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Bibliography

Last update: 2023-11-09

A (non-exhaustive) list of references about sea ice - Atmospheric Boundary Layer coupling.

I sorted out the references in the 6 categories below:

1. Impact of prescribed sea ice boundary conditions on the ABL in atmospheric models (often LES).

The focus of these studies is on the response of the ABL to various sea ice boundary conditions (but nothing i could find about the feedback effects on sea ice).

Spensberger, C.; Spengler, T. (2021).- Sensitivity of Air-Sea Heat Exchange in Cold-Air Outbreaks to Model Resolution and Sea-Ice Distribution. (Click to read more).
SI Watanabe, H. Niino, T. Spengler (2022).- Formation of maritime convergence zones within cold air outbreaks due to the shape of the coastline or sea ice edge. (Click to read more).
Lorenz T. et al (2021).- The stable atmospheric boundary layer over snow-covered sea ice: Model evaluation with fine-scale ISOBAR18 observations. (Click to read more).
Michaelis, Lupkes et al (2022).- Modelling and parametrization of the convective flow over leads in sea ice and comparison with airborne observations. (Click to read more).
Wenta, M., & Herman, A. (2018).- The influence of the spatial distribution of leads and ice floes on the atmospheric boundary layer over fragmented sea ice. (Click to read more).
  • Investigate the response of the atmospheric boundary layer (ABL) to subgrid-scale variations of sea ice properties and fracturing analyze three-dimensional air circulation within the ABL over fragmented sea ice. A series of idealized high-resolution simulations with Polar WRF is performed for several spatial distributions of ice floes and leads for two values of sea ice concentration (0.5 and 0.9) and several ambient wind speed profiles. Suggests the need for developing suitable parametrizations of ABL effects related to subgrid scale sea ice features for these models.
  • https://www.cambridge.org/core/services/aop-cambridge-core/content/view/4961E8E20BC30618A7849378985EA7FA/S0260305518000150a.pdf/
Spall M. (2019).- Dynamics and Thermodynamics of the Mean Transpolar Drift and Ice Thickness in the Arctic Ocean. (Click to read more).
  • A theory for the mean ice thickness and the Transpolar Drift in the Arctic Ocean is developed. Two distinct regimes: a thin ice regime in the eastern Arctic and a thick ice regime in the western Arctic. In the eastern Arctic, the ice drift is controlled by a balance between wind and ocean drag, while the ice thickness is controlled by heat loss to the atmosphere. In contrast, in the western Arctic, the ice thickness is determined by a balance between wind and internal ice stress, while the drift is indirectly controlled by heat loss to the atmosphere. The basic predictions for ice thickness, heat loss, ice volume, and ice export from the theory compare well with an idealized, coupled ocean–ice numerical model over a wide range of parameter space.
  • Analytical work compared to an idealised MITgcm simulation (EVP rheology) forced with bulks._
  • https://doi.org/10.1175/JCLI-D-19-0252.1
Renfrew et al (2019).- Atmospheric sensitivity to marginal-ice-zone drag: Local and global responses. (Click to read more).
Tetzlaff et al (2016)- Convective processes in the polar atmospheric boundary layer: a study based on measurements and modelling. (Click to read more).
  • PhD. thesis, Alfred-Wegener-Institut Helmholtz-Zentrum fümlr Polar- und Meeresforschung), p. 136.
  • https://media.suub.uni-bremen.de/handle/elib/992
  • Goal of this thesis lies on improving our current understanding of convective processes and the related turbulent fluxes in the polar atmospheric boundary layer (ABL) over both the sea ice covered regions and over the open ocean at the sea ice edge. Obs (aircraft)-based results are supplemented by modeling studies using a simple boxmodel and a one-dimensional mesoscale model. For this purpose, we use a 1D version of the MEsoscale TRAnsport and Stream model (METRAS, Schlünzen, 1988), which is non-hydrostatic and anelastic. The applied parametrisations are a mixing length closure (ML), a counter-gradient closure (CG), and a so-called eddy-diffusivity mass-flux closure (EDMF).
Pithan et al (2016)- Select strengths and biases of models in representing the Arctic winter boundary layer over sea ice: the Larcform 1 single column model intercomparison (Click to read more).
Sea & Yang (2013)- Dynamical response of the Arctic atmospheric boundary layer process to uncertainties in sea-ice concentration (Click to read more).
Bromwich et al (2009)- Development and testing of Polar Weather Research and Forecasting model: 2. Arctic Ocean (Click to read more).
Moeng et al (2007)- Examining Two-Way Grid Nesting for Large Eddy Simulation of the PBL Using the WRF Model (Click to read more).
  • Two-way nesting for large eddy simulation (LES) of PBL turbulence
  • A pair of LES-within-LES experiments are performed where a finer-grid LES covering a smaller horizontal domain is nested inside a coarser-grid LES covering a larger horizontal domain. Free-convection and pure shear-driven PBLs. The free-convection case has zero mean wind and the only driving force for turbulence is uniform surface heating.
  • https://journals.ametsoc.org/view/journals/mwre/135/6/mwr3406.1.xml
Zulauf et al (2002)- Two-dimensional cloud-resolving modeling of the atmospheric effects of Arctic leads based upon midwinter conditions at the Surface Heat Budget of the Arctic Ocean ice camp (Click to read more).
Birnbaum & Lüpkes (2002)- A new parameterization of surface drag in the marginal sea ice zone* (Click to read more).

