scholarly journals Inter-comparison and evaluation of sea ice algorithms: towards further identification of challenges and optimal approach using passive microwave observations

2015 ◽  
Vol 9 (5) ◽  
pp. 1797-1817 ◽  
Author(s):  
N. Ivanova ◽  
L. T. Pedersen ◽  
R. T. Tonboe ◽  
S. Kern ◽  
G. Heygster ◽  
...  

Abstract. Sea ice concentration has been retrieved in polar regions with satellite microwave radiometers for over 30 years. However, the question remains as to what is an optimal sea ice concentration retrieval method for climate monitoring. This paper presents some of the key results of an extensive algorithm inter-comparison and evaluation experiment. The skills of 30 sea ice algorithms were evaluated systematically over low and high sea ice concentrations. Evaluation criteria included standard deviation relative to independent validation data, performance in the presence of thin ice and melt ponds, and sensitivity to error sources with seasonal to inter-annual variations and potential climatic trends, such as atmospheric water vapour and water-surface roughening by wind. A selection of 13 algorithms is shown in the article to demonstrate the results. Based on the findings, a hybrid approach is suggested to retrieve sea ice concentration globally for climate monitoring purposes. This approach consists of a combination of two algorithms plus dynamic tie points implementation and atmospheric correction of input brightness temperatures. The method minimizes inter-sensor calibration discrepancies and sensitivity to the mentioned error sources.

2015 ◽  
Vol 9 (1) ◽  
pp. 1269-1313 ◽  
Author(s):  
N. Ivanova ◽  
L. T. Pedersen ◽  
R. T. Tonboe ◽  
S. Kern ◽  
G. Heygster ◽  
...  

Abstract. Sea ice concentration has been measured globally with satellite microwave radiometers for over 30 years. However there is still a need for better understanding of corresponding challenges and consequently identifying an optimal method for sea ice concentration retrieval suitable for climate monitoring. The method should minimize inter-sensor calibration discrepancies and sensitivity to error sources with climatic trends (e.g. atmospheric water vapour and water surface roughening by wind). This article presents the results of an extensive algorithm inter-comparison and validation experiment. Thirty sea ice algorithms entered the experiment where their skills were evaluated over low and high sea ice concentrations, thin ice and areas covered by melt ponds. In addition, atmospheric correction of input brightness temperatures and dynamic tie-points approach were suggested. A selection of thirteen algorithms is shown in the article to demonstrate the results. Based on the findings, an optimal approach was suggested to retrieve sea ice concentration globally for climate monitoring purposes.


2020 ◽  
Vol 12 (7) ◽  
pp. 1060 ◽  
Author(s):  
Lise Kilic ◽  
Catherine Prigent ◽  
Filipe Aires ◽  
Georg Heygster ◽  
Victor Pellet ◽  
...  

Over the last 25 years, the Arctic sea ice has seen its extent decline dramatically. Passive microwave observations, with their ability to penetrate clouds and their independency to sunlight, have been used to provide sea ice concentration (SIC) measurements since the 1970s. The Copernicus Imaging Microwave Radiometer (CIMR) is a high priority candidate mission within the European Copernicus Expansion program, with a special focus on the observation of the polar regions. It will observe at 6.9 and 10.65 GHz with 15 km spatial resolution, and at 18.7 and 36.5 GHz with 5 km spatial resolution. SIC algorithms are based on empirical methods, using the difference in radiometric signatures between the ocean and sea ice. Up to now, the existing algorithms have been limited in the number of channels they use. In this study, we proposed a new SIC algorithm called Ice Concentration REtrieval from the Analysis of Microwaves (IceCREAM). It can accommodate a large range of channels, and it is based on the optimal estimation. Linear relationships between the satellite measurements and the SIC are derived from the Round Robin Data Package of the sea ice Climate Change Initiative. The 6 and 10 GHz channels are very sensitive to the sea ice presence, whereas the 18 and 36 GHz channels have a better spatial resolution. A data fusion method is proposed to combine these two estimations. Therefore, IceCREAM will provide SIC estimates with the good accuracy of the 6+10GHz combination, and the high spatial resolution of the 18+36GHz combination.


