Articles | Volume 8, issue 10
https://doi.org/10.5194/gmd-8-3285-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-8-3285-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Development and technical paper
| 
20 Oct 2015
Development and technical paper |
| 20 Oct 2015
CH4 parameter estimation in CLM4.5bgc using surrogate global optimization
J. Müller
Center for Computational Sciences and Engineering, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
R. Paudel
Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA
C. A. Shoemaker
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
J. Woodbury
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
Y. Wang
School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
N. Mahowald
Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA
Related authors
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Danny M. Leung, Jasper F. Kok, Longlei Li, David M. Lawrence, Natalie M. Mahowald, Simone Tilmes, and Erik Kluzek
EGUsphere, https://doi.org/10.5194/egusphere-2024-1124, https://doi.org/10.5194/egusphere-2024-1124, 2024
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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This study derives a desert dust emission dataset for 1841–2000, by employing a combination of observed dust records from sedimentary cores as well as reanalyzed global dust cycle constraints. We evaluate the ability of global models to replicate the observed historical dust variability by using the emission dataset to force a historical simulation in an Earth system model. We show that prescribing our emissions forces the model to match better against observations than other mechanistic models.
Danny M. Leung, Jasper F. Kok, Longlei Li, Natalie M. Mahowald, David M. Lawrence, Simone Tilmes, Erik Kluzek, Martina Klose, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 24, 2287–2318, https://doi.org/10.5194/acp-24-2287-2024, https://doi.org/10.5194/acp-24-2287-2024, 2024
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This study uses a premier Earth system model to evaluate a new desert dust emission scheme proposed in our companion paper. We show that our scheme accounts for more dust emission physics, hence matching better against observations than other existing dust emission schemes do. Our scheme's dust emissions also couple tightly with meteorology, hence likely improving the modeled dust sensitivity to climate change. We believe this work is vital for improving dust representation in climate models.
Natalie M. Mahowald, Longlei Li, Julius Vira, Marje Prank, Douglas S. Hamilton, Hitoshi Matsui, Ron L. Miller, Louis Lu, Ezgi Akyuz, Daphne Meidan, Peter Hess, Heikki Lihavainen, Christine Wiedinmyer, Jenny Hand, Maria Grazia Alaimo, Célia Alves, Andres Alastuey, Paulo Artaxo, Africa Barreto, Francisco Barraza, Silvia Becagli, Giulia Calzolai, Shankarararman Chellam, Ying Chen, Patrick Chuang, David D. Cohen, Cristina Colombi, Evangelia Diapouli, Gaetano Dongarra, Konstantinos Eleftheriadis, Corinne Galy-Lacaux, Cassandra Gaston, Dario Gomez, Yenny González Ramos, Hannele Hakola, Roy M. Harrison, Chris Heyes, Barak Herut, Philip Hopke, Christoph Hüglin, Maria Kanakidou, Zsofia Kertesz, Zbiginiw Klimont, Katriina Kyllönen, Fabrice Lambert, Xiaohong Liu, Remi Losno, Franco Lucarelli, Willy Maenhaut, Beatrice Marticorena, Randall V. Martin, Nikolaos Mihalopoulos, Yasser Morera-Gomez, Adina Paytan, Joseph Prospero, Sergio Rodríguez, Patricia Smichowski, Daniela Varrica, Brenna Walsh, Crystal Weagle, and Xi Zhao
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-1, https://doi.org/10.5194/essd-2024-1, 2024
Preprint withdrawn
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Aerosol particles can interact with incoming solar radiation and outgoing long wave radiation, change cloud properties, affect photochemistry, impact surface air quality, and when deposited impact surface albedo of snow and ice, and modulate carbon dioxide uptake by the land and ocean. Here we present a new compilation of aerosol observations including composition, a methodology for comparing the datasets to model output, and show the implications of these results using one model.
Natalie M. Mahowald, Longlei Li, Samuel Albani, Douglas S. Hamilton, and Jasper F. Kok
Atmos. Chem. Phys., 24, 533–551, https://doi.org/10.5194/acp-24-533-2024, https://doi.org/10.5194/acp-24-533-2024, 2024
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Estimating past aerosol radiative effects and their uncertainties is an important topic in climate science. Aerosol radiative effects propagate into large uncertainties in estimates of how present and future climate evolves with changing greenhouse gas emissions. A deeper understanding of how aerosols interacted with the atmospheric energy budget under past climates is hindered in part by a lack of relevant paleo-observations and in part because less attention has been paid to the problem.
Katrine A. Gorham, Sam Abernethy, Tyler R. Jones, Peter Hess, Natalie M. Mahowald, Daphne Meidan, Matthew S. Johnson, Maarten M. J. W. van Herpen, Yangyang Xu, Alfonso Saiz-Lopez, Thomas Röckmann, Chloe A. Brashear, Erika Reinhardt, and David Mann
EGUsphere, https://doi.org/10.22541/essoar.169755495.51174285/v1, https://doi.org/10.22541/essoar.169755495.51174285/v1, 2023
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Rapid reduction of atmospheric methane is needed to slow the rate of global warming. Reducing anthropogenic methane emissions is the top priority. However, atmospheric methane is also impacted by rising natural emissions and changing sinks. Study of possible atmospheric methane removal approaches, such as iron salt aerosols to increase the chlorine radical sink, benefit from a roadmapped approach to understand if there may be viable and socially acceptable ways to decrease future risk.
María Gonçalves Ageitos, Vincenzo Obiso, Ron L. Miller, Oriol Jorba, Martina Klose, Matt Dawson, Yves Balkanski, Jan Perlwitz, Sara Basart, Enza Di Tomaso, Jerónimo Escribano, Francesca Macchia, Gilbert Montané, Natalie M. Mahowald, Robert O. Green, David R. Thompson, and Carlos Pérez García-Pando
Atmos. Chem. Phys., 23, 8623–8657, https://doi.org/10.5194/acp-23-8623-2023, https://doi.org/10.5194/acp-23-8623-2023, 2023
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Dust aerosols affect our climate differently depending on their mineral composition. We include dust mineralogy in an atmospheric model considering two existing soil maps, which still have large associated uncertainties. The soil data and the distribution of the minerals in different aerosol sizes are key to our model performance. We find significant regional variations in climate-relevant variables, which supports including mineralogy in our current models and the need for improved soil maps.
Danny M. Leung, Jasper F. Kok, Longlei Li, Gregory S. Okin, Catherine Prigent, Martina Klose, Carlos Pérez García-Pando, Laurent Menut, Natalie M. Mahowald, David M. Lawrence, and Marcelo Chamecki
Atmos. Chem. Phys., 23, 6487–6523, https://doi.org/10.5194/acp-23-6487-2023, https://doi.org/10.5194/acp-23-6487-2023, 2023
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Desert dust modeling is important for understanding climate change, as dust regulates the atmosphere's greenhouse effect and radiation. This study formulates and proposes a more physical and realistic desert dust emission scheme for global and regional climate models. By considering more aeolian processes in our emission scheme, our simulations match better against dust observations than existing schemes. We believe this work is vital in improving dust representation in climate models.
Longlei Li, Natalie M. Mahowald, Jasper F. Kok, Xiaohong Liu, Mingxuan Wu, Danny M. Leung, Douglas S. Hamilton, Louisa K. Emmons, Yue Huang, Neil Sexton, Jun Meng, and Jessica Wan
Geosci. Model Dev., 15, 8181–8219, https://doi.org/10.5194/gmd-15-8181-2022, https://doi.org/10.5194/gmd-15-8181-2022, 2022
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This study advances mineral dust parameterizations in the Community Atmospheric Model (CAM; version 6.1). Efforts include 1) incorporating a more physically based dust emission scheme; 2) updating the dry deposition scheme; and 3) revising the gravitational settling velocity to account for dust asphericity. Substantial improvements achieved with these updates can help accurately quantify dust–climate interactions using CAM, such as the dust-radiation and dust–cloud interactions.
Ye Wang, Natalie Mahowald, Peter Hess, Wenxiu Sun, and Gang Chen
Atmos. Chem. Phys., 22, 7575–7592, https://doi.org/10.5194/acp-22-7575-2022, https://doi.org/10.5194/acp-22-7575-2022, 2022
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PM2.5 is positively related to anticyclonic wave activity (AWA) changes close to the observing sites. Changes between current and future climates in AWA can explain up to 75 % of PM2.5 variability at some stations using a linear regression model. Our analysis indicates that higher PM2.5 concentrations occur when a positive AWA anomaly is prominent, which could be critical for understanding how pollutants respond to changing atmospheric circulation and for developing robust pollution projections.
Jasper F. Kok, Adeyemi A. Adebiyi, Samuel Albani, Yves Balkanski, Ramiro Checa-Garcia, Mian Chin, Peter R. Colarco, Douglas S. Hamilton, Yue Huang, Akinori Ito, Martina Klose, Danny M. Leung, Longlei Li, Natalie M. Mahowald, Ron L. Miller, Vincenzo Obiso, Carlos Pérez García-Pando, Adriana Rocha-Lima, Jessica S. Wan, and Chloe A. Whicker
Atmos. Chem. Phys., 21, 8127–8167, https://doi.org/10.5194/acp-21-8127-2021, https://doi.org/10.5194/acp-21-8127-2021, 2021
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Desert dust interacts with virtually every component of the Earth system, including the climate system. We develop a new methodology to represent the global dust cycle that integrates observational constraints on the properties and abundance of desert dust with global atmospheric model simulations. We show that the resulting representation of the global dust cycle is more accurate than what can be obtained from a large number of current climate global atmospheric models.
Jasper F. Kok, Adeyemi A. Adebiyi, Samuel Albani, Yves Balkanski, Ramiro Checa-Garcia, Mian Chin, Peter R. Colarco, Douglas S. Hamilton, Yue Huang, Akinori Ito, Martina Klose, Longlei Li, Natalie M. Mahowald, Ron L. Miller, Vincenzo Obiso, Carlos Pérez García-Pando, Adriana Rocha-Lima, and Jessica S. Wan
Atmos. Chem. Phys., 21, 8169–8193, https://doi.org/10.5194/acp-21-8169-2021, https://doi.org/10.5194/acp-21-8169-2021, 2021
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The many impacts of dust on the Earth system depend on dust mineralogy, which varies between dust source regions. We constrain the contribution of the world’s main dust source regions by integrating dust observations with global model simulations. We find that Asian dust contributes more and that North African dust contributes less than models account for. We obtain a dataset of each source region’s contribution to the dust cycle that can be used to constrain dust impacts on the Earth system.
