Danu Caus

Past land cover dynamics provide important context for understanding long-term ecosystem responses to climatic change, but paleoecological records are typically sparse, irregular, and site-based. Here, we present a spatial downscaling framework that transforms pollen-derived vegetation reconstructions into gridded paleo land cover maps. The pipeline first translates pollen-based vegetation composition into land cover class frequencies, then distributes these classes within pollen source areas using environmental similarity, and finally applies a U-Net-based interpolation approach to generate spatially continuous reconstructions. We apply the framework to Alaska and western Canada, producing 300 m land cover reconstructions across 15 land cover classes and 119 time slices. The source-area product preserves the direct link to pollen records and contains approximately 2.56 billion reconstructed pixels, while the U-Net-based product extends these reconstructions to continuous spatial coverage across the full study region. Reconstructed land cover trends show broad stability during the late Holocene, with stronger changes around 10 ka BP, including declining evergreen needle-leaved forest cover toward older time slices and corresponding changes in sparse vegetation and shrubland. Relative uncertainty estimates indicate higher uncertainty in forested regions and increasing uncertainty with age, reflecting both class ambiguity and reduced paleo-record support. An example analysis of elevational forest dynamics demonstrates the potential of the dataset for spatially explicit paleoecological applications. The framework provides a bridge between site-based pollen records and landscape-scale analyses, while emphasizing that reconstructed maps represent model-based estimates rather than direct observations of past environments.

Past land cover dynamics provide important context for understanding long-term ecosystem responses to climatic change, but paleoecological records are typically sparse, irregular, and site-based. Here, we present a spatial downscaling framework that transforms pollen-derived vegetation reconstructions into gridded paleo land cover maps. The pipeline first translates pollen-based vegetation composition into land cover class frequencies, then distributes these classes within pollen source areas using environmental similarity, and finally applies a U-Net-based interpolation approach to generate spatially continuous reconstructions. We apply the framework to Alaska and western Canada, producing 300 m land cover reconstructions across 15 land cover classes and 119 time slices. The source-area product preserves the direct link to pollen records and contains approximately 2.56 billion reconstructed pixels, while the U-Net-based product extends these reconstructions to continuous spatial coverage across the full study region. Reconstructed land cover trends show broad stability during the late Holocene, with stronger changes around 10 ka BP, including declining evergreen needle-leaved forest cover toward older time slices and corresponding changes in sparse vegetation and shrubland. Relative uncertainty estimates indicate higher uncertainty in forested regions and increasing uncertainty with age, reflecting both class ambiguity and reduced paleo-record support. An example analysis of elevational forest dynamics demonstrates the potential of the dataset for spatially explicit paleoecological applications. The framework provides a bridge between site-based pollen records and landscape-scale analyses, while emphasizing that reconstructed maps represent model-based estimates rather than direct observations of past environments.

Past land cover dynamics provide important context for understanding long-term ecosystem responses to climatic change, but paleoecological records are typically sparse, irregular, and site-based. Here, we present a spatial downscaling framework that transforms pollen-derived vegetation reconstructions into gridded paleo land cover maps. The pipeline first translates pollen-based vegetation composition into land cover class frequencies, then distributes these classes within pollen source areas using environmental similarity, and finally applies a U-Net-based interpolation approach to generate spatially continuous reconstructions. We apply the framework to Alaska and western Canada, producing 300 m land cover reconstructions across 15 land cover classes and 119 time slices. The source-area product preserves the direct link to pollen records and contains approximately 2.56 billion reconstructed pixels, while the U-Net-based product extends these reconstructions to continuous spatial coverage across the full study region. Reconstructed land cover trends show broad stability during the late Holocene, with stronger changes around 10 ka BP, including declining evergreen needle-leaved forest cover toward older time slices and corresponding changes in sparse vegetation and shrubland. Relative uncertainty estimates indicate higher uncertainty in forested regions and increasing uncertainty with age, reflecting both class ambiguity and reduced paleo-record support. An example analysis of elevational forest dynamics demonstrates the potential of the dataset for spatially explicit paleoecological applications. The framework provides a bridge between site-based pollen records and landscape-scale analyses, while emphasizing that reconstructed maps represent model-based estimates rather than direct observations of past environments.

