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Aerosol‐cloud interactions (ACI) in warm clouds are the primary source of uncertainty in effective radiative forcing (ERF) during the historical period and, by extension, inferred climate sensitivity. The ERF due to ACI (ERFaci) is composed of the radiative forcing due to changes in cloud microphysics and cloud adjustments to microphysics. Here, we examine the processes that drive ERFaci using a perturbed parameter ensemble (PPE) hosted in CAM6. Observational constraints on the PPE result in substantial constraints in the response of cloud microphysics and macrophysics to anthropogenic aerosol, but only minimal constraint on ERFaci. Examination of cloud and radiation processes in the PPE reveal buffering of ERFaci by the interaction of precipitation efficiency and radiative susceptibility. Plain Language Summary: Uncertainty in predicting future global temperature inferred from the historical record of warming is dominated by how much the warming due to greenhouse gases has been offset by the cooling due to aerosols. Aerosols are small liquid and solid particles that play an important role in cloud formation. The majority of cooling from aerosols is through reflecting incoming solar radiation back to space by cloud. In this study, we constrain an ensemble of possible global model configurations with observations of cloud properties and radiation to reduce uncertainty in the response of clouds and ultimately radiation to anthropogenic aerosol. While observations substantially reduce the uncertainty in both changes in the number of droplets and amount of liquid cloud, the constraint on aerosol cooling is minimal. We argue that the relatively weak constraint is because large changes in cloudiness are accompanied by small change in reflected sunlight due to increased cloudiness. Key Points: Models with lower precipitation efficiency result in larger mean‐state liquid water path (LWP) and larger adjustments in LWP to aerosolLarger mean‐state LWP coincides with lower albedo susceptibility to LWPThe combination of these processes results in buffering of the radiative effect of LWP adjustments [ABSTRACT FROM AUTHOR]

This work presents the first step in the development of the VISION toolkit, a set of python tools that allows for easy, efficient and more meaningful comparison between global atmospheric models and observational data. Whilst observational data and modelling capabilities are expanding in parallel, there are still barriers preventing these two data sources to be used in synergy. This arises from differences in spatial and temporal sampling between models and observational platforms: observational data from a research aircraft, for example, is sampled on specified flight trajectories at very high temporal resolution. Proper comparison with model data requires generating, storing and handling a large amount of highly temporally resolved model files, resulting in a process which is data, labour, and time intensive. In this paper we focus on comparison between model data and in-situ observations (from aircrafts, ships, buoys, sondes etc.). A stand-alone code, In-Situ Observation simulator, or ISO_simulator in short, is described here: this software reads modelled variables and observational data files and outputs model data interpolated in space and time to match observations. This model data is then written to NetCDF files that can be efficiently archived, due to their small sizes, and directly compared to observations. This method achieves a large reduction in the size of model data being produced for comparison with flight and other in-situ data. By interpolating global, gridded, hourly files onto observations locations, we reduce data output for a typical climate resolution run, from ~3 Gb per model variable per month to ~15 Mb per model variable per month (a 200 times reduction in data volume). The VISION toolkit is fast and easy to use, therefore enabling the exploitation of large observational datasets spanning decades, to be used for large scale model evaluation. Although this code has been initially tested within the Unified Model (UM) framework, which is shared by the UK Earth System Model (UKESM), it was written as a flexible tool and it can be extended to work with other models. [ABSTRACT FROM AUTHOR]

Global climate change projections are subject to substantial modelling uncertainties. A variety of emergent constraints, as well as several other statistical model evaluation approaches, have been suggested to address these uncertainties. However, they remain heavily debated in the climate science community. Still, the central idea to relate future model projections to already observable quantities has no real substitute. Here we highlight the validation perspective of predictive skill in the machine learning community as a promising alternative viewpoint. Building on this perspective, we review machine learning ideas for new types of controlling factor analyses (CFA). The principal idea behind these CFA is to use machine learning to find climate-invariant relationships in historical data, which also hold approximately under strong climate change scenarios. On the basis of existing data archives, these climate-invariant relationships can be validated in perfect-climate-model frameworks. From a machine learning perspective, we argue that such approaches are promising for three reasons: (a) they can be objectively validated both for past data and future data, (b) they provide more direct – by design physically-plausible – links between historical observations and potential future climates and (c) they can take higher dimensional relationships into account that better characterize the still complex nature of large-scale emerging relationships. We demonstrate these advantages for two recently published CFA examples in the form of constraints on climate feedback mechanisms (clouds, stratospheric water vapour), and discuss further challenges and opportunities using the example of a climate forcing (aerosol-cloud interactions). [ABSTRACT FROM AUTHOR]

Emulators, or reduced complexity climate models, are surrogate Earth system models (ESMs) that produce projections of key climate quantities with minimal computational resources. Using time‐series modeling or more advanced machine learning techniques, data‐driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state‐of‐the‐art ESMs. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data‐driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (a) enjoys the flexibility of statistical machine learning models and can learn from data, and (b) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform EBMs, while its robust physical foundation safeguards against the pitfalls of purely data‐driven models. We also illustrate how FaIRGP can be used to obtain estimates of top‐of‐atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data‐driven methods in climate emulation. Plain Language Summary: Emulators are simplified climate models that can be used to rapidly explore climate scenarios—they can run in less than a minute on an average computer. They are key tools used by the Intergovernmental Panel on Climate Change to explore the diversity of possible future climates. Data‐driven emulators use advanced machine learning techniques to produce climate predictions that look very similar to the predictions of complex climate models. However, they are not easy to interpret, and therefore to trust in practice. In this work, we introduce FaIRGP, a data‐driven emulator based on physics. The emulator is flexible and can learn from data to improve its predictions, but is also grounded on physical energy balance relationships, which makes it robust and interpretable. The model performs well in predicting future global and local temperatures under realistic future scenarios, outperforming purely physics‐driven or purely data‐driven models. Further, the probabilistic nature of our model allows for mathematically tractable uncertainty quantification. By gaining trust in such a data‐driven yet physically grounded model, we hope the climate science community can benefit more widely from their potential. Key Points: We introduce FaIRGP, a Bayesian machine learning emulator for global and local mean surface temperatures that builds upon a physically based simple climate modelThe model improves upon both purely physically‐driven and purely data‐driven baseline emulators on several metrics across realistic future scenariosThe model is fully mathematically tractable, which makes it a convenient and easy‐to‐use probabilistic tool for the emulation of surface temperatures, but also for downstream applications such as detection and attribution or precipitation emulation [ABSTRACT FROM AUTHOR]

