Articles | Volume 16, issue 2
https://doi.org/10.5194/gmd-16-449-2023
© Author(s) 2023. 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-16-449-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
23 Jan 2023
Model description paper |
| 23 Jan 2023
| 23 Jan 2023
URANOS v1.0 – the Ultra Rapid Adaptable Neutron-Only Simulation for Environmental Research
Markus Köhli
CORRESPONDING AUTHOR
Physikalisches Institut, Heidelberg University, Heidelberg, Germany
Physikalisches Institut, University of Bonn, Bonn, Germany
Martin Schrön
Department of Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Steffen Zacharias
Department of Monitoring and Exploration Technologies, Helmholtz Centre for Environmental Research – UFZ, Leipzig, Germany
Ulrich Schmidt
Physikalisches Institut, Heidelberg University, Heidelberg, Germany
Related authors
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Daniel Rasche, Jannis Weimar, Martin Schrön, Markus Köhli, Markus Morgner, Andreas Güntner, and Theresa Blume
Hydrol. Earth Syst. Sci., 27, 3059–3082, https://doi.org/10.5194/hess-27-3059-2023, https://doi.org/10.5194/hess-27-3059-2023, 2023
Short summary
Short summary
We introduce passive downhole cosmic-ray neutron sensing (d-CRNS) as an approach for the non-invasive estimation of soil moisture in deeper layers of the unsaturated zone which exceed the observational window of above-ground CRNS applications. Neutron transport simulations are used to derive mathematical descriptions and transfer functions, while experimental measurements in an existing groundwater observation well illustrate the feasibility and applicability of the approach.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Martin Schrön, Markus Köhli, and Steffen Zacharias
Hydrol. Earth Syst. Sci., 27, 723–738, https://doi.org/10.5194/hess-27-723-2023, https://doi.org/10.5194/hess-27-723-2023, 2023
Short summary
Short summary
This paper presents a new analytical concept to answer long-lasting questions of the cosmic-ray neutron sensing community, such as
what is the influence of a distant area or patches of different land use on the measurement signal?or
is the detector sensitive enough to detect a change of soil moisture (e.g. due to irrigation) in a remote field at a certain distance?The concept may support signal interpretation and sensor calibration, particularly in heterogeneous terrain.
Cosimo Brogi, Heye Reemt Bogena, Markus Köhli, Johan Alexander Huisman, Harrie-Jan Hendricks Franssen, and Olga Dombrowski
Geosci. Instrum. Method. Data Syst., 11, 451–469, https://doi.org/10.5194/gi-11-451-2022, https://doi.org/10.5194/gi-11-451-2022, 2022
Short summary
Short summary
Accurate monitoring of water in soil can improve irrigation efficiency, which is important considering climate change and the growing world population. Cosmic-ray neutrons sensors (CRNSs) are a promising tool in irrigation monitoring due to a larger sensed area and to lower maintenance than other ground-based sensors. Here, we analyse the feasibility of irrigation monitoring with CRNSs and the impact of the irrigated field dimensions, of the variations of water in soil, and of instrument design.
Till Francke, Maik Heistermann, Markus Köhli, Christian Budach, Martin Schrön, and Sascha E. Oswald
Geosci. Instrum. Method. Data Syst., 11, 75–92, https://doi.org/10.5194/gi-11-75-2022, https://doi.org/10.5194/gi-11-75-2022, 2022
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools like soil moisture, snow, or vegetation. This study presents a directional shielding approach, aiming to measure in specific directions only. The results show that non-directional neutron transport blurs the signal of the targeted direction. For typical instruments, this does not allow acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates is feasible.
Daniel Rasche, Markus Köhli, Martin Schrön, Theresa Blume, and Andreas Güntner
Hydrol. Earth Syst. Sci., 25, 6547–6566, https://doi.org/10.5194/hess-25-6547-2021, https://doi.org/10.5194/hess-25-6547-2021, 2021
Short summary
Short summary
Cosmic-ray neutron sensing provides areal average soil moisture measurements. We investigated how distinct differences in spatial soil moisture patterns influence the soil moisture estimates and present two approaches to improve the estimate of soil moisture close to the instrument by reducing the influence of soil moisture further afield. Additionally, we show that the heterogeneity of soil moisture can be assessed based on the relationship of different neutron energies.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
Martin Schrön, Steffen Zacharias, Gary Womack, Markus Köhli, Darin Desilets, Sascha E. Oswald, Jan Bumberger, Hannes Mollenhauer, Simon Kögler, Paul Remmler, Mandy Kasner, Astrid Denk, and Peter Dietrich
Geosci. Instrum. Method. Data Syst., 7, 83–99, https://doi.org/10.5194/gi-7-83-2018, https://doi.org/10.5194/gi-7-83-2018, 2018
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a unique technology to monitor water storages in complex environments, non-invasively, continuously, autonomuously, and representatively in large areas. However, neutron detector signals are not comparable per se: there is statistical noise, technical differences, and locational effects. We found out what it takes to make CRNS consistent in time and space to ensure reliable data quality. We further propose a method to correct for sealed areas in the footrint.
Martin Schrön, Markus Köhli, Lena Scheiffele, Joost Iwema, Heye R. Bogena, Ling Lv, Edoardo Martini, Gabriele Baroni, Rafael Rosolem, Jannis Weimar, Juliane Mai, Matthias Cuntz, Corinna Rebmann, Sascha E. Oswald, Peter Dietrich, Ulrich Schmidt, and Steffen Zacharias
Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, https://doi.org/10.5194/hess-21-5009-2017, 2017
Short summary
Short summary
A field-scale average of near-surface water content can be sensed by cosmic-ray neutron detectors. To interpret, calibrate, and validate the integral signal, it is important to account for its sensitivity to heterogeneous patterns like dry or wet spots. We show how point samples contribute to the neutron signal based on their depth and distance from the detector. This approach robustly improves the sensor performance and data consistency, and even reveals otherwise hidden hydrological features.
Till Francke, Cosimo Brogi, Alby Duarte Rocha, Michael Förster, Maik Heistermann, Markus Köhli, Daniel Rasche, Marvin Reich, Paul Schattan, Lena Scheiffele, and Martin Schrön
Geosci. Model Dev., 18, 819–842, https://doi.org/10.5194/gmd-18-819-2025, https://doi.org/10.5194/gmd-18-819-2025, 2025
Short summary
Short summary
Multiple methods for measuring soil moisture beyond the point scale exist. Their validation is generally hindered by not knowing the truth. We propose a virtual framework in which this truth is fully known and the sensor observations for cosmic ray neutron sensing, remote sensing, and hydrogravimetry are simulated. This allows for the rigorous testing of these virtual sensors to understand their effectiveness and limitations.
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
Hydrol. Earth Syst. Sci., 29, 465–483, https://doi.org/10.5194/hess-29-465-2025, https://doi.org/10.5194/hess-29-465-2025, 2025
Short summary
Short summary
The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two endmembers of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented supersites.
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Short summary
This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Daniel Altdorff, Maik Heistermann, Till Francke, Martin Schrön, Sabine Attinger, Albrecht Bauriegel, Frank Beyrich, Peter Biró, Peter Dietrich, Rebekka Eichstädt, Peter Martin Grosse, Arvid Markert, Jakob Terschlüsen, Ariane Walz, Steffen Zacharias, and Sascha E. Oswald
EGUsphere, https://doi.org/10.5194/egusphere-2024-3848, https://doi.org/10.5194/egusphere-2024-3848, 2024
Short summary
Short summary
The German federal state of Brandenburg is particularly prone to soil moisture droughts. To support the management of related risks, we introduce a novel soil moisture and drought monitoring network based on cosmic-ray neutron sensing technology. This initiative is driven by a collaboration of research institutions and federal state agencies, and it is the first of its kind in Germany to have started operation. In this brief communication, we outline the network design and share first results.
Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 28, 989–1000, https://doi.org/10.5194/hess-28-989-2024, https://doi.org/10.5194/hess-28-989-2024, 2024
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a non-invasive technique used to obtain estimates of soil water content (SWC) at a horizontal footprint of around 150 m and a vertical penetration depth of up to 30 cm. However, typical CRNS applications require the local calibration of a function which converts neutron counts to SWC. As an alternative, we propose a generalized function as a way to avoid the use of local reference measurements of SWC and hence a major source of uncertainty.
Daniel Rasche, Jannis Weimar, Martin Schrön, Markus Köhli, Markus Morgner, Andreas Güntner, and Theresa Blume
Hydrol. Earth Syst. Sci., 27, 3059–3082, https://doi.org/10.5194/hess-27-3059-2023, https://doi.org/10.5194/hess-27-3059-2023, 2023
Short summary
Short summary
We introduce passive downhole cosmic-ray neutron sensing (d-CRNS) as an approach for the non-invasive estimation of soil moisture in deeper layers of the unsaturated zone which exceed the observational window of above-ground CRNS applications. Neutron transport simulations are used to derive mathematical descriptions and transfer functions, while experimental measurements in an existing groundwater observation well illustrate the feasibility and applicability of the approach.
Maik Heistermann, Till Francke, Lena Scheiffele, Katya Dimitrova Petrova, Christian Budach, Martin Schrön, Benjamin Trost, Daniel Rasche, Andreas Güntner, Veronika Döpper, Michael Förster, Markus Köhli, Lisa Angermann, Nikolaos Antonoglou, Manuela Zude-Sasse, and Sascha E. Oswald
Earth Syst. Sci. Data, 15, 3243–3262, https://doi.org/10.5194/essd-15-3243-2023, https://doi.org/10.5194/essd-15-3243-2023, 2023
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) allows for the non-invasive estimation of root-zone soil water content (SWC). The signal observed by a single CRNS sensor is influenced by the SWC in a radius of around 150 m (the footprint). Here, we have put together a cluster of eight CRNS sensors with overlapping footprints at an agricultural research site in north-east Germany. That way, we hope to represent spatial SWC heterogeneity instead of retrieving just one average SWC estimate from a single sensor.
Martin Schrön, Markus Köhli, and Steffen Zacharias
Hydrol. Earth Syst. Sci., 27, 723–738, https://doi.org/10.5194/hess-27-723-2023, https://doi.org/10.5194/hess-27-723-2023, 2023
Short summary
Short summary
This paper presents a new analytical concept to answer long-lasting questions of the cosmic-ray neutron sensing community, such as
what is the influence of a distant area or patches of different land use on the measurement signal?or
is the detector sensitive enough to detect a change of soil moisture (e.g. due to irrigation) in a remote field at a certain distance?The concept may support signal interpretation and sensor calibration, particularly in heterogeneous terrain.
Cosimo Brogi, Heye Reemt Bogena, Markus Köhli, Johan Alexander Huisman, Harrie-Jan Hendricks Franssen, and Olga Dombrowski
Geosci. Instrum. Method. Data Syst., 11, 451–469, https://doi.org/10.5194/gi-11-451-2022, https://doi.org/10.5194/gi-11-451-2022, 2022
Short summary
Short summary
Accurate monitoring of water in soil can improve irrigation efficiency, which is important considering climate change and the growing world population. Cosmic-ray neutrons sensors (CRNSs) are a promising tool in irrigation monitoring due to a larger sensed area and to lower maintenance than other ground-based sensors. Here, we analyse the feasibility of irrigation monitoring with CRNSs and the impact of the irrigated field dimensions, of the variations of water in soil, and of instrument design.
Friedrich Boeing, Oldrich Rakovec, Rohini Kumar, Luis Samaniego, Martin Schrön, Anke Hildebrandt, Corinna Rebmann, Stephan Thober, Sebastian Müller, Steffen Zacharias, Heye Bogena, Katrin Schneider, Ralf Kiese, Sabine Attinger, and Andreas Marx
Hydrol. Earth Syst. Sci., 26, 5137–5161, https://doi.org/10.5194/hess-26-5137-2022, https://doi.org/10.5194/hess-26-5137-2022, 2022
Short summary
Short summary
In this paper, we deliver an evaluation of the second generation operational German drought monitor (https://www.ufz.de/duerremonitor) with a state-of-the-art compilation of observed soil moisture data from 40 locations and four different measurement methods in Germany. We show that the expressed stakeholder needs for higher resolution drought information at the one-kilometer scale can be met and that the agreement of simulated and observed soil moisture dynamics can be moderately improved.
Maik Heistermann, Heye Bogena, Till Francke, Andreas Güntner, Jannis Jakobi, Daniel Rasche, Martin Schrön, Veronika Döpper, Benjamin Fersch, Jannis Groh, Amol Patil, Thomas Pütz, Marvin Reich, Steffen Zacharias, Carmen Zengerle, and Sascha Oswald
Earth Syst. Sci. Data, 14, 2501–2519, https://doi.org/10.5194/essd-14-2501-2022, https://doi.org/10.5194/essd-14-2501-2022, 2022
Short summary
Short summary
This paper presents a dense network of cosmic-ray neutron sensing (CRNS) to measure spatio-temporal soil moisture patterns during a 2-month campaign in the Wüstebach headwater catchment in Germany. Stationary, mobile, and airborne CRNS technology monitored the root-zone water dynamics as well as spatial heterogeneity in the 0.4 km2 area. The 15 CRNS stations were supported by a hydrogravimeter, biomass sampling, and a wireless soil sensor network to facilitate holistic hydrological analysis.
Andreas Wieser, Andreas Güntner, Peter Dietrich, Jan Handwerker, Dina Khordakova, Uta Ködel, Martin Kohler, Hannes Mollenhauer, Bernhard Mühr, Erik Nixdorf, Marvin Reich, Christian Rolf, Martin Schrön, Claudia Schütze, and Ute Weber
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-131, https://doi.org/10.5194/hess-2022-131, 2022
Preprint withdrawn
Short summary
Short summary
We present an event-triggered observation concept which covers the entire process chain from heavy precipitation to flooding at the catchment scale. It combines flexible and mobile observing systems out of the fields of meteorology, hydrology and geophysics with stationary networks to capture atmospheric transport processes, heterogeneous precipitation patterns, land surface and subsurface storage processes, and runoff dynamics.
Mandy Kasner, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-123, https://doi.org/10.5194/hess-2022-123, 2022
Publication in HESS not foreseen
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a non-invasive technique that is used to quantify field-scale root-zone soil moisture. We hypothesize that unaccounted spatiotemporal changes of soil density may have impact on the quality of CRNS soil moisture products. Our results indicate a significant dependency of neutrons on soil density, which also depends on the soil moisture state. A correction approach is provided that can be recommended for practical use.
Heye Reemt Bogena, Martin Schrön, Jannis Jakobi, Patrizia Ney, Steffen Zacharias, Mie Andreasen, Roland Baatz, David Boorman, Mustafa Berk Duygu, Miguel Angel Eguibar-Galán, Benjamin Fersch, Till Franke, Josie Geris, María González Sanchis, Yann Kerr, Tobias Korf, Zalalem Mengistu, Arnaud Mialon, Paolo Nasta, Jerzy Nitychoruk, Vassilios Pisinaras, Daniel Rasche, Rafael Rosolem, Hami Said, Paul Schattan, Marek Zreda, Stefan Achleitner, Eduardo Albentosa-Hernández, Zuhal Akyürek, Theresa Blume, Antonio del Campo, Davide Canone, Katya Dimitrova-Petrova, John G. Evans, Stefano Ferraris, Félix Frances, Davide Gisolo, Andreas Güntner, Frank Herrmann, Joost Iwema, Karsten H. Jensen, Harald Kunstmann, Antonio Lidón, Majken Caroline Looms, Sascha Oswald, Andreas Panagopoulos, Amol Patil, Daniel Power, Corinna Rebmann, Nunzio Romano, Lena Scheiffele, Sonia Seneviratne, Georg Weltin, and Harry Vereecken
Earth Syst. Sci. Data, 14, 1125–1151, https://doi.org/10.5194/essd-14-1125-2022, https://doi.org/10.5194/essd-14-1125-2022, 2022
Short summary
Short summary
Monitoring of increasingly frequent droughts is a prerequisite for climate adaptation strategies. This data paper presents long-term soil moisture measurements recorded by 66 cosmic-ray neutron sensors (CRNS) operated by 24 institutions and distributed across major climate zones in Europe. Data processing followed harmonized protocols and state-of-the-art methods to generate consistent and comparable soil moisture products and to facilitate continental-scale analysis of hydrological extremes.