Snow on Antarctic sea ice strongly affects thermodynamic processes, sea ice mass balance, and microwave remote sensing, yet its spatial variability and characteristic length scales remain poorly quantified. The aim of this study is to provide a spatially extensive, layer-resolved characterization of Antarctic late-summer snow on sea ice and, for the first time, to quantify the variability of snow properties and their horizontal correlation length scales on first-year (FYI) and multi-year ice (MYI). We use a unique combination of manual snow pit observations and more than 900 SnowMicroPen (SMP) profiles collected along meter-scale transects during three expeditions in the Weddell Sea between 2018 and 2021. Snow stratigraphy and microstructural classes were derived from SMP force data using a supervised one-dimensional convolutional neural network trained on manually classified SMP profiles. Across both sea-ice regimes, intrinsic properties of individual snow types, including density and specific surface area, were broadly similar. Differences between FYI and MYI instead arise from contrasting snowpack structure, snow type fractions, and spatial coherence, with MYI characterized by a higher prevalence of dense melt-freeze layers and enhanced vertical heterogeneity. Spatial autocorrelation analyses reveal pronounced scale-dependent variability, with snow properties on FYI decorrelating over short distances, while MYI exhibits substantially higher spatial coherence. Individual ice floes capture only about 50 % of the snow density variability characteristic of their respective ice regime, underscoring fundamental limits to the representativeness of point measurements. A hierarchy of variability emerges, in which snow type fractions and layer thickness dominate snowpack heterogeneity, while bulk snow density is comparatively homogeneous across spatial scales. These results demonstrate that Antarctic summer snow variability is governed primarily by stratigraphic composition and ice-regime-dependent snowpack evolution rather than bulk-integrated properties. These findings emphasize the need for spatially distributed observations and stratigraphy-aware parameterizations to improve the representation of snow on Antarctic sea ice in remote-sensing applications and sea ice and climate models. In particular, the relative consistency of specific surface area within snow types across ice regimes provides observational constraints that can be evaluated for future microwave-emission and radiative-transfer modeling studies.

Past land cover dynamics provide important context for understanding long-term ecosystem responses to climatic change, but paleoecological records are typically sparse, irregular, and site-based. Here, we present a spatial downscaling framework that transforms pollen-derived vegetation reconstructions into gridded paleo land cover maps. The pipeline first translates pollen-based vegetation composition into land cover class frequencies, then distributes these classes within pollen source areas using environmental similarity, and finally applies a U-Net-based interpolation approach to generate spatially continuous reconstructions. We apply the framework to Alaska and western Canada, producing 300 m land cover reconstructions across 15 land cover classes and 119 time slices. The source-area product preserves the direct link to pollen records and contains approximately 2.56 billion reconstructed pixels, while the U-Net-based product extends these reconstructions to continuous spatial coverage across the full study region. Reconstructed land cover trends show broad stability during the late Holocene, with stronger changes around 10 ka BP, including declining evergreen needle-leaved forest cover toward older time slices and corresponding changes in sparse vegetation and shrubland. Relative uncertainty estimates indicate higher uncertainty in forested regions and increasing uncertainty with age, reflecting both class ambiguity and reduced paleo-record support. An example analysis of elevational forest dynamics demonstrates the potential of the dataset for spatially explicit paleoecological applications. The framework provides a bridge between site-based pollen records and landscape-scale analyses, while emphasizing that reconstructed maps represent model-based estimates rather than direct observations of past environments.

