Multiple Climate Research Articles

Effects of multiple mammalian herbivores and climate on grassland-shrubland transitions in the Chihuahuan Desert.

The replacement of grasses by shrubs or bare ground (xerification) is a primary form of landscape change in drylands globally with consequences for ecosystem services. The potential for wild herbivores to trigger or reinforce shrubland states may be underappreciated, however, and comparative analyses across herbivore taxa are sparse. We sought to clarify the relative effects of domestic cattle, native rodents, native lagomorphs, and exotic African oryx (Oryx gazella) on a Chihuahuan Desert grassland undergoing shrub encroachment. We then asked whether drought periods, wet season precipitation, or interspecific grass-shrub competition modified herbivore effects to alter plant cover, species diversity, or community composition. We established a long-term experiment with hierarchical herbivore exclosure treatments and surveyed plant foliar cover over 25 years. Cover of honey mesquite (Prosopis glandulosa) proliferated, responding primarily to climate, and was unaffected by herbivore treatments. Surprisingly, cattle and African oryx exclusion had only marginal effects on perennial grass cover at their current densities. Native lagomorphs interacted with climate to limit perennial grass cover during wet periods. Native rodents strongly decreased plant diversity, decreased evenness, and altered community composition. Overall, we found no evidence of mammalian herbivores facilitating or inhibiting shrub encroachment, but native small mammals interacting with climate drove dynamics of herbaceous plant communities. Ongoing monitoring will determine whether increased perennial grass cover from exclusion of native lagomorphs and rodents slows the transition to a dense shrubland.

Climate change is intensifying rainfall erosivity and soil erosion in West Africa

Soil erosion is a critical environmental challenge with significant implications for agriculture, water quality, and ecosystem stability. Understanding its dynamics is essential for sustainable environmental management and societal welfare. Here, we analyze rainfall erosivity and erosion patterns across West Africa (WAF) during the historical (1982–2014), near future (2028–2060), and far future (2068–2100) periods under Shared Socioeconomic Pathways (SSPs 370 and 585). Using bias-corrected-downscaled (BCD) climate models validated against reference data, we ensure an accurate representation of rainfall—a key driver of erosivity (R-factor) and soil erosion. We compare Renard's approach and the Modified Fournier Index (MFI) to calculate the R-factor and note a strong correlation. However, Renard's method shows slightly lower accuracy in Sierra Leone, Guinea, and The Gambia, likely due to its inability to capture high-intensity, short-duration rainfall events. In contrast, the MFI, utilizing continuous rain gauge data, proves more reliable for these regions. We also attribute fluctuations in erosivity, such as those seen during the 2003 West Africa floods, to synoptic weather patterns influenced by multiple climate processes.Furthermore, our analysis reveals regions where future soil erosion could exceed 20 t/ha/yr due to climate change. Under the SSP 370 scenario, soil erosion in WAF is projected to rise by 14.84 % in the near future and 18.65 % in the far future, increasing further under SSP 585 to 19.86 % and 23.49 %, respectively. The most severe increases are expected in Benin and Nigeria, with Nigeria potentially facing a 66.41 % rise in erosion by the far future under SSP 585. These findings highlight the region's exposure to intensified climatic conditions and underscore the urgent need for targeted soil management and climate adaptation strategies to mitigate erosion's ecological and socioeconomic impacts.

Early emergence and determinants of human-induced Walker circulation weakening

The Walker circulation is projected to slow down in response to greenhouse gas warming. However, detecting the impact of human activities on changes in the Walker circulation is challenging due to the significant influence of internal variability. Here, based on ensembles of multiple climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show evidence that the emergence of the human-induced weakening of Walker circulation tends to occur earlier in the middle-upper troposphere than at the surface. This earlier emergence is attributed to a more pronounced initial weakening response of the middle-upper tropospheric Walker circulation to atmospheric CO2 radiative forcing. We further reveal that the emergence time of a weaker Walker circulation varies across models. This intermodel spread is governed by an ocean thermostat that operates by modulating the zonal sea surface temperature gradient over the tropical Indo-Pacific region. Our findings address the key question of whether and how to detect human-induced large-scale atmospheric circulation changes and provide valuable insights for assessing the associated risks.

