Approaching of May maximum surface air temperature to characteristic summer season for Baghdad city

Seasonal variability is the complex non-linear response of the physical climate system. There are two types of natural variability: those external and internal to the climate system. In any given season, natural variability may cause the climate to be different than its long-term average. This study examines with the seasonal variation of the maximum temperatures during the summer season. In addition, the maximum temperatures in May become close to the characteristics of the summer season. The monthly data for maximum temperature of May, June and July were used from Iraqi Meteorological Organization and Seismology (IMOS) for 47 years from 1970 to 2017 for Baghdad city. This period was long enough to estimate the range of approaching maximum temperature (Tmax) May to summer. Results revealed a significant Tmax for Baghdad during the second period (1992–2017) and ‎shown similar behavior of Tmax in May to June and July; on the contrary that first period (1970–1991). In second period, two phases have been found out, positive phase and negative phase. The positive phase were happened in 1995, 1999, and 2006, and the negative phase was four cases (1992, 2004, 2013, and 2016), while a few cases recorded in first period. The amplitudes of monthly variability had same distance of leaner correlation especially in 1999 and 2013 that represent coherent wave with summer seasons. The variance difference for Tmax between May and June approximately was 2°C for second study’s period, while exceed this range in first period. This variance change to 7.5°C when found difference between July and May.

Evolution of phenotypic variance provides insights into the genetic basis of adaption

Most traits are polygenic and the contributing loci can be identified by GWAS. Their adaptive architecture is, however, poorly characterized. Here, we propose a new approach to study the adaptive architecture, which does not depend on genomic data. Relying on experimental evolution we measure the phenotypic variance in replicated populations during adaptation to a new environment. Extensive computer simulations show that the evolution of phenotypic variance in a replicated experimental evolution setting is a powerful approach to distinguish between oligogenic and polygenic adaptive architectures. We apply this new method to gene expression variance in male Drosophila simulans before and after 100 generations of adaptation to a novel hot environment. The variance change in gene expression was indistinguishable for genes with and without a significant change in mean expression after 100 generations of evolution. We conclude that adaptive gene expression evolution is best explained by a highly polygenic adaptive architecture. We propose that the evolution of phenotypic variance provides a powerful approach to characterize the adaptive architecture, in particular when combined with genomic data.

Uncertainty quantification and reduction using Jacobian and Hessian information

Robust design methods have expanded from experimental techniques to include sampling methods, sensitivity analysis and probabilistic optimisation. Such methods typically require many evaluations. We study design and noise variable cross-term second derivatives of a response to quickly identify design variables that reduce response variability. We first compute the response uncertainty and variance decomposition to determine contributing noise variables of an initial design. Then we compute the Hessian second-derivative matrix cross-terms between the variance-contributing noise variables and proposed design change variables. Design variable with large Hessian terms are those that can reduce response variability. We relate the Hessian coefficients to reduction in Sobol indices and response variance change. Next, the first derivative Jacobian terms indicate which design variable can shift the mean to maintain a desired nominal target value. Thereby, design changes can be proposed to reduce variability while maintaining a targeted nominal value. This workflow finds changes that improve robustness with a minimal four runs per design change. We also explore further computation reductions achieved through compounding variables. An example is shown on a Stirling engine where the top four variance-contributing tolerances and design changes identified through 16 Hessian terms generated a design with 20% less variance.

Randomised Pseudolikelihood Ratio Change Point Estimator in Garch Models

In this paper, a randomised pseudolikelihood ratio change point estimator for GARCH model is presented. Derivation of a randomised change point estimator for the GARCH model and its consistency are given. Simulation results that support the validity of the estimator are also presented. It was observed that the randomised estimator outperforms the ordinary CUSUM of squares test, and it is optimal with large variance change ratios.