Climatology and Evolution of the Antarctic Peninsula Fohn Wind-Induced Melt Regime From 1979-2018

M. K. Laffin; C. S. Zender; S. Singh; J. M. Van Wessem; C. J.P.P. Smeets; C. H. Reijmer

doi:10.1029/2020jd033682

Climatology and Evolution of the Antarctic Peninsula Fohn Wind-Induced Melt Regime From 1979-2018

M. K. Laffin^*
, C. S. Zender
, S. Singh
, J. M. Van Wessem
, C. J.P.P. Smeets
, C. H. Reijmer

^*Corresponding author for this work

University of California at Irvine

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

Abstract

Warm and dry föhn winds on the Antarctic Peninsula (AP) cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these downslope winds through mountain passes and canyons can produce localized wind-induced melt that is difficult to quantify without direct measurements. Our Föhn Detection Algorithm (FöhnDA) identifies the surface föhn signature that causes melt from measurement by 12 Automatic Weather Stations on the AP, that train a machine learning model to detect föhn in 5 km Regional Atmospheric Climate Model 2 (RACMO2.3p2) simulations and in the ERA5 reanalysis model. We estimate the fraction of AP surface melt attributed to föhn and possibly katabatic winds and identify the drivers of melt, temporal variability, and long-term trends and evolution from 1979–2018. We find that föhn wind-induced melt accounts for 3.1% of the total melt on the AP and can be as high at 18% close to the mountains where the winds funnel through mountain canyons. Föhn-induced surface melt does not significantly increase from 1979–2018, despite a warmer atmosphere and more positive Southern Annular Mode. However, a significant increase (+0.1 Gt y-1) and subsequent decrease/stabilization occur in 1979–1998 and 1999–2018, consistent with the AP warming and cooling trends during the same time periods. Föhn occurrence, more than föhn strength, drives the annual variability in föhn-induced melt. Long-term föhn-induced melt trends and evolution are attributable to seasonal changes in föhn occurrence, with increased occurrence in summer, and decreased occurrence in fall, winter, and early spring over the past 20 years.

Original language	English
Article number	e2020JD033682
Number of pages	19
Journal	Journal of Geophysical Research: Atmospheres
Volume	126
Issue number	4
DOIs	https://doi.org/10.1029/2020jd033682
Publication status	Published - 10 Feb 2021

Bibliographical note

Funding Information:
MKL was supported by the National Science Foundation (NRT‐1633631) and NASA AIST (80NSSC17K0540). CSZ gratefully acknowledges support from the DOE BER ESM and SciDAC programs (DE‐SC0019278, LLNL‐B639667, LANL‐520117). JMVW acknowledges support by PROTECT and was partly funded by the NWO (Netherlands Organisation for Scientific Research) VENI grant VI.Veni.192.083. We thank the Institute for Marine and Atmospheric Research (IMAU) at Utrecht University, the Antarctic Meteorological Research Center (AMRC) at the University of Wisconsin‐Madison, and National Snow and Ice Data Center (NSIDC) for providing Automatic Weather Station data. We thank the European Center for Medium‐Range Weather Forecasts (ECMWF) for providing ERA5 reanalysis data and the Institute for Marine and Atmospheric research Utrecht (IMAU) for providing RACMO2 output. We also thank the reviewers, whose suggestions greatly improved the manuscript. ERA5 reanalysis data are freely available through the European Center for Medium‐Range Weather Forecasts ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ).

Publisher Copyright:
© 2021. American Geophysical Union. All Rights Reserved.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Funding

MKL was supported by the National Science Foundation (NRT‐1633631) and NASA AIST (80NSSC17K0540). CSZ gratefully acknowledges support from the DOE BER ESM and SciDAC programs (DE‐SC0019278, LLNL‐B639667, LANL‐520117). JMVW acknowledges support by PROTECT and was partly funded by the NWO (Netherlands Organisation for Scientific Research) VENI grant VI.Veni.192.083. We thank the Institute for Marine and Atmospheric Research (IMAU) at Utrecht University, the Antarctic Meteorological Research Center (AMRC) at the University of Wisconsin‐Madison, and National Snow and Ice Data Center (NSIDC) for providing Automatic Weather Station data. We thank the European Center for Medium‐Range Weather Forecasts (ECMWF) for providing ERA5 reanalysis data and the Institute for Marine and Atmospheric research Utrecht (IMAU) for providing RACMO2 output. We also thank the reviewers, whose suggestions greatly improved the manuscript. ERA5 reanalysis data are freely available through the European Center for Medium‐Range Weather Forecasts ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ).

