Causality and Evolution of Summer Polynyas off the Coast of Northern Greenland

2020 ◽  
Author(s):  
Younjoo Lee ◽  
Wieslaw Maslowski ◽  
Robert Osinski ◽  
Jaclyn Clement Kinney ◽  
Anthony Craig ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
National Laboratory ◽  
Arctic Region ◽  
Open Water ◽  
Horizontal Resolution ◽  
The Arctic ◽  
Northern Coast ◽  
Infiltration Capacity ◽  
Land Hydrology

<p>The summer polynya along the northern coast of Greenland has been observed only six months later after the winter polynya in 2018, which has prompted concerns about the stability of some of the thickest sea-ice in the Arctic region. This study combines retrospective remotely sensed sea-ice measurements with results from the Regional Arctic System Model (RASM) to examine the causes, effect, and evolution of open-water areas/polynyas in the region.</p><p>RASM is a limited-domain, fully-coupled climate model, consisting of the atmosphere (Weather Research and Forecasting, WRF3.7), ocean (Los Alamos National Laboratory Parallel Ocean Program, POP2), sea-ice (Community Sea Ice Model, CICE5), land hydrology (Variable Infiltration Capacity, VIC4) and streamflow routing (RVIC) components. The ocean and sea-ice models are configured with the horizontal resolution of 1/12-degree with 45 vertical levels and 5 sea-ice thickness categories, respectively. The atmosphere and land hydrology components are set up on a 50-km grid with 40-vertical levels and 3-soil layers, respectively. The Climate Forecast System Reanalysis (CFSR) and version 2 (CFSv2) output are used as boundary conditions for dynamic downscaling.</p><p>Analysis of the sea-ice conditions off the coast of northern Greenland revealed that RASM, in agreement with satellite measurements, has simulated five summer polynya events, i.e. in August of 1984, 1985, 2002, 2004 and 2018, over the 39-year period (1980-2018). All these events were primarily dynamically forced, with the thermodynamic forcing playing the secondary, yet still important role. While the thermodynamically driven sea-ice melting exhibited a relatively little year-to-year variability, between 87 km<sup>3</sup> and 115 km<sup>3</sup>, its relative contribution to the total sea-ice loss increased by 2.5 times, from 16% in 1984 to 40% in 2018. This implies that with continuing thinning of sea-ice, increasingly less mechanical forcing may be required to generate and maintain a polynya or open water north of Greenland in summers to come.  </p>

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 Incorporation of the C-GOLDSTEIN efficient climate model into the GENIE framework: "eb_go_gs" configurations of GENIE

2011 ◽  
Vol 4 (4) ◽  
pp. 957-992 ◽  
Author(s):  
R. Marsh ◽  
S. A. Müller ◽  
A. Yool ◽  
N. R. Edwards
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Horizontal Resolution ◽  
Wind Forcing ◽  
The Arctic ◽  
System Modelling ◽  
State Variables ◽  
Time Step ◽  
Transport Pathways ◽  
Ocean Atmosphere

Abstract. A computationally efficient, intermediate complexity ocean-atmosphere-sea ice model (C-GOLDSTEIN) has been incorporated into the Grid ENabled Integrated Earth system modelling (GENIE) framework. This involved decoupling of the three component modules that were re-coupled in a modular way, to allow replacement with alternatives and coupling of further components within the framework. The climate model described here (referred to as "eb_go_gs" for short) is the most basic version of GENIE in which atmosphere, ocean and sea ice all play an active role. Among improvements on the original C-GOLDSTEIN model, latitudinal grid resolution is generalized to allow a wider range of surface grids to be used. The ocean, atmosphere and sea-ice components of the "eb_go_gs" configuration of GENIE are individually described, along with details of their coupling. The setup and results from simulations using four different meshes are presented. The four alternative meshes comprise the widely-used 36 × 36 equal-area-partitioning of the Earth surface with 16 depth layers in the ocean, a version in which horizontal and vertical resolution are doubled, a setup matching the horizontal resolution of the dynamic atmospheric component available in the GENIE framework, and a setup with enhanced resolution in high-latitude areas. Results are presented for a spin-up experiment with a baseline parameter set and wind forcing typically used for current studies in which "eb_go_gs" is coupled with the ocean biogeochemistry module of GENIE, as well as for an experiment with a modified parameter set, revised wind forcing, and additional cross-basin transport pathways (Indonesian and Bering Strait throughflows). The latter experiment is repeated with the four mesh variants, with common parameter settings throughout, except for time-step length. Selected state variables and diagnostics are compared in two regards: (i) between simulations at lowest resolution that are obtained with the baseline and modified configurations, predominantly in order to evaluate the revision of the wind forcing, the modification of some key parameters, and the effect of additional transport pathways across the Arctic Ocean and the Indonesian Archipelago; (ii) between simulations with the four meshes, in order to explore various effects of mesh choice.

