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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 and targeted simulations to understand climate forcing due to aerosols and fast climate feedbacks are analyzed. 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 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 Ships brighten low marine clouds from emissions of sulfur and aerosols, resulting in visible “ship tracks”. In 2020, new shipping regulations mandated an ∼80% reduction in the allowed fuel sulfur content. Recent observations indicate that visible ship tracks have decreased. Model simulations indicate that since 2020 shipping regulations have induced a net radiative forcing of +0.12 Wm−2. Analysis of recent temperature anomalies indicates Northern Hemisphere surface temperature anomalies in 2022–2023 are correlated with observed cloud radiative forcing and the cloud radiative forcing is spatially correlated with the simulated radiative forcing from the 2020 shipping emission changes. Shipping emissions changes could be accelerating global warming. To better constrain these estimates, better access to ship position data and understanding of ship aerosol emissions are needed. Understanding the risks and benefits of emissions reductions and the difficultly in robust attribution highlights the large uncertainty in attributing proposed deliberate climate intervention.

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.

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 (µg m−3 of SO2 / µg m−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.

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.

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 (CCN0.2 %), 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 CCN0.2 % in the marine boundary layer (∼ 26 %), followed by biomass burning (BB, ∼ 17 %). In the cloud layer, BB contributes about 34 % of the annual CCN0.2 %, 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 CCN0.2 % 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 CCN0.2 %, 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 RFari (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.

While carbon dioxide is the main cause for global warming, modeling short-lived climate forcers (SLCFs) such as methane, ozone, and particles in the Arctic allows us to simulate near-term climate and health impacts for a sensitive, pristine region that is warming at 3 times the global rate. Atmospheric modeling is critical for understanding the long-range transport of pollutants to the Arctic, as well as the abundance and distribution of SLCFs throughout the Arctic atmosphere. Modeling is also used as a tool to determine SLCF impacts on climate and health in the present and in future emissions scenarios. In this study, we evaluate 18 state-of-the-art atmospheric and Earth system models by assessing their representation of Arctic and Northern Hemisphere atmospheric SLCF distributions, considering a wide range of different chemical species (methane, tropospheric ozone and its precursors, black carbon, sulfate, organic aerosol, and particulate matter) and multiple observational datasets. Model simulations over 4 years (2008–2009 and 2014–2015) conducted for the 2022 Arctic Monitoring and Assessment Programme (AMAP) SLCF assessment report are thoroughly evaluated against satellite, ground, ship, and aircraft-based observations. The annual means, seasonal cycles, and 3-D distributions of SLCFs were evaluated using several metrics, such as absolute and percent model biases and correlation coefficients. The results show a large range in model performance, with no one particular model or model type performing well for all regions and all SLCF species. The multi-model mean (mmm) was able to represent the general features of SLCFs in the Arctic and had the best overall performance. For the SLCFs with the greatest radiative impact (CH4, O3, BC, and SO42-), the mmm was within ±25 % of the measurements across the Northern Hemisphere. Therefore, we recommend a multi-model ensemble be used for simulating climate and health impacts of SLCFs. Of the SLCFs in our study, model biases were smallest for CH4 and greatest for OA. For most SLCFs, model biases skewed from positive to negative with increasing latitude. Our analysis suggests that vertical mixing, long-range transport, deposition, and wildfires remain highly uncertain processes. These processes need better representation within atmospheric models to improve their simulation of SLCFs in the Arctic environment. As model development proceeds in these areas, we highly recommend that the vertical and 3-D distribution of SLCFs be evaluated, as that information is critical to improving the uncertain processes in models.

Aerosol–cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well-defined sources provide “opportunistic experiments” (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change.

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.

