Articles | Volume 3, issue 2
https://doi.org/10.5194/gmd-3-603-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-3-603-2010
© Author(s) 2010. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
02 Nov 2010
Description of the Earth system model of intermediate complexity LOVECLIM version 1.2
H. Goosse
Université Catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Chemin du Cyclotron, 2, 1348 Louvain-la-Neuve, Belgium
V. Brovkin
Max Planck Institute for Meteorology, Hamburg, Germany
T. Fichefet
Université Catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Chemin du Cyclotron, 2, 1348 Louvain-la-Neuve, Belgium
R. Haarsma
Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
P. Huybrechts
Earth System Sciences & Departement Geografie, Vrije Universiteit Brussel, Brussel, Belgium
J. Jongma
Section Climate Change and Landscape Dynamics, Dept. of Earth Sciences, Vrije Universiteit Amsterdam, The Netherlands
A. Mouchet
Laboratoire de Physique Atmosphérique et Planétaire, Université de Liège, Liège, Belgium
F. Selten
Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
P.-Y. Barriat
Université Catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Chemin du Cyclotron, 2, 1348 Louvain-la-Neuve, Belgium
J.-M. Campin
Massachusetts Institute of Technology, Cambridge, USA
E. Deleersnijder
Université Catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Chemin du Cyclotron, 2, 1348 Louvain-la-Neuve, Belgium
Université Catholique de Louvain, Institute of Mechanics, Materials and Civil Engineering, Louvain-la-Neuve, Belgium
E. Driesschaert
International Polar Foundation, Brussels, Belgium
H. Goelzer
Earth System Sciences & Departement Geografie, Vrije Universiteit Brussel, Brussel, Belgium
I. Janssens
Earth System Sciences & Departement Geografie, Vrije Universiteit Brussel, Brussel, Belgium
M.-F. Loutre
Université Catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Chemin du Cyclotron, 2, 1348 Louvain-la-Neuve, Belgium
M. A. Morales Maqueda
Proudman Oceanographic Laboratory, Liverpool, UK
T. Opsteegh
Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
P.-P. Mathieu
Université Catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Chemin du Cyclotron, 2, 1348 Louvain-la-Neuve, Belgium
G. Munhoven
Laboratoire de Physique Atmosphérique et Planétaire, Université de Liège, Liège, Belgium
E. J. Pettersson
Université Catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Chemin du Cyclotron, 2, 1348 Louvain-la-Neuve, Belgium
H. Renssen
Section Climate Change and Landscape Dynamics, Dept. of Earth Sciences, Vrije Universiteit Amsterdam, The Netherlands
D. M. Roche
Section Climate Change and Landscape Dynamics, Dept. of Earth Sciences, Vrije Universiteit Amsterdam, The Netherlands
Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, Laboratoire CEA/INSU-CNRS/UVSQ, CE Saclay, l'Orme des Merisiers, Gif-sur-Yvette Cedex, France
M. Schaeffer
Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
currently at: Climate Analytics, Potsdam, Germany
B. Tartinville
Numeca International, Brussels, Belgium
A. Timmermann
International Pacific Research Center, SOEST, University of Hawai'i, Honolulu, USA
S. L. Weber
Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands
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Yangke Liu, Qing Bao, Bian He, Xiaofei Wu, Jing Yang, Yimin Liu, Guoxiong Wu, Tao Zhu, Siyuan Zhou, Yao Tang, Ankang Qu, Yalan Fan, Anling Liu, Dandan Chen, Zhaoming Luo, Xing Hu, and Tongwen Wu
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Laurent Brodeau, Pierre Rampal, Einar Ólason, and Véronique Dansereau
Geosci. Model Dev., 17, 6051–6082, https://doi.org/10.5194/gmd-17-6051-2024, https://doi.org/10.5194/gmd-17-6051-2024, 2024
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Geosci. Model Dev., 17, 5913–5938, https://doi.org/10.5194/gmd-17-5913-2024, https://doi.org/10.5194/gmd-17-5913-2024, 2024
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Hong Li, Yi Yang, Jian Sun, Yuan Jiang, Ruhui Gan, and Qian Xie
Geosci. Model Dev., 17, 5883–5896, https://doi.org/10.5194/gmd-17-5883-2024, https://doi.org/10.5194/gmd-17-5883-2024, 2024
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Vertical atmospheric motions play a vital role in convective-scale precipitation forecasts by connecting atmospheric dynamics with cloud development. A three-dimensional variational vertical velocity assimilation scheme is developed within the high-resolution CMA-MESO model, utilizing the adiabatic Richardson equation as the observation operator. A 10 d continuous run and an individual case study demonstrate improved forecasts, confirming the scheme's effectiveness.
Matthias Nützel, Laura Stecher, Patrick Jöckel, Franziska Winterstein, Martin Dameris, Michael Ponater, Phoebe Graf, and Markus Kunze
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We extended the infrastructure of our modelling system to enable the use of an additional radiation scheme. After calibrating the model setups to the old and the new radiation scheme, we find that the simulation with the new scheme shows considerable improvements, e.g. concerning the cold-point temperature and stratospheric water vapour. Furthermore, perturbations of radiative fluxes associated with greenhouse gas changes, e.g. of methane, tend to be improved when the new scheme is employed.
Yibing Wang, Xianhong Xie, Bowen Zhu, Arken Tursun, Fuxiao Jiang, Yao Liu, Dawei Peng, and Buyun Zheng
Geosci. Model Dev., 17, 5803–5819, https://doi.org/10.5194/gmd-17-5803-2024, https://doi.org/10.5194/gmd-17-5803-2024, 2024
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Urban expansion intensifies challenges like urban heat and urban dry islands. To address this, we developed an urban module, VIC-urban, in the Variable Infiltration Capacity (VIC) model. Tested in Beijing, VIC-urban accurately simulated turbulent heat fluxes, runoff, and land surface temperature. We provide a reliable tool for large-scale simulations considering urban environment and a systematic urban modelling framework within VIC, offering crucial insights for urban planners and designers.
Jeremy Carter, Erick A. Chacón-Montalván, and Amber Leeson
Geosci. Model Dev., 17, 5733–5757, https://doi.org/10.5194/gmd-17-5733-2024, https://doi.org/10.5194/gmd-17-5733-2024, 2024
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Climate models are essential tools in the study of climate change and its wide-ranging impacts on life on Earth. However, the output is often afflicted with some bias. In this paper, a novel model is developed to predict and correct bias in the output of climate models. The model captures uncertainty in the correction and explicitly models underlying spatial correlation between points. These features are of key importance for climate change impact assessments and resulting decision-making.
Anna Martin, Veronika Gayler, Benedikt Steil, Klaus Klingmüller, Patrick Jöckel, Holger Tost, Jos Lelieveld, and Andrea Pozzer
Geosci. Model Dev., 17, 5705–5732, https://doi.org/10.5194/gmd-17-5705-2024, https://doi.org/10.5194/gmd-17-5705-2024, 2024
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The study evaluates the land surface and vegetation model JSBACHv4 as a replacement for the simplified submodel SURFACE in EMAC. JSBACH mitigates earlier problems of soil dryness, which are critical for vegetation modelling. When analysed using different datasets, the coupled model shows strong correlations of key variables, such as land surface temperature, surface albedo and radiation flux. The versatility of the model increases significantly, while the overall performance does not degrade.
Hugo Banderier, Christian Zeman, David Leutwyler, Stefan Rüdisühli, and Christoph Schär
Geosci. Model Dev., 17, 5573–5586, https://doi.org/10.5194/gmd-17-5573-2024, https://doi.org/10.5194/gmd-17-5573-2024, 2024
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We investigate the effects of reduced-precision arithmetic in a state-of-the-art regional climate model by studying the results of 10-year-long simulations. After this time, the results of the reduced precision and the standard implementation are hardly different. This should encourage the use of reduced precision in climate models to exploit the speedup and memory savings it brings. The methodology used in this work can help researchers verify reduced-precision implementations of their model.
David Fuchs, Steven C. Sherwood, Abhnil Prasad, Kirill Trapeznikov, and Jim Gimlett
Geosci. Model Dev., 17, 5459–5475, https://doi.org/10.5194/gmd-17-5459-2024, https://doi.org/10.5194/gmd-17-5459-2024, 2024
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Machine learning (ML) of unresolved processes offers many new possibilities for improving weather and climate models, but integrating ML into the models has been an engineering challenge, and there are performance issues. We present a new software plugin for this integration, TorchClim, that is scalable and flexible and thereby allows a new level of experimentation with the ML approach. We also provide guidance on ML training and demonstrate a skillful hybrid ML atmosphere model.
Minjin Lee, Charles A. Stock, John P. Dunne, and Elena Shevliakova
Geosci. Model Dev., 17, 5191–5224, https://doi.org/10.5194/gmd-17-5191-2024, https://doi.org/10.5194/gmd-17-5191-2024, 2024
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Modeling global freshwater solid and nutrient loads, in both magnitude and form, is imperative for understanding emerging eutrophication problems. Such efforts, however, have been challenged by the difficulty of balancing details of freshwater biogeochemical processes with limited knowledge, input, and validation datasets. Here we develop a global freshwater model that resolves intertwined algae, solid, and nutrient dynamics and provide performance assessment against measurement-based estimates.
