Articles | Volume 6, issue 1
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Optimising the FAMOUS climate model: inclusion of global carbon cycling
J. H. T. Williams
BRIDGE, School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
R. S. Smith
Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
P. J. Valdes
BRIDGE, School of Geographical Sciences, University of Bristol, Bristol, BS8 1SS, UK
B. B. B. Booth
Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK
Department of Meteorology, University of Reading, Reading, RG6 6BB, UK
J. H. T. Williams, I. J. Totterdell, P. R. Halloran, and P. J. Valdes
Geosci. Model Dev., 7, 1419–1431,
Sam Sherriff-Tadano, Ruza Ivanovic, Lauren Gregoire, Charlotte Lang, Niall Gandy, Jonathan Gregory, Tamsin L. Edwards, Oliver Pollard, and Robin S. Smith
This preprint is open for discussion and under review for Climate of the Past (CP).Short summary
Ensemble simulations of the climate and ice sheets of the Last Glacial Maximum (LGM) are performed with a new coupled climate-ice sheet model. Results show a strong sensitivity of the North American ice sheet to the albedo scheme, while the global temperature and the Greenland ice sheet appeared more sensitive to cloud and basal sliding schemes. Our result implies a potential connection between the North American ice sheet at the LGM and the future Greenland ice sheet through the albedo scheme.
Brooke Snoll, Ruza Ivanovic, Lauren Gregoire, Sam Sherriff-Tadano, Laurie Menviel, Takashi Obase, Ayako Abe-Ouchi, Nathaelle Bouttes, Chengfei He, Feng He, Marie Kapsch, Uwe Mikolajewicz, Juan Muglia, and Paul Valdes
This preprint is open for discussion and under review for Climate of the Past (CP).Short summary
Geological records show rapid climate change throughout the recent deglaciation. The drivers of these changes are still misunderstood, but often attributed to shifts in the Atlantic Ocean circulation from meltwater input. A cumulative effort to understand these processes prompted numerous simulations of this period. We use these to better explain the chain of events and our collective ability to simulate them. The results demonstrate the importance of the meltwater amount used in the simulation.
Hélène Seroussi, Vincent Verjans, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hatterman, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiametta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Peter Van Katwyk, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere Discuss.,
Preprint under review for TCShort summary
Mass loss from Antarctica is a key contributor to sea level rise over the 21st century and the associated uncertainty dominates sea level projections. We highlight here the Antarctic glaciers showing the largest changes and we quantify the main sources of uncertainty in their future evolution using an ensemble of ice flow models. We show that on top of Pine Island and Thwaites glaciers, Totten and Moscow University glaciers show rapid changes and a strong sensitivity to warmer ocean conditions.
Nina Raoult, Tim Jupp, Ben Booth, and Peter Cox
Earth Syst. Dynam., 14, 723–731,Short summary
Climate models are used to predict the impact of climate change. However, poorly constrained parameters used in the physics of the models mean that we simulate a large spread of possible future outcomes. We can use real-world observations to reduce the uncertainty of parameter values, but we do not have observations to reduce the spread of possible future outcomes directly. We present a method for translating the reduction in parameter uncertainty into a reduction in possible model projections.
Tamzin E. Palmer, Carol F. McSweeney, Ben B. B. Booth, Matthew D. K. Priestley, Paolo Davini, Lukas Brunner, Leonard Borchert, and Matthew B. Menary
Earth Syst. Dynam., 14, 457–483,Short summary
We carry out an assessment of an ensemble of general climate models (CMIP6) based on the ability of the models to represent the key physical processes that are important for representing European climate. Filtering the models with the assessment leads to more models with less global warming being removed, and this shifts the lower part of the projected temperature range towards greater warming. This is in contrast to the affect of weighting the ensemble using global temperature trends.
Caitlyn R. Witkowski, Vittoria Lauretano, Alex Farnsworth, Shufeng Li, Shi-Hu Li, Jan Peter Mayser, B. David A. Naafs, Robert A. Spicer, Tao Su, He Tang, Zhe-Kun Zhou, Paul J. Valdes, and Richard D. Pancost
Untangling the complex tectonic evolution in the Tibetan region can help us understand its impacts on climate, the Asian monsoon system, and the development of major biodiversity hotspots. We show that this “missing link” site between high elevation Tibet and low elevation coastal China had a dynamic environment but no temperature change, meaning its been at its current-day elevation for the past 34 million years.
Suzanne Robinson, Ruza F. Ivanovic, Lauren J. Gregoire, Julia Tindall, Tina van de Flierdt, Yves Plancherel, Frerk Pöppelmeier, Kazuyo Tachikawa, and Paul J. Valdes
Geosci. Model Dev., 16, 1231–1264,Short summary
We present the implementation of neodymium (Nd) isotopes into the ocean model of FAMOUS (Nd v1.0). Nd fluxes from seafloor sediment and incorporation of Nd onto sinking particles represent the major global sources and sinks, respectively. However, model–data mismatch in the North Pacific and northern North Atlantic suggest that certain reactive components of the sediment interact the most with seawater. Our results are important for interpreting Nd isotopes in terms of ocean circulation.
Michael P. Erb, Nicholas P. McKay, Nathan Steiger, Sylvia Dee, Chris Hancock, Ruza F. Ivanovic, Lauren J. Gregoire, and Paul Valdes
Clim. Past, 18, 2599–2629,Short summary
To look at climate over the past 12 000 years, we reconstruct spatial temperature using natural climate archives and information from model simulations. Our results show mild global mean warmth around 6000 years ago, which differs somewhat from past reconstructions. Undiagnosed seasonal biases in the data could explain some of the observed temperature change, but this still would not explain the large difference between many reconstructions and climate models over this period.
Antony Siahaan, Robin S. Smith, Paul R. Holland, Adrian Jenkins, Jonathan M. Gregory, Victoria Lee, Pierre Mathiot, Antony J. Payne, Jeff K. Ridley, and Colin G. Jones
The Cryosphere, 16, 4053–4086,Short summary
The UK Earth System Model is the first to fully include interactions of the atmosphere and ocean with the Antarctic Ice Sheet. Under the low-greenhouse-gas SSP1–1.9 (Shared Socioeconomic Pathway) scenario, the ice sheet remains stable over the 21st century. Under the strong-greenhouse-gas SSP5–8.5 scenario, the model predicts strong increases in melting of large ice shelves and snow accumulation on the surface. The dominance of accumulation leads to a sea level fall at the end of the century.
Amy H. Peace, Ben B. B. Booth, Leighton A. Regayre, Ken S. Carslaw, David M. H. Sexton, Céline J. W. Bonfils, and John W. Rostron
Earth Syst. Dynam., 13, 1215–1232,Short summary
Anthropogenic aerosol emissions have been linked to driving climate responses such as shifts in the location of tropical rainfall. However, the interaction of aerosols with climate remains one of the most uncertain aspects of climate modelling and limits our ability to predict future climate change. We use an ensemble of climate model simulations to investigate what impact the large uncertainty in how aerosols interact with climate has on predicting future tropical rainfall shifts.
Jie Zhang, Kalli Furtado, Steven T. Turnock, Jane P. Mulcahy, Laura J. Wilcox, Ben B. Booth, David Sexton, Tongwen Wu, Fang Zhang, and Qianxia Liu
Atmos. Chem. Phys., 21, 18609–18627,Short summary
The CMIP6 ESMs systematically underestimate TAS anomalies in the NH midlatitudes, especially from 1960 to 1990. The anomalous cooling is concurrent in time and space with anthropogenic SO2 emissions. The spurious drop in TAS is attributed to the overestimated aerosol concentrations. The aerosol forcing sensitivity cannot well explain the inter-model spread of PHC biases. And the cloud-amount term accounts for most of the inter-model spread in aerosol forcing sensitivity.
Charles J. R. Williams, Alistair A. Sellar, Xin Ren, Alan M. Haywood, Peter Hopcroft, Stephen J. Hunter, William H. G. Roberts, Robin S. Smith, Emma J. Stone, Julia C. Tindall, and Daniel J. Lunt
Clim. Past, 17, 2139–2163,Short summary
Computer simulations of the geological past are an important tool to improve our understanding of climate change. We present results from a simulation of the mid-Pliocene (approximately 3 million years ago) using the latest version of the UK’s climate model. The simulation reproduces temperatures as expected and shows some improvement relative to previous versions of the same model. The simulation is, however, arguably too warm when compared to other models and available observations.
Robin S. Smith, Steve George, and Jonathan M. Gregory
Geosci. Model Dev., 14, 5769–5787,Short summary
Many of the complex computer models used to study the physics of the natural world treat ice sheets as fixed and unchanging, capable of only simple interactions with the rest of the climate. This is partly because it is technically very difficult to usefully do anything more realistic. We have adapted a climate model so it can be joined together with a dynamical model of the Greenland ice sheet. This gives us a powerful tool to help us better understand how ice sheets and the climate interact.
