Articles | Volume 17, issue 4
https://doi.org/10.5194/gmd-17-1525-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-1525-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
21 Feb 2024
Development and technical paper |
| 21 Feb 2024
| 21 Feb 2024
Quantifying wildfire drivers and predictability in boreal peatlands using a two-step error-correcting machine learning framework in TeFire v1.0
Rongyun Tang
Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA
Department of Biological & Agricultural Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Daniel M. Ricciuto
Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
Anping Chen
Department of Biology and Graduate Degree Program in Ecology, Colorado State University, Fort Collins, CO 80523, USA
Yulong Zhang
Institute for a Secure and Sustainable Environment and Department of Industrial and Systems Engineering, University of Tennessee, Knoxville, TN 37996, USA
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Kelsey T. Foster, Wu Sun, Yoichi P. Shiga, Jiafu Mao, and Anna M. Michalak
Biogeosciences, 21, 869–891, https://doi.org/10.5194/bg-21-869-2024, https://doi.org/10.5194/bg-21-869-2024, 2024
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Assessing agreement between bottom-up and top-down methods across spatial scales can provide insights into the relationship between ensemble spread (difference across models) and model accuracy (difference between model estimates and reality). We find that ensemble spread is unlikely to be a good indicator of actual uncertainty in the North American carbon balance. However, models that are consistent with atmospheric constraints show stronger agreement between top-down and bottom-up estimates.
Chao Wang, Stephen Leisz, Li Li, Xiaoying Shi, Jiafu Mao, Yi Zheng, and Anping Chen
Earth Syst. Dynam., 15, 75–90, https://doi.org/10.5194/esd-15-75-2024, https://doi.org/10.5194/esd-15-75-2024, 2024
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Climate change can significantly impact river runoff; however, predicting future runoff is challenging. Using historical runoff gauge data to evaluate model performances in runoff simulations for the Mekong River, we quantify future runoff changes in the Mekong River with the best simulation combination. Results suggest a significant increase in the annual runoff, along with varied seasonal distributions, thus heightening the need for adapted water resource management measures.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
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We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Shuang Ma, Lifen Jiang, Rachel M. Wilson, Jeff P. Chanton, Scott Bridgham, Shuli Niu, Colleen M. Iversen, Avni Malhotra, Jiang Jiang, Xingjie Lu, Yuanyuan Huang, Jason Keller, Xiaofeng Xu, Daniel M. Ricciuto, Paul J. Hanson, and Yiqi Luo
Biogeosciences, 19, 2245–2262, https://doi.org/10.5194/bg-19-2245-2022, https://doi.org/10.5194/bg-19-2245-2022, 2022
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The relative ratio of wetland methane (CH4) emission pathways determines how much CH4 is oxidized before leaving the soil. We found an ebullition modeling approach that has a better performance in deep layer pore water CH4 concentration. We suggest using this approach in land surface models to accurately represent CH4 emission dynamics and response to climate change. Our results also highlight that both CH4 flux and belowground concentration data are important to constrain model parameters.
Jiehao Zhang, Yulong Zhang, Ge Sun, Conghe Song, Matthew P. Dannenberg, Jiangfeng Li, Ning Liu, Kerong Zhang, Quanfa Zhang, and Lu Hao
Hydrol. Earth Syst. Sci., 25, 5623–5640, https://doi.org/10.5194/hess-25-5623-2021, https://doi.org/10.5194/hess-25-5623-2021, 2021
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To quantify how vegetation greening impacts the capacity of water supply, we built a hybrid model and conducted a case study using the upper Han River basin (UHRB) that serves as the water source area to the world’s largest water diversion project. Vegetation greening in the UHRB during 2001–2018 induced annual water yield (WY) greatly decreased. Vegetation greening also increased the possibility of drought and reduced a quarter of WY on average during drought periods.
Yaoping Wang, Jiafu Mao, Mingzhou Jin, Forrest M. Hoffman, Xiaoying Shi, Stan D. Wullschleger, and Yongjiu Dai
Earth Syst. Sci. Data, 13, 4385–4405, https://doi.org/10.5194/essd-13-4385-2021, https://doi.org/10.5194/essd-13-4385-2021, 2021
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We developed seven global soil moisture datasets (1970–2016, monthly, half-degree, and multilayer) by merging a wide range of data sources, including in situ and satellite observations, reanalysis, offline land surface model simulations, and Earth system model simulations. Given the great value of long-term, multilayer, gap-free soil moisture products to climate research and applications, we believe this paper and the presented datasets would be of interest to many different communities.
