Hamish Gordon

Global model studies and observations have shown that downward transport of aerosol nucleated in the free troposphere is a major source of cloud condensation nuclei (CCN) to the global boundary layer. In Amazonia, observations show that this downward transport can occur during strong convective activity. However, it is not clear from these studies over what spatial scale this cycle of aerosol formation and downward supply of CCN is occurring. Here, we aim to quantify the extent to which the supply of aerosol to the Amazonian boundary layer is generated from nucleation within a 1000 km regional domain or from aerosol produced further afield, and the effectiveness of the transport by deep convection. We run the atmosphere-only configuration of the HadGEM3 climate model incorporating a 440 km × 1080 km regional domain over Amazonia with 4 km resolution. Simulations were performed over several diurnal cycles of convection. Below 1 km altitude in the regional domain, our results show that nucleation within the regional domain accounts for only 1.8 % of all Aitken and accumulation mode aerosol particles, whereas nucleation that occurred outside the domain (in the global model) accounts for 81 %. The remaining aerosol is primary in origin. Above 10 km, the regional-domain nucleation accounts for up to 64 % of Aitken and accumulation mode aerosol, but over several days very few particles nucleated above 10 km in the regional domain are transported into the boundary layer within the domain, and in fact very little air is mixed that far down. Rather, particles transported downwards into the boundary layer originated from outside the …

Aerosol‐cloud interactions refer to the group of atmospheric processes by which aerosols influence cloud properties, and sometimes also processes by which clouds affect aerosols. The effect of these atmospheric processes on Earth's radiative balance is potentially large, but uncertain. When combined with uncertainties in aerosol concentrations that result from emissions and aerosol processes, the uncertainty in aerosol‐cloud interactions dominates the overall uncertainty in our knowledge of radiative forcing of Earth's climate. Aerosols affect clouds primarily by changing the number of cloud condensation and ice nuclei, “indirect effects,” and sometimes also the temperature of the cloud, “semi‐direct effects.” Changes in cloud processes in response to aerosol‐cloud interactions may cause significant adjustments to cloud macrophysical properties such as …

Atmospheric aerosols influence the Earth’s climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining the parameters of expensive climate models by comparing model outputs with observations. Using the C3.AI Suite, a cloud computing platform, we use a perturbed parameter ensemble of the UKESM1 climate model to efficiently train a surrogate model. A method for estimating a data-driven model discrepancy term is described. The strict bounds method is applied to quantify parametric uncertainty in a principled way. We demonstrate the scalability of this framework with 2 weeks’ worth of simulated aerosol optical depth data over the South Atlantic and Central …

We describe and evaluate a system for regional modeling of atmospheric composition with the Met Office Unified Model (UM), suitable for climate, weather forecasting and air quality applications. In this system, named NUMAC (“Nested UM with Aerosols and Chemistry”), a global model provides boundary conditions for regional models nested within it, using the Met Office's Regional Nesting Suite for multi‐scale simulations. The regional models, which can run at convection‐permitting or cloud‐resolving scales, use the same code as the global model. The system includes double‐moment prognostic aerosol microphysics with interactive chemistry of sulfur species, ozone, NOx, and CO as in the UK Earth System Model. Double‐moment prognostic cloud microphysics is optional. To test NUMAC, we compare simulations to surface and aircraft measurements from NASA's Korea‐United States Air Quality campaign …

Cloud microphysics parametrizations control the transfer of water between phases and hydrometeor species in numerical weather prediction and climate models. As a fundamental component of weather modelling systems cloud microphysics can determine the intensity and timing of precipitation, the extent and longevity of cloud cover and its impact on radiative balance, and directly influence near surface weather metrics such as temperature and wind. In this paper we introduce and demonstrate the performance of a double moment cloud microphysical scheme (CASIM: Cloud AeroSol Interacting Microphysics) in both midlatitude and tropical settings using the same model configuration. Comparisons are made against a control configuration using the current operational single moment cloud microphysics, and CASIM configurations that use fixed in‐cloud droplet number or compute cloud droplet number …

Aerosol particles in the atmosphere profoundly influence public health and climate. Ultrafine particles enter the body through the lungs and can translocate to essentially all organs, and they represent a major yet poorly understood health risk. Human activities have considerably increased aerosols and cloudiness since preindustrial times, but they remain persistently uncertain and underrepresented in global climate models. Here we present a synthesis of the current understanding of atmospheric new particle formation derived from laboratory measurements at the CERN CLOUD chamber. Whereas the importance of sulfuric acid has long been recognized, condensable vapours such as highly oxygenated organics and iodine oxoacids also play key roles, together with stabilizers such as ammonia, amines and ions from galactic cosmic rays. We discuss how insights from CLOUD experiments are helping to interpret …

Biogenic vapors form new particles in the atmosphere, affecting global climate. The contributions of monoterpenes and isoprene to new particle formation (NPF) have been extensively studied. However, sesquiterpenes have received little attention despite a potentially important role due to their high molecular weight. Via chamber experiments performed under atmospheric conditions, we report biogenic NPF resulting from the oxidation of pure mixtures of β-caryophyllene, α-pinene, and isoprene, which produces oxygenated compounds over a wide range of volatilities. We find that a class of vapors termed ultralow-volatility organic compounds (ULVOCs) are highly efficient nucleators and quantitatively determine NPF efficiency. When compared with a mixture of isoprene and monoterpene alone, adding only 2% sesquiterpene increases the ULVOC yield and doubles the formation rate. Thus, sesquiterpene …

Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill in terms of making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample the uncertainty in 37 model parameters related to aerosols, clouds, and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian process emulators) against satellite-derived observations over several cloudy regions. Our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations, of an order that may be evaluated during model-tuning efforts. Incorporating observations associated with these inconsistencies weakens any forcing constraint because they require a wider range of parameter values to accommodate conflicting information. We show that, by neglecting variables associated with these inconsistencies, it is possible to reduce the parametric uncertainty in global mean aerosol …