Aerosol-induced absorption of shortwave radiation can modify the climate through local atmospheric heating, which affects lapse rates, precipitation, and cloud formation. Presently, the total amount of aerosol absorption is poorly constrained, and the main absorbing aerosol species (black carbon (BC), organic aerosols (OA), and mineral dust) are diversely quantified in global climate models. As part of the third phase of the Aerosol Comparisons between Observations and Models (AeroCom) intercomparison initiative (AeroCom phase III), we here document the distribution and magnitude of aerosol absorption in current global aerosol models and quantify the sources of intermodel spread, highlighting the difficulties of attributing absorption to different species. In total, 15 models have provided total present-day absorption at 550 nm (using year 2010 emissions), 11 of which have provided absorption per absorbing species. The multi-model global annual mean total absorption aerosol optical depth (AAOD) is 0.0054 (0.0020 to 0.0098; 550 nm), with the range given as the minimum and maximum model values. This is 28 % higher compared to the 0.0042 (0.0021 to 0.0076) multi-model mean in AeroCom phase II (using year 2000 emissions), but the difference is within 1 standard deviation, which, in this study, is 0.0023 (0.0019 in Phase II). Of the summed component AAOD, 60 % (range 36 %–84 %) is estimated to be due to BC, 31 % (12 %–49 %) is due to dust, and 11 % (0 %–24 %) is due to OA; however, the components are not independent in terms of their absorbing efficiency. In models with internal mixtures of absorbing aerosols, a major challenge is the lack of a common and simple method to attribute absorption to the different absorbing species. Therefore, when possible, the models with internally mixed aerosols in the present study have performed simulations using the same method for estimating absorption due to BC, OA, and dust, namely by removing it and comparing runs with and without the absorbing species. We discuss the challenges of attributing absorption to different species; we compare burden, refractive indices, and density; and we contrast models with internal mixing to models with external mixing. The model mean BC mass absorption coefficient (MAC) value is 10.1 (3.1 to 17.7) m2 g−1 (550 nm), and the model mean BC AAOD is 0.0030 (0.0007 to 0.0077). The difference in lifetime (and burden) in the models explains as much of the BC AAOD spread as the difference in BC MAC values. The difference in the spectral dependency between the models is striking. Several models have an absorption Ångstrøm exponent (AAE) close to 1, which likely is too low given current knowledge of spectral aerosol optical properties. Most models do not account for brown carbon and underestimate the spectral dependency for OA.

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.

The representations of clouds, aerosols, and cloud–aerosol–radiation impacts remain some of the largest uncertainties in climate change, limiting our ability to accurately reconstruct past climate and predict future climate. The south-east Atlantic is a region where high atmospheric aerosol loadings and semi-permanent stratocumulus clouds are co-located, providing an optimum region for studying the full range of aerosol–radiation and aerosol–cloud interactions and their perturbations of the Earth's radiation budget. While satellite measurements have provided some useful insights into aerosol–radiation and aerosol–cloud interactions over the region, these observations do not have the spatial and temporal resolution, nor the required level of precision to allow for a process-level assessment. Detailed measurements from high spatial and temporal resolution airborne atmospheric measurements in the region are very sparse, limiting their use in assessing the performance of aerosol modelling in numerical weather prediction and climate models. CLARIFY-2017 was a major consortium programme consisting of five principal UK universities with project partners from the UK Met Office and European- and USA-based universities and research centres involved in the complementary ORACLES, LASIC, and AEROCLO-sA projects. The aims of CLARIFY-2017 were fourfold: (1) to improve the representation and reduce uncertainty in model estimates of the direct, semi-direct, and indirect radiative effect of absorbing biomass burning aerosols; (2) to improve our knowledge and representation of the processes determining stratocumulus cloud microphysical and radiative properties and their transition to cumulus regimes; (3) to challenge, validate, and improve satellite retrievals of cloud and aerosol properties and their radiative impacts; (4) to improve the impacts of aerosols in weather and climate numerical models. This paper describes the modelling and measurement strategies central to the CLARIFY-2017 deployment of the FAAM BAe146 instrumented aircraft campaign, summarizes the flight objectives and flight patterns, and highlights some key results from our initial analyses.

