Articles | Volume 14, issue 5
Model description paper 20 May 2021
Model description paper | 20 May 2021
TransEBM v. 1.0: description, tuning, and validation of a transient model of the Earth's energy balance in two dimensions
Elisa Ziegler and Kira Rehfeld
No articles found.
Janica C. Bühler, Carla Roesch, Moritz Kirschner, Louise Sime, Max D. Holloway, and Kira Rehfeld
Clim. Past, 17, 985–1004,Short summary
We present three new isotope-enabled simulations for the last millennium (850–1850 CE) and compare them to records from a global speleothem database. Offsets between the simulated and measured oxygen isotope ratios are fairly small. While modeled oxygen isotope ratios are more variable on decadal timescales, proxy records are more variable on (multi-)centennial timescales. This could be due to a lack of long-term variability in complex model simulations, but proxy biases cannot be excluded.
Raphaël Hébert, Kira Rehfeld, and Thomas Laepple
Nonlin. Processes Geophys. Discuss.,
Revised manuscript accepted for NPGShort summary
Paleoclimate proxy data is essential to broaden our understanding of climate variability. There remains however challenges for traditional methods of variability analysis to be applied to such data which is usually irregular. We perform a comparative analysis of different methods of scaling analysis, which provide variability estimates as a function of timescales, applied to irregular paleoclimate proxy data.
Laia Comas-Bru, Kira Rehfeld, Carla Roesch, Sahar Amirnezhad-Mozhdehi, Sandy P. Harrison, Kamolphat Atsawawaranunt, Syed Masood Ahmad, Yassine Ait Brahim, Andy Baker, Matthew Bosomworth, Sebastian F. M. Breitenbach, Yuval Burstyn, Andrea Columbu, Michael Deininger, Attila Demény, Bronwyn Dixon, Jens Fohlmeister, István Gábor Hatvani, Jun Hu, Nikita Kaushal, Zoltán Kern, Inga Labuhn, Franziska A. Lechleitner, Andrew Lorrey, Belen Martrat, Valdir Felipe Novello, Jessica Oster, Carlos Pérez-Mejías, Denis Scholz, Nick Scroxton, Nitesh Sinha, Brittany Marie Ward, Sophie Warken, Haiwei Zhang, and SISAL Working Group members
Earth Syst. Sci. Data, 12, 2579–2606,Short summary
This paper presents an updated version of the SISAL (Speleothem Isotope Synthesis and Analysis) database. This new version contains isotopic data from 691 speleothem records from 294 cave sites and new age–depth models, including their uncertainties, for 512 speleothems.
Kira Rehfeld, Raphaël Hébert, Juan M. Lora, Marcus Lofverstrom, and Chris M. Brierley
Earth Syst. Dynam., 11, 447–468,Short summary
Under continued anthropogenic greenhouse gas emissions, it is likely that global mean surface temperature will continue to increase. Little is known about changes in climate variability. We analyze surface climate variability and compare it to mean change in colder- and warmer-than-present climate model simulations. In most locations, but not on subtropical land, simulated temperature variability up to decadal timescales decreases with mean temperature, and precipitation variability increases.
Laia Comas-Bru, Sandy P. Harrison, Martin Werner, Kira Rehfeld, Nick Scroxton, Cristina Veiga-Pires, and SISAL working group members
Clim. Past, 15, 1557–1579,Short summary
We use an updated version of the Speleothem Isotopes Synthesis and Analysis (SISAL) database and palaeoclimate simulations generated using the ECHAM5-wiso isotope-enabled climate model to provide a protocol for using speleothem isotopic data for model evaluation, including screening the observations and the optimum period for the modern observational baseline. We also illustrate techniques through which the absolute isotopic values during any time period could be used for model evaluation.
Matthias M. May and Kira Rehfeld
Earth Syst. Dynam., 10, 1–7,Short summary
Current CO2 emission rates are incompatible with the 2 °C target for global warming. Negative emission technologies are therefore an important basis for climate policy scenarios. We show that photoelectrochemical CO2 reduction might be a viable, high-efficiency alternative to biomass-based approaches, which reduce competition for arable land. To develop them, chemical reactions have to be optimized for CO2 removal, which deviates from energetic efficiency optimization in solar fuel applications.
