Articles | Volume 11, issue 8
https://doi.org/10.5194/gmd-11-3131-2018
© Author(s) 2018. 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-11-3131-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
03 Aug 2018
Methods for assessment of models |
| 03 Aug 2018
| 03 Aug 2018
Fast sensitivity analysis methods for computationally expensive models with multi-dimensional output
Lancaster Environment Centre, Lancaster University, Lancaster, UK
Oliver Wild
Lancaster Environment Centre, Lancaster University, Lancaster, UK
Apostolos Voulgarakis
Department of Physics, Imperial College London, London, UK
Lindsay Lee
School of Earth and Environment, University of Leeds, Leeds, UK
Related authors
Edmund M. Ryan, Kiona Ogle, Heather Kropp, Kimberly E. Samuels-Crow, Yolima Carrillo, and Elise Pendall
Geosci. Model Dev., 11, 1909–1928, https://doi.org/10.5194/gmd-11-1909-2018, https://doi.org/10.5194/gmd-11-1909-2018, 2018
Short summary
Short summary
Our work evaluated the appropriateness of the common steady-state (SS) assumption, for example when partitioning soil respiration of CO2 into recently photosynthesized carbon (C) and older C. Using a new model of soil CO2 production and transport we found that the SS assumption is valid most of the time, especially in sand/silt soils. Non-SS conditions occurred mainly for the few days following large rain events in all soil types, but the non-SS period was prolonged and magnified in clay soils.
Zhenze Liu, Ke Li, Oliver Wild, Ruth M. Doherty, Fiona M. O’Connor, and Steven T. Turnock
EGUsphere, https://doi.org/10.5194/egusphere-2025-1250, https://doi.org/10.5194/egusphere-2025-1250, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
Our research aimed to enhance predictions of ozone levels in the atmosphere, a gas that influences air quality and climate. We used a computer model called UKESM1 to simulate ozone, but its estimates were often inaccurate. By applying deep learning, we improved the accuracy of these predictions. This advance helps us understand how ozone might shift as the climate warms. Better predictions are vital for shaping policies on air quality and climate.
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025, https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Short summary
The multi-model experiment design of the HTAP3 Fires project takes a multi-pollutant approach to improving our understanding of transboundary transport of wildland fire and agricultural burning emissions and their impacts. The experiments are designed with the goal of answering science policy questions related to fires. The options for the multi-model approach, including inputs, outputs, and model setup, are discussed, and the official recommendations for the project are presented.
Anastasios Rovithakis, Eleanor Burke, Chantelle Burton, Matthew Kasoar, Manolis G. Grillakis, Konstantinos D. Seiradakis, and Apostolos Voulgarakis
EGUsphere, https://doi.org/10.5194/egusphere-2025-274, https://doi.org/10.5194/egusphere-2025-274, 2025
Short summary
Short summary
JULES-INFERNO captures observed burned area across Greece fairly well for the present-day. Drastic future changes in burnt area in Eastern continental and southern Greece, especially under severe climate change scenarios. Static vegetation leads to larger burnt area compared to dynamic vegetation due to the lower concentration of flammable needleleaf trees.
Pierluigi Renan Guaita, Riccardo Marzuoli, Leiming Zhang, Steven Turnock, Gerbrand Koren, Oliver Wild, Paola Crippa, and Giacomo Alessandro Gerosa
EGUsphere, https://doi.org/10.5194/egusphere-2024-2573, https://doi.org/10.5194/egusphere-2024-2573, 2024
Preprint archived
Short summary
Short summary
This study assesses the global impact of tropospheric ozone on wheat crops in the 21st century under various climate scenarios. The research highlights that ozone damage to wheat varies by region and depends on both ozone levels and climate. Vulnerable regions include East Asia, Northern Europe, and the Southern and Eastern edges of the Tibetan Plateau. Our results emphasize the need of policies to reduce ozone levels and mitigate climate change to protect global food security.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
Short summary
Short summary
Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Christopher D. Wells, Matthew Kasoar, Majid Ezzati, and Apostolos Voulgarakis
Atmos. Chem. Phys., 24, 1025–1039, https://doi.org/10.5194/acp-24-1025-2024, https://doi.org/10.5194/acp-24-1025-2024, 2024
Short summary
Short summary
Human-driven emissions of air pollutants, mostly caused by burning fossil fuels, impact both the climate and human health. Millions of deaths each year are caused by air pollution globally, and the future trends are uncertain. Here, we use a global climate model to study the effect of African pollutant emissions on surface level air pollution, and resultant impacts on human health, in several future emission scenarios. We find much lower health impacts under cleaner, lower-emission futures.
Ailish M. Graham, Richard J. Pope, Martyn P. Chipperfield, Sandip S. Dhomse, Matilda Pimlott, Wuhu Feng, Vikas Singh, Ying Chen, Oliver Wild, Ranjeet Sokhi, and Gufran Beig
Atmos. Chem. Phys., 24, 789–806, https://doi.org/10.5194/acp-24-789-2024, https://doi.org/10.5194/acp-24-789-2024, 2024
Short summary
Short summary
Our paper uses novel satellite datasets and high-resolution emissions datasets alongside a back-trajectory model to investigate the balance of local and external sources influencing NOx air pollution changes in Delhi. We find in the post-monsoon season that NOx from local and non-local transport emissions contributes most to poor air quality in Delhi. Therefore, air quality mitigation strategies in Delhi and surrounding regions are used to control this issue.
Xuewei Hou, Oliver Wild, Bin Zhu, and James Lee
Atmos. Chem. Phys., 23, 15395–15411, https://doi.org/10.5194/acp-23-15395-2023, https://doi.org/10.5194/acp-23-15395-2023, 2023
Short summary
Short summary
In response to the climate crisis, many countries have committed to net zero in a certain future year. The impacts of net-zero scenarios on tropospheric O3 are less well studied and remain unclear. In this study, we quantified the changes of tropospheric O3 budgets, spatiotemporal distributions of future surface O3 in east Asia and regional O3 source contributions for 2060 under a net-zero scenario using the NCAR Community Earth System Model (CESM) and online O3-tagging methods.
Zhenze Liu, Oliver Wild, Ruth M. Doherty, Fiona M. O'Connor, and Steven T. Turnock
Atmos. Chem. Phys., 23, 13755–13768, https://doi.org/10.5194/acp-23-13755-2023, https://doi.org/10.5194/acp-23-13755-2023, 2023
Short summary
Short summary
We investigate the impact of net-zero policies on surface ozone pollution in China. A chemistry–climate model is used to simulate ozone changes driven by local and external emissions, methane, and warmer climates. A deep learning model is applied to generate more robust ozone projection, and we find that the benefits of net-zero policies may be overestimated with the chemistry–climate model. Nevertheless, it is clear that the policies can still substantially reduce ozone pollution in future.
Joao Carlos Martins Teixeira, Chantelle Burton, Douglas I. Kelly, Gerd A. Folberth, Fiona M. O'Connor, Richard A. Betts, and Apostolos Voulgarakis
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-136, https://doi.org/10.5194/bg-2023-136, 2023
Revised manuscript not accepted
Short summary
Short summary
Representing socio-economic impacts on fires is crucial to underpin the confidence in global fire models. Introducing these into INFERNO, reduces biases and improves the modelled burnt area (BA) trends when compared to observations. Including socio-economic factors in the representation of fires in Earth System Models is important for realistically simulating BA, quantifying trends in the recent past, and for understanding the main drivers of those at regional scales.
Ernesto Reyes-Villegas, Douglas Lowe, Jill S. Johnson, Kenneth S. Carslaw, Eoghan Darbyshire, Michael Flynn, James D. Allan, Hugh Coe, Ying Chen, Oliver Wild, Scott Archer-Nicholls, Alex Archibald, Siddhartha Singh, Manish Shrivastava, Rahul A. Zaveri, Vikas Singh, Gufran Beig, Ranjeet Sokhi, and Gordon McFiggans
Atmos. Chem. Phys., 23, 5763–5782, https://doi.org/10.5194/acp-23-5763-2023, https://doi.org/10.5194/acp-23-5763-2023, 2023
Short summary
Short summary
Organic aerosols (OAs), their sources and their processes remain poorly understood. The volatility basis set (VBS) approach, implemented in air quality models such as WRF-Chem, can be a useful tool to describe primary OA (POA) production and aging. However, the main disadvantage is its complexity. We used a Gaussian process simulator to reproduce model results and to estimate the sources of model uncertainty. We do this by comparing the outputs with OA observations made at Delhi, India, in 2018.
Christopher D. Wells, Matthew Kasoar, Nicolas Bellouin, and Apostolos Voulgarakis
Atmos. Chem. Phys., 23, 3575–3593, https://doi.org/10.5194/acp-23-3575-2023, https://doi.org/10.5194/acp-23-3575-2023, 2023
Short summary
Short summary
The climate is altered by greenhouse gases and air pollutant particles, and such emissions are likely to change drastically in the future over Africa. Air pollutants do not travel far, so their climate effect depends on where they are emitted. This study uses a climate model to find the climate impacts of future African pollutant emissions being either high or low. The particles absorb and scatter sunlight, causing the ground nearby to be cooler, but elsewhere the increased heat causes warming.
Zixuan Jia, Carlos Ordóñez, Ruth M. Doherty, Oliver Wild, Steven T. Turnock, and Fiona M. O'Connor
Atmos. Chem. Phys., 23, 2829–2842, https://doi.org/10.5194/acp-23-2829-2023, https://doi.org/10.5194/acp-23-2829-2023, 2023
Short summary
Short summary
This study investigates the influence of the winter large-scale circulation on daily concentrations of PM2.5 and their sensitivity to emissions. The new proposed circulation index can effectively distinguish different levels of air pollution and explain changes in PM2.5 sensitivity to emissions from local and surrounding regions. We then project future changes in PM2.5 concentrations using this index and find an increase in PM2.5 concentrations over the region due to climate change.
David S. Stevenson, Richard G. Derwent, Oliver Wild, and William J. Collins
Atmos. Chem. Phys., 22, 14243–14252, https://doi.org/10.5194/acp-22-14243-2022, https://doi.org/10.5194/acp-22-14243-2022, 2022
Short summary
Short summary
Atmospheric methane’s growth rate rose by 50 % in 2020 relative to 2019. Lower nitrogen oxide (NOx) emissions tend to increase methane’s atmospheric residence time; lower carbon monoxide (CO) and non-methane volatile organic compound (NMVOC) emissions decrease its lifetime. Combining model sensitivities with emission changes, we find that COVID-19 lockdown emission reductions can explain over half the observed increases in methane in 2020.
Zhenze Liu, Ruth M. Doherty, Oliver Wild, Fiona M. O'Connor, and Steven T. Turnock
Atmos. Chem. Phys., 22, 12543–12557, https://doi.org/10.5194/acp-22-12543-2022, https://doi.org/10.5194/acp-22-12543-2022, 2022
Short summary
Short summary
Weaknesses in process representation in chemistry–climate models lead to biases in simulating surface ozone and to uncertainty in projections of future ozone change. We develop a deep learning model to demonstrate the feasibility of ozone bias correction and show its capability in providing improved assessments of the impacts of climate and emission changes on future air quality, along with valuable information to guide future model development.
Zixuan Jia, Ruth M. Doherty, Carlos Ordóñez, Chaofan Li, Oliver Wild, Shipra Jain, and Xiao Tang
Atmos. Chem. Phys., 22, 6471–6487, https://doi.org/10.5194/acp-22-6471-2022, https://doi.org/10.5194/acp-22-6471-2022, 2022
Short summary
Short summary
This study investigates the modulation of daily PM2.5 over three major populated regions in China by regional meteorology and large-scale circulation during winter. These results demonstrate the benefits of considering the large-scale circulation for air quality studies. The novel circulation indices proposed here can explain a considerable fraction of the day-to-day variability of PM2.5 and can be combined with regional meteorology to improve our capability to predict the variability of PM2.5.
Zhenze Liu, Ruth M. Doherty, Oliver Wild, Fiona M. O'Connor, and Steven T. Turnock
Atmos. Chem. Phys., 22, 1209–1227, https://doi.org/10.5194/acp-22-1209-2022, https://doi.org/10.5194/acp-22-1209-2022, 2022
Short summary
Short summary
Tropospheric ozone is important to future air quality and climate, and changing emissions and climate influence ozone. We investigate the evolution of ozone and ozone sensitivity from the present day (2004–2014) to the future (2045–2055) and explore the main drivers of ozone changes from global and regional perspectives. This helps guide suitable emission control strategies to mitigate ozone pollution.
João C. Teixeira, Gerd A. Folberth, Fiona M. O'Connor, Nadine Unger, and Apostolos Voulgarakis
Geosci. Model Dev., 14, 6515–6539, https://doi.org/10.5194/gmd-14-6515-2021, https://doi.org/10.5194/gmd-14-6515-2021, 2021
Short summary
Short summary
Fire constitutes a key process in the Earth system, being driven by climate as well as affecting climate. However, studies on the effects of fires on atmospheric composition and climate have been limited to date. This work implements and assesses the coupling of an interactive fire model with atmospheric composition, comparing it to an offline approach. This approach shows good performance at a global scale. However, regional-scale limitations lead to a bias in modelling fire emissions.
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, David Carruthers, Sue Grimmond, Yiqun Han, Pingqing Fu, and Simone Kotthaus
Atmos. Chem. Phys., 21, 13687–13711, https://doi.org/10.5194/acp-21-13687-2021, https://doi.org/10.5194/acp-21-13687-2021, 2021
Short summary
Short summary
Heat-related illnesses are of increasing concern in China given its rapid urbanisation and our ever-warming climate. We examine the relative impacts that land surface properties and anthropogenic heat have on the urban heat island (UHI) in Beijing using ADMS-Urban. Air temperature measurements and satellite-derived land surface temperatures provide valuable means of evaluating modelled spatiotemporal variations. This work provides critical information for urban planners and UHI mitigation.
Edmund Ryan and Oliver Wild
Geosci. Model Dev., 14, 5373–5391, https://doi.org/10.5194/gmd-14-5373-2021, https://doi.org/10.5194/gmd-14-5373-2021, 2021
Short summary
Short summary
Atmospheric chemistry transport models are important tools to investigate the local, regional and global controls on atmospheric composition and air quality. In this study, we estimate some of the model parameters using machine learning and statistics. Our findings identify the level of error and spatial coverage in the O2 and CO data that are needed to achieve good parameter estimates. We also highlight the benefits of using multiple constraints to calibrate atmospheric chemistry models.
Zhenze Liu, Ruth M. Doherty, Oliver Wild, Michael Hollaway, and Fiona M. O’Connor
Atmos. Chem. Phys., 21, 10689–10706, https://doi.org/10.5194/acp-21-10689-2021, https://doi.org/10.5194/acp-21-10689-2021, 2021
Short summary
Short summary
Surface ozone (O3) has become the main cause of atmospheric pollution in the summertime in China since 2013. We find that 70 % reductions in NOx emissions are required to reduce O3 pollution in most of industrial regions of China, and controls in VOC emissions are very important. The new chemical scheme developed for a global chemistry–climate model not only captures the regional air pollution but also benefits the future studies of regional air-quality–climate interactions.
