Preprints
https://doi.org/10.5194/tc-2022-5
https://doi.org/10.5194/tc-2022-5
 
25 Jan 2022
25 Jan 2022
Status: this preprint is currently under review for the journal TC.

Validation of Pan-Arctic Soil Temperatures in Modern Reanalysis and Data Assimilation Systems

Tyler C. Herrington1, Christopher G. Fletcher1, and Heather Kropp2 Tyler C. Herrington et al.
  • 1Department of Geography and Environmental Management, University of Waterloo, 200 University Ave., Waterloo, Ontario, Canada, N2L 3G1
  • 2Environmental Studies Program, Hamilton College, 198 College Hill Road, Clinton, 13323, New York, U.S.A.

Abstract. Reanalysis products provide spatially homogeneous coverage for a variety of climate variables in regions where observational data are limited. However, very little validation of reanalysis soil temperatures in the Arctic has been performed to date, because widespread in situ reference observations have historically been unavailable there. Here we validate pan-Arctic soil temperatures from eight reanalysis and Land Data Assimilation System (LDAS) products, using a newly-assembled database of in situ data from diverse measurement networks across Eurasia and North America. We find that most products have soil temperatures that are biased cold by 2–7 K across the Arctic, and that biases and Root Mean Square Error (RMSE) are generally largest in the cold season. Monthly mean values from most products correlate well with in situ data (R > 0.9) in the warm season, but show lower correlations (r = 0.6–0.8), in many cases, over the cold season. Similarly, the magnitude of monthly variability in soil temperatures is well captured in summer, but overestimated by 20 % to 50 % for several products in winter. The suggestion is that soil temperatures in reanalysis products are subject to much higher uncertainty when the soil is frozen and/or when the ground is snow-covered. We also validate the ensemble mean of all products, and find that when all seasons, and metrics are considered, the ensemble mean generally outperforms any individual product in terms of its correlation and variability, while maintaining relatively low biases. As such, we recommend the ensemble mean soil temperature product for a wide range of applications – such as the validation of soil temperatures in climate models, and to inform models that require soil temperature inputs, such as hydrological models, or for permafrost simulations.

Tyler C. Herrington et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-5', Anonymous Referee #1, 11 Feb 2022
  • RC2: 'Comment on tc-2022-5', Anonymous Referee #2, 30 Jun 2022

Tyler C. Herrington et al.

Data sets

Ensemble Mean Reanalysis Soil Temperature Dataset (1981–2018) Tyler Herrington and Christopher G. Fletcher https://doi.org/10.18739/A2RN3085P

Tyler C. Herrington et al.

Viewed

Total article views: 432 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
318 97 17 432 43 14 12
  • HTML: 318
  • PDF: 97
  • XML: 17
  • Total: 432
  • Supplement: 43
  • BibTeX: 14
  • EndNote: 12
Views and downloads (calculated since 25 Jan 2022)
Cumulative views and downloads (calculated since 25 Jan 2022)

Viewed (geographical distribution)

Total article views: 405 (including HTML, PDF, and XML) Thereof 405 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 03 Jul 2022
Download
Short summary
Here we validate soil temperatures from eight reanalysis products across the pan-Arctic, and compare their performance to a newly calculated ensemble mean soil temperature product. We find that most products are biased cold by 2–7 K, especially in the cold season, and that the ensemble mean product outperforms individual reanalysis products. As such, we recommend the product for a wide range of applications – including validation of climate models, or as input to permafrost models.