Longlei Li, Natalie M. Mahowald, Ron L. Miller, Carlos Pérez García-Pando, Martina Klose, Douglas S. Hamilton, Maria Gonçalves Ageitos, Paul Ginoux, Yves Balkanski, Robert O. Green, Olga Kalashnikova, Jasper F. Kok, Vincenzo Obiso, David Paynter, and David R. Thompson
Atmos. Chem. Phys., 21, 3973–4005, https://doi.org/10.5194/acp-21-3973-2021, https://doi.org/10.5194/acp-21-3973-2021, 2021
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For the first time, this study quantifies the range of the dust direct radiative effect due to uncertainty in the soil mineral abundance using all currently available information. We show that the majority of the estimated direct radiative effect range is due to uncertainty in the simulated mass fractions of iron oxides and thus their soil abundance, which is independent of the model employed. We therefore prove the necessity of considering mineralogy for understanding dust–climate interactions.
Douglas S. Hamilton, Rachel A. Scanza, Yan Feng, Joseph Guinness, Jasper F. Kok, Longlei Li, Xiaohong Liu, Sagar D. Rathod, Jessica S. Wan, Mingxuan Wu, and Natalie M. Mahowald
Geosci. Model Dev., 12, 3835–3862, https://doi.org/10.5194/gmd-12-3835-2019, https://doi.org/10.5194/gmd-12-3835-2019, 2019
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MIMI v1.0 was designed for use within Earth system models to simulate the 3-D emission, atmospheric processing, and deposition of iron and its soluble fraction. Understanding the iron cycle is important due to its role as an essential micronutrient for ocean phytoplankton; its supply limits primary productivity in many of the world's oceans. Human activity has perturbed the iron cycle, and MIMI is capable of diagnosing many of these impacts; hence, it is important for future climate studies.
George S. Fanourgakis, Maria Kanakidou, Athanasios Nenes, Susanne E. Bauer, Tommi Bergman, Ken S. Carslaw, Alf Grini, Douglas S. Hamilton, Jill S. Johnson, Vlassis A. Karydis, Alf Kirkevåg, John K. Kodros, Ulrike Lohmann, Gan Luo, Risto Makkonen, Hitoshi Matsui, David Neubauer, Jeffrey R. Pierce, Julia Schmale, Philip Stier, Kostas Tsigaridis, Twan van Noije, Hailong Wang, Duncan Watson-Parris, Daniel M. Westervelt, Yang Yang, Masaru Yoshioka, Nikos Daskalakis, Stefano Decesari, Martin Gysel-Beer, Nikos Kalivitis, Xiaohong Liu, Natalie M. Mahowald, Stelios Myriokefalitakis, Roland Schrödner, Maria Sfakianaki, Alexandra P. Tsimpidi, Mingxuan Wu, and Fangqun Yu
Atmos. Chem. Phys., 19, 8591–8617, https://doi.org/10.5194/acp-19-8591-2019, https://doi.org/10.5194/acp-19-8591-2019, 2019
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Effects of aerosols on clouds are important for climate studies but are among the largest uncertainties in climate projections. This study evaluates the skill of global models to simulate aerosol, cloud condensation nuclei (CCN) and cloud droplet number concentrations (CDNCs). Model results show reduced spread in CDNC compared to CCN due to the negative correlation between the sensitivities of CDNC to aerosol number concentration (air pollution) and updraft velocity (atmospheric dynamics).
Stelios Myriokefalitakis, Akinori Ito, Maria Kanakidou, Athanasios Nenes, Maarten C. Krol, Natalie M. Mahowald, Rachel A. Scanza, Douglas S. Hamilton, Matthew S. Johnson, Nicholas Meskhidze, Jasper F. Kok, Cecile Guieu, Alex R. Baker, Timothy D. Jickells, Manmohan M. Sarin, Srinivas Bikkina, Rachel Shelley, Andrew Bowie, Morgane M. G. Perron, and Robert A. Duce
Biogeosciences, 15, 6659–6684, https://doi.org/10.5194/bg-15-6659-2018, https://doi.org/10.5194/bg-15-6659-2018, 2018
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The first atmospheric iron (Fe) deposition model intercomparison is presented in this study, as a result of the deliberations of the United Nations Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP; http://www.gesamp.org/) Working Group 38. We conclude that model diversity over remote oceans reflects uncertainty in the Fe content parameterizations of dust aerosols, combustion aerosol emissions and the size distribution of transported aerosol Fe.
Rachel A. Scanza, Douglas S. Hamilton, Carlos Perez Garcia-Pando, Clifton Buck, Alex Baker, and Natalie M. Mahowald
Atmos. Chem. Phys., 18, 14175–14196, https://doi.org/10.5194/acp-18-14175-2018, https://doi.org/10.5194/acp-18-14175-2018, 2018
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Soluble iron input to remote oceans from dust and combustion aerosols may significantly impact the ability of the ocean to remove carbon dioxide from the atmosphere. In this paper, the processing of insoluble iron during atmospheric transport is simulated using parameterizations that can be implemented in most Earth system models. Our mechanism reasonably matches observations and is computationally efficient, enabling the study of trends and climate impacts due to the Fe–C cycle.
Masa Kageyama, Pascale Braconnot, Sandy P. Harrison, Alan M. Haywood, Johann H. Jungclaus, Bette L. Otto-Bliesner, Jean-Yves Peterschmitt, Ayako Abe-Ouchi, Samuel Albani, Patrick J. Bartlein, Chris Brierley, Michel Crucifix, Aisling Dolan, Laura Fernandez-Donado, Hubertus Fischer, Peter O. Hopcroft, Ruza F. Ivanovic, Fabrice Lambert, Daniel J. Lunt, Natalie M. Mahowald, W. Richard Peltier, Steven J. Phipps, Didier M. Roche, Gavin A. Schmidt, Lev Tarasov, Paul J. Valdes, Qiong Zhang, and Tianjun Zhou
Geosci. Model Dev., 11, 1033–1057, https://doi.org/10.5194/gmd-11-1033-2018, https://doi.org/10.5194/gmd-11-1033-2018, 2018
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The Paleoclimate Modelling Intercomparison Project (PMIP) takes advantage of the existence of past climate states radically different from the recent past to test climate models used for climate projections and to better understand these climates. This paper describes the PMIP contribution to CMIP6 (Coupled Model Intercomparison Project, 6th phase) and possible analyses based on PMIP results, as well as on other CMIP6 projects.
Bette L. Otto-Bliesner, Pascale Braconnot, Sandy P. Harrison, Daniel J. Lunt, Ayako Abe-Ouchi, Samuel Albani, Patrick J. Bartlein, Emilie Capron, Anders E. Carlson, Andrea Dutton, Hubertus Fischer, Heiko Goelzer, Aline Govin, Alan Haywood, Fortunat Joos, Allegra N. LeGrande, William H. Lipscomb, Gerrit Lohmann, Natalie Mahowald, Christoph Nehrbass-Ahles, Francesco S. R. Pausata, Jean-Yves Peterschmitt, Steven J. Phipps, Hans Renssen, and Qiong Zhang
Geosci. Model Dev., 10, 3979–4003, https://doi.org/10.5194/gmd-10-3979-2017, https://doi.org/10.5194/gmd-10-3979-2017, 2017
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The PMIP4 and CMIP6 mid-Holocene and Last Interglacial simulations provide an opportunity to examine the impact of two different changes in insolation forcing on climate at times when other forcings were relatively similar to present. This will allow exploration of the role of feedbacks relevant to future projections. Evaluating these simulations using paleoenvironmental data will provide direct out-of-sample tests of the reliability of state-of-the-art models to simulate climate changes.
Masa Kageyama, Samuel Albani, Pascale Braconnot, Sandy P. Harrison, Peter O. Hopcroft, Ruza F. Ivanovic, Fabrice Lambert, Olivier Marti, W. Richard Peltier, Jean-Yves Peterschmitt, Didier M. Roche, Lev Tarasov, Xu Zhang, Esther C. Brady, Alan M. Haywood, Allegra N. LeGrande, Daniel J. Lunt, Natalie M. Mahowald, Uwe Mikolajewicz, Kerim H. Nisancioglu, Bette L. Otto-Bliesner, Hans Renssen, Robert A. Tomas, Qiong Zhang, Ayako Abe-Ouchi, Patrick J. Bartlein, Jian Cao, Qiang Li, Gerrit Lohmann, Rumi Ohgaito, Xiaoxu Shi, Evgeny Volodin, Kohei Yoshida, Xiao Zhang, and Weipeng Zheng
Geosci. Model Dev., 10, 4035–4055, https://doi.org/10.5194/gmd-10-4035-2017, https://doi.org/10.5194/gmd-10-4035-2017, 2017
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The Last Glacial Maximum (LGM, 21000 years ago) is an interval when global ice volume was at a maximum, eustatic sea level close to a minimum, greenhouse gas concentrations were lower, atmospheric aerosol loadings were higher than today, and vegetation and land-surface characteristics were different from today. This paper describes the implementation of the LGM numerical experiment for the PMIP4-CMIP6 modelling intercomparison projects and the associated sensitivity experiments.
Molly B. Smith, Natalie M. Mahowald, Samuel Albani, Aaron Perry, Remi Losno, Zihan Qu, Beatrice Marticorena, David A. Ridley, and Colette L. Heald
Atmos. Chem. Phys., 17, 3253–3278, https://doi.org/10.5194/acp-17-3253-2017, https://doi.org/10.5194/acp-17-3253-2017, 2017
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Using different meteorology reanalyses to drive dust in climate modeling can produce dissimilar global dust distributions, especially in the Southern Hemisphere (SH). It may therefore not be advisable for SH dust studies to base results on simulations driven by one reanalysis. Northern Hemisphere dust varies mostly on seasonal timescales, while SH dust varies on interannual timescales. Dust is an important part of climate modeling, and we hope this contributes to understanding these simulations.
Bette L. Otto-Bliesner, Pascale Braconnot, Sandy P. Harrison, Daniel J. Lunt, Ayako Abe-Ouchi, Samuel Albani, Patrick J. Bartlein, Emilie Capron, Anders E. Carlson, Andrea Dutton, Hubertus Fischer, Heiko Goelzer, Aline Govin, Alan Haywood, Fortunat Joos, Allegra N. Legrande, William H. Lipscomb, Gerrit Lohmann, Natalie Mahowald, Christoph Nehrbass-Ahles, Jean-Yves Peterschmidt, Francesco S.-R. Pausata, Steven Phipps, and Hans Renssen
Clim. Past Discuss., https://doi.org/10.5194/cp-2016-106, https://doi.org/10.5194/cp-2016-106, 2016
Preprint retracted
Robert Raiswell, Jon R. Hawkings, Liane G. Benning, Alex R. Baker, Ros Death, Samuel Albani, Natalie Mahowald, Michael D. Krom, Simon W. Poulton, Jemma Wadham, and Martyn Tranter
Biogeosciences, 13, 3887–3900, https://doi.org/10.5194/bg-13-3887-2016, https://doi.org/10.5194/bg-13-3887-2016, 2016
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Iron is an essential nutrient for plankton growth. One important source of iron is wind-blown dust. The polar oceans are remote from dust sources but melting icebergs supply sediment that contains iron which is potentially available to plankton. We show that iceberg sediments contain more potentially bioavailable iron than wind-blown dust. Iceberg sources will become increasingly important with climate change and increased plankton growth can remove more carbon dioxide from the atmosphere.
