Non-eddy-resolving modelling and parametrization of turbulent convection over sea ice leads and evaluation with airborne observations

2021 ◽  
Author(s):  
Janosch Michaelis ◽  
Christof Lüpkes ◽  
Amelie Schmitt ◽  
Jörg Hartmann
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
The Arctic ◽  
Large Temperature ◽  
Strong Impact ◽  
Near Surface ◽  
Local Closure ◽  
Microscale Model ◽  
Non Local ◽  
Polar Ocean

<p><span><span>The polar ocean regions are characterised by a large variety of interactions between sea ice surfaces</span><span>, open water</span><span>, and the atmosphere. Especially between late autumn and spring, leads (open-water channels in sea ice) may play a crucial role within this system: Due to large temperature differences between the surface of leads and the near-surface atmosphere, strong turbulent convective plumes are generated with an enhanced turbulent transport of heat, moisture, and momentum. In consequence, lead-generated convection has a strong impact on the characteristics of the polar atmospheric boundary layer (ABL). </span></span></p><p><span><span>We apply a plume- but non-eddy-resolving, microscale model to study the convection over three different leads, which had been observed during the aircraft campaign STABLE over the Arctic Marginal Sea Ice Zone in March 2013. Model simulations are performed using a local and a non-local turbulence closure. The latter represents a lead-width-dependent approach for </span><span>the </span><span>turbulent fluxes </span><span>based on large eddy simulation </span><span>and it is</span><span> designed for an idealised, </span><span>lead-perpendicular</span><span>, and near-neutral inflow in an ABL of 300m </span><span>thickness</span><span>. </span><span>The observed cases from STABLE are also characterised by lead-perpendicular inflow conditions</span><span>, but the ABL is much shallower than in the ideali</span><span>s</span><span>ed cases and the inflow stratification is </span><span>partly</span><span> (slightly) stable. </span><span>Our main goal is to study the quality of both parametrizations and to evaluate, if the non-local parametrization shows advantages as compared to the local closure.</span></span></p><p><span><span>We show that the basic</span><span> observed features of the lead-generated convection are represented with both closures </span><span>despite some minor differences that will be explained</span><span>. However, the advantages of the non-local closure become clearly obvious by the physically more realistic representation of regions with observed vertical entrainment or where the observations hint at counter-gradient transport. Moreover, we also show that some weaknesses of the simulations can be </span><span>almost </span><span>overcome by introducing two further modifications </span><span>of</span><span> the non-local closure. We consider our results as another important step in the development of atmospheric turbulence parametrizations </span><span>for </span><span>non-eddy-resolving, microscale simulations of</span><span> strongly inhomogeneous convective </span><span>boundary layers</span><span>.</span></span></p>

scholarly journals Trends, abrupt shifts and interannual variability of the Arctic Wintertime Seasonal Sea Ice from 1979 to 2019

2020 ◽  
pp. 1-13
Author(s):  
Guanghua Hao ◽  
Jie Su ◽  
Timo Vihma ◽  
Fei Huang
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Large Scale ◽  
Open Water ◽  
The Arctic ◽  
Atlantic Sector ◽  
Near Surface ◽  
The Pacific ◽  
Pressure Anomaly ◽  
Abrupt Shifts

Abstract The Arctic winter seasonal sea ice (WSSI) concentration from 1979 to 2019 is derived from passive microwave data. Based on Empirical Orthogonal Function (EOF) analysis, the WSSI time series includes regionally different trends, abrupt shifts and interannual variations. The time series of the first EOF mode (PC1) mainly represents the WSSI trend, which is characterized by an increase, particularly in the Pacific sector. PC1 confirms two abrupt shifts in WSSI in 1989 and 2007, with a variance of 31%. After 2007, the large-scale atmospheric circulation anomaly shows a strengthened wavenumber 3 structure at high latitudes associated with a mid-tropospheric low-pressure anomaly in central and western Siberia and a high-pressure anomaly in eastern Siberia in summer and autumn. These patterns have promoted the increased transport of moist static energy to the central Arctic and contributed to increased near-surface air temperatures that may enhance ice melting in summer and reduce ice growth in autumn and winter. The changes in ice melt and growth have had opposite effects in the Pacific and Atlantic sectors: WSSI has increased in the Pacific sector due to the replacement of multi-year ice by WSSI, and decreased in the Atlantic sector due to the replacement of WSSI by open water.