Kira Rehfeld, Mathias Trachsel, Richard J. Telford, and Thomas Laepple
Clim. Past, 12, 2255–2270,Short summary
Indirect evidence on past climate comes from the former composition of ecological communities such as plants, preserved as pollen grains in sediments of lakes. Transfer functions convert relative counts of species to a climatologically meaningful scale (e.g. annual mean temperature in degrees C). We show that the fundamental assumptions in the algorithms impact the reconstruction results in he idealized model world, in particular if the reconstructed variables were not ecologically relevant.
K. Rehfeld, N. Molkenthin, and J. Kurths
Nonlin. Processes Geophys., 21, 691–703,
L. Tupikina, K. Rehfeld, N. Molkenthin, V. Stolbova, N. Marwan, and J. Kurths
Nonlin. Processes Geophys., 21, 705–711,
N. Molkenthin, K. Rehfeld, V. Stolbova, L. Tupikina, and J. Kurths
Nonlin. Processes Geophys., 21, 651–657,
K. Rehfeld and J. Kurths
Clim. Past, 10, 107–122,
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Geosci. Model Dev., 14, 4335–4355,Short summary
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Geosci. Model Dev., 14, 4307–4317,Short summary
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Geosci. Model Dev., 14, 4187–4223,Short summary
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Geosci. Model Dev., 14, 4159–4185,Short summary
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Geosci. Model Dev., 14, 4117–4141,Short summary
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Dirk Barbi, Nadine Wieters, Paul Gierz, Miguel Andrés-Martínez, Deniz Ural, Fatemeh Chegini, Sara Khosravi, and Luisa Cristini
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Cléa Denamiel, Petra Pranić, Damir Ivanković, Iva Tojčić, and Ivica Vilibić
Geosci. Model Dev., 14, 3995–4017,Short summary
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Geosci. Model Dev., 14, 3603–3631,Short summary
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Max Kulinich, Yanan Fan, Spiridon Penev, Jason P. Evans, and Roman Olson
Geosci. Model Dev., 14, 3539–3551,Short summary
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Jens Pfafferott, Sascha Rißmann, Matthias Sühring, Farah Kanani-Sühring, and Björn Maronga
Geosci. Model Dev., 14, 3511–3519,Short summary
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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,Short summary
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,Short summary
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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,Short summary
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.
Meng-Zhuo Zhang, Zhongfeng Xu, Ying Han, and Weidong Guo
Geosci. Model Dev., 14, 3079–3094,Short summary
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,Short summary
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.
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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.
Qun Liu, Matthew Collins, Penelope Maher, Stephen I. Thomson, and Geoffrey K. Vallis
Geosci. Model Dev., 14, 2801–2826,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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,Short summary
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.
Hui Wan, Shixuan Zhang, Philip J. Rasch, Vincent E. Larson, Xubin Zeng, and Huiping Yan
Geosci. Model Dev., 14, 1921–1948,Short summary
Numerical models used in weather and climate research and prediction unavoidably contain numerical errors resulting from temporal discretization, and the impact of such errors can be substantial. Complex process interactions often make it difficult to pinpoint the exact sources of such errors. This study uses a series of sensitivity experiments to identify components in a global atmosphere model that are responsible for time step sensitivities in various cloud regimes.
Johannes Horak, Marlis Hofer, Ethan Gutmann, Alexander Gohm, and Mathias W. Rotach
Geosci. Model Dev., 14, 1657–1680,Short summary
This process-based evaluation of the atmospheric model ICAR is conducted to derive recommendations to increase the likelihood of its results being correct for the right reasons. We conclude that a different diagnosis of the atmospheric background state is necessary, as well as a model top at an elevation of at least 10 km. Alternative boundary conditions at the top were not found to be effective in reducing this model top elevation. The results have wide implications for future ICAR studies.
Yong Wang, Guang J. Zhang, Shaocheng Xie, Wuyin Lin, George C. Craig, Qi Tang, and Hsi-Yen Ma
Geosci. Model Dev., 14, 1575–1593,Short summary
A stochastic deep convection parameterization is implemented into the US Department of Energy Energy Exascale Earth System Model Atmosphere Model version 1 (EAMv1). Compared to the default model, the well-known problem of
too much light rain and too little heavy rainis largely alleviated over the tropics with the stochastic scheme. Results from this study provide important insights into the model performance of EAMv1 when stochasticity is included in the deep convective parameterization.
