Publications


tobac v1.0: towards a flexible framework for tracking and analysis of clouds in diverse datasets

Geoscientific Model Development Discussions Copernicus GmbH (2019) 1-31

M Heikenfeld, PJ Marinescu, M Christensen, D Watson-Parris, F Senf, SC van den Heever, P Stier

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


Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation

Atmospheric Chemistry and Physics Copernicus GmbH 19 (2019) 8591-8617

GS Fanourgakis, M Kanakidou, A Nenes, SE Bauer, T Bergman, KS Carslaw, A Grini, DS Hamilton, JS Johnson, VA Karydis, A Kirkevåg, JK Kodros, U Lohmann, G Luo, R Makkonen, H Matsui, D Neubauer, JR Pierce, J Schmale, P Stier, K Tsigaridis, T van Noije, H Wang, D Watson-Parris, DM Westervelt, Y Yang, M Yoshioka, N Daskalakis, S Decesari, M Gysel-Beer, N Kalivitis, X Liu, NM Mahowald, S Myriokefalitakis, R Schrödner, M Sfakianaki, AP Tsimpidi, M Wu, F Yu

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011–2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties.&lt;/p&gt; &lt;p&gt;There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of &lt;span class="inline-formula"&gt;−24&lt;/span&gt;&amp;amp;thinsp;% and &lt;span class="inline-formula"&gt;−35&lt;/span&gt;&amp;amp;thinsp;% for particles with dry diameters &lt;span class="inline-formula"&gt;&amp;amp;gt;50&lt;/span&gt; and &lt;span class="inline-formula"&gt;&amp;amp;gt;120&lt;/span&gt;&amp;amp;thinsp;nm, as well as &lt;span class="inline-formula"&gt;−36&lt;/span&gt;&amp;amp;thinsp;% and &lt;span class="inline-formula"&gt;−34&lt;/span&gt;&amp;amp;thinsp;% for CCN at supersaturations of 0.2&amp;amp;thinsp;% and 1.0&amp;amp;thinsp;%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (&lt;span class="inline-formula"&gt;&amp;amp;lt;0.1&lt;/span&gt;&amp;amp;thinsp;%) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated &lt;span class="inline-formula"&gt;N&lt;sub&gt;3&lt;/sub&gt;&lt;/span&gt; (number concentration of particles with dry diameters larger than 3&amp;amp;thinsp;nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of &lt;span class="inline-formula"&gt;0.2&lt;/span&gt;&amp;amp;thinsp;% (CCN&lt;span class="inline-formula"&gt;&lt;sub&gt;0.2&lt;/sub&gt;&lt;/span&gt;) compared to that for &lt;span class="inline-formula"&gt;N&lt;sub&gt;3&lt;/sub&gt;&lt;/span&gt;, maximizing over regions where new particle formation is important.&lt;/p&gt; &lt;p&gt;An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter.&lt;/p&gt; &lt;p&gt;Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120&amp;amp;thinsp;nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40&amp;amp;thinsp;% during winter and 20&amp;amp;thinsp;% in summer.&lt;/p&gt; &lt;p&gt;In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB &lt;span class="inline-formula"&gt;−13&lt;/span&gt;&amp;amp;thinsp;% and &lt;span class="inline-formula"&gt;−22&lt;/span&gt;&amp;amp;thinsp;% for updraft velocities 0.3 and 0.6&amp;amp;thinsp;m&amp;amp;thinsp;s&lt;span class="inline-formula"&gt;&lt;sup&gt;−1&lt;/sup&gt;&lt;/span&gt;, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (&lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M15" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;d&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;a&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="48pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="9faa6b9bc700a00532091cfd69cae419"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-8591-2019-ie00001.svg" width="48pt" height="14pt" src="acp-19-8591-2019-ie00001.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt;) and to updraft velocity (&lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M16" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;d&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="9a0c289e263af38b17f8d2715a056c8f"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-8591-2019-ie00002.svg" width="43pt" height="14pt" src="acp-19-8591-2019-ie00002.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt;). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities &lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M17" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;d&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;a&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="48pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="a47c1357bf9f8959859c5d28931197ed"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-8591-2019-ie00003.svg" width="48pt" height="14pt" src="acp-19-8591-2019-ie00003.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt; and &lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M18" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;N&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;d&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;/&lt;/mo&gt;&lt;mo&gt;∂&lt;/mo&gt;&lt;mi&gt;w&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="43pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="2e76a96aacff9b259f027c6bf554be27"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-8591-2019-ie00004.svg" width="43pt" height="14pt" src="acp-19-8591-2019-ie00004.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt;; models may be predisposed to be too “aerosol sensitive” or “aerosol insensitive” in aerosol–cloud–climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.&lt;/p&gt; </jats:p>


