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The nature and origin of organic aerosol in the atmosphere remain unclear. The gas–particle partitioning of semi-volatile organic compounds (SVOCs) that constitute primary organic aerosols (POAs) and the multigenerational chemical aging of SVOCs are particularly poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem (Weather Research and Forecasting model with Chemistry), can be a useful tool to describe emissions of POA and its chemical evolution. However, the evaluation of model uncertainty and the optimal model parameterization may be expensive to probe using only WRF-Chem simulations. Gaussian process emulators, trained on simulations from relatively few WRF-Chem simulations, are capable of reproducing model results and estimating the sources of model uncertainty within a defined range of model parameters. In this study, a WRF-Chem VBS parameterization is proposed; we then generate a perturbed parameter ensemble of 111 model runs, perturbing 10 parameters of the WRF-Chem model relating to organic aerosol emissions and the VBS oxidation reactions. This allowed us to cover the model's uncertainty space and to compare outputs from each run to aerosol mass spectrometer observations of organic aerosol concentrations and O:C ratios measured in New Delhi, India. The simulations spanned the organic aerosol concentrations measured with the aerosol mass spectrometer (AMS). However, they also highlighted potential structural errors in the model that may be related to unsuitable diurnal cycles in the emissions and/or failure to adequately represent the dynamics of the planetary boundary layer. While the structural errors prevented us from clearly identifying an optimized VBS approach in WRF-Chem, we were able to apply the emulator in the following two periods: the full period (1–29 May) and a subperiod period of 14:00–16:00 h LT (local time) on 1–29 May. The combination of emulator analysis and model evaluation metrics allowed us to identify plausible parameter combinations for the analyzed periods. We demonstrate that the methodology presented in this study can be used to determine the model uncertainty and to identify the appropriate parameter combination for the VBS approach and hence to provide valuable information to improve our understanding of OA production.

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.

Ice crystal formation in the mixed-phase region of deep convective clouds can affect the properties of climatically important convectively generated anvil clouds. Small ice crystals in the mixed-phase cloud region can be formed by heterogeneous ice nucleation by ice-nucleating particles (INP) and secondary ice production (SIP) by, for example, the Hallett-Mossop process. We quantify the effects of INP number concentration, the temperature dependence of the INP number concentration at mixedphase temperatures, and the Hallett-Mossop splinter production efficiency on the anvil of an idealised deep convective cloud using a Latin hypercube sampling method, which allows optimal coverage of a multidimensional parameter space, and statistical emulation, which allows us to identify interdependencies between the three uncertain inputs. Our results show that anvil ice crystal number concentration (ICNC) is determined predominately by INP number concentration, with the temperature dependence of ice-nucleating aerosol activity having a secondary role. Conversely, ice crystal size is determined predominately by the temperature dependence of ice-nucleating aerosol activity, with INP number concentration having a secondary role. This is because in our simulations ICNC is predominately controlled by the number concentration of cloud droplets reaching the homogeneous freezing level which is in turn determined by INP number concentrations at low temperatures. Ice crystal size however is more strongly affected by the amount of liquid available for riming and the time available for deposition growth which is determined by INP number concentrations at higher temperatures. This work indicates that the amount of ice particle production by the Hallett-Mossop process is determined jointly by the prescribed Hallett-Mossop splinter production efficiency and the temperature dependence of ice-nucleating aerosol activity. In particular, our sampling of the joint parameter space shows that high rates of SIP do not occur unless the INP parameterisation slope (the temperature dependence of the number concentration of particles which nucleate ice) is shallow, regardless of the prescribed Hallett-Mossop splinter production efficiency. The effect of a shallow INP parameterisation slope and consequently high ice particle production by the Hallett-Mossop process in our simulations leads to a sharp transition to a cloud with extensive glaciation at warm temperatures, higher cloud updrafts, enhanced vertical mass flux and condensate divergence at the outflow level, all of which leads to a larger convectively generated anvil comprised of larger ice crystals. This work highlights the importance of quantifying the full spectrum of INP number concentrations across all mixed-phase altitudes, and the ways in which INP and SIP interact to control anvil properties.

