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Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. "Cloud controlling factor" (CCF) analysis derives relationships between large-scale meteorological drivers and cloud radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear (ΔU300). We identify an optimal configuration for predicting high-cloud radiative anomalies that includes SUT and ΔU300 and show that spatial domain size is more important than the selection of CCFs for predictive skill. We also find an important discrepancy between the optimal domain sizes required for predicting locally and globally aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks and deduce that the inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback. [ABSTRACT FROM AUTHOR]

There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous 2 decades that attempt to address this need but most have limitations on the type of data they can be used with, feature computational and/or memory expenses that make them unwieldy with larger datasets, or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed. [ABSTRACT FROM AUTHOR]

We assessed the performance of 11 AeroCom models in simulating biomass burning (BB) smoke aerosol optical depth (AOD) in the vicinity of fires over 13 regions globally. By comparing multi-model outputs and satellite observations, we aim to: (1) assess the factors affecting model-simulated, BB AOD performance using a common emissions inventory, (2) identify regions where the emission inventory might underestimate or overestimate smoke sources, and (3) identify anomalies that might point to model-specific smoke emission, dispersion, or removal, issues. Using satellite-derived AOD snapshots to constrain source strength works best where BB smoke from active sources dominates background aerosol, such as in boreal forest regions and over South America and southern-hemisphere Africa. The comparison is poor where 40 the total AOD is low, as in many agricultural burning areas or where background, non-BB AOD is high, such as parts of India and China. Many inter-model BB AOD differences can be traced to differences in model-assumed values for the mass ratio of organic aerosol to organic carbon, the BB aerosol mass extinction efficiency, and the aerosol loss-rate. The results point to the need for increased numbers of available BB cases for study in some regions, and especially to the need for more extensive, regional45 to-global-scale measurements of aerosol loss rates and of detailed microphysical and optical properties; this would better constrain models and help distinguish BB from other aerosols in satellite retrievals. More generally, there is the need for additional efforts at constraining aerosol source strength and other model attributes with multi-platform observations. [ABSTRACT FROM AUTHOR]

The assessment of aerosol-cloud interactions remains a major source of uncertainty in understanding climate change, partly due to the difficulty in making accurate observations of aerosol impacts on clouds. Ships can release large numbers of aerosols that serve as cloud condensation nuclei, which can create artificially brightened clouds known as ship tracks. These aerosol emissions offer a “natural”, or “opportunistic”, experiment to explore aerosol effects on clouds while disentangling meteorological influences. Utilising ship positions and reanalysis winds, we predict ship track locations, collocating them with satellite data to depict the temporal evolution of cloud properties after an aerosol perturbation. Repeating our analysis for a null experiment does not necessarily recover zero signal as expected, but instead reveals subtleties between different null experiment methodologies. This study uncovers a systematic bias in prior ship track research, due to the assumption that background gradients will, on average, be linear. We correct for this bias, which is linked to the correlation between wind fields and cloud properties, to reveal the true ship track response. We find that the liquid water path (LWP) response after an aerosol pertubation is weak on average, once this bias is corrected for. This has important implications for estimates of radiative forcings due to LWP adjustments, as previous responses in unstable cases were overestimated. A noticeable LWP response is only recovered in specific cases, such as marine stratocumulus clouds, where a positive LWP response is found in precipitating or clean clouds. This work highlights subtleties in the analysis of isolated opportunistic experiments, reconciling differences in the LWP response to aerosols reported in previous studies. [ABSTRACT FROM AUTHOR]

The anvil clouds of tropical deep convection have large radiative effects in both the shortwave (SW) and longwave (LW) spectra with the average magnitudes of both over 100 Wm-2. Despite this, due to the opposite sign of these fluxes, the net average of the anvil cloud radiative effect (CRE) over the tropics is observed to be neutral. Research into the response of the anvil CRE to climate change has primarily focused on the feedbacks of anvil cloud height and anvil cloud area, in particular regarding the LW feedback. However, tropical deep convection over land has a strong diurnal cycle which may couple with the shortwave component of the anvil cloud radiative effect. As this diurnal cycle is poorly represented in climate models it is vital to gain a better understanding of how its changes impact the anvil CRE. To study the connection between the deep convective cloud (DCC) lifecycle and CRE, we investigate the behaviour of both isolated and organised DCCs in a 4-month case study over sub-Saharan Africa (May–August 2016). Using a novel cloud tracking algorithm, we detect and track growing convective cores and their associated anvil clouds using geostationary satellite observations from the Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI). Retrieved cloud properties and derived broadband radiative fluxes are provided by the Community Cloud retrieval for CLimate (CC4CL) algorithm. By collecting the cloud properties of the tracked DCCs, we produce a dataset of anvil cloud properties along their lifetimes. While the majority of DCCs tracked in this dataset are isolated, with only a single core, the overall coverage of anvil clouds is dominated by those of clustered, multi-core anvils due to their larger areas and lifetimes. We find that the anvil cloud CRE of our tracked DCCs has a bimodal distribution. The interaction between the lifecycles of DCCs and the diurnal cycle of insolation results in a wide range of the SW anvil CRE, while the LW component remains in a comparatively narrow range of values. The CRE of individual anvil clouds varies widely, with isolated DCCs tending to have large negative or positive CREs, while larger, organised systems tend to have a CRE closer to 0. Despite this, we find that the net anvil cloud CRE across all tracked DCCs is close to neutral (- 0.94 ± 0.91 Wm-2). Changes in the lifecycle of DCCs, such as shifts in the time of triggering, or the length of the dissipating phase, could have large impacts on the SW anvil CRE and lead to complex responses that are not considered by theories of LW anvil CRE feedbacks. [ABSTRACT FROM AUTHOR]

Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. "Cloud Controlling Factor" (CCF) analysis derives relationships between large-scale meteorological drivers and cloud-radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear. All combinations of tested CCFs predict historical, monthly variability well for most locations at grid-cell scales. Differences between configurations for predicting globally-aggregated radiative anomalies are more pronounced, where configurations including SUT outperform others. We show that for predicting local, historical anomalies, spatial domain size is more important than the selection of CCFs, finding an important discrepancy between optimal domain sizes for local and globally-aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks, and thus deduce that inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback. [ABSTRACT FROM AUTHOR]

