Validation of pan-Arctic soil temperatures in modern reanalysis and data assimilation systems

• Published in: The Cryosphere
• Main Authors: T. C. Herrington, C. G. Fletcher, H. Kropp
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2024
• Subjects: Environmental sciences; GE1-350; Geology; QE1-996.5; permafrost; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-18-1835-2024; https://doaj.org/article/56b5b1818b774002a151ab59cbb298a9

Reanalysis products provide spatially homogeneous coverage for a variety of climate variables in regions such as the Arctic where observational data are limited. Soil temperatures are an important control of many land–atmosphere exchanges and hydrological processes, and permafrost soils are thawing as the climate warms. 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 limited there. Here we validate pan-Arctic soil temperatures from eight reanalysis and land data assimilation system products, using a newly assembled database of in situ observations from diverse measurement networks across Eurasia and North America. We examine product performance across the extratropical Northern Hemisphere between 1982 and 2018, and find that most products have soil temperatures that are biased cold by 1–5 K, with an RMSE of 2–9 K, and that biases and 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 =0.55 –0.85) in the cold season. Similarly, the magnitude of monthly variability in soil temperatures is well captured in summer but overestimated by 20 %–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, suggesting that the representation of processes controlling snow cover in reanalysis systems should be urgently studied. We also validate the ensemble mean of all available 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 ...