This is the first of two papers that describe the generation of a 25-member perturbed parameter ensemble (PPE) of high-resolution, global coupled simulations for the period 1900–2100, using CMIP5 historical and RCP8.5 emissions. Fifteen of these 25 coupled simulations now form a subset of the global projections provided for the UK Climate Projections 2018 (UKCP18). This first paper describes the selection of 25 variants (combinations of 47 parameters) using a set of cheap, coarser-resolution atmosphere-only simulations from a large sample of nearly 3000 variants. Retrospective 5-day weather forecasts run at climate resolution, and simulations of 2004–2009 with prescribed SST and sea ice are evaluated to filter out poor performance. We opted for a single design choice and sensitivity tests were done after the PPE was generated to demonstrate the effect of design choices on the filtering. Given our choice, only 38 of the parameter combinations were found to have acceptable performance at this stage. Idealised atmosphere-only simulations were then used to select the subset of 25 members that were as diverse as possible in terms of their CO2 and aerosol forcing, and their response to warmer SSTs. Using our parallel set of atmosphere-only and coupled PPEs (the latter from paper 2), we show that local biases in the atmosphere-only experiments are generally informative about the biases in the coupled PPE. Biases in radiative fluxes and cloud amounts are strongly informative for most regions, whereas this is only true for a smaller fraction of the globe for precipitation and dynamical variables. Therefore, the cheap experiments are an affordable way to search for promising parameter combinations but have limitations.

Data-driven quantification and parameterization of cloud physics in general, and of aerosol-cloud interactions in particular, rely on input data from observations or detailed simulations. These data sources have complementary limitations in terms of their spatial and temporal coverage and resolution; simulation data has the advantage of readily providing causality but cannot represent the full process complexity. In order to base data-driven approaches on comprehensive information, we therefore need ways to integrate different data sources. We discuss how the classical statistical technique of Gaussian-process emulation can be combined with specifically initialized ensembles of detailed cloud simulations (large-eddy simulations, LES) to provide a framework for evaluating data-driven descriptions of cloud characteristics and processes across different data sources. We specifically illustrate this approach for integrating LES and satellite data of aerosol-cloud interactions in subtropical stratocumulus cloud decks. We furthermore explore the extension of our framework to ground-based observations of Arctic mixed-phase clouds.- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -References:Glassmeier, F., F. Hoffmann, J. S. Johnson, T. Yamaguchi, K. S. Carslaw and G. Feingold (2019): “An emulator approach to stratocumulus susceptibility”, Atmos. Chem. Phys., 19, 10191- 10203, doi: 10.5194/acp-19-10191-2019 Hoffmann, F., F. Glassmeier, T. Yamaguchi and G. Feingold (2020): “Liquid water path steady states in stratocumulus: insights from process-level emulation and mixed-layer theory”, J. Atmos. Sci., 77, 2203-2215, doi: 10.1175/JAS-D-19-0241.1 Glassmeier, F., F. Hoffmann, J.S. Johnson, T. Yamaguchi, K. S. Carslaw, and G. Feingold (2021): “Aerosol-cloud climate cooling overestimated by ship-track data”, Science 371, 485–489, doi: 10.1126/science.abd3980

A global model perturbed parameter ensemble of 60 simulations was used to explore how combinations of six parameters related to secondary organic aerosol (SOA) formation affect particle number concentrations and organic aerosol mass. The parameters represent the formation of organic compounds with different volatilities from biogenic and anthropogenic sources. The most plausible parameter combinations were determined by comparing the simulations against observations of the number concentration of particles larger than 3 nm diameter (N3), the number concentration of particles larger than 50 nm diameter (N50), and the organic aerosol (OA) mass concentration. The simulations expose a high degree of model equifinality in which the skill of widely different parameter combinations cannot be distinguished against observations. We therefore conclude that, based on the observations we have used, a six-parameter SOA scheme is under-determined. Nevertheless, the model skill in simulating N3 and N50 is clearly determined by the low-volatility and extremely low-volatility compounds that affect new particle formation and growth, and the skill in simulating OA mass is determined by the low-volatility and semi-volatile compounds. The biogenic low-volatility class of compounds that grow nucleated clusters and condense on all particles is found to have the strongest effect on the model skill in simulating N3, N50, and OA. The simulations also expose potential structural deficiencies in the model: we find that parameter combinations that are best for N3 and N50 are worst for OA mass, and the ensemble exaggerates the observed seasonal cycle of particle concentrations – a deficiency that we conclude requires an additional anthropogenic source of either primary or secondary particles.

Many methods for modelling functions over high-dimensional spaces assume global smoothness properties; such assumptions are often violated in practice. We introduce a method for modelling functions that display heterogeneity or contain discontinuities. The heterogeneity is dealt with by using a combination of Voronoi tessellation, to partition the input space, and separate Gaussian processes to model the function over different regions of the partitioned space. The proposed method is highly flexible since it allows the Voronoi cells to combine to form regions, which enables non-convex and disconnected regions to be considered. In such problems, identifying the borders between regions is often of great importance and we propose an adaptive sampling method to gain extra information along such borders. The method is illustrated by simulated examples and an application to real data, in which we see improvements in prediction error over the commonly used stationary Gaussian process and other non-stationary variations. In our application, a computationally expensive computer model that simulates the formation of clouds is investigated, the proposed method more accurately predicts the underlying process at unobserved locations than existing emulation methods. Supplementary materials accompanying this paper are available online.

