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Cathy Hohenegger

Department Climate Physics
Group Climate Surface Interaction
Position Group Leader
phone +49 40 41173-302
Email cathy.hohenegger@mpimet.mpg.de
Room B 411

 

Research interests

 

My research focuses on precipitating convection. One question that particularly interests me is the role that the surface, being the ocean or the land, plays in setting basic features of the climatological precipitation distribution. Given my interests, being able to represent  convection explicitly and the surface dynamically is of crucial importance. This explains my strong involvement in the use and development of coupled km-scale Earth System Models. Besides, I like using more simple, idealized approaches, like the radiative convective equilibrium (RCE) conceptualization or box models to study problems of climate related to the coupling between convection and the underlying surface. Recently, I have been involved in the design of field experiments with the aim to sample the near-surface air properties at kilometer-scale resolution.

Whether it rains more over wet or over dry soils is set by the representation of convection

Whether an increase in soil moisture leads to an increase (positive feedback) or a decrease (negative feedback) of precipitation has long been debated. Positive feedbacks are of particular relevance for the climate system as they can amplify an existing perturbation, thus affecting the climate system on longer time scales. One difficulty in diagnosing the strength and sign of the feedback between soil moisture and convection is that it involves convection, which, in traditional climate model, is not explicitly resolved but parameterized. There is no guarantee that a convective parameterization can faithfully capture this feedback. In this study, we for the first time investigated the soil moisture-precipitation feedback in regional climate simulations using an explicit representation of convection and compared the results to similar integrations conducted at coarser resolution with parameterized convection. Whereas the simulations with convective parameterizations revealed a positive feedback, in agreement with past studies, the simulations with explicit convection revealed a negative feedback. The negative feedback was the result of a strong capping inversion at the top of the planetary boundary layer that inhibits the development of convection over wet soils due to missing boundary layer growth. Moreover, using different versions of the convective parameterization, either positive, neutral or negative feedbacks could be obtained, depending whether the convective parameterization was able to see the capping inversion. Even though biases in the representation of the soil moisture-precipitation feedback in the explicit simulation cannot be excluded, in any case, the feedback sign should not depend on the design of the convective parameterization and on the chosen representation of convection.

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

Congestus moistening is too inefficient to explain the daily transition to deep convection

As the buoyant convective plume rises in the atmosphere, it mixes with its environment, leading to the detrainment of updraft air in its environment.  This process moistens the environment, making it less hostile to the ascent of the next convective plume, a process called preconditioning. But other processes, in particular moisture-convergence and forced vertical ascent resulting from larger scale forcing, can also promote the transition to deep convection. In this study, we investigated the importance of  moistening by previous congestus clouds, a thermodynamic control on the transition to deep convection, versus moisture convergence, a dynamical control on this transition. We used the idea that only fast enough processes can be of relevance for the transition as it has to happen within one day. Comparing theoretical estimates of transition time due to congestus moistening or moisture convergence to observed transition times revealed that congestus moistening, with a theoretical transition time scale of  35-38 h, is too slow to explain observed transition times (2-4 h). The results are confirmed by estimates derived from idealized large-eddy simulations.  Further evidence speaking against the efficiency of preconditioning by congestus clouds were the fact that (i) most congestus clouds do not transition to deep convection actually; that (ii) the probability of transition does not increase with their lifetime and that (iii) the presence of cumuli congestus over a region does not enhance the likelihood for deep convection development. The results indicate that previous work might have overemphasized the idea of convection randomly developing in a moist and unstable atmosphere, on its own, through moistening by previous shallower clouds.

Hohenegger C. and B. Stevens, 2013: Preconditioning deep convection with cumulus congestus. J. Atmos. Sci., 70, 448-464 doi:10.1175/JAS-D-12-089.1.

 

The ability of the soil to dry out ensures a homogeneous precipitation distribution over land

Imagine the simplest planet with only three fundamental processes at play: radiation, that acts to destabilize the atmosphere, convection, that acts against it and stabilizes the atmosphere, and soil moisture. How would precipitation be spatially distributed on such a planet? Past studies that have used this conceptualization of radiative convective equilibrium (RCE) over a surface of fixed SST have found that precipitation ends up strongly organized in a blob. Does this remain true over land? A priori, two distinct final precipitation distributions are possible.  Soil moisture may act to keep precipitation to the precipitating region through a positive thermodynamical soil moisture-precipitation feedback, or to homogenize the precipitation distribution through a negative dynamical feedback requiring the generation of strong enough soil moisture-induced circulations. We found that, at least when the soil dries out, the disaggregating effect of soil moisture wins. This result could be explained theoretically combining a bulk model of the planetary boundary layer, the linearized version of the surface energy budget equation and gravity current theory. The results are surprising as they indicate that, on its own, the drying out of the soil terminates heat waves and acts against the formation of deserts. We nevertheless showed that the results are consistent with observations of the propagation of the monsoon.

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.

