Articles | Volume 6, issue 3
Geosci. Model Dev., 6, 849–859, 2013
https://doi.org/10.5194/gmd-6-849-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 6, 849–859, 2013
https://doi.org/10.5194/gmd-6-849-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper 22 Jun 2013
Development and technical paper | 22 Jun 2013
Impacts of using spectral nudging on regional climate model RCA4 simulations of the Arctic
P. Berg et al.
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Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
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This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
Jonas Olsson, Peter Berg, and Remco van de Beek
Adv. Sci. Res., 18, 59–64, https://doi.org/10.5194/asr-18-59-2021, https://doi.org/10.5194/asr-18-59-2021, 2021
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We have developed a tool to visualize rainfall observations, based on a combination of meteorological stations and weather radars, over Sweden in near real-time. By accumulating the rainfall in time (1–12 h) and space (hydrological basins), the tool is designed mainly for hydrological applications, e.g. to support flood forecasters and to facilitate post-event analyses. Despite evident uncertainties, different users have confirmed an added value of the tool in case studies.
Peter Berg, Fredrik Almén, and Denica Bozhinova
Earth Syst. Sci. Data, 13, 1531–1545, https://doi.org/10.5194/essd-13-1531-2021, https://doi.org/10.5194/essd-13-1531-2021, 2021
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HydroGFD3.0 (Hydrological Global Forcing Data) is a data set of daily precipitation and temperature intended for use in hydrological modelling. The method uses different observational data sources to correct the variables from a model estimation of precipitation and temperature. An openly available data set covers the years 1979–2019, and times after this are available by request.
Torben Schmith, Peter Thejll, Peter Berg, Fredrik Boberg, Ole Bøssing Christensen, Bo Christiansen, Jens Hesselbjerg Christensen, Marianne Sloth Madsen, and Christian Steger
Hydrol. Earth Syst. Sci., 25, 273–290, https://doi.org/10.5194/hess-25-273-2021, https://doi.org/10.5194/hess-25-273-2021, 2021
Short summary
Short summary
European extreme precipitation is expected to change in the future; this is based on climate model projections. But, since climate models have errors, projections are uncertain. We study this uncertainty in the projections by comparing results from an ensemble of 19 climate models. Results can be used to give improved estimates of future extreme precipitation for Europe.
Marc Schleiss, Jonas Olsson, Peter Berg, Tero Niemi, Teemu Kokkonen, Søren Thorndahl, Rasmus Nielsen, Jesper Ellerbæk Nielsen, Denica Bozhinova, and Seppo Pulkkinen
Hydrol. Earth Syst. Sci., 24, 3157–3188, https://doi.org/10.5194/hess-24-3157-2020, https://doi.org/10.5194/hess-24-3157-2020, 2020
Short summary
Short summary
A multinational assessment of radar's ability to capture heavy rain events is conducted. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. Results show a fair agreement, with radar underestimating by 17 %-44 % on average compared with gauges. Despite being adjusted for bias, five of six radar products still exhibited strong conditional biases with intensities of 1–2% per mm/h. Median peak intensity bias was significantly higher, reaching 44 %–67%.
Peter Berg, Ole B. Christensen, Katharina Klehmet, Geert Lenderink, Jonas Olsson, Claas Teichmann, and Wei Yang
Nat. Hazards Earth Syst. Sci., 19, 957–971, https://doi.org/10.5194/nhess-19-957-2019, https://doi.org/10.5194/nhess-19-957-2019, 2019
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A state-of-the-art regional climate model ensemble for Europe is investigated for extreme precipitation intensities. The models poorly reproduce short duration events of less than a few hours. Further, there is poor connection to some known hotspots for extreme cases. The model performance is much improved at 12 h durations. Projected future increases scale with seasonal mean temperature change, within a range from a few percent to over 10 percent per degree Celsius.
Stephen Blenkinsop, Hayley J. Fowler, Renaud Barbero, Steven C. Chan, Selma B. Guerreiro, Elizabeth Kendon, Geert Lenderink, Elizabeth Lewis, Xiao-Feng Li, Seth Westra, Lisa Alexander, Richard P. Allan, Peter Berg, Robert J. H. Dunn, Marie Ekström, Jason P. Evans, Greg Holland, Richard Jones, Erik Kjellström, Albert Klein-Tank, Dennis Lettenmaier, Vimal Mishra, Andreas F. Prein, Justin Sheffield, and Mari R. Tye
Adv. Sci. Res., 15, 117–126, https://doi.org/10.5194/asr-15-117-2018, https://doi.org/10.5194/asr-15-117-2018, 2018
Short summary
Short summary
Measurements of sub-daily (e.g. hourly) rainfall totals are essential if we are to understand short, intense bursts of rainfall that cause flash floods. We might expect the intensity of such events to increase in a warming climate but these are poorly realised in projections of future climate change. The INTENSE project is collating a global dataset of hourly rainfall measurements and linking with new developments in climate models to understand the characteristics and causes of these events.
Peter Berg, Chantal Donnelly, and David Gustafsson
Hydrol. Earth Syst. Sci., 22, 989–1000, https://doi.org/10.5194/hess-22-989-2018, https://doi.org/10.5194/hess-22-989-2018, 2018
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Short summary
A new product (Global Forcing Data, GFD) that provides bias-adjusted meteorological forcing data for impact models, such as hydrological models, is presented. The main novelty with the product is the near-real time updating of the data which allows more up-to-date impact modeling. This is performed by combining climatological data sets with climate monitoring data sets. The potential in using the data to initialize hydrological forecasts is further investigated.
B. Eggert, P. Berg, J. O. Haerter, D. Jacob, and C. Moseley
Atmos. Chem. Phys., 15, 5957–5971, https://doi.org/10.5194/acp-15-5957-2015, https://doi.org/10.5194/acp-15-5957-2015, 2015
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
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This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
Klaus Wyser, Torben Koenigk, Uwe Fladrich, Ramon Fuentes-Franco, Mehdi Pasha Karami, and Tim Kruschke
Geosci. Model Dev., 14, 4781–4796, https://doi.org/10.5194/gmd-14-4781-2021, https://doi.org/10.5194/gmd-14-4781-2021, 2021
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This paper describes the large ensemble done by SMHI with the EC-Earth3 climate model. The ensemble comprises 50 realizations for each of the historical experiments after 1970 and four different future projections for CMIP6. We describe the creation of the initial states for the ensemble and the reduced set of output variables. A first look at the results illustrates the changes in the climate during this century and puts them in relation to the uncertainty from the model's internal variability.
Tian Tian, Shuting Yang, Mehdi Pasha Karami, François Massonnet, Tim Kruschke, and Torben Koenigk
Geosci. Model Dev., 14, 4283–4305, https://doi.org/10.5194/gmd-14-4283-2021, https://doi.org/10.5194/gmd-14-4283-2021, 2021
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Three decadal prediction experiments with EC-Earth3 are performed to investigate the impact of ocean, sea ice concentration and thickness initialization, respectively. We find that the persistence of perennial thick ice in the central Arctic can affect the sea ice predictability in its adjacent waters via advection process or wind, despite those regions being seasonally ice free during two recent decades. This has implications for the coming decades as the thinning of Arctic sea ice continues.
