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

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.

scholarly journals New insight from CryoSat-2 sea ice thickness for sea ice modelling

2018 ◽  
Author(s):  
David Schröder ◽  
Danny L. Feltham ◽  
Michel Tsamados ◽  
Andy Ridout ◽  
Rachel Tilling
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Atmospheric Conditions ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Ice Growth ◽  
Melt Pond ◽  
The Impact

Abstract. Estimates of Arctic sea ice thickness are available from the CryoSat-2 (CS2) radar altimetry mission during ice growth seasons since 2010. We derive the sub-grid scale ice thickness distribution (ITD) with respect to 5 ice thickness categories used in a sea ice component (CICE) of climate simulations. This allows us to initialize the ITD in stand-alone simulations with CICE and to verify the simulated cycle of ice thickness. We find that a default CICE simulation strongly underestimates ice thickness, despite reproducing the inter-annual variability of summer sea ice extent. We can identify the underestimation of winter ice growth as being responsible and show that increasing the ice conductive flux for lower temperatures (bubbly brine scheme) and accounting for the loss of drifting snow results in the simulated sea ice growth being more realistic. Sensitivity studies provide insight into the impact of initial and atmospheric conditions and, thus, on the role of positive and negative feedback processes. During summer, atmospheric conditions are responsible for 50 % of September sea ice thickness variability through the positive sea ice and melt pond albedo feedback. However, atmospheric winter conditions have little impact on winter ice growth due to the dominating negative conductive feedback process: the thinner the ice and snow in autumn, the stronger the ice growth in winter. We conclude that the fate of Arctic summer sea ice is largely controlled by atmospheric conditions during the melting season rather than by winter temperature. Our optimal model configuration does not only improve the simulated sea ice thickness, but also summer sea ice concentration, melt pond fraction, and length of the melt season. It is the first time CS2 sea ice thickness data have been applied successfully to improve sea ice model physics.

scholarly journals Better constraints on the sea-ice state using global sea-ice data assimilation

2012 ◽  
Vol 5 (2) ◽  
pp. 1627-1667 ◽  
Author(s):  
P. Mathiot ◽  
C. König Beatty ◽  
T. Fichefet ◽  
H. Goosse ◽  
F. Massonnet ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ice Thickness ◽  
Initial State ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
The Impact

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice-ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an Ensemble Kalman Filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged sea ice) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the assimilation of synthetic data (i.e., model-generated data) and, secondly, through the assimilation of satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

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

2017 ◽  
Vol 30 (21) ◽  
pp. 8429-8446 ◽  
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.

scholarly journals Improving Met Office seasonal predictions of Arctic sea ice using assimilation of CryoSat-2 thickness

2018 ◽  
Vol 12 (11) ◽  
pp. 3419-3438 ◽  
Author(s):  
Edward W. Blockley ◽  
K. Andrew Peterson
Keyword(s):  
Sea Ice ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Forecast Errors ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Near Surface ◽  
Arctic Sea ◽  
The Impact

Abstract. Interest in seasonal predictions of Arctic sea ice has been increasing in recent years owing, primarily, to the sharp reduction in Arctic sea-ice cover observed over the last few decades, a decline that is projected to continue. The prospect of increased human industrial activity in the region, as well as scientific interest in the predictability of sea ice, provides important motivation for understanding, and improving, the skill of Arctic predictions. Several operational forecasting centres now routinely produce seasonal predictions of sea-ice cover using coupled atmosphere–ocean–sea-ice models. Although assimilation of sea-ice concentration into these systems is commonplace, 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. Here, for the first time, we directly assess the impact of winter sea-ice thickness initialization on the skill of summer seasonal predictions by assimilating CryoSat-2 thickness data into the Met Office's coupled seasonal prediction system (GloSea). We show a significant improvement in predictive skill of Arctic sea-ice extent and ice-edge location for forecasts of September Arctic sea ice made from the beginning of the melt season. The improvements in sea-ice cover lead to further improvement of near-surface air temperature and pressure fields across the region. A clear relationship between modelled winter thickness biases and summer extent errors is identified which supports the theory that Arctic winter thickness provides some predictive capability for summer ice extent, and further highlights the importance that modelled winter thickness biases can have on the evolution of forecast errors through the melt season.

