Description: In order to enhance our understanding of clouds and their microphysical processes, it is crucial to exploit both observations and models. Local observations from ground‐based remote sensing sites provide detailed information on clouds, but as they are limited in dimension, there is no straightforward way to use them to guide large‐scale model development. We show that large‐eddy simulations (LES) performed on similar temporal and spatial scales as the local observations can bridge this gap. Recently, LES with realistic topography and lateral boundary conditions became feasible for domains spanning several 100 km. In this study, we show how these simulations can be linked to observations of the Jülich Observatory for Cloud Evolution (JOYCE) for a 9‐day period in spring 2013. We discuss the advantages and disadvantages of very large versus small but more constrained domains as well as the differences compared to more idealized setups. The semi‐idealized LES include time‐varying forcing but are run with homogeneous surfaces and periodic boundary conditions. These assumptions seem to be the reason why they struggle to represent the observed varying conditions. The simulations using the “realistic” setup are able to represent the general cloud structure (timing, height, phase). It seems that the smaller and more constrained domain allows for a tighter control on the synoptic situation and is the preferred choice to ensure the comparability to the local observations. These simulations together with measures as the shown Hellinger distance will allow us to gain more insights into the representativeness of column measurements in the future.

Description: Plain Language Summary: Clouds are still a cause for uncertainty in our understanding of climate and climate feedbacks. Due to the large range of involved scales—from small droplets up to storm systems—their representation in weather and climate models is an ongoing challenge. While new and sophisticated measurements of the atmospheric column could provide new insights into important processes, their linking to models is not trivial and is ongoing research. In this study, we are presenting and exploring different approaches to combine local observations of clouds with state‐of‐the‐art high‐resolution simulations. And we are presenting a setup, which shows a promising representation of the observed clouds and is constrained enough to be applicable for long‐term statistics—one of the key requirements for improvements and evaluation clouds in of weather and climate models.

Description: Key Points: Large‐eddy simulations including external variability can bridge the gap between ground‐based observations of clouds and large‐scale models. For comparison with local observations, it is important to take external variability (e.g., large‐scale forcing and surface) into account. ICON‐LEM offers new possibilities to simulate small scales while considering external variability.

Description: The relationship between mesoscale convective organization, quantified by the spatial arrangement of convection, and oceanic precipitation in the tropical belt is examined using the output of a global storm-resolving simulation. The analysis uses a 2D watershed segmentation algorithm based on local precipitation maxima to isolate individual precipitation cells and derive their properties. 10° by 10° scenes are analyzed using a phase-space representation made of the number of cells per scene and the mean area of the cells per scene to understand the controls on the spatial arrangement of convection and its precipitation. The presence of few and large cells in a scene indicates the presence of a more clustered distribution of cells, whereas many small cells in a scene tend to be randomly distributed. In general, the degree of clustering of a scene (Iorg) is positively correlated to the mean area of the cells and negatively correlated to the number of cells. Strikingly, the degree of clustering, whether the cells are randomly distributed or closely spaced, to a first order does not matter for the precipitation amounts produced. Scenes of similar precipitation amounts appear as hyperbolae in our phase-space representation, hyperbolae that follow the contours of the precipitating area fraction. Finally, including the scene-averaged water vapour path (WVP) in our phase-space analysis reveals that scenes with larger WVP contain more cells than drier scenes, whereas the mean area of the cells only weakly varies with WVP. Dry scenes can contain both small and large cells, but they can contain only few cells of each category.

Description: The tropical temperature in the free troposphere deviates from a theoretical moist‐adiabat. The overall deviations are attributed to the entrainment of dry surrounding air. The deviations gradually approach zero in the upper troposphere, which we explain with a buoyancy‐sorting mechanism: the height to which individual convective parcels rise depends on parcel buoyancy, which is closely tied to the impact of entrainment during ascent. In higher altitudes, the temperature is increasingly controlled by the convective parcels that are warmer and more buoyant because of weaker entrainment effects. We represent such temperature deviations from moist‐adiabats in a clear‐sky one‐dimensional radiative‐convective equilibrium model. Compared with a moist‐adiabatic adjustment, having the entrainment‐induced temperature deviations lead to higher clear‐sky climate sensitivity. As the impact of entrainment depends on the saturation deficit, which increases with warming, our model predicts even more amplified surface warming from entrainment in a warmer climate.

