scholarly journals Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

2017 ◽  
Author(s):  
André Ehrlich ◽  
Eike Bierwirth ◽  
Larysa Istomina ◽  
Manfred Wendisch
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Sea Ice ◽  
Liquid Water ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Cloud Properties ◽  
Snow Properties ◽  
Surface Snow ◽  
Effective Grain Size

Abstract. In the Arctic, the passive solar remote sensing of cloud properties over highly reflecting ground is challenging due to the low contrast between the clouds and underlying surfaces (sea ice and snow). Uncertainties in retrieved cloud optical thickness τ and cloud droplet effective radius reff,C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the commonly unknown snow effective grain size reff,S. Therefore, in a first step this snow grain size effect is quantified systematically for a conventional bi-spectral retrieval of τ and reff,C for liquid water clouds. The largest impact of reff,S of up to 83 % on τ and 62 % on reff,C was found in case of small reff,S and optically thin clouds. In the second part of the paper a retrieval method is presented that simultaneously retrieves all three parameters (τ, reff,C, reff,S) in order to account for changes of the snow grain size in the cloud retrieval algorithm. Spectral cloud reflectivities at the three wavelength λ1 = 1040 nm (sensitive to reff,S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff,C) were normalized to reflectivity ratios and combined in a tri-spectral retrieval algorithm. Measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART-Albedometer) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct sea ice edge were analyzed. The retrieved values of τ, reff,C, and reff,S consistently represented the cloud properties across this transition from snow-covered sea ice to the open water and were comparable to estimates based on satellite data. Analysis showed, that the uncertainties of the tri-spectral retrieval increase for high τ, and low reff,S, but nevertheless allows a simultaneous retrieval of cloud and surface snow properties providing snow effective grain size estimates in cloud-covered areas.

scholarly journals Combined retrieval of Arctic liquid water cloud and surface snow properties using airborne spectral solar remote sensing

2017 ◽  
Vol 10 (9) ◽  
pp. 3215-3230 ◽  
Author(s):  
André Ehrlich ◽  
Eike Bierwirth ◽  
Larysa Istomina ◽  
Manfred Wendisch
Keyword(s):  
Remote Sensing ◽  
Grain Size ◽  
Sea Ice ◽  
Optical Thickness ◽  
Liquid Water ◽  
Retrieval Algorithm ◽  
Continuous Transition ◽  
Cloud Properties ◽  
Cloud Optical Thickness ◽  
The Impact

Abstract. The passive solar remote sensing of cloud properties over highly reflecting ground is challenging, mostly due to the low contrast between the cloud reflectivity and that of the underlying surfaces (sea ice and snow). Uncertainties in the retrieved cloud optical thickness τ and cloud droplet effective radius reff, C may arise from uncertainties in the assumed spectral surface albedo, which is mainly determined by the generally unknown effective snow grain size reff, S. Therefore, in a first step the effects of the assumed snow grain size are systematically quantified for the conventional bispectral retrieval technique of τ and reff, C for liquid water clouds. In general, the impact of uncertainties of reff, S is largest for small snow grain sizes. While the uncertainties of retrieved τ are independent of the cloud optical thickness and solar zenith angle, the bias of retrieved reff, C increases for optically thin clouds and high Sun. The largest deviations between the retrieved and true original values are found with 83 % for τ and 62 % for reff, C. In the second part of the paper a retrieval method is presented that simultaneously derives all three parameters (τ, reff, C, reff, S) and therefore accounts for changes in the snow grain size. Ratios of spectral cloud reflectivity measurements at the three wavelengths λ1 = 1040 nm (sensitive to reff, S), λ2 = 1650 nm (sensitive to τ), and λ3 = 2100 nm (sensitive to reff, C) are combined in a trispectral retrieval algorithm. In a feasibility study, spectral cloud reflectivity measurements collected by the Spectral Modular Airborne Radiation measurement sysTem (SMART) during the research campaign Vertical Distribution of Ice in Arctic Mixed-Phase Clouds (VERDI, April/May 2012) were used to test the retrieval procedure. Two cases of observations above the Canadian Beaufort Sea, one with dense snow-covered sea ice and another with a distinct snow-covered sea ice edge are analysed. The retrieved values of τ, reff, C, and reff, S show a continuous transition of cloud properties across snow-covered sea ice and open water and are consistent with estimates based on satellite data. It is shown that the uncertainties of the trispectral retrieval increase for high values of τ, and low reff, S but nevertheless allow the effective snow grain size in cloud-covered areas to be estimated.

