• Energy Research

Abstract Spatio-temporal variability in energy fluxes at the earth's surface implies spatial and temporal changes in observed land surface temperatures (LST). These fluxes are largely determined by variation in meteorological conditions, surface cover and soil characteristics. Consequently, a change in these parameters will be reflected in a different temporal LST behavior which can be observed by remotely sensed time series. Therefore, the objective of this paper is to perform a quantitative analysis on the parameters that determine this variability in LST to estimate the impact of changes in these parameters on the surface thermal regime. This study was conducted in the Russian Altay Mountains, an area characterized by strong gradients in meteorological conditions and surface cover. Spatio-temporal variability in LST was assessed by applying the fast Fourier transform (FFT) on 8 year of MODIS Aqua LST time series, herein considering both day and nighttime series as well as the diurnal difference. This FFT method was chosen as it allows to discriminate significant periodics, and as such enables distinction between short-term weather components, and strong, climate related, periodic patterns. A quantitative analysis was based on multiple linear regression models between the calculated, significant Fourier components (i.e. the annual and average component) and five physiographic variables representing the regional variability in meteorological conditions and surface cover. Physiographic predictors were elevation, potential solar insolation, topographic convergence, vegetation cover and snow cover duration. Results illustrated the strong inverse relationship between averaged daytime and diurnal difference LST and snow duration, with a R adj 2 of 0.85 and 0.60, respectively. On the other hand, nocturnal LST showed a strong connection with elevation and the amount of vegetation cover. Amplitudes of the annual harmonic experienced both for daytime and for nighttime LST similar trends with the set of physiographic variables – with stronger relationships at night. As such, topographic convergence was found to be the principal single predictor which demonstrated the importance of severe temperature inversions in the region. Furthermore, limited contribution of the physiographic predictors to the observed variation in the annual signal of the diurnal difference was retrieved, although a significant phase divergence was noticed between the majority of the study region and the perennial snowfields. Hence, this study gives valuable insights into the complexity of the spatio-temporal variability in LST, which can be used in future studies to estimate the ecosystems’ response on changing climatic conditions.

Abstract Spatio-temporal variability in energy fluxes at the earth's surface implies spatial and temporal changes in observed land surface temperatures (LST). These fluxes are largely determined by variation in meteorological conditions, surface cover and soil characteristics. Consequently, a change in these parameters will be reflected in a different temporal LST behavior which can be observed by remotely sensed time series. Therefore, the objective of this paper is to perform a quantitative analysis on the parameters that determine this variability in LST to estimate the impact of changes in these parameters on the surface thermal regime. This study was conducted in the Russian Altay Mountains, an area characterized by strong gradients in meteorological conditions and surface cover. Spatio-temporal variability in LST was assessed by applying the fast Fourier transform (FFT) on 8 year of MODIS Aqua LST time series, herein considering both day and nighttime series as well as the diurnal difference. This FFT method was chosen as it allows to discriminate significant periodics, and as such enables distinction between short-term weather components, and strong, climate related, periodic patterns. A quantitative analysis was based on multiple linear regression models between the calculated, significant Fourier components (i.e. the annual and average component) and five physiographic variables representing the regional variability in meteorological conditions and surface cover. Physiographic predictors were elevation, potential solar insolation, topographic convergence, vegetation cover and snow cover duration. Results illustrated the strong inverse relationship between averaged daytime and diurnal difference LST and snow duration, with a R adj 2 of 0.85 and 0.60, respectively. On the other hand, nocturnal LST showed a strong connection with elevation and the amount of vegetation cover. Amplitudes of the annual harmonic experienced both for daytime and for nighttime LST similar trends with the set of physiographic variables – with stronger relationships at night. As such, topographic convergence was found to be the principal single predictor which demonstrated the importance of severe temperature inversions in the region. Furthermore, limited contribution of the physiographic predictors to the observed variation in the annual signal of the diurnal difference was retrieved, although a significant phase divergence was noticed between the majority of the study region and the perennial snowfields. Hence, this study gives valuable insights into the complexity of the spatio-temporal variability in LST, which can be used in future studies to estimate the ecosystems’ response on changing climatic conditions.

