Since 1995, the Institute for Marine and Atmospheric research Utrecht (IMAU) at Utrecht University has operated automatic weather stations (AWS) at 20 different locations on the Antarctic ice sheet. In cooperation with multiple institutes, AWS were installed in Dronning Maud Land, on the East Antarctic Plateau, on the remnants of the Larsen B ice shelf, and on the Larsen C and Roi Baudouin ice shelves.  Besides standard meteorological observations (wind speed, wind direction, air temperature, humidity, surface pressure), these stations also recorded the four components of net surface radiation, as well as surface height change. That allows for a reliable estimation of the surface energy balance (SEB) and surface mass balance (SMB) at hourly temporal resolution. Due to the harsh climatic conditions and limited number of maintenance visits, the data require a thorough quality control procedure and specific sensor corrections.

Here we present the corrections that were applied to the measurements, as well as the procedure that was implemented to flag suspicious samples. We give an overview of the first quantification of the long-term variability in SEB components, as well as the strong contrast between the high-melt locations near the grounding lines of ice shelves and the dry interior of the Antarctic ice sheet.  In total, 152 station-years of observations are available, of which 78% are non-flagged simultaneous observations of all meteorological and radiation parameters.

This dataset may be used for the evaluation of climate models and for the interpretation and validation of remote sensing products, but also for the quantification of climatological changes and for process understanding in general. The data are openly available at  https://doi.pangaea.de/10.1594/PANGAEA.974080.

How to cite: Van Tiggelen, M., Smeets, P., Reijmer, C., Kuipers Munneke, P., and van den Broeke, M.: 30 years of Antarctic weather station observations by the IMAU network (1995-2025), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9051, https://doi.org/10.5194/egusphere-egu25-9051, 2025.

Antarctica and the Southern Ocean have an important role in Earth's climate, influencing global heat balance and carbon uptake. Recent anomalies, such as drastic sea ice decline, anomalous snowfall, and unprecedented heat waves challenge our understanding of the region's climate response. Both internal (local processes) and external (influence from lower latitudes) factors have been suggested as drivers of this variability, but the relative contributions of these remain unknown due to the lack of observations as well as shortcomings in climate models. We aim to enhance the understanding of this system by making use of recent advances in causal effect estimation. Going beyond correlation, causal network reconstruction aims to detect cause-effect links and their strength from observational datasets, including satellite records and reanalysis data. For selected sectors of Antarctica, the interconnections between ice sheet surface mass balance (SMB), sea ice, ocean temperature, and meridional transport of heat and water from lower latitudes are examined and causal relationships identified and quantified.

How to cite: Berghald, S., Van Lipzig, N., Goosse, H., and Lhermitte, S.: Interconnections between the components of the Antarctic climate system: a causal inference approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9646, https://doi.org/10.5194/egusphere-egu25-9646, 2025.

Denman Glacier Basin, a critical region for studying polar ice dynamics and climate change impacts, is heavily influenced by the combination of topographic and atmospheric conditions, particularly experiencing strong downslope winds. This study examines the structure and variability of near-surface winds in the basin, focusing on the influence of large-scale circulation, synoptic weathers, and local orographic effects. Through high-resolution atmospheric simulation experiments, we demonstrate the forced components of near-surface winds during prevalent synoptic systems in the area, quantifying the roles of large-scale and locally driven forces in shaping wind structure and variability. We also conduct perturbation experiments with topographies of varying resolutions to examine the orographic controls on the spatial climatology of downslope winds, in response to a range of synoptic systems typical to the region. Our findings can be used to clarify uncertainties in interpreting snow accumulation variability in ice cores and determining whether modern regional mass balance trends result from increased glacial discharge or shifts in synoptic circulation. This research findings will be used to interpret the Denman Glacier discharge, snow accumulation over the basin, aiding in the interpretation of recent ice core data collected in the recent field season.

How to cite: Wang, Z., Menviel, L., Sen Gupta, A., Goodwin, I., Chen, Z., and Caton Harrison, T.: Coupled Influence of Synoptic Weather and Topographic Control on Near-surface Wind Variability in the Denman Glacier Basin, East Antarctica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14448, https://doi.org/10.5194/egusphere-egu25-14448, 2025.

