scholarly journals SITool (v1.0) – a new evaluation tool for large-scale sea ice simulations: application to CMIP6 OMIP

2021 ◽  
Vol 14 (10) ◽  
pp. 6331-6354
Author(s):  
Xia Lin ◽  
François Massonnet ◽  
Thierry Fichefet ◽  
Martin Vancoppenolle
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Model Performance ◽  
Evaluation Tool ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Ice Concentration ◽  
Edge Location ◽  
Intercomparison Project ◽  
The Mean

Abstract. The Sea Ice Evaluation Tool (SITool) described in this paper is a performance metrics and diagnostics tool developed to evaluate the skill of Arctic and Antarctic model reconstructions of sea ice concentration, extent, edge location, drift, thickness, and snow depth. It is a Python-based software and consists of well-documented functions used to derive various sea ice metrics and diagnostics. Here, SITool version 1.0 (v1.0) is introduced and documented, and is then used to evaluate the performance of global sea ice reconstructions from nine models that provided sea ice output under the experimental protocols of the Coupled Model Intercomparison Project phase 6 (CMIP6) Ocean Model Intercomparison Project with two different atmospheric forcing datasets: the Coordinated Ocean-ice Reference Experiments version 2 (CORE-II) and the updated Japanese 55-year atmospheric reanalysis (JRA55-do). Two sets of observational references for the sea ice concentration, thickness, snow depth, and ice drift are systematically used to reflect the impact of observational uncertainty on model performance. Based on available model outputs and observational references, the ice concentration, extent, and edge location during 1980–2007, as well as the ice thickness, snow depth, and ice drift during 2003–2007 are evaluated. In general, model biases are larger than observational uncertainties, and model performance is primarily consistent compared to different observational references. By changing the atmospheric forcing from CORE-II to JRA55-do reanalysis data, the overall performance (mean state, interannual variability, and trend) of the simulated sea ice areal properties in both hemispheres, as well as the mean ice thickness simulation in the Antarctic, the mean snow depth, and ice drift simulations in both hemispheres are improved. The simulated sea ice areal properties are also improved in the model with higher spatial resolution. For the cross-metric analysis, there is no link between the performance in one variable and the performance in another. SITool is an open-access version-controlled software that can run on a wide range of CMIP6-compliant sea ice outputs. The current version of SITool (v1.0) is primarily developed to evaluate atmosphere-forced simulations and it could be eventually extended to fully coupled models.

scholarly journals SITool (v1.0) – a new evaluation tool for large-scale sea ice simulations: application to CMIP6 OMIP

2021 ◽  
Author(s):  
Xia Lin ◽  
François Massonnet ◽  
Thierry Fichefet ◽  
Martin Vancoppenolle
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
Ice Thickness ◽  
Evaluation Tool ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Ice Concentration ◽  
Edge Location ◽  
Intercomparison Project ◽  
The Mean

Abstract. The Sea Ice Evaluation Tool (SITool) described in this paper is a performance metrics and diagnostics tool developed to evaluate the skill of bi-polar model reconstructions of sea ice concentration, extent, edge location, drift, thickness, and snow depth. It is a Python-based software and consists of well-documented functions used to derive various sea ice metrics and diagnostics. Here, the SITool version 1.0 (v1.0) is introduced and documented, and is then used to evaluate the performance of global sea ice reconstructions from nine models that provided sea ice output under the experimental protocols of the Coupled Model Intercomparison Project 6 (CMIP6) Ocean Model Intercomparison Project with two different atmospheric forcing datasets: the Coordinated Ocean-ice Reference Experiments version 2 (CORE-II) and the updated Japanese 55-year atmospheric reanalysis (JRA55-do). Two sets of observational references for sea ice concentration, thickness, snow depth, and ice drift are systematically used to reflect the impact of observational uncertainty on model performance. Based on available model outputs and observational references, the ice concentration, extent, and edge location during 1980–2007, as well as the ice thickness, snow depth, and ice drift during 2003–2007 are evaluated. It is found that (1) in general, model biases are larger than observational uncertainties and model performances are primarily consistent compared to different observational references, (2) By changing the atmospheric forcing from CORE-II to JRA55-do reanalysis data, the overall performance (mean state, interannual variability and trend) of the simulated sea ice areal properties in both hemispheres, as well as the mean ice thickness simulation in the Antarctic, the mean snow depth and ice drift simulations in both hemispheres are improved, (3) the simulated sea ice areal properties are also improved in the model with increased spatial resolution, (4) for the cross-metric analysis, there is no link between the performance in one variable and the performance in another. The SITool is an open-access version-controlled software that can run on a wide range of CMIP6 compliant sea ice outputs. The current version of SITool (v1.0) is primarily developed to evaluate atmosphere-forced simulations and it could be eventually extended to fully coupled models.

scholarly journals Improving Multiyear Sea Ice Concentration Estimates with Sea Ice Drift

2016 ◽  
Vol 8 (5) ◽  
pp. 397 ◽  
Author(s):  
Yufang Ye ◽  
Mohammed Shokr ◽  
Georg Heygster ◽  
Gunnar Spreen
Keyword(s):  
Sea Ice ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Ice Concentration ◽  
Sea Ice Drift

scholarly journals Exponentially decaying modes and long-term prediction of sea ice concentration using Koopman mode decomposition