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 The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3‐HAM2.3 global atmosphere‐aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON‐A (icon‐aes‐1.3.00) global atmosphere model to yield the new ICON‐A‐HAM2.3 atmosphere‐aerosol model. The ICON‐A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub‐grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6‐HAM2.3 two‐moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON‐A‐HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON‐A‐HAM2.3 and ECHAM6.3‐HAM2.3, but the bias is larger in ICON‐A‐HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub‐tropical oceans. ICON‐A‐HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON‐A‐HAM2.3 compared to standard ICON‐A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON‐A‐HAM2.3 and in ECHAM6.3‐HAM2.3. In the extra‐tropics, a smaller multivariable bias is found for ICON‐A‐HAM2.3 than for ECHAM6.3‐HAM2.3.

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.

Water vapour in the atmosphere is the source of a major climate feedback mechanism and potential increases in the availability of water vapour could have important consequences for mean and extreme precipitation. Future precipitation changes further depend on how the hydrological cycle responds to different drivers of climate change, such as greenhouse gases and aerosols. Currently, neither the total anthropogenic influence on the hydrological cycle nor that from individual drivers is constrained sufficiently to make solid projections. We investigate how integrated water vapour (IWV) responds to different drivers of climate change. Results from 11 global climate models have been used, based on simulations where CO2, methane, solar irradiance, black carbon (BC), and sulfate have been perturbed separately. While the global-mean IWV is usually assumed to increase by ∼7 % per kelvin of surface temperature change, we find that the feedback response of IWV differs somewhat between drivers. Fast responses, which include the initial radiative effect and rapid adjustments to an external forcing, amplify these differences. The resulting net changes in IWV range from 6.4±0.9 % K−1 for sulfate to 9.8±2 % K−1 for BC. We further calculate the relationship between global changes in IWV and precipitation, which can be characterized by quantifying changes in atmospheric water vapour lifetime. Global climate models simulate a substantial increase in the lifetime, from 8.2±0.5 to 9.9±0.7 d between 1986–2005 and 2081–2100 under a high-emission scenario, and we discuss to what extent the water vapour lifetime provides additional information compared to analysis of IWV and precipitation separately. We conclude that water vapour lifetime changes are an important indicator of changes in precipitation patterns and that BC is particularly efficient in prolonging the mean time, and therefore likely the distance, between evaporation and precipitation.

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.

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

We introduce and evaluate 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 the 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.

The Community Intercomparison Suite (CIS) is an easy-to-use command-line tool which has been developed to allow the straightforward intercomparison of remote sensing, in situ and model data. While there are a number of tools available for working with climate model data, the large diversity of sources (and formats) of remote sensing and in situ measurements necessitated a novel software solution. Developed by a professional software company, CIS supports a large number of gridded and ungridded data sources "out-of-the-box", including climate model output in NetCDF or the UK Met Office pp file format, CloudSat, CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization), MODIS (MODerate resolution Imaging Spectroradiometer), Cloud and Aerosol CCI (Climate Change Initiative) level 2 satellite data and a number of in situ aircraft and ground station data sets. The open-source architecture also supports user-defined plugins to allow many other sources to be easily added. Many of the key operations required when comparing heterogenous data sets are provided by CIS, including subsetting, aggregating, collocating and plotting the data. Output data are written to CF-compliant NetCDF files to ensure interoperability with other tools and systems. The latest documentation, including a user manual and installation instructions, can be found on our website (http://cistools.net). Here, we describe the need which this tool fulfils, followed by descriptions of its main functionality (as at version 1.4.0) and plugin architecture which make it unique in the field.

The ability of anthropogenic aerosols to freeze supercooled cloud droplets remains debated. In this work, we present observational evidence for the glaciation of supercooled liquid-water clouds at industrial aerosol hot spots at temperatures between -10° and -24°C. Compared with the nearby liquid-water clouds, shortwave reflectance was reduced by 14% and longwave radiance was increased by 4% in the glaciation-affected regions. There was an 8% reduction in cloud cover and an 18% reduction in cloud optical thickness. Additionally, daily glaciation-induced snowfall accumulations reached 15 millimeters. Glaciation events downwind of industrial aerosol hot spots indicate that anthropogenic aerosols likely serve as ice-nucleating particles. However, rare glaciation events downwind of nuclear power plants indicate that factors other than aerosol emissions may also play a role in the observed glaciation events.

