Greenland Ice Sheet Daily Surface Melt Flux Observed From Space

Zheng, Liu; Cheng, Xiao; Shang, Xinyi; Chen, Zhuoqi; Liang, Qi; Wang, Kang

doi:10.1029/2021gl096690

Cited by 13 publications

(9 citation statements)

References 80 publications

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“…Uncertainties of the discharge estimates in this study stem from the relationship between discharge and water extent, the interpolation, the determination of the inundation period, and the effect of snow cover on the reflectance of MODIS data. Our discharge estimates failed to capture the high discharge signal in 2012 when there was an extreme melt event in the GrIS [32], [33]. Under the ultra-high discharge condition, the is almost saturated and it is not very sensitive to high flows.…”

Section: A Uncertainty Analysismentioning

confidence: 74%

Discharge Estimation With Improved Methods Using MODIS Data in Greenland: An Application in the Watson River

Lin

Cheng

Zheng

et al. 2022

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

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Greenland's river discharge has important implications for the Greenland Ice Sheet (GrIS) mass balance, global sea-level rise, and climate change. However, long-term and continuous in-situ discharge data for Greenland are scarce. The water extent is an important proxy to estimate discharge using remote sensing, but previous studies on estimating the discharge in Greenland required in-situ reflectance data to construct the water extent and suffered from inefficient processing. Here, we derived the water extent solely from the Moderate Resolution Imaging Spectroradiometer (MODIS) daily reflectance product on the Google Earth Engine (GEE) cloud platform. To improve the accuracy and efficiency, we optimized the strategies for water extent estimation and the optimal gauge pixel selection. Our improved method was applied in the Watson River. The runoff data from the regional climate model RACMO2 were employed to compare with the estimated results. Our results provide the daily discharge of the Watson River from 2002 to 2021, covering the period when field observations are unavailable. The correlation coefficient (R) and the fractional root mean square error (fRMSE) between the daily estimated discharge and the in-situ discharge are 0.69 and 0.73, respectively, whereas the R and fRMSE are 0.85 and 0.53 at a monthly timescale, respectively. Comparisons between our results and the RACMO2 runoff data suggest that the RACMO2 may generally underestimate the annual ice sheet melt runoff but overestimates the monthly runoff in July by 30% on average. The proposed method is highly automated, efficient and has the potential to be applied in other rivers with field measurements to provide continuous and long-term discharge observations. It contributes to a better understanding of the response of the GrIS to climate change

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Section: A Uncertainty Analysismentioning

confidence: 74%

Discharge Estimation With Improved Methods Using MODIS Data in Greenland: An Application in the Watson River

Lin

Cheng

Zheng

et al. 2022

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

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“…By applying data from three different sensors, Nghiem et al found that the melting extent in Greenland was as high as 98.6% on 12 July 2012 [10]. Zheng et al combined a combination of regional climate models, passive microwave data, and machine learning to find melting across Greenland in mid-July 2012 [38]. The extreme melting event in July 2012 may be related to anomalous warm air ridges due to high blockages in Greenland [10].…”

Section: Discussionmentioning

confidence: 99%

Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands

Li,

Wang,

et al. 2024

Remote Sensing

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The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis.

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“…A similar (and substantially increased) network of surface-based energy balance-enabled weather stations with radiation sensors will be needed to improve model-based and satellite-based estimates of AIS surface melt and firn hydrology. A similar effort on the GrIS under the guidance of the Geological Survey of Denmark and Greenland has led to excellent ice sheet wide coverage from around 2010, enabling the calibration of satellite-based surface melt rate estimates using machine learning techniques 227 . Additional arrays of in situ observations are required to improve model representations of ice shelf flexure and hydrofracture in response to surface meltwater ponding and drainage.…”

Section: Nature Reviews Earth and Environmentmentioning

confidence: 99%

Short- and long-term variability of the Antarctic and Greenland ice sheets

Hanna,

Topál,

Box

et al. 2024

Nat Rev Earth Environ

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The variability of Antarctic Ice Sheet and Greenland Ice Sheet occurs in various timescales and is important for projections of sea level rise; however, there are substantial uncertainties concerning future ice sheet mass changes. In this Review, we explore the degree to which shortterm fluctuations and extreme glaciological events reflect the ice sheets' long-term evolution and response to ongoing climate change. Short-term (decadal or shorter) variations in atmospheric or oceanic conditions can trigger amplifying feedbacks that increase the sensitivity of ice sheets to climate change. For example, variability in ocean-induced and atmosphere-induced melting can trigger ice thinning, retreat and/or collapse of ice shelves, grounding-line retreat, and ice flow acceleration. Antarctica is especially prone to increased melting and ice sheet collapse from warm ocean currents, which could be accentuated with increased climate variability. In Greenland both high and low melt anomalies have been observed since 2012, highlighting the influence of increased interannual climate variability on extreme glaciological events and ice sheet evolution. Failing to adequately account for such variability can result in biased projections of multi-decadal ice mass loss. Therefore, future research should aim to improve climate and ocean observations and models, and develop sophisticated ice sheet models that are directly constrained by observational records and can capture ice dynamical changes across various timescales.Sections 'weather' from 'climate' changes. Oceanic forcingOceanic forcing drives ice sheet mass loss by melting marine-terminating glaciers and ice shelves (Fig. 2). Both the GrIS and AIS exhibit interannual-scale to decadal-scale variability in response to oceanic forcing, potentially related to internal climate variability 34,35 . In Greenland, for example, such oceanic forcing has been implicated in the multi-decadal retreat and thinning of several coastal Related linksArgo: https://argo.ucsd.edu/

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Greenland Ice Sheet Daily Surface Melt Flux Observed From Space

Cited by 13 publications

References 80 publications

Discharge Estimation With Improved Methods Using MODIS Data in Greenland: An Application in the Watson River

Discharge Estimation With Improved Methods Using MODIS Data in Greenland: An Application in the Watson River

Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands

Short- and long-term variability of the Antarctic and Greenland ice sheets

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