Aerosol characteristics over the Southern Ocean are surprisingly heterogeneous because of the distinct regional dynamics and marine microbial regimes. Satellite observations and model simulations underestimate the abundance of cloud condensation nuclei. Uncertainty in radiative forcing caused by aerosol-cloud interactions is about twice as large as for CO2 and remains the least well-understood anthropogenic contribution to climate change. A major cause of uncertainty is the poorly-quantified state of aerosols in the pristine-preindustrial atmosphere, which defines the baseline against which anthropogenic effects are calculated. The Southern Ocean is one of the few remaining near-pristine aerosol environments on Earth, but there are very few measurements to evaluate models. The Antarctic Circumnavigation Expedition: Study of Preindustrial-like Aerosols and their Climate Effects (ACE-SPACE) took place between December 2016 and March 2017 and covered the entire Southern Ocean region (Indian, Pacific and Atlantic Oceans, ship track > 33,000 km) including previously unexplored areas. In situ measurements covered aerosol characteristics (e.g., chemical composition, size distributions, and cloud condensation nuclei (CCN) number concentrations), trace gases and meteorological variables. Remote sensing observations of cloud properties, the physical and microbial ocean state, as well as back trajectory analyses are used to interpret the in situ data. The contribution of sea spray to CCN in the westerly wind belt can be larger than 50%. The abundance of methanesulfonic acid indicates local and regional microbial influence on CCN abundance in Antarctic coastal waters and in the open ocean. We use the in situ data to evaluate simulated CCN concentrations from a global aerosol model. The extensive, available ACE-SPACE dataset (https://zenodo.org/communities/spi-ace?page=1&size=20) provides an unprecedented opportunity to evaluate models and to reduce the uncertainty in radiative forcing associated with the natural processes of aerosol emission, formation, transport and processing occurring over the pristine Southern Ocean.

Tropospheric aerosol radiative forcing has persisted for many years as one of the major causes of uncertainty in global climate model simulations. To sample the range of plausible aerosol and atmospheric states and perform robust statistical analyses of the radiative forcing, it is important to account for the combined effects of many sources of model uncertainty, which is rarely done due to the high computational cost. This paper describes the designs of two ensembles of the Met Office Hadley Centre Global Environment Model-U.K. Chemistry and Aerosol global climate model and provides the first analyses of the uncertainties in aerosol radiative forcing and their causes. The first ensemble was designed to comprehensively sample uncertainty in the aerosol state, while the other samples additional uncertainties in the physical model related to clouds, humidity, and radiation, thereby allowing an analysis of uncertainty in the aerosol effective radiative forcing. Each ensemble consists of around 200 simulations of the preindustrial and present-day atmospheres. The uncertainty in aerosol radiative forcing in our ensembles is comparable to the range of estimates from multimodel intercomparison projects. The mean aerosol effective radiative forcing is −1.45 W/m2 (credible interval of −2.07 to −0.81 W/m2), which encompasses but is more negative than the −1.17 W/m2 in the 2013 Atmospheric Chemistry and Climate Model Intercomparison Project and −0.90 W/m2 in the Intergovernmental Panel on Climate Change Fifth Assessment Report. The ensembles can be used to reduce aerosol radiative forcing uncertainty by challenging them with multiple measurements as well as to isolate potential causes of multimodel differences.

Funder: U.K. China Research and Innovation Partnership Fund, Funder: National Centre for Atmospheric Science (NCAS); Id: http://dx.doi.org/10.13039/501100000662, Reconstructions of volcanic aerosol radiative forcing are required to understand past climate variability. Currently, reconstructions of pre‐20th century volcanic forcing are derived from sulfate concentrations measured in polar ice cores, mainly using a relationship between the average ice‐sheet sulfate deposition and stratospheric sulfate aerosol burden based on a single explosive eruption—the 1991 eruption of Mt. Pinatubo. Here we estimate volcanic radiative forcings and associated uncertainty ranges from ice‐core sulfate records of eight of the largest bipolar deposition signals in the last 2,500 years using statistical emulation of a perturbed parameter ensemble of aerosol‐climate model simulations of explosive eruptions. Extensive sampling of different combinations of eruption source parameters using the emulators reveals that a very wide range of eruptions in different seasons with different sulfur dioxide emissions, eruption latitudes, and emission altitudes produce ice‐sheet sulfate deposition consistent with ice‐core records. Consequently, we find a large range in the volcanic forcing that can be directly attributed to the unknown eruption source parameters. We estimate that the uncertainty in volcanic forcing caused by many plausible eruption realizations leads to uncertainties in the global mean surface cooling of around 1°C for the largest unidentified historical eruptions. Our emulators are available online (https://cemac.github.io/volcanic-forcing-deposition) where eruption realizations for given ice‐sheet sulfate depositions can be explored.