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.

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.

Eddy covariance flux measurements of momentum, heat and carbon dioxide in Hudson Bay at the onset of sea ice melt 

2021 ◽  
Author(s):  
Richard Sims ◽  
Brian Butterworth ◽  
Tim Papakyriakou ◽  
Mohamed Ahmed ◽  
Brent Else
Keyword(s):  
Carbon Dioxide ◽  
Gas Exchange ◽  
Sea Ice ◽  
Eddy Covariance ◽  
Open Water ◽  
The Arctic ◽  
Gas Transfer ◽  
Hudson Bay ◽  
Flux Measurements ◽  
Ice Fraction

<p>Remoteness and tough conditions have made the Arctic Ocean historically difficult to access; until recently this has resulted in an undersampling of trace gas and gas exchange measurements. The seasonal cycle of sea ice completely transforms the air sea interface and the dynamics of gas exchange. To make estimates of gas exchange in the presence of sea ice, sea ice fraction is frequently used to scale open water gas transfer parametrisations. It remains unclear whether this scaling is appropriate for all sea ice regions. Ship based eddy covariance measurements were made in Hudson Bay during the summer of 2018 from the icebreaker CCGS Amundsen. We will present fluxes of carbon dioxide (CO<sub>2</sub>), heat and momentum and will show how they change around the Hudson Bay polynya under varying sea ice conditions. We will explore how these fluxes change with wind speed and sea ice fraction. As freshwater stratification was encountered during the cruise, we will compare our measurements with other recent eddy covariance flux measurements made from icebreakers and also will compare our turbulent CO<sub>2 </sub>fluxes with bulk fluxes calculated using underway and surface bottle pCO<sub>2</sub> data. </p><p> </p>

scholarly journals Numerical investigation of the Arctic ice-ocean boundary layer; implications for air-sea gas fluxes

2016 ◽  
Author(s):  
A. Bigdeli ◽  
B. Loose ◽  
S. T. Cole
Keyword(s):  
Sea Ice ◽  
Water Column ◽  
Mixed Layer ◽  
Mixed Layer Depth ◽  
The Arctic ◽  
Layer Depth ◽  
Near Surface ◽  
Resolution Model ◽  
Simplifying Assumptions ◽  
Vertical Spacing

Abstract. In ice-covered regions it can be challenging to determine air-sea exchange – for heat and momentum, but also for gases like carbon dioxide and methane. The harsh environment and relative data scarcity make it difficult to characterize even the physical properties of the ocean surface. Here, we seek a mechanistic interpretation for the rate of air-sea gas exchange (k) derived from radon-deficits. These require an estimate of the water column history extending 30 days prior to sampling. We used coarse resolution (36 km) regional configuration of the MITgcm with fine near surface vertical spacing (2 m) to evaluate the capability of the model to reproduce conditions prior to sampling. The model is used to estimate sea-ice velocity, concentration and mixed-layer depth experienced by the water column. We then compared the model results to existing field data including satellite, moorings and Ice-tethered profilers. We found that model-derived sea-ice coverage is 88 to 98 % accurate averaged over Beaufort Gyre, sea-ice velocities have 78 % correlation which resulted in 2 km/day error in 30 day trajectory of sea-ice. The model demonstrated the capacity to capture the broad trends in the mixed layer although with a bias and model water velocities showed only 29 % correlation with actual data. Overall, we find the course resolution model to be an inadequate surrogate for sparse data, however the simulation results are a slight improvement over several of the simplifying assumptions that are often made when surface ocean geochemistry, including the use of a constant mixed layer depth and a velocity profile that is purely wind-driven.

scholarly journals Importance of open-water ice growth and ice concentration evolution: a study based on FESOM-ECHAM6

2015 ◽  
Vol 6 (2) ◽  
pp. 2137-2179
Author(s):  
X. Shi ◽  
G. Lohmann
Keyword(s):  
Sea Ice ◽  
Surface Albedo ◽  
Global Climate Model ◽  
Open Water ◽  
Arctic Sea Ice ◽  
Meridional Overturning Circulation ◽  
The Arctic ◽  
Water Ice ◽  
Ice Growth ◽  
Ice Concentration

