Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.

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Sea ice and atmosphere interactions and predictability: preliminary results using HadGEM3

10.5194/egusphere-egu2020-13771 ◽

2020 ◽

Author(s):

Daniela Flocco ◽

Ed Hawkins ◽

Leandro Ponsoni ◽

Francois Massonnet ◽

Daniel Feltham ◽

...

Keyword(s):

Sea Ice ◽

Initial Conditions ◽

Complete Information ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Extent ◽

Sea Level Pressure ◽

Sea Ice Thickness ◽

Climate Forecasts ◽

Ice Conditions

Arctic sea ice extent has steadily declined in the past 30 years. Aside from the global impact on climate change, regional information on the sea ice presence and on its impact on oceanic and atmospheric patterns has witnessed a growing interest. There is a growing need for seasonal-to-decadal timescale climate forecasts to help inform local communities and industry stakeholders.Here we examine the influence of sea-ice thickness observations on the predictability of the sea-ice and atmospheric circulation. We perform paired sets of ensembles with the HadGEM3 GCM starting from different initial conditions in a present-day control run. One set of ensembles start with complete information about the sea-ice conditions, and one set have degraded information. We investigate how the pairs of ensembles predict the subsequent evolution of the sea-ice, sea level pressure and circulation within the Arctic and beyond with the aim of quantifying the value of sea-ice observations for improving predictions.

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Presentation and evaluation of the Arctic sea ice forecasting system neXtSIM-F

The Cryosphere ◽

10.5194/tc-15-3207-2021 ◽

2021 ◽

Vol 15 (7) ◽

pp. 3207-3227

Author(s):

Timothy Williams ◽

Anton Korosov ◽

Pierre Rampal ◽

Einar Ólason

Keyword(s):

Sea Ice ◽

Initial Conditions ◽

Thickness Distribution ◽

Arctic Sea Ice ◽

The Arctic ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Ice Concentration ◽

Forecasting System ◽

Ice Model

Abstract. The neXtSIM-F (neXtSIM forecast) forecasting system consists of a stand-alone sea ice model, neXtSIM (neXt-generation Sea Ice Model), forced by the TOPAZ ocean forecast and the ECMWF atmospheric forecast, combined with daily data assimilation of sea ice concentration. It uses the novel brittle Bingham–Maxwell (BBM) sea ice rheology, making it the first forecast based on a continuum model not to use the viscous–plastic (VP) rheology. It was tested in the Arctic for the time period November 2018–June 2020 and was found to perform well, although there are some shortcomings. Despite drift not being assimilated in our system, the sea ice drift is good throughout the year, being relatively unbiased, even for longer lead times like 5 d. The RMSE in speed and the total RMSE are also good for the first 3 or so days, although they both increase steadily with lead time. The thickness distribution is relatively good, although there are some regions that experience excessive thickening with negative implications for the summertime sea ice extent, particularly in the Greenland Sea. The neXtSIM-F forecasting system assimilates OSI SAF sea ice concentration products (both SSMIS and AMSR2) by modifying the initial conditions daily and adding a compensating heat flux to prevent removed ice growing back too quickly. The assimilation greatly improves the sea ice extent for the forecast duration.

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Can we extend local sea-ice measurements to satellite scale? An example from the N-ICE2015 expedition

Annals of Glaciology ◽

10.1017/aog.2017.37 ◽

2017 ◽

Vol 59 (76pt2) ◽

pp. 163-172 ◽

Cited By ~ 1

Author(s):

Anja Rösel ◽

Jennifer King ◽

Anthony P. Doulgeris ◽

Penelope M. Wagner ◽

A. Malin Johansson ◽

...

