Articles | Volume 13, issue 6
Geosci. Model Dev., 13, 2671–2694, 2020
https://doi.org/10.5194/gmd-13-2671-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue: The Modular Earth Submodel System (MESSy) (ACP/GMD inter-journal...
Geosci. Model Dev., 13, 2671–2694, 2020
https://doi.org/10.5194/gmd-13-2671-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper 18 Jun 2020
Model evaluation paper | 18 Jun 2020
Superparameterised cloud effects in the EMAC general circulation model (v2.50) – influences of model configuration
Harald Rybka and Holger Tost
Related authors
Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
Atmos. Chem. Phys., 21, 4285–4318, https://doi.org/10.5194/acp-21-4285-2021, https://doi.org/10.5194/acp-21-4285-2021, 2021
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Estimating the impact of convection on the upper-tropospheric water budget remains a problem for models employing resolutions of several kilometers or more. A sub-kilometer high-resolution model is used to study summertime convection. The results suggest mostly close agreement with ground- and satellite-based observational data while slightly overestimating total frozen water path and anvil lifetime. The simulations are well suited to supplying information for parameterization development.
H. Rybka and H. Tost
Atmos. Chem. Phys., 14, 5561–5576, https://doi.org/10.5194/acp-14-5561-2014, https://doi.org/10.5194/acp-14-5561-2014, 2014
Andrea Pozzer, Simon Reifenberg, Vinod Kumar, Bruno Franco, Domenico Taraborrelli, Sergy Gromov, Sebastian Ehrhart, Patrick Jöckel, Rolf Sander, Veronica Fall, Simon Rosanka, Vlassis Karydis, Dimitris Akritidis, Tamara Emmerichs, Monica Crippa, Diego Guizzardi, Johannes W. Kaiser, Lieven Clarisse, Astrid Kiendler-Scharr, Holger Tost, and Alexandra Tsimpidi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-295, https://doi.org/10.5194/gmd-2021-295, 2021
Revised manuscript under review for GMD
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A newly developed set-up of the chemistry general circulation model EMAC (ECHAM5/MESSy for Atmospheric Chemistry) is here evaluated; a comprehensive organic degradation mechanism is used and coupled with a volatility base model. The results show that the model is reproducing most of the tracer and aerosols satisfactorily, although with discrepancies for oxygenated organic gases and that this model configuration can be used for further research in atmospheric chemistry.
Ralf Weigel, Christoph Mahnke, Manuel Baumgartner, Martina Krämer, Peter Spichtinger, Nicole Spelten, Armin Afchine, Christian Rolf, Silvia Viciani, Francesco D'Amato, Holger Tost, and Stephan Borrmann
Atmos. Chem. Phys., 21, 13455–13481, https://doi.org/10.5194/acp-21-13455-2021, https://doi.org/10.5194/acp-21-13455-2021, 2021
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In July and August 2017, the StratoClim mission took place in Nepal with eight flights of the M-55 Geophysica at up to 20 km in the Asian monsoon anticyclone. New particle formation (NPF) next to cloud ice was detected in situ by abundant nucleation-mode aerosols (> 6 nm) along with ice particles (> 3 µm). NPF was observed mainly below the tropopause, down to 15 % being non-volatile residues. Observed intra-cloud NPF indicates its importance for the composition in the tropical tropopause layer.
Vinod Kumar, Julia Remmers, Steffen Beirle, Joachim Fallmann, Astrid Kerkweg, Jos Lelieveld, Mariano Mertens, Andrea Pozzer, Benedikt Steil, Marc Barra, Holger Tost, and Thomas Wagner
Atmos. Meas. Tech., 14, 5241–5269, https://doi.org/10.5194/amt-14-5241-2021, https://doi.org/10.5194/amt-14-5241-2021, 2021
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We present high-resolution regional atmospheric chemistry model simulations focused around Germany. We highlight the importance of spatial resolution of the model itself as well as the input emissions inventory and short-scale temporal variability of emissions for simulations. We propose a consistent approach for evaluating the simulated vertical distribution of NO2 using MAX-DOAS measurements while also considering its spatial sensitivity volume and change in sensitivity within this volume.
Harald Rybka, Ulrike Burkhardt, Martin Köhler, Ioanna Arka, Luca Bugliaro, Ulrich Görsdorf, Ákos Horváth, Catrin I. Meyer, Jens Reichardt, Axel Seifert, and Johan Strandgren
Atmos. Chem. Phys., 21, 4285–4318, https://doi.org/10.5194/acp-21-4285-2021, https://doi.org/10.5194/acp-21-4285-2021, 2021
Short summary
Short summary
Estimating the impact of convection on the upper-tropospheric water budget remains a problem for models employing resolutions of several kilometers or more. A sub-kilometer high-resolution model is used to study summertime convection. The results suggest mostly close agreement with ground- and satellite-based observational data while slightly overestimating total frozen water path and anvil lifetime. The simulations are well suited to supplying information for parameterization development.
Sara Bacer, Sylvia C. Sullivan, Odran Sourdeval, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Atmos. Chem. Phys., 21, 1485–1505, https://doi.org/10.5194/acp-21-1485-2021, https://doi.org/10.5194/acp-21-1485-2021, 2021
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We investigate the relative importance of the rates of both microphysical processes and unphysical correction terms that act as sources or sinks of ice crystals in cold clouds. By means of numerical simulations performed with a global chemistry–climate model, we assess the relevance of these rates at global and regional scales. This estimation is of fundamental importance to assign priority to the development of microphysics parameterizations and compare model output with observations.
Edward Groot and Holger Tost
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-1142, https://doi.org/10.5194/acp-2020-1142, 2020
Publication in ACP not foreseen
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Sensitivities and variability of upper tropospheric flow (~10 km height) resulting immediately and as a direct consequence of (thunder)storm activity have been modeled in detail down to resolutions of 100–200 m and explored for different (organisation/) storm types. It is shown that the amount of water condensation explains much of emerging variability in upper atmospheric flow. Part of the effects on the nearby upper atmospheric flow is suggested to be explained by (organisation/) storm type.
Matthew Forrest, Holger Tost, Jos Lelieveld, and Thomas Hickler
Geosci. Model Dev., 13, 1285–1309, https://doi.org/10.5194/gmd-13-1285-2020, https://doi.org/10.5194/gmd-13-1285-2020, 2020
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We have integrated the LPJ-GUESS dynamic global vegetation model into the EMAC atmospheric chemistry-enabled GCM (general circulation model). This combined framework will enable the investigation of many land–atmosphere interactions and feedbacks with state-of-the-art simulation models. Initial results show that using the climate produced by EMAC together with LPJ-GUESS produces an acceptable representation of the global vegetation.
Julie M. Nicely, Bryan N. Duncan, Thomas F. Hanisco, Glenn M. Wolfe, Ross J. Salawitch, Makoto Deushi, Amund S. Haslerud, Patrick Jöckel, Béatrice Josse, Douglas E. Kinnison, Andrew Klekociuk, Michael E. Manyin, Virginie Marécal, Olaf Morgenstern, Lee T. Murray, Gunnar Myhre, Luke D. Oman, Giovanni Pitari, Andrea Pozzer, Ilaria Quaglia, Laura E. Revell, Eugene Rozanov, Andrea Stenke, Kane Stone, Susan Strahan, Simone Tilmes, Holger Tost, Daniel M. Westervelt, and Guang Zeng
Atmos. Chem. Phys., 20, 1341–1361, https://doi.org/10.5194/acp-20-1341-2020, https://doi.org/10.5194/acp-20-1341-2020, 2020
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Differences in methane lifetime among global models are large and poorly understood. We use a neural network method and simulations from the Chemistry Climate Model Initiative to quantify the factors influencing methane lifetime spread among models and variations over time. UV photolysis, tropospheric ozone, and nitrogen oxides drive large model differences, while the same factors plus specific humidity contribute to a decreasing trend in methane lifetime between 1980 and 2015.
Jianzhong Ma, Christoph Brühl, Qianshan He, Benedikt Steil, Vlassis A. Karydis, Klaus Klingmüller, Holger Tost, Bin Chen, Yufang Jin, Ningwei Liu, Xiangde Xu, Peng Yan, Xiuji Zhou, Kamal Abdelrahman, Andrea Pozzer, and Jos Lelieveld
Atmos. Chem. Phys., 19, 11587–11612, https://doi.org/10.5194/acp-19-11587-2019, https://doi.org/10.5194/acp-19-11587-2019, 2019
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We find a pronounced maximum in aerosol extinction in the upper troposphere and lower stratosphere over the Tibetan Plateau during the Asian summer monsoon, caused mainly by mineral dust emitted from the northern Tibetan Plateau and slope area, lofted to and accumulating within the anticyclonic circulation. Mineral dust, water-soluble compounds, such as nitrate and sulfate, and associated liquid water dominate aerosol extinction around the tropopause within the Asian summer monsoon anticyclone.
Mega Octaviani, Holger Tost, and Gerhard Lammel
Geosci. Model Dev., 12, 3585–3607, https://doi.org/10.5194/gmd-12-3585-2019, https://doi.org/10.5194/gmd-12-3585-2019, 2019
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This work presents a submodel description for the atmospheric cycling and air–surface exchange processes of semivolatile organic compounds. The submodel is meant to be applied within a global atmospheric chemistry–climate model. The simulation results for polycyclic aromatic hydrocarbons confirm progress in modelling semivolatile species, verified by comparison with surface monitoring data. The significance of new modelling features for tracer distributions was quantified in a sensitivity study.
J. Christopher Kaiser, Johannes Hendricks, Mattia Righi, Patrick Jöckel, Holger Tost, Konrad Kandler, Bernadett Weinzierl, Daniel Sauer, Katharina Heimerl, Joshua P. Schwarz, Anne E. Perring, and Thomas Popp
Geosci. Model Dev., 12, 541–579, https://doi.org/10.5194/gmd-12-541-2019, https://doi.org/10.5194/gmd-12-541-2019, 2019
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The implementation of the aerosol microphysics submodel MADE3 into the global atmospheric chemistry model EMAC is described and evaluated against an extensive pool of observational data, focusing on aerosol mass and number concentrations, size distributions, composition, and optical properties. EMAC (MADE3) is able to reproduce main aerosol properties reasonably well, in line with the performance of other global aerosol models.
Sara Bacer, Sylvia C. Sullivan, Vlassis A. Karydis, Donifan Barahona, Martina Krämer, Athanasios Nenes, Holger Tost, Alexandra P. Tsimpidi, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 11, 4021–4041, https://doi.org/10.5194/gmd-11-4021-2018, https://doi.org/10.5194/gmd-11-4021-2018, 2018
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The complexity of ice nucleation mechanisms and aerosol--ice interactions makes their representation still challenging in atmospheric models. We have implemented a comprehensive ice crystal formation parameterization in the global chemistry-climate model EMAC to improve the representation of ice crystal number concentrations. The newly implemented parameterization takes into account processes which were previously neglected by the standard version of the model.
Mohamed Abdelkader, Swen Metzger, Benedikt Steil, Klaus Klingmüller, Holger Tost, Andrea Pozzer, Georgiy Stenchikov, Leonard Barrie, and Jos Lelieveld
Atmos. Chem. Phys., 17, 3799–3821, https://doi.org/10.5194/acp-17-3799-2017, https://doi.org/10.5194/acp-17-3799-2017, 2017
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We present a modeling study on the impacts of the key processes (dust emission flux, convection and dust aging parameterizations) that control the transatlantic dust transport using an advanced version of the EMAC atmospheric chemistry general circulation model. We define the
direct effect of dust agingas an increase in the AOD as a result of hygroscopic growth. We define the
indirect effectas a reduction in the dust AOD due to the higher removal of the aged dust particles.