Effects of aerosol in simulations of realistic shallow cumulus cloud fields in a large domain

ATMOSPHERIC CHEMISTRY AND PHYSICS 19 (2019) 13507-13517

G Spill, P Stier, PR Field, G Dagan


Surprising similarities in model and observational aerosol radiative forcing estimates

Atmospheric Chemistry and Physics Discussions Copernicus GmbH (2019) 1-18

E Gryspeerdt, J Mülmenstädt, A Gettelman, FF Malavelle, H Morrison, D Neubauer, DG Partridge, P Stier, T Takemura, H Wang, M Wang, K Zhang

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The radiative forcing from aerosols (particularly through their interaction with clouds) remains one of the most uncertain components of the human forcing of the climate. Observation-based studies have typically found a smaller aerosol effective radiative forcing than in model simulations and were given preferential weighting in the IPCC AR5 report. With their own sources of uncertainty, it is not clear that observation-based estimates are more reliable. Understanding the source of the model-observational difference is thus vital to reduce uncertainty in the impact of aerosols on the climate.&lt;/p&gt; &lt;p&gt;These reported discrepancies arise from the different decompositions of the aerosol forcing used in model and observational studies. Applying the observational decomposition to global climate model output, the two different lines of evidence are surprisingly similar, with a much better agreement on the magnitude of aerosol impacts on cloud properties. Cloud adjustments remain a significant source of uncertainty, particularly for ice clouds. However, they are consistent with the uncertainty from observation-based methods, with the liquid water path adjustment usually enhancing the Twomey effect by less than 50&amp;amp;thinsp;%. Depending on different sets of assumptions, this work suggests that model and observation-based estimates could be more equally weighted in future synthesis studies.&lt;/p&gt; </jats:p>


Analysis of the Atmospheric Water Budget for Elucidating the Spatial Scale of Precipitation Changes Under Climate Change

Geophysical Research Letters American Geophysical Union (AGU) 46 (2019) 10504-10511

G Dagan, P Stier, D Watson‐Parris


tobac 1.2: towards a flexible framework for tracking and analysis of clouds in diverse datasets

GEOSCIENTIFIC MODEL DEVELOPMENT 12 (2019) 4551-4570

M Heikenfeld, PJ Marinescu, M Christensen, D Watson-Parris, F Senf, SC van den Heever, P Stier


Global response of parameterised convective cloud fields to anthropogenic aerosol forcing

Atmospheric Chemistry and Physics Discussions Copernicus GmbH (2019) 1-25

Z Kipling, L Labbouz, P Stier

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The interactions between aerosols and convective clouds represent some of the greatest uncertainties in the climate impact of aerosols in the atmosphere. A wide variety of mechanisms have been proposed by which aerosols may invigorate, suppress, or change the properties of individual convective clouds, some of which can be reproduced in high-resolution limited-area models. However, there may also be mesoscale, regional or global adjustments which modulate or dampen such impacts which cannot be captured in the limited domain of such models. The Convective Cloud Field Model (CCFM) provides a mechanism to explicitly simulate a population of convective clouds within each grid column at resolutions used for global climate modelling, so that a representation of the microphysical aerosol response within each parameterised cloud type is possible.&lt;/p&gt; &lt;p&gt;Using CCFM within the global aerosol–climate model ECHAM–HAM, we demonstrate how the parameterised cloud field responds to the present-day anthropogenic aerosol perturbation in different regions. In particular, we show that in regions with strongly-forced deep convection and/or significant aerosol effects via large-scale processes, the changes in the convective cloud field due to microphysical effects is rather small; however in a more weakly-forced regime such as the Caribbean, where large-scale aerosol effects are small, a signature of convective invigoration does become apparent.&lt;/p&gt; </jats:p>


Ensembles of Global Climate Model Variants Designed for the Quantification and Constraint of Uncertainty in Aerosols and Their Radiative Forcing

JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS (2019)

