Articles | Volume 14, issue 1
https://doi.org/10.5194/gmd-14-151-2021
© Author(s) 2021. 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-14-151-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
12 Jan 2021
Development and technical paper |
| 12 Jan 2021
Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2)
Brigitta Szabó
Institute for Soil Sciences, Centre for
Agricultural Research, Herman Ottó út 15, 1022 Budapest, Hungary
Melanie Weynants
European Commission Joint Research Centre, via Enrico Fermi 2749,
21027 Ispra, Italy
Tobias K. D. Weber
Institute of Soil Science and Land Evaluation, University of
Hohenheim, Emil-Wolff-Straße 27, 70593 Stuttgart, Germany
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Geosci. Model Dev., 16, 5825–5845, https://doi.org/10.5194/gmd-16-5825-2023, https://doi.org/10.5194/gmd-16-5825-2023, 2023
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Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
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Pedotransfer functions (PTFs) are used to predicts parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically rely on the nature of the datasets for training the PTFs as well the physical comprehensiveness of the models. This roadmap papers is addressed at PTF developers and users, and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTFs research.
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Hydrol. Earth Syst. Sci., 23, 2615–2635, https://doi.org/10.5194/hess-23-2615-2019, https://doi.org/10.5194/hess-23-2615-2019, 2019
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This paper analyzes differences in the performance of the indirect and direct mapping method to derive 3-D soil hydraulic maps. Maps of saturated water content, field capacity and wilting point are presented for a 5775 km2 catchment at 100 m resolution. Advantages and disadvantages of the two methods are discussed. The absolute difference in soil water retention values is less than 0.025 cm3 cm−3 between maps derived with indirect and direct methods for 65–86 % of the catchment.
Mehdi Rahmati, Lutz Weihermüller, Jan Vanderborght, Yakov A. Pachepsky, Lili Mao, Seyed Hamidreza Sadeghi, Niloofar Moosavi, Hossein Kheirfam, Carsten Montzka, Kris Van Looy, Brigitta Toth, Zeinab Hazbavi, Wafa Al Yamani, Ammar A. Albalasmeh, Ma'in Z. Alghzawi, Rafael Angulo-Jaramillo, Antônio Celso Dantas Antonino, George Arampatzis, Robson André Armindo, Hossein Asadi, Yazidhi Bamutaze, Jordi Batlle-Aguilar, Béatrice Béchet, Fabian Becker, Günter Blöschl, Klaus Bohne, Isabelle Braud, Clara Castellano, Artemi Cerdà, Maha Chalhoub, Rogerio Cichota, Milena Císlerová, Brent Clothier, Yves Coquet, Wim Cornelis, Corrado Corradini, Artur Paiva Coutinho, Muriel Bastista de Oliveira, José Ronaldo de Macedo, Matheus Fonseca Durães, Hojat Emami, Iraj Eskandari, Asghar Farajnia, Alessia Flammini, Nándor Fodor, Mamoun Gharaibeh, Mohamad Hossein Ghavimipanah, Teamrat A. Ghezzehei, Simone Giertz, Evangelos G. Hatzigiannakis, Rainer Horn, Juan José Jiménez, Diederik Jacques, Saskia Deborah Keesstra, Hamid Kelishadi, Mahboobeh Kiani-Harchegani, Mehdi Kouselou, Madan Kumar Jha, Laurent Lassabatere, Xiaoyan Li, Mark A. Liebig, Lubomír Lichner, María Victoria López, Deepesh Machiwal, Dirk Mallants, Micael Stolben Mallmann, Jean Dalmo de Oliveira Marques, Miles R. Marshall, Jan Mertens, Félicien Meunier, Mohammad Hossein Mohammadi, Binayak P. Mohanty, Mansonia Pulido-Moncada, Suzana Montenegro, Renato Morbidelli, David Moret-Fernández, Ali Akbar Moosavi, Mohammad Reza Mosaddeghi, Seyed Bahman Mousavi, Hasan Mozaffari, Kamal Nabiollahi, Mohammad Reza Neyshabouri, Marta Vasconcelos Ottoni, Theophilo Benedicto Ottoni Filho, Mohammad Reza Pahlavan-Rad, Andreas Panagopoulos, Stephan Peth, Pierre-Emmanuel Peyneau, Tommaso Picciafuoco, Jean Poesen, Manuel Pulido, Dalvan José Reinert, Sabine Reinsch, Meisam Rezaei, Francis Parry Roberts, David Robinson, Jesús Rodrigo-Comino, Otto Corrêa Rotunno Filho, Tadaomi Saito, Hideki Suganuma, Carla Saltalippi, Renáta Sándor, Brigitta Schütt, Manuel Seeger, Nasrollah Sepehrnia, Ehsan Sharifi Moghaddam, Manoj Shukla, Shiraki Shutaro, Ricardo Sorando, Ajayi Asishana Stanley, Peter Strauss, Zhongbo Su, Ruhollah Taghizadeh-Mehrjardi, Encarnación Taguas, Wenceslau Geraldes Teixeira, Ali Reza Vaezi, Mehdi Vafakhah, Tomas Vogel, Iris Vogeler, Jana Votrubova, Steffen Werner, Thierry Winarski, Deniz Yilmaz, Michael H. Young, Steffen Zacharias, Yijian Zeng, Ying Zhao, Hong Zhao, and Harry Vereecken
Earth Syst. Sci. Data, 10, 1237–1263, https://doi.org/10.5194/essd-10-1237-2018, https://doi.org/10.5194/essd-10-1237-2018, 2018
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This paper presents and analyzes a global database of soil infiltration data, the SWIG database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists or they were digitized from published articles. We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models.