2. Sea-ice - ABL interactions in fully coupled systems (sea-ice-ocean-atmoshpere)

Ren et al (2021)- A fully coupled Arctic sea-ice–ocean–atmosphere model (ArcIOAM v1.0) based on C-Coupler2: model description** **and preliminary results (Click to read more).
  • Polar WRF coupled to MITGCM (VP rheology) at 18km.
  • Goal: provide reliable Arctic sea ice prediction on SEASONAL timescales. Compare a MITGCM forced config with a coupled config with Polar WRF. “The two-way coupling has better performance in terms of sea ice extent, concen- tration, thickness and sea surface temperature (SST), especially in summer. This result indicates that sea-ice–ocean– atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution.
  • https://doi.org/10.5194/gmd-14-1101-2021
Day et al (2022)- Day et all Benefits and challenges of dynamic sea ice for weather forecasts (Click to read more).
  • https://wcd.copernicus.org/articles/3/713/2022/
  • ECMWF IFS atmospheric forecast experiments. one in which dynamic coupling with sea ice concentration and ocean is switched on (coup-SSTSIC), one atmosphere-only where sea ice concentration and SST anomalies are persisted from the initial time(pers-SSTSIC), and another atmosphere-only with updated observed sea ice concentration and SSTs (obs-SSTSIC).
  • For the coupled forecasts (coup-SSTSIC), the IFS atmosphere is coupled to NEMO (Madec, 2008) model version 3.4.1 and LIM2, using the ORCA025 horizontal grid (with a resolution of approximately ∼ 10 km in the Arctic) with 75 levels in the vertical.
  • Demonstrate that using a dynamically coupled ocean and sea ice model in the European Centre for Medium- Range Weather Forecasts (ECMWF) Integrated Forecasting System results in improved sea ice edge position forecasts in the Northern Hemisphere in the medium range. Further, this improves forecasts of boundary layer temperature and humidity downstream of the sea ice edge in some regions during periods of rapid change in the sea ice, compared to forecasts in which the sea surface temperature anomalies and sea ice concentration do not evolve throughout the forecasts.
Smith et al (2018)- Impact of Coupling with an Ice–Ocean Model on Global Medium-Range NWP Forecast Skill (Click to read more).
Yang et al (2016)- Taking into Account Atmospheric Uncertainty Improves Sequential Assimilation of SMOS Sea Ice Thickness Data in an Ice–Ocean Model (Click to read more).
Horvat et al (2016)- Interaction of sea ice floe size, ocean eddies, and sea ice melting (Click to read more).
Pellerin et al (2014)- Impact of a Two-Way Coupling between an Atmospheric and an Ocean-Ice Model over the Gulf of St. Lawrence. (Click to read more).
  • https://doi.org/10.1175/1520-0493(2004)132<1379:IOATCB>2.0.CO;2
  • Abstract: The purpose of this study is to present the impacts of a fully interactive coupling between an atmospheric and a sea ice model over the Gulf of St. Lawrence, Canada. The impacts are assessed in terms of the atmospheric and sea ice forecasts produced by the coupled numerical system. The ocean-ice model has been developed at the Maurice Lamontagne Institute, where it runs operationally at a horizontal resolution of 5 km and is driven (one-way coupling) by atmospheric model forecasts provided by the Meteorological Service of Canada (MSC). In this paper the importance of two-way coupling is assessed by comparing the one-way coupled version with a two-way coupled version in which the atmospheric model interacts with the sea ice model during the simulation. The impacts are examined for a case in which the sea ice conditions are changing rapidly. Two atmospheric model configurations have been studied. The first one has a horizontal grid spacing of 24 km, which is the operational configuration used at the Canadian Meteorological Centre. The second one is a high-resolution configuration with a 4-km horizontal grid spacing. A 48-h forecast has been validated using satellite images for the ice and the clouds, and also using the air temperature and precipitation observations. It is shown that the two-way coupled system improves the atmospheric forecast and has a direct impact on the sea ice forecast. It is also found that forecasts are improved with a fine resolution that better resolves the physical events, fluxes, and forcing. The coupling technique is also briefly described and discussed.

3. Observations of the Arctic ABL

Mchedlishvili et al (2023)- New estimates of the pan-Arctic sea ice–atmosphere neutral drag coefficients from ICESat-2 elevation data (Click to read more).
Overland et al (1999)- Regional sensible and radiative heat flux estimates for the winter Arctic during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment (Click to read more).
  • SHEBA campaign
  • link
Lupkes et al (2012)- A parametrization, based on sea ice morphology, of the neutral atmospheric drag coefficients for weather prediction and climate models (Click to read more).
  • A hierarchy of parametrizations of the neutral 10 m drag coefficients over polar sea ice with different morphology regimes is derived on the basis of a partitioning concept that splits the total surface drag into contributions of skin drag and form drag.
  • link
Andreas et al (2009)- Parameterizing Turbulent Exchange over Sea Ice in Winter**. *Based on SHEBA campaign data. This paper develops a bulk turbulent flux algorithm to explain the winter data. (Click to read more).
  • comment
  • link

4. Impact of atmospheric boundary conditions on sea-ice models

Heorton et al (2014)- The Response of the Sea Ice Edge to Atmospheric and Oceanic Jet Formation (Click to read more).

5. Impact of simple ABL model coupled to sea ice model

Lemarié et al (2021)- A simplified atmospheric boundary layer model for an improved representation of air–sea interactions in eddying oceanic models: implementation and first evaluation in NEMO (4.0) (Click to read more).

6. Some ABL models

Lemarié et al (2021)- A simplified atmospheric boundary layer model for an improved representation of air–sea interactions in eddying oceanic models: implementation and first evaluation in NEMO (4.0) (Click to read more).
Davy et al (2009)- A model of dust in the Martian lower atmosphere (Click to read more).
Deremble et al (2013)- CheapAML: A Simple, Atmospheric Boundary Layer Model for Use in Ocean-Only Model Calculations (Click to read more).