Clouds strongly modulate the top-of-the-atmosphere energy budget and are a major source of uncertainty in climate projections. “Cloud Controlling Factor” (CCF) analysis derives relationships between large-scale meteorological drivers and cloud-radiative anomalies, which can be used to constrain cloud feedback. However, the choice of meteorological CCFs is crucial for a meaningful constraint. While there is rich literature investigating ideal CCF setups for low-level clouds, there is a lack of analogous research explicitly targeting high clouds. Here, we use ridge regression to systematically evaluate the addition of five candidate CCFs to previously established core CCFs within large spatial domains to predict longwave high-cloud radiative anomalies: upper-tropospheric static stability (SUT), sub-cloud moist static energy, convective available potential energy, convective inhibition, and upper-tropospheric wind shear. All combinations of tested CCFs predict historical, monthly variability well for most locations at grid-cell scales. Differences between configurations for predicting globally-aggregated radiative anomalies are more pronounced, where configurations including SUT outperform others. We show that for predicting local, historical anomalies, spatial domain size is more important than the selection of CCFs, finding an important discrepancy between optimal domain sizes for local and globally-aggregated radiative anomalies. Finally, we scientifically interpret the ridge regression coefficients, where we show that SUT captures physical drivers of known high-cloud feedbacks, and thus deduce that inclusion of SUT into observational constraint frameworks may reduce uncertainty associated with changes in anvil cloud amount as a function of climate change. Therefore, we highlight SUT as an important CCF for high clouds and longwave cloud feedback. [ABSTRACT FROM AUTHOR]

Determining number concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, and a process key for aerosol–cloud interactions (ACI). However, due to sparse coverage of in situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source as well as temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN numbers is one of the goals for quantifying ACI processes and achieving uncertainty-reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMSRA are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatiotemporal quantification of CCN numbers. The derived CCN dataset captures the general trend and spatial and temporal distribution of total CCN number concentrations and CCN from different aerosol species. A brief evaluation with ground-based in situ measurements demonstrates the improvement of the modelled CCN over the sole use of AOD as a proxy for CCN as the overall correlation coefficient improved from 0.37 to 0.71. However, we find the modelled CCN from CAMSRA to be generally high biased and find a particular erroneous overestimation at one heavily polluted site which emphasises the need for further validation. The CCN dataset (10.26050/WDCC/QUAERERE_CCNCAMS_v1,), which is now freely available to users, features 3-D CCN number concentrations of global coverage for various supersaturations and aerosol species covering the years 2003–2021 with daily frequency. This dataset is one of its kind as it offers lots of opportunities to be used for evaluation in models and in ACI studies. [ABSTRACT FROM AUTHOR]

The anvil clouds of tropical deep convection have large radiative effects in both the shortwave (SW) and longwave (LW) spectra with the average magnitudes of both over 100 Wm-2. Despite this, due to the opposite sign of these fluxes, the net average of anvil cloud radiative effect (CRE) over the tropics has been found to be neutral. Research into the response of anvil CRE to climate change has primarily focused on the feedbacks of anvil cloud height and anvil cloud area, in particular regarding the LW feedback. However, tropical deep convection over land has a strong diurnal cycle which may couple with the shortwave component of anvil cloud radiative effect. As this diurnal cycle is poorly represented in climate models it is vital to gain a better understanding of how its changes impact anvil CRE. To study the connection between deep convective cloud (DCC) lifecycle and CRE, we investigate the behaviour of both isolated and organised DCCs in a 4-month case study over sub-Saharan Africa (May–August 2016). Using a novel cloud tracking algorithm, we detect and track growing convective cores and their associated anvil clouds using geostationary satellite observations from Meteosat SEVIRI. Retrieved cloud properties and derived broadband radiative fluxes are provided by the CC4CL algorithm. By collecting the cloud properties of the tracked DCCs, we produce a dataset of anvil cloud properties along their lifetimes. While the majority of DCCs tracked in this dataset are isolated, with only a single core, the overall coverage of anvil clouds is dominated by those of clustered, multi-core anvils due to their larger areas and lifetimes. We find that the distribution of anvil cloud CRE of our tracked DCCs has a bimodal distribution. The interaction between the lifecycles of DCCs and the diurnal cycle of insolation results in a wide range of SW anvil CRE, while the LW component remains in a comparatively narrow range of values. The CRE of individual anvil clouds varies widely, with isolated DCCs tending to have large negative or positive CREs while larger, organised systems tend to have CRE closer to zero. Despite this, we find that the net anvil cloud CRE across all tracked DCCs is indeed neutral within our range of uncertainty (0.86 ± 0.91 Wm-2). Changes in the lifecycle of DCCs, such as shifts in the time of triggering, or the length of the dissipating phase, could have large impacts on the SW anvil CRE and lead to complex responses that are not considered by theories of LW anvil CRE feedbacks. [ABSTRACT FROM AUTHOR]

The anvil clouds of tropical deep convection have large radiative effects in both the shortwave (SW) and longwave (LW) spectra with the average magnitudes of both over 100Wm-2. Despite this, due to the opposite sign of these fluxes, the net average of anvil cloud radiative effect (CRE) over the tropics has been found to be neutral. Research into the response of anvil CRE to climate change has primarily focused on the feedbacks of anvil cloud height and anvil cloud area, in particular regarding the LWfeedback. However, tropical deep convection over land has a strong diurnal cycle which may couple with the shortwave component of anvil cloud radiative effect. As this diurnal cycle is poorly represented in climate models it is vital to gain a better understanding of how its changes impact anvil CRE. To study the connection between deep convective cloud (DCC) lifecycle and CRE, we investigate the behaviour of both isolated and organised DCCs in a 4-month case study over sub-Saharan Africa (May-August 2016). Using a novel cloud tracking algorithm, we detect and track growing convective cores and their associated anvil clouds using geostationary satellite observations from Meteosat SEVIRI. Retrieved cloud properties and derived broadband radiative fluxes are provided by the CC4CL algorithm. By collecting the cloud properties of the tracked DCCs, we produce a dataset of anvil cloud properties along their lifetimes. While the majority of DCCs tracked in this dataset are isolated, with only a single core, the overall coverage of anvil clouds is dominated by those of clustered, multi-core anvils due to their larger areas and lifetimes. We find that the distribution of anvil cloud CRE of our tracked DCCs has a bimodal distribution. The interaction between the lifecycles of DCCs and the diurnal cycle of insolation results in a wide range of SW anvil CRE, while the LW component remains in a comparatively narrow range of values. The CRE of individual anvil clouds varies widely, with isolated DCCs tending to have large negative or positive CREs while larger, organised systems tend to have CRE closer to zero. Despite this, we find that the net anvil cloud CRE across all tracked DCCs is indeed neutral within our range of uncertainty (0.86 ± 0.91Wm-2). Changes in the lifecycle of DCCs, such as shifts in the time of triggering, or the length of the dissipating phase, could have large impacts on the SW anvil CRE and lead to complex responses that are not considered by theories of LW anvil CRE feedbacks. [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]

There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous two decades that attempt to address this need, but most have limitations on the type of data they can be used with; computational and/or memory expenses that make them unwieldy with larger datasets; or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed. [ABSTRACT FROM AUTHOR]

