Model description paper
28 Jul 2022
Model description paper
| 28 Jul 2022
The Earth system model CLIMBER-X v1.0 – Part 1: Climate model description and validation
Matteo Willeit et al.
Related authors
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. Discuss., https://doi.org/10.5194/gmd-2022-307, https://doi.org/10.5194/gmd-2022-307, 2023
Preprint under review for GMD
Short summary
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 available as open-source code and 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.
Johanna Beckmann, Mahé Perrette, Sebastian Beyer, Reinhard Calov, Matteo Willeit, and Andrey Ganopolski
The Cryosphere, 13, 2281–2301, https://doi.org/10.5194/tc-13-2281-2019, https://doi.org/10.5194/tc-13-2281-2019, 2019
Short summary
Short summary
Submarine melting (SM) has been discussed as potentially triggering the recently observed retreat at outlet glaciers in Greenland. How much it may contribute in terms of future sea level rise (SLR) has not been quantified yet. When accounting for SM in our experiments, SLR contribution of 12 outlet glaciers increases by over 3-fold until the year 2100 under RCP8.5. Scaling up from 12 to all of Greenland's outlet glaciers increases future SLR contribution of Greenland by 50 %.
Reinhard Calov, Sebastian Beyer, Ralf Greve, Johanna Beckmann, Matteo Willeit, Thomas Kleiner, Martin Rückamp, Angelika Humbert, and Andrey Ganopolski
The Cryosphere, 12, 3097–3121, https://doi.org/10.5194/tc-12-3097-2018, https://doi.org/10.5194/tc-12-3097-2018, 2018
Short summary
Short summary
We present RCP 4.5 and 8.5 projections for the Greenland glacial system with the new glacial system model IGLOO 1.0, which incorporates the ice sheet model SICOPOLIS 3.3, a model of basal hydrology and a parameterization of submarine melt of outlet glaciers. Surface temperature and mass balance anomalies from the MAR climate model serve as forcing delivering projections for the contribution of the Greenland ice sheet to sea level rise and submarine melt of Helheim and Store outlet glaciers.
Matteo Willeit and Andrey Ganopolski
Clim. Past, 14, 697–707, https://doi.org/10.5194/cp-14-697-2018, https://doi.org/10.5194/cp-14-697-2018, 2018
Short summary
Short summary
The surface energy and mass balance of ice sheets strongly depends on surface albedo. Here, using an Earth system model of intermediate complexity, we explore the role played by surface albedo for the simulation of glacial cycles. We show that the evolution of the Northern Hemisphere ice sheets over the last glacial cycle is very sensitive to the parameterization of snow grain size and the effect of dust deposition on snow albedo.
Matteo Willeit and Andrey Ganopolski
Geosci. Model Dev., 9, 3817–3857, https://doi.org/10.5194/gmd-9-3817-2016, https://doi.org/10.5194/gmd-9-3817-2016, 2016
Short summary
Short summary
PALADYN is presented; it is a new comprehensive and computationally efficient land surface–vegetation–carbon cycle model designed to be used in Earth system models of intermediate complexity for long-term simulations and paleoclimate studies.
M. Willeit and A. Ganopolski
Clim. Past, 11, 1165–1180, https://doi.org/10.5194/cp-11-1165-2015, https://doi.org/10.5194/cp-11-1165-2015, 2015
Short summary
Short summary
In this paper we explore the permafrost–ice-sheet interaction using the fully coupled climate–ice-sheet model CLIMBER-2 with the addition of a newly developed permafrost module. We find that permafrost has a moderate but significant effect on ice sheet dynamics during the last glacial cycle. In particular at the Last Glacial Maximum the inclusion of permafrost leads to a 15m sea level equivalent increase in Northern Hemisphere ice volume when permafrost is included.
D. Dalmonech, A. M. Foley, A. Anav, P. Friedlingstein, A. D. Friend, M. Kidston, M. Willeit, and S. Zaehle
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-2083-2014, https://doi.org/10.5194/bgd-11-2083-2014, 2014
Revised manuscript has not been submitted
M. Willeit, A. Ganopolski, and G. Feulner
Biogeosciences, 11, 17–32, https://doi.org/10.5194/bg-11-17-2014, https://doi.org/10.5194/bg-11-17-2014, 2014
M. Willeit, A. Ganopolski, and G. Feulner
Clim. Past, 9, 1749–1759, https://doi.org/10.5194/cp-9-1749-2013, https://doi.org/10.5194/cp-9-1749-2013, 2013
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. Discuss., https://doi.org/10.5194/gmd-2022-307, https://doi.org/10.5194/gmd-2022-307, 2023
Preprint under review for GMD
Short summary
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 available as open-source code and 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.
Daniel Moreno, Jorge Alvarez-Solas, Javier Blasco, Marisa Montoya, and Alexander Robinson
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-215, https://doi.org/10.5194/tc-2022-215, 2022
Preprint under review for TC
Short summary
Short summary
We have reconstructed the Laurentide Ice Sheet, placed in North America during the Last Glacial Maximum (21,000 years ago). The absence of direct measurements raises a number of uncertainties. Here we study the impact of different physical laws that describe the friction as the ice slides over its base. We found that the Laurentide Ice Sheet is closest to prior reconstructions when the basal friction takes into account whether the base is frozen or thawed during its motion.
Negar Vakilifard, Richard G. Williams, Philip B. Holden, Katherine Turner, Neil R. Edwards, and David J. Beerling
Biogeosciences, 19, 4249–4265, https://doi.org/10.5194/bg-19-4249-2022, https://doi.org/10.5194/bg-19-4249-2022, 2022
Short summary
Short summary
To remain within the Paris climate agreement, there is an increasing need to develop and implement carbon capture and sequestration techniques. The global climate benefits of implementing negative emission technologies over the next century are assessed using an Earth system model covering a wide range of plausible climate states. In some model realisations, there is continued warming after emissions cease. This continued warming is avoided if negative emissions are incorporated.
Daniel Moreno, Alexander Robinson, Marisa Montoya, and Jorge Alvarez-Solas
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-97, https://doi.org/10.5194/tc-2022-97, 2022
Revised manuscript under review for TC
Short summary
Short summary
Our study tries to understand how the ice temperature evolves in a large mass as in the case of Antarctica. We found a relation that tells us the ice temperature at any point. These results are important because they also determine how the ice moves. In general, ice moves due to slow deformation (as if pouring honey from a jar). Nevertheless, in some regions the ice base warms enough and melts. The liquid water then serves as lubricant and the ice slides and its velocity increases rapidly.
Alexander Robinson, Daniel Goldberg, and William H. Lipscomb
The Cryosphere, 16, 689–709, https://doi.org/10.5194/tc-16-689-2022, https://doi.org/10.5194/tc-16-689-2022, 2022
Short summary
Short summary
Here we investigate the numerical stability of several commonly used methods in order to determine which of them are capable of resolving the complex physics of the ice flow and are also computationally efficient. We find that the so-called DIVA solver outperforms the others. Its representation of the physics is consistent with more complex methods, while it remains computationally efficient at high resolution.
Stefanie Talento and Andrey Ganopolski
Earth Syst. Dynam., 12, 1275–1293, https://doi.org/10.5194/esd-12-1275-2021, https://doi.org/10.5194/esd-12-1275-2021, 2021
Short summary
Short summary
We propose a model for glacial cycles and produce an assessment of possible trajectories for the next 1 million years. Under natural conditions, the next glacial inception would most likely occur ∼50 kyr after present. We show that fossil-fuel CO2 releases can have an extremely long-term effect. Potentially achievable CO2 anthropogenic emissions during the next centuries will most likely provoke ice-free conditions in the Northern Hemisphere landmasses throughout the next half a million years.
Andreas Born and Alexander Robinson
The Cryosphere, 15, 4539–4556, https://doi.org/10.5194/tc-15-4539-2021, https://doi.org/10.5194/tc-15-4539-2021, 2021
Short summary
Short summary
Ice penetrating radar reflections from the Greenland ice sheet are the best available record of past accumulation and how these layers have been deformed over time by the flow of ice. Direct simulations of this archive hold great promise for improving our models and for uncovering details of ice sheet dynamics that neither models nor data could achieve alone. We present the first three-dimensional ice sheet model that explicitly simulates individual layers of accumulation and how they deform.
Javier Blasco, Jorge Alvarez-Solas, Alexander Robinson, and Marisa Montoya
The Cryosphere, 15, 215–231, https://doi.org/10.5194/tc-15-215-2021, https://doi.org/10.5194/tc-15-215-2021, 2021
Short summary
Short summary
During the Last Glacial Maximum the Antarctic Ice Sheet was larger and more extended than at present. However, neither its exact position nor the total ice volume are well constrained. Here we investigate how the different climatic boundary conditions, as well as basal friction configurations, affect the size and extent of the Antarctic Ice Sheet and discuss its potential implications.
Alexander Robinson, Jorge Alvarez-Solas, Marisa Montoya, Heiko Goelzer, Ralf Greve, and Catherine Ritz
Geosci. Model Dev., 13, 2805–2823, https://doi.org/10.5194/gmd-13-2805-2020, https://doi.org/10.5194/gmd-13-2805-2020, 2020
Short summary
Short summary
Here we describe Yelmo v1.0, an intuitive and state-of-the-art hybrid ice sheet model. The model design and physics are described, and benchmark simulations are provided to validate its performance. Yelmo is a versatile ice sheet model that can be applied to a wide variety of problems.
Andreas Wernecke, Tamsin L. Edwards, Isabel J. Nias, Philip B. Holden, and Neil R. Edwards
The Cryosphere, 14, 1459–1474, https://doi.org/10.5194/tc-14-1459-2020, https://doi.org/10.5194/tc-14-1459-2020, 2020
Short summary
Short summary
We investigate how the two-dimensional characteristics of ice thickness change from satellite measurements can be used to judge and refine a high-resolution ice sheet model of Antarctica. The uncertainty in 50-year model simulations for the currently most drastically changing part of Antarctica can be reduced by nearly 40 % compared to a simpler, non-spatial approach and nearly 90 % compared to the original spread in simulations.
Philip B. Holden, Neil R. Edwards, Thiago F. Rangel, Elisa B. Pereira, Giang T. Tran, and Richard D. Wilkinson
Geosci. Model Dev., 12, 5137–5155, https://doi.org/10.5194/gmd-12-5137-2019, https://doi.org/10.5194/gmd-12-5137-2019, 2019
Short summary
Short summary
We describe the development of the Paleoclimate PLASIM-GENIE emulator and its application to derive a high-resolution spatio-temporal description of the climate of the last 5 x 106 years. Spatial fields of bioclimatic variables are emulated at 1000-year intervals, driven by time series of scalar boundary-condition forcing (CO2, orbit, and ice volume). Emulated anomalies are interpolated into modern climatology to produce a high-resolution climate reconstruction of the Pliocene–Pleistocene.
Johanna Beckmann, Mahé Perrette, Sebastian Beyer, Reinhard Calov, Matteo Willeit, and Andrey Ganopolski
The Cryosphere, 13, 2281–2301, https://doi.org/10.5194/tc-13-2281-2019, https://doi.org/10.5194/tc-13-2281-2019, 2019
Short summary
Short summary
Submarine melting (SM) has been discussed as potentially triggering the recently observed retreat at outlet glaciers in Greenland. How much it may contribute in terms of future sea level rise (SLR) has not been quantified yet. When accounting for SM in our experiments, SLR contribution of 12 outlet glaciers increases by over 3-fold until the year 2100 under RCP8.5. Scaling up from 12 to all of Greenland's outlet glaciers increases future SLR contribution of Greenland by 50 %.
Jamie D. Wilson, Stephen Barker, Neil R. Edwards, Philip B. Holden, and Andy Ridgwell
Biogeosciences, 16, 2923–2936, https://doi.org/10.5194/bg-16-2923-2019, https://doi.org/10.5194/bg-16-2923-2019, 2019
Short summary
Short summary
The remains of plankton rain down from the surface ocean to the deep ocean, acting to store CO2 in the deep ocean. We used a model of biology and ocean circulation to explore the importance of this process in different regions of the ocean. The amount of CO2 stored in the deep ocean is most sensitive to changes in the Southern Ocean. As plankton in the Southern Ocean are likely those most impacted by future climate change, the amount of CO2 they store in the deep ocean could also be affected.
Ilaria Tabone, Alexander Robinson, Jorge Alvarez-Solas, and Marisa Montoya
The Cryosphere, 13, 1911–1923, https://doi.org/10.5194/tc-13-1911-2019, https://doi.org/10.5194/tc-13-1911-2019, 2019
Short summary
Short summary
Recent reconstructions show that the North East Greenland Ice Stream (NEGIS) retreated away from its present-day position by 20–40 km during MIS-3. Atmospheric and external forcings were proposed as potential causes of this retreat, but the role of the ocean was not considered. Here, using a 3-D ice-sheet model, we suggest that oceanic warming is sufficient to induce a retreat of the NEGIS margin of many tens of kilometres during MIS-3, helping to explain this conundrum.
Krista M. S. Kemppinen, Philip B. Holden, Neil R. Edwards, Andy Ridgwell, and Andrew D. Friend
Clim. Past, 15, 1039–1062, https://doi.org/10.5194/cp-15-1039-2019, https://doi.org/10.5194/cp-15-1039-2019, 2019
Short summary
Short summary
We simulate the Last Glacial Maximum atmospheric CO2 decrease with a large ensemble of parameter sets to investigate the range of possible physical and biogeochemical Earth system changes accompanying the CO2 decrease. Amongst the dominant ensemble changes is an increase in terrestrial carbon, which we attribute to a slower soil respiration rate, and the preservation of carbon by the LGM ice sheets. Further investigation into the role of terrestrial carbon is warranted.
Jorge Alvarez-Solas, Rubén Banderas, Alexander Robinson, and Marisa Montoya
Clim. Past, 15, 957–979, https://doi.org/10.5194/cp-15-957-2019, https://doi.org/10.5194/cp-15-957-2019, 2019
Short summary
Short summary
The last glacial period was marked by the existence of of abrupt climatic changes; it is generally accepted that the presence of ice sheets played an important role in their occurrence. While an important effort has been made to investigate the dynamics and evolution of the Laurentide ice sheet during this period, the Eurasian ice sheet (EIS) has not received much attention. Here we investigate the response of the EIS to millennial-scale climate variability using a hybrid 3-D ice-sheet model.
Ilaria Tabone, Alexander Robinson, Jorge Alvarez-Solas, and Marisa Montoya
Clim. Past, 15, 593–609, https://doi.org/10.5194/cp-15-593-2019, https://doi.org/10.5194/cp-15-593-2019, 2019
Short summary
Short summary
By using a 3-D hybrid ice-sheet–shelf model, we investigate the impact of millennial-scale oceanic variability on the Greenland Ice Sheet (GrIS) evolution during the last glacial period (LGP). We show that the GrIS may have strongly reacted to oceanic temperature fluctuations associated with Dansgaard–Oeschger cycles, contributing to sea-level variations of more than 1 m. Our results open the chance for a non-negligible role of the GrIS in millennial-scale oceanic reorganisations during the LGP.