 
Many basic climate statistics already converge at relatively coarse resolution

Convective processes happen on scales  that are too fine to be explicitly resolved by state-of-the-art General Circulation Models (GCMs) with their typical grid spacing of O(100 km). But how fine is fine enough to capture basic climate statistics? In this study, we made use of a unique dataset of eight global storm-resolving models integrated for 40 days, called DYAMOND, and of simulations conducted with the model ICON using an explicit representation of convection across resolution, to answer this question. As the determination of the convergence is actually ill-posed for the climate system, we proposed a novel measure of convergence. We quantified the convergence by comparing resolution-induced differences to the spread obtained in the DYAMOND ensemble. Looking at large-scale features of the atmosphere, such as location of the ITCZ, precipitation amount, energy budget, we found that the resolution dependency is much smaller than initially thought. A grid spacing of 5 km already allows capturing 26 out of the 27 investigated statistics. Resolution down to 5 km especially matters for radiation, which systematically increases with resolution because of reductions in the low cloud amount over the subtropical oceans. Even for nine of the investigated statistics, including the global mean precipitation amount, the latitudinal position and  the width of the Atlantic ITCZ or the longitudinal position of the Pacific ITCZ, a grid spacing of 80 km does not lead to significant differences. On the one hand and given the cost of high-resolution simulations, this is good news for the prospect of conducting storm-resolving simulations on climatic time scales. On the other hand, the results do question the importance of the small scales for determining large-scale features of the atmosphere.

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.

 

Land steals precipitation from the ocean

It rains in mean 3 mm/day over tropical land and 3 mm/day over tropical ocean, giving the impression that the nature of the underlying surface, being land or ocean, does not matter. This is surprising as for instance the nature of the underlying surface affects the time of peak precipitation. In this study, we investigate what the partitioning of precipitation between land and ocean tells us about the controls of the underlying surface. We define the precipitation ratio as the ratio between mean precipitation averaged over tropical land and mean precipitation averaged over tropical ocean. Realizing that a rainbelt is a very good conceptualization of the tropical precipitation distribution, we demonstrate that a ratio of 0.86, not 1.0 as originally thought, would be expected if the surface would not modify the way it rains. This is so because the latitudes visited by the rainbelt have a deficit in land fraction compared to the tropical mean. As the observations show values larger than 0.86, we can infer that the surface forces the atmosphere to rain more over land. This happens by enlarging the rainbelt and attracting it to land points far north, but not through changes in intensity. As the land is drier than the ocean but gets more rain, our results leads to the interpretation that the feedback between water storage and precipitation is negative. In contrast, in the ensemble mean of CMIP models, land looses rain to the ocean and the feedback is positive. This questions the ability of such models to answer more complicated questions such as local precipitation changes.

Hohenegger, C. and B. Stevens, 2022: Tropical continents rainier than expected from geometrical constraints. AGU Advances, 3, Article e2021AV000636. https://doi.org/10.1029/2021AV000636

 
Simulating the components of the Earth system and their interactions at kilometer and subkilometer scales

State-of-the-art Earth system models typically employ grid spacings of O(100km), which is too coarse to explicitly resolve main drivers of the flow of energy and matter across the Earth system. At the Max Planck Institute for Meteorology, a new model configuration, ICON-Sapphire, has been developped. This configuration targets a representation of the components of the Earth system and their interactions with a grid spacing of 10 km and finer. Through the use of selected simulation examples, we demonstrate in this study that ICON-Sapphire can (i) be run coupled globally on seasonal time scales with a grid spacing of 5 km, on monthly time scales with a grid spacing of 2.5 km and on daily time scales with a grid spacing of 1.25 km, (ii) resolve large eddies in the atmosphere using hectometer grid spacings on limited-area domains in atmosphere-only simulations, (iii) resolve submesoscale ocean eddies by using a global uniform grid of 1.25 km or a telescoping grid  with a finest grid spacing of 530 m, the latter coupled to a uniform atmosphere and (iv) simulate biogeochemistry in an ocean-only simulation integrated for 4 years at 10 km. Comparison of basic features of the climate system to observations reveals no obvious pitfall, even though some observed aspects remain difficult to capture. The throughput of the coupled 5-km global simulation is 126 simulated days per day employing 21% of the latest machine of the German Climate Computing Center. Extrapolating from these results, multi-decadal global simulations including interactive carbon are now possible and short global simulations resolving large eddies in the atmosphere and submesoscale eddies in the ocean are within reach.

Hohenegger, C., P. Korn, L. Linardakis, R. Redler, R. Schnur, P. Adamidis, J. Bao, S. Bastin, M. Behravesh, M. Bergemann, J. Biercamp, H. Bockelmann, R. Brokopf, N. Brüggemann, L. Casaroli, F. Chegini, G. Datseris, M. Esch, G. George, M. Giorgetta, O. Gutjahr, H. Haak, M. Hanke, T. Ilyina, T. Jahns, J. Junglcaus, M. Kern, D. Klocke, L. Kluft, T. Kölling, L. Kornblueh, S. Kosukhin, C. Kroll, J. Lee, T. Mauristen, C. Mehlmann, T. Mieslinger, A. K. Naumann, L. Paccini, A. Peinado, D. Sri Praturi, D. Putrasahan, S. Rast, T. Riddick, N. Röber, H. Schmidt, U. Schulzweida, F. Schütte, H. Segura, R. Shevchenko, V. Singh, M. Spechts, C. C. Stephan, J.-S. von Storch, R. Vogel, C. Wengel, M. Winkler, F. Ziemen, J. Marotzke and B. Stevens, 2023: ICON-Sapphire: simulating the components of the Earth System and their interactions at kilometer and subkilometer scales. Geo. Model Dev., 16, 779-811, https://doi.org/10.5194/gmd-16-779-2023.

