Forcing mechanisms governing diurnal, seasonal, and interannual variability in the boundary layer depths: Five years of continuous lidar observations over a suburban site near Paris

Abstract

The atmospheric boundary layer (ABL) depth, zi, is a fundamental variable of ABL and a climatologically important quantity. The exchange of energy between the Earth's surface and the atmosphere is governed by turbulent mixing processes in the daytime ABL, and thus, zi is important for scaling turbulence and diffusion in both meteorological and air quality models. A long‐term data set of zi was derived at the Site Instrumental de Recherche par Télédétection Atmosphérique (SIRTA) observatory near Paris, using measurements obtained from a ground‐based vertically pointing aerosol lidar and an autonomous algorithm STRAT+. Using multiparameter observational data sets covering a 5 year period (October 2008 to September 2013), this study aims to explore two interconnected ABL research topics: brief climatology involving multiscale temporal zi variability (diurnal, seasonal, annual, and interannual) and the relationship between zi and near‐surface thermodynamic parameters to determine meteorological processes governing zi variability. Both the zi and the growth rate over SIRTA showed large seasonal variability with higher mean values in spring (1633 m and 225 m h−1) and summer (1947 m and 247 m h−1) than in autumn (1439 m and 196 m h−1) and winter (1033 m and 149 m h−1). Seasonal variability of daytime maximum zi is found to be strongly and linearly correlated with downwelling solar radiation at the surface (r = 0.92), while the dependence between daytime maximum zi and sensible heat flux (SHF) at seasonal scales is not fully linear, in particular, for summer months. Interannual variability is studied using deseasonalized monthly‐mean anomalies of each variable. Conditional sampling and linear regression analyses between the anomalies of deseasonalized SHF and daytime maximum zi, show (1) stronger correlation between the two parameters for the soil conditions compared to the wet soil conditions, (2) that zi anomalies were more dependent on SHF anomalies for negative than for positive boundary layer wind speed anomalies, and (3) in the summer season, zi anomalies varied more consistently with SHF anomalies for conditions with negative cloud cover anomalies than in conditions with positive cloud cover anomalies.

ReOBS: a new approach to synthesize long-term multi-variable dataset and application to the SIRTA supersite

“…But this mld can be associated with a weak sensible heat flux. One reason for this is that the dominant timescale of variability for the boundary layer depth is the daily timescale, the maximum value being reached generally near 16:00 UTC in summer above SIRTA (Pal and Haeffelin, 2015), while the timescale of variability of the boundary layer forcers is hourly or less (radiative and heat fluxes). The temporal variability around the mld maximal value is often weak during this time lapse because it reacts with a delay.…”

“…This synergy aspect has been exploited in previous studies using the SIRTA-ReOBS data; for instance to study the diurnal cycle, the annual cycle and the interannual variability but for multiple variables, (Cheruy et al, 2012 andBastin et al, 2016), to study the different components and scales of the mixing layer depth variability (Pal and Haeffelin, 2015), and to perform in addition a dynamical analysis (Dione et al, 2016 andChiriaco et al, 2014). Figure 9 illustrates a possible synergy of multi-variables.…”

“…In these studies, using SIRTA-ReOBS has led to identifying the processes responsible of the model biases. Still in term of processes, Pal and Haeffelin (2015) used SIRTAReOBS to study the different components and scales of the mixing layer depth variability. Dione et al (2016) and Chiriaco et al (2014) have benefited from the SIRTA-ReOBS to study specific season anomalies.…”

ReOBS: a new approach to synthetize long-term multi-variable dataset and application to the SIRTA supersite

“…One of the main advantages of ReOBS is that all variables are synthetized in a single file at the same temporal resolution, facilitating studies with multi-variables synergy particularly useful for the understanding of atmospheric processes. This synergy aspect has been exploited in previous studies using the SIRTA-ReOBS data, for instance to study the diurnal cycle, the annual cycle, and the interannual variability but for multiple variables, (Cheruy et al (2012) and Bastin et al (2016)), to study the different components and scales of the mixing layer depth variability (Pal and Haeffelin, 2015), and to perform in addition a dynamical analysis (Dione et al 2016, andChiriaco et al 2014). In summary, low mld are induced by strong cloud albedo effect and thus by low temperature and weak sensible heat flux due to weak energy reaching the surface.…”

“…But they are under-used, in particular the observation synergy aspects, because of their complexity and diversity in terms of calibration procedures, quality control, data treatment, file format, temporal representativeness, collecting data for fifteen years from active and passive remote-sensing, in situ measurements at the surface, in the ground, and in the planetary boundary layer. Early versions of SIRTA ReOBS dataset ("Re" stands for different steps of re-processing, see next sections, and "OBS" stands for observations) have already been used in scientific studies that required the multi-variables and multi-temporal scales available in the SIRTA-ReOBS dataset (Cheruy et al 2012, Chiriaco et al 2014, Pal and Haeffelin 2015, Bastin et al 2016, Dione et al 2016. The ReOBS method has also been tested for other Dione et al (2016).…”