Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models

Bayesian analysis of rapid climate change during the last glacial using Greenland δ18O data

We present statistical methods to determine climate regimes for the last glacial period using three temperature proxy records from Greenland: measurements of δ18O from the Greenland Ice Sheet Project 2 (GISP2), the Greenland Ice Core Project (GRIP) and the North Greenland Ice Core Project (NGRIP) using different timescales. A Markov Chain Monte Carlo method is presented to infer the number of states in a latent variable model along with their associated parameters. By using Bayesian model comparison methods we find that a model with 3 states is sufficient. These states correspond to a gradual cooling during the Greenland Interstadials, more rapid temperature decrease into Greenland Stadial and to the sudden rebound temperature increase at the onset of Greenland Interstadials. We investigate the recurrence properties of the onset of Greenland Interstadials and find no evidence to reject the null hypothesis of randomly timed events

A digital process optimization, process design and process informatics system for high-energy abrasive mass finishing

This research describes a new digital-based system to improve the efficiency and to reduce costs of high-energy abrasive mass finishing processes. The system is developed from a rigorous programme of theoretical analyses, technical experiments and industry validations. The system is able to predict the response of the process, in the context of component surface roughness and cycle time, due to employed input parameters: machining speeds, rotational velocities, immersion depth and abrasive media type. A graphical user interface (GUI) was designed to permit on-screen analyses and determination of system response under a wide range of parameters. The system converges to an optimized machining solution using optimization methods and convergence theory. The output from the system associates optimized machining parameters with output criteria that may be a target surface roughness, a minimum cycle time or a production planning period. This facilitates use of the system as a cycle design tool, as production decision support or as a process cost model. The system is generic in design and with minor modification of input and output criteria and has potential application to many other processes and applications including for example, pharmaceutical, food processing, agriculture and automotive. © 2017 Springer-Verlag Londo

Evidence for two abrupt warming events of SST in the last century

We have recently suggested that the warming in the sea surface temperature (SST) since 1900, did not occur smoothly and slowly, but with two rapid shifts in 1925/1926 and 1987/1988, which are more obvious over the tropics and the northern midlatitudes. Apart from these shifts, most of the remaining SST variability can be explained by the El Niño Southern Oscillation and the Pacific Decadal Oscillation (PDO). Here, we provide evidence that the timing of these two SST shifts (around 60 years) corresponds well to the quasi-periodicity of many natural cycles, like that of the PDO, the global and Northern Hemisphere annual mean temperature, the Atlantic Multi-decadal Oscillation, the Inter-Tropical Convergence Zone, the Southwest US Drought data, the length of day, the air surface temperature, the Atlantic meridional overturning circulation and the change in the location of the centre of mass of the solar system. In addition, we show that there exists a strong seasonal link between SST and ENSO over the tropics and the NH midlatitudes, which becomes stronger in autumn of the Northern Hemisphere. Finally, we found that before and after each SST shift, the intrinsic properties of the SST time series obey stochastic dynamics, which is unaffected by the modulation of these two shifts. In particular, the SST fluctuations for the time period between the two SST shifts exhibit 1/f-type long-range correlations, which are frequently encountered in a large variety of natural systems. Our results have potential implications for future climate shifts and crossing tipping points due to an interaction of intrinsic climate cycles and anthropogenic greenhouse gas emissions