The case for a Framework for UnderStanding Ice-Ocean iNteractions (FUSION) in the Antarctic-Southern Ocean system

We are in a period of rapidly accelerating change across the Antarctic continent and Southern Ocean, with land ice loss leading to sea level rise and multiple other climate impacts. The ice-ocean interactions that dominate the current ice loss signal are a key underdeveloped area of knowledge. The paucity of direct and continuous observations leads to high uncertainty in the glaciological, oceanographic and atmospheric fields required to constrain ice-ocean interactions, and there is a lack of standardised protocols for reconciling observations across different platforms and technologies and modelled outputs. Funding to support observational campaigns is under increasing pressure, including for long-term, internationally coordinated monitoring plans for the Antarctic continent and Southern Ocean. In this Practice Bridge article, we outline research priorities highlighted by the international ice-ocean community and propose the development of a Framework for UnderStanding Ice-Ocean iNteractions (FUSION), using a combined observational-modelling approach, to address these issues. Finally, we propose an implementation plan for putting FUSION into practice by focusing first on an essential variable in ice-ocean interactions: ocean-driven ice shelf melt.

Assessing the influence of climate change on multiple climate indices in Nepal using CMIP6 global climate models

Global climate models (GCMs) serve as essential tools for projecting future climate trends, but their coarse resolution limits localized impact assessments in sectors like hydrology, agriculture, and biodiversity. Observation data with a spatial resolution of a few kilometers are crucial for downscaling and bias-correcting GCMs at finer resolutions. However, Nepal's extreme topography and organizational challenges have led to uneven distribution of meteorological stations and inconsistent data quality. Moreover, CMIP6-based climate extremes projections for the entire country are currently unavailable. To tackle these challenges, we developed a comprehensive national database for Nepal, offering high-resolution historical and projected precipitation and temperature data analyzed through 25 climate extreme indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Initially, observation grid data were prepared at a daily timescale with a spatial resolution of 0.05° × 0.05° for baseline period (1981–2010) using the Asian Precipitation High-Resolved Observational Data Integration Toward Evaluation (APHRODITE), the fifth generation of the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5), and available good quality observed climate data. This data was then utilized to downscale and bias-correct 18 CMIP6 GCMs for 2015–2100 under four SSPs (1–2.6, 2–4.5, 3–7.0, 5–8.5). Quantile mapping was employed for the bias correction of the CMIP6 GCMs. The performance of the multimodal ensemble (MME) indicated better Nash-Sutcliffe Efficiency (NSE), root mean square error ratio (RSR), and Percent Bias (PBIAS) of climate extreme indices for the historical period. A comparative analysis was conducted across Nepal's major geographic regions to account for spatial variability in regional climate systems. The finer-resolution dataset can be crucial to deepen our understanding of climate impacts, and climate change, and eventually informing the policy-making in Nepal. Moreover, the methodology can be effectively replicated in data-scarce developing nations to promote climate research and adaptation efforts.

Actionable human–water system modelling under uncertainty

Abstract. This paper develops an actionable interdisciplinary model that quantifies and assesses uncertainties in water resource allocation under climate change. To achieve this objective, we develop an innovative socio-ecological grand ensemble that combines climate, hydrological, and microeconomic ensemble experiments with a widely used decision support system for water resource planning and management. Each system is populated with multiple models (multi-model), which we use to evaluate the impacts of multiple climate scenarios and policies (multi-scenario, multi-forcing) across systems so as to identify plausible futures where water management policies meet or miss their objectives and to explore potential tipping points. The application of the methods is exemplified by a study conducted in the Douro River basin (DRB), an agricultural basin located in central Spain. Our results show how marginal climate changes can trigger non-linear water allocation changes in the decision support systems (DSSs) and/or non-linear adaptive responses of irrigators to water shortages. For example, while some irrigators barely experience economic losses (average profit and employment fall by < 0.5 %) under mild water allocation reductions of 5 % or lower, profit and employment fall by up to 12 % (∼ 24 ×) when water allocation is reduced by 10 % or less (∼ 2×). This substantiates the relevance of informing the potential natural and socio-economic impacts of adaptation strategies and related uncertainties for identifying robust decisions.