2020 ◽  
Vol 12 (10) ◽  
pp. 1594
Author(s):  
Catherine Prigent ◽  
Lise Kilic ◽  
Filipe Aires ◽  
Victor Pellet ◽  
Carlos Jimenez

A new methodology has been described in Kilic et al. (Ice Concentration Retrieval from the Analysis of Microwaves: A New Methodology Designed for the Copernicus Imaging Microwave Radiometer, Remote Sensing 2020, 12, 1060, Part 1 of this study) to estimate Sea Ice Concentration (SIC) from satellite passive microwave observations between 6 and 36 GHz. The Ice Concentration Retrieval from the Analysis of Microwaves (IceCREAM) algorithm is based on an optimal estimation, with a simple radiative transfer model derived from satellite observations at 0% and 100% SIC. Observations at low and high frequencies have different spatial resolutions, and a scheme is developed to benefit from the low errors of the low frequencies and the high spatial resolutions of the high frequencies. This effort is specifically designed for the Copernicus Imaging Microwave Radiometer (CIMR) project, equipped with a large deployable antenna to provide a spatial resolution of ∼5 km at 18 and 36 GHz, and ∼15 km at 6 and 10 GHz. The algorithm is tested with Advanced Microwave Scanning Radiometer 2 (AMSR2) observations, for a clear scene over the north polar region, with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) estimates and the Ocean Sea Ice—Satellite Application Facilities (OSI SAF) operational products. Several algorithm options are tested, and the study case shows that both high spatial resolution and low errors are obtained with the IceCREAM method. It is also tested for the full polar regions, winter and summer, under clear and cloudy conditions. Our method is globally applicable, without fine-tuning or further weather filtering. The systematic use of all channels from 6 to 36 GHz makes it robust to changes in ice surface conditions and to weather interactions.


2020 ◽  
Vol 12 (6) ◽  
pp. 1043
Author(s):  
Liyuan Jiang ◽  
Yong Ma ◽  
Fu Chen ◽  
Jianbo Liu ◽  
Wutao Yao ◽  
...  

Polynyas are an important factor in the Antarctic and Arctic climate, and their changes are related to the ecosystems in the polar regions. The phenomenon of polynyas is influenced by the combination of inherent persistence and dynamic factors. The dynamics of polynyas are greatly affected by temporal dynamical factors, and it is difficult to objectively reflect the internal characteristics of their formation. Separating the two factors effectively is necessary in order to explore their essence. The Special Sensor Microwave/Imager (SSM/I) passive microwave sensor has been making observations of Antarctica for more than 20 years, but it is difficult for existing current sea ice concentration (SIC) products to objectively reflect how the inherent persistence factors affect the formation of polynyas. In this paper, we proposed a long-term multiple spatial smoothing method to remove the influence of dynamic factors and obtain stable annual SIC products. A halo located on the border of areas of low and high ice concentration around the Antarctic coast, which has a strong similarity with the local seabed in outline, was found using the spatially smoothed SIC products and seabed. The relationship of the polynya location to the wind and topography is a long-understood relationship; here, we quantify that where there is an abrupt slope and wind transitions, new polynyas are best generated. A combination of image expansion and threshold segmentation was used to extract the extent of sea ice and coastal polynyas. The adjusted record of changes in the extent of coastal polynyas and sea ice in the Southern Ocean indicate that there is a negative correlation between them.


