Combining machine learning and SMILEs to classify, better understand, and project changes in ENSO events

The El Niño Southern Oscillation (ENSO) occurs in three phases: neutral, warm (El Niño) and cool (La Niña). While classifying El Niño and La Niña is relatively straightforward, El Niño events can be broadly classified into two types: Central Pacific (CP) and Eastern Pacific (EP). Differentiating between CP and EP events is currently dependent on both the method and observational dataset used. In this study, we create a new classification scheme using supervised machine learning trained on 18 observational and reanalysis products. This builds on previous work by identifying classes of events using the temporal evolution of sea surface temperature in multiple regions across the tropical Pacific. By applying this new classifier to seven single model initial-condition large ensembles (SMILEs) we investigate both the internal variability and forced changes in each type of ENSO event, where events identified behave similar to those observed. It is currently debated whether the observed increase in the frequency of CP events after the late 1970s is due to climate change. We found it to be within the range of internal variability in the SMILEs. When considering future changes, we do not project a change in CP frequency or amplitude under a strong warming scenario (RCP8.5/SSP370) and we find model differences in EP El Niño and La Niña frequency and amplitude projections. Finally, we find that models show differences in projected precipitation and SST pattern changes for each event type that do not seem to be linked to the Pacific mean state SST change, although the SST and precipitation changes in individual SMILEs are linked. Our work demonstrates the value of combining machine learning with climate models, and highlights the need to use SMILEs when evaluating ENSO in climate models due to the large spread of results found within a single model due to internal variability alone

Internal variability in a changing climate: A large ensemble perspective on tropical Atlantic rainfall

In this dissertation I investigate the temporal development of internal variability under global warming. Understanding internal variability is essential to understand the past and possible future trajectories of our climate, yet it is often assumed to be a property of the climate system that does not change under global warming. I use a novel large ensemble, the Max Planck Institute Grand Ensemble, to introduce a new perspective on internal variability. Internal variability can be described as the seemingly random fluc- tuations of the climate system over time. Due to nonlinearity in the climate system, small perturbations may grow to large anomalies over time that are associated with anomalous or even extreme events. In my first chapter, I quantify internal variability and investigate whether it changes under global warming. The change in the external forcing is the same for all of these realisations, the initial conditions are different for each realisation. Thus, each realisation follows its own, unique tra- jectory. For each time step, the distribution of all realisations provides an estimate of the possible states of the climate system. I develop an analysis framework based on a large ensemble to detect, quantify and attribute changes in internal variability in a transient climate. Rather than analysing variability over time, I use the ensemble dimension of a large ensemble to quantify internal variability. This approach allows a clean separation of the forced signal from internal variability and ensures stationarity of the statistics even when the forcing is changing with time. My non-parametric approach provides an objective quantification of changes in internal variability and their robustness. In my second chapter I apply this analysis framework to investigate rainfall in the tropical Atlantic region in the past and its possible future trajectories. I can show that simulated internal variability in the Sahel encompasses all observed values for the 20th century. The model suggests an externally forced increase in rainfall towards the end of the 20th century. However, due to large internal variability, it is not possible to detect this forced change in a single realisation. In future projections, I find an increase in the mean rainfall over the Sahel, accompanied by an increase in the variability. This implies that the average rainfall will increase, but individual years may show deviations from this mean value that are larger than under present-day conditions. In the tropical Atlantic region, most state-of-the-art coupled climate models show large biases in the simulated sea surface temperature and rainfall when compared to observations. These model error

A note on quantization operators on Nichols algebra model for Schubert calculus on Weyl groups

We give a description of the (small) quantum cohomology ring of the flag variety as a certain commutative subalgebra in the tensor product of the Nichols algebras. Our main result can be considered as a quantum analog of a result by Y. Bazlov

High atmospheric horizontal resolution eliminates the wind-driven coastal warm bias in the southeastern tropical Atlantic

We investigate the strong warm bias in sea surface temperatures (SST) of the southeastern tropical Atlantic that occurs in most of the current global climate models. We analyse this bias in the Max Planck Institute Earth System Model at different horizontal resolutions ranging from 0.1° to 0.4° in the ocean and 0.5° to 1.8° in the atmosphere. High atmospheric horizontal resolution eliminates the SST bias close to the African coast, due to an improved representation of surface wind-stress near the coast. This improvement affects coastal upwelling and horizontal ocean circulation, as confirmed with dedicated sensitivity experiments. The wind-stress improvements are partly caused by the better represented orography at higher horizontal resolution in the spectral atmospheric model. The reductions of the coastal SST bias obtained through higher horizontal resolution do not, however, translate to a reduction of the large-scale bias extending westward from the African coast into the southeastern tropical Atlantic

A risk-seeking future

The 2014 IPCC Assessment expresses doubt that the global surface temperature increase will remain within the 2 °C target without deploying risky carbon-capturing or solar radiation-deflecting technologies. New behavioural research suggests that, if the IPCC is right, citizens and policymakers will support such risk-taking

The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences

Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century.</p

Experimental study of pedestrian flow through a bottleneck

In this work the results of a bottleneck experiment with pedestrians are presented in the form of total times, fluxes, specific fluxes, and time gaps. A main aim was to find the dependence of these values from the bottleneck width. The results show a linear decline of the specific flux with increasing width as long as only one person at a time can pass, and a constant value for larger bottleneck widths. Differences between small (one person at a time) and wide bottlenecks (two persons at a time) were also found in the distribution of time gaps.Comment: accepted for publication in J. Stat. Mec