Carl Thomas, Apostolos Voulgarakis, Gerald Lim, Joanna Haigh, and Peer Nowack
Weather Clim. Dynam., 2, 581–608, https://doi.org/10.5194/wcd-2-581-2021, https://doi.org/10.5194/wcd-2-581-2021, 2021
Short summary
Short summary
Atmospheric blocking events are complex large-scale weather patterns which block the path of the jet stream. They are associated with heat waves in summer and cold snaps in winter. Blocking is poorly understood, and the effect of climate change is not clear. Here, we present a new method to study blocking using unsupervised machine learning. We show that this method performs better than previous methods used. These results show the potential for unsupervised learning in atmospheric science.
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
Short summary
Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Baozhu Ge, Danhui Xu, Oliver Wild, Xuefeng Yao, Junhua Wang, Xueshun Chen, Qixin Tan, Xiaole Pan, and Zifa Wang
Atmos. Chem. Phys., 21, 9441–9454, https://doi.org/10.5194/acp-21-9441-2021, https://doi.org/10.5194/acp-21-9441-2021, 2021
Short summary
Short summary
In this study, an improved sequential sampling method is developed and implemented to estimate the contribution of below-cloud and in-cloud wet deposition over four years of measurements in Beijing. We find that the contribution of below-cloud scavenging for Ca2+, SO4 2–, and NH4+ decreases from above 50 % in 2014 to below 40 % in 2017. This suggests that the Action Plan has mitigated particulate matter pollution in the surface layer and hence decreased scavenging due to the washout process.
Yawei Qu, Apostolos Voulgarakis, Tijian Wang, Matthew Kasoar, Chris Wells, Cheng Yuan, Sunil Varma, and Laura Mansfield
Atmos. Chem. Phys., 21, 5705–5718, https://doi.org/10.5194/acp-21-5705-2021, https://doi.org/10.5194/acp-21-5705-2021, 2021
Short summary
Short summary
The meteorological effect of aerosols on tropospheric ozone is investigated using global atmospheric modelling. We found that aerosol-induced meteorological effects act to reduce modelled ozone concentrations over China, which brings the simulation closer to observed levels. Our work sheds light on understudied processes affecting the levels of tropospheric gaseous pollutants and provides a basis for evaluating such processes using a combination of observations and model sensitivity experiments.
Tabish Umar Ansari, Oliver Wild, Edmund Ryan, Ying Chen, Jie Li, and Zifa Wang
Atmos. Chem. Phys., 21, 4471–4485, https://doi.org/10.5194/acp-21-4471-2021, https://doi.org/10.5194/acp-21-4471-2021, 2021
Short summary
Short summary
We use novel modelling approaches to quantify the lingering effects of 1 d local and regional emission controls on subsequent days, the effects of longer continuous emission controls of individual sectors over different regions, and the effects of combined emission controls of multiple sectors and regions on air quality in Beijing under varying weather conditions to inform precise short-term emission control policies for avoiding heavy haze pollution in Beijing.
Paul T. Griffiths, Lee T. Murray, Guang Zeng, Youngsub Matthew Shin, N. Luke Abraham, Alexander T. Archibald, Makoto Deushi, Louisa K. Emmons, Ian E. Galbally, Birgit Hassler, Larry W. Horowitz, James Keeble, Jane Liu, Omid Moeini, Vaishali Naik, Fiona M. O'Connor, Naga Oshima, David Tarasick, Simone Tilmes, Steven T. Turnock, Oliver Wild, Paul J. Young, and Prodromos Zanis
Atmos. Chem. Phys., 21, 4187–4218, https://doi.org/10.5194/acp-21-4187-2021, https://doi.org/10.5194/acp-21-4187-2021, 2021
Short summary
Short summary
We analyse the CMIP6 Historical and future simulations for tropospheric ozone, a species which is important for many aspects of atmospheric chemistry. We show that the current generation of models agrees well with observations, being particularly successful in capturing trends in surface ozone and its vertical distribution in the troposphere. We analyse the factors that control ozone and show that they evolve over the period of the CMIP6 experiments.
Rutambhara Joshi, Dantong Liu, Eiko Nemitz, Ben Langford, Neil Mullinger, Freya Squires, James Lee, Yunfei Wu, Xiaole Pan, Pingqing Fu, Simone Kotthaus, Sue Grimmond, Qiang Zhang, Ruili Wu, Oliver Wild, Michael Flynn, Hugh Coe, and James Allan
Atmos. Chem. Phys., 21, 147–162, https://doi.org/10.5194/acp-21-147-2021, https://doi.org/10.5194/acp-21-147-2021, 2021
Short summary
Short summary
Black carbon (BC) is a component of particulate matter which has significant effects on climate and human health. Sources of BC include biomass burning, transport, industry and domestic cooking and heating. In this study, we measured BC emissions in Beijing, finding a dominance of traffic emissions over all other sources. The quantitative method presented here has benefits for revising widely used emissions inventories and for understanding BC sources with impacts on air quality and climate.
W. Joe F. Acton, Zhonghui Huang, Brian Davison, Will S. Drysdale, Pingqing Fu, Michael Hollaway, Ben Langford, James Lee, Yanhui Liu, Stefan Metzger, Neil Mullinger, Eiko Nemitz, Claire E. Reeves, Freya A. Squires, Adam R. Vaughan, Xinming Wang, Zhaoyi Wang, Oliver Wild, Qiang Zhang, Yanli Zhang, and C. Nicholas Hewitt
Atmos. Chem. Phys., 20, 15101–15125, https://doi.org/10.5194/acp-20-15101-2020, https://doi.org/10.5194/acp-20-15101-2020, 2020
Short summary
Short summary
Air quality in Beijing is of concern to both policy makers and the general public. In order to address concerns about air quality it is vital that the sources of atmospheric pollutants are understood. This work presents the first top-down measurement of volatile organic compound (VOC) emissions in Beijing. These measurements are used to evaluate the emissions inventory and assess the impact of VOC emission from the city centre on atmospheric chemistry.
Freya A. Squires, Eiko Nemitz, Ben Langford, Oliver Wild, Will S. Drysdale, W. Joe F. Acton, Pingqing Fu, C. Sue B. Grimmond, Jacqueline F. Hamilton, C. Nicholas Hewitt, Michael Hollaway, Simone Kotthaus, James Lee, Stefan Metzger, Natchaya Pingintha-Durden, Marvin Shaw, Adam R. Vaughan, Xinming Wang, Ruili Wu, Qiang Zhang, and Yanli Zhang
Atmos. Chem. Phys., 20, 8737–8761, https://doi.org/10.5194/acp-20-8737-2020, https://doi.org/10.5194/acp-20-8737-2020, 2020
Short summary
Short summary
Significant air quality problems exist in megacities like Beijing, China. To manage air pollution, legislators need a clear understanding of pollutant emissions. However, emissions inventories have large uncertainties, and reliable field measurements of pollutant emissions are required to constrain them. This work presents the first measurements of traffic-dominated emissions in Beijing which suggest that inventories overestimate these emissions in the region during both winter and summer.
Stijn Hantson, Douglas I. Kelley, Almut Arneth, Sandy P. Harrison, Sally Archibald, Dominique Bachelet, Matthew Forrest, Thomas Hickler, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Lars Nieradzik, Sam S. Rabin, I. Colin Prentice, Tim Sheehan, Stephen Sitch, Lina Teckentrup, Apostolos Voulgarakis, and Chao Yue
Geosci. Model Dev., 13, 3299–3318, https://doi.org/10.5194/gmd-13-3299-2020, https://doi.org/10.5194/gmd-13-3299-2020, 2020
Short summary
Short summary
Global fire–vegetation models are widely used, but there has been limited evaluation of how well they represent various aspects of fire regimes. Here we perform a systematic evaluation of simulations made by nine FireMIP models in order to quantify their ability to reproduce a range of fire and vegetation benchmarks. While some FireMIP models are better at representing certain aspects of the fire regime, no model clearly outperforms all other models across the full range of variables assessed.
Tao Tang, Drew Shindell, Yuqiang Zhang, Apostolos Voulgarakis, Jean-Francois Lamarque, Gunnar Myhre, Camilla W. Stjern, Gregory Faluvegi, and Bjørn H. Samset
Atmos. Chem. Phys., 20, 8251–8266, https://doi.org/10.5194/acp-20-8251-2020, https://doi.org/10.5194/acp-20-8251-2020, 2020
Short summary
Short summary
By using climate simulations, we found that both CO2 and black carbon aerosols could reduce low-level cloud cover, which is mainly due to changes in relative humidity, cloud water, dynamics, and stability. Because the impact of cloud on solar radiation is in effect only during daytime, such cloud reduction could enhance solar heating, thereby raising the daily maximum temperature by 10–50 %, varying by region, which has great implications for extreme climate events and socioeconomic activity.
Nicolas Bellouin, Will Davies, Keith P. Shine, Johannes Quaas, Johannes Mülmenstädt, Piers M. Forster, Chris Smith, Lindsay Lee, Leighton Regayre, Guy Brasseur, Natalia Sudarchikova, Idir Bouarar, Olivier Boucher, and Gunnar Myhre
Earth Syst. Sci. Data, 12, 1649–1677, https://doi.org/10.5194/essd-12-1649-2020, https://doi.org/10.5194/essd-12-1649-2020, 2020
Short summary
Short summary
Quantifying the imbalance in the Earth's energy budget caused by human activities is important to understand and predict climate changes. This study presents new estimates of the imbalance caused by changes in atmospheric concentrations of carbon dioxide, methane, ozone, and particles of pollution. Over the period 2003–2017, the overall imbalance has been positive, indicating that the climate system has gained energy and will warm further.
Marielle Saunois, Ann R. Stavert, Ben Poulter, Philippe Bousquet, Josep G. Canadell, Robert B. Jackson, Peter A. Raymond, Edward J. Dlugokencky, Sander Houweling, Prabir K. Patra, Philippe Ciais, Vivek K. Arora, David Bastviken, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Kimberly M. Carlson, Mark Carrol, Simona Castaldi, Naveen Chandra, Cyril Crevoisier, Patrick M. Crill, Kristofer Covey, Charles L. Curry, Giuseppe Etiope, Christian Frankenberg, Nicola Gedney, Michaela I. Hegglin, Lena Höglund-Isaksson, Gustaf Hugelius, Misa Ishizawa, Akihiko Ito, Greet Janssens-Maenhout, Katherine M. Jensen, Fortunat Joos, Thomas Kleinen, Paul B. Krummel, Ray L. Langenfelds, Goulven G. Laruelle, Licheng Liu, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Joe McNorton, Paul A. Miller, Joe R. Melton, Isamu Morino, Jurek Müller, Fabiola Murguia-Flores, Vaishali Naik, Yosuke Niwa, Sergio Noce, Simon O'Doherty, Robert J. Parker, Changhui Peng, Shushi Peng, Glen P. Peters, Catherine Prigent, Ronald Prinn, Michel Ramonet, Pierre Regnier, William J. Riley, Judith A. Rosentreter, Arjo Segers, Isobel J. Simpson, Hao Shi, Steven J. Smith, L. Paul Steele, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Francesco N. Tubiello, Aki Tsuruta, Nicolas Viovy, Apostolos Voulgarakis, Thomas S. Weber, Michiel van Weele, Guido R. van der Werf, Ray F. Weiss, Doug Worthy, Debra Wunch, Yi Yin, Yukio Yoshida, Wenxin Zhang, Zhen Zhang, Yuanhong Zhao, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 12, 1561–1623, https://doi.org/10.5194/essd-12-1561-2020, https://doi.org/10.5194/essd-12-1561-2020, 2020
Short summary
Short summary
Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. We have established a consortium of multidisciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate new research aimed at improving and regularly updating the global methane budget. This is the second version of the review dedicated to the decadal methane budget, integrating results of top-down and bottom-up estimates.
Oliver Wild, Apostolos Voulgarakis, Fiona O'Connor, Jean-François Lamarque, Edmund M. Ryan, and Lindsay Lee
Atmos. Chem. Phys., 20, 4047–4058, https://doi.org/10.5194/acp-20-4047-2020, https://doi.org/10.5194/acp-20-4047-2020, 2020
Short summary
Short summary
Global models of tropospheric chemistry and transport show a persistent diversity in results that has not been fully explained. We demonstrate the first use of global sensitivity analysis consistently across three independent models to explore these differences and reveal both clear similarities and surprising differences which have important implications for our assessment of future atmospheric composition change.
Alexander T. Archibald, Fiona M. O'Connor, Nathan Luke Abraham, Scott Archer-Nicholls, Martyn P. Chipperfield, Mohit Dalvi, Gerd A. Folberth, Fraser Dennison, Sandip S. Dhomse, Paul T. Griffiths, Catherine Hardacre, Alan J. Hewitt, Richard S. Hill, Colin E. Johnson, James Keeble, Marcus O. Köhler, Olaf Morgenstern, Jane P. Mulcahy, Carlos Ordóñez, Richard J. Pope, Steven T. Rumbold, Maria R. Russo, Nicholas H. Savage, Alistair Sellar, Marc Stringer, Steven T. Turnock, Oliver Wild, and Guang Zeng
Geosci. Model Dev., 13, 1223–1266, https://doi.org/10.5194/gmd-13-1223-2020, https://doi.org/10.5194/gmd-13-1223-2020, 2020
Short summary
Short summary
Here we present a description and evaluation of the UKCA stratosphere–troposphere chemistry scheme (StratTrop vn 1.0) implemented in the UK Earth System Model (UKESM1). UKCA StratTrop represents a substantial step forward compared to previous versions of UKCA. We show here that it is fully suited to the challenges of representing interactions in a coupled Earth system model and identify key areas and components for future development that will make it even better in the future.
Marios Panagi, Zoë L. Fleming, Paul S. Monks, Matthew J. Ashfold, Oliver Wild, Michael Hollaway, Qiang Zhang, Freya A. Squires, and Joshua D. Vande Hey
Atmos. Chem. Phys., 20, 2825–2838, https://doi.org/10.5194/acp-20-2825-2020, https://doi.org/10.5194/acp-20-2825-2020, 2020
Short summary
Short summary
In this paper, using dispersion modelling with emission inventories it was determined that on average 45 % of the total CO pollution that affects Beijing is transported from other areas. About half of the CO comes from beyond the immediate surrounding areas. Finally three classification types of pollution were identified and used to analyse the APHH winter campaign. The results can inform targeted control measures to be implemented in Beijing and the other regions to tackle air quality problems.