Qianqian Han, Yijian Zeng, Lijie Zhang, Calimanut-Ionut Cira, Egor Prikaziuk, Ting Duan, Chao Wang, Brigitta Szabó, Salvatore Manfreda, Ruodan Zhuang, and Bob Su
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Using machine learning, we estimated global surface soil moisture (SSM) to aid in understanding water, energy, and carbon exchange. Ensemble models outperformed individual algorithms in predicting SSM under different climates. The best-performing ensemble included K-neighbours Regressor, Random Forest Regressor, and Extreme Gradient Boosting. This is important for hydrological and climatological applications such as water cycle monitoring, irrigation management, and crop yield prediction.
Tobias Karl David Weber, Lutz Weihermüller, Attila Nemes, Michel Bechtold, Aurore Degré, Efstathios Diamantopoulos, Simone Fatichi, Vilim Filipović, Surya Gupta, Tobias L. Hohenbrink, Daniel R. Hirmas, Conrad Jackisch, Quirijn de Jong van Lier, John Koestel, Peter Lehmann, Toby R. Marthews, Budiman Minasny, Holger Pagel, Martine van der Ploeg, Simon Fiil Svane, Brigitta Szabó, Harry Vereecken, Anne Verhoef, Michael Young, Yijian Zeng, Yonggen Zhang, and Sara Bonetti
EGUsphere, https://doi.org/10.5194/egusphere-2023-1860, https://doi.org/10.5194/egusphere-2023-1860, 2023
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Pedotransfer functions (PTFs) are used to predicts parameters of models describing the hydraulic properties of soils. The appropriateness of these predictions critically rely on the nature of the datasets for training the PTFs as well the physical comprehensiveness of the models. This roadmap papers is addressed at PTF developers and users, and critically reflects the utility and future of PTFs. To this end, we present a manifesto aiming at a paradigm shift in PTFs research.
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Michelle Viswanathan, Tobias K. D. Weber, Sebastian Gayler, Juliane Mai, and Thilo Streck
Biogeosciences, 19, 2187–2209, https://doi.org/10.5194/bg-19-2187-2022, https://doi.org/10.5194/bg-19-2187-2022, 2022
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We analysed the evolution of model parameter uncertainty and prediction error as we updated parameters of a maize phenology model based on yearly observations, by sequentially applying Bayesian calibration. Although parameter uncertainty was reduced, prediction quality deteriorated when calibration and prediction data were from different maize ripening groups or temperature conditions. The study highlights that Bayesian methods should account for model limitations and inherent data structures.
Tobias K. D. Weber, Joachim Ingwersen, Petra Högy, Arne Poyda, Hans-Dieter Wizemann, Michael Scott Demyan, Kristina Bohm, Ravshan Eshonkulov, Sebastian Gayler, Pascal Kremer, Moritz Laub, Yvonne Funkiun Nkwain, Christian Troost, Irene Witte, Tim Reichenau, Thomas Berger, Georg Cadisch, Torsten Müller, Andreas Fangmeier, Volker Wulfmeyer, and Thilo Streck
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Presented are measurement results from six agricultural fields operated by local farmers in southwestern Germany over 9 years. Six eddy-covariance stations measuring water, energy, and carbon fluxes between the vegetated soil surface and the atmosphere provided the backbone of the measurement sites and were supplemented by extensive soil and vegetation state monitoring. The dataset is ideal for testing process models characterizing fluxes at the vegetated soil surface and in the atmosphere.
Brigitta Szabó, Gábor Szatmári, Katalin Takács, Annamária Laborczi, András Makó, Kálmán Rajkai, and László Pásztor
Hydrol. Earth Syst. Sci., 23, 2615–2635, https://doi.org/10.5194/hess-23-2615-2019, https://doi.org/10.5194/hess-23-2615-2019, 2019
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This paper analyzes differences in the performance of the indirect and direct mapping method to derive 3-D soil hydraulic maps. Maps of saturated water content, field capacity and wilting point are presented for a 5775 km2 catchment at 100 m resolution. Advantages and disadvantages of the two methods are discussed. The absolute difference in soil water retention values is less than 0.025 cm3 cm−3 between maps derived with indirect and direct methods for 65–86 % of the catchment.
