Articles | Volume 16, issue 4
https://doi.org/10.5194/gmd-16-1277-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-1277-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
22 Feb 2023
Model description paper |
| 22 Feb 2023
| 22 Feb 2023
A new precipitation emulator (PREMU v1.0) for lower-complexity models
Gang Liu
Sino-French Institute for Earth System Science, College of Urban and
Environmental Sciences, Peking University, Beijing, China
Sino-French Institute for Earth System Science, College of Urban and
Environmental Sciences, Peking University, Beijing, China
Chris Huntingford
UK Centre for Ecology and Hydrology, Wallingford, Oxfordshire OX10
8BB, United Kingdom
Sino-French Institute for Earth System Science, College of Urban and
Environmental Sciences, Peking University, Beijing, China
Related authors
No articles found.
Daniel Bampoh, Bartlomiej Arendarczyk, Sam Rabin, Thomas Pugh, Gregory Munday, Chris Huntingford, Garry Hayman, Peter Anthoni, Peter Alexander, Mark Rounsevell, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2026-5230, https://doi.org/10.5194/egusphere-2026-5230, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Agriculture and land use are major sources and sinks of greenhouse gases, yet how these emissions feed back through the climate to reshape ecosystems is rarely traced end-to-end. We built a coupled modelling framework that follows this link from land-management decisions to emissions, to the resulting climate change, and back to the land. It reproduces observed emissions, concentrations and climate, and shows that careful land management could slow global warming by a few tenths of a degree.
Chris Huntingford, Jesse F. Abrams, Peter Ashwin, Niklas Boers, Chris A. Boulton, Joshua E. Buxton, Joseph Clarke, Peter M. Cox, Andrew M. Cunliffe, Henk A. Dijkstra, Bahar Emirzade, Darren Ghent, Daniel Giles, Sneha Kachhara, Timothy M. Lenton, Valerio Lucarini, Helen Millman, Robert J. Parker, Sergei Petrovskii, Paul D. L. Ritchie, Cristina Ruiz Villena, Mark S. Williamson, and Niccolò Zagli
EGUsphere, https://doi.org/10.5194/egusphere-2026-5606, https://doi.org/10.5194/egusphere-2026-5606, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
Future tipping of key parts of the Earth system, driven by human-induced climate change, poses serious risks to society. These concerns have spurred the development of Early Warning Systems (EWSs) that use data to show how close we are to tipping. However, existing EWSs are often generic statistical forms. We first review current EWS methods, then present a broad set of hypotheses that may help improve them. We argue strongly for process knowledge to guide more refined EWS forms.
Hui Li, Philippe Ciais, Frédéric Chevallier, Bo Zheng, Paul I. Palmer, Frank Hase, Morgan Lopez, Elsa M. Ordway, Shushi Peng, Danielle Monteverde, Michel Ramonet, Jason M. St. Clair, Le Bienfaiteur Sagang, and Benjamin Poulter
Atmos. Chem. Phys., 26, 11893–11908, https://doi.org/10.5194/acp-26-11893-2026, https://doi.org/10.5194/acp-26-11893-2026, 2026
Short summary
Short summary
We studied how to best place methane monitoring instruments across Africa to better understand where emissions come from. Because observations can overlap in what they detect, adding more sites does not always improve results equally. Using a statistical approach, we identified both the ideal number and locations of sites (ten new sites can reduce emission uncertainty by over 65 % for total African CH4). Our results help guide cost-effective monitoring strategies in regions with limited data.
Chris Huntingford, Rebecca J. Oliver, Lina M. Mercado, Stephen A. Sitch, Douglas B. Clark, Annikki Mäkelä, Carolina Mayoral, and Richard J. Norby
EGUsphere, https://doi.org/10.5194/egusphere-2026-4392, https://doi.org/10.5194/egusphere-2026-4392, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Understanding carbon allocation among land-surface components in a changing climate is important, but it remains poorly understood. As a surrogate for missing knowledge and related equations, optimal allocation hypotheses are proposed. Makela et al. (2008) propose foliage nitrogen adjusted to maximise gross primary productivity. We add to that framework models of photosynthesis and respiration, creating an optimal structure that responds to different near-surface meteorological conditions.
Andrew J. Nicoll, Hannah M. Christensen, Chris Huntingford, and Doug Smith
Earth Syst. Dynam., 17, 1061–1079, https://doi.org/10.5194/esd-17-1061-2026, https://doi.org/10.5194/esd-17-1061-2026, 2026
Short summary
Short summary
We use artificial intelligence to learn simple equations from historical climate data that describe how North Atlantic ocean temperature, air pressure and rainfall vary and influence each other over decades. Analysing the model's behaviour and equation terms, we find rainfall strongly feeds back on both the ocean and the atmosphere. These interactions are well captured by the models and allow rainfall to be predicted over the ocean, and nearby regions such as Europe over coming decades.
Chris Huntingford, Paul D. L. Ritchie, and Joseph Clarke
Nonlin. Processes Geophys., 33, 385–399, https://doi.org/10.5194/npg-33-385-2026, https://doi.org/10.5194/npg-33-385-2026, 2026
Short summary
Short summary
The risk of climate change triggering tipping points is a concern. While Earth System Models (ESMs) predict these points, their timing often varies. Equations of nonlinear dynamical systems can model tipping, hysteresis, and inertia, enabling comparison with ESMs. We propose a method to map tipping timing estimates onto a dynamical system that simulates a full hysteresis loop. We focus on solutions to the equation under overshoot forcing in global temperature and different inertia levels.