Michael Biggart, Jenny Stocker, Ruth M. Doherty, Oliver Wild, Michael Hollaway, David Carruthers, Jie Li, Qiang Zhang, Ruili Wu, Simone Kotthaus, Sue Grimmond, Freya A. Squires, James Lee, and Zongbo Shi
Atmos. Chem. Phys., 20, 2755–2780, https://doi.org/10.5194/acp-20-2755-2020, https://doi.org/10.5194/acp-20-2755-2020, 2020
Short summary
Short summary
Ambient air pollution is a major cause of premature death in China. We examine the street-scale variation of pollutant levels in Beijing using air pollution dispersion and chemistry model ADMS-Urban. Campaign measurements are compared with simulated pollutant levels, providing a valuable means of evaluating the impact of key processes on urban air quality. Air quality modelling at such fine scales is essential for human exposure studies and for informing choices on future emission controls.
Ying Chen, Oliver Wild, Edmund Ryan, Saroj Kumar Sahu, Douglas Lowe, Scott Archer-Nicholls, Yu Wang, Gordon McFiggans, Tabish Ansari, Vikas Singh, Ranjeet S. Sokhi, Alex Archibald, and Gufran Beig
Atmos. Chem. Phys., 20, 499–514, https://doi.org/10.5194/acp-20-499-2020, https://doi.org/10.5194/acp-20-499-2020, 2020
Short summary
Short summary
PM2.5 and O3 are two major air pollutants. Some mitigation strategies focusing on reducing PM2.5 may lead to substantial increase in O3. We use statistical emulation combined with atmospheric transport model to perform thousands of sensitivity numerical studies to identify the major sources of PM2.5 and O3 and to develop strategies targeted at both pollutants. Our scientific evidence suggests that regional coordinated emission control is required to mitigate PM2.5 whilst preventing O3 increase.
Øivind Hodnebrog, Gunnar Myhre, Bjørn H. Samset, Kari Alterskjær, Timothy Andrews, Olivier Boucher, Gregory Faluvegi, Dagmar Fläschner, Piers M. Forster, Matthew Kasoar, Alf Kirkevåg, Jean-Francois Lamarque, Dirk Olivié, Thomas B. Richardson, Dilshad Shawki, Drew Shindell, Keith P. Shine, Philip Stier, Toshihiko Takemura, Apostolos Voulgarakis, and Duncan Watson-Parris
Atmos. Chem. Phys., 19, 12887–12899, https://doi.org/10.5194/acp-19-12887-2019, https://doi.org/10.5194/acp-19-12887-2019, 2019
Short summary
Short summary
Different greenhouse gases (e.g. CO2) and aerosols (e.g. black carbon) impact the Earth’s water cycle differently. Here we investigate how various gases and particles impact atmospheric water vapour and its lifetime, i.e., the average number of days that water vapour stays in the atmosphere after evaporation and before precipitation. We find that this lifetime could increase substantially by the end of this century, indicating that important changes in precipitation patterns are excepted.
Michael Hollaway, Oliver Wild, Ting Yang, Yele Sun, Weiqi Xu, Conghui Xie, Lisa Whalley, Eloise Slater, Dwayne Heard, and Dantong Liu
Atmos. Chem. Phys., 19, 9699–9714, https://doi.org/10.5194/acp-19-9699-2019, https://doi.org/10.5194/acp-19-9699-2019, 2019
Short summary
Short summary
This study, for the first time, uses combinations of aerosol and lidar data to drive an offline photolysis scheme. Absorbing species are shown to have the greatest impact on photolysis rate constants in the winter and scattering aerosol are shown to dominate responses in the summer. During haze episodes, aerosols are shown to produce a greater impact than cloud cover. The findings demonstrate the potential photochemical impacts of haze pollution in a highly polluted urban environment.
Tabish Umar Ansari, Oliver Wild, Jie Li, Ting Yang, Weiqi Xu, Yele Sun, and Zifa Wang
Atmos. Chem. Phys., 19, 8651–8668, https://doi.org/10.5194/acp-19-8651-2019, https://doi.org/10.5194/acp-19-8651-2019, 2019
Short summary
Short summary
We explore the effectiveness of short-term emission controls on haze events in Beijing in October–November 2014 with high-resolution model studies. The model captures observed hourly variation in key pollutants well, but representation of boundary layer processes remains a key constraint. The controls contributed to improved air quality in early November but would not have been sufficient had the meteorology been less favourable. We quantify the much more stringent controls needed in that case.
Zongbo Shi, Tuan Vu, Simone Kotthaus, Roy M. Harrison, Sue Grimmond, Siyao Yue, Tong Zhu, James Lee, Yiqun Han, Matthias Demuzere, Rachel E. Dunmore, Lujie Ren, Di Liu, Yuanlin Wang, Oliver Wild, James Allan, W. Joe Acton, Janet Barlow, Benjamin Barratt, David Beddows, William J. Bloss, Giulia Calzolai, David Carruthers, David C. Carslaw, Queenie Chan, Lia Chatzidiakou, Yang Chen, Leigh Crilley, Hugh Coe, Tie Dai, Ruth Doherty, Fengkui Duan, Pingqing Fu, Baozhu Ge, Maofa Ge, Daobo Guan, Jacqueline F. Hamilton, Kebin He, Mathew Heal, Dwayne Heard, C. Nicholas Hewitt, Michael Hollaway, Min Hu, Dongsheng Ji, Xujiang Jiang, Rod Jones, Markus Kalberer, Frank J. Kelly, Louisa Kramer, Ben Langford, Chun Lin, Alastair C. Lewis, Jie Li, Weijun Li, Huan Liu, Junfeng Liu, Miranda Loh, Keding Lu, Franco Lucarelli, Graham Mann, Gordon McFiggans, Mark R. Miller, Graham Mills, Paul Monk, Eiko Nemitz, Fionna O'Connor, Bin Ouyang, Paul I. Palmer, Carl Percival, Olalekan Popoola, Claire Reeves, Andrew R. Rickard, Longyi Shao, Guangyu Shi, Dominick Spracklen, David Stevenson, Yele Sun, Zhiwei Sun, Shu Tao, Shengrui Tong, Qingqing Wang, Wenhua Wang, Xinming Wang, Xuejun Wang, Zifang Wang, Lianfang Wei, Lisa Whalley, Xuefang Wu, Zhijun Wu, Pinhua Xie, Fumo Yang, Qiang Zhang, Yanli Zhang, Yuanhang Zhang, and Mei Zheng
Atmos. Chem. Phys., 19, 7519–7546, https://doi.org/10.5194/acp-19-7519-2019, https://doi.org/10.5194/acp-19-7519-2019, 2019
Short summary
Short summary
APHH-Beijing is a collaborative international research programme to study the sources, processes and health effects of air pollution in Beijing. This introduction to the special issue provides an overview of (i) the APHH-Beijing programme, (ii) the measurement and modelling activities performed as part of it and (iii) the air quality and meteorological conditions during joint intensive field campaigns as a core activity within APHH-Beijing.
Arlene M. Fiore, Emily V. Fischer, George P. Milly, Shubha Pandey Deolal, Oliver Wild, Daniel A. Jaffe, Johannes Staehelin, Olivia E. Clifton, Dan Bergmann, William Collins, Frank Dentener, Ruth M. Doherty, Bryan N. Duncan, Bernd Fischer, Stefan Gilge, Peter G. Hess, Larry W. Horowitz, Alexandru Lupu, Ian A. MacKenzie, Rokjin Park, Ludwig Ries, Michael G. Sanderson, Martin G. Schultz, Drew T. Shindell, Martin Steinbacher, David S. Stevenson, Sophie Szopa, Christoph Zellweger, and Guang Zeng
Atmos. Chem. Phys., 18, 15345–15361, https://doi.org/10.5194/acp-18-15345-2018, https://doi.org/10.5194/acp-18-15345-2018, 2018
Short summary
Short summary
We demonstrate a proof-of-concept approach for applying northern midlatitude mountaintop peroxy acetyl nitrate (PAN) measurements and a multi-model ensemble during April to constrain the influence of continental-scale anthropogenic precursor emissions on PAN. Our findings imply a role for carefully coordinated multi-model ensembles in helping identify observations for discriminating among widely varying (and poorly constrained) model responses of atmospheric constituents to changes in emissions.
Jill S. Johnson, Leighton A. Regayre, Masaru Yoshioka, Kirsty J. Pringle, Lindsay A. Lee, David M. H. Sexton, John W. Rostron, Ben B. B. Booth, and Kenneth S. Carslaw
Atmos. Chem. Phys., 18, 13031–13053, https://doi.org/10.5194/acp-18-13031-2018, https://doi.org/10.5194/acp-18-13031-2018, 2018
Short summary
Short summary
We estimate the uncertainty in an aerosol–climate model that has been tuned to match several common types of observations. We used a large set of model simulations and built emulators so that we could generate 4 million “variants” of our climate model. Even after using nine aerosol and cloud observations to constrain the model, the uncertainty remains large. We conclude that estimates of aerosol forcing from multi-model studies are likely to be more uncertain than currently estimated.
Leighton A. Regayre, Jill S. Johnson, Masaru Yoshioka, Kirsty J. Pringle, David M. H. Sexton, Ben B. B. Booth, Lindsay A. Lee, Nicolas Bellouin, and Kenneth S. Carslaw
Atmos. Chem. Phys., 18, 9975–10006, https://doi.org/10.5194/acp-18-9975-2018, https://doi.org/10.5194/acp-18-9975-2018, 2018
Short summary
Short summary
We sample uncertainty in one climate model by perturbing aerosol and physical atmosphere parameters. Our uncertainty is comparable to multi-model studies. Atmospheric parameters cause most of the top-of-atmosphere flux uncertainty; uncertainty in aerosol forcing is mostly caused by aerosols: both are important. The strongest aerosol forcings are inconsistent with top-of-atmosphere flux observations. Better constraint requires observations that share causes of uncertainty with aerosol forcing.
Claudia Timmreck, Graham W. Mann, Valentina Aquila, Rene Hommel, Lindsay A. Lee, Anja Schmidt, Christoph Brühl, Simon Carn, Mian Chin, Sandip S. Dhomse, Thomas Diehl, Jason M. English, Michael J. Mills, Ryan Neely, Jianxiong Sheng, Matthew Toohey, and Debra Weisenstein
Geosci. Model Dev., 11, 2581–2608, https://doi.org/10.5194/gmd-11-2581-2018, https://doi.org/10.5194/gmd-11-2581-2018, 2018
Short summary
Short summary
The paper describes the experimental design of the Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP). ISA-MIP will improve understanding of stratospheric aerosol processes, chemistry, and dynamics and constrain climate impacts of background aerosol variability and small and large volcanic eruptions. It will help to asses the stratospheric aerosol contribution to the early 21st century global warming hiatus period and the effects from hypothetical geoengineering schemes.
Steven T. Turnock, Oliver Wild, Frank J. Dentener, Yanko Davila, Louisa K. Emmons, Johannes Flemming, Gerd A. Folberth, Daven K. Henze, Jan E. Jonson, Terry J. Keating, Sudo Kengo, Meiyun Lin, Marianne Lund, Simone Tilmes, and Fiona M. O'Connor
Atmos. Chem. Phys., 18, 8953–8978, https://doi.org/10.5194/acp-18-8953-2018, https://doi.org/10.5194/acp-18-8953-2018, 2018
Short summary
Short summary
A simple parameterisation was developed in this study to provide a rapid assessment of the impacts and uncertainties associated with future emission control strategies by predicting changes to surface ozone air quality and near-term climate forcing of ozone. Future emissions scenarios based on currently implemented legislation are shown to worsen surface ozone air quality and enhance near-term climate warming, with changes in methane becoming increasingly important in the future.
Tao Tang, Drew Shindell, Bjørn H. Samset, Oliviér Boucher, Piers M. Forster, Øivind Hodnebrog, Gunnar Myhre, Jana Sillmann, Apostolos Voulgarakis, Timothy Andrews, Gregory Faluvegi, Dagmar Fläschner, Trond Iversen, Matthew Kasoar, Viatcheslav Kharin, Alf Kirkevåg, Jean-Francois Lamarque, Dirk Olivié, Thomas Richardson, Camilla W. Stjern, and Toshihiko Takemura
Atmos. Chem. Phys., 18, 8439–8452, https://doi.org/10.5194/acp-18-8439-2018, https://doi.org/10.5194/acp-18-8439-2018, 2018
Edmund M. Ryan, Kiona Ogle, Heather Kropp, Kimberly E. Samuels-Crow, Yolima Carrillo, and Elise Pendall
Geosci. Model Dev., 11, 1909–1928, https://doi.org/10.5194/gmd-11-1909-2018, https://doi.org/10.5194/gmd-11-1909-2018, 2018
Short summary
Short summary
Our work evaluated the appropriateness of the common steady-state (SS) assumption, for example when partitioning soil respiration of CO2 into recently photosynthesized carbon (C) and older C. Using a new model of soil CO2 production and transport we found that the SS assumption is valid most of the time, especially in sand/silt soils. Non-SS conditions occurred mainly for the few days following large rain events in all soil types, but the non-SS period was prolonged and magnified in clay soils.
Fernando Iglesias-Suarez, Douglas E. Kinnison, Alexandru Rap, Amanda C. Maycock, Oliver Wild, and Paul J. Young
Atmos. Chem. Phys., 18, 6121–6139, https://doi.org/10.5194/acp-18-6121-2018, https://doi.org/10.5194/acp-18-6121-2018, 2018
Short summary
Short summary
This study explores future ozone radiative forcing (RF) and the relative contribution due to different drivers. Climate-induced ozone RF is largely the result of the interplay between lightning-produced ozone and enhanced ozone destruction in a warmer and wetter atmosphere. These results demonstrate the importance of stratospheric–tropospheric interactions and the stratosphere as a key region controlling a large fraction of the tropospheric ozone RF.
Ruth M. Doherty, Clara Orbe, Guang Zeng, David A. Plummer, Michael J. Prather, Oliver Wild, Meiyun Lin, Drew T. Shindell, and Ian A. Mackenzie
Atmos. Chem. Phys., 17, 14219–14237, https://doi.org/10.5194/acp-17-14219-2017, https://doi.org/10.5194/acp-17-14219-2017, 2017
Short summary
Short summary
We investigate how climate change impacts global air pollution transport. To study transport changes, we use a carbon monoxide (CO) tracer species emitted from global sources. We find robust and consistent changes in CO-tracer distributions in climate change simulations performed by four chemistry–climate models in different seasons. We highlight the importance of the co-location of emission source regions and controlling transport processes in determining future pollution transport.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Ray Weiss, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Atmos. Chem. Phys., 17, 11135–11161, https://doi.org/10.5194/acp-17-11135-2017, https://doi.org/10.5194/acp-17-11135-2017, 2017
Short summary
Short summary
Following the Global Methane Budget 2000–2012 published in Saunois et al. (2016), we use the same dataset of bottom-up and top-down approaches to discuss the variations in methane emissions over the period 2000–2012. The changes in emissions are discussed both in terms of trends and quasi-decadal changes. The ensemble gathered here allows us to synthesise the robust changes in terms of regional and sectorial contributions to the increasing methane emissions.