Trajectories of Terrestrial Vegetation Productivity and Its Driving Factors in China's Drylands

AbstractClimate change and large‐scale ecological restoration programs have profoundly influenced vegetation greening and gross primary productivity (GPP) in China's drylands. However, the specific pathways through which climatic factors and vegetation greening influence GPP remain poorly understood. This study examines the spatiotemporal changes in GPP across China's drylands from 2001 to 2020 and investigates the direct and indirect effects of climatic factors and leaf area index (LAI) on GPP. The results reveal that the overall improvement in vegetation cover has positively increased GPP in these regions. Although the direct effects of climatic factors on GPP are minimal, they exert a substantial indirect effect by regulating vegetation growth, highlighting that LAI is a key intermediary in mediating the effects of climatic factors on GPP. Furthermore, these complex interactions vary significantly along the aridity gradient. This study emphasizes the necessity of comprehensively considering the intricate interactions among multiple climate and vegetation factors.

Multi-scale drought variability over West Africa and the associated large-scale circulation patterns

This study addresses the critical research gap in understanding regional drought dynamics in West Africa (WA) using advanced multivariate wavelet coherence (MWC) techniques, revealing critical insights into the complex interplay between large-scale climate indices and regional drought patterns. Utilizing the Standardized Precipitation Evapotranspiration Index (SPEI) and self-calibrating Palmer Drought Severity Index (scPDSI), we examined drought characteristics across WA from 1950 to 2018. Our analysis revealed significant drying trends across all seasons, with the Arid zone experiencing the most severe and prolonged drought (158 months) from August 1981 to September 1994, affecting 79.77% of the area. The entire WA region faced its most extensive drought during this period, impacting over 60% of the study area. MWC analysis demonstrated enhanced drought predictability when combining climate indices, particularly the Southern Oscillation Index with the Atlantic Multidecadal Oscillation, yielding high Average Wavelet Coherence values of 0.52 (SPEI) and 0.53 (scPDSI), with substantial Percentage Area of Significant Coherence of 40.19% and 43.68%, respectively. These findings highlight the crucial role of oceanic-atmospheric interactions in modulating drought conditions across WA. Our results provide valuable insights for improving drought forecasting and management strategies in the region, emphasizing the need for integrated approaches that consider multiple climate indices and their combined effects on drought dynamics.

Energy-water-carbon system management in response to climate change in China under uncertainties (2026–2050)

Climate change has seriously affected the resource utilization of the energy-water-carbon system, while the system carbon emissions continuously act on climate change. Given the interaction between the two, there is an urgent need to facilitate the rational allocation of system resources to realize the positive feedback between climate change and energy-water-carbon system development. In this study, an interval stochastic fuzzy integer programming (ISFIP) model is developed to support resource planning and management of the energy-water-carbon system in response to climate change in China. The developed model has advantages in characterizing the complexity and uncertainty of the energy-water-carbon system and providing optimal system decision-making options under multiple climate change scenarios. The results show that the SSP126-NZE scenario power system would reduce carbon emissions by 16.23 %, air pollutant emissions by 19.31 %, water consumption by 30.96 %, and increase economic benefits by 10.97 %, respectively, compared to the SSP585-STEPS baseline scenario.