©2018. The Authors. The radiative forcing caused by a volcanic eruption is dependent on several eruption source parameters such as the mass of sulfur dioxide (SO 2 ) emitted, the eruption column height, and the eruption latitude. General circulation models with prognostic aerosol and chemistry schemes can be used to investigate how each parameter influences the volcanic forcing. However, the range of multidimensional parameter space that can be explored is restricted because such simulations are computationally expensive. Here we use statistical emulation to explore the radiative impact of eruptions over a wide covarying range of SO 2 emission magnitudes, injection heights, and eruption latitudes based on only 30 simulations. We use the emulators to build response surfaces to visualize and predict the sulfate aerosol e-folding decay time, the stratospheric aerosol optical depth, and net radiative forcing of thousands of different eruptions. We find that the volcanic stratospheric aerosol optical depth and net radiative forcing are primarily determined by the mass of SO 2 emitted, but eruption latitude is the most important parameter in determining the sulfate aerosol e-folding decay time. The response surfaces reveal joint effects of the eruption source parameters in influencing the net radiative forcing, such as a stronger influence of injection height for tropical eruptions than high-latitude eruptions. We also demonstrate how the emulated response surfaces can be used to find all combinations of eruption source parameters that produce a particular volcanic response, often revealing multiple solutions.

The effect of anthropogenic aerosol on the reflectivity of stratocumulus cloud decks through changes in cloud amount is a major uncertainty in climate projections. In frequently occurring nonprecipitating stratocumulus, cloud amount can decrease through aerosol-enhanced cloud-top mixing. The climatological relevance of this effect is debated because ship exhaust only marginally reduces stratocumulus amount. By comparing detailed numerical simulations with satellite analyses, we show that ship-track studies cannot be generalized to estimate the climatological forcing of anthropogenic aerosol. The ship track-derived sensitivity of the radiative effect of nonprecipitating stratocumulus to aerosol overestimates their cooling effect by up to 200%. The offsetting warming effect of decreasing stratocumulus amount needs to be taken into account if we are to constrain the cloud-mediated radiative forcing of anthropogenic aerosol.

The uncertainty in present-day anthropogenic forcing is dominated by uncertainty in the strength of the contribution from aerosol. Much of the uncertainty in the direct aerosol forcing can be attributed to uncertainty in the anthropogenic fraction of aerosol in the present-day atmosphere, due to a lack of historical observations. Here, we present a robust relationship between total present-day aerosol optical depth and the anthropogenic contribution across three multimodel ensembles and a large single-model perturbed parameter ensemble. Using observations of aerosol optical depth, we determine a reduced likely range of the anthropogenic component and hence a reduced uncertainty in the direct forcing of aerosol.

Purpose of Review\ud \ud We assess the current understanding of the state and behaviour of aerosols under pre-industrial conditions and the importance for climate.\ud \ud \ud \ud Recent Findings\ud \ud Studies show that the magnitude of anthropogenic aerosol radiative forcing over the industrial period calculated by climate models is strongly affected by the abundance and properties of aerosols in the pre-industrial atmosphere. The low concentration of aerosol particles under relatively pristine conditions means that global mean cloud albedo may have been twice as sensitive to changes in natural aerosol emissions under pre-industrial conditions compared to present-day conditions. Consequently, the discovery of new aerosol formation processes and revisions to aerosol emissions have large effects on simulated historical aerosol radiative forcing.\ud \ud \ud \ud Summary\ud \ud We review what is known about the microphysical, chemical, and radiative properties of aerosols in the pre-industrial atmosphere and the processes that control them. Aerosol properties were controlled by a combination of natural emissions, modification of the natural emissions by human activities such as land-use change, and anthropogenic emissions from biofuel combustion and early industrial processes. Although aerosol concentrations were lower in the pre-industrial atmosphere than today, model simulations show that relatively high aerosol concentrations could have been maintained over continental regions due to biogenically controlled new particle formation and wildfires. Despite the importance of pre-industrial aerosols for historical climate change, the relevant processes and emissions are given relatively little consideration in climate models, and there have been very few attempts to evaluate them. Consequently, we have very low confidence in the ability of models to simulate the aerosol conditions that form the baseline for historical climate simulations. Nevertheless, it is clear that the 1850s should be regarded as an early industrial reference period, and the aerosol forcing calculated from this period is smaller than the forcing since 1750. Improvements in historical reconstructions of natural and early anthropogenic emissions, exploitation of new Earth system models, and a deeper understanding and evaluation of the controlling processes are key aspects to reducing uncertainties in future.