Javier Blasco, Ilaria Tabone, Jorge Alvarez-Solas, Alexander Robinson, and Marisa Montoya
Clim. Past, 15, 121–133, https://doi.org/10.5194/cp-15-121-2019, https://doi.org/10.5194/cp-15-121-2019, 2019
Short summary
Short summary
The LGP is a period punctuated by the presence of several abrupt climate events and sea-level variations of up to 20 m at millennial timescales. The origin of those fluctuations is attributed to NH paleo ice sheets, but a contribution from the AIS cannot be excluded. Here, for the first time, we investigate the response of the AIS to millennial climate variability using an ice sheet–shelf model. We shows that the AIS produces substantial sea-level rises and grounding line migrations.
Reinhard Calov, Sebastian Beyer, Ralf Greve, Johanna Beckmann, Matteo Willeit, Thomas Kleiner, Martin Rückamp, Angelika Humbert, and Andrey Ganopolski
The Cryosphere, 12, 3097–3121, https://doi.org/10.5194/tc-12-3097-2018, https://doi.org/10.5194/tc-12-3097-2018, 2018
Short summary
Short summary
We present RCP 4.5 and 8.5 projections for the Greenland glacial system with the new glacial system model IGLOO 1.0, which incorporates the ice sheet model SICOPOLIS 3.3, a model of basal hydrology and a parameterization of submarine melt of outlet glaciers. Surface temperature and mass balance anomalies from the MAR climate model serve as forcing delivering projections for the contribution of the Greenland ice sheet to sea level rise and submarine melt of Helheim and Store outlet glaciers.
Rubén Banderas, Jorge Alvarez-Solas, Alexander Robinson, and Marisa Montoya
Geosci. Model Dev., 11, 2299–2314, https://doi.org/10.5194/gmd-11-2299-2018, https://doi.org/10.5194/gmd-11-2299-2018, 2018
Short summary
Short summary
Here we present a new approach to force ice-sheet models offline, which accounts for a more realistic treatment of millennial-scale climate variability as compared to the existing methods. Our results reveal that an incorrect representation of the characteristic pattern of millennial-scale climate variability within the climate forcing not only affects NH ice-volume variations at millennial timescales but has consequences for glacial–interglacial ice-volume changes too.
Matteo Willeit and Andrey Ganopolski
Clim. Past, 14, 697–707, https://doi.org/10.5194/cp-14-697-2018, https://doi.org/10.5194/cp-14-697-2018, 2018
Short summary
Short summary
The surface energy and mass balance of ice sheets strongly depends on surface albedo. Here, using an Earth system model of intermediate complexity, we explore the role played by surface albedo for the simulation of glacial cycles. We show that the evolution of the Northern Hemisphere ice sheets over the last glacial cycle is very sensitive to the parameterization of snow grain size and the effect of dust deposition on snow albedo.
Ilaria Tabone, Javier Blasco, Alexander Robinson, Jorge Alvarez-Solas, and Marisa Montoya
Clim. Past, 14, 455–472, https://doi.org/10.5194/cp-14-455-2018, https://doi.org/10.5194/cp-14-455-2018, 2018
Short summary
Short summary
The response of the Greenland Ice Sheet (GrIS) to palaeo-oceanic changes on a glacial–interglacial timescale is studied from a modelling perspective. A 3-D hybrid ice-sheet–shelf model which includes a parameterization of the basal melting rate at the GrIS marine margins is used. The results show that the oceanic forcing plays a key role in the GrIS evolution, not only by controlling the ice retreat during the deglaciation but also by driving the ice expansion in glacial periods.
John S. Keery, Philip B. Holden, and Neil R. Edwards
Clim. Past, 14, 215–238, https://doi.org/10.5194/cp-14-215-2018, https://doi.org/10.5194/cp-14-215-2018, 2018
Short summary
Short summary
In the Eocene (~ 55 million years ago), the Earth had high levels of atmospheric CO2, so studies of the Eocene can provide insights into the likely effects of present-day fossil fuel burning. We ran a low-resolution but very fast climate model with 50 combinations of CO2 and orbital parameters, and an Eocene layout of the oceans and continents. Climatic effects of CO2 are dominant but precession and obliquity strongly influence monsoon rainfall and ocean–land temperature contrasts, respectively.
Johanna Beckmann, Mahé Perrette, and Andrey Ganopolski
The Cryosphere, 12, 301–323, https://doi.org/10.5194/tc-12-301-2018, https://doi.org/10.5194/tc-12-301-2018, 2018
Short summary
Short summary
Greenland's glaciers that are in contact with the ocean undergo a special ice–ocean melting. To project numerically Greenland's centennial contribution to sea level rise, it is crucial to incorporate this special melting. We demonstrate that a numerically cheap model shows the qualitative same behavior as numerical expensive 2–3-dimensional models and calculates the same melting as empirical data show. Our analytical solution gives some insight in the yet poorly understood melting behavior.
Andrey Ganopolski and Victor Brovkin
Clim. Past, 13, 1695–1716, https://doi.org/10.5194/cp-13-1695-2017, https://doi.org/10.5194/cp-13-1695-2017, 2017
Short summary
Short summary
Ice cores reveal that atmospheric CO2 concentration varied synchronously with the global ice volume. Explaining the mechanism of glacial–interglacial variations of atmospheric CO2 concentrations and the link between CO2 and ice sheets evolution still remains a challenge. Here using the Earth system model of intermediate complexity we performed for the first time simulations of co-evolution of climate, ice sheets and carbon cycle using the astronomical forcing as the only external forcing.
Jorge Alvarez-Solas, Rubén Banderas, Alexander Robinson, and Marisa Montoya
Clim. Past Discuss., https://doi.org/10.5194/cp-2017-143, https://doi.org/10.5194/cp-2017-143, 2017
Revised manuscript not accepted
Short summary
Short summary
The last glacial period was marked by the existence of of abrupt climatic changes. It is generally accepted that the presence of ice sheets played an
important role in their occurrence. While an important effort has been made to investigate the dynamics and evolution of the Laurentide Ice Sheet during this period, the Eurasian Ice Sheet (EIS) has not received much attention. Here we investigate the response of the EIS to millennial-scale climate variability. We use a hybrid 3D ice-sheet model.
Eva Bauer and Andrey Ganopolski
Clim. Past, 13, 819–832, https://doi.org/10.5194/cp-13-819-2017, https://doi.org/10.5194/cp-13-819-2017, 2017
Short summary
Short summary
Transient glacial cycle simulations with an EMIC and the PDD method require smaller melt factors for inception than for termination and larger factors for American than European ice sheets. The PDD online method with standard values simulates a sea level drop of 250 m at the LGM. The PDD online run reproducing the LGM ice volume has deficient ablation for reversing from glacial to interglacial climate, so termination is delayed. The SEB method with dust impact on snow albedo is seen as superior.
Mario Krapp, Alexander Robinson, and Andrey Ganopolski
The Cryosphere, 11, 1519–1535, https://doi.org/10.5194/tc-11-1519-2017, https://doi.org/10.5194/tc-11-1519-2017, 2017
Short summary
Short summary
We present the snowpack model SEMIC. It calculates snow height, surface temperature, surface albedo, and the surface mass balance of snow- and ice-covered surfaces while using meteorological data as input. In this paper we describe how SEMIC works and how well it compares with snowpack data of a more sophisticated regional climate model applied to the Greenland ice sheet. Because of its simplicity and efficiency, SEMIC can be used as a coupling interface between atmospheric and ice sheet models.
Matteo Willeit and Andrey Ganopolski
Geosci. Model Dev., 9, 3817–3857, https://doi.org/10.5194/gmd-9-3817-2016, https://doi.org/10.5194/gmd-9-3817-2016, 2016
Short summary
Short summary
PALADYN is presented; it is a new comprehensive and computationally efficient land surface–vegetation–carbon cycle model designed to be used in Earth system models of intermediate complexity for long-term simulations and paleoclimate studies.
Philip B. Holden, Neil R. Edwards, Klaus Fraedrich, Edilbert Kirk, Frank Lunkeit, and Xiuhua Zhu
Geosci. Model Dev., 9, 3347–3361, https://doi.org/10.5194/gmd-9-3347-2016, https://doi.org/10.5194/gmd-9-3347-2016, 2016
Short summary
Short summary
We describe the development, tuning and climate of PLASIM–GENIE, a new intermediate complexity Atmosphere–Ocean General Circulation Model (AOGCM), built by coupling the Planet Simulator to the GENIE Earth system model.
Giang T. Tran, Kevin I. C. Oliver, András Sóbester, David J. J. Toal, Philip B. Holden, Robert Marsh, Peter Challenor, and Neil R. Edwards
Adv. Stat. Clim. Meteorol. Oceanogr., 2, 17–37, https://doi.org/10.5194/ascmo-2-17-2016, https://doi.org/10.5194/ascmo-2-17-2016, 2016
Short summary
Short summary
In this work, we combine the information from a complex and a simple atmospheric model to efficiently build a statistical representation (an emulator) of the complex model and to study the relationship between them. Thanks to the improved efficiency, this process is now feasible for complex models, which are slow and costly to run. The constructed emulator provide approximations of the model output, allowing various analyses to be made without the need to run the complex model again.
A. M. Foley, P. B. Holden, N. R. Edwards, J.-F. Mercure, P. Salas, H. Pollitt, and U. Chewpreecha
Earth Syst. Dynam., 7, 119–132, https://doi.org/10.5194/esd-7-119-2016, https://doi.org/10.5194/esd-7-119-2016, 2016
Short summary
Short summary
We introduce GENIEem-PLASIM-ENTSem (GPem), a climate-carbon cycle emulator, showing how model emulation can be used in integrated assessment modelling to resolve regional climate impacts and systematically capture uncertainty. In a case study, we couple GPem to FTT:Power-E3MG, a non-equilibrium economic model with technology diffusion. We find that when the electricity sector is decarbonised by 90 %, further emissions reductions must be achieved in other sectors to avoid dangerous climate change.
M. Willeit and A. Ganopolski
Clim. Past, 11, 1165–1180, https://doi.org/10.5194/cp-11-1165-2015, https://doi.org/10.5194/cp-11-1165-2015, 2015
Short summary
Short summary
In this paper we explore the permafrost–ice-sheet interaction using the fully coupled climate–ice-sheet model CLIMBER-2 with the addition of a newly developed permafrost module. We find that permafrost has a moderate but significant effect on ice sheet dynamics during the last glacial cycle. In particular at the Last Glacial Maximum the inclusion of permafrost leads to a 15m sea level equivalent increase in Northern Hemisphere ice volume when permafrost is included.
A. Robinson and M. Perrette
Geosci. Model Dev., 8, 1877–1883, https://doi.org/10.5194/gmd-8-1877-2015, https://doi.org/10.5194/gmd-8-1877-2015, 2015
Short summary
Short summary
Here we present a concise interface to the NetCDF library designed to simplify reading and writing tasks of up to 6-D arrays in Fortran programs.
R. Calov, A. Robinson, M. Perrette, and A. Ganopolski
The Cryosphere, 9, 179–196, https://doi.org/10.5194/tc-9-179-2015, https://doi.org/10.5194/tc-9-179-2015, 2015
Short summary
Short summary
Ice discharge into the ocean from outlet glaciers is an important
component of mass loss of the Greenland ice sheet. Here, we present a
simple parameterization of ice discharge for coarse resolution ice
sheet models, suitable for large ensembles or long-term palaeo
simulations. This parameterization reproduces in a good approximation
the present-day ice discharge compared with estimates, and the
simulation of the present-day ice sheet elevation is considerably
improved.
