Performance of air temperature from ERAS-Land reanalysis in coastal urban agglomeration of Southeast China

Near-surface air temperature is an important indicator of climate change and extreme events. ERAS-Land reanalysis products feature liner spatial and temporal resolutions, and have been widely adopted in global climate-related research. However, the performance of ERAS-Land air temperature data in coastal urban agglomerations has received little attention. In this study, a comprehensive evaluation is conducted in the Guangdong-Hong Kong-Macau Greater Bay Area (GSA) using the observations of 1080 automatic weather stations in 2018 as reference. Generally, ERAS-Land underestimates temperature (an average bias of 0.90 degrees C), and performs better at low temperatures than at high temperatures. At the station level, it is observed that the correlation shows a strong positive linear relationship with the distance to the coastline in summer, and that the bias increases with increasing altitude throughout the year. With respect to different land cover types, data accuracy over urban and built-up lands is the lowest. The spatial pattern of ERAS-land is generally consistent with that of stations but relatively poor in urban areas. In addition, ERAS-land properly captures daily and monthly variations, as well as intraday temperature fluctuations. These conclusions provide a reference for the implementation of ERAS-land in coastal urban agglomerations.

Automated summary

This study conducted a comprehensive evaluation of air temperature data in the Guangdong-Hong Kong-Macao Greater Bay Area, revealing that ERAS-Land underestimates temperatures with varying performance at different temperature levels. Data accuracy is lowest over urban and built-up lands, but the overall spatial pattern of ERAS-Land is generally consistent with station observations.