Abstract Uncertainty is inherent to all sciences and can be studied from several different perspectives. However, best practices for climate scientists communicating uncertainty in climate projections are unclear. As anthropogenic greenhouse gas emissions continue to rise, the impacts of human activity on the climate system have become more apparent. This makes the communication of uncertainty in the climate projections critical to decision-makers. Further, the public often equates science to certainty. Yet, it is critical to understand that climate projections are not the future and that projections themselves contain several sources of uncertainty. As such, this review makes four primary recommendations to guide future research and considerations when communicating uncertainty. The goal is to provide a central place for climate scientists to have crucial conversations on how to communicate this uncertainty to facilitate decision-making. First, a standardized uncertainty communication framework, specific to climate projections, should be developed and implemented. Second, research is needed to determine how to communicate which climate projections are best representing current reality. This is critical given recent funding uncertainties that could impact the ability to make more certain climate projections. Third, there is a lack of research on how different decision-makers perceive uncertainty, which could point to a need to develop industry-specific uncertainty communication practices when developing a larger, universal uncertainty communication framework. Finally, we recommend investigating whether too much emphasis is placed on the potential impacts of climate change (negative framing of uncertainty) rather than on actions the public can take to reduce the projected warming (positive framing). Significance Statement Communicating uncertainty in the climate projections is crucial to informing and enhancing decision-making processes in sectors that are exceptionally impacted by changes in the climate (e.g., water resource managers, farmers, insurance companies, etc.). Yet, best practices for communicating climate projection uncertainty are not universal and unclear. The goal for this review is to help facilitate critical conversations around the need to standardize climate projection uncertainty communication. This review makes four primary recommendations to guide future research efforts.

Abstract Two-dimensional (2D) estimates of the upper-ocean vertical velocity w have been commonly performed based on single hydrographic distance–depth sections. However, biases of these estimates have seldom been investigated. We conduct such an investigation employing a 2-month dataset (including temperature, salinity, and horizontal velocity) at a typical front, the Almeria–Oran Front in the Mediterranean Sea, which was collected by a glider fleet piloted in parallel across-front sections. Specifically, using daily objective maps constructed from the dataset, we perform three-dimensional (3D) and 2D estimates of the balanced w ( w 3D and w 2D ) through the quasigeostrophic omega equation and evaluate w 2D against w 3D justified previously. Results show a significantly biased w 2D that is estimated assuming a straight front without curvature. Generally, in the 400-m upper ocean, w 2D and w 3D have a weak spatial correlation of 0.4–0.6; w 2D also presents a notably different magnitude, less than 50% of w 3D (even less than 20% in many cases). We find a pronounced curvature-induced shearing deformation (of horizontal density gradients by geostrophic flows) effect destroying the geostrophic balance and so is the associated w to restore the balance; precluding this effect in w 2D leads to the biases. These biases are also analyzed using the potential vorticity conservation principle: As the curvature causes the across-section vorticity advection, water parcels advected by the across-section flow change their vorticity; they have to be vertically compressed/stretched, requiring w that is neglected in w 2D . Therefore, the biased w 2D may be insufficient for understanding the vertical heat transport and its impact on the climate system. Significance Statement The difficulty of directly measuring ocean vertical motion ( w ), which is weak but plays a crucial role in regulating Earth’s climate, has necessitated the indirect estimation of w from observable variables. The distance–depth two-dimensional (2D) estimate has been a common procedure based on a hydrographic section. However, the bias of this procedure has seldom been examined. We perform such an examination using a 2-month in situ dataset at a frontal site. The 2D estimate ( w 2D ) is found to be significantly biased in both distribution and magnitude due to the neglect of frontal curvature, indicating that w 2D may be insufficient for understanding vertical transports of heat and biogeochemical tracers, as well as their impacts on the climate system and marine ecosystem.