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
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In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Eva Sinha, Kate Calvin, Ben Bond-Lamberty, Beth Drewniak, Dan Ricciuto, Khachik Sargsyan, Yanyan Cheng, Carl Bernacchi, and Caitlin Moore
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2021-244, https://doi.org/10.5194/gmd-2021-244, 2021
Preprint withdrawn
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Perennial bioenergy crops are not well represented in global land models, despite projected increase in their production. Our study expands Energy Exascale Earth System Model (E3SM) Land Model (ELM) to include perennial bioenergy crops and calibrates the model for miscanthus and switchgrass. The calibrated model captures the seasonality and magnitude of carbon and energy fluxes. This study provides the foundation for future research examining the impact of perennial bioenergy crop expansion.
Daniel M. Ricciuto, Xiaojuan Yang, Dali Wang, and Peter E. Thornton
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-163, https://doi.org/10.5194/bg-2021-163, 2021
Publication in BG not foreseen
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This paper uses a novel approach to quantify the impacts of the choice of decomposition model on carbon and nitrogen cycling. We compare the models to experimental data that examined litter decomposition over five different biomes. Despite widely differing assumptions, the models produce similar patterns of decomposition when nutrients are limiting. This differs from past analyses that did not consider the impacts of changing environmental conditions or nutrients.
Xiaoying Shi, Daniel M. Ricciuto, Peter E. Thornton, Xiaofeng Xu, Fengming Yuan, Richard J. Norby, Anthony P. Walker, Jeffrey M. Warren, Jiafu Mao, Paul J. Hanson, Lin Meng, David Weston, and Natalie A. Griffiths
Biogeosciences, 18, 467–486, https://doi.org/10.5194/bg-18-467-2021, https://doi.org/10.5194/bg-18-467-2021, 2021
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The Sphagnum mosses are the important species of a wetland ecosystem. To better represent the peatland ecosystem, we introduced the moss species to the land model component (ELM) of the Energy Exascale Earth System Model (E3SM) by developing water content dynamics and nonvascular photosynthetic processes for moss. We tested the model against field observations and used the model to make projections of the site's carbon cycle under warming and atmospheric CO2 concentration scenarios.
Binghao Jia, Xin Luo, Ximing Cai, Atul Jain, Deborah N. Huntzinger, Zhenghui Xie, Ning Zeng, Jiafu Mao, Xiaoying Shi, Akihiko Ito, Yaxing Wei, Hanqin Tian, Benjamin Poulter, Dan Hayes, and Kevin Schaefer
Earth Syst. Dynam., 11, 235–249, https://doi.org/10.5194/esd-11-235-2020, https://doi.org/10.5194/esd-11-235-2020, 2020
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We quantitatively examined the relative contributions of climate change, land
use and land cover change, and elevated CO2 to interannual variations and seasonal cycle amplitude of gross primary productivity (GPP) in China based on multi-model ensemble simulations. The contributions of major subregions to the temporal change in China's total GPP are also presented. This work may help us better understand GPP spatiotemporal patterns and their responses to regional changes and human activities.
Elias C. Massoud, Chonggang Xu, Rosie A. Fisher, Ryan G. Knox, Anthony P. Walker, Shawn P. Serbin, Bradley O. Christoffersen, Jennifer A. Holm, Lara M. Kueppers, Daniel M. Ricciuto, Liang Wei, Daniel J. Johnson, Jeffrey Q. Chambers, Charlie D. Koven, Nate G. McDowell, and Jasper A. Vrugt
Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, https://doi.org/10.5194/gmd-12-4133-2019, 2019
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We conducted a comprehensive sensitivity analysis to understand behaviors of a demographic vegetation model within a land surface model. By running the model 5000 times with changing input parameter values, we found that (1) the photosynthetic capacity controls carbon fluxes, (2) the allometry is important for tree growth, and (3) the targeted carbon storage is important for tree survival. These results can provide guidance on improved model parameterization for a better fit to observations.