Benjamin M. Sanderson, Angeline G. Pendergrass, Charles D. Koven, Florent Brient, Ben B. B. Booth, Rosie A. Fisher, and Reto Knutti
Earth Syst. Dynam., 12, 899–918,Short summary
Emergent constraints promise a pathway to the reduction in climate projection uncertainties by exploiting ensemble relationships between observable quantities and unknown climate response parameters. This study considers the robustness of these relationships in light of biases and common simplifications that may be present in the original ensemble of climate simulations. We propose a classification scheme for constraints and a number of practical case studies.
Paul J. Valdes, Christopher R. Scotese, and Daniel J. Lunt
Clim. Past, 17, 1483–1506,Short summary
Deep ocean temperatures are widely used as a proxy for global mean surface temperature in the past, but the underlying assumptions have not been tested. We use two unique sets of 109 climate model simulations for the last 545 million years to show that the relationship is valid for approximately the last 100 million years but breaks down for older time periods when the continents (and hence ocean circulation) are in very different positions.
Daniel J. Lunt, Deepak Chandan, Alan M. Haywood, George M. Lunt, Jonathan C. Rougier, Ulrich Salzmann, Gavin A. Schmidt, and Paul J. Valdes
Geosci. Model Dev., 14, 4307–4317,Short summary
Often in science we carry out experiments with computers in which several factors are explored, for example, in the field of climate science, how the factors of greenhouse gases, ice, and vegetation affect temperature. We can explore the relative importance of these factors by
swapping in and outdifferent values of these factors, and can also carry out experiments with many different combinations of these factors. This paper discusses how best to analyse the results from such experiments.
Masa Kageyama, Sandy P. Harrison, Marie-L. Kapsch, Marcus Lofverstrom, Juan M. Lora, Uwe Mikolajewicz, Sam Sherriff-Tadano, Tristan Vadsaria, Ayako Abe-Ouchi, Nathaelle Bouttes, Deepak Chandan, Lauren J. Gregoire, Ruza F. Ivanovic, Kenji Izumi, Allegra N. LeGrande, Fanny Lhardy, Gerrit Lohmann, Polina A. Morozova, Rumi Ohgaito, André Paul, W. Richard Peltier, Christopher J. Poulsen, Aurélien Quiquet, Didier M. Roche, Xiaoxu Shi, Jessica E. Tierney, Paul J. Valdes, Evgeny Volodin, and Jiang Zhu
Clim. Past, 17, 1065–1089,Short summary
The Last Glacial Maximum (LGM; ~21 000 years ago) is a major focus for evaluating how well climate models simulate climate changes as large as those expected in the future. Here, we compare the latest climate model (CMIP6-PMIP4) to the previous one (CMIP5-PMIP3) and to reconstructions. Large-scale climate features (e.g. land–sea contrast, polar amplification) are well captured by all models, while regional changes (e.g. winter extratropical cooling, precipitations) are still poorly represented.
Adam T. Blaker, Manoj Joshi, Bablu Sinha, David P. Stevens, Robin S. Smith, and Joël J.-M. Hirschi
Geosci. Model Dev., 14, 275–293,Short summary
FORTE 2.0 is a flexible coupled atmosphere–ocean general circulation model that can be run on modest hardware. We present two 2000-year simulations which show that FORTE 2.0 is capable of producing a stable climate. Earlier versions of FORTE were used for a wide range of studies, ranging from aquaplanet configurations to investigating the cold European winters of 2009–2010. This paper introduces the updated model for which the code and configuration are now publicly available.
Daniel J. Lunt, Fran Bragg, Wing-Le Chan, David K. Hutchinson, Jean-Baptiste Ladant, Polina Morozova, Igor Niezgodzki, Sebastian Steinig, Zhongshi Zhang, Jiang Zhu, Ayako Abe-Ouchi, Eleni Anagnostou, Agatha M. de Boer, Helen K. Coxall, Yannick Donnadieu, Gavin Foster, Gordon N. Inglis, Gregor Knorr, Petra M. Langebroek, Caroline H. Lear, Gerrit Lohmann, Christopher J. Poulsen, Pierre Sepulchre, Jessica E. Tierney, Paul J. Valdes, Evgeny M. Volodin, Tom Dunkley Jones, Christopher J. Hollis, Matthew Huber, and Bette L. Otto-Bliesner
Clim. Past, 17, 203–227,Short summary
This paper presents the first modelling results from the Deep-Time Model Intercomparison Project (DeepMIP), in which we focus on the early Eocene climatic optimum (EECO, 50 million years ago). We show that, in contrast to previous work, at least three models (CESM, GFDL, and NorESM) produce climate states that are consistent with proxy indicators of global mean temperature and polar amplification, and they achieve this at a CO2 concentration that is consistent with the CO2 proxy record.
Irene Malmierca-Vallet, Louise C. Sime, Paul J. Valdes, and Julia C. Tindall
Clim. Past, 16, 2485–2508,
Jonathan M. Gregory, Steven E. George, and Robin S. Smith
The Cryosphere, 14, 4299–4322,Short summary
Melting of the Greenland ice sheet as a consequence of global warming could raise global-mean sea level by up to 7 m. We have studied this using a newly developed computer model. With recent climate maintained, sea level would rise by 0.5–2.5 m over many millennia due to Greenland ice loss: the warmer the climate, the greater the sea level rise. Beyond about 3.5 m it would become partially irreversible. In order to avoid this outcome, anthropogenic climate change must be reversed soon enough.
Heiko Goelzer, Sophie Nowicki, Anthony Payne, Eric Larour, Helene Seroussi, William H. Lipscomb, Jonathan Gregory, Ayako Abe-Ouchi, Andrew Shepherd, Erika Simon, Cécile Agosta, Patrick Alexander, Andy Aschwanden, Alice Barthel, Reinhard Calov, Christopher Chambers, Youngmin Choi, Joshua Cuzzone, Christophe Dumas, Tamsin Edwards, Denis Felikson, Xavier Fettweis, Nicholas R. Golledge, Ralf Greve, Angelika Humbert, Philippe Huybrechts, Sebastien Le clec'h, Victoria Lee, Gunter Leguy, Chris Little, Daniel P. Lowry, Mathieu Morlighem, Isabel Nias, Aurelien Quiquet, Martin Rückamp, Nicole-Jeanne Schlegel, Donald A. Slater, Robin S. Smith, Fiamma Straneo, Lev Tarasov, Roderik van de Wal, and Michiel van den Broeke
The Cryosphere, 14, 3071–3096,Short summary
In this paper we use a large ensemble of Greenland ice sheet models forced by six different global climate models to project ice sheet changes and sea-level rise contributions over the 21st century. The results for two different greenhouse gas concentration scenarios indicate that the Greenland ice sheet will continue to lose mass until 2100, with contributions to sea-level rise of 90 ± 50 mm and 32 ± 17 mm for the high (RCP8.5) and low (RCP2.6) scenario, respectively.
Hélène Seroussi, Sophie Nowicki, Antony J. Payne, Heiko Goelzer, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Torsten Albrecht, Xylar Asay-Davis, Alice Barthel, Reinhard Calov, Richard Cullather, Christophe Dumas, Benjamin K. Galton-Fenzi, Rupert Gladstone, Nicholas R. Golledge, Jonathan M. Gregory, Ralf Greve, Tore Hattermann, Matthew J. Hoffman, Angelika Humbert, Philippe Huybrechts, Nicolas C. Jourdain, Thomas Kleiner, Eric Larour, Gunter R. Leguy, Daniel P. Lowry, Chistopher M. Little, Mathieu Morlighem, Frank Pattyn, Tyler Pelle, Stephen F. Price, Aurélien Quiquet, Ronja Reese, Nicole-Jeanne Schlegel, Andrew Shepherd, Erika Simon, Robin S. Smith, Fiammetta Straneo, Sainan Sun, Luke D. Trusel, Jonas Van Breedam, Roderik S. W. van de Wal, Ricarda Winkelmann, Chen Zhao, Tong Zhang, and Thomas Zwinger
The Cryosphere, 14, 3033–3070,Short summary
The Antarctic ice sheet has been losing mass over at least the past 3 decades in response to changes in atmospheric and oceanic conditions. This study presents an ensemble of model simulations of the Antarctic evolution over the 2015–2100 period based on various ice sheet models, climate forcings and emission scenarios. Results suggest that the West Antarctic ice sheet will continue losing a large amount of ice, while the East Antarctic ice sheet could experience increased snow accumulation.
Lee de Mora, Alistair A. Sellar, Andrew Yool, Julien Palmieri, Robin S. Smith, Till Kuhlbrodt, Robert J. Parker, Jeremy Walton, Jeremy C. Blackford, and Colin G. Jones
Geosci. Commun., 3, 263–278,Short summary
We use time series data from the first United Kingdom Earth System Model (UKESM1) to create six procedurally generated musical pieces for piano. Each of the six pieces help to explain either a scientific principle or a practical aspect of Earth system modelling. We describe the methods that were used to create these pieces, discuss the limitations of this pilot study and list several approaches to extend and expand upon this work.