Till Francke, Maik Heistermann, Markus Köhli, Christian Budach, Martin Schrön, and Sascha E. Oswald
Geosci. Instrum. Method. Data Syst., 11, 75–92, https://doi.org/10.5194/gi-11-75-2022, https://doi.org/10.5194/gi-11-75-2022, 2022
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a non-invasive tool for measuring hydrogen pools like soil moisture, snow, or vegetation. This study presents a directional shielding approach, aiming to measure in specific directions only. The results show that non-directional neutron transport blurs the signal of the targeted direction. For typical instruments, this does not allow acceptable precision at a daily time resolution. However, the mere statistical distinction of two rates is feasible.
Daniel Rasche, Markus Köhli, Martin Schrön, Theresa Blume, and Andreas Güntner
Hydrol. Earth Syst. Sci., 25, 6547–6566, https://doi.org/10.5194/hess-25-6547-2021, https://doi.org/10.5194/hess-25-6547-2021, 2021
Short summary
Short summary
Cosmic-ray neutron sensing provides areal average soil moisture measurements. We investigated how distinct differences in spatial soil moisture patterns influence the soil moisture estimates and present two approaches to improve the estimate of soil moisture close to the instrument by reducing the influence of soil moisture further afield. Additionally, we show that the heterogeneity of soil moisture can be assessed based on the relationship of different neutron energies.
Maik Heistermann, Till Francke, Martin Schrön, and Sascha E. Oswald
Hydrol. Earth Syst. Sci., 25, 4807–4824, https://doi.org/10.5194/hess-25-4807-2021, https://doi.org/10.5194/hess-25-4807-2021, 2021
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil moisture in footprints extending over hectometres in the horizontal and decimetres in the vertical. This study, however, demonstrates the potential of CRNS to obtain spatio-temporal patterns of soil moisture beyond isolated footprints. To that end, we analyse data from a unique observational campaign that featured a dense network of more than 20 neutron detectors in an area of just 1 km2.
Edoardo Martini, Matteo Bauckholt, Simon Kögler, Manuel Kreck, Kurt Roth, Ulrike Werban, Ute Wollschläger, and Steffen Zacharias
Earth Syst. Sci. Data, 13, 2529–2539, https://doi.org/10.5194/essd-13-2529-2021, https://doi.org/10.5194/essd-13-2529-2021, 2021
Short summary
Short summary
We present the in situ data available from the soil monitoring network
STH-net, recently implemented at the Schäfertal Hillslope site (Germany). The STH-net provides data (soil water content, soil temperature, water level, and meteorological variables – measured at a 10 min interval since 1 January 2019) for developing and testing modelling approaches in the context of vadose zone hydrology at spatial scales ranging from the pedon to the hillslope.
Benjamin Fersch, Till Francke, Maik Heistermann, Martin Schrön, Veronika Döpper, Jannis Jakobi, Gabriele Baroni, Theresa Blume, Heye Bogena, Christian Budach, Tobias Gränzig, Michael Förster, Andreas Güntner, Harrie-Jan Hendricks Franssen, Mandy Kasner, Markus Köhli, Birgit Kleinschmit, Harald Kunstmann, Amol Patil, Daniel Rasche, Lena Scheiffele, Ulrich Schmidt, Sandra Szulc-Seyfried, Jannis Weimar, Steffen Zacharias, Marek Zreda, Bernd Heber, Ralf Kiese, Vladimir Mares, Hannes Mollenhauer, Ingo Völksch, and Sascha Oswald
Earth Syst. Sci. Data, 12, 2289–2309, https://doi.org/10.5194/essd-12-2289-2020, https://doi.org/10.5194/essd-12-2289-2020, 2020
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.
Roland Baatz, Pamela L. Sullivan, Li Li, Samantha R. Weintraub, Henry W. Loescher, Michael Mirtl, Peter M. Groffman, Diana H. Wall, Michael Young, Tim White, Hang Wen, Steffen Zacharias, Ingolf Kühn, Jianwu Tang, Jérôme Gaillardet, Isabelle Braud, Alejandro N. Flores, Praveen Kumar, Henry Lin, Teamrat Ghezzehei, Julia Jones, Henry L. Gholz, Harry Vereecken, and Kris Van Looy
Earth Syst. Dynam., 9, 593–609, https://doi.org/10.5194/esd-9-593-2018, https://doi.org/10.5194/esd-9-593-2018, 2018
Short summary
Short summary
Focusing on the usage of integrated models and in situ Earth observatory networks, three challenges are identified to advance understanding of ESD, in particular to strengthen links between biotic and abiotic, and above- and below-ground processes. We propose developing a model platform for interdisciplinary usage, to formalize current network infrastructure based on complementarities and operational synergies, and to extend the reanalysis concept to the ecosystem and critical zone.
Martin Schrön, Steffen Zacharias, Gary Womack, Markus Köhli, Darin Desilets, Sascha E. Oswald, Jan Bumberger, Hannes Mollenhauer, Simon Kögler, Paul Remmler, Mandy Kasner, Astrid Denk, and Peter Dietrich
Geosci. Instrum. Method. Data Syst., 7, 83–99, https://doi.org/10.5194/gi-7-83-2018, https://doi.org/10.5194/gi-7-83-2018, 2018
Short summary
Short summary
Cosmic-ray neutron sensing (CRNS) is a unique technology to monitor water storages in complex environments, non-invasively, continuously, autonomuously, and representatively in large areas. However, neutron detector signals are not comparable per se: there is statistical noise, technical differences, and locational effects. We found out what it takes to make CRNS consistent in time and space to ensure reliable data quality. We further propose a method to correct for sealed areas in the footrint.
Martin Schrön, Markus Köhli, Lena Scheiffele, Joost Iwema, Heye R. Bogena, Ling Lv, Edoardo Martini, Gabriele Baroni, Rafael Rosolem, Jannis Weimar, Juliane Mai, Matthias Cuntz, Corinna Rebmann, Sascha E. Oswald, Peter Dietrich, Ulrich Schmidt, and Steffen Zacharias
Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, https://doi.org/10.5194/hess-21-5009-2017, 2017
Short summary
Short summary
A field-scale average of near-surface water content can be sensed by cosmic-ray neutron detectors. To interpret, calibrate, and validate the integral signal, it is important to account for its sensitivity to heterogeneous patterns like dry or wet spots. We show how point samples contribute to the neutron signal based on their depth and distance from the detector. This approach robustly improves the sensor performance and data consistency, and even reveals otherwise hidden hydrological features.
Edoardo Martini, Ulrike Werban, Steffen Zacharias, Marco Pohle, Peter Dietrich, and Ute Wollschläger
Hydrol. Earth Syst. Sci., 21, 495–513, https://doi.org/10.5194/hess-21-495-2017, https://doi.org/10.5194/hess-21-495-2017, 2017
Short summary
Short summary
With a process-based interpretation of electromagnetic induction measurements, we discussed the potential and limitations of such a method for soil moisture mapping. Results will help clarify the complex and time-varying effect of stable soil properties and dynamic state variables on the physical parameters measured, with implications for future studies. We highlighted the importance of time-series data and the need for a multidisciplinary approach for proper interpretation.