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 Is it ‘boom times’ for baleen whales in the Pacific Arctic region?

2016 ◽  
Vol 12 (9) ◽  
pp. 20160251 ◽  
Author(s):  
Sue E. Moore
Keyword(s):  
Sea Ice ◽  
Marine Ecosystem ◽  
Late Summer ◽  
Arctic Region ◽  
Open Water ◽  
Bowhead Whale ◽  
The Arctic ◽  
Ecosystem Structure ◽  
Baleen Whales ◽  
The Pacific

The marine ecosystem in the Pacific Arctic region has experienced dramatic transformation, most obvious by the loss of sea ice volume (75%), late-summer areal extent (50%) and change in phenology (four to six weeks longer open-water period). This alteration has resulted in an opening of habitat for subarctic species of baleen whales, many of which are recovering in number from severe depletions from commercial whaling in the nineteenth and twentieth centuries. Specifically, humpback, fin and minke whales ( Megaptera novaeangliae , Balaenoptera physalus and Balaenoptera acutorostrata ) are now regularly reported during summer and autumn in the southern Chukchi Sea. These predators of zooplankton and forage fishes join the seasonally resident grey whale ( Eschrichtius robustus ) and the arctic-endemic bowhead whale ( Balaena mysticetus ) in the expanding open-ocean habitat of the Pacific Arctic. Questions arising include: (i) what changes in whale-prey production and delivery mechanisms have accompanied the loss of sea ice, and (ii) how are these five baleen whale species partitioning the expanding ice-free habitat? While there has been no programme of research specifically focused on these questions, an examination of seasonal occurrence, foraging plasticity and (for bowhead whales) body condition suggests that the current state of Pacific Arctic marine ecosystem may be ‘boom times’ for baleen whales. These favourable conditions may be moderated, however, by future shifts in ecosystem structure and/or negative impacts to cetaceans related to increased commercial activities in the region.

scholarly journals Southern Ocean polynyas in CMIP6 models

2021 ◽  
Author(s):  
Martin Mohrmann ◽  
Céline Heuzé ◽  
Sebastiaan Swart
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Ocean Circulation ◽  
Deep Water ◽  
Climate Models ◽  
Climate Model ◽  
Open Water ◽  
Horizontal Resolution ◽  
Wind Stress Curl ◽  
Coastal Polynya

Abstract. Polynyas facilitate air-sea fluxes, impacting climate-relevant properties such as sea ice formation and deep water production. Despite their importance, polynyas have been poorly represented in past generations of climate models. Here we present a method to track the presence, frequency and spatial distribution of polynyas in the Southern Ocean in 27 models participating in the Climate Model Intercomparison Project phase 6 (CMIP6) and two satellite based sea ice products. Only half of the 27 models form open water polynyas (OWP), and most underestimate their area. As in satellite observations, three models show episodes of high OWP activity separated by decades of no OWPs, while other models unrealistically create OWPs nearly every year. The coastal polynya area in contrast is often overestimated, with the least accurate representations occurring in the models with the coarsest horizontal resolution. We show that the presence or absence of OWPs are linked to changes in the regional hydrography, specifically the linkages between polynya activity with deep water convection and/or the shoaling of the upper water column thermocline. Models with an accurate Antarctic Circumpolar Current (ACC) transport and wind stress curl have too frequent OWPs. Biases in polynya representation continue to exist in climate models, which has an impact on the regional ocean circulation and ventilation that require to be addressed. However, emerging iceberg discharge schemes, vertical discretisation or overflow parameterisation, are anticipated to improve polynya representations and associated climate prediction in the future.

The Arctic System Reanalysis, Version 2

2018 ◽  
Vol 99 (4) ◽  
pp. 805-828 ◽  
Author(s):  
D. H. Bromwich ◽  
A. B. Wilson ◽  
L. Bai ◽  
Z. Liu ◽  
M. Barlage ◽  
...  
Keyword(s):  
Sea Ice ◽  
Land Surface ◽  
Global Climate ◽  
Rapid Change ◽  
Wrf Model ◽  
Arctic Region ◽  
Horizontal Resolution ◽  
The Arctic ◽  
Near Surface ◽  
Environmental Models

