DEUTSCH

Climate Surface Interaction

It is hard to spend a day without thinking about precipitation. While it is most generally related to the mundane question of having to take an umbrella or not, more crucially, a lack of precipitation puts the existence of terrestrial ecosystems at sake. In contrast to the marine life and habitats, the terrestrial biosphere cannot tap into an unlimited reservoir of water molecules that can be recycled to support life. The long-term mean of the spatial distribution of precipitation is often taken for granted, but with the Earth warming, shifts in precipitation patterns threaten ecosystems. Towards understanding such shifts, we aim to understand basic features of the mean distribution of precipitation, with a particular emphasis on the role that interactions between convective storms (e.g. thunderstorms) and the underlying surface play. Our research focuses on two aspects of the precipitation distribution: the role of the land surface in setting the mean precipitation amount over land; and the role of ocean-atmosphere interactions in shaping tropical rain belts. 

 

World map: Observed precipitation
Figure 1: observed precipitation

Even though visual examples of the imprint of the land surface on precipitation exist, like the enhancement of precipitation over mountainous regions, the strength of the coupling between convective storms and the underlying surface remains virtually unknown. Imagine that a farmer waters his field and a couple of hours later a severe thunderstorm develops. Is the thunderstorm a result of the watering? Or was it pure coincidence? Or did the watering maybe make the thunderstorm more severe than it would have been otherwise? Moreover, can these localized interactions between the surface and the atmosphere scale up to affect the large-scale distribution of precipitation, both in terms of location and shape of the tropical rain belts as well as the amount of precipitation the land is receiving?

Climate models are often employed to answer such questions, but the use of conventional climate models appears inappropriate in this context. On the global scale, climate models employ a typical grid spacing of O (100 km). As most convective clouds are smaller in size, they cannot be directly represented by solving the underlying fluid-dynamical equations at each grid point, but have to be “guessed” using a set of simplified rules of thumb, called parameterization. The design of such parameterizations, and not the underlying physical processes, ends up setting the strength of the coupling between convective storms and the underlying surface. On the regional scale, models with kilometer-scale grid spacing can be used, allowing them to predict convective storms by explicitly solving the underlying fluid-dynamical equations. Yet the required imposed conditions at the boundaries of the simulated domain prevent interaction with scales larger than the domain itself. This can lead to the wrong solution within the domain and incorrect conclusions regarding the interaction between the surface and storms.

Given our interests, being able to represent convective storms explicitly on global domains and the surface dynamically is of crucial importance for our research. It explains the leadership role that we take in the institute´s internal Sapphire project, in the development of a coupled storm-resolving earth system model operating with a grid spacing of O (1 km) on decadal time scales and in the development of the ICON large-eddy model version (ICON-LEM). Our interests in how the surface influences convective storms naturally extends to the dynamics of that surface, both on the land and ocean side. This brute-force modelling strategy, which requires using the strongest computers due to the number of grid points needed, is paired with more conceptual work. One example of it is the application of storm-resolving simulations of radiative convective equilibrium (RCE) to problems of climate. Radiative convective equilibrium describes a conceptualization of the atmosphere based on the idea that a balance exists on large scale in the atmosphere between the destabilization of the atmosphere by radiation and its stabilization by convection. It is a useful conceptualization to investigate interactions between convective storms, the surface and the resulting spatial distribution of precipitation. It is also paired with the design and execution of field campaigns, such as FESSTVaL in Germany or a suite of ship campaigns (link) taking place in the tropical Atlantic.

World map: monthly mean precipitation from a coupled global simulation with a grid spacing of 5 km.
Figure 2: Monthly mean precipitation from a coupled global simulation with a grid spacing of 5 km.

Further reading

Results of previous research interests:

Below is a summary of some of our research findings related to our past research interests on the organization of convection as well as to our long-lasting research interests on land-atmosphere interactions.

 

Organization of convection

The tendency of convection to organize is a well-known ability of convection, clearly visible in satellite images. But even in purely homogeneous conditions, as in studies of radiative convective equilibrium (RCE), convection likes to spontaneously organize (Fig. 3), a process referred to as convective self-aggregation. Our research on the organization of convection aimed to better understand the mechanisms underlying the self-aggregation of convection and investigate the importance of convective organization for climate. The RCE conceptualization helps us isolate mechanisms that may control the spatial distribution of convection in the real world, and through this provides us with hypotheses testable against observations or more complex simulations. In contrast to most studies on the self-aggregation of convection, we applied the RCE concept to less idealized bottom boundary conditions such as including sea surface temperature heterogeneities [1], a dynamical land surface [2] or islands [3].

 

Evolution of convection in a month: the organization increases over time.
Fig. 3 The organization of convection over time. Each image shows one day and the color indicates how often convection is triggered at a grid point. On the last plot, the daily mean of the horizontal wind from the lowest atmospheric layer is added. The destabilization of the atmosphere by radiation leads to a random triggering of convection at the beginning of the simulation. With time, a shallow circulation develops from the non-convective towards the convective region. The shallow circulation leads to an expansion of the non-convective region, visible in the Figure from the expansion of the white holes, a circulation that forces the convection to organize. From Hohenegger et al. (2016).

Our research shows that convection fundamentally strives to organize. Its resulting spatial distribution is set dynamically by shallow circulations, confined in the planetary boundary layer, the lowest part of the atmosphere. These circulations are triggered by boundary layer heterogeneities (Fig. 4) which lead to spatial variations in density. They behave like gravity currents with the denser fluid moving into the lighter fluid. The boundary layer heterogeneities are generated by:

  • the convection itself (cold pools, which are small-scale areas of evaporatively cooled downdraft air that spread at the surface underneath precipitating clouds, and radiative heating anomalies that develop between convective and non-convective regions)
  • the underlying surface (spatial variations in sea surface temperature or in soil moisture)
Schematic representation of a shallow circulation.
Fig. 4 Schematic illustration of a shallow circulation. Shallow circulations are confined in the boundary layer and generated by density anomalies resulting from heating anomalies in the boundary layer. The denser air propagates into the lighter air and forces the air to rise, favoring the triggering of convection at the edge of the shallow circulation. (Credit: C. Hohenegger)

Those circulations compete with each other for determining the spatial distribution of convection. In a nutshell, the circulation associated with the largest density difference between the two fluids wins, a result that can be understood using a unified framework of gravity current theory (see [1,2,4]). The consequence is that convection is more organized over ocean than over land, and strongly dynamical surfaces cause precipitation to distribute more evenly [2]. We also expect that differences in radiative heating between equatorial and subtropical regions lead to a zonal contraction of the tropical rain belt [1] and that rain belts are wider over land than over ocean [2], an observed but yet unexplained fact.

Organization matters for climate as, in the theoretical RCE world, it is requested to achieve a stable climate [5]. Its change with warming, together with changes in the shallow cloud fraction, the percentage of each gridbox in a climate model that is covered with clouds, also explains more than 70% of the intermodel spread in climate sensitivity [6]. Organization matters for climate in the real world as the large-scale degree of organization of the ITCZ correlates with the degree of dryness in the subtropics [7], as expected from the results of the RCE studies. But, perhaps surprisingly, mesoscale organization does not matter for tropical precipitation amounts [8], and even coarse-resolution simulations with explicit convection, as coarse as 80 km, can still capture many basic statistics of the climate systems [9].

