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Author

Robert Jacob

Other affiliations: University of Chicago
Bio: Robert Jacob is an academic researcher from Argonne National Laboratory. The author has contributed to research in topics: Climate model & Community Climate System Model. The author has an hindex of 34, co-authored 77 publications receiving 4235 citations. Previous affiliations of Robert Jacob include University of Chicago.


Papers
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Journal ArticleDOI
17 Jul 2009-Science
TL;DR: The first synchronously coupled atmosphere-ocean general circulation model simulation from the Last Glacial Maximum to the Bølling-Allerød (BA) warming reproduces several major features of the deglacial climate evolution, suggesting a good agreement in climate sensitivity between the model and observations.
Abstract: We conducted the first synchronously coupled atmosphere-ocean general circulation model simulation from the Last Glacial Maximum to the Bolling-Allerod (BA) warming. Our model reproduces several major features of the deglacial climate evolution, suggesting a good agreement in climate sensitivity between the model and observations. In particular, our model simulates the abrupt BA warming as a transient response of the Atlantic meridional overturning circulation (AMOC) to a sudden termination of freshwater discharge to the North Atlantic before the BA. In contrast to previous mechanisms that invoke AMOC multiple equilibrium and Southern Hemisphere climate forcing, we propose that the BA transition is caused by the superposition of climatic responses to the transient CO 2 forcing, the AMOC recovery from Heinrich Event 1, and an AMOC overshoot.

873 citations

Journal ArticleDOI
Jean-Christophe Golaz1, Peter M. Caldwell1, Luke Van Roekel2, Mark R. Petersen2, Qi Tang1, Jonathan Wolfe2, G. W. Abeshu3, Valentine G. Anantharaj4, Xylar Asay-Davis2, David C. Bader1, Sterling Baldwin1, Gautam Bisht5, Peter A. Bogenschutz1, Marcia L. Branstetter4, Michael A. Brunke6, Steven R. Brus2, Susannah M. Burrows7, Philip Cameron-Smith1, Aaron S. Donahue1, Michael Deakin8, Michael Deakin9, Richard C. Easter7, Katherine J. Evans4, Yan Feng10, Mark Flanner11, James G. Foucar8, Jeremy Fyke2, Brian M. Griffin12, Cecile Hannay13, Bryce E. Harrop7, Mattthew J. Hoffman2, Elizabeth Hunke2, Robert Jacob10, Douglas W. Jacobsen2, Nicole Jeffery2, Philip W. Jones2, Noel Keen5, Stephen A. Klein1, Vincent E. Larson12, L. Ruby Leung7, Hongyi Li3, Wuyin Lin14, William H. Lipscomb2, William H. Lipscomb13, Po-Lun Ma7, Salil Mahajan4, Mathew Maltrud2, Azamat Mametjanov10, Julie L. McClean15, Renata B. McCoy1, Richard Neale13, Stephen Price2, Yun Qian7, Philip J. Rasch7, J. E. Jack Reeves Eyre6, William J. Riley5, Todd D. Ringler2, Todd D. Ringler16, Andrew Roberts2, Erika Louise Roesler8, Andrew G. Salinger8, Zeshawn Shaheen1, Xiaoying Shi4, Balwinder Singh7, Jinyun Tang5, Mark A. Taylor8, Peter E. Thornton4, Adrian K. Turner2, Milena Veneziani2, Hui Wan7, Hailong Wang7, Shanlin Wang2, Dean N. Williams1, Phillip J. Wolfram2, Patrick H. Worley4, Shaocheng Xie1, Yang Yang7, Jin-Ho Yoon17, Mark D. Zelinka1, Charles S. Zender18, Xubin Zeng6, Chengzhu Zhang1, Kai Zhang7, Yuying Zhang1, X. Zheng1, Tian Zhou7, Qing Zhu5 
TL;DR: Energy Exascale Earth System Model (E3SM) project as mentioned in this paper is a project of the U.S. Department of Energy that aims to develop and validate the E3SM model.
Abstract: Energy Exascale Earth System Model (E3SM) project - U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; Climate Model Development and Validation activity - Office of Biological and Environmental Research in the US Department of Energy Office of Science; Regional and Global Modeling and Analysis Program of the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; National Research Foundation [NRF_2017R1A2b4007480]; Office of Science of the U.S. Department of Energy [DE-AC02-05CH11231]; DOE Office of Science User Facility [DE-AC05-00OR22725]; U.S. Department of Energy by Lawrence Livermore National Laboratory [DE-AC52-07NA27344]; DOE [DE-AC05-76RLO1830]; National Center for Atmospheric Research - National Science Foundation [1852977];[DE-SC0012778]

437 citations

Journal ArticleDOI
01 Aug 2005
TL;DR: The package, the Model Coupling Toolkit (MCT), is described, which is developed to meet the general requirements and the specific requirements of a parallel climate model, and results are presented that measure MCT’s scalability, performance portability, and a proxy for coupling overhead.
Abstract: Many problems in science and engineering are best simulated as a set of mutually interacting models, resulting in a coupled or multiphysics model. These models present challenges stemming from their interdisciplinary nature and from their computational and algorithmic complexities. The computational complexity of individual models, combined with the popularity of the distributed-memory parallel programming model used on commodity micro-processor-based clusters, results in a parallel coupling problem when building a coupled model. We define and elucidate this problem and how it results in a set of requirements for software capable of simplifying the construction of parallel coupled models. We describe the package, the Model Coupling Toolkit (MCT), which we have developed to meet these general requirements and the specific requirements of a parallel climate model. We present the MCT programming model with illustrative code examples. We present representative results that measure MCT's scalability, performance portability, and a proxy for coupling overhead.

