Past and projected weather pattern persistence with associated multi-hazards in the British Isles

Hazards such as heatwaves, droughts and floods are often associated with persistent weather patterns. Atmosphere-Ocean General Circulation Models (AOGCMs) are important tools for evaluating projected changes in extreme weather. Here, we demonstrate that 2-day weather pattern persistence, derived from the Lamb Weather Types (LWTs) objective scheme, is a useful concept for both investigating climate risks from multi-hazard events as well as for assessing AOGCM realism. This study evaluates the ability of a Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model sub-ensemble of 10 AOGCMs at reproducing seasonal LWTs persistence and frequencies over the British Isles (BI). Changes in persistence are investigated under two Representative Concentration Pathways (RCP8.5 and RCP4.5) up to 2100. The ensemble broadly replicates historical LWTs persistence observed in reanalyses (1971-2000). Future persistence and frequency of summer anticyclonic LWT are found to increase, implying heightened risk of drought and heatwaves. On the other hand, the cyclonic LWT decreases in autumn suggesting reduced likelihood of flooding and severe gales. During winter, AOGCMs point to increased risk of concurrent fluvial flooding-wind hazards by 2100, however, they also tend to over-estimate such risks when compared to reanalyses. In summer, the strength of the nocturnal Urban Heat Island (UHI) of London could intensify, enhancing the likelihood of combined heatwave-poor air quality events. Further research is needed to explore other multi-hazards in relation to changing weather pattern persistence and how best to communicate such threats to vulnerable communities

Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models

Long-Range Dependence (LRD) and heavy-tailed distributions are ubiquitous in natural and socio-economic data. Such data can be self-similar whereby both LRD and heavy-tailed distributions contribute to the self-similarity as measured by the Hurst exponent. Some methods widely used in the physical sciences separately estimate these two parameters, which can lead to estimation bias. Those which do simultaneous estimation are based on frequentist methods such as Whittle’s approximate maximum likelihood estimator. Here we present a new and systematic Bayesian framework for the simultaneous inference of the LRD and heavy-tailed distribution parameters of a parametric ARFIMA model with non-Gaussian innovations. As innovations we use the α-stable and t-distributions which have power law tails. Our algorithm also provides parameter uncertainty estimates. We test our algorithm using synthetic data, and also data from the Geostationary Operational Environmental Satellite system (GOES) solar X-ray time series. These tests show that our algorithm is able to accurately and robustly estimate the LRD and heavy-tailed distribution parameters

The Structure of Climate Variability Across Scales

One of the most intriguing facets of the climate system is that it exhibits variability across all temporal and spatial scales; pronounced examples are temperature and precipitation. The structure of this variability, however, is not arbitrary. Over certain spatial and temporal ranges it can be described by scaling relationships in the form of power‐laws in probability density distributions and autocorrelation functions. These scaling relationships can be quantified by scaling exponents which measure how the variability changes across scales and how the intensity changes with frequency of occurrence. Scaling determines the relative magnitudes and persistence of natural climate fluctuations. Here, we review various scaling mechanisms and their relevance for the climate system. We show observational evidence of scaling and discuss the application of scaling properties and methods in trend detection, climate sensitivity analyses, and climate predictio

The Lorenz energy cycle: trends and the impact of modes of climate variability

The atmospheric circulation is driven by heat transport from the tropics to the polar regions, embedding energy conversions between available potential and kinetic energy through various mechanisms. The processes of energy transformations related to the dynamics of the atmosphere can be quantitatively investigated through the Lorenz energy cycle formalism. Here we examine these variations and the impacts of modes of climate variability on the Lorenz energy cycle by using reanalysis data from the Japanese Meteorological Agency (JRA-55). We show that the atmospheric circulation is overall becoming more energetic and efficient. For instance, we find a statistically significant trend in the eddy available potential energy, especially in the transient eddy available potential energy in the Southern Hemisphere. We find significant trends in the conversion rates between zonal available potential and kinetic energy, consistent with an expansion of the Hadley cell, and in the conversion rates between eddy available potential and kinetic energy, suggesting an increase in mid-latitudinal baroclinic instability. We also show that planetary-scale waves dominate the stationary eddy energy, while synoptic-scale waves dominate the transient eddy energy with a significant increasing trend. Our results suggest that interannual variability of the Lorenz energy cycle is determined by modes of climate variability. We find that significant global and hemispheric energy fluctuations are caused by the El Nino-Southern Oscillation, the Arctic Oscillation, the Southern Annular Mode, and the meridional temperature gradient over the Southern Hemisphere

Predictions of critical transitions with non-stationary reduced order models

Here we demonstrate the ability of stochastic reduced order models to predict the statistics of non-stationary systems undergoing critical transitions. First, we show that the reduced order models are able to accurately predict the autocorrelation function and probability density functions (PDF) of higher dimensional systems with time-dependent slow forcing of either the resolved or unresolved modes. Second, we demonstrate that whether the system tips early or repeatedly jumps between the two equilibrium points (flickering) depends on the strength of the coupling between the resolved and unresolved modes and the time scale separation. Both kinds of behaviour have been found to preceed critical transitions in earlier studies. Furthermore, we demonstrate that the reduced order models are also able to predict the timing of critical transitions. The skill of various proposed tipping indicators are discussed

Perspectives on tipping points in integrated models of the natural and human Earth system: cascading effects and telecoupling

The Earth system and the human system are intrinsically linked. Anthropogenic greenhouse gas emissions have led to the climate crisis, which is causing unprecedented extreme events and could trigger Earth system tipping elements. Physical and social forces can lead to tipping points and cascading effects via feedbacks and telecoupling, but the current generation of climate-economy models do not generally take account of these interactions and feedbacks. Here, we show the importance of the interplay between human societies and Earth systems in creating tipping points and cascading effects and the way they in turn affect sustainability and security. The lack of modeling of these links can lead to an underestimation of climate and societal risks as well as how societal tipping points can be harnessed to moderate physical impacts. This calls for the systematic development of models for a better integration and understanding of Earth and human systems at different spatial and temporal scales, specifically those that enable decision-making to reduce the likelihood of crossing local or global tipping points.ISSN:1748-9326ISSN:1748-931