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
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This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
Jonas Olsson, Peter Berg, and Remco van de Beek
Adv. Sci. Res., 18, 59–64, https://doi.org/10.5194/asr-18-59-2021, https://doi.org/10.5194/asr-18-59-2021, 2021
Short summary
Short summary
We have developed a tool to visualize rainfall observations, based on a combination of meteorological stations and weather radars, over Sweden in near real-time. By accumulating the rainfall in time (1–12 h) and space (hydrological basins), the tool is designed mainly for hydrological applications, e.g. to support flood forecasters and to facilitate post-event analyses. Despite evident uncertainties, different users have confirmed an added value of the tool in case studies.
Peter Berg, Fredrik Almén, and Denica Bozhinova
Earth Syst. Sci. Data, 13, 1531–1545, https://doi.org/10.5194/essd-13-1531-2021, https://doi.org/10.5194/essd-13-1531-2021, 2021
Short summary
Short summary
HydroGFD3.0 (Hydrological Global Forcing Data) is a data set of daily precipitation and temperature intended for use in hydrological modelling. The method uses different observational data sources to correct the variables from a model estimation of precipitation and temperature. An openly available data set covers the years 1979–2019, and times after this are available by request.
Ralf Döscher, Mario Acosta, Andrea Alessandri, Peter Anthoni, Almut Arneth, Thomas Arsouze, Tommi Bergmann, Raffaele Bernadello, Souhail Bousetta, Louis-Philippe Caron, Glenn Carver, Miguel Castrillo, Franco Catalano, Ivana Cvijanovic, Paolo Davini, Evelien Dekker, Francisco J. Doblas-Reyes, David Docquier, Pablo Echevarria, Uwe Fladrich, Ramon Fuentes-Franco, Matthias Gröger, Jost v. Hardenberg, Jenny Hieronymus, M. Pasha Karami, Jukka-Pekka Keskinen, Torben Koenigk, Risto Makkonen, Francois Massonnet, Martin Ménégoz, Paul A. Miller, Eduardo Moreno-Chamarro, Lars Nieradzik, Twan van Noije, Paul Nolan, Declan O’Donnell, Pirkka Ollinaho, Gijs van den Oord, Pablo Ortega, Oriol Tintó Prims, Arthur Ramos, Thomas Reerink, Clement Rousset, Yohan Ruprich-Robert, Philippe Le Sager, Torben Schmith, Roland Schrödner, Federico Serva, Valentina Sicardi, Marianne Sloth Madsen, Benjamin Smith, Tian Tian, Etienne Tourigny, Petteri Uotila, Martin Vancoppenolle, Shiyu Wang, David Wårlind, Ulrika Willén, Klaus Wyser, Shuting Yang, Xavier Yepes-Arbós, and Qiong Zhang
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2020-446, https://doi.org/10.5194/gmd-2020-446, 2021
Revised manuscript accepted for GMD
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The Earth System Model EC-Earth3 is documented here. Key performance metrics show physical behaviour and biases well within the frame known from recent models. With improved physical and dynamic features, new ESM components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
Torben Schmith, Peter Thejll, Peter Berg, Fredrik Boberg, Ole Bøssing Christensen, Bo Christiansen, Jens Hesselbjerg Christensen, Marianne Sloth Madsen, and Christian Steger
Hydrol. Earth Syst. Sci., 25, 273–290, https://doi.org/10.5194/hess-25-273-2021, https://doi.org/10.5194/hess-25-273-2021, 2021
Short summary
Short summary
European extreme precipitation is expected to change in the future; this is based on climate model projections. But, since climate models have errors, projections are uncertain. We study this uncertainty in the projections by comparing results from an ensemble of 19 climate models. Results can be used to give improved estimates of future extreme precipitation for Europe.
Stelios Myriokefalitakis, Matthias Gröger, Jenny Hieronymus, and Ralf Döscher
Ocean Sci., 16, 1183–1205, https://doi.org/10.5194/os-16-1183-2020, https://doi.org/10.5194/os-16-1183-2020, 2020
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Global inorganic and organic nutrient deposition fields are coupled to PISCES to investigate their effect on ocean biogeochemistry. Pre-industrial deposition fluxes are lower compared to the present day, resulting in lower oceanic productivity. Future changes result in a modest decrease in the nutrients put into the global ocean. This work provides a first assessment of the atmospheric organic nutrients' contribution, highlighting the importance of their representation in biogeochemistry models.
Rein Haarsma, Mario Acosta, Rena Bakhshi, Pierre-Antoine Bretonnière, Louis-Philippe Caron, Miguel Castrillo, Susanna Corti, Paolo Davini, Eleftheria Exarchou, Federico Fabiano, Uwe Fladrich, Ramon Fuentes Franco, Javier García-Serrano, Jost von Hardenberg, Torben Koenigk, Xavier Levine, Virna Loana Meccia, Twan van Noije, Gijs van den Oord, Froila M. Palmeiro, Mario Rodrigo, Yohan Ruprich-Robert, Philippe Le Sager, Etienne Tourigny, Shiyu Wang, Michiel van Weele, and Klaus Wyser
Geosci. Model Dev., 13, 3507–3527, https://doi.org/10.5194/gmd-13-3507-2020, https://doi.org/10.5194/gmd-13-3507-2020, 2020
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HighResMIP is an international coordinated CMIP6 effort to investigate the improvement in climate modeling caused by an increase in horizontal resolution. This paper describes EC-Earth3P-(HR), which has been developed for HighResMIP. First analyses reveal that increasing resolution does improve certain aspects of the simulated climate but that many other biases still continue, possibly related to phenomena that are still not yet resolved and need to be parameterized.
Klaus Wyser, Twan van Noije, Shuting Yang, Jost von Hardenberg, Declan O'Donnell, and Ralf Döscher
Geosci. Model Dev., 13, 3465–3474, https://doi.org/10.5194/gmd-13-3465-2020, https://doi.org/10.5194/gmd-13-3465-2020, 2020
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The EC-Earth model used for CMIP6 is found to have a higher equilibrium climate sensitivity (ECS) than its predecessor used for CMIP5. In a series of sensitivity experiments, we investigate which model updates since CMIP5 have contributed to the increase in ECS. The main reason for the higher sensitivity in the EC-Earth model is the improved representation of the aerosol–radiation and aerosol–cloud interactions.
Marc Schleiss, Jonas Olsson, Peter Berg, Tero Niemi, Teemu Kokkonen, Søren Thorndahl, Rasmus Nielsen, Jesper Ellerbæk Nielsen, Denica Bozhinova, and Seppo Pulkkinen
Hydrol. Earth Syst. Sci., 24, 3157–3188, https://doi.org/10.5194/hess-24-3157-2020, https://doi.org/10.5194/hess-24-3157-2020, 2020
Short summary
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A multinational assessment of radar's ability to capture heavy rain events is conducted. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. Results show a fair agreement, with radar underestimating by 17 %-44 % on average compared with gauges. Despite being adjusted for bias, five of six radar products still exhibited strong conditional biases with intensities of 1–2% per mm/h. Median peak intensity bias was significantly higher, reaching 44 %–67%.