A. Robinson and H. Goelzer
The Cryosphere, 8, 1419–1428, https://doi.org/10.5194/tc-8-1419-2014, https://doi.org/10.5194/tc-8-1419-2014, 2014
E. Bauer and A. Ganopolski
Clim. Past, 10, 1333–1348, https://doi.org/10.5194/cp-10-1333-2014, https://doi.org/10.5194/cp-10-1333-2014, 2014
D. Dalmonech, A. M. Foley, A. Anav, P. Friedlingstein, A. D. Friend, M. Kidston, M. Willeit, and S. Zaehle
Biogeosciences Discuss., https://doi.org/10.5194/bgd-11-2083-2014, https://doi.org/10.5194/bgd-11-2083-2014, 2014
Revised manuscript has not been submitted
M. Willeit, A. Ganopolski, and G. Feulner
Biogeosciences, 11, 17–32, https://doi.org/10.5194/bg-11-17-2014, https://doi.org/10.5194/bg-11-17-2014, 2014
M. Willeit, A. Ganopolski, and G. Feulner
Clim. Past, 9, 1749–1759, https://doi.org/10.5194/cp-9-1749-2013, https://doi.org/10.5194/cp-9-1749-2013, 2013
Related subject area
Climate and Earth system modeling
WRF–ML v1.0: a bridge between WRF v4.3 and machine learning parameterizations and its application to atmospheric radiative transfer
The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system
Climate impacts of parameterizing subgrid variation and partitioning of land surface heat fluxes to the atmosphere with the NCAR CESM1.2
Accelerated photosynthesis routine in LPJmL4
Improving scalability of Earth system models through coarse-grained component concurrency – a case study with the ICON v2.6.5 modelling system
Temperature forecasting by deep learning methods
Pathfinder v1.0.1: a Bayesian-inferred simple carbon–climate model to explore climate change scenarios
Inclusion of a cold hardening scheme to represent frost tolerance is essential to model realistic plant hydraulics in the Arctic–boreal zone in CLM5.0-FATES-Hydro
Implementation and evaluation of the GEOS-Chem chemistry module version 13.1.2 within the Community Earth System Model v2.1
Assessment of JSBACHv4.30 as a land component of ICON-ESM-V1 in comparison to its predecessor JSBACHv3.2 of MPI-ESM1.2
Global biomass burning fuel consumption and emissions at 500 m spatial resolution based on the Global Fire Emissions Database (GFED)
Impact of increased resolution on the representation of the Canary upwelling system in climate models
Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI): protocol and initial results from the first simulations
Introducing the VIIRS-based Fire Emission Inventory version 0 (VFEIv0)
Impact of physical parameterizations on wind simulation with WRF V3.9.1.1 under stable conditions at planetary boundary layer gray-zone resolution: a case study over the coastal regions of North China
Advancing precipitation prediction using a new-generation storm-resolving model framework – SIMA-MPAS (V1.0): a case study over the western United States
SURFER v2.0: a flexible and simple model linking anthropogenic CO2 emissions and solar radiation modification to ocean acidification and sea level rise
A new bootstrap technique to quantify uncertainty in estimates of ground surface temperature and ground heat flux histories from geothermal data
Modeling the topographic influence on aboveground biomass using a coupled model of hillslope hydrology and ecosystem dynamics
Impacts of the ice-particle size distribution shape parameter on climate simulations with the Community Atmosphere Model Version 6 (CAM6)
A modeling framework to understand historical and projected ocean climate change in large coupled ensembles
TriCCo v1.1.0 – a cubulation-based method for computing connected components on triangular grids
Estimation of missing building height in OpenStreetMap data: a French case study using GeoClimate 0.0.1
The Moist Quasi-Geostrophic Coupled Model: MQ-GCM 2.0
Cell tracking of convective rainfall: sensitivity of climate-change signal to tracking algorithm and cell definition (Cell-TAO v1.0)
Transport parameterization of the Polar SWIFT model (version 2)
Analog data assimilation for the selection of suitable general circulation models
Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0
Grid refinement in ICON v2.6.4
Classification of tropical cyclone containing images using a convolutional neural network: performance and sensitivity to the learning dataset
The ICON-A model for direct QBO simulations on GPUs (version icon-cscs:baf28a514)
Evaluation of Native Earth System Model Output with ESMValTool v2.6.0
Further improvement and evaluation of nudging in the E3SM Atmosphere Model version 1 (EAMv1): simulations of the mean climate, weather events, and anthropogenic aerosol effects
HORAYZON v1.2: an efficient and flexible ray-tracing algorithm to compute horizon and sky view factor
LPJ-GUESS/LSMv1.0: a next-generation land surface model with high ecological realism
Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44
Intercomparison of four algorithms for detecting tropical cyclones using ERA5
Inland lake temperature initialization via coupled cycling with atmospheric data assimilation
wavetrisk-2.1: an adaptive dynamical core for ocean modelling
Representing surface heterogeneity in land–atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime
AWI-CM3 coupled climate model: description and evaluation experiments for a prototype post-CMIP6 model
The Seasonal-to-Multiyear Large Ensemble (SMYLE) prediction system using the Community Earth System Model version 2
Combining Regional Mesh Refinement With Vertically Enhanced Physics to Target Marine Stratocumulus Biases
Comparison and evaluation of updates to WRF-Chem (v3.9) biogenic emissions using MEGAN
Checkerboard patterns in E3SMv2 and E3SM-MMFv2
AttentionFire_v1.0: interpretable machine learning fire model for burned area predictions over tropics
MIdASv0.2.1 – MultI-scale bias AdjuStment
Assessing methods for representing soil heterogeneity through a flexible approach within the Joint UK Land Environment Simulator (JULES) at version 3.4.1
Monthly-Scale Extended Predictions Using the Atmospheric Model Coupled with a Slab-Ocean
FOCI-MOPS v1 – integration of marine biogeochemistry within the Flexible Ocean and Climate Infrastructure version 1 (FOCI 1) Earth system model
Xiaohui Zhong, Zhijian Ma, Yichen Yao, Lifei Xu, Yuan Wu, and Zhibin Wang
Geosci. Model Dev., 16, 199–209, https://doi.org/10.5194/gmd-16-199-2023, https://doi.org/10.5194/gmd-16-199-2023, 2023
Short summary
Short summary
More and more researchers use deep learning models to replace physics-based parameterizations to accelerate weather simulations. However, embedding the ML models within the weather models is difficult as they are implemented in different languages. This work proposes a coupling framework to allow ML-based parameterizations to be coupled with the Weather Research and Forecasting (WRF) model. We also demonstrate using the coupler to couple the ML-based radiation schemes with the WRF model.
Dario Nicolì, Alessio Bellucci, Paolo Ruggieri, Panos J. Athanasiadis, Stefano Materia, Daniele Peano, Giusy Fedele, Riccardo Hénin, and Silvio Gualdi
Geosci. Model Dev., 16, 179–197, https://doi.org/10.5194/gmd-16-179-2023, https://doi.org/10.5194/gmd-16-179-2023, 2023
Short summary
Short summary
Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of the near-term climate and are useful for informing decision-makers on future climate-related risks. The predictive skill for key variables is assessed from the operational decadal prediction system compared with non-initialized historical simulations so as to quantify the added value of initialization.
Ming Yin, Yilun Han, Yong Wang, Wenqi Sun, Jianbo Deng, Daoming Wei, Ying Kong, and Bin Wang
Geosci. Model Dev., 16, 135–156, https://doi.org/10.5194/gmd-16-135-2023, https://doi.org/10.5194/gmd-16-135-2023, 2023
Short summary
Short summary
All global climate models (GCMs) use the grid-averaged surface heat fluxes to drive the atmosphere, and thus their horizontal variations within the grid cell are averaged out. In this regard, a novel scheme considering the variation and partitioning of the surface heat fluxes within the grid cell is developed. The scheme reduces the long-standing rainfall biases on the southern and eastern margins of the Tibetan Plateau. The performance of key variables at the global scale is also evaluated.
Jenny Niebsch, Werner von Bloh, Kirsten Thonicke, and Ronny Ramlau
Geosci. Model Dev., 16, 17–33, https://doi.org/10.5194/gmd-16-17-2023, https://doi.org/10.5194/gmd-16-17-2023, 2023
Short summary
Short summary
The impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are used to study the effects of multiple processes in the biosphere under climate change. There is a demand for a better computational performance of the models. In this paper, the photosynthesis model in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We found a better numerical solution of a nonlinear equation. A significant run time reduction was possible.
Leonidas Linardakis, Irene Stemmler, Moritz Hanke, Lennart Ramme, Fatemeh Chegini, Tatiana Ilyina, and Peter Korn
Geosci. Model Dev., 15, 9157–9176, https://doi.org/10.5194/gmd-15-9157-2022, https://doi.org/10.5194/gmd-15-9157-2022, 2022
Short summary
Short summary
In Earth system modelling, we are facing the challenge of making efficient use of very large machines, with millions of cores. To meet this challenge we will need to employ multi-level and multi-dimensional parallelism. Component concurrency, being a function parallel technique, offers an additional dimension to the traditional data-parallel approaches. In this paper we examine the behaviour of component concurrency and identify the conditions for its optimal application.
Bing Gong, Michael Langguth, Yan Ji, Amirpasha Mozaffari, Scarlet Stadtler, Karim Mache, and Martin G. Schultz
Geosci. Model Dev., 15, 8931–8956, https://doi.org/10.5194/gmd-15-8931-2022, https://doi.org/10.5194/gmd-15-8931-2022, 2022
Short summary
Short summary
Inspired by the success of deep learning in various domains, we test the applicability of video prediction methods by generative adversarial network (GAN)-based deep learning to predict the 2 m temperature over Europe. Our video prediction models have skill in predicting the diurnal cycle of 2 m temperature up to 12 h ahead. Complemented by probing the relevance of several model parameters, this study confirms the potential of deep learning in meteorological forecasting applications.
Thomas Bossy, Thomas Gasser, and Philippe Ciais
Geosci. Model Dev., 15, 8831–8868, https://doi.org/10.5194/gmd-15-8831-2022, https://doi.org/10.5194/gmd-15-8831-2022, 2022
Short summary
Short summary
We developed a new simple climate model designed to fill a perceived gap within the existing simple climate models by fulfilling three key requirements: calibration using Bayesian inference, the possibility of coupling with integrated assessment models, and the capacity to explore climate scenarios compatible with limiting climate impacts. Here, we describe the model and its calibration using the latest data from complex CMIP6 models and the IPCC AR6, and we assess its performance.
Marius S. A. Lambert, Hui Tang, Kjetil S. Aas, Frode Stordal, Rosie A. Fisher, Yilin Fang, Junyan Ding, and Frans-Jan W. Parmentier
Geosci. Model Dev., 15, 8809–8829, https://doi.org/10.5194/gmd-15-8809-2022, https://doi.org/10.5194/gmd-15-8809-2022, 2022
Short summary
Short summary
In this study, we implement a hardening mortality scheme into CTSM5.0-FATES-Hydro and evaluate how it impacts plant hydraulics and vegetation growth. Our work shows that the hydraulic modifications prescribed by the hardening scheme are necessary to model realistic vegetation growth in cold climates, in contrast to the default model that simulates almost nonexistent and declining vegetation due to abnormally large water loss through the roots.
Thibaud M. Fritz, Sebastian D. Eastham, Louisa K. Emmons, Haipeng Lin, Elizabeth W. Lundgren, Steve Goldhaber, Steven R. H. Barrett, and Daniel J. Jacob
Geosci. Model Dev., 15, 8669–8704, https://doi.org/10.5194/gmd-15-8669-2022, https://doi.org/10.5194/gmd-15-8669-2022, 2022
Short summary
Short summary
We bring the state-of-the-science chemistry module GEOS-Chem into the Community Earth System Model (CESM). We show that some known differences between results from GEOS-Chem and CESM's CAM-chem chemistry module may be due to the configuration of model meteorology rather than inherent differences in the model chemistry. This is a significant step towards a truly modular Earth system model and allows two strong but currently separate research communities to benefit from each other's advances.
Rainer Schneck, Veronika Gayler, Julia E. M. S. Nabel, Thomas Raddatz, Christian H. Reick, and Reiner Schnur
Geosci. Model Dev., 15, 8581–8611, https://doi.org/10.5194/gmd-15-8581-2022, https://doi.org/10.5194/gmd-15-8581-2022, 2022
Short summary
Short summary
The versions of ICON-A and ICON-Land/JSBACHv4 used for this study constitute the first milestone in the development of the new ICON Earth System Model ICON-ESM. JSBACHv4 is the successor of JSBACHv3, and most of the parameterizations of JSBACHv4 are re-implementations from JSBACHv3. We assess and compare the performance of JSBACHv4 and JSBACHv3. Overall, the JSBACHv4 results are as good as JSBACHv3, but both models reveal the same main shortcomings, e.g. the depiction of the leaf area index.
Dave van Wees, Guido R. van der Werf, James T. Randerson, Brendan M. Rogers, Yang Chen, Sander Veraverbeke, Louis Giglio, and Douglas C. Morton
Geosci. Model Dev., 15, 8411–8437, https://doi.org/10.5194/gmd-15-8411-2022, https://doi.org/10.5194/gmd-15-8411-2022, 2022
Short summary
Short summary
We present a global fire emission model based on the GFED model framework with a spatial resolution of 500 m. The higher resolution allowed for a more detailed representation of spatial heterogeneity in fuels and emissions. Specific modules were developed to model, for example, emissions from fire-related forest loss and belowground burning. Results from the 500 m model were compared to GFED4s, showing that global emissions were relatively similar but that spatial differences were substantial.
Adama Sylla, Emilia Sanchez Gomez, Juliette Mignot, and Jorge López-Parages
Geosci. Model Dev., 15, 8245–8267, https://doi.org/10.5194/gmd-15-8245-2022, https://doi.org/10.5194/gmd-15-8245-2022, 2022
Short summary
Short summary
Increasing model resolution depends on the subdomain of the Canary upwelling considered. In the Iberian Peninsula, the high-resolution (HR) models do not seem to better simulate the upwelling indices, while in Morocco to the Senegalese coast, the HR models show a clear improvement. Thus increasing the resolution of a global climate model does not necessarily have to be the only way to better represent the climate system. There is still much work to be done in terms of physical parameterizations.
Jadwiga H. Richter, Daniele Visioni, Douglas G. MacMartin, David A. Bailey, Nan Rosenbloom, Brian Dobbins, Walker R. Lee, Mari Tye, and Jean-Francois Lamarque
Geosci. Model Dev., 15, 8221–8243, https://doi.org/10.5194/gmd-15-8221-2022, https://doi.org/10.5194/gmd-15-8221-2022, 2022
Short summary
Short summary
Solar climate intervention using stratospheric aerosol injection is a proposed method of reducing global mean temperatures to reduce the worst consequences of climate change. We present a new modeling protocol aimed at simulating a plausible deployment of stratospheric aerosol injection and reproducibility of simulations using other Earth system models: Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI).
Gonzalo A. Ferrada, Meng Zhou, Jun Wang, Alexei Lyapustin, Yujie Wang, Saulo R. Freitas, and Gregory R. Carmichael
Geosci. Model Dev., 15, 8085–8109, https://doi.org/10.5194/gmd-15-8085-2022, https://doi.org/10.5194/gmd-15-8085-2022, 2022
Short summary
Short summary
The smoke from fires is composed of different compounds that interact with the atmosphere and can create poor air-quality episodes. Here, we present a new fire inventory based on satellite observations from the Visible Infrared Imaging Radiometer Suite (VIIRS). We named this inventory the VIIRS-based Fire Emission Inventory (VFEI). Advantages of VFEI are its high resolution (~500 m) and that it provides information for many species. VFEI is publicly available and has provided data since 2012.
Entao Yu, Rui Bai, Xia Chen, and Lifang Shao
Geosci. Model Dev., 15, 8111–8134, https://doi.org/10.5194/gmd-15-8111-2022, https://doi.org/10.5194/gmd-15-8111-2022, 2022
Short summary
Short summary
A large number of simulations are conducted to investigate how different physical parameterization schemes impact surface wind simulations under stable weather conditions over the coastal regions of North China using the Weather Research and Forecasting model with a horizontal grid spacing of 0.5 km. Results indicate that the simulated wind speed is most sensitive to the planetary boundary layer schemes, followed by short-wave/long-wave radiation schemes and microphysics schemes.
Xingying Huang, Andrew Gettelman, William C. Skamarock, Peter Hjort Lauritzen, Miles Curry, Adam Herrington, John T. Truesdale, and Michael Duda
Geosci. Model Dev., 15, 8135–8151, https://doi.org/10.5194/gmd-15-8135-2022, https://doi.org/10.5194/gmd-15-8135-2022, 2022
Short summary
Short summary
We focus on the recent development of a state-of-the-art storm-resolving global climate model and investigate how this next-generation model performs for precipitation prediction over the western USA. Results show realistic representations of precipitation with significantly enhanced snowpack over complex terrains. The model evaluation advances the unified modeling of large-scale forcing constraints and realistic fine-scale features to advance multi-scale climate predictions and changes.
Marina Martínez Montero, Michel Crucifix, Victor Couplet, Nuria Brede, and Nicola Botta
Geosci. Model Dev., 15, 8059–8084, https://doi.org/10.5194/gmd-15-8059-2022, https://doi.org/10.5194/gmd-15-8059-2022, 2022
Short summary
Short summary
We present SURFER, a lightweight model that links CO2 emissions and geoengineering to ocean acidification and sea level rise from glaciers, ocean thermal expansion and Greenland and Antarctic ice sheets. The ice sheet module adequately describes the tipping points of both Greenland and Antarctica. SURFER is understandable, fast, accurate up to several thousands of years, capable of emulating results obtained by state of the art models and well suited for policy analyses.