Sonia Jerez, Laura Palacios-Peña, Claudia Gutiérrez, Pedro Jiménez-Guerrero, Jose María López-Romero, Enrique Pravia-Sarabia, and Juan Pedro Montávez
Geosci. Model Dev., 14, 1533–1551,Short summary
This research explores the role of aerosols when modeling surface solar radiation at regional scales (over Europe). A set of model experiments was performed with and without dynamical modeling of atmospheric aerosols and their direct and indirect effects on radiation. Results showed significant differences in the simulated solar radiation, mainly driven by the aerosol impact on cloudiness, which calls for caution when interpreting model experiments that do not include aerosols.
Jeremy McGibbon, Noah D. Brenowitz, Mark Cheeseman, Spencer K. Clark, Johann Dahm, Eddie Davis, Oliver D. Elbert, Rhea C. George, Lucas M. Harris, Brian Henn, Anna Kwa, W. Andre Perkins, Oliver Watt-Meyer, Tobias Wicky, Christopher S. Bretherton, and Oliver Fuhrer
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
The FV3GFS is a weather and climate model written in Fortran. It uses Fortran so it can run fast, but this makes it hard to add features if you don't (or even if you do) know Fortran. We've 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.
Sara Top, Lola Kotova, Lesley De Cruz, Svetlana Aniskevich, Leonid Bobylev, Rozemien De Troch, Natalia Gnatiuk, Anne Gobin, Rafiq Hamdi, Arne Kriegsmann, Armelle Reca Remedio, Abdulla Sakalli, Hans Van De Vyver, Bert Van Schaeybroeck, Viesturs Zandersons, Philippe De Maeyer, Piet Termonia, and Steven Caluwaerts
Geosci. Model Dev., 14, 1267–1293,Short summary
Detailed climate data are needed to assess the impact of climate change on human and natural systems. The performance of two high-resolution regional climate models, ALARO-0 and REMO2015, was investigated over central Asia, a vulnerable region where detailed climate information is scarce. In certain subregions the produced climate data are suitable for impact studies, but bias adjustment is required for subregions where significant biases have been identified.
Yaqiong Lu and Xianyu Yang
Geosci. Model Dev., 14, 1253–1265,Short summary
Crop growth in land surface models normally requires high-temporal-resolution climate data, but such high-temporal-resolution climate data are not provided by many climate model simulations due to expensive storage, which limits modeling choices if there is an interest in a particular climate simulation that only saved monthly outputs. Our work provides an alternative way to use the monthly climate for crop yield projections. Such an approach could be easily adopted by other crop models.
Rumi Ohgaito, Akitomo Yamamoto, Tomohiro Hajima, Ryouta O'ishi, Manabu Abe, Hiroaki Tatebe, Ayako Abe-Ouchi, and Michio Kawamiya
Geosci. Model Dev., 14, 1195–1217,Short summary
Using the MIROC-ES2L Earth system model, selected time periods of the past were simulated. The ability to simulate the past is also an evaluation of the performance of the model in projecting global warming. Simulations for 21 000, 6000, and 127 000 years ago, and a simulation for 1000 years starting in 850 CE were simulated. The results showed that the model can generally describe past climate change.
Qiong Zhang, Ellen Berntell, Josefine Axelsson, Jie Chen, Zixuan Han, Wesley de Nooijer, Zhengyao Lu, Qiang Li, Qiang Zhang, Klaus Wyser, and Shuting Yang
Geosci. Model Dev., 14, 1147–1169,Short summary
Paleoclimate modelling has long been regarded as a strong out-of-sample test bed of the climate models that are used for the projection of future climate changes. Here, we document the model experimental setups for the three past warm periods with EC-Earth3-LR and present the results on the large-scale features. The simulations demonstrate good performance of the model in capturing the climate response under different climate forcings.
Gill M. Martin, Richard C. Levine, José M. Rodriguez, and Michael Vellinga
Geosci. Model Dev., 14, 1007–1035,Short summary
Our study highlights a number of different techniques that can be employed to investigate the sources of model error. We demonstrate how this methodology can be used to identify the regions and model components responsible for the development of long-standing errors in the Asian summer monsoon. Once these are known, further work can be done to explore the local processes contributing to this behaviour and their sensitivity to changes in physical parameterisations and/or model resolution.
