Articles | Volume 13, issue 8
https://doi.org/10.5194/gmd-13-3475-2020
© Author(s) 2020. 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-13-3475-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
05 Aug 2020
Model experiment description paper |
| 05 Aug 2020
Numerical study of the seasonal thermal and gas regimes of the largest artificial reservoir in western Europe using the LAKE 2.0 model
Institute of Earth Sciences – ICT, University of Évora, 7000-671 Évora, Portugal
Victor Stepanenko
Lomonosov Moscow State University, Research Computing Center 119234 Moscow, Russia
Lomonosov Moscow State University, Faculty of Geography, 119234 Moscow, Russia
Moscow Center for Fundamental and Applied Mathematics, 119234 Moscow, Russia
Rui Salgado
Institute of Earth Sciences – ICT, University of Évora, 7000-671 Évora, Portugal
Department of Physics, ICT, University of Évora, 7000-671 Évora, Portugal
Miguel Potes
Institute of Earth Sciences – ICT, University of Évora, 7000-671 Évora, Portugal
Department of Physics, ICT, University of Évora, 7000-671 Évora, Portugal
Alexandra Penha
Institute of Earth Sciences – ICT, University of Évora, 7000-671 Évora, Portugal
Water Laboratory, University of Évora, P.I.T.E. Rua da Barba Rala Nº1, 7005-345 Évora, Portugal
Maria Helena Novais
Institute of Earth Sciences – ICT, University of Évora, 7000-671 Évora, Portugal
Water Laboratory, University of Évora, P.I.T.E. Rua da Barba Rala Nº1, 7005-345 Évora, Portugal
Gonçalo Rodrigues
Institute of Earth Sciences – ICT, University of Évora, 7000-671 Évora, Portugal
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Maksim Iakunin, Rui Salgado, and Miguel Potes
Hydrol. Earth Syst. Sci., 22, 5191–5210, https://doi.org/10.5194/hess-22-5191-2018, https://doi.org/10.5194/hess-22-5191-2018, 2018
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Lakes and reservoirs can affect local weather regimes but usually it is difficult to trace and assess it. In this work we used the Meso-NH atmospheric model to study the impact of the Alqueva reservoir, one of the largest artificial lakes in western Europe, located in the southeast of Portugal, on meteorological parameters and the formation of a lake breeze system. The magnitude of this impact as well as the intensity of the breeze are shown in the paper.
Jason A. Clark, Elchin E. Jafarov, Ken D. Tape, Benjamin M. Jones, and Victor Stepanenko
Geosci. Model Dev., 15, 7421–7448, https://doi.org/10.5194/gmd-15-7421-2022, https://doi.org/10.5194/gmd-15-7421-2022, 2022
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Lakes in the Arctic are important reservoirs of heat. Under climate warming scenarios, we expect Arctic lakes to warm the surrounding frozen ground. We simulate water temperatures in three Arctic lakes in northern Alaska over several years. Our results show that snow depth and lake ice strongly affect water temperatures during the frozen season and that more heat storage by lakes would enhance thawing of frozen ground.
Elena Shevnina, Miguel Potes, Timo Vihma, Tuomas Naakka, Pankaj Ramji Dhote, and Praveen Kumar Thakur
The Cryosphere, 16, 3101–3121, https://doi.org/10.5194/tc-16-3101-2022, https://doi.org/10.5194/tc-16-3101-2022, 2022
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The evaporation over an ice-free glacial lake was measured in January 2018, and the uncertainties inherent to five indirect methods were quantified. Results show that in summer up to 5 mm of water evaporated daily from the surface of the lake located in Antarctica. The indirect methods underestimated the evaporation over the lake's surface by up to 72 %. The results are important for estimating the evaporation over polar regions where a growing number of glacial lakes have recently been evident.
Victor Lomov, Victor Stepanenko, Maria Grechushnikova, and Irina Repina
EGUsphere, https://doi.org/10.5194/egusphere-2022-329, https://doi.org/10.5194/egusphere-2022-329, 2022
Preprint withdrawn
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We present the first mechanistic model LAKE2.3 for prediction of methane emissions from artificial reservoirs. Estimates of CH4 emissions from the Mozhaysk reservoir (Moscow region) provided by the model are demonstrated. Methane annual emissions through diffusion, ebullition and downstream degassing according to in situ measurements and model simulations are presented. The experiments with the model allowed to determine the most sensitive model parameters for calibration of methane fluxes.