Snow on Antarctic sea ice strongly affects thermodynamic processes, sea ice mass balance, and microwave remote sensing, yet its spatial variability and characteristic length scales remain poorly quantified. The aim of this study is to provide a spatially extensive, layer-resolved characterization of Antarctic late-summer snow on sea ice and, for the first time, to quantify the variability of snow properties and their horizontal correlation length scales on first-year (FYI) and multi-year ice (MYI). We use a unique combination of manual snow pit observations and more than 900 SnowMicroPen (SMP) profiles collected along meter-scale transects during three expeditions in the Weddell Sea between 2018 and 2021. Snow stratigraphy and microstructural classes were derived from SMP force data using a supervised one-dimensional convolutional neural network trained on manually classified SMP profiles. Across both regimes, intrinsic properties of individual snow types, including density and specific surface area, were remarkably similar. Differences between FYI and MYI instead arise from contrasting snowpack structure, snow type fractions, and spatial coherence, with MYI characterized by a higher prevalence of dense melt-freeze layers and enhanced vertical heterogeneity. Spatial autocorrelation analyses reveal pronounced scale-dependent variability, with snow properties on FYI decorrelating over short distances, while MYI exhibits substantially higher spatial coherence. Individual ice floes capture only about 50 % of the variability characteristic of their respective ice regime, underscoring fundamental limits to the representativeness of point measurements. A hierarchy of variability emerges, in which snow type fractions and layer thickness dominate snowpack heterogeneity, while bulk snow density is comparatively homogeneous across spatial scales. These results demonstrate that Antarctic summer snow variability is governed primarily by stratigraphic composition and ice-regime-dependent snowpack evolution rather than bulk-integrated properties. These findings emphasize the need for spatially distributed observations and stratigraphy-aware parameterizations to improve the representation of snow on Antarctic sea ice in remote-sensing applications and sea ice and climate models.

Snow on Antarctic sea ice strongly affects thermodynamic processes, sea ice mass balance, and microwave remote sensing, yet its spatial variability and characteristic length scales remain poorly quantified. The aim of this study is to provide a spatially extensive, layer-resolved characterization of Antarctic late-summer snow on sea ice and, for the first time, to quantify the variability of snow properties and their horizontal correlation length scales on first-year (FYI) and multi-year ice (MYI). We use a unique combination of manual snow pit observations and more than 900 SnowMicroPen (SMP) profiles collected along meter-scale transects during three expeditions in the Weddell Sea between 2018 and 2021. Snow stratigraphy and microstructural classes were derived from SMP force data using a supervised one-dimensional convolutional neural network trained on manually classified SMP profiles. Across both regimes, intrinsic properties of individual snow types, including density and specific surface area, were remarkably similar. Differences between FYI and MYI instead arise from contrasting snowpack structure, snow type fractions, and spatial coherence, with MYI characterized by a higher prevalence of dense melt-freeze layers and enhanced vertical heterogeneity. Spatial autocorrelation analyses reveal pronounced scale-dependent variability, with snow properties on FYI decorrelating over short distances, while MYI exhibits substantially higher spatial coherence. Individual ice floes capture only about 50 % of the variability characteristic of their respective ice regime, underscoring fundamental limits to the representativeness of point measurements. A hierarchy of variability emerges, in which snow type fractions and layer thickness dominate snowpack heterogeneity, while bulk snow density is comparatively homogeneous across spatial scales. These results demonstrate that Antarctic summer snow variability is governed primarily by stratigraphic composition and ice-regime-dependent snowpack evolution rather than bulk-integrated properties. These findings emphasize the need for spatially distributed observations and stratigraphy-aware parameterizations to improve the representation of snow on Antarctic sea ice in remote-sensing applications and sea ice and climate models.

Past land cover dynamics provide important context for understanding long-term ecosystem responses to climatic change, but paleoecological records are typically sparse, irregular, and site-based. Here, we present a spatial downscaling framework that transforms pollen-derived vegetation reconstructions into gridded paleo land cover maps. The pipeline first translates pollen-based vegetation composition into land cover class frequencies, then distributes these classes within pollen source areas using environmental similarity, and finally applies a U-Net-based interpolation approach to generate spatially continuous reconstructions. We apply the framework to Alaska and western Canada, producing 300 m land cover reconstructions across 15 land cover classes and 119 time slices. The source-area product preserves the direct link to pollen records and contains approximately 2.56 billion reconstructed pixels, while the U-Net-based product extends these reconstructions to continuous spatial coverage across the full study region. Reconstructed land cover trends show broad stability during the late Holocene, with stronger changes around 10 ka BP, including declining evergreen needle-leaved forest cover toward older time slices and corresponding changes in sparse vegetation and shrubland. Relative uncertainty estimates indicate higher uncertainty in forested regions and increasing uncertainty with age, reflecting both class ambiguity and reduced paleo-record support. An example analysis of elevational forest dynamics demonstrates the potential of the dataset for spatially explicit paleoecological applications. The framework provides a bridge between site-based pollen records and landscape-scale analyses, while emphasizing that reconstructed maps represent model-based estimates rather than direct observations of past environments.