Francisco José Cuesta-Valero, Hugo Beltrami, Stephan Gruber, Almudena García-García, and J. Fidel González-Rouco
Geosci. Model Dev., 15, 7913–7932, https://doi.org/10.5194/gmd-15-7913-2022, https://doi.org/10.5194/gmd-15-7913-2022, 2022
Short summary
Short summary
Inversions of subsurface temperature profiles provide past long-term estimates of ground surface temperature histories and ground heat flux histories at timescales of decades to millennia. Theses estimates complement high-frequency proxy temperature reconstructions and are the basis for studying continental heat storage. We develop and release a new bootstrap method to derive meaningful confidence intervals for the average surface temperature and heat flux histories from any number of profiles.
Yilin Fang, L. Ruby Leung, Charles D. Koven, Gautam Bisht, Matteo Detto, Yanyan Cheng, Nate McDowell, Helene Muller-Landau, S. Joseph Wright, and Jeffrey Q. Chambers
Geosci. Model Dev., 15, 7879–7901, https://doi.org/10.5194/gmd-15-7879-2022, https://doi.org/10.5194/gmd-15-7879-2022, 2022
Short summary
Short summary
We develop a model that integrates an Earth system model with a three-dimensional hydrology model to explicitly resolve hillslope topography and water flow underneath the land surface to understand how local-scale hydrologic processes modulate vegetation along water availability gradients. Our coupled model can be used to improve the understanding of the diverse impact of local heterogeneity and water flux on nutrient availability and plant communities.
Wentao Zhang, Xiangjun Shi, and Chunsong Lu
Geosci. Model Dev., 15, 7751–7766, https://doi.org/10.5194/gmd-15-7751-2022, https://doi.org/10.5194/gmd-15-7751-2022, 2022
Short summary
Short summary
The two-moment bulk cloud microphysics scheme used in CAM6 was modified to consider the impacts of the ice-crystal size distribution shape parameter (μi). After that, how the μi impacts cloud microphysical processes and then climate simulations is clearly illustrated by offline tests and CAM6 model experiments. Our results and findings are useful for the further development of μi-related parameterizations.
Yona Silvy, Clément Rousset, Eric Guilyardi, Jean-Baptiste Sallée, Juliette Mignot, Christian Ethé, and Gurvan Madec
Geosci. Model Dev., 15, 7683–7713, https://doi.org/10.5194/gmd-15-7683-2022, https://doi.org/10.5194/gmd-15-7683-2022, 2022
Short summary
Short summary
A modeling framework is introduced to understand and decompose the mechanisms causing the ocean temperature, salinity and circulation to change since the pre-industrial period and into 21st century scenarios of global warming. This framework aims to look at the response to changes in the winds and in heat and freshwater exchanges at the ocean interface in global climate models, throughout the 1850–2100 period, to unravel their individual effects on the changing physical structure of the ocean.
Aiko Voigt, Petra Schwer, Noam von Rotberg, and Nicole Knopf
Geosci. Model Dev., 15, 7489–7504, https://doi.org/10.5194/gmd-15-7489-2022, https://doi.org/10.5194/gmd-15-7489-2022, 2022
Short summary
Short summary
In climate science, it is helpful to identify coherent objects, for example, those formed by clouds. However, many models now use unstructured grids, which makes it harder to identify coherent objects. We present a new method that solves this problem by moving model data from an unstructured triangular grid to a structured cubical grid. We implement the method in an open-source Python package and show that the method is ready to be applied to climate model data.
Jérémy Bernard, Erwan Bocher, Elisabeth Le Saux Wiederhold, François Leconte, and Valéry Masson
Geosci. Model Dev., 15, 7505–7532, https://doi.org/10.5194/gmd-15-7505-2022, https://doi.org/10.5194/gmd-15-7505-2022, 2022
Short summary
Short summary
OpenStreetMap is a collaborative project aimed at creaing a free dataset containing topographical information. Since these data are available worldwide, they can be used as standard data for geoscience studies. However, most buildings miss the height information that constitutes key data for numerous fields (urban climate, noise propagation, air pollution). In this work, the building height is estimated using statistical modeling using indicators that characterize the building's environment.
Sergey Kravtsov, Ilijana Mastilovic, Andrew McC. Hogg, William K. Dewar, and Jeffrey R. Blundell
Geosci. Model Dev., 15, 7449–7469, https://doi.org/10.5194/gmd-15-7449-2022, https://doi.org/10.5194/gmd-15-7449-2022, 2022
Short summary
Short summary
Climate is a complex system whose behavior is shaped by multitudes of processes operating on widely different spatial scales and timescales. In hierarchical modeling, one goes back and forth between highly idealized process models and state-of-the-art models coupling the entire range of climate subsystems to identify specific phenomena and understand their dynamics. The present contribution highlights an intermediate climate model focussing on midlatitude ocean–atmosphere interactions.
Edmund P. Meredith, Uwe Ulbrich, and Henning W. Rust
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-202, https://doi.org/10.5194/gmd-2022-202, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
Cell tracking algorithms allow the properties of a convective cell to be studied across its lifetime and, in particular, how these respond to climate change. We investigated whether the design of the algorithm can affect the magnitude of the climate-change signal. The algorithm’s criteria for identifying a cell were found to have a strong impact on the warming response. The sensitivity of the warming response to different algorithm settings and cell types should thus be fully explored.
Ingo Wohltmann, Daniel Kreyling, and Ralph Lehmann
Geosci. Model Dev., 15, 7243–7255, https://doi.org/10.5194/gmd-15-7243-2022, https://doi.org/10.5194/gmd-15-7243-2022, 2022
Short summary
Short summary
The study evaluates the performance of the Data Assimilation Research Testbed (DART), equipped with the recently added forward operator Radiative Transfer for TOVS (RTTOV), in assimilating FY-4A visible images into the Weather Research and Forecasting (WRF) model. The ability of the WRF-DART/RTTOV system to improve the forecasting skills for a tropical storm over East Asia and the Western Pacific is demonstrated in an Observing System Simulation Experiment framework.
Juan Ruiz, Pierre Ailliot, Thi Tuyet Trang Chau, Pierre Le Bras, Valérie Monbet, Florian Sévellec, and Pierre Tandeo
Geosci. Model Dev., 15, 7203–7220, https://doi.org/10.5194/gmd-15-7203-2022, https://doi.org/10.5194/gmd-15-7203-2022, 2022
Short summary
Short summary
We present a new approach to validate numerical simulations of the current climate. The method can take advantage of existing climate simulations produced by different centers combining an analog forecasting approach with data assimilation to quantify how well a particular model reproduces a sequence of observed values. The method can be applied with different observations types and is implemented locally in space and time significantly reducing the associated computational cost.
Chahan M. Kropf, Alessio Ciullo, Laura Otth, Simona Meiler, Arun Rana, Emanuel Schmid, Jamie W. McCaughey, and David N. Bresch
Geosci. Model Dev., 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022, https://doi.org/10.5194/gmd-15-7177-2022, 2022
Short summary
Short summary
Mathematical models are approximations, and modellers need to understand and ideally quantify the arising uncertainties. Here, we describe and showcase the first, simple-to-use, uncertainty and sensitivity analysis module of the open-source and open-access climate-risk modelling platform CLIMADA. This may help to enhance transparency and intercomparison of studies among climate-risk modellers, help focus future research, and lead to better-informed decisions on climate adaptation.
Günther Zängl, Daniel Reinert, and Florian Prill
Geosci. Model Dev., 15, 7153–7176, https://doi.org/10.5194/gmd-15-7153-2022, https://doi.org/10.5194/gmd-15-7153-2022, 2022
Short summary
Short summary
This article describes the implementation of grid refinement in the ICOsahedral Nonhydrostatic (ICON) model, which has been jointly developed at several German institutions and constitutes a unified modeling system for global and regional numerical weather prediction and climate applications. The grid refinement allows using a higher resolution in regional domains and transferring the information back to the global domain by means of a feedback mechanism.
Sébastien Gardoll and Olivier Boucher
Geosci. Model Dev., 15, 7051–7073, https://doi.org/10.5194/gmd-15-7051-2022, https://doi.org/10.5194/gmd-15-7051-2022, 2022
Short summary
Short summary
Tropical cyclones (TCs) are one of the most devastating natural disasters, which justifies monitoring and prediction in the context of a changing climate. In this study, we have adapted and tested a convolutional neural network (CNN) for the classification of reanalysis outputs (ERA5 and MERRA-2 labeled by HURDAT2) according to the presence or absence of TCs. We tested the impact of interpolation and of "mixing and matching" the training and test sets on the performance of the CNN.
Marco A. Giorgetta, William Sawyer, Xavier Lapillonne, Panagiotis Adamidis, Dmitry Alexeev, Valentin Clément, Remo Dietlicher, Jan Frederik Engels, Monika Esch, Henning Franke, Claudia Frauen, Walter M. Hannah, Benjamin R. Hillman, Luis Kornblueh, Philippe Marti, Matthew R. Norman, Robert Pincus, Sebastian Rast, Daniel Reinert, Reiner Schnur, Uwe Schulzweida, and Bjorn Stevens
Geosci. Model Dev., 15, 6985–7016, https://doi.org/10.5194/gmd-15-6985-2022, https://doi.org/10.5194/gmd-15-6985-2022, 2022
Short summary
Short summary
This work presents a first version of the ICON atmosphere model that works not only on CPUs, but also on GPUs. This GPU-enabled ICON version is benchmarked on two GPU machines and a CPU machine. While the weak scaling is very good on CPUs and GPUs, the strong scaling is poor on GPUs. But the high performance of GPU machines allowed for first simulations of a short period of the quasi-biennial oscillation at very high resolution with explicit convection and gravity wave forcing.
Manuel Schlund, Birgit Hassler, Axel Lauer, Bouwe Andela, Patrick Jöckel, Rémi Kazeroni, Saskia Loosveldt Tomas, Brian Medeiros, Valeriu Predoi, Stéphane Sénési, Jérôme Servonnat, Tobias Stacke, Javier Vegas-Regidor, Klaus Zimmermann, and Veronika Eyring
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-205, https://doi.org/10.5194/gmd-2022-205, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
The Earth System Model Evaluation Tool (ESMValTool) is a community diagnostics and performance metrics tool for routine evaluation of Earth system models. Originally, ESMValTool has been designed to process reformatted output provided by large model intercomparison projects like the Coupled Model Intercomparison Project (CMIP). Here, we describe a new extension of ESMValTool that allows reading and processing native climate model output, i.e., data that has not been reformatted before.
Shixuan Zhang, Kai Zhang, Hui Wan, and Jian Sun
Geosci. Model Dev., 15, 6787–6816, https://doi.org/10.5194/gmd-15-6787-2022, https://doi.org/10.5194/gmd-15-6787-2022, 2022
Short summary
Short summary
This study investigates the nudging implementation in the EAMv1 model. We find that (1) revising the sequence of calculations and using higher-frequency constraining data to improve the performance of a simulation nudged to EAMv1’s own meteorology, (2) using the relocated nudging tendency and 3-hourly ERA5 reanalysis to obtain a better agreement between nudged simulations and observations, and (3) using wind-only nudging are recommended for the estimates of global mean aerosol effects.
Christian R. Steger, Benjamin Steger, and Christoph Schär
Geosci. Model Dev., 15, 6817–6840, https://doi.org/10.5194/gmd-15-6817-2022, https://doi.org/10.5194/gmd-15-6817-2022, 2022
Short summary
Short summary
Terrain horizon and sky view factor are crucial quantities for many geoscientific applications; e.g. they are used to account for effects of terrain on surface radiation in climate and land surface models. Because typical terrain horizon algorithms are inefficient for high-resolution (< 30 m) elevation data, we developed a new algorithm based on a ray-tracing library. A comparison with two conventional methods revealed both its high performance and its accuracy for complex terrain.
David Martín Belda, Peter Anthoni, David Wårlind, Stefan Olin, Guy Schurgers, Jing Tang, Benjamin Smith, and Almut Arneth
Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, https://doi.org/10.5194/gmd-15-6709-2022, 2022
Short summary
Short summary
We present a number of augmentations to the ecosystem model LPJ-GUESS, which will allow us to use it in studies of the interactions between the land biosphere and the climate. The new module enables calculation of fluxes of energy and water into the atmosphere that are consistent with the modelled vegetation processes. The modelled fluxes are in fair agreement with observations across 21 sites from the FLUXNET network.
Jorge Baño-Medina, Rodrigo Manzanas, Ezequiel Cimadevilla, Jesús Fernández, Jose González-Abad, Antonio S. Cofiño, and José Manuel Gutiérrez
Geosci. Model Dev., 15, 6747–6758, https://doi.org/10.5194/gmd-15-6747-2022, https://doi.org/10.5194/gmd-15-6747-2022, 2022
Short summary
Short summary
Deep neural networks are used to produce downscaled regional climate change projections over Europe for temperature and precipitation for the first time. The resulting dataset, DeepESD, is analyzed against state-of-the-art downscaling methodologies, reproducing more accurately the observed climate in the historical period and showing plausible future climate change signals with low computational requirements.
Stella Bourdin, Sébastien Fromang, William Dulac, Julien Cattiaux, and Fabrice Chauvin
Geosci. Model Dev., 15, 6759–6786, https://doi.org/10.5194/gmd-15-6759-2022, https://doi.org/10.5194/gmd-15-6759-2022, 2022
Short summary
Short summary
When studying tropical cyclones in a large dataset, one needs objective and automatic procedures to detect their specific pattern. Applying four different such algorithms to a reconstruction of the climate, we show that the choice of the algorithm is crucial to the climatology obtained. Mainly, the algorithms differ in their sensitivity to weak storms so that they provide different frequencies and durations. We review the different options to consider for the choice of the tracking methodology.
Stanley G. Benjamin, Tatiana G. Smirnova, Eric P. James, Eric J. Anderson, Ayumi Fujisaki-Manome, John G. W. Kelley, Greg E. Mann, Andrew D. Gronewold, Philip Chu, and Sean G. T. Kelley
Geosci. Model Dev., 15, 6659–6676, https://doi.org/10.5194/gmd-15-6659-2022, https://doi.org/10.5194/gmd-15-6659-2022, 2022
Short summary
Short summary
Application of 1-D lake models coupled within earth-system prediction models will improve accuracy but requires accurate initialization of lake temperatures. Here, we describe a lake initialization method by cycling within a weather prediction model to constrain lake temperature evolution. We compared these lake temperature values with other estimates and found much reduced errors (down to 1-2 K). The lake cycling initialization is now applied to two operational US NOAA weather models.