Dan Lu and Daniel Ricciuto
Geosci. Model Dev., 12, 1791–1807, https://doi.org/10.5194/gmd-12-1791-2019, https://doi.org/10.5194/gmd-12-1791-2019, 2019
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This work uses machine-learning techniques to advance the predictive understanding of large-scale Earth systems.
Junyi Liang, Gangsheng Wang, Daniel M. Ricciuto, Lianhong Gu, Paul J. Hanson, Jeffrey D. Wood, and Melanie A. Mayes
Geosci. Model Dev., 12, 1601–1612, https://doi.org/10.5194/gmd-12-1601-2019, https://doi.org/10.5194/gmd-12-1601-2019, 2019
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Soil respiration, the second largest carbon fluxes between the atmosphere and land, is not well represented in global land models. In this study, using long-term observations at a temperate forest, we identified a solution for using better soil water potential simulations to improve predictions of soil respiration in the E3SM land model. In addition, parameter calibration further improved model performance.
Yuanyuan Huang, Mark Stacy, Jiang Jiang, Nilutpal Sundi, Shuang Ma, Volodymyr Saruta, Chang Gyo Jung, Zheng Shi, Jianyang Xia, Paul J. Hanson, Daniel Ricciuto, and Yiqi Luo
Geosci. Model Dev., 12, 1119–1137, https://doi.org/10.5194/gmd-12-1119-2019, https://doi.org/10.5194/gmd-12-1119-2019, 2019
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Predicting future changes in ecosystem services is not only highly desirable but is also becoming feasible as several forces are converging to transform ecological research into quantitative forecasting. To realize ecological forecasting, we have developed an Ecological Platform for Assimilating Data (EcoPAD) into models. EcoPAD also has the potential to become an interactive tool for resource management, stimulate citizen science in ecology, and transform environmental education.
Hanbo Yun, Qingbai Wu, Qianlai Zhuang, Anping Chen, Tong Yu, Zhou Lyu, Yuzhong Yang, Huijun Jin, Guojun Liu, Yang Qu, and Licheng Liu
The Cryosphere, 12, 2803–2819, https://doi.org/10.5194/tc-12-2803-2018, https://doi.org/10.5194/tc-12-2803-2018, 2018
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Here we reported the QTP permafrost region was a CH4 sink of −0.86 ± 0.23 g CH4-C m−2 yr−1 over 2012–2016, soil temperature and soil water content were dominant factors controlling CH4 fluxes, and their correlations changed with soil depth due to cryoturbation dynamics. This region was a net CH4 sink in autumn, but a net source in spring, despite both seasons experiencing similar top soil thawing and freezing dynamics.
Donghai Wu, Philippe Ciais, Nicolas Viovy, Alan K. Knapp, Kevin Wilcox, Michael Bahn, Melinda D. Smith, Sara Vicca, Simone Fatichi, Jakob Zscheischler, Yue He, Xiangyi Li, Akihiko Ito, Almut Arneth, Anna Harper, Anna Ukkola, Athanasios Paschalis, Benjamin Poulter, Changhui Peng, Daniel Ricciuto, David Reinthaler, Guangsheng Chen, Hanqin Tian, Hélène Genet, Jiafu Mao, Johannes Ingrisch, Julia E. S. M. Nabel, Julia Pongratz, Lena R. Boysen, Markus Kautz, Michael Schmitt, Patrick Meir, Qiuan Zhu, Roland Hasibeder, Sebastian Sippel, Shree R. S. Dangal, Stephen Sitch, Xiaoying Shi, Yingping Wang, Yiqi Luo, Yongwen Liu, and Shilong Piao
Biogeosciences, 15, 3421–3437, https://doi.org/10.5194/bg-15-3421-2018, https://doi.org/10.5194/bg-15-3421-2018, 2018
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Our results indicate that most ecosystem models do not capture the observed asymmetric responses under normal precipitation conditions, suggesting an overestimate of the drought effects and/or underestimate of the watering impacts on primary productivity, which may be the result of inadequate representation of key eco-hydrological processes. Collaboration between modelers and site investigators needs to be strengthened to improve the specific processes in ecosystem models in following studies.
