Articles | Volume 12, issue 10
https://doi.org/10.5194/gmd-12-4275-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4275-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
10 Oct 2019
Methods for assessment of models |
| 10 Oct 2019
| 10 Oct 2019
What should we do when a model crashes? Recommendations for global sensitivity analysis of Earth and environmental systems models
Razi Sheikholeslami
CORRESPONDING AUTHOR
School of Environment and Sustainability, University of Saskatchewan,
Saskatoon, Canada
Global Institute for Water Security, University of Saskatchewan,
Saskatoon, Canada
Saman Razavi
School of Environment and Sustainability, University of Saskatchewan,
Saskatoon, Canada
Global Institute for Water Security, University of Saskatchewan,
Saskatoon, Canada
Department of Civil, Geological, and Environmental Engineering,
University of Saskatchewan, Saskatoon, Canada
Amin Haghnegahdar
School of Environment and Sustainability, University of Saskatchewan,
Saskatoon, Canada
Global Institute for Water Security, University of Saskatchewan,
Saskatoon, Canada
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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-618, https://doi.org/10.5194/hess-2021-618, 2022
Manuscript not accepted for further review
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Andrew R. Slaughter and Saman Razavi
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Fuad Yassin, Saman Razavi, Jefferson S. Wong, Alain Pietroniro, and Howard Wheater
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-207, https://doi.org/10.5194/hess-2019-207, 2019
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Gonzalo Sapriza-Azuri, Pablo Gamazo, Saman Razavi, and Howard S. Wheater
Hydrol. Earth Syst. Sci., 22, 3295–3309, https://doi.org/10.5194/hess-22-3295-2018, https://doi.org/10.5194/hess-22-3295-2018, 2018
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Canada is very susceptible to recurrent droughts, which have damaging impacts on regional water resources and agriculture. However, nationwide drought assessments are currently lacking and impacted by limited ground-based observations. We delineate two major drought regions (Prairies and northern central) over Canada and link drought characteristics to external factors of climate variability. This study helps to determine when the drought events occur, their duration, and how often they occur.
Jefferson S. Wong, Saman Razavi, Barrie R. Bonsal, Howard S. Wheater, and Zilefac E. Asong
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This study was conducted to quantify the spatial and temporal variability of the errors associated with various gridded precipitation products in Canada. Overall, WFDEI [GPCC] and CaPA performed best with respect to different performance measures, followed by ANUSPLIN and WEDEI [CRU]. Princeton and NARR demonstrated the lowest quality. Comparing the climate model-simulated products, PCIC ensembles generally performed better than NA-CORDEX ensembles in terms of reliability in four seasons.
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Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
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Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
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We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
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In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
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In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
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We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
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For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
EGUsphere, https://doi.org/10.5194/egusphere-2024-1, https://doi.org/10.5194/egusphere-2024-1, 2024
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This article gives an overview introduction of the IAP-CAS S2S (sub-seasonal to seasonal) ensemble forecasting system and MJO forecast evaluation of the system. Compared to other S2S models, the IAP-CAS model has its advantages but also exhibits some biases, including underdispersive ensemble, overestimated amplitude and faster propagation speed when forecasting MJO. We also provide the explanation towards these biases and prospects for further improvement of this system in the future.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Joseph P. Hollowed, Christiane Jablonowski, Hunter Y. Brown, Benjamin R. Hillman, Diana L. Bull, and Joseph L. Hart
EGUsphere, https://doi.org/10.5194/egusphere-2024-335, https://doi.org/10.5194/egusphere-2024-335, 2024
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Large volcanic eruptions deposit material into the upper-atmosphere, which is capable of altering temperature and wind patterns of the Earth's atmosphere for years following. This research describes a new method of simulating these effects in an idealized, efficient atmospheric model. A volcanic eruption of sulfur dioxide is described with a simplified set of physical rules, which eventually cools the planetary surface. This model has been designed as a testbed for climate attribution studies.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Cited articles
Annan, J. D., Hargreaves, J. C., Edwards, N. R., and Marsh, R.: Parameter
estimation in an intermediate complexity earth system model using an
ensemble Kalman filter, Ocean. Model., 8, 135–154, https://doi.org/10.1016/j.ocemod.2003.12.004, 2005.