Sam S. Rabin, Joe R. Melton, Gitta Lasslop, Dominique Bachelet, Matthew Forrest, Stijn Hantson, Jed O. Kaplan, Fang Li, Stéphane Mangeon, Daniel S. Ward, Chao Yue, Vivek K. Arora, Thomas Hickler, Silvia Kloster, Wolfgang Knorr, Lars Nieradzik, Allan Spessa, Gerd A. Folberth, Tim Sheehan, Apostolos Voulgarakis, Douglas I. Kelley, I. Colin Prentice, Stephen Sitch, Sandy Harrison, and Almut Arneth
Geosci. Model Dev., 10, 1175–1197, https://doi.org/10.5194/gmd-10-1175-2017, https://doi.org/10.5194/gmd-10-1175-2017, 2017
Short summary
Short summary
Global vegetation models are important tools for understanding how the Earth system will change in the future, and fire is a critical process to include. A number of different methods have been developed to represent vegetation burning. This paper describes the protocol for the first systematic comparison of global fire models, which will allow the community to explore various drivers and evaluate what mechanisms are important for improving performance. It also includes equations for all models.
Alba Badia, Oriol Jorba, Apostolos Voulgarakis, Donald Dabdub, Carlos Pérez García-Pando, Andreas Hilboll, María Gonçalves, and Zavisa Janjic
Geosci. Model Dev., 10, 609–638, https://doi.org/10.5194/gmd-10-609-2017, https://doi.org/10.5194/gmd-10-609-2017, 2017
Short summary
Short summary
This paper presents a comprehensive description and benchmark evaluation of the tropospheric gas-phase chemistry component of the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (NMMB-MONARCH), an online chemical weather prediction system conceived for both the regional and global scales. We provide an extensive evaluation of a global annual cycle simulation using a variety of background surface stations, ozonesondes, aircraft data and satellite observations.
Marielle Saunois, Philippe Bousquet, Ben Poulter, Anna Peregon, Philippe Ciais, Josep G. Canadell, Edward J. Dlugokencky, Giuseppe Etiope, David Bastviken, Sander Houweling, Greet Janssens-Maenhout, Francesco N. Tubiello, Simona Castaldi, Robert B. Jackson, Mihai Alexe, Vivek K. Arora, David J. Beerling, Peter Bergamaschi, Donald R. Blake, Gordon Brailsford, Victor Brovkin, Lori Bruhwiler, Cyril Crevoisier, Patrick Crill, Kristofer Covey, Charles Curry, Christian Frankenberg, Nicola Gedney, Lena Höglund-Isaksson, Misa Ishizawa, Akihiko Ito, Fortunat Joos, Heon-Sook Kim, Thomas Kleinen, Paul Krummel, Jean-François Lamarque, Ray Langenfelds, Robin Locatelli, Toshinobu Machida, Shamil Maksyutov, Kyle C. McDonald, Julia Marshall, Joe R. Melton, Isamu Morino, Vaishali Naik, Simon O'Doherty, Frans-Jan W. Parmentier, Prabir K. Patra, Changhui Peng, Shushi Peng, Glen P. Peters, Isabelle Pison, Catherine Prigent, Ronald Prinn, Michel Ramonet, William J. Riley, Makoto Saito, Monia Santini, Ronny Schroeder, Isobel J. Simpson, Renato Spahni, Paul Steele, Atsushi Takizawa, Brett F. Thornton, Hanqin Tian, Yasunori Tohjima, Nicolas Viovy, Apostolos Voulgarakis, Michiel van Weele, Guido R. van der Werf, Ray Weiss, Christine Wiedinmyer, David J. Wilton, Andy Wiltshire, Doug Worthy, Debra Wunch, Xiyan Xu, Yukio Yoshida, Bowen Zhang, Zhen Zhang, and Qiuan Zhu
Earth Syst. Sci. Data, 8, 697–751, https://doi.org/10.5194/essd-8-697-2016, https://doi.org/10.5194/essd-8-697-2016, 2016
Short summary
Short summary
An accurate assessment of the methane budget is important to understand the atmospheric methane concentrations and trends and to provide realistic pathways for climate change mitigation. The various and diffuse sources of methane as well and its oxidation by a very short lifetime radical challenge this assessment. We quantify the methane sources and sinks as well as their uncertainties based on both bottom-up and top-down approaches provided by a broad international scientific community.
Stéphane Mangeon, Apostolos Voulgarakis, Richard Gilham, Anna Harper, Stephen Sitch, and Gerd Folberth
Geosci. Model Dev., 9, 2685–2700, https://doi.org/10.5194/gmd-9-2685-2016, https://doi.org/10.5194/gmd-9-2685-2016, 2016
Short summary
Short summary
To understand the role of fires in the Earth system, global fire models are required. In this paper we describe the INteractive Fire and Emission algoRithm for Natural envirOnments (INFERNO). It follows a reduced complexity approach using mainly temperature, humidity and precipitation. INFERNO was found to perform well on a global scale and to maintain regional patterns over the 1997–2011 period of study, despite regional biases particularly linked to fuel consumption.
Matthew Kasoar, Apostolos Voulgarakis, Jean-François Lamarque, Drew T. Shindell, Nicolas Bellouin, William J. Collins, Greg Faluvegi, and Kostas Tsigaridis
Atmos. Chem. Phys., 16, 9785–9804, https://doi.org/10.5194/acp-16-9785-2016, https://doi.org/10.5194/acp-16-9785-2016, 2016
Short summary
Short summary
Computer models are our primary tool to investigate how fossil-fuel emissions are affecting the climate. Here, we used three different climate models to see how they simulate the response to removing sulfur dioxide emissions from China. We found that the models disagreed substantially on how large the climate effect is from the emissions in this region. This range of outcomes is concerning if scientists or policy makers have to rely on any one model when performing their own studies.
D. L. Finney, R. M. Doherty, O. Wild, and N. L. Abraham
Atmos. Chem. Phys., 16, 7507–7522, https://doi.org/10.5194/acp-16-7507-2016, https://doi.org/10.5194/acp-16-7507-2016, 2016
Short summary
Short summary
Lightning is a source of nitric oxide (NO) and, through chemical reactions of NO, impacts ozone production. A new method for modelling global lightning markedly alters ozone concentration in the upper troposphere and frequency characteristics of ozone production compared to earlier treatments. Simulated lightning and ozone concentrations now better match observations. Reducing uncertainties associated with lightning NO is important for understanding atmospheric composition and radiative forcing.
Stijn Hantson, Almut Arneth, Sandy P. Harrison, Douglas I. Kelley, I. Colin Prentice, Sam S. Rabin, Sally Archibald, Florent Mouillot, Steve R. Arnold, Paulo Artaxo, Dominique Bachelet, Philippe Ciais, Matthew Forrest, Pierre Friedlingstein, Thomas Hickler, Jed O. Kaplan, Silvia Kloster, Wolfgang Knorr, Gitta Lasslop, Fang Li, Stephane Mangeon, Joe R. Melton, Andrea Meyn, Stephen Sitch, Allan Spessa, Guido R. van der Werf, Apostolos Voulgarakis, and Chao Yue
Biogeosciences, 13, 3359–3375, https://doi.org/10.5194/bg-13-3359-2016, https://doi.org/10.5194/bg-13-3359-2016, 2016
Short summary
Short summary
Our ability to predict the magnitude and geographic pattern of past and future fire impacts rests on our ability to model fire regimes. A large variety of models exist, and it is unclear which type of model or degree of complexity is required to model fire adequately at regional to global scales. In this paper we summarize the current state of the art in fire-regime modelling and model evaluation, and outline what lessons may be learned from the Fire Model Intercomparison Project – FireMIP.
F. Iglesias-Suarez, P. J. Young, and O. Wild
Atmos. Chem. Phys., 16, 343–363, https://doi.org/10.5194/acp-16-343-2016, https://doi.org/10.5194/acp-16-343-2016, 2016
H. S. Chen, Z. F. Wang, J. Li, X. Tang, B. Z. Ge, X. L. Wu, O. Wild, and G. R. Carmichael
Geosci. Model Dev., 8, 2857–2876, https://doi.org/10.5194/gmd-8-2857-2015, https://doi.org/10.5194/gmd-8-2857-2015, 2015
Short summary
Short summary
A new global nested atmospheric mercury transport model was developed and introduced. Model performance was found significantly better in North America and Europe than in East Asia. Nested simulation has been conducted in East Asia and shows improved skill at capturing the high spatial variability of Hg concentrations and deposition. The trans-boundary transport of Chinese primary anthropogenic mercury emissions was quantified for the first time.
P. S. Monks, A. T. Archibald, A. Colette, O. Cooper, M. Coyle, R. Derwent, D. Fowler, C. Granier, K. S. Law, G. E. Mills, D. S. Stevenson, O. Tarasova, V. Thouret, E. von Schneidemesser, R. Sommariva, O. Wild, and M. L. Williams
Atmos. Chem. Phys., 15, 8889–8973, https://doi.org/10.5194/acp-15-8889-2015, https://doi.org/10.5194/acp-15-8889-2015, 2015
Short summary
Short summary
Ozone holds a certain fascination in atmospheric science. It is ubiquitous in the atmosphere, central to tropospheric oxidation chemistry, and yet harmful to human and ecosystem health as well as being an important greenhouse gas. It is not emitted into the atmosphere but is a byproduct of the very oxidation chemistry it largely initiates. This review examines current understanding of the processes regulating tropospheric ozone at global to local scales from both measurements and models.
C. Hardacre, O. Wild, and L. Emberson
Atmos. Chem. Phys., 15, 6419–6436, https://doi.org/10.5194/acp-15-6419-2015, https://doi.org/10.5194/acp-15-6419-2015, 2015
Short summary
Short summary
The dry deposition of ozone to the Earth's surface is an important process as it controls both the removal of this potent pollutant from the atmosphere and its uptake by vegetation. It is necessary to use numerical models to study this process at the global scale, but many models to represent dry deposition lag behind current understanding. In this paper we study the dry deposition process in global models and highlight measures that will allow these models to be critically evaluated.
R. J. Pope, M. P. Chipperfield, N. H. Savage, C. Ordóñez, L. S. Neal, L. A. Lee, S. S. Dhomse, N. A. D. Richards, and T. D. Keslake
Atmos. Chem. Phys., 15, 5611–5626, https://doi.org/10.5194/acp-15-5611-2015, https://doi.org/10.5194/acp-15-5611-2015, 2015
D. L. Finney, R. M. Doherty, O. Wild, H. Huntrieser, H. C. Pumphrey, and A. M. Blyth
Atmos. Chem. Phys., 14, 12665–12682, https://doi.org/10.5194/acp-14-12665-2014, https://doi.org/10.5194/acp-14-12665-2014, 2014
Short summary
Short summary
Lightning is important in atmospheric chemistry models as a source of
nitrogen oxides which affect the greenhouse gases ozone and methane. We
present a new approach to modelling lightning using the upward movement of
ice in clouds, an essential part of the charging mechanism in thunderstorms.
The new approach performs well compared to those already in use and provides
a novel, physically based scheme that has the potential to improve the
robustness of simulated flash rates and emissions.