 
FESSTVaL: capturing cold pools and km-scale atmospheric processes with a dense network of observations

Before reaching their impressive size, mature organized thunderstorms have to grow out of small, shallow, convective clouds. The transition typically happens around 1 km. Likewise, numerical weather prediction models on limited domains have now long been operated with a grid spacing of a few kilometers to better capture smaller-scale atmospheric phenomena. Yet, surface observations at km scale are lacking. In Germany, the mean distance between the stations of the operational surface network is approximately 25 km. The goal of FESSTVaL, the Field Experiment on Submesoscale Spatio-Temporal Variability in Lindeberg was to observe submesoscale atmsospheric variability. Submeoscale variabiliy is here loosely defined as variability occurring on scales smaller than 10 km. As source of submesoscale variability, FESSTVaL targeted cold pools, gusts and coherent patterns in the planeteray boundary layer during summertime. A unique feature was the distribution of 150  self-developed and low-cost instruments. More specifically, FESSTVaL included dense networks of 80 autonomous cold pool loggers, 19 weather stations and 83 soil sensor systems, all installed in a rural region of 15-km radius in eastern Germany, as well as self-developed weather stations handed out to citizens. Boundary layer and upper air observations were provided by 8 Doppler lidars and 4 microwave radiometers distributed at 3 supersites; water vapor and temperature were also measured by advanced lidar systems and an infrared spectrometer; and rain was observed by a X-band radar. An uncrewed aircraft, multicopters and a small radiometer network carried out additional measurements during a four-week period. In this study, we present FESSTVaL measurement's strategy and first observational results of unprecedented highly-resolved spatio-temporal cold-pool structures, both in the horizontal as well as in the vertical dimension, associated with overpassing convective systems.

Hohenegger, C., F. Ament, F. Beyrich, U. Löhnert, H. Rust, J. Bange, T. Böck, C. Böttcher, J. Boventer, F. Burgemeister, M. Clemens, C. Detring, I. Detring, N. Dewani, I. B. Duran, S. Fiedler, M. Göber, C. van Heerwaarden, B. Heusinkveld, B. Kirsch, D. Klocke, C. Knist, I. Lange, F. Lauermann, V. Lehmann, J. Lehmke, R. Leinweber, K. Lundgren, M. Masbou, M. Mauder, W. Mol, H. Nevermann, T. Nomokonova, E. Päschke, A. Platis, J. Reichardt, L. Rochette, M. Sakradzija, L. Schlemmer, J. Schmidli, N. Shokri, V. Sobottke, J. Speidel, J. Steinheuer, D. D. Turner, H. Vogelmann, C. Wedemeyer, E. Weide-Luiz, S. Wiesner, N. Wildmann, K. Wolz and T. Wetz, 2023: FESSTVaL: the Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg. Bull. Amer. Meteorol. Soc., 104, E1875-E1892, https://doi.org/10.1175/BAMS-D-21-0330.1

Data available here: https://www.cen.uni-hamburg.de/en/icdc/research/samd/observational-data/short-term-observations/fesstval.html

