Internal Variability of All‐Sky and Clear‐Sky Surface Solar Radiation on Decadal Timescales. Issue 12 (24th June 2022)

• Record Type: Journal Article
• Authors: Chtirkova, Boriana; Folini, Doris; Correa, Lucas Ferreira; Wild, Martin
• Is Part Of: Journal of geophysical research. Volume 127:Issue 12(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-06-24
• Subjects: surface solar radiation -- CMIP6 -- internal variability -- unforced trends -- dimming and brightening; Atmospheric physics -- Periodicals; Geophysics -- Periodicals; 551.5
• DOI: 10.1029/2021JD036332
• Languages: English
• ISSNs: 2169-897X

Abstract

Abstract: Internal variability comprises all processes that occur within the climate system without any natural or anthropogenic forcing. Climate‐driving variables like the surface solar radiation (SSR) are shown to exhibit unforced trends (i.e., trends due to internal variability) of magnitudes comparable to the magnitude of the forced signal even on decadal timescales. We use annual mean data from 50 models participating in the preindustrial control experiment (piControl) of the Coupled Model Intercomparison Project‐Phase 6 (CMIP6) to give quantitative grid‐box specific estimates of the magnitudes of unforced trends. To characterize a trend distribution, symmetrical around 0, we use the 75th percentile of all possible values, which corresponds to a positive trend with 25% chance of occurrence. For 30‐year periods and depending on geographical location, this trend has a magnitude between 0.15 and 2.1 W m −2 /decade for all‐sky and between 0.04 and 0.38 W m −2 /decade for clear‐sky SSR. The corresponding area‐weighted medians are 0.69 W m −2 /decade for all‐sky trends and 0.17 W m −2 /decade for clear‐sky trends. The influence of internal variability is on average six times smaller in clear‐sky, compared to all‐sky SSR. The relative uncertainties in the physical representation, derived from the CMIP6 inter‐model spread, are ±32% for all‐sky and ±43% for clear‐sky SSR trends. Reasons for differences between models like horizontal resolution, aerosol handling, and the representation of atmospheric and oceanic phenomena are investigated. The results can be used in the analysis of observational time series by attributing a probability for a trend to be caused by internal variability, given its magnitude, length, and location. Key Points: Internal variability of all‐sky and clear‐sky SSR and associated decadal‐scale unforced trends are quantified probabilistically Clouds contribute to a larger fraction of unforced trends: internal variability is ∼6 times smaller in clear‐sky as compared to all‐sky SSR Unforced SSR trends are particularly strong in some regions, the regional pattern being different for clear‐sky and all‐sky trends