Browsing by Subject "SPECTRAL ALBEDO"
Now showing items 1-8 of 8
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(2017)Snow consists of non-spherical grains of various shapes and sizes. Still, in radiative transfer calculations, snow grains are often treated as spherical. This also applies to the computation of snow albedo in the Snow, Ice, and Aerosol Radiation (SNICAR) model and in the Los Alamos sea ice model, version 4 (CICE4), both of which are employed in the Community Earth System Model and in the Norwegian Earth System Model (NorESM). In this study, we evaluate the effect of snow grain shape on climate simulated by NorESM in a slab ocean configuration of the model. An experiment with spherical snow grains (SPH) is compared with another (NONSPH) in which the snow shortwave single-scattering properties are based on a combination of three non-spherical snow grain shapes optimized using measurements of angular scattering by blowing snow. The key difference between these treatments is that the asymmetry parameter is smaller in the non-spherical case (0.77-0.78 in the visible region) than in the spherical case (similar to 0.89). Therefore, for the same effective snow grain size (or equivalently, the same specific projected area), the snow broadband albedo is higher when assuming non-spherical rather than spherical snow grains, typically by 0.02-0.03. Considering the spherical case as the baseline, this results in an instantaneous negative change in net shortwave radiation with a global-mean top-of-the-model value of ca. -0.22Wm(-2). Although this global-mean radiative effect is rather modest, the impacts on the climate simulated by NorESM are substantial. The global annual-mean 2m air temperature in NONSPH is 1.17K lower than in SPH, with substantially larger differences at high latitudes. The climatic response is amplified by strong snow and sea ice feedbacks. It is further demonstrated that the effect of snow grain shape could be largely offset by adjusting the snow grain size. When assuming non-spherical snow grains with the parameterized grain size increased by ca. 70 %, the climatic differences to the SPH experiment become very small. Finally, the impact of assumed snow grain shape on the radiative effects of absorbing aerosols in snow is discussed.
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Light-absorption of dust and elemental carbon in snow in the Indian Himalayas and the Finnish Arctic (2018)Light-absorbing impurities (LAIs) deposited in snow have the potential to substantially affect the snow radiation budget, with subsequent implications for snow melt. To more accurately quantify the snow albedo, the contribution from different LAIs needs to be assessed. Here we estimate the main LAI components, elemental carbon (EC) (as a proxy for black carbon) and mineral dust in snow from the Indian Himalayas and paired the results with snow samples from Arctic Finland. The impurities are collected onto quartz filters and are analyzed thermal-optically for EC, as well as with an additional optical measurement to estimate the light-absorption of dust separately on the filters. Laboratory tests were conducted using substrates containing soot and mineral particles, especially prepared to test the experimental setup. Analyzed ambient snow samples show EC concentrations that are in the same range as presented by previous research, for each respective region. In terms of the mass absorption cross section (MAC) our ambient EC surprisingly had about half of the MAC value compared to our laboratory standard EC (chimney soot), suggesting a less light absorptive EC in the snow, which has consequences for the snow albedo reduction caused by EC. In the Himalayan samples, larger contributions by dust (in the range of 50% or greater for the light absorption caused by the LAI) highlighted the importance of dust acting as a light absorber in the snow. Moreover, EC concentrations in the Indian samples, acquired from a 120 cm deep snow pit (possibly covering the last five years of snow fall), suggest an increase in both EC and dust deposition. This work emphasizes the complexity in determining the snow albedo, showing that LAI concentrations alone might not be sufficient, but additional transient effects on the light-absorbing properties of the EC need to be considered and studied in the snow. Equally as imperative is the confirmation of the spatial and temporal representativeness of these data by comparing data from several and deeper pits explored at the same time.
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(2017)Several types of filter-based instruments are used to estimate aerosol light absorption coefficients. Two significant results are presented based on Aethalometer measurements at six Arctic stations from 2012 to 2014. First, an alternative method of post-processing the Aethalometer data is presented, which reduces measurement noise and lowers the detection limit of the instrument more effectively than box-car averaging. The biggest benefit of this approach can be achieved if instrument drift is minimised. Moreover, by using an attenuation threshold criterion for data post-processing, the relative uncertainty from the electronic noise of the instrument is kept constant. This approach results in a time series with a variable collection time (Delta t) but with a constant relative uncertainty with regard to electronic noise in the instrument. An additional advantage of this method is that the detection limit of the instrument will be lowered at small aerosol concentrations at the expense of temporal resolution, whereas there is little to no loss in temporal resolution at high aerosol concentrations (>2.1-6.7Mm(-1) as measured by the Aethalometers). At high aerosol concentrations, minimising the detection limit of the instrument is less critical. Additionally, utilising co-located filter-based absorption photometers, a correction factor is presented for the Arctic that can be used in Aethalometer corrections available in literature. The correction factor of 3.45 was calculated for low-elevation Arctic stations. This correction factor harmonises Aethalometer attenuation coefficients with light absorption coefficients as measured by the co-located light absorption photometers. Using one correction factor for Arctic Aethalometers has the advantage that measurements between stations become more inter-comparable.
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(2019)We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms. A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI-based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth <30 cm) the mean NDSI-0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.
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(2015)Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March-May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50 degrees N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56% of variation of albedo in March, 76% in April and 92% in May. Therefore the effects of other parameters were investigated only for areas with 100% SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between -15 and -10 degrees C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100% SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.
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(2015)In order to quantify the effects of absorbing contaminants on snow, a series of spectral reflectance measurements were conducted. Chimney soot, volcanic sand, and glaciogenic silt were deposited on a natural snow surface in a controlled way as a part of the Soot on Snow (SoS) campaign. The bidirectional reflectance factors of these soiled surfaces and untouched snow were measured using the Finnish Geodetic Institute's Field Goniospectropolariradiometer, FIGIFIGO. A remarkable feature is the fact that the absorbing contaminants on snow enhanced the metamorphism of snow under strong sunlight in our experiments. Immediately after deposition, the contaminated snow surface appeared darker than the natural snow in all viewing directions, but the absorbing particles sank deep into the snow in minutes. The nadir measurement remained the darkest, but at larger zenith angles, the surface of the contaminated snow changed back to almost as white as clean snow. Thus, for a ground observer the darkening caused by impurities can be completely invisible, overestimating the albedo, but a nadir-observing satellite sees the darkest points, underestimating the albedo. Through a reciprocity argument, we predict that at noon, the albedo perturbation should be lower than in the morning or after-noon. When sunlight stimulates sinking more than melting, the albedo should be higher in the afternoon than in the morning, and vice versa when melting dominates. However, differences in the hydrophobic properties, porosity, clumping, or size of the impurities may cause different results than observed in these measurements.
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(2016)Soot has a pronounced effect on the cryosphere and experiments are still needed to reduce the associated uncertainties. This work presents a series of experiments to address this issue, with soot being deposited onto a natural snow surface after which the albedo changes were monitored. The albedo reduction was the most pronounced for the snow with higher soot content, and it was observed immediately following soot deposition. Compared with a previous laboratory study the effects of soot on the snow were not as prominent in outdoor conditions. During snowmelt, about 50% of the originally deposited soot particles were observed to remain at the snow surface. More detailed experiments are however needed to better explain soot's effect on snow and to better quantify this effect. Our albedo versus soot parameterization agreed relatively well with previously published relationships.