Sea breeze fronts propagate inland from the coastline, driving convective initiation and aerosol redistribution. Forecasting sea breezes is challenging due to uncertainties in the initial conditions, as well as the covariance and interaction of various meteorological and surface parameters. Using the Regional Atmospheric Modeling System coupled to an interactive land-surface model, we conduct an ensemble of 130 idealized cloud-resolving simulations by simultaneously perturbing six atmospheric and four surface parameters describing the initial conditions. To identify the key parameters impacting the inland characteristics and the intensity of the sea breeze convection in a tropical rainforest, we apply statistical emulation and variance-based sensitivity analysis techniques. This study extends a previous study which explored the impacts of various parameters on sea breeze characteristics in arid environments devoid of moist convection. Wind speed is identified as the main contributor to the inland extent, similar to the arid environment study. However, the relative impacts of surface properties on the inland extent are less significant in the moist environment where land-surface heating can be suppressed via moist convective processes and vegetation-atmosphere interactions. Two sea breeze-initiated convection regimes are also identified: shallow and deep. Over the shallow regime, where convective available potential energy is limited, the inversion layer strength is the primary control of the convective intensity. Over the deep regime, boundary layer temperature exerts a robust control over the convective available potential energy and hence the convective intensity. The potential vertical redistribution of aerosols is closely related to the convective intensity.

Severe hailstorms have the potential to damage buildings and crops. However, important processes for the prediction of hailstorms are insufficiently represented in operational weather forecast models. Therefore, our goal is to identify model input parameters describing environmental conditions and cloud microphysics, such as the vertical wind shear and strength of ice multiplication, which lead to large uncertainties in the prediction of deep convective clouds and precipitation. We conduct a comprehensive sensitivity analysis simulating deep convective clouds in an idealized setup of a cloud-resolving model. We use statistical emulation and variance-based sensitivity analysis to enable a Monte Carlo sampling of the model outputs across the multi-dimensional parameter space. The results show that the model dynamical and microphysical properties are sensitive to both the environmental and microphysical uncertainties in the model. The microphysical parameters lead to larger uncertainties in the output of integrated hydrometeor mass contents and precipitation variables. In particular, the uncertainty in the fall velocities of graupel and hail account for more than 65 % of the variance of all considered precipitation variables and for 30 %–90 % of the variance of the integrated hydrometeor mass contents. In contrast, variations in the environmental parameters – the range of which is limited to represent model uncertainty – mainly affect the vertical profiles of the diabatic heating rates.

Observational constraint of simulated aerosol and cloud properties is an essential part of building trustworthy climate models for calculating aerosol radiative forcing. Models are usually tuned to achieve good agreement with observations, but tuning produces just one of many potential variants of a model, so the model uncertainty cannot be determined. Here we estimate the uncertainty in aerosol effective radiative forcing (ERF) in a tuned climate model by constraining 4 million variants of the HadGEM3-UKCA aerosol–climate model to match nine common observations (top-of-atmosphere shortwave flux, aerosol optical depth, PM2.5, cloud condensation nuclei at 0.2 % supersaturation (CCN0.2), and concentrations of sulfate, black carbon and organic carbon, as well as decadal trends in aerosol optical depth and surface shortwave radiation.) The model uncertainty is calculated by using a perturbed parameter ensemble that samples 27 uncertainties in both the aerosol model and the physical climate model, and we use synthetic observations generated from the model itself to determine the potential of each observational type to constrain this uncertainty. Focusing over Europe in July, we show that the aerosol ERF uncertainty can be reduced by about 30 % by constraining it to the nine observations, demonstrating that producing climate models with an observationally plausible “base state” can contribute to narrowing the uncertainty in aerosol ERF. However, the uncertainty in the aerosol ERF after observational constraint is large compared to the typical spread of a multi-model ensemble. Our results therefore raise questions about whether the underlying multi-model uncertainty would be larger if similar approaches as adopted here were applied more widely. The approach presented in this study could be used to identify the most effective observations for model constraint. It is hoped that aerosol ERF uncertainty can be further reduced by introducing process-related constraints; however, any such results will be robust only if the enormous number of potential model variants is explored.