Hunter York Brown, Benjamin Wagman, Diana Bull, Kara Peterson, Benjamin Hillman, Xiaohong Liu, Ziming Ke, and Lin Lin
Geosci. Model Dev., 17, 5087–5121, https://doi.org/10.5194/gmd-17-5087-2024, https://doi.org/10.5194/gmd-17-5087-2024, 2024
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Explosive volcanic eruptions lead to long-lived, microscopic particles in the upper atmosphere which act to cool the Earth's surface by reflecting the Sun's light back to space. We include and test this process in a global climate model, E3SM. E3SM is tested against satellite and balloon observations of the 1991 eruption of Mt. Pinatubo, showing that with these particles in the model we reasonably recreate Pinatubo and its global effects. We also explore how particle size leads to these effects.
Carl Svenhag, Moa K. Sporre, Tinja Olenius, Daniel Yazgi, Sara M. Blichner, Lars P. Nieradzik, and Pontus Roldin
Geosci. Model Dev., 17, 4923–4942, https://doi.org/10.5194/gmd-17-4923-2024, https://doi.org/10.5194/gmd-17-4923-2024, 2024
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Our research shows the importance of modeling new particle formation (NPF) and growth of particles in the atmosphere on a global scale, as they influence the outcomes of clouds and our climate. With the global model EC-Earth3 we show that using a new method for NPF modeling, which includes new detailed processes with NH3 and H2SO4, significantly impacts the number of particles in the air and clouds and changes the radiation balance of the same magnitude as anthropogenic greenhouse emissions.
Mengjie Han, Qing Zhao, Xili Wang, Ying-Ping Wang, Philippe Ciais, Haicheng Zhang, Daniel S. Goll, Lei Zhu, Zhe Zhao, Zhixuan Guo, Chen Wang, Wei Zhuang, Fengchang Wu, and Wei Li
Geosci. Model Dev., 17, 4871–4890, https://doi.org/10.5194/gmd-17-4871-2024, https://doi.org/10.5194/gmd-17-4871-2024, 2024
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The impact of biochar (BC) on soil organic carbon (SOC) dynamics is not represented in most land carbon models used for assessing land-based climate change mitigation. Our study develops a BC model that incorporates our current understanding of BC effects on SOC based on a soil carbon model (MIMICS). The BC model can reproduce the SOC changes after adding BC, providing a useful tool to couple dynamic land models to evaluate the effectiveness of BC application for CO2 removal from the atmosphere.
Kalyn Dorheim, Skylar Gering, Robert Gieseke, Corinne Hartin, Leeya Pressburger, Alexey N. Shiklomanov, Steven J. Smith, Claudia Tebaldi, Dawn L. Woodard, and Ben Bond-Lamberty
Geosci. Model Dev., 17, 4855–4869, https://doi.org/10.5194/gmd-17-4855-2024, https://doi.org/10.5194/gmd-17-4855-2024, 2024
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Hector is an easy-to-use, global climate–carbon cycle model. With its quick run time, Hector can provide climate information from a run in a fraction of a second. Hector models on a global and annual basis. Here, we present an updated version of the model, Hector V3. In this paper, we document Hector’s new features. Hector V3 is capable of reproducing historical observations, and its future temperature projections are consistent with those of more complex models.
Fangxuan Ren, Jintai Lin, Chenghao Xu, Jamiu A. Adeniran, Jingxu Wang, Randall V. Martin, Aaron van Donkelaar, Melanie S. Hammer, Larry W. Horowitz, Steven T. Turnock, Naga Oshima, Jie Zhang, Susanne Bauer, Kostas Tsigaridis, Øyvind Seland, Pierre Nabat, David Neubauer, Gary Strand, Twan van Noije, Philippe Le Sager, and Toshihiko Takemura
Geosci. Model Dev., 17, 4821–4836, https://doi.org/10.5194/gmd-17-4821-2024, https://doi.org/10.5194/gmd-17-4821-2024, 2024
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We evaluate the performance of 14 CMIP6 ESMs in simulating total PM2.5 and its 5 components over China during 2000–2014. PM2.5 and its components are underestimated in almost all models, except that black carbon (BC) and sulfate are overestimated in two models, respectively. The underestimation is the largest for organic carbon (OC) and the smallest for BC. Models reproduce the observed spatial pattern for OC, sulfate, nitrate and ammonium well, yet the agreement is poorer for BC.
Yi Xi, Chunjing Qiu, Yuan Zhang, Dan Zhu, Shushi Peng, Gustaf Hugelius, Jinfeng Chang, Elodie Salmon, and Philippe Ciais
Geosci. Model Dev., 17, 4727–4754, https://doi.org/10.5194/gmd-17-4727-2024, https://doi.org/10.5194/gmd-17-4727-2024, 2024
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The ORCHIDEE-MICT model can simulate the carbon cycle and hydrology at a sub-grid scale but energy budgets only at a grid scale. This paper assessed the implementation of a multi-tiling energy budget approach in ORCHIDEE-MICT and found warmer surface and soil temperatures, higher soil moisture, and more soil organic carbon across the Northern Hemisphere compared with the original version.
Georgia Lazoglou, Theo Economou, Christina Anagnostopoulou, George Zittis, Anna Tzyrkalli, Pantelis Georgiades, and Jos Lelieveld
Geosci. Model Dev., 17, 4689–4703, https://doi.org/10.5194/gmd-17-4689-2024, https://doi.org/10.5194/gmd-17-4689-2024, 2024
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This study focuses on the important issue of the drizzle bias effect in regional climate models, described by an over-prediction of the number of rainy days while underestimating associated precipitation amounts. For this purpose, two distinct methodologies are applied and rigorously evaluated. These results are encouraging for using the multivariate machine learning method random forest to increase the accuracy of climate models concerning the projection of the number of wet days.
Xu Yue, Hao Zhou, Chenguang Tian, Yimian Ma, Yihan Hu, Cheng Gong, Hui Zheng, and Hong Liao
Geosci. Model Dev., 17, 4621–4642, https://doi.org/10.5194/gmd-17-4621-2024, https://doi.org/10.5194/gmd-17-4621-2024, 2024
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We develop the interactive Model for Air Pollution and Land Ecosystems (iMAPLE). The model considers the full coupling between carbon and water cycles, dynamic fire emissions, wetland methane emissions, biogenic volatile organic compound emissions, and trait-based ozone vegetation damage. Evaluations show that iMAPLE is a useful tool for the study of the interactions among climate, chemistry, and ecosystems.
Malte Meinshausen, Carl-Friedrich Schleussner, Kathleen Beyer, Greg Bodeker, Olivier Boucher, Josep G. Canadell, John S. Daniel, Aïda Diongue-Niang, Fatima Driouech, Erich Fischer, Piers Forster, Michael Grose, Gerrit Hansen, Zeke Hausfather, Tatiana Ilyina, Jarmo S. Kikstra, Joyce Kimutai, Andrew D. King, June-Yi Lee, Chris Lennard, Tabea Lissner, Alexander Nauels, Glen P. Peters, Anna Pirani, Gian-Kasper Plattner, Hans Pörtner, Joeri Rogelj, Maisa Rojas, Joyashree Roy, Bjørn H. Samset, Benjamin M. Sanderson, Roland Séférian, Sonia Seneviratne, Christopher J. Smith, Sophie Szopa, Adelle Thomas, Diana Urge-Vorsatz, Guus J. M. Velders, Tokuta Yokohata, Tilo Ziehn, and Zebedee Nicholls
Geosci. Model Dev., 17, 4533–4559, https://doi.org/10.5194/gmd-17-4533-2024, https://doi.org/10.5194/gmd-17-4533-2024, 2024
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The scientific community is considering new scenarios to succeed RCPs and SSPs for the next generation of Earth system model runs to project future climate change. To contribute to that effort, we reflect on relevant policy and scientific research questions and suggest categories for representative emission pathways. These categories are tailored to the Paris Agreement long-term temperature goal, high-risk outcomes in the absence of further climate policy and worlds “that could have been”.
Ross Mower, Ethan D. Gutmann, Glen E. Liston, Jessica Lundquist, and Soren Rasmussen
Geosci. Model Dev., 17, 4135–4154, https://doi.org/10.5194/gmd-17-4135-2024, https://doi.org/10.5194/gmd-17-4135-2024, 2024
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Higher-resolution model simulations are better at capturing winter snowpack changes across space and time. However, increasing resolution also increases the computational requirements. This work provides an overview of changes made to a distributed snow-evolution modeling system (SnowModel) to allow it to leverage high-performance computing resources. Continental simulations that were previously estimated to take 120 d can now be performed in 5 h.
Jiaxu Guo, Juepeng Zheng, Yidan Xu, Haohuan Fu, Wei Xue, Lanning Wang, Lin Gan, Ping Gao, Wubing Wan, Xianwei Wu, Zhitao Zhang, Liang Hu, Gaochao Xu, and Xilong Che
Geosci. Model Dev., 17, 3975–3992, https://doi.org/10.5194/gmd-17-3975-2024, https://doi.org/10.5194/gmd-17-3975-2024, 2024
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To enhance the efficiency of experiments using SCAM, we train a learning-based surrogate model to facilitate large-scale sensitivity analysis and tuning of combinations of multiple parameters. Employing a hybrid method, we investigate the joint sensitivity of multi-parameter combinations across typical cases, identifying the most sensitive three-parameter combination out of 11. Subsequently, we conduct a tuning process aimed at reducing output errors in these cases.
Yung-Yao Lan, Huang-Hsiung Hsu, and Wan-Ling Tseng
Geosci. Model Dev., 17, 3897–3918, https://doi.org/10.5194/gmd-17-3897-2024, https://doi.org/10.5194/gmd-17-3897-2024, 2024
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This study uses the CAM5–SIT coupled model to investigate the effects of SST feedback frequency on the MJO simulations with intervals at 30 min, 1, 3, 6, 12, 18, 24, and 30 d. The simulations become increasingly unrealistic as the frequency of the SST feedback decreases. Our results suggest that more spontaneous air--sea interaction (e.g., ocean response within 3 d in this study) with high vertical resolution in the ocean model is key to the realistic simulation of the MJO.
