Observed microphysical changes in Arctic mixed-phase clouds when transitioning from sea ice to open ocean

Abstract. In situ airborne observations of cloud microphysics, aerosol properties and thermodynamic structure over the transition from sea ice to ocean are presented from the Aerosol-Cloud Coupling and Climate Interactions in the Arctic (ACCACIA) campaign. A case study from 23 March 2013 provides a unique view of the cloud microphysical changes over this transition under cold air outbreak conditions. Cloud base and depth both increased over this transition, and mean droplet number concentrations also increased from approximately 80 cm−3 over the sea ice to 90 cm−3 over the ocean. The ice properties of the cloud remained approximately constant. Observed ice crystal concentrations averaged approximately 0.5–1.5 L−1, suggesting only primary ice nucleation was active; however, there was evidence of crystal fragmentation at cloud base over the ocean. The liquid-water content increased almost four-fold over the transition and this, in conjunction with the deeper cloud layer, allowed rimed snowflakes to develop which precipitated out of cloud base. Little variation in aerosol particle number concentrations was observed between the different surface conditions; however, some variability with altitude was observed, with notably greater concentrations measured at higher altitudes (> 800 m) over the sea ice. Near-surface boundary layer temperatures increased by 13 °C from sea ice to ocean, with corresponding increases in surface heat fluxes and turbulent kinetic energy. These significant thermodynamic changes were concluded to be the primary driver of the microphysical evolution of the cloud. This study represents the first investigation, using in situ airborne observations, of cloud microphysical changes with changing sea ice cover and addresses the question of how the microphysics of Arctic stratiform clouds may change as the region warms and sea ice extent reduces.

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Observed microphysical changes in Arctic mixed-phase clouds when transitioning from sea ice to open ocean

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-13945-2016 ◽

2016 ◽

Vol 16 (21) ◽

pp. 13945-13967 ◽

Cited By ~ 14

Author(s):

Gillian Young ◽

Hazel M. Jones ◽

Thomas W. Choularton ◽

Jonathan Crosier ◽

Keith N. Bower ◽

...

Keyword(s):

Sea Ice ◽

Cloud Microphysics ◽

Heat Fluxes ◽

Cloud Layer ◽

The Arctic ◽

Cloud Base ◽

Surface Boundary ◽

Sea Ice Extent ◽

Near Surface

Abstract. In situ airborne observations of cloud microphysics, aerosol properties, and thermodynamic structure over the transition from sea ice to ocean are presented from the Aerosol-Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) campaign. A case study from 23 March 2013 provides a unique view of the cloud microphysical changes over this transition under cold-air outbreak conditions. Cloud base lifted and cloud depth increased over the transition from sea ice to ocean. Mean droplet number concentrations, Ndrop, also increased from 110 ± 36 cm−3 over the sea ice to 145 ± 54 cm−3 over the marginal ice zone (MIZ). Downstream over the ocean, Ndrop decreased to 63 ± 30 cm−3. This reduction was attributed to enhanced collision-coalescence of droplets within the deep ocean cloud layer. The liquid water content increased almost four fold over the transition and this, in conjunction with the deeper cloud layer, allowed rimed snowflakes to develop and precipitate out of cloud base downstream over the ocean. The ice properties of the cloud remained approximately constant over the transition. Observed ice crystal number concentrations averaged approximately 0.5–1.5 L−1, suggesting only primary ice nucleation was active; however, there was evidence of crystal fragmentation at cloud base over the ocean. Little variation in aerosol particle number concentrations was observed between the different surface conditions; however, some variability with altitude was observed, with notably greater concentrations measured at higher altitudes ( >  800 m) over the sea ice. Near-surface boundary layer temperatures increased by 13 °C from sea ice to ocean, with corresponding increases in surface heat fluxes and turbulent kinetic energy. These significant thermodynamic changes were concluded to be the primary driver of the microphysical evolution of the cloud. This study represents the first investigation, using in situ airborne observations, of cloud microphysical changes with changing sea ice cover and addresses the question of how the microphysics of Arctic stratiform clouds may change as the region warms and sea ice extent reduces.

