Articles | Volume 16, issue 7
https://doi.org/10.5194/gmd-16-1857-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1857-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
04 Apr 2023
Methods for assessment of models |
| 04 Apr 2023
| 04 Apr 2023
Nudging allows direct evaluation of coupled climate models with in situ observations: a case study from the MOSAiC expedition
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven/Potsdam, Germany
Marylou Athanase
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven/Potsdam, Germany
Sandro Dahlke
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven/Potsdam, Germany
Antonio Sánchez-Benítez
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven/Potsdam, Germany
Matthew D. Shupe
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
National Oceanic and Atmospheric Administration Physical Science Laboratory, Boulder, Colorado
Anne Sledd
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado
National Oceanic and Atmospheric Administration Physical Science Laboratory, Boulder, Colorado
Jan Streffing
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven/Potsdam, Germany
Department of Mathematics and Logistics, Jacobs University Bremen, Bremen, Germany
Gunilla Svensson
Department of Meteorology and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden
Thomas Jung
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research (AWI), Bremerhaven/Potsdam, Germany
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Related authors
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schäfer, Vera Schemannn, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
EGUsphere, https://doi.org/10.5194/egusphere-2024-783, https://doi.org/10.5194/egusphere-2024-783, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to the mid-latitude weather. In our article we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented wit state-of-the-art remote sensing and in-situ measurement devices.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Alexei Koldunov, Tobias Kölling, Josh Kousal, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Domokos Sármány, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
EGUsphere, https://doi.org/10.5194/egusphere-2024-913, https://doi.org/10.5194/egusphere-2024-913, 2024
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale"), and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Manfred Wendisch, Susanne Crewell, André Ehrlich, Andreas Herber, Benjamin Kirbus, Christof Lüpkes, Mario Mech, Steven J. Abel, Elisa F. Akansu, Felix Ament, Clémantyne Aubry, Sebastian Becker, Stephan Borrmann, Heiko Bozem, Marlen Brückner, Hans-Christian Clemen, Sandro Dahlke, Georgios Dekoutsidis, Julien Delanoë, Elena De La Torre Castro, Henning Dorff, Regis Dupuy, Oliver Eppers, Florian Ewald, Geet George, Irina V. Gorodetskaya, Sarah Grawe, Silke Groß, Jörg Hartmann, Silvia Henning, Lutz Hirsch, Evelyn Jäkel, Philipp Joppe, Olivier Jourdan, Zsofia Jurányi, Michail Karalis, Mona Kellermann, Marcus Klingebiel, Michael Lonardi, Johannes Lucke, Anna Luebke, Maximilian Maahn, Nina Maherndl, Marion Maturilli, Bernhard Mayer, Johanna Mayer, Stephan Mertes, Janosch Michaelis, Michel Michalkov, Guillaume Mioche, Manuel Moser, Hanno Müller, Roel Neggers, Davide Ori, Daria Paul, Fiona Paulus, Christian Pilz, Felix Pithan, Mira Pöhlker, Veronika Pörtge, Maximilian Ringel, Nils Risse, Gregory C. Roberts, Sophie Rosenburg, Johannes Röttenbacher, Janna Rückert, Michael Schäfer, Jonas Schäfer, Vera Schemannn, Imke Schirmacher, Jörg Schmidt, Sebastian Schmidt, Johannes Schneider, Sabrina Schnitt, Anja Schwarz, Holger Siebert, Harald Sodemann, Tim Sperzel, Gunnar Spreen, Bjorn Stevens, Frank Stratmann, Gunilla Svensson, Christian Tatzelt, Thomas Tuch, Timo Vihma, Christiane Voigt, Lea Volkmer, Andreas Walbröl, Anna Weber, Birgit Wehner, Bruno Wetzel, Martin Wirth, and Tobias Zinner
EGUsphere, https://doi.org/10.5194/egusphere-2024-783, https://doi.org/10.5194/egusphere-2024-783, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
The Arctic is warming faster than the rest of the globe. Warm air intrusions (WAIs) into the Arctic may play an important role in explaining this phenomenon. Cold air outbreaks (CAOs) out of the Arctic may link the Arctic climate changes to the mid-latitude weather. In our article we describe how to observe air mass transformations during CAOs and WAIs using three research aircraft instrumented wit state-of-the-art remote sensing and in-situ measurement devices.
Michael Lonardi, Elisa F. Akansu, André Ehrlich, Mauro Mazzola, Christian Pilz, Matthew D. Shupe, Holger Siebert, and Manfred Wendisch
Atmos. Chem. Phys., 24, 1961–1978, https://doi.org/10.5194/acp-24-1961-2024, https://doi.org/10.5194/acp-24-1961-2024, 2024
Short summary
Short summary
Profiles of thermal-infrared irradiance were measured at two Arctic sites. The presence or lack of clouds influences the vertical structure of these observations. In particular, the cloud top region is a source of radiative energy that can promote cooling and mixing in the cloud layer. Simulations are used to further characterize how the amount of water in the cloud modifies this forcing. A case study additionally showcases the evolution of the radiation profiles in a dynamic atmosphere.
Maximilian Maahn, Dmitri Moisseev, Isabelle Steinke, Nina Maherndl, and Matthew D. Shupe
Atmos. Meas. Tech., 17, 899–919, https://doi.org/10.5194/amt-17-899-2024, https://doi.org/10.5194/amt-17-899-2024, 2024
Short summary
Short summary
The open-source Video In Situ Snowfall Sensor (VISSS) is a novel instrument for characterizing particle shape, size, and sedimentation velocity in snowfall. It combines a large observation volume with relatively high resolution and a design that limits wind perturbations. The open-source nature of the VISSS hardware and software invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer
Atmos. Chem. Phys., 24, 1429–1450, https://doi.org/10.5194/acp-24-1429-2024, https://doi.org/10.5194/acp-24-1429-2024, 2024
Short summary
Short summary
Observations collected during MOSAiC were used to identify the range in vertical structure and stability of the central Arctic lower atmosphere through a self-organizing map analysis. Characteristics of wind features (such as low-level jets) and atmospheric moisture features (such as clouds) were analyzed in the context of the varying vertical structure and stability. Thus, the results of this paper give an overview of the thermodynamic and kinematic features of the central Arctic atmosphere.
Zen Mariani, Sara Morris, Taneil Uttal, Elena Akish, Robert Crawford, Laura Huang, Jonathan Day, Johanna Tjernström, Øystein Godøy, Lara Ferrighi, Leslie Hartten, Jareth Holt, Christopher Cox, Ewan O'Connor, Roberta Pirazzini, Marion Maturilli, Giri Prakash, James Mather, Kimberly Strong, Pierre Fogal, Vasily Kustov, Gunilla Svensson, Michael Gallagher, and Brian Vasel
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-497, https://doi.org/10.5194/essd-2023-497, 2024
Preprint under review for ESSD
Short summary
Short summary
During the Year of Polar Prediction (YOPP) we increased measurements in the polar regions and have made dedicated efforts to centralize and standardize all of the different types of data sets that have been collected to facilitate user uptake and model-observation comparisons. This paper is an overview of those efforts and a description of the novel standardized Merged Observation Data Files (MODF), including a description of the sites, data format, and instruments.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
Short summary
Short summary
Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Jonathan Day, Gunilla Svensson, Barbara Casati, Taniel Uttal, Siri-Jodha Khalsa, Eric Bazile, Elena Akish, Niramson Azouz, Lara Ferrighi, Helmut Frank, Michael Gallagher, Øystein Godøy, Leslie Hartten, Laura X. Huang, Jareth Holt, Massimo Di Stefano, Irene Suomi, Zen Mariani, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Teresa Remes, Rostislav Fadeev, Amy Solomon, Johanna Tjernström, and Mikhail Tolstykh
EGUsphere, https://doi.org/10.5194/egusphere-2023-1951, https://doi.org/10.5194/egusphere-2023-1951, 2023
Short summary
Short summary
The YOPP site Model Inter-comparison Project (YOPPsiteMIP) which was designed to facilitate enhanced weather forecast evaluation in polar regions. The paper focusses on describing the archive of forecast data and presents a multi-model evaluation at Arctic supersites during February and March 2018. The study highlights an underestimation in boundary layer temperature variance which is common across models and their related inability to forecast cold extremes at several of the sites.
Elisa F. Akansu, Sandro Dahlke, Holger Siebert, and Manfred Wendisch
Atmos. Chem. Phys., 23, 15473–15489, https://doi.org/10.5194/acp-23-15473-2023, https://doi.org/10.5194/acp-23-15473-2023, 2023
Short summary
Short summary
The height of the mixing layer is an important measure of the surface-level distribution of energy or other substances. The experimental determination of this height is associated with large uncertainties, particularly under stable conditions that we often find during the polar night or in the presence of clouds. We present a reference method using turbulence measurements on a tethered balloon, which allows us to evaluate approaches based on radiosondes or surface observations.
Taneil Uttal, Leslie M. Hartten, Siri Jodha Khalsa, Barbara Casati, Gunilla Svensson, Jonathan Day, Jareth Holt, Elena Akish, Sara Morris, Ewan O'Connor, Roberta Pirazzini, Laura X. Huang, Robert Crawford, Zen Mariani, Øystein Godøy, Johanna A. K. Tjernström, Giri Prakash, Nicki Hickmon, Marion Maturilli, and Christopher J. Cox
EGUsphere, https://doi.org/10.5194/egusphere-2023-2413, https://doi.org/10.5194/egusphere-2023-2413, 2023
Short summary
Short summary
A Merged Observatory Data File (MODF) format is presented to systematically collate complex atmosphere, ocean and terrestrial data sets collected by multiple instruments during field campaigns. The MODF format is designed to be applicable to model output data to create format-matching Merged Model Data Files (MMDFs). MODFs + MMDFs will augment and accelerate the synergistic use of model results with observational data to increase understanding and predictive skill.
Gina C. Jozef, Robert Klingel, John J. Cassano, Björn Maronga, Gijs de Boer, Sandro Dahlke, and Christopher J. Cox
Earth Syst. Sci. Data, 15, 4983–4995, https://doi.org/10.5194/essd-15-4983-2023, https://doi.org/10.5194/essd-15-4983-2023, 2023
Short summary
Short summary
Observations from the MOSAiC expedition relating to lower-atmospheric temperature, wind, stability, moisture, and surface radiation budget from radiosondes, a meteorological tower, radiation station, and ceilometer were compiled to create a dataset which describes the thermodynamic and kinematic state of the central Arctic lower atmosphere between October 2019 and September 2020. This paper describes the methods used to develop this lower-atmospheric properties dataset.