Nicholas K.-R. Kevlahan and Florian Lemarié
Geosci. Model Dev., 15, 6521–6539, https://doi.org/10.5194/gmd-15-6521-2022, https://doi.org/10.5194/gmd-15-6521-2022, 2022
Short summary
Short summary
WAVETRISK-2.1 is an innovative climate model for the world's oceans. It uses state-of-the-art techniques to change the model's resolution locally, from O(100 km) to O(5 km), as the ocean changes. This dynamic adaptivity makes optimal use of available supercomputer resources, and allows two-dimensional global scales and three-dimensional submesoscales to be captured in the same simulation. WAVETRISK-2.1 is designed to be coupled its companion global atmosphere model, WAVETRISK-1.x.
Meng Huang, Po-Lun Ma, Nathaniel W. Chaney, Dalei Hao, Gautam Bisht, Megan D. Fowler, Vincent E. Larson, and L. Ruby Leung
Geosci. Model Dev., 15, 6371–6384, https://doi.org/10.5194/gmd-15-6371-2022, https://doi.org/10.5194/gmd-15-6371-2022, 2022
Short summary
Short summary
The land surface in one grid cell may be diverse in character. This study uses an explicit way to account for that subgrid diversity in a state-of-the-art Earth system model (ESM) and explores its implications for the overlying atmosphere. We find that the shallow clouds are increased significantly with the land surface diversity. Our work highlights the importance of accurately representing the land surface and its interaction with the atmosphere in next-generation ESMs.
Jan Streffing, Dmitry Sidorenko, Tido Semmler, Lorenzo Zampieri, Patrick Scholz, Miguel Andrés-Martínez, Nikolay Koldunov, Thomas Rackow, Joakim Kjellsson, Helge Goessling, Marylou Athanase, Qiang Wang, Jan Hegewald, Dmitry V. Sein, Longjiang Mu, Uwe Fladrich, Dirk Barbi, Paul Gierz, Sergey Danilov, Stephan Juricke, Gerrit Lohmann, and Thomas Jung
Geosci. Model Dev., 15, 6399–6427, https://doi.org/10.5194/gmd-15-6399-2022, https://doi.org/10.5194/gmd-15-6399-2022, 2022
Short summary
Short summary
We developed a new atmosphere–ocean coupled climate model, AWI-CM3. Our model is significantly more computationally efficient than its predecessors AWI-CM1 and AWI-CM2. We show that the model, although cheaper to run, provides results of similar quality when modeling the historic period from 1850 to 2014. We identify the remaining weaknesses to outline future work. Finally we preview an improved simulation where the reduction in computational cost has to be invested in higher model resolution.
Stephen G. Yeager, Nan Rosenbloom, Anne A. Glanville, Xian Wu, Isla Simpson, Hui Li, Maria J. Molina, Kristen Krumhardt, Samuel Mogen, Keith Lindsay, Danica Lombardozzi, Will Wieder, Who M. Kim, Jadwiga H. Richter, Matthew Long, Gokhan Danabasoglu, David Bailey, Marika Holland, Nicole Lovenduski, Warren G. Strand, and Teagan King
Geosci. Model Dev., 15, 6451–6493, https://doi.org/10.5194/gmd-15-6451-2022, https://doi.org/10.5194/gmd-15-6451-2022, 2022
Short summary
Short summary
The Earth system changes over a range of time and space scales, and some of these changes are predictable in advance. Short-term weather forecasts are most familiar, but recent work has shown that it is possible to generate useful predictions several seasons or even a decade in advance. This study focuses on predictions over intermediate timescales (up to 24 months in advance) and shows that there is promising potential to forecast a variety of changes in the natural environment.
Peter A. Bogenschutz, Hsiang-He Lee, Qi Tang, and Takanobu Yamaguchi
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-175, https://doi.org/10.5194/gmd-2022-175, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
Models that are used to simulated and predict climate often have trouble representing specific cloud types, such as stratocumulus, that are particularly thin in the vertical direction. It has been found that increasing the model resolution can help improve this problem. In this paper we develop a novel framework that increases the horizontal and vertical resolution only for areas of the globe that contain stratocumulus, hence reducing model run-time while providing better results.
Mauro Morichetti, Sasha Madronich, Giorgio Passerini, Umberto Rizza, Enrico Mancinelli, Simone Virgili, and Mary Barth
Geosci. Model Dev., 15, 6311–6339, https://doi.org/10.5194/gmd-15-6311-2022, https://doi.org/10.5194/gmd-15-6311-2022, 2022
Short summary
Short summary
In the present study, we explore the effect of making simple changes to the existing WRF-Chem MEGAN v2.04 emissions to provide MEGAN updates that can be used independently of the land surface model chosen. The changes made to the MEGAN algorithm implemented in WRF-Chem were the following: (i) update of the emission activity factors, (ii) update of emission factor values for each plant functional type (PFT), and (iii) the assignment of the emission factor by PFT to isoprene.
Walter Hannah, Kyle Pressel, Mikhail Ovchinnikov, and Gregory Elsaesser
Geosci. Model Dev., 15, 6243–6257, https://doi.org/10.5194/gmd-15-6243-2022, https://doi.org/10.5194/gmd-15-6243-2022, 2022
Short summary
Short summary
An unphysical checkerboard signal is identified in two configurations of the atmospheric component of E3SM. The signal is very persistent and visible after averaging years of data. The signal is very difficult to study because it is often mixed with realistic weather. A method is presented to detect checkerboard patterns and compare the model with satellite observations. The causes of the signal are identified, and a solution for one configuration is discussed.
Fa Li, Qing Zhu, William Riley, Lei Zhao, Li Xu, Kunxiaojia Yuan, Min Chen, Huayi Wu, Zhipeng Gui, Jianya Gong, and James Randerson
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-195, https://doi.org/10.5194/gmd-2022-195, 2022
Preprint under review for GMD
Short summary
Short summary
In this work, we developed an interpretable machine learning model to predict sub-seasonal and near future wildfire burned area over African and South American regions. We found strong time-lagged controls (up to 6–8 month) from local climate wetness on burned areas. A skillful use of such time-lagged controls in machine learning model result in high accurate predictions of wildfire burned area, also will help develop relevant early warming and management system for tropical wildfire.
Peter Berg, Thomas Bosshard, Wei Yang, and Klaus Zimmermann
Geosci. Model Dev., 15, 6165–6180, https://doi.org/10.5194/gmd-15-6165-2022, https://doi.org/10.5194/gmd-15-6165-2022, 2022
Short summary
Short summary
When performing impact analyses with climate models, one is often confronted with the issue that the models have significant bias. Commonly, the modelled climatological temperature deviates from the observed climate by a few degrees or it rains excessively in the model. MIdAS employs a novel statistical model to translate the model climatology toward that observed using novel methodologies and modern tools. The coding platform allows opportunities to develop methods for high-resolution models.
Heather Suzanne Rumbold, Richard J. J. Gilham, and Martin John Best
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-139, https://doi.org/10.5194/gmd-2022-139, 2022
Preprint under review for GMD
Short summary
Short summary
The Joint UK Land Environment Simulator (JULES) uses a tiled representation of land cover but can only model a single dominant soil type within a grid box, hence there is no representation of sub-grid soil heterogeneity. This paper evaluates a new surface-soil tiling scheme in JULES and demonstrates the impacts of the scheme using several soil tiling approaches. Results show that soil tiling has an impact on the water and energy exchanges due to the way vegetation accesses the soil moisture.
Zhenming Wang, Shaoqing Zhang, Yishuai Jin, Yinglai Jia, Yangyang Yu, Yang Gao, Xiaolin Yu, Mingkui Li, Xiaopei Lin, and Lixin Wu
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2022-159, https://doi.org/10.5194/gmd-2022-159, 2022
Revised manuscript accepted for GMD
Short summary
Short summary
To improve the numerical model predictability of monthly extended-range scales, we use the simplified SOM to restrict the complicated SST bias from 3-D dynamical ocean model. As for SST prediction, whether in space or time, the WRF-SOM is verified to have the performance than that of the WRF-ROMS, which has a significant impact on the atmosphere. For the extreme weather event such as typhoons, the predictions of WRF-SOM are in good agreement with WRF-ROMS.
Chia-Te Chien, Jonathan V. Durgadoo, Dana Ehlert, Ivy Frenger, David P. Keller, Wolfgang Koeve, Iris Kriest, Angela Landolfi, Lavinia Patara, Sebastian Wahl, and Andreas Oschlies
Geosci. Model Dev., 15, 5987–6024, https://doi.org/10.5194/gmd-15-5987-2022, https://doi.org/10.5194/gmd-15-5987-2022, 2022
Short summary
Short summary
We present the implementation and evaluation of a marine biogeochemical model, Model of Oceanic Pelagic Stoichiometry (MOPS) in the Flexible Ocean and Climate Infrastructure (FOCI) climate model. FOCI-MOPS enables the simulation of marine biological processes, the marine carbon, nitrogen and oxygen cycles, and air–sea gas exchange of CO2 and O2. As shown by our evaluation, FOCI-MOPS shows an overall adequate performance that makes it an appropriate tool for Earth climate system simulations.
Cited articles
Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C. O.,
Dunne, J. P., Griffies, S. M., Hallberg, R., Harrison, M. J., Held, I. M.,
Jansen, M. F., John, J. G., Krasting, J. P., Langenhorst, A. R., Legg, S.,
Liang, Z., McHugh, C., Radhakrishnan, A., Reichl, B. G., Rosati, T., Samuels,
B. L., Shao, A., Stouffer, R., Winton, M., Wittenberg, A. T., Xiang, B.,
Zadeh, N., and Zhang, R.: The GFDL Global Ocean and Sea Ice Model OM4.0:
Model Description and Simulation Features, J. Adv. Model.
Earth Sy., 11, 3167–3211, https://doi.org/10.1029/2019MS001726, 2019. a, b
Adkins, J. F., McIntyre, K., and Schrag, D. P.: The salinity, temperature, and
δ18O of the glacial deep ocean, Science, 298, 1769–1773,
https://doi.org/10.1126/science.1076252, 2002. a
Bala, G., Caldeira, K., Mirin, A., Wickett, M., Delire, C., and Phillips,
T. J.: Biogeophysical effects of CO2 fertilization on global climate,
Tellus B, 58, 620–627,
https://doi.org/10.1111/j.1600-0889.2006.00210.x, 2006. a
Bauer, E. and Ganopolski, A.: Aeolian dust modeling over the past four glacial
cycles with CLIMBER-2, Global Planet. Change, 74, 49–60,
https://doi.org/10.1016/j.gloplacha.2010.07.009, 2010. a, b
Bauer, E., Petoukhov, V., Ganopolski, A., and Eliseev, A. V.: Climatic
response to anthropogenic sulphate aerosols versus well-mixed greenhouse
gases from 1850 to 2000 AD in CLIMBER-2, Tellus B, 60B, 82–97, https://doi.org/10.1111/j.1600-0889.2007.00318.x,
2008. a, b
Bereiter, B., Shackleton, S., Baggenstos, D., Kawamura, K., and Severinghaus,
J.: Mean global ocean temperatures during the last glacial transition,
Nature, 553, 39–44, https://doi.org/10.1038/nature25152, 2018. a
Bock, L., Lauer, A., Schlund, M., Barreiro, M., Bellouin, N., Jones, C., Meehl,
G. A., Predoi, V., Roberts, M. J., and Eyring, V.: Quantifying Progress
Across Different CMIP Phases With the ESMValTool, J. Geophys.
Res.-Atmos., 125, e2019JD032321, https://doi.org/10.1029/2019JD032321, 2020. a
Bohm, E., Lippold, J., Gutjahr, M., Frank, M., Blaser, P., Antz, B.,
Fohlmeister, J., Frank, N., Andersen, M. B., and Deininger, M.: Strong and
deep Atlantic meridional overturning circulation during the last glacial
cycle, Nature, 517, 73–76, https://doi.org/10.1038/nature14059, 2015. a
Bonan, G. B.: Forests and climate change: forcings, feedbacks, and the climate
benefits of forests, Science, 320, 1444–1449,
https://doi.org/10.1126/science.1155121, 2008. a
Bony, S., Colman, R., Kattsov, V. M., Allan, R. P., Bretherton, C. S.,
Dufresne, J.-L. L., Hall, A., Hallegatte, S., Holland, M. M., Ingram, W.,
Randall, D. a., Soden, B. J., Tselioudis, G., and Webb, M. J.: How Well Do
We Understand and Evaluate Climate Change Feedback Processes?, J.
Climate, 19, 3445–3482, https://doi.org/10.1175/JCLI3819.1, 2006. a, b
Bouillon, S., Morales Maqueda, M. Á., Legat, V., and Fichefet, T.: An
elastic–viscous–plastic sea ice model formulated on Arakawa B and C
grids, Ocean Model., 27, 174–184, https://doi.org/10.1016/j.ocemod.2009.01.004,
2009. a, b
Brown, J., Ferrians, O., Heginbottom, J. A., and Melnikov, E.: Circum-Arctic Map of Permafrost and Ground-Ice Conditions, Version 2, Boulder, Colorado USA, NSIDC: National Snow and Ice Data Center [data set], https://doi.org/https://doi.org/10.7265/skbg-kf16, 1998. a
Bryan, K. and Lewis, L. J.: A water mass model of the World Ocean, J.
Geophys. Res., 84, 2503–2517, https://doi.org/10.1029/JC084iC05p02503, 1979. a
Burton, C., Betts, R., Cardoso, M., Feldpausch, T. R., Harper, A., Jones, C. D., Kelley, D. I., Robertson, E., and Wiltshire, A.: Representation of fire, land-use change and vegetation dynamics in the Joint UK Land Environment Simulator vn4.9 (JULES), Geosci. Model Dev., 12, 179–193, https://doi.org/10.5194/gmd-12-179-2019, 2019. a
Caballero, R. and Hanley, J.: Midlatitude eddies, storm-track diffusivity, and
poleward moisture transport in warm climates, J. Atmos.