Ke Yu, Yang Su, Ronny Lauerwald, Philippe Ciais, Yi Xi, Haoran Xu, Xianglin Zhang, Nicolas Viovy, Amie Pickering, Marie Collard, and Daniel S. Goll
Geosci. Model Dev., 19, 7217–7237, https://doi.org/10.5194/gmd-19-7217-2026, https://doi.org/10.5194/gmd-19-7217-2026, 2026
Short summary
Short summary
Coupling crop and land surface models helps quantify the climate effects of agriculture, but lacks crop-specific management processes. We enhanced a land surface model with time-varying albedo from foliar yellowing and residue cover, improving the simulation of energy and water fluxes. Results show cooler surfaces and slightly wetter soils during residue cover, highlighting how managements improve climate mitigation and adaptation, advancing the development of climate-smart agriculture.
Nikola Besic, Philippe Ciais, Fa Li, Robert Jackson, Kunxiaojia Yuan, Shushi Peng, Benjamin Poulter, Zhen Zhang, and Qing Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2026-2604, https://doi.org/10.5194/egusphere-2026-2604, 2026
Short summary
Short summary
Wetlands are the largest natural source of methane, an important greenhouse gas, but current estimates of their emissions remain highly uncertain because models often disagree. We combined field measurements from wetlands around the world with advanced statistical methods to identify where different models perform best. Our results show that accounting for regional differences in model skill can substantially change local methane estimates and potentially improve future climate assessments.
Patric J. L. Boardman, Joseph Clarke, Peter M. Cox, Chris Huntingford, Chris D. Jones, and Mark S. Williamson
Earth Syst. Dynam., 17, 829–841, https://doi.org/10.5194/esd-17-829-2026, https://doi.org/10.5194/esd-17-829-2026, 2026
Short summary
Short summary
Climate sensitivity quantifies how much Earth warms for a given radiative forcing, and is often described by the Transient Climate Response (TCR) value. Although the Intergovernmental Panel on Climate Change (IPCC) estimates the most likely TCR to be 1.8 K, some models predict values >2.4 K. Record warmth in 2023–2024 raises questions as to whether the TCR may be larger than previously suggested. Using up-to-date data, we estimate the TCR to be ~1.8 K. We also show that future warming falls within the low–mid model range, making the 2°C Paris target still feasible.
Xinyu Li, Shushi Peng, Victor M. Stepanenko, Liu Liu, and Dan Zhu
EGUsphere, https://doi.org/10.5194/egusphere-2026-2349, https://doi.org/10.5194/egusphere-2026-2349, 2026
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Lakes release methane, but global totals are uncertain. We used a process-based computer model to simulate how methane forms and escapes. We calibrated the model in 15 lakes with continuous observations, and validated the model in 176 lakes. The model showed global lakes over ten hectares emit 17.7 to 20.1 million metric tons annually. Most gas escapes through bubbles, particularly from shallow areas. This study helps us track these emissions and predict how they might rise as the world warms.
Mengze Li, Robert B. Jackson, Marielle Saunois, Philippe Ciais, Ben Poulter, Josep G. Canadell, Prabir K. Patra, Hanqin Tian, Zhen Zhang, Etienne Fluet-Chouinard, Zutao Ouyang, Ting Zhang, David J. Beerling, Dmitry A. Belikov, Philippe Bousquet, Danilo Custodio, Naveen Chandra, Xinyu Dou, Nicola Gedney, Peter O. Hopcroft, Alison M. Hoyt, Kazuhito Ichii, Akihito Ito, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Masayuki Kondo, Fa Li, Tingting Li, Xiangyu Liu, Shamil Maksyutov, Avni Malhotra, Adrien Martinez, Kyle McDonald, Joe R. Melton, Jurek Müller, Yosuke Niwa, Shufen Pan, Shushi Peng, Changhui Peng, Zhangcai Qin, Peter Raymond, William Riley, Arjo Segers, Rona L. Thompson, Aki Tsuruta, Yi Xi, Kunxiaojia Yuan, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 18, 3507–3524, https://doi.org/10.5194/essd-18-3507-2026, https://doi.org/10.5194/essd-18-3507-2026, 2026
Short summary
Short summary
We proposed a framework that combines machine-learning and climate data to predict global natural vegetated wetland methane emissions for 2000–2025. We found that although total global emissions remained stable in the post-2020s, Northern Hemisphere emissions surged whilst tropical emissions fell. This approach allows us to rapidly monitor emissions and provides early warnings for climate impacts.
Liyang Liu, Philippe Ciais, Thu Hang Nguyen, Yi Xi, Chunjing Qiu, Elodie Salmon, Aram Kalhori, Christophe Guimbaud, Matthias Peichl, Joshua L. Ratcliffe, Koffi Dodji Noumonvi, and Xuefei Li
Biogeosciences, 23, 2277–2308, https://doi.org/10.5194/bg-23-2277-2026, https://doi.org/10.5194/bg-23-2277-2026, 2026
Short summary
Short summary
We simulate virtual idealized drainage at 10 pristine peatland sites. Over time, the emission factors of CO2 flux decrease and the reduction of CH4 emissions is amplified. The sensitivities of flux changes to drainage are primarily controlled by initial CO2 and CH4 fluxes, initial soil carbon content, peat vegetation community, air temperature and initial water table depth. Using GWP100 (100-year Global Warming Potential), our simulation suggested only very small net GHG (greenhouse gas) emission changes when peatland is drained for 50 years.