Case study of a small-scale grid-connected PV system in semi-arid climate: performance, diagnostics, and economic insights

Environmental impacts and benefits of vertical greenery systems in humid subtropical climate: A comprehensive life cycle perspective

ABSTRACT Antarctic icebergs are critical components of the Antarctic ice sheet-ice shelf-ocean system and play a key role in understanding the impacts of climate change, particularly in estimating iceberg volume through the freeboard measurements. This study presents an innovative deep learning-based approach (U-Net) for automatically measuring iceberg freeboard from optical remote sensing data in the Antarctic coastal region, following data acquisition. Landsat 8 imagery from September 2022 was used to test the method, successfully extracting 85,083 icebergs, the maximum freeboard reaching 95.16 m at (66.44°S, 91.28°E). Results revealed significant regional disparities in iceberg distribution, with the West Antarctic region exhibiting a higher iceberg density than the East Antarctica region. Iceberg coverage was notably sparse around large ice shelves. Freeboard analysis categorized icebergs into five height levels, with small to medium-sized icebergs (less than 25 m) comprising 80.07% of the total. A decreasing trend in freeboard was observed with increasing distance from the coastline. The study also identified the Bellingshausen-Amundsen Sea region as having the highest iceberg concentration, with consistent density patterns across height categories in regions between 90°W–160°W and 15°W–30°W. This research provides valuable data for understanding iceberg formation and its potential impacts on oceanic and climatic systems.

Abstract The Intergovernmental Panel on Climate Change (IPCC) pointed out that climate adaptability should be a major measure to deal with climate change in the future, and cities should be the main areas to cope with the risks. However, cities in severe cold regions are affected by both regional macro-climate and climate changes. Meanwhile the related mechanisms have not been well explored. This paper takes a typical winter city, Harbin, as the research objective. First, the change trend of the urban temperature in Harbin in the last 30 years is analyzed using meteorological and remote sensing data, and then the projection results of the future climate change in Harbin are illustrated according to the global climate system model (BCC_CSM1_1). Second, this paper discusses the relationship between meteorological parameters and some typical urban morphology factors. The results show that the urban temperature in Harbin gradually intensifies in both the summer and the winter, but the regional macro-climate background changes little. Low temperature, frequent snowfall, and insufficient sunshine in winter are still the dominant climatic environmental characteristics in Harbin. Meanwhile, the meteorological parameters have significant relationships with the urban morphology factors, with a big difference between winter and summer. Finally, this paper advances some urban planning and design strategies under the dual background of adapting to climate change and regional severe cold climate based on the analysis.

Abstract Gu et al. (2024, https://doi.org/10.1029/2023GL107401 ) recently provided the first quantitative assessment of oceanic and atmospheric forcing on decadal sea surface temperature (SST) variability globally in observations. However, the validity of their simplified statistical method in the complex climate system remains uncertain. Here, we test the fidelity of their results dynamically using wind‐stress overriding experiments in a fully coupled climate model. Our results confirm the dominant role of wind‐driven oceanic forcing in mid‐latitudes, as indicated by the sign reversal of decadal SST–surface heat flux correlation after removing wind‐driven oceanic forcing. The strong oceanic forcing is further validated quantitatively by the agreement of the forcing ratio (ocean vs. atmosphere) between the dynamical and statistical estimates. Our dynamical results support the reliability of the statistical approach in Gu et al. (2024, https://doi.org/10.1029/2023GL107401 ) and their finding that mid‐latitude decadal SST variability is primarily driven by wind‐driven oceanic forcing, particularly wind‐driven geostrophic circulation.

Climate change poses one of the most pressing challenges to global sustainability, necessitating comprehensive mitigation strategies informed by robust scientific analysis. This article examines the role of advanced modeling techniques in enhancing climate change mitigation efforts across multiple scales and sectors. We explore recent developments in integrated assessment models, machine learning algorithms, and high-resolution climate simulations that enable more accurate projections of future climate scenarios and their socioeconomic impacts. The study discusses how these sophisticated computational approaches facilitate the evaluation of mitigation pathways, including renewable energy transitions, carbon capture technologies, and nature-based solutions. Particular attention is given to the integration of uncertainty quantification methods and the coupling of physical climate models with economic and land-use models to support evidence-based policy decisions. Case studies demonstrate the application of ensemble modeling techniques, deep learning frameworks, and scenario analysis in identifying cost-effective mitigation strategies at regional and global levels. Results indicate that advanced modeling approaches significantly improve the accuracy of emission reduction projections and enhance our understanding of feedback mechanisms within the climate system. The article also addresses current limitations in data availability, computational constraints, and the challenges of downscaling global projections to local contexts. We conclude that continued refinement of modeling techniques, combined with improved interdisciplinary collaboration and stakeholder engagement, is essential for designing effective climate mitigation policies that can achieve the goals outlined in international climate agreements.

