scholarly journals Observations and Simulations of Meteorological Conditions over Arctic Thick Sea Ice in Late Winter during the Transarktika 2019 Expedition

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 174
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Open Water ◽  
The Arctic ◽  
Late Winter ◽  
Good Representation ◽  
Hourly Temperature ◽  
Ice Model

The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model COSMO-CLM (CCLM). In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5 km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data show a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.

scholarly journals Observations and simulations of meteorological conditions over Arctic thick sea ice in late winter during the Transarktika 2019 expedition

2021 ◽  
Author(s):  
Günther Heinemann ◽  
Sascha Willmes ◽  
Lukas Schefczyk ◽  
Alexander Makshtas ◽  
Vasilii Kustov ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Open Water ◽  
The Arctic ◽  
Late Winter ◽  
Good Representation ◽  
Hourly Temperature ◽  
Ice Model

<p>The parameterization of ocean/sea-ice/atmosphere interaction processes is a challenge for regional climate models (RCMs) of the Arctic, particularly for wintertime conditions, when small fractions of thin ice or open water cause strong modifications of the boundary layer. Thus, the treatment of sea ice and sub-grid flux parameterizations in RCMs is of crucial importance. However, verification data sets over sea ice for wintertime conditions are rare. In the present paper, data of the ship-based experiment Transarktika 2019 during the end of the Arctic winter for thick one-year ice conditions are presented. The data are used for the verification of the regional climate model CCLM. In addition, Moderate Resolution Imaging Spectroradiometer (MODIS) data are used for the comparison of ice surface temperature (IST) simulations of the CCLM sea ice model. CCLM is used in a forecast mode (nested in ERA5) for the Norwegian and Barents Seas with 5km resolution and is run with different configurations of the sea ice model and sub-grid flux parameterizations. The use of a new set of parameterizations yields improved results for the comparisons with in-situ data. Comparisons with MODIS IST allow for a verification over large areas and show also a good performance of CCLM. The comparison with twice-daily radiosonde ascents during Transarktika 2019, hourly microwave water vapor measurements of first 5 km in the atmosphere and hourly temperature profiler data shows a very good representation of the temperature, humidity and wind structure of the whole troposphere for CCLM.</p>

scholarly journals Impact of ocean circulation on regional polar climate simulations using the Arctic Region Climate System Model

1997 ◽  
Vol 25 ◽  
pp. 203-207 ◽  
Author(s):  
David A. Bailey ◽  
Amanda H. Lynch ◽  
Katherine S. Hedström
Keyword(s):  
Sea Ice ◽  
Mixed Layer ◽  
Climate Models ◽  
Regional Climate ◽  
The Arctic ◽  
Water Runoff ◽  
Polar Regions ◽  
Ice Dynamics ◽  
Climate System Model ◽  
Ice Model

Global climate models have pointed to the polar regions as very sensitive areas in response to climate change. However, these models often do not contain representations of processes peculiar to the polar regions such as dynamic sea ice, permafrost, and Arctic stratus clouds. Further, global models do not have the resolution necessary to model accurately many of the important processes and feedbacks. Thus, there is a need for regional climate models of higher resolution. Our such model (ARCSy M) has been developed by A. Lynch and W. Chapman. This model incorporates the NCAR Regional Climate Model (RegCM2) with the addition of Flato–Hibler cavitating fluid sea-ice dynamics and Parkinson–Washington ice thermodynamic formulation. Recently work has been conducted to couple a mixed-layer ocean to the atmosphere–ice model, and a three-dimensional (3-D) dynamical ocean model, in this case the S-Coordinate Primitive Equation Model (SPEM), to the ice model. Simulations including oceanic circulation will allow investigations of the feedbacks involved in fresh-water runoff from sea-ice melt and sea-ice transport. Further, it is shown that the definition of the mixed-layer depth has significant impact on ice thermodynamics.

scholarly journals Impact of ocean circulation on regional polar climate simulations using the Arctic Region Climate System Model