Trang Van Pham, Christian Steger, Burkhardt Rockel, Klaus Keuler, Ingo Kirchner, Mariano Mertens, Daniel Rieger, Günther Zängl, and Barbara Früh
Geosci. Model Dev., 14, 985–1005,Short summary
A new regional climate model was prepared based on a weather forecast model. Slow processes of the climate system such as ocean state development and greenhouse gas emissions were implemented. A model infrastructure and evaluation tools were also prepared to facilitate long-term simulations and model evalution. The first ICON-CLM results were close to observations and comparable to those from COSMO-CLM, the recommended model being used at the Deutscher Wetterdienst and CLM Community.
Klaus Wyser, Torben Koenigk, Uwe Fladrich, Ramon Fuentes-Franco, Mehdi Pasha Karami, and Tim Kruschke
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
This paper describes the large ensemble done by SMHI with the EC-Earth3 climate model. The ensemble comprises 50 realisations of the historical experiment and four different future projections for CMIP6. We describe the creation of the initial states for the large 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.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
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.
Michael Steiner, Beiping Luo, Thomas Peter, Michael C. Pitts, and Andrea Stenke
Geosci. Model Dev., 14, 935–959,Short summary
We evaluate polar stratospheric clouds (PSCs) as simulated by the chemistry–climate model (CCM) SOCOLv3.1 in comparison with measurements by the CALIPSO satellite. A cold bias results in an overestimated PSC area and mountain-wave ice is underestimated, but we find overall good temporal and spatial agreement of PSC occurrence and composition. This work confirms previous studies indicating that simplified PSC schemes may also achieve good approximations of the fundamental properties of PSCs.
Zhaoyuan Yu, Dongshuang Li, Zhengfang Zhang, Wen Luo, Yuan Liu, Zengjie Wang, and Linwang Yuan
Geosci. Model Dev., 14, 875–887,Short summary
Few lossy compression methods consider both the global and local multidimensional coupling correlations, which could lead to information loss in data compression. Here we develop an adaptive lossy compression method, Adaptive-HGFDR, to capture both the global and local variation of multidimensional coupling correlations and improve approximation accuracy. The method can achieve good compression performances for most flux variables with significant spatiotemporal heterogeneity.
Franziska Winterstein and Patrick Jöckel
Geosci. Model Dev., 14, 661–674,Short summary
Atmospheric methane is currently a hot topic in climate research. This is partly due to its chemically active nature. We introduce a simplified approach to simulate methane in climate models to enable large sensitivity studies by reducing computational cost but including the crucial feedback of methane on stratospheric water vapour. We further provide options to simulate the isotopic content of methane and to generate output for an inverse optimization technique for emission estimation.
Ruth Petrie, Sébastien Denvil, Sasha Ames, Guillaume Levavasseur, Sandro Fiore, Chris Allen, Fabrizio Antonio, Katharina Berger, Pierre-Antoine Bretonnière, Luca Cinquini, Eli Dart, Prashanth Dwarakanath, Kelsey Druken, Ben Evans, Laurent Franchistéguy, Sébastien Gardoll, Eric Gerbier, Mark Greenslade, David Hassell, Alan Iwi, Martin Juckes, Stephan Kindermann, Lukasz Lacinski, Maria Mirto, Atef Ben Nasser, Paola Nassisi, Eric Nienhouse, Sergey Nikonov, Alessandra Nuzzo, Clare Richards, Syazwan Ridzwan, Michel Rixen, Kim Serradell, Kate Snow, Ag Stephens, Martina Stockhause, Hans Vahlenkamp, and Rick Wagner
Geosci. Model Dev., 14, 629–644,Short summary
This paper describes the infrastructure that is used to distribute Coupled Model Intercomparison Project Phase 6 (CMIP6) data around the world for analysis by the climate research community. It is expected that there will be ~20 PB (petabytes) of data available for analysis. The operations team performed a series of preparation "data challenges" to ensure all components of the infrastructure were operational for when the data became available for timely data distribution and subsequent analysis.
Camilla Mathison, Andrew J. Challinor, Chetan Deva, Pete Falloon, Sébastien Garrigues, Sophie Moulin, Karina Williams, and Andy Wiltshire
Geosci. Model Dev., 14, 437–471,Short summary
Sequential cropping (also known as multiple or double cropping) is a common cropping system, particularly in tropical regions. Typically, land surface models only simulate a single crop per year. To understand how sequential crops influence surface fluxes, we implement sequential cropping in JULES to simulate all the crops grown within a year at a given location in a seamless way. We demonstrate the method using a site in Avignon, four locations in India and a regional run for two Indian states.