Shengxi Bai, Huilin Chen, Zhen Zhang, Shushi Peng, Fei Jiang, Fei Li, Shuzhuang Feng, Yingqi Yan, Qidan Huang, and Yongguang Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-4610, https://doi.org/10.5194/egusphere-2025-4610, 2026
Short summary
Short summary
We examined methane emissions from coal mines in Shanxi Province, China’s major coal production region. Using satellite observations and advanced analysis, we found that rare but very large methane releases play a major role in total emissions. Policies to cut coal capacity initially reduced methane, but emissions rose again with production recovery. Our findings improve understanding of how energy policies affect climate pollution and offer a framework for better methane monitoring worldwide.
Joseph Clarke, Chris Huntingford, Paul D. L. Ritchie, Rebecca Varney, Mark S. Williamson, and Peter Cox
Earth Syst. Dynam., 16, 2087–2100, https://doi.org/10.5194/esd-16-2087-2025, https://doi.org/10.5194/esd-16-2087-2025, 2025
Short summary
Short summary
An increase in CO2 in the atmosphere warms the climate through the greenhouse effect, but also leads to uptake of CO2 by the land and ocean. However, the warming is also expected to suppress carbon uptake. If this suppression were strong enough, it could overwhelm the uptake of carbon, leading to a runaway feedback loop causing severe global warming. We find it is possible that this runaway could be relevant in complex climate models and even at the end of the last ice age.
Yi Xi, Philippe Ciais, Dan Zhu, Chunjing Qiu, Yuan Zhang, Shushi Peng, Gustaf Hugelius, Simon P. K. Bowring, Daniel S. Goll, and Ying-Ping Wang
Geosci. Model Dev., 18, 6043–6062, https://doi.org/10.5194/gmd-18-6043-2025, https://doi.org/10.5194/gmd-18-6043-2025, 2025
Short summary
Short summary
Including high-latitude deep carbon is critical for projecting future soil carbon emissions, yet it is absent in most land surface models. Here we propose a new carbon accumulation protocol by integrating deep carbon from Yedoma deposits and representing the observed history of peat carbon formation in ORCHIDEE-MICT. Our results show an additional 157 Pg C in present-day Yedoma deposits and a 1–5 m shallower peat depth and 43 % less passive soil carbon in peatlands compared to the conventional protocol.
Paul D. L. Ritchie, Chris Huntingford, and Peter M. Cox
Earth Syst. Dynam., 16, 1523–1526, https://doi.org/10.5194/esd-16-1523-2025, https://doi.org/10.5194/esd-16-1523-2025, 2025
Short summary
Short summary
Climate tipping points are not committed upon crossing critical thresholds in global warming, as is often assumed. Instead, it is possible to temporarily overshoot a threshold without causing tipping, provided the duration of the overshoot is short. In this Idea, we demonstrate that restricting the time over 1.5 °C would considerably reduce tipping point risks.
Juliette Bernard, Catherine Prigent, Carlos Jimenez, Etienne Fluet-Chouinard, Bernhard Lehner, Elodie Salmon, Philippe Ciais, Zhen Zhang, Shushi Peng, and Marielle Saunois
Earth Syst. Sci. Data, 17, 2985–3008, https://doi.org/10.5194/essd-17-2985-2025, https://doi.org/10.5194/essd-17-2985-2025, 2025
Short summary
Short summary
Wetlands are responsible for about a third of global emissions of methane, a potent greenhouse gas. We have developed the Global Inundation Extent from Multi-Satellites-MethaneCentric (GIEMS-MC) dataset to represent the dynamics of wetland extent on a global scale (0.25° × 0.25° resolution, monthly time step). This updated resource combines satellite data and existing wetland databases, covering 1992 to 2020. Consistent maps of other methane-emitting surface waters (lakes, rivers, reservoirs, rice paddies) are also provided.
