Victoria R. Dutch

Wintertime carbon dioxide (CO₂) emissions from tundra soils likely make a sizable contribution to terrestrial annual Arctic carbon budgets. However, poor availability of wintertime CO₂ flux measurements limits current capacity to quantify the magnitude and variability of non-growing season carbon emissions from Arctic soils, and identify key environmental drivers of CO₂ production and release. To address this, low-cost CO₂ logging systems were developed and deployed in snowpacks at Trail Valley Creek, NT, Canada during late winter periods in 2021–2022 and 2022–2023. CO₂ fluxes calculated from sub-hourly gradients in CO₂ concentrations demonstrated consistent positive (ground-to-atmosphere) emissions, with median flux rates of 0.03 to 0.35 g C m^-2 d^-1, across a range of vegetation types (shrub, tree, tussock) and snow depths (34–136 cm). Stable carbon isotope (δ¹³C-CO₂) analyses confirmed isotopic enrichment of CO₂ at the base of the snowpack originating from soil emission, and that snowpack gases mixed sequentially with atmospheric CO₂ as they travelled through upper snow layers. Snowpack CO₂ concentration gradients and resultant fluxes were impacted by seasonal increases in air and soil temperatures, especially as near-ground gas temperatures approached the zero-curtain (0 °C) threshold during the winter-spring transition. Positive relationships between CO₂ flux and warming subnivean temperatures across the study site were similar in magnitude to those observed in pan-Arctic winter syntheses. Our results highlight that low-cost sensors may provide a robust and scalable method for monitoring the spatial variability of in-situ winter CO₂ fluxes through snow at high temporal resolution and provide unique insights into winter carbon processes.

Wintertime carbon dioxide (CO2) emissions from tundra soils likely make a sizable contribution to terrestrial annual Arctic carbon budgets. However, poor availability of wintertime CO2 flux measurements limits current capacity to quantify the magnitude and variability of non-growing season carbon emissions from Arctic soils, and identify key environmental drivers of CO2 production and release. To address this, low-cost CO2 logging systems were developed and deployed in snowpacks at Trail Valley Creek, NT, Canada during late winter periods in 2021–2022 and 2022–2023. CO2 fluxes calculated from sub-hourly gradients in CO2 concentrations demonstrated consistent positive (ground-to-atmosphere) emissions, with median flux rates of 0.03 to 0.35 g C m-2 d-1, across a range of vegetation types (shrub, tree, tussock) and snow depths (34–136 cm). Stable carbon isotope (δ13C-CO2) analyses confirmed isotopic enrichment of CO2 at the base of the snowpack originating from soil emission, and that snowpack gases mixed sequentially with atmospheric CO2 as they travelled through upper snow layers. Snowpack CO2 concentration gradients and resultant fluxes were impacted by seasonal increases in air and soil temperatures, especially as near-ground gas temperatures approached the zero-curtain (0 °C) threshold during the winter-spring transition. Positive relationships between CO2 flux and warming subnivean temperatures across the study site were similar in magnitude to those observed in pan-Arctic winter syntheses. Our results highlight that low-cost sensors may provide a robust and scalable method for monitoring the spatial variability of in-situ winter CO2 fluxes through snow at high temporal resolution and provide unique insights into winter carbon processes.

In ocean carbon budget assessments, sea ice is still treated as an impermeable barrier, rather than a dynamic interface mediating CO₂ exchange between ocean and atmosphere. We compiled more than 6000 chamber-based air–ice CO₂ flux measurements from the Arctic and Southern Oceans between 2003 and 2021, spanning diverse ice, snow, and seasonal conditions. These data show that sea ice releases CO₂ in winter and absorbs it in summer, with summer uptake offsetting winter emissions. On an annual basis, sea ice represents a small net CO₂ source of +4 Tg C yr⁻¹ in the Arctic and +2 Tg C yr⁻¹ in the Southern Ocean, challenging earlier views of a major sink. Although these fluxes are negligible at basin scales relative to open-ocean uptake, sea ice exchanges gases even in cold winter conditions, with implications for small-scale processes and other trace gases, underscoring the need for sustained, process-resolving observations.