Ravshan Eshonkulov, Arne Poyda, Joachim Ingwersen, Hans-Dieter Wizemann, Tobias K. D. Weber, Pascal Kremer, Petra Högy, Alim Pulatov, and Thilo Streck
Biogeosciences, 16, 521–540, https://doi.org/10.5194/bg-16-521-2019, https://doi.org/10.5194/bg-16-521-2019, 2019
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We compared the energy balance closure (EBC) under varying environmental conditions and investigated a wide range of possible reasons for the energy imbalance. As measures for the imbalance, we used ordinary linear regression, the energy balance ratio (EBR), and the energy residual. The EBR was also investigated as a function of buoyancy, friction velocity, and atmospheric stability. Moreover, the relationship between the EBC and flux source area or footprint was also investigated.
Mehdi Rahmati, Lutz Weihermüller, Jan Vanderborght, Yakov A. Pachepsky, Lili Mao, Seyed Hamidreza Sadeghi, Niloofar Moosavi, Hossein Kheirfam, Carsten Montzka, Kris Van Looy, Brigitta Toth, Zeinab Hazbavi, Wafa Al Yamani, Ammar A. Albalasmeh, Ma'in Z. Alghzawi, Rafael Angulo-Jaramillo, Antônio Celso Dantas Antonino, George Arampatzis, Robson André Armindo, Hossein Asadi, Yazidhi Bamutaze, Jordi Batlle-Aguilar, Béatrice Béchet, Fabian Becker, Günter Blöschl, Klaus Bohne, Isabelle Braud, Clara Castellano, Artemi Cerdà, Maha Chalhoub, Rogerio Cichota, Milena Císlerová, Brent Clothier, Yves Coquet, Wim Cornelis, Corrado Corradini, Artur Paiva Coutinho, Muriel Bastista de Oliveira, José Ronaldo de Macedo, Matheus Fonseca Durães, Hojat Emami, Iraj Eskandari, Asghar Farajnia, Alessia Flammini, Nándor Fodor, Mamoun Gharaibeh, Mohamad Hossein Ghavimipanah, Teamrat A. Ghezzehei, Simone Giertz, Evangelos G. Hatzigiannakis, Rainer Horn, Juan José Jiménez, Diederik Jacques, Saskia Deborah Keesstra, Hamid Kelishadi, Mahboobeh Kiani-Harchegani, Mehdi Kouselou, Madan Kumar Jha, Laurent Lassabatere, Xiaoyan Li, Mark A. Liebig, Lubomír Lichner, María Victoria López, Deepesh Machiwal, Dirk Mallants, Micael Stolben Mallmann, Jean Dalmo de Oliveira Marques, Miles R. Marshall, Jan Mertens, Félicien Meunier, Mohammad Hossein Mohammadi, Binayak P. Mohanty, Mansonia Pulido-Moncada, Suzana Montenegro, Renato Morbidelli, David Moret-Fernández, Ali Akbar Moosavi, Mohammad Reza Mosaddeghi, Seyed Bahman Mousavi, Hasan Mozaffari, Kamal Nabiollahi, Mohammad Reza Neyshabouri, Marta Vasconcelos Ottoni, Theophilo Benedicto Ottoni Filho, Mohammad Reza Pahlavan-Rad, Andreas Panagopoulos, Stephan Peth, Pierre-Emmanuel Peyneau, Tommaso Picciafuoco, Jean Poesen, Manuel Pulido, Dalvan José Reinert, Sabine Reinsch, Meisam Rezaei, Francis Parry Roberts, David Robinson, Jesús Rodrigo-Comino, Otto Corrêa Rotunno Filho, Tadaomi Saito, Hideki Suganuma, Carla Saltalippi, Renáta Sándor, Brigitta Schütt, Manuel Seeger, Nasrollah Sepehrnia, Ehsan Sharifi Moghaddam, Manoj Shukla, Shiraki Shutaro, Ricardo Sorando, Ajayi Asishana Stanley, Peter Strauss, Zhongbo Su, Ruhollah Taghizadeh-Mehrjardi, Encarnación Taguas, Wenceslau Geraldes Teixeira, Ali Reza Vaezi, Mehdi Vafakhah, Tomas Vogel, Iris Vogeler, Jana Votrubova, Steffen Werner, Thierry Winarski, Deniz Yilmaz, Michael H. Young, Steffen Zacharias, Yijian Zeng, Ying Zhao, Hong Zhao, and Harry Vereecken
Earth Syst. Sci. Data, 10, 1237–1263, https://doi.org/10.5194/essd-10-1237-2018, https://doi.org/10.5194/essd-10-1237-2018, 2018
Short summary
Short summary
This paper presents and analyzes a global database of soil infiltration data, the SWIG database, for the first time. In total, 5023 infiltration curves were collected across all continents in the SWIG database. These data were either provided and quality checked by the scientists or they were digitized from published articles. We are convinced that the SWIG database will allow for a better parameterization of the infiltration process in land surface models and for testing infiltration models.
Reuven B. Simhayov, Tobias K. D. Weber, and Jonathan S. Price
SOIL, 4, 63–81, https://doi.org/10.5194/soil-4-63-2018, https://doi.org/10.5194/soil-4-63-2018, 2018
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Lab experiments were performed to understand solute transport in peat from an experimental fen. Transport was analyzed under saturated and unsaturated conditions using NaCl (salt). We tested the applicability of a physical-based model which finds a wide consensus vs. alternative models. Evidence indicated that Cl transport can be explained using a simple transport model. Hence, use of the physical transport mechanism in peat should be evidence based and not automatically assumed.