Marielle Saunois, Adrien Martinez, Benjamin Poulter, Zhen Zhang, Peter A. Raymond, Pierre Regnier, Josep G. Canadell, Robert B. Jackson, Prabir K. Patra, Philippe Bousquet, Philippe Ciais, Edward J. Dlugokencky, Xin Lan, George H. Allen, David Bastviken, David J. Beerling, Dmitry A. Belikov, Donald R. Blake, Simona Castaldi, Monica Crippa, Bridget R. Deemer, Fraser Dennison, Giuseppe Etiope, Nicola Gedney, Lena Höglund-Isaksson, Meredith A. Holgerson, Peter O. Hopcroft, Gustaf Hugelius, Akihiko Ito, Atul K. Jain, Rajesh Janardanan, Matthew S. Johnson, Thomas Kleinen, Paul B. Krummel, Ronny Lauerwald, Tingting Li, Xiangyu Liu, Kyle C. McDonald, Joe R. Melton, Jens Mühle, Jurek Müller, Fabiola Murguia-Flores, Yosuke Niwa, Sergio Noce, Shufen Pan, Robert J. Parker, Changhui Peng, Michel Ramonet, William J. Riley, Gerard Rocher-Ros, Judith A. Rosentreter, Motoki Sasakawa, Arjo Segers, Steven J. Smith, Emily H. Stanley, Joël Thanwerdas, Hanqin Tian, Aki Tsuruta, Francesco N. Tubiello, Thomas S. Weber, Guido R. van der Werf, Douglas E. J. Worthy, Yi Xi, Yukio Yoshida, Wenxin Zhang, Bo Zheng, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Earth Syst. Sci. Data, 17, 1873–1958, https://doi.org/10.5194/essd-17-1873-2025, https://doi.org/10.5194/essd-17-1873-2025, 2025
Short summary
Short summary
Methane (CH4) is the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). A consortium of multi-disciplinary scientists synthesise and update the budget of the sources and sinks of CH4. This edition benefits from important progress in estimating emissions from lakes and ponds, reservoirs, and streams and rivers. For the 2010s decade, global CH4 emissions are estimated at 575 Tg CH4 yr-1, including ~65 % from anthropogenic sources.
Chris Huntingford, Andrew J. Nicoll, Cornelia Klein, and Jawairia A. Ahmad
Earth Syst. Dynam., 16, 475–495, https://doi.org/10.5194/esd-16-475-2025, https://doi.org/10.5194/esd-16-475-2025, 2025
Short summary
Short summary
AI is impacting science, providing key data insights, but most algorithms are statistical requiring cautious "out-of-sample" extrapolation. Yet climate research concerns predicting future climatic states. We consider a new method of AI-led equation discovery. Equations offer process interpretation and more robust predictions. We recommend this method for climate analysis, suggesting illustrative application to atmospheric convection, land–atmosphere CO2 flux, and global ocean circulation models.
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.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
Short summary
Short summary
Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara H. Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
Short summary
Short summary
This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 yr-1 in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Mark S. Williamson, Peter M. Cox, Chris Huntingford, and Femke J. M. M. Nijsse
Earth Syst. Dynam., 15, 829–852, https://doi.org/10.5194/esd-15-829-2024, https://doi.org/10.5194/esd-15-829-2024, 2024
Short summary
Short summary
Emergent constraints on equilibrium climate sensitivity (ECS) have generally got statistically weaker in the latest set of state-of-the-art climate models (CMIP6) compared to past sets (CMIP5). We look at why this weakening happened for one particular study (Cox et al, 2018) and attribute it to an assumption made in the theory that when corrected for restores there is a stronger relationship between predictor and ECS.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
Short summary
Short summary
The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Emma L. Robinson, Chris Huntingford, Valyaveetil Shamsudheen Semeena, and James M. Bullock
Earth Syst. Sci. Data, 15, 5371–5401, https://doi.org/10.5194/essd-15-5371-2023, https://doi.org/10.5194/essd-15-5371-2023, 2023
Short summary
Short summary
CHESS-SCAPE is a suite of high-resolution climate projections for the UK to 2080, derived from United Kingdom Climate Projections 2018 (UKCP18), designed to support climate impact modelling. It contains four realisations of four scenarios of future greenhouse gas levels (RCP2.6, 4.5, 6.0 and 8.5), with and without bias correction to historical data. The variables are available at 1 km resolution and a daily time step, with monthly, seasonal and annual means and 20-year mean-monthly time slices.
Chris Huntingford, Peter M. Cox, Mark S. Williamson, Joseph J. Clarke, and Paul D. L. Ritchie
Earth Syst. Dynam., 14, 433–442, https://doi.org/10.5194/esd-14-433-2023, https://doi.org/10.5194/esd-14-433-2023, 2023
Short summary
Short summary
Emergent constraints (ECs) reduce the spread of projections between climate models. ECs estimate changes to climate features impacting adaptation policy, and with this high profile, the method is under scrutiny. Asking
What is an EC?, we suggest they are often the discovery of parameters that characterise hidden large-scale equations that climate models solve implicitly. We present this conceptually via two examples. Our analysis implies possible new paths to link ECs and physical processes.
Louise J. Slater, Chris Huntingford, Richard F. Pywell, John W. Redhead, and Elizabeth J. Kendon
Earth Syst. Dynam., 13, 1377–1396, https://doi.org/10.5194/esd-13-1377-2022, https://doi.org/10.5194/esd-13-1377-2022, 2022
Short summary
Short summary
This work considers how wheat yields are affected by weather conditions during the three main wheat growth stages in the UK. Impacts are strongest in years with compound weather extremes across multiple growth stages. Future climate projections are beneficial for wheat yields, on average, but indicate a high risk of unseen weather conditions which farmers may struggle to adapt to and mitigate against.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
Short summary
Short summary
We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Cited articles
Allen, M. R. and Ingram, W. J.: Constraints on future changes in climate and
the hydrologic cycle, Nature, 419, 228–232, https://doi.org/10.1038/nature01092, 2002.
An, L., Hao, Y., Yeh, T.-C. J., and Zhang, B.: Annual to multidecadal
climate modes linking precipitation of the northern and southern slopes of
the Tianshan Mts, Theor. Appl. Climatol., 140, 453–465,
https://doi.org/10.1007/s00704-020-03100-y, 2020.