Henrique F. Duarte, Brett M. Raczka, Daniel M. Ricciuto, John C. Lin, Charles D. Koven, Peter E. Thornton, David R. Bowling, Chun-Ta Lai, Kenneth J. Bible, and James R. Ehleringer
Biogeosciences, 14, 4315–4340, https://doi.org/10.5194/bg-14-4315-2017, https://doi.org/10.5194/bg-14-4315-2017, 2017
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We evaluate the Community Land Model (CLM4.5) against observations at an old-growth coniferous forest site that is subjected to water stress each summer. We found that, after calibration, CLM was able to reasonably simulate the observed fluxes of energy and carbon, carbon stocks, carbon isotope ratios, and ecosystem response to water stress. This study demonstrates that carbon isotopes can expose structural weaknesses in CLM and provide a key constraint that may guide future model development.
Dan Lu, Daniel Ricciuto, Anthony Walker, Cosmin Safta, and William Munger
Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017, https://doi.org/10.5194/bg-14-4295-2017, 2017
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Calibration of terrestrial ecosystem models (TEMs) is important but challenging. This study applies an advanced sampling technique for parameter estimation of a TEM. The results improve the model fit and predictive performance.
Paul J. Hanson, Jeffery S. Riggs, W. Robert Nettles, Jana R. Phillips, Misha B. Krassovski, Leslie A. Hook, Lianhong Gu, Andrew D. Richardson, Donald M. Aubrecht, Daniel M. Ricciuto, Jeffrey M. Warren, and Charlotte Barbier
Biogeosciences, 14, 861–883, https://doi.org/10.5194/bg-14-861-2017, https://doi.org/10.5194/bg-14-861-2017, 2017
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This paper describes operational methods to achieve whole-ecosystem warming (WEW) for tall-stature, high-carbon, boreal forest peatlands. The methods enable scientists to study immediate and longer-term (1 decade) responses of organisms (microbes to trees) and ecosystem functions (carbon, water and nutrient cycles). The WEW technology allows researchers to have a plausible glimpse of future environmental conditions for study that are not available in the current observational record.
Reindert J. Haarsma, Malcolm J. Roberts, Pier Luigi Vidale, Catherine A. Senior, Alessio Bellucci, Qing Bao, Ping Chang, Susanna Corti, Neven S. Fučkar, Virginie Guemas, Jost von Hardenberg, Wilco Hazeleger, Chihiro Kodama, Torben Koenigk, L. Ruby Leung, Jian Lu, Jing-Jia Luo, Jiafu Mao, Matthew S. Mizielinski, Ryo Mizuta, Paulo Nobre, Masaki Satoh, Enrico Scoccimarro, Tido Semmler, Justin Small, and Jin-Song von Storch
Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, https://doi.org/10.5194/gmd-9-4185-2016, 2016
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Recent progress in computing power has enabled climate models to simulate more processes in detail and on a smaller scale. Here we present a common protocol for these high-resolution runs that will foster the analysis and understanding of the impact of model resolution on the simulated climate. These runs will also serve as a more reliable source for assessing climate risks that are associated with small-scale weather phenomena such as tropical cyclones.
Brett Raczka, Henrique F. Duarte, Charles D. Koven, Daniel Ricciuto, Peter E. Thornton, John C. Lin, and David R. Bowling
Biogeosciences, 13, 5183–5204, https://doi.org/10.5194/bg-13-5183-2016, https://doi.org/10.5194/bg-13-5183-2016, 2016
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We use carbon isotopes of CO2 to improve the performance of a land surface model, a component with earth system climate models. We found that isotope observations can provide important information related to the exchange of carbon and water from vegetation driven by environmental stress from low atmospheric moisture and nitrogen limitation. It follows that isotopes have a unique potential to improve model performance and provide insight into land surface model development.