Malgorzata Golub, Wim Thiery, Rafael Marcé, Don Pierson, Inne Vanderkelen, Daniel Mercado-Bettin, R. Iestyn Woolway, Luke Grant, Eleanor Jennings, Benjamin M. Kraemer, Jacob Schewe, Fang Zhao, Katja Frieler, Matthias Mengel, Vasiliy Y. Bogomolov, Damien Bouffard, Marianne Côté, Raoul-Marie Couture, Andrey V. Debolskiy, Bram Droppers, Gideon Gal, Mingyang Guo, Annette B. G. Janssen, Georgiy Kirillin, Robert Ladwig, Madeline Magee, Tadhg Moore, Marjorie Perroud, Sebastiano Piccolroaz, Love Raaman Vinnaa, Martin Schmid, Tom Shatwell, Victor M. Stepanenko, Zeli Tan, Bronwyn Woodward, Huaxia Yao, Rita Adrian, Mathew Allan, Orlane Anneville, Lauri Arvola, Karen Atkins, Leon Boegman, Cayelan Carey, Kyle Christianson, Elvira de Eyto, Curtis DeGasperi, Maria Grechushnikova, Josef Hejzlar, Klaus Joehnk, Ian D. Jones, Alo Laas, Eleanor B. Mackay, Ivan Mammarella, Hampus Markensten, Chris McBride, Deniz Özkundakci, Miguel Potes, Karsten Rinke, Dale Robertson, James A. Rusak, Rui Salgado, Leon van der Linden, Piet Verburg, Danielle Wain, Nicole K. Ward, Sabine Wollrab, and Galina Zdorovennova
Geosci. Model Dev., 15, 4597–4623, https://doi.org/10.5194/gmd-15-4597-2022, https://doi.org/10.5194/gmd-15-4597-2022, 2022
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Carlos Miranda Rodrigues, Madalena Moreira, Rita Cabral Guimarães, and Miguel Potes
Hydrol. Earth Syst. Sci., 24, 5973–5984, https://doi.org/10.5194/hess-24-5973-2020, https://doi.org/10.5194/hess-24-5973-2020, 2020
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In Mediterranean environments, evaporation is a key component of reservoir water budgets. Prediction of surface evaporation becomes crucial for adequate reservoir water management. This study provides an applicable method for calculating evaporation based on pan measurements applied at Alqueva Reservoir (southern Portugal), one of the largest artificial lakes in Europe. Moreover, the methodology presented here could be applied to other Mediterranean reservoirs.
Sofya Guseva, Tobias Bleninger, Klaus Jöhnk, Bruna Arcie Polli, Zeli Tan, Wim Thiery, Qianlai Zhuang, James Anthony Rusak, Huaxia Yao, Andreas Lorke, and Victor Stepanenko
Hydrol. Earth Syst. Sci., 24, 697–715, https://doi.org/10.5194/hess-24-697-2020, https://doi.org/10.5194/hess-24-697-2020, 2020
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We compare lake models with different complexity focusing on the key factors (e.g., eddy diffusivity) which can have an influence on the distribution of the dissolved gases in water. For the first time, we compare the biogeochemical modules in the ALBM and LAKE models. The result showed a good agreement with observed data (O2), but not for CO2. It indicates the need to improve the representation of physical and biogeochemical processes in lake models.
Miguel Potes, Gonçalo Rodrigues, Alexandra Marchã Penha, Maria Helena Novais, Maria João Costa, Rui Salgado, and Maria Manuela Morais
Proc. IAHS, 380, 73–79, https://doi.org/10.5194/piahs-380-73-2018, https://doi.org/10.5194/piahs-380-73-2018, 2018
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The Alqueva reservoir is located in the southeast of Portugal. This region is facing an increase in droughts and a degradation in water quality mainly due to more frequent and lasting phytoplancton blooms. With satellite remote sensing is possible to monitoring such reservoirs. In this study, Sentinel-2 presents very good estimation for chlorophyll a, cyanobacteria and water turbidity during a micro algae bloom that vanished due to the approximation of hurricane Ophelia to Iberian Peninsula.
Maksim Iakunin, Rui Salgado, and Miguel Potes
Hydrol. Earth Syst. Sci., 22, 5191–5210, https://doi.org/10.5194/hess-22-5191-2018, https://doi.org/10.5194/hess-22-5191-2018, 2018
Short summary
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Lakes and reservoirs can affect local weather regimes but usually it is difficult to trace and assess it. In this work we used the Meso-NH atmospheric model to study the impact of the Alqueva reservoir, one of the largest artificial lakes in western Europe, located in the southeast of Portugal, on meteorological parameters and the formation of a lake breeze system. The magnitude of this impact as well as the intensity of the breeze are shown in the paper.