Stuart Riddick, Daniel Ward, Peter Hess, Natalie Mahowald, Raia Massad, and Elisabeth Holland
Biogeosciences, 13, 3397–3426, https://doi.org/10.5194/bg-13-3397-2016, https://doi.org/10.5194/bg-13-3397-2016, 2016
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Future increases are predicted in the amount of nitrogen produced as manure or used as synthetic fertilizer in agriculture. However, the impact of climate on the subsequent fate of this nitrogen has not been evaluated. Here we describe, analyze and evaluate the FAN (flows of agricultural nitrogen) process model that simulates the the climate-dependent flows of nitrogen from agriculture. The FAN model is suitable for use within a global terrestrial climate model.
Natalie Mahowald, Fiona Lo, Yun Zheng, Laura Harrison, Chris Funk, Danica Lombardozzi, and Christine Goodale
Earth Syst. Dynam., 7, 211–229, https://doi.org/10.5194/esd-7-211-2016, https://doi.org/10.5194/esd-7-211-2016, 2016
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This paper evaluates the model predictions of leaf area index in the current climate, compared against satellite observations. It also summarizes the predicted changes in leaf area index in the future, and identifies whether some of the uncertainty in future predictions can be decreased.
Y. Zhang, N. Mahowald, R. A. Scanza, E. Journet, K. Desboeufs, S. Albani, J. F. Kok, G. Zhuang, Y. Chen, D. D. Cohen, A. Paytan, M. D. Patey, E. P. Achterberg, J. P. Engelbrecht, and K. W. Fomba
Biogeosciences, 12, 5771–5792, https://doi.org/10.5194/bg-12-5771-2015, https://doi.org/10.5194/bg-12-5771-2015, 2015
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A new technique to determine a size-fractionated global soil elemental emission inventory based on a global soil and mineralogical data set is introduced. Spatial variability of mineral dust elemental fractions (8 elements, e.g., Ca, Fe, Al) is identified on a global scale, particularly for Ca. The Ca/Al ratio ranged between 0.1 and 5.0 and is confirmed as an indicator of dust source regions by a global dust model. Total and soluble dust element fluxes into different ocean basins are estimated.
L. Meng, R. Paudel, P. G. M. Hess, and N. M. Mahowald
Biogeosciences, 12, 4029–4049, https://doi.org/10.5194/bg-12-4029-2015, https://doi.org/10.5194/bg-12-4029-2015, 2015
S. Albani, N. M. Mahowald, G. Winckler, R. F. Anderson, L. I. Bradtmiller, B. Delmonte, R. François, M. Goman, N. G. Heavens, P. P. Hesse, S. A. Hovan, S. G. Kang, K. E. Kohfeld, H. Lu, V. Maggi, J. A. Mason, P. A. Mayewski, D. McGee, X. Miao, B. L. Otto-Bliesner, A. T. Perry, A. Pourmand, H. M. Roberts, N. Rosenbloom, T. Stevens, and J. Sun
Clim. Past, 11, 869–903, https://doi.org/10.5194/cp-11-869-2015, https://doi.org/10.5194/cp-11-869-2015, 2015
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We propose an innovative framework to organize paleodust records, formalized in a publicly accessible database, and discuss the emerging properties of the global dust cycle during the Holocene by integrating our analysis with simulations performed with the Community Earth System Model. We show how the size distribution of dust is intrinsically related to the dust mass accumulation rates and that only considering a consistent size range allows for a consistent analysis of the global dust cycle.
D. S. Ward and N. M. Mahowald
Earth Syst. Dynam., 6, 175–194, https://doi.org/10.5194/esd-6-175-2015, https://doi.org/10.5194/esd-6-175-2015, 2015
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The radiative forcing of land use and land cover change activities has recently been computed for a set of forcing agents including long-lived greenhouse gases, short-lived agents (ozone and aerosols), and land surface albedo change. Here we address where the global forcing comes from and what land use activities, such as deforestation or agriculture, contribute the most forcing. We find that changes in forest and crop area can be used to predict the land use radiative forcing in some regions.
R. A. Scanza, N. Mahowald, S. Ghan, C. S. Zender, J. F. Kok, X. Liu, Y. Zhang, and S. Albani
Atmos. Chem. Phys., 15, 537–561, https://doi.org/10.5194/acp-15-537-2015, https://doi.org/10.5194/acp-15-537-2015, 2015
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The main purpose of this study was to build a framework in the Community Atmosphere Models version 4 and 5 within the Community Earth System Model to simulate dust aerosols as their component minerals. With this framework, we investigate the direct radiative forcing that results from the mineral speciation. We find that adding mineralogy results in a small positive forcing at the top of the atmosphere, while simulations without mineralogy have a small negative forcing.
J. F. Kok, N. M. Mahowald, G. Fratini, J. A. Gillies, M. Ishizuka, J. F. Leys, M. Mikami, M.-S. Park, S.-U. Park, R. S. Van Pelt, and T. M. Zobeck
Atmos. Chem. Phys., 14, 13023–13041, https://doi.org/10.5194/acp-14-13023-2014, https://doi.org/10.5194/acp-14-13023-2014, 2014
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We developed an improved model for the emission of dust particulates ("aerosols") emitted by wind erosion from the world's deserts. The implementation of our improved dust emission model into a climate model improves its agreement against measurements. We furthermore find that dust emissions are substantially more sensitive to the soil state than most current climate models account for.
D. S. Ward, N. M. Mahowald, and S. Kloster
Atmos. Chem. Phys., 14, 12701–12724, https://doi.org/10.5194/acp-14-12701-2014, https://doi.org/10.5194/acp-14-12701-2014, 2014
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While climate change mitigation policy often focuses on the energy sector, we find that 40% of the historical human-caused change in the Earth’s radiative balance can be attributed to land use activities, such as deforestation and agriculture. Since pressure on land resources is expected to increase, we compute a theoretical upper bound on the radiative balance impacts from future land use which suggests that both energy policy and land policy are necessary to minimize future climate change.
B. Foereid, D. S. Ward, N. Mahowald, E. Paterson, and J. Lehmann
Earth Syst. Dynam., 5, 211–221, https://doi.org/10.5194/esd-5-211-2014, https://doi.org/10.5194/esd-5-211-2014, 2014
S. K. Clark, D. S. Ward, and N. M. Mahowald
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acpd-13-23691-2013, https://doi.org/10.5194/acpd-13-23691-2013, 2013
Revised manuscript not accepted
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The 4DEnVar-based weakly coupled land data assimilation system for E3SM version 2
Continental-scale bias-corrected climate and hydrological projections for Australia
G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies
Modeling the effects of tropospheric ozone on the growth and yield of global staple crops with DSSAT v4.8.0
A one-dimensional urban flow model with an eddy-diffusivity mass-flux (EDMF) scheme and refined turbulent transport (MLUCM v3.0)
DCMIP2016: the tropical cyclone test case
Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP
CD-type discretization for sea ice dynamics in FESOM version 2
CSDMS Data Components: data–model integration tools for Earth surface processes modeling
A generic algorithm to automatically classify urban fabric according to the local climate zone system: implementation in GeoClimate 0.0.1 and application to French cities
Modelling water isotopologues (1H2H16O, 1H217O) in the coupled numerical climate model iLOVECLIM (version 1.1.5)
Accurate assessment of land–atmosphere coupling in climate models requires high-frequency data output
Towards variance-conserving reconstructions of climate indices with Gaussian process regression in an embedding space
A diatom extension to the cGEnIE Earth system model – EcoGEnIE 1.1
Carbon isotopes in the marine biogeochemistry model FESOM2.1-REcoM3
Flux coupling approach on an exchange grid for the IOW Earth System Model (version 1.04.00) of the Baltic Sea region
Using EUREC4A/ATOMIC field campaign data to improve trade wind regimes in the Community Atmosphere Model
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Benchmarking GOCART-2G in the Goddard Earth Observing System (GEOS)
Energy-conserving physics for nonhydrostatic dynamics in mass coordinate models
Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)
Assessing the sensitivity of aerosol mass budget and effective radiative forcing to horizontal grid spacing in E3SMv1 using a regional refinement approach
Towards the definition of a solar forcing dataset for CMIP7
ibicus: a new open-source Python package and comprehensive interface for statistical bias adjustment and evaluation in climate modelling (v1.0.1)
Disentangling the hydrological and hydraulic controls on streamflow variability in Energy Exascale Earth System Model (E3SM) V2 – a case study in the Pantanal region
Constraining the carbon cycle in JULES-ES-1.0
The utility of simulated ocean chlorophyll observations: a case study with the Chlorophyll Observation Simulator Package (version 1) in CESMv2.2
GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data
The prototype NOAA Aerosol Reanalysis version 1.0: description of the modeling system and its evaluation
Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1
Monsoon Mission Coupled Forecast System version 2.0: model description and Indian monsoon simulations
Exploring the ocean mesoscale at reduced computational cost with FESOM 2.5: efficient modeling strategies applied to the Southern Ocean
Truly conserving with conservative remapping methods
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Earth system modeling on modular supercomputing architecture: coupled atmosphere–ocean simulations with ICON 2.6.6-rc
Global Downscaled Projections for Climate Impacts Research (GDPCIR): preserving quantile trends for modeling future climate impacts
Understanding changes in cloud simulations from E3SM version 1 to version 2
WRF (v4.0)–SUEWS (v2018c) coupled system: development, evaluation and application
Scenario setup and forcing data for impact model evaluation and impact attribution within the third round of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3a)
Deep learning model based on multi-scale feature fusion for precipitation nowcasting
The Framework for Assessing Changes To Sea-level (FACTS) v1.0: a platform for characterizing parametric and structural uncertainty in future global, relative, and extreme sea-level change
Getting the leaves right matters for estimating temperature extremes
The Southern Ocean Freshwater Input from Antarctica (SOFIA) Initiative: scientific objectives and experimental design
Modeling and evaluating the effects of irrigation on land–atmosphere interaction in southwestern Europe with the regional climate model REMO2020–iMOVE using a newly developed parameterization
The Regional Climate-Chemistry-Ecology Coupling Model RegCM-Chem (v4.6)-YIBs (v1.0): Development and Application
Process-oriented models of autumn leaf phenology: ways to sound calibration and implications of uncertain projections
An evaluation of the LLC4320 global-ocean simulation based on the submesoscale structure of modeled sea surface temperature fields
An emulation-based approach for interrogating reactive transport models
NEWTS1.0: Numerical model of coastal Erosion by Waves and Transgressive Scarps
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
Tian Gan, Gregory E. Tucker, Eric W. H. Hutton, Mark D. Piper, Irina Overeem, Albert J. Kettner, Benjamin Campforts, Julia M. Moriarty, Brianna Undzis, Ethan Pierce, and Lynn McCready
Geosci. Model Dev., 17, 2165–2185, https://doi.org/10.5194/gmd-17-2165-2024, https://doi.org/10.5194/gmd-17-2165-2024, 2024
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This study presents the design, implementation, and application of the CSDMS Data Components. The case studies demonstrate that the Data Components provide a consistent way to access heterogeneous datasets from multiple sources, and to seamlessly integrate them with various models for Earth surface process modeling. The Data Components support the creation of open data–model integration workflows to improve the research transparency and reproducibility.