Global shipping accounts for 13% of global emissions of SO 2 , 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 SO[Formula: see text] emissions causes only a 25% reduction in the number of tracks detected.

Cloud reflectivity is sensitive to atmospheric aerosol concentrations because aerosols provide the condensation nuclei on which water condenses 1 . Increased aerosol concentrations due to human activity affect droplet number concentration, liquid water and cloud fraction 2 , but these changes are subject to large uncertainties 3 . Ship tracks, long lines of polluted clouds that are visible in satellite images, are one of the main tools for quantifying aerosol-cloud interactions 4 . However, only a small fraction of the clouds polluted by shipping show ship tracks 5,6 . Here we show that even when no ship tracks are visible in satellite images, aerosol emissions change cloud properties substantially. We develop a new method to quantify the effect of shipping on all clouds, showing a cloud droplet number increase and a more positive liquid water response when there are no visible tracks. We directly detect shipping-induced cloud property changes in the trade cumulus regions of the Atlantic, which are known to display almost no visible tracks. Our results indicate that previous studies of ship tracks were suffering from selection biases by focusing only on visible tracks from satellite imagery. The strong liquid water path response we find translates to a larger aerosol cooling effect on the climate, potentially masking a higher climate sensitivity than observed temperature trends would otherwise suggest., (© 2022. The Author(s).)

The Hamburg Aerosol Module version 2.3 (HAM2.3) from the ECHAM6.3-HAM2.3 global atmosphere-aerosol model is coupled to the recently developed icosahedral nonhydrostatic ICON-A (icon-aes-1.3.00) global atmosphere model to yield the new ICON-A-HAM2.3 atmosphere-aerosol model. The ICON-A and ECHAM6.3 host models use different dynamical cores, parameterizations of vertical mixing due to sub-grid scale turbulence, and parameter settings for radiation balance tuning. Here, we study the role of the different host models for simulated aerosol optical thickness (AOT) and evaluate impacts of using HAM2.3 and the ECHAM6-HAM2.3 two-moment cloud microphysics scheme on several meteorological variables. Sensitivity runs show that a positive AOT bias over the subtropical oceans is remedied in ICON-A-HAM2.3 because of a different default setting of a parameter in the moist convection parameterization of the host models. The global mean AOT is biased low compared to MODIS satellite instrument retrievals in ICON-A-HAM2.3 and ECHAM6.3-HAM2.3, but the bias is larger in ICON-A-HAM2.3 because negative AOT biases over the Amazon, the African rain forest, and the northern Indian Ocean are no longer compensated by high biases over the sub-tropical oceans. ICON-A-HAM2.3 shows a moderate improvement with respect to AOT observations at AERONET sites. A multivariable bias score combining biases of several meteorological variables into a single number is larger in ICON-A-HAM2.3 compared to standard ICON-A and standard ECHAM6.3. In the tropics, this multivariable bias is of similar magnitude in ICON-A-HAM2.3 and in ECHAM6.3-HAM2.3. In the extra-tropics, a smaller multivariable bias is found for ICON-A-HAM2.3 than for ECHAM6.3-HAM2.3., (© 2022 The Authors. Journal of Advances in Modeling Earth Systems published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

This data descriptor reports the main scientific values from General Circulation Models (GCMs) in the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). The purpose of the GCM simulations has been to enhance the scientific understanding of how changes in greenhouse gases, aerosols, and incoming solar radiation perturb the Earth's radiation balance and its climate response in terms of changes in temperature and precipitation. Here we provide global and annual mean results for a large set of coupled atmospheric-ocean GCM simulations and a description of how to easily extract files from the dataset. The simulations consist of single idealized perturbations to the climate system and have been shown to achieve important insight in complex climate simulations. We therefore expect this data set to be valuable and highly used to understand simulations from complex GCMs and Earth System Models for various phases of the Coupled Model Intercomparison Project., (© 2022. The Author(s).)