Alfonso J. Fernández, Michaël Sicard, Maria J. Costa, Juan L. Guerrero-Rascado, José L. Gómez-Amo, Francisco Molero, Rubén Barragán, Daniele Bortoli, Andrés E. Bedoya-Velásquez, María P. Utrillas, Pedro Salvador, María J. Granados-Muñoz, Miguel Potes, Pablo Ortiz-Amezcua, José A. Martínez-Lozano, Begoña Artíñano, Constantino Muñoz-Porcar, Rui Salgado, Roberto Román, Francesc Rocadenbosch, Vanda Salgueiro, José A. Benavent-Oltra, Alejandro Rodríguez-Gómez, Lucas Alados-Arboledas, Adolfo Comerón, and Manuel Pujadas
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-370, https://doi.org/10.5194/acp-2018-370, 2018
Revised manuscript not accepted
V. Masson, P. Le Moigne, E. Martin, S. Faroux, A. Alias, R. Alkama, S. Belamari, A. Barbu, A. Boone, F. Bouyssel, P. Brousseau, E. Brun, J.-C. Calvet, D. Carrer, B. Decharme, C. Delire, S. Donier, K. Essaouini, A.-L. Gibelin, H. Giordani, F. Habets, M. Jidane, G. Kerdraon, E. Kourzeneva, M. Lafaysse, S. Lafont, C. Lebeaupin Brossier, A. Lemonsu, J.-F. Mahfouf, P. Marguinaud, M. Mokhtari, S. Morin, G. Pigeon, R. Salgado, Y. Seity, F. Taillefer, G. Tanguy, P. Tulet, B. Vincendon, V. Vionnet, and A. Voldoire
Geosci. Model Dev., 6, 929–960, https://doi.org/10.5194/gmd-6-929-2013, https://doi.org/10.5194/gmd-6-929-2013, 2013
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ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
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Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
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Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
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To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
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Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
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This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
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How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
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Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
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This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
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Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
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A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
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This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
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Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136, https://doi.org/10.5194/gmd-16-4113-2023, https://doi.org/10.5194/gmd-16-4113-2023, 2023
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In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112, https://doi.org/10.5194/gmd-16-4083-2023, https://doi.org/10.5194/gmd-16-4083-2023, 2023
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Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040, https://doi.org/10.5194/gmd-16-4017-2023, https://doi.org/10.5194/gmd-16-4017-2023, 2023
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Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995, https://doi.org/10.5194/gmd-16-3953-2023, https://doi.org/10.5194/gmd-16-3953-2023, 2023
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High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872, https://doi.org/10.5194/gmd-16-3849-2023, https://doi.org/10.5194/gmd-16-3849-2023, 2023
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The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906, https://doi.org/10.5194/gmd-16-3893-2023, https://doi.org/10.5194/gmd-16-3893-2023, 2023
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A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023, https://doi.org/10.5194/gmd-16-3785-2023, 2023
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Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, https://doi.org/10.5194/gmd-16-3809-2023, 2023
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Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748, https://doi.org/10.5194/gmd-16-3723-2023, https://doi.org/10.5194/gmd-16-3723-2023, 2023