This study investigates the characteristics and mechanisms of strong winds at Jang Bogo Station (74°37'S, 164°12'E) in Terra Nova Bay, East Antarctica, using 8 years (2015-2022) of Automated Synoptic Observation System (ASOS) data and ERA5 reanalysis data. Analysis of strong wind patterns reveals two distinct strong wind regimes: southwesterly (180-270°) and northwesterly (270-360°) winds. Strong wind events show clear seasonal variation, with peak frequencies occurring in March and July. Synoptic analysis using ERA5 reanalysis data indicates that these strong winds are primarily driven by the interaction between the Amundsen Sea Low and the Antarctic continental high pressure system. The intensity and positioning of these pressure systems significantly influence both wind direction and speed at Jang Bogo Station. Notably, the strongest winds (top 1%) are predominantly northwesterly, associated with enhanced pressure gradients near the station. Case studies of extreme wind events reveal two distinct generating mechanisms: one associated with intense pressure gradients from passing cyclonic systems, and another linked to katabatic flows descending from the Antarctic interior. These findings provide important insights into the wind regime of Terra Nova Bay and contribute to our understanding of Antarctic meteorological patterns, which has implications for both operational forecasting and regional climate studies.

How to cite: Kwon, H., Choi, Y., and Park, S.-J.: Characteristics of Strong Winds at Jang Bogo Station in East Antarctica: An 8-Year Observational Study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16387, https://doi.org/10.5194/egusphere-egu25-16387, 2025.

In December, 2011 the REFIR-PAD Fourier transform spectroradiometer was installed in Concordia Station, Antarctica to perform continuous monitoring of the atmospheric downwelling emitted radiance in the middle-far infrared region. The spectroradiometer is supported by several auxiliary instruments to monitor ground and sky conditions and, since 2020, by a compact lidar sensor to provide cloud structure in the lower troposphere and boundary layer region, thus establishing a complete and integrated set of sensors for the monitoring of the Antarctic troposphere.

The main product in the data set provided by the observing system consists in high-resolution spectral radiances measured in the 100-1500 cm-1 region with a 0.4 cm^-1 resolution. This allows us not only to separate the contributions to the radiation budget due to H₂O, CO₂, O₃ and clouds, but also to retrieve vertical profiles of water vapor and temperature, columnar amounts of minor constituents and cloud properties through a data inversion process.

The production of a consistent long-term dataset needs to front multiple challenges which are intrinsic in long period continuous operation in extreme environment, methods for the correction of systematic effects and to perform automatic data quality assessment had been developed in order to be able to make the data available for use by the atmospheric science community.

An example of the results that can be obtained exploiting the advantage of long term measurement and high temporal resolution provided by the dataset is the identification and analysis of extreme events: not only it is possible to perform a detailed analysis of the most prominent events on an hourly timescale, but also it is possible to search the dataset for the occurrence and statistics of minor events that could be of similar origin.

How to cite: Bianchini, G., Di Natale, G., Palchetti, L., and De Pas, M.: Decadal time series of high-resolution downwelling spectral radiancemeasurements from Concordia Station, Antarctica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3313, https://doi.org/10.5194/egusphere-egu25-3313, 2025.

Altitude-driven gradients of air temperature, humidity, wind, and surface mass balance play a critical role in understanding glacier-climate interactions, particularly in regions of rapid environmental change like the Arctic. In this study, we compare datasets from Alfred Wegener’s last expedition to the west coast of Greenland in 1930/31 with a modern measurement network established at the same locations in 2022. This unique comparison offers insights into how the atmospheric and glacial conditions have changed within a century.  
The measurement network consists of one automatic weather station at the coast over bare ground in vicinity of the outlet glacier Qaamarujup Sermia and another at 940 m a.sl. on the Greenland Ice Sheet. For both locations observations exist during the Wegener expedition and since 2022. Additionally, temperature and humidity sensors and surface mass balance measurements distributed between these two points provide high-resolution spatial data.  
The observed gradients in air temperature, humidity, wind speed, and wind direction are analysed at multiple temporal scales, from diurnal cycles to annual variations. Preliminary results show that the air temperature gradient between the coastal and the glacier station follows a seasonal cycle by being the smallest in spring (on average –6.5 °C) and the largest in winter (on average –11°C). Although this is true in the historic and modern dataset, the gradient in spring is colder in 2023 and 2024 with –7.0°C and –6.7°C respectively versus –5.7°C in 1931. The summer gradient is warmer in the modern dataset from -8.3°C in 1930, -9.3°C in 1931 to -7.7°C in 2023 and -7.8°C in 2024.  
Our goal is to understand the key factors shaping these gradients, including the influence of large-scale atmospheric patterns such as the Greenlandic Blocking Index and North Atlantic Oscillation and the prevailing regional conditions identified through self-organizing maps. By comparing historical and modern datasets, we further examine how changes in glacier geometry and a frontal retreat of approximately 2 km since the 1930s have shaped climatic gradients. A particular focus is placed on whether this influence is more pronounced at the coastal or the glacier station.  
This work contributes to the broader understanding of how glacier-climate interactions are influenced by both local and large-scale factors and underscores the value of historic observational records in assessing climate change impacts. 