Aerosol-cloud interactions (ACIs) are considered to be the most uncertain driver of present-day radiative forcing due to human activities. The nonlinearity of cloud-state changes to aerosol perturbations make it challenging to attribute causality in observed relationships of aerosol radiative forcing. Using correlations to infer causality can be challenging when meteorological variability also drives both aerosol and cloud changes independently. Natural and anthropogenic aerosol perturbations from well-defined sources provide "opportunistic experiments" (also known as natural experiments) to investigate ACI in cases where causality may be more confidently inferred. These perturbations cover a wide range of locations and spatiotemporal scales, including point sources such as volcanic eruptions or industrial sources, plumes from biomass burning or forest fires, and tracks from individual ships or shipping corridors. We review the different experimental conditions and conduct a synthesis of the available satellite datasets and field campaigns to place these opportunistic experiments on a common footing, facilitating new insights and a clearer understanding of key uncertainties in aerosol radiative forcing. Cloud albedo perturbations are strongly sensitive to background meteorological conditions. Strong liquid water path increases due to aerosol perturbations are largely ruled out by averaging across experiments. Opportunistic experiments have significantly improved process-level understanding of ACI, but it remains unclear how reliably the relationships found can be scaled to the global level, thus demonstrating a need for deeper investigation in order to improve assessments of aerosol radiative forcing and climate change., Competing Interests: Competing interests. At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

Modern weather and climate models share a common heritage and often even components; however, they are used in different ways to answer fundamentally different questions. As such, attempts to emulate them using machine learning should reflect this. While the use of machine learning to emulate weather forecast models is a relatively new endeavour, there is a rich history of climate model emulation. This is primarily because while weather modelling is an initial condition problem, which intimately depends on the current state of the atmosphere, climate modelling is predominantly a boundary condition problem. To emulate the response of the climate to different drivers therefore, representation of the full dynamical evolution of the atmosphere is neither necessary, or in many cases, desirable. Climate scientists are typically interested in different questions also. Indeed emulating the steady-state climate response has been possible for many years and provides significant speed increases that allow solving inverse problems for e.g. parameter estimation. Nevertheless, the large datasets, non-linear relationships and limited training data make climate a domain which is rich in interesting machine learning challenges. Here, I seek to set out the current state of climate model emulation and demonstrate how, despite some challenges, recent advances in machine learning provide new opportunities for creating useful statistical models of the climate. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

The North Atlantic warming hole (NAWH) is referred to as a reduced warming, or even cooling, of the North Atlantic during an anthropogenic-driven global warming. A NAWH is predicted by climate models during the 21st century, and its pattern is already emerging in observations. Despite the known key role of the North Atlantic surface temperatures in setting the Northern Hemisphere climate, the mechanisms behind the NAWH are still not fully understood. Using state-of-the-art climate models, we show that anthropogenic aerosol forcing opposes the formation of the NAWH (by leading to a local warming) and delays its emergence by about 30 years. In agreement with previous studies, we also demonstrate that the relative warming of the North Atlantic under aerosol forcing is due to changes in ocean heat fluxes, rather than air-sea fluxes. These results suggest that the predicted reduction in aerosol forcing during the 21st century may accelerate the formation of the NAWH., (©2020. The Authors.)

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., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Aerosols interact with radiation and clouds. Substantial progress made over the past 40 years in observing, understanding, and modeling these processes helped quantify the imbalance in the Earth's radiation budget caused by anthropogenic aerosols, called aerosol radiative forcing, but uncertainties remain large. This review provides a new range of aerosol radiative forcing over the industrial era based on multiple, traceable, and arguable lines of evidence, including modeling approaches, theoretical considerations, and observations. Improved understanding of aerosol absorption and the causes of trends in surface radiative fluxes constrain the forcing from aerosol-radiation interactions. A robust theoretical foundation and convincing evidence constrain the forcing caused by aerosol-driven increases in liquid cloud droplet number concentration. However, the influence of anthropogenic aerosols on cloud liquid water content and cloud fraction is less clear, and the influence on mixed-phase and ice clouds remains poorly constrained. Observed changes in surface temperature and radiative fluxes provide additional constraints. These multiple lines of evidence lead to a 68% confidence interval for the total aerosol effective radiative forcing of -1.6 to -0.6 W m -2 , or -2.0 to -0.4 W m -2 with a 90% likelihood. Those intervals are of similar width to the last Intergovernmental Panel on Climate Change assessment but shifted toward more negative values. The uncertainty will narrow in the future by continuing to critically combine multiple lines of evidence, especially those addressing industrial-era changes in aerosol sources and aerosol effects on liquid cloud amount and on ice clouds., (©2019. The Authors.)