Gina C. Jozef, John J. Cassano, Sandro Dahlke, Mckenzie Dice, Christopher J. Cox, and Gijs de Boer
Atmos. Chem. Phys., 23, 13087–13106, https://doi.org/10.5194/acp-23-13087-2023, https://doi.org/10.5194/acp-23-13087-2023, 2023
Short summary
Short summary
Observations from the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) were used to determine the frequency of occurrence of various central Arctic lower atmospheric stability regimes and how the stability regimes transition between each other. Wind and radiation observations were analyzed in the context of stability regime and season to reveal the relationships between Arctic atmospheric stability and mechanically and radiatively driven turbulent forcings.
Albert Ansmann, Kevin Ohneiser, Ronny Engelmann, Martin Radenz, Hannes Griesche, Julian Hofer, Dietrich Althausen, Jessie M. Creamean, Matthew C. Boyer, Daniel A. Knopf, Sandro Dahlke, Marion Maturilli, Henriette Gebauer, Johannes Bühl, Cristofer Jimenez, Patric Seifert, and Ulla Wandinger
Atmos. Chem. Phys., 23, 12821–12849, https://doi.org/10.5194/acp-23-12821-2023, https://doi.org/10.5194/acp-23-12821-2023, 2023
Short summary
Short summary
The 1-year MOSAiC (2019–2020) expedition with the German ice breaker Polarstern was the largest polar field campaign ever conducted. The Polarstern, with our lidar aboard, drifted with the pack ice north of 85° N for more than 7 months (October 2019 to mid-May 2020). We measured the full annual cycle of aerosol conditions in terms of aerosol optical and cloud-process-relevant properties. We observed a strong contrast between polluted winter and clean summer aerosol conditions.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Veronique Bouchet, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Detlef Stammer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2023-376, https://doi.org/10.5194/essd-2023-376, 2023
Revised manuscript accepted for ESSD
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines are proposed as international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Olivia Linke, Johannes Quaas, Finja Baumer, Sebastian Becker, Jan Chylik, Sandro Dahlke, André Ehrlich, Dörthe Handorf, Christoph Jacobi, Heike Kalesse-Los, Luca Lelli, Sina Mehrdad, Roel A. J. Neggers, Johannes Riebold, Pablo Saavedra Garfias, Niklas Schnierstein, Matthew D. Shupe, Chris Smith, Gunnar Spreen, Baptiste Verneuil, Kameswara S. Vinjamuri, Marco Vountas, and Manfred Wendisch
Atmos. Chem. Phys., 23, 9963–9992, https://doi.org/10.5194/acp-23-9963-2023, https://doi.org/10.5194/acp-23-9963-2023, 2023
Short summary
Short summary
Lapse rate feedback (LRF) is a major driver of the Arctic amplification (AA) of climate change. It arises because the warming is stronger at the surface than aloft. Several processes can affect the LRF in the Arctic, such as the omnipresent temperature inversion. Here, we compare multimodel climate simulations to Arctic-based observations from a large research consortium to broaden our understanding of these processes, find synergy among them, and constrain the Arctic LRF and AA.
Manfred Wendisch, Johannes Stapf, Sebastian Becker, André Ehrlich, Evelyn Jäkel, Marcus Klingebiel, Christof Lüpkes, Michael Schäfer, and Matthew D. Shupe
Atmos. Chem. Phys., 23, 9647–9667, https://doi.org/10.5194/acp-23-9647-2023, https://doi.org/10.5194/acp-23-9647-2023, 2023
Short summary
Short summary
Atmospheric radiation measurements have been conducted during two field campaigns using research aircraft. The data are analyzed to see if the near-surface air in the Arctic is warmed or cooled if warm–humid air masses from the south enter the Arctic or cold–dry air moves from the north from the Arctic to mid-latitude areas. It is important to study these processes and to check if climate models represent them well. Otherwise it is not possible to reliably forecast the future Arctic climate.
Shijie Peng, Qinghua Yang, Matthew D. Shupe, Xingya Xi, Bo Han, Dake Chen, Sandro Dahlke, and Changwei Liu
Atmos. Chem. Phys., 23, 8683–8703, https://doi.org/10.5194/acp-23-8683-2023, https://doi.org/10.5194/acp-23-8683-2023, 2023
Short summary
Short summary
Due to a lack of observations, the structure of the Arctic atmospheric boundary layer (ABL) remains to be further explored. By analyzing a year-round radiosonde dataset collected over the Arctic sea-ice surface, we found the annual cycle of the ABL height (ABLH) is primarily controlled by the evolution of ABL thermal structure, and the surface conditions also show a high correlation with ABLH variation. In addition, the Arctic ABLH is found to be decreased in summer compared with 20 years ago.
Kameswara S. Vinjamuri, Marco Vountas, Luca Lelli, Martin Stengel, Matthew D. Shupe, Kerstin Ebell, and John P. Burrows
Atmos. Meas. Tech., 16, 2903–2918, https://doi.org/10.5194/amt-16-2903-2023, https://doi.org/10.5194/amt-16-2903-2023, 2023
Short summary
Short summary
Clouds play an important role in Arctic amplification. Cloud data from ground-based sites are valuable but cannot represent the whole Arctic. Therefore the use of satellite products is a measure to cover the entire Arctic. However, the quality of such cloud measurements from space is not well known. The paper discusses the differences and commonalities between satellite and ground-based measurements. We conclude that the satellite dataset, with a few exceptions, can be used in the Arctic.
Ulrike Egerer, John J. Cassano, Matthew D. Shupe, Gijs de Boer, Dale Lawrence, Abhiram Doddi, Holger Siebert, Gina Jozef, Radiance Calmer, Jonathan Hamilton, Christian Pilz, and Michael Lonardi
Atmos. Meas. Tech., 16, 2297–2317, https://doi.org/10.5194/amt-16-2297-2023, https://doi.org/10.5194/amt-16-2297-2023, 2023
Short summary
Short summary
This paper describes how measurements from a small uncrewed aircraft system can be used to estimate the vertical turbulent heat energy exchange between different layers in the atmosphere. This is particularly important for the atmosphere in the Arctic, as turbulent exchange in this region is often suppressed but is still important to understand how the atmosphere interacts with sea ice. We present three case studies from the MOSAiC field campaign in Arctic sea ice in 2020.
Océane Hames, Mahdi Jafari, David Nicholas Wagner, Ian Raphael, David Clemens-Sewall, Chris Polashenski, Matthew D. Shupe, Martin Schneebeli, and Michael Lehning
Geosci. Model Dev., 15, 6429–6449, https://doi.org/10.5194/gmd-15-6429-2022, https://doi.org/10.5194/gmd-15-6429-2022, 2022
Short summary
Short summary
This paper presents an Eulerian–Lagrangian snow transport model implemented in the fluid dynamics software OpenFOAM, which we call snowBedFoam 1.0. We apply this model to reproduce snow deposition on a piece of ridged Arctic sea ice, which was produced during the MOSAiC expedition through scan measurements. The model appears to successfully reproduce the enhanced snow accumulation and deposition patterns, although some quantitative uncertainties were shown.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
Short summary
Short summary
We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Gina Jozef, John Cassano, Sandro Dahlke, and Gijs de Boer
Atmos. Meas. Tech., 15, 4001–4022, https://doi.org/10.5194/amt-15-4001-2022, https://doi.org/10.5194/amt-15-4001-2022, 2022
Short summary
Short summary
During the MOSAiC expedition, meteorological conditions over the lowest 1 km of the atmosphere were sampled with the DataHawk2 uncrewed aircraft system. These data were used to identify the best method for atmospheric boundary layer height detection by comparing visually identified subjective boundary layer height to that identified by several objective automated detection methods. The results show a bulk Richardson number-based approach gives the best estimate of boundary layer height.
Assia Arouf, Hélène Chepfer, Thibault Vaillant de Guélis, Marjolaine Chiriaco, Matthew D. Shupe, Rodrigo Guzman, Artem Feofilov, Patrick Raberanto, Tristan S. L'Ecuyer, Seiji Kato, and Michael R. Gallagher
Atmos. Meas. Tech., 15, 3893–3923, https://doi.org/10.5194/amt-15-3893-2022, https://doi.org/10.5194/amt-15-3893-2022, 2022
Short summary
Short summary
We proposed new estimates of the surface longwave (LW) cloud radiative effect (CRE) derived from observations collected by a space-based lidar on board the CALIPSO satellite and radiative transfer computations. Our estimate appropriately captures the surface LW CRE annual variability over bright polar surfaces, and it provides a dataset more than 13 years long.
David N. Wagner, Matthew D. Shupe, Christopher Cox, Ola G. Persson, Taneil Uttal, Markus M. Frey, Amélie Kirchgaessner, Martin Schneebeli, Matthias Jaggi, Amy R. Macfarlane, Polona Itkin, Stefanie Arndt, Stefan Hendricks, Daniela Krampe, Marcel Nicolaus, Robert Ricker, Julia Regnery, Nikolai Kolabutin, Egor Shimanshuck, Marc Oggier, Ian Raphael, Julienne Stroeve, and Michael Lehning
The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, https://doi.org/10.5194/tc-16-2373-2022, 2022
Short summary
Short summary
Based on measurements of the snow cover over sea ice and atmospheric measurements, we estimate snowfall and snow accumulation for the MOSAiC ice floe, between November 2019 and May 2020. For this period, we estimate 98–114 mm of precipitation. We suggest that about 34 mm of snow water equivalent accumulated until the end of April 2020 and that at least about 50 % of the precipitated snow was eroded or sublimated. Further, we suggest explanations for potential snowfall overestimation.
Sara Pasqualetto, Luisa Cristini, and Thomas Jung
Geosci. Commun., 5, 87–100, https://doi.org/10.5194/gc-5-87-2022, https://doi.org/10.5194/gc-5-87-2022, 2022
Short summary
Short summary
Many projects in their reporting phase are required to provide a clear plan for evaluating the results of those efforts aimed at translating scientific results to a broader audience. This paper illustrates methodologies and strategies used in the framework of a European research project to assess the impact of knowledge transfer activities, both quantitatively and qualitatively, and provides recommendations and hints for developing a useful impact plan for scientific projects.
Michael R. Gallagher, Matthew D. Shupe, Hélène Chepfer, and Tristan L'Ecuyer
The Cryosphere, 16, 435–450, https://doi.org/10.5194/tc-16-435-2022, https://doi.org/10.5194/tc-16-435-2022, 2022
Short summary
Short summary
By using direct observations of snowfall and mass changes, the variability of daily snowfall mass input to the Greenland ice sheet is quantified for the first time. With new methods we conclude that cyclones west of Greenland in summer contribute the most snowfall, with 1.66 Gt per occurrence. These cyclones are contextualized in the broader Greenland climate, and snowfall is validated against mass changes to verify the results. Snowfall and mass change observations are shown to agree well.
Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, Qiang Wang, Nikolay Koldunov, Dmitry Sein, and Thomas Jung
Geosci. Model Dev., 15, 335–363, https://doi.org/10.5194/gmd-15-335-2022, https://doi.org/10.5194/gmd-15-335-2022, 2022
Short summary
Short summary
Structured-mesh ocean models are still the most mature in terms of functionality due to their long development history. However, unstructured-mesh ocean models have acquired new features and caught up in their functionality. This paper continues the work by Scholz et al. (2019) of documenting the features available in FESOM2.0. It focuses on the following two aspects: (i) partial bottom cells and embedded sea ice and (ii) dealing with mixing parameterisations enabled by using the CVMix package.
Sonja Murto, Rodrigo Caballero, Gunilla Svensson, and Lukas Papritz
Weather Clim. Dynam., 3, 21–44, https://doi.org/10.5194/wcd-3-21-2022, https://doi.org/10.5194/wcd-3-21-2022, 2022
Short summary
Short summary
This study uses reanalysis data to investigate the role of atmospheric blocking, prevailing high-pressure systems and mid-latitude cyclones in driving high-Arctic wintertime warm extreme events. These events are mainly preceded by Ural and Scandinavian blocks, which are shown to be significantly influenced and amplified by cyclones in the North Atlantic. It also highlights processes that need to be well captured in climate models for improving their representation of Arctic wintertime climate.
Heather Guy, Ian M. Brooks, Ken S. Carslaw, Benjamin J. Murray, Von P. Walden, Matthew D. Shupe, Claire Pettersen, David D. Turner, Christopher J. Cox, William D. Neff, Ralf Bennartz, and Ryan R. Neely III
Atmos. Chem. Phys., 21, 15351–15374, https://doi.org/10.5194/acp-21-15351-2021, https://doi.org/10.5194/acp-21-15351-2021, 2021
Short summary
Short summary
We present the first full year of surface aerosol number concentration measurements from the central Greenland Ice Sheet. Aerosol concentrations here have a distinct seasonal cycle from those at lower-altitude Arctic sites, which is driven by large-scale atmospheric circulation. Our results can be used to help understand the role aerosols might play in Greenland surface melt through the modification of cloud properties. This is crucial in a rapidly changing region where observations are sparse.
Ronny Engelmann, Albert Ansmann, Kevin Ohneiser, Hannes Griesche, Martin Radenz, Julian Hofer, Dietrich Althausen, Sandro Dahlke, Marion Maturilli, Igor Veselovskii, Cristofer Jimenez, Robert Wiesen, Holger Baars, Johannes Bühl, Henriette Gebauer, Moritz Haarig, Patric Seifert, Ulla Wandinger, and Andreas Macke
Atmos. Chem. Phys., 21, 13397–13423, https://doi.org/10.5194/acp-21-13397-2021, https://doi.org/10.5194/acp-21-13397-2021, 2021
Short summary
Short summary
A Raman lidar was operated aboard the icebreaker Polarstern during MOSAiC and monitored aerosol and cloud layers in the central Arctic up to 30 km height. The article provides an overview of the spectrum of aerosol profiling observations and shows aerosol–cloud interaction studies for liquid-water and ice clouds. A highlight was the detection of a 10 km deep wildfire smoke layer over the North Pole up to 17 km height from the fire season of 2019, which persisted over the whole winter period.
Benjamin Männel, Florian Zus, Galina Dick, Susanne Glaser, Maximilian Semmling, Kyriakos Balidakis, Jens Wickert, Marion Maturilli, Sandro Dahlke, and Harald Schuh
Atmos. Meas. Tech., 14, 5127–5138, https://doi.org/10.5194/amt-14-5127-2021, https://doi.org/10.5194/amt-14-5127-2021, 2021
Short summary
Short summary
Within the MOSAiC expedition, GNSS was used to monitor variations in atmospheric water vapor. Based on 15 months of continuously tracked data, coordinates and hourly zenith total delays (ZTDs) were determined using kinematic precise point positioning. The derived ZTD values agree within few millimeters with ERA5 and terrestrial GNSS and VLBI stations. The derived integrated water vapor corresponds to the frequently launched radiosondes (0.08 ± 0.04 kg m−2, rms of the differences of 1.47 kg m−2).
Jessie M. Creamean, Gijs de Boer, Hagen Telg, Fan Mei, Darielle Dexheimer, Matthew D. Shupe, Amy Solomon, and Allison McComiskey
Atmos. Chem. Phys., 21, 1737–1757, https://doi.org/10.5194/acp-21-1737-2021, https://doi.org/10.5194/acp-21-1737-2021, 2021
Short summary
Short summary
Arctic clouds play a role in modulating sea ice extent. Importantly, aerosols facilitate cloud formation, and thus it is crucial to understand the interactions between aerosols and clouds. Vertical measurements of aerosols and clouds are needed to tackle this issue. We present results from balloon-borne measurements of aerosols and clouds over the course of 2 years in northern Alaska. These data shed light onto the vertical distributions of aerosols relative to clouds spanning multiple seasons.
Lena Frey, Frida A.-M. Bender, and Gunilla Svensson
Atmos. Chem. Phys., 21, 577–595, https://doi.org/10.5194/acp-21-577-2021, https://doi.org/10.5194/acp-21-577-2021, 2021
Short summary
Short summary
We investigate the vertical distribution of aerosol in the climate model NorESM1-M in five regions of marine stratocumulus clouds. We thereby analyze the total aerosol extinction to facilitate a comparison with satellite data. We find that the model underestimates aerosol extinction throughout the troposphere, especially elevated aerosol layers. Further, we perform sensitivity experiments to identify the processes most important for vertical aerosol distribution in our model.
Peggy Achtert, Ewan J. O'Connor, Ian M. Brooks, Georgia Sotiropoulou, Matthew D. Shupe, Bernhard Pospichal, Barbara J. Brooks, and Michael Tjernström
Atmos. Chem. Phys., 20, 14983–15002, https://doi.org/10.5194/acp-20-14983-2020, https://doi.org/10.5194/acp-20-14983-2020, 2020
Short summary
Short summary
We present observations of precipitating and non-precipitating Arctic liquid and mixed-phase clouds during a research cruise along the Russian shelf in summer and autumn of 2014. Active remote-sensing observations, radiosondes, and auxiliary measurements are combined in the synergistic Cloudnet retrieval. Cloud properties are analysed with respect to cloud-top temperature and boundary layer structure. About 8 % of all liquid clouds show a liquid water path below the infrared black body limit.
Xavier Fettweis, Stefan Hofer, Uta Krebs-Kanzow, Charles Amory, Teruo Aoki, Constantijn J. Berends, Andreas Born, Jason E. Box, Alison Delhasse, Koji Fujita, Paul Gierz, Heiko Goelzer, Edward Hanna, Akihiro Hashimoto, Philippe Huybrechts, Marie-Luise Kapsch, Michalea D. King, Christoph Kittel, Charlotte Lang, Peter L. Langen, Jan T. M. Lenaerts, Glen E. Liston, Gerrit Lohmann, Sebastian H. Mernild, Uwe Mikolajewicz, Kameswarrao Modali, Ruth H. Mottram, Masashi Niwano, Brice Noël, Jonathan C. Ryan, Amy Smith, Jan Streffing, Marco Tedesco, Willem Jan van de Berg, Michiel van den Broeke, Roderik S. W. van de Wal, Leo van Kampenhout, David Wilton, Bert Wouters, Florian Ziemen, and Tobias Zolles
The Cryosphere, 14, 3935–3958, https://doi.org/10.5194/tc-14-3935-2020, https://doi.org/10.5194/tc-14-3935-2020, 2020
Short summary
Short summary
We evaluated simulated Greenland Ice Sheet surface mass balance from 5 kinds of models. While the most complex (but expensive to compute) models remain the best, the faster/simpler models also compare reliably with observations and have biases of the same order as the regional models. Discrepancies in the trend over 2000–2012, however, suggest that large uncertainties remain in the modelled future SMB changes as they are highly impacted by the meltwater runoff biases over the current climate.
Rosa Gierens, Stefan Kneifel, Matthew D. Shupe, Kerstin Ebell, Marion Maturilli, and Ulrich Löhnert
Atmos. Chem. Phys., 20, 3459–3481, https://doi.org/10.5194/acp-20-3459-2020, https://doi.org/10.5194/acp-20-3459-2020, 2020
Short summary
Short summary
Multiyear statistics of persistent low-level mixed-phase clouds observed at an Arctic fjord environment in Svalbard are presented. The effects the local boundary layer (i.e. the fjords' wind climate and surface coupling), regional wind direction, and seasonality have on the cloud occurrence and properties are evaluated using a synergy of ground-based remote sensing methods and auxiliary data. The phenomena considered were found to modify the amount of liquid and ice in the studied clouds.
Patrick Scholz, Dmitry Sidorenko, Ozgur Gurses, Sergey Danilov, Nikolay Koldunov, Qiang Wang, Dmitry Sein, Margarita Smolentseva, Natalja Rakowsky, and Thomas Jung
Geosci. Model Dev., 12, 4875–4899, https://doi.org/10.5194/gmd-12-4875-2019, https://doi.org/10.5194/gmd-12-4875-2019, 2019
Short summary
Short summary
This paper is the first in a series documenting and assessing important key components of the Finite-volumE Sea ice-Ocean Model version 2.0 (FESOM2.0). We assess the hydrographic biases, large-scale circulation, numerical performance and scalability of FESOM2.0 compared with its predecessor, FESOM1.4. The main conclusion is that the results of FESOM2.0 compare well to FESOM1.4 in terms of model biases but with a remarkable performance speedup with a 3 times higher throughput.
Nikolay V. Koldunov, Vadym Aizinger, Natalja Rakowsky, Patrick Scholz, Dmitry Sidorenko, Sergey Danilov, and Thomas Jung
Geosci. Model Dev., 12, 3991–4012, https://doi.org/10.5194/gmd-12-3991-2019, https://doi.org/10.5194/gmd-12-3991-2019, 2019
Short summary
Short summary
We measure how computational performance of the global FESOM2 ocean model (formulated on an unstructured mesh) changes with the increase in the number of computational cores. We find that for many components of the model the performance increases linearly but we also identify two bottlenecks: sea ice and ssh submodules. We show that FESOM2 is on par with the state-of-the-art ocean models in terms of throughput that reach 16 simulated years per day for eddy resolving configuration (1/10°).
