Abstract
Abstract. Local short-term temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy balance terms, which are well known but whose specific contribution and importance on the hourly scale still need to be further analyzed. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective being to estimate their importance in hourly near-surface temperature variations at the SIRTA observatory, near Paris. Almost all terms are estimated from the multi-year dataset SIRTA-ReOBS, following a few parametrizations. The four main terms acting on temperature variations are radiative forcing (separated into clear-sky and cloudy-sky radiation), atmospheric heat exchange, ground heat exchange, and advection. Compared to direct measurements of hourly temperature variations, it is shown that the sum of the four terms gives a good estimate of the hourly temperature variations, allowing a better assessment of the contribution of each term to the variation, with an accurate diurnal and annual cycle representation, especially for the radiative terms. A random forest analysis shows that whatever the season, clouds are the main modulator of the clear-sky radiation for 1 h temperature variations during the day and mainly drive these 1 h temperature variations during the night. Then, the specific role of clouds is analyzed exclusively in cloudy conditions considering the behavior of some classical meteorological variables along with lidar profiles. Cloud radiative effect in shortwave and longwave and lidar profiles show a consistent seasonality during the daytime, with a dominance of mid- and high-level clouds detected at the SIRTA observatory, which also affects near-surface temperatures and upward sensible heat flux. During the nighttime, despite cloudy conditions and having a strong cloud longwave radiative effect, temperatures are the lowest and are therefore mostly controlled by larger-scale processes at this time.
Highlights
Regional climate variability is to the first order driven by large-scale atmospheric conditions
Temperature variations at the surface are related to the surface energy balance (SEB) following surface–atmosphere interactions and solar radiation (Wang and Dickinson, 2013) that are separated into different components: latent and sensible heat fluxes (Flat and Fsens, respectively), ground heat
The use of the model developed in the current study considers all the variables acting within the atmospheric boundary layer (ABL) and controlling surface temperature variations, all of them estimated almost exclusively from surface-based observations
Summary
Regional climate variability is to the first order driven by large-scale atmospheric conditions. The study is based on direct measurements from the SIRTA-ReOBS dataset (Chiriaco et al, 2018), which includes many variables collected since 2002 at SIRTA (Site Instrumental de Recherche en Télédetection Active; Haeffelin et al 2005), an observatory located in a semi-urban area in the southwest of Paris, France This dataset is well suited to the current study objective because (i) it allows the use of a multi-variable synergistic compilation to study and compare different characteristics of the atmosphere or the surface (e.g., Bastin et al, 2018; Dione et al, 2017; Cheruy et al, 2013; Chiriaco et al, 2014), and (ii) it is located in western Europe and allows access to the hourly timescale, i.e., the scales of the local processes of the current study. It allows us to separately study the influence of each SEB term in a local scale This allows a realistic and reliable estimation of the contribution of each term (radiative fluxes, turbulent heat fluxes, etc.) on hourly temperature variations, and it would be possible to have that at different sites since each term will present a different behavior and importance.
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