1997 ◽  
Vol 25 ◽  
pp. 203-207 ◽  
Author(s):  
David A. Bailey ◽  
Amanda H. Lynch ◽  
Katherine S. Hedström
Keyword(s):  
Sea Ice ◽  
Mixed Layer ◽  
Climate Models ◽  
Regional Climate ◽  
The Arctic ◽  
Water Runoff ◽  
Polar Regions ◽  
Ice Dynamics ◽  
Climate System Model ◽  
Ice Model

Global climate models have pointed to the polar regions as very sensitive areas in response to climate change. However, these models often do not contain representations of processes peculiar to the polar regions such as dynamic sea ice, permafrost, and Arctic stratus clouds. Further, global models do not have the resolution necessary to model accurately many of the important processes and feedbacks. Thus, there is a need for regional climate models of higher resolution. Our such model (ARCSy M) has been developed by A. Lynch and W. Chapman. This model incorporates the NCAR Regional Climate Model (RegCM2) with the addition of Flato–Hibler cavitating fluid sea-ice dynamics and Parkinson–Washington ice thermodynamic formulation. Recently work has been conducted to couple a mixed-layer ocean to the atmosphere–ice model, and a three-dimensional (3-D) dynamical ocean model, in this case the S-Coordinate Primitive Equation Model (SPEM), to the ice model. Simulations including oceanic circulation will allow investigations of the feedbacks involved in fresh-water runoff from sea-ice melt and sea-ice transport. Further, it is shown that the definition of the mixed-layer depth has significant impact on ice thermodynamics.

scholarly journals The Granular Sea Ice Model in Spherical Coordinates and Its Application to a Global Climate Model

2007 ◽  
Vol 20 (24) ◽  
pp. 5946-5961 ◽  
Author(s):  
Jan Sedlacek ◽  
Jean-François Lemieux ◽  
Lawrence A. Mysak ◽  
L. Bruno Tremblay ◽  
David M. Holland
Keyword(s):  
Growth Rate ◽  
Sea Ice ◽  
Climate Model ◽  
Heat Budget ◽  
Global Climate ◽  
Global Climate Model ◽  
Internal Heat ◽  
The Arctic ◽  
Spherical Coordinates ◽  
Ice Model

Abstract The granular sea ice model (GRAN) from Tremblay and Mysak is converted from Cartesian to spherical coordinates. In this conversion, the metric terms in the divergence of the deviatoric stress and in the strain rates are included. As an application, the GRAN is coupled to the global Earth System Climate Model from the University of Victoria. The sea ice model is validated against standard datasets. The sea ice volume and area exported through Fram Strait agree well with values obtained from in situ and satellite-derived estimates. The sea ice velocity in the interior Arctic agrees well with buoy drift data. The thermodynamic behavior of the sea ice model over a seasonal cycle at one location in the Beaufort Sea is validated against the Surface Heat Budget of the Arctic Ocean (SHEBA) datasets. The thermodynamic growth rate in the model is almost twice as large as the observed growth rate, and the melt rate is 25% lower than observed. The larger growth rate is due to thinner ice at the beginning of the SHEBA period and the absence of internal heat storage in the ice layer in the model. The simulated lower summer melt is due to the smaller-than-observed surface melt.

scholarly journals Atmospheric rivers and associated precipitation patterns during the ACLOUD/PASCAL campaigns near Svalbard (May–June 2017): case studies using observations, reanalyses, and a regional climate model

2021 ◽  
Author(s):  
Carolina Viceto ◽  
Irina V. Gorodetskaya ◽  
Annette Rinke ◽  
Marion Maturilli ◽  
Alfredo Rocha ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Climate Model ◽  
Regional Climate ◽  
The Arctic ◽  
Phase Precipitation ◽  
Atmospheric Rivers ◽  
Tracking Algorithms ◽  
North Eastern ◽  
Sea Ice Edge

