scholarly journals A description of the FAMOUS (version XDBUA) climate model and control run

2008 ◽  
Vol 1 (1) ◽  
pp. 53-68 ◽  
Author(s):  
R. S. Smith ◽  
J. M. Gregory ◽  
A. Osprey
Keyword(s):  
General Circulation ◽  
High Performance ◽  
Climate Model ◽  
Computational Cost ◽  
Circulation Model ◽  
Cold Bias ◽  
Northern Latitudes ◽  
Climate Simulations ◽  
Atmosphere General Circulation Model ◽  
And Control

Abstract. FAMOUS is an ocean-atmosphere general circulation model of low resolution, capable of simulating approximately 120 years of model climate per wallclock day using current high performance computing facilities. It uses most of the same code as HadCM3, a widely used climate model of higher resolution and computational cost, and has been tuned to reproduce the same climate reasonably well. FAMOUS is useful for climate simulations where the computational cost makes the application of HadCM3 unfeasible, either because of the length of simulation or the size of the ensemble desired. We document a number of scientific and technical improvements to the original version of FAMOUS. These improvements include changes to the parameterisations of ozone and sea-ice which alleviate a significant cold bias from high northern latitudes and the upper troposphere, and the elimination of volume-averaged drifts in ocean tracers. A simple model of the marine carbon cycle has also been included. A particular goal of FAMOUS is to conduct millennial-scale paleoclimate simulations of Quaternary ice ages; to this end, a number of useful changes to the model infrastructure have been made.

scholarly journals A description of the FAMOUS (version XDBUA) climate model and control run

2008 ◽  
Vol 1 (1) ◽  
pp. 147-185 ◽  
Author(s):  
R. S. Smith ◽  
J. M. Gregory ◽  
A. Osprey
Keyword(s):  
General Circulation ◽  
High Performance ◽  
Climate Model ◽  
Computational Cost ◽  
Circulation Model ◽  
Cold Bias ◽  
Northern Latitudes ◽  
Climate Simulations ◽  
Atmosphere General Circulation Model ◽  
And Control

Abstract. FAMOUS is an ocean-atmosphere general circulation model of low resolution, capable of simulating well in excess of 100 years of model climate per wallclock day using current high performance computing facilities. It uses most of the same code as HadCM3, a widely used climate model of higher resolution and computational cost, and has been tuned to reproduce the same climate reasonably well. FAMOUS is useful for climate simulations where the computational cost makes the application of HadCM3 unfeasible, either because of the length of simulation or the size of the ensemble desired. We document a number of scientific and technical improvements to the original version of FAMOUS. These improvements include changes to the parameterisations of ozone and sea-ice which remove a significant cold bias from high northern latitudes and the upper troposphere, and the elimination of volume-averaged drifts in ocean tracers. There are also changes to the model infrastructure which facilitate paleoclimate simulations.

scholarly journals The FAMOUS climate model (versions XFXWB and XFHCC): description update to version XDBUA

2011 ◽  
Vol 4 (4) ◽  
pp. 3047-3065
Author(s):  
R. S. Smith
Keyword(s):  
Water Budget ◽  
General Circulation ◽  
Climate Model ◽  
Circulation Model ◽  
Model Description ◽  
Model Intercomparison ◽  
Ocean Atmosphere ◽  
Atmosphere General Circulation Model ◽  
Intercomparison Project ◽  
The Uk

Abstract. FAMOUS is an ocean-atmosphere general circulation model of low resolution, based on version 4.5 of the UK MetOffice Unified Model. Here we update the model description to account for changes in the model as it is used in the CMIP5 EMIC model intercomparison project (EMICmip) and a number of other studies. Most of these changes correct errors found in the code. The EMICmip version of the model (XFXWB) has a better-conserved water budget and additional cooling in some high latitude areas, but otherwise has a similar climatology to previous versions of FAMOUS. A variant of XFXWB is also described, with changes to the dynamics at the top of the model which improve the model climatology (XFHCC).

scholarly journals Development cycle 2 of the Modular Earth Submodel System (MESSy2)

2010 ◽  
Vol 3 (2) ◽  
pp. 717-752 ◽  
Author(s):  
P. Jöckel ◽  
A. Kerkweg ◽  
A. Pozzer ◽  
R. Sander ◽  
H. Tost ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
General Circulation ◽  
Atmospheric General Circulation Model ◽  
Climate Model ◽  
Circulation Model ◽  
Second Development ◽  
Development Cycle ◽  
Climate Simulations ◽  
Diagnostic Capabilities ◽  
Earth System Models