Jiwoo Lee, Peter J. Gleckler, Min-Seop Ahn, Ana Ordonez, Paul A. Ullrich, Kenneth R. Sperber, Karl E. Taylor, Yann Y. Planton, Eric Guilyardi, Paul Durack, Celine Bonfils, Mark D. Zelinka, Li-Wei Chao, Bo Dong, Charles Doutriaux, Chengzhu Zhang, Tom Vo, Jason Boutte, Michael F. Wehner, Angeline G. Pendergrass, Daehyun Kim, Zeyu Xue, Andrew T. Wittenberg, and John Krasting
Geosci. Model Dev., 17, 3919–3948, https://doi.org/10.5194/gmd-17-3919-2024, https://doi.org/10.5194/gmd-17-3919-2024, 2024
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We introduce an open-source software, the PCMDI Metrics Package (PMP), developed for a comprehensive comparison of Earth system models (ESMs) with real-world observations. Using diverse metrics evaluating climatology, variability, and extremes simulated in thousands of simulations from the Coupled Model Intercomparison Project (CMIP), PMP aids in benchmarking model improvements across generations. PMP also enables efficient tracking of performance evolutions during ESM developments.
Haoyue Zuo, Yonggang Liu, Gaojun Li, Zhifang Xu, Liang Zhao, Zhengtang Guo, and Yongyun Hu
Geosci. Model Dev., 17, 3949–3974, https://doi.org/10.5194/gmd-17-3949-2024, https://doi.org/10.5194/gmd-17-3949-2024, 2024
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Compared to the silicate weathering fluxes measured at large river basins, the current models tend to systematically overestimate the fluxes over the tropical region, which leads to an overestimation of the global total weathering flux. The most possible cause of such bias is found to be the overestimation of tropical surface erosion, which indicates that the tropical vegetation likely slows down physical erosion significantly. We propose a way of taking this effect into account in models.
Quentin Pikeroen, Didier Paillard, and Karine Watrin
Geosci. Model Dev., 17, 3801–3814, https://doi.org/10.5194/gmd-17-3801-2024, https://doi.org/10.5194/gmd-17-3801-2024, 2024
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All accurate climate models use equations with poorly defined parameters, where knobs for the parameters are turned to fit the observations. This process is called tuning. In this article, we use another paradigm. We use a thermodynamic hypothesis, the maximum entropy production, to compute temperatures, energy fluxes, and precipitation, where tuning is impossible. For now, the 1D vertical model is used for a tropical atmosphere. The correct order of magnitude of precipitation is computed.
Sarah Schöngart, Lukas Gudmundsson, Mathias Hauser, Peter Pfleiderer, Quentin Lejeune, Shruti Nath, Sonia Isabelle Seneviratne, and Carl-Friedrich Schleußner
EGUsphere, https://doi.org/10.5194/egusphere-2024-278, https://doi.org/10.5194/egusphere-2024-278, 2024
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Precipitation and temperature are two of the most impact-relevant climatic variables. Their joint distribution largely determines the division into climate regimes. Yet, projecting precipitation and temperature data under different emission scenarios relies on complex models that are computationally expensive. In this study, we propose a method that allows to generate monthly means of local precipitation and temperature at low computational costs.
Jishi Zhang, Peter Bogenschutz, Qi Tang, Philip Cameron-smith, and Chengzhu Zhang
Geosci. Model Dev., 17, 3687–3731, https://doi.org/10.5194/gmd-17-3687-2024, https://doi.org/10.5194/gmd-17-3687-2024, 2024
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We developed a regionally refined climate model that allows resolved convection and performed a 20-year projection to the end of the century. The model has a resolution of 3.25 km in California, which allows us to predict climate with unprecedented accuracy, and a resolution of 100 km for the rest of the globe to achieve efficient, self-consistent simulations. The model produces superior results in reproducing climate patterns over California that typical modern climate models cannot resolve.
Xiaohui Zhong, Xing Yu, and Hao Li
Geosci. Model Dev., 17, 3667–3685, https://doi.org/10.5194/gmd-17-3667-2024, https://doi.org/10.5194/gmd-17-3667-2024, 2024
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In order to forecast localized warm-sector rainfall in the south China region, numerical weather prediction models are being run with finer grid spacing. The conventional convection parameterization (CP) performs poorly in the gray zone, necessitating the development of a scale-aware scheme. We propose a machine learning (ML) model to replace the scale-aware CP scheme. Evaluation against the original CP scheme has shown that the ML-based CP scheme can provide accurate and reliable predictions.
Taufiq Hassan, Kai Zhang, Jianfeng Li, Balwinder Singh, Shixuan Zhang, Hailong Wang, and Po-Lun Ma
Geosci. Model Dev., 17, 3507–3532, https://doi.org/10.5194/gmd-17-3507-2024, https://doi.org/10.5194/gmd-17-3507-2024, 2024
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Anthropogenic aerosol emissions are an essential part of global aerosol models. Significant errors can exist from the loss of emission heterogeneity. We introduced an emission treatment that significantly improved aerosol emission heterogeneity in high-resolution model simulations, with improvements in simulated aerosol surface concentrations. The emission treatment will provide a more accurate representation of aerosol emissions and their effects on climate.
Feng Zhu, Julien Emile-Geay, Gregory J. Hakim, Dominique Guillot, Deborah Khider, Robert Tardif, and Walter A. Perkins
Geosci. Model Dev., 17, 3409–3431, https://doi.org/10.5194/gmd-17-3409-2024, https://doi.org/10.5194/gmd-17-3409-2024, 2024
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Climate field reconstruction encompasses methods that estimate the evolution of climate in space and time based on natural archives. It is useful to investigate climate variations and validate climate models, but its implementation and use can be difficult for non-experts. This paper introduces a user-friendly Python package called cfr to make these methods more accessible, thanks to the computational and visualization tools that facilitate efficient and reproducible research on past climates.
Rose V. Palermo, J. Taylor Perron, Jason M. Soderblom, Samuel P. D. Birch, Alexander G. Hayes, and Andrew D. Ashton
Geosci. Model Dev., 17, 3433–3445, https://doi.org/10.5194/gmd-17-3433-2024, https://doi.org/10.5194/gmd-17-3433-2024, 2024
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Models of rocky coastal erosion help us understand the controls on coastal morphology and evolution. In this paper, we present a simplified model of coastline erosion driven by either uniform erosion where coastline erosion is constant or wave-driven erosion where coastline erosion is a function of the wave power. This model can be used to evaluate how coastline changes reflect climate, sea-level history, material properties, and the relative influence of different erosional processes.
Safae Oumami, Joaquim Arteta, Vincent Guidard, Pierre Tulet, and Paul David Hamer
Geosci. Model Dev., 17, 3385–3408, https://doi.org/10.5194/gmd-17-3385-2024, https://doi.org/10.5194/gmd-17-3385-2024, 2024
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In this paper, we coupled the SURFEX and MEGAN models. The aim of this coupling is to improve the estimation of biogenic fluxes by using the SURFEX canopy environment model. The coupled model results were validated and several sensitivity tests were performed. The coupled-model total annual isoprene flux is 442 Tg; this value is within the range of other isoprene estimates reported. The ultimate aim of this coupling is to predict the impact of climate change on biogenic emissions.
Lars Ackermann, Thomas Rackow, Kai Himstedt, Paul Gierz, Gregor Knorr, and Gerrit Lohmann
Geosci. Model Dev., 17, 3279–3301, https://doi.org/10.5194/gmd-17-3279-2024, https://doi.org/10.5194/gmd-17-3279-2024, 2024
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We present long-term simulations with interactive icebergs in the Southern Ocean. By melting, icebergs reduce the temperature and salinity of the surrounding ocean. In our simulations, we find that this cooling effect of iceberg melting is not limited to the surface ocean but also reaches the deep ocean and propagates northward into all ocean basins. Additionally, the formation of deep-water masses in the Southern Ocean is enhanced.
Nanhong Xie, Tijian Wang, Xiaodong Xie, Xu Yue, Filippo Giorgi, Qian Zhang, Danyang Ma, Rong Song, Beiyao Xu, Shu Li, Bingliang Zhuang, Mengmeng Li, Min Xie, Natalya Andreeva Kilifarska, Georgi Gadzhev, and Reneta Dimitrova
Geosci. Model Dev., 17, 3259–3277, https://doi.org/10.5194/gmd-17-3259-2024, https://doi.org/10.5194/gmd-17-3259-2024, 2024
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For the first time, we coupled a regional climate chemistry model, RegCM-Chem, with a dynamic vegetation model, YIBs, to create a regional climate–chemistry–ecology model, RegCM-Chem–YIBs. We applied it to simulate climatic, chemical, and ecological parameters in East Asia and fully validated it on a variety of observational data. Results show that RegCM-Chem–YIBs model is a valuable tool for studying the terrestrial carbon cycle, atmospheric chemistry, and climate change on a regional scale.
Ha Thi Minh Ho-Hagemann, Vera Maurer, Stefan Poll, and Irina Fast
EGUsphere, https://doi.org/10.5194/egusphere-2024-923, https://doi.org/10.5194/egusphere-2024-923, 2024
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The regional Earth system model GCOAST-AHOI version 2.0 including the regional climate model ICON-CLM coupled with the ocean model NEMO and the hydrological discharge model HD via the OASIS3-MCT coupler can be a useful tool for conducting long-term regional climate simulations over the EURO-CORDEX domain. The new OASIS3-MCT coupling interface implemented in the ICON-CLM model makes it more flexible to couple with an external ocean model and an external hydrological discharge model.