There is a continuously increasing need for reliable feature detection and tracking tools based on objective analysis principles for use with meteorological data. Many tools have been developed over the previous two decades that attempt to address this need, but most have limitations on the type of data they can be used with; computational and/or memory expenses that make them unwieldy with larger datasets; or require some form of data reduction prior to use that limits the tool's utility. The Tracking and Object-Based Analysis of Clouds (tobac) Python package is a modular, open-source tool that improves on the overall generality and utility of past tools. A number of scientific improvements (three spatial dimensions, splits and mergers of features, an internal spectral filtering tool) and procedural enhancements (increased computational efficiency, internal regridding of data, and treatments for periodic boundary conditions) have been included in tobac as a part of the tobac v1.5 update. These improvements have made tobac one of the most robust, powerful, and flexible identification and tracking tools in our field to date and expand its potential use in other fields. Future plans for tobac v2 are also discussed. [ABSTRACT FROM AUTHOR]

The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in clouds constitute the dominant source of uncertainty in the global warming response to carbon dioxide forcing. To characterize and constrain this uncertainty, cloud-controlling factor (CCF) analyses have been suggested that estimate sensitivities of clouds to large-scale environmental changes, typically in cloud-regime-specific multiple linear regression frameworks. Here, local sensitivities of cloud radiative effects to a large number of controlling factors are estimated in a regime-independent framework from 20 years (2001–2020) of near-global (60 ∘ N–60 ∘ S) satellite observations and reanalysis data using statistical learning. A regularized linear regression (ridge regression) is shown to skillfully predict anomalies of shortwave (R2=0.63) and longwave cloud radiative effects (CREs) (R2=0.72) in independent test data on the basis of 28 CCFs, including aerosol proxies. The sensitivity of CREs to selected CCFs is quantified and analyzed. CRE sensitivities to sea surface temperature and estimated inversion strength are particularly pronounced in low-cloud regions and generally in agreement with previous studies. The analysis of CRE sensitivities to three-dimensional wind field anomalies reflects the fact that CREs in tropical ascent regions are mainly driven by variability of large-scale vertical velocity in the upper troposphere. In the subtropics, CRE is sensitive to free-tropospheric zonal and meridional wind anomalies, which are likely to encapsulate information on synoptic variability that influences subtropical cloud systems by modifying wind shear and thus turbulence and dry-air entrainment in stratocumulus clouds, as well as variability related to midlatitude cyclones. Different proxies for aerosols are analyzed as CCFs, with satellite-derived aerosol proxies showing a larger CRE sensitivity than a proxy from an aerosol reanalysis, likely pointing to satellite aerosol retrieval biases close to clouds, leading to overestimated aerosol sensitivities. Sensitivities of shortwave CREs to all aerosol proxies indicate a pronounced cooling effect from aerosols in stratocumulus regions that is counteracted to a varying degree by a longwave warming effect. The analysis may guide the selection of CCFs in future sensitivity analyses aimed at constraining cloud feedback and climate forcings from aerosol–cloud interactions using data from both observations and global climate models. [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]

Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill in terms of making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample the uncertainty in 37 model parameters related to aerosols, clouds, and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian process emulators) against satellite-derived observations over several cloudy regions. Our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations, of an order that may be evaluated during model-tuning efforts. Incorporating observations associated with these inconsistencies weakens any forcing constraint because they require a wider range of parameter values to accommodate conflicting information. We show that, by neglecting variables associated with these inconsistencies, it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range (around - 1.3 to - 0.1 Wm-2) in close agreement with energy balance constraints. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then very likely narrow the uncertainty further and possibly alter the central estimate. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach. [ABSTRACT FROM AUTHOR]

Determining concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, a process key for aerosol-cloud interactions (ACI). However, due to sparse coverage of in-situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source, temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN is one of the goals for quantifying ACI processes and achieving uncertainty reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (RA: EAC4) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMS are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatio-temporal quantification of CCN. This paper illustrates the temporal and spatial structure of CCN and their abundance in the atmosphere. A brief evaluation with ground based in-situ measurements demonstrates the improvement of the modeled CCN over the sole use of AOD as a proxy for CCN. The CCN dataset, which is now freely available to users (Block, 2023), features 3-D CCN concentrations of global coverage for various supersaturations and aerosol species covering the years from 2003 to 2021 with daily frequency. This dataset is one of its kind as it offers lots of opportunities to be used for evaluation in models and in ACI studies. [ABSTRACT FROM AUTHOR]

The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in clouds constitute the dominant source of uncertainty in the global warming response to carbon dioxide forcing. To characterize and constrain this uncertainty, cloud controlling factor (CCF) analyses have been suggested that estimate sensitivities of clouds to large-scale environmental changes, typically in cloud-regime specific multiple linear regression frameworks. Here, local sensitivities of cloud radiative effects to a large number of controlling factors are estimated in a regime-independent framework from 20 years of near-global satellite observations and reanalysis data using statistical learning. A regularized linear regression (ridge regression) is shown to skillfully predict anomalies of shortwave (R² = 0.63) and longwave CRE (R² = 0.72) in independent test data on the basis of 28 CCFs, including aerosol proxies. The sensitivity of CRE to selected CCFs is quantified and analyzed. CRE sensitivities to sea-surface temperature and estimated inversion strength are particularly pronounced in low-cloud regions and generally in agreement with previous studies. The analysis of CRE sensitivities to three-dimensional wind field anomalies reflects that CREs in tropical ascent regions are mainly driven by variability of large-scale vertical velocity in the upper troposphere. In the subtropics, CRE is sensitive to free-tropospheric zonal and meridional wind anomalies, which are likely to encapsulate information on synoptic variability that influences subtropical cloud systems by modifying wind shear and thus turbulence and dry-air entrainment in stratocumulus clouds, as well as variability related to midlatitude cyclones. Different proxies for aerosols are analyzed as CCFs, with satellite-derived aerosol proxies showing a larger CRE sensitivity than a proxy from an aerosol reanalysis, likely pointing to satellite aerosol retrieval biases close to clouds leading to overestimated aerosol sensitivities. Sensitivities of shortwave CRE to all aerosol proxies indicate a pronounced cooling effect from aerosols in stratocumulus regions that is counteracted to a varying degree by a longwave warming effect. The analysis may guide the selection of CCFs in future sensitivity analyses aimed at constraining cloud feedback and climate forcings from aerosol-cloud interactions, using both data from observations and global climate models. [ABSTRACT FROM AUTHOR]

