Arctic climate and snow cover trends – Comparing Global Circulation Models with remote sensing observations

Eythorsson, Darri; Garðarsson, Sigurður M.; Ahmad, S.; Hossain, Faisal; Nijssen, Bart

doi:10.1016/j.jag.2019.04.003

Cited by 17 publications

(14 citation statements)

References 39 publications

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“…This aligns with observations of winter mass balance of Icelandic glaciers in recent years with a slight significant positive trend for the past 20 years (Pálsson and Gunnarsson, 2016c) as well as an observed precipitation increase (Björnsson et al, 2018). These results are consistent with previous findings that suggest that an slight increase in snow cover extent/area is observed in Iceland (Eythorsson et al, 2019;Wang et al, 2018) even though a general decreasing snow cover extent/area and shortening of the melt season in the Northern Hemisphere is reported in many other studies (Choi et al, 2010;Hori et al, 2017).…”

Section: Discussionsupporting

confidence: 92%

“…The two main effects of snow on the cryosphere are its control on the reflection of radiation, reaching the surface of Earth and balancing its radiation budget (Barry, 2002;Warren, 1982) and isolating properties which can influence the length of the growing season (Keller et al, 2005;Barichivich et al, 2013). Snow albedo dominates the control of its irradiance feedback, which depends on various factors such as snow depth, snow cover extent, vegetation and cloud cover (Fernandes et al, 2009;Qu and Hall, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…In the Northern Hemisphere the spring snow cover extent has decreased significantly, influencing the dynamics of spring melt intensity and timing in recent years (Adam et al, 2008;Barnett et al, 2005;Choi et al, 2010;Hori et al, 2017). Various studies using remotely sensed data, observations and climate models unanimously agree that on the Northern Hemisphere scale snow cover extent is receding by 2.5 to 10 days per decade depending on the study period (Eythorsson et al, 2019;Fontrodona Bach et al, 2018;Choi et al, 2010;Hori et al, 2017;Wang et al, 2018;Liston and Hiemstra, 2011). On regional scales snow cover changes can vary depending on local climatology and its variability.…”

Section: Introductionmentioning

confidence: 99%

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Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

Gunnarsson

Garðarsson

Sveinsson

2019

Hydrol. Earth Syst. Sci.

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This study presents a spatio-temporal continuous data set for snow cover in Iceland based on the Moderate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2018. Cloud cover and polar darkness are the main limiting factors for data availability of remotely sensed optical data at higher latitudes. In Iceland the average cloud cover is 75 % with some spatial variations, and polar darkness reduces data availability from the MODIS sensor from late November until mid January. In this study MODIS snow cover data were validated over Iceland with comparison to manned in situ observations and Landsat 7/8 and Sentinel 2 data. Overall a good agreement was found between in situ observed snow cover, with an average agreement of 0.925. Agreement of Landsat 7/8 and Sentinel 2 was found to be acceptable, with R 2 values 0.96, 0.92 and 0.95, respectively, and in agreement with other studies. By applying daily data merging from Terra and Aqua and a temporal aggregation of 7 d, unclassified pixels were reduced from 75 % to 14 %. The remaining unclassified pixels after daily merging and temporal aggregation were removed with classification learners trained with classified data, pixel location, aspect and elevation. Various snow cover characteristic metrics were derived for each pixel such as snow cover duration, first and last snow-free dates, deviation and dynamics of snow cover and trends during the study period. On average the first snow-free date in Iceland is 27 June, with a standard deviation of 19.9 d. For the study period a trend of increasing snow cover duration was observed for all months except October and November. However, statistical testing of the trends indicated that there was only a significant trend in June.

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

Gunnarsson

Garðarsson

Sveinsson

2019

Hydrol. Earth Syst. Sci.