Chao Wang, Xingqin An, Qing Hou, Zhaobin Sun, Yanjun Li, and Jiangtao Li
Geosci. Model Dev., 14, 337–350,
Kalyn Dorheim, Steven J. Smith, and Ben Bond-Lamberty
Geosci. Model Dev., 14, 365–375,Short summary
Simple climate models are frequently used in research and decision-making communities because of their tractability and low computational cost. Simple climate models are diverse, including highly idealized and process-based models. Here we present a hybrid approach that combines the strength of two types of simple climate models in a flexible framework. This hybrid approach has provided insights into the climate system and opens an avenue for investigating radiative forcing uncertainties.
David N. Bresch and Gabriela Aznar-Siguan
Geosci. Model Dev., 14, 351–363,Short summary
Climate change is a fact and adaptation a necessity. The Economics of Climate Adaptation methodology provides a framework to integrate risk and reward perspectives of different stakeholders, underpinned by the CLIMADA impact modelling platform. This extended version of CLIMADA enables risk assessment and options appraisal in a modular form and occasionally bespoke fashion yet with high reusability of functionalities to foster usage in interdisciplinary studies and international collaboration.
Adam T. Blaker, Manoj Joshi, Bablu Sinha, David P. Stevens, Robin S. Smith, and Joël J.-M. Hirschi
Geosci. Model Dev., 14, 275–293,Short summary
FORTE 2.0 is a flexible coupled atmosphere–ocean general circulation model that can be run on modest hardware. We present two 2000-year simulations which show that FORTE 2.0 is capable of producing a stable climate. Earlier versions of FORTE were used for a wide range of studies, ranging from aquaplanet configurations to investigating the cold European winters of 2009–2010. This paper introduces the updated model for which the code and configuration are now publicly available.
Ah-Hyun Kim, Seong Soo Yum, Dong Yeong Chang, and Minsu Park
Geosci. Model Dev., 14, 259–273,Short summary
A new method to estimate the sulfate aerosol hygroscopicity parameter (κSO4) is suggested that can consider κSO4 for two different sulfate species instead of prescribing a single κSO4 value, as in most previous studies. The new method simulates more realistic cloud droplet concentrations and, thus, a more realistic cloud albedo effect than the original method. The new method is simple and readily applicable to modeling studies investigating sulfate aerosols’ effect in aerosol–cloud interactions.
Brigitta Szabó, Melanie Weynants, and Tobias K. D. Weber
Geosci. Model Dev., 14, 151–175,Short summary
This paper presents updated European prediction algorithms (euptf2) to compute soil hydraulic parameters from easily available soil properties. The new algorithms lead to significantly better predictions and provide a built-in prediction uncertainty computation. The influence of predictor variables on predicted soil hydraulic properties is explored and practical guidance on how to use the derived PTFs is provided. A website and an R package facilitate easy application of the updated predictions.
