Subseasonal Temporal Clustering of Extreme Precipitation in the Northern Hemisphere: Regionalization and Physical Drivers

Temporal clustering of extreme precipitation (TCEP) at subseasonal time scales often results in major impactson humans and ecosystems. Assessment and mitigation of the risk of such events requires characterization of their weather/climate drivers and their spatial dependence. Here, we introduce a regionalization method that identifies coherent regions in which the likelihood of subseasonal TCEP exhibits similar dependence to large-scale dynamics. We apply this method to each season in the Northern Hemisphere using ERA5 reanalysis data. The analysis yields spatially coherent regions, primarily at high latitudes and along the eastern margins of ocean basins. We analyze the large-scale and synoptic conditions associated with TCEP in several of the identified regions, in light of three key ingredients: lifting, moisture availability, and persistence in synoptic conditions. We find that TCEP is often directly related to distinct cyclone and blocking frequency anomalies and upper-level wave patterns. Blocking and associated Rossby wave breaking are particularly relevant at high latitudes and midlatitudes. At upper levels, meridional wave patterns dominate; however, in western Europe and parts of North America, TCEP is sometimes associated with zonally extended wave patterns. The flow features associated with TCEP in the eastern Pacific and eastern Atlantic Oceans exhibit similarities. For some regions, moisture flux anomalies are present during clustering episodes whereas in others forced lifting alone is sufficient to trigger heavy precipitation. Our results provide new information on the dynamics and spatial dependence of TCEP that may be relevant for the subseasonal prediction of clustering episodes

Accessing extremes of mid-latitudinal wave activity: methodology and application

A statistical methodology is proposed and tested for the analysis of extreme values of atmospheric wave activity at mid-latitudes. The adopted methods are the classical block-maximum and peak over threshold, respectively based on the generalized extreme value (GEV) distribution and the generalized Pareto distribution (GPD). Time-series of the ‘Wave Activity Index’ (WAI) and the ‘Baroclinic Activity Index’ (BAI) are computed from simulations of the General Circulation Model ECHAM4.6, which is run under perpetual January conditions. Both the GEV and the GPD analyses indicate that the extremes ofWAI and BAI areWeibull distributed, this corresponds to distributions with an upper bound. However, a remarkably large variability is found in the tails of such distributions; distinct simulations carried out under the same experimental setup provide sensibly different estimates of the 200-yr WAI return level. The consequences of this phenomenon in applications of the methodology to climate change studies are discussed. The atmospheric configurations characteristic of the maxima and minima of WAI and BAI are also examined

Recurrent Rossby waves: drivers and links to persistent weather

Upper-level Rossby wave packets (RWPs) are one of the key drivers of surface weather. RWPs can lead to extreme surface weather events. However, extreme impacts can also arise from long spells of persistent weather. Recurrence of synoptic-scale RWPs, termed RRWPs, where RWPs recur in the same phase over a short period, can also lead to persistent weather. The importance of RRWPs has only been identified recently. This thesis aims to explore the role of RRWPs in modulating persistent weather events and discover the atmospheric processes driving RRWPs. First, the thesis quantifies the significance of RRWPs for dry and wet spells across the globe. Persistent dry spells can lead to droughts, make heatwaves more extreme, and increase the risk of forest fires. The thesis finds that RRWPs are significantly associated with longer dry and wet spells across the globe. The case studies further demonstrate the role of RRWPs during episodes of dry and wet spells, respectively. Next, the thesis explores the role of RRWPs for hot spells in the SH. For the hot spells, RRWPs are significantly associated with longer hot spells over several regions, including south-eastern Australia (SEA), a region that has seen increasingly extreme heatwaves in recent decades. Motivated by that, the importance of RRWPs for the set of most persistent and extreme SEA heatwaves is explored further. The role of RRWPs during SEA heatwaves is demonstrated by two case studies of 2004 and 2009 SEA heatwaves, where RRWPs help to build recurrent ridges over SEA. Furthermore, days with RRWP conditions over SEA are associated with an increased probability of SEA heatwaves. Given the vital role of RRWPs for SEA heatwaves, the thesis sets out to find the association of RRWPs with other atmospheric drivers of persistent weather, namely, atmospheric blocks and quasi-resonance amplification (QRA). QRA conditions were also detected during some episodes of the most persistent and extreme SEA heatwaves. We find that RRWPs and QRA are closely associated in the SH, with 40% of QRA days also featuring RRWP conditions. We study their close association with upper-level composite maps and discuss the similarities and differences in the algorithm used to identify them. For the link of RRWPs and QRA with blocks in SH, we find an insignificant increase in the median area of blocks for days with RRWP than without and a slight decrease for QRA days than non-QRA days. We also find substantial differences in the spatial distribution of blocks between QRA days and RRWP days. Motivated by the vital role of the RRWPs, the latter part of this thesis investigates the causal drivers of RRWPs in the North Atlantic for the winter and summer seasons. RRWP episodes for summer and winter are used to identify possible causal drivers of RRWPs, whose relevance is subsequently examined in a causal network (CN) framework. The CNs reveal that local changes over the Atlantic in atmospheric blocking and low wavenumber background flow primarily drive RRWPs for both seasons. RRWPs also have feedback on background flow and blocks. In winter, tropical forcing has an indirect link with RRWPs, which drives background flow changes over the Pacific, and subsequently modifies background flow over the Atlantic. In summer, a direct link from background flow over the Pacific to RRWPs exists. CNs also reveal a robust link from extratropics to tropics in the summer, where background flow over North Atlantic drives changes in the background flow over the Pacific