Jérémy Bernard, Erwan Bocher, Matthieu Gousseff, François Leconte, and Elisabeth Le Saux Wiederhold
Geosci. Model Dev., 17, 2077–2116, https://doi.org/10.5194/gmd-17-2077-2024, https://doi.org/10.5194/gmd-17-2077-2024, 2024
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Geographical features may have a considerable effect on local climate. The local climate zone (LCZ) system proposed by Stewart and Oke (2012) is seen as a standard approach for classifying any zone according to a set of geographic indicators. While many methods already exist to map the LCZ, only a few tools are openly and freely available. We present the algorithm implemented in GeoClimate software to identify the LCZ of any place in the world using OpenStreetMap data.
Thomas Extier, Thibaut Caley, and Didier M. Roche
Geosci. Model Dev., 17, 2117–2139, https://doi.org/10.5194/gmd-17-2117-2024, https://doi.org/10.5194/gmd-17-2117-2024, 2024
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Stable water isotopes are used to infer changes in the hydrological cycle for different time periods in climatic archive and climate models. We present the implementation of the δ2H and δ17O water isotopes in the coupled climate model iLOVECLIM and calculate the d- and 17O-excess. Results of a simulation under preindustrial conditions show that the model correctly reproduces the water isotope distribution in the atmosphere and ocean in comparison to data and other global circulation models.
Kirsten L. Findell, Zun Yin, Eunkyo Seo, Paul A. Dirmeyer, Nathan P. Arnold, Nathaniel Chaney, Megan D. Fowler, Meng Huang, David M. Lawrence, Po-Lun Ma, and Joseph A. Santanello Jr.
Geosci. Model Dev., 17, 1869–1883, https://doi.org/10.5194/gmd-17-1869-2024, https://doi.org/10.5194/gmd-17-1869-2024, 2024
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We outline a request for sub-daily data to accurately capture the process-level connections between land states, surface fluxes, and the boundary layer response. This high-frequency model output will allow for more direct comparison with observational field campaigns on process-relevant timescales, enable demonstration of inter-model spread in land–atmosphere coupling processes, and aid in targeted identification of sources of deficiencies and opportunities for improvement of the models.
Marlene Klockmann, Udo von Toussaint, and Eduardo Zorita
Geosci. Model Dev., 17, 1765–1787, https://doi.org/10.5194/gmd-17-1765-2024, https://doi.org/10.5194/gmd-17-1765-2024, 2024
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Reconstructions of climate variability before the observational period rely on climate proxies and sophisticated statistical models to link the proxy information and climate variability. Existing models tend to underestimate the true magnitude of variability, especially if the proxies contain non-climatic noise. We present and test a promising new framework for climate-index reconstructions, based on Gaussian processes, which reconstructs robust variability estimates from noisy and sparse data.
Aaron A. Naidoo-Bagwell, Fanny M. Monteiro, Katharine R. Hendry, Scott Burgan, Jamie D. Wilson, Ben A. Ward, Andy Ridgwell, and Daniel J. Conley
Geosci. Model Dev., 17, 1729–1748, https://doi.org/10.5194/gmd-17-1729-2024, https://doi.org/10.5194/gmd-17-1729-2024, 2024
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As an extension to the EcoGEnIE 1.0 Earth system model that features a diverse plankton community, EcoGEnIE 1.1 includes siliceous plankton diatoms and also considers their impact on biogeochemical cycles. With updates to existing nutrient cycles and the introduction of the silicon cycle, we see improved model performance relative to observational data. Through a more functionally diverse plankton community, the new model enables more comprehensive future study of ocean ecology.
Martin Butzin, Ying Ye, Christoph Völker, Özgür Gürses, Judith Hauck, and Peter Köhler
Geosci. Model Dev., 17, 1709–1727, https://doi.org/10.5194/gmd-17-1709-2024, https://doi.org/10.5194/gmd-17-1709-2024, 2024
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In this paper we describe the implementation of the carbon isotopes 13C and 14C into the marine biogeochemistry model FESOM2.1-REcoM3 and present results of long-term test simulations. Our model results are largely consistent with marine carbon isotope reconstructions for the pre-anthropogenic period, but also exhibit some discrepancies.
Sven Karsten, Hagen Radtke, Matthias Gröger, Ha T. M. Ho-Hagemann, Hossein Mashayekh, Thomas Neumann, and H. E. Markus Meier
Geosci. Model Dev., 17, 1689–1708, https://doi.org/10.5194/gmd-17-1689-2024, https://doi.org/10.5194/gmd-17-1689-2024, 2024
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This paper describes the development of a regional Earth System Model for the Baltic Sea region. In contrast to conventional coupling approaches, the presented model includes a flux calculator operating on a common exchange grid. This approach automatically ensures a locally consistent treatment of fluxes and simplifies the exchange of model components. The presented model can be used for various scientific questions, such as studies of natural variability and ocean–atmosphere interactions.
Skyler Graap and Colin M. Zarzycki
Geosci. Model Dev., 17, 1627–1650, https://doi.org/10.5194/gmd-17-1627-2024, https://doi.org/10.5194/gmd-17-1627-2024, 2024
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A key target for improving climate models is how low, bright clouds are predicted over tropical oceans, since they have important consequences for the Earth's energy budget. A climate model has been updated to improve the physical realism of the treatment of how momentum is moved up and down in the atmosphere. By comparing this updated model to real-world observations from balloon launches, it can be shown to more accurately depict atmospheric structure in trade-wind areas close to the Equator.
Marika M. Holland, Cecile Hannay, John Fasullo, Alexandra Jahn, Jennifer E. Kay, Michael Mills, Isla R. Simpson, William Wieder, Peter Lawrence, Erik Kluzek, and David Bailey
Geosci. Model Dev., 17, 1585–1602, https://doi.org/10.5194/gmd-17-1585-2024, https://doi.org/10.5194/gmd-17-1585-2024, 2024
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Climate evolves in response to changing forcings, as prescribed in simulations. Models and forcings are updated over time to reflect new understanding. This makes it difficult to attribute simulation differences to either model or forcing changes. Here we present new simulations which enable the separation of model structure and forcing influence between two widely used simulation sets. Results indicate a strong influence of aerosol emission uncertainty on historical climate.
Rongyun Tang, Mingzhou Jin, Jiafu Mao, Daniel M. Ricciuto, Anping Chen, and Yulong Zhang
Geosci. Model Dev., 17, 1525–1542, https://doi.org/10.5194/gmd-17-1525-2024, https://doi.org/10.5194/gmd-17-1525-2024, 2024
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Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of rare peatland fire events are investigated by constructing a two-step error-correcting machine learning framework to tackle such complex systems. Fire occurrence and impacts are highly predictable with our approach. Factor-controlling simulations revealed that temperature, moisture, and freeze–thaw cycles control boreal peatland fires, indicating thermal impacts on causing peat fires.
Allison B. Collow, Peter R. Colarco, Arlindo M. da Silva, Virginie Buchard, Huisheng Bian, Mian Chin, Sampa Das, Ravi Govindaraju, Dongchul Kim, and Valentina Aquila
Geosci. Model Dev., 17, 1443–1468, https://doi.org/10.5194/gmd-17-1443-2024, https://doi.org/10.5194/gmd-17-1443-2024, 2024
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The GOCART aerosol module within the Goddard Earth Observing System recently underwent a major refactoring and update to the representation of physical processes. Code changes that were included in GOCART Second Generation (GOCART-2G) are documented, and we establish a benchmark simulation that is to be used for future development of the system. The 4-year benchmark simulation was evaluated using in situ and spaceborne measurements to develop a baseline and prioritize future development.
Oksana Guba, Mark A. Taylor, Peter A. Bosler, Christopher Eldred, and Peter H. Lauritzen
Geosci. Model Dev., 17, 1429–1442, https://doi.org/10.5194/gmd-17-1429-2024, https://doi.org/10.5194/gmd-17-1429-2024, 2024
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We want to reduce errors in the moist energy budget in numerical atmospheric models. We study a few common assumptions and mechanisms that are used for the moist physics. Some mechanisms are more consistent with the underlying equations. Separately, we study how assumptions about models' thermodynamics affect the modeled energy of precipitation. We also explain how to conserve energy in the moist physics for nonhydrostatic models.
Konstantin Aiteew, Jarno Rouhiainen, Claas Nendel, and René Dechow
Geosci. Model Dev., 17, 1349–1385, https://doi.org/10.5194/gmd-17-1349-2024, https://doi.org/10.5194/gmd-17-1349-2024, 2024
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This study evaluated the biogeochemical model MONICA and its performance in simulating soil organic carbon changes. MONICA can reproduce plant growth, carbon and nitrogen dynamics, soil water and temperature. The model results were compared with five established carbon turnover models. With the exception of certain sites, adequate reproduction of soil organic carbon stock change rates was achieved. The MONICA model was capable of performing similar to or even better than the other models.
Jianfeng Li, Kai Zhang, Taufiq Hassan, Shixuan Zhang, Po-Lun Ma, Balwinder Singh, Qiyang Yan, and Huilin Huang
Geosci. Model Dev., 17, 1327–1347, https://doi.org/10.5194/gmd-17-1327-2024, https://doi.org/10.5194/gmd-17-1327-2024, 2024
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By comparing E3SM simulations with and without regional refinement, we find that model horizontal grid spacing considerably affects the simulated aerosol mass budget, aerosol–cloud interactions, and the effective radiative forcing of anthropogenic aerosols. The study identifies the critical physical processes strongly influenced by model resolution. It also highlights the benefit of applying regional refinement in future modeling studies at higher or even convection-permitting resolutions.
