UP2.4

Arctic sea ice is steadily retreating due to climate warming, but regional and seasonal variations in Arctic sea ice are important. This study aims in understanding how the winter atmospheric circulation affects the sea ice drift and how ice motion contributes to regional sea ice concentration and thickness anomalies. Sea ice conditions in late winter and spring are crucial for predicting summer sea ice. Understanding the mechanism that affect the spring sea ice concentration and thickness have potential to improve sea ice predictions year-round.  

Atmospheric pressure patterns affect the thermodynamic vertical structure of the atmosphere, heat and moisture transport to the Arctic, and radiative and turbulent fluxes at the surface (eg. Nygård et al 2021). Circulation types also control surface wind speed and direction, and are closely linked to ice drift speed (eg. Mallett et al 2021). Winter atmospheric circulation can precondition spring sea ice anomalies and summer melt by ice dynamics (eg. sea ice transport to lower latitudes where it is more vulnerable to melt) and thermodynamics (eg. positive surface energy balance anomalies prohibiting ice growth in winter leading to thinner ice in spring).

In this study we present a Self Organizing Maps (SOM) clustering of the winter (December-March) mean sea level pressure to detect the typical circulation patterns. The SOM-analysis covers the period from December 2000 to March 2021. The circulation patterns, or SOM nodes, are linked to atmospheric conditions (surface energy balance and wind speed) and sea ice conditions (concentration, drift speed and thickness). We use data from ERA5, ORAS5, and PIOMAS reanalyses, and Polar Pathfinder Sea Ice Motion and Cryosat2-SMOS ice thickness remote sensing products. We show how the circulation types are linked to near surface wind speed and direction, and consequent sea ice drift.  As a result, we analyze if the circulation patterns can be linked to sea ice anomalies thought sea ice dynamics or thermodynamics.

How to cite: Välisuo, I., Nygård, T., and Uotila, P.: Impact of atmospheric winter circulation on sea ice anomalies in the Arctic seas, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-152, https://doi.org/10.5194/ems2022-152, 2022.

The web portal for cryospheric information of the Norwegian Meteorological Institute (MET Norway), https://cryo.met.no, provides access to the latest operational data and products, as well as the current state of sea ice, snow, and permafrost in Norway, the Arctic, and the Antarctic. This contribution focuses on the operational permafrost monitoring at MET Norway and the new permafrost monitoring products on cryo.met.no.

Systematic long-term monitoring of permafrost on Svalbard and in Norway essentially began 23 years ago under the European Union-funded Permafrost and Climate in Europe (PACE) project, with the installation of ground temperature measurements in deep boreholes. Since then, more than 35 additional instrumented boreholes have been drilled in Norway and on Svalbard. In recent years, five new permafrost boreholes have been established at remote locations on Svalbard.

Here we present methods for visualising real-time permafrost temperature data from eight operational monitoring sites on Svalbard and in Norway. The most recent permafrost temperatures are compared to the climatology generated from the station's data record, which includes median, confidence intervals, extremes, and trends. There are additional operational weather stations with extended measurement programs at these locations. The collocated monitoring provides daily updated data to study and monitor the current state, trends, and the effects of e.g. extreme climate events on permafrost temperatures. The operational monitoring provides information faster than ever before, potentially assisting in the early detection of e.g. record-high active layer thickness, pronounced permafrost temperature increases, and in early warning systems for natural hazards associated with permafrost warming and degradation. Currently, data and metadata are submitted manually to the international Global Terrestrial Network for Permafrost. Work is in progress to develop operational permafrost data services through the WMO Global Telecommunication System to support e.g. the WMO Global Cryosphere Watch datastream.

How to cite: Isaksen, K., Lutz, J., Eastwood, S., Godøy, Ø., Aaboe, S., Sørensen, A., and Ferrighi, L.: Climate-related operational permafrost monitoring in Svalbard and Norway, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-217, https://doi.org/10.5194/ems2022-217, 2022.

The Antarctic sea ice variability and its underlying drivers remain overall unsettled, particularly since the sea ice extent (SIE) during the last two decades has first exhibited a slight increase, somewhat in contrast with the global warming trend, followed by a rapid reduction in the more recent years. The unprecedented SIE minimum registered in February 2022 has received great attention and already constitutes an important case study, as the prior record low in 2017. However, other extreme anomalous events are present in the observational record, and a comprehensive analysis of both minima and maxima in the summer SIE is essential to identify and separate potential common drivers from event-specific dynamics, ultimately advancing our general understanding of the Antarctic sea ice and climate variability.