Snow on Antarctic sea ice strongly affects thermodynamic processes, sea ice mass balance, and microwave remote sensing, yet its spatial variability and characteristic length scales remain poorly quantified. The aim of this study is to provide a spatially extensive, layer-resolved characterization of Antarctic late-summer snow on sea ice and, for the first time, to quantify the variability of snow properties and their horizontal correlation length scales on first-year (FYI) and multi-year ice (MYI). We use a unique combination of manual snow pit observations and more than 900 SnowMicroPen (SMP) profiles collected along meter-scale transects during three expeditions in the Weddell Sea between 2018 and 2021. Snow stratigraphy and microstructural classes were derived from SMP force data using a supervised one-dimensional convolutional neural network trained on manually classified SMP profiles. Across both regimes, intrinsic properties of individual snow types, including density and specific surface area, were remarkably similar. Differences between FYI and MYI instead arise from contrasting snowpack structure, snow type fractions, and spatial coherence, with MYI characterized by a higher prevalence of dense melt-freeze layers and enhanced vertical heterogeneity. Spatial autocorrelation analyses reveal pronounced scale-dependent variability, with snow properties on FYI decorrelating over short distances, while MYI exhibits substantially higher spatial coherence. Individual ice floes capture only about 50 % of the variability characteristic of their respective ice regime, underscoring fundamental limits to the representativeness of point measurements. A hierarchy of variability emerges, in which snow type fractions and layer thickness dominate snowpack heterogeneity, while bulk snow density is comparatively homogeneous across spatial scales. These results demonstrate that Antarctic summer snow variability is governed primarily by stratigraphic composition and ice-regime-dependent snowpack evolution rather than bulk-integrated properties. These findings emphasize the need for spatially distributed observations and stratigraphy-aware parameterizations to improve the representation of snow on Antarctic sea ice in remote-sensing applications and sea ice and climate models.

Snow on Antarctic sea ice strongly affects thermodynamic processes, sea ice mass balance, and microwave remote sensing, yet its spatial variability and characteristic length scales remain poorly quantified. The aim of this study is to provide a spatially extensive, layer-resolved characterization of Antarctic late-summer snow on sea ice and, for the first time, to quantify the variability of snow properties and their horizontal correlation length scales on first-year (FYI) and multi-year ice (MYI). We use a unique combination of manual snow pit observations and more than 900 SnowMicroPen (SMP) profiles collected along meter-scale transects during three expeditions in the Weddell Sea between 2018 and 2021. Snow stratigraphy and microstructural classes were derived from SMP force data using a supervised one-dimensional convolutional neural network trained on manually classified SMP profiles. Across both regimes, intrinsic properties of individual snow types, including density and specific surface area, were remarkably similar. Differences between FYI and MYI instead arise from contrasting snowpack structure, snow type fractions, and spatial coherence, with MYI characterized by a higher prevalence of dense melt-freeze layers and enhanced vertical heterogeneity. Spatial autocorrelation analyses reveal pronounced scale-dependent variability, with snow properties on FYI decorrelating over short distances, while MYI exhibits substantially higher spatial coherence. Individual ice floes capture only about 50 % of the variability characteristic of their respective ice regime, underscoring fundamental limits to the representativeness of point measurements. A hierarchy of variability emerges, in which snow type fractions and layer thickness dominate snowpack heterogeneity, while bulk snow density is comparatively homogeneous across spatial scales. These results demonstrate that Antarctic summer snow variability is governed primarily by stratigraphic composition and ice-regime-dependent snowpack evolution rather than bulk-integrated properties. These findings emphasize the need for spatially distributed observations and stratigraphy-aware parameterizations to improve the representation of snow on Antarctic sea ice in remote-sensing applications and sea ice and climate models.