Articles | Volume 7, issue 1
https://doi.org/10.5194/gmd-7-249-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-7-249-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
30 Jan 2014
Development and technical paper |
| 30 Jan 2014
Methodological aspects of a pattern-scaling approach to produce global fields of monthly means of daily maximum and minimum temperature
S. Kremser
Bodeker Scientific, Alexandra, New Zealand
G. E. Bodeker
Bodeker Scientific, Alexandra, New Zealand
Bodeker Scientific, Alexandra, New Zealand
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The MAPM project showcases a method to improve estimates of PM2.5 emissions through an advanced statistical technique that is still new to the aerosol community. Using Christchurch, NZ, as a test bed, measurements from a field campaign in winter 2019 are incorporated into this new approach. An overestimation from local inventory estimates is identified. This technique may be exported to other urban areas in need.
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Ozone in Earth's atmosphere has undergone significant changes since first measured systematically from space in the late 1970s. The purpose of the paper is to present a new, spatially filled, global total column ozone climate data record spanning from October 1978 to December 2016. The database is compiled from measurements from 17 different satellite-based instruments where offsets and drifts between the instruments have been corrected using ground-based measurements.
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Aerosol–cloud interactions over the Southern Ocean are poorly understood and remain a major source of uncertainty in climate models. This study presents ship-borne measurements, collected during a 6-week voyage into the Southern Ocean in 2018, that are an important supplement to satellite-based measurements. For example, these measurements include data on low-level clouds and aerosol composition in the marine boundary layer, which can be used in climate model evaluation efforts.
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021, https://doi.org/10.5194/essd-13-2053-2021, 2021
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MAPM is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from PM concentration measurements. In support of MAPM, we conducted a winter field campaign in New Zealand. In addition to two types of instruments measuring PM, an array of other meteorological sensors were deployed, measuring temperature and wind speed as well as probing the vertical structure of the lower atmosphere. In this article, we present the measurements taken during this campaign.
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This paper presents measures of the severity of the Antarctic ozone hole covering the period 1979 to 2019. The paper shows that while the severity of Antarctic ozone depletion grew rapidly through the last two decades of the 20th century, the severity declined thereafter and faster than expected from declines in stratospheric concentrations of the chlorine- and bromine-containing chemical compounds that destroy ozone.
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Earth Syst. Sci. Data, 12, 1419–1435, https://doi.org/10.5194/essd-12-1419-2020, https://doi.org/10.5194/essd-12-1419-2020, 2020
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Since space-based measurements of stratospheric composition started, a plethora of
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Sinikka T. Lennartz, Christa A. Marandino, Marc von Hobe, Meinrat O. Andreae, Kazushi Aranami, Elliot Atlas, Max Berkelhammer, Heinz Bingemer, Dennis Booge, Gregory Cutter, Pau Cortes, Stefanie Kremser, Cliff S. Law, Andrew Marriner, Rafel Simó, Birgit Quack, Günther Uher, Huixiang Xie, and Xiaobin Xu
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Sulfur-containing trace gases in the atmosphere influence atmospheric chemistry and the energy budget of the Earth by forming aerosols. The ocean is an important source of the most abundant sulfur gas in the atmosphere, carbonyl sulfide (OCS) and its most important precursor carbon disulfide (CS2). In order to assess global variability of the sea surface concentrations of both gases to calculate their oceanic emissions, we have compiled a database of existing shipborne measurements.
Laura E. Revell, Stefanie Kremser, Sean Hartery, Mike Harvey, Jane P. Mulcahy, Jonny Williams, Olaf Morgenstern, Adrian J. McDonald, Vidya Varma, Leroy Bird, and Alex Schuddeboom
Atmos. Chem. Phys., 19, 15447–15466, https://doi.org/10.5194/acp-19-15447-2019, https://doi.org/10.5194/acp-19-15447-2019, 2019
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Aerosols over the Southern Ocean consist primarily of sea salt and sulfate, yet are seasonally biased in our model. We test three sulfate chemistry schemes to investigate DMS oxidation, which forms sulfate aerosol. Simulated cloud droplet number concentrations improve using more complex sulfate chemistry. We also show that a new sea spray aerosol source function, developed from measurements made on a recent Southern Ocean research voyage, improves the model's simulation of aerosol optical depth.
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Are regional and seasonal averages from only a few satellite measurements, all aligned along a specific path, representative? Probably not. We present a method to adjust for the so-called
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Birgit Hassler, Stefanie Kremser, Greg E. Bodeker, Jared Lewis, Kage Nesbit, Sean M. Davis, Martyn P. Chipperfield, Sandip S. Dhomse, and Martin Dameris
Earth Syst. Sci. Data, 10, 1473–1490, https://doi.org/10.5194/essd-10-1473-2018, https://doi.org/10.5194/essd-10-1473-2018, 2018
Sandip S. Dhomse, Douglas Kinnison, Martyn P. Chipperfield, Ross J. Salawitch, Irene Cionni, Michaela I. Hegglin, N. Luke Abraham, Hideharu Akiyoshi, Alex T. Archibald, Ewa M. Bednarz, Slimane Bekki, Peter Braesicke, Neal Butchart, Martin Dameris, Makoto Deushi, Stacey Frith, Steven C. Hardiman, Birgit Hassler, Larry W. Horowitz, Rong-Ming Hu, Patrick Jöckel, Beatrice Josse, Oliver Kirner, Stefanie Kremser, Ulrike Langematz, Jared Lewis, Marion Marchand, Meiyun Lin, Eva Mancini, Virginie Marécal, Martine Michou, Olaf Morgenstern, Fiona M. O'Connor, Luke Oman, Giovanni Pitari, David A. Plummer, John A. Pyle, Laura E. Revell, Eugene Rozanov, Robyn Schofield, Andrea Stenke, Kane Stone, Kengo Sudo, Simone Tilmes, Daniele Visioni, Yousuke Yamashita, and Guang Zeng
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Jared Lewis, Greg E. Bodeker, Stefanie Kremser, and Andrew Tait
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Laura E. Revell, Andrea Stenke, Beiping Luo, Stefanie Kremser, Eugene Rozanov, Timofei Sukhodolov, and Thomas Peter
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Compiling stratospheric aerosol data sets after a major volcanic eruption is difficult as the stratosphere becomes too optically opaque for satellite instruments to measure accurately. We performed ensemble chemistry–climate model simulations with two stratospheric aerosol data sets compiled for two international modelling activities and compared the simulated volcanic aerosol-induced effects from the 1991 Mt Pinatubo eruption on tropical stratospheric temperature and ozone with observations.
