scholarly journals The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation

2020 ◽  
Author(s):  
Clara Burgard ◽  
Dirk Notz ◽  
Leif T. Pedersen ◽  
Rasmus T. Tonboe
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Climate Model ◽  
System Model ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Brightness Temperatures ◽  
Climate Model Evaluation ◽  
Observational Uncertainty ◽  
Ice Concentration

Abstract. The observational uncertainty in sea-ice-concentration estimates from remotely-sensed passive-microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarisation from standard output of an Earth System Model. We evaluate ARC3O by simulating brightness temperatures based on three assimilation runs of the MPI Earth System Model (MPI-ESM) assimilated with three different sea-ice concentration products. We then compare these three sets of simulated brightness temperatures to brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that they differ up to 10 K in the period between October and June, depending on the region and the assimilation run. However, we show that these discrepancies between simulated and observed brightness temperature can be mainly attributed to the underlying observational uncertainty in sea-ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea-ice concentration but also by the melt-pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable to realistically translate the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization.

scholarly journals The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 2: Development and evaluation

2020 ◽  
Vol 14 (7) ◽  
pp. 2387-2407 ◽  
Author(s):  
Clara Burgard ◽  
Dirk Notz ◽  
Leif T. Pedersen ◽  
Rasmus T. Tonboe
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Climate Model ◽  
System Model ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Brightness Temperatures ◽  
Climate Model Evaluation ◽  
Observational Uncertainty ◽  
Ice Concentration

Abstract. The observational uncertainty in sea ice concentration estimates from remotely sensed passive microwave brightness temperatures is a challenge for reliable climate model evaluation and initialization. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarization from standard output of an Earth System Model. To evaluate sources of uncertainties when applying ARC3O, we compare brightness temperatures simulated by applying ARC3O on three assimilation runs of the MPI Earth System Model (MPI-ESM), assimilated with three different sea ice concentration products, with brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that the simulated and observed brightness temperatures differ up to 10 K in the period between October and June, depending on the region and the assimilation run. We show that these discrepancies between simulated and observed brightness temperature can be attributed mainly to the underlying observational uncertainty in sea ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea ice concentration and the melt pond parametrization in MPI-ESM, which is not necessarily realistic. ARC3O is therefore capable of realistically translating the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialization.

The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O)

2020 ◽  
Author(s):  
Clara Burgard ◽  
Dirk Notz ◽  
Leif T. Pedersen ◽  
Rasmus T. Tonboe
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Earth System Model ◽  
System Model ◽  
The Arctic ◽  
Earth System ◽  
Sea Ice Concentration ◽  
Brightness Temperatures ◽  
Observational Uncertainty ◽  
Ice Concentration

<p>The diversity in sea-ice concentration observational estimates retrieved from brightness temperatures measured from space is a challenge for our understanding of past and future sea-ice evolution as it inhibits reliable climate model evaluation and initialisation. To address this challenge, we introduce a new tool: the Arctic Ocean Observation Operator (ARC3O). </p><p>ARC3O allows us to simulate brightness temperatures at 6.9 GHz at vertical polarisation from standard output of an Earth System Model to be compared to observations from space at this frequency. We use simple temperature and salinity profiles inside the snow and ice column based on the output of the Earth System Model to compute these brightness temperatures. </p><p>In this study, we evaluate ARC3O by simulating brightness temperatures based on three assimilation runs of the MPI Earth System Model (MPI-ESM) assimilated with three different sea-ice concentration products. We then compare these three sets of simulated brightness temperatures to brightness temperatures measured by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) from space. We find that they differ up to 10 K in the period between October and June, depending on the region and the assimilation run. However, we show that these discrepancies between simulated and observed brightness temperature can be mainly attributed to the underlying observational uncertainty in sea-ice concentration and, to a lesser extent, to the data assimilation process, rather than to biases in ARC3O itself. In summer, the discrepancies between simulated and observed brightness temperatures are larger than in winter and locally reach up to 20 K. This is caused by the very large observational uncertainty in summer sea-ice concentration but also by the melt-pond parametrisation in MPI-ESM, which is not necessarily realistic. </p><p>ARC3O is therefore capable to realistically translate the simulated Arctic Ocean climate state into one observable quantity for a more comprehensive climate model evaluation and initialisation, an exciting perspective for further developing this and similar methods.</p>

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.