Austin, G. L. and Dirks, K. N.: Topographic Effects on Precipitation, in:
Encyclopedia of Hydrological Sciences,
https://doi.org/10.1002/0470848944.hsa033, 2005.
Berg, P., Moseley, C., and Haerter, J. O.: Strong increase in convective
precipitation in response to higher temperatures, Nat. Geosci., 6, 181–185,
https://doi.org/10.1038/ngeo1731, 2013.
Beusch, L., Nicholls, Z., Gudmundsson, L., Hauser, M., Meinshausen, M., and Seneviratne, S. I.: From emission scenarios to spatially resolved projections with a chain of computationally efficient emulators: coupling of MAGICC (v7.5.1) and MESMER (v0.8.3), Geosci. Model Dev., 15, 2085–2103, https://doi.org/10.5194/gmd-15-2085-2022, 2022.
Bollasina, M. A., Ming, Y., and Ramaswamy, V.: Anthropogenic Aerosols and
the Weakening of the South Asian Summer Monsoon, Science, 334, 502–505,
https://doi.org/10.1126/science.1204994, 2011.
Brunner, L., Hauser, M., Lorenz, R., and Beyerle, U.: The ETH Zurich CMIP6 next
generation archive: technical documentation, Zenodo, https://doi.org/10.5281/zenodo.3734128,
2020.
Cai, W., Whetton, P. H., and Pittock, A. B.: Fluctuations of the
relationship between ENSO and northeast Australian rainfall, Clim. Dynam.,
17, 421–432, https://doi.org/10.1007/PL00013738, 2001.
Chadwick, R. and Good, P.: Understanding nonlinear tropical precipitation
responses to CO2 forcing, Geophys. Res. Lett., 40, 4911–4915,
https://doi.org/10.1002/grl.50932, 2013.
Collins, M., Knutti, R., Arblaster, J., Dufresne, J. L., Fichefet, T.,
Friedlingstein, P., Gao, X., Gutowski, W. J., Johns, T., Krinner, G.,
Shongwe, M., Tebaldi, C., Weaver, A. J., and Wehner, M.: Long-term Climate
Change: Projections, Commitments and Irreversibility, in: Climate Change
2013: The Physical Science Basis, Contribution of Working Group I to the
Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Stocker, T. F., Qin, D., Plattner, G. K., Tignor, M., Allen, S.
K., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M., Cambridge
University Press, Cambridge, UK, New York, NY, USA, https://doi.org/10.1017/CBO9781107415324.024, 2013.
Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J.,
Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P.,
Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman,
P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri,
M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L.,
Woodruff, S. D., and Worley, S. J.: The Twentieth Century Reanalysis
Project, Q. J. Roy. Meteor. Soc., 137, 1–28, https://doi.org/10.1002/qj.776,
2011.
Dai, A.: The influence of the inter-decadal Pacific oscillation on US
precipitation during 1923–2010, Clim. Dynam., 41, 633–646,
https://doi.org/10.1007/s00382-012-1446-5, 2013.
Danabasoglu, G., Lamarque, J.-F., Bacmeister, J., Bailey, D. A., DuVivier,
A. K., Edwards, J., Emmons, L. K., Fasullo, J., Garcia, R., Gettelman, A.,
Hannay, C., Holland, M. M., Large, W. G., Lauritzen, P. H., Lawrence, D. M.,
Lenaerts, J. T. M., Lindsay, K., Lipscomb, W. H., Mills, M. J., Neale, R.,
Oleson, K. W., Otto-Bliesner, B., Phillips, A. S., Sacks, W., Tilmes, S.,
van Kampenhout, L., Vertenstein, M., Bertini, A., Dennis, J., Deser, C.,
Fischer, C., Fox-Kemper, B., Kay, J. E., Kinnison, D., Kushner, P. J.,
Larson, V. E., Long, M. C., Mickelson, S., Moore, J. K., Nienhouse, E.,
Polvani, L., Rasch, P. J., and Strand, W. G.: The Community Earth System
Model Version 2 (CESM2), J. Adv. Model. Earth Syst., 12, e2019MS001916,
https://doi.org/10.1029/2019MS001916, 2020.
Efthymiadis, D., Jones, P. D., Briffa, K. R., Böhm, R., and Maugeri, M.:
Influence of large-scale atmospheric circulation on climate variability in
the Greater Alpine Region of Europe, J. Geophys. Res.-Atmos., 112, D12104,
https://doi.org/10.1029/2006JD008021, 2007.
Eltahir, E. A. B. and Bras, R. L.: Precipitation recycling, Rev. Geophys.,
34, 367–378, https://doi.org/10.1029/96RG01927, 1996.
Fereday, D., Chadwick, R., Knight, J., and Scaife, A. A.: Atmospheric
Dynamics is the Largest Source of Uncertainty in Future Winter European
Rainfall, J. Climate, 31, 963–977, https://doi.org/10.1175/jcli-d-17-0048.1, 2018.
Gasser, T., Ciais, P., Boucher, O., Quilcaille, Y., Tortora, M., Bopp, L., and Hauglustaine, D.: The compact Earth system model OSCAR v2.2: description and first results, Geosci. Model Dev., 10, 271–319, https://doi.org/10.5194/gmd-10-271-2017, 2017.