Gerald E. Nedoluha, Brian J. Connor, Thomas Mooney, James W. Barrett, Alan Parrish, R. Michael Gomez, Ian Boyd, Douglas R. Allen, Michael Kotkamp, Stefanie Kremser, Terry Deshler, Paul Newman, and Michelle L. Santee
Atmos. Chem. Phys., 16, 10725–10734, https://doi.org/10.5194/acp-16-10725-2016, https://doi.org/10.5194/acp-16-10725-2016, 2016
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Chlorine monoxide (ClO) is central to the formation of the springtime Antarctic ozone hole since it is the catalytic agent in the most important ozone-depleting chemical cycle. We present 20 years of measurements of ClO from the Chlorine monOxide Experiment at Scott Base, Antarctica, and 12 years of measurements from the Aura Microwave Limb Sounder to show that the trends in ClO during the ozone hole season are consistent with changes in stratospheric chlorine observed elsewhere.
Yuting Wang, Nicholas M. Deutscher, Mathias Palm, Thorsten Warneke, Justus Notholt, Ian Baker, Joe Berry, Parvadha Suntharalingam, Nicholas Jones, Emmanuel Mahieu, Bernard Lejeune, James Hannigan, Stephanie Conway, Joseph Mendonca, Kimberly Strong, J. Elliott Campbell, Adam Wolf, and Stefanie Kremser
Atmos. Chem. Phys., 16, 2123–2138, https://doi.org/10.5194/acp-16-2123-2016, https://doi.org/10.5194/acp-16-2123-2016, 2016
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OCS could provide an additional constraint on the carbon cycle. The FTIR networks have existed for more than 20 years. For the first time, we used FTIR measurements of OCS and CO2 to study their relationship. We put the coupled CO2 and OCS land fluxes from the Simple Biosphere Model (SiB) into a transport model, and compared the simulations to the measurements. Looking at OCS and CO2 together inspires some new thoughts in how the biospheric models reproduce the carbon cycle in the real world.
G. E. Bodeker and S. Kremser
Atmos. Meas. Tech., 8, 1673–1684, https://doi.org/10.5194/amt-8-1673-2015, https://doi.org/10.5194/amt-8-1673-2015, 2015
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This paper discusses, and demonstrates, the methodology for reliably determining long-term trends in upper air climate data records and how measurement uncertainties should be used in trend analyses. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper describes the construction of linear least squares regression models for trend analysis and the boot-strapping approach to determine the uncertainty on the derived trends.
M. Rex, S. Kremser, P. Huck, G. Bodeker, I. Wohltmann, M. L. Santee, and P. Bernath
Atmos. Chem. Phys., 14, 6545–6555, https://doi.org/10.5194/acp-14-6545-2014, https://doi.org/10.5194/acp-14-6545-2014, 2014
P. E. Huck, G. E. Bodeker, S. Kremser, A. J. McDonald, M. Rex, and H. Struthers
Atmos. Chem. Phys., 13, 3237–3243, https://doi.org/10.5194/acp-13-3237-2013, https://doi.org/10.5194/acp-13-3237-2013, 2013
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatimah Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-176, https://doi.org/10.5194/gmd-2023-176, 2023
Preprint under review for GMD
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For the next generation of Earth System Model runs to project future climate change, the scientific community considers new scenarios to succeed RCPs and SSPs. As a contribution to that debate, we reflect on relevant policy and scientific research questions and suggest categories for Representative Emission Pathways (REP). These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Quyen Nguyen, Antoni Moore, Ivan Diaz-Rainey, Greg Bodeker, Simon C. Cox, Murray Cadzow, and Paul Thorsnes
Abstr. Int. Cartogr. Assoc., 6, 187, https://doi.org/10.5194/ica-abs-6-187-2023, https://doi.org/10.5194/ica-abs-6-187-2023, 2023
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
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Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Brian Nathan, Stefanie Kremser, Sara Mikaloff-Fletcher, Greg Bodeker, Leroy Bird, Ethan Dale, Dongqi Lin, Gustavo Olivares, and Elizabeth Somervell
Atmos. Chem. Phys., 21, 14089–14108, https://doi.org/10.5194/acp-21-14089-2021, https://doi.org/10.5194/acp-21-14089-2021, 2021
Short summary
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The MAPM project showcases a method to improve estimates of PM2.5 emissions through an advanced statistical technique that is still new to the aerosol community. Using Christchurch, NZ, as a test bed, measurements from a field campaign in winter 2019 are incorporated into this new approach. An overestimation from local inventory estimates is identified. This technique may be exported to other urban areas in need.