Abstract. Recently, a significant increase in the moisture content has been documented over the Arctic, where both local contributions and poleward moisture transport from lower latitudes can play a role. This study focuses on the anomalous moisture transport events confined to long and narrow corridors, known as atmospheric rivers (ARs) which are expected to have a strong influence on Arctic moisture amounts, precipitation and energy budget. During the two concerted intensive measurement campaigns, Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) and the Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL), which took place from May 22 to June 28, 2017, at and near Svalbard, three high water vapour transport events were identified as ARs, based on two tracking algorithms: on 30 May, 6 and 9 June. We explore in detail the temporal and spatial evolution of the events identified as ARs and the associated precipitation patterns, using measurements from the AWIPEV research station in Ny-Ålesund, satellite-borne measurements, several reanalysis products (ERA5, ERA-Interim, MERRA-2, CFSv2 and JRA-55) and HIRHAM5 regional climate model. Results show that the tracking algorithms detected the events differently partly due to differences in spatial resolution, ranging from 0.25 to 1.25 degree, in temporal resolution, ranging from 1 hour to 6 hours, and in the criteria used in the tracking algorithms. Despite being consecutive, these events showed different synoptic evolution and precipitation characteristics. The first event extended from western Siberia to Svalbard, causing mixed-phase precipitation and was associated with a retreat of the sea-ice edge. The second event a week later had a similar trajectory and most precipitation occurred as rain, although in some areas mixed-phase precipitation or only snowfall occurred, mainly over the north-eastern Greenland’s coast and northeast of Iceland and no differences were noted in the sea-ice edge. The third event showed a different pathway extending from north-eastern Atlantic towards Greenland, and then turning southeastward reaching Svalbard. This last AR caused high precipitation amounts in the east coast of Greenland in the form of rain and snow and showed no precipitation in Svalbard region. The vertical profiles of specific humidity show layers of enhanced moisture, simultaneously with dry layers during the first two events, which were not captured by all reanalysis datasets, while the model misrepresented the entire vertical profiles. Regarding the wind speed, there was an increase of values with height during the first and last events, while during the second event there were no major changes in the wind speed. The accuracy of the representation of wind speed by the reanalyses and the model depended on the event. This study shows the importance of both the Atlantic and Siberian pathways of ARs during spring-beginning of summer in the Arctic, AR-associated strong heat and moisture increase as well as precipitation phase transition, and the need of using high spatiotemporal resolution datasets when studying these intense short duration events.

scholarly journals Atmospheric rivers and associated precipitation patterns during the ACLOUD and PASCAL campaigns near Svalbard (May–June 2017): case studies using observations, reanalyses, and a regional climate model

2022 ◽  
Vol 22 (1) ◽  
pp. 441-463
Author(s):  
Carolina Viceto ◽  
Irina V. Gorodetskaya ◽  
Annette Rinke ◽  
Marion Maturilli ◽  
Alfredo Rocha ◽  
...  
Keyword(s):  
Wind Speed ◽  
Sea Ice ◽  
Climate Model ◽  
Regional Climate ◽  
The Arctic ◽  
Atmospheric Rivers ◽  
The North ◽  
Tracking Algorithms ◽  
North Eastern ◽  
Sea Ice Edge