Holger Tost
Atmos. Chem. Phys., 17, 1125–1142, https://doi.org/10.5194/acp-17-1125-2017, https://doi.org/10.5194/acp-17-1125-2017, 2017
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The paper describes the impact of lightning and the associated NOx emissions on upper-tropospheric aerosol nitrate. The consequences for both the chemical composition of the atmosphere as well as climatic impacts, which originate from lightning and modified aerosol particles, are analysed and discussed.
Mariano Mertens, Astrid Kerkweg, Patrick Jöckel, Holger Tost, and Christiane Hofmann
Geosci. Model Dev., 9, 3545–3567, https://doi.org/10.5194/gmd-9-3545-2016, https://doi.org/10.5194/gmd-9-3545-2016, 2016
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This fourth part in a series of publications describing the newly developed regional chemistry–climate system MECO(n) is dedicated to the evaluation of MECO(n) with respect to tropospheric gas-phase chemistry. For this, a simulation incorporating two regional instances, one over Europe with 50 km resolution and one over Germany with 12 km resolution, is conducted. The model results are compared with satellite, ground-based and aircraft in situ observations.
Simone Dietmüller, Patrick Jöckel, Holger Tost, Markus Kunze, Catrin Gellhorn, Sabine Brinkop, Christine Frömming, Michael Ponater, Benedikt Steil, Axel Lauer, and Johannes Hendricks
Geosci. Model Dev., 9, 2209–2222, https://doi.org/10.5194/gmd-9-2209-2016, https://doi.org/10.5194/gmd-9-2209-2016, 2016
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Four new radiation related submodels (RAD, AEROPT, CLOUDOPT, and ORBIT) are available within the MESSy framework now. They are largely based on the original radiation scheme of ECHAM5. RAD simulates radiative transfer, AEROPT calculates aerosol optical properties, CLOUDOPT calculates cloud optical properties, and ORBIT is responsible for Earth orbit calculations. Multiple diagnostic calls of the radiation routine are possible, so radiative forcing can be calculated during the model simulation.
Patrick Jöckel, Holger Tost, Andrea Pozzer, Markus Kunze, Oliver Kirner, Carl A. M. Brenninkmeijer, Sabine Brinkop, Duy S. Cai, Christoph Dyroff, Johannes Eckstein, Franziska Frank, Hella Garny, Klaus-Dirk Gottschaldt, Phoebe Graf, Volker Grewe, Astrid Kerkweg, Bastian Kern, Sigrun Matthes, Mariano Mertens, Stefanie Meul, Marco Neumaier, Matthias Nützel, Sophie Oberländer-Hayn, Roland Ruhnke, Theresa Runde, Rolf Sander, Dieter Scharffe, and Andreas Zahn
Geosci. Model Dev., 9, 1153–1200, https://doi.org/10.5194/gmd-9-1153-2016, https://doi.org/10.5194/gmd-9-1153-2016, 2016
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With an advanced numerical global chemistry climate model (CCM) we performed several detailed
combined hind-cast and projection simulations of the period 1950 to 2100 to assess the
past, present, and potential future dynamical and chemical state of the Earth atmosphere.
The manuscript documents the model and the various applied model set-ups and provides
a first evaluation of the simulation results from a global perspective as a quality check of the data.
N. I. Kristiansen, A. Stohl, D. J. L. Olivié, B. Croft, O. A. Søvde, H. Klein, T. Christoudias, D. Kunkel, S. J. Leadbetter, Y. H. Lee, K. Zhang, K. Tsigaridis, T. Bergman, N. Evangeliou, H. Wang, P.-L. Ma, R. C. Easter, P. J. Rasch, X. Liu, G. Pitari, G. Di Genova, S. Y. Zhao, Y. Balkanski, S. E. Bauer, G. S. Faluvegi, H. Kokkola, R. V. Martin, J. R. Pierce, M. Schulz, D. Shindell, H. Tost, and H. Zhang
Atmos. Chem. Phys., 16, 3525–3561, https://doi.org/10.5194/acp-16-3525-2016, https://doi.org/10.5194/acp-16-3525-2016, 2016
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Processes affecting aerosol removal from the atmosphere are not fully understood. In this study we investigate to what extent atmospheric transport models can reproduce observed loss of aerosols. We compare measurements of radioactive isotopes, that attached to ambient sulfate aerosols during the 2011 Fukushima nuclear accident, to 19 models using identical emissions. Results indicate aerosol removal that is too fast in most models, and apply to aerosols that have undergone long-range transport.
A. J. G. Baumgaertner, P. Jöckel, A. Kerkweg, R. Sander, and H. Tost
Geosci. Model Dev., 9, 125–135, https://doi.org/10.5194/gmd-9-125-2016, https://doi.org/10.5194/gmd-9-125-2016, 2016
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The Community Earth System Model (CESM1) is connected to the the Modular Earth Submodel System (MESSy) as a new base model. This allows MESSy users the option to utilize either the state-of-the art spectral element atmosphere dynamical core or the finite volume core of CESM1. Additionally, this makes several other component models available to MESSy users.
H. G. Ouwersloot, A. Pozzer, B. Steil, H. Tost, and J. Lelieveld
Geosci. Model Dev., 8, 2435–2445, https://doi.org/10.5194/gmd-8-2435-2015, https://doi.org/10.5194/gmd-8-2435-2015, 2015
A. Pozzer, A. de Meij, J. Yoon, H. Tost, A. K. Georgoulias, and M. Astitha
Atmos. Chem. Phys., 15, 5521–5535, https://doi.org/10.5194/acp-15-5521-2015, https://doi.org/10.5194/acp-15-5521-2015, 2015
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Thanks to numerical simulations and satellite observations, it is shown that aerosol optical depth (AOD) trends (2000--2010 period) over the US and Europe are due to emission decrease, while over the Sahara Desert and the Middle East they are due to meteorological changes. Over Southeast Asia, both meteorology and emission changes are important for the AOD trends.
It is shown that soluble components strongly influence AOD, as their contribution is enhanced by the aerosol water content.
K. Klingmüller, B. Steil, C. Brühl, H. Tost, and J. Lelieveld
Geosci. Model Dev., 7, 2503–2516, https://doi.org/10.5194/gmd-7-2503-2014, https://doi.org/10.5194/gmd-7-2503-2014, 2014
D. Y. Chang, H. Tost, B. Steil, and J. Lelieveld
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-14-21975-2014, https://doi.org/10.5194/acpd-14-21975-2014, 2014
Preprint withdrawn
H. Rybka and H. Tost
Atmos. Chem. Phys., 14, 5561–5576, https://doi.org/10.5194/acp-14-5561-2014, https://doi.org/10.5194/acp-14-5561-2014, 2014
G. W. Mann, K. S. Carslaw, C. L. Reddington, K. J. Pringle, M. Schulz, A. Asmi, D. V. Spracklen, D. A. Ridley, M. T. Woodhouse, L. A. Lee, K. Zhang, S. J. Ghan, R. C. Easter, X. Liu, P. Stier, Y. H. Lee, P. J. Adams, H. Tost, J. Lelieveld, S. E. Bauer, K. Tsigaridis, T. P. C. van Noije, A. Strunk, E. Vignati, N. Bellouin, M. Dalvi, C. E. Johnson, T. Bergman, H. Kokkola, K. von Salzen, F. Yu, G. Luo, A. Petzold, J. Heintzenberg, A. Clarke, J. A. Ogren, J. Gras, U. Baltensperger, U. Kaminski, S. G. Jennings, C. D. O'Dowd, R. M. Harrison, D. C. S. Beddows, M. Kulmala, Y. Viisanen, V. Ulevicius, N. Mihalopoulos, V. Zdimal, M. Fiebig, H.-C. Hansson, E. Swietlicki, and J. S. Henzing
Atmos. Chem. Phys., 14, 4679–4713, https://doi.org/10.5194/acp-14-4679-2014, https://doi.org/10.5194/acp-14-4679-2014, 2014
C. Liu, S. Beirle, T. Butler, P. Hoor, C. Frankenberg, P. Jöckel, M. Penning de Vries, U. Platt, A. Pozzer, M. G. Lawrence, J. Lelieveld, H. Tost, and T. Wagner
Atmos. Chem. Phys., 14, 1717–1732, https://doi.org/10.5194/acp-14-1717-2014, https://doi.org/10.5194/acp-14-1717-2014, 2014
Y. F. Elshorbany, P. J. Crutzen, B. Steil, A. Pozzer, H. Tost, and J. Lelieveld
Atmos. Chem. Phys., 14, 1167–1184, https://doi.org/10.5194/acp-14-1167-2014, https://doi.org/10.5194/acp-14-1167-2014, 2014
C. Brühl, J. Lelieveld, M. Höpfner, and H. Tost
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-11395-2013, https://doi.org/10.5194/acpd-13-11395-2013, 2013
Revised manuscript not accepted
D. Kunkel, H. Tost, and M. G. Lawrence
Atmos. Chem. Phys., 13, 4203–4222, https://doi.org/10.5194/acp-13-4203-2013, https://doi.org/10.5194/acp-13-4203-2013, 2013
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Geosci. Model Dev., 15, 251–268, https://doi.org/10.5194/gmd-15-251-2022, https://doi.org/10.5194/gmd-15-251-2022, 2022
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We develop a new method for climate downscaling, i.e. transforming low-resolution climate model output to high-resolution projections, using a deep-learning model known as a convolutional conditional neural process. This model is shown to outperform an ensemble of baseline methods for downscaling daily maximum temperature and precipitation and provides a powerful new downscaling framework for climate impact studies.
Eduardo Moreno-Chamarro, Louis-Philippe Caron, Saskia Loosveldt Tomas, Javier Vegas-Regidor, Oliver Gutjahr, Marie-Pierre Moine, Dian Putrasahan, Christopher D. Roberts, Malcolm J. Roberts, Retish Senan, Laurent Terray, Etienne Tourigny, and Pier Luigi Vidale
Geosci. Model Dev., 15, 269–289, https://doi.org/10.5194/gmd-15-269-2022, https://doi.org/10.5194/gmd-15-269-2022, 2022
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Climate models do not fully reproduce observations: they show differences (biases) in regional temperature, precipitation, or cloud cover. Reducing model biases is important to increase our confidence in their ability to reproduce present and future climate changes. Model realism is set by its resolution: the finer it is, the more physical processes and interactions it can resolve. We here show that increasing resolution of up to ~ 25 km can help reduce model biases but not remove them entirely.
Manuel C. Almeida, Yurii Shevchuk, Georgiy Kirillin, Pedro M. M. Soares, Rita M. Cardoso, José P. Matos, Ricardo M. Rebelo, António C. Rodrigues, and Pedro S. Coelho
Geosci. Model Dev., 15, 173–197, https://doi.org/10.5194/gmd-15-173-2022, https://doi.org/10.5194/gmd-15-173-2022, 2022
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In this study, we have evaluated the importance of the input of energy conveyed by river inflows into lakes and reservoirs when modeling surface water energy fluxes. Our results suggest that there is a strong correlation between water residence time and the surface water temperature prediction error and that the combined use of process-based physical models and machine-learning models will considerably improve the modeling of air–lake heat and moisture fluxes.