Bryce E. Harrop, Jian Lu, L. Ruby Leung, William K. M. Lau, Kyu-Myong Kim, Brian Medeiros, Brian J. Soden, Gabriel A. Vecchi, Bosong Zhang, and Balwinder Singh
Geosci. Model Dev., 17, 3111–3135, https://doi.org/10.5194/gmd-17-3111-2024, https://doi.org/10.5194/gmd-17-3111-2024, 2024
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Seven new experimental setups designed to interfere with cloud radiative heating have been added to the Energy Exascale Earth System Model (E3SM). These experiments include both those that test the mean impact of cloud radiative heating and those examining its covariance with circulations. This paper documents the code changes and steps needed to run these experiments. Results corroborate prior findings for how cloud radiative heating impacts circulations and rainfall patterns.
Mario C. Acosta, Sergi Palomas, Stella V. Paronuzzi Ticco, Gladys Utrera, Joachim Biercamp, Pierre-Antoine Bretonniere, Reinhard Budich, Miguel Castrillo, Arnaud Caubel, Francisco Doblas-Reyes, Italo Epicoco, Uwe Fladrich, Sylvie Joussaume, Alok Kumar Gupta, Bryan Lawrence, Philippe Le Sager, Grenville Lister, Marie-Pierre Moine, Jean-Christophe Rioual, Sophie Valcke, Niki Zadeh, and Venkatramani Balaji
Geosci. Model Dev., 17, 3081–3098, https://doi.org/10.5194/gmd-17-3081-2024, https://doi.org/10.5194/gmd-17-3081-2024, 2024
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We present a collection of performance metrics gathered during the Coupled Model Intercomparison Project Phase 6 (CMIP6), a worldwide initiative to study climate change. We analyse the metrics that resulted from collaboration efforts among many partners and models and describe our findings to demonstrate the utility of our study for the scientific community. The research contributes to understanding climate modelling performance on the current high-performance computing (HPC) architectures.
Sabine Doktorowski, Jan Kretzschmar, Johannes Quaas, Marc Salzmann, and Odran Sourdeval
Geosci. Model Dev., 17, 3099–3110, https://doi.org/10.5194/gmd-17-3099-2024, https://doi.org/10.5194/gmd-17-3099-2024, 2024
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Especially over the midlatitudes, precipitation is mainly formed via the ice phase. In this study we focus on the initial snow formation process in the ICON-AES, the aggregation process. We use a stochastical approach for the aggregation parameterization and investigate the influence in the ICON-AES. Therefore, a distribution function of cloud ice is created, which is evaluated with satellite data. The new approach leads to cloud ice loss and an improvement in the process rate bias.
Katie R. Blackford, Matthew Kasoar, Chantelle Burton, Eleanor Burke, Iain Colin Prentice, and Apostolos Voulgarakis
Geosci. Model Dev., 17, 3063–3079, https://doi.org/10.5194/gmd-17-3063-2024, https://doi.org/10.5194/gmd-17-3063-2024, 2024
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Peatlands are globally important stores of carbon which are being increasingly threatened by wildfires with knock-on effects on the climate system. Here we introduce a novel peat fire parameterization in the northern high latitudes to the INFERNO global fire model. Representing peat fires increases annual burnt area across the high latitudes, alongside improvements in how we capture year-to-year variation in burning and emissions.
Pengfei Shi, L. Ruby Leung, Bin Wang, Kai Zhang, Samson M. Hagos, and Shixuan Zhang
Geosci. Model Dev., 17, 3025–3040, https://doi.org/10.5194/gmd-17-3025-2024, https://doi.org/10.5194/gmd-17-3025-2024, 2024
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Improving climate predictions have profound socio-economic impacts. This study introduces a new weakly coupled land data assimilation (WCLDA) system for a coupled climate model. We demonstrate improved simulation of soil moisture and temperature in many global regions and throughout the soil layers. Furthermore, significant improvements are also found in reproducing the time evolution of the 2012 US Midwest drought. The WCLDA system provides the groundwork for future predictability studies.
Justin Peter, Elisabeth Vogel, Wendy Sharples, Ulrike Bende-Michl, Louise Wilson, Pandora Hope, Andrew Dowdy, Greg Kociuba, Sri Srikanthan, Vi Co Duong, Jake Roussis, Vjekoslav Matic, Zaved Khan, Alison Oke, Margot Turner, Stuart Baron-Hay, Fiona Johnson, Raj Mehrotra, Ashish Sharma, Marcus Thatcher, Ali Azarvinand, Steven Thomas, Ghyslaine Boschat, Chantal Donnelly, and Robert Argent
Geosci. Model Dev., 17, 2755–2781, https://doi.org/10.5194/gmd-17-2755-2024, https://doi.org/10.5194/gmd-17-2755-2024, 2024
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We detail the production of datasets and communication to end users of high-resolution projections of rainfall, runoff, and soil moisture for the entire Australian continent. This is important as previous projections for Australia were for small regions and used differing techniques for their projections, making comparisons difficult across Australia's varied climate zones. The data will be beneficial for research purposes and to aid adaptation to climate change.
Daniele Visioni, Alan Robock, Jim Haywood, Matthew Henry, Simone Tilmes, Douglas G. MacMartin, Ben Kravitz, Sarah J. Doherty, John Moore, Chris Lennard, Shingo Watanabe, Helene Muri, Ulrike Niemeier, Olivier Boucher, Abu Syed, Temitope S. Egbebiyi, Roland Séférian, and Ilaria Quaglia
Geosci. Model Dev., 17, 2583–2596, https://doi.org/10.5194/gmd-17-2583-2024, https://doi.org/10.5194/gmd-17-2583-2024, 2024
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This paper describes a new experimental protocol for the Geoengineering Model Intercomparison Project (GeoMIP). In it, we describe the details of a new simulation of sunlight reflection using the stratospheric aerosols that climate models are supposed to run, and we explain the reasons behind each choice we made when defining the protocol.
Sabin I. Taranu, David M. Lawrence, Yoshihide Wada, Ting Tang, Erik Kluzek, Sam Rabin, Yi Yao, Steven J. De Hertog, Inne Vanderkelen, and Wim Thiery
EGUsphere, https://doi.org/10.5194/egusphere-2024-362, https://doi.org/10.5194/egusphere-2024-362, 2024
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In this study, we improve an existing climate model to account for human water usage across domestic, industrial, and agriculture purposes. With the new capabilities, the model is now better equipped for studying questions related to water scarcity in both present and future conditions under climate change. Despite the advancements, there remains important limitations in our modelling framework which requires further work.
Jose Rafael Guarin, Jonas Jägermeyr, Elizabeth A. Ainsworth, Fabio A. A. Oliveira, Senthold Asseng, Kenneth Boote, Joshua Elliott, Lisa Emberson, Ian Foster, Gerrit Hoogenboom, David Kelly, Alex C. Ruane, and Katrina Sharps
Geosci. Model Dev., 17, 2547–2567, https://doi.org/10.5194/gmd-17-2547-2024, https://doi.org/10.5194/gmd-17-2547-2024, 2024
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The effects of ozone (O3) stress on crop photosynthesis and leaf senescence were added to maize, rice, soybean, and wheat crop models. The modified models reproduced growth and yields under different O3 levels measured in field experiments and reported in the literature. The combined interactions between O3 and additional stresses were reproduced with the new models. These updated crop models can be used to simulate impacts of O3 stress under future climate change and air pollution scenarios.
Jiachen Lu, Negin Nazarian, Melissa Anne Hart, E. Scott Krayenhoff, and Alberto Martilli
Geosci. Model Dev., 17, 2525–2545, https://doi.org/10.5194/gmd-17-2525-2024, https://doi.org/10.5194/gmd-17-2525-2024, 2024
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This study enhances urban canopy models by refining key assumptions. Simulations for various urban scenarios indicate discrepancies in turbulent transport efficiency for flow properties. We propose two modifications that involve characterizing diffusion coefficients for momentum and turbulent kinetic energy separately and introducing a physics-based
mass-fluxterm. These adjustments enhance the model's performance, offering more reliable temperature and surface flux estimates.
Justin L. Willson, Kevin A. Reed, Christiane Jablonowski, James Kent, Peter H. Lauritzen, Ramachandran Nair, Mark A. Taylor, Paul A. Ullrich, Colin M. Zarzycki, David M. Hall, Don Dazlich, Ross Heikes, Celal Konor, David Randall, Thomas Dubos, Yann Meurdesoif, Xi Chen, Lucas Harris, Christian Kühnlein, Vivian Lee, Abdessamad Qaddouri, Claude Girard, Marco Giorgetta, Daniel Reinert, Hiroaki Miura, Tomoki Ohno, and Ryuji Yoshida
Geosci. Model Dev., 17, 2493–2507, https://doi.org/10.5194/gmd-17-2493-2024, https://doi.org/10.5194/gmd-17-2493-2024, 2024
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Accurate simulation of tropical cyclones (TCs) is essential to understanding their behavior in a changing climate. One way this is accomplished is through model intercomparison projects, where results from multiple climate models are analyzed to provide benchmark solutions for the wider climate modeling community. This study describes and analyzes the previously developed TC test case for nine climate models in an intercomparison project, providing solutions that aid in model development.
