the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI): protocol and initial results from the first simulations
Jadwiga H. Richter
Daniele Visioni
Douglas G. MacMartin
David A. Bailey
Nan Rosenbloom
Brian Dobbins
Walker R. Lee
Jean-Francois Lamarque
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hiddensource of inter-model variability and may be leading to bias in some climate model results.
hottest20 % of parcels.
geoengineeringwould also impact other variables like precipitation and sea ice. In this study, we model various climate impacts of geoengineering on a 3-D graph to show how trying to meet one climate goal will affect other variables. We also present two computer simulations which validate our model and show that geoengineering could regulate precipitation as well as temperature.
agetracers. The largest variability occurs near the surface close to the tropical convergence zones, but the peak is further south and there is a smaller tropical–extratropical contrast for tracers with more rapid loss. Hence the variability of trace gases in the southern extratropics will vary with their chemical lifetime.
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Inaccuracies in air–sea heat fluxes severely degrade the accuracy of ocean numerical simulations. Here, we use artificial neural networks to correct air–sea heat fluxes as a function of oceanic and atmospheric state predictors. The correction successfully improves surface and subsurface ocean temperatures beyond the training period and in prediction experiments.
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.