Tobias Karl David Weber, Sascha Christian Iden, and Wolfgang Durner
Hydrol. Earth Syst. Sci., 21, 6185–6200, https://doi.org/10.5194/hess-21-6185-2017, https://doi.org/10.5194/hess-21-6185-2017, 2017
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The present paper introduces a floodplain scheme for a high-resolution land surface model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land–atmosphere fluxes and highlights the potential impact of floodplains on land–atmosphere interactions and the importance of integrating this module in coupled simulations.
Jérémy Bernard, Fredrik Lindberg, and Sandro Oswald
Geosci. Model Dev., 16, 5703–5727, https://doi.org/10.5194/gmd-16-5703-2023, https://doi.org/10.5194/gmd-16-5703-2023, 2023
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The UMEP plug-in integrated in the free QGIS software can now calculate the spatial variation of the wind speed within urban settings. This paper shows that the new wind model, URock, generally fits observations well and highlights the main needed improvements. According to this work, pedestrian wind fields and outdoor thermal comfort can now simply be estimated by any QGIS user (researchers, students, and practitioners).
Jonathan King, Jessica Tierney, Matthew Osman, Emily J. Judd, and Kevin J. Anchukaitis
Geosci. Model Dev., 16, 5653–5683, https://doi.org/10.5194/gmd-16-5653-2023, https://doi.org/10.5194/gmd-16-5653-2023, 2023
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Paleoclimate data assimilation is a useful method that allows researchers to combine climate models with natural archives of past climates. However, it can be difficult to implement in practice. To facilitate this method, we present DASH, a MATLAB toolbox. The toolbox provides routines that implement common steps of paleoclimate data assimilation, and it can be used to implement assimilations for a wide variety of time periods, spatial regions, data networks, and analytical algorithms.
Siddhartha Bishnu, Robert R. Strauss, and Mark R. Petersen
Geosci. Model Dev., 16, 5539–5559, https://doi.org/10.5194/gmd-16-5539-2023, https://doi.org/10.5194/gmd-16-5539-2023, 2023
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Here we test Julia, a relatively new programming language, which is designed to be simple to write, but also fast on advanced computer architectures. We found that Julia is both convenient and fast, but there is no free lunch. Our first attempt to develop an ocean model in Julia was relatively easy, but the code was slow. After several months of further development, we created a Julia code that is as fast on supercomputers as a Fortran ocean model.
Tyler Kukla, Daniel E. Ibarra, Kimberly V. Lau, and Jeremy K. C. Rugenstein
Geosci. Model Dev., 16, 5515–5538, https://doi.org/10.5194/gmd-16-5515-2023, https://doi.org/10.5194/gmd-16-5515-2023, 2023
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The CH2O-CHOO TRAIN model can simulate how climate and the long-term carbon cycle interact across millions of years on a standard PC. While efficient, the model accounts for many factors including the location of land masses, the spatial pattern of the water cycle, and fundamental climate feedbacks. The model is a powerful tool for investigating how short-term climate processes can affect long-term changes in the Earth system.
Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448, https://doi.org/10.5194/gmd-16-5427-2023, https://doi.org/10.5194/gmd-16-5427-2023, 2023
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The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399, https://doi.org/10.5194/gmd-16-5383-2023, https://doi.org/10.5194/gmd-16-5383-2023, 2023
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Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382, https://doi.org/10.5194/gmd-16-5365-2023, https://doi.org/10.5194/gmd-16-5365-2023, 2023
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This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151, https://doi.org/10.5194/gmd-16-5131-2023, https://doi.org/10.5194/gmd-16-5131-2023, 2023
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Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178, https://doi.org/10.5194/gmd-16-5153-2023, https://doi.org/10.5194/gmd-16-5153-2023, 2023
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We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Andrew Gettelman
Geosci. Model Dev., 16, 4937–4956, https://doi.org/10.5194/gmd-16-4937-2023, https://doi.org/10.5194/gmd-16-4937-2023, 2023
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A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866, https://doi.org/10.5194/gmd-16-4853-2023, https://doi.org/10.5194/gmd-16-4853-2023, 2023
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ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833, https://doi.org/10.5194/gmd-16-4811-2023, https://doi.org/10.5194/gmd-16-4811-2023, 2023
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Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747, https://doi.org/10.5194/gmd-16-4715-2023, https://doi.org/10.5194/gmd-16-4715-2023, 2023
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Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697, https://doi.org/10.5194/gmd-16-4677-2023, https://doi.org/10.5194/gmd-16-4677-2023, 2023
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To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
EGUsphere, https://doi.org/10.5194/egusphere-2023-1476, https://doi.org/10.5194/egusphere-2023-1476, 2023
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere-ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 59 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597, https://doi.org/10.5194/gmd-16-4581-2023, https://doi.org/10.5194/gmd-16-4581-2023, 2023
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The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616, https://doi.org/10.5194/gmd-16-4599-2023, https://doi.org/10.5194/gmd-16-4599-2023, 2023
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This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519, https://doi.org/10.5194/gmd-16-4501-2023, https://doi.org/10.5194/gmd-16-4501-2023, 2023
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How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479, https://doi.org/10.5194/gmd-16-4451-2023, https://doi.org/10.5194/gmd-16-4451-2023, 2023
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Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366, https://doi.org/10.5194/gmd-16-4331-2023, https://doi.org/10.5194/gmd-16-4331-2023, 2023
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This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313, https://doi.org/10.5194/gmd-16-4283-2023, https://doi.org/10.5194/gmd-16-4283-2023, 2023
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Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329, https://doi.org/10.5194/gmd-16-4315-2023, https://doi.org/10.5194/gmd-16-4315-2023, 2023
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A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264, https://doi.org/10.5194/gmd-16-4249-2023, https://doi.org/10.5194/gmd-16-4249-2023, 2023
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This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247, https://doi.org/10.5194/gmd-16-4233-2023, https://doi.org/10.5194/gmd-16-4233-2023, 2023
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Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2023-1263, https://doi.org/10.5194/egusphere-2023-1263, 2023
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We performed systematic evaluation of clouds simulated in the E3SMv2 to document model performance on clouds and understand what updates in E3SMv2 have caused the changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved primarily due to the re-tuning of cloud macrophysics parameters. This study offers additional insights about clouds simulated in E3SMv2 and will benefit the future E3SM developments.