Related subject area
Climate and Earth system modeling
Enhancing winter climate simulations of the Great Lakes: insights from a new coupled lake–ice–atmosphere (CLIAv1) system on the importance of integrating 3D hydrodynamics with a regional climate model
Modelling emission and transport of key components of primary marine organic aerosol using the global aerosol–climate model ECHAM6.3–HAM2.3
Assessing the climate impact of an improved volcanic sulfate aerosol representation in E3SM
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
ICON-HAM-lite 1.0: simulating the Earth system with interactive aerosols at kilometer scales
Process-based modeling framework for sustainable irrigation management at the regional scale: integrating rice production, water use, and greenhouse gas emissions
Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
Reducing time and computing costs in EC-Earth: an automatic load-balancing approach for coupled Earth system models
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
OpenBench: a land models evaluation system
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Evaluation of Ozone and its Precursors using the Multi-Scale Infrastructure for Chemistry and Aerosols Version 0 (MUSICAv0) during the Michigan-Ontario Ozone Source Experiment (MOOSE)
Impact of spatial resolution on CMIP6-driven Mediterranean climate simulations: a focus on precipitation distribution over Italy
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Statistical summaries for streamed data from climate simulations: One-pass algorithms (v0.6.2)
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev., 18, 4293–4316, https://doi.org/10.5194/gmd-18-4293-2025, https://doi.org/10.5194/gmd-18-4293-2025, 2025
Short summary
Short summary
This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025, https://doi.org/10.5194/gmd-18-4183-2025, 2025
Short summary
Short summary
This study represents the primary marine organic aerosol (PMOA) emissions, focusing on their sea–atmosphere transfer. Using the FESOM2.1–REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol–climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the southern oceans.
Ziming Ke, Qi Tang, Jean-Christophe Golaz, Xiaohong Liu, and Hailong Wang
Geosci. Model Dev., 18, 4137–4153, https://doi.org/10.5194/gmd-18-4137-2025, https://doi.org/10.5194/gmd-18-4137-2025, 2025
Short summary
Short summary
This study assesses volcanic aerosol representation in E3SM (Energy Exascale Earth System Model), showing that an emission-based approach moderately improves temperature variability and cloud responses compared to a prescribed forcing approach, yet significant bias persists.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev., 18, 4009–4021, https://doi.org/10.5194/gmd-18-4009-2025, https://doi.org/10.5194/gmd-18-4009-2025, 2025
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Philipp Weiss, Ross Herbert, and Philip Stier
Geosci. Model Dev., 18, 3877–3894, https://doi.org/10.5194/gmd-18-3877-2025, https://doi.org/10.5194/gmd-18-3877-2025, 2025
Short summary
Short summary
Aerosols strongly influence Earth's climate as they interact with radiation and clouds. New Earth system models run at resolutions of a few kilometers. To simulate the Earth system with interactive aerosols, we developed a new aerosol module. It represents aerosols as an ensemble of lognormal modes with given sizes and compositions. We present a year-long simulation with four modes at a resolution of 5 km. It captures key processes like the formation of dust storms in the Sahara.
Yan Bo, Hao Liang, Tao Li, and Feng Zhou
Geosci. Model Dev., 18, 3799–3817, https://doi.org/10.5194/gmd-18-3799-2025, https://doi.org/10.5194/gmd-18-3799-2025, 2025
Short summary
Short summary
This study proposed an advancing framework for modeling regional rice production, water use, and greenhouse gas emissions. The framework integrated a process-based soil-crop model with vital physiological effects, a novel model upscaling method, and the NSGA-II multi-objective optimization algorithm at a parallel computing platform. The framework provides a valuable tool for multi-objective optimization of rice irrigation schemes at a large scale.
Carolina A. Bieri, Francina Dominguez, Gonzalo Miguez-Macho, and Ying Fan
Geosci. Model Dev., 18, 3755–3779, https://doi.org/10.5194/gmd-18-3755-2025, https://doi.org/10.5194/gmd-18-3755-2025, 2025
Short summary
Short summary
Access to deep moisture below the Earth's surface is important for vegetation in areas of the Amazon where there is little precipitation for part of the year. Most existing numerical models of the Earth system do not adequately capture where and when deep root water uptake occurs. We address this by adding deep soil layers and a root water uptake feature to an existing model. Out modifications lead to increased dry-month transpiration and improved simulation of the annual transpiration cycle.
Sergi Palomas, Mario C. Acosta, Gladys Utrera, and Etienne Tourigny
Geosci. Model Dev., 18, 3661–3679, https://doi.org/10.5194/gmd-18-3661-2025, https://doi.org/10.5194/gmd-18-3661-2025, 2025
Short summary
Short summary
We present an automatic tool that optimizes resource distribution in coupled climate models, enhancing speed and reducing computational costs without requiring expert knowledge. Users can set energy/time criteria or limit resource usage. Tested on various European Community Earth System Model (EC-Earth) configurations and high-performance computing (HPC) platforms, it achieved up to 34 % faster simulations with fewer resources.
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
Short summary
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
Short summary
Short summary
We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
Short summary
Short summary
NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Zhongwang Wei, Qingchen Xu, Fan Bai, Xionghui Xu, Zixin Wei, Wenzong Dong, Hongbin Liang, Nan Wei, Xingjie Lu, Lu Li, Shupeng Zhang, Hua Yuan, Laibo Liu, and Yongjiu Dai
EGUsphere, https://doi.org/10.5194/egusphere-2025-1380, https://doi.org/10.5194/egusphere-2025-1380, 2025
Short summary
Short summary
Land surface models are used for simulating earth's surface interacts with the atmosphere. As models grow more complex and detailed, researchers need better tools to evaluate their performance. OpenBench, a new software system that makes evaluation process more comprehensive and efficient. It stands out by incorporating various factors and working with data at any scale which enabling scientists to incorporate new types of models and measurements as our understanding of Earth’s systems evolves.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
Short summary
Short summary
We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
Short summary
Short summary
Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Noribeth Mariscal, Louisa K. Emmons, Duseong S. Jo, Ying Xiong, Laura M. Judd, Scott J. Janz, Jiajue Chai, and Yaoxian Huang
EGUsphere, https://doi.org/10.5194/egusphere-2025-228, https://doi.org/10.5194/egusphere-2025-228, 2025
Short summary
Short summary
The distribution of ozone (O3) and its precursors (NOx, VOCs) is explored using the chemistry-climate model, MUSICAv0, and evaluated using measurements from the Michigan-Ontario Ozone Source Experiment. A custom grid of ~7 km was created over Michigan. A sector-based diurnal cycle for anthropogenic nitric oxide was included in the model. This work shows that grid resolution played a more important role for O3 precursors, and the diurnal cycle significantly impacted nighttime O3 formation.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
Short summary
Short summary
We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Katherine Grayson, Stephan Thober, Aleksander Lacima-Nadolnik, Ehsan Sharifi, Llorenç Lledó, and Francisco Doblas-Reyes
EGUsphere, https://doi.org/10.5194/egusphere-2025-28, https://doi.org/10.5194/egusphere-2025-28, 2025
Short summary
Short summary
To provide the most accurate climate adaptation information, climate models are being run with finer grid resolution, resulting in larger data output. This paper presents intelligent data reduction algorithms that act on streamed data, a novel way of processing climate data as soon as it is produced. Using these algorithms to calculate statistics, we show that the accuracy provided is well within acceptable bounds while still providing memory savings that bypass unfeasible storage requirements.
Cited articles
Agostinelli, S., Allison, J., Amako, K., et al.: GEANT4 – a simulation toolkit, Nucl. Instrum.