The roles and unexplored potential of policy experimentation in climate adaptation governance: A systematic literature review

AbstractPolicy experimentation has emerged globally as a novel governance approach to address complex socio‐environmental problems. In the climate adaptation literature, policy experiments that test technical and governance innovations on a small scale in real‐world conditions are increasingly utilized to explore new pathways for climate adaptation. However, there is a lack of empirical evidence on how policy experiments lead to transformative change in climate adaptation governance, particularly regarding their role as a change strategy. This systematic literature review aims to thoroughly investigate the topic by mapping the empirical characteristics of policy experiments, their role and their outcomes. An existing analytical framework was adapted to fulfill this objective by qualitatively and quantitatively analyzing 27 empirical papers. The findings reveal that policy experiments in climate adaptation often address multiple climate hazards, sectors and actors, yet they are spatially and temporally limited, being predominantly located in Europe. Moreover, the study highlights the transformative potential of policy experiments in climate adaptation governance, emphasizing their effectiveness in testing technical and governing innovations, as well as implementing adaptation policies. Policy experiments predominantly contribute to social learning rather than direct policy changes, requiring specific strategies to upscale the knowledge generated. We conclude the paper with a research agenda that stresses the need for more cumulative and comparative (post)assessments of climate adaptation experiments. This is important given the potential of policy experiments as governing approaches in the advancement of climate adaptation.

Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method

AbstractMore frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a F1‐score of 0.846 ± 0.012, surpassing process‐driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.

Interaction Between Climate Change Scenarios and Biological Invasion Reveals Complex Cascading Effects in Freshwater Ecosystems.

Climate change often facilitates biological invasions, leading to potential interactive impacts of these global drivers on freshwater ecosystems. Although climatic mitigation efforts may reduce the magnitude of these interactive impacts, we are still missing experimental evidence for such effects under multiple climate change scenarios within a multi-trophic framework. To address this knowledge gap, we experimentally compared the independent and interactive effects of two climate change scenarios (mitigation and business-as-usual) and biological invasion on the biomass of major freshwater trophic groups (phytoplankton, zooplankton, periphyton, macroinvertebrates, and a native macrophyte) and the decomposition rate of allochthonous material. Among the independent effects, we found that the business-as-usual climate treatment resulted in lower native macrophyte biomass and higher periphyton biomass compared to the climatic baseline and mitigation treatments. This indicates the potential of climate change to alter the relative dominance of different freshwater producers and demonstrates that climate mitigation efforts can counteract these effects. Biological invasion alone increased the biomass of chironomids, a dominant macroinvertebrate group in tropical freshwater ecosystems, demonstrating a compensatory effect on climate change. Climate change and biological invasion interactively reduced the decomposition rate of allochthonous detritus, likely mediated by the feeding preference of abundant chironomids for periphytic algae associated with the presence of non-native macrophytes. We concluded that (i) climatic mitigation can maintain climate baseline conditions in freshwater ecosystems, and (ii) the interactive effects between future climate scenarios and biological invasion are related to complex cascading interactions among trophic groups on ecosystem processes.

Parameterization of Tree and Shrub Stem Wood Density Adaptions to Multiple Climate and Soil Factor Gradients

Parameterization of Tree and Shrub Stem Wood Density Adaptions to Multiple Climate and Soil Factor Gradients

Wildfire Particulates Enhance Phytoplankton Growth and Alter Communities in the South China Sea Under Wind‐Driven Upwelling

AbstractExtreme wildfire events and cyclones are on the rise across tropical regions in response to climate change. Despite assumptions about their impact on phytoplankton through nutrient supplies, field evidence is lacking, and their combined effects remain unclear. In an on‐site microcosm experiment conducted in the Xisha Islands, South China Sea (SCS) after Typhoon Noru, we observed enhanced phytoplankton growth in response to exposure to total suspended particulates (TSP) from wildfires (2 mg/L and 6 mg/L) under wind‐driven upwelling conditions. Upwelled nutrients had a limited effect on Chl‐a concentration due to phosphate depletion, by contrast, wildfire TSP contributed nutrients enriched in nitrogen and phosphate, resulting in a 3.30–5.61‐fold increase in Chl‐a. However, upwelled nutrients increased the diatom‐to‐dinoflagellate ratio from the initial 11.0 to 12.7, TSP at low and high levels reduced the ratio to 0.3–0.8 and significantly altered the communities, with 61.8% of species, including two dominant diatoms, negatively correlated with N and/or P supplies. Species diversity declined significantly at high TSP levels. These findings suggest that enhanced primary productivity by wildfires may come at the cost of an altered phytoplankton community. This field study improves understanding of the effects of simultaneous occurrences of multiple extreme climate events on marine ecosystems.