Bathiany, S., Dijkstra, H., Crucifix, M., Dakos, V., Brovkin, V., Williamson, M. S., Lenton, T. M., and Scheffer, M.: Beyond bifurcation: using complex models to understand and predict abrupt climate change, Dynamics and Statistics of the Climate System, https://doi.org/10.1093/climsys/dzw004, 2016. a, b
Berger, A. L.: Long-Term Variations of Daily Insolation and Quaternary Climatic Changes, J. Atmos. Sci., 35, 2362–2367, https://doi.org/10.1175/1520-0469(1978)035<2362:LTVODI>2.0.CO;2, 1978. a, b, c, d
Bühler, J. C., Roesch, C., Kirschner, M., Sime, L., Holloway, M. D., and Rehfeld, K.: Comparison of the oxygen isotope signatures in speleothem records and iHadCM3 model simulations for the last millennium, Clim. Past Discuss. [preprint], https://doi.org/10.5194/cp-2020-121, in review, 2020. a, b, c
Collins, M., Knutti, R., Arblaster, J., Dufresne, J.-L., Fichefet, T., Friedlingstein, P., Gao, X., Gutowski, W., Johns, T., Krinner, G., Shongwe, M., Tebaldi, C., Weaver, A., and Wehner, M.: Long-term Climate Change: Projections, Commitments and Irreversibility, book section 12, Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1029–1136, https://doi.org/10.1017/CBO9781107415324.024, 2013. a
Crowley, T. J. and North, G. R.: Abrupt climate change and Extinction Events in Earth History, Science, 240, 996–1002, 1988. a
Crowley, T. J., Hyde, W. T., and Short, D. A.: Seasonal cycle variations on the supercontinent of Pangaea, Geology, 17, 457–460, https://doi.org/10.1130/0091-7613(1989)017<0457:SCVOTS>2.3.CO;2, 1989. a, b, c, d
Dommenget, D., Nice, K., Bayr, T., Kasang, D., Stassen, C., and Rezny, M.: The Monash Simple Climate Model experiments (MSCM-DB v1.0): an interactive database of mean climate, climate change, and scenario simulations, Geosci. Model Dev., 12, 2155–2179, https://doi.org/10.5194/gmd-12-2155-2019, 2019. a
Ghil, M.: A Mathematical Theory of Climate Sensitivity or, How to Deal With Both Anthropogenic Forcing and Natural Variability?, in: Climate Change: Multidecadal and Beyond, edited by: Chang, C.-P., Ghil, M., Latif, M., and Wallace, J. M., chap. 2, World Scientific, 31–51, https://doi.org/10.1142/9789814579933_0002, 2015. a, b
Goosse, H., Brovkin, V., Fichefet, T., Haarsma, R., Huybrechts, P., Jongma, J., Mouchet, A., Selten, F., Barriat, P.-Y., Campin, J.-M., Deleersnijder, E., Driesschaert, E., Goelzer, H., Janssens, I., Loutre, M.-F., Morales Maqueda, M. A., Opsteegh, T., Mathieu, P.-P., Munhoven, G., Pettersson, E. J., Renssen, H., Roche, D. M., Schaeffer, M., Tartinville, B., Timmermann, A., and Weber, S. L.: Description of the Earth system model of intermediate complexity LOVECLIM version 1.2, Geosci. Model Dev., 3, 603–633, https://doi.org/10.5194/gmd-3-603-2010, 2010. a
Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M., Johns, T. C., Mitchell, J. F. B., and Wood, R. A.: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Clim. Dynam., 16, 147–168, https://doi.org/10.1007/s003820050010, 2000. a, b, c
Harvey, L. D. and Schneider, S. H.: Transient climate response to external forcing on 100-104 year time scales, 2. Sensitivity experiments with a seasonal, hemispherically averaged, coupled atmosphere, land, and ocean energy balance model, J. Geophys. Res., 90, 2207–2222, https://doi.org/10.1029/JD090iD01p02207, 1985. a
Kyle, H. L., Weiss, M., and Ardanuy, P.: Cloud, Surface Temperature, and Outgoing Longwave Radiation for the Period from 1979 to 1990, J. Climate, 8, 2644–2658, https://doi.org/10.1175/1520-0442(1995)008<2644:CSTAOL>2.0.CO;2, 1995. a, b
Liu, Z., Otto-Bliesner, B. L., He, F., Brady, E. C., Tomas, R., Clark, P. U., Carlson, A. E., Lynch-Stieglitz, J., Curry, W., Brook, E., Erickson, D., Jacob, R., Kutzbach, J., and Cheng, J.: Transient simulation of last deglaciation with a new mechanism for bolling-allerød warming, Science, 325, 310–314, https://doi.org/10.1126/science.1171041, 2009. a
