Abstract

Ta (Near-surface air temperature) is an important physical parameter that reflects climate change. Although there are currently many methods to obtain the daily maximum (Tmax), minimum (Tmin), and average (Tavg) temperature (meteorological stations, remote sensing, and reanalysis data), these methods are affected by many factors. In order to obtain daily Ta data (Tmax, Tmin, and Tavg) with high spatial and temporal resolution in China, we fully analyzed the advantages and disadvantages of various existing data (reanalysis, remote sensing, and in situ data). Different Ta reconstruction models are constructed for different weather conditions, and we further improve data accuracy through building correction equations for different regions. Finally, a dataset of daily temperature (Tmax, Tmin, and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1°. For Tmax, validation using in situ data shows that the root mean square error (RMSE) ranges from 0.86 °C to 1.78 °C, the mean absolute error (MAE) varies from 0.63 °C to 1.40 °C, and the Pearson coefficient (R2) ranges from 0.96 to 0.99. For Tmin, RMSE ranges from 0.78 °C to 2.09 °C, the MAE varies from 0.58 °C to 1.61 °C, and the R2 ranges from 0.95 to 0.99. For Tavg, RMSE ranges from 0.35 °C to 1.00 °C, the MAE varies from 0.27 °C to 0.68 °C, and the R2 ranges from 0.99 to 1.00. Furthermore, a variety of evaluation indicators were used to analyze the temporal and spatial variation trends of Ta, and the Tavg increase was more than 0.0 °C/a, which is consistent with the general global warming trend. In conclusion, this dataset had a high spatial resolution and reliable accuracy, which makes up for the previous missing temperature value (Tmax, Tmin, and Tavg) at high spatial resolution. This dataset also provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage, which is publicly available with the following DOI: https://doi.org/10.5281/zenodo.5502275 (Fang et al., 2021a).

Highlights

  • Ta (Near-surface air temperature) is an important variable that reflects global climate change, and it significantly affects the cyclical conversion of energy and matter in all spheres of the earth (Gao et al, 2012, 2014)

  • The R2 fluctuated from 0.91 to 0.99, the MAE ranged from 1.69 °C to 2.71 °C, and the RMSE ranged from 2.15 °C to 3.20 °C

  • The R2 fluctuated from 0.93 to 0.97, the MAE ranged from 1.34 °C to 2.17 °C, and the RMSE fluctuated from 1.68 °C to 2.79 °C

Read more

Summary

Introduction

Ta (Near-surface air temperature) is an important variable that reflects global climate change, and it significantly affects the cyclical conversion of energy and matter in all spheres of the earth (Gao et al, 2012, 2014). Ta refers to the daily maximum (Tmax), minimum (Tmin), and average temperatures (Tavg) of daily near-surface air temperature, which are important input parameters for hydrological, environmental, and crop models (Han et al, 2020; He et al, 2020; Mostovoy et al, 2006; Schaer 55 et al, 2004). They can accurately reflect the frequency and extent of the occurrence and development of extreme climate events (Zhang et al, 2017; Miao et al, 2016). It is essential to obtain the spatio-temporal changes of Ta for studying extreme weather events, meteorological disasters leading to agricultural production reduction

Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call