The composition of organic aerosol has a pivotal influence on aerosol properties such as toxicity, and cloud droplets formation capability, which could affect both climate and air quality. However, a comprehensive and fundamental understanding of the chemical and physical processes that occur in nanometre-sized atmospheric particles remains a challenge that severely limits the quantification and predictive capabilities of aerosol formation pathways. Here, we investigated the effects of a fundamental and hitherto unconsidered physical property of nanoparticlesthe Laplace pressure. By studying the reaction of glyoxal with ammonium sulphate, both ubiquitous and important atmospheric constituents, we show that high pressure can significantly affect the chemical processes that occur in atmospheric ultrafine particles (i.e., particles < 100 nm). Using high-resolution mass spectrometry and UV/Vis spectroscopy, we demonstrated that the formation of reaction products is strongly (i.e., up to a factor of 2) slowed down under high pressures typical of atmospheric nanoparticles. A size-dependent relative rate constant is determined and numerical simulations illustrate the reduction in the production of the main glyoxal reaction products. These results established that the high pressure inside nanometre-sized aerosols must be considered as a key property that significantly impacts chemical processes that govern atmospheric aerosol growth and evolution. Short SynopsisHigh pressure reached inside atmospheric nanometre-sized particles affects their chemistry and therefore their formation and growth.

The climate is a non-equilibrium system undergoing the continuous action of forcing and dissipation. Under the effect of a spatially inhomogeneous absorption of solar energy, all the climate components dynamically respond until an approximate steady state (or attractor) is reached. However, multiple steady states can co-exist for a given forcing and with the same boundary conditions. Here, we apply the Thermodynamic Diagnostic Tool (TheDiaTo) to investigate the statistical properties of five co-existing climates, ranging from a snowball to an ice-free aquaplanet, obtained in MITgcm coupled simulations. The aim is to explore the multistability of the climate model setup by highlighting differences in competing steady states and their characteristic signatures regarding the meridional transport of heat and water mass, the Lorenz energy cycle and the material entropy production. We also investigate how such attractors change when the model configuration is varied. We consider, in particular, the effect of changing the representation of the cloud albedo, and of implementing an improved closure of the energy budget. We find that, even if the dynamics remains on the same attractor, state variables are modified. The set of metrics in TheDiaTo quantify such modifications and represent a valuable tool for model evaluation.

The climate is a complex nonequilibrium dynamical system that relaxes toward a steady state under the continuous input of solar radiation and dissipative mechanisms. The steady state is not necessarily unique. A useful tool to describe the possible steady states under different forcing is the bifurcation diagram, which reveals the regions of multistability, the position of tipping points, and the range of stability of each steady state. However, its construction is highly time consuming in climate models with a dynamical deep ocean, whose relaxation time is of the order of thousand years, or other feedback mechanisms that act on even longer time scales, like continental ice or carbon cycle. Using a coupled setup of the MIT general circulation model, we test two techniques for the construction of bifurcation diagrams with complementary advantages and reduced execution time. The first is based on the introduction of random fluctuations in the forcing and permits to explore a wide part of phase space. The second reconstructs the stable branches using estimates of the internal variability and of the surface energy imbalance on each attractor, and is more precise in finding the position of tipping points.

&lt;p&gt;&lt;span&gt;The climate can be regarded as a stationary non-equilibrium statistical system (Gallavotti 2006): a continuous and spatially inhomogeneous input of solar energy enters at the top-of-atmosphere and compensates the action of non-conservative forces, mainly occurring at small scales, to give rise to a statistically steady state (or attractor) for the whole climate. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Depending on the initial conditions and the range of forcing, all other parameters being the same, some climate models have the property to settle down on different attractors. &lt;/span&gt;&lt;span&gt;Multi-stability reflects how energy, water mass and entropy can be re-distributed in multiple ways among the climate components, such as the atmosphere, the ocean or the ice, through a different balance between nonlinear mechanisms. &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Starting from a configuration where competing climate attractors occur under the same forcing, we have explored their robustness performing two kinds of numerical experiment. &lt;/span&gt;&lt;span&gt;First, we have investigated the impact of frictional heating on the overall energy balance and we have shown that such contribution, generally neglected in the atmospheric component of climate models, has crucial &lt;/span&gt;&lt;span&gt;consequences on conservation properties: it improves the energy imbalance at top-of-atmosphere, typically non negligible in coarse simulations (Wild et al. 2020), strengthens the hydrological cycle, &lt;/span&gt;&lt;span&gt;mitigates the mechanical work associated to atmospheric circulation intensity &lt;/span&gt;&lt;span&gt;and reduces the heat transport peaks in the ocean. &lt;/span&gt;&lt;span&gt;Second, we have compared two bulk formulas for the cloud albedo, one where it is constant everywhere and the other where it increases with latitude, as implemented in the new version of the atmospheric module SPEEDY in order to improve comparisons with observational data (Kucharski 2013). We have che&lt;/span&gt;&lt;span&gt;cked that this new parameterization does not affect energy and water-mass imbalances, while reduces global temperature and water-mass transport on the attractor, giving rise to a larger conversion of heat into mechanical work in the atmosphere.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;In order to perform such studies, we have run the climate model MITgcm on coupled aquaplanets at 2.8 horizontal resolution until steady states are reached (Brunetti el al. 2019) and we have applied the Thermodynamic Diagnostic Tool (&lt;em&gt;TheDiaTo&lt;/em&gt;, Lembo et al. 2019). &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;span&gt;References: &lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Brunetti, Kasparian, V&amp;#233;rard, Climate Dynamics 53, 6293 (2019)&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Gallavotti, &lt;/span&gt;Math. Phys. 3, 530&lt;span&gt; (2006)&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Kucharski&lt;span&gt; et al.&lt;/span&gt;, Bulletin of the American Meteorological Society 94, 25&lt;span&gt; (2013)&lt;/span&gt;&lt;/p&gt;&lt;p&gt;Lembo, Lunkeit, Lucarini, Geoscientific Model Development 12, 3805&lt;span&gt; (2019)&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Wild, &lt;/span&gt;Climate Dynamics 55, 553&lt;span&gt; (2020)&lt;/span&gt;&lt;/p&gt;