Figure 6 - available via license: Creative Commons Attribution 4.0 International
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Diurnal cycle of the predictor importance estimate for each term of the model, for (a) winter, (b) spring, (c) summer and (d) fall. Same colors as in Fig. 2a and b. Vertical dashed black lines are for sunrise and sunset mean hours.
Source publication
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
observ...
Contexts in source publication
Context 1
... to better evaluate the influence of each term on hourly temperature variations at different times of the day, the importance estimation value is determined for each hour using the random forest method described previously in Section 4.1. 300 Figure 6 presents the results of this method for each season. As expected (and previously exposed), for all the seasons í µí± í µí° ¶í µí°¿ is the term dominating during nighttime, just after sunset, and before sunrise (indicated by vertical dashed lines in black). ...
Context 2
... sunrise, the surface heating produced by the sun in the early morning enhances a high growth rate in the temperature variations, whose effect makes R CS the dominant term driving í µí¼í µí± 2í µí± í µí¼í µí±¡ í µí±í µí±í µí± at those hours of the day for all seasons, except for winter. For this latter season (Figure 6a), the growth rate of R CS is almost the same as that of R CL and thus it does not expose 305 an important estimation value. This effect is due to the weak mean solar zenith angle (SZA) for this season, and the surface heating by the sun in clear-sky conditions is not strong enough to modulate temperature variations. ...
Context 3
... shift in importance between R CL and R CS occurs during the rest of the day for the four seasons of the year. This effect is 310 explained by the variation of the data for these two terms: R CS standard deviation for a given hour/season is weak, and thus its influence to explain the difference between one day to another at a specific hour remains minimal and it is not a strong predictor at diurnal cycle scale, especially in the summer and spring (Figure 6b and c, respectively) when other variables will modulate temperature variations. On the other hand, R CL turns into the main modulator of í µí¼í µí± 2í µí± í µí¼í µí±¡ í µí±í µí±í µí± for all the seasons due to its strong standard deviation for a given hour/season, reaching its maximum importance in summer (Figure 6c). ...
Context 4
... effect is 310 explained by the variation of the data for these two terms: R CS standard deviation for a given hour/season is weak, and thus its influence to explain the difference between one day to another at a specific hour remains minimal and it is not a strong predictor at diurnal cycle scale, especially in the summer and spring (Figure 6b and c, respectively) when other variables will modulate temperature variations. On the other hand, R CL turns into the main modulator of í µí¼í µí± 2í µí± í µí¼í µí±¡ í µí±í µí±í µí± for all the seasons due to its strong standard deviation for a given hour/season, reaching its maximum importance in summer (Figure 6c). Therefore, the hourly 315 temperature variations are more sensitive to cloud changes rather than solar radiation which does not vary significantly for a specific hour from day to day. ...
Context 5
... the other terms, HA importance grows along the day. Its variation is greater than that of the other terms making it the second most important modulator most of the time (except for autumn, Figure 6d). Its contribution is weak in winter due to the lack of solar radiation and vegetation whose absence will diminish the turbulent heat fluxes measured at the surface, 320 along with a weak MLD. ...
Context 6
... SIRTA observatory, located in a suburban area at 20 km from the center of Paris could be indeed regularly affected by this modulation of circulation. 330 Figure 6 supports a clear and reliable estimation of the importance of each term split into seasons for every hour of the day, which is not seen by estimating the diurnal and annual cycle contribution of each term to temperature variations (cf. Section 3). ...
Citations
... Once all the datasets are organized and the variables needed are identified, the Observation-based Temperature Evolution Model (OTEM) that estimates one-hour temperature variations at the surface is developed (Rojas Muñoz et al., 2021). This model considers the surface variables mentioned in Chapter 1 that control temperature variations, in an hourly local scale. ...
Large-scale dynamics dominate the surface temperature variations and atmospheric conditions in Western Europe to the first order. However, this large-scale air mass circulation alone does not explain all the temperature and precipitation variability. At the second order, this variability depends on small-scale processes via the atmospheric boundary layer and the surface energy balance (SEB), which itself depends largely on radiation and thus on cloud properties.The objective of this thesis is to better understand local processes and their influence on local climate variability, with a particular focus on the role of clouds.To do so, the first objective is to quantify the specific local contribution of the main SEB terms acting on short-term (i.e. hourly) temperature variations in Ile de France, and to determine their importance and the conditions under which one or the other of these terms will be preponderant. The four terms acting on the temperature variations are radiation (which can be separated into clear sky and cloud contribution), heat exchange with the atmosphere, heat exchange with the ground, and temperature advection. We develop the OTEM model that allows us to estimate these terms almost exclusively from observations, using the SIRTA-ReOBS dataset. We show that the sum of these four terms gives a good estimate of the hourly temperature variations. The weight of each term of the SEB on the hourly temperature variations is analyzed using the random forest method, whose main advantage is its ability to handle thousands of input variables and identify the most significant ones. This analysis showed that regardless of the season, clouds are the main modulator of the sun's effect on hourly temperature variations during the day, and they completely dominate during the night.The second objective is to study the specific role of clouds in temperature variations. For this purpose, other observations including lidar profiles have been used, exclusively under cloudy conditions. Several cases were created from the radiative effect of clouds during the day and night to (i) better understand how they affect the state of the atmosphere and thus other variables at the surface, and (ii) characterize the type of predominant clouds according to their radiative effect.Finally, we investigate the spatio-temporal variability of the previously obtained results: (1) spatial variability by applying the same method to the Meteopole site in Toulouse to understand how local specific conditions affect each of the terms involved in the surface temperature variations; (2) variability as a function of large-scale air circulation conditions by separating our results as a function of North Atlantic weather patterns.
Clouds warm the surface in the longwave (LW), and this warming effect can be quantified through the surface LW cloud radiative
effect (CRE). The global surface LW CRE has been estimated over more than
2 decades using space-based radiometers (2000–2021) and over the 5-year period ending in 2011 using the combination of radar, lidar and space-based
radiometers. Previous work comparing these two types of retrievals has shown that the radiometer-based cloud amount has some bias over icy surfaces. Here we propose new estimates of the global surface LW CRE from space-based lidar
observations over the 2008–2020 time period. We show from 1D atmospheric
column radiative transfer calculations that surface LW CRE linearly decreases with increasing cloud altitude. These computations allow us to
establish simple parameterizations between surface LW CRE and five cloud properties that are well observed by the Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observations (CALIPSO) space-based lidar: opaque cloud cover and altitude and thin cloud cover, altitude, and emissivity. We evaluate this new surface LWCRE–LIDAR product by comparing it to existing
satellite-derived products globally on instantaneous collocated data at
footprint scale and on global averages as well as to ground-based observations at specific locations. This evaluation shows good correlations
between this new product and other datasets. Our estimate appears to be an
improvement over others as it appropriately captures the annual variability
of the surface LW CRE over bright polar surfaces and it provides a dataset
more than 13 years long.