  • Yoon, A. and C. Hohenegger, 2025: Muted Amazon rainfall response to deforestation in a global storm-resolving model. Geophys. Res. Let., 52, e2024GL110503, https://doi.org/10.1029/2024GL110503
  • Byrne, M., G. Hegerl, J. Scheff, O. Adam, A. Berg, M. Biasutti, S. Bordoni, A. Dai, R. Geen, M. Henry, S. Hill, C. Hohenegger, V. Humphrey, M. Joshi, A. Konings, M. Laguë, F. Lambert, F. Lehner, J. Mankin, K. McColl, K. McKinnon, A. Pendergrass, M. Pietschnig, L. Schmidt, A. Schurer, E. Scott, D. Sexton, S. Sherwood, L, Vargas Zeppetello and Y. Zhang, Y, 2024:  Theory and the future of land-climate science. Nature Geoscience, 17, 1079-1086, https://doi.org/10.1038/s41561-024-01553-8
  • Kirsch, B., C. Hohenegger and F. Ament, 2024:  Morphology and growth of convective cold pools observed by a dense station network in Gemany. Quart. J. Roy. Meteorol. Soc., 150, 857-876, https://doi.org/10.1002/qj.4626.
  • Lee, J. and C. Hohenegger, 2024: Weaker land-atmosphere coupling in global storm-resolving simulation. Proc. Natl. Acad. Sci., 121, 5692-5697, https://doi.org/10.1073/pnas.2314265121
  • Schmidt, L. and C. Hohenegger, 2024: Precipitation enhancement over tropical land through the lens of the moisture-precipitation relationship. Quart. J. Roy. Meteorol. Soc.., 150, 4737-4749, https://doi.org/10.1002/qj.4838.
  • Segura, H. and C. Hohenegger, 2024: How do the tropics precipitate? Daily variations in precipitation and cloud distribution. J. Meteorol. Society Japan, 102, 525-537, https://doi.org/10.2151/jmsj.2024-028
  • Hohenegger, C., F. Ament, F. Beyrich, U. Löhnert, H. Rust, J. Bange, T. Böck, C. Böttcher, J. Boventer, F. Burgemeister, M. Clemens, C. Detring, I. Detring, N. Dewani, I. B. Duran, S. Fiedler, M. Göber, C. van Heerwaarden, B. Heusinkveld, B. Kirsch, D. Klocke, C. Knist, I. Lange, F. Lauermann, V. Lehmann, J. Lehmke, R. Leinweber, K. Lundgren, M. Masbou, M. Mauder, W. Mol, H. Nevermann, T. Nomokonova, E. P\"oschke, A. Platis, J. Reichardt, L. Rochette, M. Sakradzija, L. Schlemmer, J. Schmidli, N. Shokri, V. Sobottke, J. Speidel, J. Steinheuer, D. D. Turner, H. Vogelmann, C. Wedemeyer, E. Weide-Luiz, S. Wiesner, N. Wildmann, K. Wolz and T. Wetz, 2023: FESSTVaL: the Field Experiment on Submesoscale Spatio-Temporal Variability in Lindenberg. Bull. Amer. Meteorol. Soc., 104, E1875-E1892, doi.org/10.1175/BAMS-D-21-0330.1.
  • Hohenegger, C., P. Korn, L. Linardakis, R. Redler, R. Schnur, P. Adamidis, J. Bao, S. Bastin, M. Behravesh, M. Bergemann, J. Biercamp, H. Bockelmann, R. Brokopf, N. Brüggemann, L. Casaroli, F. Chegini, G. Datseris, M. Esch, G. George, M. Giorgetta, O. Gutjahr, H. Haak, M. Hanke, T. Ilyina, T. Jahns, J. Junglcaus, M. Kern, D. Klocke, L. Kluft, T. Kölling, L. Kornblueh, S. Kosukhin, C. Kroll, J. Lee, T. Mauristen, C. Mehlmann, T. Mieslinger, A. K. Naumann, L. Paccini, A. Peinado, D. Sri Praturi, D. Putrasahan, S. Rast, T. Riddick, N. Röber, H. Schmidt, U. Schulzweida, F. Schütte, H. Segura, R. Shevchenko, V. Singh, M. Spechts, C. C. Stephan, J.-S. von Storch, R. Vogel, C. Wengel, M. Winkler, F. Ziemen, J. Marotzke and B. Stevens, 2023: ICON-Sapphire: simulating the components of the Earth System and their interactions at kilometer and subkilometer scales. Geo. Model Dev., 16, 779-811, doi.org/10.5194/gmd-16-779-2023.
  • Jungandreas, L., M. Claussen and C. Hohenegger, 2023: How does the explicit treatment of convection alter the precipitation-soil hydrology interaction in the mid-Holocene African humid period? Clim. Past, 19, 637-664, https://doi.org/10.5194/cp-19-637-2023.
  • Schmidt, L. and C. Hohenegger, 2023: Constraints on the ratio between tropical land and ocean precipitation derived from a conceptual water balance model. J. Hydrometeorol., 24, 1103-1117, https://doi.org/10.1175/JHM-D-22-0162.1.
  • Shevchenko, R., C. Hohenegger and M. Schmitt, 2023: Impact of diurnal warm layers on atmospheric convection. J. Geophys. Res. Atm., 128, e2022JD038473, doi.org/10.1029/2022JD038473.
  • Dauhut, T. and C. Hohenegger, 2022: The contribution of convection to the stratospheric water vapor: the first budget using a global storm-resolving model. J. Geophys. Res., 127, e2021JD036295, doi.org/10.1029/2021JD036295.
  • Hohenegger, C. and B. Stevens, 2022:  Tropical continents rainier than expected from geometrical constraints. AGU Advances, 3, e2021AV000636, doi.org/10.1029/2021AV000636.
  • Kirsch, B., C. Hohenegger, D. Klocke, R. Senke, M. Offermann and F. Ament, 2022: Sub-mesoscale observations of convective cold pools with a dense station network in Hamburg, Germany. Earth Syst. Sci. Data, 14, 3531-3548, doi.org/10.5194/essd-14-3531-2022.
  • Lee, J., C. Hohenegger,  A. Chlond and R. Schnur, 2022: The climatic role of interactive leaf phenology in the vegetation-atmosphere system of radiative-convective equilibrium storm-resolving simulations. Tellus B, 74, 164-175, doi.org/10.16993/tellusb.26.
  • Mauritsen, T., R. Redler, M. Esch, B. Stevens, C. Hohenegger, D. Klocke, R. Brokopf, H. Haak, L. Linardakis, N. Röber, R. Schnur, 2022: Early development and tuning of a global coupled cloud resolving model, and its fast response to increasing CO2, Tellus A, 74, 346-363, doi.org/10.16993/tellusa.54.
  • Segura, H., C. Hohenegger, C. Wengel and B. Stevens, 2022: Learning by doing: seasonal and diurnal features of tropical precipitation in a global-coupled storm-resolving model. Geophysical Research Letters, 49: e2022GL101796, doi.org/10.1029/2022GL101796.
