Search

Abstract. New particle formation (NPF) involving organic compounds has been identified as an important process affecting aerosol particle number concentrations in the global atmosphere. Laboratory studies have shown that highly oxygenated organic molecules (HOMs) can make a substantial contribution to NPF, but there is a lack of global model studies of NPF with detailed HOM chemistry. Here, we incorporate a state-of-the-art biogenic HOM chemistry scheme with 96 chemical reactions to a global chemistry–climate model and quantify the contribution to global aerosols through HOM-driven NPF. The updated model captures the frequency of NPF events observed at continental surface sites (normalized mean bias changes from −96 % to −15 %) and shows reasonable agreement with measured rates of NPF and sub-20 nm particle growth. Sensitivity simulations show that compared to turning off the organic nucleation rate, turning off organic initial growth results in a more substantial decrease in aerosol number concentrations. Globally, organics contribute around 45 % of the annual mean vertically integrated nucleation rate (at 1.7 nm) and 25 % of the vertically averaged growth rate. The inclusion of HOM-related processes leads to a 39 % increase in the global annual mean aerosol number burden and a 33 % increase in cloud condensation nuclei (CCN) burden at 0.5 % supersaturation compared to a simulation with only inorganic nucleation. Our work predicts a greater contribution of organic nucleation to NPF than previous studies due to the semi-explicit HOM mechanism and an updated inorganic NPF scheme. The large contribution of biogenic HOMs to NPF on a global scale could make aerosol sensitive to changes in biogenic emissions.

Large uncertainties persist in modeling shallow, low clouds because of many interacting nonlinear processes and multiple cloud‐controlling environmental factors. In addition, sharp changes in behavior occur when environmental thresholds are met. Model studies that follow a traditional approach of exploring the effects of factors "one‐at‐a‐time" are unable to capture interactions between factors. We simulate a stratocumulus cloud based on the Second Dynamics and Chemistry of Marine Stratocumulus field study using a large‐eddy simulation model coupled with a two‐moment cloud microphysics scheme. The simulations are used to train a Gaussian process emulator, which we then use to visualize the relationships between two cloud‐controlling factors and domain‐averaged cloud properties. Only 29 model simulations were required to train the emulators, which then predicted cloud properties at thousands of new combinations of the two factors. Emulator response surfaces of cloud liquid water path and cloud fraction show two behavioral regimes, one of thin and patchy yet steady stratocumulus and one of thick, growing stratocumulus with cloud fraction near 1. Internal variability (initial‐condition uncertainty) creates unrealistic "bumpy" response surfaces. However, we show that the variability causing the bumpiness can be characterized in an emulator "nugget term" that is adjusted to match the distribution of a small number of initial‐condition ensemble simulations at various points on the surface, thereby allowing a smoother, deterministic response surface to be constructed. Accounting for variability allows the transition between regimes, and the joint interactions of parameters, to be visualized in a more deterministic way that has not been done before. Plain Language Summary: Modeling shallow clouds is important because these clouds have an overall cooling effect on the planet, but the magnitude of this effect is very uncertain. Shallow cloud behaviors are made up of many interacting processes that work at a large range of scales, and we are not able to fully describe all these processes in large climate models. We use a machine learning technique called Gaussian process emulation to approximate output from the high‐resolution cloud model so that we can study the cloud behavior at a much‐reduced computational cost. By perturbing two cloud‐controlling factors, we create a set of training data to train the emulator how to approximate the relationship between these factors and cloud properties of interest. We produce color maps that visually describe this relationship and we show that there are two regimes of cloud behavior. We assess the best way to account for cloud natural variability in the approximation. Gaussian process emulation is a vital tool for cloud modeling because it allows us to inspect whole cloud processes and the relationships between different inputs as they influence the output of interest. In this way we can better understand cloud processes and their uncertainties. Key Points: Gaussian process emulation of large‐eddy simulations can be used to visualize shallow cloud response to cloud‐controlling factorsEmulator response surfaces show how above‐cloud temperature and moisture have a combined effect on stratocumulus cloud liquid water pathCloud natural variability can be accounted for so that the emulator smoothly captures the deterministic variation in cloud properties [ABSTRACT FROM AUTHOR]

The regulation introduced in 2020 that limits the sulfur content in shipping fuel has reduced sulfur emissions over global open oceans by about 80%. This is expected to have reduced aerosols that both reflect solar radiation directly and affect cloud properties, with the latter also changing the solar radiation balance. Here we investigate the impacts of this regulation on aerosols and climate in the HadGEM3-GC3.1 climate model. The global aerosol effective radiative forcing caused by reduced shipping emissions is estimated to be 0.13 W m-2, which is equivalent to about 50% of the positive forcing caused by the global reduction in all anthropogenic aerosols since late 20th century. Ensembles of global coupled simulations from 2020-2049 predict a global mean warming of 0.04 K averaged over this period. Our simulations are not clear on whether the global impact is yet to emerge or has already emerged because the present-day impact is masked by variability. Nevertheless, the impact of shipping emission reductions will have either already committed us to warming above the 1.5 K Paris target or will represent an important contribution that may help explain part of the rapid jump in global temperatures over the last 12 months. Consistent with previous aerosol perturbation simulations, the warming is greatest in the Arctic, reaching a mean of 0.15 K Arctic-wide and 0.3 K in the Atlantic sector of the Arctic (which represents greater than 10% increase in the total anthropogenic warming since pre-industrial times). [ABSTRACT FROM AUTHOR]

