Documentation for Calculating Uncertainty in Biomass Emissions Model, Version 1.0 (CUBE 1.0): Contents and Use

2014 ◽  
Author(s):  
Aimee E. Curtright ◽  
Henry H. Willis ◽  
David R. Johnson ◽  
David S. Ortiz ◽  
Nicholas Burger ◽  
...  
Keyword(s):  
Model Version

A User's Guide for the Functional Economic Analysis Model (Version 2.2A)

1992 ◽  
Author(s):  
Thomas P. Frazier ◽  
John J. Cloos ◽  
Matthew S. Goldberg ◽  
Alec W. Salerno ◽  
Kathryn L. Wilson
Keyword(s):  
Economic Analysis ◽  
Analysis Model ◽  
Model Version

GFDL’s ESM2 Global Coupled Climate–Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics

2012 ◽  
Vol 25 (19) ◽  
pp. 6646-6665 ◽  
Author(s):  
John P. Dunne ◽  
Jasmin G. John ◽  
Alistair J. Adcroft ◽  
Stephen M. Griffies ◽  
Robert W. Hallberg ◽  
...  
Keyword(s):  
Climate Changes ◽  
Southern Oscillation ◽  
Heat Content ◽  
Ocean Dynamics ◽  
Thermocline Depth ◽  
Earth System ◽  
System Models ◽  
Model Version ◽  
Earth System Models

Abstract The physical climate formulation and simulation characteristics of two new global coupled carbon–climate Earth System Models, ESM2M and ESM2G, are described. These models demonstrate similar climate fidelity as the Geophysical Fluid Dynamics Laboratory’s previous Climate Model version 2.1 (CM2.1) while incorporating explicit and consistent carbon dynamics. The two models differ exclusively in the physical ocean component; ESM2M uses Modular Ocean Model version 4p1 with vertical pressure layers while ESM2G uses Generalized Ocean Layer Dynamics with a bulk mixed layer and interior isopycnal layers. Differences in the ocean mean state include the thermocline depth being relatively deep in ESM2M and relatively shallow in ESM2G compared to observations. The crucial role of ocean dynamics on climate variability is highlighted in El Niño–Southern Oscillation being overly strong in ESM2M and overly weak in ESM2G relative to observations. Thus, while ESM2G might better represent climate changes relating to total heat content variability given its lack of long-term drift, gyre circulation, and ventilation in the North Pacific, tropical Atlantic, and Indian Oceans, and depth structure in the overturning and abyssal flows, ESM2M might better represent climate changes relating to surface circulation given its superior surface temperature, salinity, and height patterns, tropical Pacific circulation and variability, and Southern Ocean dynamics. The overall assessment is that neither model is fundamentally superior to the other, and that both models achieve sufficient fidelity to allow meaningful climate and earth system modeling applications. This affords the ability to assess the role of ocean configuration on earth system interactions in the context of two state-of-the-art coupled carbon–climate models.

Mechanisms of Low Cloud–Climate Feedback in Idealized Single-Column Simulations with the Community Atmospheric Model, Version 3 (CAM3)

2008 ◽  
Vol 21 (18) ◽  
pp. 4859-4878 ◽  
Author(s):  
Minghua Zhang ◽  
Christopher Bretherton
Keyword(s):  
Dynamic Effect ◽  
Atmospheric Model ◽  
Cloud Feedback ◽  
Climate Feedback ◽  
Stratus Clouds ◽  
Model Version ◽  
Boundary Layer Turbulence ◽  
Thermodynamic Effect ◽  
Single Column ◽  
Low Cloud

Abstract This study investigates the physical mechanism of low cloud feedback in the Community Atmospheric Model, version 3 (CAM3) through idealized single-column model (SCM) experiments over the subtropical eastern oceans. Negative cloud feedback is simulated from stratus and stratocumulus that is consistent with previous diagnostics of cloud feedbacks in CAM3 and its predecessor versions. The feedback occurs through the interaction of a suite of parameterized processes rather than from any single process. It is caused by the larger amount of in-cloud liquid water in stratus clouds from convective sources, and longer lifetimes of these clouds in a warmer climate through their interaction with boundary layer turbulence. Thermodynamic effects are found to dominate the negative cloud feedback in the model. The dynamic effect of weaker subsidence in a warmer climate also contributes to the negative cloud feedback, but with about one-quarter of the magnitude of the thermodynamic effect, owing to increased low-level convection in a warmer climate.

The Madden-Julian Oscillation in the Energy Exascale Earth System Model Version 1

2021 ◽  
Author(s):  
Daehyun Kim ◽  
Daehyun Kang ◽  
Min-Seop Ahn ◽  
Charlotte DeMott ◽  
Chia-Wei Hsu ◽  
...  
Keyword(s):  
Earth System Model ◽  
System Model ◽  
Madden Julian Oscillation ◽  
Earth System ◽  
Model Version

scholarly journals Using a virtual machine environment for developing, testing, and training for the UM-UKCA composition-climate model, using Unified Model version 10.9 and above

2018 ◽  
Vol 11 (9) ◽  
pp. 3647-3657 ◽  
Author(s):  
Nathan Luke Abraham ◽  
Alexander T. Archibald ◽  
Paul Cresswell ◽  
Sam Cusworth ◽  
Mohit Dalvi ◽  
...  
Keyword(s):  
Virtual Machine ◽  
Climate Model ◽  
State Of The Art ◽  
Model Development ◽  
Unified Model ◽  
Model Version ◽  
Weather And Climate ◽  
Interactive Composition ◽  
Comprehensive Test ◽  
And Training

Abstract. The Met Office Unified Model (UM) is a state-of-the-art weather and climate model that is used operationally worldwide. UKCA is the chemistry and aerosol sub model of the UM that enables interactive composition and physical atmosphere interactions, but which adds an additional 120 000 lines of code to the model. Ensuring that the UM code and UM-UKCA (the UM running with interactive chemistry and aerosols) is well tested is thus essential. While a comprehensive test harness is in place at the Met Office and partner sites to aid in development, this is not available to many UM users. Recently, the Met Office have made available a virtual machine environment that can be used to run the UM on a desktop or laptop PC. Here we describe the development of a UM-UKCA configuration that is able to run within this virtual machine while only needing 6 GB of memory, before discussing the applications of this system for model development, testing, and training.

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