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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.

Simple climate models can be valuable if they are able to replicate aspects of complex fully coupled earth system models. Larger ensembles can be produced, enabling a probabilistic view of future climate change. A simple emissions-based climate model, FAIR, is presented, which calculates atmospheric concentrations of greenhouse gases and effective radiative forcing (ERF) from greenhouse gases, aerosols, ozone and other agents. Model runs are constrained to observed temperature change from 1880 to 2016 and produce a range of future projections under the Representative Concentration Pathway (RCP) scenarios. The constrained estimates of equilibrium climate sensitivity (ECS), transient climate response (TCR) and transient climate response to cumulative CO2 emissions (TCRE) are 2.86 (2.01 to 4.22) K, 1.53 (1.05 to 2.41) K and 1.40 (0.96 to 2.23) K (1000 GtC)-1 (median and 5–95 % credible intervals). These are in good agreement with the likely Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) range, noting that AR5 estimates were derived from a combination of climate models, observations and expert judgement. The ranges of future projections of temperature and ranges of estimates of ECS, TCR and TCRE are somewhat sensitive to the prior distributions of ECS/TCR parameters but less sensitive to the ERF from a doubling of CO2 or the observational temperature dataset used to constrain the ensemble. Taking these sensitivities into account, there is no evidence to suggest that the median and credible range of observationally constrained TCR or ECS differ from climate model-derived estimates. The range of temperature projections under RCP8.5 for 2081–2100 in the constrained FAIR model ensemble is lower than the emissions-based estimate reported in AR5 by half a degree, owing to differences in forcing assumptions and ECS/TCR distributions.

Aerosol measurements over the Southern Ocean are used to constrain aerosol–cloud interaction radiative forcing (RFaci) uncertainty in a global climate model. Forcing uncertainty is quantified using 1 million climate model variants that sample the uncertainty in nearly 30 model parameters. Measurements of cloud condensation nuclei and other aerosol properties from an Antarctic circumnavigation expedition strongly constrain natural aerosol emissions: default sea spray emissions need to be increased by around a factor of 3 to be consistent with measurements. Forcing uncertainty is reduced by around 7 % using this set of several hundred measurements, which is comparable to the 8 % reduction achieved using a diverse and extensive set of over 9000 predominantly Northern Hemisphere measurements. When Southern Ocean and Northern Hemisphere measurements are combined, uncertainty in RFaci is reduced by 21 %, and the strongest 20 % of forcing values are ruled out as implausible. In this combined constraint, observationally plausible RFaci is around 0.17 W m−2 weaker (less negative) with 95 % credible values ranging from −2.51 to −1.17 W m−2 (standard deviation of −2.18 to −1.46 W m−2). 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.

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 forcing by more than 50 %, constraining it to a range (around −1.3 to −0.1 W m−2) in close agreement with energy balance constraints. 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 very likely narrow the uncertainty further and possibly alter the central estimate. 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.

The change in planetary albedo due to aerosol–cloud interactions during the industrial era is the leading source of uncertainty in inferring Earth’s climate sensitivity to increased greenhouse gases from the historical record. The variable that controls aerosol–cloud interactions in warm clouds is droplet number concentration. Global climate models demonstrate that the present-day hemispheric contrast in cloud droplet number concentration between the pristine Southern Hemisphere and the polluted Northern Hemisphere oceans can be used as a proxy for anthropogenically driven change in cloud droplet number concentration. Remotely sensed estimates constrain this change in droplet number concentration to be between 8 cm−3 and 24 cm−3. By extension, the radiative forcing since 1850 from aerosol–cloud interactions is constrained to be −1.2 W·m−2 to −0.6 W·m−2. The robustness of this constraint depends upon the assumption that pristine Southern Ocean droplet number concentration is a suitable proxy for preindustrial concentrations. Droplet number concentrations calculated from satellite data over the Southern Ocean are high in austral summer. Near Antarctica, they reach values typical of Northern Hemisphere polluted outflows. These concentrations are found to agree with several in situ datasets. In contrast, climate models show systematic underpredictions of cloud droplet number concentration across the Southern Ocean. Near Antarctica, where precipitation sinks of aerosol are small, the underestimation by climate models is particularly large. This motivates the need for detailed process studies of aerosol production and aerosol–cloud interactions in pristine environments. The hemispheric difference in satellite estimated cloud droplet number concentration implies preindustrial aerosol concentrations were higher than estimated by most models.

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.

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.

The effect of observational constraint on the ranges of uncertain physical and chemical process parameters was explored in a global aerosol–climate model. The study uses 1 million variants of the Hadley Centre General Environment Model version 3 (HadGEM3) that sample 26 sources of uncertainty, together with over 9000 monthly aggregated grid-box measurements of aerosol optical depth, PM2.5, particle number concentrations, sulfate and organic mass concentrations. Despite many compensating effects in the model, the procedure constrains the probability distributions of parameters related to secondary organic aerosol, anthropogenic SO2 emissions, residential emissions, sea spray emissions, dry deposition rates of SO2 and aerosols, new particle formation, cloud droplet pH and the diameter of primary combustion particles. Observational constraint rules out nearly 98 % of the model variants. On constraint, the ±1σ (standard deviation) range of global annual mean direct radiative forcing (RFari) is reduced by 33 % to −0.14 to −0.26 W m−2, and the 95 % credible interval (CI) is reduced by 34 % to −0.1 to −0.32 W m−2. For the global annual mean aerosol–cloud radiative forcing, RFaci, the ±1σ range is reduced by 7 % to −1.66 to −2.48 W m−2, and the 95 % CI by 6 % to −1.28 to −2.88 W m−2. The tightness of the constraint is limited by parameter cancellation effects (model equifinality) as well as the large and poorly defined “representativeness error” associated with comparing point measurements with a global model. The constraint could also be narrowed if model structural errors that prevent simultaneous agreement with different measurement types in multiple locations and seasons could be improved. For example, constraints using either sulfate or PM2.5 measurements individually result in RFari±1σ ranges that only just overlap, which shows that emergent constraints based on one measurement type may be overconfident.

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.

Interactions between aerosols, clouds, and radiation remain a major source of uncertainty in effective radiative forcing (ERF), limiting the accuracy of climate projections. This study aims to quantify parametric uncertainties in aerosol-cloud and aerosol-radiation interactions using a perturbed parameter ensemble (PPE) of 221 simulations with the ECHAM6.3-HAM2.3 climate model, varying 23 aerosol-related parameters that control emissions, removal, chemistry, and microphysics.