Aerosol measurements over the Southern Ocean are used to constrain aerosol-cloud interaction radiative forcing uncertainty in a global climate model. Aerosol forcing uncertainty is quantified using one million climate model variants that sample the uncertainty in nearly 30 model parameters. Ship-based measurements of cloud condensation nuclei, particle number concentrations and sulfate mass concentrations from the Antarctic Circumnavigation Expedition: Study of Preindustrial-like Aerosols and Their Climate Effects (ACE-SPACE) are used to identify observationally implausible variants and thereby reduce the spread in the simulated forcing. Southern Ocean measurements strongly constrain natural aerosol emissions: default sea spray emissions in the model need to be increased by around a factor of 3 to be consistent with measurements. Aerosol forcing uncertainty is reduced by around 7 % using these measurements, which is comparable to the 8 % reduction achieved using an extensive set of over 9000 predominantly Northern Hemisphere measurements. The radiative forcing due to aerosol–cloud interactions (RFaci) is constrained to −2.61 to −1.10 W m−2 (95 % confidence) and the effective radiative forcing from aerosol-cloud interactions (ERFaci) is constrained to −2.43 to −0.54 W m−2. When Southern Ocean and Northern Hemisphere measurements are combined, the uncertainty in RFaci is reduced by 21 % and the strongest 20 % of forcing values are ruled out as implausible. In this combined constraint the observationally plausible RFaci is around 0.17 W m−2 weaker (less negative) with credible values ranging from −2.51 to −1.17 W m−2 and from −2.18 to −1.46 W m−2 when using one standard deviation to quantify the uncertainty. The Southern Ocean and Northern Hemisphere measurement datasets are complementary because they constrain different processes. These results highlight the value of remote marine aerosol measurements.

The properties of sea breeze circulations are influenced by a variety of meteorological and geophysical factors that interact with one another. These circulations can redistribute aerosol particles and pollution and therefore can play an important role in local air quality, as well as impact remote sensing. In this study, we select 11 factors that have the potential to impact either the sea breeze circulation properties and/or the spatial distribution of aerosols. Simulations are run to identify which of the 11 factors have the largest influence on the sea breeze properties and aerosol concentrations and to subsequently understand the mean response of these variables to the selected factors. All simulations are designed to be representative of conditions in coastal sub tropical environments and are thus relatively dry, as such they do not support deep convection associated with the sea breeze front. For this dry sea breeze regime, we find that the background wind speed was the most influential factor for the sea breeze propagation, with the soil saturation fraction also being important. For the spatial aerosol distribution, the most important factors were the soil moisture, sea‐air temperature difference, and the initial boundary layer height. The importance of these factors seems to be strongly tied to the development of the surface‐based mixed layer both ahead of and behind the sea breeze front. This study highlights potential avenues for further research regarding sea breeze dynamics and the impact of sea breeze circulations on pollution dispersion and remote sensing algorithms.

The climatic relevance of aerosol–cloud interactions depends on the sensitivity of the radiative effect of clouds to cloud droplet number N, and liquid water path LWP. We derive the dependence of cloud fraction CF, cloud albedo AC, and the relative cloud radiative effect rCRE=CF⋅AC on N and LWP from 159 large-eddy simulations of nocturnal stratocumulus. These simulations vary in their initial conditions for temperature, moisture, boundary-layer height, and aerosol concentration but share boundary conditions for surface fluxes and subsidence. Our approach is based on Gaussian-process emulation, a statistical technique related to machine learning. We succeed in building emulators that accurately predict simulated values of CF, AC, and rCRE for given values of N and LWP. Emulator-derived susceptibilities ∂ln⁡rCRE/∂ln⁡N and ∂ln⁡rCRE/∂ln⁡LWP cover the nondrizzling, fully overcast regime as well as the drizzling regime with broken cloud cover. Theoretical results, which are limited to the nondrizzling regime, are reproduced. The susceptibility ∂ln⁡rCRE/∂ln⁡N captures the strong sensitivity of the cloud radiative effect to cloud fraction, while the susceptibility ∂ln⁡rCRE/∂ln⁡LWP describes the influence of cloud amount on cloud albedo irrespective of cloud fraction. Our emulation-based approach provides a powerful tool for summarizing complex data in a simple framework that captures the sensitivities of cloud-field properties over a wide range of states.

