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 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 Assimilation of sea surface temperature, sea ice concentration and sea ice drift in a model of the Southern Ocean

2015 ◽  
Vol 93 ◽  
pp. 22-39 ◽  
Author(s):  
Alexander Barth ◽  
Martin Canter ◽  
Bert Van Schaeybroeck ◽  
Stéphane Vannitsem ◽  
François Massonnet ◽  
...  
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Sea Surface ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Ice Concentration ◽  
Sea Ice Drift

scholarly journals A new tracking algorithm for sea ice age distribution estimation

2018 ◽  
Vol 12 (6) ◽  
pp. 2073-2085 ◽  
Author(s):  
Anton Andreevich Korosov ◽  
Pierre Rampal ◽  
Leif Toudal Pedersen ◽  
Roberto Saldo ◽  
Yufang Ye ◽  
...  
Keyword(s):  
Sea Ice ◽  
Age Distribution ◽  
Weighted Average ◽  
Ice Age ◽  
Sea Ice Concentration ◽  
Advection Scheme ◽  
Statistical Measures ◽  
Ice Drift ◽  
Ice Concentration ◽  
Sea Ice Drift

Abstract. A new algorithm for estimating sea ice age (SIA) distribution based on the Eulerian advection scheme is presented. The advection scheme accounts for the observed divergence or convergence and freezing or melting of sea ice and predicts consequent generation or loss of new ice. The algorithm uses daily gridded sea ice drift and sea ice concentration products from the Ocean and Sea Ice Satellite Application Facility. The major advantage of the new algorithm is the ability to generate individual ice age fractions in each pixel of the output product or, in other words, to provide a frequency distribution of the ice age allowing to apply mean, median, weighted average or other statistical measures. Comparison with the National Snow and Ice Data Center SIA product revealed several improvements of the new SIA maps and time series. First, the application of the Eulerian scheme provides smooth distribution of the ice age parameters and prevents product undersampling which may occur when a Lagrangian tracking approach is used. Second, utilization of the new sea ice drift product void of artifacts from EUMETSAT OSI SAF resulted in more accurate and reliable spatial distribution of ice age fractions. Third, constraining SIA computations by the observed sea ice concentration expectedly led to considerable reduction of multi-year ice (MYI) fractions. MYI concentration is computed as a sum of all MYI fractions and compares well to the MYI products based on passive and active microwave and SAR products.

scholarly journals An analytical model for wind-driven Arctic summer sea ice drift

2015 ◽  
Vol 9 (2) ◽  
pp. 2101-2133
Author(s):  
H.-S. Park ◽  
A. L. Stewart
Keyword(s):  
Sea Ice ◽  
Analytical Model ◽  
Arctic Sea Ice ◽  
Sea Ice Concentration ◽  
Ice Drift ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Sea Ice Drift ◽  
Ice Velocity ◽  
Arctic Summer

Abstract. The authors present an approximate analytical model for wind-induced sea-ice drift that includes an ice–ocean boundary layer with an Ekman spiral in the ocean velocity. This model provides an analytically tractable solution that is most applicable to the marginal ice zone, where sea-ice concentration is substantially below 100%. The model closely reproduces the ice and upper-ocean velocities observed recently by the first ice-tethered profiler equipped with a velocity sensor (ITPV). The analytical tractability of our model allows efficient calculation of the sea-ice velocity provided that the surface wind field is known and that the ocean surface geostrophic velocity is relatively weak. The model is applied to estimate intraseasonal variations in Arctic sea ice cover due to short-timescale (around 1 week) intensification of the southerly winds. Utilizing 10 m surface winds from ERA-Interim reanalysis, the wind-induced sea-ice velocity and the associated changes in sea-ice concentration are calculated and compared with satellite observations. The analytical model captures the observed reduction of Arctic sea-ice concentration associated with the strengthening of southerlies on intraseasonal time scales. Further analysis indicates that the wind-induced surface Ekman flow in the ocean increases the sea-ice drift speed by 50% in the Arctic summer. It is proposed that the southerly wind-induced sea-ice drift, enhanced by the ocean's surface Ekman transport, can lead to substantial reduction in sea-ice concentration over a timescale of one week.

scholarly journals Cryospheric Impacts of Soviet River Diversion Schemes

1984 ◽  
Vol 5 ◽  
pp. 61-68 ◽  
Author(s):  
T. Holt ◽  
P. M. Kelly ◽  
B. S. G. Cherry
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
River Discharge ◽  
Large Scale ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
The Impact

Soviet plans to divert water from rivers flowing into the Arctic Ocean have led to research into the impact of a reduction in discharge on Arctic sea ice. We consider the mechanisms by which discharge reductions might affect sea-ice cover and then test various hypotheses related to these mechanisms. We find several large areas over which sea-ice concentration correlates significantly with variations in river discharge, supporting two particular hypotheses. The first hypothesis concerns the area where the initial impacts are likely to which is the Kara Sea. Reduced riverflow is associated occur, with decreased sea-ice concentration in October, at the time of ice formation. This is believed to be the result of decreased freshening of the surface layer. The second hypothesis concerns possible effects on the large-scale current system of the Arctic Ocean and, in particular, on the inflow of Atlantic and Pacific water. These effects occur as a result of changes in the strength of northward-flowing gradient currents associated with variations in river discharge. Although it is still not certain that substantial transfers of riverflow will take place, it is concluded that the possibility of significant cryospheric effects and, hence, large-scale climate impact should not be neglected.