Whereas the self-aggregation of convection is thought to explain some features of the large-scale organization of convection, like the tropical rainbelts which extend over thousands of kilometers, individual convective cells of a few kilometers also organize through the generation of cold pools. These cold pools are thought to take a key role in determining the lifecycle of convection, but most of what we know about them comes from model simulations, where cold pool properties depend upon the employed model parameterizations. To remedy to this gap, we designed a field experiment called FESSTVaL, using a dense network of surface observations, which, for the first time, allowed us to peer into the internal structure of cold pools (Fig. 5).

Perturbation in air temperature in the vicinity of Hamburg
Fig. 5: First pick in the internal structure of cold pool made visible by measuring the perturbation in air temperature with a dense network of self-built instruments deployed in the area of Hamburg in summer 2020. The cold pool can be recognized by its associated strong cooling and divergent wind field at its edge. It is formally defined by a temperature perturbation smaller than -2 K. From Aus Kirsch et al: Illuminating the structure of convective cold pools with a dense station network, Earth Syst. Sci. Data, https://doi.org/10.5194/essd-2021-424, 2022. CC BY 4.0

Land-atmosphere interactions

That the land surface may affect the precipitation has long been speculated. S. Aughey expressed in 1880 the idea that soil moisture may affect the precipitation amounts. And already in 1686, Halley recognized that the stronger heating of the continental land masses causes the monsoon. Still, the importance of the land surface for setting the lifecycle, distribution and climatological characteristics of convective precipitation remains debated. This is particularly due to the use of inappropriate modelling tools that may overemphasize a positive feedback between the land surface and convection [10]. With our research on land-atmosphere interactions we aimed to investigate whether the land surface, or the convection itself, ends up setting aspects of the precipitation distribution over land. For this purpose our group took advantage of models that do not have to rely on convective parameterizations.

A key property of the land surface that emerges from our research is its ability to dry out. This leads to a more even precipitation distribution over land on longer time scales (Fig. 6), in contrast to the ocean [2]. Also, it leads to a weak cooling of tropospheric temperature in the tropics by the presence of islands [3]. Beside the ability of the land to dry out, the presence of surface heterogeneity, e.g. due to different land cover types, can affect the daily triggering of convection through the generation of surface-induced mesoscale circulations. This is well known, but few studies have investigated the feedback of convection on the surface-induced circulation. Our research shows that once convection develops at the front of the surface-induced circulation, it masks the effect of the initial surface heterogeneity and fully sets the final characteristics of the circulation, such as its propagation velocity. This is partly due to the generation of cold pools [11]. It follows that biases in the representation of convection projects on the characteristics of a surface-induced circulation [12]. In other words, the use of a convective parameterization prevents a correct interaction between land surface heterogeneities and convection. As another consequence, the rate of change of precipitation with soil moisture over a heterogeneous surface does not depend upon soil moisture itself [13].

 

Schematic representation of the attraction of rain by dry regions
Figure 6: How dry regions steal rain from wet regions over land. The drying of the soil in the non-precipitating region and associated strong warming generates a shallow circulation. At least when the soil fully dries out, this circulation becomes stronger than the background circulation and moves the convection from the precipitating to the non-precipitating region. This process repeats itself with time and leads to a homogenization of the precipitation field over land.

As shown through our research, simulations that are able to explicitly resolve convective storms, by using kilometer-scale resolution, in contrast to parameterize convection, exhibit distinct interactions between the land surface and convection. In simulations with parameterized convection, the strength of the response of precipitation to a change in evapotranspiration (soil moisture) is mostly set by the design of the convective parameterization. In such simulations, an increase in soil moisture leads to an increase in precipitation, sustaining a positive feedback between soil moisture and precipitation. In contrast, an increase in soil moisture leads to a decrease in precipitation in storm-resolving simulations, implying a much weaker importance of the soil moisture state for the atmosphere  [10]. Also, convective parameterizations, through their too early triggering of convection and too strong induced circulation, prevent the triggering of circulations induced by land surface heterogeneities, such as sea breezes [12]. Finally, storm-resolving simulations produce isolated convective storms, leading to significant runoff and hence a reduced refilling of the soil in comparison to convective parameterizations whose constant drizzling keeps the soil moist, with implications for the simulation of the propagation of the monsoon [14].

 

[1] Müller, S. K. M. and C. Hohenegger, 2020:  Self-aggregation of convection in spatially-varying sea surface temperatures. J. Adv. Mod. Earth Systems, 12, e2019MS001698, doi:10.1029/2019MS001698.

[2] Hohenegger C. and B. Stevens, 2018: The role of the permanent wilting point in controlling the spatial distribution of precipitation. Proc. Natl. Acad. Sci., 115, 5692-5697, doi:10.1073/pnas.1718842115

[3] Leutwyler, D. and C. Hohenegger, 2021: Weak cooling of the troposphere by tropical islands in simulations of the radiative-convective equilibrium. Quart. J. Roy. Meteor. Soc., 147, 1788-1800, doi: 10.1002/qj.3995.

[4] Windmiller, J. and C. Hohenegger, 2019: Convection on the edge. J. Adv. Mod. Earth Systems, 11, 3959-3972, doi:10.1029/2019MS001820.

[5] Hohenegger C. and B. Stevens, 2016: Coupled radiative convective equilibrium simulations with explicit and parameterized convection. J. Adv. Mod. Earth Systems, 8, 1468-1482, doi: 10.1002/2016MS000666.

[6] Becker, T. and A. Wing, 2020: Understanding the extreme spread in climate sensitivity within the radiative-convective equilibrium model intercomparison project. J. Adv. Mod. Earth Systems, 12, e2020MS002165, doi:10.1029/2020MS002165.

[7] Hohenegger, C. and C. Jakob, 2020: A relationship between ITCZ organization and subtropical humidity. Geophys. Res. Let., 47, e2020GL088515, doi:10.1029/2020GL088515.

[8] Brueck, M., C. Hohenegger and B. Stevens, 2020: Mesoscale marine precipitation varies independently from the spatial arrangement from its convective cells. Quart. J. Roy. Meteor. Soc.,146, 1391-1402, doi:10.1002/qj.3742.

[9] Hohenegger, C., L. Kornblueh, D. Klocke, T. Becker, G. Cioni, J. F. Engels, U. Schulzweida and B. Stevens, 2020: Climate statistics in global simulations of the atmosphere, from 80 to 2.5 km grid spacing. J. Meteorol. Society Japan, 98, 73-91, doi:10.2151/jmsj.2020-005.

[10] Hohenegger C., P. Brockhaus, C. S. Bretherton, and C. Schär, 2009: The soil-moisture precipitation feedback in simulations with explicit and parameterized convection. J. Climate, 22, 5003-5020, doi: 10.1175/2009JCLI2604.1

[11] Rieck M., C. Hohenegger and P. Gentine, 2015: The effect of moist convection on thermally induced mesoscale circulations. Quart. J. Roy. Meteor. Soc., 141, 2418-2428, doi:10.1002/qj.2532

[12] Hohenegger C., L. Schlemmer and L. Silvers, 2015: Coupling of convection and circulation at various resolutions. Tellus A, 67, 26678, doi:10.3402/tellusa.v67.26678

[13] Cioni G. and C. Hohenegger, 2018: A simplified model of precipitation enhancement over a heterogeneous surface. Hydrol. Earth Syst. Sci., 22, 3197-3212,doi:10.5194/hess-22-3197-2018

[14] Jungandreas, L., C. Hohenegger and M. Claussen, 2021: Influence of the representation of convection on the mid-Holocene West African Monsoon. Clim. Past, 17, 1665-1684, doi:10.5194/cp-2020-162.

