scholarly journals Spatiotemporal variability of solar radiation introduced by clouds over Arctic sea ice

2020 ◽  
Vol 13 (4) ◽  
pp. 1757-1775 ◽  
Author(s):  
Carola Barrientos Velasco ◽  
Hartwig Deneke ◽  
Hannes Griesche ◽  
Patric Seifert ◽  
Ronny Engelmann ◽  
...  
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Arctic Sea Ice ◽  
Spatiotemporal Variability ◽  
Cloud Base ◽  
Small Scale ◽  
Global Irradiance ◽  
Synoptic Conditions ◽  
Single Station ◽  
Sky Conditions

Abstract. The role of clouds in recent Arctic warming is not fully understood, including their effects on the solar radiation and the surface energy budget. To investigate relevant small-scale processes in detail, the intensive Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL) drifting ice floe station field campaign was conducted during early summer in the central arctic. During this campaign, the small-scale spatiotemporal variability of global irradiance was observed for the first time on an ice floe with a dense network of autonomous pyranometers. A total of 15 stations were deployed covering an area of 0.83 km×1.59 km from 4–16 June 2017. This unique, open-access dataset is described here, and an analysis of the spatiotemporal variability deduced from this dataset is presented for different synoptic conditions. Based on additional observations, five typical sky conditions were identified and used to determine the values of the mean and variance of atmospheric global transmittance for these conditions. Overcast conditions were observed 39.6 % of the time predominantly during the first week, with an overall mean transmittance of 0.47. The second most frequent conditions corresponded to multilayer clouds (32.4 %), which prevailed in particular during the second week, with a mean transmittance of 0.43. Broken clouds had a mean transmittance of 0.61 and a frequency of occurrence of 22.1 %. Finally, the least frequent sky conditions were thin clouds and cloudless conditions, which both had a mean transmittance of 0.76 and occurrence frequencies of 3.5 % and 2.4 %, respectively. For overcast conditions, lower global irradiance was observed for stations closer to the ice edge, likely attributable to the low surface albedo of dark open water and a resulting reduction of multiple reflections between the surface and cloud base. Using a wavelet-based multi-resolution analysis, power spectra of the time series of atmospheric transmittance were compared for single-station and spatially averaged observations and for different sky conditions. It is shown that both the absolute magnitude and the scale dependence of variability contains characteristic features for the different sky conditions.

scholarly journals Spatiotemporal variability of shortwave radiation introduced by clouds over the Arctic sea ice

2019 ◽  
Author(s):  
Carola Barrientos Velasco ◽  
Hartwig Deneke ◽  
Hannes Griesche ◽  
Patric Seifert ◽  
Ronny Engelmann ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Spatiotemporal Variability ◽  
Shortwave Radiation ◽  
The Arctic ◽  
Cloud Base ◽  
Small Scale ◽  
Global Irradiance ◽  
Synoptic Conditions ◽  
Sky Conditions

Abstract. The role of clouds in recent Arctic warming is not fully understood, including their effects on the shortwave radiation and the surface energy budget. To investigate relevant small-scale processes in detail, an intensive field campaign was conducted during early summer in the central Arctic during the Physical feedbacks of Arctic planetary boundary layer, Sea ice, Cloud and AerosoL (PASCAL) drifting ice floe station. During this campaign, the small-scale spatiotemporal variability of global irradiance was observed for the first time on an ice floe with a dense network of autonomous pyranometers. 15 stations were deployed covering an area of 0.83 km × 1.3 km from June 4–16, 2017. This unique, open-access dataset is described here, and an analysis of the spatiotemporal variability deduced from this dataset is presented for different synoptic conditions. Based on additional observations, 5 typical sky conditions were identified and used to determine the values of the mean and variance of atmospheric global transmittance for these conditions. Overcast conditions were observed 39.6 % of the time predominantly during the first week, with an overall mean transmittance of 0.47. The second-most frequent conditions corresponded to multi-layer clouds (32.4 %) which prevailed in particular during the second week, with a mean transmittance of 0.43. Broken clouds had a mean transmittance of 0.61 and a frequency of occurrence of 22.1 %. Finally, the least frequent sky conditions were thin clouds and cloudless conditions, which both had a mean transmittance of 0.76, and occurrence frequencies of 3.5 % and 2.4 %, respectively. For overcast conditions, lower global irradiance was observed for stations closer to the ice edge, likely attributable to the low surface albedo of dark open water, and a resulting reduction of multiple reflections between the surface and cloud base. Using a wavelet-based multi-resolution analysis, power spectra of the time-series of atmospheric transmittance were compared for single-station and spatially averaged observations, and for different sky conditions. It is shown that both the absolute magnitude and the scale-dependence of variability contains characteristic features for the different sky conditions.