Greg E. Bodeker, Jan Nitzbon, Jordis S. Tradowsky, Stefanie Kremser, Alexander Schwertheim, and Jared Lewis
Earth Syst. Sci. Data, 13, 3885–3906, https://doi.org/10.5194/essd-13-3885-2021, https://doi.org/10.5194/essd-13-3885-2021, 2021
Short summary
Short summary
Ozone in Earth's atmosphere has undergone significant changes since first measured systematically from space in the late 1970s. The purpose of the paper is to present a new, spatially filled, global total column ozone climate data record spanning from October 1978 to December 2016. The database is compiled from measurements from 17 different satellite-based instruments where offsets and drifts between the instruments have been corrected using ground-based measurements.
Stefanie Kremser, Mike Harvey, Peter Kuma, Sean Hartery, Alexia Saint-Macary, John McGregor, Alex Schuddeboom, Marc von Hobe, Sinikka T. Lennartz, Alex Geddes, Richard Querel, Adrian McDonald, Maija Peltola, Karine Sellegri, Israel Silber, Cliff S. Law, Connor J. Flynn, Andrew Marriner, Thomas C. J. Hill, Paul J. DeMott, Carson C. Hume, Graeme Plank, Geoffrey Graham, and Simon Parsons
Earth Syst. Sci. Data, 13, 3115–3153, https://doi.org/10.5194/essd-13-3115-2021, https://doi.org/10.5194/essd-13-3115-2021, 2021
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Aerosol–cloud interactions over the Southern Ocean are poorly understood and remain a major source of uncertainty in climate models. This study presents ship-borne measurements, collected during a 6-week voyage into the Southern Ocean in 2018, that are an important supplement to satellite-based measurements. For example, these measurements include data on low-level clouds and aerosol composition in the marine boundary layer, which can be used in climate model evaluation efforts.
Ethan R. Dale, Stefanie Kremser, Jordis S. Tradowsky, Greg E. Bodeker, Leroy J. Bird, Gustavo Olivares, Guy Coulson, Elizabeth Somervell, Woodrow Pattinson, Jonathan Barte, Jan-Niklas Schmidt, Nariefa Abrahim, Adrian J. McDonald, and Peter Kuma
Earth Syst. Sci. Data, 13, 2053–2075, https://doi.org/10.5194/essd-13-2053-2021, https://doi.org/10.5194/essd-13-2053-2021, 2021
Short summary
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MAPM is a project whose goal is to develop a method to infer particulate matter (PM) emissions maps from PM concentration measurements. In support of MAPM, we conducted a winter field campaign in New Zealand. In addition to two types of instruments measuring PM, an array of other meteorological sensors were deployed, measuring temperature and wind speed as well as probing the vertical structure of the lower atmosphere. In this article, we present the measurements taken during this campaign.
Greg E. Bodeker and Stefanie Kremser
Atmos. Chem. Phys., 21, 5289–5300, https://doi.org/10.5194/acp-21-5289-2021, https://doi.org/10.5194/acp-21-5289-2021, 2021
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This paper presents measures of the severity of the Antarctic ozone hole covering the period 1979 to 2019. The paper shows that while the severity of Antarctic ozone depletion grew rapidly through the last two decades of the 20th century, the severity declined thereafter and faster than expected from declines in stratospheric concentrations of the chlorine- and bromine-containing chemical compounds that destroy ozone.
James Keeble, Birgit Hassler, Antara Banerjee, Ramiro Checa-Garcia, Gabriel Chiodo, Sean Davis, Veronika Eyring, Paul T. Griffiths, Olaf Morgenstern, Peer Nowack, Guang Zeng, Jiankai Zhang, Greg Bodeker, Susannah Burrows, Philip Cameron-Smith, David Cugnet, Christopher Danek, Makoto Deushi, Larry W. Horowitz, Anne Kubin, Lijuan Li, Gerrit Lohmann, Martine Michou, Michael J. Mills, Pierre Nabat, Dirk Olivié, Sungsu Park, Øyvind Seland, Jens Stoll, Karl-Hermann Wieners, and Tongwen Wu
Atmos. Chem. Phys., 21, 5015–5061, https://doi.org/10.5194/acp-21-5015-2021, https://doi.org/10.5194/acp-21-5015-2021, 2021
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Stratospheric ozone and water vapour are key components of the Earth system; changes to both have important impacts on global and regional climate. We evaluate changes to these species from 1850 to 2100 in the new generation of CMIP6 models. There is good agreement between the multi-model mean and observations, although there is substantial variation between the individual models. The future evolution of both ozone and water vapour is strongly dependent on the assumed future emissions scenario.
Ruud J. Dirksen, Greg E. Bodeker, Peter W. Thorne, Andrea Merlone, Tony Reale, Junhong Wang, Dale F. Hurst, Belay B. Demoz, Tom D. Gardiner, Bruce Ingleby, Michael Sommer, Christoph von Rohden, and Thierry Leblanc
Geosci. Instrum. Method. Data Syst., 9, 337–355, https://doi.org/10.5194/gi-9-337-2020, https://doi.org/10.5194/gi-9-337-2020, 2020
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This paper describes GRUAN's strategy for a network-wide change of the operational radiosonde from Vaisala RS92 to RS41. GRUAN's main goal is to provide long-term data records that are free of inhomogeneities due to instrumental effects, which requires proper change management. The approach is to fully characterize differences between the two radiosonde types using laboratory tests, twin soundings, and ancillary data, as well as by drawing from the various fields of expertise available in GRUAN.
Stefanie Kremser, Larry W. Thomason, and Leroy J. Bird
Earth Syst. Sci. Data, 12, 1419–1435, https://doi.org/10.5194/essd-12-1419-2020, https://doi.org/10.5194/essd-12-1419-2020, 2020
Short summary
Short summary
Since space-based measurements of stratospheric composition started, a plethora of
generally acceptedscreening methods have been developed and tailored to each measurement system and to each anticipated use of the data. These methods are often inconsistent, ad hoc, and untraceable and are seldom revised even after significant revisions to the data themselves. Here we developed new and simplified SAGE II ozone data usage rules that are based on how the measurements were made.
