Preprints
https://doi.org/10.5194/tc-2022-31
https://doi.org/10.5194/tc-2022-31
 
09 Feb 2022
09 Feb 2022
Status: this discussion paper is a preprint. It has been under review for the journal The Cryosphere (TC). The manuscript was not accepted for further review after discussion.

Global evaluation of process-based models with in situ observations to detect long-term change in lake ice

Mohammad Arshad Imrit1, Alessandro Filazzola1,2, R. Iestyn Woolway3, and Sapna Sharma1 Mohammad Arshad Imrit et al.
  • 1Department of Biology, York University, ON, Canada
  • 2Centre for Urban Environments, University of Toronto Mississauga; Mississauga, Ontario, Canada
  • 3School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey, Wales

Abstract. Lake ice phenology has been used extensively to study the impacts of anthropogenic climate change, owing to the widespread occurrence of lake ice and the length of time series available for such studies. The proliferation of process-based lake models and gridded climate data have enabled the modeling of ice phenology across broad spatial scales, for example where lakes are not sampled. In this study, we used ice phenology outputs from an ensemble of lake-climate model projections to directly compare their performance with in situ data. Generally, we found that the lake models captured the range of variability of observational records (RMSE ice on = 22.9 days [4.7, 95.4]; RMSE ice off = 17.4 days [6.1, 76.5]), and particularly the long-term trends in temperate regions. However, the models performed poorly in extremely warm years or when there were rapid short-term changes in ice phenology. The location of the lakes, such as latitude and longitude, as well as lake morphology, such as lake depth and surface area, significantly influenced model performance. For example, the models performed best in shallow small lakes and worst in deep larger lakes. Our analysis suggests that the lake models tested can reliably estimate long-term trends in lake ice cover, particularly when averaged across large spatial scales, but widespread in situ observations are critical to capture extreme events.

Mohammad Arshad Imrit et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on tc-2022-31', Zeli Tan, 15 Feb 2022
    • CC2: 'Reply on CC1', Sapna Sharma, 15 Feb 2022
  • CC3: 'Comment on tc-2022-31', Paul PUKITE, 18 Feb 2022
    • CC4: 'Reply on CC3 (broken images)', Paul PUKITE, 06 Mar 2022
      • CC5: 'Reply on CC4 (try again)', Paul PUKITE, 06 Mar 2022
        • CC6: 'Reply on CC5', Sapna Sharma, 07 Mar 2022
  • RC1: 'Comment on tc-2022-31', Anonymous Referee #1, 06 Mar 2022
  • RC2: 'Comment on tc-2022-31', Anonymous Referee #2, 21 Mar 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on tc-2022-31', Zeli Tan, 15 Feb 2022
    • CC2: 'Reply on CC1', Sapna Sharma, 15 Feb 2022
  • CC3: 'Comment on tc-2022-31', Paul PUKITE, 18 Feb 2022
    • CC4: 'Reply on CC3 (broken images)', Paul PUKITE, 06 Mar 2022
      • CC5: 'Reply on CC4 (try again)', Paul PUKITE, 06 Mar 2022
        • CC6: 'Reply on CC5', Sapna Sharma, 07 Mar 2022
  • RC1: 'Comment on tc-2022-31', Anonymous Referee #1, 06 Mar 2022
  • RC2: 'Comment on tc-2022-31', Anonymous Referee #2, 21 Mar 2022

Mohammad Arshad Imrit et al.

Mohammad Arshad Imrit et al.

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Latest update: 24 May 2022
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Short summary
Process-based models are frequently used to investigate the influence of climate change on lake ice cover, but an assessment of their validity at large spatial scales is currently lacking. Here, we provide a global assessment of lake ice models, comparing the models can accurately simulate the long-term change in lake ice but fail to capture the occurrence of extreme ice years. Model performance also differs across location and morphometric gradients.