M Yoshioka, LA Regayre, KJ Pringle, JS Johnson, GW Mann, DG Partridge, DMH Sexton, GMS Lister, N Schutgens, P Stier, Z Kipling, N Bellouin, J Browse, BBB Booth, CE Johnson, B Johnson, JDP Mollard, L Lee, KS Carslaw


The global aerosol-climate model ECHAM6.3-HAM2.3-Part 1: Aerosol evaluation

GEOSCIENTIFIC MODEL DEVELOPMENT 12 (2019) 1643-1677

I Tegen, D Neubauer, S Ferrachat, C Siegenthaler-Le Drian, I Bey, N Schutgens, P Stier, D Watson-Parris, T Stanelle, H Schmidt, S Rast, H Kokkola, M Schultz, S Schroeder, N Daskalakis, S Barthel, B Heinold, U Lohmann


Efficacy of Climate Forcings in PDRMIP Models

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES (2019)

TB Richardson, PM Forster, CJ Smith, AC Maycock, T Wood, T Andrews, O Boucher, G Faluvegi, D Flaeschner, O Hodnebrog, M Kasoar, A Kirkevag, JF Larnarque, J Muelmenstaedt, G Myhre, D Olivie, RW Portmann, BH Samset, D Shawki, D Shindell, P Stier, T Takemura, A Voulgarakis, D Watson-Parris


Water vapour adjustments and responses differ between climate drivers

ATMOSPHERIC CHEMISTRY AND PHYSICS 19 (2019) 12887-12899

O Hodnebrog, G Myhre, BH Samset, K Alterskjaer, T Andrews, O Boucher, G Faluvegi, D Flaeschner, PM Forster, M Kasoar, A Kirkevag, J-F Lamarque, D Olivie, TB Richardson, D Shawki, D Shindell, KP Shine, P Stier, T Takemura, A Voulgarakis, D Watson-Parris


Satellite inference of water vapour and above-cloud aerosol combined effect on radiative budget and cloud-Top processes in the southeastern Atlantic Ocean

Atmospheric Chemistry and Physics 19 (2019) 11613-11634

LT Deaconu, N Ferlay, F Waquet, F Peers, F Thieuleux, P Goloub

© Author(s) 2019. Aerosols have a direct effect on the Earth's radiative budget and can also affect cloud development and lifetime, and the aerosols above clouds (AAC) are particularly associated with high uncertainties in global climate models. Therefore, it is a prerequisite to improve the description and understanding of these situations. During the austral winter, large loadings of biomass burning aerosols originating from fires in the southern African subcontinent are lifted and transported westwards, across the southeastern Atlantic Ocean. The negligible wet scavenging of these absorbing aerosols leads to a near-persistent smoke layer above one of the largest stratocumulus cloud decks on the planet. Therefore, the southeastern Atlantic region is a very important area for studying the impact of above-cloud absorbing aerosols, their radiative forcing and their possible effects on clouds. In this study we aim to analyse and quantify the effect of smoke loadings on cloud properties using a synergy of different remote sensing techniques from A-Train retrievals (methods based on the passive instruments POLDER and MODIS and the operational method of the spaceborne lidar CALIOP), collocated with ERA-Interim re-analysis meteorological profiles. To analyse the possible mechanisms of AAC effects on cloud properties, we developed a high and low aerosol loading approach, which consists in evaluating the change in radiative quantities (i.e. cloud-top cooling, heating rate vertical profiles) and cloud properties with the smoke loading. During this analysis, we account for the variation in the meteorological conditions over our sample area by selecting the months associated with one meteorological regime (June-August). The results show that the region we focus on is primarily under the energetic influence of absorbing aerosols, leading to a significant positive shortwave direct effect at the top of the atmosphere. For larger loads of AACs, clouds are optically thicker, with an increase in liquid water path of 20 gm 2 and lower cloud-top altitudes by 100 m. These results do not contradict the semi-direct effect of above-cloud aerosols, explored in previous studies. Furthermore, we observe a strong covariance between the aerosol and the water vapour loadings, which has to be accounted for. A detailed analysis of the heating rate profiles shows that within the smoke layer, the absorbing aerosols are 90% responsible for warming the ambient air by approximately 5.7Kd 1. The accompanying water vapour, however, has a longwave effect at distance on the cloud top, reducing its cooling by approximately 4.7Kd 1 (equivalent to 7 %). We infer that this decreased cloud-top cooling in particular, in addition with the higher humidity above the clouds, might modify the cloud-top entrainment rate and its effect, leading to thicker clouds. Therefore, smoke (the combination of aerosol and water vapour) events would have the potential to modify and probably reinforce the underlaying cloud cover.


