Tag: Climate Change Downscaling Techniques

Abstract

Precipitation and near-surface temperature from an ensemble of 36 new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) are evaluated over Chile’s climate. The analysis is focused on four distinct climatic subregions: Northern Chile, Central Chile, Northern Patagonia, and Southern Patagonia. Over each of the subregions, first, we evaluate the performance of individual global climate models (GCMs) against a suit of precipitation and temperature observation-based gridded datasets over the historical period (1986–2014) and then we analyze the models’ projections for the end of the century (2080–2099) for four different shared socioeconomic pathways scenarios (SSP). Although the models are characterized by general wet and warm mean bias, they reproduce realistically the main spatiotemporal climatic variability over different subregions. However, none of the models is best across all subregions for both precipitation and temperature. Moreover, among the best performing models defined based on the Taylor skill score, one finds the so-called “hot models” likely exhibiting an overestimated climate sensitivity, which suggests caution in using these models for accessing future climate change in Chile. We found robust (90% of models agree in the direction of change) projected end-of-the-century reductions in mean annual precipitation for Central Chile (~ − 20 to ~ − 40%) and Northern Patagonia (~ − 10 to ~ − 30%) under scenario SSP585, but changes are strong from scenario SSP245 onwards, where precipitation is reduced by 10–20%. Northern Chile and Southern Patagonia show non-robust changes in precipitation across the models. Yet, future near-surface temperature warming presented high inter-model agreement across subregions, where the greatest increments occurred along the Andes Mountains. Northern Chile displays the strongest increment of up to ~ 6 °C in SSP585, followed by Central Chile (up to ~ 5 °C). Both Northern and Southern Patagonia show a corresponding increment by up to ~ 4 °C. We also briefly discuss about the environmental and socio-economic implications of these future changes for Chile.

Abstract

The quantitative assessment of the uncertainty components of future climate projections is critical for decision-makers and organizations to establish climate change adaptation and mitigation strategies at regional or local scales. This is the first study in which the changes in the uncertainty components of future temperature and precipitation projections are quantitatively evaluated using multiple regional climate models over East Asia, vulnerable to future climate change. For temperature, internal variability and model uncertainty were the main factors affecting the near-term projections. The scenario uncertainty continued to increase and was estimated to be the dominant factor affecting the uncertainty after the mid-term projections. Although precipitation has the same main uncertainty factors as the temperature in the near-term projections, it considerably differs from temperature because the internal variability notably contributes to the fraction to the total variance, even in the long-term projections. The internal variability of the temperature and precipitation in the near-term projections were predicted to be larger in Korea than that in East Asia. This was confirmed by regional climate models as well as previous studies using global climate models as to the importance of internal variability at smaller regional scales during the near-term projections. This study is meaningful because it provides new possibilities with respect to the consideration of climate uncertainties to the establishment of climate change policies in more detail on the regional scale.

Abstract

Expanding the South American renewable energy matrix to ensure more sustainable socio-economic development, mitigate the climate change effects, and meet the targets set in the Paris Agreement is crucial. Hence, this study sought to estimate South America’s wind speed and wind power density alterations projected by eight global climate models (GCMs) from the Coupled Model Intercomparison Project—Phase 6 (CMIP6). To this end, we applied statistical downscaling and bias correction to the GCMs outputs through the Quantile Delta Mapping method and assessed the projected changes in wind power in future climate under the Shared Socioeconomic Pathways (SSPs) SSP2-4.5 and SSP5-8.5 emission scenarios. ERA5 reanalysis data from 1995 to 2014 validated the models’ historical simulations. The CMIP6 multi-model ensemble indicated an approximate 25–50% increase in wind power density in sectors such as Northeast and South Brazil and growing wind power in regions such as Argentine Patagonia, northern Venezuela, and portions of Uruguay, Bolivia, and Paraguay. Estimates of the wind power growth for the twenty-first century in those regions reiterated their potential performance in the historical period. For the SSP5-8.5 emission scenario, the ensemble projections indicated even more favorable wind power conditions in the sectors mentioned. However, individual projections of wind intensity anomalies obtained by each ensemble member showed a large spread among the GCMs, evidencing the uncertainties associated with the prospects of change in wind power on the continent. Furthermore, this study has presented a first analysis of CMIP6 projections for South American wind power generation, providing relevant information to the energy sector decision-makers.

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.