Weather and Climate Extremes: Changes, Variations, and a Perspective from the Insurance Industry

Nutter, Franklin W.; Karl, Thomas R.; Nicholls, Neville; Ghazi, A.

doi:10.2307/2678123

Cited by 7 publications

(4 citation statements)

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“…To study the changes in climatic extremes, the Expert Team on Climate Change Detection and Indices (ETCCDI) created a core set of 27 indices [63]. The "Rclimdex" [64].…”

Section: General Climate Model Simulation the Copernicus 75 Database ...mentioning

confidence: 99%

Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

Oo,

Haishan,

Jonah

2023

International Journal of Geophysics

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The study examines the characteristics and variability of monsoon rainfall in Myanmar, focusing on the relationship between heavy rainfall, floods, and earthquakes, which impact agriculture, hydrology, and the environment. Generally, heavy rainfall can cause flooding, economic losses, and water table changes. Northern Myanmar floods occur mainly during the monsoon season from June to October and can be classified into widespread floods along major rivers like Ayeyarwady, Thanlwin, Chindwin, and Sittoung and flash floods in small streams and rivers. Climate change is expected to increase the frequency and intensity of extreme weather events, including heavy rainfall, which can trigger floods or landslides, which also can in turn cause earthquakes. Heavy rainfall over northern Myanmar and the Sagaing faults, which are the main triggers of earthquakes, has been the subject of several studies. The study uses the Copernicus 5 database of global climate model (GCM) simulations with two scenario analyses on climate change detection and indices (ETCCDI) to study changes in climatic extremes. Results show high intensity in the northern region and monsoon core regions, while the central region shows less intensity. The study also uses intensity-duration-frequency (IDF) curves to analyze the relationship between rainfall duration, intensity, and return time in major risk zones. The study finds that as short duration lengthens, rainfall intensity increases for future rainfall patterns. This information is expected to be convenient for local authorities and flood protection projects in rural and urban basins.

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“…To study the changes in climatic extremes, the Expert Team on Climate Change Detection and Indices (ETCCDI) created a core set of 27 indices [63]. The "Rclimdex" [64].…”

Section: General Climate Model Simulation the Copernicus 75 Database ...mentioning

confidence: 99%

Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

Oo,

Haishan,

Jonah

2023

International Journal of Geophysics

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“…In this study, the most effective model is chosen based on the assessment criteria presented before. A subset of (Table 4) the Team on Climate Change Detection and Indices (ETCCDI), recommended by the World Research Climate Program, was used to assess the performance of bias-corrected data in capturing the extreme climate characteristics of observed data (Karl, 1999). These indices were selected to cover a spectrum of extremes calculated for the historical simulation and target data (Peterson et al, 2001).…”

Section: Extreme Indicesmentioning

confidence: 99%

“…As stated in the Section 3.8 of the methodology, a subset of indices was used to assess the performance of biascorrected data in capturing the extreme climate characteristics of observed data, per recommendation of the World Research Climate Program (Karl, 1999). Table 10 presents the average values of the eight extreme indices introduced in section 3.78 for historical simulation (from EN-RF) and target data.…”

Section: Extreme Indicesmentioning

confidence: 99%

Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

Zebarjadian,

Dolatabadi,

Zahraie

et al. 2024

Intl Journal of Climatology

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Climate change is expected to change the frequency, duration, intensity, and pattern of precipitation, underscoring the need for accurate predictive tools. Earth system models (ESMs) serve as invaluable instruments in this endeavour, simulating climate variable variations across temporal and spatial dimensions. This study aims to develop a methodology for generating precise daily precipitation maps by rectifying biases inherent in ESM outputs. The proposed methodology includes downscaling ESM outputs to simulate historical daily grid‐based precipitation, thereby enhancing the fidelity of daily precipitation representation. For this purpose, 14 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) were employed. Random forest (RF) machine learning method was used to correct biases in these ESM outputs. This study's novelty lies in integrating results of a grid‐based RF classification model, employed to distinguish between rainy and non‐rainy days, with those obtained by two RF regression models, to estimate precipitation amounts for grid cells receiving extreme and non‐extreme precipitation, to generate an ensemble of ESM outputs. The resulting method, termed the triple coupling method (EN‐RF), was validated using precipitation data from the Divandareh‐Bijar Basin in western Iran to simulate historical climate conditions. Furthermore, the accuracy of the developed triple coupling approach was compared with that of a commonly used single machine learning‐based downscaling model (EN‐Single‐RF). Comparative analysis against a commonly used single machine learning‐based downscaling model (EN‐Single‐RF) revealed the superior performance of the EN‐RF approach in replicating the intensity and distribution of daily precipitation. Furthermore, within the triple coupling framework, support vector machine (SVM) was utilized to simulate daily historical precipitation (EN‐SVM), while the quantile mapping (QM) method served as a benchmark. Comparison of the results showed superiority of the EN‐RF to other methods (EN‐Single‐RF, EN‐SVM, and QM) in terms of various accuracy metrics (Kling‐Gupta Efficiency = 0.95, mean square error = 0.22). The findings indicated the capability of the proposed triple coupling framework using the RF algorithm to simulate the spatio‐temporal distribution of precipitation using the ESM precipitation outputs. The developed framework can be used to produce reliable projections to gain deeper insights into the potential impacts of climate change on regional precipitation patterns.

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“………………… Equation 6To study changes in climatic extremes, the Expert Team on Climate Change Detection and Indices (ETCCDI) created a core set of 27 indices [65]. The 'Rclimdex' package is used to calculate the extreme indices [66].…”

Section: General Climate Model Simulationmentioning

confidence: 99%

Climate Change Impact on the Trigger of Natural Disasters Over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

oo,

Haishan,

JONAH

2023

Preprint

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The study examines the characteristics and variability of monsoon rainfall in Myanmar, focusing on the relationship between heavy rainfall, floods, and earthquakes, which impact agriculture, hydrology, and the environment. Generally, heavy rainfall can cause flooding, economic losses, and water table changes. Northern Myanmar floods occur mainly during the monsoon season from June to October and can be classified into widespread floods along major rivers like Ayeyarwady, Thanlwin, Chindwin, and Sittoung, and flash floods in small streams and rivers. Climate change is expected to increase the frequency and intensity of extreme weather events, including heavy rainfall, which can trigger floods or landslides, which also can in turn cause earthquakes. Heavy rainfall over northern Myanmar and the Sagaing faults, which are the main triggers of earthquakes, has been the subject of several studies. The study uses the Copernicus 5 database of Global Climate Model (GCM) simulations with two scenarios analysis on Climate Change Detection and Indices (ETCCDI) to study changes in climatic extremes. Results show high intensity in the northern region and monsoon core regions, while the central region shows less intensity. The study also uses intensity-duration-frequency (IDF) curves to analyze the relationship between rainfall duration, intensity, and return time in major risk zones. The study finds that as short duration lengthens with rainfall intensity increases for future rainfall patterns. This information is expected to be convenient for local authorities and flood protection projects in rural and urban basins.

show abstract

Weather and Climate Extremes: Changes, Variations, and a Perspective from the Insurance Industry

Cited by 7 publications

References 0 publications

Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

Climate Change Impact on the Trigger of Natural Disasters over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

Triple coupling random forest approach for bias correction of ensemble precipitation data derived from Earth system models for Divandareh‐Bijar Basin (Western Iran)

Climate Change Impact on the Trigger of Natural Disasters Over South-Eastern Himalayas Foothill Region of Myanmar: Extreme Rainfall Analysis

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