Robust assessment of future changes in extreme precipitation over the Rhine basin using a GCM

Estimates of future changes in extremes of multiday precipitation sums are critical for estimates of future discharge extremes of large river basins. Here we use a large ensemble of global climate model SRES A1b scenario simulations to estimate changes in extremes of 1–20 day precipitation sums over the Rhine basin, projected for the period 2071–2100 with reference to 1961–1990. We find that in winter, an increase of order 10%, for the 99th percentile precipitation sum, is approximately fixed across the selected range of multiday sums, whereas in summer, the changes become increasingly negative as the summation time lengthens. Explanations for these results are presented that have implications for simple scaling methods for creating time series of a future climate. We show that the dependence of quantile changes on summation time is sensitive to the ensemble size and indicate that currently available discharge estimates from previous studies are based on insufficiently long time series

Complete synchronization of chaotic atmospheric models by connecting only a subset of state space

Connected chaotic systems can, under some circumstances, synchronize their states with an exchange of matter and energy between the systems. This is the case for toy models like the Lorenz 63, and more complex models. In this study we perform synchronization experiments with two connected quasi-geostrophic (QG) models of the atmosphere with 1449 degrees of freedom. The purpose is to determine whether connecting only a subset of the model state space can still lead to complete synchronization (CS). In addition, we evaluated whether empirical orthogonal functions (EOF) form efficient basis functions for synchronization in order to limit the number of connections. In this paper, we show that only the intermediate spectral wavenumbers (5-12) need to be connected in order to achieve CS. In addition, the minimum connection timescale needed for CS is 7.3 days. Both the connection subset and the connection timescale, or strength, are consistent with the time and spatial scales of the baroclinic instabilities in the model. This is in line with the fact that the baroclinic instabilities are the largest source of divergence between the two connected models. Using the Lorenz 63 model, we show that EOFs are nearly optimal basis functions for synchronization. The QG model results show that the minimum number of EOFs that need to be connected for CS is a factor of three smaller than when connecting the original state variables

Strong future increases in Arctic precipitation variability linked to poleward moisture transport

The Arctic region is projected to experience amplified warming as well as strongly increasing precipitation rates. Equally important to trends in the mean climate are changes in interannual variability, but changes in precipitation fluctuations are highly uncertain and the associated processes are unknown. Here, we use various state-of-the-art global climate model simulations to show that interannual variability of Arctic precipitation will likely increase markedly (up to 40% over the 21st century), especially in summer. This can be attributed to increased poleward atmospheric moisture transport variability associated with enhanced moisture content, possibly modulated by atmospheric dynamics. Because both the means and variability of Arctic precipitation will increase, years/seasons with excessive precipitation will occur more often, as will the associated impacts

On the recurrence and robust properties of Lorenz'63 model

Lie-Poisson structure of the Lorenz'63 system gives a physical insight on its dynamical and statistical behavior considering the evolution of the associated Casimir functions. We study the invariant density and other recurrence features of a Markov expanding Lorenz-like map of the interval arising in the analysis of the predictability of the extreme values reached by particular physical observables evolving in time under the Lorenz'63 dynamics with the classical set of parameters. Moreover, we prove the statistical stability of such an invariant measure. This will allow us to further characterize the SRB measure of the system.Comment: 44 pages, 7 figures, revised version accepted for pubblicatio

A multi-model ensemble method that combines imperfect models through learning.

Contains fulltext : 95574.pdf (publisher's version ) (Open Access)17 p

Finding the effective parameter perturbations in atmospheric models: the LORENZ63 model as case study

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

Attractor learning in synchronized chaotic systems in the presence of unresolved scales

Contains fulltext : 181584.pdf (publisher's version ) (Open Access) Contains fulltext : 181484_pos.pdf (postprint version ) (Open Access

Dynamically combining climate models to "supermodel" the tropical Pacific

Contains fulltext : 156947.pdf (publisher's version ) (Open Access