The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood, and uncertainty in climate model results persists, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. We synthesize current challenges and emphasize opportunities for advancing our understanding of the interactions between atmospheric composition, air quality, and climate change, as well as for quantifying model diversity. Our perspective is based on expert views from three multi-model intercomparison projects (MIPs) – the Precipitation Driver Response MIP (PDRMIP), the Aerosol Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specializations across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation–response paradigm through multi-model ensembles of Earth system models of varying complexity. We discuss the challenges of gaining insights from Earth system models that face computational and process representation limits and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible and machine learning approaches where they are needed, e.g., for faster and better subgrid-scale parameterizations and pattern recognition in big data. New model constraints can arise from augmented observational products that leverage multiple datasets with machine learning approaches. Future MIPs can develop smart experiment protocols that strive towards an optimal trade-off between the resolution, complexity, and number of simulations and their length and, thereby, help to advance the understanding of climate change and its impacts. [ABSTRACT FROM AUTHOR]

For over 6 months, the 2014–2015 effusive eruption at Holuhraun, Iceland, injected considerable amounts of sulfur dioxide (SO2) into the lower troposphere with a daily rate of up to one-third of the global emission rate, causing extensive air pollution across Europe. The large injection of SO2 , which oxidises to form sulfate aerosol (SO42-), provides a natural experiment offering an ideal opportunity to scrutinise state-of-the-art general circulation models' (GCMs) representation of aerosol–cloud interactions (ACIs). Here we present Part 1 of a two-part model inter-comparison using the Holuhraun eruption as a framework to analyse ACIs. We use SO2 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument and ground-based measurements of SO2 and SO42- mass concentrations across Europe, in conjunction with a trajectory analysis using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model, to assess the spatial and chemical evolution of the volcanic plume as simulated by five GCMs and a chemical transport model (CTM). IASI retrievals of plume altitude and SO2 column load reveal that the volcanic perturbation is largely contained within the lower troposphere. Compared to the satellite observations, the models capture the spatial evolution and vertical variability of the plume reasonably well, although the models often overestimate the plume altitude. HYSPLIT trajectories are used to attribute to Holuhraun emissions 111 instances of elevated sulfurous surface mass concentrations recorded at European Monitoring and Evaluation Programme (EMEP) stations during September and October 2014. Comparisons with the simulated concentrations show that the modelled ratio of SO2 to SO42- during these pollution episodes is often underestimated and overestimated for the young and mature plume, respectively. Models with finer vertical resolutions near the surface are found to better capture these elevated sulfurous ground-level concentrations. Using an exponential function to describe the decay of observed surface mass concentration ratios of SO2 to SO42- with plume age, the in-plume oxidation rate constant is estimated as 0.032 ± 0.002 h-1 (1.30 ± 0.08 d e -folding time), with a near-vent ratio of 25 ± 5 (µgm-3 of SO2 / µgm-3 of SO42-). The majority of the corresponding derived modelled oxidation rate constants are lower than the observed estimate. This suggests that the representation of the oxidation pathway/s in the simulated plumes is too slow. Overall, despite their coarse spatial resolutions, the six models show reasonable skill in capturing the spatial and chemical evolution of the Holuhraun plume. This capable representation of the underlying aerosol perturbation is essential to enable the investigation of the eruption's impact on ACIs in the second part of this study. [ABSTRACT FROM AUTHOR]

This paper documents the methodology and preliminary results from a Perturbed Parameter Ensemble (PPE) technique, where multiple parameters are varied simultaneously and the parameter values are determined with Latin hypercube sampling. This is done with the Community Atmosphere Model version 6 (CAM6), the atmospheric component of the Community Earth System Model version 2 (CESM2). We apply the PPE method to CESM2-CAM6 to understand climate sensitivity to atmospheric physics parameters. The initial simulations vary 45 parameters in the microphysics, convection, turbulence and aerosol schemes with 263 ensemble members. These atmospheric parameters are typically the most uncertain in many climate models. Control simulations are analyzed and targeted simulations to understand climate forcing due to aerosols and fast climate feedbacks. The use of various emulators is explored in the multi- dimensional space mapping input parameters to output metrics. Parameter impacts on various model outputs, such as radiation, cloud and aerosol properties are evaluated. Machine learning is also used to probe optimal parameter values against observations. Our findings show that using PPE is a valuable tool for climate uncertainty analysis. Furthermore, by varying many parameters simultaneously, we find that many different combinations of parameter values can produce results consistent with observations, and thus careful analysis of tuning is important. The CESM2-CAM6 PPE is publicly available, and extensible to other configurations to address questions of other model processes in the atmosphere and other model components (e.g. coupling to the land surface). [ABSTRACT FROM AUTHOR]