Bernd Funke, Thierry Dudok de Wit, Ilaria Ermolli, Margit Haberreiter, Doug Kinnison, Daniel Marsh, Hilde Nesse, Annika Seppälä, Miriam Sinnhuber, and Ilya Usoskin
Geosci. Model Dev., 17, 1217–1227, https://doi.org/10.5194/gmd-17-1217-2024, https://doi.org/10.5194/gmd-17-1217-2024, 2024
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We outline a road map for the preparation of a solar forcing dataset for the upcoming Phase 7 of the Coupled Model Intercomparison Project (CMIP7), considering the latest scientific advances made in the reconstruction of solar forcing and in the understanding of climate response while also addressing the issues that were raised during CMIP6.
Fiona Raphaela Spuler, Jakob Benjamin Wessel, Edward Comyn-Platt, James Varndell, and Chiara Cagnazzo
Geosci. Model Dev., 17, 1249–1269, https://doi.org/10.5194/gmd-17-1249-2024, https://doi.org/10.5194/gmd-17-1249-2024, 2024
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Before using climate models to study the impacts of climate change, bias adjustment is commonly applied to the models to ensure that they correspond with observations at a local scale. However, this can introduce undesirable distortions into the climate model. In this paper, we present an open-source python package called ibicus to enable the comparison and detailed evaluation of bias adjustment methods, facilitating their transparent and rigorous application.
Donghui Xu, Gautam Bisht, Zeli Tan, Chang Liao, Tian Zhou, Hong-Yi Li, and L. Ruby Leung
Geosci. Model Dev., 17, 1197–1215, https://doi.org/10.5194/gmd-17-1197-2024, https://doi.org/10.5194/gmd-17-1197-2024, 2024
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We aim to disentangle the hydrological and hydraulic controls on streamflow variability in a fully coupled earth system model. We found that calibrating only one process (i.e., traditional calibration procedure) will result in unrealistic parameter values and poor performance of the water cycle, while the simulated streamflow is improved. To address this issue, we further proposed a two-step calibration procedure to reconcile the impacts from hydrological and hydraulic processes on streamflow.
Douglas McNeall, Eddy Robertson, and Andy Wiltshire
Geosci. Model Dev., 17, 1059–1089, https://doi.org/10.5194/gmd-17-1059-2024, https://doi.org/10.5194/gmd-17-1059-2024, 2024
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We can run simulations of the land surface and carbon cycle, using computer models to help us understand and predict climate change and its impacts. These simulations are not perfect reproductions of the real land surface, and that can make them less effective tools. We use new statistical and computational techniques to help us understand how different our models are from the real land surface, how to make them more realistic, and how well we can simulate past and future climate.
Genevieve L. Clow, Nicole S. Lovenduski, Michael N. Levy, Keith Lindsay, and Jennifer E. Kay
Geosci. Model Dev., 17, 975–995, https://doi.org/10.5194/gmd-17-975-2024, https://doi.org/10.5194/gmd-17-975-2024, 2024
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Satellite observations of chlorophyll allow us to study marine phytoplankton on a global scale; yet some of these observations are missing due to clouds and other issues. To investigate the impact of missing data, we developed a satellite simulator for chlorophyll in an Earth system model. We found that missing data can impact the global mean chlorophyll by nearly 20 %. The simulated observations provide a more direct comparison to real-world data and can be used to improve model validation.
Jiateng Guo, Xuechuang Xu, Luyuan Wang, Xulei Wang, Lixin Wu, Mark Jessell, Vitaliy Ogarko, Zhibin Liu, and Yufei Zheng
Geosci. Model Dev., 17, 957–973, https://doi.org/10.5194/gmd-17-957-2024, https://doi.org/10.5194/gmd-17-957-2024, 2024
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This study proposes a semi-supervised learning algorithm using pseudo-labels for 3D geological modelling. We establish a 3D geological model using borehole data from a complex real urban local survey area in Shenyang and make an uncertainty analysis of this model. The method effectively expands the sample space, which is suitable for geomodelling and uncertainty analysis from boreholes. The modelling results perform well in terms of spatial morphology and geological semantics.
Shih-Wei Wei, Mariusz Pagowski, Arlindo da Silva, Cheng-Hsuan Lu, and Bo Huang
Geosci. Model Dev., 17, 795–813, https://doi.org/10.5194/gmd-17-795-2024, https://doi.org/10.5194/gmd-17-795-2024, 2024
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This study describes the modeling system and the evaluation results for the first prototype version of a global aerosol reanalysis product at NOAA, prototype NOAA Aerosol ReAnalysis version 1.0 (pNARA v1.0). We evaluated pNARA v1.0 against independent datasets and compared it with other reanalyses. We identified deficiencies in the system (both in the forecast model and in the data assimilation system) and the uncertainties that exist in our reanalysis.
Emma Howard, Chun-Hsu Su, Christian Stassen, Rajashree Naha, Harvey Ye, Acacia Pepler, Samuel S. Bell, Andrew J. Dowdy, Simon O. Tucker, and Charmaine Franklin
Geosci. Model Dev., 17, 731–757, https://doi.org/10.5194/gmd-17-731-2024, https://doi.org/10.5194/gmd-17-731-2024, 2024
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The BARPA-R modelling configuration has been developed to produce high-resolution climate hazard projections within the Australian region. When using boundary driving data from quasi-observed historical conditions, BARPA-R shows good performance with errors generally on par with reanalysis products. BARPA-R also captures trends, known modes of climate variability, large-scale weather processes, and multivariate relationships.
Deepeshkumar Jain, Suryachandra A. Rao, Ramu A. Dandi, Prasanth A. Pillai, Ankur Srivastava, Maheswar Pradhan, and Kiran V. Gangadharan
Geosci. Model Dev., 17, 709–729, https://doi.org/10.5194/gmd-17-709-2024, https://doi.org/10.5194/gmd-17-709-2024, 2024
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The present paper discusses and evaluates the new Monsoon Mission Coupled Forecast System model (MMCFS) version 2.0 which upgrades the currently operational MMCFS v1.0 at the Indian Meteorological Department, India. The individual model components have been substantially upgraded independently by their respective scientific groups. MMCFS v2.0 includes these upgrades in the operational coupled model. The new model shows significant skill improvement in simulating the Indian monsoon.
Nathan Beech, Thomas Rackow, Tido Semmler, and Thomas Jung
Geosci. Model Dev., 17, 529–543, https://doi.org/10.5194/gmd-17-529-2024, https://doi.org/10.5194/gmd-17-529-2024, 2024
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Cost-reducing modeling strategies are applied to high-resolution simulations of the Southern Ocean in a changing climate. They are evaluated with respect to observations and traditional, lower-resolution modeling methods. The simulations effectively reproduce small-scale ocean flows seen in satellite data and are largely consistent with traditional model simulations after 4 °C of warming. Small-scale flows are found to intensify near bathymetric features and to become more variable.
Karl E. Taylor
Geosci. Model Dev., 17, 415–430, https://doi.org/10.5194/gmd-17-415-2024, https://doi.org/10.5194/gmd-17-415-2024, 2024
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Remapping gridded data in a way that preserves the conservative properties of the climate system can be essential in coupling model components and for accurate assessment of the system’s energy and mass constituents. Remapping packages capable of handling a wide variety of grids can, for some common grids, calculate remapping weights that are somewhat inaccurate. Correcting for these errors, guidelines are provided to ensure conservation when the weights are used in practice.
Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova
Geosci. Model Dev., 17, 229–259, https://doi.org/10.5194/gmd-17-229-2024, https://doi.org/10.5194/gmd-17-229-2024, 2024
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This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
Abhiraj Bishnoi, Olaf Stein, Catrin I. Meyer, René Redler, Norbert Eicker, Helmuth Haak, Lars Hoffmann, Daniel Klocke, Luis Kornblueh, and Estela Suarez
Geosci. Model Dev., 17, 261–273, https://doi.org/10.5194/gmd-17-261-2024, https://doi.org/10.5194/gmd-17-261-2024, 2024
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We enabled the weather and climate model ICON to run in a high-resolution coupled atmosphere–ocean setup on the JUWELS supercomputer, where the ocean and the model I/O runs on the CPU Cluster, while the atmosphere is running simultaneously on GPUs. Compared to a simulation performed on CPUs only, our approach reduces energy consumption by 45 % with comparable runtimes. The experiments serve as preparation for efficient computing of kilometer-scale climate models on future supercomputing systems.
Diana R. Gergel, Steven B. Malevich, Kelly E. McCusker, Emile Tenezakis, Michael T. Delgado, Meredith A. Fish, and Robert E. Kopp
Geosci. Model Dev., 17, 191–227, https://doi.org/10.5194/gmd-17-191-2024, https://doi.org/10.5194/gmd-17-191-2024, 2024
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The freely available Global Downscaled Projections for Climate Impacts Research (GDPCIR) dataset gives researchers a new tool for studying how future climate will evolve at a local or regional level, corresponding to the latest global climate model simulations prepared as part of the UN Intergovernmental Panel on Climate Change’s Sixth Assessment Report. Those simulations represent an enormous advance in quality, detail, and scope that GDPCIR translates to the local level.
Yuying Zhang, Shaocheng Xie, Yi Qin, Wuyin Lin, Jean-Christophe Golaz, Xue Zheng, Po-Lun Ma, Yun Qian, Qi Tang, Christopher R. Terai, and Meng Zhang
Geosci. Model Dev., 17, 169–189, https://doi.org/10.5194/gmd-17-169-2024, https://doi.org/10.5194/gmd-17-169-2024, 2024
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We performed systematic evaluation of clouds simulated in the Energy
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Exascale Earth System Model (E3SMv2) to document model performance and understand what updates in E3SMv2 have caused changes in clouds from E3SMv1 to E3SMv2. We find that stratocumulus clouds along the subtropical west coast of continents are dramatically improved, primarily due to the retuning done in CLUBB. This study offers additional insights into clouds simulated in E3SMv2 and will benefit future E3SM developments.
Ting Sun, Hamidreza Omidvar, Zhenkun Li, Ning Zhang, Wenjuan Huang, Simone Kotthaus, Helen C. Ward, Zhiwen Luo, and Sue Grimmond
Geosci. Model Dev., 17, 91–116, https://doi.org/10.5194/gmd-17-91-2024, https://doi.org/10.5194/gmd-17-91-2024, 2024
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For the first time, we coupled a state-of-the-art urban land surface model – Surface Urban Energy and Water Scheme (SUEWS) – with the widely-used Weather Research and Forecasting (WRF) model, creating an open-source tool that may benefit multiple applications. We tested our new system at two UK sites and demonstrated its potential by examining how human activities in various areas of Greater London influence local weather conditions.