Torben Koenigk, Ramon Fuentes-Franco, Virna Meccia, Oliver Gutjahr, Laura C. Jackson, Adrian L. New, Pablo Ortega, Christopher Roberts, Malcolm Roberts, Thomas Arsouze, Doroteaciro Iovino, Marie-Pierre Moine, and Dmitry V. Sein
Ocean Sci. Discuss., https://doi.org/10.5194/os-2020-41, https://doi.org/10.5194/os-2020-41, 2020
Revised manuscript not accepted
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The mixing of water masses into the deep ocean in the North Atlantic is important for the entire global ocean circulation. We use seven global climate models to investigate the effect of increasing the model resolution on this deep ocean mixing. The main result is that increased model resolution leads to a deeper mixing of water masses in the Labrador Sea but has less effect in the Greenland Sea. However, most of the models overestimate the deep ocean mixing compared to observations.
Manu Anna Thomas, Abhay Devasthale, Tristan L'Ecuyer, Shiyu Wang, Torben Koenigk, and Klaus Wyser
Geosci. Model Dev., 12, 3759–3772, https://doi.org/10.5194/gmd-12-3759-2019, https://doi.org/10.5194/gmd-12-3759-2019, 2019
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Snow cover significantly influences the surface albedo and radiation budget. Therefore, a realistic representation of snowfall in climate models is important. Here, using decade-long estimates of snowfall derived from the satellite sensor, four climate models are evaluated to assess how well they simulate snowfall in the Arctic. It is found that light and median snowfall is overestimated by the models in comparison to the satellite observations, and extreme snowfall is underestimated.
Peter Berg, Ole B. Christensen, Katharina Klehmet, Geert Lenderink, Jonas Olsson, Claas Teichmann, and Wei Yang
Nat. Hazards Earth Syst. Sci., 19, 957–971, https://doi.org/10.5194/nhess-19-957-2019, https://doi.org/10.5194/nhess-19-957-2019, 2019
Short summary
Short summary
A state-of-the-art regional climate model ensemble for Europe is investigated for extreme precipitation intensities. The models poorly reproduce short duration events of less than a few hours. Further, there is poor connection to some known hotspots for extreme cases. The model performance is much improved at 12 h durations. Projected future increases scale with seasonal mean temperature change, within a range from a few percent to over 10 percent per degree Celsius.
Manu Anna Thomas, Abhay Devasthale, Torben Koenigk, Klaus Wyser, Malcolm Roberts, Christopher Roberts, and Katja Lohmann
Geosci. Model Dev., 12, 1679–1702, https://doi.org/10.5194/gmd-12-1679-2019, https://doi.org/10.5194/gmd-12-1679-2019, 2019
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Cloud processes occur at scales ranging from few micrometres to hundreds of kilometres. Their representation in global climate models and their fidelity are thus sensitive to the choice of spatial resolution. Here, cloud radiative effects simulated by models are evaluated using a satellite dataset, with a focus on investigating the sensitivity to spatial resolution. The evaluations are carried out using two approaches: the traditional statistical comparisons and the process-oriented evaluation.
Stephen Blenkinsop, Hayley J. Fowler, Renaud Barbero, Steven C. Chan, Selma B. Guerreiro, Elizabeth Kendon, Geert Lenderink, Elizabeth Lewis, Xiao-Feng Li, Seth Westra, Lisa Alexander, Richard P. Allan, Peter Berg, Robert J. H. Dunn, Marie Ekström, Jason P. Evans, Greg Holland, Richard Jones, Erik Kjellström, Albert Klein-Tank, Dennis Lettenmaier, Vimal Mishra, Andreas F. Prein, Justin Sheffield, and Mari R. Tye
Adv. Sci. Res., 15, 117–126, https://doi.org/10.5194/asr-15-117-2018, https://doi.org/10.5194/asr-15-117-2018, 2018
Short summary
Short summary
Measurements of sub-daily (e.g. hourly) rainfall totals are essential if we are to understand short, intense bursts of rainfall that cause flash floods. We might expect the intensity of such events to increase in a warming climate but these are poorly realised in projections of future climate change. The INTENSE project is collating a global dataset of hourly rainfall measurements and linking with new developments in climate models to understand the characteristics and causes of these events.
Peter Berg, Chantal Donnelly, and David Gustafsson
Hydrol. Earth Syst. Sci., 22, 989–1000, https://doi.org/10.5194/hess-22-989-2018, https://doi.org/10.5194/hess-22-989-2018, 2018
Short summary
Short summary
A new product (Global Forcing Data, GFD) that provides bias-adjusted meteorological forcing data for impact models, such as hydrological models, is presented. The main novelty with the product is the near-real time updating of the data which allows more up-to-date impact modeling. This is performed by combining climatological data sets with climate monitoring data sets. The potential in using the data to initialize hydrological forecasts is further investigated.
Reindert J. Haarsma, Malcolm J. Roberts, Pier Luigi Vidale, Catherine A. Senior, Alessio Bellucci, Qing Bao, Ping Chang, Susanna Corti, Neven S. Fučkar, Virginie Guemas, Jost von Hardenberg, Wilco Hazeleger, Chihiro Kodama, Torben Koenigk, L. Ruby Leung, Jian Lu, Jing-Jia Luo, Jiafu Mao, Matthew S. Mizielinski, Ryo Mizuta, Paulo Nobre, Masaki Satoh, Enrico Scoccimarro, Tido Semmler, Justin Small, and Jin-Song von Storch
Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, https://doi.org/10.5194/gmd-9-4185-2016, 2016
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Recent progress in computing power has enabled climate models to simulate more processes in detail and on a smaller scale. Here we present a common protocol for these high-resolution runs that will foster the analysis and understanding of the impact of model resolution on the simulated climate. These runs will also serve as a more reliable source for assessing climate risks that are associated with small-scale weather phenomena such as tropical cyclones.
B. Eggert, P. Berg, J. O. Haerter, D. Jacob, and C. Moseley
Atmos. Chem. Phys., 15, 5957–5971, https://doi.org/10.5194/acp-15-5957-2015, https://doi.org/10.5194/acp-15-5957-2015, 2015
R. Döscher, T. Vihma, and E. Maksimovich
Atmos. Chem. Phys., 14, 13571–13600, https://doi.org/10.5194/acp-14-13571-2014, https://doi.org/10.5194/acp-14-13571-2014, 2014
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The article reviews progress in understanding of the Arctic sea ice decline. Processes are revisited from an atmospheric, ocean and sea ice perspective. There is strong evidence for decisive atmospheric drivers of sea ice change. Large-scale ocean influences on the Arctic Ocean hydrology and circulation are highly evident. Ocean heat fluxes are clearly impacting the ice margins. Little indication exists for a direct decisive influence of the warming ocean on the central Arctic sea ice cover.