The 2015 wildfire season in Indonesia, fueled by an extremely strong El Nio, ranked as the most severe wildfire season of the 21st century, with profound implications for global greenhouse gas budgets and the broader climate system. This study first precisely quantified the extremity of the 2015 Indonesian fire, revealing that key metrics (fire count, radiative power, estimated burned area) surged to levels substantially exceeding the 20102020 baselinewith September's fire count Z-score peaking at 7.1. A high-resolution greenhouse gas emission inventory was also constructed, whose detectable contribution to atmospheric greenhouse gas concentrations was quantified; this further corroborated a strong lagged correlation (r=0.97, p=0.006) between fire emissions and atmospheric CO2concentrations. Ultimately, the direct climate impact of the fires was quantified: the emission pulse exerted an average instantaneous radiative forcing of ~0.011 W/m over the peak burning period, a quantifiable perturbation equivalent to ~0.5% of the total radiative forcing from anthropogenic CO2accumulated since 1750. This case highlights the significant, pulse-like role of extreme fire events in the global carbon-climate system.

The Digital Twins of the Ocean (DTOs) represent an emerging framework for monitoring, simulating, and predicting ocean dynamics, supporting a range of applications relevant to understanding and responding to the global climate system. By integrating large-scale, multi-sourced datasets with advanced numerical models, DTOs provide a powerful tool for climate science. This review examines the role of machine learning (ML) in advancing DTOs applications, addressing the limitations of traditional methodologies under current conditions of increasing data availability from satellites, in situ sensors, and high-resolution numerical models. We highlight how ML serves as a versatile tool for enhancing DTOs capabilities, including real-time forecasting, correcting model biases, and filling data gaps where conventional approaches fall short. Furthermore, we review surrogate models that aim to complement or replace traditional physical models, offering increasing accuracy and the appeal of much faster inference for forecasts, and the insertion of hybrid models, which couple physics-based simulations with ML algorithms and are proving to be continuously improving in accuracy for complex oceanographic tasks as bigger datasets become available and methodologies evolve. This paper provides a comprehensive review of ML applications within DTOs, focusing on key areas such as water quality and marine biodiversity, ports, marine pollution, fisheries, and renewable energy. The review concludes with a discussion of future research directions and the potential of ML to foster more robust and practical DTOs, ultimately supporting informed decision-making for sustainable ocean management.