Group members and publications

  • Lee, J. & Hohenegger, C. (2024). Weaker land-atmosphere coupling in storm-resolving simulation. Proceedings of the National Academy of Sciences, 121: e2314265121. doi:10.1073/pnas.231426512 [publisher-version]
  • Lee, J. & Hohenegger, Cathy (Climate Surface Interaction, Department Climate Physics, MPI for Meteorology, Max Planck Society), . (2024). Weaker land–atmosphere coupling in global storm-resolvingsimulation. PNAS, 121: 2314265121. doi:10.1073/pnas.2314265121 [publisher-version]
  • Yoon, A., Kim, J., Lee, J., Min Sung, H., Hong, J.-W., Min, S.-K., Lee, J. & Hong, J. (2024). Factor analysis of recent major heatwaves in East Asia. Geoscience Frontiers, 15: 101730. doi:10.1016/j.gsf.2023.101730 [publisher-version]
  • Hohenegger, C., Ament, F., Beyrich, F., Löhnert, U., Rust, H., Bange, J., Böck, T., Böttcher, C., Boventer, J., Burgemeister, F., Clemens, M., Detring, C., Detring, I., Dewani, N., Bastak-Duran, I., Fiedler, S., Göber, M., van Heerwaarden, C., Heusinkveld, B., Kirsch, B., Klocke, D., Knist, C., Lange, I., Lauermann, F., Lehmann, V., Lehmke, J., Leinweber, R., Lundgren, K., Masbou, M., Mauder, M., Mol, W., Nevermann, H., Nomokonova, T., Päschke, E., Platis, A., Reichardt, J., Rochette, L., Sakradzija, M., Schlemmer, L., Schmidli, J., Shokri, N., Sobottke, V., Speidel, J., Steinheuer, J., Turner, D., Vogelmann, H., Wedemeyer, C., Weide-Luiz, E., Wiesner, S., Wildmann, N., Wolz, K. & Wetz, T. (2023). FESSTVaL: The Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg. Bulletin of the American Meteorological Society, 104, E1875-E1892. doi:10.1175/BAMS-D-21-0330.1 [publisher-version]
  • Hohenegger, C., Korn, P., Linardakis, L., Redler, R., Schnur, R., Adamidis, P., Bao, J., Bastin, S., Behravesh, M., Bergemann, M., Biercamp, J., Bockelmann, H., Brokopf, R., Brüggemann, N., Casaroli, L., Chegini, F., Datseris, G., Esch, M., George, G., Giorgetta, M., Gutjahr, O., Haak, H., Hanke, M., Ilyina, T., Jahns, T., Jungclaus, J., Kern, M., Klocke, D., Kluft, L., Kölling, T., Kornblueh, L., Kosukhin, S., Kroll, C., Lee, J., Mauritsen, T., Mehlmann, C., Mieslinger, T., Naumann, A., Paccini, L., Peinado, A., Praturi, D., Putrasahan, D., Rast, S., Riddick, T., Roeber, N., Schmidt, H., Schulzweida, U., Schütte, F., Segura, H., Shevchenko, R., Singh, V., Specht, M., Stephan, C., von Storch, J., Vogel, R., Wengel, C., Winkler, M., Ziemen, F., Marotzke, J. & Stevens, B. (2023). ICON-Sapphire: simulating the components of the Earth System and their interactions at kilometer and subkilometer scales. Geoscientific Model Development, 16, 779-811. doi:10.5194/gmd-16-779-2023 [publisher-version]
  • Jungandreas, L., Hohenegger, C. & Claussen, M. (2023). How does the explicit treatment of convection alters the precipitation-soil hydrology interaction in the mid-Holocene African humid period?. Climate of the Past, 19, 637-664. doi:10.5194/cp-19-637-2023 [publisher-version]
  • Keil, P., Schmidt, H., Stevens, B., Byrne, M., Segura, H. & Putrasahan, D. (2023). Tropical tropospheric warming pattern explained by shifts in convective heating in the Matsuno-Gill Model. Quarterly Journal of the Royal Meteorological Society, 149, 2678-2695. doi:10.1002/qj.4526 [publisher-version]
  • Kirsch, B., Hohenegger, C. & Ament, F. (2023). Morphology and growth of convective cold pools observed by a dense station network in Germany. Quarterly Journal of the Royal Meteorological Society: early view. doi:10.1002/qj.4626
  • Lee, H., Ganbat, G., Jin, H.-G., Seo, J., Moon, S., Bok, H. & Baik, J.-J. (2023). Effects of Lake Baikal on summertime precipitation climatology over the lake surface. Geophysical Research Letters, 50: e2023GL103426. doi:10.1029/2023GL103426 [publisher-version]
  • Lee, J., Lee, H.-J., Kim, K.-B., Shin, H., Lim, J.-M., Hong, J. & Lim, K.-S. (2023). Height correction method based on the Monin–Obukhov similarity theory for better prediction of near-surface wind fields. Atmospheric Research, 292: 106882. doi:10.1016/j.atmosres.2023.106882
  • Schmidt, L. & Hohenegger, C. (2023). Constraints on the ratio between tropical land and ocean precipitation derived from a conceptual water balance model. Journal of Hydrometeorology, 24, 1103-1117. doi:10.1175/JHM-D-22-0162.1 [code][publisher-version]
  • Dauhut, T. & Hohenegger, C. (2022). The contribution of convection to the stratospheric water vapor: the first budget using a Global-Storm-Resolving Model. Journal of Geophysical Research: Atmospheres, 127: e2021JD036295. doi:10.1029/2021JD036295 [publisher-version]
  • Hohenegger, C. & Stevens, B. (2022). Tropical continents rainier than expected from geometrical constraints. AGU Advances, 3: e2021AV000636. doi:10.1029/2021AV000636 [supplementary-material][publisher-version]
  • Kirsch , B., Hohenegger, C., Klocke, D., Senke, R., Offermann, M. & Ament, F. (2022). Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany. Earth System Science Data, 14, 3531-3548. doi:10.5194/essd-14-3531-2022 [publisher-version]
  • Lee, J., Hohenegger, C., Chlond, A. & Schnur, R. (2022). The climatic role of interactive leaf phenology in the vegetation-atmosphere system of radiative-convective equilibrium storm-resolving simulations. Tellus, Series B - Chemical and Physical Meteorology, 74, 164-175. doi:10.16993/tellusb.26 [publisher-version]
  • Mauritsen, T., Redler, R., Esch, M., Stevens, B., Hohenegger, C., Klocke, D., Brokopf, R., Haak, H., Linardakis, L., Röber, N. & Schnur, R. (2022). Early development and tuning of a global coupled cloud resolving model, and its fast response to increasing CO2. Tellus Series A: Dynamic Meteorology and Oceanography, 74, 346-363 . doi:10.16993/tellusa.54 [publisher-version]
  • Muller, C., Yang, D., Craig, G., Cronin, T., Fildier, B., Haerter, J., Hohenegger, C., Mapes, B., Randall, D., Shamekh, S. & Sherwood, S. (2022). Spontaneous aggregation of convective storms. Annual Review of Fluid Mechanics, 54, 133-157. doi:10.1146/annurev-fluid-022421-011319