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>

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

scholarly journals The Influence of Cloud and Surface Properties on the Arctic Ocean Shortwave Radiation Budget in Coupled Models*

2008 ◽  
Vol 21 (5) ◽  
pp. 866-882 ◽  
Author(s):  
Irina V. Gorodetskaya ◽  
L-Bruno Tremblay ◽  
Beate Liepert ◽  
Mark A. Cane ◽  
Richard I. Cullather
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Liquid Water ◽  
Surface Albedo ◽  
Liquid Water Content ◽  
Radiation Budget ◽  
The Arctic ◽  
Coupled Models ◽  
Arctic Clouds ◽  
The Arctic Ocean

Abstract The impact of Arctic sea ice concentrations, surface albedo, cloud fraction, and cloud ice and liquid water paths on the surface shortwave (SW) radiation budget is analyzed in the twentieth-century simulations of three coupled models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report. The models are the Goddard Institute for Space Studies Model E-R (GISS-ER), the Met Office Third Hadley Centre Coupled Ocean–Atmosphere GCM (UKMO HadCM3), and the National Center for Atmosphere Research Community Climate System Model, version 3 (NCAR CCSM3). In agreement with observations, the models all have high Arctic mean cloud fractions in summer; however, large differences are found in the cloud ice and liquid water contents. The simulated Arctic clouds of CCSM3 have the highest liquid water content, greatly exceeding the values observed during the Surface Heat Budget of the Arctic Ocean (SHEBA) campaign. Both GISS-ER and HadCM3 lack liquid water and have excessive ice amounts in Arctic clouds compared to SHEBA observations. In CCSM3, the high surface albedo and strong cloud SW radiative forcing both significantly decrease the amount of SW radiation absorbed by the Arctic Ocean surface during the summer. In the GISS-ER and HadCM3 models, the surface and cloud effects compensate one another: GISS-ER has both a higher summer surface albedo and a larger surface incoming SW flux when compared to HadCM3. Because of the differences in the models’ cloud and surface properties, the Arctic Ocean surface gains about 20% and 40% more solar energy during the melt period in the GISS-ER and HadCM3 models, respectively, compared to CCSM3. In twenty-first-century climate runs, discrepancies in the surface net SW flux partly explain the range in the models’ sea ice area changes. Substantial decrease in sea ice area simulated during the twenty-first century in CCSM3 is associated with a large drop in surface albedo that is only partly compensated by increased cloud SW forcing. In this model, an initially high cloud liquid water content reduces the effect of the increase in cloud fraction and cloud liquid water on the cloud optical thickness, limiting the ability of clouds to compensate for the large surface albedo decrease. In HadCM3 and GISS-ER, the compensation of the surface albedo and cloud SW forcing results in negligible changes in the net SW flux and is one of the factors explaining moderate future sea ice area trends. Thus, model representations of cloud properties for today’s climate determine the ability of clouds to compensate for the effect of surface albedo decrease on the future shortwave radiative budget of the Arctic Ocean and, as a consequence, the sea ice mass balance.

scholarly journals Clouds damp the radiative impacts of polar sea ice loss

2020 ◽  
Vol 14 (8) ◽  
pp. 2673-2686 ◽  
Author(s):  
Ramdane Alkama ◽  
Patrick C. Taylor ◽  
Lorea Garcia-San Martin ◽  
Herve Douville ◽  
Gregory Duveiller ◽  
...  
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Surface Radiation ◽  
Radiation Budget ◽  
The Arctic ◽  
Cloud Properties ◽  
Surface Radiation Budget ◽  
Cloud Radiative Effect ◽  
Radiative Impact ◽  
The Impact

Abstract. Clouds play an important role in the climate system: (1) cooling Earth by reflecting incoming sunlight to space and (2) warming Earth by reducing thermal energy loss to space. Cloud radiative effects are especially important in polar regions and have the potential to significantly alter the impact of sea ice decline on the surface radiation budget. Using CERES (Clouds and the Earth's Radiant Energy System) data and 32 CMIP5 (Coupled Model Intercomparison Project) climate models, we quantify the influence of polar clouds on the radiative impact of polar sea ice variability. Our results show that the cloud short-wave cooling effect strongly influences the impact of sea ice variability on the surface radiation budget and does so in a counter-intuitive manner over the polar seas: years with less sea ice and a larger net surface radiative flux show a more negative cloud radiative effect. Our results indicate that 66±2% of this change in the net cloud radiative effect is due to the reduction in surface albedo and that the remaining 34±1 % is due to an increase in cloud cover and optical thickness. The overall cloud radiative damping effect is 56±2 % over the Antarctic and 47±3 % over the Arctic. Thus, present-day cloud properties significantly reduce the net radiative impact of sea ice loss on the Arctic and Antarctic surface radiation budgets. As a result, climate models must accurately represent present-day polar cloud properties in order to capture the surface radiation budget impact of polar sea ice loss and thus the surface albedo feedback.