Meth. A, 506, 250–303,
https://doi.org/10.1016/S0168-9002(03)01368-8, 2003. a
Andreasen, M., Jensen, H. K., Zreda, M., Desilets, D., Bogena, H., and Looms,
C.: Modeling cosmic ray neutron field measurements, Water Resour. Res.,
52, 6451–6471, https://doi.org/10.1002/2015wr018236, 2016. a
Badiee, A., Wallbank, J., Pulido Fentanes, J., Trill, E., Scarlet, P., Zhu,
Y., Cielniak, G., Cooper, H., Blake, J., Evans, J., Zreda, M., Köhli, M.,
and Pearson, S.: Using Additional Moderator to Control the Footprint of a
COSMOS Rover for Soil Moisture Measurement, Water Resour. Res., 57, e2020WR028478,
https://doi.org/10.1029/2020wr028478, 2021. a, b
Baroni, G., Scheiffele, L., Schrön, M., Ingwersen, J., and Oswald, S.:
Uncertainty, sensitivity and improvements in soil moisture estimation with
cosmic-ray neutron sensing, J. Hydrol., 564, 873–887,
https://doi.org/10.1016/j.jhydrol.2018.07.053, 2018. a
Battistoni, G., Boehlen, T., Cerutti, F., Chin, P., Esposito, L., Fasso, A.,
Ferrari, A., Lechner, A., Empl, A., Mairani, A., Mereghetti, A., Ortega, P.,
Ranft, J., Roesler, S., Sala, P., Vlachoudis, V., and Smirnov, G.: Overview
of the FLUKA code, Ann. Nucl. Energ., 82, 10–18,
https://doi.org/10.1016/j.anucene.2014.11.007, 2015. a
Boudard, A., Cugnon, J., David, J.-C., Leray, S., and Mancusi, D.: New
potentialities of the Liège intranuclear cascade model for reactions
induced by nucleons and light charged particles, Phys. Rev. C, 87,
014606, https://doi.org/10.1103/PhysRevC.87.014606, 2013. a
Bramblett, R. and Bonner, T.: Neutron evaporation spectra from (p, n)
reactions, Nucl. Phys., 20, 395–407, https://doi.org/10.1016/0029-5582(60)90182-6,
1960. a
Bramblett, R., Ewing, R., and Bonner, T.: A new type of neutron spectrometer,
Nucl. Instrum. Methods, 9, 1–12,
https://doi.org/10.1016/0029-554X(60)90043-4, 1960. a
Briesmeister, J.: MCNP-A general Monte Carlo N-particle
transport code, Version 4C, LA-13709-M,
https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-13709-M,
2000. a
Brown, D., Chadwick, M., Capote, R., Kahler, A., Trkov, A., Herman, M.,
Sonzogni, A., Danon, Y., Carlson, A., Dunn, M., Smith, D., Hale, G., Arbanas,
G., Arcilla, R., Bates, C., Beck, B., Becker, B., Brown, F., Casperson, R.,
Conlin, J., Cullen, D., Descalle, M.-A., Firestone, R., Gaines, T., Guber,
K., Hawari, A., Holmes, J., Johnson, T., Kawano, T., Kiedrowski, B., Koning,
A., Kopecky, S., Leal, L., Lestone, J., Lubitz, C., Damiá¡n, J. M.,
Mattoon, C., McCutchan, E., Mughabghab, S., Navratil, P., Neudecker, D.,
Nobre, G., Noguere, G., Paris, M., Pigni, M., Plompen, A., Pritychenko, B.,
Pronyaev, V., Roubtsov, D., Rochman, D., Romano, P., Schillebeeckx, P.,
Simakov, S., Sin, M., Sirakov, I., Sleaford, B., Sobes, V., Soukhovitskii,
E., Stetcu, I., Talou, P., Thompson, I., van der Marck, S.,
Welser-Sherrill, L., Wiarda, D., White, M., Wormald, J., Wright, R., Zerkle,
M., Žerovnik, G., and Zhu, Y.: ENDF/B-VIII.0: The 8th Major
Release of the Nuclear Reaction Data Library with CIELO-project Cross
Sections, New Standards and Thermal Scattering Data, Nucl. Data Sheets,
148, 1–142, https://doi.org/10.1016/j.nds.2018.02.001, 2018. a
Brun, R. and Rademakers, F.: ROOT – An Object Oriented Data Analysis
Framework, in: Proceedings of AIHENP’96 Workshop, Lausanne, vol. 389,
81–86, https://doi.org/10.1016/S0168-9002(97)00048-X, 1997. a, b
Caswell, R., Gabbard, R., Padgett, D., and Doering, W.: Attenuation of
14.1-Mev Neutrons in Water, Nucl. Sci. Eng., 2, 143–159,
https://doi.org/10.13182/NSE57-A25383, 1957. a, b, c, d
Chadwick, M., Herman, M., Obložinský, P., Dunn, M., Danon, Y.,
Kahler, A., Smith, D., Pritychenko, B., Arbanas, G., Arcilla, R., Brewer, R.,
Brown, D., Capote, R., Carlson, A., Cho, Y., Derrien, H., Guber, K., Hale,
G., Hoblit, S., Holloway, S., Johnson, T., Kawano, T., Kiedrowski, B., Kim,
H., Kunieda, S., Larson, N., Leal, L., Lestone, J., Little, R., McCutchan,
E., MacFarlane, R., MacInnes, M., Mattoon, C., McKnight, R., Mughabghab,
S., Nobre, G., Palmiotti, G., Palumbo, A., Pigni, M., Pronyaev, V., Sayer,
R., Sonzogni, A., Summers, N., Talou, P., Thompson, I., Trkov, A., Vogt, R.,
van der Marck, S., Wallner, A., White, M., Wiarda, D., and Young, P.:
ENDF/B-VII.1 Nuclear Data for Science and Technology: Cross Sections,
Covariances, Fission Product Yields and Decay Data, Nucl. Data Sheets, 112,
2887–2996, https://doi.org/10.1016/j.nds.2011.11.002, 2011. a
Cnudde, V. and Boone, M.: High-resolution X-ray computed tomography in
geosciences: A review of the current technology and applications,
Earth-Sci. Rev., 123, 1–17, https://doi.org/10.1016/j.earscirev.2013.04.003,
2013. a
Cugnon, J., Volant, C., and Vuillier, S.: Nucleon and deuteron induced
spallation reactions, Nucl. Phys. A, 625, 729–757,
https://doi.org/10.1016/S0375-9474(97)00602-7, 1997. a
Decker, A., McHale, S., Shannon, M., Clinton, J., and McClory, J.: Novel
Bonner Sphere Spectrometer Response Functions Using MCNP6, IEEE
T. Nucl. Sci., 62, 1689–1694,
https://doi.org/10.1109/TNS.2015.2416652, 2015. a
Desilets, D. and Zreda, M.: Footprint diameter for a cosmic-ray soil moisture
probe: Theory and Monte Carlo simulations, Water Resour. Res., 49,
3566–3575, https://doi.org/10.1002/wrcr.20187, 2013. a
Desilets, D., Zreda, M., and Prabu, T.: Extended scaling factors for in situ
cosmogenic nuclides: new measurements at low latitude, Earth Planet.
Sc. Lett., 246, 265–276, https://doi.org/10.1016/j.epsl.2006.03.051, 2006. a
Emmett, M.: The MORSE Monte Carlo Transport Code System, ORNL-4972/R2,
https://inis.iaea.org/collection/NCLCollectionStore/_Public/16/029/16029296.pdf,
1975. a
Federico, C., Gonçalez, O., Fonseca, E., Martin, I., and Caldas, L.:
Neutron spectra measurements in the south Atlantic anomaly region,
Radiat. Meas., 45, 1526–1528, https://doi.org/10.1016/j.radmeas.2010.06.038,
2010. a
Francke, T., Heistermann, M., Köhli, M., Budach, C., Schrön, M., and Oswald, S. E.: Assessing the feasibility of a directional cosmic-ray neutron sensing sensor for estimating soil moisture, Geosci. Instrum. Method. Data Syst., 11, 75–92, https://doi.org/10.5194/gi-11-75-2022, 2022. a, b
Franz, T. E., Zreda, M., Rosolem, R., and Ferre, T. P. A.: A universal calibration function for determination of soil moisture with cosmic-ray neutrons, Hydrol. Earth Syst. Sci., 17, 453–460, https://doi.org/10.5194/hess-17-453-2013, 2013. a
Galassi, M., Davies, J., Theiler, J., Gough, B., Jungman, G., Alken, P., Booth,
M., Rossi, F., and Ulerich, R.: GNU Scientific Library: reference manual for GSL
version 2.7, Free Software Foundation,
https://www.gnu.org/software/gsl/manual/gsl-ref.pdf (last access: 18 January 2023), 2016. a
Garny, S., Mares, V., and Rühm, W.: Response functions of a Bonner
Sphere Spectrometer calculated with GEANT4, Nucl. Instrum.