Human aerosol emissions change cloud properties by providing additional cloud condensation nuclei. This increases cloud droplet numbers, which in turn affects other cloud properties like liquid-water content and ultimately cloud albedo. These adjustments are poorly constrained, making aerosol effects the most uncertain part of anthropogenic climate forcing. Here we show that cloud droplet number and water content react differently to changing emission amounts in shipping exhausts. We use information about ship positions and modeled emission amounts together with reanalysis winds and satellite retrievals of cloud properties. The analysis reveals that cloud droplet numbers respond linearly to emission amount over a large range (1–10 kg h -1) before the response saturates. Liquid water increases in raining clouds, and the anomalies are constant over the emission ranges observed. There is evidence that this independence of emissions is due to compensating effects under drier and more humid conditions, consistent with suppression of rain by enhanced aerosol. This has implications for our understanding of cloud processes and may improve the way clouds are represented in climate models, in particular by changing parameterizations of liquid-water responses to aerosol. [ABSTRACT FROM AUTHOR]

Anthropogenic aerosol interacts strongly with incoming solar radiation, perturbing Earth's energy budget and precipitation on both local and global scales. Understanding these changes in precipitation has proven particularly difficult for the case of absorbing aerosol, which absorbs a significant amount of incoming solar radiation and hence acts as a source of localized diabatic heating to the atmosphere. In this work, we use an ensemble of atmosphere-only climate model simulations forced by identical absorbing aerosol perturbations in different geographical locations across the globe to develop a basic physical understanding of how this localized heating impacts the atmosphere and how these changes impact on precipitation both globally and locally. In agreement with previous studies we find that absorbing aerosol causes a decrease in global-mean precipitation, but we also show that even for identical aerosol optical depth perturbations, the global-mean precipitation change varies by over an order of magnitude depending on the location of the aerosol burden. Our experiments also demonstrate that the local precipitation response to absorbing aerosol is opposite in sign between the tropics and the extratropics, as found by previous work. We then show that this contrasting response can be understood in terms of different mechanisms by which the large-scale circulation responds to heating in the extratropics and in the tropics. We provide a simple theory to explain variations in the local precipitation response to absorbing aerosol in the tropics. Our work highlights that the spatial pattern of absorbing aerosol and its interactions with circulation are a key determinant of its overall climate impact and must be taken into account when developing our understanding of aerosol–climate interactions. [ABSTRACT FROM AUTHOR]

For over 6-months, the 2014–2015 effusive eruption at Holuhraun, Iceland injected considerable amounts of sulphur dioxide (SO2) into the lower troposphere with a daily rate of up to one-third of the global emission rate causing extensive air pollution across Europe. The large injection of SO2, which oxidises to form sulphate aerosol (SO42−), provides a natural experiment offering an ideal opportunity to scrutinise state-of-the-art general circulation models (GCMs) representation of aerosol-cloud interactions (ACIs). Here we present Part 1 of a two-part model inter-comparison using the Holuhraun eruption as a framework to analyse ACIs. We use SO2 retrievals from the Infrared Atmospheric Sounding Interferometer (IASI) instrument and ground-based measurements of SO2 and SO42− mass concentrations across Europe in conjunction with trajectory analysis using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model to assess the spatial and chemical evolution of the volcanic plume as simulated by 5 GCMs and a chemical transport model (CTM). IASI retrievals of plume altitude and SO2 column load reveal that the volcanic perturbation is largely contained within the lower troposphere and that the spatial evolution and vertical variability of the plume is reasonably well captured by the models, although the models underestimate the mean plume altitude. HYSPLIT trajectories are used to attribute to Holuhraun emissions 184 instances of elevated sulphurous surface mass concentrations recorded at 22 air monitoring stations across Europe. Comparisons with the simulated concentrations show that the models underestimate the elevated SO2 concentrations observed at stations closer to Holuhraun whilst overestimating those observed further away. Using a biexponential function to describe the decay of observed surface mass concentration ratios of SO2-to-SO42− with plume age, in-plume gas-phase and aqueous-phase oxidation rates are estimated as 0.031 ± 0.002 h−1 and 0.22 ± 0.16 h−1 respectively with a near-vent ratio of 31 ± 4 [μgm−3 of SO2 / ugm−3 of SO42−]. The derived gas-phase oxidation rates from the models are all lower than the observed estimate, whilst the majority of the aqueous-phase oxidation rates agree with the observed rate. This suggests that the simulated plumes capture the observed chemical behaviour in the young plume (when aqueous-phase oxidation is dominant), yet not in the mature plume (when gas-phase oxidation is dominant). Overall, despite their coarse resolution, the 6 models show reasonable skill in capturing the spatial and chemical evolution of the Holuhraun plume which is essential when exploring the eruption impact on ACIs in the second part of this study. [ABSTRACT FROM AUTHOR]

The climate science community aims to improve our understanding of climate change due to anthropogenic influences on atmospheric composition and the Earth's surface. Yet not all climate interactions are fully understood and diversity in climate model experiments persists as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article synthesizes current challenges and emphasizes opportunities for advancing our understanding of climate change and model diversity. The perspective of this article is based on expert views from three multi-model intercomparison projects (MIPs) - the Precipitation Driver Response MIP (PDRMIP), the Aerosol and Chemistry MIP (AerChemMIP), and the Radiative Forcing MIP (RFMIP). While there are many shared interests and specialisms across the MIPs, they have their own scientific foci and specific approaches. The partial overlap between the MIPs proved useful for advancing the understanding of the perturbation-response paradigm through multi-model ensembles of Earth System Models of varying complexity. It specifically facilitated contributions to the research field through sharing knowledge on best practices for the design of model diagnostics and experimental strategies across MIP boundaries, e.g., for estimating effective radiative forcing. We discuss the challenges of gaining insights from highly complex models that have specific biases and provide guidance from our lessons learned. Promising ideas to overcome some long-standing challenges in the near future are kilometer-scale experiments to better simulate circulation-dependent processes where it is possible, and machine learning approaches for faster and better sub-grid scale parameterizations where they are needed. Both would improve our ability to adopt a smart experimental design with an optimal tradeoff between resolution, complexity and simulation length. Future experiments can be evaluated and improved with sophisticated methods that leverage multiple observational datasets, and thereby, help to advance the understanding of climate change and its impacts. [ABSTRACT FROM AUTHOR]