Abstract. Climate simulations with Earth System Models (ESMs) constitute the basis of our knowledge about the projected climate change for the coming decades. They represent the major source of knowledge for the Intergovernmental Panel on Climate Change (IPCC), and are an indispensable tool in confronting climate change. Yet, ESMs are imperfect and the climate system that they simulate is highly non-linear. Therefore, small errors in the numerical representation, initial states, and boundary conditions provided for future scenarios, quickly develop large uncertainties in the trajectories of their projected climates. To improve the confidence and minimize the uncertainty, future projections use large ensembles of simulations with the same model, and ensembles with multiple models. Using these two types of ensembles concurrently addresses two kinds of uncertainty in the simulations, (1) the limited spatiotemporal accuracy of the initial states, and (2) the uncertainty in the numerical representation of the climate system (model error). The uncertainty for the future development of the anthropogenic climate drivers is addressed by projecting different Shared Socioeconomic Pathway (SSP) scenarios. Organizing multimodel ensembles to make confident statements about the future climate addressing different SSP scenarios is a tremendous collaborative effort. The Coupled Model Intercomparison Project (CMIP) addresses this challenge, with the participation of 33 modeling groups in 16 countries. As one among numerous challenges that such undertaking poses, we are addressing model replicability in this article. The anticipated number of simulated years in the 6th CMIP phase (CMIP6) accumulated to about 40 000 years. With typical values for the computational throughput of about 1 to 15 simulated years per day (SYPD), it is clear that the simulations needed to be distributed among different clusters to be completed within a reasonable amount of time. Model replicability addresses the question, whether the climate signal from different scientific scenarios generated by the same model, performed on different clusters, can be attributed exclusively to the differences in the scientific drivers. It has been shown, that even changing specific compiler flags, leads to significant changes in certain climatological fields. Model replicability holds, when the model climatologies derived from the same model under a different computing environment, are statistically indistinguishable. If replicability does not hold, we cannot be certain that differences in the model climate are exclusively attributed to differences in the scientific setups. In this article, we present a novel methodology to test replicability. We further establish an objective measure of what constitutes a different climate based on Cohen's effect size. We provide a thorough analysis of the performance of our methodology and show that we can improve the performance of a recent state-of-the-art method by 60 %. We further provide an estimate of the ensemble size that is required to prove replicability with confidence. We find that an effect size of d=0.2 can be used as a threshold for statistical indistinguishability. Our analysis, based on the Community Earth System Model 2 (CESM2) Large Ensemble Community Project (LENS2) 100-member ensemble, shows that with 50 members, we can resolve effect sizes of ∼0.3, and with ensembles of 20 members, we can still resolve effect sizes of ∼0.35. We further provide a robust methodology to objectively determine the required ensemble size, depending on the purpose and requirement of the replicability test.

Deep learning for sustainable development across climate, energy, agriculture and urban systems

Abstract. As the climate continues to warm, the Antarctic Ice Sheet (AIS) and its surrounding floating ice shelves are becoming increasingly susceptible to rapid collapse. Despite the potential impact this poses to the global climate system, the effects of ice-climate feedbacks are not directly considered by most existing coupled climate models, including those in the most recent Coupled Model Intercomparison Project (CMIP6). As such, there remains much uncertainty over the impact of this additional meltwater on current global climate change projections. Here, we use the coupled atmospheric-ocean general circulation model HadCM3-M2.1 to study the effect of a continuous meltwater discharge from the AIS on the global climate system. This involves carrying out a series of freshwater hosing experiments based on the newly proposed Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative. Due to the relative computational efficiency of the HadCM3-M2.1 model, we are able to explore longer timescales than is usual. We find that ∼ 1000 years of continuous meltwater drives global atmospheric cooling, sea ice expansion in both hemispheres and a northward shift of the Intertropical Convergence Zone (ITCZ). The resulting freshening of the global ocean results in the weakening of both the AMOC and AABW. This triggers pervasive ocean warming at depths greater than 5000 m. An additional sensitivity study is also conducted in which the sensitivity of the climate model response to a change in the horizontal distribution of AIS meltwater is tested. As a result, we find that the manner in which the AIS loses mass, whether that be predominately through iceberg mass loss or basal melt, is unlikely to affect the global climate response to AIS meltwater.

Capturing CO2 from ambient air is one of the greatest challenges of all time. Even though its concentration is approximately 0.04%, this tiny fraction still holds the power to reshape the whole climate system. The difficulty lies in the fact that the regeneration of current sorbents is highly expensive, diluted, sensitive to moisture, and suffers energy penalties. Herein, we address these challenges by developing highly efficient and thermally regenerable anion-functionalized resins (AFRs), which were synthesized by immobilizing different basic anions on the macroporous PS-DVB framework. These AFRs were characterized using FTIR, BET, and SEM methods and then tested under ambient conditions for CO2 of 400 ppm and 30 °C, where optimized AFRs [R][3ATri] exhibited a high capacity of 2.12 mmol/g and excellent recycling. The adsorption mechanism between the anion and CO2 was explained through FTIR, solid-state 13C NMR, and DFT methods. This method provides a scalable and power-efficient platform for next-generation DAC technologies to interface between molecular design and implementation.

