Dataset Information

Global Contributions of Incoming Radiation and Land Surface Conditions to Maximum Near-Surface Air Temperature Variability and Trend.

ABSTRACT: The evolution of near-surface air temperature is influenced by various dynamical, radiative, and surface-atmosphere exchange processes whose contributions are still not completely quantified. Applying stepwise multiple linear regression to Coupled Model Intercomparison Project phase 5 (CMIP5) model simulations and focusing on radiation (diagnosed by incoming shortwave and incoming longwave radiation) and land surface conditions (diagnosed by soil moisture and albedo) about 79% of the interannual variability and 99% of the multidecadal trend of monthly mean daily maximum temperature over land can be explained. The linear model captures well the temperature variability in middle-to-high latitudes and in regions close to the equator, whereas its explanatory potential is limited in deserts. While radiation is an essential explanatory variable over almost all of the analyzed domain, land surface conditions show a pronounced relation to temperature in some confined regions. These findings highlight that considering local-to-regional processes is crucial for correctly assessing interannual temperature variability and future temperature trends.

SUBMITTER: Schwingshackl C

PROVIDER: S-EPMC6049911 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:In urban areas, industrial and human activities are the prime cause that exacerbates the heating effects, also called the urban heat island (UHI) effect. The land surface temperature (LST), normalized difference vegetation index (NDVI), and the proportion of vegetation (Pv) are indicators of measurement of the heating/urbanization effects. In the present work, we investigated the impact of the COVID-19 lockdown, i.e., restricted human activities. We used Landsat-8 OLI/TIRS (level 1) data to investigate spatial and temporal heterogeneity changes in these urbanization indicators during full and partial lockdown periods in 2020 and 2021, with 2019 as the base year. We have selected three cities in India's eastern coal mining belt, Bokaro, Dhanbad, and Ranchi, for the study. Results showed a significant decrease in LST values over all sites, with a maximum reduction over mining sites, i.e., Bokaro and Dhanbad. The LST value decreased by about 13-19% during the lockdown period. Vegetation indices (i.e., NDVI and Pv) showed a substantial increase of about 15% overall sites. With decreased LST values and increased NDVI values, these quantities' correlations became more negative during the lockdown period. More positive changes are noticed over mining sites than non/less mining sites. This indirectly indicates the reduction in the heat-absorbing particles in the environment and surface of these sites, a possible cause for the reduction in LST values substantially. Reduction in coal particles at the land and vegetation surface likely contributed to decreased LST and enhanced vegetation indices. To check the statistical significance of changes in the UHI indicators in the lockdown period, statistical tests (ANOVA and Tukey's test) are performed. Results indicate that most of the case changes have been significant. The study's finding suggests the lockdown's positive impact on the heating/UHI effects. It emphasizes the need for planned lockdowns as city mitigation strategies to overcome pollution and environmental issues.

Dataset Information

Global Contributions of Incoming Radiation and Land Surface Conditions to Maximum Near-Surface Air Temperature Variability and Trend.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets