Accounting for Pacific climate variability increases projected global warming.

Observational constraint methods based on the relationship between the past global warming trend and projected warming across climate models were used to reduce uncertainties in projected warming by the Intergovernmental Panel on Climate Change. Internal climate variability in the eastern tropical Pacific associated with the so-called pattern effect weakens this relationship and has reduced the observed warming trend over recent decades. Here we show that regressing out this variability before applying the observed global mean warming trend as a constraint results in higher and narrower twenty-first century warming ranges than other methods.

An Observational Constraint on Aviation-Induced Cirrus From the COVID-19-Induced Flight Disruption.

Global aviation dropped precipitously during the covid-19 pandemic, providing an unprecedented opportunity to study aviation-induced cirrus (AIC). AIC is believed to be responsible for over half of aviation-related radiative forcing, but until now, its radiative impact has only been estimated from simulations. Here, we show that satellite observations of cirrus cloud do not exhibit a detectable global response to the dramatic aviation reductions of spring 2020.

Bispectral unfolding of the skewness of correlated additive and multiplicative noise processes.

Correlated additive and multiplicative (CAM) noise processes are well-established as general "null hypothesis" models of non-Gaussian variability in atmospheric and oceanic quantities. In this study, analytic expressions for the bispectral density (which partitions the third statistical moment into triad frequency interactions in a manner analogous to the partitioning of variance by the spectral density) are developed for discrete and continuous-time CAM processes. It is then demonstrated that under lowpass filtering, while the absolute skewness of a discrete-time CAM process may increase or decrease with decreasing cutoff frequency, the absolute skewness of continuous-time CAM processes decreases monotonically.

Reduced α-stable dynamics for multiple time scale systems forced with correlated additive and multiplicative Gaussian white noise.

Stochastic averaging problems with Gaussian forcing have been the subject of numerous studies, but far less attention has been paid to problems with infinite-variance stochastic forcing, such as an α-stable noise process. It has been shown that simple linear systems driven by correlated additive and multiplicative (CAM) Gaussian noise, which emerge in the context of reduced atmosphere and ocean dynamics, have infinite variance in certain parameter regimes. In this study, we consider the stochastic averaging of systems where a linear CAM noise process in the infinite variance parameter regime drives a comparatively slow process.

How close are time series to power tail Lévy diffusions?

This article presents a new and easily implementable method to quantify the so-called coupling distance between the law of a time series and the law of a differential equation driven by Markovian additive jump noise with heavy-tailed jumps, such as α-stable Lévy flights. Coupling distances measure the proximity of the empirical law of the tails of the jump increments and a given power law distribution. In particular, they yield an upper bound for the distance of the respective laws on path space.

Stochastic models of the meridional overturning circulation: time scales and patterns of variability.

The global meridional overturning circulation (MOC) varies over a wide range of space and time scales in response to fluctuating 'weather' perturbations that may be modelled as stochastic forcing. This study reviews model studies of the effects of climate noise on decadal to centennial MOC variability, on transitions between the MOC regimes and on the dynamics of Dansgaard-Oeschger events characteristic of glacial periods.