From red to white: the time-varying nature of ocean heat flux to Arctic sea ice

2020 ◽  
Author(s):  
Srikanth Toppaladoddi ◽  
Andrew Wells
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Time Series Data ◽  
Heat Budget ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Fluctuation Analysis ◽  
Arctic Sea

<p>Arctic sea ice is one of the most sensitive components of the Earth’s climate system. The underlying ocean plays an important role in the evolution of the ice cover through its heat flux at the ice-ocean interface which moderates ice growth and melt. Despite its importance, the spatio-temporal variations of this heat flux are not well understood. In this work, we combine direct numerical simulations of turbulent convection over fractal surfaces and analysis of time-series data from the Surface Heat Budget of the Arctic Ocean (SHEBA) program using Multifractal Detrended Fluctuation Analysis (MFDFA) to understand the nature of fluctuations in this heat flux. We identify key physical processes associated with the observed Hurst exponents calculated by the MFDFA, and how these evolve over time. We also discuss ongoing work on constructing simple stochastic models of the ocean heat flux to the ice, and potential use as a parameterisation.</p>

scholarly journals Benchmarking of Data-Driven Causality Discovery Approaches in the Interactions of Arctic Sea Ice and Atmosphere

2021 ◽  
Vol 4 ◽  
Author(s):  
Yiyi Huang ◽  
Matthäus Kleindessner ◽  
Alexey Munishkin ◽  
Debvrat Varshney ◽  
Pei Guo ◽  
...  
Keyword(s):  
Sea Ice ◽  
Time Series Data ◽  
Open Water ◽  
Meridional Wind ◽  
Arctic Sea Ice ◽  
Data Driven ◽  
The Arctic ◽  
Series Data ◽  
Sea Ice Extent ◽  
Arctic Sea

The Arctic sea ice has retreated rapidly in the past few decades, which is believed to be driven by various dynamic and thermodynamic processes in the atmosphere. The newly open water resulted from sea ice decline in turn exerts large influence on the atmosphere. Therefore, this study aims to investigate the causality between multiple atmospheric processes and sea ice variations using three distinct data-driven causality approaches that have been proposed recently: Temporal Causality Discovery Framework Non-combinatorial Optimization via Trace Exponential and Augmented lagrangian for Structure learning (NOTEARS) and Directed Acyclic Graph-Graph Neural Networks (DAG-GNN). We apply these three algorithms to 39 years of historical time-series data sets, which include 11 atmospheric variables from ERA-5 reanalysis product and passive microwave satellite retrieved sea ice extent. By comparing the causality graph results of these approaches with what we summarized from the literature, it shows that the static graphs produced by NOTEARS and DAG-GNN are relatively reasonable. The results from NOTEARS indicate that relative humidity and precipitation dominate sea ice changes among all variables, while the results from DAG-GNN suggest that the horizontal and meridional wind are more important for driving sea ice variations. However, both approaches produce some unrealistic cause-effect relationships. Additionally, these three methods cannot well detect the delayed impact of one variable on another in the Arctic. It also turns out that the results are rather sensitive to the choice of hyperparameters of the three methods. As a pioneer study, this work paves the way to disentangle the complex causal relationships in the Earth system, by taking the advantage of cutting-edge Artificial Intelligence technologies.

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

scholarly journals Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes

2020 ◽  
Vol 35 (3) ◽  
pp. 793-806
Author(s):  
William Gregory ◽  
Michel Tsamados ◽  
Julienne Stroeve ◽  
Peter Sollich
Keyword(s):  
Sea Ice ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Forecast Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Lead Times ◽  
Sea Ice Concentration ◽  
Sea Ice Extent

Abstract Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.

scholarly journals Seasonal changes in Arctic sea-ice morphology

2001 ◽  
Vol 33 ◽  
pp. 171-176 ◽  
Author(s):  
Donald K. Perovich ◽  
Jacqueline A. Richter-Menge ◽  
Walter B. Tucker
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Open Water ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Surface ◽  
Arctic Sea ◽  
Dynamic Forcing ◽  
Break Up

AbstractThe morphology of the Arctic sea-ice cover undergoes large changes over an annual cycle. These changes have a significant impact on the heat budget of the ice cover, primarily by affecting the distribution of the solar radiation absorbed in the ice-ocean system. In spring, the ice is snow-covered and ridges are the prominent features. The pack consists of large angular floes, with a small amount of open water contained primarily in linear leads. By the end of summer the ice cover has undergone a major transformation. The snow cover is gone, many of the ridges have been reduced to hummocks and the ice surface is mottled with melt ponds. One surface characteristic that changes little during the summer is the appearance of the bare ice, which remains white despite significant melting. The large floes have broken into a mosaic of smaller, rounded floes surrounded by a lace of open water. Interestingly, this break-up occurs during summer when the dynamic forcing and the internal ice stress are small During the Surface Heat Budget of the Arctic Ocean (SHEBA) field experiment we had an opportunity to observe the break-up process both on a small scale from the ice surface, and on a larger scale via aerial photographs. Floe break-up resulted in large part from thermal deterioration of the ice. The large floes of spring are riddled with cracks and leads that formed and froze during fall, winter and spring. These features melt open during summer, weakening the ice so that modest dynamic forcing can break apart the large floes into many fragments. Associated with this break-up is an increase in the number of floes, a decrease in the size of floes, an increase in floe perimeter and an increase in the area of open water.