The effects of anthropogenic aerosol, solid or liquid particles suspended in the air, are the biggest contributor to uncertainty in current climate perturbations. Heavy industry sites, such as coal power plants and steel manufacturers, large sources of greenhouse gases, also emit large amounts of aerosol in a small area. This makes them ideal places to study aerosol interactions with radiation and clouds. However, existing data sets of heavy industry locations are either not public, or suffer from reporting gaps. Here, we develop a supervised deep learning algorithm to detect unreported industry sites in high-resolution satellite data, using the existing data sets for training. For the pipeline to be viable at global scale, we employ a two-step approach. The first step uses 10 m resolution data, which is scanned for potential industry sites, before using 1.2 m resolution images to confirm or reject detections. On held-out test data, the models perform well, with the lower resolution one reaching up to 94% accuracy. Deployed to a large test region, the first stage model yields many false positive detections. The second stage, higher resolution model shows promising results at filtering these out, while keeping the true positives, improving the precision to 42% overall, so that human review becomes feasible. In the deployment area, we find five new heavy industry sites which were not in the training data. This demonstrates that the approach can be used to complement existing data sets of heavy industry sites. [ABSTRACT FROM AUTHOR]

Global shipping accounts for 13% of global emissions of SO2, which, once oxidized to sulfate aerosol, acts to cool the planet both directly by scattering sunlight and indirectly by increasing the albedo of clouds. This cooling due to sulfate aerosol offsets some of the warming effect of greenhouse gasses and is the largest uncertainty in determining the change in the Earth’s radiative balance by human activity. Ship tracks—the visible manifestation of the indirect of effect of ship emissions on clouds as quasi-linear features—have long provided an opportunity to quantify these effects. However, they have been arduous to catalog and typically studied only in particular regions for short periods of time. Using a machine-learning algorithm to automate their detection we catalog more than 1 million ship tracks to provide a global climatology. We use this to investigate the effect of stringent fuel regulations introduced by the International Maritime Organization in 2020 on their global prevalence since then, while accounting for the disruption in global commerce caused by COVID-19. We find a marked, but clearly nonlinear, decline in ship tracks globally: An 80% reduction in SOx emissions causes only a 25% reduction in the number of tracks detected. [ABSTRACT FROM AUTHOR]

The semi-permanent stratocumulus clouds over the south-eastern Atlantic Ocean (SEA) can act as an "air conditioner" to the regional and global climate system. The interaction of aerosols and clouds becomes important in this region and can lead to negative radiative effects, partially offsetting the positive radiative forcing of greenhouse gases. A key pathway by which aerosols affect cloud properties is by acting as cloud condensation nuclei (CCN). In this paper, we use the United Kingdom Earth System Model (UKESM1) to investigate the sources of CCN (from emissions and atmospheric processes) in the SEA as well as the response of the cloud droplet number concentration (CDNC), the cloud liquid water path (LWP), and radiative forcing to these sources during 2016 and 2017. Overall, free and upper troposphere nucleated aerosols are the dominant source of the boundary layer CCN concentration at 0.2 % supersaturation (CCN 0.2%), contributing an annual average of ∼ 41 % as they subside and entrain into the marine boundary layer, which is consistent with observations highlighting the important role of nucleation in the boundary layer CCN concentration. In terms of emission sources, anthropogenic emissions (from energy, industry, agriculture, etc.) contribute the most to the annual average CCN 0.2% in the marine boundary layer (∼ 26 %), followed by biomass burning (BB, ∼ 17 %). In the cloud layer, BB contributes about 34 % of the annual CCN 0.2% , midway between the contributions from aerosol nucleation (36 %) and anthropogenic sources (31 %). The contribution of aerosols from different sources to the CDNC is consistent with their contribution to CCN 0.2% within the marine boundary layer, with free and upper troposphere aerosol nucleation being the most important source of the CDNC overall. In terms of emission sources, anthropogenic sources are also the largest contributors to the annual average CDNC, closely followed by BB. However, during the BB season, BB and free and upper troposphere aerosol nucleation are equally the most important sources of the CDNC. The contribution of BB to the CDNC is more significant than its increase to CCN 0.2% , mainly because BB aerosols are mostly located directly above the inversion layer in the model; thus, they can increase the in-cloud CDNC by enhancing the supersaturation through the dynamical feedback due to short-wave absorption. An aerosol source that shows an increase in the CDNC also shows an increase in the LWP resulting from a reduction in autoconversion. Due to the absorption effect, BB aerosol can enhance existing temperature inversions and reduce the entrainment of sub-saturated air, leading to a further increase in the LWP. As a result, the contribution of BB to the LWP is second only to aerosol nucleation on annual averages. These findings demonstrate that BB is not the dominant source of CCN within the marine boundary layer from an emission source perspective. However, as most BB aerosols are located directly above the inversion layer, their effect on clouds increases due to their absorption effect (about the same as anthropogenic sources for the CDNC and more than anthropogenic sources for the LWP), highlighting the crucial role of their radiative effect on clouds. The results on the radiative effects of aerosols show that BB aerosol exhibits an overall positive RF ari (radiative forcing associated with aerosol–radiation interactions), but its net effective radiative forcing remains negative due to its effect on clouds (mainly due to its absorbing effect). By quantifying aerosol and cloud properties affected by different sources, this paper provides a framework for understanding the effects of aerosol sources on marine stratocumulus clouds and radiation in the SEA. [ABSTRACT FROM AUTHOR]