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Advective pathways of nutrients and key ecological substances in the Arctic

10.5194/egusphere-egu21-15538 ◽

2021 ◽

Author(s):

Myriel Vredenborg ◽

Benjamin Rabe ◽

Sinhue Torres-Valdès

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Ocean Circulation ◽

Environmental Changes ◽

Marine Ecosystem ◽

Ocean Model ◽

The Arctic ◽

Large Set ◽

Sea Ice Extent ◽

Near Surface

The Arctic Ocean is undergoing remarkable environmental changes due to global warming. The rise in the Arctic near-surface air temperature during the past decades is more than twice as high as the global average, a phenomenon known as the &#8220;Arctic Amplification&#8221;. As a consequence the Arctic summer sea ice extent has decreased by more than 40 % in recent decades, and moreover a year-round sea ice loss in extent and thickness was recorded. By opening up of large areas formerly covered by sea ice, the exchange of heat, moisture and momentum between the ocean and the atmosphere intensified. This resulted in changes in the ocean circulation and the water masses impacting the marine ecosystem. We investigate these changes by using a large set of hydrographic and biogeochemical data of the entire Arctic Ocean. To better quantify the current changes in the Arctic ecosystem we will compare our observational data analysis with high-resolution biogeochemical atmosphere-ice-ocean model simulations.

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Uncertainties in Arctic Sea Ice Thickness Associated with Different Atmospheric Reanalysis Datasets Using the CICE5 Model

Atmosphere ◽

10.3390/atmos10070361 ◽

2019 ◽

Vol 10 (7) ◽

pp. 361

Author(s):

Su-Bong Lee ◽

Baek-Min Kim ◽

Jinro Ukita ◽

Joong-Bae Ahn

Keyword(s):

Sea Ice ◽

Arctic Region ◽

Reanalysis Data ◽

Arctic Sea Ice ◽

Heat Fluxes ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Ice Volume ◽

Arctic Sea

Reanalysis data are known to have relatively large uncertainties in the polar region than at lower latitudes. In this study, we used a single sea-ice model (Los Alamos’ CICE5) and three sets of reanalysis data to quantify the sensitivities of simulated Arctic sea ice area and volume to perturbed atmospheric forcings. The simulated sea ice area and thickness thus volume were clearly sensitive to the selection of atmospheric reanalysis data. Among the forcing variables, changes in radiative and sensible/latent heat fluxes caused significant amounts of sensitivities. Differences in sea-ice concentration and thickness were primarily caused by differences in downward shortwave and longwave radiations. 2-m air temperature also has a significant influence on year-to-year variability of the sea ice volume. Differences in precipitation affected the sea ice volume by causing changes in the insulation effect of snow-cover on sea ice. The diversity of sea ice extent and thickness responses due to uncertainties in atmospheric variables highlights the need to carefully evaluate reanalysis data over the Arctic region.

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Recent changes in Antarctic Sea Ice

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2014.0163 ◽

2015 ◽

Vol 373 (2045) ◽

pp. 20140163 ◽

Cited By ~ 77

Author(s):

John Turner ◽

J. Scott Hosking ◽

Thomas J. Bracegirdle ◽

Gareth J. Marshall ◽

Tony Phillips

Keyword(s):

Southern Ocean ◽

Sea Ice ◽

Climate Models ◽

Surface Wind ◽

The Arctic ◽

Cmip5 Models ◽

Intrinsic Variability ◽

Sea Ice Extent ◽

Amundsen Sea ◽

Near Surface

In contrast to the Arctic, total sea ice extent (SIE) across the Southern Ocean has increased since the late 1970s, with the annual mean increasing at a rate of 186×10 3 km 2 per decade (1.5% per decade; p <0.01) for 1979–2013. However, this overall increase masks larger regional variations, most notably an increase (decrease) over the Ross (Amundsen–Bellingshausen) Sea. Sea ice variability results from changes in atmospheric and oceanic conditions, although the former is thought to be more significant, since there is a high correlation between anomalies in the ice concentration and the near-surface wind field. The Southern Ocean SIE trend is dominated by the increase in the Ross Sea sector, where the SIE is significantly correlated with the depth of the Amundsen Sea Low (ASL), which has deepened since 1979. The depth of the ASL is influenced by a number of external factors, including tropical sea surface temperatures, but the low also has a large locally driven intrinsic variability, suggesting that SIE in these areas is especially variable. Many of the current generation of coupled climate models have difficulty in simulating sea ice. However, output from the better-performing IPCC CMIP5 models suggests that the recent increase in Antarctic SIE may be within the bounds of intrinsic/internal variability.