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.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
EGUsphere, https://doi.org/10.5194/egusphere-2023-1496, https://doi.org/10.5194/egusphere-2023-1496, 2023
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Ocean models struggle to simulate small-scale ocean flows due to the computational cost of high-resolution simulations. Several cost-reducing strategies are applied to simulations of the Southern Ocean and evaluated with respect to observations and traditional, lower-resolution modelling methods. The high-resolution simulations effectively reproduce small-scale flows seen in satellite data and are largely consistent with traditional model simulations regarding their response to climate change.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
EGUsphere, https://doi.org/10.5194/egusphere-2023-890, https://doi.org/10.5194/egusphere-2023-890, 2023
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Irrigation modifies the land surface and soil conditions. The caused effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which is simulating the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
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.
Cited articles
Araya, S. N. and Ghezzehei, T. A.: Using Machine Learning for Prediction of
Saturated Hydraulic Conductivity and Its Sensitivity to Soil Structural
Perturbations, Water Resour. Res., 55, 5715–5737,
https://doi.org/10.1029/2018WR024357, 2019.
Assouline, S. and Or, D.: Conceptual and Parametric Representation of Soil
Hydraulic Properties: A Review, Vadose Zone J., 12, 1–20,
https://doi.org/10.2136/vzj2013.07.0121, 2013.
Børgesen, C. D. and Schaap, M. G.: Point and parameter pedotransfer
functions for water retention predictions for Danish soils, Geoderma,
127, 154–167, https://doi.org/10.1016/j.geoderma.2004.11.025, 2005.
Botula, Y.-D., Nemes, A., Mafuka, P., Van Ranst, E., and Cornelis, W. M.:
Prediction of Water Retention of Soils from the Humid Tropics by the
Nonparametric -Nearest Neighbor Approach, Vadose Zone J., 12, 1–17,
https://doi.org/10.2136/vzj2012.0123, 2013.
Boulesteix, A. L., Janitza, S., Kruppa, J., and König, I. R.: Overview of
random forest methodology and practical guidance with emphasis on
computational biology and bioinformatics, Wires. Data Min.
Knowl., 2, 493–507, https://doi.org/10.1002/widm.1072, 2012.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, 2001.
Deng, H., Ye, M., Schaap, M. G., and Khaleel, R.: Quantification of
uncertainty in pedotransfer function-based parameter estimation for
unsaturated flow modeling, Water Resour. Res., 45, W04409,
https://doi.org/10.1029/2008WR007477, 2009.
van Genuchten, M. T.: A closed-form equation for predicting the hydraulic
conductivity of unsaturated soils, Soil Sci. Soc. Am. J., 44, 892–898,
1980.
Greenwell, B.: Partial Dependence Plots, 15, available at:
https://cran.r-project.org/web/packages/pdp/pdp.pdf (last access: 4 January 2021),
2017a.
Greenwell, B. M.: pdp: An R Package for Constructing Partial Dependence
Plots, R J., 9, 421–436, available at:
https://journal.r-project.org/archive/2017/RJ-2017-016/RJ-2017-016.pdf (last access: 4 January 2021),
2017b.
Hastie, T., Tibshirani, R., and Friedman, J.: The Elements of Statistical
Learning, Data Mining, Inference, and Prediction, 2nd Edn., Springer,
available at: https://web.stanford.edu/~hastie/ElemStatLearn//printings/ESLII_print10.pdf (last access:
19 November 2018), 2009.
Hengl, T., Nussbaum, M., Wright, M. N., and Heuvelink, B. M.: Random Forest
as a Generic Framework for Predictive Modeling of Spatial and
Spatio-temporal Variables, PeerJ Prepr., 6, e5518,
https://doi.org/10.7287/peerj.preprints.26693v3, 2018.