Meth. A, 604, 612–617, https://doi.org/10.1016/j.nima.2009.02.044,
2009. a
Gelbard, E.: Epithermal scattering in VIM, Tech. Rep. FRA-TM-123, Argonne
National Laboratory, IL, USA, technical Memorandum, 1979. a
Goldhagen, P., Reginatto, M., Kniss, T., Wilson, J., Singleterry, R., Jones,
I., and Van Steveninck, W.: Measurement of the energy spectrum of
cosmic-ray induced neutrons aboard an ER-2 high-altitude airplane, Nucl. Instrum.
Meth. A, 476, 42–51,
https://doi.org/10.1016/S0168-9002(01)01386-9, 2002. a
Goldman, L.: Principles of CT and CT Technology, J. Nucl.
Med. Tech., 35, 115–128, https://doi.org/10.2967/jnmt.107.042978, 2007. a
Goorley, T., James, M., Booth, T., Brown, F., Bull, J., Cox, L., Durkee, J.,
Elson, J., Fensin, M., Forster, R., Hendricks, J., Hughes, H., Johns, R.,
Kiedrowski, B., Martz, R., Mashnik, S., McKinney, G., Pelowitz, D., Prael,
R., Sweezy, J., Waters, L., Wilcox, T., and Zukaitis, T.: Initial MCNP6
Release Overview, Nucl. Tech., 180, 298–315, https://doi.org/10.13182/NT11-135,
2012. a, b
Gudima, K., Mashnik, S., and Toneev, V.: Cascade-exciton model of nuclear
reactions, Nucl. Phys. A, 401, 329–361,
https://doi.org/10.1016/0375-9474(83)90532-8, 1983. a
Gudima, K., Mashnik, S., and Sierk, A.: User Manual for the Code LAQGSM,
LA-UR-01-6804,
https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-01-6804,
2001. a
Hébert, A.: Multigroup Neutron Transport and Diffusion Computations,
Springer US, Boston, 751–911, https://doi.org/10.1007/978-0-387-98149-9_8, 2010. a
Heidbüchel, I., Güntner, A., and Blume, T.: Use of cosmic-ray neutron sensors for soil moisture monitoring in forests, Hydrol. Earth Syst. Sci., 20, 1269–1288, https://doi.org/10.5194/hess-20-1269-2016, 2016. a
Hertel, N. and Davidson, J.: The response of Bonner Spheres to neutrons
from thermal energies to 17.3 MeV, Nucl. Instrum. Meth. A, 238, 509–516, https://doi.org/10.1016/0168-9002(85)90494-2,
1985. a
ISO group 85/SC 2 Radiological protection: Reference neutron radiations –
Part 1, Standard, International Organization for Standardization, Geneva, CH,
https://www.iso.org/standard/25666.html (last access: 18 January 2023), 2001. a
Iwamoto, O., Iwamoto, N., Kunieda, S., Minato, F., and Shibata, K.: The CCONE
Code System and its Application to Nuclear Data Evaluation for Fission and
Other Reactions, Nucl. Data Sheets, 131, 259–288,
https://doi.org/10.1016/j.nds.2015.12.004,
2016. a
Iwase, H., Niita, K., and Nakamura, T.: Development of General-Purpose Particle
and Heavy Ion Transport Monte Carlo Code, J. Nucl. Sci.
Technol., 39, 1142–1151, https://doi.org/10.1080/18811248.2002.9715305, 2002. a, b
Iwema, J., Schrön, M., Da Silva, J. K., Schweiser De Paiva Lopes, R.,
and Rosolem, R.: Accuracy and precision of the cosmic-ray neutron sensor for
soil moisture estimation at humid environments, Hydrol. Process., 35, e14419,
https://doi.org/10.1002/hyp.14419, 2021. a
Iyer, M. and Ganguly, A.: Neutron Evaporation and Energy Distribution in
Individual Fission Fragments, Phys. Rev. C, 5, 1410–1421,
https://doi.org/10.1103/PhysRevC.5.1410, 1972. a
Jakobi, J., Huisman, J., Köhli, M., Rasche, D., Vereecken, H., and Bogena,
H.: The Footprint Characteristics of Cosmic Ray Thermal Neutrons, Geophys.
Res. Lett., 48, e2021GL094281, https://doi.org/10.1029/2021gl094281, 2021. a, b
Kawano, T., Talou, P., Stetcu, I., and Chadwick, M.: Statistical and
evaporation models for the neutron emission energy spectrum in the
center-of-mass system from fission fragments, Nucl. Phys. A, 913, 51–70,
https://doi.org/10.1016/j.nuclphysa.2013.05.020, 2013. a
Kittelmann, T. and Boin, M.: Polycrystalline neutron scattering for Geant4:
NXSG4, Comput. Phys. Commun., 189, 114–118,
https://doi.org/10.1016/j.cpc.2014.11.009, 2015. a
Knuth, D.: The Art of Computer Programming, Volume 2, 3rd Edn.,
Seminumerical Algorithms, Addison-Wesley Longman Publishing Co., Inc.,
Boston, MA, USA, ISBN 9780321635778, 1997. a
Köhli, M.: URANOS v1.0, Harvard Dataverse V1 [code], https://doi.org/10.7910/DVN/THPNZW, 2022a. a, b
Köhli, M.: mkoehli/uranos: URANOS v1.0 (URANOS), Zenodo [data set], https://doi.org/10.5281/zenodo.6578668, 2022b. a
Köhli, M. and Schmoldt, J.-P.: Feasibility of UXO detection via pulsed
neutron-neutron logging, Appl. Radiat. Isotopes, 188, 110403,
https://doi.org/10.1016/j.apradiso.2022.110403, 2022. a, b
Köhli, M., Schrön, M., Zreda, M., Schmidt, U., Dietrich, P., and
Zacharias, S.: Footprint characteristics revised for field-scale soil
moisture monitoring with cosmic-ray neutrons, Water Resour. Res., 51,
5772–5790, https://doi.org/10.1002/2015WR017169, 2015. a, b, c
Köhli, M., Allmendinger, F., Häußler, W., Schröder, T., Klein,
M., Meven, M., and Schmidt, U.: Efficiency and spatial resolution of the
CASCADE thermal neutron detector, Nucl. Instrum. Meth. A, 828, 242–249, https://doi.org/10.1016/j.nima.2016.05.014, 2016. a, b
Köhli, M., Desch, K., Gruber, M., Kaminski, J., Schmidt, F., and Wagner,
T.: Novel neutron detectors based on the time projection method, Physica B, 551, 517–522, https://doi.org/10.1016/j.physb.2018.03.026, 2018a. a, b
Köhli, M., Schrön, M., and Schmidt, U.: Response functions for
detectors in cosmic ray neutron sensing, Nucl. Instrum. Meth. A, 902, 184–189, https://doi.org/10.1016/j.nima.2018.06.052,
2018b. a, b, c
Köhli, M., Weimar, J., Schrön, M., Baatz, R., and Schmidt, U.: Soil
Moisture and Air Humidity Dependence of the Above-Ground Cosmic-Ray Neutron
Intensity, Front. Water, 2, 15, https://doi.org/10.3389/frwa.2020.544847, 2021. a, b, c
Koning, A., Bauge, E., Dean, C., Dupont, E., Nordborg, C., Rugama, Y., Fischer,
U., Forrest, R., Kellett, M., Jacqmin, R., Leeb, H., Mills, R., Pescarini,
M., and Rullhusen, P.: Status of the JEFF Nuclear Data Library, J.