Abstract. The Modular Earth Submodel System (MESSy) is an open, multi-institutional project providing a strategy for developing comprehensive Earth System Models (ESMs) with highly flexible complexity. The first version of the MESSy infrastructure and process submodels, mainly focusing on atmospheric chemistry, has been successfully coupled to an atmospheric General Circulation Model (GCM) expanding it into an Atmospheric Chemistry GCM (AC-GCM) for nudged simulations and into a Chemistry Climate Model (CCM) for climate simulations. Here, we present the second development cycle of MESSy, which comprises (1) an improved and extended infrastructure for the basemodel independent coupling of process-submodels, (2) new, highly valuable diagnostic capabilities for the evaluation with observational data and (3) an improved atmospheric chemistry setup. With the infrastructural changes, we place the headstone for further model extensions from a CCM towards a comprehensive ESM. The new diagnostic submodels will be used for regular re-evaluations of the continuously further developing model system. The updates of the chemistry setup are briefly evaluated.

scholarly journals Sensitivity of Pliocene climate simulations in MRI-CGCM2.3 to respective boundary conditions

2016 ◽  
Vol 12 (8) ◽  
pp. 1619-1634 ◽  
Author(s):  
Youichi Kamae ◽  
Kohei Yoshida ◽  
Hiroaki Ueda
Keyword(s):  
Boundary Conditions ◽  
General Circulation ◽  
Model Comparison ◽  
Climate Model ◽  
Phase 1 ◽  
Regional Climate ◽  
Circulation Model ◽  
Poleward Heat Transport ◽  
Climate Simulations ◽  
Intercomparison Project

Abstract. Accumulations of global proxy data are essential steps for improving reliability of climate model simulations for the Pliocene warming climate. In the Pliocene Model Intercomparison Project phase 2 (PlioMIP2), a part project of the Paleoclimate Modelling Intercomparison Project phase 4, boundary forcing data have been updated from the PlioMIP phase 1 due to recent advances in understanding of oceanic, terrestrial and cryospheric aspects of the Pliocene palaeoenvironment. In this study, sensitivities of Pliocene climate simulations to the newly archived boundary conditions are evaluated by a set of simulations using an atmosphere–ocean coupled general circulation model, MRI-CGCM2.3. The simulated Pliocene climate is warmer than pre-industrial conditions for 2.4 °C in global mean, corresponding to 0.6 °C warmer than the PlioMIP1 simulation by the identical climate model. Revised orography, lakes, and shrunk ice sheets compared with the PlioMIP1 lead to local and remote influences including snow and sea ice albedo feedback, and poleward heat transport due to the atmosphere and ocean that result in additional warming over middle and high latitudes. The amplified higher-latitude warming is supported qualitatively by the proxy evidences, but is still underestimated quantitatively. Physical processes responsible for the global and regional climate changes should be further addressed in future studies under systematic intermodel and data–model comparison frameworks.

scholarly journals Decadal variability in high northern latitudes as simulated by an intermediate-complexity climate model

2001 ◽  
Vol 33 ◽  
pp. 525-532 ◽  
Author(s):  
H. Goosse ◽  
F. M. Selten ◽  
R. J. Haarsma ◽  
J. D. Opsteegh
Keyword(s):  
Sea Ice ◽  
General Circulation ◽  
Model Comparison ◽  
Climate Model ◽  
Decadal Variability ◽  
Hydrological Cycle ◽  
Circulation Model ◽  
The Arctic ◽  
Intermediate Complexity ◽  
Northern Latitudes

AbstractA 2500 year integration has been performed with a global coupled atmospheric-sea-ice-ocean model of intermediate complexity with the main objective of studying the climate variability in polar regions on decadal time-scales and longer. The atmospheric component is the ECBILT model, a spectral T21 three-level quasi-geostrophic model that includes a representation of horizontal and vertical heat transfers as well as of the hydrological cycle. ECBILT is coupled to the CLIO model, which consists of a primitive-equation free-surface ocean general circulation model and a dynamic-thermodynamic sea-ice model. Comparison of model results with observations shows that the ECBILT-CLIO model is able to reproduce reasonably well the climate of the high northern latitudes. The dominant mode of coupled variability between the atmospheric circulation and sea-ice cover in the simulation consists of an annular mode for geopotential height at 800 hPa and of a dipole between the Barents and Labrador Seas for the sea-ice concentration which are similar to observed patterns of variability. In addition, the simulation displays strong decadal variability in the sea-ice volume, with a significant peak at about 18 years. Positive volume anomalies are caused by (1) a decrease in ice export through Fram Strait associated with more anticyclonic winds at high latitudes, (2) modifications in the freezing/melting rates in the Arctic due to lower air temperature and higher surface albedo, and (3) a weaker heat flux at the ice base in the Barents and Kara seas caused by a lower inflow of warm Atlantic water. Opposite anomalies occur during the volume-decrease phase of the oscillation.