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
James Hogg ◽  
Maria Fonoberova ◽  
Igor Mezić
Keyword(s):  
Sea Ice ◽  
Annual Variation ◽  
Temporal Dynamics ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Mode Decomposition ◽  
Spatial Modes ◽  
Ice Concentration ◽  
The Mean ◽  
Insight Into

Abstract Sea ice cover in the Arctic and Antarctic is an important indicator of changes in the climate, with important environmental, economic and security consequences. The complexity of the spatio-temporal dynamics of sea ice makes it difficult to assess the temporal nature of the changes—e.g. linear or exponential—and their precise geographical loci. In this study, Koopman Mode Decomposition (KMD) is applied to satellite data of sea ice concentration for the Northern and Southern hemispheres to gain insight into the temporal and spatial dynamics of the sea ice behavior and to predict future sea ice behavior. We observe spatial modes corresponding to the mean and annual variation of Arctic and Antarctic sea ice concentration and observe decreases in the mean sea ice concentration from early to later periods, as well as corresponding shifts in the locations that undergo significant annual variation in sea ice concentration. We discover exponentially decaying spatial modes in both hemispheres and discuss their precise spatial extent, and also perform predictions of future sea ice concentration. The Koopman operator-based, data-driven decomposition technique gives insight into spatial and temporal dynamics of sea ice concentration not apparent in traditional approaches.

scholarly journals Simulated impacts of relative climate change and river discharge regulation on sea ice and oceanographic conditions in the Hudson Bay Complex

Elem Sci Anth ◽  
2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Jennifer V. Lukovich ◽  
Shabnam Jafarikhasragh ◽  
Paul G. Myers ◽  
Natasha A. Ridenour ◽  
Laura Castro de la Guardia ◽  
...  
Keyword(s):  
Climate Change ◽  
Sea Ice ◽  
River Discharge ◽  
Hudson Bay ◽  
Sea Ice Concentration ◽  
Changing Climate ◽  
Ice Drift ◽  
Ice Concentration ◽  
Sea Ice Drift ◽  
Discharge Regulation

In this analysis, we examine relative contributions from climate change and river discharge regulation to changes in marine conditions in the Hudson Bay Complex using a subset of five atmospheric forcing scenarios from the Coupled Model Intercomparison Project Phase 5 (CMIP5), river discharge data from the Hydrological Predictions for the Environment (HYPE) model, both naturalized (without anthropogenic intervention) and regulated (anthropogenically controlled through diversions, dams, reservoirs), and output from the Nucleus for European Modeling of the Ocean Ice-Ocean model for the 1981–2070 time frame. Investigated in particular are spatiotemporal changes in sea surface temperature, sea ice concentration and thickness, and zonal and meridional sea ice drift in response to (i) climate change through comparison of historical (1981–2010) and future (2021–2050 and 2041–2070) simulations, (ii) regulation through comparison of historical (1981–2010) naturalized and regulated simulations, and (iii) climate change and regulation combined through comparison of future (2021–2050 and 2041–2070) naturalized and regulated simulations. Also investigated is use of the diagnostic known as e-folding time spatial distribution to monitor changes in persistence in these variables in response to changing climate and regulation impacts in the Hudson Bay Complex. Results from this analysis highlight bay-wide and regional reductions in sea ice concentration and thickness in southwest and northeast Hudson Bay in response to a changing climate, and east-west asymmetry in sea ice drift response in support of past studies. Regulation is also shown to amplify or suppress the climate change signal. Specifically, regulation amplifies sea surface temperatures from April to August, suppresses sea ice loss by approximately 30% in March, contributes to enhanced sea ice drift speed by approximately 30%, and reduces meridional circulation by approximately 20% in January due to enhanced zonal drift. Results further suggest that the offshore impacts of regulation are amplified in a changing climate.

scholarly journals Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems

2015 ◽  
Vol 9 (4) ◽  
pp. 1735-1745 ◽  
Author(s):  
P. G. Posey ◽  
E. J. Metzger ◽  
A. J. Wallcraft ◽  
D. A. Hebert ◽  
R. A. Allard ◽  
...  
Keyword(s):  
Sea Ice ◽  
Horizontal Resolution ◽  
Forecast System ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
The Us ◽  
Ice Concentration ◽  
Edge Location ◽  
High Horizontal Resolution ◽  
Ice Edge