  • Paccini Pena, L. (2022). Sensitivity of resolved convection to ocean and land surfaces in the Tropical Atlantic and Amazon Basin. Phd Thesis, Hamburg: Universität Hamburg. Berichte zur Erdsystemforschung, 250. doi:10.17617/2.3367420 [publisher-version]
  • Segura, H., Hohenegger, C., Wengel , C. & Stevens, B. (2022). Learning by doing: seasonal and diurnal features of tropical precipitation in a global-coupled storm-resolving model. Geophysical Research Letters, 49: e2022GL101796. doi:10.1029/2022GL101796 [supplementary-material][publisher-version]
  • Shin, J., Ahn, J., Chowdhry Beeman, J., Lee, H.-G., Seo, J. & Brook, E. (2022). Millennial variations in atmospheric CO2 during the early Holocene (11.7-7.4 ka). Climate of the Past, 18, 2063-2075. doi:10.5194/cp-18-2063-2022 [publisher-version][supplementary-material]
  • Ban, N., Caillaud, C., Coppola, E., Pichelli, E., Sobolowski, S., Adinolfi, M., Ahrens, B., Alias, A., Anders, I., Bastin, S., Belušić, D., Berthou, S., Brisson, E., Cardoso, R., Chan, S., Christensen, O., Fernández, J., Fita, L., Frisius, T., Gašparac, G., Giorgi, F., Goergen, K., Haugen, J., Hodnebrog, Ø., Kartsios, S., Katragkou, E., Kendon, E., Keuler, K., Lavin-Gullon, A., Lenderink, G., Leutwyler, D., Lorenz, T., Maraun, D., Mercogliano, P., Milovac, J., Panitz, H.-J., Raffa, M., Remedio, A., Schär, C., Soares, P., Srnec, L., Steensen, B., Stocchi, P., Tölle, M., Truhetz, H., Vergara-Temprado, J., de Vries, H., Warrach-Sagi, K., Wulfmeyer, V. & Zander, M. (2021). The first multi-model ensemble of regional climate simulations at kilometer-scale resolution. Part I: Evaluation of precipitation. Climate Dynamics, 57, 275-302. doi:10.1007/s00382-021-05708-w [publisher-version]
  • Bao , J. & Windmiller, J. (2021). Impact of microphysics on tropical precipitation extremes in a global storm-resolving model. Geophysical Research Letters, 48: e2021GL094206. doi:10.1029/2021GL094206 [publisher-version][supplementary-material]
  • Becker, T. & Hohenegger, C. (2021). Entrainment and its dependency on environmental conditions and convective organization in convection-permitting simulations. Monthly Weather Review, 121, 537-550 . doi:10.1175/MWR-D-20-0229.1 [supplementary-material][publisher-version]
  • Dion, I.-A., Dallet, C., Ricaud, P., Carminati, F., Dauhut, T. & Haynes, P. (2021). Ice injected into the tropopause by deep convection - Part 2: Over the Maritime Continent. Atmospheric Chemistry and Physics, 21, 2191-2210. doi:10.5194/acp-21-2191-2021 [publisher-version]
  • Judt, F., Klocke, D., Rios-Berrios, R., Vanniere, B., Ziemen, F., Auger, L., Biercamp, J., Bretherton, C., Chen, X., Düben, P., Hohenegger, C., Khairoutdinov, M., Kodama, C., Kornblueh, L., Lin, S.-J., Nakano, M., Neumann, P., Putman, W., Röber, N., Roberts, M., Satoh, M., Shibuya, R., Stevens, B., Vidale, P., Wedi, N. & Zhou, L. (2021). Tropical cyclones in global storm-resolving models. Journal of the Meteorological Society of Japan, 99, 579-602. doi:10.2151/jmsj.2021-029 [publisher-version]
  • Jung, H., Naumann, A. & Stevens, B. (2021). Convective self-aggregation in a mean flow. Atmospheric Chemistry and Physics, 21, 10337-10345. doi:10.5194/acp-21-10337-2021 [publisher-version]
  • Jungandreas, L., Hohenegger, C. & Claussen, M. (2021). Influence of the representation of convection on the mid-Holocene West African Monsoon. Climate of the Past, 17, 1665-1684. doi:10.5194/cp-17-1665-2021 [supplementary-material][publisher-version]
  • Kim, S.-M., Koo, J.-H., Lee, H., Mok, J., Choi, M., Go, S., Lee, S., Cho, Y., Hong, J., Seo, S., Lee, J., Hong, J.-W. & Kim, J. (2021). Comparison of pm2.5 in Seoul, Korea estimated from the various ground-based and satellite aod. Applied Sciences (Switzerland), 11. doi:10.3390/app112210755 [publisher-version]
  • Kirsch, B., Ament, F. & Hohenegger, C. (2021). Convective cold pools in long-term boundary layer mast observations. Monthly Weather Review, 149, 811-820. doi:10.1175/MWR-D-20-0197.1 [publisher-version]
  • Leutwyler, D., Imamovic, A. & Schär, C. (2021). The continental-scale soil-moisture precipitation feedback in Europe with parameterized and explicit convection. Journal of Climate, 34, 5303-5320. doi:10.1175/JCLI-D-20-0415.1 [supplementary-material][publisher-version]
  • Leutwyler, D. & Hohenegger, C. (2021). Weak cooling of the troposphere by tropical islands in simulations of the radiative-convective equilibrium. Quarterly Journal of the Royal Meteorological Society, 147, 1788-1800. doi:10.1002/qj.3995 [supplementary-material][publisher-version]
  • Moon, S., Baik, J.-J. & Seo, J. (2021). Chaos synchronization in generalized Lorenz systems and an application to image encryption. Communications in Nonlinear Science and Numerical Simulation, 96: 105708. doi:10.1016/j.cnsns.2021.105708 [publisher-version]
  • Moon, S., Baik, J.-J., Seo, J. & Han, B.-S. (2021). Effects of density affecting scalar on the onset of chaos in a simplified model of thermal convection: a nonlinear dynamical perspective. The European Physical Journal Plus, 136: 92. doi:10.1140/epjp/s13360-020-01047-7
  • Paccini, L., Hohenegger, C. & Stevens, B. (2021). Explicit versus parameterized convection in response to the Atlantic Meridional Mode. Journal of Climate, 34, 3343-3354. doi:10.1175/JCLI-D-20-0224.1 [supplementary-material][publisher-version]
  • Park, J., Moon, S., Seo, J. & Baik, J.-J. (2021). Systematic comparison between the generalized Lorenz equations and DNS in the two-dimensional Rayleigh–Bénard convection. Chaos, 31: 073119. doi:10.1063/5.0051482 [publisher-version]
  • Radtke, J., Mauritsen, T. & Hohenegger, C. (2021). Shallow cumulus cloud feedback in large eddy simulations - Bridging the gap to storm resolving models. Atmospheric Chemistry and Physics, 21, 3278-3288. doi:10.5194/acp-21-3275-2021 [publisher-version]
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  • Cioni, G., Cerrai, D. & Klocke, D. (2018). Investigating the predictability of a Mediterranean tropical-like cyclone using a storm-resolving model. Quarterly Journal of the Royal Meteorological Society, 144, 1598-1610. doi:10.1002/qj.3322