Abstract Forest characteristics, structure, and dynamics within the North American boreal region are heavily influenced by wildfire intensity, severity, and frequency. Increasing temperatures are likely to result in drier conditions and longer fire seasons, potentially leading to more intense and frequent fires. However, an increase in deciduous forest cover is also predicted across the region, potentially decreasing flammability. In this study, we use an individual tree-based forest model to test bottom-up (i.e. fuels) vs top-down (i.e. climate) controls on fire activity and project future forest and wildfire dynamics. The University of Virginia Forest Model Enhanced is an individual tree-based forest model that has been successfully updated and validated within the North American boreal zone. We updated the model to better characterize fire ignition and behavior in relation to litter and fire weather conditions, allowing for further interactions between vegetation, soils, fire, and climate. Model output following updates showed good agreement with combustion observations at individual sites within boreal Alaska and western Canada. We then applied the updated model at sites within interior Alaska and the Northwest Territories to simulate wildfire and forest response to climate change under moderate (RCP 4.5) and extreme (RCP 8.5) scenarios. Results suggest that changing climate will act to decrease biomass and increase deciduous fraction in many regions of boreal North America. These changes are accompanied by decreases in fire probability and average fire intensity, despite fuel drying, indicating a negative feedback of fuel loading on wildfire. These simulations demonstrate the importance of dynamic fuels and dynamic vegetation in predicting future forest and wildfire conditions. The vegetation and wildfire changes predicted here have implications for large-scale changes in vegetation composition, biomass, and wildfire severity across boreal North America, potentially resulting in further feedbacks to regional and even global climate and carbon cycling.

Abstract Forest characteristics, structure, and dynamics within the North American boreal region are heavily influenced by wildfire intensity, severity, and frequency. Increasing temperatures are likely to result in drier conditions and longer fire seasons, potentially leading to more intense and frequent fires. However, an increase in deciduous forest cover is also predicted across the region, potentially decreasing flammability. In this study, we use an individual tree-based forest model to test bottom-up (i.e. fuels) vs top-down (i.e. climate) controls on fire activity and project future forest and wildfire dynamics. The University of Virginia Forest Model Enhanced is an individual tree-based forest model that has been successfully updated and validated within the North American boreal zone. We updated the model to better characterize fire ignition and behavior in relation to litter and fire weather conditions, allowing for further interactions between vegetation, soils, fire, and climate. Model output following updates showed good agreement with combustion observations at individual sites within boreal Alaska and western Canada. We then applied the updated model at sites within interior Alaska and the Northwest Territories to simulate wildfire and forest response to climate change under moderate (RCP 4.5) and extreme (RCP 8.5) scenarios. Results suggest that changing climate will act to decrease biomass and increase deciduous fraction in many regions of boreal North America. These changes are accompanied by decreases in fire probability and average fire intensity, despite fuel drying, indicating a negative feedback of fuel loading on wildfire. These simulations demonstrate the importance of dynamic fuels and dynamic vegetation in predicting future forest and wildfire conditions. The vegetation and wildfire changes predicted here have implications for large-scale changes in vegetation composition, biomass, and wildfire severity across boreal North America, potentially resulting in further feedbacks to regional and even global climate and carbon cycling.

ABSTRACTBoreal forest regions, including East Siberia, have experienced elevated fire activity in recent years, leading to record‐breaking greenhouse gas emissions and severe air pollution. However, our understanding of the factors that eventually halt fire spread and thus limit fire growth remains incomplete, hindering our ability to model their dynamics and predict their impacts. We investigated the locations and timing of 2.2 million fire stops—defined as 300 m unburned pixels along fire perimeters—across the vast East Siberian taiga. Fire stops were retrieved from remote sensing data covering over 27,000 individual fires that collectively burned 80 Mha between 2012 and 2022. Several geospatial datasets, including hourly fire weather data and landscape variables, were used to identify the factors contributing to individual fire stops. Our analysis attributed 87% of all fire stops to a statistically significant (p < 0.01) change in one or more of these drivers, with fire‐weather drivers limiting fire growth over time and landscape drivers constraining it across space. We found clear regional and temporal variations in the importance of these drivers. For instance, landscape drivers—such as less flammable land cover and the presence of roads—were key constraints on fire growth in southeastern Siberia, where the landscape is more populated and fragmented. In contrast, fire weather was the primary constraint on fire growth in the remote northern taiga. Additionally, in central Yakutia, a major fire hotspot in recent years, fuel limitations from previous fires increasingly restricted fire spread. The methodology we present is adaptable to other biomes and can be applied globally, providing a framework for future attribution studies on global fire growth limitations. In northeast Siberia, we found that with increasing droughts and heatwaves, remote northern fires could potentially grow even larger in the future, with major implications for the global carbon cycle and climate.