The South Indian Convergence Zone and relationship with rainfall variability in Mozambique

Includes abstract.Includes bibliographical references (leaves 137-145).Southern Hemisphere atmospheric circulation and sea surface temperatures in the South Indian Ocean, associated with the South Indian Convergence Zone (SICZ), the main rain producing system over southern Africa, and links with rainfall over Mozambique, are analysed. Cluster analysis applied to gridded outgoing long-wave radiation data, were used to identify convective activity related to the tropical temperate troughs that collectively form the SICZ. Intraseasonal and inter-annual variability of these systems and their contribution to the anomalies of rainfall over Mozambique were explored using composite, correlation and regression analyses for both the early (October to December) and the late (January to March) summer

High-resolution modeling of typhoon morakot (2009): Vortex rossby waves and their role in extreme precipitation over Taiwan

A high-resolution nonhydrostatic numerical model, the Advanced Regional Prediction System (ARPS), was used to simulate Typhoon Morakot (2009) as it made landfall over Taiwan, producing record rainfall totals. In particular, the mesoscale structure of the typhoon was investigated, emphasizing its associated deep convection, the development of inner rainbands near the center, and the resultant intense rainfall over western Taiwan. Simulations at 15- and 3-km grid spacing revealed that, following the decay of the initial inner eyewall, a new, much larger eyewall developed as the typhoon made landfall over Taiwan. Relatively large-amplitude wave structures developed in the outer eyewall and are identified as vortex Rossby waves (VRWs), based on the wave characteristics and their similarity to VRWs identified in previous studies. Moderate to strong vertical shear over the typhoon system produced a persistent wavenumber-1 (WN1) asymmetric structure during the landfall period, with upward motion and deep convection in the downshear and downshear-left sides, consistent with earlier studies. This strong asymmetry masks the effects of WN1 VRWs. WN2 and WN3 VRWs apparently are associated with the development of deep convective bands in Morakot's southwestern quadrant. This occurs as the waves move cyclonically into the downshear side of the cyclone. Although the typhoon track and topographic enhancement contribute most to the recordbreaking rainfall totals, the location of the convective bands, and their interaction with the mountainous terrain of Taiwan, also affect the rainfall distribution. Quantitatively, the 3-km ARPS rainfall forecasts are superior to those obtained from coarser-resolution models. © 2013 American Meteorological Society

Advection and autocatalysis as organizing principles for banded vegetation patterns

We motivate and analyze a simple model for the formation of banded vegetation patterns. The model incorporates a minimal number of ingredients for vegetation growth in semi-arid landscapes. It allows for comprehensive analysis and sheds new light onto phenomena such as the migration of vegetation bands and the interplay between their upper and lower edges. The key ingredient is the formulation as a closed reaction-diffusion system, thus introducing a conservation law that both allows for analysis and provides ready intuition and understanding through analogies with characteristic speeds of propagation and shock waves.Comment: 25

Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence

The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use datasets that are temporally subsampled relative to the time steps required for the numerical integration of differential equations. Here, we investigate how this often overlooked processing step affects the quality of an emulator's predictions. We implement two ML architectures from a class of methods called reservoir computing: (1) a form of Nonlinear Vector Autoregression (NVAR), and (2) an Echo State Network (ESN). Despite their simplicity, it is well documented that these architectures excel at predicting low dimensional chaotic dynamics. We are therefore motivated to test these architectures in an idealized setting of predicting high dimensional geophysical turbulence as represented by Surface Quasi-Geostrophic dynamics. In all cases, subsampling the training data consistently leads to an increased bias at small spatial scales that resembles numerical diffusion. Interestingly, the NVAR architecture becomes unstable when the temporal resolution is increased, indicating that the polynomial based interactions are insufficient at capturing the detailed nonlinearities of the turbulent flow. The ESN architecture is found to be more robust, suggesting a benefit to the more expensive but more general structure. Spectral errors are reduced by including a penalty on the kinetic energy density spectrum during training, although the subsampling related errors persist. Future work is warranted to understand how the temporal resolution of training data affects other ML architectures