  • Becker, T. and C. Hohenegger, 2021: Entrainment and its dependency on environmental conditions and convective organization in convection-permitting simulations. Mon. Wea. Rev., 149, 537-550, doi.org/10.1175/MWR-D-20-0229.1.
  • Judt F., D. Klocke, R. Rios-Berrios, B. Vanniere, F. Ziemen, L. Auger, J. Biercamp, C. Bretherton, X. Chen, P. Düben, C. Hohenegger, M. Khairoutdinov, C. Kodama, L. Kornblueh, S.-J. Lin, M. Nakano, P. Neumann, W. Putman, N. Röber, M. Roberts, M. Satoh, R. Shibuya, B. Stevens, P. L. Vidale, N. Wedi and L. Zhou, 2021: Tropical cyclones in global storm-resolving models. J. Meteorol. Soc. Japan, 99, 579-602, doi.org/10.2151/jmsj.2021-029.
  • 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, https://doi.org/10.5194/cp-17-1665-2021. Highlight
  • Kirsch, B., F. Ament and C. Hohenegger, 2021: Convective cold pools in long-term boundary layer mast observations. Mon. Wea. Rev., 149, 811-820, doi.org/10.1175/MWR-D-20-0197.1.
  • 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.org/10.1002/qj.3995.
  • Muller, C., D. Yang, G. Craig, T. Cronin, B. Fildier, J. O. Haerter, C. Hohenegger, B. Mapes, D. Randall, S. Shamekh, S. Sherwood, 2021: Spontaneous aggregation of convective storms. Annual Rev. Fluid Mech., 54, 133-157, https://doi.org/10.1146/annurev-fluid-022421-011319.
  • Paccini, L., C. Hohenegger and B. Stevens, 2021: Explicit versus parameterized convection in response to the Atlantic Meridional Mode. J. Climate, 34, 3343-3354, doi.org/10.1175/JCLI-D-20-0224.1.
  • Radtke, J., T . Mauritsen and C. Hohenegger, 2021: Shallow cumulus cloud feedback in large eddy simulations - bridging the gap to storm resovling models. Atmos. Chem. Phys., 21, 3275-3288, doi.org/10.5194/acp-21-3275-2021.
  • Rochetin, N., C. Hohenegger, L. Touzé-Peiffer and N. Villefranque, 2021: A physically-based definition of convectively generated density currents: detection and characterization in convection-permitting simulations. J. Adv. Mod. Earth Systems, 13, doi.org/10.1029/2020MS002402.
  • Roh, W., M. Satoh and C. Hohenegger, 2021: Intercomparison of cloud properties in DYAMOND simulations over the Atlantic ocean. J. Meteorol. Soc. Japan, 99, 1439-1451, doi.org/10.2151/jmsj.2021-070.
  • Brueck, M., C. Hohenegger and B. Stevens, 2020: Mesoscale marine precipitation varies indepdently from the spatial arrangement from its convective cells. Quart. J. Roy. Meteor. Soc., 146, 1391-1402, doi.org/10.1002/qj.3742
  • Hohenegger, C., 2020: Land-atmosphere interactions in tropical Africa, in Oxford Research Encyclopedia of Climate Science. Oxford University Press. doi.org/10.1093/acrefore/9780190228620.013.779.
  • Hohenegger, C. and C. Jakob, 2020: A relationship between ITCZ organization and subtropical humidity. Geophys. Res. Let., 47, e2020GL088515, doi.org/10.1029/2020GL088515.
  • Hohenegger, C. and D. Klocke, 2020: Stürmische Zeiten für die Klimaforschung. Physik in unserer Zeit, 5, 238-235, doi.org/10.1002/piuz.202001580.
  • Hohenegger, C. and C. Schär, 2020: Clouds and land. Book chapter in Clouds and climate. Edited by A. P. Siebesma, S. Bony, C. Jakob and B. Stevens. Cambridge University Press, doi.org/10.1017/9781107447738, 27pp.
  • 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.org/10.2151/jmsj.2020-005.
  • 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.org/10.1029/2019MS001698.
  • Stevens, B., C. Acquistapace, A. Hansen, R. Heinze, C. Klinger, D. Klocke, H. Rybka, W. Schubotz, J. Windmiller, P. Adamidis, I. Arka, V. Barlakas, J. Biercamp, M. Brueck, S. Brune, S. A. Buehler, U. Burkhardt, G. Cioni, M. Costa-Suros, S. Crewell, T, Crüger, H. Deneke, P. Friederichs, C. C. Henken, C. Hohenegger, M. Jacob, F. Jakub, N. Kalthoff, M. Köhler, T. W. van Laar, P. Li, U. Löhnert, A. Macke, N. Madenach, B. Mayer, C. Nam, A. K. Naumann, K. Peters, S. Poll, J. Quaas, N. Röber, N. Rochetin, L. Scheck, V. Schemann, S. Schnitt, A. Seifert, F. Senf, M. Shapkalijevski, C. Simmer, S. Singh, O. Sourdeval, D. Spickermann, J. Strandgren, O. Tessiot, N. Vercauteren, J. Vial, A. Voigt and G. Zängl, 2020: The added value of large-eddy and storm-resolving models for simulating clouds and precipitation. J. Meteorol. Society Japan, 98, 395-435, doi.org/10.2151/jmsj.2020-021, editor's choice.
  • Wing, A., C. L. Stauffer, T. Becker, K. A. Reed, M.-S. Ahn, N. P. Arnold, S. Bony, M. Branson, G. H. Bryan, J.-P. Chaboureau, S. R. de Roode, K. Gayatri, C. Hohenegger, I.-K. Hu, F. Jansson, T. R. Jones, M. Khairoutdinov, D. Kim, Z. K. Martin, S. Matsugishi, B. Medeiros, H. Miura, Y. Moon, S. K. Müller, T. Ohno, M. Popp, T. Prabhakaran, D. Randall, R. Rios-Berrios, N. Rochetin, R. Roehrig, D. M. Romps, J. H. Ruppert, M. Satoh, L. G. Silvers, M. S. Singh, B. Stevens, L. Tomassini, C. C. van Heerwaarden, S. Wang and M. Zhao,  2020: Clouds and Convective Self‐Aggregation in a Multi‐Model Ensemble of Radiative‐Convective Equilibrium Simulations. J. Adv. Mod. Earth Systems, 12, e2020MS002138, https://doi.org/10.1029/2020MS002138.