Charles J. R. Williams, Maria-Vittoria Guarino, Emilie Capron, Irene Malmierca-Vallet, Joy S. Singarayer, Louise C. Sime, Daniel J. Lunt, and Paul J. Valdes
Clim. Past, 16, 1429–1450,Short summary
Computer simulations of the geological past are an important tool to improve our understanding of climate change. We present results from two simulations using the latest version of the UK's climate model, the mid-Holocene (6000 years ago) and Last Interglacial (127 000 years ago). The simulations reproduce temperatures consistent with the pattern of incoming radiation. Model–data comparisons indicate that some regions (and some seasons) produce better matches to the data than others.
Sophie Nowicki, Heiko Goelzer, Hélène Seroussi, Anthony J. Payne, William H. Lipscomb, Ayako Abe-Ouchi, Cécile Agosta, Patrick Alexander, Xylar S. Asay-Davis, Alice Barthel, Thomas J. Bracegirdle, Richard Cullather, Denis Felikson, Xavier Fettweis, Jonathan M. Gregory, Tore Hattermann, Nicolas C. Jourdain, Peter Kuipers Munneke, Eric Larour, Christopher M. Little, Mathieu Morlighem, Isabel Nias, Andrew Shepherd, Erika Simon, Donald Slater, Robin S. Smith, Fiammetta Straneo, Luke D. Trusel, Michiel R. van den Broeke, and Roderik van de Wal
The Cryosphere, 14, 2331–2368,Short summary
This paper describes the experimental protocol for ice sheet models taking part in the Ice Sheet Model Intercomparion Project for CMIP6 (ISMIP6) and presents an overview of the atmospheric and oceanic datasets to be used for the simulations. The ISMIP6 framework allows for exploring the uncertainty in 21st century sea level change from the Greenland and Antarctic ice sheets.
Doug McNeall, Jonny Williams, Richard Betts, Ben Booth, Peter Challenor, Peter Good, and Andy Wiltshire
Geosci. Model Dev., 13, 2487–2509,Short summary
In the climate model FAMOUS, matching the modelled Amazon rainforest to observations required different land surface parameter settings than for other forests. It was unclear if this discrepancy was due to a bias in the modelled climate or an error in the land surface component of the model. Correcting the climate of the model with a statistical model corrects the simulation of the Amazon forest, suggesting that the land surface component of the model is not the source of the discrepancy.
Alan T. Kennedy-Asser, Daniel J. Lunt, Paul J. Valdes, Jean-Baptiste Ladant, Joost Frieling, and Vittoria Lauretano
Clim. Past, 16, 555–573,Short summary
Global cooling and a major expansion of ice over Antarctica occurred ~ 34 million years ago at the Eocene–Oligocene transition (EOT). A large secondary proxy dataset for high-latitude Southern Hemisphere temperature before, after and across the EOT is compiled and compared to simulations from two coupled climate models. Although there are inconsistencies between the models and data, the comparison shows amongst other things that changes in the Drake Passage were unlikely the cause of the EOT.
Jennifer E. Dentith, Ruza F. Ivanovic, Lauren J. Gregoire, Julia C. Tindall, Laura F. Robinson, and Paul J. Valdes
Publication in BG not foreseenShort summary
We have added three new tracers (a dye tracer and two representations of radiocarbon, 14C) into the ocean of the FAMOUS climate model to study large-scale circulation and the marine carbon cycle. The model performs well compared to modern 14C observations, both spatially and temporally. Proxy 14C records are interpreted in terms of water age, but comparing our dye tracer to our 14C tracer, we find that this is only valid in certain areas; elsewhere, the carbon cycle complicates the signal.
David C. Wade, Nathan Luke Abraham, Alexander Farnsworth, Paul J. Valdes, Fran Bragg, and Alexander T. Archibald
Clim. Past, 15, 1463–1483,Short summary
The amount of O2 in the atmosphere may have varied from as little as 10 % to as much as 35 % during the last 541 Myr. These changes are large enough to have led to changes in atmospheric mass, which may alter the radiative budget of the atmosphere. We present the first fully 3-D numerical model simulations to investigate the climate impacts of changes in O2 during different climate states. We identify a complex new mechanism causing increases in surface temperature when O2 levels were higher.
Mario Krapp, Robert Beyer, Stephen L. Edmundson, Paul J. Valdes, and Andrea Manica
Clim. Past Discuss.,
Revised manuscript not acceptedShort summary
The local response of the global climate system to the external drivers of the glacial–interglacial climates throughout the Quaternary can be approximated by a simple linear regression model. Based on numerical climate model simulations for the last glacial cycle, our global climate model emulator (GCMET) is able to reconstruct the climate of the last 800 000 years, in good agreement with long-term terrestrial and marine proxy records.
David J. Wilton, Marcus P. S. Badger, Euripides P. Kantzas, Richard D. Pancost, Paul J. Valdes, and David J. Beerling
Geosci. Model Dev., 12, 1351–1364,Short summary
Methane is an important greenhouse gas naturally produced in wetlands (areas of land inundated with water). Models of the Earth's past climate need estimates of the amounts of methane wetlands produce; and in order to calculate those we need to model wetlands. In this work we develop a method for modelling the fraction of an area of the Earth that is wetland, repeat this over all the Earth's land surface and apply this to a study of the Earth as it was around 50 million years ago.
Sarah Shannon, Robin Smith, Andy Wiltshire, Tony Payne, Matthias Huss, Richard Betts, John Caesar, Aris Koutroulis, Darren Jones, and Stephan Harrison
The Cryosphere, 13, 325–350,Short summary
We present global glacier volume projections for the end of this century, under a range of high-end climate change scenarios, defined as exceeding 2 °C global average warming. The ice loss contribution to sea level rise for all glaciers excluding those on the peripheral of the Antarctic ice sheet is 215.2 ± 21.3 mm. Such large ice losses will have consequences for sea level rise and for water supply in glacier-fed river systems.
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,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,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.
Masa Kageyama, Pascale Braconnot, Sandy P. Harrison, Alan M. Haywood, Johann H. Jungclaus, Bette L. Otto-Bliesner, Jean-Yves Peterschmitt, Ayako Abe-Ouchi, Samuel Albani, Patrick J. Bartlein, Chris Brierley, Michel Crucifix, Aisling Dolan, Laura Fernandez-Donado, Hubertus Fischer, Peter O. Hopcroft, Ruza F. Ivanovic, Fabrice Lambert, Daniel J. Lunt, Natalie M. Mahowald, W. Richard Peltier, Steven J. Phipps, Didier M. Roche, Gavin A. Schmidt, Lev Tarasov, Paul J. Valdes, Qiong Zhang, and Tianjun Zhou
Geosci. Model Dev., 11, 1033–1057,Short summary
The Paleoclimate Modelling Intercomparison Project (PMIP) takes advantage of the existence of past climate states radically different from the recent past to test climate models used for climate projections and to better understand these climates. This paper describes the PMIP contribution to CMIP6 (Coupled Model Intercomparison Project, 6th phase) and possible analyses based on PMIP results, as well as on other CMIP6 projects.
Taraka Davies-Barnard, Andy Ridgwell, Joy Singarayer, and Paul Valdes
Clim. Past, 13, 1381–1401,Short summary
We present the first model analysis using a fully coupled dynamic atmosphere–ocean–vegetation GCM over the last 120 kyr that quantifies the net effect of vegetation on climate. This analysis shows that over the whole period the biogeophysical effect (albedo, evapotranspiration) is dominant, and that the biogeochemical impacts may have a lower possible range than typically estimated. This emphasises the temporal reliance of the balance between biogeophysical and biogeochemical effects.
Paul J. Valdes, Edward Armstrong, Marcus P. S. Badger, Catherine D. Bradshaw, Fran Bragg, Michel Crucifix, Taraka Davies-Barnard, Jonathan J. Day, Alex Farnsworth, Chris Gordon, Peter O. Hopcroft, Alan T. Kennedy, Natalie S. Lord, Dan J. Lunt, Alice Marzocchi, Louise M. Parry, Vicky Pope, William H. G. Roberts, Emma J. Stone, Gregory J. L. Tourte, and Jonny H. T. Williams
Geosci. Model Dev., 10, 3715–3743,Short summary
In this paper we describe the family of climate models used by the BRIDGE research group at the University of Bristol as well as by various other institutions. These models are based on the UK Met Office HadCM3 models and here we describe the various modifications which have been made as well as the key features of a number of configurations in use.
Doug McNeall, Jonny Williams, Ben Booth, Richard Betts, Peter Challenor, Andy Wiltshire, and David Sexton
Earth Syst. Dynam., 7, 917–935,Short summary
We compare simulated with observed forests to constrain uncertain input parameters of the land surface component of a climate model. We find that the model is unlikely to be able to simulate the Amazon and other major forests simultaneously at any one parameter set, suggesting a bias in the model's representation of the Amazon. We find we cannot constrain parameters individually, but we can rule out large areas of joint parameter space.
