Climate Change Research Articles

PERANAN SISTEM AGROFORESTRI POLA DUSUNG TERHADAP PENDAPATAN PETANI DAN KELESTARIAN LINGKUNGAN DI DESA HARUKU

This research aims to assess the role of agroforestry in farmers' income and environmental sustainability in Haruku Village. The study used a purposive sampling method, selecting 20 respondents who apply the dusung agroforestry system in Haruku Village. Interviews and observations were conducted to collect primary data, while secondary data were obtained from the Haruku village government and other literature sources. The results indicate that there are 29 types of plants, including vegetables, fruits, tubers, and timber plants, commonly cultivated by the community in Haruku Village. Through the implementation of the agroforestry system, farmers' income is higher than non-agroforestry income. Agroforestry practices also contribute to environmental sustainability by reducing climate change impacts and decreasing the use of inorganic fertilizers among farmers.

The Future of Artificial Intelligence: Trends and Predictions

Artificial Intelligence (AI) has evolved rapidly, transforming diverse industries and societal functions. This paper provides a comprehensive overview of AI's current landscape, examining its advancements, applications, and ethical challenges. Key trends are explored, including innovations in machine learning and deep learning, AI’s expanding role across industries, and its potential for addressing climate change and sustainability. Furthermore, the paper highlights AI's role in enhancing human-machine collaboration, paving the way for systems that augment rather than replace human capabilities. Predictions for AI’s future are discussed, such as the emergence of artificial general intelligence (AGI), advancements in autonomous systems, the impact of quantum computing on AI, and innovations in AI-specific hardware. The paper also examines ethical and societal challenges, such as privacy, algorithmic bias, and the need for global governance, addressing the urgent call for responsible AI. In light of these trends, the paper emphasizes future research directions, encouraging interdisciplinary collaboration and a focus on explainable, robust, and resilient AI models. This work aims to shed light on the transformative potential of AI while advocating for ethical practices to ensure a positive and sustainable impact on society.

Measuring butterfly persistence in the face of deep uncertainty: a case study using the regal fritillary

Scientists have documented effects of climate and land use change across a range butterfly species. However, incorporating future climate and land use change into butterfly conservation plans is a difficult task. These difficulties arise mainly from assumptions that future processes are the same as past processes (stationarity) and because scientists cannot reliably predict the future (deep uncertainty). In this case study, I used land use and climate change scenarios to compare possible futures for the regal fritillary, a grassland butterfly in the central United States. My analysis indicated that climate and land use change have the potential to influence species persistence, but that climate change has the larger effect. Moderate warming scenarios may improve the possibility of persistence, whereas extreme warming reduces this possibility. My analysis demonstrates the importance of considering nonstationarity and alternative plausible futures in butterfly conservation planning.

Climate change mitigation and adaptation for rice-based farming systems in the Red River Delta, Vietnam

Abstract Background Rice is a major contributor to anthropogenic greenhouse gas (GHG) emissions, primarily methane, and at the same time will be negatively impacted by regional climate changes. Identifying rice management interventions to reduce methane emissions while improving productivity is, therefore, critical for climate change mitigation, adaptation, and food security. However, it can be challenging to conduct multivariate assessments of rice interventions in the field owing to the intensiveness of data collection and/or the challenges in testing long-term changes in meteorological and climate conditions. Process-based modeling, evaluated against site-based data, provides an entry point for evaluating the impacts of climate change on rice systems and assessing the impacts, co-benefits, and trade-offs of interventions under historical and future climate conditions. Methods We leverage existing site-based management data to model combined rice yields, methane emissions, and water productivity using a suite of process-based coupled crop-soil model experiments for 83 growing sites across the Red River Delta, Vietnam. We test three rice management interventions with our coupled crop-soil model, characterized by Alternate Wetting and Drying (AWD) water management and other principles representing the System of Rice Intensification (SRI). Our simulations are forced with historical as well as future climate conditions, represented by five Earth System Models for a high-emission climate scenario centered on the year 2050. We evaluate the efficacy of these interventions for combined climate change mitigation and adaptation under historical and future climate change. Results Two SRI interventions significantly increased yields (one by over 50%) under historical climate conditions while also reducing (or not increasing) methane emissions. These interventions also increase yields under future climate conditions relative to baseline management practices, although climate change decreases absolute yields across all management practices. Generally, where yield improved, so did crop water-use efficiency. However, impacts on methane emissions were mixed across the sites under future climate conditions. Two of the interventions resulted in increased methane emissions, depending on the baseline management point of comparison. Nevertheless, one intervention reduced (or did not significantly increase) methane under both historical and future climate conditions and relative to all baseline management systems, although there was considerable variation across five selected climate models. Conclusions SRI management principles combined with high-yielding varieties, implemented for site-specific conditions, can serve climate change adaptation and mitigation goals, although the magnitude of future climate changes, particularly warming, may reduce the efficacy of these interventions with respect to methane reductions. Future work should better bracket important sensitivities of coupled crop-soil models and disentangle which management and climate factors drive the responses shown. Furthermore, future analyses that integrate these findings into socio-economic assessment can better inform if and how SRI/AWD can potentially benefit farmer livelihoods now and in the future, which will be critical to the adoption and scaling of these management principles.

