the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements
Karina E. Williams
Patrick C. McGuire
Maria Carolina Duran Rojas
Debbie Hemming
Anne Verhoef
Chris Huntingford
Lucy Rowland
Toby Marthews
Cleiton Breder Eller
Camilla Mathison
Rodolfo L. B. Nobrega
Nicola Gedney
Pier Luigi Vidale
Fred Otu-Larbi
Divya Pandey
Sebastien Garrigues
Azin Wright
Darren Slevin
Martin G. De Kauwe
Eleanor Blyth
Jonas Ardö
Andrew Black
Damien Bonal
Nina Buchmann
Benoit Burban
Kathrin Fuchs
Agnès de Grandcourt
Ivan Mammarella
Lutz Merbold
Leonardo Montagnani
Yann Nouvellon
Natalia Restrepo-Coupe
Georg Wohlfahrt
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sapfluxnetrwill facilitate new data syntheses on the ecological factors driving water use and drought responses of trees and forests.
sluggish. In some circumstances, this causes stomata to be more open – a concern during drought conditions – by increasing transpiration. To guide interpretation and modelling of field measurements, we present an equation for sluggish effects, via a single tau parameter.
IMOGEN uses "pattern scaling" to emulate GCMs, and with such linearity enables projections to be made for alternative future scenarios of atmospheric greenhouse gas concentrations. It is also coupled to the JULES land surface model, to allow impact assessments.
We describe a recent iteration of these configurations: GA6/GL6. This includes ENDGame: a new dynamical core designed to improve the model's accuracy, stability and scalability. GA6 is now operational in a variety of Met Office and UM collaborators applications and hence its documentation is important.
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FINAM is not a model), a new coupling framework written in Python to dynamically connect independently developed models. Python, as the ultimate glue language, enables the use of codes from nearly any programming language like Fortran, C++, Rust, and others. FINAM is designed to simplify the integration of various models with minimal effort, as demonstrated through various examples ranging from simple to complex systems.
This study introduces a new 3D lake–ice–atmosphere coupled model that significantly improves winter climate simulations for the Great Lakes compared to traditional 1D lake model coupling. The key contribution is the identification of critical hydrodynamic processes – ice transport, heat advection, and shear-driven turbulence production – that influence lake thermal structure and ice cover and explain the superior performance of 3D lake models to their 1D counterparts.