Keyword(s):

Sea Ice ◽

Satellite Data ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Remotely Sensed Data ◽

Sea Ice Thickness ◽

In Situ Data ◽

Ice Conditions ◽

Area North

ABSTRACTKnowledge of Arctic sea-ice conditions is of great interest for Arctic residents, as well as for commercial usage, and to study the effects of climate change. Information gained from analysis of satellite data contributes to this understanding. In the course of using in situ data in combination with remotely sensed data, the question of how representative local scale measurements are of a wider region may arise. We compare in situ total sea-ice thickness measurements from the Norwegian young sea ICE expedition in the area north of Svalbard with airborne-derived total sea-ice thickness from electromagnetic soundings. A segmented and classified synthetic aperture radar (SAR) quad-pol ALOS-2 Palsar-2 satellite scene was grouped into three simplified ice classes. The area fractions of the three classes are: 11.2% ‘thin’, 74.4% ‘level’, and 14.4% ‘deformed’. The area fractions of the simplified classes from ground- and helicopter-based measurements are comparable with those achieved from the SAR data. Thus, this study shows that there is potential for a stepwise upscaling from in situ, to airborne, to satellite data, which allow us to assess whether in situ data collected are representative of a wider region as observed by satellites.

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Impact of sea-ice thickness initialized in April on Arctic sea-ice extent predictability with the MIROC climate model

Annals of Glaciology ◽

10.1017/aog.2020.13 ◽

2020 ◽

Vol 61 (82) ◽

pp. 97-105

Author(s):

Jun Ono ◽

Yoshiki Komuro ◽

Hiroaki Tatebe

Keyword(s):

Sea Ice ◽

Climate Model ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Predictive Skill ◽

The Pacific ◽

The Impact

AbstractThe impact of April sea-ice thickness (SIT) initialization on the predictability of September sea-ice extent (SIE) is investigated based on a series of perfect model ensemble experiments using the MIROC5.2 climate model. Ensembles with April SIT initialization can accurately predict the September SIE for greater lead times than in cases without the initialization – up to 2 years ahead. The persistence of SIT correctly initialized in April contributes to the skilful prediction of SIE in the first September. On the other hand, errors in the initialization of SIT in April cause errors in the predicted sea-ice concentration and thickness in the Pacific sector from July to September and consequently influence the predictive skill with respect to SIE in September. The present study suggests that initialization of the April SIT in the Pacific sector significantly improves the accuracy of the September SIE forecasts by decreasing the errors in sea-ice fields from July to September.

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Impacts of Assimilating Satellite Sea Ice Concentration and Thickness on Arctic Sea Ice Prediction in the NCEP Climate Forecast System

Journal of Climate ◽

10.1175/jcli-d-17-0093.1 ◽

2017 ◽

Vol 30 (21) ◽

pp. 8429-8446 ◽

Cited By ~ 16

Author(s):

Zhiqiang Chen ◽

Jiping Liu ◽

Mirong Song ◽

Qinghua Yang ◽

Shiming Xu

Keyword(s):

Sea Ice ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Forecast System ◽

Climate Forecast System ◽

Sea Ice Concentration ◽

Sea Ice Extent ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Ice Concentration

Here sea ice concentration derived from the Special Sensor Microwave Imager/Sounder and thickness derived from the Soil Moisture and Ocean Salinity and CryoSat-2 satellites are assimilated in the National Centers for Environmental Prediction Climate Forecast System using a localized error subspace transform ensemble Kalman filter (LESTKF). Three ensemble-based hindcasts are conducted to examine impacts of the assimilation on Arctic sea ice prediction, including CTL (without any assimilation), LESTKF-1 (with initial sea ice assimilation only), and LESTKF-E5 (with every 5-day sea ice assimilation). Assessment with the assimilated satellite products and independent sea ice thickness datasets shows that assimilating sea ice concentration and thickness leads to improved Arctic sea ice prediction. LESTKF-1 improves sea ice forecast initially. The initial improvement gradually diminishes after ~3-week integration for sea ice extent but remains quite steady through the integration for sea ice thickness. Large biases in both the ice extent and thickness in CTL are remarkably reduced through the hindcast in LESTKF-E5. Additional numerical experiments suggest that the hindcast with sea ice thickness assimilation dramatically reduces systematic bias in the predicted ice thickness compared with sea ice concentration assimilation only or without any assimilation, which also benefits the prediction of sea ice extent and concentration due to their covariability. Hence, the corrected state of sea ice thickness would aid in the forecast procedure. Increasing the number of ensemble members or extending the integration period to generate estimates of initial model states and uncertainties seems to have small impacts on sea ice prediction relative to LESTKF-E5.