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“…The data file contained 36 calibrated and geolocated spectral bands from the optical to TIR wavelength regions and have a spatial resolution of 1 km comprised of 1354 by 2030 pixels. Cloud pixels in the MOD021KM were identified with MODIS cloud mask product (MOD35_L2) and pixels with a zenith angle > 25° were not used to reduce the panoramic bowtie effect (Eythorsson et al, 2019). The TIR bands 31 and 32 which are respectively centered on 11.03 and 12.02 µm, were used to retrieve the SST using a split-window algorithm (Hall and Riggs, 2001) that is adapted for use with MODIS data, whose accuracy was reported within 2°C.…”

Section: Modis Datamentioning

confidence: 99%

Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

Yin¹,

Li²,

Ge³

et al. 2021

Preprint

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Abstract. Turbulent heat flux (THF) over leads is an important variable used for monitoring climate change in the Arctic. Presently, THF over leads is often calculated from satellite imagery. The accuracy of the estimated THF is low for mixed pixels that consist of ice and leads, because the mixed pixels along lead boundaries will lower the accuracy of the surface temperature measured over leads and the corresponding lead map. To address this problem, a deep residual convolutional neural network (CNN)-based framework is proposed to estimate THF over leads at the subpixel scale (DeepSTHF) with remotely sensed imagery. The DeepSTHF allows the production of a sea surface temperature (SST) image and a corresponding lead map with a finer spatial resolution than the input SST image using two CNNs, so that the subpixel scale THF can be estimated from them. The proposed approach is assessed using simulated and real MODIS imagery and compared against the conventional bicubic interpolation and pixel-based methods. The results demonstrate that the proposed CNN-based method can effectively estimate subpixel-scale information from the coarse data and performs well in producing fine spatial resolution SST images and lead maps, thereby allowing researchers to obtain more accurate and reliable THF over leads.

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“…Gao et al [36] utilized GEE to obtain the optimal threshold for identifying snow cover under different land cover types on the Tibetan Plateau, particularly in sparse vegetation and grasslands. Eythorsson et al [37] assessed the impact of climate changes on snow cover frequency over the Arctic for the period of 2001-2016 by using GEE for all spatial analyses in their study. As a promising platform, GEE enables researchers to fully assess the performance of MOD10A1 at large scales by using long-time series Landsat images, therefore guaranteeing a robust and comprehensive evaluation of the MOD10A1 product.…”

Section: Introductionmentioning

confidence: 99%

An Assessment and Error Analysis of MOD10A1 Snow Product Using Landsat and Ground Observations Over China During 2000–2016

Liu

Zhang

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The MOD10A1 daily snow cover product offers a fine temporal resolution, which is particularly important for timecritical applications. Although the reliability of this product was mostly verified by ground measurements before use, the inadequate representativeness of point-scale observations may lead to errors in the assessment of MOD10A1. In this study, based on the Google Earth Engine, MOD10A1 was thoroughly evaluated by a total of 20 131 binary snow maps generated from 30 m Landsat imagery for the 2000-2016 snow seasons in three typical snow regions across China. The three typical snow regions included Northern Xinjiang, Northeast China, and the Tibetan Plateau. In general, the accuracy of MOD10A1 under clear sky conditions is 86.5% based on ground observations from meteorological stations and 90.3% based on Landsat imagery. Compared with ground measurements, snow cover derived from Landsat images is in better agreement with MOD10A1 due to a better spatial match. The error analyses also indicate that 1) compared with land cover, topography has larger effects on the accuracy of MOD10A1 over the three snow regions with statistically significant negative relationships, particularly in Northern Xinjiang (R = −0.604, P < 0.05); and 2) MOD10A1 shows a relatively lower accuracy of 81.8% from 2006 to 2008 due to the decrease in snow depth during this period (R = 0.58, P < 0.05) based on the Mann-Kendall test.

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Arctic climate and snow cover trends – Comparing Global Circulation Models with remote sensing observations

Cited by 17 publications

References 39 publications

Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

Icelandic snow cover characteristics derived from a gap-filled MODIS daily snow cover product

Estimating subpixel turbulent heat flux over leads from MODIS thermal infrared imagery with deep learning

An Assessment and Error Analysis of MOD10A1 Snow Product Using Landsat and Ground Observations Over China During 2000–2016

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