in determining the location and intensity of the hydrological cycle in tropical and subtropical latitudes. Thus, the human-induced historical and projected weakening of the Northern Hemisphere Hadley circulation has considerable societal impacts. Yet, it is currently unknown how unparalleled this weakening is relative to the response of the circulation to natural forcings in past centuries. Here, using state-of-the-art climate models, we show that in contrast to the recent and future human-induced Hadley circulation weakening, natural forcings acted to intensify the circulation by cooling the climate over the last millennium. The reversal of a naturally-forced multi-centennial trend by human emissions highlights their unprecedented impacts on the atmospheric circulation. Given the amplifying effect of natural forcings on the Hadley circulation, our analysis stresses the importance of adequately incorporating natural forcings in climate model projections to better constrain future tropical climate changes.

Performance evaluation of varies climate models using observed and regional climate models for the Katar Watershed, Ethiopia

Climate models are fundamental tools to estimates the reliable future climate change and its effects on the water resources and agriculture in basins. However, all climate models are not equally performed for all areas. Therefore, determining the most appropriate climate models for a specific study area is essential. The focus of this study was to evaluate the performance of the regional climate models with regard to simulating precipitation, and temperatures at Katar watershed. This study examines the performance of fourteen CORDEX-AFRICA-220 Regional Climate Models (RCMs) for the period of 1984–2005 using statistical metrics such as Pearson correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and bias. The findings indicated that GERICS-MPI was better performed in representing Areta, and Bokoji station, GERICS-IPSL was better representing in Assela, Ketergenet, and Sagure station, CCCma-CanESM2-AFR22, and RCA4-ICHEC performed relatively better in representing the mean annual observed rainfall at the Kulumsa, and Ogolcho station respectively. However, RCA4-CSIRO performed weakly in estimation of annual rainfall at all stations. RCM model such as GERICS-MPI was relatively better than the others in replicating the annual pattern of the maximum temperature at Areta, Bokoji, and Ketergenet stations. Similarly, GERICS-IPSL were relatively better in replicating the annual maximum temperature at Assela, and Sagure stations, CCCma-CanESM2-AFR22 at Kulumsa station, and RCA4-ICHEC at Ogolcho station performed well in capturing the observed and simulated annual maximum temperature. Better performance was observed on minimum temperature at CCCma-CanESM2-AFR22 at Areta, Assela, and Ketergenet stations, GERICS-MOHE-AFR-22 at Bokoji station, GERICS-MPI at Kulumsa, and Ogolcho stations, RAC4-NOAA-2G at Sagure stations. However, weak performance was observed RCA4-CSIRO at all stations. RCM models of GERICS-MPI, and CCLM4-NCC-AFR-22 performed better than the other RCM models for correction of annual rainfall in Katar watershed. However, poor performance was observed at RCA4-ICHEC model on Katar watershed. The GERICS-MPI model performed well. However, poor performance was observed at RCA4-ICHEC on maximum temperature, and GERICS-NOAA-2M on minimum temperature in Katar watershed.

Seasonal Scale Climatic Factors on Grassland Phenology in Arid and Semi-Arid Zones

