Christopher Kadow

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.

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.

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.

In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. We track twelve key sets of indicators of the state of the climate system, closely following Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment report (AR6) methods, to produce our fourth annual publication. One of the indicators, the Earth's energy imbalance (EEI) provides a crucial integrative measure of the overall heating of the planet and the pace of climate change – this has more than doubled since the 1976–1995 period. A newly added indicator of temperature extremes, the number of days experiencing marine heatwaves, has more than tripled between 1991 and 2025. For the 2016–2025 decade average, observed warming relative to 1850–1900 was 1.26 [1.13 to 1.36] °C, of which 1.24 [1.0 to 1.5] °C was human-induced. Human-induced warming reached 1.37 °C relative to 1850–1900 in the year 2025, increasing at a rate of 0.27 [0.2–0.4] °C per decade over 2016–2025. This high rate of warming, which matches the all-time high seen last year in the instrumental record, was caused by a combination of greenhouse gas emissions being at an all-time high of 54.6 ± 5.5 GtCO ₂ e yr ⁻¹ over the last decade (2015–2024), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that CO ₂ emission growth is slowing. The continuation of these annual updates could track decreases or increases in the rate of human influence and climatic changes presented here, reflecting the outcomes of societal choices during the critical 2020s decade. The data presented herein can provide a useful reference point for the drafting of the IPCC seventh assessment report. In total, we employ analysis from over 40 global datasets (https://doi.org/10.5281/zenodo.20499280, Smith et al., 2026a). Future monitoring of these indicators, such as ocean and satellite measurements of the Earth's energy imbalance, are threatened by geopolitical and public funding decisions. Our ability to consistently track many of the indicators requires the continuity of observation programs and coordination mechanisms, including the Global Climate Observing System (GCOS) program, that enable their effective integration and use.

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.

In a rapidly changing climate, evidence-based decision-making benefits from up-to-date and timely information. We track twelve key sets of indicators of the state of the climate system, closely following Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment report (AR6) methods, to produce our fourth annual publication. One of the indicators, the Earth's energy imbalance (EEI) provides a crucial integrative measure of the pace of climate change – this has more than doubled since the 1976–1995 period. A newly updated indicator of temperature extremes, the number of days experiencing marine heatwaves, has more than tripled between 1991 and 2025. For the 2016–2025 decade average, observed warming relative to 1850–1900 was 1.26 [1.13 to 1.36] °C, of which 1.24 [1.0 to 1.5] °C was human-induced. Human-induced warming reached 1.37 °C relative to 1850-1900 in the year 2025, increasing at a rate of 0.27 [0.2–0.4] °C per decade over 2016–2025. This high rate of warming, which matches the all-time high seen last year in the instrumental record, was caused by a combination of greenhouse gas emissions being at an all-time high of 54.6 ± 5.5 GtCO2e per year over the last decade (2015–2024), as well as reductions in the strength of aerosol cooling. Despite this, there is evidence that CO2 emission growth is slowing. The continuation of these annual updates could track decreases or increases in the rate of human influence and climatic changes presented here, reflecting the outcomes of societal choices during the critical 2020s decade. In total, we employ analysis from over 40 global datasets (https://doi.org/10.5281/ZENODO.7883757 Smith et al., 2026a) These data are threatened by geopolitical and public funding decisions that are cutting support for key satellite and in-situ observing programs critical for the monitoring of the atmosphere, ocean, cryosphere and land surface, including long-term data preservation and provision that is key to understanding our changing climate for today and for future generations. Overall, the Global Climate Observing System (GCOS) framework, which supports much of our international capability, is under threat. Our ability to monitor effectively many of the indicators presented herein is not guaranteed without concerted international action to ensure the continuity of observation programs and coordination mechanisms, including the GCOS program, that enable their effective integration and use.

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.

Decadal climate predictions are essential for climate adaptation, yet remain challenging due to the complex interplay of initial conditions and external forcings. A key factor in achieving skillful forecasts is the upper ocean, which plays a central role in modulating decadal-scale climate variability, including phenomena such as ENSO, the Atlantic Multidecadal Variability, and the Indian Ocean Dipole. Accurately capturing the ocean’s memory is therefore critical, but traditional numerical models are computationally demanding and often exhibit systematic biases. While machine learning has shown promise in improving medium-range weather forecasts, its application to decadal climate prediction remains limited.This work explores the feasibility of using machine learning to predict sea surface temperature (SST) and ocean heat content (OHC) on decadal timescales. We develop an autoregressive model based on a UNet-like convolutional neural network, trained on 1,000 years of data from a pre-industrial control run from the fully coupled MPI-ESM. This simulation provides a controlled environment to study predictability arising from internal ocean dynamics. Inputs include SST, OHC, a land-sea mask, and top-of-atmosphere solar radiation to encode the seasonal cycle. We conduct a systematic study of input design, to assess how the representation of past states influences model stability and predictive skill. Our results suggest that machine learning can be a viable and flexible approach for decadal ocean prediction. Additionally, we find that longer input windows and coarser resolution may improve long-term stability, potentially offering new insights into how climate memory is encoded.