Geman, S., Bienenstock, E., and Doursat, R.: Neural Networks and the
Bias/Variance Dilemma, Neural Comput., 4, 1–58, https://doi.org/10.1162/neco.1992.4.1.1,
1992.
Gupta, V. and Jain, M. K.: Unravelling the teleconnections between ENSO and
dry/wet conditions over India using nonlinear Granger causality, Atmos.
Res., 247, 105168, https://doi.org/10.1016/j.atmosres.2020.105168, 2021.
Hausfather, Z. and Peters, P. G.: Emissions – the “business as usual” story is misleading, Nature, 577, 618–620, https://doi.org/10.1038/d41586-020-00177-3, 2020.
Heinze-Deml, C., Sippel, S., Pendergrass, A. G., Lehner, F., and Meinshausen, N.: Latent Linear Adjustment Autoencoder v1.0: a novel method for estimating and emulating dynamic precipitation at high resolution, Geosci. Model Dev., 14, 4977–4999, https://doi.org/10.5194/gmd-14-4977-2021, 2021.
Horowitz, L. W., Naik, V., Paulot, F., Ginoux, P. A., Dunne, J. P., Mao, J.,
Schnell, J., Chen, X., He, J., John, J. G., Lin, M., Lin, P., Malyshev, S.,
Paynter, D., Shevliakova, E., and Zhao, M.: The GFDL Global Atmospheric
Chemistry-Climate Model AM4.1: Model Description and Simulation
Characteristics, J. Adv. Model. Earth Syst., 12, e2019MS002032,
https://doi.org/10.1029/2019MS002032, 2020.
Hourdin, F., Rio, C., Grandpeix, J.-Y., Madeleine, J.-B., Cheruy, F.,
Rochetin, N., Jam, A., Musat, I., Idelkadi, A., Fairhead, L., Foujols,
M.-A., Mellul, L., Traore, A.-K., Dufresne, J.-L., Boucher, O., Lefebvre,
M.-P., Millour, E., Vignon, E., Jouhaud, J., Diallo, F. B., Lott, F.,
Gastineau, G., Caubel, A., Meurdesoif, Y., and Ghattas, J.: LMDZ6A: The
Atmospheric Component of the IPSL Climate Model With Improved and Better
Tuned Physics, J. Adv. Model. Earth Syst., 12, e2019MS001892,
https://doi.org/10.1029/2019MS001892, 2020.
Humphrey, V. and Gudmundsson, L.: GRACE-REC: a reconstruction of climate-driven water storage changes over the last century, Earth Syst. Sci. Data, 11, 1153–1170, https://doi.org/10.5194/essd-11-1153-2019, 2019.
Huntingford, C., Booth, B. B. B., Sitch, S., Gedney, N., Lowe, J. A., Liddicoat, S. K., Mercado, L. M., Best, M. J., Weedon, G. P., Fisher, R. A., Lomas, M. R., Good, P., Zelazowski, P., Everitt, A. C., Spessa, A. C., and Jones, C. D.: IMOGEN: an intermediate complexity model to evaluate terrestrial impacts of a changing climate, Geosci. Model Dev., 3, 679–687, https://doi.org/10.5194/gmd-3-679-2010, 2010.
Huntingford, C., Yang, H., Harper, A., Cox, P. M., Gedney, N., Burke, E. J., Lowe, J. A., Hayman, G., Collins, W. J., Smith, S. M., and Comyn-Platt, E.: Flexible parameter-sparse global temperature time profiles that stabilise at 1.5 and 2.0 ∘C, Earth Syst. Dynam., 8, 617–626, https://doi.org/10.5194/esd-8-617-2017, 2017.
IPCC: Climate Change 2013: The Physical Science Basis, Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental
Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner,
G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex, V.,
and Midgley, P. M., Cambridge University Press, Cambridge, UK and New York,
NY, USA, 1535 pp., 2013.
Jiang, Y., Cooley, D., and Wehner, M. F.: Principal Component Analysis for
Extremes and Application to U.S. Precipitation, J. Climate, 33, 6441–6451,
https://doi.org/10.1175/JCLI-D-19-0413.1, 2020.
Jones, J. M., Fogt, R. L., Widmann, M., Marshall, G. J., Jones, P. D., and
Visbeck, M.: Historical SAM Variability. Part I: Century-Length Seasonal
Reconstructions, J. Climate, 22, 5319–5345, https://doi.org/10.1175/2009jcli2785.1, 2009.
Jones, P. W.: First- and Second-Order Conservative Remapping Schemes for
Grids in Spherical Coordinates, Mon. Weather Rev., 127, 2204–2210,
https://doi.org/10.1175/1520-0493(1999)127<2204:Fasocr>2.0.Co;2, 1999.
Kim, H.: Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1), Data Integration and Analysis System (DIAS) [data set], https://doi.org/10.20783/DIAS.501, 2017.
Kim, J., Oh, H.-S., Lim, Y., and Kang, H.-S.: Seasonal precipitation
prediction via data-adaptive principal component regression, Int. J.
Climatol., 37, 75–86, https://doi.org/10.1002/joc.4979, 2017.
Korell, L., Auge, H., Chase, J. M., Harpole, W. S., and Knight, T. M.:
Responses of plant diversity to precipitation change are strongest at local
spatial scales and in drylands, Nat. Commun., 12, 2489,
https://doi.org/10.1038/s41467-021-22766-0, 2021.