T. Koenigk, A. Devasthale, and K.-G. Karlsson
Atmos. Chem. Phys., 14, 1987–1998, https://doi.org/10.5194/acp-14-1987-2014, https://doi.org/10.5194/acp-14-1987-2014, 2014
A. Devasthale, J. Sedlar, T. Koenigk, and E. J. Fetzer
Atmos. Chem. Phys., 13, 7441–7450, https://doi.org/10.5194/acp-13-7441-2013, https://doi.org/10.5194/acp-13-7441-2013, 2013
R. Döscher and T. Koenigk
Ocean Sci., 9, 217–248, https://doi.org/10.5194/os-9-217-2013, https://doi.org/10.5194/os-9-217-2013, 2013
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Physically regularized machine learning emulators of aerosol activation
Decadal climate predictions with the Canadian Earth System Model version 5 (CanESM5)
FaIRv2.0.0: a generalized impulse response model for climate uncertainty and future scenario exploration
BCC-CSM2-HR: a high-resolution version of the Beijing Climate Center Climate System Model
A Schwarz iterative method to evaluate ocean–atmosphere coupling schemes: implementation and diagnostics in IPSL-CM6-SW-VLR
Unstructured global to coastal wave modeling for the Energy Exascale Earth System Model using WAVEWATCH III version 6.07
TransEBM v. 1.0: description, tuning, and validation of a transient model of the Earth's energy balance in two dimensions
SimCloud version 1.0: a simple diagnostic cloud scheme for idealized climate models
Sensitivity of precipitation and temperature over the Mount Kenya area to physics parameterization options in a high-resolution model simulation performed with WRFV3.8.1
The GPU version of LASG/IAP Climate System Ocean Model version 3 (LICOM3) under the heterogeneous-compute interface for portability (HIP) framework and its large-scale application
Developing a common, flexible and efficient framework for weakly coupled ensemble data assimilation based on C-Coupler2.0
JULES-CN: a coupled terrestrial carbon–nitrogen scheme (JULES vn5.1)
Ensemble prediction using a new dataset of ECMWF initial states – OpenEnsemble 1.0
Global evaluation of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 (r5986)
Jinxiao Li, Qing Bao, Yimin Liu, Lei Wang, Jing Yang, Guoxiong Wu, Xiaofei Wu, Bian He, Xiaocong Wang, Xiaoqi Zhang, Yaoxian Yang, and Zili Shen
Geosci. Model Dev., 14, 6113–6133, https://doi.org/10.5194/gmd-14-6113-2021, https://doi.org/10.5194/gmd-14-6113-2021, 2021
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The configuration and simulated performance of tropical cyclones (TCs) in FGOALS-f3-L/H will be introduced firstly. The results indicate that the simulated performance of TC activities is improved globally with the increased horizontal resolution especially in TC counts, seasonal cycle, interannual variabilities and intensity aspects. It is worth establishing a high-resolution coupled dynamic prediction system based on FGOALS-f3-H (~ 25 km) to improve the prediction skill of TCs.
Liam Bindle, Randall V. Martin, Matthew J. Cooper, Elizabeth W. Lundgren, Sebastian D. Eastham, Benjamin M. Auer, Thomas L. Clune, Hongjian Weng, Jintai Lin, Lee T. Murray, Jun Meng, Christoph A. Keller, William M. Putman, Steven Pawson, and Daniel J. Jacob
Geosci. Model Dev., 14, 5977–5997, https://doi.org/10.5194/gmd-14-5977-2021, https://doi.org/10.5194/gmd-14-5977-2021, 2021
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Atmospheric chemistry models like GEOS-Chem are versatile tools widely used in air pollution and climate studies. The simulations used in such studies can be very computationally demanding, and thus it is useful if the model can simulate a specific geographic region at a higher resolution than the rest of the globe. Here, we implement, test, and demonstrate a new variable-resolution capability in GEOS-Chem that is suitable for simulations conducted on supercomputers.
Petra Pranić, Cléa Denamiel, and Ivica Vilibić
Geosci. Model Dev., 14, 5927–5955, https://doi.org/10.5194/gmd-14-5927-2021, https://doi.org/10.5194/gmd-14-5927-2021, 2021
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The Adriatic Sea and Coast model was developed due to the need for higher-resolution climate models and longer-term simulations to capture coastal atmospheric and ocean processes at climate scales in the Adriatic Sea. The ocean results of a 31-year-long simulation were compared to the observational data. The evaluation revealed that the model is capable of reproducing the observed physical properties with good accuracy and can be further used to study the dynamics of the Adriatic–Ionian basin.
Lynsay Spafford and Andrew H. MacDougall
Geosci. Model Dev., 14, 5863–5889, https://doi.org/10.5194/gmd-14-5863-2021, https://doi.org/10.5194/gmd-14-5863-2021, 2021
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Land biogeochemical cycles influence global climate change. Their influence is examined through complex computer models that account for the interaction of the land, ocean, and atmosphere. Improved models used in the recent round of model intercomparison used inconsistent validation methods to compare simulated land biogeochemistry to datasets. For the next round of model intercomparisons we recommend a validation protocol with explicit reference datasets and informative performance metrics.
Robin S. Smith, Steve George, and Jonathan M. Gregory
Geosci. Model Dev., 14, 5769–5787, https://doi.org/10.5194/gmd-14-5769-2021, https://doi.org/10.5194/gmd-14-5769-2021, 2021
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Many of the complex computer models used to study the physics of the natural world treat ice sheets as fixed and unchanging, capable of only simple interactions with the rest of the climate. This is partly because it is technically very difficult to usefully do anything more realistic. We have adapted a climate model so it can be joined together with a dynamical model of the Greenland ice sheet. This gives us a powerful tool to help us better understand how ice sheets and the climate interact.
Twan van Noije, Tommi Bergman, Philippe Le Sager, Declan O'Donnell, Risto Makkonen, María Gonçalves-Ageitos, Ralf Döscher, Uwe Fladrich, Jost von Hardenberg, Jukka-Pekka Keskinen, Hannele Korhonen, Anton Laakso, Stelios Myriokefalitakis, Pirkka Ollinaho, Carlos Pérez García-Pando, Thomas Reerink, Roland Schrödner, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 5637–5668, https://doi.org/10.5194/gmd-14-5637-2021, https://doi.org/10.5194/gmd-14-5637-2021, 2021
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This paper documents the global climate model EC-Earth3-AerChem, one of the members of the EC-Earth3 family of models participating in CMIP6. We give an overview of the model and describe in detail how it differs from its predecessor and the other EC-Earth3 configurations. The model's performance is characterized using coupled simulations conducted for CMIP6. The model has an effective equilibrium climate sensitivity of 3.9 °C and a transient climate response of 2.1 °C.
Paolo Pelucchi, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 14, 5413–5434, https://doi.org/10.5194/gmd-14-5413-2021, https://doi.org/10.5194/gmd-14-5413-2021, 2021
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Stratocumulus are thin clouds whose cloud cover is underestimated in climate models partly due to overly low vertical resolution. We develop a scheme that locally refines the vertical grid based on a physical constraint for the cloud top. Global simulations show that the scheme, implemented only in the radiation routine, can increase stratocumulus cloud cover. However, this effect is poorly propagated to the simulated cloud cover. The scheme's limitations and possible ways forward are discussed.
John G. Virgin, Christopher G. Fletcher, Jason N. S. Cole, Knut von Salzen, and Toni Mitovski
Geosci. Model Dev., 14, 5355–5372, https://doi.org/10.5194/gmd-14-5355-2021, https://doi.org/10.5194/gmd-14-5355-2021, 2021
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Equilibrium climate sensitivity, or the amount of warming the Earth would exhibit a result of a doubling of atmospheric CO2, is a common metric used in assessments of climate models. Here, we compare climate sensitivity between two versions of the Canadian Earth System Model. We find the newest iteration of the model (version 5) to have higher climate sensitivity due to reductions in low-level clouds, which reflect radiation and cool the planet, as the surface warms.