Gijs de Boer, Darielle Dexheimer, Fan Mei, John Hubbe, Casey Longbottom, Peter J. Carroll, Monty Apple, Lexie Goldberger, David Oaks, Justin Lapierre, Michael Crume, Nathan Bernard, Matthew D. Shupe, Amy Solomon, Janet Intrieri, Dale Lawrence, Abhiram Doddi, Donna J. Holdridge, Michael Hubbell, Mark D. Ivey, and Beat Schmid
Earth Syst. Sci. Data, 11, 1349–1362, https://doi.org/10.5194/essd-11-1349-2019, https://doi.org/10.5194/essd-11-1349-2019, 2019
Short summary
Short summary
This paper provides a summary of observations collected at Oliktok Point, Alaska, as part of the Profiling at Oliktok Point to Enhance YOPP Experiments (POPEYE) campaign. The Year of Polar Prediction (YOPP) is a multi-year concentrated effort to improve forecasting capabilities at high latitudes across a variety of timescales. POPEYE observations include atmospheric data collected using unmanned aircraft, tethered balloons, and radiosondes, made in parallel with routine measurements at the site.
Thomas Rackow, Dmitry V. Sein, Tido Semmler, Sergey Danilov, Nikolay V. Koldunov, Dmitry Sidorenko, Qiang Wang, and Thomas Jung
Geosci. Model Dev., 12, 2635–2656, https://doi.org/10.5194/gmd-12-2635-2019, https://doi.org/10.5194/gmd-12-2635-2019, 2019
Short summary
Short summary
Current climate models show errors in the deep ocean that are larger than the level of natural variability and the response to enhanced greenhouse gas concentrations. These errors are larger than the signals we aim to predict. With the AWI Climate Model, we show that increasing resolution to resolve eddies can lead to major reductions in deep ocean errors. AWI's next-generation (CMIP6) model configuration will thus use locally eddy-resolving computational grids for projecting climate change.
Ralf Bennartz, Frank Fell, Claire Pettersen, Matthew D. Shupe, and Dirk Schuettemeyer
Atmos. Chem. Phys., 19, 8101–8121, https://doi.org/10.5194/acp-19-8101-2019, https://doi.org/10.5194/acp-19-8101-2019, 2019
Short summary
Short summary
The Greenland Ice Sheet (GrIS) is rapidly melting. Snowfall is the only source of ice mass over the GrIS. We use satellite observations to assess how much snow on average falls over the GrIS and what the annual cycle and spatial distribution of snowfall is. We find the annual mean snowfall over the GrIS inferred from CloudSat to be 34 ± 7.5 cm yr−1 liquid equivalent.
Maximilian Maahn, Fabian Hoffmann, Matthew D. Shupe, Gijs de Boer, Sergey Y. Matrosov, and Edward P. Luke
Atmos. Meas. Tech., 12, 3151–3171, https://doi.org/10.5194/amt-12-3151-2019, https://doi.org/10.5194/amt-12-3151-2019, 2019
Short summary
Short summary
Cloud radars are unique instruments for observing cloud processes, but uncertainties in radar calibration have frequently limited data quality. Here, we present three novel methods for calibrating vertically pointing cloud radars. These calibration methods are based on microphysical processes of liquid clouds, such as the transition of cloud droplets to drizzle drops. We successfully apply the methods to cloud radar data from the North Slope of Alaska (NSA) and Oliktok Point (OLI) ARM sites.
Christopher J. Cox, David C. Noone, Max Berkelhammer, Matthew D. Shupe, William D. Neff, Nathaniel B. Miller, Von P. Walden, and Konrad Steffen
Atmos. Chem. Phys., 19, 7467–7485, https://doi.org/10.5194/acp-19-7467-2019, https://doi.org/10.5194/acp-19-7467-2019, 2019
Short summary
Short summary
Fogs are frequently reported by observers on the Greenland Ice Sheet. Fogs play a role in the hydrological and energetic balances of the ice sheet surface, but as yet the properties of Greenland fogs are not well known. We observed fogs in all months from Summit Station for 2 years and report their properties. Annually, fogs impart a slight warming to the surface and a case study suggests that they are particularly influential by providing insulation during the coldest part of the day in summer.
Doug M. Smith, James A. Screen, Clara Deser, Judah Cohen, John C. Fyfe, Javier García-Serrano, Thomas Jung, Vladimir Kattsov, Daniela Matei, Rym Msadek, Yannick Peings, Michael Sigmond, Jinro Ukita, Jin-Ho Yoon, and Xiangdong Zhang
Geosci. Model Dev., 12, 1139–1164, https://doi.org/10.5194/gmd-12-1139-2019, https://doi.org/10.5194/gmd-12-1139-2019, 2019
Short summary
Short summary
The Polar Amplification Model Intercomparison Project (PAMIP) is an endorsed contribution to the sixth Coupled Model Intercomparison Project (CMIP6). It will investigate the causes and global consequences of polar amplification through coordinated multi-model numerical experiments. This paper documents the experimental protocol.
Xiang-Yu Li, Gunilla Svensson, Axel Brandenburg, and Nils E. L. Haugen
Atmos. Chem. Phys., 19, 639–648, https://doi.org/10.5194/acp-19-639-2019, https://doi.org/10.5194/acp-19-639-2019, 2019
Short summary
Short summary
The broadening of droplet size distributions in stratiform clouds, where the updraft velocity is almost zero, is puzzling. Without turbulence, the classical treatment of condensational growth of cloud droplets fails to explain this
broadening. We investigated the time evolution of droplet size distributions using direct numerical simulations, where turbulence is resolved into the smallest scales. We found that the broadening is due to the turbulence-facilitated supersaturation fluctuations.
Amy Solomon, Gijs de Boer, Jessie M. Creamean, Allison McComiskey, Matthew D. Shupe, Maximilian Maahn, and Christopher Cox
Atmos. Chem. Phys., 18, 17047–17059, https://doi.org/10.5194/acp-18-17047-2018, https://doi.org/10.5194/acp-18-17047-2018, 2018
Short summary
Short summary
The results of this study indicate that perturbations in ice nucleating particles (INPs) dominate over cloud condensation nuclei (CCN) perturbations in Arctic mixed-phase stratocumulus; i.e., an equivalent fractional decrease in CCN and INPs results in an increase in the cloud-top longwave cooling rate, even though the droplet effective radius increases and the cloud emissivity decreases. In addition, cloud-processing causes layering of aerosols with increased concentrations of CCN at cloud top.
Kerstin Hartung, Gunilla Svensson, Hamish Struthers, Anna-Lena Deppenmeier, and Wilco Hazeleger
Geosci. Model Dev., 11, 4117–4137, https://doi.org/10.5194/gmd-11-4117-2018, https://doi.org/10.5194/gmd-11-4117-2018, 2018
Short summary
Short summary
Single-column models have been used to develop weather and climate models for several decades. They decouple small-scale processes from large-scale forcing and allow us to test models in a controlled environment with reduced computational cost. Here, we present a fully coupled atmosphere–ocean single-column model, including sea ice. We demonstrate that it is a valuable tool to advance our understanding in marine and polar boundary layer processes and interactions of their coupled components.
Matthew S. Norgren, Gijs de Boer, and Matthew D. Shupe
Atmos. Chem. Phys., 18, 13345–13361, https://doi.org/10.5194/acp-18-13345-2018, https://doi.org/10.5194/acp-18-13345-2018, 2018
Short summary
Short summary
Arctic mixed-phase clouds are a critical component of the Arctic climate system because of their ability to influence the surface radiation budget. The radiative impact of an individual cloud is closely linked to the ability of the cloud to convert liquid drops to ice. In this paper, we show through an observational record that clouds present in polluted atmospheric conditions have lower amounts of ice than similar clouds found in clean conditions.
Claire Pettersen, Ralf Bennartz, Aronne J. Merrelli, Matthew D. Shupe, David D. Turner, and Von P. Walden
Atmos. Chem. Phys., 18, 4715–4735, https://doi.org/10.5194/acp-18-4715-2018, https://doi.org/10.5194/acp-18-4715-2018, 2018
Short summary
Short summary
A novel method for classifying Arctic precipitation using ground based remote sensors is presented. The classification reveals two distinct, primary regimes of precipitation over the central Greenland Ice Sheet: snowfall coupled to deep, fully glaciated ice clouds or to shallow, mixed-phase clouds. The ice clouds are associated with low-pressure storm systems from the southeast, while the mixed-phase clouds slowly propagate from the southwest along a quiescent flow.
Qiang Wang, Claudia Wekerle, Sergey Danilov, Xuezhu Wang, and Thomas Jung
Geosci. Model Dev., 11, 1229–1255, https://doi.org/10.5194/gmd-11-1229-2018, https://doi.org/10.5194/gmd-11-1229-2018, 2018
Short summary
Short summary
For developing a system for Arctic research, we evaluate the Arctic Ocean simulated by FESOM. We use two global meshes differing in the horizontal resolution only in the Arctic Ocean (24 vs. 4.5 km). The high resolution significantly improves the model's representation of the Arctic Ocean. The most pronounced improvement is in the Arctic intermediate layer. The high resolution also improves the ocean surface circulation, mainly through a better representation of the Canadian Arctic Archipelago.
Robert A. Stillwell, Ryan R. Neely III, Jeffrey P. Thayer, Matthew D. Shupe, and David D. Turner
Atmos. Meas. Tech., 11, 835–859, https://doi.org/10.5194/amt-11-835-2018, https://doi.org/10.5194/amt-11-835-2018, 2018
Short summary
Short summary
This work focuses on making unambiguous measurements of Arctic cloud phase and assessing those measurements within the context of cloud radiative effects. It is found that effects related to lidar data recording systems can cause retrieval ambiguities that alter the interpretation of cloud phase in as much as 30 % of the available data. This misinterpretation of cloud-phase data can cause a misinterpretation of the effect of cloud phase on the surface radiation budget by as much as 10 to 30 %.
Lena Frey, Frida A.-M. Bender, and Gunilla Svensson
Atmos. Chem. Phys., 17, 9145–9162, https://doi.org/10.5194/acp-17-9145-2017, https://doi.org/10.5194/acp-17-9145-2017, 2017
Short summary
Short summary
In this study, the cloud albedo effect in climate models is investigated, separating the influence of anthropogenic sulfate and non-sulfate aerosols. Cloud albedo changes induced by added anthropogenic aerosols are found to be determined by changes in the cloud water content rather than model sensitivity to monthly aerosol variations. The results also indicate that the background aerosol is the main driver for a cloud brightening effect on the month-to-month scale.
Yinghui Liu, Matthew D. Shupe, Zhien Wang, and Gerald Mace
Atmos. Chem. Phys., 17, 5973–5989, https://doi.org/10.5194/acp-17-5973-2017, https://doi.org/10.5194/acp-17-5973-2017, 2017
Short summary
Short summary
Detailed and accurate vertical distributions of cloud properties are essential to accurately calculate the surface radiative flux and to depict the mean climate state, and such information is more desirable in the Arctic due to its recent rapid changes and the challenging observation conditions. This study presents a feasible way to provide such information by blending cloud observations from surface and space-based instruments with the understanding of their respective strength and limitations.