Sci., 69, 3237–3250, https://doi.org/10.1175/JAS-D-12-035.1, 2012. a
Calov, R., Ganopolski, A., Claussen, M., Petoukhov, V., and Greve, R.:
Transient simulation of the last glacial inception. Part I: glacial
inception as a bifurcation in the climate system, Clim. Dynam., 24,
545–561, https://doi.org/10.1007/s00382-005-0007-6, 2005. a
Charney, J., Arakawa, A., Baker, D., Bolin, B., Dickinson, R., Goody, R.,
Leith, C., Stommel, H., and Wunsch, C.: Carbon Dioxide and Climate: A
Scientific Assessment, Tech. Rep., National Academy of Sciences, Washington,
D.C., https://doi.org/10.17226/12181, 1979. a
Charney, J. G. and Eliassen, A.: A Numerical Method for Predicting the
Perturbations of the Middle Latitude Westerlies, Tellus, 1, 38–54,
https://doi.org/10.3402/tellusa.v1i2.8500, 1949. a, b
Claussen, M., Mysak, L., Weaver, A., Crucifix, M., Fichefet, T., Loutre, M. F.,
Weber, S., Alcamo, J., Alexeev, V., Berger, A., Calov, R., Ganopolski, A.,
Goosse, H., Lohmann, G., Lunkeit, F., Mokhov, I., Petoukhov, V., Stone, P.,
and Wang, Z.: Earth system models of intermediate complexity: Closing the
gap in the spectrum of climate system models, Clim. Dynam., 18,
579–586, https://doi.org/10.1007/s00382-001-0200-1, 2002. a
Colman, R. and McAvaney, B.: Climate feedbacks under a very broad range of
forcing, Geophys. Res. Lett., 36, 1–5, https://doi.org/10.1029/2008GL036268,
2009. a, b
Colman, R., Fraser, J., and Rotstayn, L.: Climate feedbacks in a general
circulation model incorporating prognostic clouds, Clim. Dynam., 18,
103–122, https://doi.org/10.1007/s003820100162, 2001. a
Crook, J. A., Forster, P. M., and Stuber, N.: Spatial patterns of modeled
climate feedback and contributions to temperature response and polar
amplification, J. Climate, 24, 3575–3592,
https://doi.org/10.1175/2011JCLI3863.1, 2011. a
Dang, C., Brandt, R. E., and Warren, S. G.: Parameterizations for narrowband
and broadband albedo of pure snow and snow containing mineral dust and black
carbon, J. Geophys. Res.-Atmos., 120, 5446–5468,
https://doi.org/10.1002/2014JD022646, 2015. a, b, c, d
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N.,
and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a, b, c, d, e, f, g, h, i, j, k, l, m, n
Delworth, T. L., Broccoli, A. J., Rosati, A., Stouffer, R. J., Balaji, V.,
Beesley, J. A., Cooke, W. F., Dixon, K. W., Dunne, J., Dunne, K. A.,
Durachta, J. W., Findell, K. L., Ginoux, P., Gnanadesikan, A., Gordon, C. T.,
Griffies, S. M., Gudgel, R., Harrison, M. J., Held, I. M., Hemler, R. S.,
Horowitz, L. W., Klein, S. A., Knutson, T. R., Kushner, P. J., Langenhorst,
A. R., Lee, H.-C., Lin, S.-J., Lu, J., Malyshev, S. L., Milly, P. C. D.,
Ramaswamy, V., Russell, J., Schwarzkopf, M. D., Shevliakova, E., Sirutis,
J. J., Spelman, M. J., Stern, W. F., Winton, M., Wittenberg, A. T., Wyman,
B., Zeng, F., and Zhang, R.: GFDL's CM2 Global Coupled Climate Models. Part
I: Formulation and Simulation Characteristics, J. Climate, 19,
643–674, https://doi.org/10.1175/JCLI3629.1, 2006. a, b
Durack, P. J., Wijffels, S. E., and Matear, R. J.: Ocean Salinities Reveal
Strong Global Water Cycle Intensification During 1950 to 2000, Science, 336,
455–458, https://doi.org/10.1126/science.1212222, 2012. a, b
Eby, M., Weaver, A. J., Alexander, K., Zickfeld, K., Abe-Ouchi, A., Cimatoribus, A. A., Crespin, E., Drijfhout, S. S., Edwards, N. R., Eliseev, A. V., Feulner, G., Fichefet, T., Forest, C. E., Goosse, H., Holden, P. B., Joos, F., Kawamiya, M., Kicklighter, D., Kienert, H., Matsumoto, K., Mokhov, I. I., Monier, E., Olsen, S. M., Pedersen, J. O. P., Perrette, M., Philippon-Berthier, G., Ridgwell, A., Schlosser, A., Schneider von Deimling, T., Shaffer, G., Smith, R. S., Spahni, R., Sokolov, A. P., Steinacher, M., Tachiiri, K., Tokos, K., Yoshimori, M., Zeng, N., and Zhao, F.: Historical and idealized climate model experiments: an intercomparison of Earth system models of intermediate complexity, Clim. Past, 9, 1111–1140, https://doi.org/10.5194/cp-9-1111-2013, 2013. a
ECCO Consortium, Fukumori, I., Wang, O., Fenty, I., Forget, G., Heimbach, P., and
Ponte, R. M.: ECCO Ocean Mixed Layer Depth – Monthly Mean 0.5 Degree
(Version 4 Release 4), Ver. V4r4, NASA [data set], https://doi.org/10.5067/ECG5M-OML44, 2021. a
Edwards, N. and Shepherd, J.: Bifurcations of the thermohaline circulation in
a simplified three-dimensional model of the world ocean and the effects of
inter-basin connectivity, Clim. Dynam., 19, 31–42,
https://doi.org/10.1007/s00382-001-0207-7, 2002. a, b
Edwards, N. R. and Marsh, R.: Uncertainties due to transport-parameter
sensitivity in an efficient 3-D ocean-climate model, Clim. Dynam., 24,
415–433, https://doi.org/10.1007/s00382-004-0508-8, 2005. a, b
Edwards, N. R., Willmott, A. J., and Killworth, P. D.: On the Role of
Topography and Wind Stress on the Stability of the Thermohaline Circulation,
J. Phys. Oceanogr., 28, 756–778,
https://doi.org/10.1175/1520-0485(1998)028<0756:OTROTA>2.0.CO;2, 1998. a, b
Etminan, M., Myhre, G., Highwood, E. J., and Shine, K. P.: Radiative forcing
of carbon dioxide, methane, and nitrous oxide: A significant revision of the
methane radiative forcing, Geophys. Res. Lett., 43, 12614–12623,
https://doi.org/10.1002/2016GL071930, 2016. a, b, c
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Falloon, P. D., Dankers, R., Betts, R. A., Jones, C. D., Booth, B. B. B., and Lambert, F. H.: Role of vegetation change in future climate under the A1B scenario and a climate stabilisation scenario, using the HadCM3C Earth system model, Biogeosciences, 9, 4739–4756, https://doi.org/10.5194/bg-9-4739-2012, 2012. a
Farneti, R. and Vallis, G. K.: An Intermediate Complexity Climate Model (ICCMp1) based on the GFDL flexible modelling system, Geosci. Model Dev., 2, 73–88, https://doi.org/10.5194/gmd-2-73-2009, 2009. a
Fasullo, J. T. and Trenberth, K. E.: The annual cycle of the energy budget.
Part II: Meridional structures and poleward transports, J. Climate,
21, 2313–2325, https://doi.org/10.1175/2007JCLI1936.1, 2008. a, b, c
Fedorovich, E. and Shapiro, A.: Structure of numerically simulated katabatic
and anabatic flows along steep slopes, Acta Geophys., 57, 981–1010,
https://doi.org/10.2478/s11600-009-0027-4, 2009. a
Feigelson, E., Ginzburg, A., Krasnokutskaya, L., and Petoukhov, V.: Effects of
clouds on the radiative heat exchange in the atmosphere, Geofís.
Int., 15, 293–326, https://doi.org/10.22201/igeof.00167169p.1975.15.4.1010,
1975. a, b
Fettweis, X., Box, J. E., Agosta, C., Amory, C., Kittel, C., Lang, C., van As, D., Machguth, H., and Gallée, H.: Reconstructions of the 1900–2015 Greenland ice sheet surface mass balance using the regional climate MAR model, The Cryosphere, 11, 1015–1033, https://doi.org/10.5194/tc-11-1015-2017, 2017. a, b
Fichefet, T. and Maqueda, M. A. M.: Sensitivity of a global sea ice model to
the treatment of ice thermodynamics and dynamics, J. Geophys.
Res.-Oceans, 102, 12609–12646, https://doi.org/10.1029/97JC00480, 1997. a, b, c
Fraedrich, K., Kirk, E., Luksch, U., and Lunkeit, F.: The portable university
model of the atmosphere (PUMA): Storm track dynamics and low-frequency
variability, Meteorol. Z., 14, 735–745,
https://doi.org/10.1127/0941-2948/2005/0074, 2005. a
Frajka-Williams, E., Moat, B., Smeed, D., Rayner, D., Johns, W., Baringer, M.,
Volkov, D., and Collins, J.: Atlantic meridional overturning circulation
observed by the RAPID-MOCHA-WBTS (RAPID-Meridional Overturning Circulation
and Heatflux Array-Western Boundary Time Series) array at 26N from 2004 to
2020 (v2020.1), National Oceanography Centre [data set], https://doi.org/10.5285/cc1e34b3-3385-662b-e053-6c86abc03444, 2021. a, b
Frierson, D. M., Lu, J., and Chen, G.: Width of the Hadley cell in simple and
comprehensive general circulation models, Geophys. Res. Lett., 34,
1–5, https://doi.org/10.1029/2007GL031115, 2007. a
Ganopolski, A. and Brovkin, V.: Simulation of climate, ice sheets and CO2 evolution during the last four glacial cycles with an Earth system model of intermediate complexity, Clim. Past, 13, 1695–1716, https://doi.org/10.5194/cp-13-1695-2017, 2017. a
Ganopolski, A., Rahmstorf, S., Petoukhov, V., and Claussen, M.: Simulation of
modern and glacial climates with a coupled global model of intermediate
complexity, Nature, 391, 351–356, https://doi.org/10.1038/34839, 1998. a
Ganopolski, A., Petoukhov, V., Rahmstorf, S., Brovkin, V., Claussen, M.,
Eliseev, A., and Kubatzki, C.: CLIMBER-2: a climate system model of
intermediate complexity. Part II: model sensitivity, Clim. Dynam., 17,
735–751, https://doi.org/10.1007/s003820000144, 2001. a
Ganopolski, A., Winkelmann, R., and Schellnhuber, H. J.: Critical
insolation–CO2 relation for diagnosing past and future glacial inception,
Nature, 529, 200–203, https://doi.org/10.1038/nature16494, 2016. a
Gent, P. R. and Mcwilliams, J. C.: Isopycnal Mixing in Ocean Circulation
Models, J. Phys. Oceanogr., 20, 150–155, https://doi.org/10.1175/1520-0485(1990)020<0150:IMIOCM>2.0.CO;2, 1990. a
Gerdes, R., Köberle, C., and Willebrand, J.: The influence of numerical
advection schemes on the results of ocean general circulation models,
Clim. Dynam., 5, 211–226, https://doi.org/10.1007/BF00210006, 1991. a
Goosse, H., Brovkin, V., Fichefet, T., Haarsma, R., Huybrechts, P., Jongma, J., Mouchet, A., Selten, F., Barriat, P.-Y., Campin, J.-M., Deleersnijder, E., Driesschaert, E., Goelzer, H., Janssens, I., Loutre, M.-F., Morales Maqueda, M. A., Opsteegh, T., Mathieu, P.-P., Munhoven, G., Pettersson, E. J., Renssen, H., Roche, D. M., Schaeffer, M., Tartinville, B., Timmermann, A., and Weber, S. L.: Description of the Earth system model of intermediate complexity LOVECLIM version 1.2, Geosci. Model Dev., 3, 603–633, https://doi.org/10.5194/gmd-3-603-2010, 2010. a
Greve, R.: Application of a Polythermal Three-Dimensional Ice Sheet Model to
the Greenland Ice Sheet: Response to Steady-State and Transient Climate
Scenarios, J. Climate, 10, 901–918,
https://doi.org/10.1175/1520-0442(1997)010<0901:AOAPTD>2.0.CO;2, 1997. a
Griffies, S. M.: The Gent–McWilliams Skew Flux, J. Phys.
Oceanogr., 28, 831–841,
https://doi.org/10.1175/1520-0485(1998)028<0831:TGMSF>2.0.CO;2, 1998. a
Hansen, J.: Efficacy of climate forcings, J. Geophys. Res.,
110, D18104, https://doi.org/10.1029/2005JD005776, 2005. a
Hansen, J., Russell, G., Rind, D., Stone, P., Lacis, A., Lebedeff, S., Ruedy,
R., and Travis, L.: Efficient Three-Dimensional Global Models for Climate
Studies: Models I and II, Mon. Weather Rev., 111, 609–662,
https://doi.org/10.1175/1520-0493(1983)111<0609:ETDGMF>2.0.CO;2, 1983. a, b
Hawkins, E., Smith, R. S., Allison, L. C., Gregory, J. M., Woollings, T. J.,
Pohlmann, H., and De Cuevas, B.: Bistability of the Atlantic overturning
circulation in a global climate model and links to ocean freshwater
transport, Geophys. Res. Lett., 38, 1–6,
https://doi.org/10.1029/2011GL047208, 2011. a
Held, I. M.: Stationary and quasi-stationary eddies in the extratropical
troposphere: Theory, in: Large-Scale Dynamical Processes in the Atmosphere,
edited by: Hoskins, B. and Pearce, R. P., Academic Press, 127–168, ISBN-10 0123566800, ISBN-13 978-0123566805, 1983. a
Held, I. M. and Soden, B. J.: Robust Responses of the Hydrological Cycle to
Global Warming, J. Climate, 19, 5686–5699,
https://doi.org/10.1175/JCLI3990.1, 2006. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on single levels from 1959 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.f17050d7, 2019. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee,
D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M.,
Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E.,
Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti,
G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut,
J. N.: The ERA5 global reanalysis, Q. J. Roy.
Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hibler, W. D.: A Dynamic Thermodynamic Sea Ice Model, J. Phys.
Oceanogr., 9, 815–846,
https://doi.org/10.1175/1520-0485(1979)009<0815:ADTSIM>2.0.CO;2, 1979. a, b
Holden, P. B., Edwards, N. R., Fraedrich, K., Kirk, E., Lunkeit, F., and Zhu, X.: PLASIM–GENIE v1.0: a new intermediate complexity AOGCM, Geosci. Model Dev., 9, 3347–3361, https://doi.org/10.5194/gmd-9-3347-2016, 2016. a, b
Holton, J. R.: Chapter 7 Atmospheric oscillations: Linear perturbation
theory, in: An Introduction to Dynamic Meteorology, edited by: Holton, J. R., vol. 88, Academic Press, 182–227,
https://doi.org/10.1016/S0074-6142(04)80041-X, 2004. a
Hoskins, B. J. and Valdes, P. J.: On the Existence of Storm-Tracks, J. Atmos. Sci., 47, 1854–1864,
https://doi.org/10.1175/1520-0469(1990)047<1854:OTEOST>2.0.CO;2, 1990. a, b
Hu, Y., Huang, H., and Zhou, C.: Widening and weakening of the Hadley
circulation under global warming, Sci. Bull., 63, 640–644,
https://doi.org/10.1016/j.scib.2018.04.020, 2018. a
Hunke, E. C. and Dukowicz, J. K.: An elastic-viscous-plastic model for sea ice
dynamics, J. Phys. Oceanogr., 27, 1849–1867,
https://doi.org/10.1175/1520-0485(1997)027<1849:AEVPMF>2.0.CO;2, 1997. a, b
Ilyina, T., Six, K. D., Segschneider, J., Maier-Reimer, E., Li, H., and
Núñez-Riboni, I.: Global ocean biogeochemistry model HAMOCC:
Model architecture and performance as component of the MPI-Earth system model
in different CMIP5 experimental realizations, J. Adv.