The radiative effects of clouds make a large contribution to the Earth's energy balance, and changes in clouds constitute the dominant source of uncertainty in the global warming response to carbon dioxide forcing. To characterize and constrain this uncertainty, cloud controlling factor (CCF) analyses have been suggested that estimate sensitivities of clouds to large-scale environmental changes, typically in cloud-regime specific multiple linear regression frameworks. Here, local sensitivities of cloud radiative effects to a large number of controlling factors are estimated in a regime-independent framework from 20 years of near-global satellite observations and reanalysis data using statistical learning. A regularized linear regression (ridge regression) is shown to skillfully predict anomalies of shortwave (R² = 0.63) and longwave CRE (R² = 0.72) in independent test data on the basis of 28 CCFs, including aerosol proxies. The sensitivity of CRE to selected CCFs is quantified and analyzed. CRE sensitivities to sea-surface temperature and estimated inversion strength are particularly pronounced in low-cloud regions and generally in agreement with previous studies. The analysis of CRE sensitivities to three-dimensional wind field anomalies reflects that CREs in tropical ascent regions are mainly driven by variability of large-scale vertical velocity in the upper troposphere. In the subtropics, CRE is sensitive to free-tropospheric zonal and meridional wind anomalies, which are likely to encapsulate information on synoptic variability that influences subtropical cloud systems by modifying wind shear and thus turbulence and dry-air entrainment in stratocumulus clouds, as well as variability related to midlatitude cyclones. Different proxies for aerosols are analyzed as CCFs, with satellite-derived aerosol proxies showing a larger CRE sensitivity than a proxy from an aerosol reanalysis, likely pointing to satellite aerosol retrieval biases close to clouds leading to overestimated aerosol sensitivities. Sensitivities of shortwave CRE to all aerosol proxies indicate a pronounced cooling effect from aerosols in stratocumulus regions that is counteracted to a varying degree by a longwave warming effect. The analysis may guide the selection of CCFs in future sensitivity analyses aimed at constraining cloud feedback and climate forcings from aerosol-cloud interactions, using both data from observations and global climate models. [ABSTRACT FROM AUTHOR]

Aerosol forcing uncertainty represents the largest climate forcing uncertainty overall. Its magnitude has remained virtually undiminished over the past 20 years despite considerable advances in understanding most of the key contributing elements. Recent work has produced modest increases only in the confidence of the uncertainty estimate itself. This review summarizes the contributions toward reducing the uncertainty in the aerosol forcing of climate made by satellite observations, measurements taken within the atmosphere, as well as modeling and data assimilation. We adopt a more measurement‐oriented perspective than most reviews of the subject in assessing the strengths and limitations of each; gaps and possible ways to fill them are considered. Currently planned programs supporting advanced, global‐scale satellite and surface‐based aerosol, cloud, and precursor gas observations, climate modeling, and intensive field campaigns aimed at characterizing the underlying physical and chemical processes involved, are all essential. But in addition, new efforts are needed: (a) to obtain systematic aircraft in situ measurements capturing the multi‐variate probability distribution functions of particle optical, microphysical, and chemical properties (and associated uncertainty estimates), as well as co‐variability with meteorology, for the major aerosol airmass types; (b) to conceive, develop, and implement a suborbital (aircraft plus surface‐based) program aimed at systematically quantifying the cloud‐scale microphysics, cloud optical properties, and cloud‐related vertical velocities associated with aerosol‐cloud interactions; and (c) to focus much more research on integrating the unique contributions of satellite observations, suborbital measurements, and modeling, to reduce the persistent uncertainty in aerosol climate forcing.Plain Language Summary: Aerosols, such as airborne wildfire smoke, desert dust, volcanic and pollution particles, affect Earth's climate by reflecting (some also absorb) sunlight. These aerosol particles also play key roles in cloud formation and evolution, further affecting the planet's energy balance. The magnitudes of these effects, and even the underlying mechanisms, represent the largest uncertainty in climate modeling. Despite two decades of advances in many aspects of aerosol‐climate science, aerosol climate forcing uncertainty is virtually undiminished. Yet, reducing this uncertainty is critical for any effort to attribute, mitigate, or predict climate changes. We adopt a measurement‐oriented perspective to assess the strengths and limitations of measurement and modeling programs, and conclude that current and planned efforts need to continue. However, in addition, new efforts are needed: (a) to obtain aircraft in situ measurements that capture systematically aerosol particle properties for the major aerosol airmass types, globally, (b) to conceive, develop, and implement an aircraft and surface‐based program aimed at filling gaps in our understanding of the interactions between aerosol particles and clouds, along with (c) much more research focused on integrating the unique contributions of satellite observations, suborbital measurements, and modeling, to reduce the uncertainty in our understanding of Earth's changing climate.Key Points: Aerosol climate forcing uncertainty is virtually undiminished despite two decades of advances in many aspects of aerosol‐climate scienceThis review concludes that current and planned aerosol modeling, satellite and ground‐based observation programs remain essentialNew, systematic aircraft aerosol‐particle and cloud‐process measurements are also needed, along with better model‐measurement integration [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 modelled 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 increases are constant over the emission ranges observed. There is evidence that this is due to compensating effects under rainy and non-rainy 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]

Convective aggregation is an important atmospheric phenomenon which frequently occurs in idealized models in radiative‐convective equilibrium (RCE), where the effects of land, rotation, sea surface temperature gradients, and the diurnal cycle are often removed. This aggregation is often triggered and maintained by self‐generated radiatively driven circulations, for which longwave feedbacks are essential. Many questions remain over how important the driving processes of aggregation in idealized models are in the real atmosphere. We approach this question by adding a continentally sized, idealized tropical rainforest island into an RCE model to investigate how land‐sea contrasts impact convective aggregation and its mechanisms. We show that convection preferentially forms over the island persistently in our simulation. This is forced by a large‐scale, thermally driven circulation. First, a sea‐breeze circulation is triggered by the land‐sea thermal contrast, driven by surface sensible heating. This sea‐breeze circulation triggers convection which then generates longwave heating anomalies. Through mechanism denial tests we find that removing the longwave feedbacks reduces the large‐scale effects of aggregation but does not prevent aggregation from occurring, and thus we highlight there must be another process aiding the aggregation of convection. We also show, by varying the island size, that the aggregated convective cluster appears to have a maximum spatial extent of O $\mathcal{O}$(10,000 km). These results highlight that the mechanisms of idealized aggregation remain relevant when land is included in the model, and therefore these mechanisms could help us understand convective organization in the real world. Plain Language Summary: Large tropical storm clouds can cluster together to form organized systems, which are associated with extreme precipitation within the cloudy region, and very dry conditions away from the cloudy region. These systems, called organized deep convection, are often studied in simplified models of the atmosphere where the land, Earth's rotation, and variations in sea‐surface temperature are removed. In this paper we take a step toward reality and look at how including a large, idealized island in a simplified model affects how convection clusters. We show that convective clouds group together over the land throughout our model simulations. We investigate the processes driving this and find that surface flux feedbacks dominate early in the simulations. The feedbacks maintaining the convective aggregation are more complex. We find there is an important role for the longwave feedbacks, but that there must be another necessary process also aiding the clustering process. Our results highlight that the processes in highly idealized modeling studies seem to be relevant in more realistic models, and thus could help us understand how convection organizes in the real world. However, we also conclude it is important that follow‐up work investigates the remaining additional feedbacks to gain a more complete understanding. Key Points: Convection preferentially aggregates over land in a global radiative‐convective equilibrium simulationA global land‐centered circulation drives the aggregation and is triggered through surface fluxes, but maintained through longwave fluxesThe land‐based convective cluster appears to have a maximum spatial scale of O $\mathcal{O}$(10,000 km) [ABSTRACT FROM AUTHOR]

