Tag: Climate Change Downscaling Techniques

Abstract

The complex topography and inherent nonlinearity affiliated with influential hydrological processes of urban catchments, coupled with limited availability of measured data, limits the prediction accuracy of conventional models. Artificial Neural Network models (ANNs) have displayed commendable progress in recognising and simulating highly complex, non-linear associations allied with input-output variables, with limited comprehension of the underlying physical processes. Therefore, this paper investigates the effectiveness and accuracy of ANN models, in estimating the urban catchment runoff, employing minimal and commonly available hydrological data variables – rainfall and upstream catchment flow data, employing two powerful supervised-learning-algorithms, Bayesian-Regularization (BR) and Levenberg-Marquardt (LM). Gardiners Creek catchment, encompassed in Melbourne, Australia, with more than thirty years of quality-checked rainfall and streamflow data was chosen as the study location. Two significant storm events that transpired within the last fifteen years - the 4th of February 2011 and the 6th of November 2018, were nominated for calibration and validation of the ANN model. The study results advocate that the use of the LM-ANN model stipulates accurate estimates of the historical storm events, with a stronger correlation and lower generalisation error, in contrast to the BR-ANN model, while the integration of upstream catchment flow alongside rainfall, vindicate for their collective impact upon the dynamics of the flow being spawned at the downstream catchment locations, significantly enhancing the model performance and providing a more cost-effective and near-realistic modelling approach that can be considered for application in studies of urban catchment responses, with limited data availability.

Abstract

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry, it has become imperative to monitor and mitigate these threats to ensure civil aviation safety. The eddy dissipation rate (EDR) has been established as the standard metric for quantifying turbulence in civil aviation. This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder (QAR) data. The detection of atmospheric turbulence is approached as an anomaly detection problem. Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events. Moreover, comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available. In summary, the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data, comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms. This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Abstract

As the risks associated with air turbulence are intensified by climate change and the growth of the aviation industry, it has become imperative to monitor and mitigate these threats to ensure civil aviation safety. The eddy dissipation rate (EDR) has been established as the standard metric for quantifying turbulence in civil aviation. This study aims to explore a universally applicable symbolic classification approach based on genetic programming to detect turbulence anomalies using quick access recorder (QAR) data. The detection of atmospheric turbulence is approached as an anomaly detection problem. Comparative evaluations demonstrate that this approach performs on par with direct EDR calculation methods in identifying turbulence events. Moreover, comparisons with alternative machine learning techniques indicate that the proposed technique is the optimal methodology currently available. In summary, the use of symbolic classification via genetic programming enables accurate turbulence detection from QAR data, comparable to that with established EDR approaches and surpassing that achieved with machine learning algorithms. This finding highlights the potential of integrating symbolic classifiers into turbulence monitoring systems to enhance civil aviation safety amidst rising environmental and operational hazards.

Abstract

Tropical cyclones (TCs) are among the most devastating natural hazards for coastal regions, and their response to human activities has broad socio-economic relevance. So far, how TC responds to climate change mitigation remains unknown, complicating the design of adaptation policies. Using net-zero and negative carbon emission experiments, we reveal a robust hemisphere-asymmetric hysteretic TC response to CO₂ reduction. During the decarbonization phase, the Northern Hemisphere TC frequency continues to decrease for several more decades, while the Southern Hemisphere oceans abruptly shifts to a stormier state, with the timescales depending on mitigation details. Such systematic changes are largely attributed to the planetary-scale reorganization of vertical wind shear and midlevel upward motion associated with the hysteretic southward migration of the Intertropical Convergence Zone, underpinned by the Atlantic Meridional Overturning Circulation and El Niño-like mean state changes. The hemispheric contrast in TC response suggests promising benefits for most of the world’s population from human action to mitigate greenhouse gas warming, but it may also exacerbate regional socioeconomic disparities, for example by putting more pressure on small open-ocean island states in the Southern Hemisphere to adapt to TC risks.

Abstract

Tropical cyclones (TCs) are among the most devastating natural hazards for coastal regions, and their response to human activities has broad socio-economic relevance. So far, how TC responds to climate change mitigation remains unknown, complicating the design of adaptation policies. Using net-zero and negative carbon emission experiments, we reveal a robust hemisphere-asymmetric hysteretic TC response to CO₂ reduction. During the decarbonization phase, the Northern Hemisphere TC frequency continues to decrease for several more decades, while the Southern Hemisphere oceans abruptly shifts to a stormier state, with the timescales depending on mitigation details. Such systematic changes are largely attributed to the planetary-scale reorganization of vertical wind shear and midlevel upward motion associated with the hysteretic southward migration of the Intertropical Convergence Zone, underpinned by the Atlantic Meridional Overturning Circulation and El Niño-like mean state changes. The hemispheric contrast in TC response suggests promising benefits for most of the world’s population from human action to mitigate greenhouse gas warming, but it may also exacerbate regional socioeconomic disparities, for example by putting more pressure on small open-ocean island states in the Southern Hemisphere to adapt to TC risks.