Sergey Danilov, Dmitry Sidorenko, Qiang Wang, and Thomas Jung
Geosci. Model Dev., 10, 765–789, https://doi.org/10.5194/gmd-10-765-2017, https://doi.org/10.5194/gmd-10-765-2017, 2017
Short summary
Short summary
Numerical models of global ocean circulation are used to learn about future climate. The ocean circulation is characterized by processes on different spatial scales which are still beyond the reach of present computers. We describe a new model setup that allows one to vary a model's spatial resolution and hence focus the computational power on regional dynamics, reaching a better description of local processes in areas of interest.
Nathaniel B. Miller, Matthew D. Shupe, Christopher J. Cox, David Noone, P. Ola G. Persson, and Konrad Steffen
The Cryosphere, 11, 497–516, https://doi.org/10.5194/tc-11-497-2017, https://doi.org/10.5194/tc-11-497-2017, 2017
Short summary
Short summary
A comprehensive observational dataset is assembled to investigate atmosphere–Greenland ice sheet interactions and characterize surface temperature variability. The amount the surface temperature warms, due to increases in cloud presence and/or elevated sun angle, varies throughout the annual cycle and is modulated by the responses of latent, sensible and ground heat fluxes. This observationally based study provides process-based relationships, which are useful for evaluation of climate models.
Robert A. Stillwell, Ryan R. Neely III, Jeffrey P. Thayer, Matthew D. Shupe, and Michael O'Neill
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2016-303, https://doi.org/10.5194/amt-2016-303, 2016
Revised manuscript not accepted
Short summary
Short summary
This work explores the observation of Arctic mixed phase clouds by lidar and the consequences of mishandling lidar signals linking the signals to their geophysical interpretation. It concludes 3 points: 1) cloud phase identification is not only linked to cloud phase but other cloud properties, 2) having more than two polarization signals can be used to quality control data not possible with only two signals, and 3) phase retrievals with more than two polarizations enhance retrieval flexibility.
Claire Pettersen, Ralf Bennartz, Mark S. Kulie, Aronne J. Merrelli, Matthew D. Shupe, and David D. Turner
Atmos. Chem. Phys., 16, 4743–4756, https://doi.org/10.5194/acp-16-4743-2016, https://doi.org/10.5194/acp-16-4743-2016, 2016
Short summary
Short summary
We examined four summers of data from a ground-based atmospheric science instrument suite at Summit Station, Greenland, to isolate the signature of the ice precipitation. By using a combination of instruments with different specialities, we identified a passive microwave signature of the ice precipitation. This ice signature compares well to models using synthetic data characteristic of the site.
Qinghua Yang, Martin Losch, Svetlana N. Losa, Thomas Jung, Lars Nerger, and Thomas Lavergne
The Cryosphere, 10, 761–774, https://doi.org/10.5194/tc-10-761-2016, https://doi.org/10.5194/tc-10-761-2016, 2016
Short summary
Short summary
We assimilate the summer SICCI sea ice concentration data with an ensemble-based Kalman Filter. Comparing with the approach using a constant data uncertainty, the sea ice concentration estimates are further improved when the SICCI-provided uncertainty are taken into account, but the sea ice thickness cannot be improved. We find the data assimilation system cannot give a reasonable ensemble spread of sea ice concentration and thickness if the provided uncertainty are directly used.
A. Solomon, G. Feingold, and M. D. Shupe
Atmos. Chem. Phys., 15, 10631–10643, https://doi.org/10.5194/acp-15-10631-2015, https://doi.org/10.5194/acp-15-10631-2015, 2015
Short summary
Short summary
The maintenance of cloud ice production in Arctic mixed-phase stratocumulus is investigated in large eddy simulations that include a prognostic ice nuclei (IN) formulation and a diurnal cycle. It is demonstrated that IN recycling through subcloud sublimation prolongs ice production. Competing feedbacks between dynamical mixing and recycling are found to slow the rate of ice lost. The results of this study have important implications for the maintenance of phase partitioning in Arctic clouds.
S. Danilov, Q. Wang, R. Timmermann, N. Iakovlev, D. Sidorenko, M. Kimmritz, T. Jung, and J. Schröter
Geosci. Model Dev., 8, 1747–1761, https://doi.org/10.5194/gmd-8-1747-2015, https://doi.org/10.5194/gmd-8-1747-2015, 2015
Short summary
Short summary
Unstructured meshes allow multi-resolution modeling of ocean dynamics. Sea ice models formulated on unstructured meshes are a necessary component of ocean models intended for climate studies. This work presents a description of a finite-element sea ice model which is used as a component of a finite-element sea ice ocean circulation model. The principles underlying its design can be of interest to other groups pursuing ocean modelling on unstructured meshes.
G. Sotiropoulou, J. Sedlar, M. Tjernström, M. D. Shupe, I. M. Brooks, and P. O. G. Persson
Atmos. Chem. Phys., 14, 12573–12592, https://doi.org/10.5194/acp-14-12573-2014, https://doi.org/10.5194/acp-14-12573-2014, 2014
Short summary
Short summary
During ASCOS, clouds are more frequently decoupled from the surface than coupled to it; when coupling occurs it is primary driven by the cloud. Decoupled clouds have a bimodal structure; they are either weakly or strongly decoupled from the surface; the enhancement of the decoupling is possibly due to sublimation of precipitation. Stable clouds (no cloud-driven mixing) are also observed; those are optically thin, often single-phase liquid, with no or negligible precipitation (e.g. fog).
J. M. Intrieri, G. de Boer, M. D. Shupe, J. R. Spackman, J. Wang, P. J. Neiman, G. A. Wick, T. F. Hock, and R. E. Hood
Atmos. Meas. Tech., 7, 3917–3926, https://doi.org/10.5194/amt-7-3917-2014, https://doi.org/10.5194/amt-7-3917-2014, 2014
Short summary
Short summary
In winter 2011, the Global Hawk unmanned aircraft system (UAS) was deployed over the Arctic to evaluate a UAS dropsonde system at high latitudes. Dropsondes deployed from the Global Hawk successfully obtained high-resolution profiles of temperature, pressure, humidity, and wind speed and direction information between the stratosphere and surface. During the 25-hour polar flight, the Global Hawk released 35 sondes between the North Slope of Alaska and 85° N latitude.
Q. Wang, S. Danilov, D. Sidorenko, R. Timmermann, C. Wekerle, X. Wang, T. Jung, and J. Schröter
Geosci. Model Dev., 7, 663–693, https://doi.org/10.5194/gmd-7-663-2014, https://doi.org/10.5194/gmd-7-663-2014, 2014
J. Sedlar and M. D. Shupe
Atmos. Chem. Phys., 14, 3461–3478, https://doi.org/10.5194/acp-14-3461-2014, https://doi.org/10.5194/acp-14-3461-2014, 2014
M. Tjernström, C. Leck, C. E. Birch, J. W. Bottenheim, B. J. Brooks, I. M. Brooks, L. Bäcklin, R. Y.-W. Chang, G. de Leeuw, L. Di Liberto, S. de la Rosa, E. Granath, M. Graus, A. Hansel, J. Heintzenberg, A. Held, A. Hind, P. Johnston, J. Knulst, M. Martin, P. A. Matrai, T. Mauritsen, M. Müller, S. J. Norris, M. V. Orellana, D. A. Orsini, J. Paatero, P. O. G. Persson, Q. Gao, C. Rauschenberg, Z. Ristovski, J. Sedlar, M. D. Shupe, B. Sierau, A. Sirevaag, S. Sjogren, O. Stetzer, E. Swietlicki, M. Szczodrak, P. Vaattovaara, N. Wahlberg, M. Westberg, and C. R. Wheeler
Atmos. Chem. Phys., 14, 2823–2869, https://doi.org/10.5194/acp-14-2823-2014, https://doi.org/10.5194/acp-14-2823-2014, 2014
G. de Boer, M. D. Shupe, P. M. Caldwell, S. E. Bauer, O. Persson, J. S. Boyle, M. Kelley, S. A. Klein, and M. Tjernström
Atmos. Chem. Phys., 14, 427–445, https://doi.org/10.5194/acp-14-427-2014, https://doi.org/10.5194/acp-14-427-2014, 2014
M. D. Shupe, P. O. G. Persson, I. M. Brooks, M. Tjernström, J. Sedlar, T. Mauritsen, S. Sjogren, and C. Leck
Atmos. Chem. Phys., 13, 9379–9399, https://doi.org/10.5194/acp-13-9379-2013, https://doi.org/10.5194/acp-13-9379-2013, 2013
Related subject area
Climate and Earth system modeling
An overview of cloud–radiation denial experiments for the Energy Exascale Earth System Model version 1
The computational and energy cost of simulation and storage for climate science: lessons from CMIP6
Subgrid-scale variability of cloud ice in the ICON-AES 1.3.00
INFERNO-peat v1.0.0: a representation of northern high-latitude peat fires in the JULES-INFERNO global fire model
The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Towards the definition of a solar forcing dataset for CMIP7
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Constraining the carbon cycle in JULES-ES-1.0
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations
Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the Southern Ocean
Implementing detailed nucleation predictions in the Earth system model EC-Earth3.3.4: sulfuric acid-ammonia nucleation
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
Short summary
Short summary
Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
Short summary
Short summary
We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
Short summary
Short summary
Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
Short summary
Short summary
Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
Short summary
Short summary
Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
Short summary
Short summary
We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
Short summary
Short summary
This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
Short summary
Short summary
The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
Short summary
Short summary
This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
Short summary
Short summary
Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
Short summary
Short summary
Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
Short summary
Short summary
Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
Short summary
Short summary