Katja Frieler, Jan Volkholz, Stefan Lange, Jacob Schewe, Matthias Mengel, María del Rocío Rivas López, Christian Otto, Christopher P. O. Reyer, Dirk Nikolaus Karger, Johanna T. Malle, Simon Treu, Christoph Menz, Julia L. Blanchard, Cheryl S. Harrison, Colleen M. Petrik, Tyler D. Eddy, Kelly Ortega-Cisneros, Camilla Novaglio, Yannick Rousseau, Reg A. Watson, Charles Stock, Xiao Liu, Ryan Heneghan, Derek Tittensor, Olivier Maury, Matthias Büchner, Thomas Vogt, Tingting Wang, Fubao Sun, Inga J. Sauer, Johannes Koch, Inne Vanderkelen, Jonas Jägermeyr, Christoph Müller, Sam Rabin, Jochen Klar, Iliusi D. Vega del Valle, Gitta Lasslop, Sarah Chadburn, Eleanor Burke, Angela Gallego-Sala, Noah Smith, Jinfeng Chang, Stijn Hantson, Chantelle Burton, Anne Gädeke, Fang Li, Simon N. Gosling, Hannes Müller Schmied, Fred Hattermann, Jida Wang, Fangfang Yao, Thomas Hickler, Rafael Marcé, Don Pierson, Wim Thiery, Daniel Mercado-Bettín, Robert Ladwig, Ana Isabel Ayala-Zamora, Matthew Forrest, and Michel Bechtold
Geosci. Model Dev., 17, 1–51, https://doi.org/10.5194/gmd-17-1-2024, https://doi.org/10.5194/gmd-17-1-2024, 2024
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Our paper provides an overview of all observational climate-related and socioeconomic forcing data used as input for the impact model evaluation and impact attribution experiments within the third round of the Inter-Sectoral Impact Model Intercomparison Project. The experiments are designed to test our understanding of observed changes in natural and human systems and to quantify to what degree these changes have already been induced by climate change.
Jinkai Tan, Qiqiao Huang, and Sheng Chen
Geosci. Model Dev., 17, 53–69, https://doi.org/10.5194/gmd-17-53-2024, https://doi.org/10.5194/gmd-17-53-2024, 2024
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This study presents a deep learning architecture, multi-scale feature fusion (MFF), to improve the forecast skills of precipitations especially for heavy precipitations. MFF uses multi-scale receptive fields so that the movement features of precipitation systems are well captured. MFF uses the mechanism of discrete probability to reduce uncertainties and forecast errors so that heavy precipitations are produced.
Robert E. Kopp, Gregory G. Garner, Tim H. J. Hermans, Shantenu Jha, Praveen Kumar, Alexander Reedy, Aimée B. A. Slangen, Matteo Turilli, Tamsin L. Edwards, Jonathan M. Gregory, George Koubbe, Anders Levermann, Andre Merzky, Sophie Nowicki, Matthew D. Palmer, and Chris Smith
Geosci. Model Dev., 16, 7461–7489, https://doi.org/10.5194/gmd-16-7461-2023, https://doi.org/10.5194/gmd-16-7461-2023, 2023
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Future sea-level rise projections exhibit multiple forms of uncertainty, all of which must be considered by scientific assessments intended to inform decision-making. The Framework for Assessing Changes To Sea-level (FACTS) is a new software package intended to support assessments of global mean, regional, and extreme sea-level rise. An early version of FACTS supported the development of the IPCC Sixth Assessment Report sea-level projections.
Gregory Duveiller, Mark Pickering, Joaquin Muñoz-Sabater, Luca Caporaso, Souhail Boussetta, Gianpaolo Balsamo, and Alessandro Cescatti
Geosci. Model Dev., 16, 7357–7373, https://doi.org/10.5194/gmd-16-7357-2023, https://doi.org/10.5194/gmd-16-7357-2023, 2023
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Some of our best tools to describe the state of the land system, including the intensity of heat waves, have a problem. The model currently assumes that the number of leaves in ecosystems always follows the same cycle. By using satellite observations of when leaves are present, we show that capturing the yearly changes in this cycle is important to avoid errors in estimating surface temperature. We show that this has strong implications for our capacity to describe heat waves across Europe.
Neil C. Swart, Torge Martin, Rebecca Beadling, Jia-Jia Chen, Christopher Danek, Matthew H. England, Riccardo Farneti, Stephen M. Griffies, Tore Hattermann, Judith Hauck, F. Alexander Haumann, André Jüling, Qian Li, John Marshall, Morven Muilwijk, Andrew G. Pauling, Ariaan Purich, Inga J. Smith, and Max Thomas
Geosci. Model Dev., 16, 7289–7309, https://doi.org/10.5194/gmd-16-7289-2023, https://doi.org/10.5194/gmd-16-7289-2023, 2023
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Current climate models typically do not include full representation of ice sheets. As the climate warms and the ice sheets melt, they add freshwater to the ocean. This freshwater can influence climate change, for example by causing more sea ice to form. In this paper we propose a set of experiments to test the influence of this missing meltwater from Antarctica using multiple different climate models.
Christina Asmus, Peter Hoffmann, Joni-Pekka Pietikäinen, Jürgen Böhner, and Diana Rechid
Geosci. Model Dev., 16, 7311–7337, https://doi.org/10.5194/gmd-16-7311-2023, https://doi.org/10.5194/gmd-16-7311-2023, 2023
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Irrigation modifies the land surface and soil conditions. The effects can be quantified using numerical climate models. Our study introduces a new irrigation parameterization, which simulates the effects of irrigation on land, atmosphere, and vegetation. We applied the parameterization and evaluated the results in terms of their physical consistency. We found an improvement in the model results in the 2 m temperature representation in comparison with observational data for our study.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Baiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
EGUsphere, https://doi.org/10.5194/egusphere-2023-1733, https://doi.org/10.5194/egusphere-2023-1733, 2023
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For the first time, we coupled a regional climate chemistry model RegCM-Chem with a dynamic vegetation model YIBs to create a regional climate-chemistry-ecology model RegCM-Chem-YIBs. We applied it to simulate climatic, chemical and ecological parameters in East Asia and fully validated it on a variety of observational data. The research results show that RegCM-Chem-YIBs model is a valuable tool for studying terrestrial carbon cycle, atmospheric chemistry, and climate change in regional scale.
Michael Meier and Christof Bigler
Geosci. Model Dev., 16, 7171–7201, https://doi.org/10.5194/gmd-16-7171-2023, https://doi.org/10.5194/gmd-16-7171-2023, 2023
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We analyzed >2.3 million calibrations and 39 million projections of leaf coloration models, considering 21 models, 5 optimization algorithms, ≥7 sampling procedures, and 26 climate scenarios. Models based on temperature, day length, and leaf unfolding performed best, especially when calibrated with generalized simulated annealing and systematically balanced or stratified samples. Projected leaf coloration shifts between −13 and +20 days by 2080–2099.
Katharina Gallmeier, J. Xavier Prochaska, Peter Cornillon, Dimitris Menemenlis, and Madolyn Kelm
Geosci. Model Dev., 16, 7143–7170, https://doi.org/10.5194/gmd-16-7143-2023, https://doi.org/10.5194/gmd-16-7143-2023, 2023
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This paper introduces an approach to evaluate numerical models of ocean circulation. We compare the structure of satellite-derived sea surface temperature anomaly (SSTa) instances determined by a machine learning algorithm at 10–80 km scales to those output by a high-resolution MITgcm run. The simulation over much of the ocean reproduces the observed distribution of SSTa patterns well. This general agreement, alongside a few notable exceptions, highlights the potential of this approach.
Angus Fotherby, Harold J. Bradbury, Jennifer L. Druhan, and Alexandra V. Turchyn
Geosci. Model Dev., 16, 7059–7074, https://doi.org/10.5194/gmd-16-7059-2023, https://doi.org/10.5194/gmd-16-7059-2023, 2023
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We demonstrate how, given a simulation of fluid and rock interacting, we can emulate the system using machine learning. This means that, for a given initial condition, we can predict the final state, avoiding the simulation step once the model has been trained. We present a workflow for applying this approach to any fluid–rock simulation and showcase two applications to different fluid–rock simulations. This approach has applications for improving model development and sensitivity analyses.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-223, https://doi.org/10.5194/gmd-2023-223, 2023
Revised manuscript accepted for GMD
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion by either uniform erosion processes where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea level history, material properties, and the relative influence of different erosional processes.
Cited articles
Adhya, T., Bharati, K., Mohanty, S., Ramakrishnan, B., Rao, V., Sethunathan, N., and Wassmann, R.: Methane emission from rice fields at Cuttack, India, Nutr. Cycl. Agroecosys., 58, 95–105, 2000.
Aleman, D., Romeijn, H., and Dempsey, J.: A response surface approach to beam orientation optimization in intensity modulated radiation therapy treatment planning, INFORMS J. Comput., 21, 62–76, 2009.
Arah, J. and Stephen, K.: A model of the processes leading to methane emission from peatland, Atmos. Environ., 32, 3257–3264, 1998.
Aselmann, I. and Crutzen, P.: Global distribution of natural fresh-water wetlands and rice paddies, their net primary productivity, seasonality and possible methane emsissions, J. Atmos. Chem., 8, 307–358, 1989.
Baird, A., Beckwith, C., Waldron, S., and Waddington, J.: Ebullition of methane-containing gas bubbles from near surface Sphagnum peat, Geophys. Res. Lett., 31, L21505, https://doi.org/10.1029/2004GL021157, 2004.
Bartlett, K. and Harriss, R.: Review and assessment of methane emissions from wetlands, Chemosphere, 26, 261–320, 1993.
Bartlett, K., Crill, P., Bonassi, J., Richey, J., and Harriss, R.: Methane flux from the Amazon River floodplain: Emissions during rising water, J. Geophys. Res., 95, 16773–16788, 1990.
Bender, M. and Conrad, R.: Kinetics of CH4 oxidation in oxic soils exposed to ambient air or high CH4 mixing ratios, FEMS Microbiol. Ecol., 101, 261–270, 1992.
Bloom, A., Palmer, P., Fraser, A., Reay, D., and Frankenberg, C.: Large-Scale Controls of Methanogenesis Inferred from Methane and Gravity Spaceborne Data, Science, 327, 322–325, 2010.
Booker, A., Dennis Jr., J., Frank, P., Serafini, D., Torczon, V., and Trosset, M.: A rigorous framework for optimization of expensive functions by surrogates, Struct. Multidiscip. O., 17, 1–13, 1999.
Bousquet, P., Ciais, P., Miller, J., Dlugokencky, E., Hauglustaine, D., Prigent, C., Van der Werf, G., Peylin, P., Brunke, E., Carouge, C., Langenfelds, R., Lathiere, J., Papa, F., Ramonet, M., Schmidt, M., Steele, L., Tyler, S., and White, J.: Contribution of anthropogenic and natural sources to atmospheric methane variability, Nature, 443, 439–443, 2006.
Butterbach-Bahl, K., Papen, H., and Rennenberg, H.: Impact of gas transport through rice cultivars on methane emission from rice paddy fields, Plant Cell Environ., 20, 1175–1183, 1997.