How to cite: Schalamon, F. R., Nicholson, L., Scher, S., Trügler, A., Schöner, W., and Abermann, J.: Glacier-Climate Interactions across Time: A West Greenland Case Study , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10147, https://doi.org/10.5194/egusphere-egu25-10147, 2025.

The Arctic is experiencing changes in precipitation, both in terms of amount and phase, due to rising temperatures. Key mechanisms contributing to these changes include increased poleward moisture transport and higher ocean evaporation resulting from the shrinking sea ice cover. In autumn, changes in precipitation over the sea ice can influence its growth by altering the insulation between the ocean and the atmosphere. A reduction in snow cover (which has lower insulating properties) enables the ocean to cool faster by releasing heat into the atmosphere, thus promoting sea ice growth. In spring, variations in snowfall and rainfall can affect the sea ice albedo, influencing its melting rate. Using the regional climate model MAR, which includes a complex snow scheme, we examine trends in precipitation and snow depth over the Arctic sea ice during the growth season. We also conduct sensitivity tests to assess the response of snow depth to changes in sea ice thickness.

How to cite: Lambin, C., Kittel, C., Maure, D., Noël, B., and Fettweis, X.: Evolution of precipitations and snow depth over the Arctic sea ice modeled by the regional climate model MAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12294, https://doi.org/10.5194/egusphere-egu25-12294, 2025.

How are polar-to-midlatitude teleconnections represented in recent large ensembles of coupled climate model simulations? And how do they evolve with global warming? Using the rich information on internal variability available from large ensembles, we investigate the relationship between sea ice amount and atmospheric circulation for both Arctic and Antarctic sea ice variability in CESM2 and ACCESS-ESM1-5, using a composite analysis. We find that the links between sea ice and sea level pressure (SLP), the midlatitude jet stream and temperature depend on the region in which sea ice varies, for instance with low Barents-Kara sea ice in January being associated with a positive North Atlantic Oscillation SLP pattern and high pressure over Northern Eurasia. These circulation patterns persist with increased levels of global warming, until around 3 or 4°C when they start to evolve in some cases, as sea ice starts to disappear. Surface air temperatures are anomalously high around the region of sea ice retreat with varying patterns of remote cooling elsewhere. Lagged analysis shows that sea-ice circulation relationships when the atmosphere leads sea ice are very similar to the instantaneous relationships, suggesting that the latter largely reflects the atmospheric patterns leading to reduced sea ice. For positive lags (sea ice leading the atmosphere), for some regions the SLP teleconnections persist in a weakened state for subsequent months, whilst for others they evolve, e.g. into a negative Arctic Oscillation response for Barents-Kara sea ice reduction. However, results for positive lags differ between the two models examined. SLP relationships with Antarctic sea ice are model dependent, but feature a negative Southern Annular Mode pattern in ACCESS-ESM1-5. In CESM2, we find a less zonally symmetric pattern which also consists of high pressure over the pole in Autumn and Winter.

How to cite: Iles, C., Samset, B., and Lund, M.: Polar-to-midlatitude teleconnections in a warming world: Statistical relationships from large ensembles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13156, https://doi.org/10.5194/egusphere-egu25-13156, 2025.