This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
Short summary
Short summary
Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
Short summary
Short summary
Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
Short summary
Short summary
We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
Short summary
Short summary
Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
Short summary
Short summary
As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
Short summary
Short summary
In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
Short summary
Short summary
This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
Short summary
Short summary
A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
Short summary
Short summary
Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
Short summary
Short summary
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
Short summary
Short summary
The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
Short summary
Short summary
We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
Short summary
Short summary
This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
Short summary
Short summary
By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
Short summary
Short summary
We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
Short summary
Short summary
Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
Short summary
Short summary
We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
Short summary
Short summary
We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
Short summary
Short summary
Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
Short summary
Short summary
This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
Short summary
Short summary
This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
Short summary
Short summary
The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
Short summary
Short summary
The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
Short summary
Short summary
Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Carl Svenhag, Moa Kristina Sporre, Tinja Olenius, Daniel Yazgi, Sara Marie Blichner, Lars Peter Nieradzik, and Pontus Roldin
EGUsphere, https://doi.org/10.5194/egusphere-2023-2665, https://doi.org/10.5194/egusphere-2023-2665, 2024
Short summary
Short summary
Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we showed that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacted the number of particles in the air and clouds. It also changed the radiation balance in the same magnitude as man-made greenhouse emissions.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
Short summary
Short summary
Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
Short summary
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
Short summary
Short summary
We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
Short summary
Short summary
The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
Short summary
Short summary
We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
Short summary
Short summary
For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
Short summary
Short summary
Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
Short summary
Short summary
This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
Short summary
Short summary
Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
Short summary
Short summary
Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
Short summary
Short summary
Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
Short summary
Short summary
Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Cited articles
Ahn, M.-S., Kim, D., Sperber, K. R., Kang, I.-S., Maloney, E., Waliser, D., and
Hendon, H.: MJO simulation in CMIP5 climate models: MJO skill metrics and
process-oriented diagnosis, Clim. Dynam., 49, 4023–4045, 2017. a
Barbi, D., Gierz, P., Andrés-Martínez, M., Ural, D., and Cristini, L.: esm_tools_release3_as_used_by_AWI-CM3_paper (3.1), Zenodo [code], https://doi.org/10.5281/zenodo.6335309, 2022. a
Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical
weather prediction, Nature, 525, 47–55, 2015. a
Blockley, E., Vancoppenolle, M., Hunke, E., Bitz, C., Feltham, D., Lemieux,
J.-F., Losch, M., Maisonnave, E., Notz, D., Rampal, P., Tietsche, S., Tremblay, B., Turner, A.,
Massonnet, F.,
Ólason, E.,
Roberts, A.,
Aksenov, Y.,
Fichefet, T.,
Garric, G.,
Iovino, D.,
Madec, G.,
Rousset, C.,
Salas y Melia, D.,
and Schroeder, D.: The future of
sea ice modeling: where do we go from here?,
B. Am. Meteorol. Soc., 101, E1304–E1311, 2020. a
Bogenschutz, P. A., Gettelman, A., Morrison, H., Larson, V. E., Schanen, D. P., Meyer, N. R., and Craig, C.: Unified parameterization of the planetary boundary layer and shallow convection with a higher-order turbulence closure in the Community Atmosphere Model: single-column experiments, Geosci. Model Dev., 5, 1407–1423, https://doi.org/10.5194/gmd-5-1407-2012, 2012. a
Box, G.: Robustness in the Strategy of Scientific Model Building, in:
Robustness in Statistics, edited by: Launer, R. L. and Wilkinson, G. N.,
201–236, Academic Press,
https://doi.org/10.1016/B978-0-12-438150-6.50018-2, 1979. a
Bretherton, C. S., Krueger, S. K., Wyant, M. C., Bechtold, P., Van Meijgaard,
E., Stevens, B., and Teixeira, J.: A GCSS boundary-layer cloud model
intercomparison study of the first ASTEX Lagrangian experiment,
Bound.-Lay. Meteorol., 93, 341–380, 1999. a
Brinkop, S. and Roeckner, E.: Sensitivity of a general circulation model to
parameterizations of cloud–turbulence interactions in the atmospheric
boundary layer, Tellus A, 47, 197–220, 1995. a
Calonne, N., Milliancourt, L., Burr, A., Philip, A., Martin, C. L., Flin, F.,
and Geindreau, C.: Thermal Conductivity of Snow, Firn, and Porous Ice From
3-D Image-Based Computations, Geophys. Res. Lett., 46,
13079–13089, 2019. a
Casati, B., Wilson, L., Stephenson, D., Nurmi, P., Ghelli, A., Pocernich, M.,
Damrath, U., Ebert, E., Brown, B., and Mason, S.: Forecast verification:
current status and future directions, Meteorological Applications: A journal
of forecasting, practical applications, training techniques and modelling,
15, 3–18, 2008. a
Ceppi, P., Brient, F., Zelinka, M. D., and Hartmann, D. L.: Cloud feedback
mechanisms and their representation in global climate models,
Wires Clim. Change, 8, e465, https://doi.org/10.1002/wcc.465, 2017. a
CESM community: CESM/CAM6 model code, GitHub, https://github.com/ESCOMP/CESM, last accessed: 28 March 2023. a
Coindreau, O., Hourdin, F., Haeffelin, M., Mathieu, A., and Rio, C.: Assessment
of physical parameterizations using a global climate model with stretchable
grid and nudging, Mon. Weather Rev., 135, 1474–1489, 2007. a
Cox, C., Gallagher, M., Shupe, M., Persson, O., Solomon, A., Blomquist, B.,
Brooks, I., Costa, D., Gottas, D., Hutchings, J., Osborn, J., Morris, S.,
Preusser, A., and Uttal, T.: 10-meter (m) meteorological flux tower
measurements (Level 1 Raw), Multidisciplinary Drifting Observatory for the
Study of Arctic Climate (MOSAiC), central Arctic, October 2019–September
2020, Arctic data center [data set], https://doi.org/10.18739/A2VM42Z5F, 2021. a, b
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D., DuVivier, A.,
Edwards, J., Emmons, L., Fasullo, J., Garcia, R., Gettelman, A., Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M., Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R., Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S., van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C., Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J., Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E., Polvani, L., Rasch, P. J., and Strand, W. G.: The
community earth system model version 2 (CESM2),
J. Adv. Model. Earth Sy., 12, e2019MS001916, https://doi.org/10.1029/2019MS001916, 2020. a, b
ECMWF: IFS Documentation CY43R3 – Part IV: Physical processes, no. 4 in IFS
Documentation, ECMWF, https://doi.org/10.21957/efyk72kl, 2017. a, b, c
Eyring, V., Harris, N., Rex, M., Shepherd, T. G., Fahey, D., Amanatidis, G.,
Austin, J., Chipperfield, M., Dameris, M., Forster, P. D. F., Gettelman, A., Graf, H. F., Nagashima, T., Newman, P. A., Pawson, S., Prather, M. J., Pyle, J. A., Salawitch, R. J., Santer, B. D., and Waugh, D. W.: A
strategy for process-oriented validation of coupled chemistry–climate
models, B. Am. Meteorol. Soc., 86, 1117–1134,
2005. a
Eyring, V.,
Cox, P. M.,
Flato, G. M.,
Gleckler, P. J.,
Abramowitz, G.,
Caldwell, P.,
Collins, W. D.,
Gier, B. K.,
Hall, A. D.,
Hoffman, F. M.,
Hurtt, G. C.,
Jahn, A.,
Jones, C. D.,
Klein, S. A.,
Krasting, J. P.,
Kwiatkowski, L.,
Lorenz, R.,
Maloney, E.,
Meehl, G. A.,
Pendergrass, A. G.,
Pincus, R.,
Ruane, A. C.,
Russell, J. L.,
Sanderson, B. M.,
Santer, B. D.,
Sherwood, S. C.,
Simpson, I. R.,
Stouffer, R. J., and
Williamson, M. S.: Taking climate model evaluation to the next level, Nat. Clim.
Change, 9, 102–110, 2019. a
Geerts, B., Giangrande, S. E., McFarquhar, G. M., Xue, L., Abel, S. J.,
Comstock, J. M., Crewell, S., DeMott, P. J., Ebell, K., Field, P., Hill, T. C. J.,
Hunzinger, A.,
Jensen, M. P.,
Johnson, K. L.,
Juliano, T. W.,
Kollias, P.,
Kosovic, B.,
Lackner, C.,
Luke, E.,
Lüpkes, C.,
Matthews, A. A.,
Neggers, R.,
Ovchinnikov, M.,
Powers, H.,
Shupe, M. D.
Spengler, T.,
Swanson, B. E.
Tjernström, M.,
Theisen, A. K.,
Wales, N. A.,
Wang, Y.,
Wendisch, M., and
Wu, P.:
The COMBLE Campaign: A Study of Marine Boundary Layer Clouds in Arctic
Cold-Air Outbreaks, B. Am. Meteorol. Soc., 103,
E1371–E1389, 2022. a
Gettelman, A., Bardeen, C., McCluskey, C. S., Järvinen, E., Stith, J.,
Bretherton, C., McFarquhar, G., Twohy, C., D'Alessandro, J., and Wu, W.:
Simulating observations of Southern Ocean clouds and implications for
climate, J. Geophys. Res.-Atmos., 125, e2020JD032619, https://doi.org/10.1029/2020JD032619, 2020. a
Guo, Z., Griffin, B. M., Domke, S., and Larson, V. E.: A parameterization of
turbulent dissipation and pressure damping time scales in stably stratified