Matthias Mengel, Simon Treu, Stefan Lange, and Katja Frieler
Geosci. Model Dev., 14, 5269–5284, https://doi.org/10.5194/gmd-14-5269-2021, https://doi.org/10.5194/gmd-14-5269-2021, 2021
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To identify the impacts of historical climate change it is necessary to separate the effect of the different impact drivers. To address this, one needs to compare historical impacts to a counterfactual world with impacts that would have been without climate change. We here present an approach that produces counterfactual climate data and can be used in climate impact models to simulate counterfactual impacts. We make these data available through the ISIMIP project.
Yixiong Lu, Tongwen Wu, Yubin Li, and Ben Yang
Geosci. Model Dev., 14, 5183–5204, https://doi.org/10.5194/gmd-14-5183-2021, https://doi.org/10.5194/gmd-14-5183-2021, 2021
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The spurious precipitation in the tropical southeastern Pacific and southern Atlantic is one of the most prominent systematic biases in coupled atmosphere–ocean general circulation models. This study significantly promotes the marine stratus simulation and largely alleviates the excessive precipitation biases through improving parameterizations of boundary-layer turbulence and shallow convection, providing an effective solution to the long-standing bias in the tropical precipitation simulation.
Silje Lund Sørland, Roman Brogli, Praveen Kumar Pothapakula, Emmanuele Russo, Jonas Van de Walle, Bodo Ahrens, Ivonne Anders, Edoardo Bucchignani, Edouard L. Davin, Marie-Estelle Demory, Alessandro Dosio, Hendrik Feldmann, Barbara Früh, Beate Geyer, Klaus Keuler, Donghyun Lee, Delei Li, Nicole P. M. van Lipzig, Seung-Ki Min, Hans-Jürgen Panitz, Burkhardt Rockel, Christoph Schär, Christian Steger, and Wim Thiery
Geosci. Model Dev., 14, 5125–5154, https://doi.org/10.5194/gmd-14-5125-2021, https://doi.org/10.5194/gmd-14-5125-2021, 2021
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We review the contribution from the CLM-Community to regional climate projections following the CORDEX framework over Europe, South Asia, East Asia, Australasia, and Africa. How the model configuration, horizontal and vertical resolutions, and choice of driving data influence the model results for the five domains is assessed, with the purpose of aiding the planning and design of regional climate simulations in the future.
Paul A. Ullrich, Colin M. Zarzycki, Elizabeth E. McClenny, Marielle C. Pinheiro, Alyssa M. Stansfield, and Kevin A. Reed
Geosci. Model Dev., 14, 5023–5048, https://doi.org/10.5194/gmd-14-5023-2021, https://doi.org/10.5194/gmd-14-5023-2021, 2021
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TempestExtremes (TE) is a multifaceted framework for feature detection, tracking, and scientific analysis of regional or global Earth system datasets. Version 2.1 of TE now provides extensive support for nodal and areal features. This paper describes the algorithms that have been added to the TE framework since version 1.0 and gives several examples of how these can be combined to produce composite algorithms for evaluating and understanding atmospheric features.
Tobias Peter Bauer, Peter Holtermann, Bernd Heinold, Hagen Radtke, Oswald Knoth, and Knut Klingbeil
Geosci. Model Dev., 14, 4843–4863, https://doi.org/10.5194/gmd-14-4843-2021, https://doi.org/10.5194/gmd-14-4843-2021, 2021
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We present the coupled atmosphere–ocean model system ICONGETM. The added value and potential of using the latest coupling technologies are discussed in detail. An exchange grid handles the different coastlines from the unstructured atmosphere and the structured ocean grids. Due to a high level of automated processing, ICONGETM requires only minimal user input. The application to a coastal upwelling scenario demonstrates significantly improved model results compared to uncoupled simulations.
Dawn L. Woodard, Alexey N. Shiklomanov, Ben Kravitz, Corinne Hartin, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 4751–4767, https://doi.org/10.5194/gmd-14-4751-2021, https://doi.org/10.5194/gmd-14-4751-2021, 2021
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We have added a representation of the permafrost carbon feedback to the simple, open-source global carbon–climate model Hector and calibrated the results to be consistent with historical data and Earth system model projections. Our results closely match previous work, estimating around 0.2 °C of warming from permafrost this century. This capability will be useful to explore uncertainties in this feedback and for coupling with integrated assessment models for policy and economic analysis.
Klaus Wyser, Torben Koenigk, Uwe Fladrich, Ramon Fuentes-Franco, Mehdi Pasha Karami, and Tim Kruschke
Geosci. Model Dev., 14, 4781–4796, https://doi.org/10.5194/gmd-14-4781-2021, https://doi.org/10.5194/gmd-14-4781-2021, 2021
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This paper describes the large ensemble done by SMHI with the EC-Earth3 climate model. The ensemble comprises 50 realizations for each of the historical experiments after 1970 and four different future projections for CMIP6. We describe the creation of the initial states for the ensemble and the reduced set of output variables. A first look at the results illustrates the changes in the climate during this century and puts them in relation to the uncertainty from the model's internal variability.
Yidi Xu, Philippe Ciais, Le Yu, Wei Li, Xiuzhi Chen, Haicheng Zhang, Chao Yue, Kasturi Kanniah, Arthur P. Cracknell, and Peng Gong
Geosci. Model Dev., 14, 4573–4592, https://doi.org/10.5194/gmd-14-4573-2021, https://doi.org/10.5194/gmd-14-4573-2021, 2021
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In this study, we implemented the specific morphology, phenology and harvest process of oil palm in the global land surface model ORCHIDEE-MICT. The improved model generally reproduces the same leaf area index, biomass density and life cycle fruit yield as observations. This explicit representation of oil palm in a global land surface model offers a useful tool for understanding the ecological processes of oil palm growth and assessing the environmental impacts of oil palm plantations.
Benjamin A. Toms, Karthik Kashinath, Prabhat, and Da Yang
Geosci. Model Dev., 14, 4495–4508, https://doi.org/10.5194/gmd-14-4495-2021, https://doi.org/10.5194/gmd-14-4495-2021, 2021
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We test whether a type of machine learning called neural networks can be used trustfully within the geosciences. We do so by challenging the networks to understand the spatial patterns of a commonly studied geoscientific phenomenon. The neural networks can correctly identify the spatial patterns, which lends confidence that similar networks can be used for more uncertain problems. The results of this study may give geoscientists confidence when using neural networks in their research.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 14, 4429–4441, https://doi.org/10.5194/gmd-14-4429-2021, https://doi.org/10.5194/gmd-14-4429-2021, 2021
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Soil moisture is of great importance for weather and climate. We present a machine learning model that produces accurate predictions of satellite-observed surface soil moisture, based on meteorological data from a climate model. It can be used as soil moisture parametrisation in climate models and to produce comprehensive global soil moisture datasets. Moreover, it may motivate similar applications of machine learning in climate science.