AbstractThe Arctic is a vital component of the global climate, and its rapid environmental evolution is an important element of climate change around the world. To detect and diagnose the changes occurring to the coupled Arctic climate system, a state-of-the-art synthesis for assessment and monitoring is imperative. This paper presents the Arctic System Reanalysis, version 2 (ASRv2), a multiagency, university-led retrospective analysis (reanalysis) of the greater Arctic region using blends of the polar-optimized version of the Weather Research and Forecasting (Polar WRF) Model and WRF three-dimensional variational data assimilated observations for a comprehensive integration of the regional climate of the Arctic for 2000–12. New features in ASRv2 compared to version 1 (ASRv1) include 1) higher-resolution depiction in space (15-km horizontal resolution), 2) updated model physics including subgrid-scale cloud fraction interaction with radiation, and 3) a dual outer-loop routine for more accurate data assimilation. ASRv2 surface and pressure-level products are available at 3-hourly and monthly mean time scales at the National Center for Atmospheric Research (NCAR). Analysis of ASRv2 reveals superior reproduction of near-surface and tropospheric variables. Broadscale analysis of forecast precipitation and site-specific comparisons of downward radiative fluxes demonstrate significant improvement over ASRv1. The high-resolution topography and land surface, including weekly updated vegetation and realistic sea ice fraction, sea ice thickness, and snow-cover depth on sea ice, resolve finescale processes such as topographically forced winds. Thus, ASRv2 permits a reconstruction of the rapid change in the Arctic since the beginning of the twenty-first century–complementing global reanalyses. ASRv2 products will be useful for environmental models, verification of regional processes, or siting of future observation networks.

scholarly journals Southern Ocean polynyas in CMIP6 models

2021 ◽  
Vol 15 (9) ◽  
pp. 4281-4313
Author(s):  
Martin Mohrmann ◽  
Céline Heuzé ◽  
Sebastiaan Swart
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Ocean Circulation ◽  
Deep Water ◽  
Climate Models ◽  
Climate Model ◽  
Open Water ◽  
Horizontal Resolution ◽  
Wind Stress Curl ◽  
Coastal Polynya

Abstract. Polynyas facilitate air–sea fluxes, impacting climate-relevant properties such as sea ice formation and deep water production. Despite their importance, polynyas have been poorly represented in past generations of climate models. Here we present a method to track the presence, frequency and spatial distribution of polynyas in the Southern Ocean in 27 models participating in the Climate Model Intercomparison Project Phase 6 (CMIP6) and two satellite-based sea ice products. Only half of the 27 models form open-water polynyas (OWPs), and most underestimate their area. As in satellite observations, three models show episodes of high OWP activity separated by decades of no OWP, while other models unrealistically create OWPs nearly every year. In contrast, the coastal polynya area is overestimated in most models, with the least accurate representations occurring in the models with the coarsest horizontal resolution. We show that the presence or absence of OWPs is linked to changes in the regional hydrography, specifically the linkages between polynya activity with deep water convection and/or the shoaling of the upper water column thermocline. Models with an accurate Antarctic Circumpolar Current transport and wind stress curl have too frequent OWPs. Biases in polynya representation continue to exist in climate models, which has an impact on the regional ocean circulation and ventilation that should be addressed. However, emerging iceberg discharge schemes, more adequate vertical grid type or overflow parameterisation are anticipated to improve polynya representations and associated climate prediction in the future.

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

Co-producing Sea-Ice Predictions with Stakeholders Using Simulation

2021 ◽  
Keyword(s):  
Sea Ice ◽  
Local Community ◽  
Economic Value ◽  
Arctic Region ◽  
Production Model ◽  
The Arctic ◽  
Simulation Game ◽  
Climate Services ◽  
Diverse Range ◽  
Uncertainty Information

Abstract Forecasts of sea-ice evolution in the Arctic region for several months ahead can be of considerable socio-economic value for a diverse range of marine sectors and for local community supply logistics. However, subseasonal-to-seasonal (S2S) forecasts represent a significant technical challenge, while translating user needs into scientifically manageable procedures and robust user confidence requires collaboration among a range of stakeholders. We developed and tested a novel, transdisciplinary co-production approach that combined socio-economic scenarios and participatory, research-driven simulation-gaming to test a new S2S sea-ice forecast system with experienced mariners in the cruise tourism sector. Our custom-developed computerized simulation-game ICEWISE integrated sea-ice parameters, forecast technology and human factors, as a participatory environment for stakeholder engagement. We explored the value of applications-relevant S2S sea-ice prediction and linked uncertainty information. Results suggest that the usefulness of S2S services is currently most evident in schedule-dependent sectors but expected to increase due to anticipated changes in the physical environment and continued growth in Arctic operations. Reliable communication of uncertainty information in sea-ice forecasts must be demonstrated and trialed before users gain confidence in emerging services and technologies. Mariners’ own intuition, experience, and familiarity with forecast service provider reputation impact the extent to which sea-ice information may reduce uncertainties and risks for Arctic mariners. Our insights into the performance of the combined foresight/simulation co-production model in brokering knowledge across a range of domains demonstrates promise. We conclude with an overview of the potential contributions from S2S sea-ice predictions and from experiential co-production models to the development of decision-driven and science-informed climate services.

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