inversions, and its effects on low clouds in global simulations,
J. Adv. Model. Earth Sy., 13, e2020MS002278, https://doi.org/10.1029/2020MS002278, 2021. a
Hartung, K., Svensson, G., Holt, J., Lewinschal, A., and Tjernström, M.:
Exploring the dynamics of an Arctic sea ice melt event using a coupled
Atmosphere-Ocean Single-Column Model (AOSCM), J. Adv. Model. Earth Sy., e2021MS002593, https://doi.org/10.1029/2021MS002593, 2022. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020. a
Holtslag, A., Svensson, G., Baas, P., Basu, S., Beare, B., Beljaars, A.,
Bosveld, F., Cuxart, J., Lindvall, J., Steeneveld, G., Holtslag, A. A. M.,
Svensson, G.,
Baas, P.,
Basu, S.,
Beare, B.,
Beljaars, A. C. M.,
Bosveld, F. C.,
Cuxart, J.,
Lindvall, J.,
Steeneveld, G. J.,
Tjernström, M., and
Van De Wiel, B. J. H.: Stable
atmospheric boundary layers and diurnal cycles: challenges for weather and
climate models, B. Am. Meteorol. Soc., 94,
1691–1706, 2013. a
Karlsson, J. and Svensson, G.: The simulation of Arctic clouds and their
influence on the winter surface temperature in present-day climate in the
CMIP3 multi-model dataset, Clim. Dynam., 36, 623–635, 2011. a
Katlein, C., Valcic, L., Lambert-Girard, S., and Hoppmann, M.: New insights into radiative transfer within sea ice derived from autonomous optical propagation measurements, The Cryosphere, 15, 183–198, https://doi.org/10.5194/tc-15-183-2021, 2021. a
Knust, R.: Polar Research and Supply Vessel POLARSTERN operated by the
Alfred-Wegener-Institute, Journal of large-scale research facilities JLSRF,
3, A119, https://doi.org/10.17815/jlsrf-3-163, 2017. a
Larson, V. E.: CLUBB-SILHS: A parameterization of subgrid variability in the
atmosphere, arXiv [preprint], https://doi.org/10.48550/arXiv.1711.03675, 2022. a
Lei, R., Cheng, B., Hoppmann, M., Zhang, F., Zuo, G., Hutchings, J. K., Lin,
L., Lan, M., Wang, H., Regnery, J., Krumpen, T., Haapala, J., Rabe, B.,
Perovich, D. K., and Nicolaus, M.: Seasonality and timing of sea ice mass
balance and heat fluxes in the Arctic transpolar drift during
2019–2020, Elementa: Science of the Anthropocene, 10, 000089,
https://doi.org/10.1525/elementa.2021.000089, 2022a. a
Lei, R., Cheng, B., Hoppmann, M., and Zuo, G.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2019T58, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940393, 2022b. a
Lei, R., Cheng, B., Hoppmann, M., Zuo, G., and Lan, M.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2019T62, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940231, 2022c. a
Lei, R., Cheng, B., Zuo, G., and Hoppmann, M.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2019T63, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940593, 2022d. a
Lei, R., Cheng, B., Hoppmann, M., and Zuo, G.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2019T64, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940617, 2022e. a
Lei, R., Cheng, B., Zuo, G., and Hoppmann, M.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2019T65, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940634, 2022f. a
Lei, R., Cheng, B., Zuo, G., Hoppmann, M., and Lan, M.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2019T70, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940659, 2022g. a
Lei, R., Cheng, B., Hoppmann, M., and Zuo, G.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2019T72, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940668, 2022h. a
Lei, R., Hutchings, J. K., Cheng, B., Hoppmann, M., and Yuan, Z.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2020T73, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940680, 2022i. a
Lei, R., Hutchings, J. K., Cheng, B., and Hoppmann, M.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2020T74, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940692, 2022j. a
Lei, R., Hutchings, J. K., Hoppmann, M., and Yuan, Z.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2020T77, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940749, 2022k. a
Lei, R., Hutchings, J. K., Hoppmann, M., and Yuan, Z.: Temperature and heating induced temperature difference measurements from SIMBA-type sea ice mass balance buoy 2020T76, deployed during MOSAiC 2019/20, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940702, 2022l. a
Lipscomb, W. H.: Modeling the thickness distribution of Arctic sea ice,
University of Washington, 1998. 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, 1996. a
Manabe, S. and Wetherald, R. T.: The effects of doubling the CO2 concentration
on the climate of a general circulation model, J. Atmos. Sci., 32, 3–15, 1975. a
Maturilli, M., Holdridge, D. J., Dahlke, S., Graeser, J., Sommerfeld, A.,
Jaiser, R., Deckelmann, H., and Schulz, A.: Initial radiosonde data from
2019-10 to 2020-09 during project MOSAiC, https://doi.org/10.1594/PANGAEA.928656, 2021. a, b
McFarquhar, G. M., Bretherton, C. S., Marchand, R., Protat, A., DeMott, P. J., Alexander, S. P., Roberts, G. C., Twohy, C. H., Toohey, D., Siems, S., Huang, Y., Wood, R., Rauber, R. M., Lasher-Trapp, S., Jensen, J., Stith, J. L., Mace, J., Um, J., Järvinen, E., Schnaiter, M., Gettelman, A., Sanchez, K. J., McCluskey, C. S., Russell, L. M., McCoy, I. L., Atlas, R. L., Bardeen, C. G., Moore, K. A., Hill, T. C. J., Humphries, R. S., Keywood, M. D., Ristovski, Z., Cravigan, L., Schofield, R., Fairall, C., Mallet, M. D., Kreidenweis, S. M., Rainwater, B., D’Alessandro, J., Wang, Y., Wu, W., Saliba, G., Levin, E. J. T., Ding, S., Lang, F., Truong, S. C. H., Wolff, C., Haggerty, J., Harvey, M. J., Klekociuk, A. R., and McDonald, A.: Observations of clouds, aerosols, precipitation, and surface
radiation over the southern ocean: An overview of CAPRICORN, MARCUS, MICRE,
and SOCRATES, B. Am. Meteorol. Soc., 102,
E894–E928, 2021. a
Medeiros, B., Deser, C., Tomas, R. A., and Kay, J. E.: Arctic inversion
strength in climate models, J. Climate, 24, 4733–4740, 2011. a
Meurdesoif, Y.: XIOS 2.0 (Revision 1297), Zenodo [code], https://doi.org/10.5281/zenodo.4905653, 2017. a
Meurdesoif, Y.: XML IO Server for Climate Models, http://forge.ipsl.jussieu.fr/ioserver, last access: 22 June 2021. a
Morris, V., Zhang, D., and Ermold, B.: Ceilometer (CEIL), ARM data center, [data], https://doi.org/10.5439/1181954, 2021. a, b, c
Nam, C., Bony, S., Dufresne, J.-L., and Chepfer, H.: The “too few, too
bright” tropical low-cloud problem in CMIP5 models, Geophys. Res. Lett., 39, L21801, https://doi.org/10.1029/2012GL053421, 2012. a
Nicolaus, M., Anhaus, P., Hoppmann, M., Tao, R., and Katlein, C.: Lightchain measurements from radiation station 2020R10. Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, PANGAEA [dataset in review], https://doi.pangaea.de/10.1594/PANGAEA.949126, 2022. a
Notz, D.: How well must climate models agree with observations?, Philos. T. R. Soc. A, 373, 20140164, https://doi.org/10.1098/rsta.2014.0164, 2015. a
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016. a
Paterson, W. and Bryce, S.: Physics of glaciers, Butterworth-Heinemann, ISBN 0750647426, 1994. a
Pithan, F. and Mauritsen, T.: Arctic amplification dominated by temperature
feedbacks in contemporary climate models, Nat. Geosci., 7, 181–184,
https://doi.org/10.1038/ngeo2071, 2014. a
Pithan, F., Angevine, W., and Mauritsen, T.: Improving a global model from the
boundary layer: Total turbulent energy and the neutral limit Prandtl number,
J. Adv. Model. Earth Sy., 7, 791–805, 2015. a
Pithan, F., Ackerman, A., Angevine, W. M., Hartung, K., Ickes, L., Kelley, M.,
Medeiros, B., Sandu, I., Steeneveld, G.-J., Sterk, H. A., Svensson, G., Vaillancourt, P. A., and Zadra, A.: Select
strengths and biases of models in representing the Arctic winter boundary
layer over sea ice: the Larcform 1 single column model intercomparison,
J. Adv. Model. Earth Sy., 8, 1345–1357, 2016. a
Pithan, F., Svensson, G., Caballero, R., Chechin, D., Cronin, T. W., Ekman,
A. M., Neggers, R., Shupe, M. D., Solomon, A., Tjernström, M., and Wendisch, M.:
Role of air-mass transformations in exchange between the Arctic and
mid-latitudes, Nat. Geosci., 11, 805–812, 2018. a
Rackow, T., Goessling, H. F., Jung, T., Sidorenko, D., Semmler, T., Barbi, D.,
and Handorf, D.: Towards multi-resolution global climate modeling with
ECHAM6-FESOM. Part II: climate variability, Clim. Dynam., 50, 2369–2394,
2018. a
Randall, D. A., Xu, K.-M., Somerville, R. J., and Iacobellis, S.: Single-column
models and cloud ensemble models as links between observations and climate
models, J. Climate, 9, 1683–1697, 1996. a
Riihimaki, L.: Radiation instruments on Ice (ICERADRIIHIMAKI),
https://doi.org/10.5439/1608608, 2019. a, b
Scholz, P., Sidorenko, D., Gurses, O., Danilov, S., Koldunov, N., Wang, Q., Sein, D., Smolentseva, M., Rakowsky, N., and Jung, T.: Assessment of the Finite-volumE Sea ice-Ocean Model (FESOM2.0) – Part 1: Description of selected key model elements and comparison to its predecessor version, Geosci. Model Dev., 12, 4875–4899, https://doi.org/10.5194/gmd-12-4875-2019, 2019. a
Scholz, P., Sidorenko, D., Danilov, S., Wang, Q., Koldunov, N., Sein, D., and Jung, T.: Assessment of the Finite-VolumE Sea ice–Ocean Model (FESOM2.0) – Part 2: Partial bottom cells, embedded sea ice and vertical mixing library CVMix, Geosci. Model Dev., 15, 335–363, https://doi.org/10.5194/gmd-15-335-2022, 2022a. a
Scholz, P., Sidorenko, D., Gurses, O., Danilov, S., Koldunov, N.,
Wang, Q., Sein, D., Smolentseva, M., Rakowsky, N., and Jung, T.: FESOM 2.0 AWI-CM3 version 3.0, Zenodo [code], AWI-CM3 version 3.0, https://doi.org/10.5281/zenodo.6335383, 2022b. a
Serreze, M. C., Kahl, J. D., and Schnell, R. C.: Low-level temperature
inversions of the Eurasian Arctic and comparisons with Soviet drifting
station data, J. Climate, 5, 615–629, 1992. a
Shepherd, T. G., Boyd, E., Calel, R. A., Chapman, S. C., Dessai, S., Dima-West,
I. M., Fowler, H. J., James, R., Maraun, D., Martius, O., Shepherd, T. G.,
Boyd, E.,
Calel, R. A.,
Chapman, S. C.,
Dessai, S.,