The Role of Climate Change and Human Intervention in Shaping Vegetation Patterns in the Fen River Basin of China: Implications of the Grain for Green Program

The Fen River Basin (FRB), an ecologically fragile region in China, exemplifies the intricate interplay between vegetation dynamics and both climatic and human-driven factors. This study leverages a 40-year (1982–2022) dataset, utilizing the kernel-based normalized difference vegetation index (kNDVI) alongside key climatic variables—rainfall (PRE), temperature (TMP), and solar radiation (SRAD)—to investigate vegetation variations and their drivers in the FRB, particularly in relation to the Grain for Green Program (GGP). Our analysis highlights significant greening across the FRB, with the kNDVI slope increasing by 0.0028 yr−1 and green-covered areas expanding by 92.8% over the study period. The GGP facilitated the greening process, resulting in a notable increase in the kNDVI slope from 0.0005 yr−1 to 0.0052 yr−1 and a marked expansion in the area of significant greening from 24.6% to 95.8%. Regional climate shifts, characterized by increased warming, heightened humidity, and a slight rise in SRAD, have further driven vegetation growth, contributing 75%, 58.7%, and 23.6% to vegetation dynamics, respectively. Notably, the GGP has amplified vegetation’s sensitivity to climatic variables, with areas significantly impacted by multiple climate factors expanding from 4.8% to 37.5%. Specially, PRE is the primary climatic influence, impacting 71.3% of the pertinent regions, followed by TMP (60.1%) and SRAD (30%). The integrated effects of climatic and anthropogenic factors, accounting for 47.8% and 52.2% of kNDVI variations, respectively, collectively influence 96% of the region’s vegetation dynamics. These findings underscore the critical role of climate change and human interventions in shaping vegetation patterns and provide a robust foundation for refining ecological conservation strategies, particularly in the context of global warming and land-use policies.

Future drought characterization using multiple drought indices and CMIP-6 Climate Models within Mille Watershed, Lower Awash Basin, Ethiopia

Future drought characterization using multiple drought indices and CMIP-6 Climate Models within Mille Watershed, Lower Awash Basin, Ethiopia

Intraspecific variation in leaf morphology of three widespread woody species along climatic gradients

Abstract The variation and plasticity of leaf morphology plays a pivotal role in the response to environmental changes for plant individuals. Discovering the large-scale pattern of such variation can reveal plants' general adaptive strategies. We analyzed leaf morphology of three widespread woody species in the northern hemisphere using specimen data from the iDigBio and GBIF databases, to investigate the variations in the individual mean traits, in the inter- and intra-individual variability of traits, and in the allometry between traits, along climatic gradients. We found that larger and wider leaves were associated with warmer, wetter and low-sunlight habitats, while smaller but wider leaves are linked to higher wind speed, indicating the response of leaf morphology to multiple climate stresses. The inter-individual variation in leaf area was smaller in colder and windier conditions, suggesting the trait convergence among individuals under environmental filtering, while the intra-individual variation in leaf relative width was smaller in warmer habitats, indicating the similar growth optimum of leaves within one individual in more favorable conditions. Finally, the allometric exponent between leaf length (X-axis) and width (Y-axis) became greater under lower solar radiation and higher wind speed, while the squared correlation coefficient (r2) indicating phenotypic integration showed a decoupling trend under colder conditions, indicating that climate affected the variation tendency of leaf relative width during leaf enlargement. These results reveal the common patterns of leaf morphology responding to climate variation spatially and underscore the necessity to consider inter- and intra-individual variability when examining plant responses to environmental changes.