In this work, we aim at assessing the relative roles of atmospheric and oceanic processes in the summer SIE extremes and at disentangling the dynamic contributions to sea ice changes - such as wind-driven transport and divergence - from the thermodynamic part (freezing and melting). Furthermore, we identify the key regions at play during such events, the local dominant mechanisms, and the mutual interactions that result in a total maximum or minimum. The timing and persistence of the sea ice, atmosphere and ocean anomalies in the prior months are also examined to clarify the time scales of the processes during the melting season that lead to the summer extremes.

We use observations and reanalysis data over the satellite period (1979-2022) and compare our main findings with results obtained from an ocean-sea ice model (NEMO-LIM) driven by prescribed atmospheric fields from ERA5 on the same period. While the model may not be able to capture all the extremes in the observational record, examining its own variability provides valuable insights on the dynamics of the Antarctic sea ice extremes.

How to cite: Mezzina, B., Goosse, H., Klein, F., and Massonnet, F.: Drivers of extreme Antarctic ice extents in summer over the period 1979-2022, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-278, https://doi.org/10.5194/ems2022-278, 2022.

Sea ice declines have been continuously observed in recent decades over the Arctic Ocean by global warming. In recent years, intense storms over the Arctic have been observed more frequently, consistent with events of extreme sea ice loss. However, the interaction of storms and sea ice is not yet fully understood due to limited observations. In August 2016, an intense summer Arctic cyclone covering the Arctic ocean was occurred and maintained for almost one month, with the recorded the minimum central surface pressure of 967 hPa. During the developing stage of the cyclone, strong baroclinic instability near the cyclone center mainly contributed to the cyclone development. In addition, enhanced upward latent heat flux is also attributable to the development of Arctic cyclone. The presence or absence of sea ice would have had a significant impact on the development of storms. A large loss of sea ice was also observed along the storm's path.

In this study, a suite of sensitivity experiments with sea ice conditions are performed using the Korean Integrated Model (KIM), a global numerical weather prediction system developed by the Korea Institute of Atmospheric Prediction Systems (KIAPS) and used as the operational system at the Korea Meteorological Administration (KMA) since April 2020. The results of control experiment based on surface cycling with the reanalysis data are compared with those of sensitivity simulations using the fixed sea ice data before and after the storm. The effects on baroclinic instability, heat flux, and cyclone intensity induced by the changes in sea ice are compared. Numerical simulations are additionally performed using the initial version of KIM coupling system with earth system components including land surface, ocean, and sea ice.

How to cite: Kim, S.-W., Shim, T., Kim, J., and Park, J.: Intense summer Arctic cyclone and its relationship with Arctic sea ice simulated by the Korean Integrated Model, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-354, https://doi.org/10.5194/ems2022-354, 2022.

Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. However, climate models have had difficulties in correctly reproducing the seasonal snow and its recent trends. A recent bias-correction method significantly reduces the uncertainty of NH snow water equivalent (SWE) estimation, which enables a more reliable analysis of the climate models’ ability to describe the snow cover. In this study, we have intercompared CMIP6 (Coupled Model Intercomparison Project Phase 6) and observation-based SWE estimates north of 40° N for the period 1982-2014 and analyzed whether temperature (T) and precipitation (P) biases could explain the SWE biases. We analyzed separately SWE in winter and SWE change rate in spring. For SWE reference data, we used bias-corrected SnowCCI data for non-mountainous regions and the mean of Brown, MERRA-2 and Crocus v7 datasets for the mountainous regions. The analysis shows that CMIP6 models tend to overestimate SWE, but large variability exists between models. In winter, the SWE model biases are mainly positive, while in spring, the variability between models increases. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. T contributes to SWE biases mainly in regions, where T is close to 0℃ in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. However, the results also showed that biases in T or P cannot explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as observation uncertainty or deficiencies in model parameterizations, also contribute to SWE biases.

How to cite: Kouki, K., Räisänen, P., Luojus, K., Luomaranta, A., and Riihelä, A.: Evaluation of Northern Hemisphere snow water equivalent in CMIP6 models during 1982-2014, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-447, https://doi.org/10.5194/ems2022-447, 2022.