  • Crueger, T., Giorgetta, M., Brokopf, R., Esch, M., Fiedler, S., Hohenegger, C., Kornblueh, L., Mauritsen, T., Nam, C., Naumann, A., Peters, K., Rast, S., Roeckner, E., Schmidt, H., Sakradzija, M., Vial, J., Vogel, R. & Stevens, B. (2018). ICON-A: the atmospheric component of the ICON Earth System Model. Part II: Model evaluation. Journal of Advances in Modeling Earth Systems, 10, 1638-1662. doi:10.1029/2017MS001233 [publisher-version]
  • Giorgetta, M., Brokopf, R., Crueger, T., Esch, M., Fiedler, S., Helmert, J., Hohenegger, C., Kornblueh, L., Köhler, M., Manzini, E., Mauritsen, T., Nam, C., Raddatz, T., Rast, S., Reinert, D., Sakradzija, M., Schmidt, H., Schneck, R., Schnur, R., Silvers, L., Wan, H., Zängl, G. & Stevens, B. (2018). ICON-A: the atmospheric component of the ICON Earth System Model. Part I: Model description. Journal of Advances in Modeling Earth Systems, 10, 1613-1637. doi:10.1029/2017MS001242 [publisher-version]
  • Gryspeerdt, E., Quaas, J., Goren, T., Klocke, D. & Brueck, M. (2018). An automated cirrus classification. Atmospheric Chemistry and Physics, 18, 6157-6169. doi:10.5194/acp-18-6157-2018 [publisher-version]
  • Hohenegger, C. & Stevens, B. (2018). The role of the permanent wilting point in controlling the spatial distribution of precipitation. Proceedings of the National Academy of Sciences of the United States of America, 115, 5692-5697. doi:10.1073/pnas.1718842115 [supplementary-material][publisher-version][supplementary-material]
  • Hohenegger, C., Stevens, B., Kornblueh, L., Brueck, M. & Röber, N. (2018). Wenn globale Klimamodelle einen Elefanten von einer großen Maus unterscheiden können. Jahrbuch / Max-Planck-Gesellschaft, 2018. [any-fulltext]
  • Mikolajewicz, U., Ziemen, F., Cioni, G., Claussen, M., Fraedrich, K., Heidkamp, M., Hohenegger, C., Jiménez de la Cuesta, D., Kapsch, M.-L., Lemburg, A., Mauritsen, T., Meraner, K., Röber, N., Schmidt, H., Six, K., Stemmler, I., Tamarin-Brodsky, T., Winkler, A., Zhu, X. & Stevens, B. (2018). The climate of a retrograde rotating earth. Earth System Dynamics, 9, 1191-1215. doi:10.5194/esd-9-1191-2018 [publisher-version]
  • Petrova, I., van Heerwaarden, C., Hohenegger, C. & Guichard, F. (2018). Regional co-variability of spatial and temporal soil moisture - precipitation coupling in North Africa: an observational perspective. Hydrology and Earth System Sciences, 22, 3275-3294. doi:10.5194/hess-22-3275-2018 [publisher-version]
  • Ruppert, J. & Hohenegger, C. (2018). Diurnal circulation adjustment and organized deep convection. Journal of Climate, 31, 4899-4916. doi:10.1175/JCLI-D-17-0693.1 [supplementary-material][publisher-version]
  • Sakradzija, M. & Klocke, D. (2018). Physically constrained stochastic shallow convection in realistic kilometer-scale simulations. Journal of Advances in Modeling Earth Systems, 10, 2755-2776. doi:10.1029/2018MS001358 [publisher-version]
  • Senf, F., Klocke, D. & Brueck, M. (2018). Size-resolved evaluation of simulated deep tropical convection. Monthly Weather Review, 146, 2161-2182. doi:10.1175/MWR-D-17-0378.1 [publisher-version]
  • Becker, T., Stevens, B. & Hohenegger, C. (2017). Imprint of the convective parameterization and sea-surface temperature on large-scale convective self-aggregation. Journal of Advances in Modeling Earth Systems, 9, 1488-1505. doi:10.1002/2016MS000865 [publisher-version]
  • Bellon , G., Reitebuch, O. & Naumann, A. (2017). Shallow circulations: relevance and strategies for satellite observation. Surveys in Geophysics, 38(SI), 1509-1528. doi:10.1007/s10712-017-9442-2
  • Berner, J., Achatz, U., Batte, L., Bengtsson, L., De La Camara, A., Christensen, H., Colangeli, M., Coleman, D., Crommelin, D., Dolaptchiev, S., Franzke, C., Friederichs, P., Imkeller, P., Järvinen, H., Juricke, S., Kitsios, V., Lott, F., Lucarini, V., Mahajan, S., Palmer, T., Penland, C., Sakradzija, M., von Storch, J.-S., Weisheimer, A., Weniger, M., Williams, P. & Yano, J.-I. (2017). Stochastic parameterization: Towards a new view of weather and climate models. Bulletin of the American Meteorological Society, 98, 565-587. doi:10.1175/BAMS-D-15-00268.1 [publisher-version]
  • Bony, S., Stevens, B., Ament, F., Crewell, S., Delanoe, J., Farrell, D., Flamant, C., Gross, S., Hirsch, L., Mayer, B., Nuijens, L., Ruppert, J., Sandu, I., Siebesma, P., Speich, S., Szczap, F., Vogel, R., Wendisch, M. & Wirth, M. (2017). EUREC4A: a field campaign to elucidate the couplings between clouds, convection and circulation. Surveys in Geophysics, 38(SI), 1529-1568. doi:10.1007/s10712-017-9428-0 [publisher-version]
  • Cherian, R., Quaas, J., Salzmann, M. & Tomassini, L. (2017). Black carbon indirect radiative effects in a climate model. Tellus B: Chemical and Physical Meteorology, 69: 1369342. doi:10.1080/16000889.2017.1369342 [supplementary-material][publisher-version]
  • Cioni, G. & Hohenegger, C. (2017). Effect of soil moisture on diurnal convection and precipitation in large-eddy simulations. Journal of Hydrometeorology, 18, 1885-1903. doi:10.1175/JHM-D-16-0241.1 [publisher-version]
  • Heinze, R., Dipankar, A., Henken, C., Moseley, C., Sourdeval, O., Trömel, S., Xie, X., Adamidis, P., Ament, F., Baars, H., Barthlott, C., Behrendt, A., Blahak, U., Bley, S., Brdar, S., Brueck, M., Crewell, S., Deneke, H., Girolamo, P., Evaristo, R., Fischer, J., Frank, C., Friederichs, P., Göcke, T., Gorges, K., Hande, L., Hanke, M., Hansen, A., Hege, H.-C., Hoose, C., Jahns, T., Kalthoff, N., Klocke, D., Kneifel, S., Knippertz, P., Kuhn, A., van Laar, T., Macke, A., Maurer, V., Mayer, B., Meyer, C., Muppa, S., Neggers, R., Orlandi, E., Pantillon, F., Pospichal, B., Röber , N., Scheck, L., Seifert, A., Seifert, P., Senf, F., Siligam, P., Simmer, C., Steinke, S., Stevens, B., Wapler, K., Weniger, M., Wulfmeyer, V., Zängl, G., Zhang, D. & Quaas, J. (2017). Large-eddy simulations over Germany using ICON: A comprehensive evaluation. Quarterly Journal of the Royal Meteorological Society, 143, 69-100. doi:10.1002/qj.2947 [publisher-version]
