Large computer models are ubiquitous in the Earth sciences. These models often have tens or hundreds of tuneable parameters and can take thousands of core hours to run to completion while generating terabytes of output. It is becoming common practice to develop emulators as fast approximations, or surrogates, of these models in order to explore the relationships between these inputs and outputs, understand uncertainties, and generate large ensembles datasets. While the purpose of these surrogates may differ, their development is often very similar. Here we introduce ESEm: an open-source tool providing a general workflow for emulating and validating a wide variety of models and outputs. It includes efficient routines for sampling these emulators for the purpose of uncertainty quantification and model calibration. It is built on well-established, high-performance libraries to ensure robustness, extensibility and scalability. We demonstrate the flexibility of ESEm through three case studies using ESEm to reduce parametric uncertainty in a general circulation model and explore precipitation sensitivity in a cloud-resolving model and scenario uncertainty in the CMIP6 multi-model ensemble.

Aerosols have a direct effect on the Earth's radiative budget and can also affect cloud development and lifetime, and the aerosols above clouds (AAC) are particularly associated with high uncertainties in global climate models. Therefore, it is a prerequisite to improve the description and understanding of these situations. During the austral winter, large loadings of biomass burning aerosols originating from fires in the southern African subcontinent are lifted and transported westwards, across the southeastern Atlantic Ocean. The negligible wet scavenging of these absorbing aerosols leads to a near-persistent smoke layer above one of the largest stratocumulus cloud decks on the planet. Therefore, the southeastern Atlantic region is a very important area for studying the impact of above-cloud absorbing aerosols, their radiative forcing and their possible effects on clouds. In this study we aim to analyse and quantify the effect of smoke loadings on cloud properties using a synergy of different remote sensing techniques from A-Train retrievals (methods based on the passive instruments POLDER and MODIS and the operational method of the spaceborne lidar CALIOP), collocated with ERA-Interim re-analysis meteorological profiles. To analyse the possible mechanisms of AAC effects on cloud properties, we developed a high and low aerosol loading approach, which consists in evaluating the change in radiative quantities (i.e. cloud-top cooling, heating rate vertical profiles) and cloud properties with the smoke loading. During this analysis, we account for the variation in the meteorological conditions over our sample area by selecting the months associated with one meteorological regime (June–August). The results show that the region we focus on is primarily under the energetic influence of absorbing aerosols, leading to a significant positive shortwave direct effect at the top of the atmosphere. For larger loads of AACs, clouds are optically thicker, with an increase in liquid water path of 20 g m⁻² and lower cloud-top altitudes by 100 m. These results do not contradict the semi-direct effect of above-cloud aerosols, explored in previous studies. Furthermore, we observe a strong covariance between the aerosol and the water vapour loadings, which has to be accounted for. A detailed analysis of the heating rate profiles shows that within the smoke layer, the absorbing aerosols are 90 % responsible for warming the ambient air by approximately 5.7 K d⁻¹. The accompanying water vapour, however, has a longwave effect at distance on the cloud top, reducing its cooling by approximately 4.7 K d⁻¹ (equivalent to 7 %). We infer that this decreased cloud-top cooling in particular, in addition with the higher humidity above the clouds, might modify the cloud-top entrainment rate and its effect, leading to thicker clouds. Therefore, smoke (the combination of aerosol and water vapour) events would have the potential to modify and probably reinforce the underlaying cloud cover.

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.