Asadzadeh, M., Razavi, S., Tolson, B. A., and Fay, D.: Pre-emption
strategies for efficient multi-objective optimization: Application to the
development of Lake Superior regulation plan, Environ. Modell. Softw., 54,
128–141, https://doi.org/10.1016/j.envsoft.2014.01.005, 2014.
Beretta, L. and Santaniello, A.: Nearest neighbor imputation algorithms: a
critical evaluation, BMC Med. Inform. Decis., 16, 74, https://doi.org/10.1186/s12911-016-0318-z, 2016.
Burnash, R. J. C.: The NWS River forecast system-catchment modeling, in:
Computer Models of Watershed Hydrology, edited by: Singh, V. P., Water
Resources Publication, Highlands Ranch, Colorado, USA, 311–366, 1995.
Choudhury, B. J. and Idso, S. B.: An empirical model for stomatal
resistance of field-grown wheat, Agr. Forest. Meteorol., 36, 65–82,
https://doi.org/10.1016/0168-1923(85)90066-8, 1985.
Camm, J. D., Raturi, A. S., and Tsubakitani, S.: Cutting big M down to size,
Interfaces, 20, 61–66, https://doi.org/10.1287/inte.20.5.61,1990.
Clark, M. P. and Kavetski, D.: Ancient numerical daemons of conceptual
hydrological modeling: 1. Fidelity and efficiency of time stepping schemes,
Water. Resour. Res., 46, W10510, https://doi.org/10.1029/2009WR008894, 2010.
Crombecq, K., Laermans, E., and Dhaene, T.: Efficient space-filling and
non-collapsing sequential design strategies for simulation-based modelling,
Eur. J. Oper. Res., 214, 683–696, https://doi.org/10.1016/j.ejor.2011.05.032, 2011.
Edwards, N. R. and Marsh, R.: Uncertainties due to transport-parameter
sensitivity in an efficient 3-D ocean-climate model, Clim. Dynam., 24,
415–433, https://doi.org/10.1007/s00382-004-0508-8, 2005.
Edwards, N. R., Cameron, D., and Rougier, J.: Precalibrating an intermediate
complexity climate model, Clim. Dynam., 37, 1469–1482, https://doi.org/10.1007/s00382-010-0921-0, 2011.
Estrada, E.: Quasirandom geometric networks from low-discrepancy sequences,
Phys. Rev. E., 96, 022314, https://doi.org/10.1103/PhysRevE.96.022314, 2017.
Fisher, M. J., Charles-Edwards, D. A., and Ludlow, M. M.: An analysis of the
effects of repeated short-term soil water deficits on stomatal conductance
to carbon dioxide and leaf photosynthesis by the legume Macroptilium
atropurpureum cv. Siratro, Funct. Plant. Biol., 8, 347–357, https://doi.org/10.1071/PP9810347, 1981.
Forrester, A. I. and Keane, A. J.: Recent advances in surrogate-based
optimization, Prog. Aerosp. Sci., 45, 50–79, https://doi.org/10.1016/j.paerosci.2008.11.001, 2009.
Gupta, H. V. and Razavi, S.: Revisiting the basis of sensitivity analysis
for dynamical Earth system models, Water. Resour. Res., 54, 8692–8717,
https://doi.org/10.1029/2018WR022668, 2018.
Haghnegahdar, A. and Razavi, S.: Insights into sensitivity analysis of
earth and environmental systems models: On the impact of parameter
perturbation scale, Environ. Modell. Softw., 95, 115–131, https://doi.org/10.1016/j.envsoft.2017.03.031, 2017.