Prediction of the potentially suitable areas of Eucommia ulmoides Oliver in China under climate change based on optimized Biomod2 and MaxEnt models

Eucommia ulmoides Oliver is a medicinal plant of significant economic importance. Its cortex has been employed for centuries to alleviate various conditions such as lumbar pain, knee pain, and osteoporosis. Additionally, E. ulmoides possesses substantial industrial value. With the growing demand for this medicinal herb, ensuring its sustainable supply has become imperative. Climate change has caused habitat restrictions or migration of medicinal plants. Therefore, predicting the impact of climate change on the distribution of E. ulmoides is crucial for its conservation and sustainable use. This study evaluated the potential distribution of E. ulmoides across China under various climate change scenarios since the last interglacial period by modeling suitable areas based on 257 distribution records and 19 major environmental factors related to E. ulmoides. The model selection process initially identified the MaxEnt model as the most suitable. The optimized MaxEnt model, with RM = 2.0 and FC = LQHPT settings, generated the most precise predictions. Results indicate that the minimum temperature of the coldest month, annual mean temperature, and annual precipitation significantly affect the distribution of E. ulmoides. Under current environmental conditions, highly suitable areas for E. ulmoides are found in Southwest and Southeast China, with a total suitable habitat area of 23.12 × 104 km2. However, the range of suitable habitat has shifted due to global warming’s negative impact. Under different climate scenarios, suitable areas for E. ulmoides have either increased or decreased, with expansions primarily in high-latitude regions. Future climate scenarios predict shifts in the centroid of suitable E. ulmoides habitat towards Yichang City in Hubei Province. The findings of this study support the development, artificial cultivation, and conservation of E. ulmoides resources.

Assessing CMIP Models’ Ability to Detect Observed Surface Warming Signals Related to Climate Change

Abstract Assessing if CMIP models can detect observed climate change signals is crucial for evaluating their realism and strengthening confidence in future projections. These signals can only be compared once distinguishable from the background climate “noise.” Built for impact studies but not detection purpose, the time of emergence (ToE) estimates when this occurs using a fixed signal-to-noise ratio. To more accurately assess models’ ability to reproduce observed climate change signals, we introduce the time of detection (ToD), which employs a statistical significance test of the nonlinear trend, accounting for the time series length. ToD and ToE are computed for sea surface temperature (SST) and relative SST (RSST) across four observational and 69 CMIP5/6 historical and shared socioeconomic pathways 5–8.5 (SSP5–8.5) scenario datasets. ToD precedes ToE and is less sensitive to the chosen threshold. While already detectable in most of the tropics, SST warming in the equatorial Pacific is detectable in ∼90% of models but only one out of four observational datasets, due to a stronger modeled warming. Because RSST signals are weaker than SST signals, they do not emerge anywhere but are detectable in two regions. The enhanced warming in the western Indian Ocean, detected in ∼45% of models, is not robust in observations when excluding pre-1960 dubious data. Conversely, the subdued southeast Pacific warming detected in observations and ∼80% of models is robust and consistent with a poleward extension of the Southern Hemisphere Hadley cell. The ToD’s ability to detect subtle signals allows for assessing the realism of the CMIP warming pattern in this region. Significance Statement Recent studies emphasize the importance of the climate change warming pattern and its apparent discrepancy between climate models and observations in the equatorial Pacific. We argue that our newly proposed time of detection (ToD) is better suited than the well-known time of emergence (ToE) for investigating where modeled and observed warming patterns can be confidently compared. In agreement with previous studies, our analysis indicates model–observations disagreement over the equatorial Pacific. Using ToD instead of ToE allows for detecting a robust subdued warming (i.e., weaker than the tropical average) in the southeast Pacific and therefore confirms the realism of the CMIP warming pattern in this region.