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 south-eastern Atlantic Ocean (SEA) is semi-permanently covered by one of the most extensive stratocumulus cloud decks on the planet and experiences about one-third of the global biomass burning emissions from the southern Africa savannah region during the fire season. To get a better understanding of the impact of these biomass burning aerosols on clouds and the radiation balance over the SEA, the latest generation of the UK Earth System Model (UKESM1) is employed. Measurements from the CLARIFY and ORACLES flight campaigns are used to evaluate the model, demonstrating that the model has good skill in reproducing the biomass burning plume. To investigate the underlying mechanisms in detail, the effects of biomass burning aerosols on the clouds are decomposed into radiative effects (via absorption and scattering) and microphysical effects (via perturbation of cloud condensation nuclei – CCN – and cloud microphysical processes). July–August means are used to characterize aerosols, clouds, and the radiation balance during the fire season. Results show that around 65 % of CCN at 0.2 % supersaturation in the SEA can be attributed to biomass burning. The absorption effect of biomass burning aerosols is the most significant on clouds and radiation. Near the continent, it increases the supersaturation diagnosed by the activation scheme, while further from the continent it reduces the altitude of the supersaturation. As a result, the cloud droplet number concentration responds with a similar pattern to the absorption effect of biomass burning aerosols. The microphysical effect, however, decreases the supersaturation and increases the cloud droplet concentration over the ocean, although this change is relatively small. The liquid water path is also significantly increased over the SEA (mainly caused by the absorption effect of biomass burning aerosols) when biomass burning aerosols are above the stratocumulus cloud deck. The microphysical pathways lead to a slight increase in the liquid water path over the ocean. These changes in cloud properties indicate the significant role of biomass burning aerosols for clouds in this region. Among the effects of biomass burning aerosols on the radiation balance, the semi-direct radiative effects (rapid adjustments induced by the radiative effects of biomass burning aerosols) have a dominant cooling impact over the SEA, which offset the warming direct radiative effect (radiative forcing from biomass burning aerosol–radiation interactions) and lead to an overall net cooling radiative effect in the SEA. However, the magnitude and the sign of the semi-direct effects are sensitive to the relative location of biomass burning aerosols and clouds, reflecting the critical task of the accurate modelling of the biomass burning plume and clouds in this region.

This study presents a comparison between the retrieval of optical properties of aerosol above clouds (AAC) from different techniques developed for the A-Train sensors CALIOP/CALIPSO and POLDER/PARASOL. The main objective is to analyse the consistency between the results derived from the active and the passive measurements. We compare the aerosol optical thickness (AOT) above optically thick clouds (cloud optical thickness (COT) larger than 3) and their Ångström exponent (AE). These parameters are retrieved with the CALIOP operational method, the POLDER operational polarization method and the CALIOP-based depolarization ratio method (DRM) – for which we also propose a calibrated version (denominated DRM_SODA, where SODA is the Synergized Optical Depth of Aerosols). We analyse 6 months of data over three distinctive regions characterized by different types of aerosols and clouds. Additionally, for these regions, we select three case studies: a biomass-burning event over the South Atlantic Ocean, a Saharan dust case over the North Atlantic Ocean and a Siberian biomass-burning event over the North Pacific Ocean. Four and a half years of data are studied over the entire globe for distinct situations where aerosol and cloud layers are in contact or vertically separated. Overall, the regional analysis shows a good correlation between the POLDER and the DRM_SODA AOTs when the microphysics of aerosols is dominated by fine-mode particles of biomass-burning aerosols from southern Africa (correlation coefficient (R²) of 0.83) or coarse-mode aerosols of Saharan dust (R² of 0.82). A good correlation between these methods (R² of 0.68) is also observed in the global treatment, when the aerosol and cloud layers are separated well. The analysis of detached layers also shows a mean difference in AOT of 0.07 at 532 nm between POLDER and DRM_SODA at a global scale. The correlation between the retrievals decreases when a complex mixture of aerosols is expected (R² of 0.37) – as in the East Asia region – and when the aerosol–cloud layers are in contact (R² of 0.36). The correlation coefficient between the CALIOP operational method and POLDER is found to be low, as the CALIOP method largely underestimates the aerosol loading above clouds by a factor that ranges from 2 to 4. Potential biases on the retrieved AOT as a function of cloud properties are also investigated. For different types of scenes, the retrieval of above-cloud AOT from POLDER and from DRM are compared for different underlying cloud properties (droplet effective radius (r_eff) and COT retrieved with MODIS). The results reveal that DRM AOT vary with r_eff. When accounting for r_eff in the DRM algorithm, the consistency between the methods increases. The sensitivity study shows that an additional polarized signal coming from aerosols located within the cloud could affect the polarization method, which leads to an overestimation of the AOT retrieved with POLDER algorithm. In addition, the aerosols attached to or within the cloud can potentially impact the DRM retrievals through the modification of the cloud droplet chemical composition and its ability to backscatter light. The next step of this work is to combine POLDER and CALIOP to investigate the impacts of aerosols on clouds and climate when these particles are transported above or within clouds.