Systematic Physics Constrained Parameter Estimation of Stochastic Differential Equations

A systematic Bayesian framework is developed for physics constrained parameter inference ofstochastic differential equations (SDE) from partial observations. The physical constraints arederived for stochastic climate models but are applicable for many fluid systems. A condition isderived for global stability of stochastic climate models based on energy conservation. Stochasticclimate models are globally stable when a quadratic form, which is related to the cubic nonlinearoperator, is negative definite. A new algorithm for the efficient sampling of such negative definite matrices is developed and also for imputing unobserved data which improve the accuracy of theparameter estimates. The performance of this framework is evaluated on two conceptual climatemodels

Evaluation of daily precipitation extremes in reanalysis and gridded observation‐based datasets over Germany

Accurate and reliable gridded datasets are important for analyzing extreme weather and climate events. Specifically, these datasets should produce extreme value statistics that are close to reality. Here we use various statistical methods to evaluate the quality of four gridded data products in representing daily precipitation extremes. The data products are the COSMO-REA6 regional reanalysis, the ERA5 global reanalysis, and the E-OBS and HYRAS gridded observation-based datasets. The statistical methods we use offer a thorough insight into the quality of the different datasets by providing temporal and spatial extreme value statistics of daily precipitation. Our results show that all datasets except HYRAS underestimate the magnitude of daily precipitation extremes when compared with weather station data. Moreover, the reanalysis datasets give generally worse extreme value statistics of daily precipitation than the gridded observation-based datasets. In particular, the reanalysis datasets often fail in reproducing the accurate timing of observed daily precipitation extremes

Spatial trend analysis of gridded temperature data at varying spatial scales

Classical assessments of trends in gridded temperature data perform independent evaluations across the grid, thus, ignoring spatial correlations in the trend estimates. In particular, this affects assessments of trend significance as evaluation of the collective significance of individual tests is commonly neglected. In this article we build a space-time hierarchical Bayesian model for temperature anomalies where the trend coefficient is modeled by a latent Gaussian random field. This enables us to calculate simultaneous credible regions for joint significance assessments. In a case study, we assess summer season trends in 65 years of gridded temperature data over Europe. We find that while spatial smoothing generally results in larger regions where the null hypothesis of no trend is rejected, this is not the case for all sub-regions

Review article: A European perspective on wind and storm damage – from the meteorological background to index-based approaches to assess impacts

Wind and windstorms cause severe damage to natural and human-made environments. Thus, wind-related risk assessment is vital for the preparation and mitigation of calamities. However, the cascade of events leading to damage depends on many factors that are environment-specific and the available methods to address wind-related damage often require sophisticated analysis and specialization. Fortunately, simple indices and thresholds are as effective as complex mechanistic models for many applications. Nonetheless, the multitude of indices and thresholds available requires a careful selection process according to the target sector. Here, we first provide a basic background on wind and storm formation and characteristics, followed by a comprehensive collection of both indices and thresholds that can be used to predict the occurrence and magnitude of wind and storm damage. We focused on five key sectors: forests, urban areas, transport, agriculture and wind-based energy production. For each sector we described indices and thresholds relating to physical properties such as topography and land cover but also to economic aspects (e.g. disruptions in transportation or energy production). In the face of increased climatic variability, the promotion of more effective analysis of wind and storm damage could reduce the impact on society and the environment

Stochastic Parameterization Toward a New View of Weather and Climate Models

Peer reviewe

Changing temporal volatility of precipitation extremes due to global warming

It is of utmost importance to understand how precipitation extremes change due to global warming. Here, we examine the volatility of precipitation extremes by analysing the waiting time distribution between events and the clustering of precipitation extremes. For this we use the ERA5 reanalysis data and high-resolution simulations with the Community Earth System Model (CESM). We find significant evidence for a power-law distribution of waiting times between precipitation extremes and of serial clustering of precipitation extremes. This suggests that precipitation extremes do not occur independently from each other. This is in contrast with previous studies which typically assume that precipitation extreme events occur independently from each other. CESM reproduces these properties well. The climate change simulations show that the waiting times between precipitation extremes become shorter and that at the same time the clustering of precipitation extremes increases. Hence, global warming affects the temporal characteristics of precipitation extremes and, thus, precipitation extremes will become more volatile.11Nsciescopu

Risk of extreme high fatalities due to weather and climate hazards and its connection to large-scale climate variability