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The NASA Eulerian Snow on Sea Ice Model (NESOSIM) v1.0: initial model development and analysis

Geoscientific Model Development ◽

10.5194/gmd-11-4577-2018 ◽

2018 ◽

Vol 11 (11) ◽

pp. 4577-4602 ◽

Cited By ~ 13

Author(s):

Alek A. Petty ◽

Melinda Webster ◽

Linette Boisvert ◽

Thorsten Markus

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Snow Depth ◽

Horizontal Resolution ◽

The Arctic ◽

Blowing Snow ◽

Sea Ice Concentration ◽

Near Surface ◽

Ice Model

Abstract. The NASA Eulerian Snow On Sea Ice Model (NESOSIM) is a new, open-source snow budget model that is currently configured to produce daily estimates of the depth and density of snow on sea ice across the Arctic Ocean through the accumulation season. NESOSIM has been developed in a three-dimensional Eulerian framework and includes two (vertical) snow layers and several simple parameterizations (accumulation, wind packing, advection–divergence, blowing snow lost to leads) to represent key sources and sinks of snow on sea ice. The model is forced with daily inputs of snowfall and near-surface winds (from reanalyses), sea ice concentration (from satellite passive microwave data) and sea ice drift (from satellite feature tracking) during the accumulation season (August through April). In this study, we present the NESOSIM formulation, calibration efforts, sensitivity studies and validation efforts across an Arctic Ocean domain (100 km horizontal resolution). The simulated snow depth and density are calibrated with in situ data collected on drifting ice stations during the 1980s. NESOSIM shows strong agreement with the in situ seasonal cycles of snow depth and density, and shows good (moderate) agreement with the regional snow depth (density) distributions. NESOSIM is run for a contemporary period (2000 to 2015), with the results showing strong sensitivity to the reanalysis-derived snowfall forcing data, with the Modern-Era Retrospective analysis for Research and Applications (MERRA) and the Japanese Meteorological Agency 55-year reanalysis (JRA-55) forced snow depths generally higher than ERA-Interim, and the Arctic System Reanalysis (ASR) generally lower. We also generate and force NESOSIM with a consensus median daily snowfall dataset from these reanalyses. The results are compared against snow depth estimates derived from NASA's Operation IceBridge (OIB) snow radar data from 2009 to 2015, showing moderate–strong correlations and root mean squared errors of ∼ 10 cm depending on the OIB snow depth product analyzed, similar to the comparisons between OIB snow depths and the commonly used modified Warren snow depth climatology. Potential improvements to this initial NESOSIM formulation are discussed in the hopes of improving the accuracy and reliability of these simulated snow depths and densities.

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Dynamic and Thermodynamic Impacts of the Winter Arctic Oscillation on Summer Sea Ice Extent

Journal of Climate ◽

10.1175/jcli-d-17-0067.1 ◽

2018 ◽

Vol 31 (4) ◽

pp. 1483-1497 ◽

Cited By ~ 5

Author(s):

Hyo-Seok Park ◽

Andrew L. Stewart ◽

Jun-Hyeok Son

Keyword(s):

