Potential predictability of precipitation over the continental United States

Our ability to predict precipitation on climate time-scales (months–decades) is limited by our ability to separate signals in the climate system (cyclical and secular) from noise — that is, variability due to processes that are inherently unpredictable on climate time-scales. This dissertation describes methods for characterizing “weather” noise — variability that arises from daily-scale processes — as well as the potential predictability of precipitation on climate time-scales. In each method, we make use of a climate-stationary null model for precipitation and determine which characteristics of the true, non-stationary system cannot be captured by a stationary assumption. This un-captured climate variability is potentially predictable, meaning that it is due to climate time-scale processes, although those processes themselves may not be entirely predictable, either practically or theoretically. The three primary methods proposed in this dissertation are 1. A stochastic framework for modeling precipitation occurrence with proper daily-scale memory representation, using variable order Markov chains and information criteria for order selection. 2. A corresponding method for representing precipitation intensity, allowing for memory in intensity processes. 3. A semi-parametric stochastic framework for precipitation which represents intensity and occurrence without separating the processes, designed to handle the issues that arise from estimating likelihoods for zero-inflated processes. Using each of these methods, potential predictability is determined across the contiguous 48 United States. Additionally, the methods of Chapter 4 are used to determine the magnitude of weather and climate variability for the “historical runs” of five global climate models for comparison against observational data. It is found that while some areas of the contiguous 48 United States are potentially very predictable (up to ∼ 70% of interannual variability), many regions are so dominated by weather noise that climate signals are effectively masked. Broadly, perhaps 20–30% of interannual variability may be potentially predictable, but this ranges considerably with geography and the annual seasonal cycle, yielding “hot spots” and “cold spots” of potential predictability. The analyzed global climate models demonstrate a fairly robust representation of weather-scale processes, and properly represent the ratio of weather-to- climate induced variability, despite some regional errors in mean precipitation totals and corresponding variability

Stochastic physical models for wind fields and precipitation extremes

A major goal of this thesis is to introduce stochastic, physically consistent models for precipitation extremes based on the moisture budget. The moisture budget describes the moisture flux convergence and is essential for the generation of precipitation and in particular extreme precipitation. The introduced models are used to extensively study to which extent the budget equation can account for characteristics of precipitation extremes. An important question in this respect is under which conditions the budget equation generates a heavy-tailed behavior. A further point is to understand whether the spatial structure of the humidity transport is essential in generating precipitation extremes. It is demonstrated that the humidity budget equation does not allow for the emergence of heavy-tailed precipitation distributions from light-tailed distribution for wind and humidity. At the same time finite sample approximations of the models suggest that asymptotic properties may be of very limited practical relevance. The models considered here show a remarkable stability to the correlation of wind and humidity. We prove the convergence of a precipitation model to its max-stable limit, which yields asymptotic spatial independence of precipitation extremes. Further, there is no prominent difference between precipitation extremes in purely rotational or purely divergent flow. The budget equation reveals a strong sensitivity to the marginal distributions of wind and humidity and further assumptions, which shows the need for well-established distributional assumptions for these variables. In order to model moisture flux convergence spatially consistent a multivariate Gaussian random field formulation is introduced. It represents the differential relations of a wind field and related variables such as the streamfunction, velocity potential, vorticity, and divergence. The covariance model of the Gaussian random field is based on a flexible bivariate Mat´ern covariance function for the streamfunction and velocity potential. It allows for different variances in the potentials, nonzero correlations between them, anisotropy, and a flexible smoothness parameter. The joint covariance function of the related variables is derived analytically. Further, it is shown that a consistent model with nonzero correlations between the potentials and positive definite covariance function is possible, rebutting a claim of Obukhov (1954). The statistical model is fitted to forecasts of the horizontal wind fields of a mesoscale numerical weather prediction system. Parameter uncertainty is assessed by a parametric bootstrap method. The estimates reveal only physically negligible correlations between the potentials. The covariance model provides opportunity for a wealth of applications in data assimilation

Hydro-meteorological risk assessment methods and management by nature-based solutions