Influenced by climate change, significant alterations in vegetation phenology have been observed globally. Grassland phenology is highly sensitive to climate change. However, research on the variations in grassland phenology and its responses to seasonal climatic changes in arid and semi-arid regions remains scarce. This study, utilizing Solar-Induced Chlorophyll Fluorescence (SIF) data, meteorological station data, and grassland type data, employs trend analysis and time series analysis to explore the trends of seasonal climatic variability and the sensitivity response of grassland phenology in Xinjiang to seasonal climates. The findings reveal the following: (1) The region experiences more pronounced warming in winter and spring than in summer and autumn, with ground temperature increments outpacing those of air temperatures. The summer season registers the peak in precipitation volume and rate of increase, where mountainous zones accrue more rainfall compared to basins and plains. The distribution of sunshine duration is characterized by higher values in eastern areas than in the west and more in the plains than in mountainous regions, potentially due to escalating cloudiness, which has contributed to a diminishing trend in sunshine hours across Xinjiang over the past 20 years. (2) Over the past two decades, the perennial greening phase of Xinjiang grasslands has predominantly occurred in early May, showing an overall trend of occurring earlier by approximately 5.47 days per decade, while the yellowing phase mainly occurs at the end of September and the beginning of October, demonstrating a delaying trend (6.61 days/decade). The average length of the growing season is 145 days, generally showing a slightly increasing trend (11.97 days/decade). (3) In spring, the rise in air and ground temperatures, along with increased sunshine duration, all promote grassland growth, leading to an earlier greening phase. Conversely, in autumn, increases in air temperature, ground temperature, and sunshine duration can inhibit grassland growth, resulting in an earlier yellowing phase. Increased precipitation in summer and autumn can delay the yellowing phase and extend the length of the grassland growing season. This research provides new insights into the factors influencing large-scale grassland phenology and offers references for grassland adaptation to future climate changes.

Late Holocene tropical cyclones linked to climatic and solar variability

Response of tropical cyclone (TC) frequency to future climate change has important implications for society but remains poorly understood due to a lack of long-term reliable observational records. Here, we use high-resolution organic geochemical proxies (OGPs) with robust chronological control from two coastal lakes, >150 km apart, to reconstruct past TC activities in the northeastern Gulf of Mexico (GOM). Results show multi-decadal-to-centennial-scale fluctuations in TC frequency over the last 5500 years. We found that TC frequency either exceeded or was comparable to modern observations during a prolonged interval (∼1500-720 cal yr BP) that encompasses the Medieval Warm Period. We also found a nearly 90% reduction in TC activity, relative to modern, during an exceptionally quiescent period (650-280 cal yr BP) that overlaps the Little Ice Age. Warmer temperatures in the North Atlantic did not always support increased TC frequency in this region. We show, for the first time, that increased TC activities in the northeastern GOM track increased solar irradiance. High TC frequency is also coupled to periods of increased sea surface temperatures in the northern GOM, enhanced El Niño-Southern Oscillation, and positive phases of the Atlantic Multi-Decadal Oscillation. Our results highlight the importance of natural variability in climate and solar activity in modulating TC frequency.

Spatio-Temporal Changes of Vegetation Net Primary Productivity and Its Driving Factors on the Tibetan Plateau from 1979 to 2018

The Net Primary Productivity (NPP) of the Tibetan Plateau (TP) has undergone significant changes since the 1980s. The investigation of the spatiotemporal changes of NPP and its driving factors is of significant importance. Here, we analyze the spatial and temporal trends of Net Primary Production (NPP) and the effects of meteorological factors on the NPP change on the Tibetan Plateau (TP) using version 5.0 of the Community Land Model. The results showed that the average NPP was 256 (g C·m2·yr−1) over the past 40 years, with a continuously increasing trend of 2.38 (g C·m2·yr−1). Precipitation was the main factor affecting NPP changes, temperature had no significant effect on NPP changes, while radiation showed a negative trend. Changes in precipitation, temperature and radiation account for approximately 91%, 5.3%, and 3.8% of NPP variation, respectively. Based on grass coverage, we categorized alpine grasslands into three types: high, medium, and low coverage. Our findings indicate the NPP change of the high-coverage grasslands was mainly affected by precipitation, and then the temperature and radiation. Comparatively, the precipitation change is the driving factor of the increased NPP of low-coverage grasslands, but the temperature increase is the negative factor. Our studies have implications for assessing and predicting vegetation responses to future climate change.