Cao, M., Marshall, S., and Gregson, K.: Global carbon exchange and methane emissions from natural wetlands: Application of a process-based model, J. Geophys. Res., 101, 14399–14414, 1996.
Cheng, W., Yagi, K., Akiyama, H., Nishimura, S., Sudo, S., Fumoto, T., Hasegawa, T., Hartley, A., and Megonigal, J.: An empirical model of soil chemical properties that regulate methane production in Japanese rice paddy soils, J. Environ. Qual., 36, 1920–1925, 2007.
Ciais, P., Gasser, T., Paris, J., Caldeira, K., Raupach, M., Canadell, J., Patwardhan, A., Friedlingstein, P., Piao, S., and Gitz, V.: Attributing the increase in atmospheric CO2 to emitters and absorbers, Nature Clim. Change, 3, 926–930, 2013.
Cicerone, R., Shetter, J., and Delwiche, C.: Seasonal-variation of methane flux from a California rice paddy, J. Geophys. Res.-Oceans, 88, 1022–1024, 1983.
Cicerone, R., Delwiche, C., Tyler, S., and Zimmerman, P.: Methane emissions from California rice paddies with varied treatments, Global Biogeochem. Cy., 6, 233–248, 1992.
Colmer, T.: Long-distance transport of gases in plants: a perspective on internal aeration and radial oxygen loss from roots, Plant Cell Environ., 26, 17–36, 2003.
Computational and Information Systems Laboratory: Yellowstone: IBM iDataPlex System (Wyoming-NCAR Alliance), Boulder, CO, USA: National Center for Atmospheric Research. http://n2t.net/ark:/85065/d7wd3xhc (last access: 15 October 2015), 2012.
Conrad, R.: Control of microbial methane production in wetland rice fields, Nutr. Cycl. Agroecosys., 64, 59–69, 2002.
Cronk, J. and Fennessy, M.: Wetland Plants: Biology and Ecology, Lewis Publishers, Boca Raton, FL., USA, 2001.
Davis, E. and Ierapetritou, M.: Kriging based method for the solution of mixed-integer nonlinear programs containing black-box functions, J. Global Optim., 43, 191–205, 2009.
Dlugokencky, E., Nisbet, E., Fisher, R., and Lowry, D.: Global atmospheric methane: budget, changes and dangers, Phil. T. R. Soc. A, 369, 2058–2072, 2011.
Dunfield, P., Knowles, R., Dumont, R., and Moore, T.: Methane production and consumption in temperate and subarctic peat soils: response to temperature and pH, Soil Biol. Biochem., 25, 321–326, 1993.
Forrester, A., Sóbester, A., and Keane, A.: Engineering Design via Surrogate Modelling – A Practical Guide, John Wiley & Sons Ltd, Chichester, UK, 2008.
Friedman, J.: Multivariate Adaptive Regression Splines, The Annals of Statistics, 19, 1–141, 1991.
Giunta, A., Balabanov, V., Haim, D., Grossman, B., Mason, W., Watson, L., and Haftka, R.: Aircraft multidisciplinary design optimisation using design of experiments theory and response surface modelling, Aeronaut. J., 101, 347–356, 1997.
Goel, T., Haftka, R. T., Shyy, W., and Queipo, N. V.: Ensemble of Surrogates, Struct. Multidiscip. O., 33, 199–216, 2007.
Grunfeld, S. and Brix, H.: Methanogenesis and methane emissions: effects of water table, substrate type and presence of Phragmites australis, Aquat. Bot., 64, 63–75, 1999.
Gutmann, H.: A Radial Basis Function Method for Global Optimization, J. Global Optim., 19, 201–227, 2001.
Han, X., Hendricks Franssen, H.-J., Montzka, C., and Vereecken, H.: Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations, Water Resources Research, 50, 6081–6105, 2014.
Huang, Y., Jaing, J., Zong, L., Sass, R., and Fisher, F.: Comparison of field measurements of CH4 emission from rice cultivation in Nanjing, China and in Texas, USA, Adv. Atmos. Sci., 18, 1121–1130, 2001.
Hurrell, J., Holland, M., Gent, P., Ghan, S., Kay, J., Kushner, P., Lamarque, J.-F., Large, W., Lawrence, D., Lindsay, K., Lipscomb, W., Long, M., Mahowald, N., Marsh, D., Neale, R., Rasch, P., Vavrus, S., Vertenstein, M., Bader, D., Collins, W., Hack, J., Kiehl, J., and Marshall, S.: The Community Earth System Model: A Framework for Collaborative Research, B. Am. Meteorol. Soc., 94, 1339–1360, 2013.
Jain, M., Kumar, S., Wassmann, R., Mitra, S., Singh, S., Singh, J., Singh, R., Yadav, A., and Gupta, S.: Methane emissions from irrigated rice fields in northern India (New Delhi), Nutr. Cycl. Agroecosys., 58, 75–83, 2000.
Jiang, C., Wang, Y., Zheng, X., Zhu, B., Huang, Y., and Hao, Q.: Methane and nitrous oxide emissions from three paddy rice based cultivation systems in southwest China, Adv. Atmos. Sci., 23, 415–424, 2006.
Jones, D., Schonlau, M., and Welch, W.: Efficient Global Optimization of Expensive Black-Box Functions, J. Global Optim., 13, 455–492, 1998.
Keller, M. M.: Biological Sources and Sinks of Methane in Tropical Habitats and Tropical Atmospheric Chemistry, PhD thesis, Princeton University, Princeton, USA, 1990.
Kellner, E., Baird, A., Oosterwoud, M., Harrison, K., and Waddington, J.: Effect of temperature and atmospheric pressure on methane (CH4) ebullition from near surface peats, Geophys. Res. Lett., 33, L18405, https://doi.org/10.1029/2006GL027509, 2006.
Knoblauch, C.: Bodenkundlich-mikrobiologische Bestandsaufnahme zur Methanoxidation in einer Flussmarsch der Tide-Elbe, Master's thesis, University of Hamburg, Hamburg, Germany, 1994.
Koven, C. D., Riley, W. J., Subin, Z. M., Tang, J. Y., Torn, M. S., Collins, W. D., Bonan, G. B., Lawrence, D. M., and Swenson, S. C.: The effect of vertically resolved soil biogeochemistry and alternate soil C and N models on C dynamics of CLM4, Biogeosciences, 10, 7109–7131, https://doi.org/10.5194/bg-10-7109-2013, 2013.
Lo, M.-H., Famiglietti, J., Yeh, P.-F., and Syed, T.: Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data, Water Resour. Res., 46, W05517, https://doi.org/10.1029/2009WR007855, 2010.
Lombardi, J., Epp, M., and Chanton, J.: Investigation of the methyl fluoride technique for determining rhizospheric methane oxidation, Biogeochemistry, 36, 153–172, 1997.
Matthews, E. and Fung, I.: Methane emisiion from natural wetlands: global distribution, area, and environmental characteristics of sources, Global Biogeochem. Cy., 1, 61–86, 1987.
Meng, L., Hess, P. G. M., Mahowald, N. M., Yavitt, J. B., Riley, W. J., Subin, Z. M., Lawrence, D. M., Swenson, S. C., Jauhiainen, J., and Fuka, D. R.: Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations, Biogeosciences, 9, 2793–2819, https://doi.org/10.5194/bg-9-2793-2012, 2012.
Moore, D., Hub, J., Sacks, W. J., Schimel, D., and Monson, R.: Estimating transpiration and the sensitivity of carbon uptake to water availability in a subalpine forest using a simple ecosystem process model informed by measured net CO2 and H2O fluxes, Agr. Forest Meteorol., 148, 1467–1477, 2008.
Mugunthan, P., Shoemaker, C., and Regis, R.: Comparison of function approximation, heuristic, and derivative-based methods for automatic calibration of computationally expensive groundwater bioremediation models, Water Resour. Res., 41, W11427, https://doi.org/10.1029/2005WR004134, 2005.
Müller, J. and Piché, R.: Mixture Surrogate Models Based on Dempster-Shafer Theory for Global Optimization Problems, J. Global Optim., 51, 79–104, 2011.
Müller, J. and Shoemaker, C.: Influence of ensemble surrogate models and sampling strategy on the solution quality of algorithms for computationally expensive black-box global optimization problems, J. Global Optim., 60, 123–144, https://doi.org/10.1007/s10898-014-0184-0, 2014.
Müller, J., Shoemaker, C., and Piché, R.: SO-MI: A Surrogate Model Algorithm for Computationally Expensive Nonlinear Mixed-Integer Black-Box Global Optimization Problems, Comput. Oper. Res., 40, 1383–1400, 2013.
Myers, R. and Montgomery, D.: Response Surface Methodology, Process and Product Optimization using Designed Experiments, Wiley-Interscience Publication, New Jersey, USA, 1995.
Myhre, G., Shindell, D., Bréon, F.-M., Collins, W., Fuglestvedt, J., Huang, J., Koch, D., Lamarque, J.-F., Lee, D., Mendoza, B., Nakajima, T., Robock, A., Stephens, G., Takemura, T., and Zhang, H.: Anthropogenic and Natural Radiative Forcing, in: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Cambridge University Press, Cambridge, UK and New York, NY, USA, 2013.
Oleson, K., Lawrence, D., Bonan, G., Drewniak, B., Huang, M., Koven, C., Levis, S., Li, F., Riley, W., Subin, Z., Swenson, S., Thornton, P., Bozbiyik, A., Fisher, R., Kluzek, E., Lamarque, J.-F., Lawrence, P., Leung, L., Lipscomb, W., Muszala, S., Ricciuto, D., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical Description of Version 4.5 of the Community Land Model (CLM), Tech. Rep. NCAR/TN-503+STR, National Center for Atmospheric Research, Boulder, CO, USA, https://doi.org/10.5065/D6RR1W7M, 2013.
Popp, T. J., Chanton, J. P., Whiting, G. J., and Grant, N.: Evaluation of Methane Oxidation in the Rhizosphere of Carex Dominated Fen in North Central Alberta, Canada, Biogeochemistry, 51, 259–281, 2000.
Powell, M.: The Theory of Radial Basis Function Approximation in 1990, Advances in Numerical Analysis, vol. 2: wavelets, subdivision algorithms and radial basis functions, Oxford University Press, Oxford, UK, 105–210, 1992.
Prigent, C., Papa, F., Aires, F., Rossow, W., and Matthews, E.: Global inundation dynamics inferred from multiple satellite observations, 1993–2000, J. Geophys. Res., 112, D12107, https://doi.org/10.1029/2006JD007847, 2007.
Prihodko, L., Denning, A., Hanan, N., Baker, I., and Davis, K.: Sensitivity, uncertainty and time dependence of parameters in a complex land surface model, Agr. Forest Meteorol., 148, 268–287, 2008.