G. W. Mann, K. S. Carslaw, C. L. Reddington, K. J. Pringle, M. Schulz, A. Asmi, D. V. Spracklen, D. A. Ridley, M. T. Woodhouse, L. A. Lee, K. Zhang, S. J. Ghan, R. C. Easter, X. Liu, P. Stier, Y. H. Lee, P. J. Adams, H. Tost, J. Lelieveld, S. E. Bauer, K. Tsigaridis, T. P. C. van Noije, A. Strunk, E. Vignati, N. Bellouin, M. Dalvi, C. E. Johnson, T. Bergman, H. Kokkola, K. von Salzen, F. Yu, G. Luo, A. Petzold, J. Heintzenberg, A. Clarke, J. A. Ogren, J. Gras, U. Baltensperger, U. Kaminski, S. G. Jennings, C. D. O'Dowd, R. M. Harrison, D. C. S. Beddows, M. Kulmala, Y. Viisanen, V. Ulevicius, N. Mihalopoulos, V. Zdimal, M. Fiebig, H.-C. Hansson, E. Swietlicki, and J. S. Henzing
Atmos. Chem. Phys., 14, 4679–4713, https://doi.org/10.5194/acp-14-4679-2014, https://doi.org/10.5194/acp-14-4679-2014, 2014
L. A. Lee, K. J. Pringle, C. L. Reddington, G. W. Mann, P. Stier, D. V. Spracklen, J. R. Pierce, and K. S. Carslaw
Atmos. Chem. Phys., 13, 8879–8914, https://doi.org/10.5194/acp-13-8879-2013, https://doi.org/10.5194/acp-13-8879-2013, 2013
V. Naik, A. Voulgarakis, A. M. Fiore, L. W. Horowitz, J.-F. Lamarque, M. Lin, M. J. Prather, P. J. Young, D. Bergmann, P. J. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, B. Josse, Y. H. Lee, I. A. MacKenzie, T. Nagashima, T. P. C. van Noije, D. A. Plummer, M. Righi, S. T. Rumbold, R. Skeie, D. T. Shindell, D. S. Stevenson, S. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 5277–5298, https://doi.org/10.5194/acp-13-5277-2013, https://doi.org/10.5194/acp-13-5277-2013, 2013
K. W. Bowman, D. T. Shindell, H. M. Worden, J.F. Lamarque, P. J. Young, D. S. Stevenson, Z. Qu, M. de la Torre, D. Bergmann, P. J. Cameron-Smith, W. J. Collins, R. Doherty, S. B. Dalsøren, G. Faluvegi, G. Folberth, L. W. Horowitz, B. M. Josse, Y. H. Lee, I. A. MacKenzie, G. Myhre, T. Nagashima, V. Naik, D. A. Plummer, S. T. Rumbold, R. B. Skeie, S. A. Strode, K. Sudo, S. Szopa, A. Voulgarakis, G. Zeng, S. S. Kulawik, A. M. Aghedo, and J. R. Worden
Atmos. Chem. Phys., 13, 4057–4072, https://doi.org/10.5194/acp-13-4057-2013, https://doi.org/10.5194/acp-13-4057-2013, 2013
D. S. Stevenson, P. J. Young, V. Naik, J.-F. Lamarque, D. T. Shindell, A. Voulgarakis, R. B. Skeie, S. B. Dalsoren, G. Myhre, T. K. Berntsen, G. A. Folberth, S. T. Rumbold, W. J. Collins, I. A. MacKenzie, R. M. Doherty, G. Zeng, T. P. C. van Noije, A. Strunk, D. Bergmann, P. Cameron-Smith, D. A. Plummer, S. A. Strode, L. Horowitz, Y. H. Lee, S. Szopa, K. Sudo, T. Nagashima, B. Josse, I. Cionni, M. Righi, V. Eyring, A. Conley, K. W. Bowman, O. Wild, and A. Archibald
Atmos. Chem. Phys., 13, 3063–3085, https://doi.org/10.5194/acp-13-3063-2013, https://doi.org/10.5194/acp-13-3063-2013, 2013
A. Voulgarakis, V. Naik, J.-F. Lamarque, D. T. Shindell, P. J. Young, M. J. Prather, O. Wild, R. D. Field, D. Bergmann, P. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. M. Doherty, V. Eyring, G. Faluvegi, G. A. Folberth, L. W. Horowitz, B. Josse, I. A. MacKenzie, T. Nagashima, D. A. Plummer, M. Righi, S. T. Rumbold, D. S. Stevenson, S. A. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 2563–2587, https://doi.org/10.5194/acp-13-2563-2013, https://doi.org/10.5194/acp-13-2563-2013, 2013
P. J. Young, A. T. Archibald, K. W. Bowman, J.-F. Lamarque, V. Naik, D. S. Stevenson, S. Tilmes, A. Voulgarakis, O. Wild, D. Bergmann, P. Cameron-Smith, I. Cionni, W. J. Collins, S. B. Dalsøren, R. M. Doherty, V. Eyring, G. Faluvegi, L. W. Horowitz, B. Josse, Y. H. Lee, I. A. MacKenzie, T. Nagashima, D. A. Plummer, M. Righi, S. T. Rumbold, R. B. Skeie, D. T. Shindell, S. A. Strode, K. Sudo, S. Szopa, and G. Zeng
Atmos. Chem. Phys., 13, 2063–2090, https://doi.org/10.5194/acp-13-2063-2013, https://doi.org/10.5194/acp-13-2063-2013, 2013
E. M. Dunne, L. A. Lee, C. L. Reddington, and K. S. Carslaw
Atmos. Chem. Phys., 12, 11573–11587, https://doi.org/10.5194/acp-12-11573-2012, https://doi.org/10.5194/acp-12-11573-2012, 2012
Related subject area
Climate and Earth system modeling
FLAME 1.0: a novel approach for modelling burned area in the Brazilian biomes using the maximum entropy concept
SURFER v3.0: a fast model with ice sheet tipping points and carbon cycle feedbacks for short- and long-term climate scenarios
NMH-CS 3.0: a C# programming language and Windows-system-based ecohydrological model derived from Noah-MP
A method for quantifying uncertainty in spatially interpolated meteorological data with application to daily maximum air temperature
Baseline Climate Variables for Earth System Modelling
PaleoSTeHM v1.0: a modern, scalable spatiotemporal hierarchical modeling framework for paleo-environmental data
The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)
ZEMBA v1.0: an energy and moisture balance climate model to investigate Quaternary climate
Development and evaluation of a new 4DEnVar-based weakly coupled ocean data assimilation system in E3SMv2
TemDeep: a self-supervised framework for temporal downscaling of atmospheric fields at arbitrary time resolutions
The ensemble consistency test: from CESM to MPAS and beyond
Presentation, calibration and testing of the DCESS II Earth system model of intermediate complexity (version 1.0)
Synthesizing global carbon–nitrogen coupling effects – the MAGICC coupled carbon–nitrogen cycle model v1.0
Historical trends and controlling factors of isoprene emissions in CMIP6 Earth system models
Investigating carbon and nitrogen conservation in reported CMIP6 Earth system model data
From weather data to river runoff: using spatiotemporal convolutional networks for discharge forecasting
A Fortran–Python interface for integrating machine learning parameterization into earth system models
ROCKE-3D 2.0: An updated general circulation model for simulating the climates of rocky planets
A rapid-application emissions-to-impacts tool for scenario assessment: Probabilistic Regional Impacts from Model patterns and Emissions (PRIME)
The DOE E3SM version 2.1: overview and assessment of the impacts of parameterized ocean submesoscales
WRF-ELM v1.0: a regional climate model to study land–atmosphere interactions over heterogeneous land use regions
Modeling commercial-scale CO2 storage in the gas hydrate stability zone with PFLOTRAN v6.0
DiuSST: a conceptual model of diurnal warm layers for idealized atmospheric simulations with interactive sea surface temperature
High-Resolution Model Intercomparison Project phase 2 (HighResMIP2) towards CMIP7
T&C-CROP: representing mechanistic crop growth with a terrestrial biosphere model (T&C, v1.5) – model formulation and validation
An updated non-intrusive, multi-scale, and flexible coupling interface in WRF 4.6.0
Monitoring and benchmarking Earth system model simulations with ESMValTool v2.12.0
The Earth Science Box Modeling Toolkit (ESBMTK 0.14.0.11): a Python library for research and teaching
CropSuite v1.0 – a comprehensive open-source crop suitability model considering climate variability for climate impact assessment
ICON ComIn – the ICON Community Interface (ComIn version 0.1.0, with ICON version 2024.01-01)
Using feature importance as an exploratory data analysis tool on Earth system models
A new metrics framework for quantifying and intercomparing atmospheric rivers in observations, reanalyses, and climate models
The real challenges for climate and weather modelling on its way to sustained exascale performance: a case study using ICON (v2.6.6)
COSP-RTTOV-1.0: Flexible radiation diagnostics to enable new science applications in model evaluation, climate change detection, and satellite mission design
Impact of spatial resolution on CMIP6-driven Mediterranean climate simulations: a focus on precipitation distribution over Italy
Improving the representation of major Indian crops in the Community Land Model version 5.0 (CLM5) using site-scale crop data
Evaluation of CORDEX ERA5-forced NARCliM2.0 regional climate models over Australia using the Weather Research and Forecasting (WRF) model version 4.1.2
Design, evaluation, and future projections of the NARCliM2.0 CORDEX-CMIP6 Australasia regional climate ensemble
The Detection and Attribution Model Intercomparison Project (DAMIP v2.0) contribution to CMIP7
Amending the algorithm of aerosol–radiation interactions in WRF-Chem (v4.4)
The very-high-resolution configuration of the EC-Earth global model for HighResMIP
GOSI9: UK Global Ocean and Sea Ice configurations
Decomposition of skill scores for conditional verification: impact of Atlantic Multidecadal Oscillation phases on the predictability of decadal temperature forecasts
Virtual Integration of Satellite and In-situ Observation Networks (VISION) v1.0: In-Situ Observations Simulator (ISO_simulator)
Climate model downscaling in central Asia: a dynamical and a neural network approach
Advanced climate model evaluation with ESMValTool v2.11.0 using parallel, out-of-core, and distributed computing
Multi-year simulations at kilometre scale with the Integrated Forecasting System coupled to FESOM2.5 and NEMOv3.4
Features of mid- and high-latitude low-level clouds and their relation to strong aerosol effects in the Energy Exascale Earth System Model version 2 (E3SMv2)
Subsurface hydrological controls on the short-term effects of hurricanes on nitrate–nitrogen runoff loading: a case study of Hurricane Ida using the Energy Exascale Earth System Model (E3SM) Land Model (v2.1)
The Development and Application of an Arctic Sea Ice Emulator v.1
Maria Lucia Ferreira Barbosa, Douglas I. Kelley, Chantelle A. Burton, Igor J. M. Ferreira, Renata Moura da Veiga, Anna Bradley, Paulo Guilherme Molin, and Liana O. Anderson
Geosci. Model Dev., 18, 3533–3557, https://doi.org/10.5194/gmd-18-3533-2025, https://doi.org/10.5194/gmd-18-3533-2025, 2025
Short summary
Short summary
As fire seasons in Brazil become increasingly severe, confidently understanding the factors driving fires is more critical than ever. To address this challenge, we developed FLAME (Fire Landscape Analysis using Maximum Entropy), a new model designed to predict fires and to analyse the spatial influence of both environmental and human factors while accounting for uncertainties. By adapting the model to different regions, we can enhance fire management strategies, making FLAME a powerful tool for protecting landscapes in Brazil and beyond.
Victor Couplet, Marina Martínez Montero, and Michel Crucifix
Geosci. Model Dev., 18, 3081–3129, https://doi.org/10.5194/gmd-18-3081-2025, https://doi.org/10.5194/gmd-18-3081-2025, 2025
Short summary
Short summary
We present SURFER v3.0, a simple climate model designed to estimate the impact of CO2 and CH4 emissions on global temperatures, sea levels, and ocean pH. We added new carbon cycle processes and calibrated the model to observations and results from more complex models, enabling use over timescales ranging from decades to millions of years. SURFER v3.0 is fast, transparent, and easy to use, making it an ideal tool for policy assessments and suitable for educational purposes.
Yong-He Liu and Zong-Liang Yang
Geosci. Model Dev., 18, 3157–3174, https://doi.org/10.5194/gmd-18-3157-2025, https://doi.org/10.5194/gmd-18-3157-2025, 2025
Short summary
Short summary
NMH-CS 3.0 is a C#-based ecohydrological model reconstructed from the WRF-Hydro/Noah-MP model by translating the Fortran code of WRF-Hydro 3.0 and integrating a parallel river routing module. It enables efficient execution on multi-core personal computers. Simulations in the Yellow River basin demonstrate its consistency with WRF-Hydro outputs, providing a reliable alternative to the original Noah-MP model.
Conor T. Doherty, Weile Wang, Hirofumi Hashimoto, and Ian G. Brosnan
Geosci. Model Dev., 18, 3003–3016, https://doi.org/10.5194/gmd-18-3003-2025, https://doi.org/10.5194/gmd-18-3003-2025, 2025
Short summary
Short summary
We present, analyze, and validate a methodology for quantifying uncertainty in gridded meteorological data products produced by spatial interpolation. In a validation case study using daily maximum near-surface air temperature (Tmax), the method works well and produces predictive distributions with closely matching theoretical versus actual coverage levels. Application of the method reveals that the magnitude of uncertainty in interpolated Tmax varies significantly in both space and time.
Martin Juckes, Karl E. Taylor, Fabrizio Antonio, David Brayshaw, Carlo Buontempo, Jian Cao, Paul J. Durack, Michio Kawamiya, Hyungjun Kim, Tomas Lovato, Chloe Mackallah, Matthew Mizielinski, Alessandra Nuzzo, Martina Stockhause, Daniele Visioni, Jeremy Walton, Briony Turner, Eleanor O'Rourke, and Beth Dingley
Geosci. Model Dev., 18, 2639–2663, https://doi.org/10.5194/gmd-18-2639-2025, https://doi.org/10.5194/gmd-18-2639-2025, 2025
Short summary
Short summary
The Baseline Climate Variables for Earth System Modelling (ESM-BCVs) are defined as a list of 135 variables which have high utility for the evaluation and exploitation of climate simulations. The list reflects the most frequently used variables from Earth system models based on an assessment of data publication and download records from the largest archive of global climate projects.
Yucheng Lin, Robert E. Kopp, Alexander Reedy, Matteo Turilli, Shantenu Jha, and Erica L. Ashe
Geosci. Model Dev., 18, 2609–2637, https://doi.org/10.5194/gmd-18-2609-2025, https://doi.org/10.5194/gmd-18-2609-2025, 2025
Short summary
Short summary
PaleoSTeHM v1.0 is a state-of-the-art framework designed to reconstruct past environmental conditions using geological data. Built on modern machine learning techniques, it efficiently handles the sparse and noisy nature of paleo-records, allowing scientists to make accurate and scalable inferences about past environmental change. By using flexible statistical models, PaleoSTeHM separates different sources of uncertainty, improving the precision of historical climate reconstructions.
Ingo Richter, Ping Chang, Ping-Gin Chiu, Gokhan Danabasoglu, Takeshi Doi, Dietmar Dommenget, Guillaume Gastineau, Zoe E. Gillett, Aixue Hu, Takahito Kataoka, Noel S. Keenlyside, Fred Kucharski, Yuko M. Okumura, Wonsun Park, Malte F. Stuecker, Andréa S. Taschetto, Chunzai Wang, Stephen G. Yeager, and Sang-Wook Yeh
Geosci. Model Dev., 18, 2587–2608, https://doi.org/10.5194/gmd-18-2587-2025, https://doi.org/10.5194/gmd-18-2587-2025, 2025
Short summary
Short summary
Tropical ocean basins influence each other through multiple pathways and mechanisms, referred to here as tropical basin interaction (TBI). Many researchers have examined TBI using comprehensive climate models but have obtained conflicting results. This may be partly due to differences in experiment protocols and partly due to systematic model errors. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) aims to address this problem by designing a set of TBI experiments that will be performed by multiple models.
Daniel F. J. Gunning, Kerim H. Nisancioglu, Emilie Capron, and Roderik S. W. van de Wal
Geosci. Model Dev., 18, 2479–2508, https://doi.org/10.5194/gmd-18-2479-2025, https://doi.org/10.5194/gmd-18-2479-2025, 2025
Short summary
Short summary
This work documents the first results from ZEMBA: an energy balance model of the climate system. The model is a computationally efficient tool designed to study the response of climate to changes in the Earth's orbit. We demonstrate that ZEMBA reproduces many features of the Earth's climate for both the pre-industrial period and the Earth's most recent cold extreme – the Last Glacial Maximum. We intend to develop ZEMBA further and investigate the glacial cycles of the last 2.5 million years.
Pengfei Shi, L. Ruby Leung, and Bin Wang
Geosci. Model Dev., 18, 2443–2460, https://doi.org/10.5194/gmd-18-2443-2025, https://doi.org/10.5194/gmd-18-2443-2025, 2025
Short summary
Short summary
Improving climate predictions has significant socio-economic impacts. In this study, we develop and apply a new weakly coupled ocean data assimilation (WCODA) system to a coupled climate model. The WCODA system improves simulations of ocean temperature and salinity across many global regions. This system is meant to advance our understanding of the ocean's role in climate predictability.
Liwen Wang, Qian Li, Qi Lv, Xuan Peng, and Wei You
Geosci. Model Dev., 18, 2427–2442, https://doi.org/10.5194/gmd-18-2427-2025, https://doi.org/10.5194/gmd-18-2427-2025, 2025
Short summary
Short summary
Our research presents a novel deep learning approach called "TemDeep" for downscaling atmospheric variables at arbitrary time resolutions based on temporal coherence. Results show that our method can accurately recover evolution details superior to other methods, reaching 53.7 % in the restoration rate. Our findings are important for advancing weather forecasting models and enabling more precise and reliable predictions to support disaster preparedness, agriculture, and sustainable development.
Teo Price-Broncucia, Allison Baker, Dorit Hammerling, Michael Duda, and Rebecca Morrison
Geosci. Model Dev., 18, 2349–2372, https://doi.org/10.5194/gmd-18-2349-2025, https://doi.org/10.5194/gmd-18-2349-2025, 2025
Short summary
Short summary
The ensemble consistency test (ECT) and its ultrafast variant (UF-ECT) have become powerful tools in the development community for the identification of unwanted changes in the Community Earth System Model (CESM). We develop a generalized setup framework to enable easy adoption of the ECT approach for other model developers and communities. This framework specifies test parameters to accurately characterize model variability and balance test sensitivity and computational cost.