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Arctic Sea Ice Extent May Shrink Below 2012 Record Low

Eos ◽

10.1029/2016eo051183 ◽

2016 ◽

Vol 97 ◽

Cited By ~ 1

Author(s):

Megan Gannon

Keyword(s):

Sea Ice ◽

Satellite Data ◽

Field Measurements ◽

Arctic Sea Ice ◽

Ice Thickness ◽

Sea Ice Extent ◽

Arctic Sea ◽

Data Field ◽

Arctic Sea Ice Extent

Satellite data, field measurements, and readings from "snow buoys" reveal ice thickness patterns similar to those preceding the lowest recorded sea ice extent, which was reached nearly 4 years ago.

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Arctic sea ice extent and thickness

Philosophical Transactions of the Royal Society of London Series A Physical and Engineering Sciences ◽

10.1098/rsta.1995.0072 ◽

1995 ◽

Vol 352 (1699) ◽

pp. 301-319 ◽

Cited By ~ 17

Keyword(s):

Sea Ice ◽

Open Water ◽

Arctic Sea Ice ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Extent ◽

Arctic Sea ◽

Ice Conditions ◽

Fast Ice ◽

Arctic Sea Ice Extent

Current knowledge on Arctic sea ice extent and thickness variability is reviewed, and we examine whether measurements to date provide evidence for the impact of climate change. The total Arctic ice extent has shown a small but significant reduction of (2.1 ± 0.9)% during the period 1978-87, after apparently increasing from a lower level in the early 1970s. However, open water within the pack ice limit has also diminished, so that the reduction of sea ice area is only (1.8 ± 1.2)%. This stability conceals large interannual variations and trends in individual regions of the Arctic Ocean and sub-Arctic seas, which are out of phase with one another and so have little net impact on the overall hemispheric ice extent. The maximum annual global extent (occurring during the Antarctic winter) shows a more significant decrease of 5% during 1972-87. Ice thickness distribution has been measured by submarine sonar profiling, moored upward sonars, airborne laser prohlometry, airborne electromagnetic techniques and drilling. Promising new techniques include: sonar mounted on an AUV or neutrally buoyant float; acoustic tomography or thermometry; and inference from a combination of microwave sensors. In relation to climate change, the most useful measurement has been repeated submarine sonar profiling under identical parts of the Arctic, which offers some evidence of a decline in mean ice thickness in the 1980s compared to the 1970s. The link between mean ice thickness and climatic warming is complex because of the effects of dynamics and deformation. Only fast ice responds primarily to air temperature changes and one can predict thinning of fast ice and extension of the open water season in fast ice areas. Another region of increasingly mild ice conditions is the central Greenland Sea where winter thermohaline convection is triggered by cyclic growth and melt of local young ice. In recent years convection to the bottom has slowed or ceased, possibly related to moderation of ice conditions.

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Sea ice and atmospheric potential predictability in coupled GCMs

10.5194/egusphere-egu21-2590 ◽

2021 ◽

Author(s):

Daniela Flocco ◽

Ed Hawkins ◽

Leandro Ponsoni ◽

François Massonnett ◽

Daniel Feltham ◽

...

Keyword(s):

Sea Ice ◽

Initial Conditions ◽

Complete Information ◽

Arctic Sea Ice ◽

Prediction Skill ◽

The Arctic ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Potential Predictability ◽