Ecological researchers and decision - makers, with few exceptions, have failed to adopt an integrated and multidisciplinary approach to analyzing the relationship between human action and the ecological systems that sustain it; although specialization is of vital importance for the systemic progress of knowledge, there is also the need for a generalizing vision, which integrates different fields of knowledge, to create bridges, in particular, between the social sciences and the natural sciences. According to the official rhetoric, which can be found in international documents, the changes in the climate system represent direct and immediate evidence of the negative consequences that atmospheric pollution generated by human activities creates on the entire environment. On the other hand, other authors state that climate warming is an exaggeration, its effects are far from only negative, the limits of the planet are far from being reached, and the gloomy forecasts are nothing more than alarmist scenarios stimulated by certain top industries or financial interests.

Abstract. Mountainous regions such as the Western Himalayas shows strong indications of climate change impact through drastic shifts in high-altitude vegetation patterns. Vegetation plays crucial roles in moderating sunlight absorption and heat exchange with land and helps maintain natural ecosystemic equilibrium. Studying vegetation changes over time across diverse locations remains pretty crucial to understand the climate system dynamics of the region. This study examines the vegetation change dynamics in high altitude Garhwal region above 3000 meters in Uttarakhand, India, and leverages satellite imagery quite extensively, examining shifts in vegetation and land cover changes between 2013 and 2025. High-resolution LISS-IV satellite images provide multispectral bands in green, red and NIR regions making it quite suitable for vegetation change analysis. NDVI data measuring vegetation health and proliferation over years with considerable accuracy can be derived from LISS-IV sensor. Results show a stark rise in vegetation mostly in areas 3000-4500 meters above sea level, with marked growth observed in these regions. Many areas exhibited remarkably low NDVI values below 0.15 in 2013, but by 2025 values had substantially improved to between 0.35 and 0.45. NDVI increases are very prominent above 4500 meters, showing extensive greening in these altitudinal areas, possibly because of rising temperatures and human activities. The study utilized modern techniques like artificial intelligence and machine learning alongside remote sensing and GIS to better understand these changes in vegetation that are occurring rapidly. AI/ML-based classification methods captured subtle changes in vegetation patterns, effectively over a period of time with considerable precision and high accuracy. The growing necessity for sustainable environmental management strategies protecting fragile mountain environments amidst rapidly changing climatic conditions becomes increasingly evident after this study.

Abstract. High-altitude vegetation change dynamics are essential for unraveling the complex interrelationship between climatic variability and energy exchange processes that occur in these sensitive ecosystems. They are also important for understanding the function of vegetation cover as an essential element of global and regional climate systems. Vegetation cover change is time-specific and represents fundamental indicators of Earth system dynamics. This research is an attempt to detect and analyze vegetation cover change patterns in various altitudinal zones (3000 m and above) of the Garhwal Himalayas during the past half century from 1972– 73 to 2022–23 and analyze their ecological implications for changes in every decade in between, employing multi-temporal Landsat satellite data, remote sensing, GIS, and AI/ML-based spatial analysis. Decadal and slope-wise (altitudinal) zonal analysis indicates a steep rise in vegetated cover, especially in the 3000–3500 m and 3500–4000 m zones. For example, in the 3000–3500 m zone, vegetated cover rose from 56.42% in 1972–73 to 66.87% in 2022–23 of the areas, whereas in the 3500–4000 m zone, it went up from 43.78% to 59.03%, covering the areas that were previously under snow and barren land. This suggests a clear upward change in vegetation distribution, which is presumably caused by warming climate. The zones above 4000 m remains covered with snow and barren land, but also exhibits moderate vegetation intrusions, indicating a slow alpine ecosystem transformation. Predictive analysis of all the six maps from 1972-73 until 2022-23 to give forecast for 2033 shows significant changes with pine forests, moist alpine pastures, dry alpine scrubs, and barren land are predicted to increase by 65%, 13%, 9%, and 14%, respectively. Conversely, snow cover is anticipated to decline by 33% and Oak forests by 3%, indicating significant environmental shifts in the Himalayas. These results are crucial for formulating specific strategies for sustainable ecosystem resilience in the high-altitude zones of the Garhwal Himalayas.