Description: Plain Language Summary: The tropical temperature structure is determined by regions with deep convection, which is believed to be moist‐adiabatic. However, both models and observations show that the temperature deviates from moist‐adiabats. This is because convective parcels often mix with dry environmental air during ascent, pushing the temperature away from the moist‐adiabatic structure. More importantly, the tropical temperature is not dominated by one or a few strongest convective plumes, but rather controlled by the combined effect of many convective plumes of different strengths and depths. Therefore, the tropical temperature structure reflects the composition of convection happening at different values of boundary‐layer energy and mixing processes of variable efficiency with the environment. Using an idealized model, we find that representing such a deviation in the temperature structure increases the surface warming, because the resulting temperature lapse rate (LR) is more similar to a constant LR, showing less temperature increases higher than a moist‐adiabatic LR. This effect is likely amplified in a warmer climate due to this mixing process becoming more efficient in pushing the temperature further away from moist‐adiabats.

Description: Key Points: The tropical temperature profile in the free troposphere deviates from that following a moist‐adiabatic lapse rate (LR). The deviations from the moist‐adiabatic LR can be explained by entrainment with a buoyancy‐sorting mechanism. The temperature deviations from moist‐adiabats increase climate sensitivity.

Description: https://doi.org/10.5281/zenodo.1313687

Description: https://cds.climate.copernicus.eu/cdsapp#%21/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=overview

Description: https://esgf-data.dkrz.de/projects/cmip6-dkrz/

Description: http://hdl.handle.net/21.11116/0000-0008-FDA6-0

Description: Four previously identified patterns of meso‐scale cloud organization in the trades — called Sugar, Gravel, Flowers, and Fish — are studied using long‐term records of ground‐based measurements, satellite observations and reanalyzes. A deep neural network trained to detect these patterns is applied to satellite imagery to identify periods during which a particular pattern is observed over the Barbados Cloud Observatory. Surface‐based remote sensing at the observatory is composited and shows that the patterns can be distinguished by differences in cloud geometry. Variations in total cloudiness among the patterns are dominated by variations in cloud‐top cloudiness. Cloud amount near cloud base varies little. Each pattern is associated with a distinct atmospheric environment whose characteristics are traced back to origins that are not solely within the trades. Sugar air‐masses are characterized by weak winds and of tropical origin. Fish are driven by convergence lines originating from synoptical disturbances. Gravel and Flowers are most native to the trades, but distinguish themselves with slightly stronger winds and stronger subsidence in the first case and greater stability in the latter. The patterns with the higher cloud amounts and more negative cloud‐radiative effects, Flowers and Fish, are selected by conditions expected to occur less frequently with greenhouse warming.

Description: Key Points: Meso‐scale patterns of trade‐wind clouds are identified with a neural network and characterized based on observations. The four analyzed patterns distinguish themselves by stratiform cloudiness and less by cloudiness at the lifting condensation level. Two patterns are imprinted by tropical, respectively extra‐tropical intrusions.