  • Heinze, R., Moseley, C., Böske, C., Muppa, S., Maurer, V., Raasch, S. & Stevens, B. (2017). Evaluation of large-eddy simulations forced with mesoscale model output for a multi-week period during a measurement campaign. Atmospheric Chemistry and Physics, 17, 7083-7109. doi:10.5194/acp-17-7083-2017 [publisher-version]
  • Klocke, D., Brueck, M., Hohenegger, C. & Stevens, B. (2017). Rediscovering the doldrums in cloud resolving simulations of the Tropical Atlantic. Nature Geoscience, 10, 891-896. doi:10.1038/s41561-017-0005-4
  • Macke, A., Seifert, P., Baars, H., Barthlott, C., Beekmans, C., Behrendt, A., Bohn, B., Brueck, M., Bühl, J., Crewell, S., Damian, T., Deneke, H., Düsing, S., Foth, A., Di Girolamo, P., Hammann, E., Heinze, R., Hirsikko, A., Kalisch, J., Kalthoff, N., Kinne, S., Kohler, M., Löhnert, U., Lakshmi Madhavan, B., Maurer, V., Kumar Muppa, S., Schween, J., Serikov, I., Siebert, H., Simmer, C., Späth, F., Steinke, S., Träumner, K., Trömel, S., Wehner, B., Wieser, A., Wulfmeyer, V. & Xie, X. (2017). The HD(CP)2 Observational Prototype Experiment (HOPE) - An overview. Atmospheric Chemistry and Physics, 17, 4887-4914. doi:10.5194/acp-17-4887-2017 [supplementary-material][publisher-version]
  • Naumann, A., Stevens, B., Hohenegger, C. & Mellado, J.-P. (2017). A conceptual model of a shallow circulation induced by prescribed low-level radiative cooling. Journal of the Atmospheric Sciences, 74, 3129-3144. doi:10.1175/JAS-D-17-0030.1 [publisher-version]
  • Peters, K., Crueger, T., Jakob , C. & Moebis, B. (2017). Improved MJO-simulation in ECHAM6.3 by coupling a stochastic multicloud model to the convection scheme. Journal of Advances in Modeling Earth Systems, 9, 193-219. doi:10.1002/2016MS000809 [publisher-version]
  • Peters, K. & Hohenegger, C. (2017). On the dependence of squall-line characteristics on surface conditions. Journal of the Atmospheric Sciences, 74, 2211-2228. doi:10.1175/JAS-D-16-0290.1 [publisher-version]
  • Rauser, F., Alqadi, M., Arowolo, S., Baker, N., Behrens, E., Bedard, J., Dogulul, N., Domingues, L., Frassoni, A., Keller, J., Kirkpatrick, S., Langendijk, G., Mohammad, S., Misafa, M., Naumann, A., Osmon, N., Reed, K., Rothmüller, M., Schemann, V., Singh, A., Sonntag, S., Tummon, F., Victor, D., Villafverte, M., Walawender, J. & Zaroug, M. (2017). Earth system science frontiers - an early career perspective. Bulletin of the American Meteorological Society, 98, 1119-1127. doi:10.1175/BAMS-D-16-0025.1 [publisher-version]
  • Retsch, M., Hohenegger, C. & Stevens, B. (2017). Vertical resolution refinement in an aqua-planet and its effect on the ITCZ. Journal of Advances in Modeling Earth Systems, 9, 2425-2436 . doi:10.1002/2017MS001010 [supplementary-material][publisher-version]
  • Sakradzija, M. & Hohenegger, C. (2017). What determines the distribution of ahallow convective mass flux through cloud base?. Journal of the Atmospheric Sciences, 74, 2615-2632. doi:10.1175/JAS-D-16-0326.1 [publisher-version]
  • Siongco, A., Hohenegger, C. & Stevens, B. (2017). Sensitivity of the summertime tropical Atlantic precipitation distribution to convective parameterization and model resolution in ECHAM6. Journal of Geophysical Research-Atmospheres, 122, 2579-2594. doi:10.1002/2016JD026093 [publisher-version]
  • Bhattacharya, R. & Stevens, B. (2016). A two Turbulence Kinetic Energy model as a scale-adaptive approach to modeling the planetary boundary layer. Journal of Advances in Modeling Earth Systems, 8, 224-243. doi:10.1002/2015MS000548 [publisher-version]
  • Bony, S., Stevens, B., Coppin, D., Becker, T., Reed, K., Voigt, A. & Medeiros, B. (2016). Thermodynamic control of anvil cloud amount. Proceedings of the National Academy of Sciences of the United States of America, 113, 8927-8932 . doi:10.1073/pnas.1601472113 [publisher-version][publisher-version]
  • Cioni, G., Malguzzi, P. & Buzzi, A. (2016). Thermal structure and dynamical precursor of a Mediterranean tropical-like cyclone. Quarterly Journal of the Royal Meteorological Society, 142, 1757-1766. doi:10.1002/qj.2773
  • Heinze, R., Mironov, D. & Raasch, S. (2016). Analysis of pressure-strain and pressure gradient-scalar covariances in cloud-topped boundary layers: A large-eddy simulation study. Journal of Advances in Modeling Earth Systems, 8, 3-30. doi:10.1002/2015MS000508 [supplementary-material][publisher-version]
  • Hohenegger, C. & Stevens, B. (2016). Coupled radiative convective equilibrium simulations with explicit and parameterized convection. Journal of Advances in Modeling Earth Systems, 8, 1468-1482. doi:10.1002/2016MS000666 [publisher-version]
  • Milinski, S., Bader, J., Haak, H., Siongco, A. & Jungclaus, J. (2016). High atmospheric horizontal resolution eliminates the wind-driven coastal warm bias in the southeastern tropical Atlantic. Geophysical Research Letters, 43, 10,455 -10,462. doi:10.1002/2016GL070530 [publisher-version]
  • Moseley, C., Hohenegger, C., Berg, P. & Haerter, J. (2016). Intensification of convective extremes driven by cloud–cloud interaction. Nature Geoscience, 9, 748-752. doi:10.1038/ngeo2789
  • Naumann, A. & Seifert, A. (2016). Recirculation and growth of raindrops in simulated shallow cumulus. Journal of Advances in Modeling Earth Systems, 8, 520-537. doi:10.1002/2016MS000631 [publisher-version][supplementary-material]
  • Naumann, A. & Seifert, A. (2016). Evolution of the shape of the raindrop size distribution in simulated shallow cumulus. Journal of the Atmospheric Sciences, 73, 2279-2297. doi:10.1175/JAS-D-15-0263.1 [publisher-version]
  • Ruppert, J. & Johnson, R. (2016). On the cumulus diurnal cycle over the tropical warm pool. Journal of Advances in Modeling Earth Systems, 8, 669-690. doi:10.1002/2015MS000610 [publisher-version]
  • Ruppert, J. (2016). Diurnal timescale feedbacks in the tropical cumulus regime. Journal of Advances in Modeling Earth Systems, 8, 1483-1500. doi:10.1002/2016MS000713 [publisher-version]