Climate Resilience in Farm Animals: Transcriptomics-Based Alterations in Differentially Expressed Genes and Stress Pathways

The livestock sector, essential for maintaining food supply and security, encounters numerous obstacles as a result of climate change. Rising global populations exacerbate competition for natural resources, affecting feed quality and availability, heightening livestock disease risks, increasing heat stress, and contributing to biodiversity loss. Although various management and dietary interventions exist to alleviate these impacts, they often offer only short-lived solutions. We must take a more comprehensive approach to understanding how animals adapt to and endure their environments. One such approach is quantifying transcriptomes under different environments, which can uncover underlying pathways essential for livestock adaptation. This review explores the progress and techniques in studies that apply gene expression analysis to livestock production systems, focusing on their adaptation to climate change. We also attempt to identify various biomarkers and transcriptomic differences between species and pure/crossbred animals. Looking ahead, integrating emerging technologies such as spatialomics could further accelerate genetic improvements, enabling more thermoresilient and productive livestock in response to future climate fluctuations. Ultimately, insights from these studies will help optimize livestock production systems by identifying thermoresilient/desired animals for use in precise breeding programs to counter climate change.

Mapping and assessing the future provision of lake ecosystem services in Lithuania

Lakes supply multiple ecosystem services (ES), key to supporting socio-ecologic systems and human well-being. In the context of future land use and climate changes, it is imperative to anticipate potential impacts on lake ES supply. Hence, studies that deal with future lake ES, such as mapping, are lacking. In this work, we mapped and assessed the future supply of three ES: (1) maintenance of nursery conditions (nursery ES), (2) maintenance of chemical conditions of freshwaters (nutrient regulation ES), and (3) direct and indirect cultural outputs (recreation ES) in Lithuania. Four future scenarios were utilised, integrating land use and climate changes: A0 - business as usual; A1-urbanisation; A2: land abandonment and afforestation; and A3 - agricultural intensification. The projected year was 2050, following the intermediate Representative Concentration Pathway (RCP 4.5). The future scenarios were simulated using the open-source software Dinamica EGO based on a 6-step modelling framework. Statistical differences among the scenarios and ES were analysed by applying a Kruskal-Wallis ANOVA test. Spatial analysis was done by performing a Moran's I and Getis-Ord (Gi∗) hotspot analysis. The results showed significant differences in nursery and nutrient regulation ES. The highest supply in nursery ES was observed for the A0 scenario. For nutrient regulation ES, the lowest ES supply was identified for the A1 scenario and for recreation ES, the highest was found in the A2 scenario. The eastern and northeastern regions of Lithuania showed a high ES supply. Hot spots were only identified in the eastern region. These regions are associated with a high area covered by forests and protected areas. The central region shows a low ES supply, identified as a cold spot where the agricultural landscape dominates. The results of the PCA analysis revealed an association between nursery and recreation ES. Nutrient regulation was not associated with the other two ES. Mapping and assessing the impact of future scenarios is vital to anticipating the potential dynamics of lake ES, especially in the context of climate change. This information is essential in the context of environmental management, helping decision-makers to ensure a sustainable ES supply and contributing to human wellbeing.

Exploring plant-microbe interactions in adapting to abiotic stress under climate change: a review

Climatic change and extreme weather events have become a major threat to global agricultural productivity. Plants coexist with microorganisms, which play a significant role in influencing their growth and functional traits. The rhizosphere serves as an ecological niche encompassing plant roots and is a chemically complex environment that supports the growth and development of diverse plant-interactive microbes. Although plant-microbe interactions have been extensively investigated however, limited exploration have been made how abiotic stresses affect the structure and assembly of microbial communities in the rhizosphere. This review highlights climate change influence on plant growth, functional traits, and microbial communities. It explores plant mechanisms for mitigating abiotic stress, such as removing reactive oxygen species (ROS), regulating antioxidant activity and indole-3-acetic acid (IAA) production, and controlling growth-inhibitory ethylene levels through colonization by bacteria producing ACC deaminase. Additionally, we elaborated the systematic communicatory network steered by hormonal crosstalk and root exudation, which can modulate and initiate the dialogues between plants and surrounding microbes. This network ultimately promotes the chemotactic movement of microbes towards the rhizosphere, facilitating their early colonization. Finally, we reviewed the recent advancements for understanding how plant-microbe interactions foster resilience under climate stress.