Hodnett, M. G. and Tomasella, J.: Marked differences between van Genuchten
soil water-retention parameters for temperate and tropical soils: a new
water-retention pedo-transfer functions developed for tropical soils,
Geoderma, 108, 155–180, https://doi.org/10.1016/S0016-7061(02)00105-2, 2002.
Khodaverdiloo, H., Homaee, M., van Genuchten, M. T., and Dashtaki, S. G.:
Deriving and validating pedotransfer functions for some calcareous soils, J.
Hydrol., 399, 93–99, https://doi.org/10.1016/j.jhydrol.2010.12.040, 2011.
Kotlar, A. M., Jong van Lier, Q., Barros, A. H. C., Iversen, B. V., and
Vereecken, H.: Development and Uncertainty Assessment of Pedotransfer
Functions for Predicting Water Contents at Specific Pressure Heads, Vadose
Zo.ne J., 18, 190063, https://doi.org/10.2136/vzj2019.06.0063, 2019.
Kuhn, M., Wing, J., Weston, S., Williams, A., Keefer, C., Engelhardt, A.,
Cooper, T., Mayer, Z., Benesty, M., Lescarbeau, R., Ziem, A., Scrucca, L.,
Tang, Y., Candan, C., and Hunt, T.: Package “caret” Classification and
Regression Training Description Misc functions for training and plotting
classification and regression models, available at:
https://cran.r-project.org/web/packages/caret/caret.pdf (last access: 4 January 2021), 2017.
Kuhn, M., Wing, J., Weston, S., Keefer, C., Engelhardt, A., Cooper, T.,
Mayer, Z., Kenkel, B., Team, R. C., Benesty, M., Lescarbeau, R., Ziem, A.,
Scrucca, L., Tang, Y., Candan, C., and Hunt, T.: caret: Classification and
Regression Training. R package version 6.0-79, available at:
https://github.com/topepo/caret/ (last access: 4 January 2021), 2018.
Lal, R. and Shukla, M. K.: Principles of soil physics, Marcel Dekker, Inc.,
New York, 2004.
Li, D., Gao, G., and Fu, B.: Predicting available water of soil from
particle-size distribution and bulk density in an oasis – desert transect
in northwestern China, J. Hydrol., 538, 539–550,
https://doi.org/10.1016/j.jhydrol.2016.04.046, 2016.
Lilly, A., Nemes, A., Rawls, W. J., and Pachepsky, Y. A.: Probabilistic
approach to the identification of input variables to estimate hydraulic
conductivity, Soil Sci. Soc. Am. J., 72, 16–24,
https://doi.org/10.2136/sssaj2006.0391, 2008.
Lin, H.: Earth's Critical Zone and hydropedology: concepts, characteristics, and advances, Hydrol. Earth Syst. Sci., 14, 25–45, https://doi.org/10.5194/hess-14-25-2010, 2010.
Van Looy, K., Bouma, J., Herbst, M., Koestel, J., Minasny, B., Mishra, U.,
Montzka, C., Nemes, A., Pachepsky, Y. A., Padarian, J., Schaap, M. G.,
Tóth, B., Verhoef, A., Vanderborght, J., van der Ploeg, M. J.,
Weihermüller, L., Zacharias, S., Zhang, Y., and Vereecken, H.:
Pedotransfer Functions in Earth System Science: Challenges and Perspectives,
Rev. Geophys., 55, 1199–1256, https://doi.org/10.1002/2017RG000581, 2017.
Malone, B. P., McBratney, A. B., Minasny, B., and Laslett, G. M.: Mapping
continuous depth functions of soil carbon storage and available water
capacity, Geoderma, 154, 138–152, https://doi.org/10.1016/j.geoderma.2009.10.007,
2009.
McNeill, S. J., Lilburne, L. R., Carrick, S., Webb, T. H., and Cuthill, T.:
Pedotransfer functions for the soil water characteristics of New Zealand
soils using S-map information, Geoderma, 326, 96–110,
https://doi.org/10.1016/j.geoderma.2018.04.011, 2018.
Meinshausen, N.: Quantile Regression Forests, J. Mach. Learn. Res., 7,
983–999, 2006.
De Mendiburu, F.: agricolae: Statistical Procedures for Agricultural
Research, R package version 1.2-8, available at:
https://cran.r-project.org/package=agricolae (last access: 4 January 2021), 2017.
Mualem, Y.: A new model for predicting the hydraulic conductivity of
unsaturated porous media, Water Resour. Res., 12, 513–522, 1976.
Nemes, A., Schaap, M., and Wösten, J.: Functional evaluation of
pedotransfer functions derived from different scales of data collection,
Soil Sci. Soc. Am., 67, 1093–1102, 2003.
Nguyen, P. M., Haghverdi, A., Pue, J. De, Botula, Y., Le, K. V, Waegeman, W.,
and Cornelis, W. M.: Comparison of statistical regression and data-mining
techniques in estimating soil water retention of tropical delta soils,
Biosyst. Eng., 153, 12–27, https://doi.org/10.1016/j.biosystemseng.2016.10.013, 2017.