Korean Phys. Soc., 59, 1057–1062, https://doi.org/10.3938/jkps.59.1057,
2011. a
Lefmann, K. and Nielsen, K.: McStas, a general software package for neutron
ray-tracing simulations, Neutron News, 10, 20–23,
https://doi.org/10.1080/10448639908233684, 1999. a
Li, D., Schrön, M., Köhli, M., Bogena, H., Weimar, J., Jiménez
Bello, M., Han, X., Martínez Gimeno, M., Zacharias, S., Vereecken,
H., and Hendricks Franssen, H.: Can Drip Irrigation be Scheduled with
Cosmic-Ray Neutron Sensing?, Vadose Zone J., 18, 190053,
https://doi.org/10.2136/vzj2019.05.0053, 2019. a, b
Liu, H., Hou, Y., Li, H., Song, Y., Hu, L., and Liang, M.: Cosmic-ray neutron
fluxes and spectra at different altitudes based on Monte Carlo
simulations, Appl. Radiat. Isotopes, 175, 109800,
https://doi.org/10.1016/j.apradiso.2021.109800, 2021. a
Maire, E. and Withers, P.: Quantitative X-ray tomography, Int.
Mater. Rev., 59, 1–43, https://doi.org/10.1179/1743280413Y.0000000023, 2013. a
Mares, V. and Schraube, H.: Evaluation of the response matrix of a Bonner
Sphere Spectrometer with LiI detector from thermal energy to 100 MeV,
Nucl. Instrum. Meth. A, 337, 461–473,
https://doi.org/10.1016/0168-9002(94)91116-9, 1994. a
Mares, V., Schraube, G., and Schraube, H.: Calculated neutron response of a
Bonner Sphere Spectrometer with 3He counter, Nucl. Instrum. Meth. A, 307, 398–412,
https://doi.org/10.1016/0168-9002(91)90210-H, 1991. a, b
Matsumoto, M. and Nishimura, T.: Mersenne Twister: A 623-dimensionally
Equidistributed Uniform Pseudo-random Number Generator, ACM T.
Model. Comput. Sim., 8, 3–30, https://doi.org/10.1145/272991.272995,
1998. a
McConn Jr., R., Gesh, C., Pagh, R., Rucker, R., and Williams III, R.:
Compendium of Material Composition Data for Radiation Transport Modeling,
Tech. Rep. PNNL-15870 Rev. 1, Pacific Northwest National Laboratory,
Richland, Washington 99352,
https://www.pnnl.gov/main/publications/external/technical_reports/pnnl-15870rev1.pdf,
2011. a
McKinney, G.: MCNP6 Cosmic and Terrestrial Background Particle Fluxes,
LA-UR-13-24293, release
3,
https://mcnp.lanl.gov/pdf_files/la-ur-13-24293.pdf, 2013. a
McKinney, G., Armstrong, H., James, M., Clem, J., and Goldhagen, P.: MCNP6
Cosmic-Source Option, LA-UR-12-22318,
https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-12-22318,
2012. a
Metropolis, N. and Ulam, S.: The Monte Carlo Method, J. Am.
Stat. Assoc., 44, 335–341, https://doi.org/10.2307/2280232, 1949. a
Nelms, A.: Energy loss and range of electrons and positrons, United States
Department of Commerce, national
Bureau of Standards Circular 577 and Suppl.,
https://catalog.hathitrust.org/Record/007291096 (last access: 18 January 2023), 1956. a
Niita, K.: QMD and JAM Calculations for High Energy Nucleon-Nucleus
Collisions, J. Nucl. Sci. Technol., 39, 714–719,
https://doi.org/10.1080/00223131.2002.10875198, 2002. a
Niita, K., Takada, H., Meigo, S., and Ikeda, Y.: High-energy particle transport
code NMTC/JAM, Nucl. Instrum. Meth. B, 184, 406–420,
https://doi.org/10.1016/S0168-583X(01)00784-4, 2001. a
Otuka, N., Dupont, E., Semkova, V., Pritychenko, B., Blokhin, A., Aikawa, M.,
Babykina, S., Bossant, M., Chen, G., Dunaeva, S., Forrest, R., Fukahori, T.,
Furutachi, N., Ganesan, S., Ge, Z., Gritzay, O., Herman, M., Hlavač,
S., Kato, K., Lalremruata, B., Lee, Y., Makinaga, A., Matsumoto, K.,
Mikhaylyukova, M., Pikulina, G., Pronyaev, V., Saxena, A., Schwerer, O.,
Simakov, S., Soppera, N., Suzuki, R., Takács, S., Tao, X., Taova, S.,
Tárkányi, F., Varlamov, V., Wang, J., Yang, S., Zerkin, V., and
Zhuang, Y.: Towards a More Complete and Accurate Experimental Nuclear
Reaction Data Library (EXFOR): International Collaboration Between Nuclear
Reaction Data Centres (NRDC), Nucl. Data Sheets, 120, 272–276,
https://doi.org/10.1016/j.nds.2014.07.065, 2014. a
Pazianotto, M., Cortés-Giraldo, M., Federico, C., Gonçalez, O.,
Quesada, J., and Carlson, B.: Determination of the cosmic-ray-induced neutron
flux and ambient dose equivalent at flight altitude, J. Phys.
Conf. Ser., 630, 012022, https://doi.org/10.1088/1742-6596/630/1/012022, 2015. a
Peggs, S., et al.: European Spallation Source Technical Design Report,
Technical Design Report ESS, ESS, Lundt, ESS-2013-001,
http://docdb01.esss.lu.se/DocDB/0002/000274/006/TDR_final_130423_print_ch1.pdf,
2013. a
Prael, R. and Lichtenstein, H.: User Guide to LCS: The LAHET Code System,
LA-UR-89-3014,
https://permalink.lanl.gov/object/tr?what=info:lanl-repo/lareport/LA-UR-89-3014,
1989. a
Rasche, D., Köhli, M., Schrön, M., Blume, T., and Güntner, A.: Towards disentangling heterogeneous soil moisture patterns in cosmic-ray neutron sensor footprints, Hydrol. Earth Syst. Sci., 25, 6547–6566, https://doi.org/10.5194/hess-25-6547-2021, 2021. a
Romano, P. and Forget, B.: The OpenMC Monte Carlo particle transport code,
Ann. Nucl. Energ., 51, 274–281, https://doi.org/10.1016/j.anucene.2012.06.040,
2013. a, b
Roth, S.: Ray casting for modeling solids, Comput. Vision Graph., 18, 109–144, https://doi.org/10.1016/0146-664X(82)90169-1, 1982. a
Šaroun, J. and Kulda, J.: RESTRAX – a program for TAS
resolution calculation and scan profile simulation, Physica B, 234-236, 1102–1104, https://doi.org/10.1016/s0921-4526(97)00037-9, 1997. a
Sato, T.: Analytical Model for Estimating Terrestrial Cosmic Ray Fluxes Nearly
Anytime and Anywhere in the World: Extension of PARMA/EXPACS, PLOS ONE, 10,
1–33, https://doi.org/10.1371/journal.pone.0144679, 2015. a
Sato, T. and Niita, K.: Analytical Functions to Predict Cosmic-Ray Neutron
Spectra in the Atmosphere, Radiation Research, 166, 544–555,
https://doi.org/10.1667/RR0610.1, 2006. a
Sato, T., Yasuda, H., Niita, K., Endo, A., and Sihver, L.: Development of
PARMA: PHITS-based Analytical Radiation Model in the Atmosphere,
Radiat. Res., 170, 244–259, https://doi.org/10.1667/RR1094.1, 2008. a, b, c
Schattan, P., Köhli, M., Schrön, M., Baroni, G., and Oswald, S.:
Sensing Area-Average Snow Water Equivalent with Cosmic-Ray Neutrons: The
Influence of Fractional Snow Cover, Water Resour. Res., 55,
10796–10812, https://doi.org/10.1029/2019WR025647, 2019. a, b