In situ meteorological data are essential for a better understanding of the ongoing environmental changes in the Arctic. In order to increase the scientific value of discussions on understanding the actual state of environmental change in a given area, it is necessary to appropriately remove the anomalous values recorded due to external factors resulting from low temperature and icing. Here we present methods for quality control (QC) of meteorological observation datasets from two automatic weather stations in northwest Greenland, where drastic glaciological and meteorological environmental changes have occurred. The stations were installed in the accumulation area of the Greenland Ice Sheet (SIGMA-A site, 1490 m a.s.l.) and near the equilibrium line of the Qaanaaq Ice Cap (SIGMA-B site, 944 m a.s.l.). We describe the two-step sequence of QC procedures we used to produce increasingly reliable data sets by masking erroneous records. This method was developed for the climatic conditions of Greenland, however, it is designed to be as universally applicable as possible, with a basis in meteorology and glaciology, and with the intention of removing the subjectivity of the person performing the QC. The QC is divided into two processes: Initial Control and Secondary Control. Initial Control removes values that violate physical laws and also serves as a preliminary process to improve the accuracy of Secondary Control. Secondary Control removes abnormal values using stricter statistical criteria than Initial Control. As a result of this two-step process, controlled by scientifically objective criteria, we were able to successfully remove erroneous data sets and greatly reduce the time required for QC. In addition, by using a generally applicable process, we were able to successfully establish an algorithm that could be applied to multiple sites. The data sets from both the SIGMA-A and SIGMA-B sites were classified into three levels (Level 1.1 to Level 1.3) according to the stage of data processing. Level 1.1 is the so-called raw data, in which the data for the period when the logger was stopped are masked (processed to flag them as missing or abnormal), the so-called raw data. Level 1.2 and Level 1.3 are datasets to which Initial Control and Secondary Control have been applied to the Level 1.1 and Level 1.2 datasets, respectively, and the Level 1.3 dataset is a dataset from which all abnormal values have been removed. These datasets have been archived in the Arctic Data Archive System (ADS) operated by the National Institute of Polar Research in Japan (e.g., Level 1.3 dataset: SIGMA-A - https://doi.org/10.17592/001.2022041303 and SIGMA-B - https://doi.org/10.17592/001.2022041306).

How to cite: Nishimura, M., Aoki, T., Niwano, M., Matoba, S., Tanikawa, T., Yamasaki, T., Yamaguchi, S., and Fujita, K.: Quality-controlled meteorological datasets from SIGMA automatic weather stations in northwest Greenland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13984, https://doi.org/10.5194/egusphere-egu25-13984, 2025.

As global mean temperatures exceeded the 1.5 °C threshold in 2024, the urgency to better quantify the impacts of global warming, including sea level rise contributions from polar ice sheets, has intensified. The Greenland Ice Sheet (GrIS) has experienced significant mass loss over recent decades, primarily driven by surface melting, a process expected to accelerate under continued warming. Surface melt is influenced by a combination of factors and complex interactions between atmosphere and ice sheet surface, but simulating these processes using coupled climate models is computationally expensive and often impractical.

In this study, we develop a graph neural network (GNN) as an emulator for GrIS surface melt, trained on output from the Community Earth System Model version 2 (CESM2), which explicitly calculates surface melt through a downscaled surface energy balance framework. GNNs are uniquely suited to this task, as they capture spatial and relational dependencies across the ice sheet, enabling the emulator to reproduce spatially resolved melt fields and identify the influence of key atmospheric patterns.

We will first evaluate the emulator’s performance in replicating CESM2 simulated melt under different climatic conditions and employ explainability techniques to identify the relative importance of key atmospheric patterns in driving surface melt. This work aims to demonstrate the utility of machine learning emulators in enhancing our understanding of GrIS surface melt dynamics and advancing projections of sea level rise under future climate scenarios.

How to cite: Yin, Z., Subramanian, A., and Datta, R.: Emulating Greenland Ice Sheet Surface Melt Using Graph Neural Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15568, https://doi.org/10.5194/egusphere-egu25-15568, 2025.

In recent decades, the Arctic has warmed nearly four times faster than the global average, undergoing profound changes as a result. A key factor in this accelerated warming is the meridional transport of atmospheric water vapour. Particularly, intense intrusions of moisture and heat, so-called atmospheric rivers (ARs), are rare phenomena to reach the high latitudes, but can have severe impacts on the Arctic environment.

In this study, we examine an AR pair in April 2020 using a combination of Eulerian and Lagrangian methods alongside observational data from Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. The event consisted of two distinct ARs that followed separate pathways - one across Siberia and the other across the Atlantic - before converging in the central Arctic within the span of one week. Large-scale atmospheric circulation patterns associated with these ARs show a combination of low and high pressure systems on the flanks of the ARs, channelling moisture and heat northward. Notably, our results show that the Siberian AR was linked to extreme heat anomalies, whereas the Atlantic AR primarily transported abundant moisture.