Maximillian Van Wyk de Vries, Tom Matthews, L. Baker Perry, Nirakar Thapa, and Rob Wilby
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-36, https://doi.org/10.5194/gmd-2024-36, 2024
Revised manuscript accepted for GMD
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This paper introduces the AtsMOS workflow, a new tool for improving weather forecasts in mountainous areas. By combining advanced statistical techniques with local weather data, AtsMOS can provide more accurate predictions of weather conditions. Using data from Mount Everest as an example, AtsMOS has shown promise in better forecasting hazardous weather conditions, making it a valuable tool for communities in mountainous regions and beyond.
Cited articles
Anderson, L. A. and Sarmiento, J. L.: Redfield ratios of remineralization determined by nutrient data analysis, Global Biogeochem. Cy., 8, 65–80, 1994.
Bacastow, R. and Maier-Reimer, E.: Ocean-circulation model of the carbon cycle, Clim. Dynam., 4, 95–125, 1990.
Barnola, J.-M., Anklin, M., Porcheron, J., Raynaud, D., Schwander, J., and Stauffer, B.: CO2 evolution during the last millennium as recorded from Antarctica and Greenland ice, Tellus B, 47, 264–272, 1995.
Bazilevich, N. I.: Biological Productivity of Ecosystems of Northern Eurasia, Nauka, Moscow, 293 pp., 1993 (in Russian).
Beckmann, A. and Goosse, H.: A parameterization of ice shelf–ocean interactions for climate models, Ocean Model., 5, 157–170, 2003.
Berger, A. L.: Long-term variations of daily insolation and Quaternary climatic changes, J. Atmos. Sci., 35, 2363–2367, 1978.
Bigg, G. R., Wadley, M. R., Stevens, D. P., and Johnson, J. A.: Prediction of iceberg trajectories for the North Atlantic and Arctic Oceans, Geophys. Res. Lett., 23, 3587–3590, 1996.
Bigg, G. R., Wadley, M. R., Stevens, D. P., and Johnson, J. A.: Modelling the dynamics and thermodynamics of icebergs, Cold Reg. Sci. Technol., 26, 113–135, 1997.
Braconnot, P., Otto-Bliesner, B., Harrison, S., Joussaume, S., Peterchmitt, J.-Y., Abe-Ouchi, A., Crucifix, M., Driesschaert, E., Fichefet, Th., Hewitt, C. D., Kageyama, M., Kitoh, A., Loutre, M.-F., Marti, O., Merkel, U., Ramstein, G., Valdes, P., Weber, L., Yu, Y., and Zhao, Y.: Results of PMIP2 coupled simulations of the Mid-Holocene and Last Glacial Maximum - Part 2: feedbacks with emphasis on the location of the ITCZ and mid- and high latitudes heat budget, Clim. Past, 3, 279–296, https://doi.org/10.5194/cp-3-279-2007, 2007.
Briffa, K. R.: Annual climate variability in the Holocene: interpreting the message of ancient trees, Quaternary Sci. Rev., 19(1–5), 87–105, 2000.
Briffa, K. R., Osborn, T., Schweingruber, F., Harris, I., Jones, P., Shiyatov, S., and Vaganov, E.: Low-frequency temperature variations from a northern tree ring density network, J. Geophys. Res., 106(D3), 2929–2941, 2001.
Briffa, K. R., Osborn, T. J., and Schweingruber, F. H.: Large-scale temperature inferences from tree rings: a review, Global Planet. Change, 40(1–2), 11–26, 2004.
Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. B., Jones, P. D.: Uncertainty estimates in regional and global observed temperature changes: A new data set from 1850, J. Geophys. Res., 111, D12106, https://doi.org/10.1029/2005JD006548, 2006.
Brovkin, V., Ganopolski, A., and Svirezhev, Y.: A continuous climate-vegetation classification for use in climate-biosphere studies, Ecol. Model., 101, 251–261, 1997.
Brovkin, V., Bendtsen, J., Claussen, M., Ganopolski, A., Kubatzki, C., Petoukhov, V., and Andreev, A.: Carbon cycle, vegetation and Clim. Dynam. in the Holocene: experiments with the CLIMBER-2 model, Global Biogeochem. Cy., 16(4), 1139, https://doi.org/10.1029/2001GB001662, 2002.
Bryan, K. and Lewis, L. J.: A water mass model of the world ocean, J. Geophys. Res., 84, 2503–2517, 1979.
Campin, J. M. and Goosse, H.: A parameterization of density driven downsloping flow for coarse resolution model in z-coordinate, Tellus A, 51, 412–430, 1999.
Charlson, R. J., Langner, J., Rodhe, H., Leovy, C. B., and Warren, S. G.: Perturbation of the Northern Hemisphere radiative balance by backscattering from anthropogenic sulfate aerosols, Tellus AB, 43, 152–163, 1991.
Chapin, F. S., Bret-Harte, M. S., Hobbie, S. E., and Zhong, H. L.: Plant functional types as predictors of transient responses of arctic vegetation to global change, J. Veg. Sci., 7, 347–358, 1996.
Chou, C. and Neelin, J. D.: Linearization of a long-wave radiation scheme for intermediate tropical atmospheric model, J. Geophys. Res., 101, 15129–15145, 1996.
Claussen, M., Mysak, L. A., Weaver, A. J., Crucifix, M., Fichefet, T., Loutre, M. F., Weber, S. L., Alcamo, J., Alexeev, V. A., Berger, A., Calov, R., Ganopolski, A., Goosse, H., Lohman, G., Lunkeit, F., Mohkov, I. 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, 2002.
Comiso, J. C. and Nishio, F.: Trends in the sea ice cover using enhanced and compatible AMSR-E, SSM/I, and SMMR data, J. Geophys. Res., 113, C02S07, https://doi.org/10.1029/2007JC004257, 2008.
Cook, E. R., Esper, J., and D'Arrigo, R. D.: Extra-tropical Northern Hemisphere land temperature variability over the past 1000 years, Quaternary Sci. Rev., 23(20–22), 2063–2074, 2004.
Cox, M.: Isopycnal diffusion in a z-coordinate ocean model, Ocean Model., 74, 1–5, 1987.
Cramer, W., Bondeau, A., Woodward, F. I., Prentice, I. C., Betts, R. A., Brovkin, V., Cox, P. M., Fisher, V., Foley, J. A., Friend, A. D., Kucharik, C., Lomas, M. R., Ramankutty, N., Sitch, S., Smith, B., White, A., and Young-Molling, C.: Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models, Glob. Change Biol., 7, 357–373, 2001.
Crespin, E., Goosse, H., Fichefet, T., and Mann, M. E.: The 15th century Arctic warming in coupled model simulations with data assimilation, Clim. Past, 5, 389–401, https://doi.org/10.5194/cp-5-389-2009, 2009.
Crowley, T. J., Baum, S. K., Kim, K. Y., Hegerl, G. C., and Hyde, W. T.: Modeling ocean heat content changes during the last millennium, Geophys. Res. Lett., 30(18), 1932, https://doi.org/10.1029/2003GL017801, 2003.
D'Arrigo, R., Wilson, R., and Jacoby, G., : On the long-term context for late twentieth century warming, J. Geophys. Res., 111, D03103, https://doi.org/10.1029/2005JD006352, 2006.
Deleersnijder, E., Beckers, J.-M., Campin, J.-M., El Mohajir, M., Fichefet, T., and Luyten, P.: Some mathematical problems associated with the development and use of marine models, in: The mathematics of model for climatology and environment, edited by: Diaz, J. I., NATO ASI Series, Springer-Verlag, I(48), 39-86, 1997.
Deleersnijder, E. and Campin, J.-M.: On the computation of the barotropic mode of a free-surface world ocean model, Ann. Geophys., 13, 675–688, https://doi.org/10.1007/s00585-995-0675-x, 1995.
den Elzen, M., Beusen, A., and Rothmans, J.: Modelling Global Biogeochemical Cycles: an integrated assessment approach, Bilthoven, RIVM report no. 461502007, 104 pp., 1995.
de Vries, P. and Weber, S. L.: The Atlantic freshwater budget as a diagnostic for the existence of a stable shut-down of the meridional overturning circulation, Geophys. Res. Lett., 32, L09606, https://doi.org/10.1029/2004GL021450, 2005.
Dickson, A. G. and Riley, J. P.: The estimation of acid dissociation constants in seawater media from potentiometric titrations with strong base. I. The ionic product of water – KW, Mar. Chem., 7, 89–99, 1979.
Dickson, A. G.: An exact definition of total alkalinity and a procedure for the estimation of alkalinity and total inorganic carbon from titration data, Deep-Sea Res., 28A, 609–623, 1981.
Dickson, A. G. and Millero, F.: A comparison of the equilibrium constants for the dissociation of carbonic acid in seawater media, Deep-Sea Res., 34, 1733–1743, 1987.
Dickson, A. G.: Thermodynamics of the dissociation of borid acid in synthetic seawater from 273.15 to 318.15 K, Deep-Sea Res., 37, 755–766, 1990.
Driesschaert, E.: Climate change over the next millennia using LOVECLIM, a new Earth system model including the polar ice sheets, Ph.D. thesis, Université catholique de Louvain, available at: http://hdl.handle.net/2078.1/5375 (last access: 2009), 2005.
Driesschaert, E., Fichefet, T., Goosse, H., Huybrechts, P., Janssens, I., Mouchet, A., Munhoven, G., Brovkin, V., and Weber, S. L.: Modeling the influence of Greenland ice sheet melting on the Atlantic meridional overturning circulation during the next millennia, Geophys. Res. Lett., 34, L10707, https://doi.org/10.1029/2007GL029516, 2007.