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.

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>

scholarly journals Impact of the Assimilation of Sea Ice Concentration Data on an Atmosphere-Ocean-Sea Ice Coupled Simulation of the Arctic Ocean Climate

SOLA ◽  
2011 ◽  
Vol 7 ◽  
pp. 37-40 ◽  
Author(s):  
Takahiro Toyoda ◽  
Toshiyuki Awaji ◽  
Nozomi Sugiura ◽  
Shuhei Masuda ◽  
Hiromichi Igarashi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
The Arctic ◽  
Coupled Simulation ◽  
Concentration Data ◽  
Sea Ice Concentration ◽  
Ocean Climate ◽  
Ice Concentration ◽  
The Arctic Ocean

scholarly journals Measuring sea ice concentration in the Arctic Ocean using SMOS

2016 ◽  
Author(s):  
Carolina Gabarro ◽  
Antonio Turiel ◽  
Pedro Elosegui ◽  
Joaquim A. Pla-Resina ◽  
Marcos Portabella
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Radiative Properties ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Wide Range ◽  
Ice Concentration ◽  
The Arctic Ocean ◽  
Ice Conditions ◽  
Microwave Imager

Abstract. We present a new method to estimate sea ice concentration in the Arctic Ocean using brightness temperature observations from the Soil Moisture Ocean Salinity (SMOS) interferometric satellite. The method, which employs a Maximum Likelihood Estimator (MLE), exploits the marked difference in radiative properties between sea ice and seawater, in particular when observed over the wide range of satellite viewing angles afforded by SMOS. Observations at L-band frequencies such as those from SMOS (i.e., 1.4 GHz, or equivalently 21-cm wavelength) are advantageous to remote sensing of sea ice because the atmosphere is virtually transparent at that frequency. We find that sea ice concentration is well determined (correlations of about 0.75) as compared to estimates from other sensors such as the Special Sensor Microwave/Imager (SSM/I and SSMIS). We also find that the efficacy of the method decreases under thin sea ice conditions (ice thickness

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.

Relationships between Sea Ice Concentration and Wind Speed over the Arctic Ocean during 1979–2015

2019 ◽  
Vol 32 (22) ◽  
pp. 7783-7796 ◽  
Author(s):  
Liisi Jakobson ◽  
Timo Vihma ◽  
Erko Jakobson
Keyword(s):  
Surface Roughness ◽  
Wind Speed ◽  
Sea Ice ◽  
Arctic Ocean ◽  
Time Scales ◽  
The Arctic ◽  
Sea Ice Concentration ◽  
Atmospheric Stratification ◽  
Ice Concentration ◽  
The Arctic Ocean

Abstract NCEP CFSR reanalysis 6-hourly fields from 1979 to 2015 were used to investigate the relationships of sea ice concentration (SIC), atmospheric stratification, surface roughness, and wind speed at 10-m height (W10) and 850-hPa level (W850). We found that in autumn (September–November), winter (December–February), and spring (March–May) a lower SIC favors less-stable stratification and a higher W10. In autumn, the decrease in SIC is strongest, and SIC has its strongest correlation with the atmospheric stratification, W10, and the ratio of W10 and W850 (WSR). W10 and WSR have increased in autumn, and the negative trends in SIC typically are collocated with positive trends in W10 and WSR. In winter, W850 has negative trends over the Arctic Ocean, which, together with the lack of decrease of SIC in the central Arctic, has prevented W10 from increasing in winter. The winter trends are notably different from those for autumn, but the correlations are fairly similar. In autumn, winter, and spring, the negative correlation between SIC and W10 originated from the reduction of both stratification and aerodynamic surface roughness z0 with a reduction of SIC. The dependence of z0 on SIC is, however, weak in NCEP CFSR. In summer, the ratio of W10 and W850 has increased over large areas. The correlations between SIC and atmospheric variables were stronger on interannual time scales than on subseasonal time scales. The causal relationships are complicated by the two-way interaction between SIC and W10. In most cases, especially in summer, SIC increases after periods of W10 exceeding 5 m s−1.

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