Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation

Snow cover trends for Canada over the 1955–2017 period for the daily snow depth–observing network of Environment and Climate Change Canada (ECCC) are presented based on an updated quality-controlled historical daily in situ snow depth dataset. The period since approximately 1995 is characterized by...

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Main Authors: R. D. Brown (10688568), C. Smith (2247166), C. Derksen (10688571), L. Mudryk (10688574)
Format: Other Non-Article Part of Journal/Newspaper
Language:unknown
Published: 2021
Subjects:
Online Access:https://doi.org/10.6084/m9.figshare.14470788.v1
id ftsmithonian:oai:figshare.com:article/14470788
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spelling ftsmithonian:oai:figshare.com:article/14470788 2023-05-15T15:17:52+02:00 Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation R. D. Brown (10688568) C. Smith (2247166) C. Derksen (10688571) L. Mudryk (10688574) 2021-04-22T20:40:02Z https://doi.org/10.6084/m9.figshare.14470788.v1 unknown https://figshare.com/articles/journal_contribution/Canadian_In_Situ_Snow_Cover_Trends_for_1955_2017_Including_an_Assessment_of_the_Impact_of_Automation/14470788 doi:10.6084/m9.figshare.14470788.v1 CC BY 4.0 CC-BY Ecology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified snow depth Canada ruler sonic sensor trends Text Journal contribution 2021 ftsmithonian https://doi.org/10.6084/m9.figshare.14470788.v1 2021-05-05T17:41:38Z Snow cover trends for Canada over the 1955–2017 period for the daily snow depth–observing network of Environment and Climate Change Canada (ECCC) are presented based on an updated quality-controlled historical daily in situ snow depth dataset. The period since approximately 1995 is characterized by a rapid decline in manual observations (loss of over 800 manual observing sites between 1995 and 2017) and an increasing number of automated stations equipped with sonic snow depth sensors. In 2017 these accounted for approximately 45% of the network and more than 80% of the snow depth–observing network north of latitude 55°N. Automated stations are characterized by more frequent missing and anomalous data than manual ruler observations, particularly at Arctic sites. A comparison of closely located automated sonic and manual ruler observations showed similar numbers of days with snow cover but the sonic sensors detected significantly lower snow depths. For time series analysis of annual snow cover variables, the systematic difference between ruler and sonic snow depth can be removed using a common 2003–2016 reference period to compute snow cover anomalies. The updated trend results are broadly similar to previously published assessments showing long-term decreases in annual snow cover duration (SCD) and snow depth over most of Canada, with the largest decreases observed in spring snow cover and seasonal maximum snow depth (SDmax). Significant declines in SCD and SDmax of −1.7 (±1.1) days decade -1 and −1.8 cm (±0.8) cm decade −1 were observed in the Canada–averaged series over the 1955–2017 period. These trends mainly reflect snow cover conditions over southern Canada where the observing network is concentrated and where there are significant negative correlations between snow cover and winter air temperature. Declining numbers of stations reporting snow depth, issues with sonic sensor data quality, and systematic differences between ruler and sonic sensor measurements are major challenges for continued climate monitoring with the current ECCC snow depth–observing network. Other Non-Article Part of Journal/Newspaper Arctic Climate change Unknown Arctic Canada
institution Open Polar
collection Unknown
op_collection_id ftsmithonian
language unknown
topic Ecology
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
snow depth
Canada
ruler
sonic sensor
trends
spellingShingle Ecology
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
snow depth
Canada
ruler
sonic sensor
trends
R. D. Brown (10688568)
C. Smith (2247166)
C. Derksen (10688571)
L. Mudryk (10688574)
Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation
topic_facet Ecology
Environmental Sciences not elsewhere classified
Biological Sciences not elsewhere classified
snow depth
Canada
ruler
sonic sensor
trends
description Snow cover trends for Canada over the 1955–2017 period for the daily snow depth–observing network of Environment and Climate Change Canada (ECCC) are presented based on an updated quality-controlled historical daily in situ snow depth dataset. The period since approximately 1995 is characterized by a rapid decline in manual observations (loss of over 800 manual observing sites between 1995 and 2017) and an increasing number of automated stations equipped with sonic snow depth sensors. In 2017 these accounted for approximately 45% of the network and more than 80% of the snow depth–observing network north of latitude 55°N. Automated stations are characterized by more frequent missing and anomalous data than manual ruler observations, particularly at Arctic sites. A comparison of closely located automated sonic and manual ruler observations showed similar numbers of days with snow cover but the sonic sensors detected significantly lower snow depths. For time series analysis of annual snow cover variables, the systematic difference between ruler and sonic snow depth can be removed using a common 2003–2016 reference period to compute snow cover anomalies. The updated trend results are broadly similar to previously published assessments showing long-term decreases in annual snow cover duration (SCD) and snow depth over most of Canada, with the largest decreases observed in spring snow cover and seasonal maximum snow depth (SDmax). Significant declines in SCD and SDmax of −1.7 (±1.1) days decade -1 and −1.8 cm (±0.8) cm decade −1 were observed in the Canada–averaged series over the 1955–2017 period. These trends mainly reflect snow cover conditions over southern Canada where the observing network is concentrated and where there are significant negative correlations between snow cover and winter air temperature. Declining numbers of stations reporting snow depth, issues with sonic sensor data quality, and systematic differences between ruler and sonic sensor measurements are major challenges for continued climate monitoring with the current ECCC snow depth–observing network.
format Other Non-Article Part of Journal/Newspaper
author R. D. Brown (10688568)
C. Smith (2247166)
C. Derksen (10688571)
L. Mudryk (10688574)
author_facet R. D. Brown (10688568)
C. Smith (2247166)
C. Derksen (10688571)
L. Mudryk (10688574)
author_sort R. D. Brown (10688568)
title Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation
title_short Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation
title_full Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation
title_fullStr Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation
title_full_unstemmed Canadian In Situ Snow Cover Trends for 1955–2017 Including an Assessment of the Impact of Automation
title_sort canadian in situ snow cover trends for 1955–2017 including an assessment of the impact of automation
publishDate 2021
url https://doi.org/10.6084/m9.figshare.14470788.v1
geographic Arctic
Canada
geographic_facet Arctic
Canada
genre Arctic
Climate change
genre_facet Arctic
Climate change
op_relation https://figshare.com/articles/journal_contribution/Canadian_In_Situ_Snow_Cover_Trends_for_1955_2017_Including_an_Assessment_of_the_Impact_of_Automation/14470788
doi:10.6084/m9.figshare.14470788.v1
op_rights CC BY 4.0
op_rightsnorm CC-BY
op_doi https://doi.org/10.6084/m9.figshare.14470788.v1
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