Mohamed H. Salim, Sebastian Schubert, Jaroslav Resler, Pavel Krč, Björn Maronga, Farah Kanani-Sühring, Matthias Sühring, and Christoph Schneider
Geosci. Model Dev., 15, 145–171, https://doi.org/10.5194/gmd-15-145-2022, https://doi.org/10.5194/gmd-15-145-2022, 2022
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Radiative transfer processes are the main energy transport mechanism in urban areas which influence the surface energy budget and drive local convection. We show here the importance of each process to help modellers decide on how much detail they should include in their models to parameterize radiative transfer in urban areas. We showed how the flow field may change in response to these processes and the essential processes needed to assure acceptable quality of the numerical simulations.
Alexey V. Eliseev, Rustam D. Gizatullin, and Alexandr V. Timazhev
Geosci. Model Dev., 14, 7725–7747, https://doi.org/10.5194/gmd-14-7725-2021, https://doi.org/10.5194/gmd-14-7725-2021, 2021
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A stationary, computationally efficient scheme, ChAP 1.0 (Chemical and Aerosol Processes, version 1.0), is developed for the sulfur cycle in the troposphere. This scheme is designed for Earth system models of intermediate complexity (EMICs). The scheme model reasonably reproduces characteristics of the tropospheric sulfur cycle. Despite its simplicity, ChAP may be successfully used to simulate anthropogenic sulfur pollution in the atmosphere at coarse spatial scales and timescales.
Erika Coppola, Paolo Stocchi, Emanuela Pichelli, Jose Abraham Torres Alavez, Russell Glazer, Graziano Giuliani, Fabio Di Sante, Rita Nogherotto, and Filippo Giorgi
Geosci. Model Dev., 14, 7705–7723, https://doi.org/10.5194/gmd-14-7705-2021, https://doi.org/10.5194/gmd-14-7705-2021, 2021
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In this work we describe the development of a non-hydrostatic version of the regional climate model RegCM4-NH, implemented to allow simulations at convection-permitting scales of <4 km for climate applications. The new core is described, and three case studies of intense convection are carried out to illustrate the model performances. Comparison with observations is much improved with respect to with coarse grid runs. RegCM4-NH offers a promising tool for climate investigations at a local scale.
Alexei Belochitski and Vladimir Krasnopolsky
Geosci. Model Dev., 14, 7425–7437, https://doi.org/10.5194/gmd-14-7425-2021, https://doi.org/10.5194/gmd-14-7425-2021, 2021
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There is a lot interest in using machine learning (ML) techniques to improve environmental models by replacing physically based model components with ML-derived ones. The latter ordinarily demonstrate excellent results when tested in a stand-alone setting but can break their host model either outright when coupled to it or eventually when the model changes. We built an ML component that not only does not destabilize its host model but is also robust with respect to substantial changes in it.
Ingo Bethke, Yiguo Wang, François Counillon, Noel Keenlyside, Madlen Kimmritz, Filippa Fransner, Annette Samuelsen, Helene Langehaug, Lea Svendsen, Ping-Gin Chiu, Leilane Passos, Mats Bentsen, Chuncheng Guo, Alok Gupta, Jerry Tjiputra, Alf Kirkevåg, Dirk Olivié, Øyvind Seland, Julie Solsvik Vågane, Yuanchao Fan, and Tor Eldevik
Geosci. Model Dev., 14, 7073–7116, https://doi.org/10.5194/gmd-14-7073-2021, https://doi.org/10.5194/gmd-14-7073-2021, 2021
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The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It adds data assimilation capability to the Norwegian Earth System Model version 1 (NorESM1) and has contributed output to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). We describe the system and evaluate its baseline, reanalysis and prediction performance.
Bin Mu, Bo Qin, and Shijin Yuan
Geosci. Model Dev., 14, 6977–6999, https://doi.org/10.5194/gmd-14-6977-2021, https://doi.org/10.5194/gmd-14-6977-2021, 2021
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Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air–sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.
Luan F. Santos and Pedro S. Peixoto
Geosci. Model Dev., 14, 6919–6944, https://doi.org/10.5194/gmd-14-6919-2021, https://doi.org/10.5194/gmd-14-6919-2021, 2021
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The Andes act as a wall in atmospheric flows and play an important role in the weather of South America but are currently underrepresented in weather and climate models. In this work, we propose grids that better capture the mountains and, using idealized dynamical models, study the effects caused by the use of such grids. While possibly improving forecasts for short periods, the grids introduce spurious numerical (nonphysical) effects, which can demand added caution from model developers.
Reinel Sospedra-Alfonso, William J. Merryfield, George J. Boer, Viatsheslav V. Kharin, Woo-Sung Lee, Christian Seiler, and James R. Christian
Geosci. Model Dev., 14, 6863–6891, https://doi.org/10.5194/gmd-14-6863-2021, https://doi.org/10.5194/gmd-14-6863-2021, 2021
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CanESM5 decadal predictions that started from observed climate states represent the observed evolution of upper-ocean temperatures, surface climate, and the carbon cycle better than ones not started from observed climate states for several years into the forecast. This is due both to better representations of climate internal variability and to corrections of the model response to external forcing including changes in GHG emissions and aerosols.
Hao-Jhe Hong and Thomas Reichler
Geosci. Model Dev., 14, 6647–6660, https://doi.org/10.5194/gmd-14-6647-2021, https://doi.org/10.5194/gmd-14-6647-2021, 2021
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The Arctic wintertime circulation of the stratosphere has pronounced impacts on the troposphere and surface climate. Changes in the stratospheric circulation can lead to either increases or decreases in Arctic ozone. Understanding the interactions between ozone and the circulation will have the benefit of model prediction for the climate. This study introduces an economical and fast simplified model that represents the realistic distribution of ozone and its interaction with the circulation.
Arseniy Karagodin-Doyennel, Eugene Rozanov, Timofei Sukhodolov, Tatiana Egorova, Alfonso Saiz-Lopez, Carlos A. Cuevas, Rafael P. Fernandez, Tomás Sherwen, Rainer Volkamer, Theodore K. Koenig, Tanguy Giroud, and Thomas Peter
Geosci. Model Dev., 14, 6623–6645, https://doi.org/10.5194/gmd-14-6623-2021, https://doi.org/10.5194/gmd-14-6623-2021, 2021
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Here, we present the iodine chemistry module in the SOCOL-AERv2 model. The obtained iodine distribution demonstrated a good agreement when validated against other simulations and available observations. We also estimated the iodine influence on ozone in the case of present-day iodine emissions, the sensitivity of ozone to doubled iodine emissions, and when considering only organic or inorganic iodine sources. The new model can be used as a tool for further studies of iodine effects on ozone.
João C. Teixeira, Gerd A. Folberth, Fiona M. O'Connor, Nadine Unger, and Apostolos Voulgarakis
Geosci. Model Dev., 14, 6515–6539, https://doi.org/10.5194/gmd-14-6515-2021, https://doi.org/10.5194/gmd-14-6515-2021, 2021
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Fire constitutes a key process in the Earth system, being driven by climate as well as affecting climate. However, studies on the effects of fires on atmospheric composition and climate have been limited to date. This work implements and assesses the coupling of an interactive fire model with atmospheric composition, comparing it to an offline approach. This approach shows good performance at a global scale. However, regional-scale limitations lead to a bias in modelling fire emissions.
Jiali Wang, Zhengchun Liu, Ian Foster, Won Chang, Rajkumar Kettimuthu, and V. Rao Kotamarthi
Geosci. Model Dev., 14, 6355–6372, https://doi.org/10.5194/gmd-14-6355-2021, https://doi.org/10.5194/gmd-14-6355-2021, 2021
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Downscaling, the process of generating a higher spatial or time dataset from a coarser observational or model dataset, is a widely used technique. Two common methodologies for performing downscaling are to use either dynamic (physics-based) or statistical (empirical). Here we develop a novel methodology, using a conditional generative adversarial network (CGAN), to perform the downscaling of a model's precipitation forecasts and describe the advantages of this method compared to the others.
Dalei Hao, Gautam Bisht, Yu Gu, Wei-Liang Lee, Kuo-Nan Liou, and L. Ruby Leung
Geosci. Model Dev., 14, 6273–6289, https://doi.org/10.5194/gmd-14-6273-2021, https://doi.org/10.5194/gmd-14-6273-2021, 2021
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Topography exerts significant influence on the incoming solar radiation at the land surface. This study incorporated a well-validated sub-grid topographic parameterization in E3SM land model (ELM) version 1.0. The results demonstrate that sub-grid topography has non-negligible effects on surface energy budget, snow cover, and surface temperature over the Tibetan Plateau and that the ELM simulations are sensitive to season, elevation, and spatial scale.
Claude-Michel Nzotungicimpaye, Kirsten Zickfeld, Andrew H. MacDougall, Joe R. Melton, Claire C. Treat, Michael Eby, and Lance F. W. Lesack
Geosci. Model Dev., 14, 6215–6240, https://doi.org/10.5194/gmd-14-6215-2021, https://doi.org/10.5194/gmd-14-6215-2021, 2021
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In this paper, we describe a new wetland methane model (WETMETH) developed for use in Earth system models. WETMETH consists of simple formulations to represent methane production and oxidation in wetlands. We also present an evaluation of the model performance as embedded in the University of Victoria Earth System Climate Model (UVic ESCM). WETMETH is capable of reproducing mean annual methane emissions consistent with present-day estimates from the regional to the global scale.
Jinxiao Li, Qing Bao, Yimin Liu, Lei Wang, Jing Yang, Guoxiong Wu, Xiaofei Wu, Bian He, Xiaocong Wang, Xiaoqi Zhang, Yaoxian Yang, and Zili Shen
Geosci. Model Dev., 14, 6113–6133, https://doi.org/10.5194/gmd-14-6113-2021, https://doi.org/10.5194/gmd-14-6113-2021, 2021
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The configuration and simulated performance of tropical cyclones (TCs) in FGOALS-f3-L/H will be introduced firstly. The results indicate that the simulated performance of TC activities is improved globally with the increased horizontal resolution especially in TC counts, seasonal cycle, interannual variabilities and intensity aspects. It is worth establishing a high-resolution coupled dynamic prediction system based on FGOALS-f3-H (~ 25 km) to improve the prediction skill of TCs.
Liam Bindle, Randall V. Martin, Matthew J. Cooper, Elizabeth W. Lundgren, Sebastian D. Eastham, Benjamin M. Auer, Thomas L. Clune, Hongjian Weng, Jintai Lin, Lee T. Murray, Jun Meng, Christoph A. Keller, William M. Putman, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 14, 5977–5997, https://doi.org/10.5194/gmd-14-5977-2021, https://doi.org/10.5194/gmd-14-5977-2021, 2021
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Atmospheric chemistry models like GEOS-Chem are versatile tools widely used in air pollution and climate studies. The simulations used in such studies can be very computationally demanding, and thus it is useful if the model can simulate a specific geographic region at a higher resolution than the rest of the globe. Here, we implement, test, and demonstrate a new variable-resolution capability in GEOS-Chem that is suitable for simulations conducted on supercomputers.