Model. Earth Sy., 5, 287–315, https://doi.org/10.1029/2012MS000178, 2013. a
IPCC: Annex II: Climate System Scenario Tables, edited by: Prather, M., Flato, G., Friedlingstein, P., Jones, C., Lamarque, J.-F., Liao, H., and Rasch, P., 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, United Kingdom and New York, NY, USA, 1395–1446, https://doi.org/10.1017/CBO9781107415324.030, 2013. a
IPCC: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K., Lonnoy, E.,
Matthews, J. B. R., Maycock, T. K., Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, in press, https://doi.org/10.1017/9781009157896, 2021. a
Jackett, D. R. and McDougall, T. J.: Minimal Adjustment of Hydrographic
Profiles to Achieve Static Stability, J. Atmos. Ocean. Tech., 12, 381–389,
https://doi.org/10.1175/1520-0426(1995)012<0381:maohpt>2.0.co;2, 1995. a, b
Jackson, L. C., Kahana, R., Graham, T., Ringer, M. A., Woollings, T., Mecking,
J. V., and Wood, R. A.: Global and European climate impacts of a slowdown of
the AMOC in a high resolution GCM, Clim. Dynam., 45, 3299–3316,
https://doi.org/10.1007/s00382-015-2540-2, 2015. a
Johns, W. E., Baringer, M. O., Beal, L. M., Cunningham, S. A., Kanzow, T.,
Bryden, H. L., Hirschi, J. J., Marotzke, J., Meinen, C. S., Shaw, B., and
Curry, R.: Continuous, array-based estimates of atlantic ocean heat
transport at 26.5∘ N, J. Climate, 24, 2429–2449,
https://doi.org/10.1175/2010JCLI3997.1, 2011. a
Kageyama, M., Albani, S., Braconnot, P., Harrison, S. P., Hopcroft, P. O., Ivanovic, R. F., Lambert, F., Marti, O., Peltier, W. R., Peterschmitt, J.-Y., Roche, D. M., Tarasov, L., Zhang, X., Brady, E. C., Haywood, A. M., LeGrande, A. N., Lunt, D. J., Mahowald, N. M., Mikolajewicz, U., Nisancioglu, K. H., Otto-Bliesner, B. L., Renssen, H., Tomas, R. A., Zhang, Q., Abe-Ouchi, A., Bartlein, P. J., Cao, J., Li, Q., Lohmann, G., Ohgaito, R., Shi, X., Volodin, E., Yoshida, K., Zhang, X., and Zheng, W.: The PMIP4 contribution to CMIP6 – Part 4: Scientific objectives and experimental design of the PMIP4-CMIP6 Last Glacial Maximum experiments and PMIP4 sensitivity experiments, Geosci. Model Dev., 10, 4035–4055, https://doi.org/10.5194/gmd-10-4035-2017, 2017. a
Kageyama, M., Harrison, S. P., Kapsch, M.-L., Lofverstrom, M., Lora, J. M., Mikolajewicz, U., Sherriff-Tadano, S., Vadsaria, T., Abe-Ouchi, A., Bouttes, N., Chandan, D., Gregoire, L. J., Ivanovic, R. F., Izumi, K., LeGrande, A. N., Lhardy, F., Lohmann, G., Morozova, P. A., Ohgaito, R., Paul, A., Peltier, W. R., Poulsen, C. J., Quiquet, A., Roche, D. M., Shi, X., Tierney, J. E., Valdes, P. J., Volodin, E., and Zhu, J.: The PMIP4 Last Glacial Maximum experiments: preliminary results and comparison with the PMIP3 simulations, Clim. Past, 17, 1065–1089, https://doi.org/10.5194/cp-17-1065-2021, 2021. a, b, c
Klemann, V., Martinec, Z., and Ivins, E. R.: Glacial isostasy and plate
motion, J. Geodyn., 46, 95–103,
https://doi.org/10.1016/j.jog.2008.04.005, 2008. a
Köhler, P., Nehrbass-Ahles, C., Schmitt, J., Stocker, T. F., and Fischer, H.: A 156 kyr smoothed history of the atmospheric greenhouse gases CO2, CH4, and N2O and their radiative forcing, Earth Syst. Sci. Data, 9, 363–387, https://doi.org/10.5194/essd-9-363-2017, 2017. a
Krapp, M., Robinson, A., and Ganopolski, A.: SEMIC: an efficient surface energy and mass balance model applied to the Greenland ice sheet, The Cryosphere, 11, 1519–1535, https://doi.org/10.5194/tc-11-1519-2017, 2017. a, b
Kraus, E. B. and Turner, J. S.: A one-dimensional model of the seasonal
thermocline II. The general theory and its consequences, Tellus, 19,
98–106, https://doi.org/10.3402/tellusa.v19i1.9753, 1967. a, b
Lacis, A. A. and Hansen, J.: A Parameterization for the Absorption of Solar
Radiation in the Earth's Atmosphere, J. Atmos. Sci.,
31, 118–133, https://doi.org/10.1175/1520-0469(1974)031<0118:APFTAO>2.0.CO;2, 1974. a
Lenton, T. M., Marsh, R., Price, A. R., Lunt, D. J., Aksenov, Y., Annan, J. D.,
Cooper-Chadwick, T., Cox, S. J., Edwards, N. R., Goswami, S., Hargreaves,
J. C., Harris, P. P., Jiao, Z., Livina, V. N., Payne, A. J., Rutt, I. C.,
Shepherd, J. G., Valdes, P. J., Williams, G., Williamson, M. S., and Yool,
A.: Effects of atmospheric dynamics and ocean resolution on bi-stability of
the thermohaline circulation examined using the Grid ENabled Integrated Earth
system modelling (GENIE) framework, Clim. Dynam., 29, 591–613,
https://doi.org/10.1007/s00382-007-0254-9, 2007. a
Levis, S., Foley, J. A., and Pollard, D.: Potential high-latitude vegetation
feedbacks on CO2-induced climate change, Geophys. Res. Lett., 26,
747–750, https://doi.org/10.1029/1999GL900107, 1999. a
Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., Garcia, H. E.,
Locarnini, R. A., Mishonov, A. V., Reagan, J. R., Seidov, D., Yarosh, E. S.,
and Zweng, M. M.: World ocean heat content and thermosteric sea level change
(0–2000 m), 1955–2010, Geophys. Res. Lett., 39, 1–5,
https://doi.org/10.1029/2012GL051106, 2012. a, b
Levitus, S., Boyer, T. P., and Garcia, Hernan E. Locarnini, Ricardo A. Zweng,
Melissa M. Mishonov, Alexey V. Reagan, James R. Antonov, John I. Baranova,
Olga K. Biddle, Mathew Hamilton, Melanie Johnson, Daphne R. Paver,
Christopher R. Seidov, D.: World Ocean Atlas 2013 (NCEI Accession
0114815), NCEI [data set], https://doi.org/10.7289/v5f769gt, 2015. a, b
Lhardy, F., Bouttes, N., Roche, D. M., Crosta, X., Waelbroeck, C., and Paillard, D.: Impact of Southern Ocean surface conditions on deep ocean circulation during the LGM: a model analysis, Clim. Past, 17, 1139–1159, https://doi.org/10.5194/cp-17-1139-2021, 2021. a, b
Loeb, N. G., Doelling, D. R., Wang, H., Su, W., Nguyen, C., Corbett, J. G.,
Liang, L., Mitrescu, C., Rose, F. G., and Kato, S.: Clouds and the Earth'S
Radiant Energy System (CERES) Energy Balanced and Filled (EBAF)
top-of-atmosphere (TOA) edition-4.0 data product, J. Climate, 31,
895–918, https://doi.org/10.1175/JCLI-D-17-0208.1, 2018. a, b, c, d, e
Lucazeau, F.: Analysis and Mapping of an Updated Terrestrial Heat Flow Data
Set, Geochem. Geophy. Geosy., 20, 4001–4024,
https://doi.org/10.1029/2019GC008389, 2019. a
Ma, L., Hurtt, G. C., Chini, L. P., Sahajpal, R., Pongratz, J., Frolking, S., Stehfest, E., Klein Goldewijk, K., O'Leary, D., and Doelman, J. C.: Global rules for translating land-use change (LUH2) to land-cover change for CMIP6 using GLM2, Geosci. Model Dev., 13, 3203–3220, https://doi.org/10.5194/gmd-13-3203-2020, 2020. a
Maier-Reimer, E. and Hasselmann, K.: Transport and storage of CO2 in the ocean
– an inorganic ocean-circulation carbon cycle model, Clim. Dynam.,
2, 63–90, https://doi.org/10.1007/BF01054491, 1987. a
Manabe, S. and Stouffer, R. J.: Two Stable Equilibria of a Coupled
Ocean-Atmosphere Model, J. Climate, 841–866,
https://doi.org/10.1175/1520-0442(1988)001<0841:TSEOAC>2.0.CO;2, 1988. a
Marsh, R., Müller, S. A., Yool, A., and Edwards, N. R.: Incorporation of the C-GOLDSTEIN efficient climate model into the GENIE framework: “eb_go_gs” configurations of GENIE, Geosci. Model Dev., 4, 957–992, https://doi.org/10.5194/gmd-4-957-2011, 2011. a, b
Marsland, S., Haak, H., Jungclaus, J., Latif, M., and Röske, F.: The
Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear
coordinates, Ocean Model., 5, 91–127,
https://doi.org/10.1016/S1463-5003(02)00015-X, 2003. a
Martinec, Z., Klemann, V., van der Wal, W., Riva, R. E., Spada, G., Sun, Y.,
Melini, D., Kachuck, S. B., Barletta, V., Simon, K., A, G., and James, T. S.:
A benchmark study of numerical implementations of the sea level equation in
GIA modelling, Geophys. J. Int., 215, 389–414,
https://doi.org/10.1093/gji/ggy280, 2018. a
Matthes, K., Funke, B., Andersson, M. E., Barnard, L., Beer, J., Charbonneau, P., Clilverd, M. A., Dudok de Wit, T., Haberreiter, M., Hendry, A., Jackman, C. H., Kretzschmar, M., Kruschke, T., Kunze, M., Langematz, U., Marsh, D. R., Maycock, A. C., Misios, S., Rodger, C. J., Scaife, A. A., Seppälä, A., Shangguan, M., Sinnhuber, M., Tourpali, K., Usoskin, I., van de Kamp, M., Verronen, P. T., and Versick, S.: Solar forcing for CMIP6 (v3.2), Geosci. Model Dev., 10, 2247–2302, https://doi.org/10.5194/gmd-10-2247-2017, 2017. a
Mauritsen, T., Stevens, B., Roeckner, E., Crueger, T., Esch, M., Giorgetta, M.,
Haak, H., Jungclaus, J., Klocke, D., Matei, D., Mikolajewicz, U., Notz, D.,
Pincus, R., Schmidt, H., and Tomassini, L.: Tuning the climate of a global
model, J. Adv. Model. Earth Sy., 4, 1–18,
https://doi.org/10.1029/2012MS000154, 2012. a
McManus, J. F., Francois, R., Gherardi, J.-M., Keigwin, L. D., and Brown-Leger,
S.: Collapse and rapid resumption of Atlantic meridional circulation linked
to deglacial climate changes., Nature, 428, 834–837,
https://doi.org/10.1038/nature02494, 2004. a
McPhee, M. G.: Turbulent heat flux in the upper ocean under sea ice, J. Geophys. Res., 97, 5365–5379, https://doi.org/10.1029/92JC00239, 1992. a, b
Meier, W. N., Fetterer, F., Windnagel, A., and Stewart, J.: NOAA/NSIDC Climate
Data Record of Passive Microwave Sea Ice Concentration, Version 4, National Snow & Ice Data Center [data set],
https://doi.org/10.7265/efmz-2t65, 2021. a, b
Meinshausen, M., Vogel, E., Nauels, A., Lorbacher, K., Meinshausen, N., Etheridge, D. M., Fraser, P. J., Montzka, S. A., Rayner, P. J., Trudinger, C. M., Krummel, P. B., Beyerle, U., Canadell, J. G., Daniel, J. S., Enting, I. G., Law, R. M., Lunder, C. R., O'Doherty, S., Prinn, R. G., Reimann, S., Rubino, M., Velders, G. J. M., Vollmer, M. K., Wang, R. H. J., and Weiss, R.: Historical greenhouse gas concentrations for climate modelling (CMIP6), Geosci. Model Dev., 10, 2057–2116, https://doi.org/10.5194/gmd-10-2057-2017, 2017. a
Millero, F. J. and Poisson, A.: International one-atmosphere equation of state
of seawater, Deep-Sea Res. Pt. I, 28,
625–629, https://doi.org/10.1016/0198-0149(81)90122-9, 1981. a, b, c
Montoya, M., Griesel, A., Levermann, A., Mignot, J., Hofmann, M., Ganopolski,
A., and Rahmstorf, S.: The earth system model of intermediate complexity
CLIMBER-3α. Part I: Description and performance for present-day conditions,
Clim. Dynam., 25, 237–263, https://doi.org/10.1007/s00382-005-0044-1, 2005. a
Morice, C. P., Kennedy, J. J., Rayner, N. A., and Jones, P. D.: Quantifying
uncertainties in global and regional temperature change using an ensemble of
observational estimates: The HadCRUT4 data set, J. Geophys.
Res.-Atmos., 117, 1–22, https://doi.org/10.1029/2011JD017187, 2012. a, b
Müller, S. A., Joos, F., Edwards, N. R., and Stocker, T. F.: Water Mass
Distribution and Ventilation Time Scales in a Cost-Efficient,
Three-Dimensional Ocean Model, J. Climate, 19, 5479–5499,
https://doi.org/10.1175/JCLI3911.1, 2006. a, b, c, d
Myhre, G., Highwood, E. J., Shine, K. P., and Stordal, F.: New estimates of
radiative forcing due to well mixed greenhouse gases, Geophys. Res.