Contrasting Response of Precipitation to Aerosol Perturbation in the Tropics and Extratropics Explained by Energy Budget Considerations.

Geophysical research letters 46 (2019) 7828-7837

G Dagan, P Stier, D Watson-Parris

Precipitation plays a crucial role in the Earth's energy balance, the water cycle, and the global atmospheric circulation. Aerosols, by direct interaction with radiation and by serving as cloud condensation nuclei, may affect clouds and rain formation. This effect can be examined in terms of energetic constraints, that is, any aerosol-driven diabatic heating/cooling of the atmosphere will have to be balanced by changes in precipitation, radiative fluxes, or divergence of dry static energy. Using an aqua-planet general circulation model (GCM), we show that tropical and extratropical precipitation have contrasting responses to aerosol perturbations. This behavior can be explained by contrasting ability of the atmosphere to diverge excess dry static energy in the two different regions. It is shown that atmospheric heating in the tropics leads to large-scale thermally driven circulation and a large increase in precipitation, while the excess energy from heating in the extratropics is constrained due to the effect of the Coriolis force, causing the precipitation to decrease.


In situ constraints on the vertical distribution of global aerosol

ATMOSPHERIC CHEMISTRY AND PHYSICS 19 (2019) 11765-11790

D Watson-Parris, N Schutgens, C Reddington, KJ Pringle, D Liu, JD Allan, H Coe, KS Carslaw, P Stier


Constraining the aerosol influence on cloud liquid water path

ATMOSPHERIC CHEMISTRY AND PHYSICS 19 (2019) 5331-5347

E Gryspeerdt, T Goren, O Sourdeval, J Quaas, J Muelmenstaedt, S Dipu, C Unglaub, A Gettelman, M Christensen


Weak average liquid-cloud-water response to anthropogenic aerosols.

Nature 572 (2019) 51-55

V Toll, M Christensen, J Quaas, N Bellouin

The cooling of the Earth's climate through the effects of anthropogenic aerosols on clouds offsets an unknown fraction of greenhouse gas warming. An increase in the amount of water inside liquid-phase clouds induced by aerosols, through the suppression of rain formation, has been postulated to lead to substantial cooling, which would imply that the Earth's surface temperature is highly sensitive to anthropogenic forcing. Here we provide direct observational evidence that, instead of a strong increase, aerosols cause a relatively weak average decrease in the amount of water in liquid-phase clouds compared with unpolluted clouds. Measurements of polluted clouds downwind of various anthropogenic sources-such as oil refineries, smelters, coal-fired power plants, cities, wildfires and ships-reveal that aerosol-induced cloud-water increases, caused by suppressed rain formation, and decreases, caused by enhanced evaporation of cloud water, partially cancel each other out. We estimate that the observed decrease in cloud water offsets 23% of the global climate-cooling effect caused by aerosol-induced increases in the concentration of cloud droplets. These findings invalidate the hypothesis that increases in cloud water cause a substantial climate cooling effect and translate into reduced uncertainty in projections of future climate.


Core and margin in warm convective clouds – Part 1: Core types and evolution during a cloud's lifetime