Description: European Union's Horizon 2020 Research and Innovation Programme

Description: NASA

Description: https://doi.org/10.5281/zenodo.4767674

Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Stevens, B., Bony, S., Farrell, D., Ament, F., Blyth, A., Fairall, C., Karstensen, J., Quinn, P. K., Speich, S., Acquistapace, C., Aemisegger, F., Albright, A. L., Bellenger, H., Bodenschatz, E., Caesar, K.-A., Chewitt-Lucas, R., de Boer, G., Delanoë, J., Denby, L., Ewald, F., Fildier, B., Forde, M., George, G., Gross, S., Hagen, M., Hausold, A., Heywood, K. J., Hirsch, L., Jacob, M., Jansen, F., Kinne, S., Klocke, D., Kölling, T., Konow, H., Lothon, M., Mohr, W., Naumann, A. K., Nuijens, L., Olivier, L., Pincus, R., Pöhlker, M., Reverdin, G., Roberts, G., Schnitt, S., Schulz, H., Siebesma, A. P., Stephan, C. C., Sullivan, P., Touzé-Peiffer, L., Vial, J., Vogel, R., Zuidema, P., Alexander, N., Alves, L., Arixi, S., Asmath, H., Bagheri, G., Baier, K., Bailey, A., Baranowski, D., Baron, A., Barrau, S., Barrett, P. A., Batier, F., Behrendt, A., Bendinger, A., Beucher, F., Bigorre, S., Blades, E., Blossey, P., Bock, O., Böing, S., Bosser, P., Bourras, D., Bouruet-Aubertot, P., Bower, K., Branellec, P., Branger, H., Brennek, M., Brewer, A., Brilouet , P.-E., Brügmann, B., Buehler, S. A., Burke, E., Burton, R., Calmer, R., Canonici, J.-C., Carton, X., Cato Jr., G., Charles, J. A., Chazette, P., Chen, Y., Chilinski, M. T., Choularton, T., Chuang, P., Clarke, S., Coe, H., Cornet, C., Coutris, P., Couvreux, F., Crewell, S., Cronin, T., Cui, Z., Cuypers, Y., Daley, A., Damerell, G. M., Dauhut, T., Deneke, H., Desbios, J.-P., Dörner, S., Donner, S., Douet, V., Drushka, K., Dütsch, M., Ehrlich, A., Emanuel, K., Emmanouilidis, A., Etienne, J.-C., Etienne-Leblanc, S., Faure, G., Feingold, G., Ferrero, L., Fix, A., Flamant, C., Flatau, P. J., Foltz, G. R., Forster, L., Furtuna, I., Gadian, A., Galewsky, J., Gallagher, M., Gallimore, P., Gaston, C., Gentemann, C., Geyskens, N., Giez, A., Gollop, J., Gouirand, I., Gourbeyre, C., de Graaf, D., de Groot, G. E., Grosz, R., Güttler, J., Gutleben, M., Hall, K., Harris, G., Helfer, K. C., Henze, D., Herbert, C., Holanda, B., Ibanez-Landeta, A., Intrieri, J., Iyer, S., Julien, F., Kalesse, H., Kazil, J., Kellman, A., Kidane, A. T., Kirchner, U., Klingebiel, M., Körner, M., Kremper, L. A., Kretzschmar, J., Krüger, O., Kumala, W., Kurz, A., L'Hégaret, P., Labaste, M., Lachlan-Cope, T., Laing, A., Landschützer, P., Lang, T., Lange, D., Lange, I., Laplace, C., Lavik, G., Laxenaire, R., Le Bihan, C., Leandro, M., Lefevre, N., Lena, M., Lenschow, D., Li, Q., Lloyd, G., Los, S., Losi, N., Lovell, O., Luneau, C., Makuch, P., Malinowski, S., Manta, G., Marinou, E., Marsden, N., Masson, S., Maury, N., Mayer, B., Mayers-Als, M., Mazel, C., McGeary, W., McWilliams, J. C., Mech, M., Mehlmann, M., Meroni, A. N., Mieslinger, T., Minikin, A., Minnett, P., Möller, G., Morfa Avalos, Y., Muller, C., Musat, I., Napoli, A., Neuberger, A., Noisel, C., Noone, D., Nordsiek, F., Nowak, J. L., Oswald, L., Parker, D. J., Peck, C., Person, R., Philippi, M., Plueddemann, A., Pöhlker, C., Pörtge, V., Pöschl, U., Pologne, L., Posyniak, M., Prange, M., Quiñones Meléndez, E., Radtke, J., Ramage, K., Reimann, J., Renault, L., Reus, K., Reyes, A., Ribbe, J., Ringel, M., Ritschel, M., Rocha, C. B., Rochetin, N., Röttenbacher, J., Rollo, C., Royer, H., Sadoulet, P., Saffin, L., Sandiford, S., Sandu, I., Schäfer, M., Schemann, V., Schirmacher, I., Schlenczek, O., Schmidt, J., Schröder, M., Schwarzenboeck, A., Sealy, A., Senff, C. J., Serikov, I., Shohan, S., Siddle, E., Smirnov, A., Späth, F., Spooner, B., Stolla, M. K., Szkółka, W., de Szoeke, S. P., Tarot, S., Tetoni, E., Thompson, E., Thomson, J., Tomassini, L., Totems, J., Ubele, A. A., Villiger, L., von Arx, J., Wagner, T., Walther, A., Webber, B., Wendisch, M., Whitehall, S., Wiltshire, A., Wing, A. A., Wirth, M., Wiskandt, J., Wolf, K., Worbes, L., Wright, E., Wulfmeyer, V., Young, S., Zhang, C., Zhang, D., Ziemen, F., Zinner, T., and Zöger, M.: EUREC4A. Earth System Science Data, 13(8), (2021): 4067–4119, https://doi.org/10.5194/essd-13-4067-2021.

