Gridded Arctic sea ice concentration reconstruction for the first half of the 20th century based on different proxy data

2021 ◽  
Author(s):  
Vladimir Semenov ◽  
Tatiana Matveeva
Keyword(s):  
Sea Ice ◽  
20Th Century ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
The North ◽  
Arctic Sea ◽  
Ice Concentration

<p>Global warming in the recent decades has been accompanied by a rapid recline of the Arctic sea ice area most pronounced in summer (10% per decade). To understand the relative contribution of external forcing and natural variability to the modern and future sea ice area changes, it is necessary to evaluate a range of long-term variations of the Arctic sea ice area in the period before a significant increase in anthropogenic emissions of greenhouse gases into the atmosphere. Available observational data on the spatiotemporal dynamics of Arctic sea ice until 1950s are characterized by significant gaps and uncertainties. In the recent years, there have appeared several reconstructions of the early 20<sup>th</sup> century Arctic sea ice area that filled the gaps by analogue methods or utilized combined empirical data and climate model’s output. All of them resulted in a stronger that earlier believed negative sea ice area anomaly in the 1940s concurrent with the early 20<sup>th</sup> century warming (ETCW) peak. In this study, we reconstruct the monthly average gridded sea ice concentration (SIC) in the first half of the 20th century using the relationship between the spatiotemporal features of SIC variability, surface air temperature over the Northern Hemisphere extratropical continents, sea surface temperature in the North Atlantic and North Pacific, and sea level pressure. In agreement with a few previous results, our reconstructed data also show a significant negative anomaly of the Arctic sea ice area in the middle of the 20th century, however with some 15% to 30% stronger amplitude, about 1.5 million km<sup>2</sup> in September and 0.7 million km<sup>2</sup> in March. The reconstruction demonstrates a good agreement with regional Arctic sea ice area data when available and suggests that ETWC in the Arctic has been accompanied by a concurrent sea ice area decline of a magnitude that have been exceeded only in the beginning of the 21<sup>st</sup> century.</p>

Arctic Sea Ice Reemergence: The Role of Large-Scale Oceanic and Atmospheric Variability*

2015 ◽  
Vol 28 (14) ◽  
pp. 5477-5509 ◽  
Author(s):  
Mitchell Bushuk ◽  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Sea Ice ◽  
Large Scale ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Spring Sea Ice ◽  
Phase Relationships

Abstract Arctic sea ice reemergence is a phenomenon in which spring sea ice anomalies are positively correlated with fall anomalies, despite a loss of correlation over the intervening summer months. This work employs a novel data analysis algorithm for high-dimensional multivariate datasets, coupled nonlinear Laplacian spectral analysis (NLSA), to investigate the regional and temporal aspects of this reemergence phenomenon. Coupled NLSA modes of variability of sea ice concentration (SIC), sea surface temperature (SST), and sea level pressure (SLP) are studied in the Arctic sector of a comprehensive climate model and in observations. It is found that low-dimensional families of NLSA modes are able to efficiently reproduce the prominent lagged correlation features of the raw sea ice data. In both the model and observations, these families provide an SST–sea ice reemergence mechanism, in which melt season (spring) sea ice anomalies are imprinted as SST anomalies and stored over the summer months, allowing for sea ice anomalies of the same sign to reappear in the growth season (fall). The ice anomalies of each family exhibit clear phase relationships between the Barents–Kara Seas, the Labrador Sea, and the Bering Sea, three regions that compose the majority of Arctic sea ice variability. These regional phase relationships in sea ice have a natural explanation via the SLP patterns of each family, which closely resemble the Arctic Oscillation and the Arctic dipole anomaly. These SLP patterns, along with their associated geostrophic winds and surface air temperature advection, provide a large-scale teleconnection between different regions of sea ice variability. Moreover, the SLP patterns suggest another plausible ice reemergence mechanism, via their winter-to-winter regime persistence.