Li, G., Gao, C., Lu, B., and Chen, H.: Inter-annual variability of spring
precipitation over the Indo-China Peninsula and its asymmetric relationship
with El Niño-Southern Oscillation, Clim. Dynam., 56, 2651–2665,
https://doi.org/10.1007/s00382-020-05609-4, 2021.
Li, L., Li, J., and Yu, R.: Evaluation of CMIP6 HighResMIP models in
simulating precipitation over Central Asia, Adv. Clim. Change Res., 13,
1–13, https://doi.org/10.1016/j.accre.2021.09.009, 2022.
Li, W., Zhai, P., and Cai, J.: Research on the Relationship of ENSO and the
Frequency of Extreme Precipitation Events in China, Adv. Clim. Change. Res.,
2, 101–107, https://doi.org/10.3724/SP.J.1248.2011.00101, 2011.
Liu, G., Peng, S. S., Huntingford, C., and Xi, Y.: GangLiulg/PreMU: v1.0.0 (PREMU), Zenodo [code], https://doi.org/10.5281/zenodo.7545350, 2023.
Lu, B., Li, H., Wu, J., Zhang, T., Liu, J., Liu, B., Chen, Y., and Baishan,
J.: Impact of El Niño and Southern Oscillation on the summer
precipitation over Northwest China, Atmos. Sci. Lett, 20, e928,
https://doi.org/10.1002/asl.928, 2019.
McKinnon, K. A. and Deser, C.: Internal Variability and Regional Climate
Trends in an Observational Large Ensemble, J. Climate, 31, 6783–6802,
https://doi.org/10.1175/JCLI-D-17-0901.1, 2018.
McKinnon, K. A. and Deser, C.: The Inherent Uncertainty of Precipitation
Variability, Trends, and Extremes due to Internal Variability, with
Implications for Western U.S. Water Resources, J. Climate, 34, 9605–9622,
https://doi.org/10.1175/JCLI-D-21-0251.1, 2021.
Medvigy, D. and Beaulieu, C.: Trends in Daily Solar Radiation and
Precipitation Coefficients of Variation since 1984, J. Climate, 25,
1330–1339, https://doi.org/10.1175/2011jcli4115.1, 2012.
Meinshausen, M., Raper, S. C. B., and Wigley, T. M. L.: Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration, Atmos. Chem. Phys., 11, 1417–1456, https://doi.org/10.5194/acp-11-1417-2011, 2011.
Michel, S., Swingedouw, D., Chavent, M., Ortega, P., Mignot, J., and Khodri, M.: Reconstructing climatic modes of variability from proxy records using ClimIndRec version 1.0, Geosci. Model Dev., 13, 841–858, https://doi.org/10.5194/gmd-13-841-2020, 2020.
Nath, S., Lejeune, Q., Beusch, L., Seneviratne, S. I., and Schleussner, C.-F.: MESMER-M: an Earth system model emulator for spatially resolved monthly temperature, Earth Syst. Dynam., 13, 851–877, https://doi.org/10.5194/esd-13-851-2022, 2022.
Nicholls, Z. R. J., Meinshausen, M., Lewis, J., Gieseke, R., Dommenget, D., Dorheim, K., Fan, C.-S., Fuglestvedt, J. S., Gasser, T., Golüke, U., Goodwin, P., Hartin, C., Hope, A. P., Kriegler, E., Leach, N. J., Marchegiani, D., McBride, L. A., Quilcaille, Y., Rogelj, J., Salawitch, R. J., Samset, B. H., Sandstad, M., Shiklomanov, A. N., Skeie, R. B., Smith, C. J., Smith, S., Tanaka, K., Tsutsui, J., and Xie, Z.: Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response, Geosci. Model Dev., 13, 5175–5190, https://doi.org/10.5194/gmd-13-5175-2020, 2020.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Prein, A. F., Rasmussen, R. M., Ikeda, K., Liu, C., Clark, M. P., and
Holland, G. J.: The future intensification of hourly precipitation extremes,
Nat. Clim. Change, 7, 48–52, https://doi.org/10.1038/nclimate3168, 2017.
Rahaman, W., Chatterjee, S., Ejaz, T., and Thamban, M.: Increased influence
of ENSO on Antarctic temperature since the Industrial Era, Sci. Rep., 9, 6006,
https://doi.org/10.1038/s41598-019-42499-x, 2019.
Riahi, K., van Vuuren, D. P., Kriegler, E., Edmonds, J., O'Neill, B. C.,
Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., Lutz, W.,
Popp, A., Cuaresma, J. C., Kc, S., Leimbach, M., Jiang, L., Kram, T., Rao,
S., Emmerling, J., Ebi, K., Hasegawa, T., Havlik, P., Humpenöder, F., Da
Silva, L. A., Smith, S., Stehfest, E., Bosetti, V., Eom, J., Gernaat, D.,
Masui, T., Rogelj, J., Strefler, J., Drouet, L., Krey, V., Luderer, G.,
Harmsen, M., Takahashi, K., Baumstark, L., Doelman, J. C., Kainuma, M.,
Klimont, Z., Marangoni, G., Lotze-Campen, H., Obersteiner, M., Tabeau, A.,
and Tavoni, M.: The Shared Socioeconomic Pathways and their energy, land
use, and greenhouse gas emissions implications: An overview, Glob. Environ.