Charles Pelletier, Thierry Fichefet, Hugues Goosse, Konstanze Haubner, Samuel Helsen, Pierre-Vincent Huot, Christoph Kittel, François Klein, Sebastien Le clec'h, Nicole P. M. van Lipzig, Sylvain Marchi, François Massonnet, Pierre Mathiot, Ehsan Moravveji, Eduardo Moreno-Chamarro, Pablo Ortega, Frank Pattyn, Niels Souverijns, Guillian Van Achter, Sam Vanden Broucke, Alexander Vanhulle, Deborah Verfaillie, and Lars Zipf
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-315, https://doi.org/10.5194/gmd-2021-315, 2021
Revised manuscript accepted for GMD
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We present PARASO, a circumpolar model for simulating the Antarctic climate. PARASO features five distinct models, each covering different Earth system subcomponents (ice sheet, atmosphere, land, sea ice, ocean). In this technical article, we describe how this tool has been developed, with a focus on the "coupling interfaces" representing the feedbacks between the distinct models put to contribution. PARASO is stable and ready-to-use, but is still characterized by significant biases.
Petra Pranić, Cléa Denamiel, and Ivica Vilibić
Geosci. Model Dev., 14, 5927–5955, https://doi.org/10.5194/gmd-14-5927-2021, https://doi.org/10.5194/gmd-14-5927-2021, 2021
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The Adriatic Sea and Coast model was developed due to the need for higher-resolution climate models and longer-term simulations to capture coastal atmospheric and ocean processes at climate scales in the Adriatic Sea. The ocean results of a 31-year-long simulation were compared to the observational data. The evaluation revealed that the model is capable of reproducing the observed physical properties with good accuracy and can be further used to study the dynamics of the Adriatic–Ionian basin.
Lynsay Spafford and Andrew H. MacDougall
Geosci. Model Dev., 14, 5863–5889, https://doi.org/10.5194/gmd-14-5863-2021, https://doi.org/10.5194/gmd-14-5863-2021, 2021
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Land biogeochemical cycles influence global climate change. Their influence is examined through complex computer models that account for the interaction of the land, ocean, and atmosphere. Improved models used in the recent round of model intercomparison used inconsistent validation methods to compare simulated land biogeochemistry to datasets. For the next round of model intercomparisons we recommend a validation protocol with explicit reference datasets and informative performance metrics.
Robin S. Smith, Steve George, and Jonathan M. Gregory
Geosci. Model Dev., 14, 5769–5787, https://doi.org/10.5194/gmd-14-5769-2021, https://doi.org/10.5194/gmd-14-5769-2021, 2021
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Many of the complex computer models used to study the physics of the natural world treat ice sheets as fixed and unchanging, capable of only simple interactions with the rest of the climate. This is partly because it is technically very difficult to usefully do anything more realistic. We have adapted a climate model so it can be joined together with a dynamical model of the Greenland ice sheet. This gives us a powerful tool to help us better understand how ice sheets and the climate interact.
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
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This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
Kai-Yuan Cheng, Lucas M. Harris, and Yong Qiang Sun
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-239, https://doi.org/10.5194/gmd-2021-239, 2021
Revised manuscript accepted for GMD
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This paper presents the implementation of container technology for the System for High‐resolution prediction on Earth‐to‐Local Domains (SHiELD), a unified atmospheric model that can be used as a global, a global-nest, and a regional model for weather-to-seasonal prediction. Container technology makes SHiELD cross-platform and easy to use, which opens opportunities for collaborative research and development. Performance and scalability of the containerized SHiELD are evaluated and discussed.
Lukas Strebel, Heye Bogena, Harry Vereecken, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-38, https://doi.org/10.5194/gmd-2021-38, 2021
Revised manuscript accepted for GMD
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We present the technical coupling between a land surface model (CLM5) and the Parallel Data Assimilation Framework (PDAF). This coupling enables measurement data to update simulated model states and parameters in a statistically optimal way. We demonstrate the viability of the model framework using an application in a forested catchment where the inclusion of soil water measurements significantly improved the simulation quality.
Paolo Pelucchi, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 14, 5413–5434, https://doi.org/10.5194/gmd-14-5413-2021, https://doi.org/10.5194/gmd-14-5413-2021, 2021
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Stratocumulus are thin clouds whose cloud cover is underestimated in climate models partly due to overly low vertical resolution. We develop a scheme that locally refines the vertical grid based on a physical constraint for the cloud top. Global simulations show that the scheme, implemented only in the radiation routine, can increase stratocumulus cloud cover. However, this effect is poorly propagated to the simulated cloud cover. The scheme's limitations and possible ways forward are discussed.
John G. Virgin, Christopher G. Fletcher, Jason N. S. Cole, Knut von Salzen, and Toni Mitovski
Geosci. Model Dev., 14, 5355–5372, https://doi.org/10.5194/gmd-14-5355-2021, https://doi.org/10.5194/gmd-14-5355-2021, 2021
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Equilibrium climate sensitivity, or the amount of warming the Earth would exhibit a result of a doubling of atmospheric CO2, is a common metric used in assessments of climate models. Here, we compare climate sensitivity between two versions of the Canadian Earth System Model. We find the newest iteration of the model (version 5) to have higher climate sensitivity due to reductions in low-level clouds, which reflect radiation and cool the planet, as the surface warms.
Matthias Mengel, Simon Treu, Stefan Lange, and Katja Frieler
Geosci. Model Dev., 14, 5269–5284, https://doi.org/10.5194/gmd-14-5269-2021, https://doi.org/10.5194/gmd-14-5269-2021, 2021
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To identify the impacts of historical climate change it is necessary to separate the effect of the different impact drivers. To address this, one needs to compare historical impacts to a counterfactual world with impacts that would have been without climate change. We here present an approach that produces counterfactual climate data and can be used in climate impact models to simulate counterfactual impacts. We make these data available through the ISIMIP project.
Christopher Holder, Anand Gnanadesikan, and Marie Aude-Pradal
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-167, https://doi.org/10.5194/gmd-2021-167, 2021
Revised manuscript accepted for GMD
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It can be challenging to understand why Earth System Models (ESMs) produce specific results, because one can arrive at the same result simply by changing the values of the parameters. In our manuscript, we demonstrated that it was possible to use machine learning to figure out how and why particular components of an ESM (such as biology or ocean circulations) were affecting the output. This work could be applied to observations to improve the accuracy of the formulations used in ESMs.
Yixiong Lu, Tongwen Wu, Yubin Li, and Ben Yang
Geosci. Model Dev., 14, 5183–5204, https://doi.org/10.5194/gmd-14-5183-2021, https://doi.org/10.5194/gmd-14-5183-2021, 2021
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The spurious precipitation in the tropical southeastern Pacific and southern Atlantic is one of the most prominent systematic biases in coupled atmosphere–ocean general circulation models. This study significantly promotes the marine stratus simulation and largely alleviates the excessive precipitation biases through improving parameterizations of boundary-layer turbulence and shallow convection, providing an effective solution to the long-standing bias in the tropical precipitation simulation.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
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We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Paul A. Ullrich, Colin M. Zarzycki, Elizabeth E. McClenny, Marielle C. Pinheiro, Alyssa M. Stansfield, and Kevin A. Reed
Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, https://doi.org/10.5194/gmd-14-5023-2021, 2021
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TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets. Version 2.1 of TE now provides extensive support for nodal and areal features. This paper describes the algorithms that have been added to the TE framework since version 1.0 and gives several examples of how these can be combined to produce composite algorithms for evaluating and understanding atmospheric features.
Israel Silber, Robert C. Jackson, Ann M. Fridlind, Andrew S. Ackerman, Scott Collis, Johannes Verlinde, and Jiachen Ding
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-194, https://doi.org/10.5194/gmd-2021-194, 2021
Revised manuscript accepted for GMD
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The Earth Model Column Collaboratory (EMC2) is an open-source ground-based (and air- or space-borne) lidar and radar simulator and subcolumn generator designed for large-scale models, in particular climate models, applicable also for high-resolution models. EMC2 emulates measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. We demonstrate the use of EMC2 to compare AWARE measurements with the NASA GISS ModelE3 and LES.
Tobias Peter Bauer, Peter Holtermann, Bernd Heinold, Hagen Radtke, Oswald Knoth, and Knut Klingbeil
Geosci. Model Dev., 14, 4843–4863, https://doi.org/10.5194/gmd-14-4843-2021, https://doi.org/10.5194/gmd-14-4843-2021, 2021
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We present the coupled atmosphere–ocean model system ICONGETM. The added value and potential of using the latest coupling technologies are discussed in detail. An exchange grid handles the different coastlines from the unstructured atmosphere and the structured ocean grids. Due to a high level of automated processing, ICONGETM requires only minimal user input. The application to a coastal upwelling scenario demonstrates significantly improved model results compared to uncoupled simulations.
Dawn L. Woodard, Alexey N. Shiklomanov, Ben Kravitz, Corinne Hartin, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 4751–4767, https://doi.org/10.5194/gmd-14-4751-2021, https://doi.org/10.5194/gmd-14-4751-2021, 2021
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We have added a representation of the permafrost carbon feedback to the simple, open-source global carbon–climate model Hector and calibrated the results to be consistent with historical data and Earth system model projections. Our results closely match previous work, estimating around 0.2 °C of warming from permafrost this century. This capability will be useful to explore uncertainties in this feedback and for coupling with integrated assessment models for policy and economic analysis.
Klaus Wyser, Torben Koenigk, Uwe Fladrich, Ramon Fuentes-Franco, Mehdi Pasha Karami, and Tim Kruschke
Geosci. Model Dev., 14, 4781–4796, https://doi.org/10.5194/gmd-14-4781-2021, https://doi.org/10.5194/gmd-14-4781-2021, 2021
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This paper describes the large ensemble done by SMHI with the EC-Earth3 climate model. The ensemble comprises 50 realizations for each of the historical experiments after 1970 and four different future projections for CMIP6. We describe the creation of the initial states for the ensemble and the reduced set of output variables. A first look at the results illustrates the changes in the climate during this century and puts them in relation to the uncertainty from the model's internal variability.
Yidi Xu, Philippe Ciais, Le Yu, Wei Li, Xiuzhi Chen, Haicheng Zhang, Chao Yue, Kasturi Kanniah, Arthur P. Cracknell, and Peng Gong
Geosci. Model Dev., 14, 4573–4592, https://doi.org/10.5194/gmd-14-4573-2021, https://doi.org/10.5194/gmd-14-4573-2021, 2021
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In this study, we implemented the specific morphology, phenology and harvest process of oil palm in the global land surface model ORCHIDEE-MICT. The improved model generally reproduces the same leaf area index, biomass density and life cycle fruit yield as observations. This explicit representation of oil palm in a global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.
Benjamin A. Toms, Karthik Kashinath, Prabhat, and Da Yang
Geosci. Model Dev., 14, 4495–4508, https://doi.org/10.5194/gmd-14-4495-2021, https://doi.org/10.5194/gmd-14-4495-2021, 2021
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We test whether a type of machine learning called neural networks can be used trustfully within the geosciences. We do so by challenging the networks to understand the spatial patterns of a commonly studied geoscientific phenomenon. The neural networks can correctly identify the spatial patterns, which lends confidence that similar networks can be used for more uncertain problems. The results of this study may give geoscientists confidence when using neural networks in their research.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 14, 4429–4441, https://doi.org/10.5194/gmd-14-4429-2021, https://doi.org/10.5194/gmd-14-4429-2021, 2021
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Soil moisture is of great importance for weather and climate. We present a machine learning model that produces accurate predictions of satellite-observed surface soil moisture, based on meteorological data from a climate model. It can be used as soil moisture parametrisation in climate models and to produce comprehensive global soil moisture datasets. Moreover, it may motivate similar applications of machine learning in climate science.
