scholarly journals Airborne measurements of directional reflectivity over the marginal sea ice zone

2021 ◽  
Author(s):  
Sebastian Becker ◽  
André Ehrlich ◽  
Evelyn Jäkel ◽  
Tim Carlsen ◽  
Michael Schäfer ◽  
...  
Keyword(s):  
Sea Ice ◽  
Digital Camera ◽  
Open Ocean ◽  
The Arctic ◽  
Data Sets ◽  
Satellite Measurements ◽  
Inhomogeneous Surface ◽  
Airborne Measurements ◽  
Marginal Sea ◽  
Reflectance Factor

Abstract. The directional reflection of solar radiation by the Arctic Ocean is dominated by two main surface types: sea ice (often snow-covered) and ice-free (open) ocean. However, in the transitional marginal sea ice zone (MIZ), the reflection properties of both surface types are mixed, which might cause uncertainties in the results of retrieval methods of atmospheric parameters over the MIZ using airborne and satellite measurements. To quantify these uncertainties, respective measurements of reflection properties of the MIZ are needed. Therefore, in this study, an averaged hemispherical-directional reflectance factor (HDRF) of the inhomogeneous surface (mixture of sea ice and open ocean surfaces) in the MIZ is derived using airborne measurements collected with a digital fish-eye camera. For this purpose, a sea ice mask was constructed to separate the reflectivity measurements from sea ice and open ocean pixels. The separated data sets were accumulated and averaged to provide separate HDRFs for sea ice and open ocean surfaces. The respective results were compared with simulations and independent measurements available from the literature. Using the sea ice fraction derived in parallel from the digital camera images, the mixed HDRF describing the directional reflectivity of the inhomogeneous surface of the MIZ was reconstructed by a linear weighting procedure. The result was compared with the original measurements of directional reflectivity over the MIZ. It is concluded that the HDRF of the MIZ can be well reconstructed by linear combination of the HDRFs of homogeneous sea ice and open ocean surfaces, accounting for the special conditions present in the MIZ compared to homogeneous surfaces.

scholarly journals Radiative energy budget and cloud radiative forcing in the daytime marginal sea ice zone during Arctic spring and summer

2021 ◽  
Author(s):  
Johannes Stapf ◽  
André Ehrlich ◽  
Christof Lüpkes ◽  
Manfred Wendisch
Keyword(s):  
Boundary Layer ◽  
Sea Ice ◽  
Energy Budget ◽  
Radiative Forcing ◽  
The Arctic ◽  
Radiative Energy ◽  
Cloud Radiative Forcing ◽  
Airborne Measurements ◽  
Marginal Sea ◽  
The Impact

Abstract. Airborne measurements of the surface radiative energy budget (REB) collected in the area of the marginal sea ice zone (MIZ) close to Svalbard (Norway) during two campaigns conducted in early spring and and early summer are presented. From the data, the cloud radiative forcing was derived. The analysis is focussed on the impact of changing atmospheric thermodynamic conditions on the REB and on the linkage of sea ice properties and cloud radiative forcing (CRF). The observed two-mode longwave net irradiance frequency distributions above sea ice are compared with measurements from previous studies. The transition of both states (cloudy and cloud-free) from winter towards summer and the associated broadening of the modes is discussed as a function of the seasonal thermodynamic profiles and the surface type. The influence of cold air outbreaks (CAO) and warm air intrusions on the REB is illustrated for several case studies, whereby the source and sink terms of REB in the evolving CAO boundary layer are quantified. Furthermore, the role of thermodynamic profiles and the vertical location of clouds during on-ice flow is illustrated. The sea ice concentration was identified as the main driver of the shortwave cooling by the clouds. The longwave warming of clouds, estimated to about 75 W m−2, seems to be representative for this region, as compared to other studies. Simplified radiative transfer simulations of the frequently observed low-level boundary layer clouds and average thermodynamic profiles represent the observed radiative quantities fairly well. The simulations illustrate the delicate interplay of surface and cloud properties that modify the REB and CRF, and the challenges in quantifying trends in the Arctic REB induced by potential changes of the cloud optical thickness.