Description: The science guiding the EUREC4A campaign and its measurements is presented. EUREC4A comprised roughly 5 weeks of measurements in the downstream winter trades of the North Atlantic – eastward and southeastward of Barbados. Through its ability to characterize processes operating across a wide range of scales, EUREC4A marked a turning point in our ability to observationally study factors influencing clouds in the trades, how they will respond to warming, and their link to other components of the earth system, such as upper-ocean processes or the life cycle of particulate matter. This characterization was made possible by thousands (2500) of sondes distributed to measure circulations on meso- (200 km) and larger (500 km) scales, roughly 400 h of flight time by four heavily instrumented research aircraft; four global-class research vessels; an advanced ground-based cloud observatory; scores of autonomous observing platforms operating in the upper ocean (nearly 10 000 profiles), lower atmosphere (continuous profiling), and along the air–sea interface; a network of water stable isotopologue measurements; targeted tasking of satellite remote sensing; and modeling with a new generation of weather and climate models. In addition to providing an outline of the novel measurements and their composition into a unified and coordinated campaign, the six distinct scientific facets that EUREC4A explored – from North Brazil Current rings to turbulence-induced clustering of cloud droplets and its influence on warm-rain formation – are presented along with an overview of EUREC4A's outreach activities, environmental impact, and guidelines for scientific practice. Track data for all platforms are standardized and accessible at https://doi.org/10.25326/165 (Stevens, 2021), and a film documenting the campaign is provided as a video supplement.

Description: This research has been supported by the people and government of Barbados; the Max Planck Society and its supporting members; the German Research Foundation (DFG) and the German Federal Ministry of Education and Research (grant nos. GPF18-1_69 and GPF18-2_50); the European Research Council (ERC) advanced grant EUREC4A (grant agreement no. 694768) under the European Union’s Horizon 2020 research and innovation program (H2020), with additional support from CNES (the French National Centre for Space Studies) through the EECLAT proposal, Météo-France, the CONSTRAIN H2020 project (grant agreement no. 820829), and the French AERIS Research Infrastructure; the Natural Environment Research Council (NE/S015868/1, NE/S015752/1, and NE/S015779/1); ERC under the European Union’s H2020 program (COMPASS, advanced grant agreement no. 74110); the French national program LEFE INSU, by IFREMER, the French research fleet, CNES, the French research infrastructures AERIS and ODATIS, IPSL, the Chaire Chanel program of the Geosciences Department at ENS, and the European Union's Horizon 2020 research and innovation program under grant agreement no. 817578 TRIATLAS; NOAA’s Climate Variability and Prediction Program within the Climate Program Office (grant nos. GC19-305 and GC19-301); NOAA cooperative agreement NA15OAR4320063; NOAA's Climate Program Office and base funds to NOAA/AOML's Physical Oceanography Division; Swiss National Science Foundation grant no. 188731; the UAS Program Office, Climate Program Office, and Physical Sciences Laboratory and by the US National Science Foundation (NSF) through grant AGS-1938108; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy – EXC 2037 “CLICCS – Climate, Climatic Change, and Society” – project no. 390683824; and Poland’s National Science Centre grant no. UMO-2018/30/M/ST10/00674 and Foundation for Polish Science grant no. POIR.04.04.00-00-3FD6/17-02.

Description: © The Author(s), 2017. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Surveys in Geophysics 38 (2017): 1529–1568, doi:10.1007/s10712-017-9428-0.

Description: Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization.

Description: The EUREC4A project is supported by the European Research Council (ERC), under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 694768), by the Max Planck Society and by DFG (Deutsche Forschungsgemeinschaft, German Research Foundation) Priority Program SPP 1294.

