Cao et al. (2025) Brief communication: Reanalyses underperform in cold regions, raising concerns for climate services and research

Identification

• Journal: ˜The œcryosphere
• Year: 2025
• Date: 2025-10-14
• Authors: Bin Cao, Stephan Gruber
• DOI: 10.5194/tc-19-4525-2025

Research Groups

• State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), National Tibetan Plateau Data Center, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China
• Department of Geography and Environmental Studies, Carleton University, Ottawa, Ontario, Canada

Short Summary

This study quantifies the relative quality of five state-of-the-art reanalyses in cold regions, revealing that the ensemble spread for mean annual air temperature and maximum snow water equivalent is significantly higher in these areas compared to other regions, raising concerns for climate services and research.

Objective

• To quantify the relative quality of five state-of-the-art reanalyses in cold regions to inform the application of reanalysis products and motivate further improvements specific to cold environments.

Study Configuration

• Spatial Scale: Global terrestrial areas, with a focus on "cold regions" defined as areas with a mean annual air temperature (MAAT) below 0 °C, including the Greenland and Antarctic ice sheets. Analysis conducted on a 0.25° grid.
• Temporal Scale: The three most recent decades (1991–2020) for primary analysis, with some temporal trends analyzed from the 1960s to the 2010s.

Methodology and Data

• Models used: JRA-3Q, ERA5 (including its 10-member ensemble), MERRA-2, JRA-55, and NCEP2 reanalyses.
• Data sources:
  • Reanalysis products: JRA-3Q, ERA5, MERRA-2, JRA-55, NCEP2.
  • In situ observations and population data: Copernicus Climate Change Service (C3S) Climate Data Store (2021) and Center for International Earth Science Information Network-CIESIN-Columbia University (2018).
  • Cryosphere occurrence data: Randolph Glacier Inventory (glaciers), Estilow et al. (2015) (snow cover extent), Kim et al. (2017) (seasonally frozen ground).
  • Permafrost zonation index (PZI) derived from Gruber (2012) model.
  • Downscaling algorithms for near-surface air temperature from Cao et al. (2017, 2019).

Main Results

• The average ensemble spread of mean annual air temperature (MAAT) in cold regions is 1.5 °C (0.5–3.0 °C), which is 90 % greater than in other regions (0.8 °C, 0.3–1.5 °C).
• The ensemble spread of the mean MAAT warming trend in cold regions is 0.24 °C per decade (0.10–0.42 °C per decade), approximately 60 % higher than in other terrestrial areas (0.15 °C per decade, 0.06–0.25 °C per decade).
• The relative average ensemble spread of maximum snow water equivalent (maxSWE) is 105 % (51 %–206 %), indicating that the variation between reanalyses is, on average, greater than their ensemble mean.
• The greatest maxSWE spread occurs in high-altitude regions (e.g., High-Mountain Asia) and continental ice sheets, despite flat terrain in the latter, suggesting inadequate representation of ice, snow, and firn processes.
• Even among the more advanced 4DVar reanalyses, the MAAT spread in cold regions remains significant at 1.3 °C (0.3–2.9 °C), and its trend spread is 0.13 °C per decade (0.04–0.24 °C per decade).
• The spread of the ERA5 ensemble, representing parametric uncertainty within a single system, is notably smaller (0.1 °C for MAAT, 1.0 % for relative maxSWE) compared to the spread between different reanalyses.
• While the overall spread in MAAT and maxSWE generally reduced after 1980 due to increased satellite data assimilation, a persistent spread since then suggests underlying process representation issues in numerical weather prediction models.
• Ice sheet areas exhibit the most significant spread, with MAAT spread 2.3 times greater and maxSWE spread 1.7 times greater than in seasonally frozen-ground-dominated areas.
• The low density of in situ observations in cold regions (0.08 stations per 10^4 km^2) limits the ability to constrain reanalyses, contributing to their reduced quality.

Contributions

• Provides the first comprehensive, observation-independent quantification of the relative quality of multiple state-of-the-art reanalyses in cold regions for key variables (mean annual air temperature and maximum snow water equivalent).
• Highlights a critical and persistent gap in the ability of current reanalysis systems to accurately represent and simulate cold-region phenomena, despite advancements in data assimilation.
• Connects the reduced reanalysis quality in cold regions to sparse in situ observations and the complex, non-linear cryospheric processes, emphasizing the need for improved process representation.
• Underscores the implications of these limitations for climate research and services in regions disproportionately affected by climate change, motivating targeted improvements in numerical weather prediction models and reanalysis products.

Funding

• National Natural Science Foundation of China (grant no. 42422608)
• Youth Innovation Promotion Association of the Chinese Academy of Sciences (grant no. 2023075)
• Natural Sciences and Engineering Research Council of Canada (grant nos. NETGP 523228-18 and RGPIN-2020-04783)

Citation

@article{Cao2025Brief,
  author = {Cao, Bin and Gruber, Stephan},
  title = {Brief communication: Reanalyses underperform in cold regions, raising concerns for climate services and research},
  journal = {˜The œcryosphere},
  year = {2025},
  doi = {10.5194/tc-19-4525-2025},
  url = {https://doi.org/10.5194/tc-19-4525-2025}
}