Impacts of Climate Change-Induced Temperature Rise on Phenology, Physiology, and Yield in Three Red Grape Cultivars: Malbec, Bonarda, and Syrah

Climate change has significant implications for agriculture, especially in viticulture, where temperature plays a crucial role in grapevine (Vitis vinifera) growth. Mendoza’s climate is ideal for producing high-quality wines, but 21st-century climate change is expected to have negative impacts. This study aimed to evaluate the effects of increased temperature on the phenology, physiology, and yield of Malbec, Bonarda, and Syrah. A field trial was conducted over two seasons (2019–2020 and 2020–2021) in an experimental vineyard with an active canopy heating system (+2–4 °C). Phenological stages (budburst, flowering, fruit set, veraison, harvest), shoot growth (SG), number of shoots (NS), stomatal conductance (gs), chlorophyll content (CC), chlorophyll fluorescence (CF), and water potential (ψa) were measured. Additionally, temperature, relative humidity, light intensity, and canopy temperature were recorded. Heat treatment advanced all phenological stages by approximately two weeks, increased SG and NS, and reduced gs and ψa during the hottest months. CC and CF remained unaffected. The treatment also resulted in lower yields, reduced acidity, and increased °Brix in both seasons. Overall, rising temperatures due to climate change advance the phenological phases of Malbec, Syrah, and Bonarda, leading to lower yields, higher °Brix, and lower acidity, although physiological variables remained largely unchanged.

Climate change worries and fertility intentions: Insights from three EU countries

AbstractObjectiveThis study investigates the relationship between climate change concern and fertility intentions in Finland, Estonia, and Sweden. It explores whether climate worries are associated with a greater inclination to remain childfree or a reduction in the number of intended children, and whether the association varies across the three countries.BackgroundClimate change concern is said to influence fertility intentions either by prompting individuals to reduce their intended fertility to alleviate pressure on the planet or by instilling a sense of guilt and concern about bringing a child into a perceived doomed world. However, scientific evidence on this association is still limited and inconsistent.MethodThe analysis draws on nationally representative data from the 2021/22 Gender and Generation Survey, focusing on individuals aged 18–40. It examines certainty in future fertility intentions and the intended number of children through ordinal and multinomial logistic regressions, distinguishing between first‐birth and higher‐order birth intentions. Interactions between climate change concern and country are explored to assess potential cross‐country differences.ResultsThe results provide evidence that strong concern for climate change is linked to reduced fertility intentions, driven by childless individuals' intentions to forgo childbearing altogether rather than reducing the number of intended children. This pattern holds across all three countries, with a more pronounced association in Finland and Estonia, and somewhat weaker in Sweden.ConclusionThis study highlights, for the first time in the European context and on a representative sample, that climate change concern is negatively associated with fertility intentions.

Climate Change and Mental Health in Bhutan

Bhutan remains a largely non-industrialized country, making minimal contributions to global warming and climate change. Yet, the country suffers from the clutches of the adverse effects of climate change. Among them, the impact of climate change on mental health including psychological distress, acute stress disorder, post-traumatic stress disorder, major depressive disorder and anxiety disorder is well known. In this article, we aim to describe the various mental health issues that the country is facing or is likely to face in the future as a result of climate change.

Processes and controls of regional floods over eastern China

Abstract. Mounting evidence points to elevated regional flood hazards in a changing climate, but existing knowledge about their processes and controls is limited. This is partially attributed to inadequate characterizations of the spatial extent and potential drivers of these floods. Here we develop a machine-learning-based framework (mainly including the Density Based Spatial Clustering Applications with Noise (DBSCAN) clustering algorithm and a conditional random forest model) to examine the processes and controls of regional floods over eastern China. Our empirical analyses are based on a dense network of stream gauging stations with continuous observations of annual maximum flood peaks (i.e. magnitude and timing) during the period 1980–2017. A comprehensive catalogue of 318 regional floods is developed. We reveal a pronounced clustering of regional floods in both space and time over eastern China. This is dictated by cyclonic precipitating systems and/or their interactions with topography. We highlight contrasting behaviours of regional floods in terms of their spatial extents and intensities. These contrasts are determined by fine-scale structures of flood-producing storms and anomalous soil moisture. While land surface properties might play a role in basin-scale flood processes, it is more critical to capture spatial–temporal rainfall variabilities and soil moisture anomalies for reliable large-scale flood hazard modelling and impact assessments. Our analyses contribute to flood science by better characterizing the spatial dimension of flood hazards and can serve as a basis for collaborative flood risk management in a changing climate.

