Session D64: Extreme Events, Tipping Points, and Abrupt Changes in the Climate System

Extreme precipitation in a changing climate

As greenhouse gas concentrations increase and the world warms, not only temperature but also precipitation is changing. Governments, and more recently courts and businesses, are demanding information about what impacts they will face in the coming years and decades. Providing this information is challenging for any aspect of future climate because of the diverse sources of uncertainty – including natural variability, structural differences among climate models and their projections, and the range of trajectories society might choose to follow. Precipitation varies strongly in space and time, integrates over many physical processes unfolding at many different scales, and is highly non-gaussian. In this talk I will discuss work to advance our understanding of changes in precipitation, using climate models as well as observations and developing new approaches to quantify precipitation change, along the way rethinking what we mean by precipitation variability and its extremes.   

Typicality of the 2021 Western North America summer heatwave

Elucidating the statistical properties of extreme meteo-climatic events and capturing the physical processes responsible for their occurrence are key steps for improving our understanding of climate variability and climate change and for better evaluating the associated hazards. It has recently become apparent that large deviation theory (LDT) is very useful for investigating persistent extreme events, and specifically, for flexibly estimating long return periods and for introducing a notion of dynamical typicality. Using a methodological framework based on LDT and taking advantage of long simulations by a state-of-the-art Earth system model, we investigate the 2021 Western North America summer heatwave. Indeed, our analysis shows that the 2021 event can be seen as an unlikely but possible manifestation of climate variability, whilst its probability of occurrence is greatly amplified by the ongoing climate change. We also clarify the properties of spatial coherence of the 2021 heatwave and elucidate the role played by the Rocky Mountains in modulating hot, dry, and persistent extreme events in the Western Pacific region of North America.   

Studies of Extreme Weather using Huge Ensembles of Machine-Learning-based Climate Emulators

Studying low-likelihood high-impact climate events in a warming world requires massive ensembles of hindcasts to capture their statistics. It is currently not feasible to generate these ensembles using traditional weather or climate models, especially at sufficiently high spatial resolution.

We describe how to bring the power of machine learning (ML) to generate climate hindcasts at four to five orders-of-magnitude lower computational cost than conventional numerical methods. We show how to evaluate ML climate emulators using the same rigorous metrics developed for operational numerical weather prediction.  We conclude by discussing the prospects for studying the causes and statistics of low-likelihood high-impact extremes using huge ensembles generated using these ML emulators.   

An investigation of tipping mechanisms in a carbon cycle model

Rate-induced tipping (R-tipping) occurs when a ramp parameter changes rapidly enough to cause the system to tip between co-existing, attracting states, while noise-induced tipping (N-tipping) occurs when there are random transitions between two attractors of the underlying deterministic system. We investigate R-tipping and N-tipping events in a carbonate system in the upper ocean, in which the key objective is understanding how the system undergoes tipping away from a stable fixed point in a bistable regime. While R-tipping away from the fixed point is straightforward, N-tipping poses challenges due to a periodic orbit forming the basin boundary for the attracting fixed point of the underlying deterministic system. Furthermore, in the case of N-tipping, we are interested in the case where noise is away from the small noise limit, as it is more appropriate for the application. We compute the most probable escape path (MPEP) for our system, resulting in a firm grasp on the least action path in an asymmetric system of higher scale. Our analysis shows that the carbon cycle model is susceptible to both tipping mechanisms.   

A Modeling Framework to Investigate Impact of Increased Storm Variability on Self-Organized Vegetation Patterns in Drylands

Banded patterns of vegetation growth on gentle slopes can be found in certain dryland regions. The soil water and biomass dynamics act on a slow timescale, and infrequent rainstorms inject water into the system on a fast timescale. We model the slow subsystem as the time-evolution of a reaction-diffusion equation, and we treat the storms as instantaneous kicks of added water. The water is deposited inhomogeneously due to differences in the infiltration rate and downhill flow speed in areas with dense biomass versus bare soil. Specifically, biomass impedes the downhill flow of surface water and increases infiltration, which leads to the surface water left by a storm being concentrated in the soil near the uphill edge of a vegetation band. We explore the effect of storm variability by introducing randomness into the timing and the amount of water deposited by storms. We are particularly interested in how storm variability affects the resilience of the vegetation patterns compared to the idealized case of identical, regularly-timed storms, as this may give insight into potential risks of ecosystem collapse due to increased variability brought about by climate change.   

Long-term instabilities and unphysical drifts of AI weather models: Challenges of learning multi-scale dynamics

AI weather models, deep neural networks trained only on observation-derived data (reanalysis), have shown remarkable forecast skills for up to 10 days, even outperforming the state-of-the-art numerical weather prediction models. However, these models have been found to go unstable (blow up) or drift to unphysical circulation patterns when run for weeks and months. These models have also been found to represent the small scales (high wavenumbers) incorrectly. Here, we show that the two problems are related, and due to a well-known inductive bias of neural networks called spectral bias. When applied to nonlinear multi-scale dynamical systems such as turbulence and climate, spectral bias leads to errors in small scales, which then grow and accumulate over time and affect all scales. We propose a new algorithm, FouRKS (Fourier regularized loss function with Runge-Kutta integration and Self-supervising layer), which significantly reduces the spectral bias and enables long-term stable, physically consistent integrations. We demonstrate the performance of FouRKS on two-layer quasi-geostrophic turbulent flows and global weather (reanalysis).   

Observations of localized submesoscale kinetic energy fluxes

Ocean dynamics at the submesoscale play a key role in mediating upper-ocean energy dissipation and dispersion of tracers. Observations of ocean currents from synoptic mesoscale surveys at submesoscale resolution (250~m--100~km) from a novel airborne instrument (MASS DoppVis) reveal that the kinetic energy spectrum in the California Current System is nearly continuous from 100~km to sub-kilometer scales, with a $k^{-2}$ spectral slope. Although there is not a transition in the kinetic energy spectral slope, there is a transition in the dynamics to non-linear ageostrophic interactions at scales of $mathcal{O}$(1~km). Kinetic energy transfer across spatial scales is enabled by interactions between the rotational and divergent components of the flow field at the submesoscale. Kinetic energy flux is patchy and localized at submesoscale fronts. Kinetic energy is transferred both downscale and upscale from 1~km in the observations of a cold filament.   

Cloud Absorption and Fog Heating by Visible Light due to Photomolecular Effect

Despite that the bulk water is nearly transparent to visible light, we recently discovered that visible light can be absorbed at water-air interface via a process we call the photomolecular effect, in which a photon can cleave off water molecular clusters.  We hypothesize that this effect is behind the experimental observation of anomalously high clouds absorption, which has been controversial for nearly 80 years since the large cloud absorption cannot be explained based on bulk water optical constants.  We show experimentally that the temperature of a dense thin fog increases significantly under LEDs of multiple wavelengths in the visible spectrum with intensity comparable to solar radiation, peaking at the 520 nm (green) where bulk water absorbs least.  Green laser irradiation of the fog further confirms heating effect. Monte Carlo further supports the photomolecular absorption mechanism can explain the anomalous cloud absorption.  Our study suggests that photomolecular effect can highly influence the global climates.