Change, 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017.
Samset, B. H., Myhre, G., Forster, P. M., Hodnebrog, Ø., Andrews, T.,
Faluvegi, G., Fläschner, D., Kasoar, M., Kharin, V., Kirkevåg, A.,
Lamarque, J.-F., Olivié, D., Richardson, T., Shindell, D., Shine, K. P.,
Takemura, T., and Voulgarakis, A.: Fast and slow precipitation responses to
individual climate forcers: A PDRMIP multimodel study, Geophys Res. Lett.,
43, 2782–2791, https://doi.org/10.1002/2016GL068064, 2016.
Shepherd, T. G.: Atmospheric circulation as a source of uncertainty in
climate change projections, Nat. Geosci., 7, 703–708, https://doi.org/10.1038/ngeo2253,
2014.
Singh, C. V.: Pattern characteristics of Indian monsoon rainfall using
principal component analysis (PCA), Atmos. Res., 79, 317–326,
https://doi.org/10.1016/j.atmosres.2005.05.006, 2006.
Song, F., Leung, L. R., Lu, J., and Dong, L.: Seasonally dependent responses
of subtropical highs and tropical rainfall to anthropogenic warming, Nat.
Clim. Change, 8, 787–792, https://doi.org/10.1038/s41558-018-0244-4, 2018.
Storch, H. and Zwiers, F.: Statistical Analysis in Climate Research, Cambridge University Press,
Cambridge, https://doi.org/10.1017/CBO9780511612336, 2011.
Sun, Q., Miao, C., Duan, Q., Ashouri, H., Sorooshian, S., and Hsu, K.-L.: A
Review of Global Precipitation Data Sets: Data Sources, Estimation, and
Intercomparisons, Rev. Geophys., 56, 79–107,
https://doi.org/10.1002/2017RG000574, 2018.
Tebaldi, C. and Arblaster, J. M.: Pattern scaling: Its strengths and
limitations, and an update on the latest model simulations, Clim. Change,
122, 459–471, https://doi.org/10.1007/s10584-013-1032-9, 2014.
Tebaldi, C. and Knutti, R.: Evaluating the accuracy of climate change
pattern emulation for low warming targets, Environ. Res. Lett., 13, 055006,
https://doi.org/10.1088/1748-9326/aabef2, 2018.
Trenberth, K. E. and Shea, D. J.: Relationships between precipitation and
surface temperature, Geophys. Res. Lett., 32, L14703,
https://doi.org/10.1029/2005GL022760, 2005.
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The
Changing Character of Precipitation, B. Am. Meteorol. Soc., 84, 1205–1218,
10.1175/bams-84-9-1205, 2003.
Tsanis, I. and Tapoglou, E.: Winter North Atlantic Oscillation impact on
European precipitation and drought under climate change, Theor. Appl.
Climatol., 135, 323–330, https://doi.org/10.1007/s00704-018-2379-7, 2019.
van Oldenborgh, G. J. and Burgers, G.: Searching for decadal variations in
ENSO precipitation teleconnections, Geophys. Res. Lett., 32,
https://doi.org/10.1029/2005GL023110, 2005.
WCRP: CMIP6, WCRP [data set], https://esgf-node.llnl.gov/search/cmip6/, last access: 19 February 2023.
Yan, Z., Wu, B., Li, T., Collins, M., Clark, R., Zhou, T., Murphy, J., and
Tan, G.: Eastward shift and extension of ENSO-induced tropical precipitation
anomalies under global warming, Sci. Adv., 6, eaax4177,
https://doi.org/10.1126/sciadv.aax4177, 2020.
Yang, X., Yong, B., Yu, Z., and Zhang, Y.: An evaluation of CMIP5
precipitation simulations using ground observations over ten river basins in
China, Hydrol. Res., 52, 676–698, https://doi.org/10.2166/nh.2021.151, 2021.
Zelazowski, P., Huntingford, C., Mercado, L. M., and Schaller, N.: Climate pattern-scaling set for an ensemble of 22 GCMs – adding uncertainty to the IMOGEN version 2.0 impact system, Geosci. Model Dev., 11, 541–560, https://doi.org/10.5194/gmd-11-541-2018, 2018.
Zhang, W., Furtado, K., Wu, P., Zhou, T., Chadwick, R., Marzin, C., Rostron,
J., and Sexton, D.: Increasing precipitation variability on
daily-to-multiyear time scales in a warmer world, Sci. Adv., 7, eabf8021,
https://doi.org/10.1126/sciadv.abf8021, 2021.
Zhou, P., Liu, Z., and Cheng, L.: An alternative approach for quantitatively
estimating climate variability over China under the effects of ENSO events,
Atmos. Res., 238, 104897, https://doi.org/10.1016/j.atmosres.2020.104897,
2020.
Short summary
Due to computational limits, lower-complexity models (LCMs) were developed as a complementary tool for accelerating comprehensive Earth system models (ESMs) but still lack a good precipitation emulator for LCMs. Here, we developed a data-calibrated precipitation emulator (PREMU), a computationally effective way to better estimate historical and simulated precipitation by current ESMs. PREMU has potential applications related to land surface processes and their interactions with climate change.
Due to computational limits, lower-complexity models (LCMs) were developed as a complementary...