Backward air parcel trajectories calculated using LAGRANTO provide new insights into the complex dynamics of Arctic ARs, revealing details of their distinct pathways and moisture source regions. Analysis of these trajectories also uncovers a strong connection between the observed sea ice melt in the Barents-Kara Sea and the interaction of an AR with the ice edge, underscoring the significant influence of ARs on the Arctic climate system.

How to cite: Aviles Podgurski, L. E., Martineau, P., Lu, H., Yamamoto, A., Phillips, T., Bracegirdle, T., Maycock, A. C., Orr, A., Fleming, A., Hogg, A. E., and Muszynski, G.: Pathways of Atmospheric Rivers in the Arctic: Dynamics, Moisture Transport, and Impacts on Sea Ice during April 2020, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15984, https://doi.org/10.5194/egusphere-egu25-15984, 2025.

Clouds modulate the net radiative flux interacting with both shortwave and longwave radiation, but the uncertainties regarding their effect in polar regions are especially high, because ground observations are lacking and evaluation through satellites is made difficult by the high surface reflectance. In this work, the radiative regimes and sky conditions for five different stations, two in the Arctic (Ny-Ålesund, 78.92°N, 11.93°E,  Barrow, 71.32°N, 156.61° W) and four in Antarctica (Neumayer, 70.68°S, 8.27°W; Syowa,  69.01°S, 39.58°E; South Pole, 90°S, 0°E ; DomeC, 75.01°S, 123.33°E) will be presented, considering the decade between 2010 and 2020. Measurements of broadband shortwave and longwave radiation components (both downwelling and upwelling) are collected within the frame of the Baseline Surface Radiation Network (BSRN) (Driemel et al. 2018). Observations, together with  identification of the clear sky and overcast conditions will be compared with ERA5 reanalysis (Herschbach et al., 2023). Furthermore, the identified conditions based on estimated cloud fraction will serve as labels for a machine learning classification task, leveraging algorithms such as Random Forest and Long Short-Term Memory (LSTM) networks (i.e. Zeng et al., 2021; Sedlar et al., 2021). These models incorporate features including global and diffuse shortwave radiation, downward longwave radiation, solar zenith angle, surface air temperature, relative humidity, and the ratio of water vapor pressure to surface temperature. The Random Forest model will also compute feature importance, identifying the most influential variables in predicting sky conditions and providing insights into the relationships between these meteorological factors.

Bibliography

Driemel et al. (2018): Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017). 

Riihimaki et al. (2019): Radiative Flux Analysis (RADFLUXANAL) Value-Added Product.

Hersbach, H. et al. (2023): ERA5 hourly data on single levels from 1940 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) 

Zeng, Z. et al. (2021): Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station. Adv. Atmos. Sci. 38, 1497–1509. 

Sedlar, J. et al. (2021): Development of a Random-Forest Cloud-Regime Classification Model Based on Surface Radiation and Cloud Products. J. Appl. Meteor. Climatol., 60, 477–491.

How to cite: Cavaliere, A., Frangipani, C., Baracchi, D., Becherini, F., Lupi, A., Mazzola, M., Pulimeno, S., Shullani, D., and Vitale, V.: Surface radiation budget data in a bipolar perspective: observations, comparison and exploiting for products., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11681, https://doi.org/10.5194/egusphere-egu25-11681, 2025.

This study examines long-term climatic and hydrological trends in River Oulankajoki, in Finland. The aim is to understand the impacts of changing climate conditions on river systems. Using 57 years (1966–2023) of daily air temperature and snow depth data from the Finnish Meteorological Institute (FMI) and hydrological observations from the Finnish Environment Institute (SYKE), the analysis incorporates longwave (LW) and shortwave (SW) radiation data from ERA5, accessed through Google Earth Engine (GEE). The Mann-Kendall trend test was employed to detect significant temporal changes, that reveals a significant decreasing trend in both air temperature values and discharge Phase Change Timing (i.e., PCT) over the study period. The results show that the river ice break-up timing has been shifting about 3-weeks in time, meaning that the break-up season occurs earlier than 57 years ago. These changes indicate potential shifts in regional climate dynamics, likely influenced by global climate change. Correlation heatmaps showed strong positive relationships between air temperature (AT) and river ice Break-Up Days (i.e., BUDs).

How to cite: Jalali Shahrood, A., Ahrari, A., and Torabi Haghighi, A.: Assessment of Long-Term Climatic, Hydrological, and River Ice Dynamics in River Oulankajoki , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3634, https://doi.org/10.5194/egusphere-egu25-3634, 2025.