scholarly journals Deep Learning Convolutional Neural Network Applying for the Arctic Acoustic Tomography Current Inversion Accuracy Improvement

2021 ◽  
Vol 9 (7) ◽  
pp. 755
Author(s):  
Kangkang Jin ◽  
Jian Xu ◽  
Zichen Wang ◽  
Can Lu ◽  
Long Fan ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sea Ice ◽  
Convolutional Neural Network ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Small Scale ◽  
Acoustic Tomography ◽  
Arctic Sea ◽  
Current Inversion

Warm current has a strong impact on the melting of sea ice, so clarifying the current features plays a very important role in the Arctic sea ice coverage forecasting study field. Currently, Arctic acoustic tomography is the only feasible method for the large-range current measurement under the Arctic sea ice. Furthermore, affected by the high latitudes Coriolis force, small-scale variability greatly affects the accuracy of Arctic acoustic tomography. However, small-scale variability could not be measured by empirical parameters and resolved by Regularized Least Squares (RLS) in the inverse problem of Arctic acoustic tomography. In this paper, the convolutional neural network (CNN) is proposed to enhance the prediction accuracy in the Arctic, and especially, Gaussian noise is added to reflect the disturbance of the Arctic environment. First, we use the finite element method to build the background ocean model. Then, the deep learning CNN method constructs the non-linear mapping relationship between the acoustic data and the corresponding flow velocity. Finally, the simulation result shows that the deep learning convolutional neural network method being applied to Arctic acoustic tomography could achieve 45.87% accurate improvement than the common RLS method in the current inversion.

scholarly journals Investigation of spatiotemporal variability of melt pond fraction and its relationship with sea ice extent during 2000–2017 using a new data

2019 ◽  
Author(s):  
Yifan Ding ◽  
Xiao Cheng ◽  
Jiping Liu ◽  
Fengming Hui ◽  
Zhenzhan Wang
Keyword(s):  
Sea Ice ◽  
Arctic Basin ◽  
Beaufort Sea ◽  
Arctic Sea Ice ◽  
Spatiotemporal Variability ◽  
First Year ◽  
Spatial And Temporal Variability ◽  
Sea Ice Extent ◽  
Melt Pond ◽  
Melt Ponds

Abstract. The accurate knowledge of variations of melt ponds is important for understanding Arctic energy budget due to its albedo-transmittance-melt feedback. In this study, we develop and validate a new method for retrieving melt pond fraction (MPF) from the MODIS surface reflectance. We construct an ensemble-based deep neural network and use in-situ observations of MPF from multi-sources to train the network. The results show that our derived MPF is in good agreement with the observations, and relatively outperforms the MPF retrieved by University of Hamburg. Built on this, we create a new MPF data from 2000 to 2017 (the longest data in our knowledge), and analyze the spatial and temporal variability of MPF. It is found that the MPF has significant increasing trends from late July to early September, which is largely contributed by the MPF over the first-year sea ice. The analysis based on our MPF during 2000–2017 confirms that the integrated MPF to late June does promise to improve the prediction skill of seasonal Arctic sea ice minimum. However, our MPF data shows concentrated significant correlations first appear in a band, extending from the eastern Beaufort Sea, through the central Arctic, to the northern East Siberian and Laptev Seas in early-mid June, and then shifts towards large areas of the Beaufort Sea, Canadian Arctic, the northern Greenland Sea and the central Arctic basin.

scholarly journals Multiresolution analysis of the spatiotemporal variability in global radiation observed by a dense network of 99 pyranometers during the HOPE campaign

2016 ◽  
Author(s):  
Bomidi Lakshmi Madhavan ◽  
Hartwig Deneke ◽  
Jonas Witthuhn ◽  
Andreas Macke
Keyword(s):  
Autocorrelation Function ◽  
Multiresolution Analysis ◽  
Global Radiation ◽  
Power Spectra ◽  
Spatiotemporal Variability ◽  
Discrete Wavelet ◽  
Dense Network ◽  
Point Measurement ◽  
Single Station ◽  
Sky Conditions