Esteban Fernández Villanueva and Gary Shaffer
Geosci. Model Dev., 18, 2161–2192, https://doi.org/10.5194/gmd-18-2161-2025, https://doi.org/10.5194/gmd-18-2161-2025, 2025
Short summary
Short summary
We describe, calibrate and test the Danish Center for Earth System Science (DCESS) II model, a new, broad, adaptable and fast Earth system model. DCESS II is designed for global simulations over timescales of years to millions of years using limited computer resources like a personal computer. With its flexibility and comprehensive treatment of the global carbon cycle, DCESS II is a useful, computationally friendly tool for simulations of past climates as well as for future Earth system projections.
Gang Tang, Zebedee Nicholls, Alexander Norton, Sönke Zaehle, and Malte Meinshausen
Geosci. Model Dev., 18, 2193–2230, https://doi.org/10.5194/gmd-18-2193-2025, https://doi.org/10.5194/gmd-18-2193-2025, 2025
Short summary
Short summary
We studied carbon–nitrogen coupling in Earth system models by developing a global carbon–nitrogen cycle model (CNit v1.0) within the widely used emulator MAGICC. CNit effectively reproduced the global carbon–nitrogen cycle dynamics observed in complex models. Our results show persistent nitrogen limitations on plant growth (net primary production) from 1850 to 2100, suggesting that nitrogen deficiency may constrain future land carbon sequestration.
Ngoc Thi Nhu Do, Kengo Sudo, Akihiko Ito, Louisa K. Emmons, Vaishali Naik, Kostas Tsigaridis, Øyvind Seland, Gerd A. Folberth, and Douglas I. Kelley
Geosci. Model Dev., 18, 2079–2109, https://doi.org/10.5194/gmd-18-2079-2025, https://doi.org/10.5194/gmd-18-2079-2025, 2025
Short summary
Short summary
Understanding historical isoprene emission changes is important for predicting future climate, but trends and their controlling factors remain uncertain. This study shows that long-term isoprene trends vary among Earth system models mainly due to partially incorporating CO2 effects and land cover changes rather than to climate. Future models that refine these factors’ effects on isoprene emissions, along with long-term observations, are essential for better understanding plant–climate interactions.
Gang Tang, Zebedee Nicholls, Chris Jones, Thomas Gasser, Alexander Norton, Tilo Ziehn, Alejandro Romero-Prieto, and Malte Meinshausen
Geosci. Model Dev., 18, 2111–2136, https://doi.org/10.5194/gmd-18-2111-2025, https://doi.org/10.5194/gmd-18-2111-2025, 2025
Short summary
Short summary
We analyzed carbon and nitrogen mass conservation in data from various Earth system models. Our findings reveal significant discrepancies between flux and pool size data, where cumulative imbalances can reach hundreds of gigatons of carbon or nitrogen. These imbalances appear primarily due to missing or inconsistently reported fluxes – especially for land-use and fire emissions. To enhance data quality, we recommend that future climate data protocols address this issue at the reporting stage.
Florian Börgel, Sven Karsten, Karoline Rummel, and Ulf Gräwe
Geosci. Model Dev., 18, 2005–2019, https://doi.org/10.5194/gmd-18-2005-2025, https://doi.org/10.5194/gmd-18-2005-2025, 2025
Short summary
Short summary
Forecasting river runoff, which is crucial for managing water resources and understanding climate impacts, can be challenging. This study introduces a new method using convolutional long short-term memory (ConvLSTM) networks, a machine learning model that processes spatial and temporal data. Focusing on the Baltic Sea region, our model uses weather data as input to predict daily river runoff for 97 rivers.
Tao Zhang, Cyril Morcrette, Meng Zhang, Wuyin Lin, Shaocheng Xie, Ye Liu, Kwinten Van Weverberg, and Joana Rodrigues
Geosci. Model Dev., 18, 1917–1928, https://doi.org/10.5194/gmd-18-1917-2025, https://doi.org/10.5194/gmd-18-1917-2025, 2025
Short summary
Short summary
Earth system models (ESMs) struggle with the uncertainties associated with parameterizing subgrid physics. Machine learning (ML) algorithms offer a solution by learning the important relationships and features from high-resolution models. To incorporate ML parameterizations into ESMs, we develop a Fortran–Python interface that allows for calling Python functions within Fortran-based ESMs. Through two case studies, this interface demonstrates its feasibility, modularity, and effectiveness.
Kostas Tsigaridis, Andrew S. Ackerman, Igor Aleinov, Mark A. Chandler, Thomas L. Clune, Christopher M. Colose, Anthony D. Del Genio, Maxwell Kelley, Nancy Y. Kiang, Anthony Leboissetier, Jan P. Perlwitz, Reto A. Ruedy, Gary L. Russell, Linda E. Sohl, Michael J. Way, and Eric T. Wolf
EGUsphere, https://doi.org/10.5194/egusphere-2025-925, https://doi.org/10.5194/egusphere-2025-925, 2025
Short summary
Short summary
We present the second generation of ROCKE-3D, a generalized 3-dimensional model for use in Solar System and exoplanetary simulations of rocky planet climates. We quantify how the different component choices affect model results, and discuss strengths and limitations of using each component, together with how one can select which component to use. ROCKE-3D is publicly available and tutorial sessions are available for the community, greatly facilitating its use by any interested group.
Camilla Mathison, Eleanor J. Burke, Gregory Munday, Chris D. Jones, Chris J. Smith, Norman J. Steinert, Andy J. Wiltshire, Chris Huntingford, Eszter Kovacs, Laila K. Gohar, Rebecca M. Varney, and Douglas McNeall
Geosci. Model Dev., 18, 1785–1808, https://doi.org/10.5194/gmd-18-1785-2025, https://doi.org/10.5194/gmd-18-1785-2025, 2025
Short summary
Short summary
We present PRIME (Probabilistic Regional Impacts from Model patterns and Emissions), which is designed to take new emissions scenarios and rapidly provide regional impact information. PRIME allows large ensembles to be run on multi-centennial timescales, including the analysis of many important variables for impact assessments. Our evaluation shows that PRIME reproduces the climate response for known scenarios, providing confidence in using PRIME for novel scenarios.
Katherine M. Smith, Alice M. Barthel, LeAnn M. Conlon, Luke P. Van Roekel, Anthony Bartoletti, Jean-Christophe Golaz, Chengzhu Zhang, Carolyn Branecky Begeman, James J. Benedict, Gautam Bisht, Yan Feng, Walter Hannah, Bryce E. Harrop, Nicole Jeffery, Wuyin Lin, Po-Lun Ma, Mathew E. Maltrud, Mark R. Petersen, Balwinder Singh, Qi Tang, Teklu Tesfa, Jonathan D. Wolfe, Shaocheng Xie, Xue Zheng, Karthik Balaguru, Oluwayemi Garuba, Peter Gleckler, Aixue Hu, Jiwoo Lee, Ben Moore-Maley, and Ana C. Ordoñez
Geosci. Model Dev., 18, 1613–1633, https://doi.org/10.5194/gmd-18-1613-2025, https://doi.org/10.5194/gmd-18-1613-2025, 2025
Short summary
Short summary
Version 2.1 of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) adds the Fox-Kemper et al. (2011) mixed-layer eddy parameterization, which restratifies the ocean surface layer through an overturning streamfunction. Results include surface layer bias reduction in temperature, salinity, and sea ice extent in the North Atlantic; a small strengthening of the Atlantic meridional overturning circulation; and improvements to many atmospheric climatological variables.
Huilin Huang, Yun Qian, Gautam Bisht, Jiali Wang, Tirthankar Chakraborty, Dalei Hao, Jianfeng Li, Travis Thurber, Balwinder Singh, Zhao Yang, Ye Liu, Pengfei Xue, William J. Sacks, Ethan Coon, and Robert Hetland
Geosci. Model Dev., 18, 1427–1443, https://doi.org/10.5194/gmd-18-1427-2025, https://doi.org/10.5194/gmd-18-1427-2025, 2025
Short summary
Short summary
We integrate the E3SM Land Model (ELM) with the WRF model through the Lightweight Infrastructure for Land Atmosphere Coupling (LILAC) Earth System Modeling Framework (ESMF). This framework includes a top-level driver, LILAC, for variable communication between WRF and ELM and ESMF caps for ELM initialization, execution, and finalization. The LILAC–ESMF framework maintains the integrity of the ELM's source code structure and facilitates the transfer of future ELM model developments to WRF-ELM.
Michael Nole, Jonah Bartrand, Fawz Naim, and Glenn Hammond
Geosci. Model Dev., 18, 1413–1425, https://doi.org/10.5194/gmd-18-1413-2025, https://doi.org/10.5194/gmd-18-1413-2025, 2025
Short summary
Short summary
Safe carbon dioxide (CO2) storage is likely to be critical for mitigating some of the most severe effects of climate change. We present a simulation framework for modeling CO2 storage beneath the seafloor, where CO2 can form a solid. This can aid in permanent CO2 storage for long periods of time. Our models show what a commercial-scale CO2 injection would look like in a marine environment. We discuss what would need to be considered when designing a subsea CO2 injection.
Reyk Börner, Jan O. Haerter, and Romain Fiévet
Geosci. Model Dev., 18, 1333–1356, https://doi.org/10.5194/gmd-18-1333-2025, https://doi.org/10.5194/gmd-18-1333-2025, 2025
Short summary
Short summary
The daily cycle of sea surface temperature (SST) impacts clouds above the ocean and could influence the clustering of thunderstorms linked to extreme rainfall and hurricanes. However, daily SST variability is often poorly represented in modeling studies of how clouds cluster. We present a simple, wind-responsive model of upper-ocean temperature for use in atmospheric simulations. Evaluating the model against observations, we show that it performs significantly better than common slab models.
Malcolm J. Roberts, Kevin A. Reed, Qing Bao, Joseph J. Barsugli, Suzana J. Camargo, Louis-Philippe Caron, Ping Chang, Cheng-Ta Chen, Hannah M. Christensen, Gokhan Danabasoglu, Ivy Frenger, Neven S. Fučkar, Shabeh ul Hasson, Helene T. Hewitt, Huanping Huang, Daehyun Kim, Chihiro Kodama, Michael Lai, Lai-Yung Ruby Leung, Ryo Mizuta, Paulo Nobre, Pablo Ortega, Dominique Paquin, Christopher D. Roberts, Enrico Scoccimarro, Jon Seddon, Anne Marie Treguier, Chia-Ying Tu, Paul A. Ullrich, Pier Luigi Vidale, Michael F. Wehner, Colin M. Zarzycki, Bosong Zhang, Wei Zhang, and Ming Zhao
Geosci. Model Dev., 18, 1307–1332, https://doi.org/10.5194/gmd-18-1307-2025, https://doi.org/10.5194/gmd-18-1307-2025, 2025
Short summary
Short summary
HighResMIP2 is a model intercomparison project focusing on high-resolution global climate models, that is, those with grid spacings of 25 km or less in the atmosphere and ocean, using simulations of decades to a century in length. We are proposing an update of our simulation protocol to make the models more applicable to key questions for climate variability and hazard in present-day and future projections and to build links with other communities to provide more robust climate information.
Jordi Buckley Paules, Simone Fatichi, Bonnie Warring, and Athanasios Paschalis
Geosci. Model Dev., 18, 1287–1305, https://doi.org/10.5194/gmd-18-1287-2025, https://doi.org/10.5194/gmd-18-1287-2025, 2025
Short summary
Short summary
We present and validate enhancements to the process-based T&C model aimed at improving its representation of crop growth and management practices. The updated model, T&C-CROP, enables applications such as analysing the hydrological and carbon storage impacts of land use transitions (e.g. conversions between crops, forests, and pastures) and optimizing irrigation and fertilization strategies in response to climate change.
Sébastien Masson, Swen Jullien, Eric Maisonnave, David Gill, Guillaume Samson, Mathieu Le Corre, and Lionel Renault
Geosci. Model Dev., 18, 1241–1263, https://doi.org/10.5194/gmd-18-1241-2025, https://doi.org/10.5194/gmd-18-1241-2025, 2025
Short summary
Short summary
This article details a new feature we implemented in the popular regional atmospheric model WRF. This feature allows for data exchange between WRF and any other model (e.g. an ocean model) using the coupling library Ocean–Atmosphere–Sea–Ice–Soil Model Coupling Toolkit (OASIS3-MCT). This coupling interface is designed to be non-intrusive, flexible and modular. It also offers the possibility of taking into account the nested zooms used in WRF or in the models with which it is coupled.
Axel Lauer, Lisa Bock, Birgit Hassler, Patrick Jöckel, Lukas Ruhe, and Manuel Schlund
Geosci. Model Dev., 18, 1169–1188, https://doi.org/10.5194/gmd-18-1169-2025, https://doi.org/10.5194/gmd-18-1169-2025, 2025
Short summary
Short summary
Earth system models are important tools to improve our understanding of current climate and to project climate change. Thus, it is crucial to understand possible shortcomings in the models. New features of the ESMValTool software package allow one to compare and visualize a model's performance with respect to reproducing observations in the context of other climate models in an easy and user-friendly way. We aim to help model developers assess and monitor climate simulations more efficiently.
Ulrich G. Wortmann, Tina Tsan, Mahrukh Niazi, Irene A. Ma, Ruben Navasardyan, Magnus-Roland Marun, Bernardo S. Chede, Jingwen Zhong, and Morgan Wolfe
Geosci. Model Dev., 18, 1155–1167, https://doi.org/10.5194/gmd-18-1155-2025, https://doi.org/10.5194/gmd-18-1155-2025, 2025
Short summary
Short summary
The Earth Science Box Modeling Toolkit (ESBMTK) is a user-friendly Python library that simplifies the creation of models to study earth system processes, such as the carbon cycle and ocean chemistry. It enhances learning by emphasizing concepts over programming and is accessible to students and researchers alike. By automating complex calculations and promoting code clarity, ESBMTK accelerates model development while improving reproducibility and the usability of scientific research.
Florian Zabel, Matthias Knüttel, and Benjamin Poschlod
Geosci. Model Dev., 18, 1067–1087, https://doi.org/10.5194/gmd-18-1067-2025, https://doi.org/10.5194/gmd-18-1067-2025, 2025
Short summary
Short summary
CropSuite is a new open-source crop suitability model. It provides a GUI and a wide range of options, including a spatial downscaling of climate data. We apply CropSuite to 48 staple and opportunity crops at a 1 km spatial resolution in Africa. We find that climate variability significantly impacts suitable areas but also affects optimal sowing dates and multiple cropping potential. The results provide valuable information for climate impact assessments, adaptation, and land-use planning.