scholarly journals Iodine observed in new particle formation events in the Arctic atmosphere during ACCACIA

2014 ◽  
Vol 14 (21) ◽  
pp. 28949-28972 ◽  
Author(s):  
J. D. Allan ◽  
P. I. Williams ◽  
J. Najera ◽  
J. D. Whitehead ◽  
M. J. Flynn ◽  
...  
Keyword(s):  
Sea Ice ◽  
Molecular Iodine ◽  
Particle Formation ◽  
The Arctic ◽  
New Particle Formation ◽  
Sulphur Compounds ◽  
Cloud Properties ◽  
Coastal Sea ◽  
Arctic Atmosphere ◽  
Initial Source

Abstract. Accurately accounting for new particle formation (NPF) is crucial to our ability to predict aerosol number concentrations and thus cloud properties, which is in turn vital in simulating radiative transfer and climate. Here we present an analysis of NPF events observed in the Greenland Sea during the summertime as part of the Aerosol-Cloud Coupling And Climate Interactions in the Arctic (ACCACIA) project. While NPF events have been reported in the Arctic before, we were able, for the first time, to detect iodine in the growing particles using an Aerosol Mass Spectrometer (AMS) during a persistent event in the region of the coastal sea ice near Greenland. Given the potency of iodine as a nucleation precursor, the results imply that iodine was responsible for the initial NPF, a phenomenon that has been reported at lower latitudes and associated with molecular iodine emissions from coastal macroalgae. The initial source of iodine in this instance is not clear, but it was associated with air originating approximately 1 day previously over melting coastal sea ice. These results show that atmospheric models must consider iodine as a source of new particles in addition to established precursors such as sulphur compounds.

scholarly journals Numerical simulation of extreme snow melt observed at the SIGMA-A site, northwest Greenland, during summer 2012

2015 ◽  
Vol 9 (1) ◽  
pp. 495-539
Author(s):  
M. Niwano ◽  
T. Aoki ◽  
S. Matoba ◽  
S. Yamaguchi ◽  
T. Tanikawa ◽  
...  
Keyword(s):  
Heat Flux ◽  
Grain Size ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
In Situ Measurements ◽  
The Arctic ◽  
Snow Surface ◽  
Near Surface ◽  
Surface Snow

Abstract. The surface energy balance (SEB) from 30 June to 14 July 2012 at site SIGMA (Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic)-A, (78°03' N, 67°38' W; 1490 m a.s.l.) on the northwest Greenland Ice Sheet (GrIS) was investigated by using in situ atmospheric and snow measurements, as well as numerical modeling with a one-dimensional, multi-layered, physical snowpack model called SMAP (Snow Metamorphism and Albedo Process). At SIGMA-A, remarkable near-surface snowmelt and continuous heavy rainfall (accumulated precipitation between 10 and 14 July was estimated to be 100 mm) were observed after 10 July 2012. Application of the SMAP model to the GrIS snowpack was evaluated based on the snow temperature profile, snow surface temperature, surface snow grain size, and shortwave albedo, all of which the model simulated reasonably well. However, comparison of the SMAP-calculated surface snow grain size with in situ measurements during the period when surface hoar with small grain size was observed on-site revealed that it was necessary to input air temperature, relative humidity, and wind speed data from two heights to simulate the latent heat flux into the snow surface and subsequent surface hoar formation. The calculated latent heat flux was always directed away from the surface if data from only one height were input to the SMAP model, even if the value for roughness length of momentum was perturbed between the possible maximum and minimum values in numerical sensitivity tests. This result highlights the need to use two-level atmospheric profiles to obtain realistic latent heat flux. Using such profiles, we calculated the SEB at SIGMA-A from 30 June to 14 July 2012. Radiation-related fluxes were obtained from in situ measurements, whereas other fluxes were calculated with the SMAP model. By examining the components of the SEB, we determined that low-level clouds accompanied by a significant temperature increase played an important role in the melt event observed at SIGMA-A. These conditions induced a remarkable surface heating via cloud radiative forcing in the polar region.

scholarly journals Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

2019 ◽  
Vol 11 (19) ◽  
pp. 2280 ◽  
Author(s):  
Alexander Kokhanovsky ◽  
Maxim Lamare ◽  
Olaf Danne ◽  
Carsten Brockmann ◽  
Marie Dumont ◽  
...  
Keyword(s):  
Grain Size ◽  
Specific Surface ◽  
Surface Area ◽  
Specific Surface Area ◽  
Environmental Changes ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
The Arctic ◽  
Observation Data ◽  
Snow Properties

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

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