Duplessy, J. C., Roche, D. M., and Kageyama, M.: The deep ocean during the last interglacial period, Science, 316(5821), 89–91, 2007.
Eltahan, M., Eltahan, H., and Venkatesh, S.: Forecast of Iceberg Ensemble Drift, Offshore, 43, 94–94, 1983.
Esper, J., Cook, E. R., and Schweingruber, F. H.: Low-frequency signals in long tree-ring chronologies for reconvariability, Science, 295(5563), 2250–2253, 2002.
Etheridge, D. M., Steele, L. P., Langenfelds, R. L., Francey, R. J., Barnola, J.-M., and Morgan, V. I.: Historical CO2 records from the Law Dome DE08, DE08-2, and DSS ice cores, in: Trends: A Compendium of Data on Global Change, Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tenn., USA, 1998.
Fasham, M. J.: Modelling the marine biota, in: The Global Carbon Cycle, edited by: Heimann, M., Springer Verlag, Berlin Heidelberg, volume I15 of NATO ASI Series, 457–504, 1993.
Fichefet, T. and Morales Maqueda, M. A.: Sensitivity of a global sea ice model to the treatment of ice thermodynamics and dynamics, J. Geophys. Res. 102(C6), 12609–12646, 1997.
Fichefet, T. and Morales Maqueda, M. A.: Modelling the influence of snow accumulation and snow-ice formation on the seasonal cycle of the Antarctic sea-ice cover, Clim. Dynam., 15(4), 251–268, 1999.
Flückiger, J., Knutti, R., White, J. W. C., and Renssen, H.: Modeled seasonality of glacial abrupt climate change, Clim. Dynam., 31, 633–645, https://doi.org/10.1007/s00382-008-0373-y, 2008
Foukal, P., Frölich, C., Spruit, H., and Wigley, T. M. L.: Variations in solar luminosity and their effect on the Earth's climate, Nature, 443, 161–166, 2006.
Frank, D. C., Esper, J., Raible, C. C., Buntgen, U., Trouet, V., Stocker, B., and Joos, F.: Ensemble reconstruction constraints on the global carbon cycle sensitivity to climate, Nature, 463, 527–U143, 2010.
Gadd, A. J.: A split-explicit integration scheme for numerical weather prediction, Q. J. Roy. Meteor. Soc., 104, 569–582, 1978.
Gent, P. R. and McWilliams, J. C.: Isopycnal mixing in ocean general circulation models, J. Phys. Oceanogr., 20, 150–155, 1990.
Gladstone, R. M., Bigg, G. R., and Nicholls, K. W.: Iceberg trajectory modeling and meltwater injection in the Southern Ocean, J. Geophys. Res.-Oceans, 106, 19903–19915, 2001.
Glen, J. W.: The creep of polycrystalline ice, P. R. Soc. Lond.-B, 228, 519–538, 1955.
Goelzer, H., Huybrechts, P., Loutre, M. F., Goosse, H., Fichefet, T., and Mouchet, A.: Impact of Greenland and Antarctic ice sheet interactions on climate sensitivity, Clim. Dynam., in press, https://doi.org/10.1007/s00382-010-0885-0, 2010.
Goosse, H., Campin, J.-M., Fichefet, T., and Deleersnijder, E.: Sensitivity of a global ice-ocean model to the Bering Strait throughflow, Clim. Dynam., 13, 349–358, 1997.
Goosse, H. and Fichefet, T.: Importance of ice-ocean interactions for the global ocean circulation: a model study, J. Geophys. Res., 104, 23337–23355, 1999.
Goosse, H., Selten, F. M., Haarsma, R.J., and Opsteegh, J. D.: Decadal variability in high northern latitudes as simulated by an intermediate complexity climate model, Ann. Glaciol., 33, 525–532, 2001.
Goosse, H., Selten, F. M., Haarsma, R. J., and Opsteegh, J. D.: A mechanism of decadal variability of the sea-ice volume in the Northern Hemisphere, Clim. Dynam., 19, 61–83, https://doi.org/10.1007/s00382-001-0209-5, 2002.
Goosse, H., Renssen, H., Timmermann, A., and Bradley, R. S.: Internal and forced climate variability during the last millennium: a model-data comparison using ensemble simulations. Quaternary Sci. Rev., 24, 1345–1360, 2005.
Goosse, H., Driesschaert, E., Fichefet, T., and Loutre, M.-F.: Information on the early Holocene climate constrains the summer sea ice projections for the 21st century, Clim. Past, 3, 683–692, https://doi.org/10.5194/cp-3-683-2007, 2007.
Goosse, H., Crespin, E., de Montety, A., Mann, M. E., Renssen, H., and Timmermann, A.: Reconstructing surface temperature changes over the past 600 years using climate model simulations with data assimilation, J. Geophys. Res.-Atmos., 115, D09108, https://doi.org/10.1029/2009JD012737, 2010.
Gregory, J. M., Dixon, K. W., Stouffer, R. J., Weaver, A. J., Driesschaert, E., Eby, M., Fichefet, T., Hasumi, H., Hu, A., Jungclaus, J. H., Kamenkovich, I. V., Levermann, A., Montoya, M., Murakami, S., Nawrath, S., Oka, A., Sokolov, A. P., and Thorpe, R. B.: A model intercomparison of changes in the Atlantic thermohaline circulation in response to increasing atmospheric CO2 concentration, Geophys. Res. Lett., 32, L12703, https://doi.org/10.1029/2005GL023209, 2005.
Greenfell, T. C. and Perovich, D. K.: Spectral albedos of sea ice and incident solar irradiance in the southern Beaufort Sea, J. Geophys., Res., 89, 3573–3580, 1984.
Hegerl, G. C., Crowley, T. J., Hyde, W. T., and Frame, D. J.: Climate sensitivity constrained by temperature reconstructions over the past seven centuries, Nature, 440, 1029–1032, 2006.
Haarsma, R. J., Selten, F. M., Opsteegh, J. D., Lenderink, G., and Liu, Q.: ECBilt, a coupled atmosphere ocean sea-ice model for climate predictability studies, KNMI, De Bilt, The Netherlands, 31 pp., 1996.
Heinze, C., Hupe, A., Maier-Reimer, E., Dittert, N., and Ragueneau, O.: Sensitivity of the marine biospheric Si cycle for biogeochemical parameter variations, Global Biogeochem. Cy., 17(3), 1086, https://doi.org/10.1029/2002GB001943, 2003.
Held, I. M. and Suarez, M. J.: A two level primitive equation atmosphere model designed for climate sensitivities experiments, J. Atmos. Sci., 35, 206–229, 1978.
Hibler, W. D.: A dynamic thermodynamic sea ice model, J. Phys. Oceanogr., 9, 815–846, 1979.
Holdridge, L. R.: Determination of world plant formation from simple climate data, Science, 105, 367–368, 1947.
Holton, J. R.: An introduction to dynamical meteorology, 4th edn., Elsevier, International Geophisics Series, 88, 535 pp., 2004.
Hutter, K.: Theoretical Glaciology, D. Reidel, Dordrecht, 510 pp., 1983.
Huybrechts, P.: The Antarctic ice sheet and environmental change: a three-dimensional modelling study, Berichte zur Polarforschung, 99, 241 pp., 1992.
Huybrechts, P.: Sea-level changes at LGM from ice-dynamic reconstructions of the Greenland and Antarctic ice sheets during glacial cycles, Quaternary Sci. Rev., 21, 203–231, 2002.
Huybrechts, P. and de Wolde, J.: The dynamic response of the Greenland and Antarctic ice sheets to multiple-century climatic warming, J. Climate, 12(8), 2169–2188, 1999.
Huybrechts, P., and Miller, H.: Flow and balance of the polar ice sheets, in: Observed global climate, Landolt-Boernstein/ New Series (Numerical data and functional relationships in Science and Technology), V/6, edited by: Hantel, M., Springer Verlag, Berlin, Heidelberg, New York, 13/1–13, 2005.
Indermühle, A., Stocker, T. F., Joos, F., Fischer, H., Smith, H. J., Wahlen, M., Deck, B., Mastroianni, D., Tschumi, J., Blunier, T., Meyer, R., and Stauffer, B.: Holocene carbon-cycle dynamics based on CO2 trapped in ice at Taylor Dome, Antarctica, Nature, 398, 121–126, 1999
International GEWEX Project Office: GSWP-2: The Second Global Soil Wetness Project Science and Implementation Plan. IGPO Publication Series No. 37, 65 pp., 2002.
Janssens, I. and Huybrechts, P.: The treatment of meltwater retention in mass-balance parameterizations of the Greenland ice sheet, Ann. Glaciol., 31, 133–140, 2000.
Jiang, H., Eiriksson, J., Schulz, M., Knudsen, K.-L., and Seidenkrantz, M. S.: Evidence for solar forcing of sea-surface temperature on the North Icelandic shelf during the late Holocene, Geology, 33, 73–76, 2005.
Jones, P. D., Briffa, K. R., Barnett, T. P., and Tett, S. F. B.: High-resolution palaeoclimatic records for the last millennium: interpretation, integration and comparison with General Circulation Model control-run temperatures, The Holocene, 8(4), 455–471, 1998.
Jones, P. D., Osborn, T. J., and Briffa, K. R.: The evolution of climate over the last millennium, Science, 292(5517), 662–667, 2001.
Jongma, J. I., Driesschaert, E., Fichefet, T., Goosse, H., and Renssen, H.: The effect of dynamic-thermodynamic icebergs on the Southern Ocean climate in a three-dimensional model, Ocean Model., 26, 104–113, 2009.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, B. Am. Meteorol. Soc., 77, 437–471, 1996.
