Aerosol–cloud interaction is the most uncertain component of the overall anthropogenic forcing of the climate, in which cloud droplet number concentration (Nd) sensitivity to aerosol (S) is a key term for the overall estimation. However, satellite-based estimates of S are especially challenging, mainly due to the difficulty in disentangling aerosol effects on Nd from possible confounders. By combining multiple satellite observations and reanalysis, this study investigates the impacts of (a) updraft, (b) precipitation, (c) retrieval errors, and (d) vertical co-location between aerosol and cloud on the assessment of S in the context of marine warm (liquid) clouds. Our analysis suggests that S increases remarkably with both cloud-base height and cloud geometric thickness (proxies for vertical velocity at cloud base), consistent with stronger aerosol–cloud interactions at larger updraft velocity for midlatitude and low-latitude clouds. In turn, introducing the confounding effect of aerosol–precipitation interaction can artificially amplify S by an estimated 21 %, highlighting the necessity of removing precipitating clouds from analyses of S. It is noted that the retrieval biases in aerosol and cloud appear to underestimate S, in which cloud fraction acts as a key modulator, making it practically difficult to balance the accuracies of aerosol–cloud retrievals at aggregate scales (e.g., 1◦ × 1 ◦ grid). Moreover, we show that using column-integrated sulfate mass concentration (SO4C) to approximate sulfate concentration at cloud base (SO4B) can result in a degradation of correlation with Nd, along with a nearly twofold enhancement of S, mostly attributed to the inability of SO4C to capture the full spatiotemporal variability of SO4B. These findings point to several potential ways forward to practically account for the major influential factors by means of satellite observations and reanalysis, aiming at optimal observational estimates of global radiative forcings due to the Twomey effect and also cloud adjustments.

Land-use changes impact the energy balance of the Earth system, and feedbacks in the Earth system can dampen or amplify this perturbation. We analyze here from satellite data the response of clouds and subsequently radiation to a change of land use for the example of deforestation in the Amazon Basin. In this region, the characteristics of different cloud types over two vegetation types (forest and crop-/grasslands) were calculated for a time period of five years by using satellite data from the instruments MODIS and CERES. The cloud types are defined according to height, optical thickness, and fraction of cloud cover. For calculating the radiative forcing caused by deforestation, the dependency of spatial and temporal averages for the reflected shortwave and outgoing longwave radiation of the top of the atmosphere on vegetation types were determined as well. The results show distinct differences in cloud cover and radiative forcing over crop-/grasslands and forests for the two vegetation regimes, implying a potentially significant positive cloud feedback to deforestation.