scholarly journals Uncertainties of satellite-derived surface skin temperatures in the polar oceans: MODIS, AIRS/AMSU, and AIRS only

2015 ◽  
Vol 8 (10) ◽  
pp. 4025-4041 ◽  
Author(s):  
H.-J. Kang ◽  
J.-M. Yoo ◽  
M.-J. Jeong ◽  
Y.-I. Won
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Data Sets ◽  
High Latitudes ◽  
Ice Surface ◽  
Moderate Resolution ◽  
Microwave Sounding ◽  
Polar Oceans ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Ice Surface Temperature

Abstract. Uncertainties in the satellite-derived surface skin temperature (SST) data in the polar oceans during two periods (16–24 April and 15–23 September) 2003–2014 were investigated and the three data sets were intercompared as follows: MODerate Resolution Imaging Spectroradiometer Ice Surface Temperature (MODIS IST), the SST of the Atmospheric Infrared Sounder/Advanced Microwave Sounding Unit-A (AIRS/AMSU), and AIRS only. The AIRS only algorithm was developed in preparation for the degradation of the AMSU-A. MODIS IST was systematically warmer up to 1.65 K at the sea ice boundary and colder down to −2.04 K in the polar sea ice regions of both the Arctic and Antarctic than that of the AIRS/AMSU. This difference in the results could have been caused by the surface classification method. The spatial correlation coefficient of the AIRS only to the AIRS/AMSU (0.992–0.999) method was greater than that of the MODIS IST to the AIRS/AMSU (0.968–0.994). The SST of the AIRS only compared to that of the AIRS/AMSU had a bias of 0.168 K with a RMSE of 0.590 K over the Northern Hemisphere high latitudes and a bias of −0.109 K with a RMSE of 0.852 K over the Southern Hemisphere high latitudes. There was a systematic disagreement between the AIRS retrievals at the boundary of the sea ice, because the AIRS only algorithm utilized a less accurate GCM forecast over the seasonally varying frozen oceans than the microwave data. The three data sets (MODIS, AIRS/AMSU and AIRS only) showed significant warming rates (2.3 ± 1.7 ~ 2.8 ± 1.9 K decade−1) in the northern high regions (70–80° N) as expected from the ice-albedo feedback. The systematic temperature disagreement associated with surface type classification had an impact on the resulting temperature trends.

Pan-Arctic surface ozone seasonality modified by sea-ice-sourced bromine: modelling vs measurements

2021 ◽  
Author(s):  
Xin Yang ◽  
Anne-M Blechschmidt2 ◽  
Kristof Bognar ◽  
Audra McClure–Begley ◽  
Sara Morris ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Sea Ice ◽  
Surface Ozone ◽  
Open Ocean ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Spray ◽  
Research Station ◽  
Blowing Snow ◽  
Model Control