Historical and Future global warming under different RCP emission scenarios

Understanding the relationship between carbon emissions and climate change has become increasingly urgent amidst the universal endorsement of sustainable development and the establishment of net-zero carbon emission target (The Paris Agreement). In this study, model projections based on Coupled Model Intercomparison Project Phase 5 (CMIP5) are used to examine future climate change under different representative concentration pathway (RCP) emission scenarios till 2300. Besides analysis of future global average temperature change under the emission scenarios of RCP2.6, RCP4.5, RCP6.0, and RCP 8.5, historical data is first collected during the past three decades (1990-2021). The results show that the simulated global warming ranges from 0.9 to 1.3 between 1990 and 2021, consistent with the observations (0.93). Carbon emissions in China arose mostly from manufacturing and construction, electricity and heat and transport. Emissions from electricity, heat and transport increase by a high ratio of 7.7 and 3.5 times due to industry development. Under different RCP scenarios, the global surface temperature anomaly will increase by 0.2-1.0, 1.2-2.0, 3.5-4.3 and 3.0-8.5 , respectively. The surface temperature anomaly values under RCP8.5 are much higher than those under other emission scenarios. Only in the RCP2.6 scenario (sustainability), the surface warming in all simulations stays below a 2 threshold at the end of the 21st century. To attain net zero emissions, it is highly demanded to develop strict emission policies, chart a course toward decarbonization, and institute effective measures aimed at realizing net zero emissions within the short to medium term. Concurrently striving for economic objectives, managing the equilibrium between economic growth and environmental sustainability emerges as a critical imperative.

Predicting the geographical distribution and niche characteristics of Cotoneaster multiflorus based on future climate change

IntroductionArid and semi-arid regions are climate-sensitive areas, which account for about 40% of the world’s land surface area. Future environment change will impact the environment of these area, resulting in a sharp expansion of arid and semi-arid regions. Cotoneaster multiflorus is a multi-functional tree species with extreme cold, drought and barren resistance, as well as ornamental and medicinal functions. It was found to be one of the most important tree species for ecological restoration in arid and semi-arid areas. However, bioclimatic factors play an important role in the growth, development and distribution of plants. Therefore, exploring the response pattern and ecological adaptability of C. multiflorus to future climate change is important for the long-term ecological restoration of C. multiflorus in arid and semi-arid areas.MethodsIn this study, we predicted the potential distribution of C. multiflorus in China under different climate scenarios based on the MaxEnt 2.0 model, and discussed its adaptability and the major factors affecting its geographical distribution.ResultsThe major factors that explained the geographical distribution of C. multiflorus were Annual precipitation (Bio12), Min air temperature of the coldest month (Bio6), and Mean air temperature of the coldest quarter (Bio11). However, C. multiflorus could thrive in environments where Annual precipitation (Bio12) >150 mm, Min air temperature of the coldest month (Bio6) > -42.5°C, and Mean air temperature of the coldest quarter (Bio11) > -20°C, showcasing its characteristics of cold and drought tolerance. Under different future climate scenarios, the total suitable area for C. multiflorus ranged from 411.199×104 km² to 470.191×104 km², which was 0.8~6.14 percentage points higher than the current total suitable area. Additionally, it would further shift towards higher latitude.DiscussionThe MaxEnt 2.0 model predicted the potential distribution pattern of C. multiflorus in the context of future climate change, and identified its ecological adaptability and the main climatic factors affecting its distribution. This study provides an important theoretical basis for natural vegetation restoration in arid and semi-arid areas.

The impact of future UK offshore wind farm distribution and climate change on generation performance and variability.