A total of sixteen global chemistry transport models and general circulation models have participated in this study. Fourteen models have been evaluated with regard to their ability to reproduce near-surface observed number concentration of aerosol particle and cloud condensation nuclei (CCN), and derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations, located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments, and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24 % and −35 % for particles with dry diameters > 50 nm and > 120 nm and −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. Fifteen models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −17 % and −22 % for updraft velocities 0.3 and 0.6 m s−1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally-derived value at lower than at higher updraft velocities (index-of-agreement of 0.47 vs 0.50). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration and to updraft velocity. Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high.

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of −24 % and −35 % for particles with dry diameters >50 and >120 nm, as well as −36 % and −34 % for CCN at supersaturations of 0.2 % and 1.0 %, respectively. However, they seem to behave differently for particles activating at very low supersaturations ( %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2 % (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40 % during winter and 20 % in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB −13 % and −22 % for updraft velocities 0.3 and 0.6 m s−1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂Nd/∂Na) and to updraft velocity (∂Nd/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂Nd/∂Na and ∂Nd/∂w; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

The topic of cloud radiative forcing associated with the atmospheric aerosol has been the focus of intense scrutiny for decades. The enormity of the problem is reflected in the need to understand aspects such as aerosol composition, optical properties, cloud condensation, and ice nucleation potential, along with the global distribution of these properties, controlled by emissions, transport, transformation, and sinks. Equally daunting is that clouds themselves are complex, turbulent, microphysical entities and, by their very nature, ephemeral and hard to predict. Atmospheric general circulation models represent aerosol−cloud interactions at ever-increasing levels of detail, but these models lack the resolution to represent clouds and aerosol−cloud interactions adequately. There is a dearth of observational constraints on aerosol−cloud interactions. We develop a conceptual approach to systematically constrain the aerosol−cloud radiative effect in shallow clouds through a combination of routine process modeling and satellite and surface-based shortwave radiation measurements. We heed the call to merge Darwinian and Newtonian strategies by balancing microphysical detail with scaling and emergent properties of the aerosol−cloud radiation system.

© 2020 The Author(s). Published by IOP Publishing Ltd. Anthropogenic aerosol emissions are predicted to decline sharply throughout the 21st century, in line with climate change and air quality mitigation policies, causing a near-term warming of climate that will impact our trajectory towards 1.5 °C above pre-industrial temperatures. However, the persistent uncertainty in aerosol radiative forcing limits our understanding of how much the global mean temperature will respond to near-term reductions in anthropogenic aerosol emissions. We quantify the model and scenario uncertainty in global mean aerosol radiative forcing up to 2050 using statistical emulation of a perturbed parameter ensemble for emission reduction scenarios consistent with three Shared Socioeconomic Pathways. We then use a simple climate model to translate the uncertainty in aerosol radiative forcing into uncertainty in global mean temperature projections, accounting additionally for the potential correlation of aerosol radiative forcing and climate sensitivity. Near-term aerosol radiative forcing uncertainty alone causes an uncertainty window of around 5 years (2034-2039) on the projected year of exceeding a global temperature rise of 1.5 °C above pre-industrial temperatures for a middle of the road emissions scenario (SSP2-RCP4.5). A correlation between aerosol radiative forcing and climate sensitivity would increase the 1.5 °C exceedance window by many years. The results highlight the importance of quantifying aerosol radiative forcing and any relationship with climate sensitivity in climate models in order to reduce uncertainty in temperature projections.

Model simulations of many climate phenomena remain highly uncertain despite scientific advances and huge amounts of data. Scientists must do more to tackle model uncertainty head-on.