Jeremy McGibbon, Noah D. Brenowitz, Mark Cheeseman, Spencer K. Clark, Johann P. S. Dahm, Eddie C. Davis, Oliver D. Elbert, Rhea C. George, Lucas M. Harris, Brian Henn, Anna Kwa, W. Andre Perkins, Oliver Watt-Meyer, Tobias F. Wicky, Christopher S. Bretherton, and Oliver Fuhrer
Geosci. Model Dev., 14, 4401–4409, https://doi.org/10.5194/gmd-14-4401-2021, https://doi.org/10.5194/gmd-14-4401-2021, 2021
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FV3GFS is a weather and climate model written in Fortran. It uses Fortran so that it can run fast, but this makes it hard to add features if you do not (or even if you do) know Fortran. We have written a Python interface to FV3GFS that lets you import the Fortran model as a Python package. We show examples of how this is used to write
modelscripts, which reproduce or build on what the Fortran model can do. You could do this same wrapping for any compiled model, not just FV3GFS.
Bin Mu, Bo Qin, and Shijin Yuan
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-213, https://doi.org/10.5194/gmd-2021-213, 2021
Revised manuscript accepted for GMD
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Considering the sophisticated energy exchanges and multivariate coupling in ENSO, we subjectively incorporate the prior physical knowledge into the modeling process and build up an ENSO deep learning forecast model with a multivariate air-sea coupler, named ENSO-ASC, the performance of which outperforms the other state-of-the-art models. The extensive experiments indicate that ENSO-ASC is a powerful tool for both the ENSO prediction and for the analysis of the underlying complex mechanisms.
Alexander Pasternack, Jens Grieger, Henning W. Rust, and Uwe Ulbrich
Geosci. Model Dev., 14, 4335–4355, https://doi.org/10.5194/gmd-14-4335-2021, https://doi.org/10.5194/gmd-14-4335-2021, 2021
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Decadal climate ensemble forecasts are increasingly being used to guide adaptation measures. To ensure the applicability of these probabilistic predictions, inherent systematic errors of the prediction system must be adjusted. Since it is not clear which statistical model is optimal for this purpose, we propose a recalibration strategy with a systematic model selection based on non-homogeneous boosting for identifying the most relevant features for both ensemble mean and ensemble spread.
Daniel J. Lunt, Deepak Chandan, Alan M. Haywood, George M. Lunt, Jonathan C. Rougier, Ulrich Salzmann, Gavin A. Schmidt, and Paul J. Valdes
Geosci. Model Dev., 14, 4307–4317, https://doi.org/10.5194/gmd-14-4307-2021, https://doi.org/10.5194/gmd-14-4307-2021, 2021
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Often in science we carry out experiments with computers in which several factors are explored, for example, in the field of climate science, how the factors of greenhouse gases, ice, and vegetation affect temperature. We can explore the relative importance of these factors by
swapping in and outdifferent values of these factors, and can also carry out experiments with many different combinations of these factors. This paper discusses how best to analyse the results from such experiments.
Markus Adloff, Andy Ridgwell, Fanny M. Monteiro, Ian J. Parkinson, Alexander J. Dickson, Philip A. E. Pogge von Strandmann, Matthew S. Fantle, and Sarah E. Greene
Geosci. Model Dev., 14, 4187–4223, https://doi.org/10.5194/gmd-14-4187-2021, https://doi.org/10.5194/gmd-14-4187-2021, 2021
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We present the first representation of the trace metals Sr, Os, Li and Ca in a 3D Earth system model (cGENIE). The simulation of marine metal sources (weathering, hydrothermal input) and sinks (deposition) reproduces the observed concentrations and isotopic homogeneity of these metals in the modern ocean. With these new tracers, cGENIE can be used to test hypotheses linking these metal cycles and the cycling of other elements like O and C and simulate their dynamic response to external forcing.
Alessandro Anav, Adriana Carillo, Massimiliano Palma, Maria Vittoria Struglia, Ufuk Utku Turuncoglu, and Gianmaria Sannino
Geosci. Model Dev., 14, 4159–4185, https://doi.org/10.5194/gmd-14-4159-2021, https://doi.org/10.5194/gmd-14-4159-2021, 2021
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The Mediterranean Basin is a complex region, characterized by the presence of pronounced topography and a complex land–sea distribution including a considerable number of islands and straits; these features generate strong local atmosphere–sea interactions.
Regional Earth system models have been developed and used to study both present and future Mediterranean climate systems. The main aims of this paper are to present and evaluate the newly developed regional Earth system model ENEA-REG.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
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In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Dirk Barbi, Nadine Wieters, Paul Gierz, Miguel Andrés-Martínez, Deniz Ural, Fatemeh Chegini, Sara Khosravi, and Luisa Cristini
Geosci. Model Dev., 14, 4051–4067, https://doi.org/10.5194/gmd-14-4051-2021, https://doi.org/10.5194/gmd-14-4051-2021, 2021
Cléa Denamiel, Petra Pranić, Damir Ivanković, Iva Tojčić, and Ivica Vilibić
Geosci. Model Dev., 14, 3995–4017, https://doi.org/10.5194/gmd-14-3995-2021, https://doi.org/10.5194/gmd-14-3995-2021, 2021
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The atmospheric results of the Adriatic Sea and Coast (AdriSC) climate simulation (1987–2017) are evaluated against available observational datasets in the Adriatic region. Generally, the AdriSC model performs better than regional climate models that have resolutions that are 4 times more coarse, except concerning summer temperatures, which are systematically underestimated. High-resolution climate models may thus provide new insights about the local impacts of global warming in the Adriatic.
Guy Munhoven
Geosci. Model Dev., 14, 3603–3631, https://doi.org/10.5194/gmd-14-3603-2021, https://doi.org/10.5194/gmd-14-3603-2021, 2021
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Sea-floor sediments play an important role in biogeochemical cycling of elements (e.g. carbon, silicon, nutrients) in the ocean. Realistic sediment modules are, however, not yet commonly used in global ocean biogeochemical models. Here we present MEDUSA, a model of the processes taking place in the surface sea-floor sediments which control the interaction between the sediments and the ocean. MEDUSA can be configured to meet the exact needs of any given ocean biogeochemical model.
Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson
Geosci. Model Dev., 14, 3539–3551, https://doi.org/10.5194/gmd-14-3539-2021, https://doi.org/10.5194/gmd-14-3539-2021, 2021
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We present a novel stochastic approach based on Markov chains to estimate climate model weights of multi-model ensemble means. This approach showed improved performance (better correlation with observations) over existing alternatives during cross-validation and model-as-truth tests. The results of this comparative analysis should serve to motivate further studies in applications of Markov chain and other nonlinear methods to find optimal model weights for constructing ensemble means.
Arseniy Karagodin-Doyennel, Eugene Rozanov, Timofei Sukhodolov, Tatiana Egorova, Alfonso Saiz-Lopez, Carlos A. Cuevas, Rafael P. Fernandez, Tomás Sherwen, Rainer Volkamer, Theodore K. Koenig, Tanguy Giroud, and Thomas Peter
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-107, https://doi.org/10.5194/gmd-2021-107, 2021
Revised manuscript accepted for GMD
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Here, we present the iodine chemistry module in the SOCOL-AERv2 model. The obtained iodine distribution showed a good agreement when validated against other simulations and available observations. We also estimated the contribution of iodine to ozone loss in the case of present-day iodine emissions, the sensitivity of ozone to doubled iodine emissions, and when considering only organic or inorganic iodine sources. The new model can be used as a tool to further studies of iodine effect on ozone.