The Amazon rainforest routinely experiences intense and long-lived biomass burning events that result in smoke plumes that cover vast regions. The spatial and temporal extent of the plumes and the complex pathways through which they interact with the atmosphere have proved challenging to measure for purposes of gaining a representative understanding of smoke impacts on the Amazonian atmosphere. In this study, we use multiple collocated satellite sensors on board AQUA and TERRA platforms to study the underlying smoke–cloud–radiation interactions during the diurnal cycle. An 18-year time series for both morning and afternoon overpasses is constructed, providing collocated measurements of aerosol optical depth (AOD; column-integrated aerosol extinction), cloud properties, top-of-atmosphere radiative fluxes, precipitation, and column water vapour content from independent sources. The long-term time series reduces the impact of interannual variability and provides robust evidence that smoke significantly modifies the Amazonian atmosphere. Low loadings of smoke (AOD ≤ 0.4) enhance convective activity, cloudiness, and precipitation, but higher loadings (AOD > 0.4) strongly suppress afternoon convection and promote low-level cloud occurrence. Accumulated precipitation increases with convective activity but remains elevated under high smoke loadings, suggesting fewer but more intense convective cells. Contrasting morning and afternoon cloud responses to smoke are observed, in line with recent simulations. Observations of top-of-atmosphere radiative fluxes support the findings and show that the response of low-level cloud properties and cirrus coverage to smoke results in a pronounced and consistent increase in top-of-atmosphere outgoing radiation (cooling) of up to 50 W m -2 for an AOD perturbation of + 1.0. The results demonstrate that smoke strongly modifies the atmosphere over the Amazon via widespread changes to the cloud field properties. Rapid adjustments work alongside instantaneous radiative effects to drive a stronger cooling effect from smoke than previously thought, whilst contrasting morning and afternoon responses of liquid and ice water paths highlight a potential method for constraining aerosol impacts on climate. Increased drought susceptibility, land use change, and deforestation will have important and widespread impacts on the region over the coming decades. Based on this analysis, we anticipate that further increases in anthropogenic fire activity will associated with an overall reduction in regional precipitation and a negative forcing (cooling) on the Earth's energy budget. [ABSTRACT FROM AUTHOR]

Automated methods for the detection and tracking of deep convective clouds in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds (DCC), however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting DCCs (including detection of growing cores through cloud top cooling and detection of anvil clouds using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire life cycle of DCCs and their associated anvils. Furthermore, we expect this framework to be applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data. [ABSTRACT FROM AUTHOR]

Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill at making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy-balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties in the model, limit the effectiveness of observational constraint of the uncertain physical processes. We sample uncertainty in 37 model parameters related to aerosols, clouds and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate 1 million model variants (different parameter settings from Gaussian Process emulators) against satellite-derived observations over several cloudy regions. We show that it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 %, constraining it to a range in close agreement with energy-balance constraints (around −1.3 to −0.1 W m−2). However, our analysis of a very large set of model variants exposes model internal inconsistencies that would not be apparent in a small set of model simulations. Incorporating observations associated with these inconsistencies weakens the forcing constraint because they require a wider range of parameter values to accommodate conflicting information. Our estimated aerosol forcing range is the maximum feasible constraint using our structurally imperfect model and the chosen observations. Structural model developments targeted at the identified inconsistencies would enable a larger set of observations to be used for constraint, which would then narrow the uncertainty further. Such an approach provides a rigorous pathway to improved model realism and reduced uncertainty that has so far not been achieved through the normal model development approach. [ABSTRACT FROM AUTHOR]

Atmospheric aerosols influence the Earth’s climate, primarily by affecting cloud formation and scattering visible radiation. However, aerosol-related physical processes in climate simulations are highly uncertain. Constraining these processes could help improve model-based climate predictions. We propose a scalable statistical framework for constraining the parameters of expensive climate models by comparing model outputs with observations. Using the C3.AI Suite, a cloud computing platform, we use a perturbed parameter ensemble of the UKESM1 climate model to efficiently train a surrogate model. A method for estimating a data-driven model discrepancy term is described. The strict bounds method is applied to quantify parametric uncertainty in a principled way. We demonstrate the scalability of this framework with 2 weeks’ worth of simulated aerosol optical depth data over the South Atlantic and Central African region, written from the model every 3 hr and matched in time to twice-daily MODIS satellite observations. When constraining the model using real satellite observations, we establish constraints on combinations of two model parameters using much higher time-resolution outputs from the climate model than previous studies. This result suggests that within the limits imposed by an imperfect climate model, potentially very powerful constraints may be achieved when our framework is scaled to the analysis of more observations and for longer time periods. [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]

Estimates of the anthropogenic effective radiative forcing (ERF) trend have increased by 50% since 2000 (from +0.4 W m−2 decade−1 in 2000–09 to +0.6 W m−2 decade−1 in 2010–19), the majority of which is driven by changes in the aerosol ERF trend, as a result of aerosol emissions reductions. Here we study the extent to which observations of the climate system agree with these ERF assumptions. We use a large ERF ensemble from the IPCC's Sixth Assessment Report (AR6) to attribute the anthropogenic contributions to global mean surface temperature (GMST), top-of-atmosphere radiative flux, and we use aerosol optical depth observations. The GMST trend has increased from +0.18°C decade−1 in 2000–09 to +0.35°C decade−1 in 2010–19, coinciding with the anthropogenic warming trend rising from +0.19°C decade−1 in 2000–09 to +0.24°C decade−1 in 2010–19. This, as well as observed trends in top-of-atmosphere radiative fluxes and aerosol optical depths, supports the claim of an aerosol-induced temporary acceleration in the rate of warming. However, all three observation datasets additionally suggest that smaller aerosol ERF trend changes are compatible with observations since 2000, since radiative flux and GMST trends are significantly influenced by internal variability over this period. A zero-trend-change aerosol ERF scenario results in a much smaller anthropogenic warming acceleration since 2000 but is poorly represented in AR6's ERF ensemble. Short-term ERF trends are difficult to verify using observations, so caution is required in predictions or policy judgments that depend on them, such as estimates of current anthropogenic warming trend, and the time remaining to, or the outstanding carbon budget consistent with, 1.5°C warming. Further systematic research focused on quantifying trends and early identification of acceleration or deceleration is required. [ABSTRACT FROM AUTHOR]