Karsten, R. and Marshall, J.: Constructing the residual circulation of the Antarctic Circumpolar Current from observations, J. Phys. Oceanogr., 32(12), 3315–3327, 2002.
Lean, J. L., Wang, Y.-M., and Sheeley, N. R.: The effect of increasing solar activity on the Sun's total and open magnetic flux during multiple cycles: implications for solar forcing of climate, Geophys. Res. Let., 29(24), 2224, https://doi.org/10.1029/2002GL015880, 2002.
Levitus, S., Antonov, J. I., Boyer, T. P., Locarnini, R. A., Garcia, H. E., and Mishonov, A. V.: Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems, Geophys. Res. Lett., 36, L07608, https://doi.org/10.1029/2008GL037155, 2009.
Leemans, R. and Cramer, W. P.: The IIASA database for mean monthly values of temperature, precipitation and cloudiness on a global terrestrial grid, Int. Inst. for Appl. Syst. Anal., Laxenburg, Austria, Research rep. RR-91-18, 1991.
Lieth, H.: Modeling the primary productivity of the world, in Primary Productivity of the Biosphere, edited by: Lieth, H. and Whittaker, R. H., Springer-Verlag, New York, 237–263, 1975.
Lorenzo, M. N., Taboada, J. J., Iglesias, I., and Álvarez, I.: The role of stochastic forcing on the behaviour of the thermohaline circulation, Annals of the New York Academy of Sciences, in: Trends and Directions in Climate Research, 1146(1), 60–86, ISBN:1-57331-732-2, 2008.
Loset, S.: Thermal-Energy Conservation in Icebergs and Tracking by Temperature, J. Geophys. Res.-Oceans, 98, 10001–10012, 1993.
Loutre, M. F., Mouchet, A., Fichefet, T., Goosse, H., Goelzer, H., and Huybrechts, P.: Evaluating climate model performance with various parameter sets using observations over the last centuries, Clim. Past Discuss., 6, 711–765, https://doi.org/10.5194/cpd-6-711-2010, 2010.
Lynch-Stieglitz, J., Adkins, J. F. , Curry, W. B., Dokken, T., Hall, I. R., Herguera, J. C., Hirschi, J. J. M., Ivanova, E. V., Kissel, C., Marchal, O., Marchitto, T. M., McCave, I. N., McManus, J. F., Mulitza, S., Ninnemann, U., Peeters, F., Yu, E. F., and Zahn, R.: Atlantic Meridional Overturning Circulation During the Last Glacial Maximum, Science, 316, 66–69, 2007.
Maier-Reimer, E.: Geochemical cycles in an ocean general circulation model. Preindustrial tracer distributions, Global Biogeochem. Cy., 7, 645–677, 1993.
Mann, M. E., Bradley, R. S., and Hughes, M. K.: Northern hemisphere temperatures during the past millennium: Inferences, uncertainties, and limitations, Geophys. Res. Lett., 26(6), 759–762, 1999.
Mann, M. E. and Jones, P. D.: Global surface temperatures over the past two millennia, Geophys. Res. Lett., 30(15), 1820, https://doi.org/10.1029/ 2003GL017814, 2003.
Marland, G., Boden, T., and Andres, R.: Global, Regional, and National Annual CO2 Emissions from Fossil-Fuel Burning, Cement Production, and Gas Flaring: 1751–-2000, in: Trends: A compendium of data on global change, published by Carbon Dioxide Information Analysis Center, Oak Ridge, Tennessee, 2003.
Marshall, J. and Molteni, F.: Towards a dynamic understanding of planetary-scale flow regimes, J. Atmos. Sc., 50, 1792–1818, 1993.
Martin, J. H., Knauer, G. A., Karl, D. M., and Broenkow, W. W.: VERTEX: carbon cycling in the northeast Pacific, Deep-Sea Res., 34, 267–285, 1987.
Mathieu, P. P. and Deleersnijder, E.: Accuracy and stability of the discretised isopycnal-mixing equation, Appl. Math. Lett., 12(4) 81–88, 1999.
Mellor, G. L. and Yamada, T.: Development of a turbulence closure model for geophysical fluid problems, Rev. Geophys. Space Phys., 20(4), 851–875, 1982.
Menviel, L., Timmermann, A., Mouchet, A., and Timm, O.: Meridional reorganizations of marine and terrestrial productivity during Heinrich events, Paleoceanography, 23, PA1203, https://doi.org/10.1029/2007PA001445, 2008.
Menviel, L.: Climate-carbon cycle interactions on millennial to glacial timescales as simulated by a model of intermediate complexity, Ph.D. thesis, University of Hawaii, 179 pp., available at: http://www.soest.hawaii.edu/oceanography/students/lmenviel/Menvielthesis2008.pdf (last access: 2009), 2008
Mesinger, F. and Arakawa, A.: Numerical methods used in atmospheric models, WMO-ISCU Joint Organizing Committee, GARP Publications Series 17, 37 pp., 1976.
Millero, F. J.: Thermodynamics of the carbon dioxide system in the oceans, Geochim. Cosmochim. Ac., 59, 661–677, 1995.
Moberg, A., Sonechkin, D. M., Holmgren, K., Datsenko, N. M., and Karlén, W.: Highly variable Northern Hemisphere temperatures reconstructed from low- and high-resolution proxy data, Nature, 433(7026), 613–617, 2005.
Mouchet, A. and François, L. M.: Sensitivity of a global oceanic carbon cycle model to the circulation and the fate of organic matter: preliminary results, Phys. Chem. Earth, 21, 511–516, 1996.
Mouchet, A.: A 3D model of ocean biogeochemical cycles and climate sensitivity studies, Ph.D. thesis, Université de Liège, Liège, Belgium, in preparation, 2010.
Mucci, A.: The solubility of calcite and aragonite in seawater at various salinities, temperatures, and one atmosphere total pressure, Am. J. Sci., 283, 780–799, 1983.
Muscheler, R., Joos, F., Beer, J., Muller, S. A., Vonmoos, M., and Snowball, I.: Solar activity during the last 1000 yr inferred from radionuclide records, Quaternary Sci. Rev., 26(1–2), 82–97, 2007.
Najjar, R. J., Jin, X., Louanchi, F., Aumont, O., Caldeira, K., Doney, S. C., Dutay, J.-C., Follows, M., Gruber, N., Joos, F., Lindsay, K., Maier-Reimer, E., Matear, R. J., Matsumoto, K., Mouchet, A., Orr, J. C., Plattner, G.-K., Sarmiento, J. L., Schlitzer, R., Weirig, M. F., Yamanaka, Y., and Yool, A.: Impact of circulation on export production, dissolved organic matter, and dissolved oxygen in the ocean: Results from Phase II of the Ocean Carbon-cycle Model Intercomparison Project (OCMIP-2), Global Biogeochem. Cy., 21, GB3007, https://doi.org/10.1029/2006GB002857, 2007.
Neftel, A., Friedli, H., Moor, E., Lotscher, H., Oeschger, H., Siegenthaler, U., and Stauffer, B.: Historic CO2 record from the Siple Station ice core, in: Trends '93: A Compendium of Data on Global Change, edited by: Boden, T. A., Kaiser, D. P., Sepanski, R. J., and Stoss, F. W., Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, OakRidge, Tenn., USA, ORNL/CDIAC-65, 11–14, 1994.
Nelson, D. M., Tréguer, P., Brzezinski, M. A., Leynaert, A., and Quéguiner, B.: Production and dissolution of biogenic silica in the ocean: revised global estimates, comparison with regional data and relationship to biogenic sedimentation, Global Biogeochem. Cy., 9, 359–372, 1995.
Olson, J., Watts, J. A., and Allison, L. J.: Major world ecosystem complexes ranked by carbon in live vegetation: A database, CDIAC Numerical Data Collection, Oak Ridge Natl. Lab., Oak Ridge, Tenn., NDP-017, 164 pp., 1985.
Opsteegh, J. D., Haarsma, R. J., Selten, F. M., and Kattenberg, A.: ECBilt: A dynamic alternative to mixed boundary conditions in ocean models, Tellus A, 50, 348–367, 1998.
Paterson, W. S. B.: The physics of glaciers, 3rd edn., Pergamon Press, Oxford, 480 pp., 1994.
Peltier, W. R.: Global isostasy and the surface of the ice-age Earth: the ice-5g (Vm2) Model and Grace, Annu. Rev. Earth Pl. Sc., 32, 111–149, 2004.
Petoukhov, V., Claussen, M., Berger, A., Crucifix, M., Eby, M., Eliseev, A., Fichefet, T., Ganopolski, A., Goosse, H., Kamenkovich, I., Mokhov, I., Montoya, M., Mysak, L. A., Sokolov, A., Stone, P., Wang, Z., and Weaver, A. J.: EMIC Inter-comparison Project (EMIP-CO2): Comparative analysis of EMIC simulations of climate, and of equilibrium and transient responses to atmospheric CO2 doubling, Clim. Dynam., 25 363–385, 2005.
Pollack, H. N. and Smerdon, J. E.: Borehole climate reconstructions: Spatial structure and hemispheric averages, J. Geophys. Res., 109(D11), D11106, https://doi.org/10.1029/2003JD004163, 2004.
Post, W. M., King, A. W., and Wullschleger, S. D.: Historical variations in terrestrial biospheric carbon cycle, Global Biogeochem. Cy., 11, 99–109, 1997.