<p>Within the framework of the International Arctic Systems for Observing the Atmosphere (IASOA), we report a modelling-based study on surface ozone across the Arctic. We use surface ozone from six sites: Summit (Greenland), Pallas (Finland), Barrow (USA), Alert (Canada), Tiksi (Russia), and Villum Research Station (VRS) at Station Nord (North Greenland, Danish Realm), and ozonesonde data from three Canadian sites: Resolute, Eureka, and Alert. Two global chemistry models: a global chemistry transport model (p-TOMCAT) and a global chemistry climate model (UKCA), are used for model-data comparisons. Remotely sensed data of BrO from the GOME-2 satellite instrument at Eureka, Canada are used for model validation.</p><p>The observed climatology data show that spring surface ozone at coastal Arctic is heavily depleted, making ozone seasonality at Arctic coastal sites distinctly different from that at inland sites. Model simulations show that surface ozone can be greatly reduced by bromine chemistry. In April, bromine chemistry can cause a net ozone loss (monthly mean) of 10-20 ppbv, with almost half attributable to open-ocean-sourced bromine and the rest to sea-ice-sourced bromine. However, the open-ocean-sourced bromine, via sea spray bromide depletion, cannot by itself produce ozone depletion events (ODEs) (defined as ozone volume mixing ratios VMRs < 10 ppbv). In contrast, sea-ice-sourced bromine, via sea salt aerosol (SSA) production from blowing snow, can produce ODEs even without bromine from sea spray, highlighting the importance of sea ice surface in polar boundary layer chemistry.</p><p>Modelled total inorganic bromine (Br<sub>Y</sub>) over the Arctic sea ice  is sensitive to model configuration, e.g., under the same bromine loading, Br<sub>Y</sub> in the Arctic spring boundary layer in the p-TOMCAT control run (i.e., with all bromine emissions) can be 2 times that in the UKCA control run. Despite the model differences, both model control runs can successfully reproduce large bromine explosion events (BEEs) and ODEs in polar spring. Model-integrated tropospheric column BrO generally matches GOME-2 tropospheric columns within ~50% in UKCA and a factor of 2 in p-TOMCAT. The success of the models in reproducing both ODEs and BEEs in the Arctic indicates that the relevant parameterizations implemented in the models work reasonably well, which supports the proposed mechanism of SSA production and bromide release on sea ice. Given that sea ice is a large source of SSA and halogens, changes in sea ice type and extent in a warming climate will influence Arctic boundary layer chemistry, including the oxidation of atmospheric elemental mercury. Note that this work dose not necessary rule out other possibilities that may act as a source of reactive bromine from sea ice zone.</p>

scholarly journals On the Arctic Wintertime Climate in Global Climate Models

2011 ◽  
Vol 24 (22) ◽  
pp. 5757-5771 ◽  
Author(s):  
Gunilla Svensson ◽  
Johannes Karlsson
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Precipitable Water ◽  
Open Ocean ◽  
Global Climate Models ◽  
The Arctic ◽  
Turbulent Heat ◽  
Skin Temperatures ◽  
Longwave Flux ◽  
The Arctic Region

Abstract Energy fluxes important for determining the Arctic surface temperatures during winter in present-day simulations from the Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel dataset are investigated. The model results are evaluated over different surfaces using satellite retrievals and ECMWF interim reanalysis (ERA-Interim). The wintertime turbulent heat fluxes vary substantially between models and different surfaces. The monthly median net turbulent heat flux (upward) is in the range 100–200 W m−2 and −15 to 15 W m−2 over open ocean and sea ice, respectively. The simulated net longwave radiative flux at the surface is biased high over both surfaces compared to observations but for different reasons. Over open ocean, most models overestimate the outgoing longwave flux while over sea ice it is rather the downwelling flux that is underestimated. Based on the downwelling longwave flux over sea ice, two categories of models are found. One group of models that shows reasonable downwelling longwave fluxes, compared with observations and ERA-Interim, is also associated with relatively high amounts of precipitable water as well as surface skin temperatures. This group also shows more uniform airmass properties over the Arctic region possibly as a result of more frequent events of warm-air intrusion from lower latitudes. The second group of models underestimates the downwelling longwave radiation and is associated with relatively low surface skin temperatures as well as low amounts of precipitable water. These models also exhibit a larger decrease in the moisture and temperature profiles northward in the Arctic region, which might be indicative of too stagnant conditions in these models.

scholarly journals Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison: Closed Ice and Ship-Based Observations

2019 ◽  
Author(s):  
Stefan Kern ◽  
Thomas Lavergne ◽  
Dirk Notz ◽  
Leif Toudal Pedersen ◽  
Rasmus Tage Tonboe ◽  
...  
Keyword(s):  
Sea Ice ◽  
The Arctic ◽  
Data Sets ◽  
Sea Ice Concentration ◽  
Data Set ◽  
Group I ◽  
Ice Concentration ◽  
Data Products ◽  
Group Ii ◽  
The Antarctic