The semi-permanent stratocumulus clouds over the South-eastern Atlantic Ocean (SEA) can act as an “air conditioners” to the regional and global climate system. The interaction of aerosols and clouds become important in this region, and can lead to negative radiative effects, partially offsetting the positive radiative forcing of greenhouse gases. A key pathway of aerosols affecting cloud properties is by acting as cloud condensation nuclei (CCN). In this paper, we use the United Kingdom Earth System Model to investigate the sources of CCN (from atmospheric processes and emission sources) in the SEA, and the response of cloud droplet number concentration (CDNC), cloud liquid water path (LWP), and radiative forcing to those sources. Overall, total nucleation (binary nucleation) is the most important source of CCN0.2 % in the marine boundary layer, contributing an annual average of 50 % of CCN0.2 %. In terms of emission sources, anthropogenic emissions (from energy, industry, agriculture, etc.) contribute the most to the annual average CCN0.2 % in the marine boundary layer, followed by BB. In the free troposphere, however, BB becomes the dominant source of CCN0.2 %, accounting for 64 % of the annual average. The contribution of aerosols from different sources to CDNC is consistent with their contribution to CCN0.2 % within the marine boundary layer, with total nucleation being the most important source of CDNC overall. In terms of emissions, anthropogenic sources are also the largest contributors to the annual average of CDNC, closely followed by BB. The contribution of BB to CDNC is more significant than its increase to CCN0.2 %, mainly because BB aerosol also can increase CDNC by enhancing the maximum supersaturation through the radiative effect of shortwave absorption. For an aerosol source that shows an increase in CDNC, it also shows an increase in LWP resulting from a reduction in autoconversion. BB aerosol, due to the absorption effect, can enhance existing temperature inversions and reduce the entrainment of sub-saturated air, leading to a further increase in LWP. As a result, the contribution of BB to LWP is second only to total nucleation. These findings demonstrate that BB is not the dominant source of CCN within the marine boundary layer from an emission source perspective. However, its contribution to clouds increases due to its absorption effect (about the same as anthropogenic sources for CDNC and more than anthropogenic sources for LWP), highlighting the crucial role of its radiative effect on clouds. The results on the radiative effects of aerosols show that BB aerosol exhibits an overall positive RFari (radiative forcing associated with aerosol-radiation interaction), but its net effective radiative forcing remains negative due to its effect on clouds (mainly by absorbing effect). By quantifying aerosol and cloud properties affected by different sources, this paper provides a framework to understand aerosol sources effects on the marine cirrocumulus clouds and radiation in the SEA. [ABSTRACT FROM AUTHOR]

Aerosol particles play an important role in the climate system by absorbing and scattering radiation and influencing cloud properties. They are also one of the biggest sources of uncertainty for climate modeling. Many climate models do not include aerosols in sufficient detail due to computational constraints. To represent key processes, aerosol microphysical properties and processes have to be accounted for. This is done in the ECHAM-HAM (European Center for Medium-Range Weather Forecast-Hamburg-Hamburg) global climate aerosol model using the M7 microphysics, but high computational costs make it very expensive to run with finer resolution or for a longer time. We aim to use machine learning to emulate the microphysics model at sufficient accuracy and reduce the computational cost by being fast at inference time. The original M7 model is used to generate data of input-output pairs to train a neural network (NN) on it. We are able to learn the variables' tendencies achieving an average R² score of 77.1%. We further explore methods to inform and constrain the NN with physical knowledge to reduce mass violation and enforce mass positivity. On a Graphics processing unit (GPU), we achieve a speed-up of up to over 64 times faster when compared to the original model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Quantifying effective radiative forcing due to aerosol‐cloud interactions (EERFACI) remains a largely uncertain process, and the magnitude remains unconstrained in general circulation models. Previous studies focus on the magnitude of ERFACI arising from all cloud types, or examine it in the framework of dynamical regimes. Aerosol forcing due to aerosol‐cloud interactions in the HadGEM3‐GA7.1 global climate model is decomposed into several global observational cloud regimes. Regimes are assigned to model gridboxes and forcing due to aerosol‐cloud interactions is calculated on a regime‐by‐regime basis with a 20‐year averaging period. Patterns of regime occurrence are in good agreement with satellite observations. ERFACI is then further decomposed into three terms, representing radiative changes within a given regime, transitions between different cloud regimes, and nonlinear effects. The total global mean ERFACI is −1.03 Wm−2. When decomposed, simulated ERFACI is greatest in the thick stratocumulus regime (−0.51 Wm−2). Plain Language Summary: The effect of anthropogenic aerosol emissions on clouds is highly uncertain in climate models. Many previous attempts to reduce this uncertainty have focused on examining all cloud types as a whole. This work sets out a framework to examine one measure of aerosol‐cloud interactions when the effect is split by different cloud types. This framework is applied to the HadGEM3‐GA7.1 climate model. It is found that thick stratocumulus clouds exhibit the strongest aerosol‐cloud interactions, especially those found off the west coast of both North and South America, and West Africa. It is hoped that this will lead to a greater understanding of how these interactions manifest themselves in different cloud types, and that this methodology will promote the use of constraints on specific cloud types, to provide potentially greater reductions in the aforementioned uncertainty. Key Points: The majority of effective radiative forcing in HadGEM3‐GA7.1 comes from stratocumulus cloudsForcing from marine stratocumulus clouds is highly sensitive to aerosol perturbationsDecomposing radiative forcing by cloud regimes can be a useful technique to gain insights into climate model predictions [ABSTRACT FROM AUTHOR]