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This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534, https://doi.org/10.5194/gmd-16-3501-2023, https://doi.org/10.5194/gmd-16-3501-2023, 2023
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In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433, https://doi.org/10.5194/gmd-16-3407-2023, https://doi.org/10.5194/gmd-16-3407-2023, 2023
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We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
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We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373, https://doi.org/10.5194/gmd-16-3355-2023, https://doi.org/10.5194/gmd-16-3355-2023, 2023
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Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Feijia Yin, Volker Grewe, Federica Castino, Pratik Rao, Sigrun Matthes, Katrin Dahlmann, Simone Dietmüller, Christine Frömming, Hiroshi Yamashita, Patrick Peter, Emma Klingaman, Keith P. Shine, Benjamin Lührs, and Florian Linke
Geosci. Model Dev., 16, 3313–3334, https://doi.org/10.5194/gmd-16-3313-2023, https://doi.org/10.5194/gmd-16-3313-2023, 2023
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This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261, https://doi.org/10.5194/gmd-16-3241-2023, https://doi.org/10.5194/gmd-16-3241-2023, 2023
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Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135, https://doi.org/10.5194/gmd-16-3123-2023, https://doi.org/10.5194/gmd-16-3123-2023, 2023
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Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122, https://doi.org/10.5194/gmd-16-3103-2023, https://doi.org/10.5194/gmd-16-3103-2023, 2023
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This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028, https://doi.org/10.5194/gmd-16-3013-2023, https://doi.org/10.5194/gmd-16-3013-2023, 2023
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Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012, https://doi.org/10.5194/gmd-16-2995-2023, https://doi.org/10.5194/gmd-16-2995-2023, 2023
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Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955, https://doi.org/10.5194/gmd-16-2939-2023, https://doi.org/10.5194/gmd-16-2939-2023, 2023
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This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973, https://doi.org/10.5194/gmd-16-2957-2023, https://doi.org/10.5194/gmd-16-2957-2023, 2023
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A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913, https://doi.org/10.5194/gmd-16-2899-2023, https://doi.org/10.5194/gmd-16-2899-2023, 2023
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A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850, https://doi.org/10.5194/gmd-16-2833-2023, https://doi.org/10.5194/gmd-16-2833-2023, 2023
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C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809, https://doi.org/10.5194/gmd-16-2795-2023, https://doi.org/10.5194/gmd-16-2795-2023, 2023
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FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736, https://doi.org/10.5194/gmd-16-2719-2023, https://doi.org/10.5194/gmd-16-2719-2023, 2023
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It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-51, https://doi.org/10.5194/gmd-2023-51, 2023
Revised manuscript accepted for GMD
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To assess the ability of Earth System Model (ESM) predictions, we developed a tool called ESMAC Diags to understand the details of how aerosols, clouds, and ACI are represented in ESMs, and this paper describes its version 2 functionality. We compared the model predictions with measurements taken by airplanes, ships, satellites, and ground instruments over four regions over the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Cited articles
Adrian, R., O'Reilly, C. M., Zagarese, H., Baines, S. B., Hessen, D. O.,
Keller, W., Livingstone, D. M., Sommaruga, R., Straile, D., Van Donk, E.,
Weyhenmeyer, G. A., and Winder, M.: Lakes as sentinels of climate change,
Limnol. Oceanogr., 54, 2283–2297,
https://doi.org/10.4319/lo.2009.54.6_part_2.2283, 2009. a