Changes in aerosols cause a change in net top-of-the-atmosphere (ToA) short-wave and long-wave radiative fluxes; rapid adjustments in clouds, water vapour and temperature; and an effective radiative forcing (ERF) of the planetary energy budget. The diverse sources of model uncertainty and the computational cost of running climate models make it difficult to isolate the main causes of aerosol ERF uncertainty and to understand how observations can be used to constrain it. We explore the aerosol ERF uncertainty by using fast model emulators to generate a very large set of aerosol–climate model variants that span the model uncertainty due to 27 parameters related to atmospheric and aerosol processes. Sensitivity analyses shows that the uncertainty in the ToA flux is dominated (around 80 %) by uncertainties in the physical atmosphere model, particularly parameters that affect cloud reflectivity. However, uncertainty in the change in ToA flux caused by aerosol emissions over the industrial period (the aerosol ERF) is controlled by a combination of uncertainties in aerosol (around 60 %) and physical atmosphere (around 40 %) parameters. Four atmospheric and aerosol parameters account for around 80 % of the uncertainty in short-wave ToA flux (mostly parameters that directly scale cloud reflectivity, cloud water content or cloud droplet concentrations), and these parameters also account for around 60 % of the aerosol ERF uncertainty. The common causes of uncertainty mean that constraining the modelled planetary brightness to tightly match satellite observations changes the lower 95 % credible aerosol ERF value from −2.65 to −2.37 W m−2. This suggests the strongest forcings (below around −2.4 W m−2) are inconsistent with observations. These results show that, regardless of the fact that the ToA flux is 2 orders of magnitude larger than the aerosol ERF, the observed flux can constrain the uncertainty in ERF because their values are connected by constrainable process parameters. The key to reducing the aerosol ERF uncertainty further will be to identify observations that can additionally constrain individual parameter ranges and/or combined parameter effects, which can be achieved through sensitivity analysis of perturbed parameter ensembles.

This study aims to identify model parameters describing atmospheric conditions such as wind shear and cloud condensation nuclei (CCN) concentration, which lead to large uncertainties in the prediction of deep convective clouds. In an idealized setup of a cloud-resolving model including a two-moment microphysics scheme we use the approach of statistical emulation to allow for a Monte Carlo sampling of the parameter space, which enables a comprehensive sensitivity analysis. We analyze the impact of six uncertain input parameters on cloud properties (vertically integrated content of six hydrometeor classes), precipitation, and the size distribution of hail. Furthermore, we investigate whether the sensitivities are robust for different trigger mechanisms of convection. We find that the uncertainties of most cloud and precipitation outputs are dominated by the uncertainty in the temperature profile and the CCN concentration while the contributions of other input parameters to the uncertainties may vary. The temperature profile is also an important factor in determining the size distribution of surface hail. We also notice that the sensitivities of cloud water and hail to the CCN concentration depend on environmental conditions. Our results show that depending on the choice of the trigger mechanism, the contribution of the input parameters to the uncertainty varies, which means that studies with different trigger mechanisms might not be comparable. Overall, the emulator approach appears to be a powerful tool for the analysis of complex weather prediction models in an idealized setup.

The microphysical properties of convective clouds determine their radiative effects on climate, the amount and intensity of precipitation as well as dynamical features. Realistic simulation of these cloud properties presents a major challenge. In particular, because models are complex and slow to run, we have little understanding of how the considerable uncertainties in parameterised processes feed through to uncertainty in the cloud responses. Here we use statistical emulation to enable a Monte Carlo sampling of a convective cloud model to quantify the sensitivity of twelve cloud properties to aerosol concentrations and nine model parameters representing the main microphysical processes. We examine the response of liquid and ice-phase hydrometeor concentrations, precipitation and cloud dynamics for a deep convective cloud in a continental environment. Across all cloud responses, the concentration of the Aitken and accumulation aerosol modes and the collection efficiency of droplets by graupel particles have the most influence on the uncertainty. However, except at very high aerosol concentrations, uncertainties in precipitation intensity and amount are affected more by interactions between drops and graupel than by large variations in aerosol. The uncertainties in ice crystal mass and number are controlled primarily by the shape of the crystals, ice nucleation rates and aerosol concentrations. Overall, although aerosol particle concentrations are an important factor in deep convective clouds, uncertainties in several processes significantly affect the reliability of complex microphysical models. The results suggest that our understanding of aerosol-cloud interaction could be greatly advanced by extending the emulator approach to models of cloud systems.

We consider the use of emulator technology as an alternative method to second-order Monte Carlo (2DMC) in the uncertainty analysis for a percentile from the output of a stochastic model. 2DMC is a technique that uses repeated sampling in order to make inferences on the uncertainty and variability in a model output. The conventional 2DMC approach can often be highly computational, making methods for uncertainty and sensitivity analysis unfeasible. We explore the adequacy and efficiency of the emulation approach, and we find that emulation provides a viable alternative in this situation. We demonstrate these methods using two different examples of different input dimensions, including an application that considers contamination in pre-pasteurised milk.

The increasing use of multi-member climate model ensembles for making future climate impact assessments presents both opportunities for understanding uncertainties, and challenges for interpreting the results. We outline current approaches to assessing uncertainties in climate impacts, statistical methods for assessing uncertainties, issues regarding model integration and complexity, and ways in which uncertainty frameworks can be used to inform adaptation decisions, with case studies focused on agriculture. Finally, we highlight future research needs and provide recommendations for making further progress.