Description: Trade-wind cumuli constitute the cloud type with the highest frequency of occurrence on Earth, and it has been shown that their sensitivity to changing environmental conditions will critically influence the magnitude and pace of future global warming. Research over the last decade has pointed out the importance of the interplay between clouds, convection and circulation in controling this sensitivity. Numerical models represent this interplay in diverse ways, which translates into different responses of trade-cumuli to climate perturbations. Climate models predict that the area covered by shallow cumuli at cloud base is very sensitive to changes in environmental conditions, while process models suggest the opposite. To understand and resolve this contradiction, we propose to organize a field campaign aimed at quantifying the physical properties of trade-cumuli (e.g., cloud fraction and water content) as a function of the large-scale environment. Beyond a better understanding of clouds-circulation coupling processes, the campaign will provide a reference data set that may be used as a benchmark for advancing the modelling and the satellite remote sensing of clouds and circulation. It will also be an opportunity for complementary investigations such as evaluating model convective parameterizations or studying the role of ocean mesoscale eddies in air–sea interactions and convective organization

Description: Stratospheric sulfate aerosols from volcanic eruptions have a significant impact on the Earth's climate. To include the effects of volcanic eruptions in climate model simulations, the Easy Volcanic Aerosol (EVA) forcing generator provides stratospheric aerosol optical properties as a function of time, latitude, height, and wavelength for a given input list of volcanic eruption attributes. EVA is based on a parameterized three-box model of stratospheric transport and simple scaling relationships used to derive mid-visible (550 nm) aerosol optical depth and aerosol effective radius from stratospheric sulfate mass. Precalculated look-up tables computed from Mie theory are used to produce wavelength-dependent aerosol extinction, single scattering albedo, and scattering asymmetry factor values. The structural form of EVA and the tuning of its parameters are chosen to produce best agreement with the satellite-based reconstruction of stratospheric aerosol properties following the 1991 Pinatubo eruption, and with prior millennial-timescale forcing reconstructions, including the 1815 eruption of Tambora. EVA can be used to produce volcanic forcing for climate models which is based on recent observations and physical understanding but internally self-consistent over any timescale of choice. In addition, EVA is constructed so as to allow for easy modification of different aspects of aerosol properties, in order to be used in model experiments to help advance understanding of what aspects of the volcanic aerosol are important for the climate system.

Description: We present the first forcing interpretation of the future anthropogenic aerosol scenarios of CMIP6 with the simple plumes parameterisation MACv2-SP. The nine scenarios for 2015 to 2100 are based on anthropogenic aerosol emissions for use in CMIP6 (Riahi et al., 2017; Gidden et al., 2018). We use the emissions to scale the observationally informed anthropogenic aerosol optical properties and the associated effect on the cloud albedo of present-day (Fiedler et al., 2017; Stevens et al., 2017) into the future. The resulting scenarios in MACv2-SP are then ranked according to their strength in forcing magnitude and spatial asymmetries for anthropogenic aerosol. All scenarios, except SSP3-70 and SSP4-60, show a decrease in anthropogenic aerosol by 2100 with a range from 108 % to 36 % of the anthropogenic aerosol optical depth in 2015. We estimate the radiative forcing of anthropogenic aerosol from high- and low-end scenarios in the mid-2090s by performing ensembles of simulations with the atmosphere-only configuration of MPI-ESM1.2. MACv2-SP translates the CMIP6 emission scenarios for inducing anthropogenic aerosol forcing. With the implementation in our model, we obtain forcing estimates for both the shortwave instantaneous radiative forcing (RF) and the effective radiative forcing (ERF) of anthropogenic aerosol relative to 1850. Here, ERF accounts for rapid atmospheric adjustments and natural variability internal to the model. The ERF of anthropogenic aerosol for the mid-2090s ranges from −0.15 W m−2 for SSP1-19 to −0.54 W m−2 for SSP3-70, i.e. the mid-2090s ERF is 30 %–108 % of the value in the mid-2000s due to differences in the emission pathway alone. Assuming a stronger Twomey effect changes these ERFs to −0.39 and −0.92 W m−2, respectively, which are similar to estimates obtained from models with complex aerosol parameterisations. The year-to-year standard deviations around 0.3 W m−2 associated with natural variability highlight the necessity to average over sufficiently long time periods for estimating ERF; this is in contrast to RF that is typically well constrained after simulating just 1 year. The scenario interpretation of MACv2-SP will be used within the framework of CMIP6 and other cutting-edge scientific endeavours.