In Earth system models and ocean carbon budget assessments, sea-ice is still treated as an impermeable barrier rather than a dynamic interface mediating CO₂ exchange between the ocean and atmosphere. To quantify its contribution to the polar ocean carbon budget, we compiled over 6000 chamber-based air–ice CO2 flux collected across the Arctic and Southern Ocean between 2003 and 2021. This unprecedented database spans diverse sea-ice, snow, and seasonal conditions. Despite spatiotemporal limitations, this database enables a comprehensive reassessment of sea-ice’s role in the carbon budgets of polar oceans. Sea ice acts as a CO₂ source in winter and a sink in summer, as summer drawdowns offset most winter emissions. On an annual basis, sea-ice represents a small net CO₂ source of +4 Tg C yr⁻¹ in the Arctic and +2 Tg C yr⁻¹ in the Southern Ocean, refuting previous hypotheses of a major sea-ice carbon sink. Although these net fluxes remain negligible at basin scales relative to concurrent open-ocean uptake, our results highlight that sea-ice exchanges gases even in cold winter conditions. These exchanges may have critical implications for short-term, small-scale processes and for other trace gases, underscoring the need for continuous, process-resolving flux observations.

The Arctic Ocean covers 3 % of the Earth's surface but is estimated to contribute 5–14 % to the global ocean carbon sink. Sparse and unevenly distributed observations complicate our understanding of the size and the controlling mechanisms of this carbon sink. We adopt and advance the two‐step neural network approach of Landschützer et al. (2016, https://doi.org/10.1002/2015gb005359; Self Organizing Map—Feed Forward Network) to improve region‐specific reconstructions of the surface ocean partial pressure of carbon dioxide (()) in the Arctic Ocean and subsequently estimate the air‐sea flux. Our study shows that biogeochemical properties previously selected as predictor variables for () at the global scale are not well suited to the Arctic Ocean and a sensitivity study reveals large differences in the size of the Arctic Ocean carbon sink depending on the choice of air‐sea flux parameterization. This is most acute for those relating to sea ice cover, leading to a difference of up to 25 % (9.2–13.3 Pg C) in the total size of the Arctic Ocean carbon sink over the 32‐year duration of the study.

The Arctic winter atmospheric boundary layer often features strong and persistent low‐level stability (LLS), which arises from longwave radiative cooling of the surface during the polar night. This stable stratification results in a positive lapse rate feedback, which is a major contributor to Arctic amplification. A second state, with cloudy conditions, weaker stability, and near‐zero net surface longwave flux is also observed. Previous work has shown that many CMIP5 models fail to appropriately partition water between liquid and ice phases in mixed‐phase clouds, leading to a lack of this cloudy state. In this study, we assess the representation of the Arctic winter atmospheric boundary layer over sea ice in global climate models contributing to the latest phase of the Coupled Model Intercomparison Project (CMIP6). We compare boundary layer process relationships in these models to those in surface‐based and radiosonde observations collected during the MOSAiC (2019–2020) and SHEBA (1997–1998) expeditions, and by North Pole drifting stations (1955–1991). The majority of CMIP6 models fail to realistically represent the cloudy state over winter Arctic sea ice. Despite this, CMIP6 multimodel mean LLS falls within the observational range, and models mostly capture the observed dependence of LLS on near‐surface air temperature and wind speed. CMIP6 models predict a decline in winter LLS with Arctic warming, with mean stability falling below zero by 2100 under the SSP2‐4.5 scenario. Our results highlight the failure to accurately simulate mixed‐phase clouds as an important limitation on representing a realistic Arctic winter boundary layer in many CMIP6 models.

The precise magnitude and timing of permafrost-thaw-related emissions and their subsequent impact on the global climate system remain highly uncertain. This uncertainty stems from the complex quantification of the rate and extent of permafrost thaw, which is influenced by factors such as snow cover and other surface properties. Acting as a thermal insulator, snow cover directly influences surface energy fluxes and can significantly impact the permafrost thermal regime. However, current Earth system models often inadequately represent the nuanced effects of snow cover in permafrost regions, leading to inaccuracies in simulating soil temperatures and permafrost dynamics. Notably, the Community Land Model (CLM5.0) tends to overestimate snowpack thermal conductivity over permafrost regions, resulting in an underestimation of the snow insulating capacity. Using a snow thermal conductivity scheme better adapted for the snowpack typically found in permafrost regions, we seek to resolve thermal insulation underestimation and assess the influence of snow on simulated soil temperatures and permafrost dynamics. Evaluation using two Arctic-wide soil temperature observation datasets reveals that the new snow thermal conductivity scheme reduces the cold-soil temperature bias (root-mean-square error, RMSE = 3.17 to 2.4 °C, using remote sensing data; RMSE = 3.9 to 2.19 °C, using in situ data), demonstrates robustness through sensitivity analysis under lower tundra snow densities, and addresses the overestimation of permafrost extent in the default CLM5.0. This improvement highlights the importance of incorporating realistic snow processes in land surface models for enhanced predictions of permafrost dynamics and their response to climate change.