Dima-West, I. M.,
Fowler, H. J.,
James, R.,
Maraun, D.,
Martius, O.,
Senior, C. A.,
Sobel, A. H.,
Stainforth, D. A.,
Tett, S. F. B.,
Trenberth, K. E.,
van den Hurk, B. J. J. M.,
Watkins, N. W.,
Wilby, R. L., and Zenghelis, D. A.: Storylines:
an alternative approach to representing uncertainty in physical aspects of
climate change, Climatic Change, 151, 555–571, 2018. a
Shupe, M.: ShupeTurner cloud microphysics, ARM IOP archive, https://doi.org/10.5439/1871015, 2022. a, b
Shupe, M. D., Persson, P. O. G., Brooks, I. M., Tjernström, M., Sedlar, J., Mauritsen, T., Sjogren, S., and Leck, C.: Cloud and boundary layer interactions over the Arctic sea ice in late summer, Atmos. Chem. Phys., 13, 9379–9399, https://doi.org/10.5194/acp-13-9379-2013, 2013. a
Shupe, M., Rex, M., Dethloff, K., Damm, E., Fong, A., Gradinger, R., Heuzé,
C., Loose, B., Makarov, A., Maslowski, W., Nicolaus, M., Perovich, D., Rabe, B., Rinke, A., Sokolov, V., and Sommerfeld, A.: The MOSAiC expedition: A
year drifting with the Arctic sea ice, Arctic report card, https://doi.org/10.25923/9g3v-xh92, 2020. a
Shupe, M. D., Turner, D. D., Zwink, A., Thieman, M. M., Mlawer, E. J., and
Shippert, T.: Deriving Arctic cloud microphysics at Barrow, Alaska:
Algorithms, results, and radiative closure,
J. Appl. Meteorol. Clim., 54, 1675–1689, 2015. a
Shupe, M. D., Rex, M., Blomquist, B., Persson, P. O. G., Schmale, J., Uttal,
T., Althausen, D., Angot, H., Archer, S., Bariteau, L., Beck, I., Bilberry, J., Bucci, S., Buck, C., Boyer, M., Brasseur, Z., Brooks, I. M.,
Calmer, R., Cassano, J., Castro, V., Chu, D., Costa, D., Cox, C. J.,
Creamean, J., Crewell, S., Dahlke, S., Damm, E.,
de Boer, G., Deckelmann, H., Dethloff, K., Dütsch, M., Ebell, K., Ehrlich, A.,
Ellis, J., Engelmann, R.,
Fong, A. A., Frey, M. M., Gallagher, M. R., Ganzeveld, L., Gradinger, R., Graeser, J.,
Greenamyer, V., Griesche, H., Griffiths, S.,
Hamilton, J.,
Heinemann, G.,
Helmig, D.,
Herber, A., Heuzé, C.,
Hofer, J.,
Houchens, T.,
Howard, D.,
Inoue, J.,
Jacobi, H.-W.,
Jaiser, R.,
Jokinen, T.,
Jourdan, O.,
Jozef, G.,
King, W.,
Kirchgaessner, A.,
Klingebiel, M.,
Krassovski, M.,
Krumpen, T.,
Lampert, A.,
Landing, W.,
Laurila, T.,
Lawrence, D.,
Lonardi, M.,
Loose, B.,
Lüpkes, C.,
Maahn, M.,
Macke, A.,
Maslowski, W.,
Marsay, C.,
Maturilli, M.,
Mech, M.,
Morris, S.,
Moser, M.,
Nicolaus, M.,
Ortega, P.,
Osborn, J.,
Pätzold, F.,
K. Perovich, D.,
Petäjä, T.,
Pilz, C.,
Pirazzini, R.,
Posman, K.,
Powers, H.,
A. Pratt, K.,
Preußer, A.,
Quéléver, L.,
Radenz, M.,
Rabe, B.,
Rinke, A.,
Sachs, T.,
Schulz, A.,
Siebert, H.,
Silva, T.,
Solomon, A.,
Sommerfeld, A.,
Spreen, G.,
Stephens, M.,
Stohl, A.,
Svensson, G.,
Uin, J.,
Viegas, J.,
Voigt, C.,
von der Gathen, P.,
Wehner, B.,
Welker, J. M,
Wendisch, M.,
Werner, M., Xie, Z., and Yue, F.: Overview of
the MOSAiC expedition: Atmosphere, https://doi.org/10.1525/elementa.2021.00060, 2022. a
Sidorenko, D., Rackow, T., Jung, T., Semmler, T., Barbi, D., Danilov, S.,
Dethloff, K., Dorn, W., Fieg, K., Gößling, H. F., Sidorenko, D.,
Rackow, T.,
Jung, T.,
Semmler, T.,
Barbi, D.,
Danilov, S.,
Dethloff, K.,
Dorn, W.,
Fieg, K.,
Goessling, H. F.,
Handorf, D.,
Harig, S.,
Hiller, W.,
Juricke, S.,
Losch, M.,
Schröter, J.,
Sein, D. V.,
and Wang, Q.: Towards
multi-resolution global climate modeling with ECHAM6–FESOM. Part I: model
formulation and mean climate, Clim. Dynam., 44, 757–780, 2015. a
Stevens, B., Giorgetta, M., Esch, M., Mauritsen, T., Crueger, T., Rast, S.,
Salzmann, M., Schmidt, H., Bader, J., Block, K., Brokopf, R., Fast, I., Kinne, S., Kornblueh, L., Lohmann, U., Pincus, R., Reichler, T., and Roeckner, E.: Atmospheric
component of the MPI-M Earth system model: ECHAM6, J. Adv. Model. Earth Sy., 5, 146–172, 2013. a
Stramler, K., Del Genio, A. D., and Rossow, W. B.: Synoptically driven Arctic
winter states, J. Climate, 24, 1747–1762, 2011. a
Streffing, J. and Fladich, U.: Modifications to use OpenIFS CY43R3V1 for AWI-CM3 version 3.0, Zenodo [code], https://doi.org/10.5281/zenodo.6335498, 2022. a
Streffing, J., Sidorenko, D., Semmler, T., Zampieri, L., Scholz, P., Andrés-Martínez, M., Koldunov, N., Rackow, T., Kjellsson, J., Goessling, H., Athanase, M., Wang, Q., Hegewald, J., Sein, D., Mu, L., Fladrich, U., Barbi, D., Gierz, P., Danilov, S., Juricke, S., Lohmann, G., and Jung, T.: AWI-CM3 coupled climate model: Description and evaluation experiments for a prototype post-CMIP6 model, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2022-32, 2022. a
Sundqvist, H., Berge, E., and Kristjánsson, J. E.: Condensation and cloud
parameterization studies with a mesoscale numerical weather prediction model,
Mon. Weather Rev., 117, 1641–1657, 1989. a
Svensson, G., Murto, S., Shupe, M. D., Pithan, F., Magnusson, L., Day, J. J.,
Doyle, J. D., Renfrew, I. A., Spengler, T., and Vihma, T.: Warm air
intrusions reaching the MOSAiC expedition in April 2020 – the YOPP targeted
observing period (TOP), Elementa: Science of the Anthropocene,
submitted, 2022. a
Tjernström, M., Žagar, M., Svensson, G., Cassano, J. J., Pfeifer, S.,
Rinke, A., Wyser, K., Dethloff, K., Jones, C., Semmler, T., and Shaw, M.: Modelling
the Arctic boundary layer: an evaluation of six ARCMIP regional-scale models
using data from the SHEBA project, Bound.-Lay. Meteorol., 117, 337–381,
2005. a
Tjernström, M., Svensson, G., Magnusson, L., Brooks, I. M., Prytherch, J.,
Vüllers, J., and Young, G.: Central Arctic weather forecasting:
Confronting the ECMWF IFS with observations from the Arctic Ocean 2018
expedition, Q. J. Roy. Meteor. Soc., 147,
1278–1299, 2021. a
Uttal, T., Casati, B., Werner, K., Day, J. J., and Svensson, G.: The Year of
Polar Prediction Supersite Model Intercomparison Project (YOPPsiteMIP), in:
27 IUGG General Asssembly, 8 to 19 July 2019, Montréal, Québec, Canada, 2019. a
Valcke, S., Craig, T., Maisonnave, E., and Coquart, L.: OASIS3-MCT: The Oasis coupler between climate models, https://oasis.cerfacs.fr/en/downloads/, last access: 22 June 2021. a
van Garderen, L., Feser, F., and Shepherd, T. G.: A methodology for attributing the role of climate change in extreme events: a global spectrally nudged storyline, Nat. Hazards Earth Syst. Sci., 21, 171–186, https://doi.org/10.5194/nhess-21-171-2021, 2021. a
van Niekerk, A., Shepherd, T. G., Vosper, S. B., and Webster, S.: Sensitivity
of resolved and parametrized surface drag to changes in resolution and
parametrization, Q. J. Roy. Meteor. Soc., 142,
2300–2313, 2016. a
Wagner, D. N., Shupe, M. D., Cox, C., Persson, O. G., Uttal, T., Frey, M. M., Kirchgaessner, A., Schneebeli, M., Jaggi, M., Macfarlane, A. R., Itkin, P., Arndt, S., Hendricks, S., Krampe, D., Nicolaus, M., Ricker, R., Regnery, J., Kolabutin, N., Shimanshuck, E., Oggier, M., Raphael, I., Stroeve, J., and Lehning, M.: Snowfall and snow accumulation during the MOSAiC winter and spring seasons, The Cryosphere, 16, 2373–2402, https://doi.org/10.5194/tc-16-2373-2022, 2022. a
Wang, Q., Danilov, S., Sidorenko, D., Timmermann, R., Wekerle, C., Wang, X., Jung, T., and Schröter, J.: The Finite Element Sea Ice-Ocean Model (FESOM) v.1.4: formulation of an ocean general circulation model, Geosci. Model Dev., 7, 663–693, https://doi.org/10.5194/gmd-7-663-2014, 2014. a
Wehrli, K., Guillod, B. P., Hauser, M., Leclair, M., and Seneviratne, S. I.:
Assessing the dynamic versus thermodynamic origin of climate model biases,
Geophys. Res. Lett., 45, 8471–8479, 2018. a
Wehrli, K., Hauser, M., and Seneviratne, S. I.: Storylines of the 2018 Northern Hemisphere heatwave at pre-industrial and higher global warming levels, Earth Syst. Dynam., 11, 855–873, https://doi.org/10.5194/esd-11-855-2020, 2020.
a
Werner, K., Svensson, G., and Jung, T.: Start of Arctic YOPP Targeted Observing
Periods, YOPP Newsletter PolarPredictNews no. 14, International Coordination Office
for Polar Prediction, 2020. a
Williams, K. D., Bodas-Salcedo, A., Déqué, M., Fermepin, S., Medeiros,
B., Watanabe, M., Jakob, C., Klein, S. A., Senior, C. A., and Williamson,
D. L.: The Transpose-AMIP II experiment and its application to the
understanding of Southern Ocean cloud biases in climate models, J. Climate, 26, 3258–3274, 2013. a
Wyser, K.: EC-Earth community runoff-mapper scheme, Zenodo [code], https://doi.org/10.5281/zenodo.6335474, 2022. a
Woods, C., Caballero, R., and Svensson, G.: Large-scale circulation associated
with moisture intrusions into the Arctic during winter, Geophys. Res.
Lett., 40, 4717–4721, 2013. a
Zampieri, L., Kauker, F., Fröhle, J., Sumata, H., Hunke, E. C., and
Goessling, H. F.: Impact of Sea-Ice Model Complexity on the Performance of an
Unstructured-Mesh Sea-Ice/Ocean Model under Different Atmospheric Forcings,
J. Adv. Model. Earth Sy., 13, e2020MS002438, https://doi.org/10.1029/2020MS002438, 2021. a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M.,
Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of higher climate
sensitivity in CMIP6 models, Geophys. Res. Lett., 47, , e2019GL085782, https://doi.org/10.1029/2019GL085782, 2020. a
Zhang, Y., Xie, S., Klein, S. A., Marchand, R., Kollias, P., Clothiaux, E. E.,
Lin, W., Johnson, K., Swales, D., Bodas-Salcedo, A., Tang, S.,
Haynes, J. M.,
Collis, S.,
Jensen, M.,
Bharadwaj, N.,
Hardin, J., and Isom, B.: The ARM cloud
radar simulator for global climate models: Bridging field data and climate
models, B. Am. Meteorol. Soc., 99, 21–26, 2018. a
Short summary
Evaluating climate models usually requires long observational time series, but we present a method that also works for short field campaigns. We compare climate model output to observations from the MOSAiC expedition in the central Arctic Ocean. All models show how the arrival of a warm air mass warms the Arctic in April 2020, but two models do not show the response of snow temperature to the diurnal cycle. One model has too little liquid water and too much ice in clouds during cold days.
Evaluating climate models usually requires long observational time series, but we present a...