Emma J. Stone, Emilie Capron, Daniel J. Lunt, Antony J. Payne, Joy S. Singarayer, Paul J. Valdes, and Eric W. Wolff
Clim. Past, 12, 1919–1932,Short summary
Climate models forced only with greenhouse gas concentrations and orbital parameters representative of the early Last Interglacial are unable to reproduce the observed colder-than-present temperatures in the North Atlantic and the warmer-than-present temperatures in the Southern Hemisphere. Using a climate model forced also with a freshwater amount derived from data representing melting from the remnant Northern Hemisphere ice sheets accounts for this response via the bipolar seesaw mechanism.
William H. G. Roberts, Antony J. Payne, and Paul J. Valdes
Clim. Past, 12, 1601–1617,Short summary
There are observations from ocean sediment cores that during the last ice age the Laurentide Ice Sheet, which sat over North America, periodically surged. In this study we show the role that water at the base of an ice sheet plays in these surges. We show that with a more realistic representation of water drainage at the base of the ice sheet than usually used, these surges can still occur and that they are triggered by an internal ice sheet instability; no external trigger is needed.
Ruza F. Ivanovic, Lauren J. Gregoire, Masa Kageyama, Didier M. Roche, Paul J. Valdes, Andrea Burke, Rosemarie Drummond, W. Richard Peltier, and Lev Tarasov
Geosci. Model Dev., 9, 2563–2587,Short summary
This manuscript presents the experiment design for the PMIP4 Last Deglaciation Core experiment: a transient simulation of the last deglaciation, 21–9 ka. Specified model boundary conditions include time-varying orbital parameters, greenhouse gases, ice sheets, ice meltwater fluxes and other geographical changes (provided for 26–0 ka). The context of the experiment and the choices for the boundary conditions are explained, along with the future direction of the working group.
Stefan C. Dekker, Margriet Groenendijk, Ben B. B. Booth, Chris Huntingford, and Peter M. Cox
Earth Syst. Dynam., 7, 525–533,Short summary
Our analysis allows us to infer maps of changing plant water-use efficiency (WUE) for 1901–2010, using atmospheric observations of temperature, humidity and CO2. Our estimated increase in global WUE is consistent with the tree-ring and eddy covariance data, but much larger than the historical WUE increases simulated by Earth System Models (ESMs). We therefore conclude that the effects of increasing CO2 on plant WUE are significantly underestimated in the latest climate projections.
M. Clare Smith, Joy S. Singarayer, Paul J. Valdes, Jed O. Kaplan, and Nicholas P. Branch
Clim. Past, 12, 923–941,Short summary
We used climate modelling to estimate the biogeophysical impacts of agriculture on the climate over the last 8000 years of the Holocene. Our results show statistically significant surface temperature changes (mainly cooling) from as early as 7000 BP in the JJA season and throughout the entire annual cycle by 2–3000 BP. The changes were greatest in the areas of land use change but were also seen in other areas. Precipitation was also affected, particularly in Europe, India, and the ITCZ region.
Matthew J. Carmichael, Daniel J. Lunt, Matthew Huber, Malte Heinemann, Jeffrey Kiehl, Allegra LeGrande, Claire A. Loptson, Chris D. Roberts, Navjit Sagoo, Christine Shields, Paul J. Valdes, Arne Winguth, Cornelia Winguth, and Richard D. Pancost
Clim. Past, 12, 455–481,Short summary
In this paper, we assess how well model-simulated precipitation rates compare to those indicated by geological data for the early Eocene, a warm interval 56–49 million years ago. Our results show that a number of models struggle to produce sufficient precipitation at high latitudes, which likely relates to cool simulated temperatures in these regions. However, calculating precipitation rates from plant fossils is highly uncertain, and further data are now required.
B. A. A. Hoogakker, R. S. Smith, J. S. Singarayer, R. Marchant, I. C. Prentice, J. R. M. Allen, R. S. Anderson, S. A. Bhagwat, H. Behling, O. Borisova, M. Bush, A. Correa-Metrio, A. de Vernal, J. M. Finch, B. Fréchette, S. Lozano-Garcia, W. D. Gosling, W. Granoszewski, E. C. Grimm, E. Grüger, J. Hanselman, S. P. Harrison, T. R. Hill, B. Huntley, G. Jiménez-Moreno, P. Kershaw, M.-P. Ledru, D. Magri, M. McKenzie, U. Müller, T. Nakagawa, E. Novenko, D. Penny, L. Sadori, L. Scott, J. Stevenson, P. J. Valdes, M. Vandergoes, A. Velichko, C. Whitlock, and C. Tzedakis
Clim. Past, 12, 51–73,Short summary
In this paper we use two climate models to test how Earth’s vegetation responded to changes in climate over the last 120 000 years, looking at warm interglacial climates like today, cold ice-age glacial climates, and intermediate climates. The models agree well with observations from pollen, showing smaller forested areas and larger desert areas during cold periods. Forests store most terrestrial carbon; the terrestrial carbon lost during cold climates was most likely relocated to the oceans.
P. R. Halloran, B. B. B. Booth, C. D. Jones, F. H. Lambert, D. J. McNeall, I. J. Totterdell, and C. Völker
Biogeosciences, 12, 4497–4508,Short summary
The oceans currently take up around a quarter of the carbon dioxide (CO2) emitted by human activity. While stored in the ocean, this CO2 is not causing global warming. Here we explore high latitude North Atlantic CO2 uptake across a set of climate model simulations, and find that the models show a peak in ocean CO2 uptake around the middle of the century after which time CO2 uptake begins to decline. We identify the causes of this long-term change and interannual variability in the models.
J. H. T. Williams, I. J. Totterdell, P. R. Halloran, and P. J. Valdes
Geosci. Model Dev., 7, 1419–1431,
R. F. Ivanovic, P. J. Valdes, R. Flecker, and M. Gutjahr
Clim. Past, 10, 607–622,
P. J. Irvine, L. J. Gregoire, D. J. Lunt, and P. J. Valdes
Geosci. Model Dev., 6, 1447–1462,
M. Eby, A. J. Weaver, K. Alexander, K. Zickfeld, A. Abe-Ouchi, A. A. Cimatoribus, E. Crespin, S. S. Drijfhout, N. R. Edwards, A. V. Eliseev, G. Feulner, T. Fichefet, C. E. Forest, H. Goosse, P. B. Holden, F. Joos, M. Kawamiya, D. Kicklighter, H. Kienert, K. Matsumoto, I. I. Mokhov, E. Monier, S. M. Olsen, J. O. P. Pedersen, M. Perrette, G. Philippon-Berthier, A. Ridgwell, A. Schlosser, T. Schneider von Deimling, G. Shaffer, R. S. Smith, R. Spahni, A. P. Sokolov, M. Steinacher, K. Tachiiri, K. Tokos, M. Yoshimori, N. Zeng, and F. Zhao
Clim. Past, 9, 1111–1140,
B. B. B. Booth, D. Bernie, D. McNeall, E. Hawkins, J. Caesar, C. Boulton, P. Friedlingstein, and D. M. H. Sexton
Earth Syst. Dynam., 4, 95–108,
B. Ringeval, P. O. Hopcroft, P. J. Valdes, P. Ciais, G. Ramstein, A. J. Dolman, and M. Kageyama
Clim. Past, 9, 149–171,
Related subject area
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Jason Neil Steven Cole, Knut von Salzen, Jiangnan Li, John Scinocca, David Plummer, Vivek Arora, Norman McFarlane, Michael Lazare, Murray MacKay, and Diana Verseghy
Geosci. Model Dev., 16, 5427–5448,Short summary
The Canadian Atmospheric Model version 5 (CanAM5) is used to simulate on a global scale the climate of Earth's atmosphere, land, and lakes. We document changes to the physics in CanAM5 since the last major version of the model (CanAM4) and evaluate the climate simulated relative to observations and CanAM4. The climate simulated by CanAM5 is similar to CanAM4, but there are improvements, including better simulation of temperature and precipitation over the Amazon and better simulation of cloud.
Florian Zabel and Benjamin Poschlod
Geosci. Model Dev., 16, 5383–5399,Short summary
Today, most climate model data are provided at daily time steps. However, more and more models from different sectors, such as energy, water, agriculture, and health, require climate information at a sub-daily temporal resolution for a more robust and reliable climate impact assessment. Here we describe and validate the Teddy tool, a new model for the temporal disaggregation of daily climate model data for climate impact analysis.
Young-Chan Noh, Yonghan Choi, Hyo-Jong Song, Kevin Raeder, Joo-Hong Kim, and Youngchae Kwon
Geosci. Model Dev., 16, 5365–5382,Short summary
This is the first attempt to assimilate the observations of microwave temperature sounders into the global climate forecast model in which the satellite observations have not been assimilated in the past. To do this, preprocessing schemes are developed to make the satellite observations suitable to be assimilated. In the assimilation experiments, the model analysis is significantly improved by assimilating the observations of microwave temperature sounders.