Changes in global-mean precipitation are strongly constrained by global radiative cooling, while regional rainfall changes are less constrained because energy can be transported. Absorbing and non-absorbing aerosols have different effects on both global-mean and regional precipitation, due to the distinct effects on energetics. This study analyses the precipitation responses to large perturbations in black carbon (BC) and sulfate (SUL) by examining the changes in atmospheric energy budget terms on global and regional scales, in terms of fast (independent of changes in sea surface temperature, SST) and slow responses (mediated by changes in SST). Changes in atmospheric radiative cooling/heating are further decomposed into contributions from clouds, aerosols, and clear–clean sky (without clouds or aerosols). Both cases show a decrease in global-mean precipitation, which is dominated by fast responses in the BC case and slow responses in the SUL case. The geographical patterns are distinct too. The intertropical convergence zone (ITCZ), accompanied by tropical rainfall, shifts northward in the BC case, while it shifts southward in the SUL case. For both cases, energy transport terms from the slow response dominate the changes in tropical rainfall, which are associated with the northward (southward) shift of the Hadley cell in response to the enhanced southward (northward) cross-equatorial energy flux caused by increased BC (SUL) emission. The extra-tropical precipitation decreases in both cases. For the BC case, fast responses to increased atmospheric radiative heating contribute most to the reduced rainfall, in which absorbing aerosols directly heat the mid-troposphere, stabilise the column, and suppress precipitation. Unlike BC, non-absorbing aerosols decrease surface temperatures through slow processes, cool the whole atmospheric column, and reduce specific humidity, which leads to decreased radiative cooling from the clear–clean sky, which is consistent with the reduced rainfall. Examining the changes in large-scale circulation and local thermodynamics qualitatively explains the responses of precipitation to aerosol perturbations, whereas the energetic perspective provides a method to quantify their contributions. [ABSTRACT FROM AUTHOR]

By interacting with radiation, aerosols perturb the Earth's energy budget and thus the global precipitation amount. It was previously shown that aerosol‐radiation interactions lead to a reduction in the global‐mean precipitation amount. We have further demonstrated in aqua‐planet simulations that the local response to absorbing aerosols differs between the tropics and the extra‐tropics. In this study we incorporate an energy budget perspective to further examine the latitudinal‐dependence of the effect of aerosol‐radiation interaction on precipitation in idealized global simulations. We demonstrate that the transition between a positive local precipitation response in the tropics and a negative local precipitation response in the extra‐tropics occurs at relatively low latitudes (∼10°), indicating a transition between the deep‐tropics (in which the Coriolis force is low, hence direct thermally driven circulation, and associated divergence/convergence of energy/moisture, can form as a result of the diabatic‐heating) and their surroundings. In addition, we gradually increase the level of complexity of the simulations and demonstrate that, in the case of absorbing aerosols, the effect of land is to counteract some of the response both inside and outside the deep‐tropics due to the reduction in surface latent‐heat flux that opposes the diabatic‐heating. The effect of scattering aerosols is also examined and demonstrates a decrease in precipitation over land in both the tropics and extra‐tropics and no effect over the ocean. Finally, we examine these results in a more realistic set‐up and demonstrate that, although the physical mechanisms still operate, they are not significant enough to be discerned from the model's natural‐variability. Key Points: Energy budget perspective is used to examine the latitudinal dependence of the effect of aerosol‐radiation interaction on precipitationA transition between a positive response in the tropics and a negative response in the extra‐tropics occurs at relatively low latitudesScattering and absorbing aerosol effects on precipitation over land and ocean and under more realistic conditions are examined [ABSTRACT FROM AUTHOR]