Bart van den Hurk, Hyungjun Kim, Gerhard Krinner, Sonia I. Seneviratne, Chris Derksen, Taikan Oki, Hervé Douville, Jeanne Colin, Agnès Ducharne, Frederique Cheruy, Nicholas Viovy, Michael J. Puma, Yoshihide Wada, Weiping Li, Binghao Jia, Andrea Alessandri, Dave M. Lawrence, Graham P. Weedon, Richard Ellis, Stefan Hagemann, Jiafu Mao, Mark G. Flanner, Matteo Zampieri, Stefano Materia, Rachel M. Law, and Justin Sheffield
Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, https://doi.org/10.5194/gmd-9-2809-2016, 2016
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This manuscript describes the setup of the CMIP6 project Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP).
J. Mao, D. M. Ricciuto, P. E. Thornton, J. M. Warren, A. W. King, X. Shi, C. M. Iversen, and R. J. Norby
Biogeosciences, 13, 641–657, https://doi.org/10.5194/bg-13-641-2016, https://doi.org/10.5194/bg-13-641-2016, 2016
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The aim of this study is to implement, calibrate and evaluate the CLM4 against carbon and hydrology observations from a shading and labeling experiment in a stand of young loblolly pines. We found a combination of parameters measured on-site and calibration targeting biomass, transpiration, and 13C discrimination gave good agreement with pretreatment measurements. We also used observations from the experiment to develop a conceptual model of short-term photosynthate storage and transport.
X. Shi, P. E. Thornton, D. M. Ricciuto, P. J. Hanson, J. Mao, S. D. Sebestyen, N. A. Griffiths, and G. Bisht
Biogeosciences, 12, 6463–6477, https://doi.org/10.5194/bg-12-6463-2015, https://doi.org/10.5194/bg-12-6463-2015, 2015
W. D. Collins, A. P. Craig, J. E. Truesdale, A. V. Di Vittorio, A. D. Jones, B. Bond-Lamberty, K. V. Calvin, J. A. Edmonds, S. H. Kim, A. M. Thomson, P. Patel, Y. Zhou, J. Mao, X. Shi, P. E. Thornton, L. P. Chini, and G. C. Hurtt
Geosci. Model Dev., 8, 2203–2219, https://doi.org/10.5194/gmd-8-2203-2015, https://doi.org/10.5194/gmd-8-2203-2015, 2015
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The integrated Earth system model (iESM) has been developed as a
new tool for projecting the joint human-climate system. The
iESM is based upon coupling an integrated assessment model (IAM)
and an Earth system model (ESM) into a common modeling
infrastructure. By introducing heretofore-omitted
feedbacks between natural and societal drivers in iESM, we can improve
scientific understanding of the human-Earth system
dynamics.
C. Safta, D. M. Ricciuto, K. Sargsyan, B. Debusschere, H. N. Najm, M. Williams, and P. E. Thornton
Geosci. Model Dev., 8, 1899–1918, https://doi.org/10.5194/gmd-8-1899-2015, https://doi.org/10.5194/gmd-8-1899-2015, 2015
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In this paper we propose a probabilistic framework for an uncertainty quantification study of a carbon cycle model and focus on the comparison between steady-state and transient
simulation setups. We study model parameters via global sensitivity analysis and employ a Bayesian approach to calibrate these parameters using NEE observations at the Harvard Forest site. The calibration results are then used to assess the predictive skill of the model via posterior predictive checks.
Y. Wei, S. Liu, D. N. Huntzinger, A. M. Michalak, N. Viovy, W. M. Post, C. R. Schwalm, K. Schaefer, A. R. Jacobson, C. Lu, H. Tian, D. M. Ricciuto, R. B. Cook, J. Mao, and X. Shi
Geosci. Model Dev., 7, 2875–2893, https://doi.org/10.5194/gmd-7-2875-2014, https://doi.org/10.5194/gmd-7-2875-2014, 2014
A. V. Di Vittorio, L. P. Chini, B. Bond-Lamberty, J. Mao, X. Shi, J. Truesdale, A. Craig, K. Calvin, A. Jones, W. D. Collins, J. Edmonds, G. C. Hurtt, P. Thornton, and A. Thomson
Biogeosciences, 11, 6435–6450, https://doi.org/10.5194/bg-11-6435-2014, https://doi.org/10.5194/bg-11-6435-2014, 2014
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Economic models provide scenarios of land use and greenhouse gas emissions to earth system models to project global change. We found, and partially addressed, inconsistencies in land cover between an economic and an earth system model that effectively alter a prescribed scenario, causing significant differences in projected terrestrial carbon and atmospheric CO2 between prescribed and altered scenarios. We outline a solution to this current problem in scenario-based global change projections.