The High Arctic archipelago of Svalbard is one of the fastest-warming locations on the planet. In addition to melting snow and ice which threatens a vulnerable ecosystem, thawing permafrost destabilises slopes, coastlines and man-made structures and exposes especially cultural heritage objects. Preserving the numerous cultural heritage objects is challenging because on Svalbard they receive protected status automatically if they are older than 1946. In the PCCH-Arctic project, we study the influence of climate change on cultural heritage sites, including among others the wooden houses in Ny-Ålesund and the coal cableway in Longyearbyen. To gain meaningful climate information on specific sites we apply a high-resolution regional climate model.
Traditionally, these high-resolution convection-permitting datasets have been produced in separate time windows for historical and future periods. However, some cryospheric phenomena such as permafrost and glaciers require longer transient simulations.
We here present a high-resolution climate projection dataset covering Svalbard for the years 1991-2060 at 2.5 km horizontal resolution. The simulation is produced by the regional climate model HARMONIE Climate (HCLIM) cycle 43 featuring convection-permitting HARMONIE-AROME atmospheric physics and SURFEX land-surface model with ISBA Explicit Snow snow scheme and Simple Ice (SICE) prognostic sea ice thickness. Boundary data comes from the Norwegian Earth System model (NorESM2-MM) following the SSP5-8.5 scenario. We present projected future changes in distributions, focusing on precipitation and snow, and evaluate against ERA5 and CARRA reanalyses. We also discuss spatial variability and representation of small-scale features in a challenging landscape with steep topography and large contrasts between land, sea-ice and open ocean.

How to cite: Landgren, O., Lutz, J., Dobler, A., and Isaksen, K.: Multi-decadal convection-permitting climate simulation over Svalbard and its benefit for assessing the future of cultural heritage sites, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-556, https://doi.org/10.5194/ems2022-556, 2022.

The Last Glacial Maximum (LGM) is a global event that occurred 26 to 21 ka BP and was characterized by lower temperatures (3 to 6 °C globally) and different precipitation regimes. The environment was marked by extended glaciers which released a large set of morphologies as the systems of well-preserved moraines in the alpine foreland that testify the advanced phases of the alpine glaciers during the LGM. Mountain glaciers, quickly responding to temperature and precipitation variations, are considered excellent indicators of climate change. Here we focus on the European Alps and attempt to unveil the physical processes that sustained the glacier extent during the LGM using a multiple regional climate model (RCM) nesting approach. Toward this goal, we completed and intercompared two high-resolution (12 km) RCM simulations, one for the steady-state LGM standard (21 ka BP) and one for pre-industrial (PI) conditions. We also calculated the LGM and PI environmental equilibrium line altitude (envELA) for the whole Alpine chain starting from physically-based summer temperature a total annual precipitation. Precipitation and temperature patterns show good consistency with proxy records and other RCM studies. In particular, our results present a predominance of convection over stratiform precipitation during summer, as well as increased southwesterly stratiform precipitation in the southern alpine region compared to the PI. Frequent summer snowfall, extending to low elevations, where caused by this precipitation pattern, along with lower temperatures, and led to a substantial drop in the ELA. Our model-based evaluation of the LGM ELA showed unprecedented consistency with the estimated LGM Alpine glacier reconstructions, further proving the great potential of this method for paleoclimate applications.

How to cite: Del Gobbo, C., Colucci, R. R., Monegato, G., Žebre, M., and Giorgi, F.: Atmospheric dynamics supporting the Alpine glaciers at the Last Glacial Maximum assessed with a high-resolution regional climate model, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-655, https://doi.org/10.5194/ems2022-655, 2022.

Alpine cold ice caps are sensitive, but so far rarely studied indicators of present and past local climate. The interpretation of this archive needs detailed in situ glaciological and meteorological records. On the Weißseespitze summit ice cap (3499 m) in Austria we compared past and present climate and mass balance. The ice cap shows limited ice flow.  First ice-cores have been drilled close to Weißseespitze peak. The current ice cover has a thickness of about 10 meter and has locked nearly 6000 years of climate history. First-ever meteorological observations using an automatic weather station at the ice dome in combination with a camera setup on rocks near the summit showed that most of the accumulation took place between October and December and from April to June. In the colder winter months wind erosion prevents accumulation. Melt occurred between June and September, with ice melt taking place during a few days only, mainly in August. The melt caused ice losses of up to 0.6 m, i.e. ~ 5% of the total ice thickness. Historical maps show a loss of 34.9 ± 10.0 m between 1893 and 2018 and almost balanced conditions between 1893 and 1914. The measurement of the meteorological conditions of present ice loss lays the basis for the interpretation of past gaps in the ice core records as past warm/melt events. As glacier melt continues with increasing rates even in the highest elevations of the Eastern Alps, this significant archives trapped in the cold ice of glaciers should be lifted before they disappear as an impact of climate change.

How to cite: Fischer, A., Bohleber, P., Helfricht, K., and Stocker-Waldhuber, M.: Mass balance and meteorology on a cold Eastern Alpine ice cap as a potential link to past climate, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-709, https://doi.org/10.5194/ems2022-709, 2022.