  • Sakradzija, M., Seifert, A. & Dipankar, A. (2016). A stochastic scale-aware parameterization of shallow cumulus convection across the convective gray zone. Journal of Advances in Modeling Earth Systems, 8, 786-812. doi:10.1002/2016MS000634 [publisher-version]
  • Schlemmer, L. & Hohenegger, C. (2016). Modifications of the atmospheric moisture field as a result of cold-pool dynamics. Quarterly Journal of the Royal Meteorological Society, 142, 30-42. doi:10.1002/qj.2625
  • Simmer, C., Adrian, G., Jones, S., Wirth, V., Göber, M., Hohenegger, C., Janjic´, T., Keller, J., Ohlwein, C., Seifert, A., Trömel, S., Ulbrich, T., Wapler, K., Weissmann, M., Keller, J., Masbou, M., Meilinger, S., Riß, N., Schomburg, A., Vormann, A. & Weingärtner, C. (2016). HErZ - The German Hans-Ertel Centre for Weather Research. Bulletin of the American Meteorological Society, 97, 1057-1068. doi:10.1175/BAMS-D-13-00227.1 [publisher-version]
  • Siongco, A. (2016). Drivers of precipitation biases in the tropical Atlantic sector. Phd Thesis, Hamburg: Universität Hamburg. Berichte zur Erdsystemforschung, 181. doi:10.17617/2.2321074 [publisher-version]
  • Chen , R. & Tomassini, L. (2015). The role of moisture in summertime low-level jet formation and associated rainfall over the East Asian monsoon region. Journal of the Atmospheric Sciences, 72, 3871-3890. doi:10.1175/JAS-D-15-0064.1 [publisher-version]
  • Hohenegger, C., Schlemmer, L. & Silvers, L. (2015). Coupling of convection and circulation at various resolutions. Tellus Series A-Dynamic Meteorology and Oceanography, 67: 26678. doi:10.3402/tellusa.v67.26678 [publisher-version]
  • Naumann, A. (2015). Cloud structures and rain formation in the atmospheric boundary layer. Phd Thesis, Hamburg: Universität Hamburg. Berichte zur Erdsystemforschung, 170. doi:10.17617/2.2166985 [publisher-version]
  • Naumann, A. & Seifert, A. (2015). A Lagrangian drop model to study warm rain microphysical processes in a shallow cumulus. Journal of Advances in Modeling Earth Systems, 7, 1136-1154. doi:10.1002/2015MS000456 [publisher-version]
  • Rieck, M., Hohenegger, C. & Gentine, P. (2015). The effect of moist convection on thermally induced mesoscale circulations. Quarterly Journal of the Royal Meteorological Society, 141, 2418-2428. doi:10.1002/qj.2532
  • Rieck, M. (2015). The role of heterogeneities and land-atmosphere interactions in the development of moist convection. Phd Thesis, Hamburg: Universität Hamburg. Berichte zur Erdsystemforschung, 167. doi:10.17617/2.2095640 [publisher-version]
  • Rosch, J., Heus, T., Brueck, M., Salzmann, M., Mülmenstädt, J., Schlemmer, L. & Quaas, J. (2015). Analysis of diagnostic climate model cloud parametrizations using large-eddy simulations. Quarterly Journal of the Royal Meteorological Society, 141, 2199-2205. doi:10.1002/qj.2515
  • Sakradzija, M. (2015). A stochastic parameterization of shallow cumulus convection for high-resolution numerical weather and climate models. Phd Thesis, Hamburg: Universität Hamburg. Berichte zur Erdsystemforschung, 171. doi:10.17617/2.2183346 [publisher-version]
  • Sakradzija, M., Seifert, A. & Heus, T. (2015). Fluctuations in a quasi-stationary shallow cumulus cloud ensemble. Nonlinear Processes in Geophysics, 22, 65-85. doi:10.5194/npg-22-65-2015 [publisher-version]
  • Sant, V., Posselt, R. & Lohmann, U. (2015). Prognostic precipitation with three liquid water classes in the ECHAM5-HAM GCM. Atmospheric Chemistry and Physics, 15, 8717-8738. doi:10.5194/acp-15-8717-2015 [publisher-version]
  • Siongco, A., Hohenegger, C. & Stevens, B. (2015). The Atlantic ITCZ bias in CMIP5 models. Climate Dynamics, 45, 1169-1180. doi:10.1007/s00382-014-2366-3 [publisher-version]
  • Tomassini, L., Voigt, A. & Stevens, B. (2015). On the connection between tropical circulation, convective mixing, and climate sensitivity. Quarterly Journal of the Royal Meteorological Society, 141, 1404 -1416. doi:10.1002/qj.2450 [pre-print]
  • Bhattacharya, R. (2014). A two turbulence kinetic energy model for the scale adaptive treatment of the planetary boundary layer. Phd Thesis, Hamburg: Universität Hamburg. Berichte zur Erdsystemforschung, 154. doi:10.17617/2.2043342 [publisher-version]
  • Froidevaux, P., Schlemmer, L., Schmidli, J., Langhans, W. & Schaer, C. (2014). Influence of the background wind on the local soil moisture-precipitation feedback. Journal of the Atmospheric Sciences, 71, 782-799. doi:10.1175/JAS-D-13-0180.1 [publisher-version]
  • Rieck, M., Hohenegger, C. & van Heerwaarden, C. (2014). The influence of land surface heterogeneities on cloud size development. Monthly Weather Review, 142, 3830-3846. doi:10.1175/MWR-D-13-00354.1 [publisher-version]
  • Schemann, V. (2014). Towards a scale aware cloud process parameterization for global climate models. Phd Thesis, Hamburg: Universität Hamburg. Berichte zur Erdsystemforschung, 145. doi:10.17617/2.1932864 [publisher-version]
  • Schlemmer, L. & Hohenegger, C. (2014). The formation of wider and deeper clouds as a result of cold-pool dynamics. Journal of the Atmospheric Sciences, 71, 2842-2858. doi:10.1175/JAS-D-13-0170.1 [publisher-version]
  • Weissmann, M., Gober, M., Hohenegger, C., Janjic, T., Keller, J., Ohlwein, C., Seifert, A., Tromel, S., Ulbrich, T., Wapler, K., Bollmeyer, C. & Deneke, H. (2014). Initial phase of the Hans-Ertel Centre for Weather Research - A virtual centre at the interface of basic and applied weather and climate research. Meteorologische Zeitschrift, 23, 193-208. doi:10.1127/0941-2948/2014/0558 [publisher-version]
  • Abma, D., Heus, T. & Mellado, J.-P. (2013). Direct numerical simulations of evaporative cooling at the lateral boundary of shallow cumulus clouds. Journal of the Atmospheric Sciences, 70, 2088-2102. doi:10.1175/JAS-D-12-0230.1 [publisher-version]
  • Blossey, P., Bretherton, C., Zhang, M., Cheng, A., Endo, S., Heus, T., Liu, Y., Lock, A., De Roode, S. & Xu, K.-M. (2013). Marine low cloud sensitivity to an idealized climate change: The CGILS les intercomparison. Journal of Advances in Modeling Earth Systems, 5, 234-258. doi:10.1002/jame.20025 [publisher-version]