Atmospheric Chemistry and Physics Copernicus GmbH 19 (2019) 10717-10738

RH Heiblum, L Pinto, O Altaratz, G Dagan, I Koren

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The properties of a warm convective cloud are determined by the competition between the growth and dissipation processes occurring within it. One way to observe and follow this competition is by partitioning the cloud to core and margin regions. Here we look at three core definitions, namely positive vertical velocity (&lt;span class="inline-formula"&gt;&lt;i&gt;W&lt;/i&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt;), supersaturation (RH&lt;span class="inline-formula"&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt;), and positive buoyancy (&lt;span class="inline-formula"&gt;&lt;i&gt;B&lt;/i&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt;), and follow their evolution throughout the lifetime of warm convective clouds.&lt;/p&gt; &lt;p&gt;Using single cloud and cloud field simulations with bin-microphysics schemes, we show that the different core types tend to be subsets of one another in the following order: &lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;B&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;core&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;⊆&lt;/mo&gt;&lt;msub&gt;&lt;mi mathvariant="normal"&gt;RH&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;core&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;⊆&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;core&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="107pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="4b48f5ce235ae08f6aa376e6e7adc73c"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-10717-2019-ie00001.svg" width="107pt" height="13pt" src="acp-19-10717-2019-ie00001.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt;. This property is seen for several different thermodynamic profile initializations and is generally maintained during the growing and mature stages of a cloud's lifetime. This finding is in line with previous works and theoretical predictions showing that cumulus clouds may be dominated by negative buoyancy at certain stages of their lifetime. The RH&lt;span class="inline-formula"&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt;–&lt;span class="inline-formula"&gt;&lt;i&gt;W&lt;/i&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt; pair is most interchangeable, especially during the growing stages of the cloud.&lt;/p&gt; &lt;p&gt;For all three definitions, the core–shell model of a core (positive values) at the center of the cloud surrounded by a shell (negative values) at the cloud periphery applies to over 80&amp;amp;thinsp;% of a typical cloud's lifetime. The core–shell model is less appropriate in larger clouds with multiple cores displaced from the cloud center. Larger clouds may also exhibit buoyancy cores centered near the cloud edge. During dissipation the cores show less overlap, reduce in size, and may migrate from the cloud center.&lt;/p&gt; </jats:p>


Core and margin in warm convective clouds – Part 2: Aerosol effects on core properties

Atmospheric Chemistry and Physics Copernicus GmbH 19 (2019) 10739-10755

RH Heiblum, L Pinto, O Altaratz, G Dagan, I Koren

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; The effects of aerosol on warm convective cloud cores are evaluated using single cloud and cloud field simulations. Three core definitions are examined: positive vertical velocity (&lt;span class="inline-formula"&gt;&lt;i&gt;W&lt;/i&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt;), supersaturation (RH&lt;span class="inline-formula"&gt;&lt;sub&gt;core&lt;/sub&gt;)&lt;/span&gt;, and positive buoyancy (&lt;span class="inline-formula"&gt;&lt;i&gt;B&lt;/i&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt;). As presented in Part 1 (Heiblum et al., 2019), the property &lt;span class="inline-formula"&gt;&lt;math xmlns="http://www.w3.org/1998/Math/MathML" id="M4" display="inline" overflow="scroll" dspmath="mathml"&gt;&lt;mrow&gt;&lt;msub&gt;&lt;mi&gt;B&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;core&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;⊆&lt;/mo&gt;&lt;msub&gt;&lt;mi mathvariant="normal"&gt;RH&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;core&lt;/mi&gt;&lt;/msub&gt;&lt;mo&gt;⊆&lt;/mo&gt;&lt;msub&gt;&lt;mi&gt;W&lt;/mi&gt;&lt;mi mathvariant="normal"&gt;core&lt;/mi&gt;&lt;/msub&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;span&gt;&lt;svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="107pt" height="13pt" class="svg-formula" dspmath="mathimg" md5hash="4b48f5ce235ae08f6aa376e6e7adc73c"&gt;&lt;svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="acp-19-10739-2019-ie00001.svg" width="107pt" height="13pt" src="acp-19-10739-2019-ie00001.png"/&gt;&lt;/svg:svg&gt;&lt;/span&gt;&lt;/span&gt; is seen during growth of warm convective clouds. We show that this property is kept irrespective of aerosol concentration. During dissipation core fractions generally decrease with less overlap between cores. However, for clouds that develop in low aerosol concentrations capable of producing precipitation, &lt;span class="inline-formula"&gt;&lt;i&gt;B&lt;/i&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt; and subsequently &lt;span class="inline-formula"&gt;&lt;i&gt;W&lt;/i&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt; volume fractions may increase during dissipation (i.e., loss of cloud mass). The RH&lt;span class="inline-formula"&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt; volume fraction decreases during cloud lifetime and shows minor sensitivity to aerosol concentration.&lt;/p&gt; &lt;p&gt;It is shown that a &lt;span class="inline-formula"&gt;&lt;i&gt;B&lt;/i&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt; forms due to two processes: (i) convective updrafts – condensation within supersaturated updrafts and release of latent heat – and (ii) dissipative downdrafts – subsaturated cloudy downdrafts that warm during descent and “undershoot” the level of neutral buoyancy. The former process occurs during cloud growth for all aerosol concentrations. The latter process only occurs for low aerosol concentrations during dissipation and precipitation stages where large mean drop sizes permit slow evaporation rates and subsaturation during descent.&lt;/p&gt; &lt;p&gt;The aerosol effect on the diffusion efficiencies plays a crucial role in the development of the cloud and its partition to core and margin. Using the RH&lt;span class="inline-formula"&gt;&lt;sub&gt;core&lt;/sub&gt;&lt;/span&gt; definition, it is shown that the total cloud mass is mostly dictated by core processes, while the total cloud volume is mostly dictated by margin processes. Increase in aerosol concentration increases the core (mass and volume) due to enhanced condensation but also decreases the margin due to evaporation. In clean clouds larger droplets evaporate much slower, enabling preservation of cloud size, and even increase by detrainment and dilution (volume increases while losing mass). This explains how despite having smaller cores and less mass, cleaner clouds may live longer and grow to larger sizes.&lt;/p&gt; </jats:p>