Weather and climate hazards cause too many fatalities each year. These weather and climate hazards are projected to increase in frequency and intensity due to global warming. Here, we use a disaster database to investigate continentally aggregated fatality data for trends. We also examine whether modes of climate variability affect the propensity of fatalities. Furthermore, we quantify fatality risk by computing effective return periods which depend on modes of climate variability. We find statistically significant increasing trends for heat waves and floods for worldwide aggregated data. Significant trends occur in the number of fatalities in Asia where fatalities due to heat waves and floods are increasing, while storm-related fatalities are decreasing. However, when normalized by population size, the trends are no longer significant. Furthermore, the number of fatalities can be well described probabilistically by an extreme value distribution, a generalized Pareto distribution (GPD). Based on the GPD, we evaluate covariates which affect the number of fatalities aggregated over all hazard types. For this purpose, we evaluate combinations of modes of climate variability and socio-economic indicators as covariates. We find no evidence for a significant direct impact from socio-economic indicators; however, we find significant evidence for the impact from modes of climate variability on the number of fatalities. The important modes of climate variability affecting the number of fatalities are tropical cyclone activity, modes of sea surface temperature and atmospheric teleconnection patterns. This offers the potential of predictability of the number of fatalities given that most of these climate modes are predictable on seasonal to inter-annual time scales.Deutsche Forschungsgemeinschaft https://doi.org/10.13039/501100001659Deutsche Forschungsgemeinschaft (DE)Bundesministerium für Bildung und Forschung https://doi.org/10.13039/50110000234

Future projections of heat mortality risk for major european cities

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).Over the last few decades, heat waves have intensified and have led to excess mortality. While the probability of being affected by heat stress has significantly increased, the risk of heat mortality is rarely quantified. This quantification of heat mortality risk is necessary for systematic adaptation measures. Furthermore, heat mortality records are sparse and short, which presents a challenge for assessing heat mortality risk for future climate projections. It is therefore crucial to derive indicators for a systematic heat mortality risk assessment. Here, risk indicators based on temperature and mortality data are developed and applied to major cities in Germany, France, and Spain using regional climate model simulations. Bias-corrected daily maximum, minimum, and wet-bulb temperatures show increasing trends in future climate projections for most considered cities. In addition, we derive a relationship between daily maximum temperatures and mortality for producing future projections of heat mortality risk from extreme temperatures that is based on low (representative concentration pathway RCP2.6) and high (RCP8.5) emission scenario future climate projections. Our results illustrate that heat mortality increases by about 0.9% decade-1 in Germany, 1.7% decade-1 in France, and 7.9% decade-1 in Spain for RCP8.5 by 2050. The future climate projections also show that wet-bulb temperatures above 308C will be reached regularly, with maxima above 408Clikely by 2050. Our results suggest a significant increase of heat mortality in the future, especially in Spain. On average, our results indicate that the mortality risk trend is almost 2 times as high in all three countries for the RCP8.5 scenario relative to RCP2.6.11Nsciessciscopu

Global risks of infectious disease outbreaks and its relation to climate

Infectious disease outbreaks are recurring events which can lead to a large number of fatalities every year. Infectious disease outbreaks occur infrequently and the role of global warming and modes of climate variability for those outbreaks is not clear. Here we use an extreme value statistics approach to examine annual spatially aggregated infectious disease fatality data to compute their probability to occur using generalized Pareto distribution (GPD) models. The GPD provides a good model for modeling the fatality data and reveals that the number of fatalities follows a power-law. We find that the magnitude of Covid-19 is of an expected level given previous fatality data over the period 1900-2020. We also examined whether including co-variates in the GPD models provide better model fits. We find evidence that a pure linear trend is the best co-variate and, thus, has increased the propensity of large outbreaks to occur for most continents and world-wide. This suggests that mainly non-climate factors affect the likelihood of outbreaks. This linear trend function provides a crude representation of socio-economic trends such as improved public health. However, for South America the Atlantic multidecadal oscillation modulates the outbreak propensity as the best co-variate, showing that climate can play some role in infectious disease outbreaks in some regions.11Nsciescopu

The role of transient eddies and diabatic heating in the maintenance of European heat waves: a nonlinear quasi-stationary wave perspective

<jats:title>Abstract</jats:title><jats:p>European heat waves result from large-scale stationary waves and have major impacts on the economy and mortality. However, the dynamical processes leading to and maintaining heat waves are still not well understood. Here we use a nonlinear stationary wave model (NSWM) to examine the role played by anomalous stationary waves and how they are forced during heat waves. For our study, we use the Japanese Reanalysis (JRA-55) data for the period 1958 through 2017. We show that the NSWM can successfully reproduce the main features of the observed anomalous stationary waves in the upper troposphere. Our results indicate that the dynamics of heat waves are nonlinear, and transient momentum fluxes are the primary drivers of the observed anomalous stationary waves. The contribution from orographic forcing is moderate and mainly through nonlinear interactions with diabatic heating. Further decomposition of the transients indicates that the high-frequency transient vorticity fluxes make dominant contributions. Furthermore, our results reveal that the response to heating located in the tropical Indian Ocean and the west Pacific region is primarily responsible for maintaining the observed anomalous stationary waves linked to European heat waves. This is confirmed by exploring the relationship between heat waves and the Indian Ocean Dipole strength. The heating in the mid-latitude and tropical Atlantic region plays a secondary role. Our results suggest that European heat waves are potentially predictable by considering the nonlinear effects involved in anomalous stationary waves and the heating sources in the nearby and remote tropical region.</jats:p&gt