Haghnegahdar, A., Tolson, B. A., Craig, J. R., and Paya, K. T.: Assessing the
performance of a semi-distributed hydrological model under various watershed
discretization schemes, Hydrol. Process., 29, 4018–4031, https://doi.org/10.1002/hyp.10550, 2015.
Herrera, L. J., Pomares, H., Rojas, I., Guillén, A., Rubio, G., and
Urquiza, J.: Global and local modelling in RBF networks, Neurocomputing,
74, 2594–2602, https://doi.org/10.1016/j.neucom.2011.03.027, 2011.
Hudak, A. T., Crookston, N. L., Evans, J. S., Hall, D. E., and Falkowski, M. J.:
Nearest neighbor imputation of species-level, plot-scale forest structure
attributes from LiDAR data, Remote. Sens. Environ., 112, 2232–2245,
https://doi.org/10.1016/j.rse.2007.10.009, 2008.
Jin, R., Chen, W., and Simpson, T. W.: Comparative studies of metamodelling
techniques under multiple modelling criteria, Struct. Multidiscip. O.,
23, 1–13, https://doi.org/10.1007/s00158-001-0160-4, 2001.
Jones, D. R.: A taxonomy of global optimization methods based on response
surfaces, J. Global Optim., 21, 345–383, https://doi.org/10.1023/A:1012771025575, 2001.
Kavetski, D. and Clark, M. P.: Ancient numerical daemons of conceptual
hydrological modeling: 2. Impact of time stepping schemes on model analysis
and prediction, Water. Resour. Res., 46, W10511, https://doi.org/10.1029/2009WR008896, 2010.
Kavetski, D., Kuczera, G., and Franks, S. W.: Calibration of conceptual
hydrological models revisited: 1. Overcoming numerical artefacts, J.
Hydrol., 320, 173–186, https://doi.org/10.1016/j.jhydrol.2005.07.012, 2006.
Kelleher, C., Wagener, T., McGlynn, B., Ward, A. S., Gooseff, M. N., and
Payn, R. A.: Identifiability of transient storage model parameters along a
mountain stream, Water. Resour. Res., 49, 5290–5306, https://doi.org/10.1002/wrcr.20413, 2013.
Kitayama, S. and Yamazaki, K.: Simple estimate of the width in Gaussian
kernel with adaptive scaling technique, Appl. Soft. Comp., 11,
4726–4737, https://doi.org/10.1016/j.asoc.2011.07.011, 2011.
Kouwen, N., Soulis, E. D., Pietroniro, A., Donald, J., and Harrington, R.
A.: Grouped response units for distributed hydrologic modelling, J. Water.
Res. Plan. Man., 119, 289–305, https://doi.org/10.1061/(ASCE)0733-9496(1993)119:3(289), 1993.
Krogh, S. A., Pomeroy, J. W., and Marsh, P.: Diagnosis of the hydrology of a
small Arctic basin at the tundra-taiga transition using a physically based
hydrological model, J. Hydrol., 550, 685–703, https://doi.org/10.1016/j.jhydrol.2017.05.042, 2017.
Leroux, N. R. and Pomeroy, J. W.: Simulation of capillary overshoot in snow
combining trapping of the wetting phase with a non-equilibrium Richards
equation model, Water. Resour. Res., 54, 236–248, https://doi.org/10.1029/2018WR022969, 2019.
Li, S., Rupp, D. E., Hawkins, L., Mote, P. W., McNeall, D., Sparrow, S. N., Wallom, D. C. H., Betts, R. A., and Wettstein, J. J.: Reducing climate model biases by exploring parameter space with large ensembles of climate model simulations and statistical emulation, Geosci. Model Dev., 12, 3017–3043, https://doi.org/10.5194/gmd-12-3017-2019, 2019.
Lin, Y.: An Efficient Robust Concept Exploration Method and Sequential
Exploratory Experimental Design, PhD thesis,
Georgia Institute of Technology, USA, 2004.
Lindström, G., Johansson, B., Persson, M., Gardelin, M., and
Bergström, S.: Development and test of the distributed HBV-96
hydrological model, J. Hydrol., 201, 272–288, 1997.
