Importance of variable neutral drag coefficients for ocean-ice and air-ice fluxes in polar regions

2021 ◽  
Author(s):  
Jean Sterlin ◽  
Thierry Fichefet ◽  
Francois Massonnet ◽  
Michel Tsamados
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Surface Fluxes ◽  
The Arctic ◽  
Climate Modelling ◽  
Form Drag ◽  
Drag Coefficients ◽  
Ice Surface ◽  
Melt Ponds ◽  
Ice Surface Roughness

<p>Sea ice features a variety of obstacles to the flow of air and seawater at its top and bottom surfaces. Sea ice ridges, floe edges, ice surface roughness and melt ponds, lead to a form drag that interacts dynamically with the air-ice and ocean-ice fluxes of heat and momentum. In most climate models, surface fluxes of heat and momentum are calculated by bulk formulas using constant drag coefficients over sea ice, to reflect the mean surface roughness of the interfaces with the atmosphere and ocean. However, such constant drag coefficients do not account for the subgrid-scale variability of the sea ice surface roughness. To study the effect of form drag over sea ice on air-ice-ocean fluxes, we have implemented a formulation that estimates drag coefficients in ice-covered areas comprising the effect of sea ice ridges, floe edges and melt ponds, and ice surface skin (Tsamados et al., 2013) into the NEMO3.6-LIM3 global coupled ice-ocean model. In this work, we thoroughly analyse the impacts of this improvement on the model performance in both the Arctic and Antarctic. A particular attention is paid to the influence of this modification on the air-ice-ocean fluxes of heat and momentum, and the characteristics of the oceanic surface layers. We also formulate an assessment of the importance of variable drag coefficients over sea ice for the climate modelling community.</p>

Drone-based sea ice albedo measurements and photogrammetry during the Arctic freeze-up in the MOSAiC expedition

2021 ◽  
Author(s):  
Roberta Pirazzini ◽  
Henna-Reetta Hannula ◽  
David Brus ◽  
Ruzica Dadic ◽  
Martin Scnheebeli
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Surface Topography ◽  
Near Infrared ◽  
Snow Accumulation ◽  
The Arctic ◽  
Ocean Optics ◽  
Surface Features ◽  
Ice Surface ◽  
Melt Ponds

<p>Aerial albedo measurements and detailed surface topography of sea ice are needed to characterize the distribution of the various surface types (melt ponds of different depth and size, ice of different thicknesses, leads, ridges) and to determine how they contribute to the areal-averaged albedo on different horizontal scales. These measurements represent the bridge between the albedo measured from surface-based platforms, which typically have metre-to-tens-of-meters footprint, and satellite observations or large-grid model outputs.</p><p>Two drones were operated in synergy to measure the albedo and map the surface topography of the sea ice during the leg 5 of the MOSAiC expedition (August-September 2020), when concurrent albedo and surface roughness measurements were collected using surface-based instruments. The drone SPECTRA was equipped with paired Kipp and Zonen CM4 pyranometers measuring broadband albedo and paired Ocean Optics STS VIS (350 – 800 nm) and NIR (650-1100 nm) micro-radiometers measuring visible and near-infrared spectral albedo, and the drone Mavic 2 Pro was equipped with camera to perform photography mapping of the area measured by the SPECTRA drone.</p><p>Here we illustrate the collected data, which show a drastic change in sea ice albedo during the observing period, from the initial melting state to the freezing and snow accumulation state, and demonstrate how this change is related to the evolution of the different surface features, melt ponds and leads above all. From the data analysis we can conclude that the 30m albedo is not significantly affected by the individual surface features and, therefore, it is potentially representative of the sea ice albedo in satellite footprint and model grid areas.</p><p>The Digital Elevation Models (DEMs) of the sea ice surface obtained from UAV photogrammetry are combined with the DEMs based on Structure From Motion technique that apply photos manually taken close to the surface. This will enable us to derive the surface roughness from sub-millimeter to meter scales, which is critical to interpret the observed albedo and to develop correction methods to eliminate the artefacts caused by shadows.</p><p>The UAV-based albedo and surface roughness are highly complementary also to analogous helicopter-based observations, and will be relevant for the interpretation of all the physical and biochemical processes observed at and near the sea ice surface during the transition from melting to freezing and growing.</p>

scholarly journals C-Band Polarimetric Coherences and Ratios for Discriminating Sea Ice Roughness

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Mukesh Gupta ◽  
Randall K. Scharien ◽  
David G. Barber
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Two Dimensions ◽  
The Arctic ◽  
Rapid Decline ◽  
Ice Surface ◽  
Ice Roughness ◽  
Direct Measurements ◽  
Ice Surface Roughness ◽  
Spaceborne Radar