Abstract. This study presents the improvement in ice edge error within the US Navy's operational sea ice forecast systems gained by assimilating high horizontal resolution satellite-derived ice concentration products. Since the late 1980's, the ice forecast systems have assimilated near real-time sea ice concentration derived from the Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSMI and then SSMIS). The resolution of the satellite-derived product was approximately the same as the previous operational ice forecast system (25 km). As the sea ice forecast model resolution increased over time, the need for higher horizontal resolution observational data grew. In 2013, a new Navy sea ice forecast system (Arctic Cap Nowcast/Forecast System – ACNFS) went into operations with a horizontal resolution of ~ 3.5 km at the North Pole. A method of blending ice concentration observations from the Advanced Microwave Scanning Radiometer (AMSR2) along with a sea ice mask produced by the National Ice Center (NIC) has been developed, resulting in an ice concentration product with very high spatial resolution. In this study, ACNFS was initialized with this newly developed high resolution blended ice concentration product. The daily ice edge locations from model hindcast simulations were compared against independent observed ice edge locations. ACNFS initialized using the high resolution blended ice concentration data product decreased predicted ice edge location error compared to the operational system that only assimilated SSMIS data. A second evaluation assimilating the new blended sea ice concentration product into the pre-operational Navy Global Ocean Forecast System 3.1 also showed a substantial improvement in ice edge location over a system using the SSMIS sea ice concentration product alone. This paper describes the technique used to create the blended sea ice concentration product and the significant improvements in ice edge forecasting in both of the Navy's sea ice forecasting systems.

scholarly journals Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean–sea ice modelling system

2019 ◽  
Vol 13 (2) ◽  
pp. 491-509 ◽  
Author(s):  
Sindre Fritzner ◽  
Rune Graversen ◽  
Kai H. Christensen ◽  
Philip Rostosky ◽  
Keguang Wang
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
The Other ◽  
Ice Thickness ◽  
Initial State ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
Independent Observations

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining the best-possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, which include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean–sea-ice model is used to assess the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application Facility, thin sea ice from the European Space Agency's (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice from ESA's CryoSat-2 satellite, and a new snow-depth product derived from the National Space Agency's Advanced Microwave Scanning Radiometer (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a smaller but still positive short-term effect on snow depth and sea-ice concentration. In our study, the seasonal forecast showed that assimilating snow depth led to a less accurate long-term estimation of sea-ice extent compared to the other assimilation systems. The other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, but possibly even longer.

scholarly journals Short-Term Daily Prediction of Sea Ice Concentration Based on Deep Learning of Gradient Loss Function

2021 ◽  
Vol 8 ◽  
Author(s):  
Quanhong Liu ◽  
Ren Zhang ◽  
Yangjun Wang ◽  
Hengqian Yan ◽  
Mei Hong
Keyword(s):  
Deep Learning ◽  
Sea Ice ◽  
Loss Function ◽  
Model Performance ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Short Term ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Impact

The navigability potential of the Northeast Passage has gradually emerged with the melting of Arctic sea ice. For the purpose of navigation safety in the Arctic area, a reliable daily sea ice concentration (SIC) prediction result is required. As the mature application of deep learning technique in short-term prediction of other fields (atmosphere, ocean, and hurricane, etc.), a new model was proposed for daily SIC prediction by selecting multiple factors, adopting gradient loss function (Grad-loss) and incorporating an improved predictive recurrent neural network (PredRNN++). Three control experiments are designed to test the impact of these three improvements for model performance with multiple indicators. Results show that the proposed model has best prediction skill in our experiments by taking physical process and local SIC variation into consideration, which can continuously predict daily SIC for up to 9 days.

scholarly journals Impact of assimilating sea ice concentration, sea ice thickness and snow depth in a coupled ocean-sea ice modeling system

2018 ◽  
Author(s):  
Sindre Fritzner ◽  
Rune Graversen ◽  
Kai H. Christensen ◽  
Philip Rostosky ◽  
Keguang Wang
Keyword(s):  
Sea Ice ◽  
Snow Depth ◽  
The Other ◽  
Ice Thickness ◽  
Initial State ◽  
Sea Ice Concentration ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Ice Concentration ◽  
Independent Observations

Abstract. The accuracy of the initial state is very important for the quality of a forecast, and data assimilation is crucial for obtaining a best possible initial state. For many years, sea-ice concentration was the only parameter used for assimilation into numerical sea-ice models. Sea-ice concentration can easily be observed by satellites, and satellite observations provide a full Arctic coverage. During the last decade, an increasing number of sea-ice related variables have become available, these include sea-ice thickness and snow depth, which are both important parameters in the numerical sea-ice models. In the present study, a coupled ocean-sea-ice model is used to asses the assimilation impact of sea-ice thickness and snow depth on the model. The model system with the assimilation of these parameters is verified by comparison with a system assimilating only ice concentration and a system having no assimilation. The observations assimilated are sea ice concentration from the Ocean and Sea Ice Satellite Application facility, thin sea ice thickness from the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity mission, thick sea ice thickness from ESA’s CryoSat satellite, and a new snow depth product derived from the National Space Agency’s Advanced Microwave Scanning Radiometers (AMSR-E/AMSR-2) satellites. The model results are verified by comparing assimilated observations and independent observations of ice concentration from AMSR-E/AMSR-2, and ice thickness and snow depth from the IceBridge campaign. It is found that the assimilation of ice thickness strongly improves ice concentration, ice thickness and snow depth, while the snow observations have a positive effect on snow thickness and ice concentration. In our study, the seasonal forecast showed that assimilating snow depth lead to a worse estimation of sea-ice extent compared to the other assimilation systems, the other three gave similar results. The improvements due to assimilation were found to last for at least 3–4 months, possibly even longer.

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