Abstract The UK plans to significantly increase offshore wind generation capacity as part of the effort to achieve net zero targets. Current installation is densely located in a few areas, particularly off the east coast of England, and although current siting proposals include new offshore regions, significant volumes of wind generation capacity are yet to be located to meet 2050 installation targets. This paper uses a recent dataset of multi-decadal offshore wind power capacity factor timeseries to assess how UK offshore wind generation is likely to be affected by both the spatial distribution of future wind farms, and by the impacts of near-future (2020-2050) climate change. We determine that a wider geographic spread of offshore capacity results in a much-improved and less-variable UK-aggregated power generation profile, with substantial reductions in periods of low generation and extreme wind power ramping events, without negatively impacting mean or peak generation outputs. The impact of near-term climate change appears to be minor, slightly reducing overall generation and possibly resulting in an underestimation of future installation requirements, but this climate signal is outweighed by the effects of spatial distribution, and even more so by inherent hourly to inter-annual wind speed variability. This study implies that the intermittency of wind generation can be partly mitigated through increasing the spatial diversity of the existing wind farm distribution. Alongside a more in depth investigation of future climate change, and a holistic assessment of relevant geospatial factors such as Levelised Cost of Energy, infrastructure, and environmental constraints, this study could be used for optimisation of future offshore wind siting.

Predicting habitat suitability for the Australian cycad-attacking weevil (Siraton internatus) under climate change

AbstractCycads hold important economic and conservation value. Some species are extensively used in landscaping, while others are endangered and legally protected. The Australian cycad-attacking weevil, Siraton internatus, is notably destructive, occasionally causing infestations and invasions across various countries. This study simulated habitat suitability for S. internatus to assess its potential invasion and the impact of climate change. Habitat suitability was evaluated under current climate and four climate change scenarios over two time frames (2050 and 2090). Furthermore, we investigated the threat posed by S. internatus to cycad reserves, using Taiwanese reserves as a representative case. Our MaxEnt predictions demonstrated high accuracy, meeting multiple evaluation criteria. We explored the potential distribution of S. internatus within Australia and internationally, identifying suitable habitats in Africa, the Americas, Asia, and Europe. The case study highlighted the low habitat suitability within the two Taiwanese cycad reserves, which is projected to decrease to unsuitable levels under future climate change scenarios for this weevil species. Moreover, our results revealed that suitable habitat for S. internatus is projected to contract globally under all climate scenarios and time periods, but expansion in Chile and the southern Himalaya (e.g., Nepal). This study provides valuable insights into cycad conservation and pest invasion risks. The results support both global and local efforts to manage the invasion threats from this destructive Australian cycad-attacking weevil species. It also accentuates the urgency for continuous biosecurity inspections and prevention of exporting mature cycad caudexes from Australia.

Elevated atmospheric CO2 concentration and vegetation structural changes contributed to gross primary productivity increase more than climate and forest cover changes in subtropical forests of China

Abstract. The subtropical forests of China play a pivotal role in the global carbon cycle and in regulating the global climate. Quantifying the individual and combined effects of forest cover change (FCC), vegetation structural change (e.g. leaf area index (LAI)), CO2 fertilisation, and climate change (CC) on the annual gross primary productivity (GPP) dynamics of different subtropical forest types are essential for mitigating carbon emissions and predicting future climate changes, but these impacts remain unclear. In this study, we used a processed-based model to comprehensively investigate the impacts of these factors on GPP variations with a series of model experiments in China's subtropical forests from 2001 to 2018. Simulated GPP showed a significant increasing trend (20.67 gCm-2yr-1, p<0.001) under the interaction effects of FCC, LAI change, rising CO2, and CC. The CO2 fertilisation (6.84 gCm-2yr-1, p<0.001) and LAI change (3.79 gCm-2yr-1, p=0.004) were the two dominant drivers of total subtropical forest GPP increase, followed by the effects of FCC (0.52 gCm-2yr-1, p<0.001) and CC (0.92 gCm-2yr-1, p=0.080). We observed different responses to drivers depending on forest types. The evergreen broad-leaved forests showed the maximum carbon sequestration rate due to the positive effects of all drivers. Both the FCC (0.19 gCm-2yr-1, p<0.05) and CC (1.22 gCm-2yr-1, p<0.05) significantly decreased evergreen needle-leaved forest GPP, while their negative effects were almost offset by the positive impact of LAI changes. Our results indicated that LAI outweighed FCC in promoting GPP, which is an essential driver that needs to be accounted for in studies and ecological and management programmes. Overall, our study offers a novel perspective on different drivers of subtropical forest GPP changes and provides valuable information for policy makers to better manage subtropical forests to mitigate climate change risks.