Sea Ice ◽

Radiative Forcing ◽

Arctic Oscillation ◽

Reanalysis Data ◽

Heat Fluxes ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Extent ◽

The Pacific ◽

Basin Scale

Arctic summer sea ice extent exhibits substantial interannual variability, as is highlighted by the remarkable recovery in sea ice extent in 2013 following the record minimum in the summer of 2012. Here, the mechanism via which Arctic Oscillation (AO)-induced ice thickness changes impact summer sea ice is explored, using observations and reanalysis data. A positive AO weakens the basin-scale anticyclonic sea ice drift and decreases the winter ice thickness by 15 and 10 cm in the Eurasian and the Pacific sectors of the Arctic, respectively. Three reanalysis datasets show that the upward surface heat fluxes are reduced over wide areas of the Arctic, suppressing the ice growth during the positive AO winters. The winter dynamic and thermodynamic thinning preconditions the ice for enhanced radiative forcing via the ice–albedo feedback in late spring–summer, leading to an additional 10 cm of thinning over the Pacific sector of the Arctic. Because of these winter AO-induced dynamic and thermodynamics effects, the winter AO explains about 22% ( r = −0.48) of the interannual variance of September sea ice extent from 1980 to 2015.

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The Increasing Prevalence of High Frequency Internal Waves in an Arctic Ocean With Declining Sea Ice Cover

Volume 7B: Ocean Engineering ◽

10.1115/omae2019-96621 ◽

2019 ◽

Author(s):

Tom Rippeth ◽

Vasyl Vlasenko ◽

Nataliya Stashchuk ◽

Igor E. Kozlov ◽

Brian Scannell ◽

...

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Internal Waves ◽

High Frequency ◽

Ice Cover ◽

The Arctic ◽

Sea Ice Extent ◽

Road Map ◽

The Arctic Ocean

Abstract Receding seasonal sea ice extent over the Arctic Ocean is increasing access to what was a largely inaccessible region. At lower latitudes the complex vertical current structure associated with large amplitude, high frequency non-linear internal waves, sometimes referred to as solitons, present a significant challenge to the safe engineering design and operation of offshore infrastructure. In this paper we examine the prevalence this type of internal wave in the Arctic Ocean. To do so we will draw on both in situ and remotely sensed oceanographic data. This will be combined with state-of-the-art numerical modelling to demonstrate a link between the geographical occurrence of these waves and the tide. Whilst the link implies that these features are geographically limited, it is also likely that the geographical limits will change with declining sea ice cover. These results will then be used to provide a road map towards a methodology for forecasting the prevalence of these phenomena in a future Arctic Ocean.

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Atlantic- and Arctic Water transport across the Yermak Plateau

10.5194/egusphere-egu2020-11409 ◽

2020 ◽

Author(s):

Frank Nilsen ◽

Eli Anne Ersdal ◽

Ragnheid Skogseth

Keyword(s):

Sea Ice ◽

Arctic Ocean ◽

Ocean Circulation ◽

Heat Fluxes ◽

The Arctic ◽

Ocean Current ◽

Bottom Current ◽

The Arctic Ocean ◽

Yermak Plateau

The pathway by which Atlantic Water ultimately inflows to the Arctic Ocean via the Yermak Plateau are of great interest for improving the current understanding of the evolving state of the European Arctic. The Arctic branches of the West Spitsbergen Current (WSC), i.e. the Svalbard Branch (SB), the Yermak Pass Branch (YPB) and the Yermak Branch (YB), are the primary routes through which warm AW enters the Arctic Ocean (AO). These branches either flow around (YB) or passes (SB, YPB) over the Yermak Plateau, the Arctic Sill, which is a topographic obstacle for warm water intrusion to the Arctic and possible melting of sea ice. In addition, The Spitsbergen Polar Current (SPC), carrying fresh costal and Arctic type water from the Barents Sea has to cross the Yermak Platea along the northwestern corner of the Spitsbergen coastline. In order to reveal the dynamics across the YP and the roles of the different AW branches in heat flux variability across this arctic sill, a set of in situ ocean data, ocean climatology (UNIS HD), reanalyzed atmospheric data (NORA10) and altimetry data products from Ssalto/Duacs (CMEMS), where synthesized in order to study the seasonal and year-to-year variability in ocean currents across the YP. In situ data from the Remote Sensing of Ocean Circulation and Environmental Mass Changes (REOCIRC) project consist of water time series of temperature, salinity, ocean current and Ocean Bottom Pressure (OBP), which covered the SB and the SPC. Air-ocean interaction mechanisms for controlling volume transport and heat fluxes in the SB and SPC are presented, and further linked to the variability of the other primary AW routes towards the AO. Moreover, surface geostrophic currents from Absolute Dynamic Topography (ADT) are calibrated against the geostrophic bottom current calculated from in situ OBP recorders. Estimates of winter volume- and heat transports across the YP for the time period 1993-2019 are presented, and interannual variability in the SB linked to the WSC and other AW branches are discussed together with consequences for sea ice melting north of Svalbard.