Qian, T., Dai, A., Trenberth, K., and Oleson, K.: Simulation of global land surface conditions from 1948 to 2004. Part I: Forcing data and evaluations, J. Hydrometeorol., 7, 953–975, 2006.
Ray, J. and Swiler, L.: Bayesian calibration of the Community Land Model using surrogates, Tech. Rep. SAND2014-0867, Sandia National Laboratories, Livermore, CA, USA, 2014.
Regis, R.: Stochastic radial basis function algorithms for large-scale optimization involving expensive black-box objective and constraint functions, Comput. Oper. Res., 38, 837–853, 2011.
Regis, R. and Shoemaker, C.: A Stochastic Radial Basis Function Method for the Global Optimization of Expensive Functions, INFORMS J. Comput., 19, 497–509, 2007.
Regis, R. and Shoemaker, C.: Parallel Stochastic Global Optimization Using Radial Basis Functions, INFORMS J. Comput., 21, 411–426, 2009.
Regis, R. and Shoemaker, C.: Combining radial basis function surrogates and dynamic coordinate search in high-dimensional expensive black-box optimization, Eng. Optimiz., 45, 529–555, 2013.
Riley, W. J., Subin, Z. M., Lawrence, D. M., Swenson, S. C., Torn, M. S., Meng, L., Mahowald, N. M., and Hess, P.: Barriers to predicting changes in global terrestrial methane fluxes: analyses using CLM4Me, a methane biogeochemistry model integrated in CESM, Biogeosciences, 8, 1925–1953, https://doi.org/10.5194/bg-8-1925-2011, 2011.
Ringeval, B., de Noblet-Ducoudre, N., Ciais, P., Bousquet, P., Prigeent, C., Papa, F., and Rossow, W.: An attempt to quantify the impact of changes in wetland extent on methane emissions on the seasonal and interannual time scales, Global Biogeochem. Cy., 24, GB2003, https://doi.org/10.1029/2008gb003354, 2010.
Saarnio, S., Alm, J., Silvola, J., Lohila, A., Nykänen, H., and Martikainen, P.: Seasonal Variation in CH4 Emissions and Production and Oxidation Potentials at Microsites on an Oligotrophic Pine Fen, Oecologia, 110, 414–422, 1997.
Schuh, A. E., Denning, A. S., Corbin, K. D., Baker, I. T., Uliasz, M., Parazoo, N., Andrews, A. E., and Worthy, D. E. J.: A regional high-resolution carbon flux inversion of North America for 2004, Biogeosciences, 7, 1625–1644, https://doi.org/10.5194/bg-7-1625-2010, 2010.
Segers, R.: Methane production and methane consumption: a review of processes underlying wetland methane fluxes, Biogeochemistry, 41, 23–51, 1998.
Segers, R. and Kengen, S.: Methane production as a function of anaerobic carbon mineralization: A process model, Soil Biol. Biochem., 30, 1107–1117, 1998.
Setyanto, P., Rosenami, A., Boer, R., Fauziah, C., and Khanif, M.: The effect of rice cultivars on methane emission from irrigated rice field, Indonesian Journal of Agricultural Sciences, 5, 20–31, 2004.
Shannon, R. D. and White, J. R.: 3-Year Study of Controls on Methane Emissions from 2 Michigan Peatlands, Biogeochemistry, 27, 35–60, 1994.
Shurpali, N. J. and Verma, S. B.: Micrometeorological measurements of methane flux in a Minnesota peatland during two growing seasons, Biogeochemistry, 40, 1–15, 1998.
Sigren, L., Lewis, S., Fisher, F., and Sass, R. L.: Effects of field drainage on soil parameters related to methane production and emision from rice paddies, Global Biogeochem. Cy., 11, 151–162, 1997.
Simpson, T., Mauery, T., Korte, J., and Mistree, F.: Kriging metamodels for global approximation in simulation-based multidisciplinary design optimization, AIAA J., 39, 2233–2241, 2001.
Solonen, A., Ollinaho, P., Laine, M., Haario, H., Tamminen, J., and Järvinen, H.: Efficient MCMC for climate model parameter estimation: Parallel adaptive chains and early rejection, Bayesian Analysis, 7, 715–736, 2012.
Subin, Z., Riley, W., and Mironov, D.: Improved lake model for climate simulations, J. Adv. Model. Earth Syst., 4, M02001, https://doi.org/10.1029/2011MS000072, 2012.
Sun, Y., Hou, Z., Huang, M., Tian, F., and Ruby Leung, L.: Inverse modeling of hydrologic parameters using surface flux and runoff observations in the Community Land Model, Hydrol. Earth Syst. Sci., 17, 4995–5011, https://doi.org/10.5194/hess-17-4995-2013, 2013.
Swenson, S. and Lawrence, D.: A New Fractional Snow Covered Area Parameterization for the Community Land Model and its Effect on the Surface Energy Balance, J. Geophys. Res., 117, D21107, https://doi.org/10.1029/2012JD018178, 2012.
Swenson, S., Lawrence, D., and Lee, H.: Improved Simulation of the Terrestrial Hydrological Cycle in Permafrost Regions by the Community Land Model, J. Adv. Model. Earth Syst., 4, M08002, https://doi.org/10.1029/2012MS000165, 2012.
Thornton, P., Lamarque, J., Rosenbloom, N., and Mahowald, N.: Influence of carbon-nitrogen cycle couplng on land model response to CO2 fertilization and climate variability, Global Biogeochem. Cy., 21, GB4018, https://doi.org/10.1029/2006GB002868, 2007.
Thornton, P. E., Doney, S. C., Lindsay, K., Moore, J. K., Mahowald, N., Randerson, J. T., Fung, I., Lamarque, J.-F., Feddema, J. J., and Lee, Y.-H.: Carbon-nitrogen interactions regulate climate-carbon cycle feedbacks: results from an atmosphere-ocean general circulation model, Biogeosciences Discuss., 6, 3303–3354, https://doi.org/10.5194/bgd-6-3303-2009, 2009.
Tian, X., Xie, Z., and Dai, A.: A land surface soil moisture data assimilation system based on the dual-UKF method and the Community Land Model, J. Geophys. Res.-Atmos., 113, D14127, https://doi.org/10.1029/2007JD009650, 2008.
Turner, D., Ritts, W., Wharton, S., Thomas, C., Monson, R., Black, T., and Falk, M.: Assessing FPAR source and parameter optimization scheme in application of a diagnostic carbon flux model, Remote Sens. Environ., 113, 1529–1539, 2009.
Viana, F., Haftka, R., and Steffen Jr., V.: Multiple surrogates: how cross-validation errors can help us to obtain the best predictor, Struct. Multidiscip. O., 39, 439–457, 2009.
Walter, B. and Heimann, M.: A process-based, climate-sensitive model to derive methane emissions from natural wetlands: Application to five wetland sites, sensitivity to model parameters, and climate, Global Biogeochem. Cy., 14, 745–765, 2000.
Walter, B., Heimann, M., and Matthews, E.: Modeling modern methane emissions from natural wetlands 1. Model description and results, J. Geophys. Res.-Atmos., 106, 34189–34206, 2001.
Wang, Z., Xu, Y., Li, Z., Guo, Y., Wassmann, R., Neue, H., Lantin, R., Buendia, L., Ding, Y., and Wang, Z.: A four-year record of methane emissions from irrigated rice fields in the Beijing region of China, Nutr. Cycl. Agroecosys., 58, 55–63, 2000.
Wania, R., Ross, I., and Prentice, I. C.: Implementation and evaluation of a new methane model within a dynamic global vegetation model: LPJ-WHyMe v1.3.1, Geosci. Model Dev., 3, 565–584, https://doi.org/10.5194/gmd-3-565-2010, 2010.
Whalen, S. and Reeburgh, W.: Moisture and temperature sensitivity of CH4 oxidation in boreal soils, Soil Biol. Biochem., 28, 1271–1281, 1996.
Wild, S. and Shoemaker, C.: Global convergence of radial basis function trust-region algorithms for derivative-free optimization, SIAM Review, 55, 349–371, 2013.
Yagi, K., Tsuruta, H., Kanda, K., and Minami, K.: Effect of water management on methane emission from a Japanese rice paddy field: Automated methane monitoring, Global Biogeochem. Cy., 10, 255–267, 1996.
Yang, B., Qian, Y., Lin, G., Leung, R., and Zhang, Y.: Some issues in uncertainty quantification and parameter tuning: a case study of convective parameterization scheme in the WRF regional climate model, Atmos. Chem. Phys., 12, 2409–2427, https://doi.org/10.5194/acp-12-2409-2012, 2012.
Yang, B., Qian, Y., Lin, G., Leung, L., Rasch, P., Zhang, G., McFarlane, S., Zhao, C., Zhang, Y., Wang, H., Wang, M., and Liu, X.: Uncertainty quantification and parameter tuning in the CAM5 Zhang-McFarlane convection scheme and impact of improved convection on the global circulation and climate, J. Geophys. Res.-Atmos., 118, 395–415, 2013.
Zeng, X., Drewniak, B. A., and Constantinescu, E. M.: Calibration of the Crop model in the Community Land Model, Geosci. Model Dev. Discuss., 6, 379–398, https://doi.org/10.5194/gmdd-6-379-2013, 2013.
Zhang, Y., Li, C., Trettin, C., Li, H., and Sun, G.: An integrated model of soil, hydrology, and vegetation for carbon dynamics in wetland ecosystems, Global Biogeochem. Cy., 16, 1–17, 2002.
Zhu, Q., Liu, J., Peng, C., Chen, H., Fang, X., Jiang, H., Yang, G., Zhu, D., Wang, W., and Zhou, X.: Modelling methane emissions from natural wetlands: TRIPLEX-GHG model integration, sensitivity analysis, and calibration, Geosci. Model Dev. Discuss., 6, 5423–5473, https://doi.org/10.5194/gmdd-6-5423-2013, 2013.
Zhuang, Q., Melillo, J., Kicklighter, D., Prinn, R., McGuire, A., Steudler, P., Felzer, B., and Hu, S.: Methane fluxes between terrestrial ecosystems and the atmosphere at northern high latitudes during the past century: A retrospective analysis with a process-based biogeochemistry model, Global Biogeochem. Cy., 18, GB3010, https://doi.org/10.1029/2004GB002239, 2004.
Short summary
We tune the CH4-related parameters of the Community Land Model (CLM) using surrogate global optimization in order to reduce the discrepancies between the CLM predictions and observed CH4 emissions. This is the first application of a surrogate optimization method to calibrate a global climate model. We found that the observation data drives the model to predict more CH4 emissions in the northern latitudes and less in the tropics.
We tune the CH4-related parameters of the Community Land Model (CLM) using surrogate global...
