Hideki Kobayashi

Forest canopy models are used to simulate biosphere–atmosphere coupling in global climate models, as well as the microclimate influences of forests at the stand scale. It has recently been shown that wood structures store significant heat following radiation absorption and impact air temperature diurnal patterns inside canopies. Yet, radiation absorption by woody stems is not fully considered in current models. Here we modify the radiative transfer component of the CanVeg2 multilayer canopy model to include radiation absorption by woody stems. We evaluate the model modifications by comparing estimates against a 3D ray tracing radiative transfer model parametrized using ground lidar measurements, and against tower observations of albedo in four broadleaf forests. We found a very good agreement between the 1D and 3D models, and a good agreement between models and observations. Our approach provides a tractable and computationally efficient implementation of radiation absorption by woody stems to calculate biomass heat storage in canopy models.

Measurements of surface-atmosphere carbon dioxide (CO ₂ ) and methane (CH ₄ ) fluxes have been relatively sparse across the Arctic tundra and boreal biomes, causing significant uncertainties in carbon budget estimates from the region. While the availability of Arctic-boreal carbon flux data has increased substantially over the past decade, the data have remained spread across different repositories, scientific articles, and unpublished sources, making it difficult to leverage. Here we present a new dataset of monthly Arctic-boreal carbon fluxes (ABCFlux v2) across terrestrial (wetlands and uplands) and freshwater (lakes and rivers) ecosystems compiled from previous syntheses including the Arctic-boreal CO ₂ flux database (ABCFlux v1), the Boreal-Arctic Wetland and Lake Methane Dataset (BAWLD-CH4), and the Global River Methane Database (GRiMeDB). In addition, we consider data from general-purpose (e.g., Zenodo) and flux network repositories, literature, and site principal investigators. The dataset includes surface-atmosphere CO ₂ fluxes of gross primary production (GPP), ecosystem respiration (Reco), and net ecosystem exchange (NEE), alongside CH ₄ fluxes. For aquatic ecosystems, we split CH ₄ fluxes into diffusive and ebullitive flux pathways, and included potential emissions from transient storage in the water column (“storage fluxes”), alongside CO ₂ and CH ₄ concentrations dissolved in the surface water. Fluxes are measured through a variety of methods including chamber and eddy covariance techniques alongside bubble traps, ice-surveys, and concentration-based turbulence-driven modelling in aquatic ecosystems. The monthly flux data are reported together with supporting methodological and environmental metadata. The resulting ABCFlux v2 has 23 847 flux site-months, 8182 concentration site-months, and 199 seasonal observations from 1024 sites, and includes 56 139 reported fluxes (i.e. sum of GPP, Reco, NEE, and CH ₄ fluxes) from the years 1984 to 2024. The majority of monthly observations occurred after 1999. Wetlands had the highest number of site-month observations (8758), followed by boreal forest (6981), lotic ecosystems (6275), lentic ecosystems (3799) and upland tundra (3308). Measurements of CO ₂ dominated the dataset across most ecosystem types (25 222) except for lentic ecosystems, where CH ₄ flux site-months (3098) were more frequent than CO ₂ flux site-months (2915). Overall, ABCFlux v2 includes 160 % more site-months for terrestrial CO ₂ flux data compared to ABCFlux v1. Integrating and updating BAWLD-CH4 flux data from growing season averages to monthly fluxes resulted in 5671 site-months of chamber CH ₄ data compared to 762 site-years. This collaborative initiative, involving contributions from over 260 researchers, provides a comprehensive overview of the current state of the Arctic-boreal carbon flux network and its data, and serves as an important step in reducing uncertainties in Arctic-boreal carbon budgets and in enhancing our understanding of climate feedbacks. The data can be accessed at ORNL DAAC at https://doi.org/10.3334/ORNLDAAC/2448 (Virkkala et al., 2026).

Forest canopy models are used to simulate biosphere–atmosphere coupling in global climate models, as well as the microclimate influences of forests at the stand scale. It has recently been shown that wood structures store significant heat following radiation absorption and impact air temperature diurnal patterns inside canopies. Yet, radiation absorption by woody stems is not fully considered in current models. Here we modify the radiative transfer component of the CanVeg2 multilayer canopy model to include radiation absorption by woody stems. We evaluate the model modifications by comparing estimates against a validated 3D ray tracing radiative transfer model parametrized using ground lidar measurements, and against tower observations of albedo in four broadleaf forests. We found a very good agreement between the 1D and 3D models, and a good agreement between models and observations. Our approach provides a tractable and computationally efficient implementation of radiation absorption by woody stems to calculate biomass heat storage in canopy models.

Forest canopy models are used to simulate biosphere–atmosphere coupling in global climate models, as well as the microclimate influences of forests at the stand scale. It has recently been shown that wood structures store significant heat following radiation absorption and impact air temperature diurnal patterns inside canopies. Yet, radiation absorption by woody stems is not fully considered in current models. Here we modify the radiative transfer component of the CanVeg2 multilayer canopy model to include radiation absorption by woody stems. We evaluate the model modifications by comparing estimates against a validated 3D ray tracing radiative transfer model parametrized using ground lidar measurements, and against tower observations of albedo in four broadleaf forests. We found a very good agreement between the 1D and 3D models, and a good agreement between models and observations. Our approach provides a tractable and computationally efficient implementation of radiation absorption by woody stems to calculate biomass heat storage in canopy models.

