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.

<p lang="en-GB" align="justify">In a warming climate where the frequency and intensity of extreme events (such as droughts and floods) are increasing, a better representation and estimation of land surface variables remains a crucial step to study their response to climate change. Soil moisture is a key variable of the water cycle. Monitoring soil moisture, either by in situ measurements or by satellite observations allows better prediction and anticipation of droughts and floods, especially in agricultural regions. In order to fully exploit the growing number of satellite observations data, assimilation techniques can be used to integrate these data into land surface models.</p> <p lang="en-GB" align="justify">In this work, surface soil moisture (SSM) observations from Sentinel-1 (S1) satellite are assimilated into the ISBA model at the kilometer scale. The main objective is to evaluate the added value of the SSM assimilation and its impact on the ISBA model simulations, driven by atmospheric variables from the AROME weather forecast model. The Land Data Assimilation System tool (LDAS-Monde) of Météo-France is used. The SSM S1 product covers the period 2017-2019, over two regions in south of France and one in Spain. The native resolution of the S1 product is 10 m, and the aggregated 1 km product only covers areas where radar signal interpretation is possible. The two areas of interest in France are the Toulouse and the Montpellier regions. In these two areas, in situ soil moisture measurements are available (SMOSMANIA network and Meteopole-Flux stations of Meteo-France). The area of interest in Spain is located between Salamanca and Valladolid, where the REMEDHUS network of in-situ soil moisture measurements is located. In situ SSM observations at a depth of 5 cm were gathered from all stations at an hourly temporal resolution. The S1 SSM shows a good agreement with the in situ observations, including over the Météopole-Flux site which is located in a semi-urban area.</p> <p lang="en-GB" align="justify">The impact of assimilating SSM products is evaluated over three surface variables: SSM at the 1 – 4 cm soil deph layer (WG2), at the root zone at 30 cm soil depth (WG5) and on the Leaf Area Index (LAI). Three experiments are then carried out over the three regions: assimilation of the S1 SSM product alone, assimilation of the LAI retrieved from the Copernicus Global land Service (CGLS), and one last experience where S1 SSM is jointly assimilated with LAI.</p> <p lang="en-GB" align="justify">The results of these experiments on one hand show that when SSM alone is assimilated, almost no improvement is observed on WG2 between the ISBA model outputs and the assimilation outputs when compared to in situ measurements. On the other hand, when SSM is jointly assimilated with LAI, there is a stronger impact on WG2 and thus the outputs are closer to the in situ observations. Concerning WG5, the impact of assimilating SSM and LAI is found to be even stronger.</p> <p lang="en-GB" align="justify"> </p>

<p>The local contribution of clouds to the surface energy balance and temperature variability is an important topic in order to apprehend how this intake affects local climate variability and extreme events, how this contribution varies from one place to another, and how it evolves in a warming climate. The scope of this study is to understand how clouds impact temperature variability, to quantify their contribution, and to compare their effects to other surface processes. To do so, we develop a method to estimate the different terms that control temperature variability at the surface (∂T<sub>2m</sub> /∂t) by using this equation: <strong>∂T<sub>2m</sub> /∂t=R+HA+HG+Adv</strong> where R is the radiation that is separated into the cloud term (R<sub>cloud</sub>) and the clear sky one (R<sub>CS</sub>), HA the atmospheric heat exchange, HG the ground heat exchange, and Adv the advection. These terms are estimated hourly, almost only using direct measurements from SIRTA-ReOBS dataset (an hourly long-term multi-variables dataset retrieved from SIRTA, an observatory located in a semi-urban area 20-km South-West of Paris; Chiriaco et al., 2019) for a five-years period. The method gives good results for the hourly temperature variability, with a 0.8 correlation coefficient and a weak residual term between left part (directly measured) and right part of the equation.</p><p>A bagged decision trees analysis of this equation shows that R<sub>CS</sub> dominates temperature variability during daytime and is mainly modulated by cloud radiative effect (R<sub>cloud</sub>). During nighttime, the bagged decision trees analysis determines that R<sub>cloud</sub> is the term controlling temperature changes. When a diurnal cycle analysis (split into seasons) is performed for each term, HA becomes an important negative modulator in the late afternoon, chiefly in spring and summer, when evaporation and thermal conduction are increased. In contrast, HG and Adv terms do not play an essential role on temperature variability at this temporal scale and their contribution is barely considerable in the one-hour variability, but still they remain necessary in order to obtain the best coefficient estimator between the directly measured observations and the method estimated. All terms except advection have a marked monthly-hourly cycle.</p><p>Next steps consist in characterize the types of clouds and study their physical properties corresponding to the cases where R<sub>cloud</sub> is significant, using the Lidar profiles also available in the SIRTA-ReOBS dataset.</p>

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Abstract. Local temperature variations at the surface are mainly dominated by small-scale processes coupled through the surface energy budget terms, which depend mostly on radiation availability and thus cloud processes. A method to determine each of these terms based almost exclusively on observations is presented in this paper, with the main objective to estimate their importance in hourly 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 cloud 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 cycles 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-hour temperature variations during the day, and mainly drive these 1-hour 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 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.