Cenlin He, Prasanth Valayamkunnath, Michael Barlage, Fei Chen, David Gochis, Ryan Cabell, Tim Schneider, Roy Rasmussen, Guo-Yue Niu, Zong-Liang Yang, Dev Niyogi, and Michael Ek
Geosci. Model Dev., 16, 5131–5151,Short summary
Noah-MP is one of the most widely used open-source community land surface models in the world, designed for applications ranging from uncoupled land surface and ecohydrological process studies to coupled numerical weather prediction and decadal climate simulations. To facilitate model developments and applications, we modernize Noah-MP by adopting modern Fortran code and data structures and standards, which substantially enhance model modularity, interoperability, and applicability.
Xiaoxu Shi, Alexandre Cauquoin, Gerrit Lohmann, Lukas Jonkers, Qiang Wang, Hu Yang, Yuchen Sun, and Martin Werner
Geosci. Model Dev., 16, 5153–5178,Short summary
We developed a new climate model with isotopic capabilities and simulated the pre-industrial and mid-Holocene periods. Despite certain regional model biases, the modeled isotope composition is in good agreement with observations and reconstructions. Based on our analyses, the observed isotope–temperature relationship in polar regions may have a summertime bias. Using daily model outputs, we developed a novel isotope-based approach to determine the onset date of the West African summer monsoon.
Geosci. Model Dev., 16, 4937–4956,Short summary
A representation of rainbows is developed for a climate model. The diagnostic raises many common issues. Simulated rainbows are evaluated against limited observations. The pattern of rainbows in the model matches observations and theory about when and where rainbows are most common. The diagnostic is used to assess the past and future state of rainbows. Changes to clouds from climate change are expected to increase rainbows as cloud cover decreases in a warmer world.
Ralf Hand, Eric Samakinwa, Laura Lipfert, and Stefan Brönnimann
Geosci. Model Dev., 16, 4853–4866,Short summary
ModE-Sim is an ensemble of simulations with an atmosphere model. It uses observed sea surface temperatures, sea ice conditions, and volcanic aerosols for 1420 to 2009 as model input while accounting for uncertainties in these conditions. This generates several representations of the possible climate given these preconditions. Such a setup can be useful to understand the mechanisms that contribute to climate variability. This paper describes the setup of ModE-Sim and evaluates its performance.
Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino, Rosalia Santoleri, and Chunxue Yang
Geosci. Model Dev., 16, 4811–4833,Short summary
Regional climate models are a fundamental tool for a very large number of applications and are being increasingly used within climate services, together with other complementary approaches. Here, we introduce a new regional coupled model, intended to be later extended to a full Earth system model, for climate investigations within the Mediterranean region, coupled data assimilation experiments, and several downscaling exercises (reanalyses and long-range predictions).
Anna L. Merrifield, Lukas Brunner, Ruth Lorenz, Vincent Humphrey, and Reto Knutti
Geosci. Model Dev., 16, 4715–4747,Short summary
Using all Coupled Model Intercomparison Project (CMIP) models is unfeasible for many applications. We provide a subselection protocol that balances user needs for model independence, performance, and spread capturing CMIP’s projection uncertainty simultaneously. We show how sets of three to five models selected for European applications map to user priorities. An audit of model independence and its influence on equilibrium climate sensitivity uncertainty in CMIP is also presented.
Bin Mu, Xiaodan Luo, Shijin Yuan, and Xi Liang
Geosci. Model Dev., 16, 4677–4697,Short summary
To improve the long-term forecast skill for sea ice extent (SIE), we introduce IceTFT, which directly predicts 12 months of averaged Arctic SIE. The results show that IceTFT has higher forecasting skill. We conducted a sensitivity analysis of the variables in the IceTFT model. These sensitivities can help researchers study the mechanisms of sea ice development, and they also provide useful references for the selection of variables in data assimilation or the input of deep learning models.
Laura Muntjewerf, Richard Bintanja, Thomas Reerink, and Karin van der Wiel
Geosci. Model Dev., 16, 4581–4597,Short summary
The KNMI Large Ensemble Time Slice (KNMI–LENTIS) is a large ensemble of global climate model simulations with EC-Earth3. It covers two climate scenarios by focusing on two time slices: the present day (2000–2009) and a future +2 K climate (2075–2084 in the SSP2-4.5 scenario). We have 1600 simulated years for the two climates with (sub-)daily output frequency. The sampled climate variability allows for robust and in-depth research into (compound) extreme events such as heat waves and droughts.
Yi-Chi Wang, Wan-Ling Tseng, Yu-Luen Chen, Shih-Yu Lee, Huang-Hsiung Hsu, and Hsin-Chien Liang
Geosci. Model Dev., 16, 4599–4616,Short summary
This study focuses on evaluating the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the El Niño–Southern Oscillation (ENSO), a significant tropical climate pattern with global impacts. Our findings reveal that TaiESM1 effectively captures several characteristics of ENSO, such as its seasonal variation and remote teleconnections. Its pronounced ENSO strength bias is also thoroughly investigated, aiming to gain insights to improve climate model performance.
Raghul Parthipan, Hannah M. Christensen, J. Scott Hosking, and Damon J. Wischik
Geosci. Model Dev., 16, 4501–4519,Short summary
How can we create better climate models? We tackle this by proposing a data-driven successor to the existing approach for capturing key temporal trends in climate models. We combine probability, allowing us to represent uncertainty, with machine learning, a technique to learn relationships from data which are undiscoverable to humans. Our model is often superior to existing baselines when tested in a simple atmospheric simulation.
Laura J. Wilcox, Robert J. Allen, Bjørn H. Samset, Massimo A. Bollasina, Paul T. Griffiths, James Keeble, Marianne T. Lund, Risto Makkonen, Joonas Merikanto, Declan O'Donnell, David J. Paynter, Geeta G. Persad, Steven T. Rumbold, Toshihiko Takemura, Kostas Tsigaridis, Sabine Undorf, and Daniel M. Westervelt
Geosci. Model Dev., 16, 4451–4479,Short summary
Changes in anthropogenic aerosol emissions have strongly contributed to global and regional climate change. However, the size of these regional impacts and the way they arise are still uncertain. With large changes in aerosol emissions a possibility over the next few decades, it is important to better quantify the potential role of aerosol in future regional climate change. The Regional Aerosol Model Intercomparison Project will deliver experiments designed to facilitate this.
Nicholas Depsky, Ian Bolliger, Daniel Allen, Jun Ho Choi, Michael Delgado, Michael Greenstone, Ali Hamidi, Trevor Houser, Robert E. Kopp, and Solomon Hsiang
Geosci. Model Dev., 16, 4331–4366,Short summary
This work presents a novel open-source modeling platform for evaluating future sea level rise (SLR) impacts. Using nearly 10 000 discrete coastline segments around the world, we estimate 21st-century costs for 230 SLR and socioeconomic scenarios. We find that annual end-of-century costs range from USD 100 billion under a 2 °C warming scenario with proactive adaptation to 7 trillion under a 4 °C warming scenario with minimal adaptation, illustrating the cost-effectiveness of coastal adaptation.
Shruti Nath, Lukas Gudmundsson, Jonas Schwaab, Gregory Duveiller, Steven J. De Hertog, Suqi Guo, Felix Havermann, Fei Luo, Iris Manola, Julia Pongratz, Sonia I. Seneviratne, Carl F. Schleussner, Wim Thiery, and Quentin Lejeune
Geosci. Model Dev., 16, 4283–4313,Short summary
Tree cover changes play a significant role in climate mitigation and adaptation. Their regional impacts are key in informing national-level decisions and prioritising areas for conservation efforts. We present a first step towards exploring these regional impacts using a simple statistical device, i.e. emulator. The emulator only needs to train on climate model outputs representing the maximal impacts of aff-, re-, and deforestation, from which it explores plausible in-between outcomes itself.
Chen Zhang and Tianyu Fu
Geosci. Model Dev., 16, 4315–4329,Short summary
A new automatic calibration toolkit was developed and implemented into the recalibration of a 3-D water quality model, with observations in a wider range of hydrological variability. Compared to the model calibrated with the original strategy, the recalibrated model performed significantly better in modeled total phosphorus, chlorophyll a, and dissolved oxygen. Our work indicates that hydrological variability in the calibration periods has a non-negligible impact on the water quality models.
Camilla Mathison, Eleanor Burke, Andrew J. Hartley, Douglas I. Kelley, Chantelle Burton, Eddy Robertson, Nicola Gedney, Karina Williams, Andy Wiltshire, Richard J. Ellis, Alistair A. Sellar, and Chris D. Jones
Geosci. Model Dev., 16, 4249–4264,Short summary
This paper describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle. We have created a new configuration and set-up for the JULES-ES land surface model, driven by bias-corrected historical and future climate model output provided by the Inter-Sectoral Impacts Model Intercomparison Project (ISIMIP). This allows us to compare projections of the impacts of climate change across multiple impact models and multiple sectors.