Over the last decade, the way we communicate and engage with one another has changed on a global scale. It is now easier than ever to network and collaborate with colleagues worldwide. But, the COVID-19 pandemic led to a rapid and unplanned move to virtual platforms, resulting in several accessibility challenges and the inadvertent exclusion of several people during online events. While virtual/hybrid events have strong potential to facilitate new opportunities and networks for everyone, they are also greatly positioned to increase the inclusion of groups traditionally excluded from purely in-person conferences. However, early and careful planning is needed to achieve this, with inclusion and accessibility considered from the start. Including a virtual element in a conference does not automatically equal inclusion or accessibility. Without effective planning, virtual and hybrid events will replicate many biases and exclusions inherent to in-person events.This presentation will share lessons learned from previous events’ successes and failures, based on the combined experiences of several groups and individuals who have planned and run such events. This presentation is based on an EGU Sphere article, of the same title, that aims to provide guidance on planning online/hybrid events from an accessibility viewpoint based on the authors experiences. The goal of this presentation is to initiate discussion on event accessibility and inclusion and to help generate new ideas and knowledge from people outside of the authors network. Every event is unique and will require its own accessibility design, but early consideration is crucial to ensure everyone feels welcome and included. Our suggested accessibility considerations have been broken down into three stages of event planning: 1) Pre-event planning, 2) on the day/during the event, and 3) after the event.Ensuring accessibility and inclusivity in designing and running virtual/hybrid events can help everyone engage more meaningfully, resulting in more impactful discussions including groups with limited access to in-person events. However, while this article is intended to act as a starting place for inclusion and accessibility in online and hybrid event planning, it is not a fully comprehensive guide. As more events are run, it is expected that new insights and experiences will be gained, helping to continually update standards.

The Arctic Ocean is responsible for around 5-10% of oceanic CO2 uptake, despite the region only accounting for approximately 4% of the world's oceans (Bates & Mathis, 2009). In this study, we investigate the exchange of CO2 between the atmosphere and the ocean in the Arctic Ocean for the period 2000-2017. Our estimates are obtained using the GEOSChem-LETKF inverse model system (Chen et al. 2021), in combination with data from the NOAA surface CO2 monitoring network (ObsPack, Cooperative Global Atmospheric Data Integration Project, 2018). We evaluate the impact of alternative representations of the prior flux distribution for air-sea CO2 fluxes. These include the following datasets: Landschutzer et al. (2016), Rodenbeck et al. (2014), and Watson et al. (2020). We present estimates of the long-term trend, year-to-year fluctuations, and regional and seasonal variability in air-sea CO2 exchange in the Arctic Ocean, with a focus on the region north of 58˚N. The sea ice extent of the regional seas of the Arctic Ocean has an influence on the magnitude and seasonality of the regional air-sea CO2 flux. We also investigate the potential links between changes in sea-ice extent and changes in air-sea CO2 fluxes.

The Arctic Ocean covers only 3 % of the Earth’s surface but contributes 5 - 14 % of the global ocean carbon sink. Sparse and unevenly distributed observations of the partial pressure of CO2 (pCO2) hinder our understanding of the magnitude and the controlling mechanisms of this carbon sink. In order to constrain the magnitude of this flux, we adapt the Self-Organising Map – Feed-Forward neural Network (SOM-FFN) method of Landschützer et al. (2016) to interpolate existing observations and construct a monthly 1 x 1 degree pCO2 product for the Arctic Ocean from 1991 - 2022. We first divide the Arctic Ocean (i.e., the region ≥ 55° N) into five biogeochemical provinces; four obtained from using the SOM method and a fifth for all grid cells with greater than 85 % ice cover. For each province, we then derive non-linear relationships between pCO₂ and predictor variables (i.e., biogeochemical drivers) using the FFN method. The monthly reconstructed Arctic pCO2 product is then evaluated against existing observations of surface ocean pCO2, chiefly from SOCATv2023 and from independent timeseries stations. Our study shows that biogeochemical properties previously selected as predictor variables at the global scale are not well suited to the Arctic Ocean. Limiting the spatial domain from which relationships are derived also improves performance, with less biased p(CO2) values predicted when excluding the Baltic Sea.