Changes in global-mean precipitation are strongly constrained by global radiative cooling, while regional rainfall changes are less constrained because energy can be transported. Absorbing and non-absorbing aerosols have different effects on both global-mean and regional precipitation, due to the distinct effects on energetics. This study analyses the precipitation responses to large perturbations in black carbon (BC) and sulphate (SUL) respectively by examining the changes in atmospheric energy budget terms on global and regional scales, in terms of fast (independent of changes in sea surface temperature (SST)) and slow responses (mediated by changes in SST). Changes in atmospheric radiative cooling/heating are further decomposed into contributions from clouds, aerosols, and clear-clean sky (without clouds or aerosols). Both cases show a decrease in global-mean precipitation, dominated by fast responses in the BC case while slow responses in the SUL case. The geographical patterns are distinct too. The intertropical convergence zone (ITCZ), accompanied with tropical rainfall, shifts northward in the BC case, while southward in the SUL case. For both cases, energy transport terms from the slow response dominates the changes in tropical rainfall, which are associated with the northward (southward) shift of Hadley cell in response to the enhanced southward (northward) cross-equatorial energy flux caused by increased BC (SUL) emission. The extra-tropical precipitation decreases in both cases. For the BC case, fast responses to increased atmospheric radiative heating contribute most to the reduced rainfall, in which absorbing aerosols directly heat the mid- troposphere, stabilise the column, and suppress precipitation. Unlike BC, non- absorbing aerosols decrease surface temperatures through slow processes, cool the whole atmospheric column, and reduce specific humidity, which leads to decreased radiative cooling from the clean-clear sky, and is consistent with the reduced rainfall. Examining the changes in large-scale circulation and local thermodynamics qualitatively explains the responses of precipitation to aerosol perturbations, whereas the energetic perspective provides a method to quantify their contributions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Understanding how drivers of climate change affect precipitation remains an important area of research. Although several robust precipitation responses have been identified under continued increases in greenhouse gases (GHGs), considerable uncertainty remains. This is particularly the case at regional scales, including the West Coast of the United States and California. Here, we exploit idealized, single forcing simulations from the Precipitation Driver Response Model Intercomparison Project (PDRMIP) to address how climate drivers impact California wintertime precipitation. Consistent with recent work, GHGs including carbon dioxide and methane, as well as solar forcing, yield a robust increase in California wintertime precipitation. We also find robust California precipitation responses to aerosols but with opposite responses for sulfate versus black carbon aerosol. Sulfate aerosol increases California wintertime precipitation, whereas black carbon reduces it. Moreover, California precipitation is more sensitive to aerosols, particularly regional emissions from Europe and Asia, than to GHGs. These precipitation responses are consistent with shifts in the jet stream and altered moisture fluxes. Although the idealized nature of PDRMIP simulations precludes a formal attribution, our results suggest that aerosols can perturb precipitation and fresh water resources along the West Coast of the United States. Key Points: In addition to GHGs, CA wintertime precipitation increases under sulfate forcing, particularly European and Asian sulfateIn contrast, black carbon aerosols decrease California precipitationCalifornia precipitation is more sensitive to aerosols than to GHGs and solar forcing [ABSTRACT FROM AUTHOR]

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 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). The July-August means are used to characterise aerosols, clouds and the radiation balance during the fire season. Results show around 68% of CCN at 0.2% supersaturation in the SEA domain can be attributed to biomass burning. The absorption effect of biomass burning aerosols is the most significant in affecting clouds and radiation. Near the continent it increases the maximum supersaturation diagnosed by the activation scheme, while further from the continent it reduces the altitude of the maximum supersaturation. As a result, the cloud droplet number concentration shows a similar pattern. The microphysical effect of biomass burning aerosols decreases the maximum supersaturation and increases the cloud droplets concentration over the ocean; however, 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 on clouds in this region. Among the effects of biomass burning aerosols on radiation balance, the semi-direct radiative effects (rapid adjustments induced by biomass burning aerosols radiative effects) have a dominant cooling impact over the SEA, which offset the warming direct radiative effect (radiative forcing from biomass burning aerosol"radiation interactions). However, the magnitude and the sign of the semi-direct effects are dependent on the relative location of biomass burning aerosols and clouds. The net biomass burning aerosols radiative effect shows a negative cooling effect in the SEA, indicating the significant role of biomass burning aerosols in affecting the regional radiation balance and climate. [ABSTRACT FROM AUTHOR]

We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two- or three-dimensional time-varying input. The application of high-level data formats, such as Iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the three-dimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. The tobac framework provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data. [ABSTRACT FROM AUTHOR]

Despite ongoing efforts, the vertical distribution of aerosols globally is poorly understood. This in turn leads to large uncertainties in the contributions of the direct and indirect aerosol forcing on climate. Using the Global Aerosol Synthesis and Science Project (GASSP) database – the largest synthesised collection of in situ aircraft measurements currently available, with more than 1000 flights from 37 campaigns from around the world – we investigate the vertical structure of submicron aerosols across a wide range of regions and environments. The application of this unique dataset to assess the vertical distributions of number size distribution and cloud condensation nuclei (CCN) in the global aerosol–climate model ECHAM-HAM reveals that the model underestimates accumulation-mode particles in the upper troposphere, especially in remote regions. The processes underlying this discrepancy are explored using different aerosol microphysical schemes and a process sensitivity analysis. These show that the biases are predominantly related to aerosol ageing and removal rather than emissions. [ABSTRACT FROM AUTHOR]

Global mean precipitation changes due to climate change were previously shown to be relatively small and well constrained by the energy budget. However, local precipitation changes can be much more significant. In this paper we propose that for large enough scales, for which the water budget is closed (precipitation [P] roughly equals evaporation [E]), changes in P approach the small global mean value. However, for smaller scales, for which P and E are not necessarily equal and convergence of water vapor still plays a role, changes in P could be much larger due to dynamical contributions. Using 40 years of two reanalysis data sets, 39 Coupled Model Intercomparison Project Phase 5 (CMIP5) models and additional numerical simulations, we identify the scale of transition in the importance of the different terms in the water budget to precipitation to be ~3,500–4,000 km and demonstrate its relation to the spatial scale of precipitation changes under climate change. Plain Language Summary: Predicting precipitation changes due to climate change is of great importance for society. We propose that the present‐day characteristic scale of the hydrological cycle (for which precipitation roughly equals evaporation) predicts the spatial scale of future precipitation changes under global warming. For smaller scales than the characteristic scale of the hydrological cycle, changes in precipitation could be much larger than the global mean change due to water vapor convergence contributions. However, above this scale the precipitation changes approach the relatively small global mean change. Using reanalysis data sets, Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and additional numerical simulations, we identify the characteristic scale of the hydrological cycle to be ~3,500–4,000 km and demonstrate its relation to the spatial scale of precipitation changes under climate change. These results suggest that changes in precipitation on the regional‐continental scale could be much larger than the global mean change. Key Points: The present‐day characteristic scale of the hydrological cycle predicts the spatial scale of precipitation changes under climate changeUsing reanalysis data sets and CMIP5 models, we identify the characteristic scale of the hydrological cycle to be ~3,500–4,000 kmThese results suggest that changes in precipitation on the continental scale could be much larger than the global mean change [ABSTRACT FROM AUTHOR]