Arctic sea ice volume budget decomposition satellite product for the CryoSat-2 (2010-2020) period 

2021 ◽  
Author(s):  
Harry Heorton ◽  
Michel Tsamados ◽  
Paul Holland ◽  
Jack Landy
Keyword(s):  
Sea Ice ◽  
Climate Model ◽  
Volume Growth ◽  
Arctic Sea Ice ◽  
First Year ◽  
Sea Ice Concentration ◽  
Marginal Seas ◽  
Ice Volume ◽  
Arctic Sea ◽  
Ice Concentration

<p><span>We combine satellite-derived observations of sea ice concentration, drift, and thickness to provide the first observational decomposition of the dynamic (advection/divergence) and thermodynamic (melt/growth) drivers of wintertime Arctic sea ice volume change. Ten winter growth seasons are analyzed over the CryoSat-2 period between October 2010 and April 2020. Sensitivity to several observational products is performed to provide an estimated uncertainty of the budget calculations. The total thermodynamic ice volume growth and dynamic ice losses are calculated with marked seasonal, inter-annual and regional variations</span><span>. Ice growth is fastest during Autumn, in the Marginal Seas and over first year ice</span><span>. Our budget decomposition methodology can help diagnose the processes confounding climate model predictions of sea ice. We make our product and code available to the community in monthly pan-Arctic netcdft files for the entire October 2010 to April 2020 period.</span></p>

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.

Sensitivity of the Arctic sea ice concentration forecasts to different atmospheric forcing: a case study

2014 ◽  
Vol 33 (12) ◽  
pp. 15-23
Author(s):  
Qinghua Yang ◽  
Jiping Liu ◽  
Zhanhai Zhang ◽  
Cuijuan Sui ◽  
Jianyong Xing ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Atmospheric Forcing ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

scholarly journals Step changes in persistent organic pollutants over the Arctic and their implications

2015 ◽  
Vol 15 (6) ◽  
pp. 3479-3495 ◽  
Author(s):  
Y. Zhao ◽  
T. Huang ◽  
L. Wang ◽  
H. Gao ◽  
J. Ma
Keyword(s):  
Sea Ice ◽  
Step Change ◽  
Arctic Sea Ice ◽  
Change Points ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Exchange Flux ◽  
Air Exchange

Abstract. While some persistent organic pollutants (POPs) have been declining globally due to their worldwide ban since the 1980s, the declining trends of many of these toxic chemicals become less significant and in some cases their ambient air concentrations, e.g., polychlorinated biphenyls (PCBs), showed observable increase during the 2000s, disagreeing with their declining global emissions and environmental degradation. As part of the efforts to assess the influences of environmental factors on the long-term trend of POPs in the Arctic, step change points in the time series of ambient POP atmospheric concentrations collected from four arctic monitoring sites were examined using various statistical techniques. Results showed that the step change points of these POP data varied in different years and at different sites. Most step change points were found in 2001–2002 and 2007–2008. In particular, the step change points of many PCBs for 2007–2008 were coincident with the lowest arctic sea ice concentration occurring during the 2000s. The perturbations of air concentration and water–air exchange fluxes of several selected POPs averaged over the Arctic, simulated by a POP mass balance perturbation model, switched from negative to positive during the early 2000s, indicating a tendency for reversal of POPs from deposition to volatilization which coincides with a positive to negative reversal of arctic sea ice extent anomalies from 2001. Perturbed ice–air exchange flux of PCB 28 and 153 showed an increasing trend and a negative to positive reversal in 2007, the year with the lowest arctic sea ice concentration. On the other hand, perturbed ice–air exchange flux of α-hexachlorocyclohexane decreased over the period of 1995 to 2012, likely owing to its lower Henry's law constant which indicates its relatively lower tendency for volatilization from ice to air.

scholarly journals Model-data comparison and data assimilation of mid-Holocene Arctic sea-ice concentration

2013 ◽  
Vol 9 (6) ◽  
pp. 6515-6549 ◽  
Author(s):  
F. Klein ◽  
H. Goosse ◽  
A. Mairesse ◽  
A. de Vernal
Keyword(s):  
Data Assimilation ◽  
Sea Ice ◽  
Atmospheric Circulation ◽  
Barents Sea ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Data ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration

Abstract. The consistency between a new quantitative reconstruction of Arctic sea-ice concentration based on dinocyst assemblages and the results of climate models has been investigated for the mid-Holocene. The comparison shows that the simulated sea-ice changes are weaker and spatially more homogeneous than the recorded ones. Furthermore, although the model-data agreement is relatively good in some regions such as the Labrador Sea, the skill of the models at local scale is low. The response of the models follows mainly the increase in summer insolation at large scale. This is modulated by changes in atmospheric circulation leading to differences between regions in the models that are albeit smaller than in the reconstruction. Performing simulations with data assimilation using the model LOVECLIM amplifies those regional differences, mainly through a reduction of the southward winds in the Barents Sea and an increase in the westerly winds in the Canadian Basin of the Arctic. This leads to an increase in the ice concentration in the Barents and Chukchi Seas and a better agreement with the reconstructions. This underlines the potential role of atmospheric circulation to explain the reconstructed changes during the Holocene.