Little, R. J. A. and Rubin, D. B.: Statistical Analysis with Missing Data,
John Wiley & Sons, New York, USA, 1987.
Liu, Y. and Gopalakrishnan, V.: An overview and evaluation of recent
machine learning imputation methods using cardiac imaging data, Data, 2,
8, https://doi.org/10.3390/data2010008, 2017.
Lucas, D. D., Klein, R., Tannahill, J., Ivanova, D., Brandon, S., Domyancic, D., and Zhang, Y.: Failure analysis of parameter-induced simulation crashes in climate models, Geosci. Model Dev., 6, 1157–1171, https://doi.org/10.5194/gmd-6-1157-2013, 2013.
McRoberts, R. E.: Diagnostic tools for nearest neighbors techniques when
used with satellite imagery, Remote. Sens. Environ., 113, 489–499,
https://doi.org/10.1016/j.rse.2008.06.015, 2009.
McRoberts, R. E., Nelson, M. D., and Wendt, D. G.: Stratified estimation of
forest area using satellite imagery, inventory data, and the k-Nearest
Neighbors technique, Remote. Sens. Environ., 82, 457–468, https://doi.org/10.1016/S0034-4257(02)00064-0, 2002.
Metzger, C., Nilsson, M. B., Peichl, M., and Jansson, P.-E.: Parameter interactions and sensitivity analysis for modelling carbon heat and water fluxes in a natural peatland, using CoupModel v5, Geosci. Model Dev., 9, 4313–4338, https://doi.org/10.5194/gmd-9-4313-2016, 2016.
Mullur, A. A. and Messac, A.: Metamodeling using extended radial basis
functions: a comparative approach, Eng. Comput., 21, 203–217, https://doi.org/10.1007/s00366-005-0005-7, 2006.
Nossent, J., Elsen, P., and Bauwens, W.: Sobol' sensitivity analysis of a complex environmental model, Environ. Model. Software., 26, 1515–1525, https://doi.org/10.1016/j.envsoft.2011.08.010, 2011.
Paja, W., Wrzesien, M., Niemiec, R., and Rudnicki, W. R.: Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models, Geosci. Model Dev., 9, 1065–1072, https://doi.org/10.5194/gmd-9-1065-2016, 2016.
Pappenberger, F., Beven, K. J., Ratto, M., and Matgen, P.: Multi-method
global sensitivity analysis of flood inundation models, Adv. Water. Resour.,
31, 1–14, https://doi.org/10.1016/j.advwatres.2007.04.009,
2008.
Pietroniro, A., Fortin, V., Kouwen, N., Neal, C., Turcotte, R., Davison, B., Verseghy, D., Soulis, E. D., Caldwell, R., Evora, N., and Pellerin, P.: Development of the MESH modelling system for hydrological ensemble forecasting of the Laurentian Great Lakes at the regional scale, Hydrol. Earth Syst. Sci., 11, 1279–1294, https://doi.org/10.5194/hess-11-1279-2007, 2007.
Raj, R., van der Tol, C., Hamm, N. A. S., and Stein, A.: Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output, Geosci. Model Dev., 11, 83–101, https://doi.org/10.5194/gmd-11-83-2018, 2018.
Razavi, S. and Gupta, H. V.: What do we mean by sensitivity analysis? The
need for comprehensive characterization of “global” sensitivity in Earth
and Environmental systems models, Water. Resour. Res., 51, 3070–3092.
https://doi.org/10.1002/2014WR016527, 2015.
Razavi, S. and Gupta, H. V.: A new framework for comprehensive, robust, and
efficient global sensitivity analysis: 1. Theory, Water. Resour. Res., 52,
423–439, https://doi.org/10.1002/2015WR017558, 2016a.
Razavi, S. and Gupta, H. V.: A new framework for comprehensive, robust, and
efficient global sensitivity analysis: 2. Application, Water. Resour. Res.,
52, 440–455, https://doi.org/10.1002/2015WR017559, 2016b.