The Amazon rainforest routinely experiences intense and long-lived biomass burning events that result in smoke plumes that cover vast regions. The spatial and temporal extent of the plumes, and the complex pathways through which they interact with the atmosphere, has proved challenging to measure and gain a representative understanding of smoke impacts on the Amazonian atmosphere. In this study we use multiple collocated satellite sensors onboard AQUA and TERRA platforms to study the underlying smoke-cloud-radiation interactions during the diurnal cycle. An 18-year timeseries for both morning and afternoon overpasses is constructed providing collocated measurements of aerosol optical depth (column integrated aerosol extinction, AOD), cloud properties, top-of-atmosphere radiative fluxes, precipitation, and column water-vapour content from independent sources. The long-term timeseries reduces the impact of interannual variability and provides robust evidence that smoke significantly modifies the Amazonian atmosphere. Low loadings of smoke (AOD ≤ 0.4) enhance convective activity, cloudiness and precipitation, but higher loadings (AOD > 0.4) strongly suppress afternoon convection and promote low-level cloud occurrence. Accumulated precipitation increases with convective activity but remains elevated under high smoke loadings suggesting fewer but more intense convective cells. Contrasting morning and afternoon cloud responses to smoke are observed, in-line with recent simulations. Observations of top-of-atmosphere radiative fluxes support the findings, and show that the response of low-level cloud properties and cirrus coverage to smoke results in a pronounced and consistent increase in top-of-atmosphere outgoing radiation (cooling) of up to 50 Wm-2 for an AOD perturbation of +1.0. The results demonstrate that smoke strongly modifies the atmosphere over the Amazon via widespread changes to the cloud-field properties. Rapid adjustments work alongside instantaneous radiative effects to drive a stronger cooling effect from smoke than previously thought, whilst contrasting morning/afternoon responses of liquid and ice water paths highlight a potential method for constraining aerosol impacts on climate. Increased drought susceptibility, land-use change, and deforestation will have important and widespread impacts to the region over the coming decades. Based on this analysis, we anticipate further increases in anthropogenic fire activity to be associated with an overall reduction in regional precipitation, and a negative forcing (cooling) on the Earth’s energy budget. [ABSTRACT FROM AUTHOR]

Aerosol radiative forcing uncertainty affects estimates of climate sensitivity and limits model skill at making climate projections. Efforts to improve the representations of physical processes in climate models, including extensive comparisons with observations, have not significantly constrained the range of possible aerosol forcing values. A far stronger constraint, in particular for the lower (most-negative) bound, can be achieved using global mean energy-balance arguments based on observed changes in historical temperature. Here, we show that structural deficiencies in a climate model, revealed as inconsistencies among observationally constrained cloud properties, limit the effectiveness of observational constraint of the uncertain physical processes. We sample uncertainty in 37 model parameters related to aerosols, clouds and radiation in a perturbed parameter ensemble of the UK Earth System Model and evaluate one million model variants (different parameter settings from Gaussian Process emulators) against satellite-derived observations over several cloudy regions. We show it is possible to reduce the parametric uncertainty in global mean aerosol forcing by more than 50 % to a range in close agreement with energy-balance constraints (around -1.3 to -0.1 W m-2). However, incorporating observations associated with model inconsistencies weakens the constraint because the inconsistencies introduce conflicting information about relationships between model parameter values and aerosol forcing. Our estimated aerosol forcing range is the maximum feasible constraint using these observations and our structurally imperfect model. Structural model developments, targeted at the inconsistencies identified here, would enable a larger set of observations to be used for constraint, which would then narrow the uncertainty further. [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]

Cloud reflectivity is sensitive to atmospheric aerosol concentrations because aerosols provide the condensation nuclei on which water condenses1. Increased aerosol concentrations due to human activity affect droplet number concentration, liquid water and cloud fraction2, but these changes are subject to large uncertainties3. Ship tracks, long lines of polluted clouds that are visible in satellite images, are one of the main tools for quantifying aerosol–cloud interactions4. However, only a small fraction of the clouds polluted by shipping show ship tracks5,6. 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.Investigations of the effect of aerosol emissions from shipping on cloud properties when both visible and invisible ship tracks are considered implies a large negative radiative effect (and associated cooling) from liquid water path adjustments. [ABSTRACT FROM AUTHOR]

Anthropogenic aerosols exert a cooling influence that offsets part of the greenhouse gas warming. Due to their short tropospheric lifetime of only several days, the aerosol forcing responds quickly to emissions. Here, we present and discuss the evolution of the aerosol forcing since 2000. There are multiple lines of evidence that allow us to robustly conclude that the anthropogenic aerosol effective radiative forcing (ERF) – both aerosol–radiation interactions (ERFari) and aerosol–cloud interactions (ERFaci) – has become less negative globally, i.e. the trend in aerosol effective radiative forcing changed sign from negative to positive. Bottom-up inventories show that anthropogenic primary aerosol and aerosol precursor emissions declined in most regions of the world; observations related to aerosol burden show declining trends, in particular of the fine-mode particles that make up most of the anthropogenic aerosols; satellite retrievals of cloud droplet numbers show trends in regions with aerosol declines that are consistent with these in sign, as do observations of top-of-atmosphere radiation. Climate model results, including a revised set that is constrained by observations of the ocean heat content evolution show a consistent sign and magnitude for a positive forcing relative to the year 2000 due to reduced aerosol effects. This reduction leads to an acceleration of the forcing of climate change, i.e. an increase in forcing by 0.1 to 0.3 W m -2 , up to 12 % of the total climate forcing in 2019 compared to 1750 according to the Intergovernmental Panel on Climate Change (IPCC). [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]

Clouds are a key player in the global climate system, affecting the atmospheric water and energy budgets, and they are strongly coupled to the large-scale atmospheric circulation. Here, we examine the co-variability of the atmospheric energy and water budget imbalances in three different global model configurations-radiative-convective equilibrium, aqua-planet, and global simulations with land. The gradual increase in the level of complexity of the model configuration enables an investigation of the effects of rotation, meridional temperature gradient, land-sea contrast, and seasonal cycle on the co-variability of the water and energy imbalances. We demonstrate how this co-variability is linked to both the large-scale tropical atmospheric circulation and to cloud properties. Hence, we propose a co-variability-based framework that connects cloud properties to the large-scale tropical circulation and climate system and is directly linked to the top-down constrains on the system--the water and energy budgets. In addition, we examine how the water and energy budget imbalances co-variability depends on the temporal averaging scale, and explain its dependency on how stationary the circulation is in the different model configurations. Finally, we demonstrate the effect of an idealized global warming and convective aggregation on this co-variability. Plain Language Summary Understanding the link between clouds and the atmospheric circulation is considered as a major challenge in climate science. Both atmospheric circulation and clouds are intimately linked to Earth's water and energy budgets. We show that looking at how the residuals of these budgets depend on each other and on cloud properties can provide a new framework with which to study the properties of clouds, circulations, and their respective impacts on the climate system. In this paper, we present this framework, and demonstrate its usefulness for a range of different climate model complexities. We show examples of how the framework could be used to investigate the effects of climate change on clouds and circulation. Finally, we present an application for convective aggregation (the clustering of deep convective clouds in idealized climate models). [ABSTRACT FROM AUTHOR]