Jeremy McGibbon, Noah D. Brenowitz, Mark Cheeseman, Spencer K. Clark, Johann P. S. Dahm, Eddie C. Davis, Oliver D. Elbert, Rhea C. George, Lucas M. Harris, Brian Henn, Anna Kwa, W. Andre Perkins, Oliver Watt-Meyer, Tobias F. Wicky, Christopher S. Bretherton, and Oliver Fuhrer
Geosci. Model Dev., 14, 4401–4409, https://doi.org/10.5194/gmd-14-4401-2021, https://doi.org/10.5194/gmd-14-4401-2021, 2021
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FV3GFS is a weather and climate model written in Fortran. It uses Fortran so that it can run fast, but this makes it hard to add features if you do not (or even if you do) know Fortran. We have written a Python interface to FV3GFS that lets you import the Fortran model as a Python package. We show examples of how this is used to write
modelscripts, which reproduce or build on what the Fortran model can do. You could do this same wrapping for any compiled model, not just FV3GFS.
Thomas S. Ball, Naomi E. Vaughan, Thomas W. Powell, Andrew Lovett, and Timothy M. Lenton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-169, https://doi.org/10.5194/gmd-2021-169, 2021
Revised manuscript accepted for GMD
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We present C-LLAMA 1.0 (Country-Level Land Availability Model for Agriculture), a simple model of the global food and agriculture system. C-LLAMA uses publicly available dataset to make projections of diet, food demand, food production and land-use. Operating at the national level, each country acts as a 'box' with inputs and outputs. The model is designed to be simple to understand and modify, making it well-placed to explore the food system as a driver of land-use in future scenarios.
Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich
Geosci. Model Dev., 14, 4335–4355, https://doi.org/10.5194/gmd-14-4335-2021, https://doi.org/10.5194/gmd-14-4335-2021, 2021
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Decadal climate ensemble forecasts are increasingly being used to guide adaptation measures. To ensure the applicability of these probabilistic predictions, inherent systematic errors of the prediction system must be adjusted. Since it is not clear which statistical model is optimal for this purpose, we propose a recalibration strategy with a systematic model selection based on non-homogeneous boosting for identifying the most relevant features for both ensemble mean and ensemble spread.
Yan Yang, A. Anthony Bloom, Shuang Ma, Paul Levine, Alexander Norton, Nicholas C. Parazoo, John T. Reager, John Worden, Gregory R. Quetin, T. Luke Smallman, Mathew Williams, Liang Xu, and Sassan Saatchi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-190, https://doi.org/10.5194/gmd-2021-190, 2021
Revised manuscript accepted for GMD
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Global carbon and water have large uncertainties that are hard to quantify in current regional and global models. Field observations provide opportunities for better calibration and validation of current modeling of carbon and water. With the unique structure of CARDAMOM model, we have utilized the data assimilation capability and designed the benchmarking framework by taking field observations into modeling. Results show that data assimilation improves model performance in different aspects.
Daniel J. Lunt, Deepak Chandan, Alan M. Haywood, George M. Lunt, Jonathan C. Rougier, Ulrich Salzmann, Gavin A. Schmidt, and Paul J. Valdes
Geosci. Model Dev., 14, 4307–4317, https://doi.org/10.5194/gmd-14-4307-2021, https://doi.org/10.5194/gmd-14-4307-2021, 2021
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Often in science we carry out experiments with computers in which several factors are explored, for example, in the field of climate science, how the factors of greenhouse gases, ice, and vegetation affect temperature. We can explore the relative importance of these factors by
swapping in and outdifferent values of these factors, and can also carry out experiments with many different combinations of these factors. This paper discusses how best to analyse the results from such experiments.
Markus Adloff, Andy Ridgwell, Fanny M. Monteiro, Ian J. Parkinson, Alexander J. Dickson, Philip A. E. Pogge von Strandmann, Matthew S. Fantle, and Sarah E. Greene
Geosci. Model Dev., 14, 4187–4223, https://doi.org/10.5194/gmd-14-4187-2021, https://doi.org/10.5194/gmd-14-4187-2021, 2021
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We present the first representation of the trace metals Sr, Os, Li and Ca in a 3D Earth system model (cGENIE). The simulation of marine metal sources (weathering, hydrothermal input) and sinks (deposition) reproduces the observed concentrations and isotopic homogeneity of these metals in the modern ocean. With these new tracers, cGENIE can be used to test hypotheses linking these metal cycles and the cycling of other elements like O and C and simulate their dynamic response to external forcing.
Alessandro Anav, Adriana Carillo, Massimiliano Palma, Maria Vittoria Struglia, Ufuk Utku Turuncoglu, and Gianmaria Sannino
Geosci. Model Dev., 14, 4159–4185, https://doi.org/10.5194/gmd-14-4159-2021, https://doi.org/10.5194/gmd-14-4159-2021, 2021
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The Mediterranean Basin is a complex region, characterized by the presence of pronounced topography and a complex land–sea distribution including a considerable number of islands and straits; these features generate strong local atmosphere–sea interactions.
Regional Earth system models have been developed and used to study both present and future Mediterranean climate systems. The main aims of this paper are to present and evaluate the newly developed regional Earth system model ENEA-REG.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
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In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Dirk Barbi, Nadine Wieters, Paul Gierz, Miguel Andrés-Martínez, Deniz Ural, Fatemeh Chegini, Sara Khosravi, and Luisa Cristini
Geosci. Model Dev., 14, 4051–4067, https://doi.org/10.5194/gmd-14-4051-2021, https://doi.org/10.5194/gmd-14-4051-2021, 2021
Cléa Denamiel, Petra Pranić, Damir Ivanković, Iva Tojčić, and Ivica Vilibić
Geosci. Model Dev., 14, 3995–4017, https://doi.org/10.5194/gmd-14-3995-2021, https://doi.org/10.5194/gmd-14-3995-2021, 2021
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The atmospheric results of the Adriatic Sea and Coast (AdriSC) climate simulation (1987–2017) are evaluated against available observational datasets in the Adriatic region. Generally, the AdriSC model performs better than regional climate models that have resolutions that are 4 times more coarse, except concerning summer temperatures, which are systematically underestimated. High-resolution climate models may thus provide new insights about the local impacts of global warming in the Adriatic.
Cited articles
Adler, R., Sapiano, M., Huffman, G., Wang, J., Gu, G., Bolvin, D., Chiu, L.,
Schneider, U., Becker, A., Nelkin, E., Xie, P., Ferraro, R., and Shin, D.:
The Global Precipitation Climatology Project (GPCP) monthly analysis (New
Version 2.3) and a review of 2017 global precipitation, Atmosphere, 9, 138,
https://doi.org/10.3390/atmos9040138, 2018. a
Arakawa, A., Jung, J.-H., and Wu, C.-M.: Toward unification of the multiscale modeling of the atmosphere, Atmos. Chem. Phys., 11, 3731–3742, https://doi.org/10.5194/acp-11-3731-2011, 2011. a, b
Baumgaertner, A. J. G., Jöckel, P., Kerkweg, A., Sander, R., and Tost, H.: Implementation of the Community Earth System Model (CESM) version 1.2.1 as a new base model into version 2.50 of the MESSy framework, Geosci. Model Dev., 9, 125–135, https://doi.org/10.5194/gmd-9-125-2016, 2016. a
Bechtold, P., Chaboureau, J. P., Beljaars, A., Betts, A. K., Kohler, M.,
Miller, M., and Redelsperger, J. L.: The simulation of the diurnal cycle of
convective precipitation over land in a global model, Q. J. Roy. Meteor. Soc., 130, 3119–3137, 2004. a
Beheng, K.: A parameterization of warm cloud microphysical conversion
processes, Atmos. Res., 33, 193–206,
https://doi.org/10.1016/0169-8095(94)90020-5, 11th International Conference on Clouds
and Precipitation, Part II, 1994. a
Blossey, P. N., Bretherton, C. S., and Wyant, M. C.: Subtropical Low Cloud
Response to a Warmer Climate in a Superparameterized Climate Model. Part II:
Column Modeling with a Cloud Resolving Model, J. Adv. Model.
Earth Syst., 1, 8, https://doi.org/10.3894/JAMES.2009.1.8, 2009. a
Bodas-Salcedo, A., Webb, M. J., Bony, S., Chepfer, H., Dufresne, J.-L., Klein,
S. A., Zhang, Y., Marchand, R., Haynes, J. M., Pincus, R., and John, V. O.:
COSP: Satellite simulation software for model assessment, B.
Am. Meteor. Soc., 92, 1023–1043,
https://doi.org/10.1175/2011BAMS2856.1, 2011. a
Bodas-Salcedo, A., Hill, P., Furtado, K., Williams, K., Field, P., Manners, J.,
Hyder, P., and Kato, S.: Large Contribution of Supercooled Liquid Clouds to
the Solar Radiation Budget of the Southern Ocean, J. Climate, 29, 4213–4228,
https://doi.org/10.1175/JCLI-D-15-0564.1, 2016. a
Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P., Satheesh,
S., Sherwood, S., Stevens, B., and Zhang, X.: Clouds and Aerosols, book
section 7, 571–658, Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA, https://doi.org/10.1017/CBO9781107415324.016, 2013. a
Calisto, M., Folini, D., Wild, M., and Bengtsson, L.: Cloud radiative forcing intercomparison between fully coupled CMIP5 models and CERES satellite data, Ann. Geophys., 32, 793–807, https://doi.org/10.5194/angeo-32-793-2014, 2014. a
Cheng, A. and Xu, K.-M.: An explicit representation of vertical momentum
transport in a multiscale modeling framework through its 2-D cloud-resolving
model component, J. Geophys. Res.-Atmos., 119,
2356–2374, https://doi.org/10.1002/2013JD021078, 2014. a
Cheng, A. N. and Xu, K. M.: Diurnal variability of low clouds in the Southeast
Pacific simulated by a multiscale modeling framework model, J.
Geophys. Res.-Atmos., 118, 9191–9208, https://doi.org/10.1002/jgrd.50683,
2013. a
Cole, J. N. S., Barker, H. W., Randall, D. A., Khairoutdinov, M. F., and
Clothiaux, E. E.: Global consequences of interactions between clouds and
radiation at scales unresolved by global climate models, Geophys. Res.
Lett., 32, L06703, https://doi.org/10.1029/2004GL020945, 2005. a, b, c
Collier, J. C. and Bowman, K. P.: Diurnal cycle of tropical precipitation in a
general circulation model, J. Geophys. Res.-Atmos., 109, d17105,
https://doi.org/10.1029/2004JD004818, 2004. a
Cui, X. and Li, X.: Diurnal responses of tropical convective and stratiform
rainfall to diurnally varying sea surface temperature, Meteorol.