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A Bayesian Approach for Inferring Sea Ice Loads

Journal of Applied Mechanics ◽

10.1115/1.4050281 ◽

2021 ◽

pp. 1-13

Author(s):

Matthew Parno ◽

Taylor Hodgdon ◽

Brendan West ◽

Devin O'Connor ◽

Arnold Song

Keyword(s):

Sea Ice ◽

Element Model ◽

The Arctic ◽

Internal Stresses ◽

Sea Ice Extent ◽

Measurement Framework ◽

In Situ Sensors ◽

Ice Loads ◽

Ice Pack

Abstract The Earth's climate is rapidly changing and some of the most drastic changes can be seen in the Arctic, where sea ice extent has diminished considerably in recent years. As the Arctic climate continues to change, gathering in situ sea ice measurements is increasingly important for understanding the complex evolution of the Arctic ice pack. To date, observations of ice stresses in the Arctic have been spatially and temporally sparse. We propose a measurement framework that would instrument existing sea ice buoys with strain gauges. This measurement framework uses a Bayesian inference approach to infer ice loads acting on the buoy from a set of strain gauge measurements. To test our framework, strain measurements were collected from an experiment where a buoy was frozen into ice that was subsequently compressed to simulate convergent sea ice conditions. A linear elastic finite element model was used to describe the response of the deformable buoy to mechanical loading, allowing us to link the observed strain on the buoy interior to the applied load on the buoy exterior. The approach presented in this paper presents an instrumentation framework that could use existing buoy platforms as in situ sensors of internal stresses in the ice pack.

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Sea Ice Concentration and Sea Ice Extent Mapping with L-Band Microwave Radiometry and GNSS-R Data from the FFSCat Mission Using Neural Networks

Remote Sensing ◽

10.3390/rs13061139 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1139

Author(s):

David Llaveria ◽

Juan Francesc Munoz-Martin ◽

Christoph Herbert ◽

Miriam Pablos ◽

Hyuk Park ◽

...

Keyword(s):

Sea Ice ◽

Microwave Radiometer ◽

Cost Effective ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Neural Network Approach ◽

Ice Concentration ◽

L Band ◽

Moderate Cost

CubeSat-based Earth Observation missions have emerged in recent times, achieving scientifically valuable data at a moderate cost. FSSCat is a two 6U CubeSats mission, winner of the ESA S3 challenge and overall winner of the 2017 Copernicus Masters Competition, that was launched in September 2020. The first satellite, 3Cat-5/A, carries the FMPL-2 instrument, an L-band microwave radiometer and a GNSS-Reflectometer. This work presents a neural network approach for retrieving sea ice concentration and sea ice extent maps on the Arctic and the Antarctic oceans using FMPL-2 data. The results from the first months of operations are presented and analyzed, and the quality of the retrieved maps is assessed by comparing them with other existing sea ice concentration maps. As compared to OSI SAF products, the overall accuracy for the sea ice extent maps is greater than 97% using MWR data, and up to 99% when using combined GNSS-R and MWR data. In the case of Sea ice concentration, the absolute errors are lower than 5%, with MWR and lower than 3% combining it with the GNSS-R. The total extent area computed using this methodology is close, with 2.5% difference, to those computed by other well consolidated algorithms, such as OSI SAF or NSIDC. The approach presented for estimating sea ice extent and concentration maps is a cost-effective alternative, and using a constellation of CubeSats, it can be further improved.

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