Abstract. A newly developed global climate model FESOM-ECHAM6 with an unstructured mesh and high resolution is applied to investigate to what degree the area-thickness distribution of new ice formed in open water affects the ice and ocean properties. A sensitivity experiment is performed which reduces the horizontal-to-vertical aspect ratio of open-water ice growth. The resulting decrease in the Arctic winter sea-ice concentration strongly reduces the surface albedo, enhances the ocean heat release to the atmosphere, and increases the sea-ice production. Furthermore, our simulations show a positive feedback mechanism among the Arctic sea ice, the Atlantic Meridional Overturning Circulation (AMOC), and the surface air temperature in the Arctic, as the sea ice transport affects the freshwater budget in regions of deep water formation. A warming over Europe, Asia and North America, associated with a negative anomaly of Sea Level Pressure (SLP) over the Arctic (positive phase of the Arctic Oscillation (AO)), is also simulated by the model. For the Southern Ocean, the most pronounced change is a warming along the Antarctic Circumpolar Current (ACC), especially for the Pacific sector. Additionally, a series of sensitivity tests are performed using an idealized 1-D thermodynamic model to further investigate the influence of the open-water ice growth, which reveals similar results in terms of the change of sea ice and ocean temperature. In reality, the distribution of new ice on open water relies on many uncertain parameters, for example, surface albedo, wind speed and ocean currents. Knowledge of the detailed processes is currently too crude for those processes to be implemented realistically into models. Our sensitivity experiments indicate a pronounced uncertainty related to open-water sea ice growth which could significantly affect the climate system.

Changes in phytoplankton concentration now drive increased Arctic Ocean primary production

Science ◽  
2020 ◽  
Vol 369 (6500) ◽  
pp. 198-202 ◽  
Author(s):  
K. M. Lewis ◽  
G. L. van Dijken ◽  
K. R. Arrigo
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Arctic Ocean ◽  
Open Water ◽  
Water Area ◽  
The Arctic ◽  
Carbon Export ◽  
Phytoplankton Primary Production ◽  
Open Water Area ◽  
The Arctic Ocean

Historically, sea ice loss in the Arctic Ocean has promoted increased phytoplankton primary production because of the greater open water area and a longer growing season. However, debate remains about whether primary production will continue to rise should sea ice decline further. Using an ocean color algorithm parameterized for the Arctic Ocean, we show that primary production increased by 57% between 1998 and 2018. Surprisingly, whereas increases were due to widespread sea ice loss during the first decade, the subsequent rise in primary production was driven primarily by increased phytoplankton biomass, which was likely sustained by an influx of new nutrients. This suggests a future Arctic Ocean that can support higher trophic-level production and additional carbon export.

scholarly journals A parameter model of gas exchange for the seasonal sea ice zone

Ocean Science ◽  
2014 ◽  
Vol 10 (1) ◽  
pp. 17-28 ◽  
Author(s):  
B. Loose ◽  
W. R. McGillis ◽  
D. Perovich ◽  
C. J. Zappa ◽  
P. Schlosser
Keyword(s):  
Boundary Layer ◽  
Gas Exchange ◽  
Sea Ice ◽  
Open Water ◽  
Field Studies ◽  
The Arctic ◽  
Gas Transfer ◽  
Transfer Velocity ◽  
Using Data ◽  
Effective Transfer

Abstract. Carbon budgets for the polar oceans require better constraint on air–sea gas exchange in the sea ice zone (SIZ). Here, we utilize advances in the theory of turbulence, mixing and air–sea flux in the ice–ocean boundary layer (IOBL) to formulate a simple model for gas exchange when the surface ocean is partially covered by sea ice. The gas transfer velocity (k) is related to shear-driven and convection-driven turbulence in the aqueous mass boundary layer, and to the mean-squared wave slope at the air–sea interface. We use the model to estimate k along the drift track of ice-tethered profilers (ITPs) in the Arctic. Individual estimates of daily-averaged k from ITP drifts ranged between 1.1 and 22 m d−1, and the fraction of open water (f) ranged from 0 to 0.83. Converted to area-weighted effective transfer velocities (keff), the minimum value of keff was 10−55 m d−1 near f = 0 with values exceeding keff = 5 m d−1 at f = 0.4. The model indicates that effects from shear and convection in the sea ice zone contribute an additional 40% to the magnitude of keff, beyond what would be predicted from an estimate of keff based solely upon a wind speed parameterization. Although the ultimate scaling relationship for gas exchange in the sea ice zone will require validation in laboratory and field studies, the basic parameter model described here demonstrates that it is feasible to formulate estimates of k based upon properties of the IOBL using data sources that presently exist.