Measuring the composition of the stratosphere, and understanding the processes regulating it, have become,in the last few decades, top priorities in the scientific community, particularly since the discovery ofthe ozone hole in the 1980s. While a lot has indeed been done in monitoring ozone, other constituents also influence the stratosphere’s composition, and interfere namely with ozone, affecting its chemical and dynamical balance. Among these is nitric acid (HNO3 ) which is a reservoir for ozone depleting NOx ,but also a key player in the formation of polar stratospheric clouds which, by turning inert species into active radicals, enhance the ozone depletion further. The nadir-viewing IASI instrument is a very good means of obtaining simultaneous data of nitric acid and ozone. Indeed, it measures the radiation of the Earth’s atmosphere in the thermal infrared spectral range, which allows it to measure even at night. This is crucial to the study of polar processes, since they occur mostly during the polar winter, when no light reaches these latitudes. Thanks to its design and its technical characteristics, the IASI instrument provides data all-year round, for every location on the Earth. The purpose of this work is to use this unique set of IASI data to understand what drives the variability of HNO3 in the stratosphere. No study so far has focused on the factors affecting the time and spatial distributions of nitric acid to the extent and scale we propose here. We aim to identify and quantify these factors, and to compare them with the drivers of ozone variability. Nitric acid data are thus obtained for the 10 years of IASI observation (2008 − 2017), and vertical profiles are retrieved in near-real time thanks to the FORLI algorithm developed at ULB. The first part of the present work provides a detailed characterization of the IASI FORLI-HNO3 data set in terms of vertical sensitivity and errors. We show that the HNO3 maximum is found around 20 km altitude, where we also find the maximum sensitivity of the measurements to the vertical profile. The analysis of the averaging kernels shows us that only one level of information can be extracted from the vertical profile, which constrains the rest of our analyses to the use of a total (or almost total) column. We also find that the IASI measurements tend to overestimate slightly the HNO3 column in the upper troposphere/lower stratosphere region of the profile. The data set is validated against ground-based FTIR measurements at different latitudes: we find good agreement between IASI and the FTIR data, which confirms that IASI manages to reproduce the HNO3 columns and their seasonality accurately. Comparisons with a state of the art atmospheric model data are also shown, and suggest that improvement is still largely needed in models to represent the HNO3 distributions accurately. The use of a data-assimilated model (BASCOE) shows a much better agreement with the IASI observations. The next part of the work describes the geophysical analyses carried out, and details the first time series and global distributions of HNO3 from IASI. After describing the various (mostly polar) processes at play observed in the time series, the question of the formation of the polar stratospheric clouds is raised, and further results are shown about the temperature at which these form. While a fixed threshold (195 K) is usually used for geophysical analyses, we find from the observational IASI data set that this fixed temperature can vary substantially depending on local conditions and on altitude. The last sections use multivariate linear regressions to fit the HNO3 and O3 time series, featuring various chemical and dynamical variables in order to identify what factors are responsible for their respective variability. We include the variables most commonly used in such kind of study, i.e. a linear trend, harmonical terms to account for the annual seasonality, and proxies for the quasi-biennial oscillation, the multivariate ENSO index, and the Arctic and Antarctic oscillations. The novelty of our work resides in the addition of a proxy for the volume of polar stratospheric clouds to account for the strong denitrification observed in the HNO3 time series in polar regions. We find that the annual cycle, encompassing the solar seasonality and the Brewer-Dobson circulation, is the factor explaining most of the variability of both HNO3 and O3 ,at almost all latitudes. In the polar regions, however, the volume of polar stratospheric clouds is a key factor contributing the most to their variability. Globally, the same factors explain the same portion of both HNO3 and O3 variability. In the last part of the thesis, we conclude and provide a preliminary co-analysis of HNO3 and O3 from the 10-year IASI data. The results are encouraging and highlight the potential of the IASI measurements to monitor the polar processes on various scales. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Satellite instruments currently provide the largest source of infor- mation to today's data assimilation (DA) systems for numerical weather predic- tion (NWP). With the development of high-resolution models, the efficient use of observations at high density is essential to improve small-scale information in the weather forecast. However, a large amount of satellite radiances has to be removed from DA by horizontal data thinning due to uncorrelated observation error assumptions. Moreover, satellite radiances include systematic errors (biases) that may be even larger than the observation signal itself, and must be properly removed prior to DA. Although the Variational Bias Correction (VarBC) scheme is widely used by global NWP centers, there are still open questions regarding its use in Limited-Area Models (LAMs). This thesis aims to tackle the obser- vation error difficulties in assimilating polar satellite radiances in the meso-scale ALADIN system. Firstly, we evaluate spatial- and inter-channel error correla- tions to enhance the positive effect of data thinning. Secondly, we study satellite radiance bias characteristics with the key aspects of the VarBC in LAMs, and we compare the different VarBC configurations with regards to forecast performance. This work is a step towards improving the...

Land-use changes impact the energy balance of the Earth system, and feedbacks in the Earth system can dampen or amplify this perturbation. We analyze here from satellite data the response of clouds and subsequently radiation to a change of land use for the example of deforestation in the Amazon Basin. In this region, the characteristics of different cloud types over two vegetation types (forest and crop-/grasslands) were calculated for a time period of five years by using satellite data from the instruments MODIS and CERES. The cloud types are defined according to height, optical thickness, and fraction of cloud cover. For calculating the radiative forcing caused by deforestation, the dependency of spatial and temporal averages for the reflected shortwave and outgoing longwave radiation of the top of the atmosphere on vegetation types were determined as well. The results show distinct differences in cloud cover and radiative forcing over crop-/grasslands and forests for the two vegetation regimes, implying a potentially significant positive cloud feedback to deforestation.