An observation-based scaling model for climate sensitivity estimates and global projections to 2100

Summertime carbonyl compounds in an urban area in the North China plain: Identification of sources, key precursors and their contribution to O3 formation

Carbonyl compounds are critical components of volatile organic compounds. They significantly participate in the photochemical formation of atmospheric ozone and thus threaten human health. This study measured 15 C₁-C₈ carbonyl compounds at an urban site in Linyi, a typically industrialised city in the North China Plain (NCP). Formaldehyde (3.89 ppbv), acetaldehyde (1.66 ppbv) and acetone (2.03 ppbv) were found to be the top three carbonyl compounds, accounting for 76.11% of the total concentration of carbonyl compounds. Anthropogenic secondary formation was recognised as the main source of the top five carbonyl compounds, which included formaldehyde, acetaldehyde, acetone, butyraldehyde and benzaldehyde, and accounted for 46-54% of all sources. Alkenes were the most important precursors of formaldehyde and acetaldehyde, suggesting that reducing the emission of alkenes from anthropogenic sources is an effective way to control carbonyl compound pollution in Linyi. Furthermore, the photolysis of carbonyl compounds played a significant role (68-75%) as sources of HO₂• and RO₂• and thus made a significant contribution (14.6%) to the photochemical formation of O₃. This study highlights the importance of anthropogenic secondary formation as a source of carbonyl compounds and provides a scientific basis for O₃ pollution control in carbonyl compound-enriched cities in the NCP.

Separating internal and externally forced contributions to global temperature variability using a Bayesian stochastic energy balance framework

Earth's temperature variability can be partitioned into internal and externally forced components. Yet, underlying mechanisms and their relative contributions remain insufficiently understood, especially on decadal to centennial timescales. Important reasons for this are difficulties in isolating internal and externally forced variability. Here, we provide a physically motivated emulation of global mean surface temperature (GMST) variability, which allows for the separation of internal and external variations. To this end, we introduce the "ClimBayes" software package, which infers climate parameters from a stochastic energy balance model (EBM) with a Bayesian approach. We apply our method to GMST data from temperature observations and 20 last millennium simulations from climate models of intermediate to high complexity. This yields the best estimates of the EBM's forced and forced + internal response, which we refer to as emulated variability. The timescale-dependent variance is obtained from spectral analysis. In particular, we contrast the emulated forced and forced + internal variance on interannual to centennial timescales with that of the GMST target. Our findings show that a stochastic EBM closely approximates the power spectrum and timescale-dependent variance of GMST as simulated by modern climate models. Small deviations at interannual timescales can be attributed to the simplified representation of internal variability and, in particular, the absence of (pseudo-)oscillatory modes in the stochastic EBM. Altogether, we demonstrate the potential of combining Bayesian inference with conceptual climate models to emulate statistics of climate variables across timescales.

Grid-Point Rainfall Trends, Teleconnection Patterns, and Regionalised Droughts in Portugal (1919–2019)

This paper describes the long-term grid-point rainfall trends in the context of climate change, recent regionalised rainfall decline and drought events for mainland Portugal, which is teleconnected, in most cases, to the trends of mathematical descriptions of the North Atlantic Oscillation (NAO) during the century from October 1919 to September 2019. Grid-point rainfall dataset (1919–2019, from 126 centroids in a regular mesh over the country) have been constructed from high-quality ground-based data and as such, it provides a reliable source for the analysis of rainfall trends at different timescales: October–December, January–March, December–March, and the hydrological year. The Mann–Kendall (MK) coupled with Sen’s slope estimator test are applied to quantify the trends. The Sequential Mann–Kendall (SQMK) analysis is implemented to obtain the fluctuation of the progressive trends along the studied 100-year period. Because of their pivotal role in linking and synchronising climate variability, teleconnections to the North Atlantic Ocean are also explored to explain the rainfall trends over the Portuguese continuum. The results provide a solid basis to explain the climate change effects on the Portuguese rainfall based on significant associations with strong negative correlations between changes in rainfall and in NAO indices. These strong opposing correlations are displayed in most of the winter seasons and in the year. After the late 1960s, a generalised rainfall decrease emerges against a background of significant upward trends of the NAO; such coupled behaviour has persisted for decades. Regionalised droughts at three identified climatic regions, based on factor analysis and Standardised Precipitation Index (SPI), are also discussed, concluding that the frequency of severe droughts may increase again, accompanied by a stronger influence of the recently more positive and unusual winter season and annual NAO indices.