Climate models contain numerous parameters for which the numeric values are uncertain. In the context of climate simulation and prediction, a relevant question is what range of climate outcomes is possible given the range of parameter uncertainties. Which parameter perturbation changes the climate in some predefined sense the most? In the context of the Lorenz 63 model, a method is developed that identifies effective parameter perturbations based on short integrations. Use is made of the adjoint equations to assess the sensitivity of a short integration to a parameter perturbation. A key feature is the selection of initial conditions
The sensitivity of climate models in particular, and nonlinear models in general, is a topic of grea...
The equations of climate are, in principle, known. Why then is it so hard to formulate a biasfree mo...
We report phase diagrams detailing the intransitivity observed in the climate scenarios supported by...
Climate models contain numerous parameters for which the numeric values are uncertain. In the contex...
Uncertainty in the outcome of numerical models of physical and biological processes, such as the cli...
Global climate models contain numerous parameters with uncertain values. In the context of climate s...
Representing model uncertainty is important for both numerical weather and climate prediction. Stoch...
International audiencePioneering work by Lorenz (1965, 1968, 1969) developed a number of methods for...
The adjoint method is used to calibrate the medium complexity climate model “Planet Simulator” throu...
The process of parameter estimation targeting a chosen set of observations is an essential aspect of...
Optimisation methods were successfully used to calibrate parameters in an atmospheric component of a...
The first multi thousand member "perturbed physics" ensemble simulation of present and future climat...
Many sources of uncertainty limit the accuracy of climate projections. Among them, we focus here on ...
Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's we...
Uncertainty in climate projections is large as shown by the likely uncertainty ranges in Equilibrium...
The sensitivity of climate models in particular, and nonlinear models in general, is a topic of grea...
The equations of climate are, in principle, known. Why then is it so hard to formulate a biasfree mo...
We report phase diagrams detailing the intransitivity observed in the climate scenarios supported by...
Climate models contain numerous parameters for which the numeric values are uncertain. In the contex...
Uncertainty in the outcome of numerical models of physical and biological processes, such as the cli...
Global climate models contain numerous parameters with uncertain values. In the context of climate s...
Representing model uncertainty is important for both numerical weather and climate prediction. Stoch...
International audiencePioneering work by Lorenz (1965, 1968, 1969) developed a number of methods for...
The adjoint method is used to calibrate the medium complexity climate model “Planet Simulator” throu...
The process of parameter estimation targeting a chosen set of observations is an essential aspect of...
Optimisation methods were successfully used to calibrate parameters in an atmospheric component of a...
The first multi thousand member "perturbed physics" ensemble simulation of present and future climat...
Many sources of uncertainty limit the accuracy of climate projections. Among them, we focus here on ...
Uncertainty quantification (UQ) is a fundamental challenge in the numerical simulation of Earth's we...
Uncertainty in climate projections is large as shown by the likely uncertainty ranges in Equilibrium...
The sensitivity of climate models in particular, and nonlinear models in general, is a topic of grea...
The equations of climate are, in principle, known. Why then is it so hard to formulate a biasfree mo...
We report phase diagrams detailing the intransitivity observed in the climate scenarios supported by...