Desert dust plays an important role in several components of the Earth system, mainly due to its nutrient content and interactions with radiation and clouds. Nevertheless, the airborne lifetime of coarse dust particles and their atmospheric transport from source regions are severely underestimated by current climate models. For instance, Saharan dust particles with diameters as large as 30 µm have repeatedly been detected in the Americas, while models are unable to reproduce this phenomenon. In this dissertation, we investigated the effects of vertical turbulent mixing in elevated shear layers on particle deposition rates. We focused on the Saharan Air Layer (SAL), a dust-laden air mass situated above the Marine Boundary Layer (MBL) that periodically moves from the African continent towards the Caribbean region over the tropical North Atlantic. Using an analytical approach combined with large-eddy simulations (LES), we first present a theory for dust concentration profiles evolving under the effects of gravitational settling and turbulent mixing. We found that the increase in dust airborne lifetime due to turbulence is determined uniquely by the Peclet number Pe (the ratio of the mixing timescale to the settling timescale), and it is limited to a factor of 2 when compared with laminar flows. Afterwards, we quantify the decrease in shear-induced eddy mixing rates caused by stable buoyancy stratification (which is typically present in the SAL). Wefound that, under weak stratification, SAL eddy diffusivities can be calculated as a function of layer depth, shear magnitude, and gradient Richardson number Rig . Our estimates indicate that even small diffusivity values can significantly delay the deposition of particles as large as super coarse dust (with a diameter greater than 10 µm). In fact, we show that, together with particle asphericity, turbulent mixing can explain to a great extent the presence of super coarse Saharan dust in the Caribbean observed during the SALTRACE field campaign. Finally, we also note that the diffusivities calculated for our elevated SAL set-up decay faster with Rig than typical ABL models, highlighting the importance of employing appropriate parameterization schemes in climate models to represent slow processes (particularly affected by small diffusivities values) accurately.

Turbulence in the ocean surface boundary layer (OSBL) directly controls transport and dispersion of passive materials near the ocean surface. Many such materials directly affect human and ecosystem health and human activities (e.g. microplastic, oil and nutrients and phytoplankton) as well as climate-change-relevant global budgets (e.g. carbon dioxide), making their mixing and transport mechanisms an important topic of investigation. However, the dynamics of the upper ocean are such that most of the processes and flow structures that are relevant to such transport are too small to be investigated with global circulation models and challenging to be investigated with field and laboratory experiments. As a result much is unknown about how the transport and mixing of materials is affected by OSBL turbulence in different regimes, particularly when surface waves are present, which have no analog in atmospheric boundary layers.

Based on these considerations, this dissertation is dedicated to the investigation of passive material transport and mixing in OSBLs under generalized regimes (i.e. regimes that are a combination of convection, wind-stress-driven turbulence and wave-driven turbulence). Focus is given to small-scale processes (smaller than an OSBL depth) and the tool of choice is the large-eddy simulation (LES) technique, which can resolve the relevant eddies for transport while parameterizing smaller ones.

One of the contributions of this dissertation is the exploration of a wide range of upper ocean regimes and its analysis over a general framework. This is challenging since the dynamics of these regimes can be extremely different from each other, which reflects on material transport as extremely different outcomes for different regimes. Furthermore, another major contribution is the development of a method to aid in the development of eddy-diffusivity models for OSBL mixing. While development of such models has historically depended on choices of scaling laws and shape functions that have to be made \textit{a priori} (and therefore constrain the outcome), the method developed here does not make any such assumptions. Thus, all results naturally emerge from data. Finally, after showing that results conform to current knowledge of upper ocean physics, we modify existing models accordingly and show that results are improved with the adopted modifications.

Current Earth system models (ESMs) struggle to accurately simulate the spatiotemporal variability of the desert dust cycle and the impact of dust aerosols on Earth’s climate and ecosystems. This is in part because these models lack several essential land-surface aeolian processes that couple dust with climate and land-surface changes. We address these fundamental problems and improve the representation of dust emission in ESMs using both a process-based approach and a data-driven approach. First, we develop a new process-based dust emission scheme that couples dust emission better with simulated land-surface processes in ESMs, with improved descriptions of (1) the effect of soil texture on the dust emission threshold, (2) the effects of rocks and vegetation on dissipating the surface wind stress, (3) the effects of boundary-layer turbulence on driving intermittent dust emissions, and (4) a simple methodology to rescale coarser-resolution emission simulations to match the spatial variability of higher-resolution emission simulations in ESMs. We use the revised dust emission scheme to model global dust emissions in a standalone model forced by reanalyzed meteorology and land-surface fields. The resulting simulated emissions show substantially better agreement than other existing emission models against observationally constrained regional dust emissions. Second, we implement the new emission scheme into the Community Earth System Model version 2 (CESM2) for a present-day (2004–2008) dust cycle simulation. The results show that our scheme significantly reduces the CESM2 simulation bias against observations compared to CESM’s default emission scheme and improves the correlations against observations of key dust variables such as dust aerosol optical depth (DAOD) and surface particulate matter (PM) concentration. Third, we use a data-driven approach to construct a globally gridded historical (1841–2000) dust emission inventory by empirically combining multiple sedimentary records of dust deposition with observational and modeling constraints on the modern-day dust cycle. The derived inventory contains the interdecadal variability of dust emissions as forced by the rising trends in the observed deposition time series, which show a global emission increase of $\sim$50\% for 1841–2000. We evaluate the inventory’s effectiveness in enforcing a historical dust increase in ESMs by using it to force a long-term (1851–2000) dust cycle simulation in CESM2. The simulated dust deposition is in reasonable agreement with the long-term variability in most dust deposition records and with independently measured long-term trends in dust concentrations. This contrasts with dust simulations from the Coupled Model Intercomparison Project (CMIP6), which show little to no secular trends. Our studies thus provide two approaches to advancing the Earth system modeling of desert dust aerosols. The new process-based dust emission scheme enables more accurate simulations of the dust cycle’s present-day spatial and day-to-day variability, enhancing short-term weather and air pollution forecasting power on a daily timescale. The empirically derived dust emission inventory allows realistic simulations of the historical dust increase, thereby improving the accuracy of aerosol radiative forcing predictions in climate change assessments across multidecadal timescales. Both approaches will thus improve dust cycle simulations in ESMs across multiple timescales of interest.