Deep learning for monthly Arctic sea ice concentration prediction

2020 ◽  
Author(s):  
Tom Andersson ◽  
Fruzsina Agocs ◽  
Scott Hosking ◽  
María Pérez-Ortiz ◽  
Brooks Paige ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Warming ◽  
Deep Learning ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Prediction System ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

<p>Over recent decades, the Arctic has warmed faster than any region on Earth. The rapid decline in Arctic sea ice extent (SIE) is often highlighted as a key indicator of anthropogenic climate change. Changes in sea ice disrupt Arctic wildlife and indigenous communities, and influence weather patterns as far as the mid-latitudes. Furthermore, melting sea ice attenuates the albedo effect by replacing the white, reflective ice with dark, heat-absorbing melt ponds and open sea, increasing the Sun’s radiative heat input to the Arctic and amplifying global warming through a positive feedback loop. Thus, the reliable prediction of sea ice under a changing climate is of both regional and global importance. However, Arctic sea ice presents severe modelling challenges due to its complex coupled interactions with the ocean and atmosphere, leading to high levels of uncertainty in numerical sea ice forecasts.</p><p>Deep learning (a subset of machine learning) is a family of algorithms that use multiple nonlinear processing layers to extract increasingly high-level features from raw input data. Recent advances in deep learning techniques have enabled widespread success in diverse areas where significant volumes of data are available, such as image recognition, genetics, and online recommendation systems. Despite this success, and the presence of large climate datasets, applications of deep learning in climate science have been scarce until recent years. For example, few studies have posed the prediction of Arctic sea ice in a deep learning framework. We investigate the potential of a fully data-driven, neural network sea ice prediction system based on satellite observations of the Arctic. In particular, we use inputs of monthly-averaged sea ice concentration (SIC) maps since 1979 from the National Snow and Ice Data Centre, as well as climatological variables (such as surface pressure and temperature) from the European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) dataset. Past deep learning-based Arctic sea ice prediction systems tend to overestimate sea ice in recent years - we investigate the potential to learn the non-stationarity induced by climate change with the inclusion of multi-decade global warming indicators (such as average Arctic air temperature). We train the networks to predict SIC maps one month into the future, evaluating network prediction uncertainty by ensembling independent networks with different random weight initialisations. Our model accounts for seasonal variations in the drivers of sea ice by controlling for the month of the year being predicted. We benchmark our prediction system against persistence, linear extrapolation and autoregressive models, as well as September minimum SIE predictions from submissions to the Sea Ice Prediction Network's Sea Ice Outlook. Performance is evaluated quantitatively using the root mean square error and qualitatively by analysing maps of prediction error and uncertainty.</p>

scholarly journals Multi-scale observations of the co-evolution of sea ice thermophysical properties and microwave brightness temperatures during the summer melt period in Hudson Bay

Elem Sci Anth ◽  
2020 ◽  
Vol 8 ◽  
Author(s):  
Madison L. Harasyn ◽  
Dustin Isleifson ◽  
Wayne Chan ◽  
David G. Barber
Keyword(s):  
Sea Ice ◽  
Liquid Water ◽  
Thermophysical Properties ◽  
Passive Microwave ◽  
Hudson Bay ◽  
Strong Positive Correlation ◽  
Sea Ice Concentration ◽  
Multi Scale ◽  
Ice Concentration

Monitoring the trend of sea ice breakup and formation in Hudson Bay is vital for maritime operations, such as local hunting or shipping, particularly in response to the lengthening of the ice-free period in the Bay driven by climate change. Satellite passive microwave sea ice concentration products are commonly used for large-scale sea ice monitoring and predictive modelling; however, these product algorithms are known to underperform during the summer melt period due to the changes in sea ice thermophysical properties. This study investigates the evolution of in situ and satellite-retrieved brightness temperature (TB) throughout the melt season using a combination of in situ passive microwave measurements, thermophysical sampling, unmanned aerial vehicle (UAV) surveys, and satellite-retrieved TB. In situ data revealed a strong positive correlation between the presence of liquid water in the snow matrix and in situ TB in the 37 and 89 GHz frequencies. When considering TB ratios utilized by popular sea ice concentration algorithms (e.g., NASA Team 2), liquid water presence in the snow matrix was shown to increase the in situ TB gradient ratio of 37/19V. In situ gradient ratios of 89/19V and 89/19H were shown to correlate positively with UAV-derived melt pond coverage across the ice surface. Multi-scale comparison between in situ TB measurements and satellite-retrieved TB (by Advanced Microwave Scanning Radiometer 2) showed a distinct pattern of passive microwave TB signature at different stages of melt, confirmed by data from in situ thermophysical measurements. This pattern allowed for both in situ and satellite-retrieved TB to be partitioned into three discrete stages of sea ice melt: late spring, early melt and advanced melt. The results of this study thus advance the goal of achieving more accurate modeled predictions of the sea ice cover during the critical navigation and breakup period in Hudson Bay.

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.

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