scholarly journals Analyzing the impact of CryoSat-2 ice thickness initialization on seasonal Arctic Sea Ice prediction

2020 ◽  
Vol 61 (82) ◽  
pp. 78-85
Author(s):  
Richard Allard ◽  
E. Joseph Metzger ◽  
Neil Barton ◽  
Li Li ◽  
Nathan Kurtz ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Global Ocean ◽  
Ice Thickness ◽  
Ensemble Forecasts ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Forecasting System ◽  
Ocean Forecasting ◽  
The Impact

AbstractTwin 5-month seasonal forecast experiments are performed to predict the September 2018 mean and minimum ice extent using the fully coupled Navy Earth System Prediction Capability (ESPC). In the control run, ensemble forecasts are initialized from the operational US Navy Global Ocean Forecasting System (GOFS) 3.1 but do not assimilate ice thickness data. Another set of forecasts are initialized from the same GOFS 3.1 fields but with sea ice thickness derived from CryoSat-2 (CS2). The Navy ESPC ensemble mean September 2018 minimum sea ice extent initialized with GOFS 3.1 ice thickness was over-predicted by 0.68 M km2 (5.27 M km2) vs the ensemble forecasts initialized with CS2 ice thickness that had an error of 0.40 M km2 (4.99 M km2), a 43% reduction in error. The September mean integrated ice edge error shows a 18% improvement for the Pan-Arctic with the CS2 data vs the control forecasts. Comparison against upward looking sonar ice thickness in the Beaufort Sea reveals a lower bias and RMSE with the CS2 forecasts at all three moorings. Ice concentration at these locations is also improved, but neither set of forecasts show ice free conditions as observed at moorings A and D.

scholarly journals Better constraints on the sea-ice state using global sea-ice data assimilation

2012 ◽  
Vol 5 (6) ◽  
pp. 1501-1515 ◽  
Author(s):  
P. Mathiot ◽  
C. König Beatty ◽  
T. Fichefet ◽  
H. Goosse ◽  
F. Massonnet ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Ice Thickness ◽  
Initial State ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
The Impact

Abstract. Short-term and decadal sea-ice prediction systems need a realistic initial state, generally obtained using ice–ocean model simulations with data assimilation. However, only sea-ice concentration and velocity data are currently assimilated. In this work, an ensemble Kalman filter system is used to assimilate observed ice concentration and freeboard (i.e. thickness of emerged) data into a global coupled ocean–sea-ice model. The impact and effectiveness of our data assimilation system is assessed in two steps: firstly, through the use of synthetic data (i.e. model-generated data), and secondly, through the assimilation of real satellite data. While ice concentrations are available daily, freeboard data used in this study are only available during six one-month periods spread over 2005–2007. Our results show that the simulated Arctic and Antarctic sea-ice extents are improved by the assimilation of synthetic ice concentration data. Assimilation of synthetic ice freeboard data improves the simulated sea-ice thickness field. Using real ice concentration data enhances the model realism in both hemispheres. Assimilation of ice concentration data significantly improves the total hemispheric sea-ice extent all year long, especially in summer. Combining the assimilation of ice freeboard and concentration data leads to better ice thickness, but does not further improve the ice extent. Moreover, the improvements in sea-ice thickness due to the assimilation of ice freeboard remain visible well beyond the assimilation periods.

Mechanisms for and Predictability of a Drastic Reduction in the Arctic Sea Ice: APPOSITE Data with Climate Model MIROC

2019 ◽  
Vol 32 (5) ◽  
pp. 1361-1380 ◽  
Author(s):  
J. Ono ◽  
H. Tatebe ◽  
Y. Komuro
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Forecast Errors ◽  
Drastic Reduction ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Arctic Sea

