Tag: Climate Change Downscaling Techniques

Abstract

Purpose

Eastern Africa has a complex hydroclimate and socio-economic context, making it vulnerable to climate change-induced hydrological extremes. This review presents recent research on drivers and typologies of extremes across different geographies and highlights challenges and improvements in forecasting hydrological extremes at various timescales.

Recent Findings

Droughts and floods remain the major challenges of the region. Recently, frequent alterations between droughts and floods have been a common occurrence and concern. Research underlines the heterogeneity of extremes and the impact of climate change as increased intensity and duration of extremes. Moreover, the importance of local and antecedent conditions in changing the characteristics of extremes is emphasized.

Summary

A better understanding of these drivers and how they interact is required. Observational and modeling tools must capture these relationships and extremes on short timescales. Although there are improvements in forecasting these extremes, providing relevant information beyond meteorological variables requires further research.

Abstract

This study assesses the suitability of convolutional neural networks (CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September (JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa, particularly in providing improved forecast products which are essential for end users.

Abstract

While political debates on climate change become increasingly heated, our houses and city infrastructure continue to suffer from an increasing trend of damages due to adverse atmospheric events, from heavier-than-usual rainfalls to heat waves, droughts, and floods. Adapting our homes and critical infrastructure to sustain the effects of climate dynamics requires novel data-driven interdisciplinary approaches for efficient risk mitigation. We develop a new systematic framework based on the machinery of statistical and machine learning tools to evaluate water-related home insurance risks and quantify uncertainty due to varying climate model projections. Furthermore, we introduce the concept of data depth to the analysis of weather and climate ensembles, which remains a novel territory for statistical depth methodology as well as the field of environmental risk and ensemble forecasting in general. We illustrate the new data-driven methodology for risk analysis in application to rainfall-related home insurance in the Canadian Prairies over 2002–2011.

Supplementary materials accompanying this paper appear online.