Max Planck Institute für Meteorologie: Climate Data Operators, available at: https://code.mpimet.mpg.de/projects/cdo, last access: 30 April 2021. a
Neukom, R., Barboza, L. A., Erb, M. P., Shi, F., Emile-Geay, J., Evans, M. N., Franke, J., Kaufman, D. S., Lücke, L., Rehfeld, K., Schurer, A., Zhu, F., Brönnimann, S., Hakim, G. J., Henley, B. J., Ljungqvist, F. C., McKay, N., Valler, V., and von Gunten, L.: Consistent multidecadal variability in global temperature reconstructions and simulations over the Common Era, Nat. Geosci., 12, 643–649, https://doi.org/10.1038/s41561-019-0400-0, 2019. a, b, c, d
North, G. R.: The small ice cap instability in diffusive climate models, J. Atmos. Sci., 41, 3390–3395, https://doi.org/10.1175/1520-0469(1984)041<3390:TSICII>2.0.CO;2, 1984. a, b
North, G. R. and Coakley, J. A.: Differences between seasonal and mean annual energy balance model calculations of climate and climate sensitivity, J. Atmos. Sci., 36, 1189–1204, https://doi.org/10.1175/1520-0469(1979)036<1189:DBSAMA>2.0.CO;2, 1979. a
North, G. R., Cahalan, R. F., and Coakley, J. A.: Energy-Balance Climate Models, Rev. Geophys. Space Phys., 16, 2000–2001, 1980. a
Otto-Bliesner, B. L., Brady, E. C., Fasullo, J., Jahn, A., Landrum, L., Stevenson, S., Rosenbloom, N., Mai, A., and Strand, G.: Climate variability and change since 850 CE. An ensemble approach with the community earth system model, B. Am. Meteorol. Soc., 97, 787–801, https://doi.org/10.1175/BAMS-D-14-00233.1, 2016. a, b
Otto-Bliesner, B. L., Braconnot, P., Harrison, S. P., Lunt, D. J., Abe-Ouchi, A., Albani, S., Bartlein, P. J., Capron, E., Carlson, A. E., Dutton, A., Fischer, H., Goelzer, H., Govin, A., Haywood, A., Joos, F., LeGrande, A. N., Lipscomb, W. H., Lohmann, G., Mahowald, N., Nehrbass-Ahles, C., Pausata, F. S. R., Peterschmitt, J.-Y., Phipps, S. J., Renssen, H., and Zhang, Q.: The PMIP4 contribution to CMIP6 – Part 2: Two interglacials, scientific objective and experimental design for Holocene and Last Interglacial simulations, Geosci. Model Dev., 10, 3979–4003, https://doi.org/10.5194/gmd-10-3979-2017, 2017. a
Rohling, E. J., Sluijs, A., Dijkstra, H. A., Köhler, P., Van De Wal, R. S., Von Der Heydt, A. S., Beerling, D. J., Berger, A., Bijl, P. K., Crucifix, M., Deconto, R., Drijfhout, S. S., Fedorov, A., Foster, G. L., Ganopolski, A., Hansen, J., Hönisch, B., Hooghiemstra, H., Huber, M., Huybers, P., Knutti, R., Lea, D. W., Lourens, L. J., Lunt, D., Masson-Demotte, V., Medina-Elizalde, M., Otto-Bliesner, B., Pagani, M., Pälike, H., Renssen, H., Royer, D. L., Siddall, M., Valdes, P., Zachos, J. C., and Zeebe, R. E.: Making sense of palaeoclimate sensitivity, Nature, 491, 683–691, https://doi.org/10.1038/nature11574, 2012. a
Ruddiman, W. F.: Earth's Climate. Past and Future, W. H. Freeman and Company, 3rd Edn., 2014. a
Schmidt, G. A., Annan, J. D., Bartlein, P. J., Cook, B. I., Guilyardi, E., Hargreaves, J. C., Harrison, S. P., Kageyama, M., LeGrande, A. N., Konecky, B., Lovejoy, S., Mann, M. E., Masson-Delmotte, V., Risi, C., Thompson, D., Timmermann, A., Tremblay, L.-B., and Yiou, P.: Using palaeo-climate comparisons to constrain future projections in CMIP5, Clim. Past, 10, 221–250, https://doi.org/10.5194/cp-10-221-2014, 2014. a, b, c
Sellers, W. D.: A Global Climatic Model Based on the Energy Balance of the Earth-Atmosphere System, J. Appl. Meteorol., 8, 392–400, https://doi.org/10.1175/1520-0450(1969)008<0392:AGCMBO>2.0.CO;2, 1969. a, b, c, d
Stap, L. B., van de Wal, R. S., de Boer, B., Köhler, P., Hoencamp, J. H., Lohmann, G., Tuenter, E., and Lourens, L. J.: Modeled Influence of Land Ice and CO2 on Polar Amplification and Paleoclimate Sensitivity During the Past 5 Million Years, Paleoceanogr. Paleoclim., 33, 381–394, https://doi.org/10.1002/2017PA003313, 2018. a
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.
Past climate changes are the only record of how the climate responds to changes in conditions on...