Sinikka T. Lennartz, Christa A. Marandino, Marc von Hobe, Meinrat O. Andreae, Kazushi Aranami, Elliot Atlas, Max Berkelhammer, Heinz Bingemer, Dennis Booge, Gregory Cutter, Pau Cortes, Stefanie Kremser, Cliff S. Law, Andrew Marriner, Rafel Simó, Birgit Quack, Günther Uher, Huixiang Xie, and Xiaobin Xu
Earth Syst. Sci. Data, 12, 591–609, https://doi.org/10.5194/essd-12-591-2020, https://doi.org/10.5194/essd-12-591-2020, 2020
Short summary
Short summary
Sulfur-containing trace gases in the atmosphere influence atmospheric chemistry and the energy budget of the Earth by forming aerosols. The ocean is an important source of the most abundant sulfur gas in the atmosphere, carbonyl sulfide (OCS) and its most important precursor carbon disulfide (CS2). In order to assess global variability of the sea surface concentrations of both gases to calculate their oceanic emissions, we have compiled a database of existing shipborne measurements.
Laura E. Revell, Stefanie Kremser, Sean Hartery, Mike Harvey, Jane P. Mulcahy, Jonny Williams, Olaf Morgenstern, Adrian J. McDonald, Vidya Varma, Leroy Bird, and Alex Schuddeboom
Atmos. Chem. Phys., 19, 15447–15466, https://doi.org/10.5194/acp-19-15447-2019, https://doi.org/10.5194/acp-19-15447-2019, 2019
Short summary
Short summary
Aerosols over the Southern Ocean consist primarily of sea salt and sulfate, yet are seasonally biased in our model. We test three sulfate chemistry schemes to investigate DMS oxidation, which forms sulfate aerosol. Simulated cloud droplet number concentrations improve using more complex sulfate chemistry. We also show that a new sea spray aerosol source function, developed from measurements made on a recent Southern Ocean research voyage, improves the model's simulation of aerosol optical depth.
Corinna Kloss, Marc von Hobe, Michael Höpfner, Kaley A. Walker, Martin Riese, Jörn Ungermann, Birgit Hassler, Stefanie Kremser, and Greg E. Bodeker
Atmos. Meas. Tech., 12, 2129–2138, https://doi.org/10.5194/amt-12-2129-2019, https://doi.org/10.5194/amt-12-2129-2019, 2019
Short summary
Short summary
Are regional and seasonal averages from only a few satellite measurements, all aligned along a specific path, representative? Probably not. We present a method to adjust for the so-called
sampling biasand investigate its influence on derived long-term trends. The method is illustrated and validated for a long-lived trace gas (carbonyl sulfide), and it is shown that the influence of the sampling bias is too small to change scientific conclusions on long-term trends.
Jordis S. Tradowsky, Gregory E. Bodeker, Richard R. Querel, Peter J. H. Builtjes, and Jürgen Fischer
Earth Syst. Sci. Data, 10, 2195–2211, https://doi.org/10.5194/essd-10-2195-2018, https://doi.org/10.5194/essd-10-2195-2018, 2018
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A best-estimate data set of the temperature profile above the atmospheric measurement facility at Lauder, New Zealand, has been developed. This site atmospheric state best estimate (SASBE) combines atmospheric measurements made at two locations and includes an estimate of uncertainty on every data point. The SASBE enhances the value of measurements made by a reference-quality climate observing network and may be used for a variety of purposes in research and education.
Birgit Hassler, Stefanie Kremser, Greg E. Bodeker, Jared Lewis, Kage Nesbit, Sean M. Davis, Martyn P. Chipperfield, Sandip S. Dhomse, and Martin Dameris
Earth Syst. Sci. Data, 10, 1473–1490, https://doi.org/10.5194/essd-10-1473-2018, https://doi.org/10.5194/essd-10-1473-2018, 2018
Sandip S. Dhomse, Douglas Kinnison, Martyn P. Chipperfield, Ross J. Salawitch, Irene Cionni, Michaela I. Hegglin, N. Luke Abraham, Hideharu Akiyoshi, Alex T. Archibald, Ewa M. Bednarz, Slimane Bekki, Peter Braesicke, Neal Butchart, Martin Dameris, Makoto Deushi, Stacey Frith, Steven C. Hardiman, Birgit Hassler, Larry W. Horowitz, Rong-Ming Hu, Patrick Jöckel, Beatrice Josse, Oliver Kirner, Stefanie Kremser, Ulrike Langematz, Jared Lewis, Marion Marchand, Meiyun Lin, Eva Mancini, Virginie Marécal, Martine Michou, Olaf Morgenstern, Fiona M. O'Connor, Luke Oman, Giovanni Pitari, David A. Plummer, John A. Pyle, Laura E. Revell, Eugene Rozanov, Robyn Schofield, Andrea Stenke, Kane Stone, Kengo Sudo, Simone Tilmes, Daniele Visioni, Yousuke Yamashita, and Guang Zeng
Atmos. Chem. Phys., 18, 8409–8438, https://doi.org/10.5194/acp-18-8409-2018, https://doi.org/10.5194/acp-18-8409-2018, 2018
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We analyse simulations from the Chemistry-Climate Model Initiative (CCMI) to estimate the return dates of the stratospheric ozone layer from depletion by anthropogenic chlorine and bromine. The simulations from 20 models project that global column ozone will return to 1980 values in 2047 (uncertainty range 2042–2052). Return dates in other regions vary depending on factors related to climate change and importance of chlorine and bromine. Column ozone in the tropics may continue to decline.