Schrön, M., Köhli, M., Scheiffele, L., Iwema, J., Bogena, H. R., Lv, L., Martini, E., Baroni, G., Rosolem, R., Weimar, J., Mai, J., Cuntz, M., Rebmann, C., Oswald, S. E., Dietrich, P., Schmidt, U., and Zacharias, S.: Improving calibration and validation of cosmic-ray neutron sensors in the light of spatial sensitivity, Hydrol. Earth Syst. Sci., 21, 5009–5030, https://doi.org/10.5194/hess-21-5009-2017, 2017. a, b
Schrön, M., Rosolem, R., Köhli, M., Piussi, L., Schröter, I.,
Iwema, J., Kögler, S., Oswald, S., Wollschläger, U., Samaniego, L.,
Dietrich, P., and Zacharias, S.: Cosmic-ray Neutron Rover Surveys of Field
Soil Moisture and the Influence of Roads, Water Resour. Res., 54,
6441–6459, https://doi.org/10.1029/2017WR021719, 2018. a
Schrön, M., Zacharias, S., Womack, G., Köhli, M., Desilets, D., Oswald, S. E., Bumberger, J., Mollenhauer, H., Kögler, S., Remmler, P., Kasner, M., Denk, A., and Dietrich, P.: Intercomparison of cosmic-ray neutron sensors and water balance monitoring in an urban environment, Geosci. Instrum. Method. Data Syst., 7, 83–99, https://doi.org/10.5194/gi-7-83-2018, 2018. a, b, c
Schrön, M., Oswald, S., Zacharias, S., Kasner, M., Dietrich, P., and
Attinger, S.: Neutrons on Rails: Transregional Monitoring of Soil Moisture
and Snow Water Equivalent, Geophys. Res. Lett., 48, e2021GL093924,
https://doi.org/10.1029/2021gl093924, 2021. a
Shibata, K., Iwamoto, O., Nakagawa, T., Iwamoto, N., Ichihara, A., Kunieda, S.,
Chiba, S., Furutaka, K., Otuka, N., Ohsawa, T., Murata, T., Matsunobu, H.,
Zukeran, A., Kamada, S., and Katakura, J.: JENDL-4.0: A New Library for
Nuclear Science and Engineering, J. Nucl. Sci. Technol.,
48, 1–30, https://doi.org/10.1080/18811248.2011.9711675, 2011. a
Smith, A., Fields, P., and Roberts, J.: Spontaneous Fission Neutron Spectrum of
252Cf, Phys. Rev., 108, 411–413,
https://doi.org/10.1103/PhysRev.108.411, 1957. a
Solovyev, A., Fedorov, V., Kharlov, V., and Stepanova, U.: Comparative analysis
of MCNPX and GEANT4 codes for fast-neutron radiation treatment planning,
Nucl. Energ. Technol., 1, 14–19, https://doi.org/10.1016/j.nucet.2015.11.004,
2015. a
Sun Programmers Group: Fortran 77 Reference Manual, Tech. rep., SunSoft,
http://wwwcdf.pd.infn.it/localdoc/f77_sun.pdf,
1995a. a
Sun Programmers Group: Fortran 90 User's Guide, Tech. rep., SunSoft,
http://smdc.sinp.msu.ru/doc/Fortran90UsersGuide.pdf,
1995b. a
Sweezy, J., Hertel, N., and Veinot, K.: BUMS – Bonner Sphere Unfolding
Made Simple: an HTML based multisphere neutron spectrometer unfolding
package, Nucl. Instrum. Meth. A, 476,
263–269, https://doi.org/10.1016/S0168-9002(01)01466-8,
2002. a
Terrell, J.: Fission Neutron Spectra and Nuclear Temperatures, Phys. Rev.,
113, 527–541, https://doi.org/10.1103/PhysRev.113.527, 1959. a
Thomas, D. and Alevra, A.: Bonner Sphere Spectrometers – a critical
review, Nucl. Instrum. Meth. A, 476, 12–20,
https://doi.org/10.1016/S0168-9002(01)01379-1, 2002. a
van der Ende, B., Atanackovic, J., Erlandson, A., and Bentoumi, G.: Use of
GEANT4 vs. MCNPX for the characterization of a boron-lined neutron
detector, Nucl. Instrum. Meth. A, 820, 40–47,
https://doi.org/10.1016/j.nima.2016.02.082, 2016. a
Walsh, J., Forget, B., and Smith, K.: Accelerated sampling of the free gas
resonance elastic scattering kernel, Ann. Nucl. Energ., 69, 116–124,
https://doi.org/10.1016/j.anucene.2014.01.017, 2014. a
Waters, L., McKinney, G., Durkee, J., Fensin, M., Hendricks, J., James, M.,
Johns, R., and Pelowitz, D.: The MCNPX Monte Carlo Radiation Transport
Code, AIP Conf. Proc., 896, 81–90, https://doi.org/10.1063/1.2720459,
2007. a, b
Watt, B.: Energy Spectrum of Neutrons from Thermal Fission of
235U, Phys. Rev., 87, 1037–1041,
https://doi.org/10.1103/PhysRev.87.1037, 1952. a
Wechsler, D., Zsigmond, G., Streffer, F., and Mezei, F.: VITESS: Virtual
instrumentation tool for pulsed and continuous sources, Neutron News, 11,
25–28, https://doi.org/10.1080/10448630008233764, 2000. a
Weimar, J., Köhli, M., Budach, C., and Schmidt, U.: Large-Scale Boron-Lined
Neutron Detection Systems as a 3He Alternative for Cosmic Ray Neutron
Sensing, Front. Water, 2, 16, https://doi.org/10.3389/frwa.2020.00016, 2020. a, b
Weisskopf, V.: Statistics and Nuclear Reactions, Phys. Rev., 52, 295–303,
https://doi.org/10.1103/PhysRev.52.295, 1937. a
X-5 Monte Carlo Team: MCNP-A general Monte Carlo N-particle transport
code, Version 5, LA-UR-03-1987, volume I: Overview and Theory,
https://laws.lanl.gov/vhosts/mcnp.lanl.gov/pdf_files/la-ur-03-1987.pdf,
2003.
a
Yamashita, M., Stephens, L., and Patterson, H.: Cosmic-ray-produced neutrons at
ground level: Neutron production rate and flux distribution, J.
Geophys. Res., 71, 3817–3834, https://doi.org/10.1029/JZ071i016p03817, 1966. a
Ziegler, J.: Terrestrial cosmic ray intensities, IBM J. Res.
Dev., 42, 117–140, https://doi.org/10.1147/rd.421.0117, 1998. a
Zreda, M., Desilets, D., Ferré, T., and Scott, R.: Measuring soil moisture
content non-invasively at intermediate spatial scale using cosmic-ray
neutrons, Geophys. Res. Lett., 35, L21402,
https://doi.org/10.1029/2008GL035655, 2008. a
Zreda, M., Shuttleworth, W. J., Zeng, X., Zweck, C., Desilets, D., Franz, T., and Rosolem, R.: COSMOS: the COsmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4079–4099, https://doi.org/10.5194/hess-16-4079-2012, 2012. a
Zweck, C., Zreda, M., and Desilets, D.: Snow shielding factors for cosmogenic
nuclide dating inferred from Monte Carlo neutron transport simulations,
Earth Planet. Sc. Lett., 379, 64–71,
https://doi.org/10.1016/j.epsl.2013.07.023, 2013. a
Short summary
In the last decades, Monte Carlo codes were often consulted to study neutrons near the surface. As an alternative for the growing community of CRNS, we developed URANOS. The main model features are tracking of particle histories from creation to detection, detector representations as layers or geometric shapes, a voxel-based geometry model, and material setup based on color codes in ASCII matrices or bitmap images. The entire software is developed in C++ and features a graphical user interface.
In the last decades, Monte Carlo codes were often consulted to study neutrons near the surface....