Abstract. The time series of global radiation observed by a dense network of 99 autonomous pyranometers are investigated with a multiresolution analysis based on the maximum overlap discrete wavelet transform and the Haar wavelet. For different sky conditions, typical wavelet power spectra are calculated to quantify the timescale dependence of variability in global transmittance. The power spectra of global transmittance are found to be dominated by the direct irradiance component under all sky conditions. Distinctly higher variability is observed at all frequencies in the power spectra of global transmittance under broken cloud conditions compared to clear, cirrus or overcast skies. The spatial autocorrelation function including its frequency-dependence is determined to quantify the degree of similarity of two measurements as a function of their spatial separation. Distances ranging from 100 m to 10 km are considered, and a rapid decrease of the autocorrelation function is found with increasing frequency and distance. For frequencies below 1.0 min−1, variations in transmittance become completely uncorrelated already after several hundred meters. A method is introduced to estimate the deviation between a point measurement and a spatially averaged value for a surrounding domain, which takes into account domain size and averaging period, and is used to explore the representativeness of a single pyranometer observation for its surrounding region. Two distinct mechanisms are identified, which limit the representativeness: on the one hand, spatial averaging reduces variability and thus modifies the shape of the power spectrum. On the other hand, the correlation of variations of the spatially averaged field and a point measurement decreases rapidly with increasing temporal frequency. For a grid-box of 10 x 10 km2 and averaging periods of 1.5–3 h, the deviation of global transmittance between a point measurement and an area-averaged value depends on the prevailing sky conditions: 2.8 % (clear), 1.8 % (cirrus), 1.5 % (overcast) and 4.2 % (broken clouds). The global radiation observed at a single station is found to deviate from the spatial average by as much as 14–23 Wm−2 (clear), 8–26 Wm−2 (cirrus), 4–23 Wm−2 (overcast), and 31–79 Wm−2 (broken clouds) from domain averages ranging from 1 x 1 km2 to 10 x 10 km2 in area.

scholarly journals Spatiotemporal Variability of Barium in Arctic Sea‐Ice and Seawater

2018 ◽  
Vol 123 (5) ◽  
pp. 3507-3522 ◽  
Author(s):  
Katharine R. Hendry ◽  
Kimberley M. Pyle ◽  
G. Barney Butler ◽  
Adam Cooper ◽  
Agneta Fransson ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Spatiotemporal Variability ◽  
Arctic Sea

scholarly journals Advances in understanding and parameterization of small-scale physical processes in the marine Arctic climate system: a review

2014 ◽  
Vol 14 (17) ◽  
pp. 9403-9450 ◽  
Author(s):  
T. Vihma ◽  
R. Pirazzini ◽  
I. Fer ◽  
I. A. Renfrew ◽  
J. Sedlar ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Sea Ice ◽  
Surface Fluxes ◽  
Climate System ◽  
Arctic Climate ◽  
The Arctic ◽  
Small Scale ◽  
Climate Modelling ◽  
Physical Processes ◽  
Made In

Abstract. The Arctic climate system includes numerous highly interactive small-scale physical processes in the atmosphere, sea ice, and ocean. During and since the International Polar Year 2007–2009, significant advances have been made in understanding these processes. Here, these recent advances are reviewed, synthesized, and discussed. In atmospheric physics, the primary advances have been in cloud physics, radiative transfer, mesoscale cyclones, coastal, and fjordic processes as well as in boundary layer processes and surface fluxes. In sea ice and its snow cover, advances have been made in understanding of the surface albedo and its relationships with snow properties, the internal structure of sea ice, the heat and salt transfer in ice, the formation of superimposed ice and snow ice, and the small-scale dynamics of sea ice. For the ocean, significant advances have been related to exchange processes at the ice–ocean interface, diapycnal mixing, double-diffusive convection, tidal currents and diurnal resonance. Despite this recent progress, some of these small-scale physical processes are still not sufficiently understood: these include wave–turbulence interactions in the atmosphere and ocean, the exchange of heat and salt at the ice–ocean interface, and the mechanical weakening of sea ice. Many other processes are reasonably well understood as stand-alone processes but the challenge is to understand their interactions with and impacts and feedbacks on other processes. Uncertainty in the parameterization of small-scale processes continues to be among the greatest challenges facing climate modelling, particularly in high latitudes. Further improvements in parameterization require new year-round field campaigns on the Arctic sea ice, closely combined with satellite remote sensing studies and numerical model experiments.