  • Mauritsen, T., J. Bader, T. Becker, J. Behrens, M. Bittner, R. Brokopf, V. Brovkin, M. Claussen, T. Crueger, M. Esch, I. Fast, S. Fiedler, D. Fläschner, V. Gayler, M. Giorgetta, D. S. Goll, H. Haak, S. Hagemann, C. Hedemann, C. Hohenegger, T. Ilyina, T. Jahns, D. Jimenez-de-la-Cuesta, J. Jungclaus, T. Kleinen, S. Kloster, D. Kracher, S. Kinne, D. Kleberg, G. Lasslop, L. Kornblueh, J. Marotzke, D. Matei, K. Meraner, U. Mikolajewicz, K. Modali, B. Möbis, W. A. Müller, J. E. M. S. Nabel, C. C. W. Nam, D. Notz, S.-S. Nyavira, H. Paulsen, K. Peters, R. Pincus, H. Pohlmann, J. Pongratz, M. Popp, T. J. Raddatz, S. Rast, R. Redler, C. H. Reick, T. Rohrschneider, V. Schemann, H. Schmidt, R. Schnur, U. Schulzweida, K. D. Six, L. Stein, I. Stemmler, B. Stevens, J.-S. von Storch, F. Tian, A. Voigt, P. Vrese, K.-H. Wieners, S. Wilkenskjeld, A. Winkler and E. Roeckner, 2019: Developments in the MPI-M Earth System Model version 1.2 (MPI-ESM1.2) and its response to increasing CO2, J. Adv. Mod. Earth Systems, 11, 998-1038, doi.org/10.1029/2018MS001400.
  • Naumann, A. K., B. Stevens and C. Hohenegger, 2019: A moist conceptual model for the boundary layer structure and radiatively driven shallow circulations in the Trades. J. Atmos. Sci., 76, 1289-1306, doi.org/10.1175/JAS-D-18-0226.1
  • Peters, K., C. Hohenegger and D. Klocke, 2019: Different representation of mesoscale convective systems in convection-permitting and convection-parameterizing NWP models and its implications for large-scale forecast evolution. Atmosphere, 10, 503, doi.org/10.3390/atmos10090503.
  • Pscheidt, I., F. Senf, R. Heinze, H. Deneke, S. Trömmel and C. Hohenegger, 2019: How organized is deep convection over Germany? Quart. J. Roy. Meteor. Soc., 145, 2366-2384, doi.org/10.1002/qj.3552
  • Retsch, M.H., T. Mauritsen and C. Hohenegger, 2019:  Climate change feedbacks in aquaplanet experiments with explicit and parametrised convection for horizontal resolutions of 2525 km up to 5 km. J. Adv. Mod. Earth Systems, 11, 2070-2088, doi.org/10.1029/2019MS001677.
  • Stevens, B., S. Bony, H. Brogniez, L. Hentgen, C. Hohenegger, C. Kiemle, T. S. L'Ecuyer, A. K. Naumann, H. Schulz, P. A. Siebesma, J. Vial, D. M. Winker and P. Zuidema, 2019: Sugar, gravel, fish, and flowers: mesoscale cloud patterns in the Tradewinds. Quart. J. Roy. Meteor. Soc., 146, 141-152, doi.org/10.1002/qj.3662.
  • Vial, J., R. Vogel, S. Bony, B. Stevens, D. M. Winker, X. Cai, C. Hohenegger, A. K. Naumann, H. Brogniez, 2019: A new look at the daily cycle of tradewind cumuli. J. Adv. Mod. Earth Systems, 11, 3148-3166, doi.org/10.1029/2019MS001746.
  • Windmiller, J. and C. Hohenegger, 2019: Convection on the edge. J. Adv. Mod. Earth Systems, 11, 3959-3972, doi.org/10.1029/2019MS001820.
  • Becker T., C. S. Bretherton, C. Hohenegger and B. Stevens, 2018: Estimating bulk entrainment with unaggregated and aggregated convection. Geophys. Res. Let., 45, 455-462, doi.org/10.1002/2017GL076640.
  • Cioni G. and C. Hohenegger, 2018: A simplified model of precipitation enhancement over a heterogeneous surface. Hydrol. Earth Syst. Sci., 22, 3197-3212, doi.org/10.5194/hess-22-3197-2018
  • Crueger, T., M. A. Giorgetta, R. Brokopf, M. Esch, S. Fiedler, C. Hohenegger, L. Kornblueh, T. Mauritsen, C. Nam, A. K. Naumann, K. Peters, S. Rast, E. Roeckner, M. Sakradzija, H. Schmidt, J. Vial, R. Vogel and B. Stevens, 2018: ICON-A, the atmosphere component of the ICON Earth System Model. Part II. Model evaluation. J. Adv. Mod. Earth Systems, 10, 1638-1662, doi.org/10.1029/2017MS001233.
  • Giorgetta, M., R. Brokopf, T. Crueger, M. Esch, S. Fiedler, J. Helmert, C. Hohenegger, L. Kornblueh, M. Köhler, E. Manzini, T. Mauritsen, C. Nam, T. Raddatz, S. Rast, D. Reinert, M. Sakradzija, H. Schmidt, R. Schneck, R. Schnur, L. Silvers, H. Wan, G. Zängl and B. Stevens 2018: ICON-A, the atmosphere component of the ICON Earth System Model. Part I: Model description. J. Adv. Mod. Earth Systems, 10, 1613-1637, doi.org/10.1029/2017MS001242.
  • 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.org/10.1073/pnas.1718842115
  • Mikolajewicz U., F. Ziemen, G. Cioni, M. Claussen, K. Fraedrich, M. Heidkamp, C. Hohenegger, D. Jimenez de la Cuesta, M.-L. Kapsch, A. Lemburg, T. Mauritsen, K. Meraner, N. Röber, H. Schmidt, K. D. Six, I. Stemmler, T. Tamarin-Brodsky, A. Winkler, X. Zhu and B. Stevens, 2018: The climate of a retrograde rotating Earth. Earth Syst. Dynam., 9, 1191-1215, doi.org/10.5194/esd-9-1191-2018.
  • Petrova I. Y., C. C. van Heerwaarden, C. Hohenegger and F. Guichard, 2018: Regional co-variability of spatial and temporal soil moisture-precipitation coupling in North Africa: an observational perspective. Hydrol. Earth Syst. Sci., 22, 3275-3294, doi.org/10.5194/hess-22-3275-2018.
  • Ruppert J. H. and C. Hohenegger, 2018: Diurnal cycle adjustment and organized deep convection. J. Climate, 31, 4899-4916, doi.org/10.1175/JCLI-D-17-0693.1
  • Becker T., B. Stevens and C. Hohenegger, 2017: Imprint of the convective parameterization and sea-surface temperature on large-scale convective self-aggregation. J. Adv. Mod. Earth Systems, 9, 1488-1505, doi.org/10.1002/2016MS000865.
  • Cioni G. and C. Hohenegger, 2017: Effect of soil moisture on diurnal convection and precipitation in large-eddy simulations. J. Hydrometeorol., 18, 1885-1903, doi.org/10.1175/JHM-D-16-0241.1
  • Klocke D., M. Brueck, C. Hohenegger and B. Stevens, 2017: Rediscovery of the doldrums in sotrm-resolving simulations over the tropical Atlantic. Nat. Geoscience, 10, 891-896, doi.org/10.1038/s41561-017-0005-4
  • Naumann A. K., B. Stevens, C. Hohenegger and J. P. Mellado, 2017: A conceptual model of a shallow circulation induced by prescribed low-level radiative cooling. J. Atmos. Sci., 74, 3129-3144, doi.org/10.1175/JAS-D-17-0030.1
