Anna Rutgersson

A statistical evaluation of daily downwelling radiation data from the Global Tropical Moored Buoy Array (GTMBA), collocated with Clouds and Earth's Radiant Energy Systems (CERES) satellite data, was conducted from 2000 to 2023. This study addressed systematic biases and spurious data downwelling shortwave (Qs) and longwave (Q_L) radiation, which are crucial for understanding the ocean's energy budget and climate system. Two filtering methods were applied: a fixed threshold (Qs > 350 Wm^-2, Q_L > 450 Wm^-2) and a dynamic threshold (mean + 2 standard deviations per station) to check the spuriousness. For Qs, both methods had a negligible effect or resulted in a slight increase in bias, with no significant improvement in root mean squared error (RMSE) or correlation across regions. However, for Q_L showing large discrepancies in buoy observations in peaks (420–440 Wm^-2) compared to satellite (400–410 Wm^-2), both fixed and dynamic threshold filtering consistently improved correlation, bias, and RMSE. This greater effectiveness for Q_L is attributed to its strong influence by atmospheric temperature and humidity profiles, creating systematic biases that filtering effectively addresses. Overall, threshold filtering proved more effective for Q_L than for Qs, with fixed methods delivering consistently positive results for Q_L. The study highlights the need for customised threshold strategies to validate in situ data and advocates for further advancements in satellite retrieval algorithms and data assimilation to enhance the accuracy of radiative flux products and mitigate existing biases.

A statistical evaluation of daily downwelling radiation data from the Global Tropical Moored Buoy Array (GTMBA), collocated with Clouds and Earth's Radiant Energy Systems (CERES) satellite data, was conducted from 2000 to 2023. This study addressed systematic biases and spurious data downwelling shortwave (Qs) and longwave (Q_L) radiation, which are crucial for understanding the ocean's energy budget and climate system. Two filtering methods were applied: a fixed threshold (Qs > 350 Wm^-2, Q_L > 450 Wm^-2) and a dynamic threshold (mean + 2 standard deviations per station) to check the spuriousness. For Qs, both methods had a negligible effect or resulted in a slight increase in bias, with no significant improvement in root mean squared error (RMSE) or correlation across regions. However, for Q_L showing large discrepancies in buoy observations in peaks (420–440 Wm^-2) compared to satellite (400–410 Wm^-2), both fixed and dynamic threshold filtering consistently improved correlation, bias, and RMSE. This greater effectiveness for Q_L is attributed to its strong influence by atmospheric temperature and humidity profiles, creating systematic biases that filtering effectively addresses. Overall, threshold filtering proved more effective for Q_L than for Qs, with fixed methods delivering consistently positive results for Q_L. The study highlights the need for customised threshold strategies to validate in situ data and advocates for further advancements in satellite retrieval algorithms and data assimilation to enhance the accuracy of radiative flux products and mitigate existing biases.

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).

A statistical evaluation of daily downwelling radiation data from the Global Tropical Moored Buoy Array (GTMBA), collocated with Clouds and Earth's Radiant Energy Systems (CERES) satellite data, was conducted from 2000 to 2023. This study addressed systematic biases and spurious data downwelling shortwave (Qs) and longwave (Q_L) radiation, which are crucial for understanding the ocean's energy budget and climate system. Two filtering methods were applied: a fixed threshold (Qs > 350 Wm^-2, Q_L > 450 Wm^-2) and a dynamic threshold (mean + 2 standard deviations per station) to check the spuriousness. For Qs, both methods had a negligible effect or resulted in a slight increase in bias, with no significant improvement in root mean squared error (RMSE) or correlation across regions. However, for Q_L showing large discrepancies in buoy observations in peaks (420–440 Wm^-2) compared to satellite (400–410 Wm^-2), both fixed and dynamic threshold filtering consistently improved correlation, bias, and RMSE. This greater effectiveness for Q_L is attributed to its strong influence by atmospheric temperature and humidity profiles, creating systematic biases that filtering effectively addresses. Overall, threshold filtering proved more effective for Q_L than for Qs, with fixed methods delivering consistently positive results for Q_L. The study highlights the need for customised threshold strategies to validate in situ data and advocates for further advancements in satellite retrieval algorithms and data assimilation to enhance the accuracy of radiative flux products and mitigate existing biases.

Large-scale Euro-Atlantic variability, shaped by the polar jet stream, governs weather and climate in the Baltic Sea region, thereby impacting the physical and biogeochemical properties of the Baltic Sea ecosystem. This review synthesizes how key atmospheric circulation features and modes of climate variability, including the North Atlantic Oscillation, atmospheric blocking and the Atlantic Multidecadal Variability, influence the Baltic Sea region. By integrating evidence from the published literature, observational datasets, and both global and regional climate model simulations, we assess established as well as potential linkages to key climatic variables, including temperature, precipitation, and storm activity, across temporal scales ranging from synoptic events to multidecadal variability. We then evaluate how these climate controls cascade into ecosystem-relevant processes, namely oxygen dynamics, primary productivity and ocean acidification. Although physical links are already established, the pathways connecting large-scale atmospheric patterns to biogeochemistry are still poorly constrained, partly because dedicated field studies and targeted model experiments are limited. We outline priority research needs to enhance near-term predictability and reduce uncertainty in future projections for the Baltic Sea.