Atmos. Phys., 104, 53–61, https://doi.org/10.1007/s00703-008-0016-1, 2009. a
Dai, A.: Precipitation Characteristics in Eighteen Coupled Climate Models,
J. Climate, 19, 4605–4630, https://doi.org/10.1175/JCLI3884.1, 2006. a
Demory, M.-E., Vidale, P. L., Roberts, M. J., Berrisford, P., Strachan, J.,
Schiemann, R., and Mizielinski, M. S.: The role of horizontal resolution in
simulating drivers of the global hydrological cycle, Clim. Dynam., 42,
2201–2225, https://doi.org/10.1007/s00382-013-1924-4, 2014. a
Dietmüller, S., Jöckel, P., Tost, H., Kunze, M., Gellhorn, C., Brinkop, S., Frömming, C., Ponater, M., Steil, B., Lauer, A., and Hendricks, J.: A new radiation infrastructure for the Modular Earth Submodel System (MESSy, based on version 2.51), Geosci. Model Dev., 9, 2209–2222, https://doi.org/10.5194/gmd-9-2209-2016, 2016. a, b
Elliott, E. J., Yu, S., Kooperman, G. J., Morrison, H., Wang, M., and
Pritchard, M. S.: Sensitivity of summer ensembles of fledgling
superparameterized U.S. mesoscale convective systems to cloud resolving model
microphysics and grid configuration, J. Adv. Model. Earth
Syst., 8, 634–649, https://doi.org/10.1002/2015MS000567, 2016. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S., Collins, W.,
Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of Climate Models, book section 9, 741–866, Cambridge
University Press, Cambridge, United Kingdom and New York, NY, USA,
https://doi.org/10.1017/CBO9781107415324.020, 2013. a
Grabowski, W. W.: Coupling cloud processes with the large-scale dynamics using
the Cloud-Resolving Convection Parameterization (CRCP), J.
Atmos. Sci., 58, 978–997,
https://doi.org/10.1175/1520-0469(2001)058<0978:CCPWTL>2.0.CO;2, 2001. a
Grabowski, W. W.: An improved framework for superparameterization, J.
Atmos. Sci., 61, 1940–1952,
https://doi.org/10.1175/1520-0469(2004)061<1940:AIFFS>2.0.CO;2, 2004a. a
Grabowski, W. W.: An Improved Framework for Superparameterization, J.
Atmos. Sci., 61, 1940–1952,
https://doi.org/10.1175/1520-0469(2004)061<1940:AIFFS>2.0.CO;2, 2004b. a
Grabowski, W. W.: Towards Global Large Eddy Simulation: Super-Parameterization
Revisited, J. Meteor. Soc. Japan. Ser. II, 94,
327–344, https://doi.org/10.2151/jmsj.2016-017, 2016. a
Grabowski, W. W. and Smolarkiewicz, P. K.: CRCP: a Cloud Resolving Convection
Parameterization for modeling the tropical convecting atmosphere, Physica D,
133, 171–178, https://doi.org/10.1016/S0167-2789(99)00104-9, 1999. a
Guichard, F., Petch, J. C., Redelsperger, J.-L., Bechtold, P., Chaboureau,
J.-P., Cheinet, S., Grabowski, W., Grenier, H., Jones, C. G., Köhler, M.,
Piriou, J.-M., Tailleux, R., and Tomasini, M.: Modelling the diurnal cycle of
deep precipitating convection over land with cloud-resolving models and
single-column models, Q. J. Roy. Meteor. Soc., 130, 3139–3172,
https://doi.org/10.1256/qj.03.145, 2004. a
Gustafson, W. I., Berg, L. K., Easter, R. C., and Ghan, S. J.: The
Explicit-Cloud Parameterized-Pollutant hybrid approach for aerosol-cloud
interactions in multiscale modeling framework models: tracer transport
results, Environ. Res. Lett., 3, 025005, https://doi.org/10.1088/1748-9326/3/2/025005, 2008. a
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. a
Haynes, J. M., Jakob, C., Rossow, W. B., Tselioudis, G., and Brown, J.: Major
Characteristics of Southern Ocean Cloud Regimes and Their Effects on the
Energy Budget, J. Climate, 24, 5061–5080,
https://doi.org/10.1175/2011JCLI4052.1, 2011. a
Heinze, R., Dipankar, A., Henken, C. 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.,
Di 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. I., Muppa,
S. K., Neggers, R. A. J., 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., and Quaas, J.: Large-eddy simulations over Germany using ICON: a
comprehensive evaluation, Q. J. Roy. Meteor.
Soc., 143, 69–100, https://doi.org/10.1002/qj.2947, 2017a. a
Heinze, R., Moseley, C., Böske, L. N., Muppa, S. K., Maurer, V., Raasch, S., and Stevens, B.: Evaluation of large-eddy simulations forced with mesoscale model output for a multi-week period during a measurement campaign, Atmos. Chem. Phys., 17, 7083–7109, https://doi.org/10.5194/acp-17-7083-2017, 2017b. a
Hourdin, F., Mauritsen, T., Gettelman, A., Golaz, J.-C., Balaji, V., Duan, Q.,
Folini, D., Ji, D., Klocke, D., Qian, Y., Rauser, F., Rio, C., Tomassini, L.,
Watanabe, M., and Williamson, D.: The Art and Science of Climate Model
Tuning, B. Am. Meteor. Soc., 98, 589–602,
https://doi.org/10.1175/BAMS-D-15-00135.1, 2017. a
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu,
G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor
Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8,
38–55, https://doi.org/10.1175/JHM560.1, 2007. a
Jansson, F., van den Oord, G., Pelupessy, I., Grönqvist, J. H., Siebesma,
A. P., and Crommelin, D.: Regional Superparameterization in a Global
Circulation Model Using Large Eddy Simulations, J. Adv.
Model. Earth Syst., 11, 2958–2979, https://doi.org/10.1029/2018MS001600, 2019. a
Jiang, J. H., Su, H., Zhai, C. X., Perun, V. S., Del Genio, A., Nazarenko,
L. S., Donner, L. J., Horowitz, L., Seman, C., Cole, J., Gettelman, A.,
Ringer, M. A., Rotstayn, L., Jeffrey, S., Wu, T. W., Brient, F., Dufresne,
J. L., Kawai, H., Koshiro, T., Watanabe, M., LEcuyer, T. S., Volodin, E. M.,
Iversen, T., Drange, H., Mesquita, M. D. S., Read, W. G., Waters, J. W.,
Tian, B. J., Teixeira, J., and Stephens, G. L.: Evaluation of cloud and water
vapor simulations in CMIP5 climate models using NASA “A-Train” satellite
observations, J. Geophys. Res.-Atmos., 117, D14105,
https://doi.org/10.1029/2011JD017237, 2012. a
Jöckel, P., Sander, R., Kerkweg, A., Tost, H., and Lelieveld, J.: Technical Note: The Modular Earth Submodel System (MESSy) – a new approach towards Earth System Modeling, Atmos. Chem. Phys., 5, 433–444, https://doi.org/10.5194/acp-5-433-2005, 2005. a
Jöckel, P., Kerkweg, A., Pozzer, A., Sander, R., Tost, H., Riede, H., Baumgaertner, A., Gromov, S., and Kern, B.: Development cycle 2 of the Modular Earth Submodel System (MESSy2), Geosci. Model Dev., 3, 717–752, https://doi.org/10.5194/gmd-3-717-2010, 2010. a, b
Johnson, D.: Parametrization of the cloud topped boundary layer: aircraft
measurements, in: Workshop on Parametrization of the Cloud Topped Boundary
layer, 8–11 June 1993, 77–118, ECMWF, ECMWF, Shinfield Park, Reading,
1993. a
Jung, J. H. and Arakawa, A.: Development of a Quasi-3D Multiscale Modeling
Framework: Motivation, Basic Algorithm and Preliminary results, J.
Adv. Model. Earth Syst., 2, 11, https://doi.org/10.3894/JAMES.2010.2.11,
2010. a
Jung, J. H. and Arakawa, A.: Simulation of subgrid orographic precipitation
with an embedded 2-D cloud-resolving model, J. Adv. Model.
Earth Syst., 8, 31–40, https://doi.org/10.1002/2015MS000539, 2016. a
Kajikawa, Y., Yamaura, T., Tomita, H., and Satoh, M.: Impact of Tropical
Disturbance on the Indian Summer Monsoon Onset Simulated by a Global
Cloud-System-Resolving Model, SOLA, 11, 80–84,
https://doi.org/10.2151/sola.2015-020, 2015. a
Kajikawa, Y., Miyamoto, Y., Yoshida, R., Yamaura, T., Yashiro, H., and Tomita,
H.: Resolution dependence of deep convections in a global simulation from
over 10-kilometer to sub-kilometer grid spacing, Prog. Earth
Planet. Sci., 3, 16, https://doi.org/10.1186/s40645-016-0094-5, 2016. a
Kanamitsu, M., Ebisuzaki, W., Woollen, J., Yang, S.-K., Hnilo, J. J., Fiorino,
M., and Potter, G. L.: NCEP–DOE AMIP-II Reanalysis (R-2), B.
Am. Meteorol. Soc., 83, 1631–1643,
https://doi.org/10.1175/BAMS-83-11-1631, 2002. a
Karlsson, K.-G., Anttila, K., Trentmann, J., Stengel, M., Fokke Meirink, J., Devasthale, A., Hanschmann, T., Kothe, S., Jääskeläinen, E., Sedlar, J., Benas, N., van Zadelhoff, G.-J., Schlundt, C., Stein, D., Finkensieper, S., Håkansson, N., and Hollmann, R.: CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmos. Chem. Phys., 17, 5809–5828, https://doi.org/10.5194/acp-17-5809-2017, 2017. a
Kerkweg, A. and Jöckel, P.: The 1-way on-line coupled atmospheric chemistry model system MECO(n) – Part 1: Description of the limited-area atmospheric chemistry model COSMO/MESSy, Geosci. Model Dev., 5, 87–110, https://doi.org/10.5194/gmd-5-87-2012, 2012. a
Khairoutdinov, M., Randall, D., and DeMott, C.: Simulations of the atmospheric
general circulation using a cloud-resolving model as a superparameterization
of physical processes, J. Atmos. Sci., 62, 2136–2154,
https://doi.org/10.1175/JAS3453.1, 2005. a, b, c
Khairoutdinov, M., DeMott, C., and Randall, D.: Evaluation of the simulated
interannual and subseasonal variability in an AMIP-Style simulation using the
CSU multiscale modeling framework, J. Climate, 21, 413–431,
https://doi.org/10.1175/2007JCLI1630.1, 2008. a, b, c, d
Khairoutdinov, M. F. and Randall, D. A.: A cloud resolving model as a cloud
parameterization in the NCAR Community Climate System Model: Preliminary
results, Geophys. Res. Lett., 28, 3617–3620,
https://doi.org/10.1029/2001GL013552, 2001. a, b
Khairoutdinov, M. F. and Randall, D. A.: Cloud resolving modeling of the ARM
summer 1997 IOP: Model formulation, results, uncertainties, and
sensitivities, J. Atmos. Sci., 60, 607–625,
https://doi.org/10.1175/1520-0469(2003)060<0607:CRMOTA>2.0.CO;2, 2003. a, b, c
Khairoutdinov, M. F. and Randall, D.: High-Resolution Simulation of
Shallow-to-Deep Convection Transition over Land, J. Atmos.