Anthropogenic aerosols exert a cooling influence that offsets part of the greenhouse gas warming. Due to their short tropospheric lifetime of only up to several days, the aerosol forcing responds quickly to emissions. Here we present and discuss the evolution of the aerosol forcing since 2000. There are multiple lines of evidence that allow to robustly conclude that the anthropogenic aerosol effective radiative forcing - both aerosol-radiation and aerosol-cloud interactions - has become globally less negative, i.e. that the trend in aerosol effective radiative forcing changed sign from negative to positive. Bottom-up inventories show that anthropogenic primary aerosol and aerosol precursor emissions declined in most regions of the world; observations related to aerosol burden show declining trends, in particular of the fine-mode particles that make up most of the anthropogenic aerosols; satellite retrievals of cloud droplet numbers show trends consistent in sign, as do observations of top-of-atmosphere radiation. Climate model results, including a revised set that is constrained by observations of the ocean heat content evolution show a consistent sign and magnitude for a positive forcing relative to 2000 due to reduced aerosol effects. This reduction leads to an acceleration of the forcing of climate change, i.e. an increase in forcing by 0.1 to 0.3Wm-2, up to 12% of the total climate forcing in 2019 compared to 1750 according to IPCC. [ABSTRACT FROM AUTHOR]

Automated methods for the detection and tracking of deep convective storms in geostationary satellite imagery have a vital role in both the forecasting of severe storms and research into their behaviour. Studying the interactions and feedbacks between multiple deep convective clouds, however, poses a challenge for existing algorithms due to the necessary compromise between false detection and missed detection errors. We utilise an optical flow method to determine the motion of deep convective clouds in GOES-16 ABI imagery in order to construct a semi-Lagrangian framework for the motion of the cloud field, independently of the detection and tracking of cloud objects. The semi-Lagrangian framework allows for severe storms to be simultaneously detected and tracked in both spatial and temporal dimensions. For the purpose of this framework we have developed a novel Lagrangian convolution method and a number of novel implementations of morphological image operations that account for the motion of observed objects. These novel methods allow the accurate extension of computer vision techniques to the temporal domain for moving objects such as DCCs. By combining this framework with existing methods for detecting deep convective clouds (including detection of growing cores through cloud top cooling and detection of anvil using brightness temperature), we show that the novel framework enables reductions in errors due to both false and missed detections compared to any of the individual methods, reducing the need to compromise when compared with existing frameworks. The novel framework enables the continuous tracking of anvil clouds associated with detected deep convection after convective activity has stopped, enabling the study of the entire lifecycle of deep convective clouds and their associated anvils. Furthermore, we expect this framework to applicable to a wide range of cases including the detection and tracking of low-level clouds and other atmospheric phenomena. In addition, this framework may be used to combine observations from multiple sources, including satellite observations, weather radar and reanalysis model data. [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]

The Sahel rainfall has a close teleconnection with North Atlantic sea surface temperature (NASST) variability, which has separately been shown to be affected by aerosols. Therefore, changes in regional aerosols emission could potentially drive multidecadal Sahel rainfall variability. Here we combine ensembles of state‐of‐the‐art global climate models (the CESM and CanESM large ensemble simulations and CMIP6 models) with observational data sets to demonstrate that anthropogenic aerosols have significantly impacted 20th‐century detrended Sahel rainfall multidecadal variability through modifying NASST. We show that aerosol‐induced multidecadal variations of downward solar radiative fluxes over the North Atlantic cause NASST variability during the 20th century, altering the ITCZ position and dynamically linking aerosol effects to Sahel rainfall variability. This process chain is caused by aerosol‐induced changes in radiative surface fluxes rather than changes in ocean circulations. CMIP6 models further suggest that aerosol‐cloud interactions modulate the inter‐model uncertainty of simulated NASST and potentially the Sahel rainfall variability. Plain Language Summary: Sahel rainfall experienced significant multidecadal variability over the 20th century, with large societal implications. However, the drivers of this variability remain debated. Here we show that anthropogenic aerosols largely contributed to the Sahel rainfall variability. We propose a process chain, from changing sulfate emissions from Europe and North America, to changes in North Atlantic surface net radiative fluxes, via North Atlantic sea surface temperature variability to a shift of ITCZ and changes in West African monsoon, and finally Sahel rainfall variability. This process chain is consistently evidenced by ensembles of state‐of‐the‐art global climate models as well as observational data sets. We show that aerosol‐radiation interactions and aerosol‐cloud interactions are both important processes in this chain. These findings highlight the importance of accurate representation of regional aerosol‐cloud‐radiation interactions for the simulation of Sahel rainfall variability. Key Points: A process chain from anthropogenic aerosol emissions to the Sahel rainfall is proposedLarge ensemble and CMIP6 simulations suggest that this link is mediated by aerosol impacts on North Atlantic SSTBoth aerosol‐cloud interactions and aerosol‐radiation interactions are important in this chain process [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]

Tropical deep convection can aggregate into large clusters, which can have impacts on the local humidity and precipitation. Sea surface temperature (SST) gradients have been shown to organize convection, yet there has been little work done to investigate the impact of diabatic heating perturbations in the atmosphere on the aggregation of convection. Here we investigate how anomalous diabatic heating of the atmospheric column, through an idealized aerosol plume, affects the existence and mechanisms of convective aggregation in non‐rotating, global radiative‐convective equilibrium simulations. We show that the aerosol forcing has the ability to increase the degree of aggregation, especially at lower SSTs. Detailed investigation shows that the diabatic heating source incites a thermally driven circulation, forced by the shortwave perturbation. The increase in aggregation is caused in part by this circulation, and in part by the longwave heating anomalies occurring due to the surface convergence of moisture and convection. At higher SSTs, longwave feedbacks are crucial for the aggregation of convection, even with the shortwave heating perturbation. At lower SSTs, convection is able to aggregate with the shortwave perturbation in the absence of longwave feedbacks. These perturbations provide a link to studying the effects of absorbing aerosol plumes on convection, for example during the Indian monsoon season. We argue that, as there is aggregation for plumes with realistic aerosol absorption optical depths, this could be an analogue for real‐world organization in regions with high pollution. Plain Language Summary: Tropical clouds grouping together into large clusters in idealized climate models is an interesting topic of research. When this happens, the average temperature increases with more rain where the clouds are and less rain away from them. This paper looks at how we can force clouds to cluster together. We use plumes of aerosols to cause heating perturbations. We show that this causes a large‐scale circulation to develop, with warm air rising near to the plume and cold air sinking further away from it. This helps clouds to cluster together, through changes in the absorption and reflection of longwave and shortwave radiation. We highlight that real‐world pollution plumes or dust events may have similar effects on the clustering of clouds near to them. Key Points: Can increase the degree of aggregation by using a shortwave radiative heating perturbationRadiative heating perturbation incites a global thermally driven circulationShortwave radiative heating perturbation can aggregate convection at lower sea surface temperatures, even without typically crucial longwave feedbacks [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]