Jonathan Conway, Greg Bodeker, and Chris Cameron
Atmos. Chem. Phys., 18, 8065–8077, https://doi.org/10.5194/acp-18-8065-2018, https://doi.org/10.5194/acp-18-8065-2018, 2018
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Strong westerly winds occur in the stratosphere during winter and spring. These winds, the polar vortex, limit how much air is mixed between mid- and high-latitudes. We present a new view of the polar vortex mixing barrier in the Southern Hemisphere, revealing a frequent double-walled barrier with two distinct regions of weak mixing. This double-walled structure is expected to alter the spatial and temporal variation of trace gas concentrations (e.g. ozone) across the polar vortex.
Stefanie Kremser, Jordis S. Tradowsky, Henning W. Rust, and Greg E. Bodeker
Atmos. Meas. Tech., 11, 3021–3029, https://doi.org/10.5194/amt-11-3021-2018, https://doi.org/10.5194/amt-11-3021-2018, 2018
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We investigate the feasibility of quantifying the difference in biases of two instrument types (i.e. radiosondes) by flying the old and new instruments on alternating days, so-called interlacing, to statistically derive the systematic biases between the instruments. While it is in principle possible to estimate the difference between two instrument biases from interlaced measurements, the number of required interlaced flights is very large for reasonable autocorrelation coefficient values.
Elizabeth C. Weatherhead, Jerald Harder, Eduardo A. Araujo-Pradere, Greg Bodeker, Jason M. English, Lawrence E. Flynn, Stacey M. Frith, Jeffrey K. Lazo, Peter Pilewskie, Mark Weber, and Thomas N. Woods
Atmos. Chem. Phys., 17, 15069–15093, https://doi.org/10.5194/acp-17-15069-2017, https://doi.org/10.5194/acp-17-15069-2017, 2017
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Satellite overlap is often carried out as a check on the stability of the data collected. We looked at how length of overlap influences how much information can be derived from the overlap period. Several results surprised us: the confidence we could have in the matchup of two records was independent of the offset, and understanding of the relative drift between the two satellite data sets improved significantly with 2–3 years of overlap. Sudden jumps could easily be confused with drift.
Jared Lewis, Greg E. Bodeker, Stefanie Kremser, and Andrew Tait
Geosci. Model Dev., 10, 4563–4575, https://doi.org/10.5194/gmd-10-4563-2017, https://doi.org/10.5194/gmd-10-4563-2017, 2017
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The Ensemble Projections Incorporating Climate model uncertainty (EPIC) method uses climate pattern scaling to expand a small number of daily maximum and minimum surface temperature projections into an ensemble that captures the structural uncertainty between climate models. The method is useful for providing projections of changes in climate to users wishing to investigate the impacts of climate change in a probabilistic and computationally efficient way.
Laura E. Revell, Andrea Stenke, Beiping Luo, Stefanie Kremser, Eugene Rozanov, Timofei Sukhodolov, and Thomas Peter
Atmos. Chem. Phys., 17, 13139–13150, https://doi.org/10.5194/acp-17-13139-2017, https://doi.org/10.5194/acp-17-13139-2017, 2017
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Compiling stratospheric aerosol data sets after a major volcanic eruption is difficult as the stratosphere becomes too optically opaque for satellite instruments to measure accurately. We performed ensemble chemistry–climate model simulations with two stratospheric aerosol data sets compiled for two international modelling activities and compared the simulated volcanic aerosol-induced effects from the 1991 Mt Pinatubo eruption on tropical stratospheric temperature and ozone with observations.
Leon S. Friedrich, Adrian J. McDonald, Gregory E. Bodeker, Kathy E. Cooper, Jared Lewis, and Alexander J. Paterson
Atmos. Chem. Phys., 17, 855–866, https://doi.org/10.5194/acp-17-855-2017, https://doi.org/10.5194/acp-17-855-2017, 2017
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Information from long-duration balloons flying in the Southern Hemisphere stratosphere during 2014 as part of X Project Loon are used to assess the quality of a number of different reanalyses. This work assesses the potential of the X Project Loon observations to validate outputs from the reanalysis models. In particular, we examined how the model winds compared with those derived from the balloon GPS information. We also examined simulated trajectories compared with the true trajectories.
Ulrike Langematz, Franziska Schmidt, Markus Kunze, Gregory E. Bodeker, and Peter Braesicke
Atmos. Chem. Phys., 16, 15619–15627, https://doi.org/10.5194/acp-16-15619-2016, https://doi.org/10.5194/acp-16-15619-2016, 2016
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The extent of anthropogenically driven Antarctic ozone depletion prior to 1980 is examined using transient chemistry–climate model simulations from 1960 to 2000 with prescribed changes of ozone depleting substances in conjunction with observations. All models show a long-term, halogen-induced negative trend in Antarctic ozone from 1960 to 1980, ranging between 26 and 50 % of the total anthropogenic ozone depletion from 1960 to 2000. A stronger ozone decline of 56 % was estimated from observation.
Gerald E. Nedoluha, Brian J. Connor, Thomas Mooney, James W. Barrett, Alan Parrish, R. Michael Gomez, Ian Boyd, Douglas R. Allen, Michael Kotkamp, Stefanie Kremser, Terry Deshler, Paul Newman, and Michelle L. Santee
Atmos. Chem. Phys., 16, 10725–10734, https://doi.org/10.5194/acp-16-10725-2016, https://doi.org/10.5194/acp-16-10725-2016, 2016
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Chlorine monoxide (ClO) is central to the formation of the springtime Antarctic ozone hole since it is the catalytic agent in the most important ozone-depleting chemical cycle. We present 20 years of measurements of ClO from the Chlorine monOxide Experiment at Scott Base, Antarctica, and 12 years of measurements from the Aura Microwave Limb Sounder to show that the trends in ClO during the ozone hole season are consistent with changes in stratospheric chlorine observed elsewhere.