Bo Dong, Ross Bannister, Yumeng Chen, Alison Fowler, and Keith Haines
Geosci. Model Dev., 16, 4233–4247,Short summary
Traditional Kalman smoothers are expensive to apply in large global ocean operational forecast and reanalysis systems. We develop a cost-efficient method to overcome the technical constraints and to improve the performance of existing reanalysis products.
Makcim L. De Sisto, Andrew H. MacDougall, Nadine Mengis, and Sophia Antoniello
Geosci. Model Dev., 16, 4113–4136,Short summary
In this study, we developed a nitrogen and phosphorus cycle in an intermediate-complexity Earth system climate model. We found that the implementation of nutrient limitation in simulations has reduced the capacity of land to take up atmospheric carbon and has decreased the vegetation biomass, hence, improving the fidelity of the response of land to simulated atmospheric CO2 rise.
Manuel C. Almeida and Pedro S. Coelho
Geosci. Model Dev., 16, 4083–4112,Short summary
Water temperature (WT) datasets of low-order rivers are scarce. In this study, five different models are used to predict the WT of 83 rivers. Generally, the results show that the models' hyperparameter optimization is essential and that to minimize the prediction error it is relevant to apply all the models considered in this study. Results also show that there is a logarithmic correlation among the error of the predicted river WT and the watershed time of concentration.
Lingcheng Li, Yilin Fang, Zhonghua Zheng, Mingjie Shi, Marcos Longo, Charles D. Koven, Jennifer A. Holm, Rosie A. Fisher, Nate G. McDowell, Jeffrey Chambers, and L. Ruby Leung
Geosci. Model Dev., 16, 4017–4040,Short summary
Accurately modeling plant coexistence in vegetation demographic models like ELM-FATES is challenging. This study proposes a repeatable method that uses machine-learning-based surrogate models to optimize plant trait parameters in ELM-FATES. Our approach significantly improves plant coexistence modeling, thus reducing errors. It has important implications for modeling ecosystem dynamics in response to climate change.
Qi Tang, Jean-Christophe Golaz, Luke P. Van Roekel, Mark A. Taylor, Wuyin Lin, Benjamin R. Hillman, Paul A. Ullrich, Andrew M. Bradley, Oksana Guba, Jonathan D. Wolfe, Tian Zhou, Kai Zhang, Xue Zheng, Yunyan Zhang, Meng Zhang, Mingxuan Wu, Hailong Wang, Cheng Tao, Balwinder Singh, Alan M. Rhoades, Yi Qin, Hong-Yi Li, Yan Feng, Yuying Zhang, Chengzhu Zhang, Charles S. Zender, Shaocheng Xie, Erika L. Roesler, Andrew F. Roberts, Azamat Mametjanov, Mathew E. Maltrud, Noel D. Keen, Robert L. Jacob, Christiane Jablonowski, Owen K. Hughes, Ryan M. Forsyth, Alan V. Di Vittorio, Peter M. Caldwell, Gautam Bisht, Renata B. McCoy, L. Ruby Leung, and David C. Bader
Geosci. Model Dev., 16, 3953–3995,Short summary
High-resolution simulations are superior to low-resolution ones in capturing regional climate changes and climate extremes. However, uniformly reducing the grid size of a global Earth system model is too computationally expensive. We provide an overview of the fully coupled regionally refined model (RRM) of E3SMv2 and document a first-of-its-kind set of climate production simulations using RRM at an economic cost. The key to this success is our innovative hybrid time step method.
Anne Marie Treguier, Clement de Boyer Montégut, Alexandra Bozec, Eric P. Chassignet, Baylor Fox-Kemper, Andy McC. Hogg, Doroteaciro Iovino, Andrew E. Kiss, Julien Le Sommer, Yiwen Li, Pengfei Lin, Camille Lique, Hailong Liu, Guillaume Serazin, Dmitry Sidorenko, Qiang Wang, Xiaobio Xu, and Steve Yeager
Geosci. Model Dev., 16, 3849–3872,Short summary
The ocean mixed layer is the interface between the ocean interior and the atmosphere and plays a key role in climate variability. We evaluate the performance of the new generation of ocean models for climate studies, designed to resolve
ocean eddies, which are the largest source of ocean variability and modulate the mixed-layer properties. We find that the mixed-layer depth is better represented in eddy-rich models but, unfortunately, not uniformly across the globe and not in all models.
Duseong S. Jo, Simone Tilmes, Louisa K. Emmons, Siyuan Wang, and Francis Vitt
Geosci. Model Dev., 16, 3893–3906,Short summary
A new simple secondary organic aerosol (SOA) scheme has been developed for the Community Atmosphere Model (CAM) based on the complex SOA scheme in CAM with detailed chemistry (CAM-chem). The CAM with the new SOA scheme shows better agreements with CAM-chem in terms of aerosol concentrations and radiative fluxes, which ensures more consistent results between different compsets in the Community Earth System Model. The new SOA scheme also has technical advantages for future developments.
Leroy J. Bird, Matthew G. W. Walker, Greg E. Bodeker, Isaac H. Campbell, Guangzhong Liu, Swapna Josmi Sam, Jared Lewis, and Suzanne M. Rosier
Geosci. Model Dev., 16, 3785–3808,Short summary
Deriving the statistics of expected future changes in extreme precipitation is challenging due to these events being rare. Regional climate models (RCMs) are computationally prohibitive for generating ensembles capable of capturing large numbers of extreme precipitation events with statistical robustness. Stochastic precipitation generators (SPGs) provide an alternative to RCMs. We describe a novel single-site SPG that learns the statistics of precipitation using a machine-learning approach.
Zhe Zhang, Yanping Li, Fei Chen, Phillip Harder, Warren Helgason, James Famiglietti, Prasanth Valayamkunnath, Cenlin He, and Zhenhua Li
Geosci. Model Dev., 16, 3809–3825,Short summary
Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Abolfazl Simorgh, Manuel Soler, Daniel González-Arribas, Florian Linke, Benjamin Lührs, Maximilian M. Meuser, Simone Dietmüller, Sigrun Matthes, Hiroshi Yamashita, Feijia Yin, Federica Castino, Volker Grewe, and Sabine Baumann
Geosci. Model Dev., 16, 3723–3748,Short summary
This paper addresses the robust climate optimal trajectory planning problem under uncertain meteorological conditions within the structured airspace. Based on the optimization methodology, a Python library has been developed, which can be accessed using the following DOI: https://doi.org/10.5281/zenodo.7121862. The developed tool is capable of providing robust trajectories taking into account all probable realizations of meteorological conditions provided by an EPS computationally very fast.
Matteo Willeit, Tatiana Ilyina, Bo Liu, Christoph Heinze, Mahé Perrette, Malte Heinemann, Daniela Dalmonech, Victor Brovkin, Guy Munhoven, Janine Börker, Jens Hartmann, Gibran Romero-Mujalli, and Andrey Ganopolski
Geosci. Model Dev., 16, 3501–3534,Short summary
In this paper we present the carbon cycle component of the newly developed fast Earth system model CLIMBER-X. The model can be run with interactive atmospheric CO2 to investigate the feedbacks between climate and the carbon cycle on temporal scales ranging from decades to > 100 000 years. CLIMBER-X is expected to be a useful tool for studying past climate–carbon cycle changes and for the investigation of the long-term future evolution of the Earth system.
Jatan Buch, A. Park Williams, Caroline S. Juang, Winslow D. Hansen, and Pierre Gentine
Geosci. Model Dev., 16, 3407–3433,Short summary
We leverage machine learning techniques to construct a statistical model of grid-scale fire frequencies and sizes using climate, vegetation, and human predictors. Our model reproduces the observed trends in fire activity across multiple regions and timescales. We provide uncertainty estimates to inform resource allocation plans for fuel treatment and fire management. Altogether the accuracy and efficiency of our model make it ideal for coupled use with large-scale dynamical vegetation models.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406,Short summary
We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Alena Malyarenko, Alexandra Gossart, Rui Sun, and Mario Krapp
Geosci. Model Dev., 16, 3355–3373,Short summary
Simultaneous modelling of ocean, sea ice, and atmosphere in coupled models is critical for understanding all of the processes that happen in the Antarctic. Here we have developed a coupled model for the Ross Sea, P-SKRIPS, that conserves heat and mass between the ocean and sea ice model (MITgcm) and the atmosphere model (PWRF). We have shown that our developments reduce the model drift, which is important for long-term simulations. P-SKRIPS shows good results in modelling coastal polynyas.
Feijia Yin, Volker Grewe, Federica Castino, Pratik Rao, Sigrun Matthes, Katrin Dahlmann, Simone Dietmüller, Christine Frömming, Hiroshi Yamashita, Patrick Peter, Emma Klingaman, Keith P. Shine, Benjamin Lührs, and Florian Linke
Geosci. Model Dev., 16, 3313–3334,Short summary
This paper describes a newly developed submodel ACCF V1.0 based on the MESSy 2.53.0 infrastructure. The ACCF V1.0 is based on the prototype algorithmic climate change functions (aCCFs) v1.0 to enable climate-optimized flight trajectories. One highlight of this paper is that we describe a consistent full set of aCCFs formulas with respect to fuel scenario and metrics. We demonstrate the usage of the ACCF submodel using AirTraf V2.0 to optimize trajectories for cost and climate impact.