  • Peters K. and C. Hohenegger, 2017: On the dependence of squall line characteristics on surface conditions. J. Atmos. Sci., 74, 2211-2228, doi.org/10.1175/JAS-D-16-0290.1
  • Retsch M. H., C. Hohenegger and B. Stevens, 2017: Vertical resolution refinement in an aqua-planet and its effect on the ITCZ. J. Adv. Mod. Earth Systems, 9, 2425-2436, doi.org/10.1002/2017MS001010.
  • Sakradzija, M and C. Hohenegger, 2017: What determines the distribution of shallow convective mass flux through cloud base? J. Atmos. Sci, 74, 2615-2632, doi.org/10.1175/JAS-D-16-0326.1
  • Siongco C., C. Hohenegger and B. Stevens, 2017: Sensitivity of the summertime tropical Atlantic precipitation distribution to convective parameterization and model resolution in ECHAM6. J. Geophys. Res., 122, 2579-2594, doi.org/10.1002/2016JD026093.
  • 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.org/10.1002/2016MS000666
  • Moseley C., C. Hohenegger, P. Berg and J. O. Haerter, 2016: Intensification  of convective extremes driven by cloud-cloud interaction. Nature Geoscience, 9, 748-752, doi.org/10.1038/ngeo2789
  • Schlemmer L. and C. Hohenegger, 2016: Modifications of the atmospheric moisture field as a result of cold-pool dynamics. Quart. J. Roy. Meteor. Soc., 142, 30-42, doi.org/10.1002/qj.2625
  • Simmer C, G. Adrian, S. Jones, V. Wirth, M. Göber, C. Hohenegger, T. Janjic, J. Keller, C. Ohlwein, A. Seifert, S. Trömel, T. Ulbrich, K. Wapler, M. Weissmann, J. Keller, M. Masbou, S. Meilinger, N. Riss, A. Schomburg, A. Vormann, C. Weingärtner, 2016: HErZ: The German Hans-Ertel Centre for Weather Research. Bull. Amer. Meteoro. Soci., 1057-1068, doi.org/10.1175/BAMS-D-13-00227.1
  • Hohenegger C., L. Schlemmer and L. Silvers, 2015: Coupling of convection and circulation at various resolutions. Tellus A, 67, 26678, doi.org/10.3402/tellusa.v67.26678
  • 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.org/10.1002/qj.2532
  • Rieck M., C. Hohenegger and C. C. van Heerwaarden, 2014: The influence of land surface heterogeneities on cloud size development. Mon. Wea. Rev., 142, 3830-3846, doi.org/10.1175/MWR-D-13-00354.1
  • Schlemmer L and C. Hohenegger, 2014: The formation of wider and deeper clouds as a result of cold-pool dynamics. J. Atmos. Sci., 71, 2842-2858, doi.org/10.1175/JAS-D-13-0170.1
  • Siongco C., C. Hohenegger and B. Stevens, 2014:  The Atlantic ITCZ bias in CMIP5 models, Clim. Dyn., 45, 1169, doi.org/10.1007/s00382-014-2366-3
  • Weissmann M., M. Göber, C. Hohenegger, T. Janjic, J. Keller, C. Ohlwein, A. Seifert, S. Trömel, T. Ulbrich, K. Wapler, C. Bollmezer and H. Deneke, 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. Meteor. Z., 23, 193-208, doi.org/10.1127/0941-2948/2014/0558
  • Crueger T., W. May, and C. Hohenegger, 2013: Tropical precipitation and convection changes in the MPI-ESM in response to CO2 forcing. J. Adv. Mod. Earth Systems, 5, 85-97, doi.org/10.1002/jame.20012
  • De Rooy W. C., P. Bechtold, K. Fröhlich, C. Hohenegger, H. Jonker, D. Mironov, A. P. Siebesma, J. Teixeira, and J-I Yano, 2013: Entrainment and detrainment in cumulus convection: an overview. Quart. J. Roy. Meteor. Soc., 139, 1-19, doi.org/10.1002/qj.1959
  • Hohenegger C. and B. Stevens, 2013: Controls on and impacts of the diurnal cycle of deep convection. J. Adv. Mod. Earth Systems, 5, 801-815, doi.org/10.1002/2012MS000216
  • Hohenegger C. and B. Stevens, 2013: Preconditioning deep convection with cumulus congestus. J. Atmos. Sci., 70, 448-464, doi.org/10.1175/JAS-D-12-089.1
  • Hohenegger C. and B. Stevens, 2013: Reply to "Comments on Preconditioning deep convection with cumulus congestus". J. Atmos. Sci., 70, 4155-4156, doi.org/10.1175/JAS-D-13-0216.1
  • Schlemmer L., C. Hohenegger, J. Schmidli, and C. Schär, 2012: Diurnal equilibrium convection and land surface-atmosphere interactions in an idealized  cloud-resolving model. Quart. J. Roy. Meteor. Soc., 138, 1526-1539, doi.org/10.1002/qj.1892
  • Hohenegger C. and C. S. Bretherton, 2011: Simulating deep convection with a shallow convection scheme. Atmos. Chem. Phys., 11, 10389-10406, doi.org/10.5194/acp-11-10389-2011
  • Schlemmer L., C. Hohenegger, J. Schmidli, C. S. Bretherton, and C. Schär, 2011: An idealized cloud-resolving framework for the study of midlatitude diurnal convection over land. J. Atmos. Sci., 68, 1041-1057, doi.org/10.1175/2010JAS3640.1
  • 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.org/10.1175/2009JCLI2604.1
  • Hohenegger C., P. Brockhaus, and C. Schär, 2008: Towards climate simulations at cloud-resolving scales. Meteor. Z., 17, 383-394, doi.org/10.1127/0941-2948/2008/0303
  • Hohenegger C., A. Walser, W. Langhans, and C. Schär, 2008: Cloud-resolving ensemble simulations of the August 2005 Alpine flood. Quart. J. Roy. Meteor. Soc., 134, 889-904, doi.org/10.1002/qj.252
  • Hohenegger C. and C. Schär, 2007: Atmospheric predictability at synoptic versus cloud-resolving scales. Bull. Amer. Meteor. Soc., 88, 1783-1793, doi.org/10.1175/BAMS-88-11-1783
  • Hohenegger C. and C. Schär, 2007: Predictability and error growth dynamics in cloud-resolving models. J. Atmos. Sci., 64, 4467-4478, doi.org/10.1175/2007JAS2143.1
  • Hohenegger C., D. Lüthi, and C. Schär, 2006: Predictability mysteries in cloud-resolving models. Mon. Wea. Rev., 134, 2095-2107, doi.org/10.1175/MWR3176.1
  • Hohenegger C. and P. L. Vidale, 2005: Sensitivity of the European climate to aerosol forcing as simulated with a regional climate model.  J. Geophys. Res., 110, D06201, doi.org/10.1029/2004JD005335