Nussbaum, M., Spiess, K., Baltensweiler, A., Grob, U., Keller, A., Greiner, L., Schaepman, M. E., and Papritz, A.: Evaluation of digital soil mapping approaches with large sets of environmental covariates, SOIL, 4, 1–22, https://doi.org/10.5194/soil-4-1-2018, 2018.
Odgers, N. P., McBratney, A. B., and Minasny, B.: Digital soil property
mapping and uncertainty estimation using soil class probability rasters,
Geoderma, 237, 190–198, https://doi.org/10.1016/j.geoderma.2014.09.009, 2014.
Olson, R. S., La Cava, W., Mustahsan, Z., Varik, A., and Moore, J. H.:
Data-driven advice for applying machine learning to bioinformatics problems,
in Biocomputing 2018, edited by: Altman, R. B., Dunker, A. K., Hunter, L.,
Ritchie, M. D., Murray, T. A., and Klein, T. E., World Scientific, 192–203,
2018.
Pachepsky, Y. A. and Rawls, W. J.: Accuracy and Reliability of Pedotransfer
Functions as Affected by Grouping Soils, Soil Sci. Soc. Am. J., 63,
1748–1757, https://doi.org/10.2136/sssaj1999.6361748x, 1999.
R Core Team: R: A language and environment for statistical computing,
available at: https://www.r-project.org (last access: 4 January 2021), 2019.
Rawls, W., Pachepsky, Y., and Ritchie, J.: Effect of soil organic carbon on
soil water retention, Geoderma, 116, 61–76,
https://doi.org/10.1016/S0016-7061(03)00094-6, 2003.
Ridgeway, G.: gbm: Generalized Boosted Regression Models, R package version
2.1.3., 2017.
Román Dobarco, M., Cousin, I., Le Bas, C., and Martin, M. P.:
Pedotransfer functions for predicting available water capacity in French
soils, their applicability domain and associated uncertainty, Geoderma,
336, 81–95, https://doi.org/10.1016/J.GEODERMA.2018.08.022, 2019.
Schaap, M. G.: Models for Indirect Estimation of Soil Hydraulic Properties,
in Encyclopedia of Hydrological Sciences, edited by: Anderson, M. G. and
McDonnell, J. J., John Wiley & Sons, Ltd, Chichester, UK, 2006.
Streck, T. and Weber, T. K. D.: Analytical expressions for noncapillary soil
water retention based on popular capillary retention models, Vadose Zone J.,
19, e20042, https://doi.org/10.1002/vzj2.20042, 2020.
Szabó, B., Szatmári, G., Takács, K., Laborczi, A., Makó, A., Rajkai, K., and Pásztor, L.: Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics, Hydrol. Earth Syst. Sci., 23, 2615–2635, https://doi.org/10.5194/hess-23-2615-2019, 2019a.
Szabó, B., Gyurkó, D., Weynants, M., and Weber, T. K. D.: Web
interface for European hydraulic pedotransfer functions (euptfv2), Agártudományi Kutatóközpont, Talajtani Intézet,
https://doi.org/10.34977/euptfv2.01, 2019b.
Szabó, B., Weynants, M., and Weber, T. K.: Scripts of updated European
hydraulic pedotransfer functions (euptfv2), Zenodo, https://doi.org/10.5281/ZENODO.3759442,
2020.
Szatmári, G. and Pásztor, L.: Comparison of various uncertainty
modelling approaches based on geostatistics and machine learning algorithms,
Geoderma, 337, 1329–1340, https://doi.org/10.1016/j.geoderma.2018.09.008, 2019.
Tomasella, J., Crestana, S. and Rawls, W. J.: Comparison of Two Techniques
to Develop Pedotransfer Functions for Water Retention, Soil Sci. Soc. Am.
J., 67, 1085–1092, 2003.
Tóth, B., Weynants, M., Nemes, A., Makó, A., Bilas, G., and Tóth,
G.: New generation of hydraulic pedotransfer functions for Europe, Eur. J.
Soil Sci., 66, 226–238, https://doi.org/10.1111/ejss.12192, 2015.
Tóth, B., Weynants, M., Pásztor, L., and Hengl, T.: 3D soil hydraulic
database of Europe at 250 m resolution, Hydrol. Process., 31,
2662–2666, https://doi.org/10.1002/hyp.11203, 2017.
Tranter, G., Minasny, B., and McBratney, A. B.: Estimating Pedotransfer
Function Prediction Limits Using Fuzzy -Means with Extragrades, Soil Sci.
Soc. Am. J., 74, 1967–1975, https://doi.org/10.2136/sssaj2009.0106, 2010.
Vaysse, K. and Lagacherie, P.: Using quantile regression forest to estimate
uncertainty of digital soil mapping products, Geoderma, 291, 55–64,
https://doi.org/10.1016/j.geoderma.2016.12.017, 2017.
