The near surface freeze-thaw cycle is a fundamental characteristic of thermal changes in the land surface and an important parameter for studying the distribution of frozen soil and climate change in cold regions. In recent decades, global warming has inevitably led to changes in freeze-thaw processes and patterns, posing a threat to ecological security and regional sustainable development. To support the study of freeze-thaw cycle response and feedback characteristics under the background of climate change, we prepared a surface freeze-thaw dataset for China and surrounding areas from 2000 to 2023 based on the latest ERA5 Land hourly temperature data. This dataset contains 10 indicators of freeze-thaw cycle changes, including daily freeze-thaw status and annual freeze-thaw intensity on the near surface, with spatial resolutions of 0.1 ° and 0.01 °. The regional range is between 3 ° N-54 ° N and 60 ° E-136 ° E, covering 29 countries including China, Mongolia, Pakistan, Afghanistan, Tajikistan, and Kyrgyzstan. This dataset has the characteristics of strong current situation, multiple elements, and wide scope, which can provide data support for the evolution of near surface freeze-thaw cycles, cryosphere, hydrology, ecology, environment and other research under the background of climate change.
The European Centre for Medium Range Weather Forecasts (ECMWF) ERA5 Land dataset and the United States National Environmental Information Center Global Integrated Surface Database (ISD).
Firstly, convert the original ERA5 Land temperature unit to Celsius and the observation time to local time; Secondly, check the changing characteristics of each grid point data sequence and control the data quality; Finally, from the data sequence of 24 hours per day, extract the highest temperature (Tmax), lowest temperature (Tmin), and daily average temperature (Tavg) of the day, and then calculate and statistically analyze the freeze-thaw cycle characteristics. The freeze-thaw variables include daily air (a) and surface (b) freeze-thaw status (FTS), annual freezing days (FSD), freezing index (FSI), freeze-thaw days (FTD), freeze-thaw index (FTI), melting days (TSD), melting index (TSI), classical melting index (TI), freezing index (FI), and 10 freeze-thaw cycle characteristics of 2000-2023 surface thermal classification, providing two spatial resolutions: 0.1 ° (about 9 km) and 0.01 ° (900m) obtained through nearest neighbor interpolation algorithm.
The overall discrimination accuracy of air freeze-thaw status and surface freeze-thaw status is 87%. The root mean square errors (RMSE) of annual air freezing days (FSD), annual air melting days (TSD), and annual air freeze-thaw cycle days (FTD) are 17.8, 29.9, and 20.7 days, respectively. The annual air freezing index (FSI), annual air melting index (TSI), and annual air freeze-thaw cycle index (FTI) are 395.1, 455.3, and 104.5 days ℃, respectively. The RMSE of annual surface freezing days (FSD), annual surface melting days (TSD), and annual surface freeze-thaw cycle days (FTD) are 12, 20, and 28.6 days, respectively. The annual surface freezing index (FSI), annual surface melting index (TSI), and annual surface freeze-thaw cycle index (FTI) are 155.8, 299.1, and 160.7 days ℃, respectively.
| # | number | name | type |
| 1 | 2022YFF07117 | National Program on Key Basic Research Project (973 Program) |
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.NIEER.DB6621.2024
10.12072/ncdc.nieer.db6621.2024