scholarly journals Arctic sea-ice conditions and the distribution of solar radiation during summer

1997 ◽  
Vol 25 ◽  
pp. 445-450 ◽  
Author(s):  
Donald K. Perovich ◽  
Walter B. Tucker
Keyword(s):  
Solar Radiation ◽  
Sea Ice ◽  
Open Water ◽  
Ice Cover ◽  
Feedback Mechanism ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Melt Ponds ◽  
Arctic Ice ◽  
Median Area

Understanding the interaction of solar radiation with the ice cover is critical in determining the heat and mass balance of the Arctic ice pack, and in assessing potential impacts due to climate change. Because of the importance of the ice-albedo feedback mechanism, information on the surface state of the ice cover is needed. Observations of the surface slate of sea ice were obtained from helicopter photography missions made during the 1994 Arctic Ocean Section cruise. Photographs from one flight, taken during the height of the melt season (31 July 1994) at 76° N, 172° W, were analyzed in detail. Bare ice covered 82% of the total area, melt ponds 12%, and open water 6%, There was considerable variability in these area fractions on scales < 1 km2. Sample areas >2 3 km2gave representative values of ice concentration and pond fraction. Melt ponds were numerous, with a number density of 1800 ponds km-2. The melt ponds had a mean area of 62 m2a median area of 14 m2, and a size distribution that was well lit by a cumulative lognormal distribution. While leads make up only a small portion of the total area, they are the source of virtually all of the solar energy input to the ocean.

scholarly journals Multiresolution analysis of the spatiotemporal variability in global radiation observed by a dense network of 99 pyranometers

2017 ◽  
Vol 17 (5) ◽  
pp. 3317-3338 ◽  
Author(s):  
Bomidi Lakshmi Madhavan ◽  
Hartwig Deneke ◽  
Jonas Witthuhn ◽  
Andreas Macke
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Multiresolution Analysis ◽  
Global Radiation ◽  
Power Spectra ◽  
Spatiotemporal Variability ◽  
Dense Network ◽  
Point Measurement ◽  
Single Station ◽  
Sky Conditions

Abstract. The time series of global radiation observed by a dense network of 99 autonomous pyranometers during the HOPE campaign around Jülich, Germany, are investigated with a multiresolution analysis based on the maximum overlap discrete wavelet transform and the Haar wavelet. For different sky conditions, typical wavelet power spectra are calculated to quantify the timescale dependence of variability in global transmittance. Distinctly higher variability is observed at all frequencies in the power spectra of global transmittance under broken-cloud conditions compared to clear, cirrus, or overcast skies. The spatial autocorrelation function including its frequency dependence is determined to quantify the degree of similarity of two time series measurements as a function of their spatial separation. Distances ranging from 100 m to 10 km are considered, and a rapid decrease of the autocorrelation function is found with increasing frequency and distance. For frequencies above 1∕3 min−1 and points separated by more than 1 km, variations in transmittance become completely uncorrelated. A method is introduced to estimate the deviation between a point measurement and a spatially averaged value for a surrounding domain, which takes into account domain size and averaging period, and is used to explore the representativeness of a single pyranometer observation for its surrounding region. Two distinct mechanisms are identified, which limit the representativeness; on the one hand, spatial averaging reduces variability and thus modifies the shape of the power spectrum. On the other hand, the correlation of variations of the spatially averaged field and a point measurement decreases rapidly with increasing temporal frequency. For a grid box of 10 km  ×  10 km and averaging periods of 1.5–3 h, the deviation of global transmittance between a point measurement and an area-averaged value depends on the prevailing sky conditions: 2.8 (clear), 1.8 (cirrus), 1.5 (overcast), and 4.2 % (broken clouds). The solar global radiation observed at a single station is found to deviate from the spatial average by as much as 14–23 (clear), 8–26 (cirrus), 4–23 (overcast), and 31–79 W m−2 (broken clouds) from domain averages ranging from 1 km  ×  1 km to 10 km  ×  10 km in area.

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