scholarly journals Improved Estimation of Proxy Sea Surface Temperature in the Arctic

2020 ◽  
Vol 37 (2) ◽  
pp. 341-349 ◽  
Author(s):  
Viva Banzon ◽  
Thomas M. Smith ◽  
Michael Steele ◽  
Boyin Huang ◽  
Huai-Min Zhang
Keyword(s):  
Sea Ice ◽  
Open Water ◽  
Cold Season ◽  
Sea Surface ◽  
The Arctic ◽  
Random Errors ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
Almost All

AbstractArctic sea surface temperatures (SSTs) are estimated mostly from satellite sea ice concentration (SIC) estimates. In regions with sea ice the SST is the temperature of open water or of the water under the ice. A number of different proxy SST estimates based on SIC have been developed. In recent years more Arctic quality-control buoy SSTs have become available, allowing better validation of different estimates and the development of improved proxy estimates. Here proxy SSTs from different approaches are evaluated and an improved proxy SST method is shown. The improved proxy SSTs were tested in an SST analysis, and showed reduced bias and random errors compared to the Arctic buoy SSTs. Almost all reduction in errors is in the warm melt season. In the cold season the SIC is typically high and all estimates tend to have low errors. The improved method will be incorporated into an operational SST analysis.

scholarly journals Retrieval of Summer Sea Ice Concentration in the Pacific Arctic Ocean from AMSR2 Observations and Numerical Weather Data Using Random Forest Regression

2021 ◽  
Vol 13 (12) ◽  
pp. 2283
Author(s):  
Hyangsun Han ◽  
Sungjae Lee ◽  
Hyun-Cheol Kim ◽  
Miae Kim
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Open Water ◽  
The Arctic ◽  
Atmospheric Effects ◽  
Sea Ice Concentration ◽  
Air Temperatures ◽  
The Pacific ◽  
Ice Concentration

The Arctic sea ice concentration (SIC) in summer is a key indicator of global climate change and important information for the development of a more economically valuable Northern Sea Route. Passive microwave (PM) sensors have provided information on the SIC since the 1970s by observing the brightness temperature (TB) of sea ice and open water. However, the SIC in the Arctic estimated by operational algorithms for PM observations is very inaccurate in summer because the TB values of sea ice and open water become similar due to atmospheric effects. In this study, we developed a summer SIC retrieval model for the Pacific Arctic Ocean using Advanced Microwave Scanning Radiometer 2 (AMSR2) observations and European Reanalysis Agency-5 (ERA-5) reanalysis fields based on Random Forest (RF) regression. SIC values computed from the ice/water maps generated from the Korean Multi-purpose Satellite-5 synthetic aperture radar images from July to September in 2015–2017 were used as a reference dataset. A total of 24 features including the TB values of AMSR2 channels, the ratios of TB values (the polarization ratio and the spectral gradient ratio (GR)), total columnar water vapor (TCWV), wind speed, air temperature at 2 m and 925 hPa, and the 30-day average of the air temperatures from the ERA-5 were used as the input variables for the RF model. The RF model showed greatly superior performance in retrieving summer SIC values in the Pacific Arctic Ocean to the Bootstrap (BT) and Arctic Radiation and Turbulence Interaction STudy (ARTIST) Sea Ice (ASI) algorithms under various atmospheric conditions. The root mean square error (RMSE) of the RF SIC values was 7.89% compared to the reference SIC values. The BT and ASI SIC values had three times greater values of RMSE (20.19% and 21.39%, respectively) than the RF SIC values. The air temperatures at 2 m and 925 hPa and their 30-day averages, which indicate the ice surface melting conditions, as well as the GR using the vertically polarized channels at 23 GHz and 18 GHz (GR(23V18V)), TCWV, and GR(36V18V), which accounts for atmospheric water content, were identified as the variables that contributed greatly to the RF model. These important variables allowed the RF model to retrieve unbiased and accurate SIC values by taking into account the changes in TB values of sea ice and open water caused by atmospheric effects.

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>

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 New methodology to estimate Arctic sea ice concentration from SMOS combining brightness temperature differences in a maximum-likelihood estimator

2017 ◽  
Vol 11 (4) ◽  
pp. 1987-2002 ◽  
Author(s):  
Carolina Gabarro ◽  
Antonio Turiel ◽  
Pedro Elosegui ◽  
Joaquim A. Pla-Resina ◽  
Marcos Portabella
Keyword(s):  
Maximum Likelihood ◽  
Sea Ice ◽  
Maximum Likelihood Estimator ◽  
The Arctic ◽  
Ice Thickness ◽  
Likelihood Estimator ◽  
Sea Ice Concentration ◽  
Arctic Sea ◽  
Ice Concentration ◽  
L Band

Abstract. Monitoring sea ice concentration is required for operational and climate studies in the Arctic Sea. Technologies used so far for estimating sea ice concentration have some limitations, for instance the impact of the atmosphere, the physical temperature of ice, and the presence of snow and melting. In the last years, L-band radiometry has been successfully used to study some properties of sea ice, remarkably sea ice thickness. However, the potential of satellite L-band observations for obtaining sea ice concentration had not yet been explored. In this paper, we present preliminary evidence showing that data from the Soil Moisture Ocean Salinity (SMOS) mission can be used to estimate sea ice concentration. Our method, based on a maximum-likelihood estimator (MLE), exploits the marked difference in the radiative properties of sea ice and seawater. In addition, the brightness temperatures of 100 % sea ice and 100 % seawater, as well as their combined values (polarization and angular difference), have been shown to be very stable during winter and spring, so they are robust to variations in physical temperature and other geophysical parameters. Therefore, we can use just two sets of tie points, one for summer and another for winter, for calculating sea ice concentration, leading to a more robust estimate. After analysing the full year 2014 in the entire Arctic, we have found that the sea ice concentration obtained with our method is well determined as compared to the Ocean and Sea Ice Satellite Application Facility (OSI SAF) dataset. However, when thin sea ice is present (ice thickness ≲ 0.6 m), the method underestimates the actual sea ice concentration.

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.

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