Education

2021

Habilitation, department Earth Sciences, U. Hamburg, Hamburg, Germany

Thesis: Precipitating Convection: its daily transition, organization and interaction with the land surface

Mentor: Prof. M. Claussen, Meteorological Institute
2003-2006 

Dr. Sc. ETH, Department Earth Sciences, ETH Zürich, Zurich, Switzerland

Thesis: Dynamical analysis of atmospheric predictability in cloud-resolving models

Advisor: Prof. C. Schär, Institute for Atmospheric and Climate Science
1998-2003

Dipl. Natw. ETH with distinction, Department Earth Sciences, ETH Zürich, Zurich, Switzerland

Thesis: An assessment of the sensitivity of the European climate on aerosol forcing simulated with a regional climate model

Advisor: Dr. P.-L. Vidale, Institute for Atmospheric and Climate Science
1993-1998 High school, Lycée Collège des Creusets, Sion, Switzerland

 

Professional experience

2022- W2 research group leader Max Planck Institute for Meteorology, Germany
2011-2022 Research group leader Max Planck Institute for Meteorology, Germany
2010 Scientist Max Planck Institute for Meteorology, Germany
2009-2010 Visiting scientist Dept. Atmos. Sciences, U. of Washington, USA
2007-2009 Postdoctoral researcher IAC, ETH Zürich, Switzerland
2003-2006 Teaching assistant IAC, ETH Zürich, Switzerland
Jul-Oct 2001 Internship MeteoSwiss, Switzerland

 

Complete CV (stand January 2024)