Sci., 63, 3421–3436, https://doi.org/10.1175/JAS3810.1, 2006. a
Kikuchi, K. and Wang, B.: Diurnal Precipitation Regimes in the Global Tropics,
J. Climate, 21, 2680–2696, https://doi.org/10.1175/2007JCLI2051.1, 2008. a
Knutti, R., Stocker, T. F., Joos, F., and Plattner, G. K.: Constraints on
radiative forcing and future climate change from observations and climate
model ensembles, Nature, 416, 719–723, https://doi.org/10.1038/416719a, 2002. a
Kodama, C., Yamada, Y., Noda, A. T., Kikuchi, K., Kajikawa, Y., Nasuno, T.,
Tomita, T., Yamaura, T., Takahashi, H. G., Hara, M., Kawatani, Y., Satoh, M.,
and Sugi, M.: A 20-Year Climatology of a NICAM AMIP-Type Simulation, J. Meteor. Soc. Japan Ser. II, 93, 393–424,
https://doi.org/10.2151/jmsj.2015-024, 2015. a
Kooperman, G. J., Pritchard, M. S., and Somerville, R. C. J.: Robustness and
sensitivities of central U.S. summer convection in the super-parameterized
CAM: Multi-model intercomparison with a new regional EOF index, Geophys.
Res. Lett., 40, 3287–3291, https://doi.org/10.1002/grl.50597, 2013. a
Kooperman, G. J., Pritchard, M. S., and Somerville, R. C. J.: The response of
US summer rainfall to quadrupled CO2 climate change in conventional and
superparameterized versions of the NCAR community atmosphere model, J. Adv. Model. Earth Syst., 6, 859–882,
https://doi.org/10.1002/2014MS000306, 2014. a
Kooperman, G. J., Pritchard, M. S., Burt, M. A., Branson, M. D., and Randall,
D. A.: Robust effects of cloud superparameterization on simulated daily
rainfall intensity statistics across multiple versions of the Community Earth
System Model, J. Adv. Model. Earth Syst., 8, 140–165,
https://doi.org/10.1002/2015MS000574, 2016. a, b
Lauer, A. and Hamilton, K.: Simulating Clouds with Global Climate Models: A
Comparison of CMIP5 Results with CMIP3 and Satellite Data, J.
Climate, 26, 3823–3845, https://doi.org/10.1175/JCLI-D-12-00451.1, 2013. a
Loeb, N. G., Wielicki, B. A., Doelling, D. R., Smith, G. L., Keyes, D. F.,
Kato, S., Manalo-Smith, N., and Wong, T.: Toward optimal closure of the
Earth's top-of-atmosphere radiation budget, J. Climate, 22, 748–766,
2009. a
Lohmann, U. and Roeckner, E.: Design and performance of a new cloud
microphysics scheme developed for the ECHAM general circulation model,
Clim. Dynam., 12, 557–572, https://doi.org/10.1007/s003820050128, 1996. a
Luo, Z. and Stephens, G. L.: An enhanced convection-wind-evaporation feedback
in a superparameterization GCM (SP-GCM) depiction of the Asian summer
monsoon, Geophys. Res. Lett., 33, L06707, https://doi.org/10.1029/2005GL025060, 2006. a
Mace, G. G.: Cloud properties and radiative forcing over the maritime storm
tracks of the Southern Ocean and North Atlantic derived from A-Train, J. Geophys. Res.-Atmos., 115, D10201, https://doi.org/10.1029/2009JD012517, 2010. a
Maher, P., Vallis, G. K., Sherwood, S. C., Webb, M. J., and Sansom, P. G.: The
Impact of Parameterized Convection on Climatological Precipitation in
Atmospheric Global Climate Models, Geophys. Res. Lett., 45,
3728–3736, https://doi.org/10.1002/2017GL076826, 2018. a
Marchand, R. and Ackerman, T.: An analysis of cloud cover in multiscale
modeling framework global climate model simulations using 4 and 1 km
horizontal grids, J. Geophys. Res.-Atmos., 115, D16207,
https://doi.org/10.1029/2009JD013423, 2010. a, b, c, d
Marchand, R., Haynes, J., Mace, G. G., Ackerman, T., and Stephens, G.: A
comparison of simulated cloud radar output from the multiscale modeling
framework global climate model with CloudSat cloud radar observations,
J. Geophys. Res.-Atmos., 114, D00A20,
https://doi.org/10.1029/2008JD009790, 2009. a
Matsui, T., Chern, J.-D., Tao, W.-K., Lang, S., Satoh, M., Hashino, T., and
Kubota, T.: On the Land–Ocean Contrast of Tropical Convection and
Microphysics Statistics Derived from TRMM Satellite Signals and Global
Storm-Resolving Models, J. Hydrometeorol., 17, 1425–1445,
https://doi.org/10.1175/JHM-D-15-0111.1, 2016. a
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M.,
Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz, D.,
Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the climate of a global
model, J. Adv. Model. Earth Syst., 4, M00A01,
https://doi.org/10.1029/2012MS000154, 2012. a, b
Minghuai, W., E., L. V., Steven, G., Mikhail, O., P., S. D., Heng, X.,
Xiaohong, L., Philip, R., and Zhun, G.: A multiscale modeling framework model
(superparameterized CAM5) with a higher-order turbulence closure: Model
description and low-cloud simulations, J. Adv. Model. Earth
Syst., 7, 484–509, https://doi.org/10.1002/2014MS000375, 2015. a
Miura, H., Satoh, M., Nasuno, T., Noda, A. T., and Oouchi, K.: A Madden-Julian
Oscillation Event Realistically Simulated by a Global Cloud-Resolving Model,
Science, 318, 1763–1765, https://doi.org/10.1126/science.1148443, 2007. a
Miyakawa, T., Satoh, M., Miura, H., Tomita, H., Yashiro, H., Noda, A. T.,
Yamada, Y., Kodama, C., Kimoto, M., and Yoneyama, K.: Madden-Julian
Oscillation prediction skill of a new-generation global model demonstrated
using a supercomputer, Nat. Commun., 5, 3769,
https://doi.org/10.1038/ncomms4769, 2014. a
Miyamoto, Y., Kajikawa, Y., Yoshida, R., Yamaura, T., Yashiro, H., and Tomita,
H.: Deep moist atmospheric convection in a subkilometer global simulation,
Geophys. Res. Lett., 40, 4922–4926, https://doi.org/10.1002/grl.50944, 2013. a
Morrison, H., Thompson, G., and Tatarskii, V.: Impact of Cloud Microphysics on
the Development of Trailing Stratiform Precipitation in a Simulated Squall
Line: Comparison of One- and Two-Moment Schemes, Mon. Weather Rev., 137,
991–1007, https://doi.org/10.1175/2008MWR2556.1, 2009. a
Moss, S., Francis, P., and Johnson, D.: Calculation and parameterization of the
effective radius of ice particles using aircraft data, in: Proc. 12th Int.
Conf. on Clouds and Precipitation, 1255–1258, 1996. a
Nordeng, T. E.: Extended versions of the convective parametrization scheme at
ECMWF and their impact on the mean and transient activity of the model in the
tropics, Technical Momorandum 206, ECMWF Research Department, European
Centre for Medium Range Weather Forecasts, Reading, UK, 1994. a
O’Dell, C. W., Wentz, F. J., and Bennartz, R.: Cloud Liquid Water Path from
Satellite-Based Passive Microwave Observations: A New Climatology over the
Global Oceans, J. Climate, 21, 1721–1739,
https://doi.org/10.1175/2007JCLI1958.1, 2008. a
Parishani, H., Pritchard, M. S., Bretherton, C. S., Terai, C. R., Wyant, M. C.,
Khairoutdinov, M., and Singh, B.: Insensitivity of the Cloud Response to
Surface Warming Under Radical Changes to Boundary Layer Turbulence and Cloud
Microphysics: Results From the Ultraparameterized CAM, J. Adv.
Model. Earth Syst., 10, 3139–3158, https://doi.org/10.1029/2018MS001409, 2018. a, b, c
Pritchard, M. S. and Somerville, R. C. J.: Assessing the Diurnal Cycle of
Precipitation in a Multi-Scale Climate Model, J. Adv. Model.
Earth Syst., 1, 12, 2009a. a
Pritchard, M. S. and Somerville, R. C. J.: Empirical orthogonal function
analysis of the diurnal cycle of precipitation in a multi-scale climate
model, Geophys. Res. Lett., 36, L05812, https://doi.org/10.1029/2008GL036964, l05812,
2009b. a, b
Pritchard, M. S., Bretherton, C. S., and DeMott, C. A.: Restricting 32–128 km
horizontal scales hardly affects the MJO in the Superparameterized Community
Atmosphere Model v.3.0 but the number of cloud-resolving grid columns
constrains vertical mixing, J. Adv. Model. Earth Syst., 6,
723–739, https://doi.org/10.1002/2014MS000340, 2014. a, b, c
Probst, P., Rizzi, R., Tosi, E., Lucarini, V., and Maestri, T.: Total cloud
cover from satellite observations and climate models, Atmos.
Res., 107, 161–170, https://doi.org/10.1016/j.atmosres.2012.01.005, 2012. a
Qin, H., Pritchard, M. S., Kooperman, G. J., and Parishani, H.: Global Effects
of Superparameterization on Hydrothermal Land-Atmosphere Coupling on Multiple
Timescales, J. Adv. Model. Earth Syst., 10, 530–549,
https://doi.org/10.1002/2017MS001185, 2018. a, b, c
Randall, D., Khairoutdinov, M. F., Arakawa, A., and Grabowski, W.: Breaking the
cloud parameterization deadlock, B. Am. Meteor.