scholarly journals Step changes in persistent organic pollutants over the Arctic and their implications

2015 ◽  
Vol 15 (1) ◽  
pp. 1225-1267 ◽  
Author(s):  
Y. Zhao ◽  
T. Huang ◽  
L. Wang ◽  
H. Gao ◽  
J. Ma
Keyword(s):  
Sea Ice ◽  
Step Change ◽  
Arctic Sea Ice ◽  
Change Points ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Exchange Flux ◽  
Air Exchange

Abstract. While some persistent organic pollutants (POPs) have been declining globally due to their worldwide ban since the 1980s, the declining trends of many of these toxic chemicals become less significant and in some cases their ambient air concentrations, e.g., polychlorinated biphenyls (PCBs), showed observable increase since 2000, disagreeing with their declining global emissions and environmental degradation. As part of the efforts to assess the influences of environmental factors on long-term trend of POPs in the Arctic, step change points in the time series of ambient POPs atmospheric concentrations collected from four arctic monitoring sites were examined using various statistical techniques. Results showed that the step change points of these POPs data varied in different years and at different sites. Most step change points were found in 2001–2002 and 2007–2008, respectively. In particular, the step change points of many PCBs for 2007–2008 were coincident with the lowest arctic sea ice concentration occurring in this period of time during the 2000s. The perturbations of air concentration and water-air exchange fluxes of several selected POPs averaged over the Arctic, simulated by a POPs mass balance perturbation model, switched from negative to positive from the early 2000s, indicating a tendency for reversal of POPs from deposition to volatilization which coincides with a positive to negative reversal of arctic sea ice extent anomalies from 2001. Perturbed ice-air exchange flux of PCB-28 and 153 showed an increasing trend and the negative to positive reversal in 2007, the year with the lowest arctic sea ice concentration. On the other hand, perturbed ice-air exchange flux of α-hexachlorocyclohexane (HCH) decreased over the period of 1995 through 2012, likely owing to its lower Henry's law constant which indicates its relatively lower tendency for volatilization from ice to air.

scholarly journals Satellite-Based Prediction of Arctic Sea Ice Concentration Using a Deep Neural Network with Multi-Model Ensemble

2018 ◽  
Vol 11 (1) ◽  
pp. 19 ◽  
Author(s):  
Jiwon Kim ◽  
Kwangjin Kim ◽  
Jaeil Cho ◽  
Yong Kang ◽  
Hong-Joo Yoon ◽  
...  
Keyword(s):  
Sea Ice ◽  
Deep Neural Network ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Model Ensemble ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Future Prediction ◽  
Near Future

Warming of the Arctic leads to a decrease in sea ice, and the decrease of sea ice, in turn, results in warming of the Arctic again. Several microwave sensors have provided continuously updated sea ice data for over 30 years. Many studies have been conducted to investigate the relationships between the satellite-derived sea ice concentration (SIC) of the Arctic and climatic factors associated with the accelerated warming. However, linear equations using the general circulation model (GCM) data, with low spatial resolution, cannot sufficiently cope with the problem of complexity or non-linearity. Time-series techniques are effective for one-step-ahead forecasting, but are not appropriate for future prediction for about ten or twenty years because of increasing uncertainty when forecasting multiple steps ahead. This paper describes a new approach to near-future prediction of Arctic SIC by employing a deep learning method with multi-model ensemble. We used the regional climate model (RCM) data provided in higher resolution, instead of GCM. The RCM ensemble was produced by Bayesian model averaging (BMA) to minimize the uncertainty which can arise from a single RCM. The accuracies of RCM variables were much improved by the BMA2 method, which took into consideration temporal and spatial variations to minimize the uncertainty of individual RCMs. A deep neural network (DNN) method was used to deal with the non-linear relationships between SIC and climate variables, and to provide a near-future prediction for the forthcoming 10 to 20 years. We adjusted the DNN model for optimized SIC prediction by adopting best-fitted layer structure, loss function, optimizer algorithm, and activation function. The accuracy was much improved when the DNN model was combined with BMA2 ensemble, showing the correlation coefficient of 0.888. This study provides a viable option for monitoring Arctic sea ice change of the near future.

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>

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