Forecasting conditional climate-change using a hybrid approach

Akbar Akbari Esfahani; Michael J. Friedel

doi:10.1016/j.envsoft.2013.10.009

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Forecasting conditional climate-change using a hybrid approach

Akbar Akbari Esfahani·Michael J. Friedel

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Abstract

A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009–2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists.

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Environmental Modelling and Software Article

Source-reported bounding extent: 31.25° to 42° latitude; -124.41° to -102° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

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BibTeX RIS

Akbar Akbari Esfahani, Michael J. Friedel. 2014. Forecasting conditional climate-change using a hybrid approach. https://doi.org/10.1016/j.envsoft.2013.10.009

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