The rapid decline of sea ice in the Arctic has resulted in a variable sea ice roughness that necessitates improved methods for efficient observation using high-resolution spaceborne radar. The utility of C-band polarimetric backscatter, coherences, and ratios as a discriminator of ice surface roughness is evaluated. An existing one-dimensional backscatter model has been modified to two-dimensions (2D) by considering deviation in the orientation (i.e., the slopes) in azimuth and range direction of surface roughness simultaneously as an improvement in the model. It is shown theoretically that the circular coherence (ρRRLL) decreases exponentially with increasing surface roughness. The crosspolarized coherence (ρHHVH) is found to be less sensitive to surface roughness, whereas the copolarized coherence (ρVVHH) decreases at far-range incidence angles for all ice types. A complete validation of the adapted 2D model using direct measurements of surface roughness is suggested as an avenue for further research.

scholarly journals Arctic Sea Ice Surface Roughness Derived from Multi-Angular Reflectance Satellite Imagery

2018 ◽  
Author(s):  
Anne W. Nolin
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Ice Surface ◽  
Ice Roughness ◽  
Melt Ponds ◽  
Arctic Sea ◽  
Angular Reflectance ◽  
Ice Surface Roughness

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer melt ponds, and ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters meters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values are nearly identical for areas with roughness <20 cm but that for rougher regions, the MISR-derived roughness has a narrower range of values than the ATM data. The algorithm is able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-derived roughness data have a variance of about half that ATM roughness data.

scholarly journals Impact of Variable Atmospheric and Oceanic Form Drag on Simulations of Arctic Sea Ice*

2014 ◽  
Vol 44 (5) ◽  
pp. 1329-1353 ◽  
Author(s):  
Michel Tsamados ◽  
Daniel L. Feltham ◽  
David Schroeder ◽  
Daniela Flocco ◽  
Sinead L. Farrell ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Form Drag ◽  
Drag Coefficients ◽  
Arctic Sea ◽  
The Arctic Ocean ◽  
Range Of Values

Abstract Over Arctic sea ice, pressure ridges and floe and melt pond edges all introduce discrete obstructions to the flow of air or water past the ice and are a source of form drag. In current climate models form drag is only accounted for by tuning the air–ice and ice–ocean drag coefficients, that is, by effectively altering the roughness length in a surface drag parameterization. The existing approach of the skin drag parameter tuning is poorly constrained by observations and fails to describe correctly the physics associated with the air–ice and ocean–ice drag. Here, the authors combine recent theoretical developments to deduce the total neutral form drag coefficients from properties of the ice cover such as ice concentration, vertical extent and area of the ridges, freeboard and floe draft, and the size of floes and melt ponds. The drag coefficients are incorporated into the Los Alamos Sea Ice Model (CICE) and show the influence of the new drag parameterization on the motion and state of the ice cover, with the most noticeable being a depletion of sea ice over the west boundary of the Arctic Ocean and over the Beaufort Sea. The new parameterization allows the drag coefficients to be coupled to the sea ice state and therefore to evolve spatially and temporally. It is found that the range of values predicted for the drag coefficients agree with the range of values measured in several regions of the Arctic. Finally, the implications of the new form drag formulation for the spinup or spindown of the Arctic Ocean are discussed.

scholarly journals Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

2014 ◽  
Vol 14 (17) ◽  
pp. 9403-9450 ◽  
Author(s):  
T. Vihma ◽  
R. Pirazzini ◽  
I. Fer ◽  
I. A. Renfrew ◽  
J. Sedlar ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Surface Fluxes ◽  
Climate System ◽  
Arctic Climate ◽  
The Arctic ◽  
Small Scale ◽  
Climate Modelling ◽  
Physical Processes ◽  
Made In

Abstract. The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007–2009, significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice–ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave–turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice–ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.

scholarly journals Average cosine coefficient and spectral distribution of the light field under sea ice: Implications for primary production

Elem Sci Anth ◽  
2019 ◽  
Vol 7 ◽  
Author(s):  
L. C. Matthes ◽  
J. K. Ehn ◽  
S. L.-Girard ◽  
N. M. Pogorzelec ◽  
M. Babin ◽  
...  
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Radiation Field ◽  
Light Field ◽  
The Arctic ◽  
Daily Production ◽  
Ice Surface ◽  
Melt Ponds ◽  
The Impact ◽  
Average Cosine