Abstract. Accurate sea-ice concentration (SIC) data are a pre-requisite to reliably monitor the polar sea-ice covers. Over the last four decades, many algorithms have been developed to retrieve the SIC from satellite microwave radiometry, some of them applied to generate long-term data products. We report on results of a systematic inter-comparison of ten global SIC data products at 12.5 to 50.0 km grid resolution for both the Arctic and the Antarctic. The products are compared with each other with respect to differences in SIC, sea-ice area (SIA), and sea-ice extent (SIE), and they are compared against a global winter-time near-100 % reference SIC data set for closed pack ice conditions and against global year-round ship-based visual observations of the sea-ice cover. We can group the products based on the observed inter-product consistency and differences of the inter-comparison results. Group I consists of data sets using the self-optimizing EUMETSAT-OSISAF – ESA-CCI algorithms. Group II includes data using the NASA-Team 2 and Comiso-Bootstrap algorithms, and the NOAA-NSIDC sea-ice concentration climate data record (CDR). The standard NASA-Team and the ARTIST Sea Ice (ASI) algorithms are put into a separate group III because of their often quite diverse results. Within group I and II evaluation results and intra-product differences are mostly very similar. For instance, among group I products, SIA agrees within ±100 000 km2 in both hemispheres during maximum and minimum sea-ice cover. Among group II products, satellite- minus ship-based SIC differences agree within ±0.7 %. Standing out with large negative differences to other products and evaluation data is the standard NASA-Team algorithm, in both hemispheres. The three CDRs of group I (SICCI-25km, SICCI-50km, and OSI-450) are biased low compared to the 100 % reference SIC with biases of −0.4 % to −1.0 % (Arctic) and −0.3 % to −1.1 % (Antarctic). Products of group II appear to be mostly biased high in the Arctic by between +1.0 % and +3.5 %, while their biases in the Antarctic only range from −0.2  to +0.9 %. The standard deviation is smaller in the Arctic for the quoted group I products: 1.9 % to 2.9 % and Antarctic: 2.5 % to 3.1 %, than for group II products: Arctic: 3.6 % to 5.0 %, Antarctic: 4.5 % to 5.4 %. Products of group I exhibit larger overall satellite- minus ship-based SIC differences than group II in both hemispheres. However, compared to group II, group I products’ standard deviations are smaller, correlations higher and evaluation results are less sensitive to seasonal changes. We discuss the impact of truncating the SIC distribution, as naturally retrieved by the algorithms around the 100 % sea-ice concentration end. We show that evaluation studies of such truncated SIC products can result in misleading statistics and favour data sets that systematically overestimate SIC. We describe a method to re-construct the un-truncated distribution of SIC before the evaluation is performed. On the basis of this evaluation, we open a discussion about the overestimation of SIC in data products, with far-reaching consequences for, e.g., surface heat-flux estimations in winter. We also document inconsistencies in the behaviour of the weather filters used in products of group II, and suggest advancing studies about the influence of these weather filters on SIA and SIE time-series and their trends.

Comparison of Arctic cloud properties over sea ice and open ocean based on airborne spectral solar remote sensing

2021 ◽  
Author(s):  
Marcus Klingebiel ◽  
André Ehrlich ◽  
Elena Ruiz-Donoso ◽  
Manfred Wendisch
Keyword(s):  
Remote Sensing ◽  
Sea Ice ◽  
Open Water ◽  
Open Ocean ◽  
Arctic Sea Ice ◽  
Radiative Properties ◽  
Radiation Budget ◽  
The Arctic ◽  
Liquid Water Path ◽  
Cloud Properties

<p>Over the last decades, the Arctic has experienced an enhanced warming, which is known as Arctic amplification. This process leads to a decrease in the amount of Arctic sea ice, which is linked by different feedback mechanisms to clouds and the related radiative properties. To analyze how the properties of these Arctic clouds could change in a future sea ice free Arctic, we completed three airborne campaigns in the marginal sea ice zone between 2017 and 2020 covering summer and winter conditions. During these campaigns we performed in-situ and remote sensing measurements to study cloud micro- and macrophysical properties and analyzed how these clouds affect the radiation budget. In this study we use the passive remote sensing measurements from these airborne observations to retrieve cloud top effective radius, liquid water path and cloud optical thickness. We found that these cloud properties differ between a sea ice surface and over open water. The airborne observations are supported by an analysis of the cloud product from the MODIS satellite. The systematic differences of clouds over sea ice and the open ocean suggests that clouds may change in a future warming Arctic environment.</p>