2021 ◽  
Author(s):  
Katharina Hartmuth ◽  
Lukas Papritz ◽  
Maxi Boettcher ◽  
Heini Wernli

<p>Single extreme weather events such as intense storms or blocks can have a major impact on polar surface temperatures, the formation and melting rates of sea-ice, and, thus, on minimum and maximum sea-ice extent within a particular year. Anomalous weather conditions on the time scale of an entire season, for example resulting from an unusual sequence of storms, can affect the polar energy budget and sea-ice coverage even more. Here, we introduce the concept of an extreme season in a distinct region using an EOF analysis in the phase space spanned by anomalies of a set of surface parameters (surface temperature, precipitation, surface solar and thermal radiation and surface heat fluxes). To focus on dynamical instead of climate change aspects, we define anomalies as departures of the seasonal mean from a transient climatology. The goal of this work is to study the dynamical processes leading to such anomalous seasons in the polar regions, which have not yet been analysed. Specifically, we focus here on a detailed analysis of Arctic extreme seasons and their underlying atmospheric dynamics in the ERA5 reanalysis data set.</p><p>We find that in regions covered predominantly by sea ice, extreme seasons are mostly determined by anomalies of atmospheric dynamical features such as cyclones and blocking. In contrast, in regions including large areas of open water the formation of extreme seasons can also be partially due to preconditioning during previous seasons, leading to strong anomalies in the sea ice concentration and/or sea surface temperatures at the beginning of the extreme season.</p><p>Two particular extreme season case studies in the Kara-Barents Seas are discussed in more detail. In this region, the winter of 2011/12 shows the largest positive departure of surface temperature from the background warming trend together with a negative anomaly in the sea ice concentration. An analysis of the synoptic situation shows that the strongly reduced frequency of cold air outbreaks compared to climatology combined with several blocking events and the frequent occurrence of cyclones transporting warm air into the region favored the continuous anomalies of both parameters. In contrast, the winter of 2016/17, which shows a positive precipitation anomaly and negative anomaly in the surface energy balance, was favored by a strong surface preconditioning. An extremely warm summer and autumn in 2016 caused strongly reduced sea ice concentrations and increased sea surface temperatures in the Kara-Barents Seas at the beginning of the winter, favoring increased air-sea fluxes and precipitation during the following months.</p><p>Our results reveal a high degree of variability of the processes involved in the formation of extreme seasons in the Arctic. Quantifying and understanding these processes will also be important when considering climate change effects in polar regions and the ability of climate models in reproducing extreme seasons in the Arctic and Antarctica.</p>


2020 ◽  
Vol 12 (18) ◽  
pp. 2880
Author(s):  
Shuang Liang ◽  
Jiangyuan Zeng ◽  
Zhen Li ◽  
Dejing Qiao ◽  
Ping Zhang ◽  
...  

Sea ice concentration (SIC) plays a significant role in climate change research and ship’s navigation in polar regions. Satellite-based SIC products have become increasingly abundant in recent years; however, the uncertainty of these products still exists and needs to be further investigated. To comprehensively evaluate the consistency of the SIC derived from different SIC algorithms in long time series and the whole polar regions, we compared four passive microwave (PM) satellite SIC products with the ERA-Interim sea ice fraction dataset during the period of 2015–2018. The PM SIC products include the SSMIS/ASI, AMSR2/BT, the Chinese FY3B/NT2, and FY3C/NT2. The results show that the remotely sensed SIC products derived from different SIC algorithms are generally in good consistency. The spatial and temporal distribution of discrepancy among satellite SIC products for both Arctic and Antarctic regions are also observed. The most noticeable difference for all the four SIC products mostly occurs in summer and at the marginal ice zone, indicating that large uncertainties exist in satellite SIC products in such period and areas. The SSMIS/ASI and AMSR2/BT show relatively better consistency with ERA-Interim in the Arctic and Antarctic, respectively, but they exhibit opposite bias (dry/wet) relative to the ERA-Interim data. The sea ice extent (SIE) and sea ice area (SIA) derived from PM and ERA-Interim SIC were also compared. It is found that the difference of PM SIE and SIA varies seasonally, which is in line with that of PM SIC, and the discrepancy between PM and ERA-Interim data is larger in Arctic than in Antarctic. We also noticed that different algorithms have different performances in different regions and periods; therefore, the hybrid of multiple algorithms is a promising way to improve the accuracy of SIC retrievals. It is expected that our findings can contribute to improving the satellite SIC algorithms and thus promote the application of these useful products in global climate change studies.