Razavi, S. and Gupta, H. V.: A multi-method generalized global sensitivity
matrix approach to accounting for the dynamical nature of Earth and
environmental systems models, Environ. Modell. Softw., 114, 1–11, https://doi.org/10.1016/j.envsoft.2018.12.002, 2019.
Razavi, S., Tolson, B. A., Matott, L. S., Thomson, N. R., MacLean, A., and
Seglenieks, F. R.: Reducing the computational cost of automatic calibration
through model pre-emption, Water. Resour. Res., 46, W11523, https://doi.org/10.1029/2009WR008957, 2010.
Razavi, S., Tolson, B. A., and Burn, D. H.: Review of surrogate modeling in
water resources, Water. Resour. Res., 48, W07401, https://doi.org/10.1029/2011WR011527, 2012a.
Razavi, S., Tolson, B. A., and Burn, D. H.: Numerical assessment of
metamodelling strategies in computationally intensive optimization, Environ.
Modell. Softw., 34, 67–86, https://doi.org/10.1016/j.envsoft.2011.09.010, 2012b.
Razavi, S., Sheikholeslami, R., Gupta, H. V., and Haghnegahdar, A.:
VARS-TOOL: A toolbox for comprehensive, efficient, and robust sensitivity
and uncertainty analysis, Environ. Modell. Softw., 112, 95–107, https://doi.org/10.1016/j.envsoft.2018.10.005, 2019.
Safta, C., Ricciuto, D. M., Sargsyan, K., Debusschere, B., Najm, H. N., Williams, M., and Thornton, P. E.: Global sensitivity analysis, probabilistic calibration, and predictive assessment for the data assimilation linked ecosystem carbon model, Geosci. Model Dev., 8, 1899–1918, https://doi.org/10.5194/gmd-8-1899-2015, 2015.
Saltelli, A. and Annoni, P.: How to avoid a perfunctory sensitivity
analysis, Environ. Modell. Softw., 25, 1508–1517, https://doi.org/10.1016/j.envsoft.2010.04.012, 2010.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli, D., Saisana, M., and Tarantola, S.: Global Sensitivity Analysis. The Primer, John Wiley & Sons, Chichester, West Sussex, UK, https://doi.org/10.1002/9780470725184, 2008.
Saltelli, A., Annoni, P., Azzini, I., Campolongo, F., Ratto, M., and
Tarantola, S.: Variance based sensitivity analysis of model output. Design
and estimator for the total sensitivity index, Comput. Phys. Commun.,
181, 259–270, https://doi.org/10.1016/j.cpc.2009.09.018,
2010.
Sarrazin, F., Pianosi, F., and Wagener, T.: Global sensitivity analysis of environmental models: convergence and validation, Environ. Model. Softw., 79, 135–152, https://doi.org/10.1016/j.envsoft.2016.02.005, 2016.
Sheikholeslami, R. and Razavi, S.: Progressive Latin Hypercube Sampling: An
efficient approach for robust sampling-based analysis of environmental
models, Environ. Modell. Softw., 93, 109–126, https://doi.org/10.1016/j.envsoft.2017.03.010, 2017.
Sheikholeslami, R., Yassin, F., Lindenschmidt, K. E., and Razavi, S.:
Improved understanding of river ice processes using global sensitivity
analysis approaches, J. Hydrol. Eng., 22, 04017048, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001574, 2017.
Sheikholeslami, R., Razavi, S., Gupta, H. V., Becker, W., and Haghnegahdar,
A.: Global sensitivity analysis for high-dimensional problems: how to
objectively group factors and measure robustness and convergence while
reducing computational cost, Environ. Modell. Softw., 111, 282–299,
https://doi.org/10.1016/j.envsoft.2018.09.002, 2019.
Singh, V. P. and Frevert, D. K.: Mathematical Models of Small Watershed
Hydrology and Applications, 950 pp., Water Resources Publication, Highlands
Ranch, Colorado, USA, 2002.
