The ongoing exhaustion of fossil fuels places a limit to the total amount of anthropogenic CO2 that will be emitted into the atmosphere and therefore constraints future global warming. Here we assess the implications of fossil fuels depletion on future changes of atmospheric CO2 concentration and global-mean temperature. We find that, despite the exhaustion of fossil fuels, future global warming will likely reach a dangerous level. Deliberate actions aiming at emissions reduction are needed to avoid dangerous climate change.

The present work combines experimental and numerical analyses to improve current understanding of turbulence in stably stratified flows. An extensive literature review is presented on the mechanisms governing turbulence under stratified conditions, with a special focus on the Richardson number parameter, as it is often adopted as a switch to turn turbulence modelling on/off. Anisotropization of turbulence is investigated, as it is found to be an important mechanism for turbulence survival at any Richardson number, but usually overlooked in turbulence parameterizations. For this purpose, an experimental dataset previously collected over an Alpine glacier is used, with a focus on the anisotropy of the Reynolds stress tensor, as the scientific community has recently shown improvements in the description of the atmospheric surface layer by taking this aspect into account. Different sources leading stresses to deviate from the isotropic limit are explored, as well as energy exchanges across scales and between kinetic and potential reservoirs, in order to identify the main processes that should be included in turbulence parameterizations to properly represent anisotropic turbulence under stable conditions. High-resolution numerical simulations are then performed with the Weather Research and Forecasting (WRF) model to evaluate different PBL parameterizations in reproducing specific stable atmospheric conditions developing over complex terrain, and their influence on the local circulation. For this purpose, two wintertime case studies in a basin-like area of an Alpine valley are investigated. Both are fair-weather episodes with weak synoptic forcing and well-developed diurnal local circulations, differing by the thermal stratification in the basin. In particular, the influence of thermal stratification on the outbreak of a valley-exit wind coming from a tributary valley is investigated, and the influence of such type of flows on turbulence anisotropy in stably stratified conditions is discussed for future investigations.

Land surface models (LSMs) implemented in numerical weather prediction (NWP) models use several parameters to suitably describe the surface and its interaction with the atmosphere, whose determination is often affected by many uncertainties, strongly influencing simulation results. However, the sensitivity of meteorological model results to these parameters has not yet been studied systematically, especially in complex terrain, where uncertainty is expected to be even larger. This work aims at identifying critical LSM parameters influencing the results of NWP models, focusing in particular on the simulation of thermally-driven circulations over complex terrain. While previous sensitivity analyses employed offline LSM simulations to evaluate the sensitivity to surface parameters, this study adopts an online coupled approach, utilizing the Noah-MP LSM within the Weather Research and Forecasting (WRF) model. To overcome computational constraints, a novel tool, Machine Learning-based Automated Multi-method Parameter Sensitivity and Importance analysis Tool (ML-AMPSIT), is developed and tested. This tool allows users to explore the sensitivity of the results to model parameters using supervised machine learning regression algorithms, including Random Forest, CART, XGBoost, SVM, LASSO, Gaussian Process Regression, and Bayesian Ridge Regression. These algorithms serve as fast surrogate models, greatly accelerating sensitivity analyses while maintaining a high level of accuracy. The versatility and effectiveness of ML-AMPSIT enable the fast implementation of advanced sensitivity methods, such as the Sobol method, overcoming the computational limitations encountered in expensive models like WRF. The suitability of this tool to assess model’s sensitivity to the variation of specific parameters is first tested in an idealized sea breeze case study where six surface parameters are varied. Then, the analysis focuses on the evaluation of the sensitivity to surface parameters in the simulation of thermally-driven circulations in a mountain valley. Specifically, an idealized three-dimensional topography consisting of a valley-plain system is adopted, analyzing a complete diurnal cycle of valley and slope winds. The analysis focuses on all the key surface parameters governing the interactions between NoahMP and WRF. The proposed approach, novel in the context of LSM-NWP model coupling, draws from established applications of machine learning in various Earth science disciplines, underscoring its potential to improve the estimation of parameter sensitivities in NWP models.