Non-Monotonic Aerosol Effect on Precipitation in Convective Clouds over Tropical Oceans

Scientific Reports Springer Science and Business Media LLC 9 (2019) 7809

H Liu, J Guo, I Koren, O Altaratz, G Dagan, Y Wang, JH Jiang, P Zhai, YL Yung


Anthropogenic aerosol forcing – insights from multiple estimates from aerosol-climate models with reduced complexity

Atmospheric Chemistry and Physics Copernicus GmbH 19 (2019) 6821-6841

S Fiedler, S Kinne, WTK Huang, P Räisänen, D O&amp;apos;Donnell, N Bellouin, P Stier, J Merikanto, T van Noije, R Makkonen, U Lohmann

<jats:p>&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; This study assesses the change in anthropogenic aerosol forcing from the mid-1970s to the mid-2000s. Both decades had similar global-mean anthropogenic aerosol optical depths but substantially different global distributions. For both years, we quantify (i) the forcing spread due to model-internal variability and (ii) the forcing spread among models. Our assessment is based on new ensembles of atmosphere-only simulations with five state-of-the-art Earth system models. Four of these models will be used in the sixth Coupled Model Intercomparison Project (CMIP6; &lt;span class="cit" id="xref_altparen.1"&gt;&lt;a href="#bib1.bibx14"&gt;Eyring et al.&lt;/a&gt;, &lt;a href="#bib1.bibx14"&gt;2016&lt;/a&gt;&lt;/span&gt;). Here, the complexity of the anthropogenic aerosol has been reduced in the participating models. In all our simulations, we prescribe the same patterns of the anthropogenic aerosol optical properties and associated effects on the cloud droplet number concentration. We calculate the instantaneous radiative forcing (RF) and the effective radiative forcing (ERF). Their difference defines the net contribution from rapid adjustments. Our simulations show a model spread in ERF from &lt;span class="inline-formula"&gt;−0.4&lt;/span&gt; to &lt;span class="inline-formula"&gt;−0.9&lt;/span&gt;&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;span class="inline-formula"&gt;&lt;sup&gt;−2&lt;/sup&gt;&lt;/span&gt;. The standard deviation in annual ERF is 0.3&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;span class="inline-formula"&gt;&lt;sup&gt;−2&lt;/sup&gt;&lt;/span&gt;, based on 180 individual estimates from each participating model. This result implies that identifying the model spread in ERF due to systematic differences requires averaging over a sufficiently large number of years. Moreover, we find almost identical ERFs for the mid-1970s and mid-2000s for individual models, although there are major model differences in natural aerosols and clouds. The model-ensemble mean ERF is &lt;span class="inline-formula"&gt;−0.54&lt;/span&gt;&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;span class="inline-formula"&gt;&lt;sup&gt;−2&lt;/sup&gt;&lt;/span&gt; for the pre-industrial era to the mid-1970s and &lt;span class="inline-formula"&gt;−0.59&lt;/span&gt;&amp;amp;thinsp;W&amp;amp;thinsp;m&lt;span class="inline-formula"&gt;&lt;sup&gt;−2&lt;/sup&gt;&lt;/span&gt; for the pre-industrial era to the mid-2000s. Our result suggests that comparing ERF changes between two observable periods rather than absolute magnitudes relative to a poorly constrained pre-industrial state might provide a better test for a model's ability to represent transient climate changes.&lt;/p&gt; </jats:p>

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