Abstract. Recently, a significant increase in the atmospheric moisture content has been documented over the Arctic, where both local contributions and poleward moisture transport from lower latitudes can play a role. This study focuses on the anomalous moisture transport events confined to long and narrow corridors, known as atmospheric rivers (ARs), which are expected to have a strong influence on Arctic moisture amounts, precipitation, and the energy budget. During two concerted intensive measurement campaigns – Arctic CLoud Observations Using airborne measurements during polar Day (ACLOUD) and the Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL) – that took place at and near Svalbard, three high-water-vapour-transport events were identified as ARs, based on two tracking algorithms: the 30 May event, the 6 June event, and the 9 June 2017 event. We explore the temporal and spatial evolution of the events identified as ARs and the associated precipitation patterns in detail using measurements from the French (Polar Institute Paul Emile Victor) and German (Alfred Wegener Institute for Polar and Marine Research) Arctic Research Base (AWIPEV) in Ny-Ålesund, satellite-borne measurements, several reanalysis products (the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA) Interim (ERA-Interim); the ERA5 reanalysis; the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2); the Climate Forecast System version 2 (CFSv2); and the Japanese 55-Year Reanalysis (JRA-55)), and the HIRHAM regional climate model version 5 (HIRHAM5). Results show that the tracking algorithms detected the events differently, which is partly due to differences in the spatial and temporal resolution as well as differences in the criteria used in the tracking algorithms. The first event extended from western Siberia to Svalbard, caused mixed-phase precipitation, and was associated with a retreat of the sea-ice edge. The second event, 1 week later, had a similar trajectory, and most precipitation occurred as rain, although mixed-phase precipitation or only snowfall occurred in some areas, mainly over the coast of north-eastern Greenland and the north-east of Iceland, and no differences were noted in the sea-ice edge. The third event showed a different pathway extending from the north-eastern Atlantic towards Greenland before turning south-eastward and reaching Svalbard. This last AR caused high precipitation amounts on the east coast of Greenland in the form of rain and snow and showed no precipitation in the Svalbard region. The vertical profiles of specific humidity show layers of enhanced moisture that were concurrent with dry layers during the first two events and that were not captured by all of the reanalysis datasets, whereas the HIRHAM5 model misrepresented humidity at all vertical levels. There was an increase in wind speed with height during the first and last events, whereas there were no major changes in the wind speed during the second event. The accuracy of the representation of wind speed by the reanalyses and the model depended on the event. The objective of this paper was to build knowledge from detailed AR case studies, with the purpose of performing long-term analysis. Thus, we adapted a regional AR detection algorithm to the Arctic and analysed how well it identified ARs, we used different datasets (observational, reanalyses, and model) and identified the most suitable dataset, and we analysed the evolution of the ARs and their impacts in terms of precipitation. This study shows the importance of the Atlantic and Siberian pathways of ARs during spring and beginning of summer in the Arctic; the significance of the AR-associated strong heat increase, moisture increase, and precipitation phase transition; and the requirement for high-spatio-temporal-resolution datasets when studying these intense short-duration events.

scholarly journals Antarctic sea ice decline delayed well into the 21st century in a high-resolution climate projection

2020 ◽  
Author(s):  
Thomas Rackow ◽  
Sergey Danilov ◽  
Helge F. Goessling ◽  
Hartmut H. Hellmer ◽  
Dmitry V. Sein ◽  
...  
Keyword(s):  
Global Warming ◽  
High Resolution ◽  
Sea Ice ◽  
Climate Models ◽  
Climate Model ◽  
The Arctic ◽  
Alternative Mechanism ◽  
Sea Ice Extent ◽  
Antarctic Sea Ice ◽  
Sea Ice Decline

<p>Despite ongoing global warming and strong sea ice decline in the Arctic, the sea ice extent around the Antarctic continent has not declined during the satellite era since 1979. This is in stark contrast to existing climate models that tend to show a strong negative sea ice trend for the same period; hence the confidence in projected Antarctic sea-ice changes is considered to be low. In the years since 2016, there has been significantly lower Antarctic sea ice extent, which some consider a sign of imminent change; however, others have argued that sea ice extent is expected to regress to the weak decadal trend in the near future.</p><p>In this presentation, we show results from climate change projections with a new climate model that allows the simulation of mesoscale eddies in dynamically active ocean regions in a computationally efficient way. We find that the high-resolution configuration (HR) favours periods of stable Antarctic sea ice extent in September as observed over the satellite era. Sea ice is not projected to decline well into the 21<sup>st</sup> century in the HR simulations, which is similar to the delaying effect of, e.g., added glacial melt water in recent studies. The HR ocean configurations simulate an ocean heat transport that responds differently to global warming and is more efficient at moderating the anthropogenic warming of the Southern Ocean. As a consequence, decrease of Antarctic sea ice extent is significantly delayed, in contrast to what existing coarser-resolution climate models predict.</p><p>Other explanations why current models simulate a non-observed decline of Antarctic sea-ice have been put forward, including the choice of included sea ice physics and underestimated simulated trends in westerly winds. Our results provide an alternative mechanism that might be strong enough to explain the gap between modeled and observed trends alone.</p>

scholarly journals A Regional Seasonal Forecast Model of Arctic Minimum Sea Ice Extent: Reflected Solar Radiation vs. Late Winter Coastal Divergence