Tomppo, E., Nilsson, M., Rosengren, M., Aalto, P., and Kennedy, P.:
Simultaneous use of Landsat-TM and IRS-1C WiFS data in estimating large area
tree stem volume and aboveground biomass, Remote. Sens. Environ., 82,
156–171, https://doi.org/10.1016/S0034-4257(02)00031-7, 2002.
Treglown, C.: Predicting crashes in climate model simulations through artificial neural networks, 1st ANU Bio-inspired Computing conference (ABCs 2018), Canberra, Australia, 20 July 2018, Paper 172, 2018.
Tutz, G. and Ramzan, S.: Improved methods for the imputation of missing
data by nearest neighbor methods, Comput. Stat. Data. An., 90, 84–99,
https://doi.org/10.1016/j.csda.2015.04.009, 2015.
Vanrolleghem, P. A., Mannina, G., Cosenza, A., and Neumann, M. B.: Global
sensitivity analysis for urban water quality modelling: Terminology,
convergence and comparison of different methods, J. Hydrol., 522, 339–352,
https://doi.org/10.1016/j.jhydrol.2014.12.056, 2015.
Verseghy, D.: CLASS – the Canadian Land Surface Scheme (Version 3.6),
Technical Documentation, Science and Technology Branch, Environment and
Climate Change Canada, Toronto, Tech. Rep., 179 pp. 2012.
Verseghy, D. L.: CLASS – A Canadian land surface scheme for GCMs, I. Soil
model, Int. J. Climatol., 11, 111–133, https://doi.org/10.1002/joc.3370110202, 1991.
Verseghy, D. L., McFarlane, N. A., and Lazare, M.: CLASS – A Canadian land
surface scheme for GCMs, II. Vegetation model and coupled runs, Int. J.
Climatol., 13, 347–370, https://doi.org/10.1002/joc.3370130402, 1993.
Webster, M., Scott, J., Sokolov, A., and Stone, P.: Estimating probability
distributions from complex models with bifurcations: The case of ocean
circulation collapse, J. Environ. Syst., 31, 1–21, https://doi.org/10.2190/A518-W844-4193-4202, 2004.
Williamson, D.: Exploratory ensemble designs for environmental models using
k-extended Latin Hypercubes, Environmetrics, 26, 268–283, https://doi.org/10.1002/env.2335, 2015.
Williamson, D., Goldstein, M., Allison, L., Blaker, A., Challenor, P.,
Jackson, L., and Yamazaki, K.: History matching for exploring and reducing
climate model parameter space using observations and a large perturbed
physics ensemble, Clim. Dynam., 41, 1703–1729, https://doi.org/10.1007/s00382-013-1896-4, 2013.
Williamson, D. B., Blaker, A. T., and Sinha, B.: Tuning without over-tuning: parametric uncertainty quantification for the NEMO ocean model, Geosci. Model Dev., 10, 1789–1816, https://doi.org/10.5194/gmd-10-1789-2017, 2017.
Yassin, F., Razavi, S., Wheater, H., Sapriza-Azuri, G., Davison, B., and
Pietroniro, A.: Enhanced identification of a hydrologic model using
streamflow and satellite water storage data: a multi-criteria sensitivity
analysis and optimization approach, Hydrol. Process., 31, 3320–3333,
https://doi.org/10.1002/hyp.11267, 2017.
Zhao, D. and Xue, D.: A comparative study of metamodeling methods
considering sample quality merits, Struct. Multidiscip. O., 42, 923–938,
https://doi.org/10.1007/s00158-010-0529-3, 2010.
Short summary
The ever-growing complexity of Earth and environmental system models can pose many types of software development and implementation issues such as parameter-induced simulation crashes, which are mainly caused by the violation of numerical stability conditions. Here, we introduce a new approach to handle crashed simulations when performing sensitivity analysis. Our results show that this approach can comply well with the dimensionality of the model, sample size, and the number of crashes.
The ever-growing complexity of Earth and environmental system models can pose many types of...