Yuting Wang, Nicholas M. Deutscher, Mathias Palm, Thorsten Warneke, Justus Notholt, Ian Baker, Joe Berry, Parvadha Suntharalingam, Nicholas Jones, Emmanuel Mahieu, Bernard Lejeune, James Hannigan, Stephanie Conway, Joseph Mendonca, Kimberly Strong, J. Elliott Campbell, Adam Wolf, and Stefanie Kremser
Atmos. Chem. Phys., 16, 2123–2138, https://doi.org/10.5194/acp-16-2123-2016, https://doi.org/10.5194/acp-16-2123-2016, 2016
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OCS could provide an additional constraint on the carbon cycle. The FTIR networks have existed for more than 20 years. For the first time, we used FTIR measurements of OCS and CO2 to study their relationship. We put the coupled CO2 and OCS land fluxes from the Simple Biosphere Model (SiB) into a transport model, and compared the simulations to the measurements. Looking at OCS and CO2 together inspires some new thoughts in how the biospheric models reproduce the carbon cycle in the real world.
N. R. P. Harris, B. Hassler, F. Tummon, G. E. Bodeker, D. Hubert, I. Petropavlovskikh, W. Steinbrecht, J. Anderson, P. K. Bhartia, C. D. Boone, A. Bourassa, S. M. Davis, D. Degenstein, A. Delcloo, S. M. Frith, L. Froidevaux, S. Godin-Beekmann, N. Jones, M. J. Kurylo, E. Kyrölä, M. Laine, S. T. Leblanc, J.-C. Lambert, B. Liley, E. Mahieu, A. Maycock, M. de Mazière, A. Parrish, R. Querel, K. H. Rosenlof, C. Roth, C. Sioris, J. Staehelin, R. S. Stolarski, R. Stübi, J. Tamminen, C. Vigouroux, K. A. Walker, H. J. Wang, J. Wild, and J. M. Zawodny
Atmos. Chem. Phys., 15, 9965–9982, https://doi.org/10.5194/acp-15-9965-2015, https://doi.org/10.5194/acp-15-9965-2015, 2015
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Trends in the vertical distribution of ozone are reported for new and recently revised data sets. The amount of ozone-depleting compounds in the stratosphere peaked in the second half of the 1990s. We examine the trends before and after that peak to see if any change in trend is discernible. The previously reported decreases are confirmed. Furthermore, the downward trend in upper stratospheric ozone has not continued. The possible significance of any increase is discussed in detail.
K. Kreher, G. E. Bodeker, and M. Sigmond
Atmos. Chem. Phys., 15, 7653–7665, https://doi.org/10.5194/acp-15-7653-2015, https://doi.org/10.5194/acp-15-7653-2015, 2015
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This manuscript aims to answer the following question: which of the existing sites engaged in upper-air temperature measurements are best located to detect expected future trends within the shortest time possible? To do so, we explore one objective method for selecting the optimal locations for detecting projected 21st century trends and then demonstrate a similar technique for objectively selecting optimal locations for detecting expected future trends in total column ozone.
G. E. Bodeker and S. Kremser
Atmos. Meas. Tech., 8, 1673–1684, https://doi.org/10.5194/amt-8-1673-2015, https://doi.org/10.5194/amt-8-1673-2015, 2015
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This paper discusses, and demonstrates, the methodology for reliably determining long-term trends in upper air climate data records and how measurement uncertainties should be used in trend analyses. A pedagogical approach is taken whereby numerical recipes for key parts of the trend analysis process are explored. The paper describes the construction of linear least squares regression models for trend analysis and the boot-strapping approach to determine the uncertainty on the derived trends.
F. Tummon, B. Hassler, N. R. P. Harris, J. Staehelin, W. Steinbrecht, J. Anderson, G. E. Bodeker, A. Bourassa, S. M. Davis, D. Degenstein, S. M. Frith, L. Froidevaux, E. Kyrölä, M. Laine, C. Long, A. A. Penckwitt, C. E. Sioris, K. H. Rosenlof, C. Roth, H.-J. Wang, and J. Wild
Atmos. Chem. Phys., 15, 3021–3043, https://doi.org/10.5194/acp-15-3021-2015, https://doi.org/10.5194/acp-15-3021-2015, 2015
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Understanding ozone trends in the vertical is vital in terms of assessing the success of the Montreal Protocol. This paper compares and analyses the long-term trends in stratospheric ozone from seven new merged satellite data sets. The data sets largely agree well with each other, particularly for the negative trends seen in the early period 1984-1997. For the 1998-2011 period there is less agreement, but a clear shift from negative to mostly positive trends.
