Scientific knowledge advances through the continuous accumulation of primary research. However, its true value emerges when individual findings are synthesized, integrated, and organized into coherent conceptual frameworks that not only advance scientific understanding but also support evidence-based decision-making and policy development. Review papers play a central role by consolidating scattered findings, identifying patterns across studies, and providing interpretative frameworks that guide both research and policy. In rapidly expanding and highly interdisciplinary fields such as climate change science, review papers are not auxiliary products of scholarship but essential instruments for investigating and managing complexity, identifying knowledge gaps, highlighting uncertainty, and assessing scale mismatches. They are particularly important in translating heterogeneous evidence, from observational records, experiments, and modelling, into coherent narratives that can inform mitigation and adaptation strategies. Review papers vary widely in scope, methodology: some are narrative and expert-driven, while others adopt systematic or quantitative approaches such as meta-analysis. Each type serves a different purpose, and each carries specific strengths and limitations. Understanding these differences is essential for evaluating the scientific contribution of a review and for producing high-quality syntheses. This paper briefly discusses the scientific role of review articles, the diversity of review methodologies, and their specific relevance in climate change research. It also provides a detailed and practical discussion of how to design and write a review paper, emphasizing clarity of research questions, methodological transparency, critical synthesis, and conceptual innovation. Ultimately, this review argues that review papers are not secondary outputs of scientific research but primary engines of scientific progress, shaping conceptual frameworks, identifying critical knowledge gaps, directing future research agendas, underpinning global assessments, and translating scientific evidence into informed policy and societal action.
The transit time of carbon quantifies the time that it takes carbon atoms to travel through an ecosystem, from fixation of atmospheric CO2 via photosynthesis until loss of carbon, mostly via respiration by plants and microorganisms, but also by methane emissions, and by leaching as dissolved inorganic or organic matter. Transit times are relevant to predict the future behavior of carbon sinks and how they would respond to changes in the environment. As most carbon is lost to the atmosphere as CO2, measurements of radiocarbon in respired carbon dioxide can be used to approximate the mean transit time of carbon in ecosystems. We review here an increasing number of studies that use radiocarbon to obtain the age of respired carbon from ecosystems, and their use as a constraint for carbon cycle models. Measurements of radiocarbon from vegetation pools and soils indicate a mixture of ages of carbon in ecosystem respiration. For example, respiration to support metabolism and growth in trees comes from very recently fixed substrates in leaves, but organs like stems and roots can mix recent substrates with older storage reserves. From detrital necromass and soils, the age of respired carbon integrates large variations in the age of carbon in substrates available for decomposition, ranging from less than one year (leaf litter in tropical systems) to a few hundred years for slowly decaying wood, or carbon stabilized by mineral sorption. Integrated at the soil or ecosystem level, the age of respired carbon is generally a highly ‘right’ skewed distribution, with most CO2 released through faster cycling processes (plant respiration and rapid decomposition), but with a small component derived from very slow processes (slower decomposition). Thus, the mean age of respired CO2 can be one to two decades old, while the median age can be much younger. Linking measured radiocarbon to modeled processes requires translation of either measured values to transit times or prediction of radiocarbon by models. Evidence from radiocarbon observations and models shows that the distribution of transit times of carbon in terrestrial ecosystems is key to understand what fraction of fixed carbon will be respired quickly, what fraction can be stored for decadal to century timescales, and what fraction can accumulate over longer timescales and influence long-term carbon storage.
Snow-related processes occur over a large range of spatial scales. Studying these processes therefore requires observation techniques with different spatial coverages, from short-range (up to several meters) to medium-range (several meters to several kilometers) and long-range (several kilometers to hundreds of kilometers). However, integration of observations with different coverages remains challenging because these have different spatial resolutions, making it difficult to ensure the representativeness across distinct techniques. Although snow observations have become more accurate in recent years, many challenges still prevent the retrieval of detailed and robust snow observations in mountainous and high-latitude regions. This review provides an overview of the most recent advances in techniques used to improve snow monitoring at different spatial coverages during the last 5 years. Although the use of in-situ methods has improved observations of the Snow Water Equivalent (SWE), these observations are still limited by the presence of liquid water and the need for secondary variables. Promising developments include Global Navigation Satellite Systems (GNSS) -based antennas for measurement of SWE and microwave permittivity for measurement of snow density. Similarly, further studies are needed to examine the impact of the forest canopy on snow detection and the presence of Light-Absorbing Particles (LAP) on the snow surface and their effect on snowpack albedo. Airborne technologies, including unmanned aerial vehicles (UAVs) with Light Detection and Ranging (LiDAR) or stereo optical imagery, have provided better observations of snow depth over large areas and have higher reliability than earlier technologies. Space-borne sensors can also provide advanced monitoring of snow, including measurements of snow depth distribution using satellite photogrammetry and monitoring of SWE using radar technologies. Future efforts should focus on measurements of SWE and snow density from local to global scales, improve observations in forested areas, develop distributed data for snowpack variables, and integrate observations from techniques with different coverages. We also underscore that long-term monitoring from satellites must be maintained to achieve better forecasts of changes in snow, improve the management of snow resources, and provide a deeper understanding of the dynamics of climate change. Machine learning offers new opportunities for refining the techniques used for snow observations and integration of different techniques.
This review explores how European policy incentives, including grants, subsidies, and capacity-building programs, can support rural communities in their pursuit of sustainable economic growth and urgent adaptation to climate change. It intends to understand what policies work, where they fall short, and how these policies can be improved to build stronger and climate-resilient rural areas. A total of 592 papers published between 2000 and 2024 were reviewed, and 12 high-quality studies were selected for in-depth analysis. These studies suggest that national policy objectives should align with specific community needs. Effective approaches often utilize financial incentives, such as eco-schemes and Payments for Ecosystem Services (PES), in combination with participatory approaches. However, limited funding, inflexible bureaucracy, and the need for context-specific, tailored interventions still hinder their full potential. The review provides practical insights for policymakers and stakeholders, demonstrating that smarter, community-driven approaches incentivize economic growth while simultaneously preparing rural areas for climate-related challenges. These aspects underscore that inclusive, adaptive policies are central to realizing a fairer, more sustainable future across rural Europe.
The paper aims to explore the effects Sea-Level Rise (SLR) will have on coastal Caribbean ecosystem services. To this end, it conducts a multimethod analysis of two biodiversity hotspots recognized for their regional significance, vulnerability, and productivity. Tropical coastal marine ecosystems are highly vulnerable to the impacts of SLR, which intensifies coastal flooding, erosion, and saltwater intrusion. These environmental changes will directly affect critical ecosystem services and the communities that depend on them. Additionally, the findings suggest that small island ecosystems are more vulnerable to these effects compared to their continental counterparts. This study evaluates the projected impacts of SLR on critical ecosystem services supporting coastal communities in the Colombian Caribbean. Focusing on two key biodiversity hotspots—the Seaflower and Ciénaga Grande de Santa Marta Biosphere Reserves—we modeled extreme coastal sea levels and developed GIS-based flood maps under IPCC scenarios SSP3-7.0 and SSP5-8.5 for the years 2050 and 2100. These projections were used to inform a structured expert elicitation process aimed at assessing potential disruptions to ecosystem services. Findings reveal distinct patterns of social and ecological vulnerability across sites, underscoring the need for region-specific adaptation strategies to protect both biodiversity and human well-being.