Lett., 25, 2715–2718, https://doi.org/10.1029/98GL01908, 1998. a
Nadeau, L. P., Ferrari, R., and Jansen, M. F.: Antarctic sea ice control on
the depth of North Atlantic deep water, J. Climate, 32, 2537–2551,
https://doi.org/10.1175/JCLI-D-18-0519.1, 2019. a
Nijsse, F. J. M. M., Cox, P. M., and Williamson, M. S.: Emergent constraints on transient climate response (TCR) and equilibrium climate sensitivity (ECS) from historical warming in CMIP5 and CMIP6 models, Earth Syst. Dynam., 11, 737–750, https://doi.org/10.5194/esd-11-737-2020, 2020. a
Niu, G. Y. and Yang, Z. L.: An observation-based formulation of snow cover
fraction and its evaluation over large North American river basins, J. Geophys. Res.-Atmos., 112, 1–14, https://doi.org/10.1029/2007JD008674,
2007. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Charles,
D., Levis, S., Li, F., Riley, W. J., Zachary, M., Swenson, S. C., Thornton,
P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E., Lamarque, F.,
Lawrence, P. J., Leung, L. R., Muszala, S., Ricciuto, D. M., Sacks, W., Sun,
Y., Tang, J., and Yang, Z.-L.: Technical Description of version 4.5 of the
Community Land Model (CLM) Coordinating, Tech. Rep., No. NCAR/TN-503+STR, https://doi.org/10.5065/D6RR1W7M, 2013. a
Orr, J. C., Najjar, R. G., Aumont, O., Bopp, L., Bullister, J. L., Danabasoglu, G., Doney, S. C., Dunne, J. P., Dutay, J.-C., Graven, H., Griffies, S. M., John, J. G., Joos, F., Levin, I., Lindsay, K., Matear, R. J., McKinley, G. A., Mouchet, A., Oschlies, A., Romanou, A., Schlitzer, R., Tagliabue, A., Tanhua, T., and Yool, A.: Biogeochemical protocols and diagnostics for the CMIP6 Ocean Model Intercomparison Project (OMIP), Geosci. Model Dev., 10, 2169–2199, https://doi.org/10.5194/gmd-10-2169-2017, 2017. a
Paul, A., Mulitza, S., Stein, R., and Werner, M.: A global climatology of the ocean surface during the Last Glacial Maximum mapped on a regular grid (GLOMAP), Clim. Past, 17, 805–824, https://doi.org/10.5194/cp-17-805-2021, 2021. a, b
Pedro, J. B., Jochum, M., Buizert, C., He, F., Barker, S., and Rasmussen,
S. O.: Beyond the bipolar seesaw: Toward a process understanding of
interhemispheric coupling, Quaternary Sci. Rev., 192, 27–46,
https://doi.org/10.1016/j.quascirev.2018.05.005, 2018. a
Petoukhov, V., Ganopolski, A., Brovkin, V., Claussen, M., Eliseev, A.,
Kubatzki, C., and Rahmstorf, S.: CLIMBER-2: a climate system model of
intermediate complexity. Part I: model description and performance for
present climate, Clim. Dynam., 16, 1–17, https://doi.org/10.1007/PL00007919,
2000. a, b, c, d, e, f, g, h, i, j, k
Pinardi, N., Rosati, A., and Pacanowski, R. C.: The sea surface pressure
formulation of rigid lid models. Implications for altimetric data
assimilation studies, J. Marine Syst., 6, 109–119,
https://doi.org/10.1016/0924-7963(94)00011-Y, 1995. a
Planchon, O. and Darboux, F.: A fast, simple and versatile algorithm to fill
the depressions of digital elevation models, Catena, 46, 159–176,
https://doi.org/10.1016/S0341-8162(01)00164-3, 2002. a
Rahmstorf, S.: Bifurcations of the Atlantic thermohaline circulation in
response to changes in the hydrological cycle, Nature, 378, 145–149,
https://doi.org/10.1038/378145a0, 1995. a
Rahmstorf, S., Crucifix, M., Ganopolski, A., Goosse, H., Kamenkovich, I.,
Knutti, R., Lohmann, G., Marsh, R., Mysak, L. A., Wang, Z., and Weaver,
A. J.: Thermohaline circulation hysteresis: A model intercomparison,
Geophys. Res. Lett., 32, L23605, https://doi.org/10.1029/2005GL023655, 2005. a, b
Redi, M. H.: Oceanic isopycnal mixing by coordinate rotation, J.
Phys. Oceanogr., 12, 1154–1158,
https://doi.org/10.1175/1520-0485(1982)012<1154:OIMBCR>2.0.CO;2, 1982. a
Ritz, S. P., Stocker, T. F., and Joos, F.: A coupled dynamical ocean-energy
balance atmosphere model for paleoclimate studies, J. Climate, 24,
349–375, https://doi.org/10.1175/2010JCLI3351.1, 2011. a
Robinson, A. and Perrette, M.: NCIO 1.0: a simple Fortran NetCDF interface, Geosci. Model Dev., 8, 1877–1883, https://doi.org/10.5194/gmd-8-1877-2015, 2015. a
Robinson, A., Alvarez-Solas, J., Montoya, M., Goelzer, H., Greve, R., and Ritz, C.: Description and validation of the ice-sheet model Yelmo (version 1.0), Geosci. Model Dev., 13, 2805–2823, https://doi.org/10.5194/gmd-13-2805-2020, 2020. a
Roesch, A., Wild, M., Gilgen, H., and Ohmura, A.: A new snow cover fraction
parameterization for the ECHAM4 GCM, Clim. Dynam., 17, 933–946,
https://doi.org/10.1007/s003820100153, 2001. a, b
Rossow, W. B. and Schiffer, R. A.: Advances in Understanding Clouds from
ISCCP, B. Am. Meteorol. Soc., 80, 2261–2287,
https://doi.org/10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2, 1999. a, b
Schaffer, J., Timmermann, R., Arndt, J. E., Kristensen, S. S., Mayer, C., Morlighem, M., and Steinhage, D.: A global, high-resolution data set of ice sheet topography, cavity geometry, and ocean bathymetry, Earth Syst. Sci. Data, 8, 543–557, https://doi.org/10.5194/essd-8-543-2016, 2016. a
Semtner, A. J.: A Model for the Thermodynamic Growth of Sea Ice in Numerical
Investigations of Climate, J. Phys. Oceanogr., 6, 379–389,
https://doi.org/10.1175/1520-0485(1976)006<0379:AMFTTG>2.0.CO;2, 1976. a, b, c
Shin, S. I., Liu, Z., Otto-Bliesner, B. L., Kutzbach, J. E., and Vavrus, S. J.:
Southern Ocean sea-ice control of the glacial North Atlantic thermohaline
circulation, Geophys. Res. Lett., 30, 68–71,
https://doi.org/10.1029/2002GL015513, 2003. a
Smith, C. J., Kramer, R. J., Myhre, G., Forster, P. M., Soden, B. J., Andrews,
T., Boucher, O., Faluvegi, G., Fläschner, D., Hodnebrog, Kasoar, M.,
Kharin, V., Kirkevåg, A., Lamarque, J. F., Mülmenstädt, J.,
Olivié, D., Richardson, T., Samset, B. H., Shindell, D., Stier, P.,
Takemura, T., Voulgarakis, A., and Watson-Parris, D.: Understanding Rapid
Adjustments to Diverse Forcing Agents, Geophys. Res. Lett., 45,
12023–12031, https://doi.org/10.1029/2018GL079826, 2018. a, b, c, d
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. a
Stommel, H.: Thermohaline Convection with Two Stable Regimes of Flow, Tellus,
13, 224–230, https://doi.org/10.1111/j.2153-3490.1961.tb00079.x, 1961. a
Stouffer, R. J. and Manabe, S.: Equilibrium response of thermohaline
circulation to large changes in atmospheric CO2 concentration, Clim.
Dynam., 20, 759–773, https://doi.org/10.1007/s00382-002-0302-4, 2003. a
Subin, Z. M., Riley, W. J., and Mironov, D.: An improved lake model for
climate simulations: Model structure, evaluation, and sensitivity analyses in
CESM1, J. Adv. Model. Earth Sy., 4, 1–27,
https://doi.org/10.1029/2011MS000072, 2012. a
Tarasov, L., Dyke, A. S., Neal, R. M., and Peltier, W. R.: A data-calibrated
distribution of deglacial chronologies for the North American ice complex
from glaciological modeling, Earth Planet. Sc. Lett., 315–316,
30–40, https://doi.org/10.1016/j.epsl.2011.09.010, 2012. a
Tarnocai, C., Canadell, J. G., Schuur, E. a. G., Kuhry, P., Mazhitova, G., and
Zimov, S.: Soil organic carbon pools in the northern circumpolar permafrost
region, Global Biogeochem. Cy., 23, 2,
https://doi.org/10.1029/2008GB003327, 2009. a
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An Overview of CMIP5 and the
Experiment Design, B. Am. Meteorol. Soc., 93,
485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a
Tierney, J. E., Zhu, J., King, J., Malevich, S. B., Hakim, G. J., and Poulsen,
C. J.: Glacial cooling and climate sensitivity revisited, Nature, 584,
569–573, https://doi.org/10.1038/s41586-020-2617-x, 2020. a, b, c
Trenberth, K. E. and Caron, J. M.: Estimates of Meridional Atmosphere and
Ocean Heat Transports, J. Climate, 14, 3433–3443,
https://doi.org/10.1175/1520-0442(2001)014<3433:EOMAAO>2.0.CO;2, 2001. a
Trenberth, K. E., Smith, L., Qian, T., Dai, A., and Fasullo, J.: Estimates of
the Global Water Budget and Its Annual Cycle Using Observational and Model
Data, J. Hydrometeorol., 8, 758–769, https://doi.org/10.1175/JHM600.1,
2007. a
Vavrus, S. and Waliser, D.: An improved parameterization for simulating Arctic
cloud amount in the CCSM3 climate model, J. Climate, 21, 5673–5687,
https://doi.org/10.1175/2008JCLI2299.1, 2008. a
Vellinga, M. and Wood, R. A.: Global climatic impacts of a collapse of the
atlantic thermohaline circulation, Climatic Change, 54, 251–267,
https://doi.org/10.1023/A:1016168827653, 2002. a
Weaver, A. J., Eby, M., Wiebe, E. C., Ewen, T. L., Fanning, A. F., MacFadyen,
A., Matthews, H. D., Meissner, K. J., Saenko, O., Schmittner, A., Yoshimori,
M., Bitz, C. M., Holland, M. M., Duffy, P. B., and Wang, H.: The UVic earth
system climate model: Model description, climatology, and applications to
past, present and future climates, Atmos. Ocean, 39, 361–428,
https://doi.org/10.1080/07055900.2001.9649686, 2001. a, b, c
Weber, S. L., Drijfhout, S. S., Abe-Ouchi, A., Crucifix, M., Eby, M., Ganopolski, A., Murakami, S., Otto-Bliesner, B., and Peltier, W. R.: The modern and glacial overturning circulation in the Atlantic ocean in PMIP coupled model simulations, Clim. Past, 3, 51–64, https://doi.org/10.5194/cp-3-51-2007, 2007. a
Weijer, W., Cheng, W., Drijfhout, S. S., Fedorov, A. V., Hu, A., Jackson,
L. C., Liu, W., McDonagh, E. L., Mecking, J. V., and Zhang, J.: Stability of
the Atlantic Meridional Overturning Circulation: A Review and Synthesis,
J. Geophys. Res.-Oceans, 124, 5336–5375,
https://doi.org/10.1029/2019JC015083, 2019. a
Wetherald, R. T. and Manabe, S.: Cloud Feedback Processes in a General
Circulation Model, J. Atmos. Sci., 45, 1397–1416,
https://doi.org/10.1175/1520-0469(1988)045<1397:CFPIAG>2.0.CO;2, 1988. a
Wild, M., Folini, D., Schär, C., Loeb, N., Dutton, E. G., and
König-Langlo, G.: The global energy balance from a surface
perspective, Clim. Dynam., 40, 3107–3134,
https://doi.org/10.1007/s00382-012-1569-8, 2013. a
Willeit, M.: CLIMBER-X v1.0, Zenodo [code], https://doi.org/10.5281/zenodo.6877358, 2022. a
Willeit, M., Ganopolski, A., and Feulner, G.: Asymmetry and uncertainties in biogeophysical climate–vegetation feedback over a range of CO2 forcings, Biogeosciences, 11, 17–32, https://doi.org/10.5194/bg-11-17-2014, 2014. a
Willeit, M., Ganopolski, A., Calov, R., and Brovkin, V.: Mid-Pleistocene
transition in glacial cycles explained by declining CO2 and regolith
removal, Science Advances, 5, eaav7337, https://doi.org/10.1126/sciadv.aav7337, 2019. a
Yamamoto, G. and Tanaka, M.: Increase of Global Albedo Due to Air Pollution,
J. Atmos. Sci., 29, 1405–1412,
https://doi.org/10.1175/1520-0469(1972)029<1405:IOGADT>2.0.CO;2, 1972. a
Yang, H., Li, Q., Wang, K., Sun, Y., and Sun, D.: Decomposing the meridional
heat transport in the climate system, Clim. Dynam., 44, 2751–2768,
https://doi.org/10.1007/s00382-014-2380-5, 2015. a
Yin, J., Stouffer, R. J., Spelman, M. J., and Griffies, S. M.: Evaluating the
uncertainty induced by the virtual salt flux assumption in climate
simulations and future projections, J. Climate, 23, 80–96,
https://doi.org/10.1175/2009JCLI3084.1, 2010. a
Zalesak, S. T.: Fully multidimensional flux-corrected transport algorithms for
fluids, J. Comput. Phys., 31, 335–362,
https://doi.org/10.1016/0021-9991(79)90051-2, 1979. a, b, c
Zelinka, M. D., Klein, S. A., and Hartmann, D. L.: Computing and partitioning
cloud feedbacks using cloud property histograms. Part II: Attribution to
changes in cloud amount, altitude, and optical depth, J. Climate,
25, 3736–3754, https://doi.org/10.1175/JCLI-D-11-00249.1, 2012.
a
Zelinka, M. D., Myers, T. A., McCoy, D. T., Po-Chedley, S., Caldwell, P. M.,
Ceppi, P., Klein, S. A., and Taylor, K. E.: Causes of Higher Climate
Sensitivity in CMIP6 Models, Geophys. Res. Lett., 47, e2019GL085782,
https://doi.org/10.1029/2019GL085782, 2020. a
Zika, J. D., Skliris, N., Blaker, A. T., Marsh, R., Nurser, A. J., and Josey,
S. A.: Improved estimates of water cycle change from ocean salinity: The key
role of ocean warming, Environ. Res. Lett., 13, 074036,
https://doi.org/10.1088/1748-9326/aace42, 2018. a, b
Short summary
In this paper we present the climate component of the newly developed fast Earth system model CLIMBER-X. It has a horizontal resolution of 5°x5° and is designed to simulate the evolution of the Earth system on temporal scales ranging from decades to >100 000 years. CLIMBER-X is available as open-source code and is expected to be a useful tool for studying past climate changes and for the investigation of the long-term future evolution of the climate.
In this paper we present the climate component of the newly developed fast Earth system model...