Sea Ice Thickness

Assimilation of sea ice concentration satellite products has successfully been used to initialize sea ice models and coupled NWP systems. Sea-ice thickness observations, being much less mature, are typically not assimilated. However, many studies suggest that initialization of winter sea-ice thickness could lead to improved prediction of Arctic summer sea ice. We have examined the potential for sea ice thickness observations to improve forecast skill on timescales from days to a year ahead in two state-of-the-art coupled GCMs.Here we examine the influence of Arctic sea-ice thickness observations on the potential predictability of the sea-ice and atmospheric circulation using idealised &#8216;data denial&#8217; experiments. We perform paired sets of ensembles with the HadGEM3 and EC-Earth GCMs using different initial conditions retrieved from present-day control runs.One set of ensembles start with complete information about the sea-ice conditions and is treated as &#8220;truth&#8221;, and one set has degraded sea ice information. We investigate how the pairs of ensembles, all started in January, predict the subsequent evolution of the sea-ice state, sea level pressure and circulation within the Arctic with the aim of quantifying the value of sea-ice observations for improving predictions.We show that accurate initialization of sea ice thickness improves the model prediction skill during the first month of simulation and that several sea ice state and atmospheric variables present a re-emergence of skill in September. Prediction skill of several oceanic variables is also observed. The two models present a good agreement in terms of the regions where they show either a skill gain or loss.

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A daily to seasonal Arctic sea ice forecasting AI

10.5194/egusphere-egu21-15981 ◽

2021 ◽

Author(s):

Tom R. Andersson ◽

J. Scott Hosking ◽

Eleanor Krige ◽

Maria Pérez-Ortiz ◽

Brooks Paige ◽

...

Keyword(s):

Sea Ice ◽

Initial Conditions ◽

Arctic Sea Ice ◽

Dynamical Model ◽

The Arctic ◽

Climate Simulation ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Arctic Sea

Arctic sea ice forecasting is a major scientific effort with fundamental challenges at play. To address such challenges, we have developed a physics-informed, data-driven sea ice forecasting system, IceNet, which outperformed a leading dynamical model (ECMWF SEAS5) in monthly-averaged forecasts of pan-Arctic sea ice concentration. IceNet adopted a U-Net deep learning architecture and was trained on over 2,000 years of CMIP6 climate simulation data. Despite its state-of-the-art seasonal forecasting skill at lead times of 2-6 months, IceNet has two main limitations. First, it could not outperform the dynamical model in short-range (1-month) forecasts. This is partly caused by IceNet operating on monthly-averages, which smears the initial conditions and weather phenomena that can dominate predictability at short time scales. Second, IceNet is afflicted by the &#8216;spring predictability barrier&#8217; that affects all long range forecasts of summer. This predictability barrier arises primarily due to the importance of melt-season ice thickness conditions on summer sea ice. Here we present our early findings from IceNet2, which attempts to alleviate these issues by operating on daily-averages and including sea ice thickness as an input variable. IceNet2 paves the way for our efforts to aid the Arctic conservation community by developing the first public, operational sea ice forecasting AI.

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A data assimilation application for improving estimates of Arctic sea ice thickness variability and change since the turn of the 21st century

10.5194/egusphere-egu21-8952 ◽

2021 ◽

Author(s):

Molly Wieringa ◽

Cecilia Bitz

Keyword(s):

Data Assimilation ◽

Sea Ice ◽

Initial Conditions ◽

Arctic Sea Ice ◽

Prognostic Models ◽

Ice Thickness ◽

Sea Ice Concentration ◽

Sea Ice Thickness ◽

Arctic Sea ◽

Arctic Sea Ice Thickness

Current sea ice prediction systems exhibit significant room for improvement compared to idealized estimates of sea ice predictability, a gap that could be closed by improving the initial conditions provided to prognostic models. Sea ice volume, the area-weighted integral of sea ice thickness (SIT), in particular, demonstrates long initial value predictability; in other words, accurate forecasting of Arctic sea ice requires highly accurate SIT initial conditions. Continuous records of SIT are, unfortunately, few and far between. To address this conundrum, we have explored applications of the Data Assimilation Research Testbed (DART) to constrain the Los Alamos Sea Ice Model (CICE5) within the Community Earth System Model using satellite-derived SIT observations from 2003 to present day. Our data assimilation system has been fine-tuned using new and highly accurate freeboard measurements from NASA&#8217;s ICESat-2 mission. Using SIT information alone, we generate two assimilation products: the first using DART with CICE5 and the second with an offline assimilation method. We compare these products to one another and to the community standard SIT record, PIOMAS. Future work will introduce multivariate assimilation of SIT with other sea ice variables, including sea ice concentration, sea ice skin temperature, and sea surface temperature.

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