Atmospheric Noise: The Indefinite Urbanism of Los Angeles

This article reviews Mariana Peterson’s Atmospheric Noise, which draws jarring, cacophonous resonances between the science and engineering of acoustics, urban political economy, governmentality, the metaphysics of sound and the social construction of ecology and environment in the city of Los Angeles.

A New Optimisation Tool for Automatic InSAR Time Series Processing with MasTer.

<p>The increasing amount of SAR data available opens new challenges in terms of data storage management and processing load. Fully exploit those large databases requires the developement of automatic processing  chains. The InSAR Mass processing Toolbox for Multidimensional time series (MasTer) is able to combine any type of SAR data to produce automatic unsupervised 2D ground deformation time series, from data download up to updated displaying of 2D time series results on a web page, updated incrementally as soon as a new image is available. We present our last methodological improvement based on the computation of a coherence proxy to guide a pair selection optimization, balancing the use of each image as master and slave. Whereas this new tool reduces the number of DInSAR interferograms computed by up to 75%, it also increases the signal to noise ratio of the time series by reducing the influence of DEM errors and atmospheric noise.</p>

Dynamics and Predictability of Hemispheric-Scale Multidecadal Climate Variability in an Observationally Constrained Mechanistic Model

This paper addresses the dynamics of internal hemispheric-scale multidecadal climate variability by postulating an energy-balance (EBM) model comprising two deep-ocean oscillators in the Atlantic and Pacific basins, coupled through their surface mixed layers via atmospheric teleconnections. This system is linear and driven by the atmospheric noise. Two sets of the EBM model parameters are developed by fitting the EBM-based mixed-layer temperature covariance structure to best mimic basin-average North Atlantic/Pacific sea surface temperature (SST) covariability in either observations or control simulations of comprehensive climate models within the CMIP5 project. The differences between the dynamics underlying the observed and CMIP5-simulated multidecadal climate variability and predictability are encapsulated in the algebraic structure of the two EBM model versions so obtained: EBMCMIP5 and EBMOBS. The multidecadal variability in EBMCMIP5 is overall weaker and amounts to a smaller fraction of the total SST variability than in EBMOBS, pointing to a lower potential decadal predictability of virtual CMIP5 climates relative to that of the actual climate. The EBMCMIP5 decadal hemispheric teleconnections (and, by inference, those in CMIP5 models) are largely controlled by the variability of the Pacific, in which the ocean, due to its large thermal and dynamical memory, acts as a passive integrator of atmospheric noise. By contrast, EBMOBS features a stronger two-way coupling between the Atlantic and Pacific multidecadal oscillators, thereby suggesting the existence of a hemispheric-scale and, perhaps, global multidecadal mode associated with internal ocean dynamics. The inferred differences between the observed and CMIP5 simulated climate variability stem from a stronger communication between the deep ocean and surface processes implicit in the observational data.

Decomposition of the Atlantic Multidecadal Variability in a Historical Climate Simulation

The Atlantic multidecadal variability (AMV) modulates various climate features worldwide with enormous societal and economic implications, including variations in hurricane activity in the Atlantic, sea level, West African and Indian monsoon rainfall, European climate, and hemispheric-scale surface temperature. Leading hypotheses regarding the nature and origin of AMV focus primarily on its links with oceanic and coupled ocean–atmosphere internal variability, and on its response to external forcing. The role of another possible process, that of atmospheric noise forcing of the ocean, has received less attention. This is addressed here by means of historical coupled simulations and diagnostic experiments, which isolate the influences of external and atmospheric noise forcings. Our findings show that external forcing is an important driver of the simulated AMV. They also demonstrate that weather noise is key in driving the simulated internal AMV in the southern part (0°–60°N) of the AMV region, and that weather noise forcing is responsible for up to 10%–20% of the multidecadal internal SST variability in some isolated areas of the subpolar gyre region. Ocean dynamics independent from the weather noise forcing is found to be the dominant cause of multidecadal SST in the northern part of the AMV region.