Abstract. Titan is Saturn’s largest moon, which is uniquely positioned in planetary science due to its methane-based hydrological cycle and Earth-like surface processes. The temporal study of lake size variations along the western shoreline of the Ligeia Mare is conducted using Cassini SAR observations from the T29 (2007) and T108 (2015) flybys. The boundaries of the selected lakes were manually delineated using high-resolution BIDR-processed SAR imagery to quantify areal changes. The lakes included Logtak, Sevan, Vanern, and Ohrid Lacus. These lakes were chosen for their distinct visibility and distribution within the study area. A SAR mosaic was generated to compare the spatial extents of the lakes during the two-time frames. The results clearly suggest a loss in surface areas for certain lakes over the 8-year time interval and thus may suggest evaporation, infiltration, or subsurface exchange processes. Such variability in lake size provides a strong argument for an active climate system on Titan along with surface-atmosphere interactions. The study stands in favor of radar data as the effective parameter in monitoring geomorphologic changes on Titan, especially over regions obscured by the dense atmosphere, and adds to the study of Titan's methane cycle and surface evolution.

Abstract. The Atlantic Meridional Overturning Circulation (AMOC) plays an important role in our climate system, continuous monitoring is important and could be enhanced by combining all available information. Moored measuring arrays like RAPID divide the AMOC in near-surface contributions, western-boundary currents, and the deep ocean in the interior of the basin. For the deep-ocean component, moorings measure density and focus on the calculation through geostrophy. These moored devices come with a high maintenance effort. Existing reconstruction studies show success with near-surface variables on monthly time scales, but do not focus on the interior transport. For interannual to decadal time scales, the geostrophic contribution becomes an important contribution. Argo floats could provide required information about the geostrophic circulation as they continuously and cost-effective deliver hydrographic profiles. But they are spatially unstructured and only report instantaneous values. Here we show that the geostrophic part of the AMOC can be data-drivenly reconstructed by Argo profiles. To demonstrate this, we use a realistic and physically consistent high-resolution model VIKING20X. By simulating virtual Argo floats, we demonstrate that a learnable binning method to process the spatially variable Argo float distribution is able to reconstruct the geostrophic part of the VIKING20X AMOC by up to 80 % explained variance and a mean error of less than one Sverdrup for the geostrophic transport. Using methods of explainable AI we investigate the importance of our input components showing an increasing importance of the Argo profiles on seasonal and interannual timescales, validating the usefulness of the Argo floats for the reconstruction. Our results demonstrate how an AMOC reconstruction from unstructured Argo profiles could replace estimates of the geostrophic deep-ocean component of the AMOC from the RAPID Array in the context of high-resolution ocean and climate models.

The climate system is undergoing unprecedented and dramatic changes, with increasing frequency and intensity of extreme events such as heat waves, droughts and heavy rainfall. Climate change has triggered profound changes in the global carbon cycle and eco-hydrological processes, posing unprecedented challenges for watershed carbon management, and quantifying climate-driven eco-hydrological processes remains critical for achieving watershed-scale carbon neutrality. In this study, we developed an integrated modeling framework combining Biome-BGC, GBHM and RWEQ models, aiming to comprehensively assess the ecohydrological processes and carbon cycle changes in the west liao River Basin (WLRB). Our results suggest that the future climate of the WLRB (1991–2100) will shift towards a warmer and wetter climate, accompanied by decreasing wind speeds but increasing extreme wind events. These changes drive three key carbon-climate feedbacks: warmer maximum temperatures lead to degradation of vegetation productivity in the plains, weakening watershed carbon sequestration capacity and reducing the sensitivity of vegetation to precipitation in the semi-arid zone. Increased frequency of extreme wind speeds greatly increases the potential for wind erosion in the WLRB, threatening soil organic carbon storage. From the perspective of aquatic carbon pools, despite reduced drought risk and increased water availability, there is a strong likelihood of increased frequency and intensity of flooding, which may exacerbate lateral carbon export. Our findings highlight that climate change amplifies synergistic risks to terrestrial and aquatic carbon pools, requiring adaptive strategies such as establishing synergistic vegetation restoration models that integrate windbreak-carbon sequestration with flood regulation. These findings not only improve our understanding of the evolutionary mechanisms and potential risks of ecohydrological processes, but also provide guidance for future watershed carbon management.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13021-025-00339-8.