Greenland Ice Sheet Daily Surface Melt Flux Observed From Space

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

doi:10.1029/2021GL096690

Greenland Ice Sheet Daily Surface Melt Flux Observed From Space

Lei Zheng
, Xiao Cheng^*
, Xinyi Shang
, Zhuoqi Chen
, Qi Liang
, Kang Wang

^*Corresponding author for this work

School of Geographic Sciences

Research output: Contribution to journal › Article › peer-review

21 Scopus citations

Abstract

Greenland Ice Sheet (GrIS) surface melt has contributed to the global sea-level rise and the ongoing warming is expected to promote this process. This study provides a new strategy for the quantitative estimate of GrIS daily surface melt at enhanced resolution (3.125 km) from a remote sensing perspective beyond traditional regional climate models (RCMs). Daily melt flux is estimated from spaceborne radiometer observations with a back-propagation neural network model. The network is trained with melt fluxes that are calculated using detailed in-situ atmospheric and snow observations and a surface energy balance model. Our results provide details about the extreme melt in mid-July 2012 when surface melt occurred at Summit and the meltwater volume exceeded 20 Gt as a result of anomalous warming. Meltwater volume from the satellite is very close to that from RCMs.

Original language	English
Article number	e2021GL096690
Journal	Geophysical Research Letters
Volume	49
Issue number	6
DOIs	https://doi.org/10.1029/2021GL096690
State	Published - 28 Mar 2022

Keywords

Greenland Ice Sheet
back-propagation neural network
energy balance model
melt flux
remote sensing

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1029/2021GL096690

Cite this

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title = "Greenland Ice Sheet Daily Surface Melt Flux Observed From Space",

abstract = "Greenland Ice Sheet (GrIS) surface melt has contributed to the global sea-level rise and the ongoing warming is expected to promote this process. This study provides a new strategy for the quantitative estimate of GrIS daily surface melt at enhanced resolution (3.125 km) from a remote sensing perspective beyond traditional regional climate models (RCMs). Daily melt flux is estimated from spaceborne radiometer observations with a back-propagation neural network model. The network is trained with melt fluxes that are calculated using detailed in-situ atmospheric and snow observations and a surface energy balance model. Our results provide details about the extreme melt in mid-July 2012 when surface melt occurred at Summit and the meltwater volume exceeded 20 Gt as a result of anomalous warming. Meltwater volume from the satellite is very close to that from RCMs.",

keywords = "Greenland Ice Sheet, back-propagation neural network, energy balance model, melt flux, remote sensing",

author = "Lei Zheng and Xiao Cheng and Xinyi Shang and Zhuoqi Chen and Qi Liang and Kang Wang",

year = "2022",

month = mar,

day = "28",

doi = "10.1029/2021GL096690",

language = "英语",

volume = "49",

journal = "Geophysical Research Letters",

issn = "0094-8276",

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TY - JOUR

T1 - Greenland Ice Sheet Daily Surface Melt Flux Observed From Space

AU - Zheng, Lei

AU - Cheng, Xiao

AU - Shang, Xinyi

AU - Chen, Zhuoqi

AU - Liang, Qi

AU - Wang, Kang

PY - 2022/3/28

Y1 - 2022/3/28

N2 - Greenland Ice Sheet (GrIS) surface melt has contributed to the global sea-level rise and the ongoing warming is expected to promote this process. This study provides a new strategy for the quantitative estimate of GrIS daily surface melt at enhanced resolution (3.125 km) from a remote sensing perspective beyond traditional regional climate models (RCMs). Daily melt flux is estimated from spaceborne radiometer observations with a back-propagation neural network model. The network is trained with melt fluxes that are calculated using detailed in-situ atmospheric and snow observations and a surface energy balance model. Our results provide details about the extreme melt in mid-July 2012 when surface melt occurred at Summit and the meltwater volume exceeded 20 Gt as a result of anomalous warming. Meltwater volume from the satellite is very close to that from RCMs.

AB - Greenland Ice Sheet (GrIS) surface melt has contributed to the global sea-level rise and the ongoing warming is expected to promote this process. This study provides a new strategy for the quantitative estimate of GrIS daily surface melt at enhanced resolution (3.125 km) from a remote sensing perspective beyond traditional regional climate models (RCMs). Daily melt flux is estimated from spaceborne radiometer observations with a back-propagation neural network model. The network is trained with melt fluxes that are calculated using detailed in-situ atmospheric and snow observations and a surface energy balance model. Our results provide details about the extreme melt in mid-July 2012 when surface melt occurred at Summit and the meltwater volume exceeded 20 Gt as a result of anomalous warming. Meltwater volume from the satellite is very close to that from RCMs.

KW - Greenland Ice Sheet

KW - back-propagation neural network

KW - energy balance model

KW - melt flux

KW - remote sensing

UR - https://www.scopus.com/pages/publications/85127317631

U2 - 10.1029/2021GL096690

DO - 10.1029/2021GL096690

M3 - 文章

AN - SCOPUS:85127317631

SN - 0094-8276

VL - 49

JO - Geophysical Research Letters

JF - Geophysical Research Letters

IS - 6

M1 - e2021GL096690

ER -

Greenland Ice Sheet Daily Surface Melt Flux Observed From Space

Abstract

Keywords

UN SDGs

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