UN SDGs

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

SDG 13 Climate Action

Keywords

Föhn wind
ice shelf
surface energy budget
surface melt
Winter melt

Access to Document

10.1029/2020jd033682

2020JD033682Final published version, 2.47 MBLicence: Taverne

Cite this

@article{beb170b530254afa8b3f88c9b5d22d80,

title = "Climatology and Evolution of the Antarctic Peninsula Fohn Wind-Induced Melt Regime From 1979-2018",

abstract = "Warm and dry f{\"o}hn winds on the Antarctic Peninsula (AP) cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these downslope winds through mountain passes and canyons can produce localized wind-induced melt that is difficult to quantify without direct measurements. Our F{\"o}hn Detection Algorithm (F{\"o}hnDA) identifies the surface f{\"o}hn signature that causes melt from measurement by 12 Automatic Weather Stations on the AP, that train a machine learning model to detect f{\"o}hn in 5 km Regional Atmospheric Climate Model 2 (RACMO2.3p2) simulations and in the ERA5 reanalysis model. We estimate the fraction of AP surface melt attributed to f{\"o}hn and possibly katabatic winds and identify the drivers of melt, temporal variability, and long-term trends and evolution from 1979–2018. We find that f{\"o}hn wind-induced melt accounts for 3.1\% of the total melt on the AP and can be as high at 18\% close to the mountains where the winds funnel through mountain canyons. F{\"o}hn-induced surface melt does not significantly increase from 1979–2018, despite a warmer atmosphere and more positive Southern Annular Mode. However, a significant increase (+0.1 Gt y-1) and subsequent decrease/stabilization occur in 1979–1998 and 1999–2018, consistent with the AP warming and cooling trends during the same time periods. F{\"o}hn occurrence, more than f{\"o}hn strength, drives the annual variability in f{\"o}hn-induced melt. Long-term f{\"o}hn-induced melt trends and evolution are attributable to seasonal changes in f{\"o}hn occurrence, with increased occurrence in summer, and decreased occurrence in fall, winter, and early spring over the past 20 years.",

keywords = "F{\"o}hn wind, ice shelf, surface energy budget, surface melt, Winter melt",

author = "Laffin, \{M. K.\} and Zender, \{C. S.\} and S. Singh and \{Van Wessem\}, \{J. M.\} and Smeets, \{C. J.P.P.\} and Reijmer, \{C. H.\}",

note = "Funding Information: MKL was supported by the National Science Foundation (NRT‐1633631) and NASA AIST (80NSSC17K0540). CSZ gratefully acknowledges support from the DOE BER ESM and SciDAC programs (DE‐SC0019278, LLNL‐B639667, LANL‐520117). JMVW acknowledges support by PROTECT and was partly funded by the NWO (Netherlands Organisation for Scientific Research) VENI grant VI.Veni.192.083. We thank the Institute for Marine and Atmospheric Research (IMAU) at Utrecht University, the Antarctic Meteorological Research Center (AMRC) at the University of Wisconsin‐Madison, and National Snow and Ice Data Center (NSIDC) for providing Automatic Weather Station data. We thank the European Center for Medium‐Range Weather Forecasts (ECMWF) for providing ERA5 reanalysis data and the Institute for Marine and Atmospheric research Utrecht (IMAU) for providing RACMO2 output. We also thank the reviewers, whose suggestions greatly improved the manuscript. ERA5 reanalysis data are freely available through the European Center for Medium‐Range Weather Forecasts ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ). Publisher Copyright: {\textcopyright} 2021. American Geophysical Union. All Rights Reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = feb,

day = "10",

doi = "10.1029/2020jd033682",

language = "English",

volume = "126",

journal = "Journal of Geophysical Research: Atmospheres",

issn = "2169-8996",

publisher = "John Wiley and Sons Inc.",

number = "4",

}

TY - JOUR

T1 - Climatology and Evolution of the Antarctic Peninsula Fohn Wind-Induced Melt Regime From 1979-2018

AU - Laffin, M. K.

AU - Zender, C. S.

AU - Singh, S.

AU - Van Wessem, J. M.

AU - Smeets, C. J.P.P.

AU - Reijmer, C. H.