B. Bond-Lamberty, K. Calvin, A. D. Jones, J. Mao, P. Patel, X. Y. Shi, A. Thomson, P. Thornton, and Y. Zhou
Geosci. Model Dev., 7, 2545–2555, https://doi.org/10.5194/gmd-7-2545-2014, https://doi.org/10.5194/gmd-7-2545-2014, 2014
X. Yang, P. E. Thornton, D. M. Ricciuto, and W. M. Post
Biogeosciences, 11, 1667–1681, https://doi.org/10.5194/bg-11-1667-2014, https://doi.org/10.5194/bg-11-1667-2014, 2014
D. N. Huntzinger, C. Schwalm, A. M. Michalak, K. Schaefer, A. W. King, Y. Wei, A. Jacobson, S. Liu, R. B. Cook, W. M. Post, G. Berthier, D. Hayes, M. Huang, A. Ito, H. Lei, C. Lu, J. Mao, C. H. Peng, S. Peng, B. Poulter, D. Riccuito, X. Shi, H. Tian, W. Wang, N. Zeng, F. Zhao, and Q. Zhu
Geosci. Model Dev., 6, 2121–2133, https://doi.org/10.5194/gmd-6-2121-2013, https://doi.org/10.5194/gmd-6-2121-2013, 2013
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
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Multivariate adjustment of drizzle bias using machine learning in European climate projections
New model ensemble reveals how forcing uncertainty and model structure alter climate simulated across CMIP generations of the Community Earth System Model
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
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
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We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
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In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
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In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
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We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
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For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Stephanie Fiedler, Vaishali Naik, Fiona M. O'Connor, Christopher J. Smith, Paul Griffiths, Ryan J. Kramer, Toshihiko Takemura, Robert J. Allen, Ulas Im, Matthew Kasoar, Angshuman Modak, Steven Turnock, Apostolos Voulgarakis, Duncan Watson-Parris, Daniel M. Westervelt, Laura J. Wilcox, Alcide Zhao, William J. Collins, Michael Schulz, Gunnar Myhre, and Piers M. Forster
Geosci. Model Dev., 17, 2387–2417, https://doi.org/10.5194/gmd-17-2387-2024, https://doi.org/10.5194/gmd-17-2387-2024, 2024
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Climate scientists want to better understand modern climate change. Thus, climate model experiments are performed and compared. The results of climate model experiments differ, as assessed in the latest Intergovernmental Panel on Climate Change (IPCC) assessment report. This article gives insights into the challenges and outlines opportunities for further improving the understanding of climate change. It is based on views of a group of experts in atmospheric composition–climate interactions.
Sergey Danilov, Carolin Mehlmann, Dmitry Sidorenko, and Qiang Wang
Geosci. Model Dev., 17, 2287–2297, https://doi.org/10.5194/gmd-17-2287-2024, https://doi.org/10.5194/gmd-17-2287-2024, 2024
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Sea ice models are a necessary component of climate models. At very high resolution they are capable of simulating linear kinematic features, such as leads, which are important for better prediction of heat exchanges between the ocean and atmosphere. Two new discretizations are described which improve the sea ice component of the Finite volumE Sea ice–Ocean Model (FESOM version 2) by allowing simulations of finer scales.
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.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
EGUsphere, https://doi.org/10.5194/egusphere-2024-45, https://doi.org/10.5194/egusphere-2024-45, 2024
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This study focused on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies were applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method of Random Forest for increasing the accuracy of climate models, concerning the projection of the number of wet days.
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.
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.
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Li, F., Zhu, Q., Riley, W. J., Zhao, L., Xu, L., Yuan, K., Chen, M., Wu, H., Gui, Z., Gong, J., and Randerson, J. T.: AttentionFire_v1.0: interpretable machine learning fire model for burned-area predictions over tropics, Geosci. Model Dev., 16, 869–884, https://doi.org/10.5194/gmd-16-869-2023, 2023.
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Short summary
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.
Carbon-rich boreal peatlands are at risk of burning. The reproducibility and predictability of...