Sea ice representation in CMIP6 simulations with EC-Earth3-Veg

2020 ◽  
Author(s):  
Torben Koenigk ◽  
Evelien Dekker
Keyword(s):  
Sea Ice ◽  
Emission Scenario ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Ice Thickness ◽  
First Year ◽  
Data Sets ◽  
Sea Ice Extent ◽  
Sea Ice Thickness ◽  
Arctic Sea

<p>In this study, we compare the sea ice in ensembles of historical and future simulations with EC-Earth3-Veg to the sea ice of the NSIDC and OSA-SAF satellite data sets. The EC-Earth3-Veg Arctic sea ice extent generally matches well to the observational data sets, and the trend over 1980-2014 is captured correctly. Interestingly, the summer Arctic sea ice area minimum occurs already in August in the model. Mainly east of Greenland, sea ice area is overestimated. In summer, Arctic sea ice is too thick compared to PIOMAS. In March, sea ice thickness is slightly overestimated in the Central Arctic but in the Bering and Kara Seas, the ice thickness is lower than in PIOMAS.</p><p>While the general picture of Arctic sea ice looks good, EC-Earth suffers from a warm bias in the Southern Ocean. This is also reflected by a substantial underestimation of sea ice area in the Antarctic.</p><p>Different ensemble members of the future scenario projections of sea ice show a large range of the date of first year with a minimum ice area below 1 million square kilometers in the Arctic. The year varies between 2024 and 2056. Interestingly, this range does not differ very much with the emission scenario and even under the low emission scenario SSP1-1.9 summer Arctic sea ice almost totally disappears.</p>

scholarly journals Sea ice as a source of sea salt aerosol to Greenland ice cores: a model-based study

2017 ◽  
Vol 17 (15) ◽  
pp. 9417-9433 ◽  
Author(s):  
Rachael H. Rhodes ◽  
Xin Yang ◽  
Eric W. Wolff ◽  
Joseph R. McConnell ◽  
Markus M. Frey
Keyword(s):  
Sea Ice ◽  
Atmospheric Chemistry ◽  
Transport Model ◽  
Ice Core ◽  
Ice Cores ◽  
Open Ocean ◽  
Sea Salt ◽  
The Arctic ◽  
Sea Salt Aerosol ◽  
Annual Variability

Abstract. Growing evidence suggests that the sea ice surface is an important source of sea salt aerosol and this has significant implications for polar climate and atmospheric chemistry. It also suggests the potential to use ice core sea salt records as proxies for past sea ice extent. To explore this possibility in the Arctic region, we use a chemical transport model to track the emission, transport, and deposition of sea salt from both the open ocean and the sea ice, allowing us to assess the relative importance of each. Our results confirm the importance of sea ice sea salt (SISS) to the winter Arctic aerosol burden. For the first time, we explicitly simulate the sea salt concentrations of Greenland snow, achieving values within a factor of two of Greenland ice core records. Our simulations suggest that SISS contributes to the winter maxima in sea salt characteristic of ice cores across Greenland. However, a north–south gradient in the contribution of SISS relative to open-ocean sea salt (OOSS) exists across Greenland, with 50 % of winter sea salt being SISS at northern sites such as NEEM (77° N), while only 10 % of winter sea salt is SISS at southern locations such as ACT10C (66° N). Our model shows some skill at reproducing the inter-annual variability in sea salt concentrations for 1991–1999, particularly at Summit where up to 62 % of the variability is explained. Future work will involve constraining what is driving this inter-annual variability and operating the model under different palaeoclimatic conditions.

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