Assessment of Arctic seasonal snow cover rates of change

Arctic snow cover extent (SCE) trends and rates of change reported across recent climate assessments vary due to the time period of available data, the selection of snow products, and methodological considerations. While all reported trends are strongly negative during spring, more uncertainty exist...

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Published in:The Cryosphere
Main Authors: Derksen, Chris, Mudryk, Lawrence
Format: Text
Language:English
Published: 2023
Subjects:
Online Access:https://doi.org/10.5194/tc-17-1431-2023
https://tc.copernicus.org/articles/17/1431/2023/
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spelling ftcopernicus:oai:publications.copernicus.org:tc106892 2023-05-15T14:54:21+02:00 Assessment of Arctic seasonal snow cover rates of change Derksen, Chris Mudryk, Lawrence 2023-04-03 application/pdf https://doi.org/10.5194/tc-17-1431-2023 https://tc.copernicus.org/articles/17/1431/2023/ eng eng doi:10.5194/tc-17-1431-2023 https://tc.copernicus.org/articles/17/1431/2023/ eISSN: 1994-0424 Text 2023 ftcopernicus https://doi.org/10.5194/tc-17-1431-2023 2023-04-10T16:23:12Z Arctic snow cover extent (SCE) trends and rates of change reported across recent climate assessments vary due to the time period of available data, the selection of snow products, and methodological considerations. While all reported trends are strongly negative during spring, more uncertainty exists in autumn. Motivated to increase the confidence in SCE trends reported in climate assessments, we quantify the impact of (1) year-over-year increases in time series length over the past 2 decades, (2) the choice of reference period, (3) the application of a statistical methodology to improve inter-dataset agreement, (4) the dataset ensemble size, and (5) product version changes. Results show that the rate of change during May and June has remained consistent over the past decade as time series length has increased and is largely insensitive to the choice of reference period. Although new product versions have increased spatial resolution, use more advanced reanalysis meteorology to force snow models, and include improved remote sensing retrieval algorithms, these enhancements do not result in any notable changes in the observed rate of Arctic SCE change in any month compared to a baseline set of older products. The most impactful analysis decision involves the scaling of dataset climatologies using an updated version of the NOAA snow chart climate data record as the baseline. While minor for most months, this adjustment can influence the calculated rate of change for June by a factor of 2 relative to different climatological baselines. Text Arctic Copernicus Publications: E-Journals Arctic The Cryosphere 17 4 1431 1443
institution Open Polar
collection Copernicus Publications: E-Journals
op_collection_id ftcopernicus
language English
description Arctic snow cover extent (SCE) trends and rates of change reported across recent climate assessments vary due to the time period of available data, the selection of snow products, and methodological considerations. While all reported trends are strongly negative during spring, more uncertainty exists in autumn. Motivated to increase the confidence in SCE trends reported in climate assessments, we quantify the impact of (1) year-over-year increases in time series length over the past 2 decades, (2) the choice of reference period, (3) the application of a statistical methodology to improve inter-dataset agreement, (4) the dataset ensemble size, and (5) product version changes. Results show that the rate of change during May and June has remained consistent over the past decade as time series length has increased and is largely insensitive to the choice of reference period. Although new product versions have increased spatial resolution, use more advanced reanalysis meteorology to force snow models, and include improved remote sensing retrieval algorithms, these enhancements do not result in any notable changes in the observed rate of Arctic SCE change in any month compared to a baseline set of older products. The most impactful analysis decision involves the scaling of dataset climatologies using an updated version of the NOAA snow chart climate data record as the baseline. While minor for most months, this adjustment can influence the calculated rate of change for June by a factor of 2 relative to different climatological baselines.
format Text
author Derksen, Chris
Mudryk, Lawrence
spellingShingle Derksen, Chris
Mudryk, Lawrence
Assessment of Arctic seasonal snow cover rates of change
author_facet Derksen, Chris
Mudryk, Lawrence
author_sort Derksen, Chris
title Assessment of Arctic seasonal snow cover rates of change
title_short Assessment of Arctic seasonal snow cover rates of change
title_full Assessment of Arctic seasonal snow cover rates of change
title_fullStr Assessment of Arctic seasonal snow cover rates of change
title_full_unstemmed Assessment of Arctic seasonal snow cover rates of change
title_sort assessment of arctic seasonal snow cover rates of change
publishDate 2023
url https://doi.org/10.5194/tc-17-1431-2023
https://tc.copernicus.org/articles/17/1431/2023/
geographic Arctic
geographic_facet Arctic
genre Arctic
genre_facet Arctic
op_source eISSN: 1994-0424
op_relation doi:10.5194/tc-17-1431-2023
https://tc.copernicus.org/articles/17/1431/2023/
op_doi https://doi.org/10.5194/tc-17-1431-2023
container_title The Cryosphere
container_volume 17
container_issue 4
container_start_page 1431
op_container_end_page 1443
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