Vegetation redistribution is predicted to intensify soil organic carbon loss under future climate changes on the Tibetan Plateau

Vegetation redistribution may bring unexpected climate-soil carbon cycling in terrestrial biomes. However, whether and how vegetation redistribution alters the soil carbon pool under climate change is still poorly understood on the Tibetan Plateau. Here, we applied the G-Range model to simulate the cover of herbs, shrubs and trees, net primary productivity (NPP) and soil organic carbon density (SOCD) at the depth of 60 cm on Tibetan Plateau for the individual years 2020 and 2060, using climate projection for Representative Concentration Pathways (RCP) 4.5 and RCP8.5 scenarios with the RegCM4.6 model system. Vegetation redistribution was defined as the transitions in bare ground, herbs, shrubs and trees between 2020 and 2060, with approximately 57.9 % (RCP4.5) and 59 % (RCP8.5) of the area will redistribute vegetation over the whole Tibetan Plateau. The vegetation cover will increase by about 2.4 % (RCP4.5) and 1.9 % (RCP8.5), while the NPP and SOCD will decrease by about −14.3 g C m−2 yr−1 and −907 g C m−2 (RCP4.5), and −1.8 g C m−2 yr−1and −920 g C m−2 (RCP8.5). Shrubs and trees will expand in the east, and herbs will expand in the northwest part of the Plateau. These areas are projected to be hotspots with greater SOCD reduction in response to future climate change, and will include lower net plant carbon input due to the negative NPP. Our study indicates that the SOC pool will become a carbon source under increased air temperature and rainfall on the Tibetan Plateau by 2060, especially for the area with vegetation redistribution. These results revealed the potential risk of vegetation redistribution under climate change in alpine ecosystems, indicating the policymakers need to pay attention on the vegetation redistribution to mitigate the soil carbon emission and achieve the goal of carbon neutrality on the Tibetan Plateau.

Vulnerability of geoheritage sites in South Africa to climate change: Examples from the Eastern Cape Province

Climate change and variability have potential to impact on all types of heritage sites globally, through increasing weathering and erosion of geological and built heritage, changes in biome and ecosystem viability, sea-level rise and coastal flooding, and increased climate hazard events. This study examines the most likely impacts of climate change on different heritage sites in South Africa, with a particular regional focus on sites of potential geoheritage interest in the Eastern Cape Province of the country. The diverse examples described in this study are: (1) Klasies River cave, which is a Middle Stone Age archaeological site; (2) different localities along the coast where faunal footprints are preserved within aeolianite that dates mainly to the last interglacial period; (3) different localities inland where rock art panels are present within caves or rock shelters; and (4) Cape St Francis, which is a coastal foreland where different phases of late Quaternary coastal dunes with archaeological interest are preserved. Results show that many of these very varied sites of geoheritage interest are not protected as National or Provincial Heritage Sites under current legislation, which is weak and inconsistently applied across the country. This means that no specific management plans, under national policy guidelines, are in place for mitigating against the impacts of present and future climate change and variability, either regionally or nationally. These climate changes include: sea-level rise and increased coastal storminess and wave attack; and changes in weathering and erosion regimes as a result of increased temperatures and likely decreased rainfall or higher rainfall variability. A better appreciation of the interconnections that exist between geology and natural and cultural heritage may lead to valuing landscape and site geoheritage more highly at the local, national and international level.