The Arctic spring phytoplankton bloom has been reported to commence under a melting sea ice cover as transmission of photosynthetically active radiation (PAR; 400–700 nm) suddenly increases with the formation of surface melt ponds. Spatial variability in ice surface characteristics, i.e., snow thickness or melt pond distributions, and subsequent impact on transmitted PAR makes estimating light-limited primary production difficult during this time of year. Added to this difficulty is the interpretation of data from various sensor types, including hyperspectral, multispectral, and PAR-band irradiance sensors, with either cosine-corrected (planar) or spherical (scalar) sensor heads. To quantify the impact of the heterogeneous radiation field under sea ice, spectral irradiance profiles were collected beneath landfast sea ice during the Green Edge ice-camp campaigns in May–June 2015 and June–July 2016. Differences between PAR measurements are described using the downwelling average cosine, μd, a measure of the degree of anisotropy of the downwelling underwater radiation field which, in practice, can be used to convert between downwelling scalar, E0d, and planar, Ed, irradiance. A significantly smallerμd(PAR) was measured prior to snow melt compared to after (0.6 vs. 0.7) when melt ponds covered the ice surface. The impact of the average cosine on primary production estimates, shown in the calculation of depth-integrated daily production, was 16% larger under light-limiting conditions when E0d was used instead of Ed. Under light-saturating conditions, daily production was only 3% larger. Conversion of underwater irradiance data also plays a role in the ratio of total quanta to total energy (EQ/EW, found to be 4.25), which reflects the spectral shape of the under-ice light field. We use these observations to provide factors for converting irradiance measurements between irradiance detector types and units as a function of surface type and depth under sea ice, towards improving primary production estimates.

Mapping Arctic Sea Ice Surface Roughness with Multi-angle Imaging SpectroRadiometry

2020 ◽  
Author(s):  
Thomas Johnson ◽  
Michel Tsamados ◽  
Jan-Peter Muller ◽  
Julienne Stroeve
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Distribution Functions ◽  
Arctic Sea Ice ◽  
Ice Age ◽  
Training Dataset ◽  
Support Vector ◽  
Laser Measurements ◽  
Ice Surface ◽  
Ice Surface Roughness

<div> <div> <p>Surface roughness is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer melt pond extent, while also closely related to ice age. High resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness have remained elusive and do not extended over multi-decadal time-scales.  The MISR (Multi-angle Imaging SpectroRadiometer) instrument acquires optical imagery at 275m (red channel) and 1.1 km (all channels) resolutions from nine near-simultaneous camera view zenith angles sampling specular anisotropy, since 1999. Extending on previous work to model sea ice surface roughness from MISR angular reflectance signatures, a training dataset of cloud-free pixels and coincident probability distribution functions of lidar derived elevations from the Airborne Topographic Mapper (ATM) is generated. Surface roughness, defined as the standard deviation of the within-pixel elevations to a best-fit plane, is modelled using Support Vector Regression with a Radial Basis Function kernel, hyperparameters are tuned using GridSearchCV, and performance is assessed using nested cross-validation. We present derived instantaneous and monthly averaged sea ice roughness products at 1.1km and 17.6km resolution over the timespan of IceBridge campaigns (March and April for 2009-2018) on an EASE-2 (Equal-Area Scalable Earth) grid. These show considerable promise in detecting newly formed smooth ice from polynyas, and detailed surface features such as ridges and leads.</p> </div> </div><div> </div>

scholarly journals Sea-ice surface roughness estimates from airborne laser scanner and laser altimeter observations in Fram Strait and north of Svalbard

2015 ◽  
Vol 56 (69) ◽  
pp. 235-244 ◽  
Author(s):  
Justin F. Beckers ◽  
Angelika H.H. Renner ◽  
Gunnar Spreen ◽  
Sebastian Gerland ◽  
Christian Haas
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Laser Scanner ◽  
Ice Thickness ◽  
Fram Strait ◽  
Laser Altimeter ◽  
Airborne Laser Scanner ◽  
Ice Surface ◽  
Airborne Laser ◽  
Ice Surface Roughness

AbstractWe present sea-ice surface roughness estimates, i.e. the standard deviation of relative surface elevation, in the Arctic regions of Fram Strait and the Nansen Basin north of Svalbard acquired by an airborne laser scanner and a single-beam laser altimeter in 2010. We compare the scanner to the altimeter and compare the differences between the two survey regions. We estimate and correct sensor roll from the scanner data using the hyperbolic response of the scanner over a flat surface. Measurement surveys had to be longer than 5 km north of Svalbard and longer than 15 km in Fram Strait before the statistical distribution in surface roughness from the scanner and altimeter became similar. The shape of the surface roughness probability distributions agrees with those of airborne electromagnetic induction measurements of ice thickness. The ice in Fram Strait had a greater mean surface roughness, 0.16 m vs 0.09 m, and a wider distribution in roughness values than the ice in the Nansen Basin. An increase in surface roughness with increasing ice thickness was observed over fast ice found in Fram Strait near the coast of Greenland but not for the drift ice.

Late Summer Arctic Sea Ice Surface Roughness Signatures in C-Band SAR Data

2016 ◽  
Vol 9 (3) ◽  
pp. 1199-1215 ◽  
Author(s):  
Ane S. Fors ◽  
Camilla Brekke ◽  
Sebastian Gerland ◽  
Anthony P. Doulgeris ◽  
Justin F. Beckers
Keyword(s):  
Surface Roughness ◽  
Sea Ice ◽  
Late Summer ◽  
Arctic Sea Ice ◽  
Ice Surface ◽  
Arctic Sea ◽  
Sar Data ◽  
Ice Surface Roughness
Sign in / Sign up

Export Citation Format

Share Document