A. A. Penckwitt, G. E. Bodeker, P. Stoll, J. Lewis, T. von Clarmann, and A. Jones
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amtd-8-235-2015, https://doi.org/10.5194/amtd-8-235-2015, 2015
Preprint withdrawn
A. Parrish, I. S. Boyd, G. E. Nedoluha, P. K. Bhartia, S. M. Frith, N. A. Kramarova, B. J. Connor, G. E. Bodeker, L. Froidevaux, M. Shiotani, and T. Sakazaki
Atmos. Chem. Phys., 14, 7255–7272, https://doi.org/10.5194/acp-14-7255-2014, https://doi.org/10.5194/acp-14-7255-2014, 2014
M. Rex, S. Kremser, P. Huck, G. Bodeker, I. Wohltmann, M. L. Santee, and P. Bernath
Atmos. Chem. Phys., 14, 6545–6555, https://doi.org/10.5194/acp-14-6545-2014, https://doi.org/10.5194/acp-14-6545-2014, 2014
V. F. Sofieva, J. Tamminen, E. Kyrölä, T. Mielonen, P. Veefkind, B. Hassler, and G.E. Bodeker
Atmos. Chem. Phys., 14, 283–299, https://doi.org/10.5194/acp-14-283-2014, https://doi.org/10.5194/acp-14-283-2014, 2014
S. Brönnimann, J. Bhend, J. Franke, S. Flückiger, A. M. Fischer, R. Bleisch, G. Bodeker, B. Hassler, E. Rozanov, and M. Schraner
Atmos. Chem. Phys., 13, 9623–9639, https://doi.org/10.5194/acp-13-9623-2013, https://doi.org/10.5194/acp-13-9623-2013, 2013
H. Garny, G. E. Bodeker, D. Smale, M. Dameris, and V. Grewe
Atmos. Chem. Phys., 13, 7279–7300, https://doi.org/10.5194/acp-13-7279-2013, https://doi.org/10.5194/acp-13-7279-2013, 2013
B. Hassler, P. J. Young, R. W. Portmann, G. E. Bodeker, J. S. Daniel, K. H. Rosenlof, and S. Solomon
Atmos. Chem. Phys., 13, 5533–5550, https://doi.org/10.5194/acp-13-5533-2013, https://doi.org/10.5194/acp-13-5533-2013, 2013
P. E. Huck, G. E. Bodeker, S. Kremser, A. J. McDonald, M. Rex, and H. Struthers
Atmos. Chem. Phys., 13, 3237–3243, https://doi.org/10.5194/acp-13-3237-2013, https://doi.org/10.5194/acp-13-3237-2013, 2013
G. E. Bodeker, B. Hassler, P. J. Young, and R. W. Portmann
Earth Syst. Sci. Data, 5, 31–43, https://doi.org/10.5194/essd-5-31-2013, https://doi.org/10.5194/essd-5-31-2013, 2013
Related subject area
Climate and Earth system modeling
Assimilation of the AMSU-A radiances using the CESM (v2.1.0) and the DART (v9.11.13)–RTTOV (v12.3)
Modernizing the open-source community Noah with multi-parameterization options (Noah-MP) land surface model (version 5.0) with enhanced modularity, interoperability, and applicability
Simulated stable water isotopes during the mid-Holocene and pre-industrial periods using AWI-ESM-2.1-wiso
Rainbows and climate change: a tutorial on climate model diagnostics and parameterization
ModE-Sim – a medium-sized atmospheric general circulation model (AGCM) ensemble to study climate variability during the modern era (1420 to 2009)
MESMAR v1: a new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region
Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications
IceTFT v1.0.0: interpretable long-term prediction of Arctic sea ice extent with deep learning
The KNMI Large Ensemble Time Slice (KNMI–LENTIS)
ENSO statistics, teleconnections, and atmosphere–ocean coupling in the Taiwan Earth System Model version 1
Using probabilistic machine learning to better model temporal patterns in parameterizations: a case study with the Lorenz 96 model
The Regional Aerosol Model Intercomparison Project (RAMIP)
DSCIM-Coastal v1.1: an open-source modeling platform for global impacts of sea level rise
TIMBER v0.1: a conceptual framework for emulating temperature responses to tree cover change
Recalibration of a three-dimensional water quality model with a newly developed autocalibration toolkit (EFDC-ACT v1.0.0): how much improvement will be achieved with a wider hydrological variability?
Description and evaluation of the JULES-ES set-up for ISIMIP2b
Simplified Kalman smoother and ensemble Kalman smoother for improving reanalyses
Modelling the terrestrial nitrogen and phosphorus cycle in the UVic ESCM
Modeling river water temperature with limiting forcing data: Air2stream v1.0.0, machine learning and multiple regression
A machine learning approach targeting parameter estimation for plant functional type coexistence modeling using ELM-FATES (v2.0)
The fully coupled regionally refined model of E3SM version 2: overview of the atmosphere, land, and river results
The mixed-layer depth in the Ocean Model Intercomparison Project (OMIP): impact of resolving mesoscale eddies
A new simplified parameterization of secondary organic aerosol in the Community Earth System Model Version 2 (CESM2; CAM6.3)
Deep learning for stochastic precipitation generation – deep SPG v1.0
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Robust 4D climate-optimal flight planning in structured airspace using parallelized simulation on GPUs: ROOST V1.0
The Earth system model CLIMBER-X v1.0 – Part 2: The global carbon cycle
SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States
LandInG 1.0: a toolbox to derive input datasets for terrestrial ecosystem modelling at variable resolutions from heterogeneous sources
Conservation of heat and mass in P-SKRIPS version 1: the coupled atmosphere–ice–ocean model of the Ross Sea
Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53
Implementation of a machine-learned gas optics parameterization in the ECMWF Integrated Forecasting System: RRTMGP-NN 2.0
Differentiable programming for Earth system modeling
Evaluation of CMIP6 model performances in simulating fire weather spatiotemporal variability on global and regional scales
Data-driven aeolian dust emission scheme for climate modelling evaluated with EMAC 2.55.2
Testing the reconstruction of modelled particulate organic carbon from surface ecosystem components using PlankTOM12 and machine learning
An improved method of the Globally Resolved Energy Balance model by the Bayesian networks
Assessing predicted cirrus ice properties between two deterministic ice formation parameterizations
Various ways of using empirical orthogonal functions for climate model evaluation
C-Coupler3.0: an integrated coupler infrastructure for Earth system modelling
FEOTS v0.0.0: a new offline code for the fast equilibration of tracers in the ocean
Pace v0.2: a Python-based performance-portable atmospheric model
Introducing a new floodplain scheme in ORCHIDEE (version 7885): validation and evaluation over the Pantanal wetlands
Hydrological modelling on atmospheric grids: using graphs of sub-grid elements to transport energy and water
The sea level simulator v1.0: a model for integration of mean sea level change and sea level extremes into a joint probabilistic framework
The analysis of large-volume multi-institute climate model output at a Central Analysis Facility (PRIMAVERA Data Management Tool V2.10)
Structural k-means (S k-means) and clustering uncertainty evaluation framework (CUEF) for mining climate data
The emergence of the Gulf Stream and interior western boundary as key regions to constrain the future North Atlantic carbon uptake
Evaluating wind profiles in a numerical weather prediction model with Doppler lidar
Evaluation of bias correction methods for a multivariate drought index: case study of the Upper Jhelum Basin
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
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This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
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Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
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We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
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A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
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ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
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Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
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Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
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To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
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The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
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This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
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How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