Effects of grazing and vegetation on Daurian Pika populations in northern Mongolia

Abstract Daurian Pika (Ochotona dauurica) are steppe-dwelling burrowing mammals with the potential to have large effects on central Asian grasslands due to their extensive range, propensity to occur at extremely high density, and roles as ecosystem engineers and important prey species. The few studies that have been done are mostly from northern China and Russia, while little research has been done in the majority of their range in Mongolia. We studied a population of Daurian Pika in the Darhad Valley of northern Mongolia, near the southern edge of the permafrost, where climate change is progressing rapidly. We evaluated pika populations at 87 random plots across a large 40 × 125 km area and assessed the impact of factors related to vegetation cover, grazing, and soils that predicted their occupancy and an index of their density (number of active burrows). We found that pikas were more likely to occur in areas with taller grass and higher forb cover, and burrow densities were higher in areas with low or moderate grazing and lower soil moisture. In summer, pikas mainly foraged on grass compared with forbs—while in fall, forbs appeared to be selected for in haypiles. Dense pika burrow systems had taller grasses and forbs in their immediate vicinity, suggesting that in some cases, pika could help promote plant growth for other grazers. Long-tailed Ground Squirrel (Urocitellus undulatus) was the second most abundant small mammal in our study sight and selected for areas with high cover of overgrazing indicator species and for short forbs, providing little evidence for competition with Daurian Pika. Our results suggest that shorter grass (~1 cm) can decrease pika occupancy by 75%, while heavy grazing may decrease burrow density by 66% in dry soils. With grazing pressure in Mongolia increasing dramatically since the 1990s, future research is strongly needed to assess the impacts of grazing on this keystone species.

La sostenibilidad como valor de marca en la publicidad automotriz de España

Introduction: Climate change and Corporate Social Responsibility (CSR) have driven companies, including the automotive industry, to embrace sustainability as a fundamental part of their communication strategy. Purpose: The main objective of this study is to analyze sustainability as a brand value in digital advertising in the Spanish automotive industry. Methodology: This research follows a triangular methodology combining interviews, surveys, and analysis of advertising content to explore how experts and consumers perceive this trend, considering their gender and age. Findings: The result of this work shows that sustainability is increasingly influencing purchasing decisions, but there is a disconnect between what advertising conveys and what users value. Brands still prioritize design and price over sustainability, having to progressively adjust their messaging to better reflect the ecological and social concerns that audiences have. Originality/value: The contribution focuses on two paths: firstly, to fill an academic gap in terms of studies focused on the specific communication actions carried out by the automotive sector in Spain, and secondly, to demonstrate that sustainability has not entered as a brand value that forces higher sales.

Common Institutional Ownership and Corporate Carbon Emissions

AbstractThere has been a growing interest in comparative work exploring when and why firms embark on green paths. It has been concluded that in national contexts where inter‐firm ties are stronger, progress has been stronger. In turn, this raises questions about the impact of inter‐firm ties within, rather than between, national contexts, and in settings where progress towards renewables has been uneven and contested. Accordingly, we explore how common institutional ownership may foster collaboration among firms within the same industry against climate change. Using a sample of US‐listed firms from 2006 to 2019, we obtain robust evidence that firms with industrial peers that are owned by the same institutional investors have lower carbon emissions. In addition, we find that a threshold exists for which the impact on carbon emissions holds only when firms are commonly connected with a substantial number of peers. The existence of this threshold suggests potential free‐riding issues and highlights the beneficial role of investors in promoting cross‐industry collaboration. Overall, our results highlight the role played by institutional investors in tackling climate issues, with important implications for both climate‐ and antitrust‐related regulations.