Random Walks through Climate Networks: Sea Ice Prediction with Bayesian Inference

2020 ◽  
Author(s):  
William Gregory ◽  
Michel Tsamados ◽  
Julienne Stroeve ◽  
Peter Sollich
Keyword(s):  
Bayesian Inference ◽  
Sea Ice ◽  
Time Series Data ◽  
Gaussian Process Regression ◽  
Forecast Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Sea Ice Concentration ◽  
Spring Predictability Barrier

<p><span>Spatial predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. However, with sea ice variability likely to increase under continued anthropogenic warming, increasingly complex tools are required in order to make accurate forecasts. In this study, predictions of both Arctic and Antarctic summer sea ice extents are made using a complex network statistical approach. This method exploits statistical relationships within geo-spatial time series data in order to construct regions of spatio-temporal homogeneity -- nodes, and subsequently derive teleconnection links between them. The nodes and links of the networks here are generated from monthly sea ice concentration fields in June(November), July(December) and August(January) for Arctic(Antarctic) forecasts, hence lead times extend from 1 to 3 months. Network information is then utilised within a linear Gaussian Process Regression forecast model; a Bayesian inference technique. Network teleconnection weights are used to generate priors over functions in the form of a random walk covariance kernel; the hyperparameters of which are determined by the empirical Bayesian approach of type-II maximum likelihood. We also show predictions of all other months in order to ascertain the presence of a spring predictability barrier in observational data, for both hemispheres.</span></p>

scholarly journals A recent bifurcation in Arctic sea-ice cover

2012 ◽  
Vol 6 (4) ◽  
pp. 2621-2651 ◽  
Author(s):  
V. N. Livina ◽  
T. M. Lenton
Keyword(s):  
Sea Ice ◽  
Early Warning ◽  
Normal State ◽  
Time Series Data ◽  
Warning Signal ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Series Data ◽  
Internal Climate Variability ◽  
Arctic Sea

Abstract. There is ongoing debate over whether Arctic sea-ice has already passed a "tipping point", or whether it will do so in future, with several recent studies arguing that the loss of summer sea ice does not involve a bifurcation because it is highly reversible in models. Recently developed methods can detect and sometimes forewarn of bifurcations in time-series data, hence we applied them to satellite data for Arctic sea-ice cover. Here we show that a new low ice cover state has appeared from 2007 onwards, which is distinct from the normal state of seasonal sea ice variation, suggesting a bifurcation has occurred from one attractor to two. There was no robust early warning signal of critical slowing down prior to this bifurcation, consistent with it representing the appearance of a new ice cover state rather than the loss of stability of the existing state. The new low ice cover state has been sampled predominantly in summer-autumn and seasonal forcing combined with internal climate variability are likely responsible for triggering recent transitions between the two ice cover states. However, all early warning indicators show destabilization of the summer-autumn sea-ice since 2007. This suggests the new low ice cover state may be a transient feature and further abrupt changes in summer-autumn Arctic sea-ice cover could lie ahead; either reversion to the normal state or a yet larger ice loss.

scholarly journals A Variational Method for Computation of Sensible Heat Flux over the Arctic Sea Ice

2009 ◽  
Vol 26 (4) ◽  
pp. 838-845 ◽  
Author(s):  
Zuohao Cao ◽  
Jianmin Ma
Keyword(s):  
Heat Flux ◽  
Variational Method ◽  
Sea Ice ◽  
Variational Approach ◽  
Sensible Heat Flux ◽  
Arctic Sea Ice ◽  
Eddy Correlation ◽  
The Arctic ◽  
Sensible Heat ◽  
Arctic Sea

Abstract In this study, a variational approach was employed to compute surface sensible heat flux over the Arctic sea ice. Because the variational approach is able to take into account information from the Monin–Obukhov similarity theory (MOST) as well as the observed meteorological information, it is expected to improve the pure MOST-based approach in computation of sensible heat flux. Verifications using the direct eddy-correlation measurements over the Arctic sea ice during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment period of 1997/98 show that the variational method yields good agreement between the computed and the measured sensible heat fluxes. The variational method is also shown to be more accurate than the traditional MOST method in the computation of sensible heat flux over the Arctic sea ice.

scholarly journals Increasing riverine heat influx triggers Arctic sea ice decline and oceanic and atmospheric warming