Precipitation plays a crucial role in the Earth's energy balance, the water cycle, and the global atmospheric circulation. Aerosols, by direct interaction with radiation and by serving as cloud condensation nuclei, may affect clouds and rain formation. This effect can be examined in terms of energetic constraints, that is, any aerosol‐driven diabatic heating/cooling of the atmosphere will have to be balanced by changes in precipitation, radiative fluxes, or divergence of dry static energy. Using an aqua‐planet general circulation model (GCM), we show that tropical and extratropical precipitation have contrasting responses to aerosol perturbations. This behavior can be explained by contrasting ability of the atmosphere to diverge excess dry static energy in the two different regions. It is shown that atmospheric heating in the tropics leads to large‐scale thermally driven circulation and a large increase in precipitation, while the excess energy from heating in the extratropics is constrained due to the effect of the Coriolis force, causing the precipitation to decrease. Plain Language Summary: Precipitation, as the Earth's only natural source of fresh water, is of great importance for society. Climate change, besides changing the mean surface temperature and its distribution, is expected to change the precipitation's temporal and spatial distribution and, to a lesser extent, the global mean precipitation. One important agent in precipitation changes is anthropogenic aerosols. In this paper we study the response of precipitation to aerosol perturbations at different latitudes. Previously, it was proposed that aerosols drive a slowdown of the hydrological cycle. In addition, it was shown that, due to energy budget conservation, absorbing aerosols leads to a reduction in the global mean precipitation. Here we show that the response in the tropics is the opposite of the global mean response and of the extratropical response. Specifically, we show that the same aerosol perturbation generally increases precipitation in the tropics and decreases precipitation in the extratropics. This behavior can be explained by the contrasting ability of the atmosphere to diverge excess dry static energy in the tropics and extratropics. We also show that local aerosol perturbations could affect precipitation in remote regions due to a formation of large‐scale circulation. Key Points: Aerosol effect on precipitation is examined in terms of energetic constraintsAerosol perturbation generally increases precipitation in the tropics and decreases precipitation in the extratropicsThis behavior can be explained by contrasting ability of the atmosphere to diverge excess dry static energy in the two different regions [ABSTRACT FROM AUTHOR]

We introduce tobac (Tracking and Object-Based Analysis of Clouds), a newly developed framework for tracking and analysing individual clouds in different types of datasets, such as cloud-resolving model simulations and geostationary satellite retrievals. The software has been designed to be used flexibly with any two- or three-dimensional time-varying input. The application of high-level data formats, such as iris cubes or xarray arrays, for input and output allows for convenient use of metadata in the tracking analysis and visualisation. Comprehensive analysis routines are provided to derive properties like cloud lifetimes or statistics of cloud properties along with tools to visualise the results in a convenient way. The application of tobac is presented in two examples. We first track and analyse scattered deep convective cells based on maximum vertical velocity and the three-dimensional condensate mixing ratio field in cloud-resolving model simulations. We also investigate the performance of the tracking algorithm for different choices of time resolution of the model output. In the second application, we show how the framework can be used to effectively combine information from two different types of datasets by simultaneously tracking convective clouds in model simulations and in geostationary satellite images based on outgoing longwave radiation. tobac provides a flexible new way to include the evolution of the characteristics of individual clouds in a range of important analyses like model intercomparison studies or model assessment based on observational data. [ABSTRACT FROM AUTHOR]

We introduce and evaluate the aerosol simulations with the global aerosol-climate model ECHAM6.3-HAM2.3, which is the aerosol component of the fully coupled aerosol-chemistry-climate model ECHAM-HAMMOZ. Both the host atmospheric climate model ECHAM6.3 and the aerosol model HAM2.3 were updated from previous versions. The updated version of the HAM aerosol model contains improved parameterizations of aerosol processes such as cloud activation, as well as updated emission fields for anthropogenic aerosol species and modifications in the online computation of sea salt and mineral dust aerosol emissions. Aerosol results from nudged and free running simulations for the 10-year period 2003 to 2012 are compared to various measurements of aerosol properties. While there are regional deviations between model and observations, the model performs well overall in terms of aerosol optical thickness, but may underestimate coarse mode aerosol concentrations to some extent, so that the modeled particles are smaller than indicated by the observations. Sulfate aerosol measurements in the US and Europe are reproduced well by the model, while carbonaceous aerosol species are biased low. Both mineral dust and sea salt aerosol concentrations are improved compared to previous versions of ECHAM-HAM. The evaluation of the simulated aerosol distributions serves as a basis for the suitability of the model for simulating aerosol-climate interactions in a changing climate. [ABSTRACT FROM AUTHOR]

Abstract: The spaceborne Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument provides valuable information on the vertical distribution of global aerosol and is often used to evaluate vertical aerosol distributions in general circulation models (GCMs). Here we show, however, that the detection limit of the CALIOP retrievals mean background aerosol is not detected, leading to substantially skewed statistics that moreover differ significantly by product. In the CALIOP Level 2 product this missing low‐backscatter aerosol results in the retrieved aerosol distribution significantly overrepresenting aerosol backscatter and extinction in the middle and upper troposphere if taken to be representative of the undetected aerosol. The CALIOP Level 3 product assumes no aerosol where none is detected, which then leads to an underestimation in the aerosol extinction profile in the upper troposphere. Using the ECHAM‐HAM GCM, we estimate that the mean fraction of aerosol undetected by CALIOP daytime (nighttime) retrievals is 41% (44%) globally. [ABSTRACT FROM AUTHOR]