Soc., 84, 1547–1564, https://doi.org/10.1175/BAMS-84-11-1547, 2003. a
Randall, D. A.: Beyond deadlock, Geophys. Res. Lett., 40, 5970–5976,
https://doi.org/10.1002/2013GL057998, 2013. a
Roeckner, E., Bäuml, G., Bonaventura, L., Brokopf, R., Esch, M., Giorgetta,
M., Hagemann, S., Kirchner, I., Kornblueh, L., Manzini, E., Rhodin, A.,
Schlese, U., Schulzweida, U., and Tompkins, A.: The atmospheric general
circulation model ECHAM 5. Part I: Model description., Tech. Rep. 349,
Max-Planck-Institute for Meteorology, Hamburg,
available at: https://www.mpimet.mpg.de/fileadmin/publikationen/Reports/max_scirep_349.pdf (last access: 27 May 2020),
2003. a, b, c
Roeckner, E., Brokopf, R., Esch, M., Giorgetta, M., Hagemann, S., Kornblueh,
L., Manzini, E., Schlese, U., and Schulzweida, U.: Sensitivity of simulated
climate to horizontal and vertical resolution in the ECHAM5 atmosphere model,
J. Climate, 19, 3771–3791, https://doi.org/10.1175/JCLI3824.1, 2006. a, b
Rybka, H. and Tost, H.: SP-EMAC – analysis and plotting scripts, Zenodo,
https://doi.org/10.5281/zenodo.3387004, 2019a. a
Rybka, H. and Tost, H.: SP-EMAC – model source code and input files, Zenodo,
https://doi.org/10.5281/zenodo.3386969, 2019b. a
Sato, T., Miura, H., Satoh, M., Takayabu, Y. N., and Wang, Y.: Diurnal Cycle of
Precipitation in the Tropics Simulated in a Global Cloud-Resolving Model,
J. Climate, 22, 4809–4826, https://doi.org/10.1175/2009JCLI2890.1, 2009. a, b, c
Song, H., Zhang, Z., Ma, P.-L., Ghan, S., and Wang, M.: The importance of considering sub-grid cloud variability when using satellite observations to evaluate the cloud and precipitation simulations in climate models, Geosci. Model Dev., 11, 3147–3158, https://doi.org/10.5194/gmd-11-3147-2018, 2018. a
Stephens, G. L., L'Ecuyer, T., Forbes, R., Gettelmen, A., Golaz, J.-C.,
Bodas-Salcedo, A., Suzuki, K., Gabriel, P., and Haynes, J.: Dreary state of
precipitation in global models, J. Geophys. Res.-Atmos.,
115, D24211, https://doi.org/10.1029/2010JD014532, 2010. a
Stevens, B., Satoh, M., Auger, L., Biercamp, J., Bretherton, C. S., Chen, X.,
Düben, P., Judt, F., Khairoutdinov, M., Klocke, D., Kodama, C.,
Kornblueh, L., Lin, S.-J., Neumann, P., Putman, W. M., Röber, N.,
Shibuya, R., Vanniere, B., Vidale, P. L., Wedi, N., and Zhou, L.: DYAMOND:
the DYnamics of the Atmospheric general circulation Modeled On
Non-hydrostatic Domains, Progr. Earth Planet. Sci., 6, 61,
https://doi.org/10.1186/s40645-019-0304-z, 2019. a
Stevens, B., Acquistapace, C., Hansen, A., Heinze, R., Klinger, C., Klocke, D.,
Rybka, H., Schubotz, W., Windmiller, J., Adamidis, P., Arka, I., Barlakas,
V., Biercamp, J., Brueck, M., Brune, S., Buehler, S., Burkhardt, U., Cioni,
G., Costa-Surós, M., Crewell, S., Crueger, T., Deneke, H., Friederichs, P.,
Carbajal Henken, C., Hohenegger, C., Jacob, M., Jakub, F., Kalthoff, N.,
Kohler, M., Li, P., Lohnert, U., Macke, A., Madenach, N., Mayer, B., Nam, C.,
Naumann, A., Peters, K., Poll, S., Quaas, J., Rober, N., Rochetin, N.,
Scheck, L., Schemann, V., Schnitt, S., Seifert, A., Senf, F., Shapkalijevski,
M., Simmer, C., Singh, S., Sourdeval, O., Spickermann, D., Strandgren, J.,
Tessiot, O., Laar, T. v., Vercauteren, N., Vial, J., Voigt, A., and Zangl,
G.: The Added Value of Large-eddy and Storm-resolving Models for Simulating
Clouds and Precipitation, J. Meteor. Soc. Japan, 98, 395–435,
https://doi.org/10.2151/jmsj.2020-021, 2020. a
Stubenrauch, C. J., Rossow, W. B., Kinne, S., Ackerman, S., Cesana, G.,
Chepfer, H., Di Girolamo, L., Getzewich, B., Guignard, A., Heidinger, A.,
Maddux, B. C., Menzel, W. P., Minnis, P., Pearl, C., Platnick, S., Poulsen,
C., Riedi, J., Sun-Mack, S., Walther, A., Winker, D., Zeng, S., and Zhao, G.:
Assessment of Global Cloud Datasets from Satellites: Project and Database
Initiated by the GEWEX Radiation Panel, B. Am.
Meteorol. Soc., 94, 1031–1049, https://doi.org/10.1175/BAMS-D-12-00117.1,
2013. a
Subramanian, A., Weisheimer, A., Palmer, T., Vitart, F., and Bechtold, P.:
Impact of stochastic physics on tropical precipitation in the coupled ECMWF
model, Q. J. Roy. Meteorol. Soc., 143, 852–865,
https://doi.org/10.1002/qj.2970, 2017. a
Sui, C.-H., Lau, K.-M., Takayabu, Y. N., and Short, D. A.: Diurnal Variations
in Tropical Oceanic Cumulus Convection during TOGA COARE, J.
Atmos. Sci., 54, 639–655,
https://doi.org/10.1175/1520-0469(1997)054<0639:DVITOC>2.0.CO;2, 1997. a
Sui, C.-H., Li, X., and Lau, K.-M.: Radiative–Convective Processes in
Simulated Diurnal Variations of Tropical Oceanic Convection, J.
Atmos. Sci., 55, 2345–2357,
https://doi.org/10.1175/1520-0469(1998)055<2345:RCPISD>2.0.CO;2, 1998. a
Sun, J. and Pritchard, M. S.: Effects of Explicit Convection on Land Surface
Air Temperature and Land-Atmosphere Coupling in the Thermal Feedback Pathway,
J. Adv. Model. Earth Syst., 10, 2376–2392,
https://doi.org/10.1029/2018MS001301, 2018. a, b, c
Suzuki, K., Stephens, G., Bodas-Salcedo, A., Wang, M., Golaz, J.-C., Yokohata,
T., and Koshiro, T.: Evaluation of the Warm Rain Formation Process in Global
Models with Satellite Observations, J. Atmos. Sci., 72,
3996–4014, https://doi.org/10.1175/JAS-D-14-0265.1, 2015. a
Swales, D. J., Pincus, R., and Bodas-Salcedo, A.: The Cloud Feedback Model Intercomparison Project Observational Simulator Package: Version 2, Geosci. Model Dev., 11, 77–81, https://doi.org/10.5194/gmd-11-77-2018, 2018. a
Tao, W. K., Chern, J. D., Atlas, R., Randall, D., Khairoutdinov, M., Li, J. L.,
Waliser, D. E., Hou, A., Lin, X., Peters-Lidard, C., Lau, W., Jiang, J., and
Simpson, J.: A Multiscale Modeling System: Developments, Applications, and
Critical Issues, B. Am. Meteorol. Soc., 90,
515–534, https://doi.org/10.1175/2008BAMS2542.1, 2009. a, b, c, d
Taylor, K. E.: Summarizing multiple aspects of model performance in a single
diagram, J. Geophys. Res.-Atmos., 106, 7183–7192,
https://doi.org/10.1029/2000JD900719, 2001. a
Tiedtke, M.: A Comprehensive Mass Flux Scheme For Cumulus Parameterization In
Large-scale Models, Mon. Weather Rev., 117, 1779–1800,
https://doi.org/10.1175/1520-0493(1989)117<1779:ACMFSF>2.0.CO;2, 1989. a
Tompkins, A. M.: Organization of Tropical Convection in Low Vertical Wind
Shears: The Role of Cold Pools, J. Atmos. Sci., 58,
1650–1672, https://doi.org/10.1175/1520-0469(2001)058<1650:OOTCIL>2.0.CO;2, 2001. a
Tost, H., Jöckel, P., and Lelieveld, J.: Influence of different convection parameterisations in a GCM, Atmos. Chem. Phys., 6, 5475–5493, https://doi.org/10.5194/acp-6-5475-2006, 2006. a
Tulich, S. N.: A strategy for representing the effects of convective momentum
transport in multiscale models: Evaluation using a new superparameterized
version of the Weather Research and Forecast model (SP-WRF), J.
Adv. Model. Earth Syst., 7, 938–962, https://doi.org/10.1002/2014MS000417,
2015. a, b, c
Wang, M., Ghan, S., Easter, R., Ovchinnikov, M., Liu, X., Kassianov, E., Qian, Y., Gustafson Jr., W. I., Larson, V. E., Schanen, D. P., Khairoutdinov, M., and Morrison, H.: The multi-scale aerosol-climate model PNNL-MMF: model description and evaluation, Geosci. Model Dev., 4, 137–168, https://doi.org/10.5194/gmd-4-137-2011, 2011a. a, b, c
Wang, M., Ghan, S., Ovchinnikov, M., Liu, X., Easter, R., Kassianov, E., Qian, Y., and Morrison, H.: Aerosol indirect effects in a multi-scale aerosol-climate model PNNL-MMF, Atmos. Chem. Phys., 11, 5431–5455, https://doi.org/10.5194/acp-11-5431-2011, 2011b. a
Wielicki, B. A., Barkstrom, B. R., Harrison, E. F., III, R. B. L., Smith,
G. L., and Cooper, J. E.: Clouds and the Earth's Radiant Energy System
(CERES): An Earth Observing System Experiment, B. Am.
Meteor
. Soc., 77, 853–868,
https://doi.org/10.1175/1520-0477(1996)077<0853:CATERE>2.0.CO;2, 1996. a
Wyant, M. C., Bretherton, C. S., Bacmeister, J. T., Kiehl, J. T., Held, I. M.,
Zhao, M., Klein, S. A., and Soden, B. J.: A comparison of low-latitude cloud
properties and their response to climate change in three AGCMs sorted into
regimes using mid-tropospheric vertical velocity, Clim. Dynam., 27,
261–279, https://doi.org/10.1007/s00382-006-0138-4, 2006a. a
Wyant, M. C., Khairoutdinov, M., and Bretherton, C. S.: Climate sensitivity and
cloud response of a GCM with a superparameterization, Geophys. Res.
Lett., 33, L06714, https://doi.org/10.1029/2005GL025464, 2006b. a, b
Wyant, M. C., Bretherton, C. S., and Blossey, P. N.: Subtropical Low Cloud
Response to a Warmer Climate in a Superparameterized Climate Model. Part I:
Regime Sorting and Physical Mechanisms, J. Adv. Model. Earth
Syst., 1, 7, https://doi.org/10.3894/JAMES.2009.1.7, 2009. a, b
Yang, S. and Smith, E. A.: Mechanisms for Diurnal Variability of Global
Tropical Rainfall Observed from TRMM, J. Climate, 19, 5190–5226,
https://doi.org/10.1175/JCLI3883.1, 2006.
a, b
Yashiro, H., Kajikawa, Y., Miyamoto, Y., Yamaura, T., Yoshida, R., and Tomita, H.: Resolution Dependence of the Diurnal Cycle of Precipitation Simulated by a Global Cloud-System Resolving Model, SOLA, 12, 272–276,
https://doi.org/10.2151/sola.2016-053, 2016. a
Zhang, G. J. and McFarlane, N. A.: Sensitivity of Climate Simulations To the
Parameterization of Cumulus Convection In the Canadian Climate Center
General-circulation Model, Atmos.-Ocean, 33, 407–446, 1995. a
Zhang, T., Zhang, M., Lin, W., Lin, Y., Xue, W., Yu, H., He, J., Xin, X., Ma, H.-Y., Xie, S., and Zheng, W.: Automatic tuning of the Community Atmospheric Model (CAM5) by using short-term hindcasts with an improved downhill simplex optimization method, Geosci. Model Dev., 11, 5189–5201, https://doi.org/10.5194/gmd-11-5189-2018, 2018. a
Zhang, Y., Klein, S. A., Liu, C., Tian, B., Marchand, R. T., Haynes, J. M.,
McCoy, R. B., Zhang, Y., and Ackerman, T. P.: On the diurnal cycle of deep
convection, high-level cloud, and upper troposphere water vapor in the
Multiscale Modeling Framework, J. Geophys. Res.-Atmos.,
113, D16105, https://doi.org/10.1029/2008JD009905, 2008. a, b
Short summary
Simulating cloud processes and their interactions with their environment is one of the biggest challenges in atmospheric science. This study couples a cloud-resolving model with a global climate model to improve the representation of small-scale processes for climate simulations. Unlike conventional approaches, tropical precipitation is better simulated with the new model setup. However, the diurnal cycle of precipitation and cloud amounts can be significantly influenced by the chosen setup.
Simulating cloud processes and their interactions with their environment is one of the biggest...