  • Crueger, T., Hohenegger, C. & May, W. (2013). Tropical precipitation and convection changes in the Max Planck Institute Earth system model (MPI-ESM) in response to CO2 forcing. Journal of Advances in Modeling Earth Systems, 5, 85-97. doi:10.1002/jame.20012 [publisher-version]
  • De Rooy, W., Bechtold, P., Fröhlich, K., Hohenegger, C., Jonker, H., Mironov, D., Pier Siebesma, A., Teixeira, J. & Yano, J.-I. (2013). Entrainment and detrainment in cumulus convection: An overview. Quarterly Journal of the Royal Meteorological Society, 139, 1-19 . doi:10.1002/qj.1959
  • Giorgetta, M., Roeckner, E., Mauritsen, T., Bader, J., Crueger, T., Esch, M., Rast, S., Kornblueh, L., Schmidt, H., Kinne, S., Hohenegger, C., Möbis, B., Krismer, T., Wieners, K.-H. & Stevens, B. (2013). The atmospheric general circulation model ECHAM6 - Model description. Berichte zur Erdsystemforschung / Max-Planck-Institut für Meteorologie, 135. [publisher-version]
  • Heus, T. & Seifert, A. (2013). Automated tracking of shallow cumulus clouds in large domain, long duration Large Eddy Simulations. Geoscientific Model Development, 6, 1261-1273. doi:10.5194/gmd-6-1261-2013 [publisher-version]
  • Hohenegger, C. & Stevens, B. (2013). Preconditioning deep convection with cumulus congestus. Journal of the Atmospheric Sciences, 70, 448-464. doi:10.1175/JAS-D-12-089.1 [publisher-version]
  • Hohenegger, C. & Stevens, B. (2013). Controls on and impacts of the diurnal cycle of deep convection. Journal of Advances in Modeling Earth Systems, 5, 801-815. doi:10.1002/2012MS000216 [publisher-version]
  • Naumann, A., Seifert, A. & Mellado, J.-P. (2013). A refined statistical cloud closure using double-Gaussian probability density functions. Geoscientific Model Development, 6, 1641-1657. doi:10.5194/gmd-6-1641-2013 [publisher-version]
  • Schemann, V., Stevens, B., Gruetzun, V. & Quaas, J. (2013). Scale dependency of total water variance and its implication for cloud parameterizations. Journal of the Atmospheric Sciences, 70, 3615-3630. doi:10.1175/JAS-D-13-09.1 [publisher-version]
  • Seifert, A. & Heus, T. (2013). Large-eddy simulation of organized precipitating trade wind cumulus clouds. Atmospheric Chemistry and Physics, 13, 5631-5645. doi:10.5194/acp-13-5631-2013 [publisher-version][supplementary-material]
  • Zhang, M., Bretherton, C., Blossey, P., Austin, P., Bacmeister, J., Bony, S., Brient, F., Cheedela, S.-K., Cheng, A., Genio, A., Roode, S., Endo, S., Franklin, C., Golaz, J.-C., Hannay, C., Heus, T., Isotta, F., Dufresne, J.-L., Kang, I.-S., Kawai, H., Köhler, M., Larson, V., Liu, Y., Lock, A., Lohmann, U., Khairoutdinov, M., Molod, A., Neggers, R., Rasch, P., Sandu, I., Senkbeil, R., Siebesma, A., Drian, C.-L., Stevens, B., Suarez, M., Xu, K.-M., von Salzen, K., Webb, M., Wolf, A. & Zhao, M. (2013). CGILS: Results from the first phase of an international project to understand the physical mechanisms of low cloud feedbacks in single column models. Journal of Advances in Modeling Earth Systems, 5, 826-842. doi:10.1002/2013MS000246 [publisher-version]
  • Lieber, M., Grützun, V., Wolke, R., Müller, M. & Nagel, W. (2012). Highly scalable dynamic load balancing in the atmospheric modeling system COSMO-SPECS+FD4. In Applied Parallel and Scientific Computing : 10th International Conference, PARA 2010, Reykjavík, Iceland, June 6-9, 2010, Revised Selected Papers, Part I (pp.131-141). Berlin: Springer.
  • Naumann, A., Notz, D., Havik, L. & Sirevaag, A. (2012). Laboratory study of initial sea-ice growth: properties of grease ice and nilas. Cryosphere, 6, 729-741 . doi:10.5194/tc-6-729-2012 [publisher-version]
  • Neggers, R., Siebesma, A. & Heus, T. (2012). Continous single-column model evaluation at a permanent meteorological supersite. Bulletin of the American Meteorological Society, 93, 1389-1400. doi:10.1175/BAMS-D-11-00162.1 [publisher-version]
  • Rieck, M., Nuijens, L. & Stevens, B. (2012). Marine boundary layer cloud feedbacks in a constant relative humidity atmosphere. Journal of the Atmospheric Sciences, 69, 2538-2550. doi:10.1175/JAS-D-11-0203.1
  • Schlemmer, L., Hohenegger, C., Schmidli, J. & Schär, C. (2012). Diurnal equilibrium convection and land surface-atmosphere interactions in an idealized cloud-resolving model. Quarterly Journal of the Royal Meteorological Society, 138, 1526-1539. doi:10.1002/qj.1892
  • Hohenegger, C. & Bretherton, C. (2011). Simulating deep convection with a shallow convection scheme. Atmospheric Chemistry and Physics, 11, 10389-10406. doi:10.5194/acp-11-10389-2011 [publisher-version][any-fulltext]
  • Neggers, R., Heus, T. & Siebesma, A. (2011). Overlap statistics of cumuliform boundary-layer cloud fields in large-eddy simulations. Journal of Geophysical Research-Atmospheres, 116: D21202. doi:10.1029/2011JD015650
  • Heus, T., van Heerwaarden, C., Jonker, H., Pier Siebesma, A., Axelsen, S., van den Dries, K., Geoffroy, O., Moene, A., Pino, D., de Roode, S. & Vilà-Guerau de Arellano, J. (2010). Formulation of the Dutch Atmospheric Large-Eddy Simulation (DALES) and overview of its applications. Geoscientific Model Development, 3, 415-444. doi:10.5194/gmd-3-415-2010 [publisher-version]

Contact

Dr. Cathy Hohenegger

Group leader
Phone: +49 (0)40 41173-302
cathy.hohenegger@we dont want spammpimet.mpg.de

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Climate physics Clouds Observations Publication

When thunderstorms light up scientists' eyes: first results from the field campaign FESSTVaL

In a recent study, Dr. Cathy Hohenegger, group leader in the climate physics department, reports on the measurement effort called the Field Experiment…

Satellite image of Earth with eddy
Climate physics Clouds Observations

Wivern Earth Explorer candidate goes forward

The satellite mission Wivern has been selected by the European Space Agency ESA as one of two remaining candidates to progress to the next development…