N1 - Funding Information: MKL was supported by the National Science Foundation (NRT‐1633631) and NASA AIST (80NSSC17K0540). CSZ gratefully acknowledges support from the DOE BER ESM and SciDAC programs (DE‐SC0019278, LLNL‐B639667, LANL‐520117). JMVW acknowledges support by PROTECT and was partly funded by the NWO (Netherlands Organisation for Scientific Research) VENI grant VI.Veni.192.083. We thank the Institute for Marine and Atmospheric Research (IMAU) at Utrecht University, the Antarctic Meteorological Research Center (AMRC) at the University of Wisconsin‐Madison, and National Snow and Ice Data Center (NSIDC) for providing Automatic Weather Station data. We thank the European Center for Medium‐Range Weather Forecasts (ECMWF) for providing ERA5 reanalysis data and the Institute for Marine and Atmospheric research Utrecht (IMAU) for providing RACMO2 output. We also thank the reviewers, whose suggestions greatly improved the manuscript. ERA5 reanalysis data are freely available through the European Center for Medium‐Range Weather Forecasts ( https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 ). Publisher Copyright: © 2021. American Geophysical Union. All Rights Reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/2/10

Y1 - 2021/2/10

N2 - Warm and dry föhn winds on the Antarctic Peninsula (AP) cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these downslope winds through mountain passes and canyons can produce localized wind-induced melt that is difficult to quantify without direct measurements. Our Föhn Detection Algorithm (FöhnDA) identifies the surface föhn signature that causes melt from measurement by 12 Automatic Weather Stations on the AP, that train a machine learning model to detect föhn in 5 km Regional Atmospheric Climate Model 2 (RACMO2.3p2) simulations and in the ERA5 reanalysis model. We estimate the fraction of AP surface melt attributed to föhn and possibly katabatic winds and identify the drivers of melt, temporal variability, and long-term trends and evolution from 1979–2018. We find that föhn wind-induced melt accounts for 3.1% of the total melt on the AP and can be as high at 18% close to the mountains where the winds funnel through mountain canyons. Föhn-induced surface melt does not significantly increase from 1979–2018, despite a warmer atmosphere and more positive Southern Annular Mode. However, a significant increase (+0.1 Gt y-1) and subsequent decrease/stabilization occur in 1979–1998 and 1999–2018, consistent with the AP warming and cooling trends during the same time periods. Föhn occurrence, more than föhn strength, drives the annual variability in föhn-induced melt. Long-term föhn-induced melt trends and evolution are attributable to seasonal changes in föhn occurrence, with increased occurrence in summer, and decreased occurrence in fall, winter, and early spring over the past 20 years.

AB - Warm and dry föhn winds on the Antarctic Peninsula (AP) cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these downslope winds through mountain passes and canyons can produce localized wind-induced melt that is difficult to quantify without direct measurements. Our Föhn Detection Algorithm (FöhnDA) identifies the surface föhn signature that causes melt from measurement by 12 Automatic Weather Stations on the AP, that train a machine learning model to detect föhn in 5 km Regional Atmospheric Climate Model 2 (RACMO2.3p2) simulations and in the ERA5 reanalysis model. We estimate the fraction of AP surface melt attributed to föhn and possibly katabatic winds and identify the drivers of melt, temporal variability, and long-term trends and evolution from 1979–2018. We find that föhn wind-induced melt accounts for 3.1% of the total melt on the AP and can be as high at 18% close to the mountains where the winds funnel through mountain canyons. Föhn-induced surface melt does not significantly increase from 1979–2018, despite a warmer atmosphere and more positive Southern Annular Mode. However, a significant increase (+0.1 Gt y-1) and subsequent decrease/stabilization occur in 1979–1998 and 1999–2018, consistent with the AP warming and cooling trends during the same time periods. Föhn occurrence, more than föhn strength, drives the annual variability in föhn-induced melt. Long-term föhn-induced melt trends and evolution are attributable to seasonal changes in föhn occurrence, with increased occurrence in summer, and decreased occurrence in fall, winter, and early spring over the past 20 years.

KW - Föhn wind

KW - ice shelf

KW - surface energy budget

KW - surface melt

KW - Winter melt

UR - https://www.scopus.com/pages/publications/85101772092

U2 - 10.1029/2020jd033682

DO - 10.1029/2020jd033682

M3 - Article

AN - SCOPUS:85101772092

SN - 2169-8996

VL - 126

JO - Journal of Geophysical Research: Atmospheres

JF - Journal of Geophysical Research: Atmospheres

IS - 4

M1 - e2020JD033682

ER -

Climatology and Evolution of the Antarctic Peninsula Fohn Wind-Induced Melt Regime From 1979-2018

Abstract

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this