Kerstin Hartung, Bastian Kern, Nils-Arne Dreier, Jörn Geisbüsch, Mahnoosh Haghighatnasab, Patrick Jöckel, Astrid Kerkweg, Wilton Jaciel Loch, Florian Prill, and Daniel Rieger
Geosci. Model Dev., 18, 1001–1015, https://doi.org/10.5194/gmd-18-1001-2025, https://doi.org/10.5194/gmd-18-1001-2025, 2025
Short summary
Short summary
The ICOsahedral Non-hydrostatic (ICON) model system Community Interface (ComIn) library supports connecting third-party modules to the ICON model. Third-party modules can range from simple diagnostic Python scripts to full chemistry models. ComIn offers a low barrier for code extensions to ICON, provides multi-language support (Fortran, C/C++, and Python), and reduces the migration effort in response to new ICON releases. This paper presents the ComIn design principles and a range of use cases.
Daniel Ries, Katherine Goode, Kellie McClernon, and Benjamin Hillman
Geosci. Model Dev., 18, 1041–1065, https://doi.org/10.5194/gmd-18-1041-2025, https://doi.org/10.5194/gmd-18-1041-2025, 2025
Short summary
Short summary
Machine learning has advanced research in the climate science domain, but its models are difficult to understand. In order to understand the impacts and consequences of climate interventions such as stratospheric aerosol injection, complex models are often necessary. We use a case study to illustrate how we can understand the inner workings of a complex model. We present this technique as an exploratory tool that can be used to quickly discover and assess relationships in complex climate data.
Bo Dong, Paul Ullrich, Jiwoo Lee, Peter Gleckler, Kristin Chang, and Travis A. O'Brien
Geosci. Model Dev., 18, 961–976, https://doi.org/10.5194/gmd-18-961-2025, https://doi.org/10.5194/gmd-18-961-2025, 2025
Short summary
Short summary
A metrics package designed for easy analysis of atmospheric river (AR) characteristics and statistics is presented. The tool is efficient for diagnosing systematic AR bias in climate models and useful for evaluating new AR characteristics in model simulations. In climate models, landfalling AR precipitation shows dry biases globally, and AR tracks are farther poleward (equatorward) in the North and South Atlantic (South Pacific and Indian Ocean).
Panagiotis Adamidis, Erik Pfister, Hendryk Bockelmann, Dominik Zobel, Jens-Olaf Beismann, and Marek Jacob
Geosci. Model Dev., 18, 905–919, https://doi.org/10.5194/gmd-18-905-2025, https://doi.org/10.5194/gmd-18-905-2025, 2025
Short summary
Short summary
In this paper, we investigated performance indicators of the climate model ICON (ICOsahedral Nonhydrostatic) on different compute architectures to answer the question of how to generate high-resolution climate simulations. Evidently, it is not enough to use more computing units of the conventionally used architectures; higher memory throughput is the most promising approach. More potential can be gained from single-node optimization rather than simply increasing the number of compute nodes.
Jonah K. Shaw, Dustin J. Swales, Sergio DeSouza-Machado, David D. Turner, Jennifer E. Kay, and David P. Schneider
EGUsphere, https://doi.org/10.5194/egusphere-2025-169, https://doi.org/10.5194/egusphere-2025-169, 2025
Short summary
Short summary
Satellites have observed earth's emission of infrared radiation since the 1970s. Because infrared wavelengths interact with the atmosphere in distinct ways, these observations contain information about the earth and atmosphere. We present a tool that runs alongside global climate models and produces output that can be directly compared with satellite measurements of infrared radiation. We then use this tool for climate model evaluation, climate change detection, and satellite mission design.
Maria Vittoria Struglia, Alessandro Anav, Marta Antonelli, Sandro Calmanti, Franco Catalano, Alessandro Dell'Aquila, Emanuela Pichelli, and Giovanna Pisacane
EGUsphere, https://doi.org/10.5194/egusphere-2025-387, https://doi.org/10.5194/egusphere-2025-387, 2025
Short summary
Short summary
We present the results of downscaling global climate projections for the Mediterranean and Italian regions aiming to produce high-resolution climate information for the assessment of climate change signals, focusing on extreme events. A general warming is foreseen by the end of century with a mean precipitation reduction accompanied, over Italian Peninsula, by a strong increase in the intensity of extreme precipitation events, particularly relevant for the high emissions scenario during autumn
Kangari Narender Reddy, Somnath Baidya Roy, Sam S. Rabin, Danica L. Lombardozzi, Gudimetla Venkateswara Varma, Ruchira Biswas, and Devavat Chiru Naik
Geosci. Model Dev., 18, 763–785, https://doi.org/10.5194/gmd-18-763-2025, https://doi.org/10.5194/gmd-18-763-2025, 2025
Short summary
Short summary
The study aimed to improve the representation of wheat and rice in a land model for the Indian region. The modified model performed significantly better than the default model in simulating crop phenology, yield, and carbon, water, and energy fluxes compared to observations. The study highlights the need for global land models to use region-specific crop parameters for accurately simulating vegetation processes and land surface processes.
Giovanni Di Virgilio, Fei Ji, Eugene Tam, Jason P. Evans, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Yue Li, and Matthew L. Riley
Geosci. Model Dev., 18, 703–724, https://doi.org/10.5194/gmd-18-703-2025, https://doi.org/10.5194/gmd-18-703-2025, 2025
Short summary
Short summary
We evaluate the skill in simulating the Australian climate of some of the latest generation of regional climate models. We show when and where the models simulate this climate with high skill versus model limitations. We show how new models perform relative to the previous-generation models, assessing how model design features may underlie key performance improvements. This work is of national and international relevance as it can help guide the use and interpretation of climate projections.
Giovanni Di Virgilio, Jason P. Evans, Fei Ji, Eugene Tam, Jatin Kala, Julia Andrys, Christopher Thomas, Dipayan Choudhury, Carlos Rocha, Stephen White, Yue Li, Moutassem El Rafei, Rishav Goyal, Matthew L. Riley, and Jyothi Lingala
Geosci. Model Dev., 18, 671–702, https://doi.org/10.5194/gmd-18-671-2025, https://doi.org/10.5194/gmd-18-671-2025, 2025
Short summary
Short summary
We introduce new climate models that simulate Australia’s future climate at regional scales, including at an unprecedented resolution of 4 km for 1950–2100. We describe the model design process used to create these new climate models. We show how the new models perform relative to previous-generation models and compare their climate projections. This work is of national and international relevance as it can help guide climate model design and the use and interpretation of climate projections.
Nathan P. Gillett, Isla R. Simpson, Gabi Hegerl, Reto Knutti, Dann Mitchell, Aurélien Ribes, Hideo Shiogama, Dáithí Stone, Claudia Tebaldi, Piotr Wolski, Wenxia Zhang, and Vivek K. Arora
EGUsphere, https://doi.org/10.5194/egusphere-2024-4086, https://doi.org/10.5194/egusphere-2024-4086, 2025
Short summary
Short summary
Climate model simulations of the response to human and natural influences together, natural climate influences alone, and greenhouse gases alone, among others, are key to quantifying human influence on the climate. The last set of such coordinated simulations underpinned key findings in the last Intergovernmental Panel on Climate Change (IPCC) report. Here we propose a new set of such simulations to be used in the next generation of attribution studies, and to underpin the next IPCC report.
Jiawang Feng, Chun Zhao, Qiuyan Du, Zining Yang, and Chen Jin
Geosci. Model Dev., 18, 585–603, https://doi.org/10.5194/gmd-18-585-2025, https://doi.org/10.5194/gmd-18-585-2025, 2025
Short summary
Short summary
In this study, we improved the calculation of how aerosols in the air interact with radiation in WRF-Chem. The original model used a simplified method, but we developed a more accurate approach. We found that this method significantly changes the properties of the estimated aerosols and their effects on radiation, especially for dust aerosols. It also impacts the simulated weather conditions. Our work highlights the importance of correctly representing aerosol–radiation interactions in models.
Eduardo Moreno-Chamarro, Thomas Arsouze, Mario Acosta, Pierre-Antoine Bretonnière, Miguel Castrillo, Eric Ferrer, Amanda Frigola, Daria Kuznetsova, Eneko Martin-Martinez, Pablo Ortega, and Sergi Palomas
Geosci. Model Dev., 18, 461–482, https://doi.org/10.5194/gmd-18-461-2025, https://doi.org/10.5194/gmd-18-461-2025, 2025
Short summary
Short summary
We present the high-resolution model version of the EC-Earth global climate model to contribute to HighResMIP. The combined model resolution is about 10–15 km in both the ocean and atmosphere, which makes it one of the finest ever used to complete historical and scenario simulations. This model is compared with two lower-resolution versions, with a 100 km and a 25 km grid. The three models are compared with observations to study the improvements thanks to the increased resolution.
Catherine Guiavarc'h, David Storkey, Adam T. Blaker, Ed Blockley, Alex Megann, Helene Hewitt, Michael J. Bell, Daley Calvert, Dan Copsey, Bablu Sinha, Sophia Moreton, Pierre Mathiot, and Bo An
Geosci. Model Dev., 18, 377–403, https://doi.org/10.5194/gmd-18-377-2025, https://doi.org/10.5194/gmd-18-377-2025, 2025
Short summary
Short summary
The Global Ocean and Sea Ice configuration version 9 (GOSI9) is the new UK hierarchy of model configurations based on the Nucleus for European Modelling of the Ocean (NEMO) and available at three resolutions. It will be used for various applications, e.g. weather forecasting and climate prediction. It improves upon the previous version by reducing global temperature and salinity biases and enhancing the representation of Arctic sea ice and the Antarctic Circumpolar Current.
Andy Richling, Jens Grieger, and Henning W. Rust
Geosci. Model Dev., 18, 361–375, https://doi.org/10.5194/gmd-18-361-2025, https://doi.org/10.5194/gmd-18-361-2025, 2025
Short summary
Short summary
The performance of weather and climate prediction systems is variable in time and space. It is of interest how this performance varies in different situations. We provide a decomposition of a skill score (a measure of forecast performance) as a tool for detailed assessment of performance variability to support model development or forecast improvement. The framework is exemplified with decadal forecasts to assess the impact of different ocean states in the North Atlantic on temperature forecast.
Maria R. Russo, Sadie L. Bartholomew, David Hassell, Alex M. Mason, Erica Neininger, A. James Perman, David A. J. Sproson, Duncan Watson-Parris, and Nathan Luke Abraham
Geosci. Model Dev., 18, 181–191, https://doi.org/10.5194/gmd-18-181-2025, https://doi.org/10.5194/gmd-18-181-2025, 2025
Short summary
Short summary
Observational data and modelling capabilities have expanded in recent years, but there are still barriers preventing these two data sources from being used in synergy. Proper comparison requires generating, storing, and handling a large amount of data. This work describes the first step in the development of a new set of software tools, the VISION toolkit, which can enable the easy and efficient integration of observational and model data required for model evaluation.
Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann
Geosci. Model Dev., 18, 161–180, https://doi.org/10.5194/gmd-18-161-2025, https://doi.org/10.5194/gmd-18-161-2025, 2025
Short summary
Short summary
We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.
Manuel Schlund, Bouwe Andela, Jörg Benke, Ruth Comer, Birgit Hassler, Emma Hogan, Peter Kalverla, Axel Lauer, Bill Little, Saskia Loosveldt Tomas, Francesco Nattino, Patrick Peglar, Valeriu Predoi, Stef Smeets, Stephen Worsley, Martin Yeo, and Klaus Zimmermann
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-236, https://doi.org/10.5194/gmd-2024-236, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for the evaluation of Earth system models. Here, we describe recent significant improvements of ESMValTool’s computational efficiency including parallel, out-of-core, and distributed computing. Evaluations with the enhanced version of ESMValTool are faster, use less computational resources, and can handle input data larger than the available memory.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Willem Deconinck, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Rohit Ghosh, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Thomas Jung, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Aleksei Koldunov, Tobias Kölling, Josh Kousal, Christian Kühnlein, Pedro Maciel, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Balthasar Reuter, Domokos Sármány, Patrick Scholz, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
Geosci. Model Dev., 18, 33–69, https://doi.org/10.5194/gmd-18-33-2025, https://doi.org/10.5194/gmd-18-33-2025, 2025
Short summary
Short summary
Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale") and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Hui Wan, Abhishek Yenpure, Berk Geveci, Richard C. Easter, Philip J. Rasch, Kai Zhang, and Xubin Zeng
EGUsphere, https://doi.org/10.5194/egusphere-2024-4020, https://doi.org/10.5194/egusphere-2024-4020, 2025
Short summary
Short summary
In E3SMv2 and many other global climate models, the simulated anthropogenic aerosol influence on the Earth's energy balance is sensitive to the presence of clouds with very low droplet number concentrations. Numerical experiments conducted in this study suggest that mid- and high-latitude low-level stratus occurring under weak turbulence is an important cloud regime for understanding the causes of very low cloud droplet number concentrations in global climate simulations.
Yilin Fang, Hoang Viet Tran, and L. Ruby Leung
Geosci. Model Dev., 18, 19–32, https://doi.org/10.5194/gmd-18-19-2025, https://doi.org/10.5194/gmd-18-19-2025, 2025
Short summary
Short summary
Hurricanes may worsen water quality in the lower Mississippi River basin (LMRB) by increasing nutrient runoff. We found that runoff parameterizations greatly affect nitrate–nitrogen runoff simulated using an Earth system land model. Our simulations predicted increased nitrogen runoff in the LMRB during Hurricane Ida in 2021, albeit less pronounced than the observations, indicating areas for model improvement to better understand and manage nutrient runoff loss during hurricanes in the region.
Sian Megan Chilcott and Malte Meinshausen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-203, https://doi.org/10.5194/gmd-2024-203, 2025
Revised manuscript accepted for GMD
Short summary
Short summary
Climate models are expensive to run and often underestimate how sensitive Arctic sea ice is to climate change. To address this, we developed a simple model that emulates the response of sea ice to global warming. We find the remaining carbon dioxide (CO2) emissions that will avoid a seasonally ice-free Arctic Ocean is lower than previous estimates of 821 Gigatonnes of CO2. Our model also provides insights into the future of winter sea ice, examining a larger ensemble than previously possible.
Cited articles
Ahtikoski, A., Heikkilä, J., Alenius, V., and Siren, M.: Economic
viability of utilizing biomass energy from young stands – the case of
Finland, Biomass Bioenerg., 32, 988–996, 2008.
Ba, S., Myers, W. R., and Brenneman, W. A.: Optimal sliced Latin hypercube
designs, Technometrics, 57, 479–487, 2015.
Bailis, R., Ezzati, M., and Kammen, D. M.: Mortality and greenhouse gas
impacts of biomass and petroleum energy futures in Africa, Science, 308,
98–103, 2005.
Bastos, L. S. and O'Hagan, A.: Diagnostics for Gaussian process emulators,
Technometrics, 51, 425–438, 2009.
Campbell, J. E., Carmichael, G. R., Chai, T., Mena-Carrasco, M., Tang, Y.,
Blake, D., Blake, N., Vay, S. A., Collatz, G. J., and Baker, I.:
Photosynthetic control of atmospheric carbonyl sulfide during the growing
season, Science, 322, 1085–1088, 2008.