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Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
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This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
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Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
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A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
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This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
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Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
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In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
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Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
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A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
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Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
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This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
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In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
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We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
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We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373, https://doi.org/10.5194/gmd-16-3355-2023, https://doi.org/10.5194/gmd-16-3355-2023, 2023
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Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Feijia Yin, Volker Grewe, Federica Castino, Pratik Rao, Sigrun Matthes, Katrin Dahlmann, Simone Dietmüller, Christine Frömming, Hiroshi Yamashita, Patrick Peter, Emma Klingaman, Keith P. Shine, Benjamin Lührs, and Florian Linke
Geosci. Model Dev., 16, 3313–3334, https://doi.org/10.5194/gmd-16-3313-2023, https://doi.org/10.5194/gmd-16-3313-2023, 2023
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This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261, https://doi.org/10.5194/gmd-16-3241-2023, https://doi.org/10.5194/gmd-16-3241-2023, 2023
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Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
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Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122, https://doi.org/10.5194/gmd-16-3103-2023, https://doi.org/10.5194/gmd-16-3103-2023, 2023
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This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, https://doi.org/10.5194/gmd-16-3013-2023, 2023
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Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, https://doi.org/10.5194/gmd-16-2995-2023, 2023
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Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955, https://doi.org/10.5194/gmd-16-2939-2023, https://doi.org/10.5194/gmd-16-2939-2023, 2023
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This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973, https://doi.org/10.5194/gmd-16-2957-2023, https://doi.org/10.5194/gmd-16-2957-2023, 2023
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A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
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A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
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C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
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FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
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It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
EGUsphere, https://doi.org/10.5194/egusphere-2023-549, https://doi.org/10.5194/egusphere-2023-549, 2023
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The present paper introduces a floodplains scheme for a high resolution Land Surface Model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land atmosphere fluxes and highlights the potential impact of floodplains on land-atmosphere interactions and the importance of integrating this module in coupled simulations.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606, https://doi.org/10.5194/gmd-16-2583-2023, https://doi.org/10.5194/gmd-16-2583-2023, 2023
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The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Magnus Hieronymus
Geosci. Model Dev., 16, 2343–2354, https://doi.org/10.5194/gmd-16-2343-2023, https://doi.org/10.5194/gmd-16-2343-2023, 2023
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A statistical model called the sea level simulator is presented and made freely available. The sea level simulator integrates mean sea level rise and sea level extremes into a joint probabilistic framework that is useful for flood risk estimation. These flood risk estimates are contingent on probabilities given to different emission scenarios and the length of the planning period. The model is also useful for uncertainty quantification and in decision and adaptation problems.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-46, https://doi.org/10.5194/gmd-2023-46, 2023
Revised manuscript accepted for GMD
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The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a Central Analysis Facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large data set. We believe that similar, multi institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233, https://doi.org/10.5194/gmd-16-2215-2023, https://doi.org/10.5194/gmd-16-2215-2023, 2023
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This study proposes (i) the structural k-means (S k-means) algorithm for clustering spatiotemporally structured climate data and (ii) the clustering uncertainty evaluation framework (CUEF) based on the mutual-information concept.
Nadine Goris, Klaus Johannsen, and Jerry Tjiputra
Geosci. Model Dev., 16, 2095–2117, https://doi.org/10.5194/gmd-16-2095-2023, https://doi.org/10.5194/gmd-16-2095-2023, 2023
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Climate projections of a high-CO2 future are highly uncertain. A new study provides a novel approach to identifying key regions that dynamically explain the model uncertainty. To yield an accurate estimate of the future North Atlantic carbon uptake, we find that a correct simulation of the upper- and interior-ocean volume transport at 25–30° N is key. However, results indicate that models rarely perform well for both indicators and point towards inconsistencies within the model ensemble.
Pyry Pentikäinen, Ewan J. O'Connor, and Pablo Ortiz-Amezcua
Geosci. Model Dev., 16, 2077–2094, https://doi.org/10.5194/gmd-16-2077-2023, https://doi.org/10.5194/gmd-16-2077-2023, 2023
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We used Doppler lidar to evaluate the wind profiles generated by a weather forecast model. We first compared the Doppler lidar observations with co-located radiosonde profiles, and they agree well. The model performs best over marine and coastal locations. Larger errors were seen in locations where the surface was more complex, especially in the wind direction. Our results show that Doppler lidar is a suitable instrument for evaluating the boundary layer wind profiles in atmospheric models.
Rubina Ansari, Ana Casanueva, Muhammad Usman Liaqat, and Giovanna Grossi
Geosci. Model Dev., 16, 2055–2076, https://doi.org/10.5194/gmd-16-2055-2023, https://doi.org/10.5194/gmd-16-2055-2023, 2023
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Bias correction (BC) has become indispensable to climate model output as a post-processing step to render output more useful for impact assessment studies. The current work presents a comparison of different state-of-the-art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) for climate model simulations from three initiatives (CMIP6, CORDEX, and CORDEX-CORE) for a multivariate drought index (i.e., standardized precipitation evapotranspiration index).
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