Prather, M. C.: Numerical advection by conservation of second-order moments, J. Geophys. Res., 91(D6), 6671–6681, 1986.
Prentice, I. C., Cramer, W., Harrison, S. P., Leemans, R., Monserud, R. A., and Solomon, A. M.: A global biome model based on plant physiology and dominance, soil properties and climate, J. Biogeogr., 19, 117–134, 1992.
Qiuzhen Yin, Berger, A., Driesschaert, E., Goosse, H., Loutre, M. F., and Crucifix, M.: The Eurasian ice sheet reinforces the East Asian summer monsoon during the interglacial 500 000 years ago, Clim. Past, 4, 79–90, https://doi.org/10.5194/cp-4-79-2008, 2008.
Rahmstorf, S., Crucifix, M., Ganopolski, A., Goosse, H., Kamenkovich, I., Knutti, R., Lohmann, G., Marsh, B., Mysak, L., Wang, Z., and Weaver, A.: Thermohaline circulation hysteresis: a model intercomparison, Geophys. Res. Lett., 32, L23605, https://doi.org/10.1029/2005GL23655, 2005.
Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J., Kattsov, V., Pitman, A., Shukla, J., Noda, A., Srinivasan, J., Stouffer, R. J., Sumi, A., and Taylor, K. E.: Climate models and their evaluation. In: Climate Change 2007: The Physical Science Basis, in: Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z., Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2007.
Ramankutty, N. and Foley, J. A.: Estimating historical changes in global land cover: croplands from 1700 to 1992, Global Biogeochem. Cy., 13(4), 997–1027, 1999.
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and nigh marine air temperature since the late nineteenth century, J. Geophys. Res., 108(D14), 4407, https://doi.org/10.1029/2002JD002670, 2003.
Redfield, A., Ketchum, B., and Richards, F.: The influence of organisms on the composition of seawater, in: The Sea, edited by: Hill, M. N., Wiley Interscience, New York, 2, 26–77, 1963.
Redi, M. H.: Oceanic isopycnal mixing coordinate rotation, J. Phys. Oceanogr., 12, 1154–1158, 1982.
Renssen, H., Goosse, H., Fichefet, T., and Campin, J.-M.: The 8.2 kyr BP event simulated by a global atmosphere-sea-ice-ocean model, Geophys. Res. Lett., 28, 1567–1570, 2001.
Renssen, H., Brovkin, V., Fichefet, T., and Goosse, H.: Holocene climatic instability during the termination of the African Humid Period, Geophys. Res. Lett., 30(4), 1184, https://doi.org/1029/2002GL016636, 2003.
Renssen, H., Goosse, H., Fichefet, T., Brovkin, V., Driesschaert, E., and Wolk, F.: Simulating the Holocene climate evolution at northern high latitudes using a coupled atmosphere-sea ice-ocean-vegetation mode, Clim. Dynam., 24, 23–43, 2005.
Roche, D. M., Dokken, T. M., Goosse, H., Renssen, H., and Weber, S. L.: Climate of the Last Glacial Maximum: sensitivity studies and model-data comparison with the LOVECLIM coupled model, Clim. Past, 3, 205–224, https://doi.org/10.5194/cp-3-205-2007, 2007.
Rossow, W. B., Walker, A. W., Beuschel, D. E., and Roiter, M. D.: International satellite cloud climatology project (ISCCP) documentation of new cloud datasets, WMO/TD-No 737, World Meteorological Organisation, 1996.
Rutherford, S., Mann, M. E., Osborn, T. J., Bradley, R. S., Briffa, K. R., Hughes, M. K., and Jones, P. D.: Proxy-based Northern Hemisphere surface temperature reconstructions: Sensitivity to method, predictor network, target season, and target domain, J. Climate, 18(13), 2308–2329, 2005.
Schaeffer, M., Selten, F., and van Dorland, R.: Linking Image and ECBilt. National Institute for public health and the environment (RIVM), Bilthoven, The Netherlands, Report no 4815008008, 1998.
Schaeffer, M., Selten, F., Goosse, H., and Opsteegh, T.: On the influence of location of high-latitude ocean deep convection and Arctic sea-ice on climate change projections, J. Climate, 17(22), 4316–4329, 2004.
Schimel, D. S., Braswell, B. H., Holland, E. A., McKeown, R., Ojima, D. S., Painter, T. H., Parton, W. J., and Towsend, A. R.: Climatic, edaphic, and biotic control over storage and turnover of carbon in soil, Global Biogeochem. Cy., 8, 279–293, 1994.
Selten, F. M., Haarsma, R. J., and Opsteegh, J. D.: On the mechanism of North Atlantic decadal variability, J. Climate, 12, 1956–1973, 1999.
Shine, K. P. and Henderson-Sellers, A.: The sensitivity of a thermodynamic sea ice model to changes in surface albedo parameterization, J. Geophys. Res. Lett., 90, 2243–2250, 1985.
Siegenthaler, U., Monnin, E., Kawamura, K., Spahni, R., Schwander, J., Stauffer, B., Stocker, T. F., Barnola, J.-M., and Fischer, H.: Supporting evidence from the EPICA Dronning Maud Land ice core for atmospheric CO2 changes during the past millennium, Tellus B, 57, 51–57(7), 2005.
Smith, S. D.: Hindcasting Iceberg Drift Using Current Profiles and Winds, Cold Reg. Sci. Technol., 22, 33–45, 1993.
Svirezhev, Y.: Simplest dynamic models of the global vegetation pattern, Ecol. Model., 124, 131–144, 1999.
Swingedouw, D., Fichefet, T., Huybrechts, P., Goosse, H., Driesschaert, E., and Loutre, M.-F.: Antarctic ice-sheet melting provides negative feedbacks on future climate warming, Geophys. Res. Lett., 35, L17705, https://doi.org/10.1029/2008GL034410, 2008.
Tartinville, B., Campin, J. M., Fichefet, T., and Goosse, H.: Realistic representation of the surface freshwater flux in an ice-ocean general circulation model, Ocean Model., 3(1–2), 95–108, 2001.
Terray, L., Valcke, S., and Piacentini, A.: OASIS 2.2, Ocean Atmosphere Sea Ice Soil user's guide and reference manuel, CERFACS Tech. Rep. TR/CGMC/98-05, 69 pp., 1998.
Timm, O., Timmermann, A., Abe-Ouchi, A., Saito, F., and Segawa, T.: On the definition of seasons in paleoclimate simulations with orbital forcing, Paleoceanography, 23, PA2221, https://doi.org/10.1029/2007PA001461, 2008.
Timmermann, A. and Goosse, H.: Is the wind stress forcing essential for the meridional overturning circulation?, Geophys. Res. Lett., 31, L04303, https://doi.org/10.1029/2003GL018777, 2004.
Timmermann, A., Justino Barbosa, F., Jin, F. F., and Goosse, H.: Surface temperature control in the North Pacific during the last glacial maximum, Clim. Dynam., 23, 353–370, https://doi.org/10.1007/s00382-004-0434-9, 2004.
Timmermann, A., An, S.-I., Krebs, U., and Goosse, H.: ENSO suppression due to weakening of the North Atlantic thermohaline circulation, J. Climate, 18(16), 3122–3139, 2005.
van der Schrier, G. and Barkmeijer, J.: Bjerknes' hypothesis on the coldness during AD 1790–1820 revisited, Clim. Dynam., 24, 335–371, 2005.
van der Schrier, G., Drijfhout, S. S., Hazeleger, W., and Noulin, L.: Increasing the Atlantic subtropical jet cools the circum-North Atlantic region, Meteorol. Z., 16(6) 675–684, 2007.
Wanninkhof, R.: Relationship between wind speed and gas exchange over the ocean, J. Geophys. Res., 97, 7373–7382, 1992.
Wanninkhof, R. and Knox, M.: Chemical enhancement of CO2 exchange in natural waters, Limnol. Oceanogr., 41, 689–697, 1996.
Weber, S. L. and Oerlemans, J.: Holocene glacier variability: three case studies using an intermediate-complexity climate model, The Holocene, 13, 353–363, 2003.
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.
Weeks, W. F. and Campbell, W. J.: Towing Icebergs to Irrigate Arid Lands – Manna or Madness, Science and Public Affairs-Bulletin of the Atomic Scientists, 29, 35–39, 1973.
Weertman, J.: The theory of glacier sliding, J. Glaciol., 5(39), 287–303, 1964.
Weiss, R. F.: Carbon dioxide in water and seawater: the solubility of a non-ideal gas, Mar. Chem., 2, 203–215, 1974.
Wiersma, A. P. and Jongma, J. I.: A role for icebergs in the 8.2 ka climate event, Clim. Dynam., 35(2–3), 535–549, https://doi.org/10.1007/s00382-009-0645-1, 2009.
Woodward, F. I.: Climate and Plant Distribution, Cambridge Univ. Press, New York, 174 pp., 1987.
Xie, P. and Arkin, P. A.: Analyses of global monthly precipitation using gauge observations, satellite estimates and numerical model predictions, J. Climate, 9, 840–858, 1996.
Zhang, J. L. and Hibler, W. D.: On an efficient numerical method for modeling sea ice dynamics, J. Geophys. Res., 102(C4), 8691–8702, 1997.
Zhao, Y., Braconnot, P., Marti, O., Harrison, S. P., Hewitt, C. D., Kitoh, A., Liu, Z., Mikolajewicz, U., Otto-Bliesner, B., and Weber, S. L.: A multi-model analysis of ocean feedbacks on the African and Indian monsoon during the mid-Holocene, Clim. Dynam., 25, 777–800, 2005.