Peter Ukkonen and Robin J. Hogan
Geosci. Model Dev., 16, 3241–3261,Short summary
Climate and weather models suffer from uncertainties resulting from approximated processes. Solar and thermal radiation is one example, as it is computationally too costly to simulate precisely. This has led to attempts to replace radiation codes based on physical equations with neural networks (NNs) that are faster but uncertain. In this paper we use global weather simulations to demonstrate that a middle-ground approach of using NNs only to predict optical properties is accurate and reliable.
Maximilian Gelbrecht, Alistair White, Sebastian Bathiany, and Niklas Boers
Geosci. Model Dev., 16, 3123–3135,Short summary
Differential programming is a technique that enables the automatic computation of derivatives of the output of models with respect to model parameters. Applying these techniques to Earth system modeling leverages the increasing availability of high-quality data to improve the models themselves. This can be done by either using calibration techniques that use gradient-based optimization or incorporating machine learning methods that can learn previously unresolved influences directly from data.
Carolina Gallo, Jonathan M. Eden, Bastien Dieppois, Igor Drobyshev, Peter Z. Fulé, Jesús San-Miguel-Ayanz, and Matthew Blackett
Geosci. Model Dev., 16, 3103–3122,Short summary
This study conducts the first global evaluation of the latest generation of global climate models to simulate a set of fire weather indicators from the Canadian Fire Weather Index System. Models are shown to perform relatively strongly at the global scale, but they show substantial regional and seasonal differences. The results demonstrate the value of model evaluation and selection in producing reliable fire danger projections, ultimately to support decision-making and forest management.
Klaus Klingmüller and Jos Lelieveld
Geosci. Model Dev., 16, 3013–3028,Short summary
Desert dust has significant impacts on climate, public health, infrastructure and ecosystems. An impact assessment requires numerical predictions, which are challenging because the dust emissions are not well known. We present a novel approach using satellite observations and machine learning to more accurately estimate the emissions and to improve the model simulations.
Anna Denvil-Sommer, Erik T. Buitenhuis, Rainer Kiko, Fabien Lombard, Lionel Guidi, and Corinne Le Quéré
Geosci. Model Dev., 16, 2995–3012,Short summary
Using outputs of global biogeochemical ocean model and machine learning methods, we demonstrate that it will be possible to identify linkages between surface environmental and ecosystem structure and the export of carbon to depth by sinking organic particles using real observations. It will be possible to use this knowledge to improve both our understanding of ecosystem dynamics and of their functional representation within models.
Zhenxia Liu, Zengjie Wang, Jian Wang, Zhengfang Zhang, Dongshuang Li, Zhaoyuan Yu, Linwang Yuan, and Wen Luo
Geosci. Model Dev., 16, 2939–2955,Short summary
This study introduces an improved method of the Globally Resolved Energy Balance (GREB) model by the Bayesian network. The improved method constructs a coarse–fine structure that combines a dynamical model with a statistical model based on employing the GREB model as the global framework and utilizing Bayesian networks as the local optimization. The results show that the improved model has better applicability and stability on a global scale and maintains good robustness on the timescale.
Colin Tully, David Neubauer, and Ulrike Lohmann
Geosci. Model Dev., 16, 2957–2973,Short summary
A new method to simulate deterministic ice nucleation processes based on the differential activated fraction was evaluated against a cumulative approach. Box model simulations of heterogeneous-only ice nucleation within cirrus suggest that the latter approach likely underpredicts the ice crystal number concentration. Longer simulations with a GCM show that choosing between these two approaches impacts ice nucleation competition within cirrus but leads to small and insignificant climate effects.
Rasmus E. Benestad, Abdelkader Mezghani, Julia Lutz, Andreas Dobler, Kajsa M. Parding, and Oskar A. Landgren
Geosci. Model Dev., 16, 2899–2913,Short summary
A mathematical method known as common EOFs is not widely used within the climate research community, but it offers innovative ways of evaluating climate models. We show how common EOFs can be used to evaluate large ensembles of global climate model simulations and distill information about their ability to reproduce salient features of the regional climate. We can say that they represent a kind of machine learning (ML) for dealing with big data.
Li Liu, Chao Sun, Xinzhu Yu, Hao Yu, Qingu Jiang, Xingliang Li, Ruizhe Li, Bin Wang, Xueshun Shen, and Guangwen Yang
Geosci. Model Dev., 16, 2833–2850,Short summary
C-Coupler3.0 is an integrated coupler infrastructure with new features, i.e. a series of parallel-optimization technologies, a common halo-exchange library, a common module-integration framework, a common framework for conveniently developing a weakly coupled ensemble data assimilation system, and a common framework for flexibly inputting and outputting fields in parallel. It is able to handle coupling under much finer resolutions (e.g. more than 100 million horizontal grid cells).
Joseph Schoonover, Wilbert Weijer, and Jiaxu Zhang
Geosci. Model Dev., 16, 2795–2809,Short summary
FEOTS aims to enhance the value of data produced by state-of-the-art climate models by providing a framework to diagnose and use ocean transport operators for offline passive tracer simulations. We show that we can capture ocean transport operators from a validated climate model and employ these operators to estimate water mass budgets in an offline regional simulation, using a small fraction of the compute resources required to run a full climate simulation.
Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky, Elynn Wu, Christopher Kung, Tal Ben-Nun, Lucas Harris, Linus Groner, and Oliver Fuhrer
Geosci. Model Dev., 16, 2719–2736,Short summary
It is hard for scientists to write code which is efficient on different kinds of supercomputers. Python is popular for its user-friendliness. We converted a Fortran code, simulating Earth's atmosphere, into Python. This new code auto-converts to a faster language for processors or graphic cards. Our code runs 3.5–4 times faster on graphic cards than the original on processors in a specific supercomputer system.
Anthony Schrapffer, Jan Polcher, Anna Sörensson, and Lluís Fita
The present paper introduces a floodplains scheme for a high resolution Land Surface Model river routing. It was developed and evaluated over one of the world’s largest floodplains: the Pantanal in South America. This shows the impact of tropical floodplains on land surface conditions (soil moisture, temperature) and on land atmosphere fluxes and highlights the potential impact of floodplains on land-atmosphere interactions and the importance of integrating this module in coupled simulations.
Shuaiqi Tang, Adam C. Varble, Jerome D. Fast, Kai Zhang, Peng Wu, Xiquan Dong, Fan Mei, Mikhail Pekour, Joseph C. Hardin, and Po-Lun Ma
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
To assess the ability of Earth System Model (ESM) predictions, we developed a tool called ESMAC Diags to understand the details of how aerosols, clouds, and ACI are represented in ESMs, and this paper describes its version 2 functionality. We compared the model predictions with measurements taken by airplanes, ships, satellites, and ground instruments over four regions over the world. Results show that this new tool can help identify model problems and guide future development of ESMs.
Jan Polcher, Anthony Schrapffer, Eliott Dupont, Lucia Rinchiuso, Xudong Zhou, Olivier Boucher, Emmanuel Mouche, Catherine Ottlé, and Jérôme Servonnat
Geosci. Model Dev., 16, 2583–2606,Short summary
The proposed graphs of hydrological sub-grid elements for atmospheric models allow us to integrate the topographical elements needed in land surface models for a realistic representation of horizontal water and energy transport. The study demonstrates the numerical properties of the automatically built graphs and the simulated water flows.
Geosci. Model Dev., 16, 2343–2354,Short summary
A statistical model called the sea level simulator is presented and made freely available. The sea level simulator integrates mean sea level rise and sea level extremes into a joint probabilistic framework that is useful for flood risk estimation. These flood risk estimates are contingent on probabilities given to different emission scenarios and the length of the planning period. The model is also useful for uncertainty quantification and in decision and adaptation problems.
Jon Seddon, Ag Stephens, Matthew S. Mizielinski, Pier Luigi Vidale, and Malcolm J. Roberts
Geosci. Model Dev. Discuss.,
Revised manuscript accepted for GMDShort summary
The PRIMAVERA project aimed to develop a new generation of advanced global climate models. The large volume of data generated was uploaded to a Central Analysis Facility (CAF) and was analysed by 100 PRIMAVERA scientists there. We describe how the PRIMAVERA project used the CAF's facilities to enable users to analyse this large data set. We believe that similar, multi institute, big-data projects could also use a CAF to efficiently share, organise and analyse large volumes of data.
Quang-Van Doan, Toshiyuki Amagasa, Thanh-Ha Pham, Takuto Sato, Fei Chen, and Hiroyuki Kusaka
Geosci. Model Dev., 16, 2215–2233,Short summary
This study proposes (i) the structural k-means (S k-means) algorithm for clustering spatiotemporally structured climate data and (ii) the clustering uncertainty evaluation framework (CUEF) based on the mutual-information concept.