Jens Pfafferott, Sascha Rißmann, Matthias Sühring, Farah Kanani-Sühring, and Björn Maronga
Geosci. Model Dev., 14, 3511–3519, https://doi.org/10.5194/gmd-14-3511-2021, https://doi.org/10.5194/gmd-14-3511-2021, 2021
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The building model is integrated via an urban surface model into the urban climate model.
There is a strong interaction between the built environment and the urban climate.
According to the building energy concept, the energy demand results in a waste heat; this is directly transferred to the urban environment.
The impact of buildings on the urban climate is defined by different physical building parameters with different technical facilities for ventilation, heating and cooling.
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
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We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Katja Weigel, Lisa Bock, Bettina K. Gier, Axel Lauer, Mattia Righi, Manuel Schlund, Kemisola Adeniyi, Bouwe Andela, Enrico Arnone, Peter Berg, Louis-Philippe Caron, Irene Cionni, Susanna Corti, Niels Drost, Alasdair Hunter, Llorenç Lledó, Christian Wilhelm Mohr, Aytaç Paçal, Núria Pérez-Zanón, Valeriu Predoi, Marit Sandstad, Jana Sillmann, Andreas Sterl, Javier Vegas-Regidor, Jost von Hardenberg, and Veronika Eyring
Geosci. Model Dev., 14, 3159–3184, https://doi.org/10.5194/gmd-14-3159-2021, https://doi.org/10.5194/gmd-14-3159-2021, 2021
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This work presents new diagnostics for the Earth System Model Evaluation Tool (ESMValTool) v2.0 on the hydrological cycle, extreme events, impact assessment, regional evaluations, and ensemble member selection. The ESMValTool v2.0 diagnostics are developed by a large community of scientists aiming to facilitate the evaluation and comparison of Earth system models (ESMs) with a focus on the ESMs participating in the Coupled Model Intercomparison Project (CMIP).
Jun'ya Takakura, Shinichiro Fujimori, Kiyoshi Takahashi, Naota Hanasaki, Tomoko Hasegawa, Yukiko Hirabayashi, Yasushi Honda, Toshichika Iizumi, Chan Park, Makoto Tamura, and Yasuaki Hijioka
Geosci. Model Dev., 14, 3121–3140, https://doi.org/10.5194/gmd-14-3121-2021, https://doi.org/10.5194/gmd-14-3121-2021, 2021
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To simplify calculating economic impacts of climate change, statistical methods called emulators are developed and evaluated. There are trade-offs between model complexity and emulation performance. Aggregated economic impacts can be approximated by relatively simple emulators, but complex emulators are necessary to accommodate finer-scale economic impacts.
Hao-Jhe Hong and Thomas Reichler
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-149, https://doi.org/10.5194/gmd-2021-149, 2021
Revised manuscript accepted for GMD
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The Arctic wintertime circulation of the stratosphere has pronounced impacts on the troposphere and surface climate. Changes in the stratospheric circulation can lead to either increases or decreases in Arctic ozone. Understanding the interactions between ozone and the circulation will have the benefit of model prediction for the climate. This study introduces an economical and fast simplified model that represents the realistic distribution of ozone and its interaction with the circulation.
Meng-Zhuo Zhang, Zhongfeng Xu, Ying Han, and Weidong Guo
Geosci. Model Dev., 14, 3079–3094, https://doi.org/10.5194/gmd-14-3079-2021, https://doi.org/10.5194/gmd-14-3079-2021, 2021
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The Multivariable Integrated Evaluation Tool (MVIETool) is a simple-to-use and straightforward tool designed for evaluation and intercomparison of climate models in terms of vector fields or multiple fields. The tool incorporates some new improvements in vector field evaluation (VFE) and multivariable integrated evaluation (MVIE) methods, which are introduced in this paper.
Sam J. Silva, Po-Lun Ma, Joseph C. Hardin, and Daniel Rothenberg
Geosci. Model Dev., 14, 3067–3077, https://doi.org/10.5194/gmd-14-3067-2021, https://doi.org/10.5194/gmd-14-3067-2021, 2021
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The activation of aerosol into cloud droplets is an important but uncertain process in the Earth system. The physical and chemical interactions that govern this process are too computationally expensive to explicitly resolve in modern Earth system models. Here, we demonstrate how hybrid machine learning approaches can provide a potential path forward, enabling the representation of the more detailed physics and chemistry at a reduced computational cost while still retaining physical information.
Reinel Sospedra-Alfonso, William J. Merryfield, George J. Boer, Viatsheslav V. Kharin, Woo-Sung Lee, Christian Seiler, and James R. Christian
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-1, https://doi.org/10.5194/gmd-2021-1, 2021
Revised manuscript accepted for GMD
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CanESM5 decadal predictions started from observed climate states represent the observed evolution of upper ocean temperatures, surface climate, and the carbon cycle better than ones not started from observed climate states for several years into the forecast. This is due both to better representing climate internal variability, and to corrections of the model response to external forcing including changes in GHG emissions and aerosols.
Nicholas J. Leach, Stuart Jenkins, Zebedee Nicholls, Christopher J. Smith, John Lynch, Michelle Cain, Tristram Walsh, Bill Wu, Junichi Tsutsui, and Myles R. Allen
Geosci. Model Dev., 14, 3007–3036, https://doi.org/10.5194/gmd-14-3007-2021, https://doi.org/10.5194/gmd-14-3007-2021, 2021
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This paper presents an update of the FaIR simple climate model, which can estimate the impact of anthropogenic greenhouse gas and aerosol emissions on the global climate. This update aims to significantly increase the structural simplicity of the model, making it more understandable and transparent. This simplicity allows it to be implemented in a wide range of environments, including Excel. We suggest that it could be used widely in academia, corporate research, and education.
Tongwen Wu, Rucong Yu, Yixiong Lu, Weihua Jie, Yongjie Fang, Jie Zhang, Li Zhang, Xiaoge Xin, Laurent Li, Zaizhi Wang, Yiming Liu, Fang Zhang, Fanghua Wu, Min Chu, Jianglong Li, Weiping Li, Yanwu Zhang, Xueli Shi, Wenyan Zhou, Junchen Yao, Xiangwen Liu, He Zhao, Jinghui Yan, Min Wei, Wei Xue, Anning Huang, Yaocun Zhang, Yu Zhang, Qi Shu, and Aixue Hu
Geosci. Model Dev., 14, 2977–3006, https://doi.org/10.5194/gmd-14-2977-2021, https://doi.org/10.5194/gmd-14-2977-2021, 2021
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This paper presents the high-resolution version of the Beijing Climate Center (BCC) Climate System Model, BCC-CSM2-HR, and describes its climate simulation performance including the atmospheric temperature and wind; precipitation; and the tropical climate phenomena such as TC, MJO, QBO, and ENSO. BCC-CSM2-HR is our model version contributing to the HighResMIP. We focused on its updates and differential characteristics from its predecessor, the medium-resolution version BCC-CSM2-MR.