Forest canopy models are used to simulate biosphere–atmosphere coupling in global climate models, as well as the microclimate influences of forests at the stand scale. It has recently been shown that wood structures store significant heat following radiation absorption and impact air temperature diurnal patterns inside canopies. Yet, radiation absorption by woody stems is not fully considered in current models. Here we modify the radiative transfer component of the CanVeg2 multilayer canopy model to include radiation absorption by woody stems. We evaluate the model modifications by comparing estimates against a validated 3D ray tracing radiative transfer model parametrized using ground lidar measurements, and against tower observations of albedo in four broadleaf forests. We found a very good agreement between the 1D and 3D models, and a good agreement between models and observations. Our approach provides a tractable and computationally efficient implementation of radiation absorption by woody stems to calculate biomass heat storage in canopy models.

Forest canopy models are used to simulate biosphere–atmosphere coupling in global climate models, as well as the microclimate influences of forests at the stand scale. It has recently been shown that wood structures store significant heat following radiation absorption and impact air temperature diurnal patterns inside canopies. Yet, radiation absorption by woody stems is not fully considered in current models. Here we modify the radiative transfer component of the CanVeg2 multilayer canopy model to include radiation absorption by woody stems. We evaluate the model modifications by comparing estimates against a validated 3D ray tracing radiative transfer model parametrized using ground lidar measurements, and against tower observations of albedo in four broadleaf forests. We found a very good agreement between the 1D and 3D models, and a good agreement between models and observations. Our approach provides a tractable and computationally efficient implementation of radiation absorption by woody stems to calculate biomass heat storage in canopy models.

Forest canopy models are used to simulate biosphere–atmosphere coupling in global climate models, as well as the microclimate influences of forests at the stand scale. It has recently been shown that wood structures store significant heat following radiation absorption and impact air temperature diurnal patterns inside canopies. Yet, radiation absorption by woody stems is not fully considered in current models. Here we modify the radiative transfer component of the CanVeg2 multilayer canopy model to include radiation absorption by woody stems. We evaluate the model modifications by comparing estimates against a validated 3D ray tracing radiative transfer model parametrized using ground lidar measurements, and against tower observations of albedo in four broadleaf forests. We found a very good agreement between the 1D and 3D models, and a good agreement between models and observations. Our approach provides a tractable and computationally efficient implementation of radiation absorption by woody stems to calculate biomass heat storage in canopy models.

Forest canopy models are used to simulate biosphere–atmosphere coupling in global climate models, as well as the microclimate influences of forests at the stand scale. It has recently been shown that wood structures store significant heat following radiation absorption and impact air temperature diurnal patterns inside canopies. Yet, radiation absorption by woody stems is not fully considered in current models. Here we modify the radiative transfer component of the CanVeg2 multilayer canopy model to include radiation absorption by woody stems. We evaluate the model modifications by comparing estimates against a validated 3D ray tracing radiative transfer model parametrized using ground lidar measurements, and against tower observations of albedo in four broadleaf forests. We found a very good agreement between the 1D and 3D models, and a good agreement between models and observations. Our approach provides a tractable and computationally efficient implementation of radiation absorption by woody stems to calculate biomass heat storage in canopy models.

Recent frequent heatwaves have adversely affected the carbon cycles of terrestrial ecosystems. Solar‐induced chlorophyll fluorescence (SIF) and the photochemical reflectance index (PRI) can detect heat and drought stress in vegetation. However, the applicability of these indicators in extreme cases, such as heatwaves, needs consideration. We examined diurnal changes observed in tower‐based SIF, PRI, canopy conductance, and gross primary productivity (GPP) over 3 weeks during mid‐summer in 2022 in a cool‐temperate deciduous forest covered by Larix kaempferi (Lamb.) Carrière, in Japan. We found that half‐hourly GPP and SIF declined significantly from noon to afternoon during the latter part of clear, extremely hot days (up to 31°C) when the soil water content (SWC) was low (< 21%), and the vapor pressure deficit (VPD) was high (> 15 hPa). Values of GPP and SIF peaked at 21.7°C and 26.8°C, respectively, and they declined at higher temperatures. Canopy conductance declined with increasing air temperature and VPD, suggesting that stomatal closure caused the observed reduction in GPP. To investigate the stress response without light‐dependent variations, canopy fluorescence emission efficiency ( εF ) and light use efficiency (LUE) were calculated from SIF and GPP, respectively. VPD had the most significant negative correlation with PRI, LUE, and εF . εF was also associated with SWC, particularly under lower SWC conditions. Our results demonstrate that SIF and PRI responded to heat and drought with different sensitivities and thresholds and can serve as diagnostic tools to assess the influence of moderate to extreme environments on terrestrial ecosystems.