Hydro-meteorological risk (HMR) management involves a range of methods, such as monitoring of uncertain climate, planning and prevention by technical countermeasures, risk assessment, preparedness for risk by early-warnings, spreading knowledge and awareness, response and recovery. To execute HMR management by risk assessment, many models and tools, ranging from conceptual to sophisticated/numerical methods are currently in use. However, there is still a gap in systematically classifying and documenting them in the field of disaster risk management. This paper discusses various methods used for HMR assessment and its management via potential nature-based solutions (NBS), which are actually lessons learnt from nature. We focused on three hydro-meteorological hazards (HMHs), floods, droughts and heatwaves, and their management by relevant NBS. Different methodologies related to the chosen HMHs are considered with respect to exposure, vulnerability and adaptation interaction of the elements at risk. Two widely used methods for flood risk assessment are fuzzy logic (e.g. fuzzy analytic hierarchy process) and probabilistic methodology (e.g. univariate and multivariate probability distributions). Different kinds of indices have been described in the literature to define drought risk, depending upon the type of drought and the purpose of evaluation. For heatwave risk estimation, mapping of the vulnerable property and population-based on geographical information system is a widely used methodology in addition to a number of computational, mathematical and statistical methods, such as principal component analysis, extreme value theorem, functional data analysis, the Ornstein–Uhlenbeck process and meta-analysis. NBS (blue, green and hybrid infrastructures) are promoted for HMR management. For example, marshes and wetlands in place of dams for flood and drought risk reduction, and green infrastructure for urban cooling and combating heatwaves, are potential NBS. More research is needed into risk assessment and management through NBS, to enhance its wider significance for sustainable living, building adaptations and resilience

Modelling intense rainfall in a changing climate

Anthropogenic climate change is unequivocal, with serious implications for society. Decades of atmospheric pollution have precipitated rapid non-stationarity in the hydrosphere, changing the frequency and intensity of storms in space and time. Utilities and civil infrastructure span generations, requiring practitioners to assess the local impacts of hydro-meteorological change. Rainfall simulation and extreme value theory are necessary for water resources planning and hazard mitigation. However, purely statistical techniques lack physical realism and the estimation of larger extremes can be highly uncertain. This thesis presents a new approach for estimating short duration rainfall extremes in a changing climate with mechanistic stochastic rainfall models. Mechanistic stochastic models simulate rainfall with rectangular pulses which conceptualise the phenomenology of rainfall generation in storms. But, since their inception over 30 years ago, they have tended to under estimate rainfall extremes at fine temporal scales. Motivated by industry to improve the physical realism of extreme rainfall estimation at sub-hourly scales, a censored modelling approach is presented with Bartlett-Lewis rectangular pulse models to simulate the intense rainfall profile. With censored rainfall simulation, intense storm profiles are constructed from the superposition of cells, from which extremes are sampled. The approach is applied to two test sites in the UK and Germany and used to estimate rainfall extremes in the present and hypothesised future climates at the end of this century. A new downscaling methodology is developed in which the rainfall models are conditioned on an ensemble of CMIP5 climate model outputs for moderate and severe climate forcing. Using K-nearest neighbour sampling to identify the training data for calibration, model parameter estimators are approximated using multivariate linear regression to enable estimation outside the covariate range. The approach is introduced with conditioning on mean monthly near surface air temperature and verified with further conditioning on relative humidity.Open Acces

Impact of climate change on agricultural and natural ecosystems

This book illustrates the main results deriving from fourteen studies, dealing with the impact of climate change on different agricultural and natural ecosystems, carried out within the Impact of Climate change On agricultural and Natural Ecosystems (ICONE) project funded by the ALFA Programme of the European Commission. During this project, a common methodology on several Global Change-related matters was developed and shared among members of scientific communities coming from Latin America and Europe. In order to facilitate this interdisciplinary approach, specific mobility programmes, addressed to post-graduate, Master and PhD students, have been organized. The research, led by the research groups, was focused on the study of the impact of climate change on various environmental features (i.e. runoff in hydrological basins, soil erosion and moisture, forest canopy, sugarcane crop, land use, drought, precipitation, etc). Integrated and shared methodologies of atmospheric physics, remote sensing, eco-physiology and modelling have been applied