Law, Colonial-Capitalist Floods, and the Production of Injustices in Eastern India: Insights for Climate Adaptation

Abstract Floods are not merely ‘natural’ disasters; rather, they emerge as socio-natural phenomena shaped by political, social, and economic processes. Law plays a pivotal role in producing and sustaining these processes and contributes to the creation of unjust environments. Drawing on political ecology and environmental history, this article analyzes the role of law and its interactions with colonialism and capitalism in the Damodar river valley in Eastern India. The Damodar river valley is an intensely engineered and hazardous region, a site of multiple interventions and developmental and ecological experiments for over a century. Colonial and post-colonial legacies have left a lasting imprint on legal, policy, and institutional frameworks, establishing a path-dependent trajectory for addressing future climate change adaptation challenges. While focusing on a specific case study, the article's approach and findings have broader significance, especially in the context of climate adaptation. The central argument underscores the need to understand the political and legal dimensions of flooding, and reinforces the need for a shift beyond incremental adjustments that do not tackle the underlying structures that produce the injustices associated with floods. It highlights the importance of ‘transformative adaptation’ approaches that address the root causes of climate-related disasters, such as restructuring power relations between actors, reconfiguring governance structures, and scrutinizing ideologies that mediate how water is used and distributed.

Impact of climate change and genetic development on Iowa corn yield

The vulnerability of corn yield to high temperature and insufficient rainfall in the US mid-west is widely acknowledged. The impact of extreme weather and genetic development on corn yield is less well known. One of the main reasons is that the multicollinearity in the variables can lead to confounding results. Here we model the impact of climate and genetic development by employing an elastic net regression model to address the multicollinearity issue. This allows us to develop a more robust multiple regression model with higher predictive accuracy. Using granular data for Iowa from 1981-2018, we find that corn yield is vulnerable to high mean summer temperatures particularly in July, a widening diurnal temperature range in June and dry summer conditions (due to extremely low rainfall) from June-August. We find that overall climate impact reduced average annual yield by 0.7%. We also find that genetic development which led to earlier planting dates, widening duration of the reproductive interval, higher growing degree day accumulation and larger net planted area had a beneficial impact on the Iowa corn yield during 1981-2018 resulting in an average annual yield improvement of 1.8% per annum. This provides a basis for optimism that these genetic developments and management practices will continue to adapt and improve in the future to counter the impact of climate change on corn yield. We have also modelled the impact of future climate change using the latest climate projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). These climate projections show that the average temperature during the growing season (MayO-October) will increase by 2.4 -2.9 o C by mid-century while the average spring temperature (March and April) will increase by a relatively slower 1.9 -2.3 o C by mid-century. Additionally, climate projections show that both temperature and rainfall will also become more extreme in the future with the changes varying from spring to summer. Our results show that, just due to climate change alone in Iowa corn yield will decline between 1.4-1.7% per annum until mid-century (or 1.2-2.1% per annum until the late twenty first century).

Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE

Extreme precipitation and temperature indices under future climate change in central Asia based on CORDEX-CORE

Feeling climate injustice: Affective climate violence, greenhouse gaslighting and the whiteness of climate anxiety

This paper explores the process of feeling climate injustice. It aims to situate climate distress as an issue of justice, in order to generate more politically accountable and empowering responses. It firstly situates climate anxiety, solastalgia and climate disaster trauma as symptoms of affective climate violence, where harm that could have been prevented was instead consciously and systematically exacerbated by fossil fuelled political regimes. It articulates witnessing as a practice of affective climate justice, an approach that would recognise climate distress as violence, and offer support, apology and redress for this violence, including through seeking to prevent future climate change. However, the second section outlines how, in perverse efforts to maintain fossil fuel interests, climate distress is often further amplified through practices of greenhouse gaslighting – denying, deriding and dismissing people's experiences of harm. Greenhouse gaslighting is outlined as a patriarchal practice of emotional abuse that is enabled by and seeks to perpetuate white-colonial-extractivism. Thirdly, the paper argues that even within progressive circles, current efforts to witness climate distress potentially fail to enact affective climate justice due to discourses that centre whiteness and privilege, rather than recognising and responding to the different and unequal forms of affective climate violence experienced by diverse peoples.

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.