Jennifer R. Marlon

Aerosol radiative forcing represents the largest source of uncertainty for calculating present-day climate sensitivity. This uncertainty has been underestimated in climate models, as they have not adequately accounted for uncertainties in the emissions of aerosols prior to the satellite era. This is especially the case for natural aerosols, such as mineral dust and aerosols from biomass burning. In this paper, we take a step towards reducing this uncertainty by providing a roadmap for improved integration of proxy-based observational constraints on past aerosol variability with model simulations. We first review existing literature on the importance of natural aerosol radiative forcing during historical and paleo-time periods, with a focus on the Last Glacial Maximum and the preindustrial era (ca. 1850) to the present. We then compile existing paleo-data archives and describe their ability to constrain emissions of aerosols, highlighting existing gaps. Model-data combinations are available to estimate radiative effects for dust and volcanoes in several time periods, and for biomass burning for preindustrial to present day, but are not available for other aerosols. Modeled feedbacks of aerosols to climate (W/m²/°C) are 10–100⨉ lower than model-data derived estimates for dust, casting doubt on our ability to successfully model feedbacks in the current generation of Earth system models. Finally, we propose a framework for integrating paleo-archives with models through harmonized datasets, proxy–model translation metrics, and regularized inversion approaches, and identify priority measurements needed to reduce uncertainty in both aerosol loading and radiative-effect efficiency across climate states.

Aerosol radiative forcing represents the largest source of uncertainty for calculating present-day climate sensitivity. This uncertainty has been underestimated in climate models, as they have not adequately accounted for uncertainties in the emissions of aerosols prior to the satellite era. This is especially the case for natural aerosols, such as mineral dust and aerosols from biomass burning. In this paper, we take a step towards reducing this uncertainty by providing a roadmap for improved integration of proxy-based observational constraints on past aerosol variability with model simulations. We first review existing literature on the importance of natural aerosol radiative forcing during historical and paleo-time periods, with a focus on the Last Glacial Maximum and the preindustrial era (ca. 1850) to the present. We then compile existing paleo-data archives and describe their ability to constrain emissions of aerosols, highlighting existing gaps. Model-data combinations are available to estimate radiative effects for dust and volcanoes in several time periods, and for biomass burning for preindustrial to present day, but are not available for other aerosols. Modeled feedbacks of aerosols to climate (W/m²/°C) are 10–100⨉ lower than model-data derived estimates for dust, casting doubt on our ability to successfully model feedbacks in the current generation of Earth system models. Finally, we propose a framework for integrating paleo-archives with models through harmonized datasets, proxy–model translation metrics, and regularized inversion approaches, and identify priority measurements needed to reduce uncertainty in both aerosol loading and radiative-effect efficiency across climate states.

Flood risk management assumes that people in hazardous places recognize the danger they face, but evidence on flood risk perception remains sparse in South Asia, home to much of the world's flood-exposed population. India has more than 600 million people exposed to inland flood hazards and the highest flood-related mortality in Asia, yet no national evidence base on flood risk perception. Here we combine a nationally representative survey of 10,704 Indian adults with a Multilevel Machine Learning with Poststratification (MMLP) approach to estimate flood risk perception for all 633 districts, and compare these against assessed hazard from the flood record. Perceived and assessed risk diverge by more than 30 percentage points in over half of districts, well beyond the uncertainty in our estimates. Districts that most sharply under-perceive risk are concentrated in Assam, Kerala, and Andhra Pradesh, while over-perception is widespread across northern, central, and northeastern states. These mismatches follow state boundaries, with state explaining nearly three times as much of the variance as district-level hydrology, climate, education, vulnerability, and news combined. This makes flood risk perception a measurable input for adaptation planning, and provides a method transferable to any country where hazard is well measured but perception is not.

Aerosol radiative forcing represents the largest source of uncertainty for calculating present-day climate sensitivity. This uncertainty has been underestimated in climate models, as they have not adequately accounted for uncertainties in the emissions of aerosols prior to the satellite era. This is especially the case for natural aerosols, such as mineral dust and aerosols from biomass burning. In this paper, we take a step towards reducing this uncertainty by providing a roadmap for improved integration of proxy-based observational constraints on past aerosol variability with model simulations. We first review existing literature on the importance of natural aerosol radiative forcing during historical and paleo-time periods, with a focus on the Last Glacial Maximum and the preindustrial era (ca. 1850) to the present. We then compile existing paleo-data archives and describe their ability to constrain emissions of aerosols, highlighting existing gaps. Model-data combinations are available to estimate radiative effects for dust and volcanoes in several time periods, and for biomass burning for preindustrial to present day, but are not available for other aerosols. Modeled feedbacks of aerosols to climate (W/m2/°C) are 10–100⨉ lower than model-data derived estimates for dust, casting doubt on our ability to successfully model feedbacks in the current generation of Earth system models. Finally, we propose a framework for integrating paleo-archives with models through harmonized datasets, proxy–model translation metrics, and regularized inversion approaches, and identify priority measurements needed to reduce uncertainty in both aerosol loading and radiative-effect efficiency across climate states.