Time Series Clustering of Sea Surface Temperature in the Mediterranean and Black Sea Marine System

ABSTRACTSea surface temperature (SST) is a significant climatic variable that affects the climate of the Earth. Monitoring a location's SST pattern is useful for several research areas, including weather forecasting and climate change. In this study, the emerging hot spot and cold spot patterns of SST in the Mediterranean and Black Sea Marine System (MBMS) were examined, the spatial distribution characteristics and temporal changes of SST in the sub‐basins were analysed, and future predictions were made. A distinctive aspect of the research lies in the introduction of novel techniques, specifically the application of space time cube and evolving hot spot analysis, for visualising and evaluating SST in the MBMS. This approach sets the study apart by pioneering the utilisation of these methods in this particular context. In the examined region, SST demonstrates a decreasing trend from east to west and from south to north. The forecast suggests that this spatial distribution pattern will persist in 2033, further accentuated by the intensification of the warming effect. Nine different time series clusters are defined within this distribution pattern. Although it changes seasonally, the prevailing statistically significant hot spots in the study area are primarily characterised by new hot spots, intensifying hot spots, sporadic hot spots and oscillating hot spots. The trends of hot and cold spot clusters, along with SST values, were assessed for all sub‐basins in the MBMS. Conversely, the observed clustering category among statistically significant cold spots is identified as persistent cold spots, diminishing cold spots, sporadic cold spots, oscillating cold spots and historical cold spots. The spatiotemporal analysis in this research has provided notable insights, offering a spatial context to the previously explored temporal trends of SST in the MBMS.

Forecast skill assessment of an operational continental heat-cold-health forecasting system: New avenues for health early warning systems.

More than 110,000 Europeans died as a result of the record-breaking temperatures of 2022 and 2023. A new generation of impact-based early warning systems, using epidemiological models to transform weather forecasts into health forecasts for targeted population subgroups, is an essential adaptation strategy to increase resilience against climate change. Here, we assessed the skill of an operational continental heat-cold-health forecasting system. We used state-of-the-art temperature-lag-mortality epidemiological models to transform bias-corrected ensemble weather forecasts into daily temperature-related mortality forecasts. We found that temperature forecasts can be used to issue skillful forecasts of temperature-related mortality. However, the forecast skill varied by season and location, and it was different for temperature and temperature-related mortality due to the use of epidemiological models. Overall, our study demonstrates and quantifies the forecast skill horizon of heat-cold-health forecasting systems, which is a necessary step toward generating trust among public health authorities and end users.

Research on National Short-Term Precipitation Forecast Method Based on Machine Learning

Abstract. Climate change is closely related to human lives. With the development of information technology, meteorological data gradually show the characteristics of big data. The purpose of this study is to compare the application effects of different machine learning models in short-term precipitation prediction, to improve the accuracy and efficiency of prediction. Based on the annual average precipitation data of prefecture-level cities in China from 1990 to 2022, this paper uses Random Forest, eXtreme Gradient Boosting and Neural Networks to construct prediction models, and comprehensively evaluate them. In this study, the data is preprocessed to ensure the data quality of the input model. Next, the data is fed into three machine learning algorithm models, Random Forest, eXtreme Gradient Boosting and Neural Networks. Finally, the prediction performance of each model is evaluated by various indexes. In this paper, it is found that the Random Forest model has the best performance, and its prediction accuracy is higher than the other two models, which has great application potential in the field of short-term precipitation prediction. This study shows that a reasonable selection of machine learning methods and optimization of model parameters can effectively improve the accuracy of short-term precipitation prediction. This paper provides some empirical evidence for precipitation prediction, which will help to make more effective decisions in dealing with extreme weather events and climate change challenges in the future.

Modelling marine heatwaves impact on shallow and upper mesophotic tropical coral reefs

Abstract Coral reefs ecosystems, often compared to rain forests for their high biodiversity, are threatened by ocean warming causing coral bleaching when the symbiotic relationship between dinoflagellates and corals breaks under high ocean temperatures. Thermal stress from marine heatwaves (MHWs) occur both at the surface and subsurface with subsurface MHWs lasting longer with potentially higher cumulative intensities. However, global coral bleaching models generally ignore the differences in thermal stress between surface and sea-bed levels. Here, we define MHWs at sea-bed level to model coral bleaching with daily resolution from 6 May 1993 to 31 October 2023, for 9944 tropical coral reefs between 0 and 60 m depths. We show that deeper reefs experience on average higher thermal stress and bleaching compared to surface reefs. Using surface temperature data to model bleaching for deeper corals underestimates bleaching intensities by an average of 6% ± 9% compared to the subsurface calibrated model. Our study is a starting point for more accurate coral bleaching modelling, providing additional evidence to reshape our perception of deeper coral reefs as potential refugees from climate change.