Extreme precipitation and wind events in Western Europe are often driven by atmospheric rivers (ARs) developing over the North Atlantic Ocean. Even though research has explored AR variability in relation to large‐scale atmospheric dynamics and the North Atlantic Storm Track, gaps remain in understanding how oceanic variability influences AR activity, particularly within the eddy‐rich environment of the Gulf Stream extension. The enhanced ocean heat transport and mesoscale eddy activity associated with this western boundary current can influence large‐scale dynamics, modulate moisture availability in the lower atmosphere, and potentially control the AR activity downstream. In this study, we evaluated ocean mesoscale features, oceanic heat supply, and surface heat fluxes in the Gulf Stream extension region at monthly time‐scales. We assessed their downstream impact on AR activity using state‐of‐the‐art observational datasets. Our analysis identified winter and spring as the key seasons for interactions between Gulf Stream conditions and ARs. Higher ocean heat transport and mesoscale sea‐surface height (SSH) meandering were associated with a northward shift in downstream AR activity and a positive North Atlantic Oscillation (NAO) pattern, although the atmospheric response is weaker in the latter case. In contrast, stronger than average surface heat fluxes in the Gulf Stream were linked to a southward shift of ARs and a strong negative NAO pattern, suggesting a dominant atmospheric influence that enhanced moisture availability and modulated North Atlantic dynamics. These results show that the Gulf Stream plays an important role in controlling the latitudinal variability of ARs over the Euro‐Atlantic sector during winter and spring.

Teleconnections between the North Atlantic and the Baltic Sea region are shaped by the polar jet stream and are critical drivers of weather and climate in the region, thereby impacting the physical and biogeochemical properties of the Baltic Sea ecosystem. This review synthesizes how key circulation features and modes of climate variability, including the North Atlantic Oscillation, atmospheric blocking and the Atlantic Multidecadal Variability, influence the Baltic Sea region. By examining existing literature data and observational and climate model data, we summarize links to temperature, precipitation, storms and other key indicators from synoptic to multidecadal time scales. We then assess how these climate controls cascade into ecosystem relevant processes, namely oxygen dynamics, primary productivity and ocean acidification. Although physical links are already established, the pathways connecting large-scale atmospheric patterns to biogeochemistry are still poorly constrained, partly because dedicated field studies and targeted model experiments are limited. We outline priority research needs to enhance near-term predictability and reduce uncertainty in future projections for the Baltic Sea.

Teleconnections between the North Atlantic and the Baltic Sea region are shaped by the polar jet stream and are critical drivers of weather and climate in the region, thereby impacting the physical and biogeochemical properties of the Baltic Sea ecosystem. This review synthesizes how key circulation features and modes of climate variability, including the North Atlantic Oscillation, atmospheric blocking and the Atlantic Multidecadal Variability, influence the Baltic Sea region. By examining existing literature data and observational and climate model data, we summarize links to temperature, precipitation, storms and other key indicators from synoptic to multidecadal time scales. We then assess how these climate controls cascade into ecosystem relevant processes, namely oxygen dynamics, primary productivity and ocean acidification. Although physical links are already established, the pathways connecting large-scale atmospheric patterns to biogeochemistry are still poorly constrained, partly because dedicated field studies and targeted model experiments are limited. We outline priority research needs to enhance near-term predictability and reduce uncertainty in future projections for the Baltic Sea.

The rapid rise of data-driven weather forecasting has prompted increasing interest in how such models perform relative to traditional numerical weather prediction systems. While recent studies have highlighted the formers’ superior skill on standard forecast metrics, questions remain regarding their ability to forecast physically complex, derived variables, particularly in the context of extreme events. In this study, we assess the performance of two leading operational data-driven models (GraphCast and Pangu-Weather) in forecasting integrated vapour transport (IVT) and atmospheric rivers (ARs), using ECMWF’s IFS-HRES as a reference physics-based forecast. Forecasts are evaluated against ERA5 reanalysis over one year of global data, using three AR detection algorithms and lead times ranging from 1 to 10 days. Results show that data-driven models achieved root-mean-square errors for IVT comparable to or slightly better than IFS-HRES, particularly in the tropics and at shorter lead times. However, they achieved a poorer representation of the higher quantiles of the IVT distribution. A case study of a high-impact AR event revealed that all models could forecast the main event characteristics up to five days in advance. However, AR characteristics and detection performance varied substantially across detection algorithms. Notably, the geometrically stricter detection method highlighted a clearer advantage for IFS-HRES, especially in the midlatitudes and at shorter leads. Overall, while no model systematically outperformed the others across all AR detection algorithms, the results suggest that physics-based models may retain advantages in forecasting geometrically and physically consistent derived features like ARs, particularly under strict detection criteria. These findings underscore the importance of targeted evaluation frameworks for derived and extreme phenomena as data-driven models become more central in operational forecasting.