334 citations

Journal ArticleDOI
01 Feb 2012
TL;DR: The latest versions of the model, CCSM4 and CESM1, contain totally new coupling capabilities in the CPL7 coupler that permit additional flexibility and extensibility to address the challenges involved in earth system modeling.
Abstract: The Community Climate System Model (CCSM) has been developed over the last decade, and it is used to understand past, present, and future climates. The latest versions of the model, CCSM4 and CESM1, contain totally new coupling capabilities in the CPL7 coupler that permit additional flexibility and extensibility to address the challenges involved in earth system modeling. The CPL7 coupling architecture takes a completely new approach with respect to the high-level design of the system. CCSM4 now contains a top-level driver that calls model component initialize, run, and finalize methods through specified interfaces. The top-level driver allows the model components to be placed on relatively arbitrary hardware processor layouts and run sequentially, concurrently, or mixed. Improvements have been made to the memory and performance scaling of the coupler to support much higher resolution configurations. CCSM4 scales better to higher processor counts, and has the ability to handle global resolutions up to one-tenth of a degree.

191 citations


Cited by
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Journal ArticleDOI
13 Jun 2008-Science
TL;DR: Interdisciplinary science that integrates knowledge of the many interacting climate services of forests with the impacts of global change is necessary to identify and understand as yet unexplored feedbacks in the Earth system and the potential of forests to mitigate climate change.
Abstract: The world's forests influence climate through physical, chemical, and biological processes that affect planetary energetics, the hydrologic cycle, and atmospheric composition. These complex and nonlinear forest-atmosphere interactions can dampen or amplify anthropogenic climate change. Tropical, temperate, and boreal reforestation and afforestation attenuate global warming through carbon sequestration. Biogeophysical feedbacks can enhance or diminish this negative climate forcing. Tropical forests mitigate warming through evaporative cooling, but the low albedo of boreal forests is a positive climate forcing. The evaporative effect of temperate forests is unclear. The net climate forcing from these and other processes is not known. Forests are under tremendous pressure from global change. Interdisciplinary science that integrates knowledge of the many interacting climate services of forests with the impacts of global change is necessary to identify and understand as yet unexplored feedbacks in the Earth system and the potential of forests to mitigate climate change.

4,541 citations

Journal Article
TL;DR: In this article, the authors present a document, redatto, voted and pubblicato by the Ipcc -Comitato intergovernativo sui cambiamenti climatici - illustra la sintesi delle ricerche svolte su questo tema rilevante.
Abstract: Cause, conseguenze e strategie di mitigazione Proponiamo il primo di una serie di articoli in cui affronteremo l’attuale problema dei mutamenti climatici. Presentiamo il documento redatto, votato e pubblicato dall’Ipcc - Comitato intergovernativo sui cambiamenti climatici - che illustra la sintesi delle ricerche svolte su questo tema rilevante.

4,187 citations

Journal ArticleDOI
TL;DR: The Community Climate System Model version 3 (CCSM3) as discussed by the authors is a coupled climate model with components representing the atmosphere, ocean, sea ice, and land surface connected by a flux coupler.
Abstract: The Community Climate System Model version 3 (CCSM3) has recently been developed and released to the climate community. CCSM3 is a coupled climate model with components representing the atmosphere, ocean, sea ice, and land surface connected by a flux coupler. CCSM3 is designed to produce realistic simulations over a wide range of spatial resolutions, enabling inexpensive simulations lasting several millennia or detailed studies of continental-scale dynamics, variability, and climate change. This paper will show results from the configuration used for climate-change simulations with a T85 grid for the atmosphere and land and a grid with approximately 1° resolution for the ocean and sea ice. The new system incorporates several significant improvements in the physical parameterizations. The enhancements in the model physics are designed to reduce or eliminate several systematic biases in the mean climate produced by previous editions of CCSM. These include new treatments of cloud processes, aerosol ...

2,500 citations

Journal ArticleDOI
TL;DR: The Community Earth System Model (CESM) as discussed by the authors is a community tool used to investigate a diverse set of Earth system interactions across multiple time and space scales, including biogeochemical cycles, a variety of atmospheric chemistry options, the Greenland Ice Sheet, and an atmosphere that extends to the lower thermosphere.
Abstract: The Community Earth System Model (CESM) is a flexible and extensible community tool used to investigate a diverse set of Earth system interactions across multiple time and space scales. This global coupled model significantly extends its predecessor, the Community Climate System Model, by incorporating new Earth system simulation capabilities. These comprise the ability to simulate biogeochemical cycles, including those of carbon and nitrogen, a variety of atmospheric chemistry options, the Greenland Ice Sheet, and an atmosphere that extends to the lower thermosphere. These and other new model capabilities are enabling investigations into a wide range of pressing scientific questions, providing new foresight into possible future climates and increasing our collective knowledge about the behavior and interactions of the Earth system. Simulations with numerous configurations of the CESM have been provided to phase 5 of the Coupled Model Intercomparison Project (CMIP5) and are being analyzed by the broad com...

2,075 citations

Journal ArticleDOI
13 Feb 2019-Nature
TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.
Abstract: Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.

2,014 citations