New particle formation (NPF) involving organic compounds has been identified as an important process affecting aerosol particle number concentrations in the global atmosphere. Laboratory studies have shown that highly oxygenated organic molecules (HOMs) can make a substantial contribution to NPF, but there is a lack of global model studies of NPF with detailed HOMs chemistry. Here, we add a state-of-art biogenic HOMs chemistry scheme with 96 chemical reactions to a global chemistry-climate model and quantify the contribution to global aerosols through HOMs-driven NPF. The updated model captures the frequency of NPF events observed at continental surface sites (normalized mean bias changes from -96 % to -15 %) and shows reasonable agreement with measured rates of NPF and sub-20 nm particle growth. Sensitivity simulations show that the effect of HOMs on particle growth is more important for particle number than the effect on particle formation. Globally, organics contribute around 45 % of the annual mean vertically-integrated nucleation rate (at 1 nm) and 25 % of the vertically-averaged growth rate. The inclusion of HOMs-related processes leads to a 39 % increase in the global annual mean aerosol number burden and a 33 % increase in cloud condensation nuclei (CCN) burden at 0.5 % supersaturation compared to a simulation with only inorganic nucleation. Our work predicts a greater contribution of organic nucleation to NPF than previous studies due to the explicit HOMs mechanism and an updated inorganic NPF scheme. The large contribution of biogenic HOMs to NPF on a global scale could make global aerosol sensitive to changes in biogenic emissions. [ABSTRACT FROM AUTHOR]

Aerosols have a potentially large effect on climate, particularly through their interactions with clouds, but the magnitude of this effect is highly uncertain. Large volcanic eruptions produce sulfur dioxide, which in turn produces aerosols; these eruptions thus represent a natural experiment through which to quantify aerosolcloud interactions. Here we show that the massive 20142015 fissure eruption in Holuhraun, Iceland, reduced the size of liquid cloud dropletsconsistent with expectationsbut had no discernible effect on other cloud properties. The reduction in droplet size led to cloud brightening and global-mean radiative forcing of around 0.2 watts per square metre for September to October 2014. Changes in cloud amount or cloud liquid water path, however, were undetectable, indicating that these indirect effects, and cloud systems in general, are well buffered against aerosol changes. This result will reduce uncertainties in future climate projections, because we are now able to reject results from climate models with an excessive liquid-water-path response., Author(s): Florent F. Malavelle (corresponding author) [1]; Jim M. Haywood [1, 2]; Andy Jones [2]; Andrew Gettelman [3]; Lieven Clarisse [4]; Sophie Bauduin [4]; Richard P. Allan [5, 6]; Inger [...]

Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill at 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 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. We show that it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range in close agreement with energy-balance constraints (around −1.3 to −0.1 W m−2). However, 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. Incorporating observations associated with these inconsistencies weakens the forcing constraint because they require a wider range of parameter values to accommodate conflicting information. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then narrow the uncertainty further. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach. [ABSTRACT FROM AUTHOR]

Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill at 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, limit the effectiveness of observational constraint of the uncertain physical processes. We sample uncertainty in 37 model parameters related to aerosols, clouds and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate one million model variants (different parameter settings from Gaussian Process emulators) against satellite-derived observations over several cloudy regions. We show it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 % to a range in close agreement with energy-balance constraints (around -1.3 to -0.1 W m-2). However, incorporating observations associated with model inconsistencies weakens the constraint because the inconsistencies introduce conflicting information about relationships between model parameter values and aerosol forcing. Our estimated aerosol forcing range is the maximum feasible constraint using these observations and our structurally imperfect model. Structural model developments, targeted at the inconsistencies identified here, would enable a larger set of observations to be used for constraint, which would then narrow the uncertainty further. [ABSTRACT FROM AUTHOR]

In the Arctic, the aerosol budget plays a particular role in determining the behaviour of clouds, which are important for the surface energy balance and thus for the region's climate. A key question is the extent to which cloud condensation nuclei in the high Arctic summertime boundary layer are controlled by local emission and formation processes as opposed to transport from outside. Each of these sources is likely to respond differently to future changes in ice cover. Here we use a global model and observations from ship and aircraft field campaigns to understand the source of high Arctic aerosol in late summer. We find that particles formed remotely, i.e. at lower latitudes, outside the Arctic, are the dominant source of boundary layer Aitken mode particles during the sea ice melt period up to the end of August. Particles from such remote sources, entrained into the boundary layer from the free troposphere, account for nucleation and Aitken mode particle concentrations that are otherwise underestimated by the model. This source from outside the high Arctic declines as photochemical rates decrease towards the end of summer, and is largely replaced by local new particle formation driven by iodic acid emitted from the surface and associated with freeze-up. Such a local source is consistent with strong fluctuations in nucleation mode concentrations that occur in September. Our results suggest a high-Arctic aerosol regime shift in late summer, and only after this shift do cloud condensation nuclei become sensitive to local aerosol processes. [ABSTRACT FROM AUTHOR]

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 regional domain and entered the domain at lower altitudes. Our model results show that CCN entering the Amazonian boundary layer are transported downwards gradually over multiple convective cycles on scales much larger than 1000 km. Therefore, on a 1000 km scale in the model (approximately one-third the size of Amazonia), trace gas emission, new particle formation, transport and CCN production do not form a ‘closed loop’ regulated by the biosphere. Rather, on this scale, long-range transport of aerosol is a much more important factor controlling CCN in the boundary layer. [ABSTRACT FROM AUTHOR]