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, explore precipitation sensitivity in a cloud resolving model and scenario uncertainty in the CMIP6 multi-model ensemble. [ABSTRACT FROM AUTHOR]

Absorbing aerosol from biomass burning impacts the hydrological cycle and radiation fluxes both directly and indirectly via modifications to convective processes and cloud development. Using the ICOsahedral Non‐hydrostatic modelling framework in a regional configuration with 1,500 m convection‐permitting resolution, we isolate the response of the Amazonian atmosphere to biomass burning smoke via enhanced cloud droplet number concentrations Nd (aerosol‐cloud interactions; ACI) and changes to radiative fluxes (aerosol‐radiation interactions; ARI) over a period of 8 days. We decompose ARI into contributions from reduced shortwave radiation and localized heating of the smoke. We show ARI influences the formation and development of convective cells: surface cooling below the smoke drives suppression of convection that increases with smoke optical depth, while the elevated heating promotes initial suppression and subsequent intensification of convection overnight; a corresponding diurnal response (repeating temporal response day‐after‐day) from high precipitation rates is shown. Enhanced Nd (ACI) perturbs the bulk cloud properties and suppresses low‐to‐moderate precipitation rates. Both ACI and ARI result in enhanced high‐altitude ice clouds that have a strong positive longwave radiative effect. Changes to low‐cloud coverage (ARI) and albedo (ACI) drive an overall negative shortwave radiative effect, that slowly increases in magnitude due to a moistening of the boundary layer. The overall net radiative effect is dominated by the enhanced high‐altitude clouds, and is sensitive to the plume longevity. The considerable diurnal responses that we simulate cannot be observed by polar orbiting satellites widely used in previous work, highlighting the potential of geostationary satellites to observe large‐scale impacts of aerosols on clouds. Plain Language Summary: There remain important uncertainties on how smoke from forest and grassland fires impacts the past, present, and future climates. In this study, we use a detailed model of the atmosphere over the Amazon rainforest to understand and quantify the processes by which smoke influences clouds and rain via two pathways: the first driven by changes to the absorption of solar radiation through the smoky atmosphere, and the second driven by an increase in the number of cloud droplets due to smoke particles. We find that the diurnal cycle of convection that drives much of the Amazon rainfall is greatly affected by changes to the radiation with less activity during the day and increasing activity overnight. The more numerous cloud droplets make the clouds brighter and help suppress rainfall rates. Changes to both radiation and cloud droplet number result in more extensive and thicker ice‐phase clouds that exert a warming effect on the climate; the longer the smoke plume persists for, the stronger the warming effect. Our findings highlight important processes that are not sufficiently represented in global climate models, and also highlight a need to use time‐resolved geostationary satellite observations of the region to capture more of the diurnal cycle. Key Points: Smoke drives strong diurnal responses in convection, cloud cover, and precipitation that cannot be observed by polar‐orbiting satellitesThe radiative effect of smoke is sensitive to plume longevity and the response of high‐altitude clouds that exert a strong positive effectSimulations suggest the localized suppression of convection scales with the optical depth of smoke over the Amazon rainforest [ABSTRACT FROM AUTHOR]

Shallow clouds remain greatly significant in improving our understanding of the atmosphere. Using the Met Office Unified Model, we compare highly idealised simulations of shallow cumuli with those using more realistic domains, with open lateral boundaries and varying large‐scale forcing. We find that the realistic simulations are more capable of representing the cloud field on large spatial scales, and appear to limit the aerosol perturbations leading to impacts on the thermodynamic conditions. Aerosol perturbations lead to changes in the cloud vertical structure, and thermodynamic evolution of the idealised simulations; a central feature of behavior seen previously in idealised simulations. Modelling approaches with open boundaries and time‐varying forcing may allow for improved representation of shallow clouds in the atmosphere, and greater understanding of how they may respond to perturbations. Plain Language Summary: Clouds, and shallow clouds in particular, are responsible for much uncertainty in our understanding of the atmosphere, and the response of the climate system to anthropogenic perturbations. The representation of shallow clouds in models has long been a challenge due to the myriad processes and scales involved; from micrometer cloud droplets, to cloud fields of tens or hundreds of km, leading to many computational difficulties. Here we present and compare a number of simulations using different approaches. We show that certain modelling choices allow for an improved representation of shallow cloud fields on large scales, and also show a different response to aerosol perturbations, with implications for future development of estimations of the effects of aerosol on shallow clouds. Key Points: Simulations including time‐varying large‐scale forcing and open boundaries may improve representation of shallow cumulus fieldsAerosol effects on shallow cumuli may be constrained by large‐scale forcing, but nonetheless significantMet Office Unified Model (UM) allows for a variety of configurations to compare modelling approaches [ABSTRACT FROM AUTHOR]

Shallow clouds remain greatly significant in improving our understanding of the atmosphere. Using the Met Office Unified Model, we compare highly idealised simulations of shallow cumuli with those using more realistic domains, with open lateral boundaries and varying large‐scale forcing. We find that the realistic simulations are more capable of representing the cloud field on large spatial scales, and appear to limit the aerosol perturbations leading to impacts on the thermodynamic conditions. Aerosol perturbations lead to changes in the cloud vertical structure, and thermodynamic evolution of the idealised simulations; a central feature of behavior seen previously in idealised simulations. Modelling approaches with open boundaries and time‐varying forcing may allow for improved representation of shallow clouds in the atmosphere, and greater understanding of how they may respond to perturbations. Plain Language Summary: Clouds, and shallow clouds in particular, are responsible for much uncertainty in our understanding of the atmosphere, and the response of the climate system to anthropogenic perturbations. The representation of shallow clouds in models has long been a challenge due to the myriad processes and scales involved; from micrometer cloud droplets, to cloud fields of tens or hundreds of km, leading to many computational difficulties. Here we present and compare a number of simulations using different approaches. We show that certain modelling choices allow for an improved representation of shallow cloud fields on large scales, and also show a different response to aerosol perturbations, with implications for future development of estimations of the effects of aerosol on shallow clouds. Key Points: Simulations including time‐varying large‐scale forcing and open boundaries may improve representation of shallow cumulus fieldsAerosol effects on shallow cumuli may be constrained by large‐scale forcing, but nonetheless significantMet Office Unified Model (UM) allows for a variety of configurations to compare modelling approaches [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]