2021 ◽  
pp. 1
Author(s):  
Rachel Kim ◽  
Bruno Tremblay ◽  
Charles Brunette ◽  
Robert Newton
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Open Water ◽  
Forecast Model ◽  
Ice Cover ◽  
The Arctic ◽  
Late Winter ◽  
Coupled Models ◽  
Sea Ice Extent ◽  
Predictive Skill

AbstractThinning sea ice cover in the Arctic is associated with larger interannual variability in the minimum Sea Ice Extent (SIE). The current generation of forced or fully coupled models, however, have difficulty predicting SIE anomalies from the long-term trend, highlighting the need to better identify the mechanisms involved in the seasonal evolution of sea ice cover. One such mechanism is Coastal Divergence (CD), a proxy for ice thickness anomalies based on late winter ice motion, quantified using Lagrangian ice tracking. CD gains predictive skill through the positive feedback of surface albedo anomalies, mirrored in Reflected Solar Radiation (RSR), during melt season. Exploring the dynamic and thermodynamic contributions to minimum SIE predictability, RSR, initial SIE (iSIE) and CD are compared as predictors using a regional seasonal sea ice forecast model for July 1, June 1 and May 1 forecast dates for all Arctic peripheral seas. The predictive skill of June RSR anomalies mainly originates from open water fraction at the surface, i.e. June iSIE and June RSR have equal predictive skill for most seas. The finding is supported by the surprising positive correlation found between June Melt Pond Fraction (MPF) and June RSR in all peripheral seas: MPF anomalies indicate presence of ice or open water that is key to creating minimum SIE anomalies. This contradicts models that show correlation between melt onset, MPF and the minimum SIE. A hindcast model shows that for a May 1 forecast, CD anomalies have better predictive skill than RSR anomalies for most peripheral seas.

Intercomparison of Arctic Regional Climate Models: Modeling Clouds and Radiation for SHEBA in May 1998

2006 ◽  
Vol 19 (17) ◽  
pp. 4167-4178 ◽  
Author(s):  
Jun Inoue ◽  
Jiping Liu ◽  
James O. Pinto ◽  
Judith A. Curry
Keyword(s):  
Radiative Transfer ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Cloud Microphysics ◽  
Surface Heat ◽  
The Arctic ◽  
Surface Boundary ◽  
Simulated Surface

Abstract To improve simulations of the Arctic climate and to quantify climate model errors, four regional climate models [the Arctic Regional Climate System Model (ARCSYM), the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS), the High-Resolution Limited-Area Model (HIRHAM), and the Rossby Center Atmospheric Model (RCA)] have simulated the annual Surface Heat Budget of the Arctic Ocean (SHEBA) under the Arctic Regional Climate Model Intercomparison Project (ARCMIP). The same lateral boundary and ocean surface boundary conditions (i.e., ice concentration and surface temperature) drive all of the models. This study evaluated modeled surface heat fluxes and cloud fields during May 1998, a month that included the onset of the surface icemelt. In general, observations agreed with simulated surface pressure and near-surface air properties. Simulation errors due to surface fluxes and cloud effects biased the net simulated surface heat flux, which in turn affected the timing of the simulated icemelt. Modeled cloud geometry and precipitation suggest that the RCA model produced the most accurate cloud scheme, followed by the HIRHAM model. Evaluation of a relationship between cloud water paths and radiation showed that a radiative transfer scheme in ARCSYM was closely matched with the observation when liquid clouds were dominant. Clouds and radiation are of course closely linked, and an additional comparison of the radiative transfer codes for ARCSYM and COAMPS was performed for clear-sky conditions, thereby excluding cloud effects. Overall, the schemes for radiative transfer in ARCSYM and for cloud microphysics in RCA potentially have some advantages for modeling the springtime Arctic.

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