Balsamo, G.: Interactive lakes in the Integrated Forecasting System, ECMWF
Newsletter, 137, 30–34, https://doi.org/10.21957/rffv1gir, 2013. a
Bonan, G. B.: Sensitivity of a GCM Simulation to Inclusion of Inland Water
Surfaces, J. Climate, 8, 2691–2704,
https://doi.org/10.1175/1520-0442(1995)008<2691:SOAGST>2.0.CO;2, 1995. a, b, c
Canuto, V. M., Howard, A., Cheng, Y., and Dubovikov, M. S.: Ocean Turbulence.
Part I: One-Point Closure Model-Momentum and Heat Vertical Diffusivities,
J. Phys. Oceanogr., 31, 1413–1426,
https://doi.org/10.1175/1520-0485(2001)031<1413:OTPIOP>2.0.CO;2, 2001. a
Ekhtiari, N., Grossman-Clarke, S., Koch, H., Souza, W. M., Donner, R. V., and
Volkholz, J.: Effects of the Lake Sobradinho Reservoir (Northeastern Brazil)
on the Regional Climate, Climate, 5, 50, https://doi.org/10.3390/cli5030050, 2017. a
Hanson, P. C., Pollard, A. I., Bade, D. L., Predick, K., Carpenter, S. R., and
Foley, J. A.: A model of carbon evasion and sedimentation in temperate lakes,
Glob. Change Biol., 10, 1285–1298,
https://doi.org/10.1111/j.1529-8817.2003.00805.x, 2004. a
Heiskanen, J. J., Mammarella, I., Ojala, A., Stepanenko, V., Erkkilä, K.-M.,
Miettinen, H., Sandström, H., Eugster, W., Leppäranta, M., Järvinen, H.,
Vesala, T., and Nordbo, A.: Effects of water clarity on lake stratification
and lake-atmosphere heat exchange, J. Geophys. Res.-Atmos., 120, 7412–7428, https://doi.org/10.1002/2014JD022938, 2015. a, b
Hipsey, M. R., Bruce, L. C., Boon, C., Busch, B., Carey, C. C., Hamilton, D. P., Hanson, P. C., Read, J. S., de Sousa, E., Weber, M., and Winslow, L. A.: A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON), Geosci. Model Dev., 12, 473–523, https://doi.org/10.5194/gmd-12-473-2019, 2019. a
Hondzo, M. and Stefan, H. G.: Lake Water Temperature Simulation Model, J. Hydraul. Eng., 119, 1251–1273,
https://doi.org/10.1061/(ASCE)0733-9429(1993)119:11(1251), 1993. a
Iakunin, M., Salgado, R., and Potes, M.: Breeze effects at a large artificial lake: summer case study, Hydrol. Earth Syst. Sci., 22, 5191–5210, https://doi.org/10.5194/hess-22-5191-2018, 2018. a, b, c, d
Iakunin, M., Stepanenko, V., Salgado, R., Potes, M., Rodrigues, G., Penha, A., and Novais, M. H.: Atmospheric forcing dataset for Numerical study of the seasonal thermal and gas regimes of the large artificial lake in Western Europe using LAKE2.0 [Data set], Zenodo, https://doi.org/10.5281/zenodo.3608230, 2020. a
Imberger, J. and Patterson, J. C.: A dynamic reservoir simulation model-DYRESM:
5, in: Transport Models for Inland and Coastal Waters, edited by: Fischer, H. B.,
Academic Press, New York, 310–361, 1981. a
Le Moigne, P., Colin, J., and Decharme, B.: Impact of lake surface temperatures
simulated by the FLake scheme in the CNRM-CM5 climate model, Tellus A, 68, 31274,
https://doi.org/10.3402/tellusa.v68.31274, 2016. a, b
Lopes, F., Silva, H. G., Salgado, R., Potes, M., Nicoll, K. A., and Harrison,
R. G.: Atmospheric electrical field measurements near a fresh water reservoir
and the formation of the lake breeze, Tellus A, 68, 31592, https://doi.org/10.3402/tellusa.v68.31592, 2016. a
Mironov, D.: Parameterization of lakes in numerical weather prediction.
Description of a lake model, COSMO Technical Report, Deutscher Wetterdienst,
11, 41 pp., 2008. a
Monin, A. S. and Obukhov, A. M.: Basic regularity in turbulent mixing in the
surface layer of the atmosphere, U.S.S.R. Academy of Science, Works of the
Geophysical Institute, 151, 163–187, 1954. a
Novais, M. H., Penha, A., Morales, E., Potes, M., Salgado, R., and Morais, M.:
Vertical distribution of benthic diatoms in a large reservoir (Alqueva,
Southern Portugal) during thermal stratification, Sci. Total
Environ., 659, 1242–1255, https://doi.org/10.1016/j.scitotenv.2018.12.251, 2018. a
Patterson, J. C., Hamblin, P. F., and Imberger, J.: Classification and dynamic
simulation of the vertical density structure of lakes, Limnol.
Oceanogr., 29, 845–861, https://doi.org/10.4319/lo.1984.29.4.0845, 1984. a
Penha, A. M., Chambel, A., Murteira, M., and Morais, M.: Influence of different
land uses on groundwater quality in southern Portugal, Environ. Earth
Sci., 75, 622, https://doi.org/10.1007/s12665-015-5038-7, 2016. a, b
Pereira, H., Figueira, J. R., and Marques, R. C.: Multiobjective Irrigation
Model: Alqueva River Basin Application, J. Irrig. Drain.
