Examination on the Spatial Coverage Change and Future Projection of Climate Extreme Events during Summer and Winter in South Korea Using a Combined Climate Extreme Index

Abstract Based upon trends in observed climate, extreme events are thought to be increasing in frequency and/or magnitude. This change in extreme events is attributed to enhancement of the hydrologic cycle caused by increased greenhouse gas concentrations. Results are presented of relatively long (50 yr) regional climate model simulations of the western United States examining the sensitivity of climate and extreme events to a doubling of preindustrial atmospheric CO2 concentrations. These results indicate a shift in the temperature distribution, resulting in fewer cold days and more hot days; the largest changes occur at high elevations. The rainfall distribution is also affected; total rain increases as a result of increases in rainfall during the spring season and at higher elevations. The risk of flooding is generally increased, as is the severity of droughts and heat waves. These results, combined with results of decreased snowpack and increased evaporation, could further stress the water supply of the western United States.

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An Initialized Attribution Method for Extreme Events on Subseasonal to Seasonal Time-Scales

Journal of Climate ◽

10.1175/jcli-d-19-1021.1 ◽

2020 ◽

pp. 1-53

Author(s):

Guomin Wang ◽

Pandora Hope ◽

Eun-Pa Lim ◽

Harry H Hendon ◽

Julie M Arblaster

Keyword(s):

Extreme Events ◽

Extreme Event ◽

Global Climate ◽

Climate Extreme ◽

Weather Extremes ◽

Climate Services ◽

Weather And Climate ◽

Heat Event ◽

Climate Events ◽

Historical Levels

AbstractWhen record-breaking climate and weather extremes occur, decision-makers and planners want to know whether they are random natural events with historical levels of re-occurrence or are reflective of an altered frequency or intensity as a result of climate change. This paper describes a method to attribute extreme weather and climate events to observed increases in atmospheric CO2 using an initialized sub-seasonal to seasonal coupled global climate prediction system. Application of this method provides quantitative estimates of the contribution arising from increases in the level of atmospheric CO2 to individual weather and climate extreme events. Using a coupled sub-seasonal to seasonal forecast system differs from other methods because it has the merit of being initialized with the observed conditions and subsequently reproducing the observed events and their mechanisms. This can aid understanding when the reforecasts with and without enhanced CO2 are compared and communicated to a general audience. Atmosphere-ocean interactions are accounted for. To illustrate the method, we attribute the record Australian heat event of October 2015. We find that about half of the October 2015 Australia-wide temperature anomaly is due to the increase in atmospheric CO2 since 1960. This method has the potential to provide attribution statements for forecast events within an outlook period, i.e. before they occur. This will allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services.

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Contribution of climatic changes in means and variability to temperature and precipitation extremes

10.5194/egusphere-egu2020-7852 ◽

2020 ◽

Author(s):

Karin van der Wiel ◽

Richard Bintanja

Keyword(s):

High Temperature ◽

Extreme Events ◽

Global Climate ◽

Heavy Precipitation ◽

Climatic Changes ◽

Precipitation Extremes ◽

Climate Extreme ◽

The Mean ◽

Mean Climate ◽

Mean State

<p>Weather or climate extreme events disproportionately affect societies and ecosystems. Physical understanding of the impact of global climate change on the occurrence of such extreme events is therefore crucial. Here we separate changes in the occurrence of high-temperature and heavy-precipitation events in a part caused by climatic changes of the mean state and a part caused by climatic changes in variability. We extend the frequently used Probability Ratio (PR) framework, used to quantify changes in the occurrence of extreme events, such that it produces a 'PRmean' value for changes due to a change in mean climate and a 'PRvar' value for changes due to changes in climate variability. Large ensemble climate model simulations are used to quantify changes in extreme events in a 2C warmer world. It is found that the increased occurrence of high-temperature extremes is predominantly caused by the increase of mean temperatures, with a much smaller role for changes in variability (PRmean >> PRvar). The spatial differences are considerable, however, with the polar regions standing out as regions where changes in temperature variability do have a considerable limiting effect on extreme event occurrence. Changes in heavy-precipitation extremes are generally due to changes in both mean climate and variability (PRvar &#8776; PRmean). Despite complex feedbacks in the global climate system, the ratio of PRmean to PRvar is largely independent of the event threshold and the climate scenario. These results help to quantify robustness of projected changes in climate extremes, given that projections of changes in the mean state are in many cases much better constrained than projections of changes in variability.</p>

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A chaotically driven model climate: extreme events and snapshot attractors

Nonlinear Processes in Geophysics ◽

10.5194/npg-18-573-2011 ◽

2011 ◽

Vol 18 (5) ◽

pp. 573-580 ◽

Cited By ~ 22

Author(s):

T. Bódai ◽

Gy. Károlyi ◽

T. Tél

Keyword(s):

Extreme Events ◽

Resonance Effect ◽

Circulation Model ◽

Extreme Value Statistics ◽

Climate Extreme ◽

Resonance Effects ◽

System A ◽

Peak Over Threshold ◽

Global Atmospheric Circulation ◽

Natural Measures

Abstract. In a low-order chaotic global atmospheric circulation model the effects of deterministic chaotic driving are investigated. As a result of driving, peak-over-threshold type extreme events, e.g. cyclonic activity in the model, become more extreme, with increased frequency of recurrence. When the characteristic time of the driving is comparable to that of the undriven system, a resonance effect with amplified variance shows up. For very fast driving we find a reduced enhancement of variance, which is also the case with white noise driving. Snapshot attractors and their natural measures are determined as a function of time, and a resonance effects is also identified. The extreme value statistics of group maxima is found to follow a Weibull distribution.

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Frequency Analysis of Nonidentically Distributed Hydrometeorological Extremes Associated with Large-Scale Climate Variability Applied to South Korea

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-13-0200.1 ◽

2014 ◽

Vol 53 (5) ◽

pp. 1193-1212 ◽

Cited By ~ 8

Author(s):

Taesam Lee ◽

Changsam Jeong

Keyword(s):

Parameter Estimation ◽

Climate Variability ◽

South Korea ◽

Simulation Study ◽

Extreme Events ◽

Large Scale ◽

Southern Oscillation ◽

Estimation Method ◽

Parameter Estimation Method

AbstractIn the frequency analyses of extreme hydrometeorological events, the restriction of statistical independence and identical distribution (iid) from year to year ensures that all observations are from the same population. In recent decades, the iid assumption for extreme events has been shown to be invalid in many cases because long-term climate variability resulting from phenomena such as the Pacific decadal variability and El Niño–Southern Oscillation may induce varying meteorological systems such as persistent wet years and dry years. Therefore, the objective of the current study is to propose a new parameter estimation method for probability distribution models to more accurately predict the magnitude of future extreme events when the iid assumption of probability distributions for large-scale climate variability is not adequate. The proposed parameter estimation is based on a metaheuristic approach and is derived from the objective function of the rth power probability-weighted sum of observations in increasing order. The combination of two distributions, gamma and generalized extreme value (GEV), was fitted to the GEV distribution in a simulation study. In addition, a case study examining the annual hourly maximum precipitation of all stations in South Korea was performed to evaluate the performance of the proposed approach. The results of the simulation study and case study indicate that the proposed metaheuristic parameter estimation method is an effective alternative for accurately selecting the rth power when the iid assumption of extreme hydrometeorological events is not valid for large-scale climate variability. The maximum likelihood estimate is more accurate with a low mixing probability, and the probability-weighted moment method is a moderately effective option.

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