Abstract The mechanisms for and predictability of a drastic reduction in the Arctic sea ice extent (SIE) are investigated using the Model for Interdisciplinary Research on Climate (MIROC) version 5.2. Here, a control (CTRL) with forcing fixed at year 2000 levels and perfect-model ensemble prediction (PRED) experiments are conducted. In CTRL, three (model years 51, 56, and 57) drastic SIE reductions occur during a 200-yr-long integration. In year 56, the sea ice moves offshore in association with a positive phase of the summer Arctic dipole anomaly (ADA) index and melts due to heat input through the increased open water area, and the SIE drastically decreases. This provides the preconditioning for the lowest SIE in year 57 when the Arctic Ocean interior is in a warm state and the spring sea ice volume has a large negative anomaly due to drastic ice reduction in the previous year. Although the ADA is one of the key mechanisms behind sea ice reduction, it does not always cause a drastic reduction. Our analysis suggests that wind direction favoring offshore ice motion is a more important factor for drastic ice reduction events. In years experiencing drastic ice reduction events, the September SIE can be skillfully predicted in PRED started from July, but not from April. This is because the forecast errors for the July sea level pressure and those for the sea ice concentration and sea ice thickness along the ice edge are large in PRED started from April.

Sea-ice freeboard or thickness? Design choices in the context of data assimilation in the coupled numerical prediction system EC-Earth3 for seasonal Arctic sea ice prediction

2021 ◽  
Author(s):  
Francois Massonnet ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Ed Blockley ◽  
Pablo Ortega Montilla ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
General Circulation ◽  
Seasonal Prediction ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration

<p>It is well established that winter and spring Arctic sea-ice thickness anomalies are a key source of predictability for late summer sea-ice concentration. While numerical general circulation models (GCMs) are increasingly used to perform seasonal predictions, they are not systematically taking advantage of the wealth of polar observations available. Data assimilation, the study of how to constrain GCMs to produce a physically consistent state given observations and their uncertainties, remains, therefore, an active area of research in the field of seasonal prediction. With the recent advent of satellite laser and radar altimetry, large-scale estimates of sea-ice thickness have become available for data assimilation in GCMs. However, the sea-ice thickness is never directly observed by altimeters, but rather deduced from the measured sea-ice freeboard (the height of the emerged part of the sea ice floe) based on several assumptions like the depth of snow on sea ice and its density, which are both often poorly estimated. Thus, observed sea-ice thickness estimates are potentially less reliable than sea-ice freeboard estimates. Here, using the EC-Earth3 coupled forecasting system and an ensemble Kalman filter, we perform a set of sensitivity tests to answer the following questions: (1) Does the assimilation of late spring observed sea-ice freeboard or thickness information yield more skilful predictions than no assimilation at all? (2) Should the sea-ice freeboard assimilation be preferred over sea-ice thickness assimilation? (3) Does the assimilation of observed sea-ice concentration provide further constraints on the prediction? We address these questions in the context of a realistic test case, the prediction of 2012 summer conditions, which led to the all-time record low in Arctic sea-ice extent. We finally formulate a set of recommendations for practitioners and future users of sea ice observations in the context of seasonal prediction.</p>

scholarly journals On the timescales and length scales of the Arctic sea ice thickness anomalies: a study based on 14 reanalyses

2019 ◽  
Vol 13 (2) ◽  
pp. 521-543 ◽  
Author(s):  
Leandro Ponsoni ◽  
François Massonnet ◽  
Thierry Fichefet ◽  
Matthieu Chevallier ◽  
David Docquier
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Ice Thickness ◽  
Sea Ice Concentration ◽  
Length Scales ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Arctic Sea Ice Thickness

Abstract. The ocean–sea ice reanalyses are one of the main sources of Arctic sea ice thickness data both in terms of spatial and temporal resolution, since observations are still sparse in time and space. In this work, we first aim at comparing how the sea ice thickness from an ensemble of 14 reanalyses compares with different sources of observations, such as moored upward-looking sonars, submarines, airbornes, satellites, and ice boreholes. Second, based on the same reanalyses, we intend to characterize the timescales (persistence) and length scales of sea ice thickness anomalies. We investigate whether data assimilation of sea ice concentration by the reanalyses impacts the realism of sea ice thickness as well as its respective timescales and length scales. The results suggest that reanalyses with sea ice data assimilation do not necessarily perform better in terms of sea ice thickness compared with the reanalyses which do not assimilate sea ice concentration. However, data assimilation has a clear impact on the timescales and length scales: reanalyses built with sea ice data assimilation present shorter timescales and length scales. The mean timescales and length scales for reanalyses with data assimilation vary from 2.5 to 5.0 months and 337.0 to 732.5 km, respectively, while reanalyses with no data assimilation are characterized by values from 4.9 to 7.8 months and 846.7 to 935.7 km, respectively.

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