scholarly journals Deep learning to represent subgrid processes in climate models

2018 ◽  
Vol 115 (39) ◽  
pp. 9684-9689 ◽  
Author(s):  
Stephan Rasp ◽  
Michael S. Pritchard ◽  
Pierre Gentine
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Wave Spectrum ◽  
Computational Cost ◽  
Circulation Model ◽  
The Neural Network ◽  
Equatorial Wave

The representation of nonlinear subgrid processes, especially clouds, has been a major source of uncertainty in climate models for decades. Cloud-resolving models better represent many of these processes and can now be run globally but only for short-term simulations of at most a few years because of computational limitations. Here we demonstrate that deep learning can be used to capture many advantages of cloud-resolving modeling at a fraction of the computational cost. We train a deep neural network to represent all atmospheric subgrid processes in a climate model by learning from a multiscale model in which convection is treated explicitly. The trained neural network then replaces the traditional subgrid parameterizations in a global general circulation model in which it freely interacts with the resolved dynamics and the surface-flux scheme. The prognostic multiyear simulations are stable and closely reproduce not only the mean climate of the cloud-resolving simulation but also key aspects of variability, including precipitation extremes and the equatorial wave spectrum. Furthermore, the neural network approximately conserves energy despite not being explicitly instructed to. Finally, we show that the neural network parameterization generalizes to new surface forcing patterns but struggles to cope with temperatures far outside its training manifold. Our results show the feasibility of using deep learning for climate model parameterization. In a broader context, we anticipate that data-driven Earth system model development could play a key role in reducing climate prediction uncertainty in the coming decade.

scholarly journals Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method*

2014 ◽  
Vol 27 (11) ◽  
pp. 4002-4014 ◽  
Author(s):  
Y. Liu ◽  
Z. Liu ◽  
S. Zhang ◽  
X. Rong ◽  
R. Jacob ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Average Method ◽  
General Circulation ◽  
Climate Model ◽  
Signal To Noise Ratio ◽  
Circulation Model ◽  
Superior Performance ◽  
Spatial Average ◽  
Atmosphere General Circulation Model ◽  
Parameter Values

Abstract Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. Here, an adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.

scholarly journals Development cycle 2 of the Modular Earth Submodel System (MESSy2)

2010 ◽  
Vol 3 (3) ◽  
pp. 1423-1501 ◽  
Author(s):  
P. Jöckel ◽  
A. Kerkweg ◽  
A. Pozzer ◽  
R. Sander ◽  
H. Tost ◽  
...  
Keyword(s):  
Atmospheric Chemistry ◽  
General Circulation ◽  
Atmospheric General Circulation Model ◽  
Climate Model ◽  
Circulation Model ◽  
Second Development ◽  
Development Cycle ◽  
Climate Simulations ◽  
Diagnostic Capabilities ◽  
Earth System Models

Abstract. The Modular Earth Submodel System (MESSy) is an open, multi-institutional project providing a strategy for developing comprehensive Earth System Models (ESMs) with highly flexible complexity. The first version of the MESSy infrastructure and process submodels, mainly focusing on atmospheric chemistry, has been successfully coupled to an atmospheric General Circulation Model (GCM) expanding it into an Atmospheric Chemistry GCM (AC-GCM) for nudged simulations and into a Chemistry Climate Model (CCM) for climate simulations. Here, we present the second development cycle of MESSy, which comprises (1) an improved and extended infrastructure for the basemodel independent coupling of process-submodels, (2) new, highly valuable diagnostic capabilities for the evaluation with observational data and (3) an improved atmospheric chemistry setup. With the infrastructural changes, we place the headstone for further model extensions from a CCM towards a comprehensive ESM. The new diagnostic submodels will be used for regular re-evaluations of the continuously further developing model system. The updates of the chemistry setup are briefly evaluated.

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