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Contrasting effects of intensity and organisation on the structure and lifecycle of deep convective clouds

(2024)

Authors:

William Jones, Philip Stier

Abstract:

The anvils of deep convective clouds (DCCs) have an important impact on global radiation balance. While the anvil cloud area feedback to warming temperatures is expected to have a cooling effect, it has the largest uncertainty of any cloud-climate feedback. Differences in anvil structure contribute to this uncertainty due to changes in the proportions of thicker, cooling anvil and thinner, warming anvil cirrus. A lack of long-term observational datasets of both convective and anvil properties of DCCs has limited our understanding of the connections between these processes. Using a novel cloud tracking algorithm we detect and track the developing cores, thick and thin anvils of DCCs seen in 5 years of GOES-16 imagery, allowing investigations of their properties throughout the DCC lifecycle. Using this dataset, we compare how the amount of thin anvil cirrus changes with the intensity and organisation of observed DCCs. Previous studies of anvil structure have found that the proportion of thin cirrus increases with convective intensity across a range of regimes. We find that the thin anvil proportion increases with convective intensity both in area and lifetime. To the contrary, for more organised DCCs – those with more cores – we find, however, that the thin anvil area and lifetime both decrease as a proportion of the total anvil. While more intense DCCs have shorter growing phases and longer dissipating phases, the opposite is true for more organised DCCs. These differences in lifecycle have an important impact on thin anvil proportion. The contrast in structure and lifecycle between DCCs with increasing intensity and increasing organisation occurs despite both convective processes having positive impacts on the total anvil area, lifetime and temperature. As both the intensity and organisation of DCCs are expected to increase with warming, we may expect differences in anvil cloud area feedback between different regimes depending on the occurrence of isolated or organised DCCs.
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Comparing ML retrieved and invisible ship tracks to probe the meteorological dependence of cloud susceptibility to aerosol

(2024)

Authors:

Peter Manshausen, Duncan Watson-Parris, Philip Stier

Abstract:

Aerosol-cloud interactions continue to resist reliable quantification, partly owing to their strong dependence on cloud and weather regimes. For a long time, opportunistic experiments such as ship tracks have been used to overcome issues of confounding. Recent advances leverage (i) Machine Learning (ML) to drastically enlarge ship track data bases, and (ii) ‘invisible ship tracks’, found by advecting ship emissions, to overcome selection biases in ship track studies. Here, we combine both approaches, to advance our understanding of how meteorology controls cloud responses to aerosol emissions. Firstly, we show that even though the ML dataset is much larger than previous hand-logged data sets, it still contains only a fraction of less than 1% of the cloud regions polluted by shipping. This means less than 1% of ship tracks are visible. Secondly, we find that this fraction varies strongly with location and season, with the Southern Hemisphere winter leading to most visible tracks in the Stratocumulus regions of the SE Pacific and SE Atlantic. Thirdly, we identify meteorological regimes favourable to the visibility of tracks, using ML methods such as Random Forests and Explainable AI, alongside traditional methodsThe regime favourable to visible tracks is defined by a stable lower troposphere and little vertical movement, low sea surface temperatures, high cloud cover, and low boundary layer heights. Lastly, we quantify the link between ship track visibility and albedo change in polluted clouds, establishing to what extent days with visible tracks are those when cloud albedo is most susceptible to aerosol. Building on this relationship, a predictive model like our Random Forest has applications in deliberate Marine Cloud Brightening by predicting the days that are most susceptible to aerosol perturbations.
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Simulating the Earth system with interactive aerosols at the kilometer scale

(2024)

Authors:

Philipp Weiss, Philip Stier

Abstract:

Aerosols originate from natural processes and human activities. They scatter and absorb radiation but also act as condensation nuclei in clouds. How these interactions influence the climate is still uncertain. New climate simulations at the kilometer-scale allow us to examine long-standing questions related to these interactions such as the complex effects on convective clouds. To perform kilometer-scale simulations with interactive aerosols, we developed the reduced-complexity aerosol module HAM-lite and coupled it to the climate model ICON-Sapphire. HAM-lite is based on and fully traceable to the complex aerosol module HAM. Aerosols are represented as an ensemble of log-normal modes with prescribed sizes and compositions. We present first global simulations with ICON-Sapphire and HAM-lite at resolutions of about five kilometers and over periods of a few months. The sea surface temperature and sea ice are prescribed with boundary conditions of AMIP, and the initial conditions of the atmosphere and land are derived from the operational analysis of ECMWF. The aerosols are represented by two pure modes, one of dust and one of sea salt, and two internally mixed modes, both of organic carbon, black carbon, and sulfate. The first mixed mode represents aerosols from biomass burning emissions and the second mixed mode represents aerosols from anthropogenic and volcanic emissions. The simulations capture key elements of the global aerosol cycle, of which some are missing entirely in coarse-scale simulations. For example, cold pool fronts drive intense dust storms over the Sahara and tropical cyclones interact with sea salt aerosols in the Pacific. We observe the transport of dust aerosols across the ocean, the wash out of sea salt aerosols by rain bands, and the updraft of biomass burning aerosols over land. We evaluate the observations with a combination of remote-sensing and in-situ data. We also compare the results to coarse-scale climate simulations. To understand processes like updraft by convection or deposition by rain, we examine the distribution of aerosols throughout the vertical column.
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Supplementary material to "A systematic evaluation of high-cloud controlling factors"

(2024)

Authors:

Sarah Wilson Kemsley, Paulo Ceppi, Hendrik Andersen, Jan Cermak, Philip Stier, Peer Nowack
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Cloud condensation nuclei concentrations derived from the CAMS reanalysis

Earth System Science Data Copernicus Publications 16:1 (2024) 443-470

Authors:

Karoline Block, Mahnoosh Haghighatnasab, Daniel G Partridge, Philip Stier, Johannes Quaas

Abstract:

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 (https://doi.org/10.26050/WDCC/QUAERERE_CCNCAMS_v1, Block, 2023), 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.

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