Olivier Marti, Sébastien Nguyen, Pascale Braconnot, Sophie Valcke, Florian Lemarié, and Eric Blayo
Geosci. Model Dev., 14, 2959–2975, https://doi.org/10.5194/gmd-14-2959-2021, https://doi.org/10.5194/gmd-14-2959-2021, 2021
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State-of-the-art Earth system models, like the ones used in CMIP6, suffer from temporal inconsistencies at the ocean–atmosphere interface. In this study, a mathematically consistent iterative Schwarz method is used as a reference. Its tremendous computational cost makes it unusable for production runs, but it allows us to evaluate the error made when using legacy coupling schemes. The impact on the climate at longer timescales of days to decades is not evaluated.
Steven R. Brus, Phillip J. Wolfram, Luke P. Van Roekel, and Jessica D. Meixner
Geosci. Model Dev., 14, 2917–2938, https://doi.org/10.5194/gmd-14-2917-2021, https://doi.org/10.5194/gmd-14-2917-2021, 2021
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Wind-generated waves are an important process in the global climate system. They mediate many interactions between the ocean, atmosphere, and sea ice. Models which describe these waves are computationally expensive and have often been excluded from coupled Earth system models. To address this, we have developed a capability for the WAVEWATCH III model which allows model resolution to be varied globally across the coastal open ocean. This allows for improved accuracy at reduced computing time.
Elisa Ziegler and Kira Rehfeld
Geosci. Model Dev., 14, 2843–2866, https://doi.org/10.5194/gmd-14-2843-2021, https://doi.org/10.5194/gmd-14-2843-2021, 2021
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Past climate changes are the only record of how the climate responds to changes in conditions on Earth, but simulations with complex climate models are challenging. We extended a simple climate model such that it simulates the development of temperatures over time. In the model, changes in carbon dioxide and ice distribution affect the simulated temperatures the most. The model is very efficient and can therefore be used to examine past climate changes happening over long periods of time.
Qun Liu, Matthew Collins, Penelope Maher, Stephen I. Thomson, and Geoffrey K. Vallis
Geosci. Model Dev., 14, 2801–2826, https://doi.org/10.5194/gmd-14-2801-2021, https://doi.org/10.5194/gmd-14-2801-2021, 2021
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Clouds play an vital role in Earth's energy budget, and even a small change in cloud fields can have a large impact on the climate system. They also bring lots of uncertainties to climate models. Here we implement a simple diagnostic cloud scheme in order to reproduce the general radiative properties of clouds. The scheme can capture some key features of the cloud fraction and cloud radiative properties and thus provide a useful tool to explore unsolved problems relating to clouds.
Martina Messmer, Santos J. González-Rojí, Christoph C. Raible, and Thomas F. Stocker
Geosci. Model Dev., 14, 2691–2711, https://doi.org/10.5194/gmd-14-2691-2021, https://doi.org/10.5194/gmd-14-2691-2021, 2021
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Sensitivity experiments with the WRF model are run to find an optimal parameterization setup for precipitation around Mount Kenya at a scale that resolves convection (1 km). Precipitation is compared against many weather stations and gridded observational data sets. Both the temporal correlation of precipitation sums and pattern correlations show that fewer nests lead to a more constrained simulation with higher correlation. The Grell–Freitas cumulus scheme obtains the most accurate results.
Pengfei Wang, Jinrong Jiang, Pengfei Lin, Mengrong Ding, Junlin Wei, Feng Zhang, Lian Zhao, Yiwen Li, Zipeng Yu, Weipeng Zheng, Yongqiang Yu, Xuebin Chi, and Hailong Liu
Geosci. Model Dev., 14, 2781–2799, https://doi.org/10.5194/gmd-14-2781-2021, https://doi.org/10.5194/gmd-14-2781-2021, 2021
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Global ocean general circulation models are a fundamental tool for oceanography research, ocean forecast, and climate change research. The increasing resolution will greatly improve simulations of the models, but it also demands much more computing resources. In this study, we have ported an ocean general circulation model to a heterogeneous computing system and have developed a 3–5 km model version. A 14-year integration has been conducted and the preliminary results have been evaluated.
Chao Sun, Li Liu, Ruizhe Li, Xinzhu Yu, Hao Yu, Biao Zhao, Guansuo Wang, Juanjuan Liu, Fangli Qiao, and Bin Wang
Geosci. Model Dev., 14, 2635–2657, https://doi.org/10.5194/gmd-14-2635-2021, https://doi.org/10.5194/gmd-14-2635-2021, 2021
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Data assimilation (DA) provides better initial states of model runs by combining observations and models. This work focuses on the technical challenges in developing a coupled ensemble-based DA system and proposes a new DA framework DAFCC1 based on C-Coupler2. DAFCC1 enables users to conveniently integrate a DA method into a model with automatic and efficient data exchanges. A sample DA system that combines GSI/EnKF and FIO-AOW demonstrates the effectiveness of DAFCC1.
Andrew J. Wiltshire, Eleanor J. Burke, Sarah E. Chadburn, Chris D. Jones, Peter M. Cox, Taraka Davies-Barnard, Pierre Friedlingstein, Anna B. Harper, Spencer Liddicoat, Stephen Sitch, and Sönke Zaehle
Geosci. Model Dev., 14, 2161–2186, https://doi.org/10.5194/gmd-14-2161-2021, https://doi.org/10.5194/gmd-14-2161-2021, 2021
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Limited nitrogen availbility can restrict the growth of plants and their ability to assimilate carbon. It is important to include the impact of this process on the global land carbon cycle. This paper presents a model of the coupled land carbon and nitrogen cycle, which is included within the UK Earth System model to improve projections of climate change and impacts on ecosystems.
Pirkka Ollinaho, Glenn D. Carver, Simon T. K. Lang, Lauri Tuppi, Madeleine Ekblom, and Heikki Järvinen
Geosci. Model Dev., 14, 2143–2160, https://doi.org/10.5194/gmd-14-2143-2021, https://doi.org/10.5194/gmd-14-2143-2021, 2021
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OpenEnsemble 1.0 is a novel dataset that aims to open ensemble or probabilistic weather forecasting research up to the academic community. The dataset contains atmospheric states that are required for running model forecasts of atmospheric evolution. Our capacity to observe the atmosphere is limited; thus, a single reconstruction of the atmospheric state contains some errors. Our dataset provides sets of 50 slightly different atmospheric states so that these errors can be taken into account.
Yan Sun, Daniel S. Goll, Jinfeng Chang, Philippe Ciais, Betrand Guenet, Julian Helfenstein, Yuanyuan Huang, Ronny Lauerwald, Fabienne Maignan, Victoria Naipal, Yilong Wang, Hui Yang, and Haicheng Zhang
Geosci. Model Dev., 14, 1987–2010, https://doi.org/10.5194/gmd-14-1987-2021, https://doi.org/10.5194/gmd-14-1987-2021, 2021
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We evaluated the performance of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 against remote sensing, ground-based measurement networks and ecological databases. The simulated carbon, nitrogen and phosphorus fluxes among different spatial scales are generally in good agreement with data-driven estimates. However, the recent carbon sink in the Northern Hemisphere is substantially underestimated. Potential causes and model development priorities are discussed.
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