Global Land Surface Temperature Change (2003–2017) and Its Relationship with Climate Drivers: AIRS, MODIS, and ERA5-Land Based Analysis

Abstract

Land surface temperature (LST) plays a critical role in the water cycle and energy balance at global and regional scales. Large-scale LST estimates can be obtained from satellite observations and reanalysis data. In this study, we first investigate the long-term changes of LST during 2003–2017 on a per-pixel basis using three different datasets derived from (i) the Atmospheric Infrared Sounder (AIRS) onboard Aqua satellite, (ii) the Moderate Resolution Imaging Spectroradiometer (MODIS) also aboard Aqua, and (iii) the recently released ERA5-Land reanalysis data. It was found that the spatio-temporal patterns of these data agree very well. All three products globally showed an uptrend in the annual average LST during 2003–2017 but with considerable spatial variations. The strongest increase was found over the region north of 45° N, particularly over Asian Russia, whereas a slight decrease was observed over Australia. The regression analysis indicated that precipitation (P), incoming surface solar radiation (SW↓), and incoming surface longwave radiation (LW↓) can together explain the inter-annual LST variations over most regions, except over tropical forests, where the inter-annual LST variation is low. Spatially, the LST changes during 2003–2017 over the region north of 45° N were mainly influenced by LW↓, while P and SW↓ played a more important role over other regions. A detailed look at Asian Russia and the Amazon rainforest at a monthly time scale showed that warming in Asian Russia is dominated by LST increases in February–April, which are closely related with the simultaneously increasing LW↓ and clouds. Over the southern Amazon, the most apparent LST increase is found in the dry season (August–September), primarily affected by decreasing P. In addition, increasing SW↓ associated with decreasing atmospheric aerosols was another factor found to cause LST increases. This study shows a high level of consistency among LST trends derived from satellite and reanalysis products, thus providing more robust characteristics of the spatio-temporal LST changes during 2003–2017. Furthermore, the major climatic drivers of LST changes during 2003–2017 were identified over different regions, which might help us predict the LST in response to changing climate in the future.

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

“…Kozubek et al (2020) indicated that the trend of temperature from ERA5 data has good consistency with observations data. Liu et al (2021) confirmed that the land surface temperature data on ERA5 products are highly consistent with the trend derived from satellite data in 2003-2017. On the regional scale, the research of evaluating the credibility of reanalysis data over China has received extensive attention.…”

Four Decades of Surface Temperature, Precipitation, and Wind Speed Trends over Lakes of Greece

“…An advantage of ERA5-Land data is the high native horizontal resolution (9 km) while it also offers the capability of long-term hourly or monthly analyses as it covers a time period from 1981 to nearly present (2-3 months before). ERA5-Land provides high-quality information which can be exploited by various climatic studies and interdisciplinary analyses (e.g., [30][31][32]. For this study, we retrieved monthly averaged precipitation, skin temperature (considered as surface temperature), and wind speed data for a 39-year period from January 1981 to November 2020.…”

Global spatiotemporally continuous MODIS land surface temperature dataset

“…However, the usefulness of such data is poor when the field stations are sparsely distributed 17 . Most model reanalysis datasets, such as the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset, National Center for Environmental Prediction (NCEP) products, and the European Center for Medium-Range Weather Forecasts (ECMWF) Reanalysis product ERA-Interim 18 , can provide spatiotemporally continuous LSTs at a global scale. However, these reanalysis datasets are usually output with coarse resolutions, and the effects of surface properties on LSTs are only roughly considered in these numerical models, which are unable to meet the requirements of many applications, which demand LSTs with finer resolution.…”