ABSTRACTBoreal forest regions, including East Siberia, have experienced elevated fire activity in recent years, leading to record‐breaking greenhouse gas emissions and severe air pollution. However, our understanding of the factors that eventually halt fire spread and thus limit fire growth remains incomplete, hindering our ability to model their dynamics and predict their impacts. We investigated the locations and timing of 2.2 million fire stops—defined as 300 m unburned pixels along fire perimeters—across the vast East Siberian taiga. Fire stops were retrieved from remote sensing data covering over 27,000 individual fires that collectively burned 80 Mha between 2012 and 2022. Several geospatial datasets, including hourly fire weather data and landscape variables, were used to identify the factors contributing to individual fire stops. Our analysis attributed 87% of all fire stops to a statistically significant (p < 0.01) change in one or more of these drivers, with fire‐weather drivers limiting fire growth over time and landscape drivers constraining it across space. We found clear regional and temporal variations in the importance of these drivers. For instance, landscape drivers—such as less flammable land cover and the presence of roads—were key constraints on fire growth in southeastern Siberia, where the landscape is more populated and fragmented. In contrast, fire weather was the primary constraint on fire growth in the remote northern taiga. Additionally, in central Yakutia, a major fire hotspot in recent years, fuel limitations from previous fires increasingly restricted fire spread. The methodology we present is adaptable to other biomes and can be applied globally, providing a framework for future attribution studies on global fire growth limitations. In northeast Siberia, we found that with increasing droughts and heatwaves, remote northern fires could potentially grow even larger in the future, with major implications for the global carbon cycle and climate.

Abstract. Climate change contributes to the increased frequency and intensity of wildfires globally, with significant impacts on society and the environment. However, our understanding of the global distribution of extreme fires remains skewed, primarily influenced by media coverage and regionalised research efforts. This inaugural State of Wildfires report systematically analyses fire activity worldwide, identifying extreme events from the March 2023–February 2024 fire season. We assess the causes, predictability, and attribution of these events to climate change and land use and forecast future risks under different climate scenarios. During the 2023–2024 fire season, 3.9×106 km2 burned globally, slightly below the average of previous seasons, but fire carbon (C) emissions were 16 % above average, totalling 2.4 Pg C. Global fire C emissions were increased by record emissions in Canadian boreal forests (over 9 times the average) and reduced by low emissions from African savannahs. Notable events included record-breaking fire extent and emissions in Canada, the largest recorded wildfire in the European Union (Greece), drought-driven fires in western Amazonia and northern parts of South America, and deadly fires in Hawaii (100 deaths) and Chile (131 deaths). Over 232 000 people were evacuated in Canada alone, highlighting the severity of human impact. Our analyses revealed that multiple drivers were needed to cause areas of extreme fire activity. In Canada and Greece, a combination of high fire weather and an abundance of dry fuels increased the probability of fires, whereas burned area anomalies were weaker in regions with lower fuel loads and higher direct suppression, particularly in Canada. Fire weather prediction in Canada showed a mild anomalous signal 1 to 2 months in advance, whereas events in Greece and Amazonia had shorter predictability horizons. Attribution analyses indicated that modelled anomalies in burned area were up to 40 %, 18 %, and 50 % higher due to climate change in Canada, Greece, and western Amazonia during the 2023–2024 fire season, respectively. Meanwhile, the probability of extreme fire seasons of these magnitudes has increased significantly due to anthropogenic climate change, with a 2.9–3.6-fold increase in likelihood of high fire weather in Canada and a 20.0–28.5-fold increase in Amazonia. By the end of the century, events of similar magnitude to 2023 in Canada are projected to occur 6.3–10.8 times more frequently under a medium–high emission scenario (SSP370). This report represents our first annual effort to catalogue extreme wildfire events, explain their occurrence, and predict future risks. By consolidating state-of-the-art wildfire science and delivering key insights relevant to policymakers, disaster management services, firefighting agencies, and land managers, we aim to enhance society's resilience to wildfires and promote advances in preparedness, mitigation, and adaptation. New datasets presented in this work are available from https://doi.org/10.5281/zenodo.11400539 (Jones et al., 2024) and https://doi.org/10.5281/zenodo.11420742 (Kelley et al., 2024a).