Quantifying the efficacy of different climate forcings is important for understanding the real-world climate sensitivity. This study presents a systematic multimodel analysis of different climate driver efficacies using simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). Efficacies calculated from instantaneous radiative forcing deviate considerably from unity across forcing agents and models. Effective radiative forcing (ERF) is a better predictor of global mean near-surface air temperature (GSAT) change. Efficacies are closest to one when ERF is computed using fixed sea surface temperature experiments and adjusted for land surface temperature changes using radiative kernels. Multimodel mean efficacies based on ERF are close to one for global perturbations of methane, sulfate, black carbon, and insolation, but there is notable intermodel spread. We do not find robust evidence that the geographic location of sulfate aerosol affects its efficacy. GSAT is found to respond more slowly to aerosol forcing than CO 2 in the early stages of simulations. Despite these differences, we find that there is no evidence for an efficacy effect on historical GSAT trend estimates based on simulations with an impulse response model, nor on the resulting estimates of climate sensitivity derived from the historical period. However, the considerable intermodel spread in the computed efficacies means that we cannot rule out an efficacy-induced bias of ±0.4 K in equilibrium climate sensitivity to CO 2 doubling when estimated using the historical GSAT trend., (©2019. The Authors.)

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., (©2019. The Authors.)

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., (©2019. The Authors.)

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50 and > 120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N 3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN 0.2 ) compared to that for N 3 , maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB -13% and -22% for updraft velocities 0.3 and 0.6 ms -1 , respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration ( ∂N d / ∂N a ) and to updraft velocity ( ∂N d / ∂w ). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂N d / ∂N a and ∂N d /∂ w ; models may be predisposed to be too "aerosol sensitive" or "aerosol insensitive" in aerosol-cloud-climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect., Competing Interests: Competing interests. The authors declare that they have no conflict of interest.

Rapid adjustments are responses to forcing agents that cause a perturbation to the top of atmosphere energy budget but are uncoupled to changes in surface warming. Different mechanisms are responsible for these adjustments for a variety of climate drivers. These remain to be quantified in detail. It is shown that rapid adjustments reduce the effective radiative forcing (ERF) of black carbon by half of the instantaneous forcing, but for CO 2 forcing, rapid adjustments increase ERF. Competing tropospheric adjustments for CO 2 forcing are individually significant but sum to zero, such that the ERF equals the stratospherically adjusted radiative forcing, but this is not true for other forcing agents. Additional experiments of increase in the solar constant and increase in CH 4 are used to show that a key factor of the rapid adjustment for an individual climate driver is changes in temperature in the upper troposphere and lower stratosphere.

Different climate drivers influence precipitation in different ways. Here we use radiative kernels to understand the influence of rapid adjustment processes on precipitation in climate models. Rapid adjustments are generally triggered by the initial heating or cooling of the atmosphere from an external climate driver. For precipitation changes, rapid adjustments due to changes in temperature, water vapor, and clouds are most important. In this study we have investigated five climate drivers (CO 2 , CH 4 , solar irradiance, black carbon, and sulfate aerosols). The fast precipitation responses to a doubling of CO 2 and a 10-fold increase in black carbon are found to be similar, despite very different instantaneous changes in the radiative cooling, individual rapid adjustments, and sensible heating. The model diversity in rapid adjustments is smaller for the experiment involving an increase in the solar irradiance compared to the other climate driver perturbations, and this is also seen in the precipitation changes.