2020 ◽  
Vol 6 (45) ◽  
pp. eabc4699 ◽  
Author(s):  
Hotaek Park ◽  
Eiji Watanabe ◽  
Youngwook Kim ◽  
Igor Polyakov ◽  
Kazuhiro Oshima ◽  
...  
Keyword(s):  
Sea Ice ◽  
Land Surface ◽  
Heat Budget ◽  
Ocean Model ◽  
Early Summer ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Surface Model ◽  
Arctic Sea ◽  
Atmospheric Warming

Arctic river discharge increased over the last several decades, conveying heat and freshwater into the Arctic Ocean and likely affecting regional sea ice and the ocean heat budget. However, until now, there have been only limited assessments of riverine heat impacts. Here, we adopted a synthesis of a pan-Arctic sea ice–ocean model and a land surface model to quantify impacts of river heat on the Arctic sea ice and ocean heat budget. We show that river heat contributed up to 10% of the regional sea ice reduction over the Arctic shelves from 1980 to 2015. Particularly notable, this effect occurs as earlier sea ice breakup in late spring and early summer. The increasing ice-free area in the shelf seas results in a warmer ocean in summer, enhancing ocean–atmosphere energy exchange and atmospheric warming. Our findings suggest that a positive river heat–sea ice feedback nearly doubles the river heat effect.

scholarly journals A recent tipping point in the Arctic sea-ice cover: abrupt and persistent increase in the seasonal cycle since 2007

2013 ◽  
Vol 7 (1) ◽  
pp. 275-286 ◽  
Author(s):  
V. N. Livina ◽  
T. M. Lenton
Keyword(s):  
Sea Ice ◽  
Satellite Data ◽  
Early Warning ◽  
Seasonal Cycle ◽  
Ice Cover ◽  
Tipping Point ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Series Data ◽  
Arctic Sea

Abstract. There is ongoing debate over whether Arctic sea ice has already passed a "tipping point", or whether it will do so in the future. Several recent studies argue that the loss of summer sea ice does not involve an irreversible bifurcation, because it is highly reversible in models. However, a broader definition of a "tipping point" also includes other abrupt, non-linear changes that are neither bifurcations nor necessarily irreversible. Examination of satellite data for Arctic sea-ice area reveals an abrupt increase in the amplitude of seasonal variability in 2007 that has persisted since then. We identified this abrupt transition using recently developed methods that can detect multi-modality in time-series data and sometimes forewarn of bifurcations. When removing the mean seasonal cycle (up to 2008) from the satellite data, the residual sea-ice fluctuations switch from uni-modal to multi-modal behaviour around 2007. We originally interpreted this as a bifurcation in which a new lower ice cover attractor appears in deseasonalised fluctuations and is sampled in every summer–autumn from 2007 onwards. However, this interpretation is clearly sensitive to how the seasonal cycle is removed from the raw data, and to the presence of continental land masses restricting winter–spring ice fluctuations. Furthermore, there was no robust early warning signal of critical slowing down prior to the hypothesized bifurcation. Early warning indicators do however show destabilization of the summer–autumn sea-ice cover since 2007. Thus, the bifurcation hypothesis lacks consistent support, but there was an abrupt and persistent increase in the amplitude of the seasonal cycle of Arctic sea-ice cover in 2007, which we describe as a (non-bifurcation) "tipping point". Our statistical methods detect this "tipping point" and its time of onset. We discuss potential geophysical mechanisms behind it, which should be the subject of further work with process-based models.

scholarly journals Spectral transmission and implications for the partitioning of shortwave radiation in arctic sea ice

2006 ◽  
Vol 44 ◽  
pp. 1-6 ◽  
Author(s):  
Thomas C. Grenfell ◽  
Bonnie Light ◽  
Donald K. Perovich
Keyword(s):  
Sea Ice ◽  
Heat Budget ◽  
Particulate Material ◽  
Arctic Sea Ice ◽  
Shortwave Radiation ◽  
The Arctic ◽  
Extinction Coefficients ◽  
Arctic Sea ◽  
Suspended Particulate Material ◽  
Upper Mixed Layer

AbstractWe present a new set of values for the spectral extinction coefficients, Kλ, for the interior of first-year (FY) and multi-year (MY) Arctic sea ice during the summer melt season measured during SHEBA (Surface Heat Budget of the Arctic Ocean program) and at Barrow, Alaska, USA. Results for FY ice are consistent with previously reported values, and differences can be understood in terms of variations in the concentration of biological and suspended particulate material. The values for the interior of MY ice are lower than previously reported for both bare and ponded ice. For bare MY ice the new Kλ values predict a substantial increase in the solar radiation transmitted through the ice into the upper mixed layer. Ponded MY ice is only slightly more transparent than previously reported, and FY ice values are generally consistent with previously reported values. Assuming an asymmetry parameter of 0.94, the extinction coefficients are consistent with a volume-scattering coefficient of 77 m–1 that is constant from 400 to at least 720 nm.

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