Fires on Earth are changing in response to human activities, both through direct ecosystem management and indirect climate change-induced warming and associated shifts in regional rainfall patterns. While increased burning in forested systems often captures international media attention, the decline in burning in grassy systems––especially African savannas––receives less focus, despite their dominant contribution to total global burned area and fire emissions. Forecasting future fire activity and its impacts on local ecology and livelihoods, as well as global climate feedbacks, requires a robust mechanistic understanding of the complex interactions between climatic conditions, ecosystem functioning, human activities, and fire across a range of climate states not captured by modern satellite-based observations.This study focuses on Africa, whose environments span a diversity of climates and ecologies, from some of the driest and most sparsely vegetated regions on Earth (such as the Sahara) to some of the wettest and most biologically productive (such as the Congo Rainforest). These two ends of the rainfall gradient experience non-existent to infrequent burning. However, the most expansive biomes in Africa are tropical savannas and grasslands where precipitation is intermediate and highly seasonal, supporting rapid vegetation growth during wet seasons and drying and abundant fires in the dry season. As a result, burning in Africa constitutes more than half of all global burned area each year. Robust histories of how fires have changed in Africa through time are therefore essential to understanding changes in biomass burning at a global scale. In addition to its broad scope of environments and outmatched contributions to total global burning, Africa also has the longest history of human fire use and land-use change, making it an ideal testing ground for interrogating the combined roles climate shifts and human behaviors play in shaping fire regimes through time.Here, we present a new synthesis of African paleofire activity inferred from the accumulation of both physical and molecular proxies (e.g., charcoal and polycyclic aromatic hydrocarbons) within climate archives spanning multiple depositional contexts (e.g., lacustrine, marine, and peat sediments) which record biomass burning across a host of ecosystems. Our new reconstruction spans the last 24 thousand years, within which we focus on four key time periods: the Last Glacial Maximum and deglaciation, the mid-Holocene African Humid Period, the late-Holocene rise of metallurgy and agriculture, and the post-industrial era. We evaluate trends in biomass burning during these intervals, and, by comparison to paleoclimate and archeological datasets, we assess the extent to which these patterns are driven by climatic and/or human influences at continental, regional, and biome scales.

Climate change impacts everyone, but those impacts are not distributed equally. Frontline communities (FLC) face disproportionate climate change harms, but that may not entail greater worry about them. Worries shape community priorities, and understanding these worries can improve engagement strategies. Here we integrate Climate Change in the American Mind project (n = 6,183) data with the U.S. Climate and Environmental Justice Screening Tool to explore climate change attitudes among people located in FLC compared with those not in FLC. People in FLC and non-FLC are equally worried about global warming. However, people in FLC are slightly more worried about several climate change impacts and are less likely to think there is scientific consensus that global warming is happening. Geographic analysis shows where FLC are most and least worried about global warming. This study shows a need for strategic communication in FLC regarding the connections between climate impacts and climate change.

Extreme heat poses a risk to public health, yet illness and death from heat exposure are largely preventable through behavioral adaptation. However, individuals’ perceptions of local heat risks often diverge from expert assessments, limiting protective behaviors. Here we introduce the Risk Analysis – Perception Framework, which quantifies the alignment between assessed risk, based on a standardized public health metric from the Centers for Disease Control and Prevention Heat and Health Index, and perceived risk, modeled using a multi-year national survey (2018–2022). Applying the framework across US counties, we find substantial misalignments between assessed and perceived risk, particularly in the Pacific Northwest and in Appalachia. These gaps highlight how personal experience, contextual cues, and mental shortcuts shape risk perceptions that often diverge from expert assessments of vulnerability. Through systematic risk alignment mapping, the Risk Analysis – Perception Framework provides an empirical approach to target climate risk communication, adaptation strategies, and policy interventions.

Widespread impacts of landscape fire on ecosystems, societies, and the climate system itself have heightened the need to understand the potential future trajectory of fire under continued climate change. However, the complexity of fire makes climate change impact assessment challenging. The climate system influences fire in many ways, including through vegetation, fuel dryness, fire weather, and ignition. Furthermore, fire’s impacts are highly diverse, spanning threats to human and ecological values and beneficial ecosystem and cultural services. Here, we discuss the art and science of projecting climate change impacts on landscape fire. This not only includes how fire, its drivers, and its impacts are modeled, but critically it also includes how projections of the climate system are developed. By raising and discussing these issues, we aim to foster the development of more robust and useful fire projections, help interpret existing assessments, and support society in charting a course toward a sustainable fire future.

Addressing global climate change requires policy action by governments in every country and at every geographic scale. Yet, we lack systematic data on climate change opinions around the world, particularly at the sub-national scales critical to many policy processes. Here, we model country and sub-national concern about climate change using a Bayesian item response theory model. We draw on almost 3.9 million survey responses from 97 surveys conducted between 2002 and 2023. We estimate public concern about climate change in 166 countries and 2,188 sub-national regions, representing 97.9% of the global population. Our results indicate substantial heterogeneity in climate concern, both nationally and sub-nationally. We highlight strong sub-national heterogeneity in climate concern in some of the countries most pivotal to global climate action, contrasted with relative homogeneity in other places. Our findings offer an important resource for policymakers, scientists, advocates, and educators working to address climate change and plan for its impacts.