Booth, B. B. B., Jones, C. D., Collins, M., Totterdell, I. J., Cox, P. M., Sitch, S., Huntingford, C., Betts, R. A., Harris, G. R., and Lloyd, J.: High sensitivity of future global warming to land carbon cycle processes, Environ. Res. Lett., 7, 024002, https://doi.org/10.1088/1748-9326/7/2/024002, 2012.
Boyd, P. W., Watson, A. J., Law, C. S., Abraham, E. R., Trull, T., Murdoch, R., Bakker, D. C. E., Bowie, A. R., Buesseler, K. O., Chang, H., Charette, M., Croot, P., Downing, K., Frew, R., Gall, M., Hadfield, M., Hall, J., Harvey, M., Jameson, G., LaRoche, J., Liddicoat, M., Ling, R., Maldonado, M. T., McKay, R. M., Nodder, S., Pickmere, S., Pridmore, R., Rintoul, S., Safi, K., Sutton, P., Strzepek, R., Tanneberger, K., Turner, S., Waite A., and Zeldis, J.: A mesoscale phytoplankton bloom in the polar Southern Ocean stimulated by iron fertilisation, Nature, 407, 695–702, 2000.
Collins, W. J., Bellouin, N., Doutriaux-Boucher, M., Gedney, N., Halloran, P., Hinton, T., Hughes, J., Jones, C. D., Joshi, M., Liddicoat, S., Martin, G., O'Connor, F., Rae, J., Senior, C., Sitch, S., Totterdell, I., Wiltshire, A., and Woodward, S.: Development and evaluation of an Earth-System model – HadGEM2, Geosci. Model Dev., 4, 1051–1075, https://doi.org/10.5194/gmd-4-1051-2011, 2011.
Cox, P. M.: Description of the "TRIFFID" dynamic global vegetation model, Hadley Centre Technical Note 24, 17 pp., 2001.
Cox, P. M., Betts, R. A., Bunton, C. B., Essery, R. L. H., Rowntree, P. R., and Smith, J.: The impact of new land surface physics on the GCM sensitivity of climate and climate sensitivity, Clim. Dynam., 15, 183–203, 1999.
Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A., and Totterdell, I. J.: Acceleration of global warming due to carbon cycle feedbacks in a coupled climate model, Nature, 408, 184–187, 2000.
Doney, S. C., Lindsay, K., Caldeira, K., Campin, J.-M., Drange, H., Dutay, J.-C., Follows, M., Gao, Y., Gnanadesikan, A., Gruber, N., Ishida, A., Joos, F., Madec, G., Maier-Reimer, E., Marshall, J. C., Matear, R. J., Monfray, P., Mouchet, A., Najjar, R., Orr, J. C., Plattner, G.-K., Sarmiento, J., Schlitzer, R., Slater, R., Totterdell, I. J., Weirig, M.-F., Yamanaka, Y., and Yool, A.: Evaluating global ocean carbon models: The importance of realistic physics, Global Biogeochem. Cy., 18, GB3017, https://doi.org/10.1029/2003GB002150, 2004.
Essery, R. L. H., Best, M. J., Betts, R. A., and Cox, P. M.: Explicit representation of subgrid heterogeneity in a GCM land surface scheme, J. Hydrometeorol., 4, 530–543, 2003.
Garcia, H. E., Locarnini, R. A., Boyer, T. P., and Antonov, J. I.: World Ocean Atlas 2005, Vol. 4: Nutrients (phosphate, nitrate, silicate), edited by: Levitus, S., NOAA Atlas NESDIS 64, US Government Printing Office, Washington, DC, 396 pp., 2006.
Gordon, C., Cooper, C., Senior, C. A., Banks, H., Gregory, J. M., Johns T. C., Mitchell, J. F. B., and Wood, R. A.: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments, Clim. Dynam., 16, 147–168, 2000.
Gregoire, L. J., Valdes, P. J., Payne, A. J., and Kahana, R.: Optimal tuning of a GCM using modern and glacial constraints, Clim. Dynam., 37, 705–719, https://doi.org/10.1007/s00382-010-0934-8, 2010.
Halloran, P. R.: Does atmospheric CO2 seasonality play an important role in governing the air-sea flux of CO2?, Biogeosciences, 9, 2311–2323, https://doi.org/10.5194/bg-9-2311-2012, 2012.
Jones, C., Gregory, J., Thorpe, R., Cox, P., Murphy, J., Sexton, D., and Valdes, P.: Systematic optimisation and climate simulations of FAMOUS, a fast version of HadCM3, Clim. Dyn., 25, 189–204, 2005.
Kriest, I., Khatiwala, S., and Oschlies, A.: Towards an assessment of simple global marine biogeochemical models of different complexity, Progr. Oceanogr. 86, 337–360,https://doi.org/10.1016/j.pocean.2010.05.002, 2012.
Law, B. E., Arkebauer, T., Campbell, J. L., Chen, J., Sun, O., Schwartz, M., van Ingen, C., and Verma, S.: Terrestrial carbon observations: protocols for vegetation sampling and data submission, Food and Agricultural Organisation of the United Nations, Rome, 87 pp., 2008.
Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen. D. O., Zhu, Z., Yang, L., and Merchant, J. W.: Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data, Int. J. Remote Sens., 21, 1303–1330, 2000.
Maslin, M., Malhi, Y., Phillips, O., and Cowling, C.: New views on an old forest: assessing the longevity, resilience and future of the Amazon rainforest, T. I. Brit. Geogr., 30, 477–499, 2005.
Murphy, J. M., Sexton, D. M. H., Barnett, D., N., Jones, G. S., Webb, M. J., Collins, M., and Stainforth, D. A.: Quantification of modelling uncertainties in a large ensemble of climate change simulations, Nature, 430, 768–772, 2004.
Neale, R. and Slingo, J.: The maritime continent and its role in the global climate: A GCM Study. J. Clim., 16, 834–848, 2003.
Nishii, K., Miyasaka, T., Nakamura, H., Kosaka, Y., Yokoi, S., Takayabu, Y. N., Endo, H., Ichikawa, H., Oshima, K., Sato, N., and Tsushima, Y.: Relationship of the reproducibility of multiple variables among global climate models. J. Meteorol. Soc. Japan, 90A, 87–100, https://doi.org/10.2151/jmsj.2012-A04, 2012.
Palmer, J. R. and Totterdell, I. J.: Production and export in a global ocean ecosystem model, Deep-Sea Res. Pt. I, 48, 1169–1198, 2001.
Pope, V. D., Gallani, M. L., Rowntree P. R., and Stratton, R. A.: The impact of new physical parametrizations in the Hadley Centre climate model – HadAM3, Clim. Dynam., 16, 123–146, 2000.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century, J. Geophys. Res., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003.
Redfield, A. C.: The biological control of chemical factors in the environment, Am. Sci., 46, 205–221, 1958.
Reichler, T. and Kim, J.: How well do coupled models simulate today's climate?, Bull. Am. Met. Soc., 89, 303–311, https://doi.org/10.1175/BAMS-89-3-303, 2008.
Ridgwell, A. and Hargreaves, J. C.: Regulation of atmospheric CO2 by deep-sea sediments in an Earth system model, Global Biogeochem. Cy., 21, GB2008, https://doi.org/10.1029/2006GB002764, 2007.
Scott, V., Kettle, H., and Merchant, C., J.: Sensitivity analysis of an ocean carbon cycle model in the North Atlantic: An investigation of parameters affecting the air-sea CO2 flux, primary production and export of detritus, Ocean Sci., 7, 405–419, https://doi.org/10.5194/os-7-405-2011, 2011.
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, https://doi.org/10.1007/s00382-011-1208-9, 2012.
Smith, R. S.: The FAMOUS climate model (versions XFXWB and XFHCC): description update to version XDBUA, Geosci. Model Dev., 5, 269–276, https://doi.org/10.5194/gmd-5-269-2012, 2012.
Smith, R. S., Gregory, J. M., and Osprey, A.: A description of the FAMOUS (version XDBUA) climate model and control run, Geosci. Model Dev., 1, 53–68, https://doi.org/10.5194/gmd-1-53-2008, 2008.
Uppala, S. M., Kållberg, P. W., Simmons, A. J., Andrae, U., da Costa Bechtold, V., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Van de Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., McNally, A. P., Mahfouf, J.-F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Q. J. R. Meteorol. Soc., 131, 2961–3012, https://doi.org/10.1256/qj.04.176, 2005.
Watterson, I. G.: Non-dimensional measures of climate model performance, Int. J. Climatol., 16, 379–391, 1996.
Werth, D. and Avissar, R.: The local and global effects of Amazon deforestation, J. Geophys. Res., 107, 8087, https://doi.org/10.1029/2001JD000717, 2002.
Xie, P., and Arkin, P. A.: Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs, B. Am. Meteorol. Soc., 78, 2539–2558, 1997.