Carslaw, K., Lee, L., Reddington, C., Pringle, K., Rap, A., Forster, P.,
Mann, G., Spracklen, D., Woodhouse, M., and Regayre, L.: Large contribution
of natural aerosols to uncertainty in indirect forcing, Nature, 503, 67–71,
2013.
Chang, E. T., Strong, M., and Clayton, R. H.: Bayesian sensitivity analysis
of a cardiac cell model using a Gaussian process emulator, PloS one, 10,
e0130252, https://doi.org/10.1371/journal.pone.0137004, 2015.
Coggan, J. S., Bartol, T. M., Esquenazi, E., Stiles, J. R., Lamont, S.,
Martone, M. E., Berg, D. K., Ellisman, M. H., and Sejnowski, T. J.: Evidence
for ectopic neurotransmission at a neuronal synapse, Science, 309, 446–451,
2005.
Cressie, N.: The origins of kriging, Math. Geol., 22, 239–252,
1990.
Cukier, R., Fortuin, C., Shuler, K. E., Petschek, A., and Schaibly, J.:
Study of the sensitivity of coupled reaction systems to uncertainties in
rate coefficients. I Theory, The J. Chem. Phys., 59, 3873–3878,
1973.
Currin, C., Mitchell, T., Morris, M., and Ylvisaker, D.: Bayesian prediction
of deterministic functions, with applications to the design and analysis of
computer experiments, J. Am. Stat. Assoc., 86,
953–963, 1991.
de Gee, M., Lof, M. E., and Hemerik, L.: The effect of chemical information
on the spatial distribution of fruit flies: II parameterization,
calibration, and sensitivity, B. Math. Biol., 70, 1850–1868,
2008.
Degroote, J., Couckuyt, I., Vierendeels, J., Segers, P., and Dhaene, T.:
Inverse modelling of an aneurysm's stiffness using surrogate-based
optimization and fluid-structure interaction simulations, Struct.
Multidis. Optim., 46, 457–469, 2012.
De Lozzo, M. and Marrel, A.: Sensitivity analysis with dependence and
variance-based measures for spatio-temporal numerical simulators, Stoch.
Environ. Res. Risk Assess., 31, 1437–1453, 2017.
Ferretti, F., Saltelli, A., and Tarantola, S.: Trends in sensitivity
analysis practice in the last decade, Sci. Total Environ.,
568, 666–670, https://doi.org/10.1016/j.scitotenv.2016.02.133, 2016.
Goldstein, M. and Rougier, J.: Bayes linear calibrated prediction for
complex systems, J. Am. Stat. Assoc., 101,
1132–1143, 2006.
Gómez-Dans, J. L., Lewis, P. E., and Disney, M.: Efficient Emulation of
Radiative Transfer Codes Using Gaussian Processes and Application to Land
Surface Parameter Inferences, Remote Sens., 8, 1–32, 2016.
Hankin, R. K.: Introducing BACCO, an R package for Bayesian analysis of
computer code output, J. Stat. Softw., 14, 1–21, 2005.
Hill, T. C., Ryan, E., and Williams, M.: The use of CO2 flux time series for
parameter and carbon stock estimation in carbon cycle research, Global
Change Biol., 18, 179–193, 2012.
Homma, T. and Saltelli, A.: Importance measures in global sensitivity
analysis of nonlinear models, Reliab. Eng. Syst. Safe.,
52, 1–17, 1996.
Iooss, B. and Lemaître, P.: A review on global sensitivity analysis
methods, in: Uncertainty Management in Simulation-Optimization of Complex
Systems, Operations Research/Computer Science Interfaces Series, Vol 59.
Springer, Boston, MA, 2015.
Kennedy, M., Anderson, C., O'Hagan, A., Lomas, M., Woodward, I., Gosling, J.
P., and Heinemeyer, A.: Quantifying uncertainty in the biospheric carbon
flux for England and Wales, J. Royal Stat. Soc.
A , 171, 109–135, 2008.
Kennedy, M. C. and O'Hagan, A.: Predicting the output from a complex
computer code when fast approximations are available, Biometrika, 87, 1–13,
2000.
Koehler, J. and Owen, A.: 9 Computer experiments, Handbook of Statistics,
13, 261–308, 1996.
Lamboni, M., Monod, H., and Makowski, D.: Multivariate sensitivity analysis
to measure global contribution of input factors in dynamic models,
Reliab. Eng. Syst. Safe., 96, 450–459, 2011.
Lee, L. A., Carslaw, K. S., Pringle, K. J., and Mann, G. W.: Mapping the
uncertainty in global CCN using emulation, Atmos. Chem. Phys., 12, 9739–9751,
https://doi.org/10.5194/acp-12-9739-2012, 2012.
Lee, L. A., Pringle, K. J., Reddington, C. L., Mann, G. W., Stier, P.,
Spracklen, D. V., Pierce, J. R., and Carslaw, K. S.: The magnitude and causes
of uncertainty in global model simulations of cloud condensation nuclei,
Atmos. Chem. Phys., 13, 8879–8914, https://doi.org/10.5194/acp-13-8879-2013,
2013.
Lilburne, L. and Tarantola, S.: Sensitivity analysis of spatial models,
Int. J. Geogr. Inform. Sci., 23, 151–168,
2009.
Loeppky, J. L., Sacks, J., and Welch, W. J.: Choosing the sample size of a
computer experiment: A practical guide, Technometrics, 51, 366–376, 2009.
Mara, T. A. and Tarantola, S.: Application of global sensitivity analysis of
model output to building thermal simulations,
Building Simulation, 1, 290–302, 2008.
Marrel, A., Iooss, B., Laurent, B., and Roustant, O.: Calculations of sobol
indices for the gaussian process metamodel, Reliab. Eng.
Syst. Safe., 94, 742–751, 2009.
Oakley, J. E. and O'Hagan, A.: Probabilistic sensitivity analysis of complex
models: a Bayesian approach, J. Royal Stat. Soc.
B, 66, 751–769, 2004.
O'Hagan, A.: Bayesian analysis of computer code outputs: a tutorial,
Reliab. Eng. Syst. Safe., 91, 1290–1300, 2006.
Pistone, G. and Vicario, G.: Kriging prediction from a circular grid:
application to wafer diffusion, Appl. Stoch. Models Business
Industry, 29, 350–361, 2013.
Queipo, N. V., Haftka, R. T., Shyy, W., Goel, T., Vaidyanathan, R., and
Tucker, P. K.: Surrogate-based analysis and optimization, Prog.
Aerosp. Sci., 41, 1–28, 2005.
Rasmussen, C. E. and Williams, C. K. I.: Gaussian Processes for Machine
Learning, 2006, the MIT Press, ISBN 026218253X, 2006.
Ripley, B. D.: Spatial statistics, John Wiley & Sons,
Hoboken, New Jersey, 2005.
Roustant, O., Ginsbourger, D., and Deville, Y.: DiceKriging, DiceOptim: Two
R packages for the analysis of computer experiments by kriging-based
metamodeling and optimization, available at: https://hal.archives-ouvertes.fr/hal-00495766/document (last access: 15 June 2016),
2012.
Roy, P. T., El Moçayd, N., Ricci, S., Jouhaud, J.-C., Goutal, N., De
Lozzo, M., and Rochoux, M. C.: Comparison of Polynomial Chaos and Gaussian
Process surrogates for uncertainty quantification and correlation estimation
of spatially distributed open-channel steady flows, Stoch. Environ.
Res. Risk Assess., 2017, 1–19, 2017.
Ryan, E.: Fast sensitivity analysis methods for computationally expensive models with multi-dimensional
output, https://doi.org/10.5281/zenodo.1038667, 2017.
Ryan, E. and Wild, O.: Data for the GSA methods paper by Ryan et al.,
https://doi.org/10.5281/zenodo.1038670, 2017.
Saltelli, A.: Making best use of model evaluations to compute sensitivity
indices, Comput. Phys. Commun., 145, 280–297, 2002.
Saltelli, A., Andres, T., and Homma, T.: Sensitivity analysis of model
output: an investigation of new techniques, Comput. Stat.
Data Anal., 15, 211–238, 1993.
Saltelli, A. and Annoni, P.: How to avoid a perfunctory sensitivity
analysis, Environ. Modell. Softw., 25, 1508–1517, 2010.
Saltelli, A., Ratto, M., Andres, T., Campolongo, F., Cariboni, J., Gatelli,
D., Saisana, M., and Tarantola, S.: Global sensitivity analysis: the primer,
John Wiley & Sons, 2008.
Saltelli, A., Ratto, M., Tarantola, S., and Campolongo, F.: Update 1 of:
Sensitivity analysis for chemical models, Chem. Rev., 112, PR1–PR21,
2012.
Saltelli, A., Tarantola, S., and Chan, K.-S.: A quantitative
model-independent method for global sensitivity analysis of model output,
Technometrics, 41, 39–56, 1999.
Schmidt, G. A., Kelley, M., Nazarenko, L., Ruedy, R., Russell, G. L.,
Aleinov, I., Bauer, M., Bauer, S. E., Bhat, M. K., and Bleck, R.:
Configuration and assessment of the GISS ModelE2 contributions to the CMIP5
archive, J. Adv. Model. Earth Syst., 6, 141–184, 2014.
Sexton, D. M., Murphy, J. M., Collins, M., and Webb, M. J.: Multivariate
probabilistic projections using imperfect climate models part I: outline of
methodology, Clim. Dynam., 38, 2513–2542, 2012.
Shindell, D. T., Faluvegi, G., Unger, N., Aguilar, E., Schmidt,
G. A., Koch, D. M., Bauer, S. E., and Miller, R. L.: Simulations
of preindustrial, present-day, and 2100 conditions in the NASA
GISS composition and climate model G-PUCCINI, Atmos. Chem. Phys.,
6, 4427–4459, https://doi.org/10.5194/acp-6-4427-2006, 2006.
Sobie, E. A.: Parameter sensitivity analysis in electrophysiological models
using multivariable regression, Biophys. J., 96, 1264–1274, 2009.
Sobol, I. Y. M.: On sensitivity estimation for nonlinear mathematical
models, Matemat. Modeliro., 2, 112–118, 1990.
Stanfill, B., Mielenz, H., Clifford, D., and Thorburn, P.: Simple approach
to emulating complex computer models for global sensitivity analysis,
Environ. Modell. Softw., 74, 140–155, 2015.
Stites, E. C., Trampont, P. C., Ma, Z., and Ravichandran, K. S.: Network
analysis of oncogenic Ras activation in cancer, Science, 318, 463–467, 2007.
Strong, M., Oakley, J. E., and Brennan, A.: An efficient method for
computing the Expected Value of Sample Information, A non-parametric
regression approach, ScHARR working paper, 2015a.
Strong, M., Oakley, J. E., and Brennan, A.: Estimating multiparameter
partial expected value of perfect information from a probabilistic
sensitivity analysis sample a nonparametric regression approach, Med.
Decis. Mak., 34, 311–326, 2014.
Strong, M., Oakley, J. E., Brennan, A., and Breeze, P.: Estimating the
expected value of sample information using the probabilistic sensitivity
analysis sample a fast nonparametric regression-based method, Med.
Decis. Mak., 35, 570–583, 2015b.
Tarantola, S., Gatelli, D., and Mara, T. A.: Random balance designs for the
estimation of first order global sensitivity indices, Reliab.
Eng. Syst. Safe., 91, 717–727, 2006.
Vanuytrecht, E., Raes, D., and Willems, P.: Global sensitivity analysis of
yield output from the water productivity model, Environ. Modell.
Softw., 51, 323–332, 2014.
Verrelst, J., Sabater, N., Rivera, J. P., Muñoz-Marí, J., Vicent,
J., Camps-Valls, G., and Moreno, J.: Emulation of Leaf, Canopy and
Atmosphere Radiative Transfer Models for Fast Global Sensitivity Analysis,
Remote Sens., 8, 673–699, 2016.
Voulgarakis, A., Naik, V., Lamarque, J.-F., Shindell, D. T., Young, P. J.,
Prather, M. J., Wild, O., Field, R. D., Bergmann, D., Cameron-Smith, P.,
Cionni, I., Collins, W. J., Dalsøren, S. B., Doherty, R. M., Eyring, V.,
Faluvegi, G., Folberth, G. A., Horowitz, L. W., Josse, B., MacKenzie, I. A.,
Nagashima, T., Plummer, D. A., Righi, M., Rumbold, S. T., Stevenson, D. S.,
Strode, S. A., Sudo, K., Szopa, S., and Zeng, G.: Analysis of present day and
future OH and methane lifetime in the ACCMIP simulations, Atmos. Chem. Phys.,
13, 2563–2587, https://doi.org/10.5194/acp-13-2563-2013, 2013.
Vu-Bac, N., Rafiee, R., Zhuang, X., Lahmer, T., and Rabczuk, T.: Uncertainty
quantification for multiscale modeling of polymer nanocomposites with
correlated parameters, Composites B, 68, 446–464, 2015.
Welch, W. J., Buck, R. J., Sacks, J., Wynn, H. P., Mitchell, T. J., and
Morris, M. D.: Screening, predicting, and computer experiments,
Technometrics, 34, 15–25, 1992.
Wild, O.: Modelling the global tropospheric ozone budget: exploring the
variability in current models, Atmos. Chem. Phys., 7, 2643–2660,
https://doi.org/10.5194/acp-7-2643-2007, 2007.
Wild, O., Pochanart, P., and Akimoto, H.: Trans-Eurasian transport of ozone
and its precursors, J. Geophys. Res.-Atmos., 109, D11302, https://doi.org/10.1029/2003JD004501, 2004.
Wild, O. and Prather, M. J.: Excitation of the primary tropospheric chemical
mode in a global three-dimensional model, J. Geophys. Res.,
105, 24647–24660, 2000.
Wild, O., Ryan, E., O'Connor, F., Vougarakis, A., and Lee, L.: Reducing
Uncertainty in Model Budgets of Tropospheric Ozone and OH,
Atmos.Chem. Phys., in preparation, 2018.
Wold, S., Sjöström, M., and Eriksson, L.: PLS-regression: a basic
tool of chemometrics, Chemom. Intell. Labor. Syst., 58,
109–130, 2001.
Wood, S. N.: Generalized additive models: an introduction with R, CRC press,
New York, 2017.
Wu, J., Dhingra, R., Gambhir, M., and Remais, J. V.: Sensitivity analysis of
infectious disease models: methods, advances and their application, J.
Roy. Soc. Interf., 10, https://doi.org/10.1098/rsif.2012.1018, 2013.
Short summary
Global sensitivity analysis (GSA) identifies which parameters of a model most affect its output. We performed GSA using statistical emulators as surrogates of two slow-running atmospheric chemistry transport models. Due to the high dimension of the model outputs, we considered two alternative methods: one that reduced the output dimension and one that did not require an emulator. The alternative methods accurately performed the GSA but were significantly faster than the emulator-only method.
Global sensitivity analysis (GSA) identifies which parameters of a model most affect its output....