scholarly journals Arctic Fog Detection Using Infrared Spectral Measurements

2019 ◽  
Vol 36 (8) ◽  
pp. 1643-1656
Author(s):  
Li Yi ◽  
King-Fai Li ◽  
Xianyao Chen ◽  
Ka-Kit Tung
Keyword(s):  
Sea Ice ◽  
Chukchi Sea ◽  
Open Water ◽  
Detection Algorithm ◽  
Probability Of Detection ◽  
Satellite Observations ◽  
The Arctic ◽  
Water Surface Area ◽  
Infrared Spectral ◽  
Fog Detection

AbstractThe rapid increase in open-water surface area in the Arctic, resulting from sea ice melting during the summer likely as a result of global warming, may lead to an increase in fog [defined as a cloud with a base height below 1000 ft (~304 m)], which may imperil ships and small aircraft transportation in the region. There is a need for monitoring fog formation over the Arctic. Given that ground-based observations of fog over Arctic open water are very sparse, satellite observations may become the most effective way for Arctic fog monitoring. We developed a fog detection algorithm using the temperature difference between the cloud top and the surface, called ∂T in this work. A fog event is said to be detected if ∂T is greater than a threshold, which is typically between −6 and −12 K, depending on the time of the day (day or night) and the surface types (open water or sea ice). We applied this method to the coastal regions of Chukchi Sea and Beaufort Sea near Barrow, Alaska (now known as Utqiaġvik), during the months of March–October. Training with satellite observations between 2007 and 2014 over this region, the ∂T method can detect Arctic fog with an optimal probability of detection (POD) between 74% and 90% and false alarm rate (FAR) between 5% and 17%. These statistics are validated with data between 2015 and 2016 and are shown to be robust from one subperiod to another.

scholarly journals The Impact of Regional Arctic Sea Ice Loss on Atmospheric Circulation and the NAO

2016 ◽  
Vol 29 (2) ◽  
pp. 889-902 ◽  
Author(s):  
Rasmus A. Pedersen ◽  
Ivana Cvijanovic ◽  
Peter L. Langen ◽  
Bo M. Vinther
Keyword(s):  
Sea Ice ◽  
Atmospheric Circulation ◽  
General Circulation ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Near Surface ◽  
Arctic Sea ◽  
The Impact ◽  
Sea Ice Reduction

Abstract Reduction of the Arctic sea ice cover can affect the atmospheric circulation and thus impact the climate beyond the Arctic. The atmospheric response may, however, vary with the geographical location of sea ice loss. The atmospheric sensitivity to the location of sea ice loss is studied using a general circulation model in a configuration that allows combination of a prescribed sea ice cover and an active mixed layer ocean. This hybrid setup makes it possible to simulate the isolated impact of sea ice loss and provides a more complete response compared to experiments with fixed sea surface temperatures. Three investigated sea ice scenarios with ice loss in different regions all exhibit substantial near-surface warming, which peaks over the area of ice loss. The maximum warming is found during winter, delayed compared to the maximum sea ice reduction. The wintertime response of the midlatitude atmospheric circulation shows a nonuniform sensitivity to the location of sea ice reduction. While all three scenarios exhibit decreased zonal winds related to high-latitude geopotential height increases, the magnitudes and locations of the anomalies vary between the simulations. Investigation of the North Atlantic Oscillation reveals a high sensitivity to the location of the ice loss. The northern center of action exhibits clear shifts in response to the different sea ice reductions. Sea ice loss in the Atlantic and Pacific sectors of the Arctic cause westward and eastward shifts, respectively.

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