2021 ◽  
Author(s):  
Charel Wohl ◽  
Anna E. Jones ◽  
William T. Sturges ◽  
Philip D. Nightingale ◽  
Brent Else ◽  
...  

Abstract. The marginal sea ice zone has been identified as a source of different climate active gases to the atmosphere due to its unique biogeochemistry. However, it remains highly undersampled and the impact of changes in sea ice concentration on the distributions of these gases is poorly understood. To address this, we present measurements of dissolved methanol, acetone, acetaldehyde, dimethyl sulfide and isoprene in the sea ice zone of the Canadian Arctic from the surface down to 60 m. The measurements were made using a Segmented Flow Coil Equilibrator coupled to a Proton Transfer Reaction Mass Spectrometer. These gases varied in concentrations with depth, with the highest concentrations generally observed near the surface. Underway (3–4 m) measurements showed broadly higher concentrations in partial sea ice cover compared to ice-free waters. The large number of depth profiles at different sea ice coverages enables proposition of the likely dominant production processes of these compounds in this area. Methanol concentrations appear to be controlled by specific biological consumption processes. Acetone and acetaldehyde concentrations are influenced by the penetration depth of light and the mixed layer depth, implying dominant photochemical sources in this area. Dimethyl sulfide and isoprene both display higher surface concentrations in partial sea ice coverage compared to ice-free waters due to ice edge blooms. Dimethyl sulfide concentrations sometimes display a subsurface maximum in ice -free conditions, while isoprene displays more reliably a subsurface maximum. Surface gas concentrations were used to estimate their air – sea fluxes. Despite obvious in situ production, we estimate that the sea ice zone is absorbing methanol and acetone from the atmosphere. In contrast, DMS and isoprene are consistently emitted from the ocean, with marked episodes of high emissions during ice-free conditions, suggesting that these gases are produced in ice-covered areas and emitted once the ice has melted. Our measurements show that the seawater concentrations and air-sea fluxes of these gases are clearly impacted by sea ice concentration. These novel measurements and insights will allow us to better constrain the cycling of these gases in the polar regions and their effect on the oxidative capacity and aerosol budget in the Arctic atmosphere.


2005 ◽  
Vol 133 (12) ◽  
pp. 3678-3692 ◽  
Author(s):  
S. Dierer ◽  
K. H. Schlünzen ◽  
G. Birnbaum ◽  
B. Brümmer ◽  
G. Müller

Abstract A high-resolution atmosphere–sea ice model is used to investigate the interactions between cyclones and sea ice cover in polar regions. For this purpose, a cyclone passage observed during the 1999 Fram Strait Cyclone Experiment (FRAMZY) is simulated for two consecutive days. The results of the coupled mesoscale transport and stream model–mesoscale sea ice model (METRAS–MESIM) are compared with aircraft and ice drift measurements. With the exception of temperature, all atmospheric parameters are well simulated. Main reasons for discrepancies were found in large differences between the measurements and the forcing data taken from the results of the regional model (REMO). In addition, advection was slightly wrong as a result of a 17° deviation in wind directions. The altogether well simulated wind field is interactively used to force the sea ice model MESIM; results agree well with drift buoy measurements. Average deviations of simulated and measured ice drift are smaller than 8° for direction and smaller than 3.7 cm s−1 for speed, which is less than 10% of the average speed. The simulated ratio between ice drift and wind velocity increases slightly during cyclone passage from 2.6% to 2.9%, a tendency also known from observations. During a 36-h period, the simulated sea ice concentration locally decreases up to 20% in accordance with measurements. A neglect of changing sea ice cover causes a decrease of the heat flux to the atmosphere from 53 to 12 W m−2. The values correspond to averages over the evaluation region (approximately 228 000 km2) and period (36 h). Temperature and humidity are decreased by 2 K and 0.2 g kg−1, respectively, over the ice-covered region. In contrast, the effect on pressure and wind remains small, probably because the cyclone does not move in the vicinity of the ice edge.


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