E., 145, 05019006, https://doi.org/10.1061/(ASCE)IR.1943-4774.0001396, 2019. a
Policarpo, C., Salgado, R., and Costa, M. J.: Numerical Simulations of Fog
Events in Southern Portugal, Adv. Meteorol., 2017, 1276784, https://doi.org/10.1155/2017/1276784, 2017. a
Potes, M., Costa, M. J., da Silva, J. C. B., Silva, A. M., and Morais, M.:
Remote sensing of water quality parameters over Alqueva Reservoir in the
south of Portugal, Int. J. Remote Sens., 32, 3373–3388,
https://doi.org/10.1080/01431161003747513, 2011. a
Potes, M., Costa, M. J., and Salgado, R.: Satellite remote sensing of water turbidity in Alqueva reservoir and implications on lake modelling, Hydrol. Earth Syst. Sci., 16, 1623–1633, https://doi.org/10.5194/hess-16-1623-2012, 2012. a, b, c
Potes, M., Salgado, R., Costa, M. J., Morais, M., Bortoli, D., Kostadinov, I.,
and Mammarella, I.: Lake–atmosphere interactions at Alqueva reservoir: a
case study in the summer of 2014, Tellus A, 69, 1272787, https://doi.org/10.1080/16000870.2016.1272787, 2017. a
Potes, M., Rodrigues, G., Penha, A. M., Novais, M. H., Costa, M. J., Salgado, R., and Morais, M. M.: Use of Sentinel 2 – MSI for water quality monitoring at Alqueva reservoir, Portugal, Proc. IAHS, 380, 73–79, https://doi.org/10.5194/piahs-380-73-2018, 2018. a
Silva, A., De Lima, I., Santo, F., and Pires, V.: Assessing changes in drought
and wetness episodes in drainage basins using the Standardized Precipitation
Index, Bodenkultur, 65, 31–37, 2014. a
Stefan, H. and Fang, X.: Dissolved Oxygen Model for Regional Lake Analysis,
Ecol. Model., 71, 37–68, https://doi.org/10.1016/0304-3800(94)90075-2, 1994. a
Stepanenko, V., Jöhnk, K. D., Machulskaya, E., Perroud, M., Subin, Z.,
Nordbo, A., Mammarella, I., and Mironov, D.: Simulation of surface energy
fluxes and stratification of a small boreal lake by a set of one-dimensional
models, Tellus A, 66, 21389,
https://doi.org/10.3402/tellusa.v66.21389, 2014. a
Stepanenko, V., Mammarella, I., Ojala, A., Miettinen, H., Lykosov, V., and Vesala, T.: LAKE 2.0: a model for temperature, methane, carbon dioxide and oxygen dynamics in lakes, Geosci. Model Dev., 9, 1977–2006, https://doi.org/10.5194/gmd-9-1977-2016, 2016. a, b, c
Stepanenko, V. M., Repina, I. A., Ganbat, G., and Davaa, G.: Numerical
Simulation of Ice Cover of Saline Lakes, Izvestiya, Atmos. Ocean.
Phys., 55, 129–138, https://doi.org/10.1134/S0001433819010092, 2019. a
Su, D., Hu, X., Wen, L., Lyu, S., Gao, X., Zhao, L., Li, Z., Du, J., and Kirillin, G.: Numerical study on the response of the largest lake in China to climate change, Hydrol. Earth Syst. Sci., 23, 2093–2109, https://doi.org/10.5194/hess-23-2093-2019, 2019. a, b
Subin, Z. M., Riley, W. J., and Mironov, D.: An improved lake model for climate
simulations: Model structure, evaluation, and sensitivity analyses in CESM1,
J. Adv. Model. Earth Sy., 4, M02001, https://doi.org/10.1029/2011MS000072,
2012. a
Thiery, W., Martynov, A., Darchambeau, F., Descy, J.-P., Plisnier, P.-D., Sushama, L., and van Lipzig, N. P. M.: Understanding the performance of the FLake model over two African Great Lakes, Geosci. Model Dev., 7, 317–337, https://doi.org/10.5194/gmd-7-317-2014, 2014.
a
Tomaz, A., Patanita, M., Guerreiro, I., Boteta, L., and Palma, J. F.: Water use
and productivity of maize-based cropping systems in the Alqueva Region
(Portugal), Cereal Res. Commun., 45, 1–11,
https://doi.org/10.1556/0806.45.2017.036, 2017. a
Twine, T., Kustas, W. P., Norman, J., Cook, D., Houser, P., Teyers, T. P.,
Prueger, J. H., Starks, P., and Wesely, M.: Correcting Eddy-Covariance Flux
Underestimates over a Grassland, Agr. Forest Meteorol., 103,
https://doi.org/10.1016/S0168-1923(00)00123-4, 2000. a
Walter, K. M., Smith, L. C., and Chapin, F. S.: Methane bubbling from northern
lakes: present and future contributions to the global methane budget, Philos.
T. Roy. Soc. A, 365, 1657–1676, https://doi.org/10.1098/rsta.2007.2036, 2007. a
Short summary
The Alqueva reservoir, located in the southeast of Portugal, is the largest artificial reservoir in western Europe. It was established in 2002 to provide water and electrical resources to meet regional needs. Complex research of this reservoir is an essential scientific task in the scope of meteorology, hydrology, biology, and ecology. Two numerical models (namely, LAKE 2.0 and FLake) were used to assess the thermodynamic and biogeochemical regimes of the reservoir over 2 years of observations.
The Alqueva reservoir, located in the southeast of Portugal, is the largest artificial reservoir...