Probabilistic explanation for episodic ecological events

Abstract Climate associated ecological phenomena that occur approximately once per decade suggest the influence of decadal climate oscillations. However, the consistency and origins of such climate patterns in the Atlantic and Pacific regions is currently under debate. Here, we propose a probabilistic explanation for episodic ecological events based on the likelihood of multiple climate patterns converging in a particular phase combination. To illustrate, we apply this model to continental scale facultative migration of seed-eating finches out of the boreal forest. This \emph{irruption} phenomenon is triggered by seed crop failures stemming from two weakly correlated climate patterns occurring simultaneously in their positive phases -- the North Atlantic Oscillation (NAO) and the North Pacific Oscillation (NPO). The joint probability of NAO and NPO both being positive (above upper tercile) is about $(1/3)^2\approx 0.11$, illustrating a simple probabilistic explanation for quasi-decadal finch irruption and potentially other episodic ecological events in regions affected by multiple climate modes.

A Stepwise Multifactor Regression Analysis of the Interactive Effects of Multiple Climate Factors on the Response of Vegetation Recovery to Drought

In this study, a stepwise multifactor vegetation regression analysis (SMVRA) approach was proposed to investigate the interaction of multiple climate factors on vegetative growth in the study area from 2000 to 2020. It was developed by integrating the stepwise linear regression method, Standardized Precipitation Evapotranspiration Index (SPEI), Normalized Difference Vegetation Index (NDVI), and Pearson correlation coefficient. SMVRA can be used to intuitively understand the interactive effects of multiple correlated factors (e.g., temperature, precipitation, potential evapotranspiration, and the drought index) upon vegetation. The results show that the resilience of vegetation in the BLR basin is influenced by the severity of drought. Annual changes in SPEI over the BLR basin show an increasing trend, with rates of 3.12 × 10−2. Precipitation and NDVI had a strong positive correlation (p < 0.05), found for 34.93% of the total pixels in the study area. In the BLR basin, vegetation growth is inhibited in the 4 years following a drought event. The area near 800 m is most sensitive to drought events. It provides a theoretical basis for future drought response and effective vegetation restoration in the region.

Comprehensive assessment of the climatic and vegetation impacts of wind farms on grasslands: A case study in inner Mongolia, China

Although wind power contributes to the reduction of greenhouse gas emissions, it also has significant impacts on the local climate and vegetation. Exploring these impacts is important for the sustainable development of wind power. Therefore, based on moderate-resolution imaging spectroradiometer (MODIS) data and other remote sensing data from 2003 to 2022, this paper investigated the impacts of 101 grassland wind farms (WFs) in Inner Mongolia on land-atmosphere water and heat exchange, vegetation growth, ecosystem primary productivity, and vegetation structural characteristics during the growing season and revealed the spatial distribution patterns of the impacts of WFs as well as differences between different types of grasslands. The results indicated that WFs increased the nighttime land surface temperature (LST), decreased evapotranspiration (ET), inhibited vegetation growth, decreased gross primary productivity (GPP), and reduced the leaf area index (LAI) in growing season grasslands. This effect varied across different types of grasslands and showed significant complexity. In terms of land-atmosphere water and heat exchange, nighttime LST increases and ET decreases were significant in the typical steppe but not in the meadow steppe. In terms of vegetation change, meadow steppe had the most inhibited vegetation growth and the greatest reduction in GPP. In terms of the impact range, WFs on typical steppe and meadow steppe have opposite effects on vegetation growth and ecosystem primary productivity inside and outside of them, i.e., they inhibit vegetation growth and reduce GPP inside the WF areas but promote vegetation growth and increase GPP outside the WF areas. Compared with previous studies, this study analyzed multiple climate and vegetation indicators based on many WF samples, which reduced the uncertainty associated with a single sample and provided more comprehensive and comparable observations of different types of grasslands. These findings can help to balance the relationship between wind power development and ecological protection.