Vereecken, H., Maes, J., Feyen, J., and Darius, P.: Estimating the Soil
Moisture Retention Characteristic From Texture, Bulk Density, and Carbon
Content, Soil Sci., 148, 389–403, https://doi.org/10.1097/00010694-198912000-00001,
1989.
Vereecken, H., Huisman, J. A., Franssen Hendricks, H. J., Brüggemann,
N., Bogena, H. R., Kollet, S., Javaux, M., van der Kruk, J., and
Vanderborght, J.: Soil hydrology: Recent methodological advances,
challenges, and perspectives, Water Resour. Res., 51, 1–18,
https://doi.org/10.1002/2014WR016852.Received, 2015.
Weber, T. K. D., Durner, W., Streck, T., and Diamantopoulos, E.: A Modular
Framework for Modeling Unsaturated Soil Hydraulic Properties Over the Full
Moisture Range, Water Resour. Res., 55, 4994–5011, https://doi.org/10.1029/2018WR024584,
2019.
Weber, T. K. D., Finkel, M., Conceição Gonçalves, M., Vereecken,
H., and Diamantopoulos, E.: Pedotransfer function for the Brunswick soil
hydraulic property model and comparison to the van Genuchten-Mualem model,
Water Resour. Res., 56, e2019WR026820, https://doi.org/10.1029/2019WR026820, 2020a.
Weber, T. K. D., Weynants, M., and Szabó, B.: R package of updated
European hydraulic pedotransfer functions (euptf2), Zenodo,
https://doi.org/10.5281/zenodo.4281045, 2020b.
Weynants, M. and Tóth, B.: The euptf package, Eur. Soil Portal, 5,
available at: https://esdac.jrc.ec.europa.eu (last access: 19 May
2019), 2014.
Weynants, M., Vereecken, H., and Javaux, M.: Revisiting Vereecken
Pedotransfer Functions: Introducing a Closed-Form Hydraulic Model, Vadose
Zone J., 8, 86, https://doi.org/10.2136/vzj2008.0062, 2009.
Weynants, M., Montanarella, L., Tóth, G., Arnoldussen, A., Anaya Romero,
M., Bilas, G., Borresen, T., Cornelis, W., Daroussin, J., Gonçalves, M.
D. C., Haugen, L.-E., Hennings, V., Houskova, B., Iovino, M., Javaux, M.,
Keay, C. A., Kätterer, T., Kvaerno, S., Laktinova, T., Lamorski, K.,
Lilly, A., Mako, A., Matula, S., Morari, F., Nemes, A., Patyka, N. V.,
Romano, N., Schindler, U., Shein, E., Slawinski, C., Strauss, P., Tóth,
B., and Woesten, H.: European HYdropedological Data Inventory (EU-HYDI), Scientific and Technical Research series, ISSN 1831-9424, Luxembourg,
2013.
Wiesmeier, M., Urbanski, L., Hobley, E., Lang, B., von Lützow, M.,
Marin-Spiotta, E., van Wesemael, B., Rabot, E., Ließ, M., Garcia-Franco,
N., Wollschläger, U., Vogel, H. J. and Kögel-Knabner, I.: Soil
organic carbon storage as a key function of soils - A review of drivers and
indicators at various scales, Geoderma, 333, 149–162,
https://doi.org/10.1016/j.geoderma.2018.07.026, 2019.
Wösten, J. H. M., Lilly, A., Nemes, A., and Le Bas, C.:
Development and use of a database of hydraulic properties of European soils,
Geoderma, 90, 169–185, 1999.
Wright, M. N. and Ziegler, A.: ranger: A Fast Implementation of Random
Forests for High Dimensional Data in C
Zhang, Y. and Schaap, M. G.: Weighted recalibration of the Rosetta
pedotransfer model with improved estimates of hydraulic parameter
distributions and summary statistics (Rosetta3), J. Hydrol., 547, 39–53, https://doi.org/10.1016/j.jhydrol.2017.01.004, 2017.
Zhang, Y. and Schaap, M. G.: Estimation of saturated hydraulic conductivity
with pedotransfer functions: A review, J. Hydrol., 575, 1011–1030,
https://doi.org/10.1016/j.jhydrol.2019.05.058, 2019.
Zhao, H., Zeng, Y., Lv, S., and Su, Z.: Analysis of soil hydraulic and thermal properties for land surface modeling over the Tibetan Plateau, Earth Syst. Sci. Data, 10, 1031–1061, https://doi.org/10.5194/essd-10-1031-2018, 2018.
Ziegler, A. and König, I. R.: Mining data with random forests: Current
options for real-world applications, Wires Data Min.
Knowl., 4, 55–63, https://doi.org/10.1002/widm.1114, 2014.
Short summary
This paper presents updated European prediction algorithms (euptf2) to compute soil hydraulic parameters from easily available soil properties. The new algorithms lead to significantly better predictions and provide a built-in prediction uncertainty computation. The influence of predictor variables on predicted soil hydraulic properties is explored and practical guidance on how to use the derived PTFs is provided. A website and an R package facilitate easy application of the updated predictions.
This paper presents updated European prediction algorithms (euptf2) to compute soil hydraulic...
