scholarly journals Estimating changes in temperature extremes from millennial-scale climate simulations using generalized extreme value (GEV) distributions

2016 ◽  
Vol 2 (1) ◽  
pp. 79-103 ◽  
Author(s):  
Whitney K. Huang ◽  
Michael L. Stein ◽  
David J. McInerney ◽  
Shanshan Sun ◽  
Elisabeth J. Moyer
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Extreme Value ◽  
Temperature Extremes ◽  
Generalized Extreme Value ◽  
Climate System Model ◽  
Climate Simulations ◽  
Location Parameters ◽  
Recurrence Intervals ◽  
Cold Extremes

Abstract. Changes in extreme weather may produce some of the largest societal impacts of anthropogenic climate change. However, it is intrinsically difficult to estimate changes in extreme events from the short observational record. In this work we use millennial runs from the Community Climate System Model version 3 (CCSM3) in equilibrated pre-industrial and possible future (700 and 1400 ppm CO2) conditions to examine both how extremes change in this model and how well these changes can be estimated as a function of run length. We estimate changes to distributions of future temperature extremes (annual minima and annual maxima) in the contiguous United States by fitting generalized extreme value (GEV) distributions. Using 1000-year pre-industrial and future time series, we show that warm extremes largely change in accordance with mean shifts in the distribution of summertime temperatures. Cold extremes warm more than mean shifts in the distribution of wintertime temperatures, but changes in GEV location parameters are generally well explained by the combination of mean shifts and reduced wintertime temperature variability. For cold extremes at inland locations, return levels at long recurrence intervals show additional effects related to changes in the spread and shape of GEV distributions. We then examine uncertainties that result from using shorter model runs. In theory, the GEV distribution can allow prediction of infrequent events using time series shorter than the recurrence interval of those events. To investigate how well this approach works in practice, we estimate 20-, 50-, and 100-year extreme events using segments of varying lengths. We find that even using GEV distributions, time series of comparable or shorter length than the return period of interest can lead to very poor estimates. These results suggest caution when attempting to use short observational time series or model runs to infer infrequent extremes.

scholarly journals Estimating changes of temperatures and precipitation extremes in India using the Generalized Extreme Value (GEV) distribution

2018 ◽  
Author(s):  
Kishore Pangaluru ◽  
Isabella Velicogna ◽  
Tyler C. Sutterley ◽  
Yara Mohajerani ◽  
Enrico Ciraci ◽  
...  
Keyword(s):  
Extreme Temperature ◽  
Extreme Value ◽  
Precipitation Extremes ◽  
Temperature Extremes ◽  
Extreme Temperatures ◽  
Return Periods ◽  
Generalized Extreme Value ◽  
Gev Distribution ◽  
Cold Extreme ◽  
Temperature And Precipitation

Abstract. Changes in extreme temperature and precipitation may give some of the largest significant societal and ecological impacts. For changes in the magnitude of extreme temperature and precipitation over India, we used a statistical model of generalized extreme value (GEV) distribution. The GEV statistical distribution is a time-dependent distribution with different time scales of variability bounded by a precipitation, maximum (Tmax), and minimum (Tmin) temperature extremes and also assessed their possibility changes are evaluated and quantified over India is presented. The GEV-based method is applied on both precipitation and temperature extremes over India during the 20th and 21st centuries using multiple coupled climate models taking an interest in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and observational datasets. The regional means of historical warm extreme temperatures are 34.89, 36.42, and 38.14 °C for three different (10, 20, and 50-year) periods, respectively; whereas the cold extreme mean temperatures are 7.75, 4.19, and −1.57 °C. It indicates that 20th century cold extreme temperatures have relatively larger variations than the warm extremes. As for the future, the CMIP5 models of warm extreme regional mean values increase from 0.33 to 0.75 °C in all return periods (10-, 20-, and 50-year periods), while in the case of cold extreme means values vary between 0.58 and 2.29 °C. In the future, cold extreme values have a larger increasing rate over the northwest, northeast, some parts of north-central, and Inter Peninsula regions. The CRU precipitation extremes are larger than the historical extreme precipitation in all three (10, 20, and 50-year) return-periods.

scholarly journals Extreme events in total ozone over Arosa – Part 1: Application of extreme value theory

2010 ◽  
Vol 10 (20) ◽  
pp. 10021-10031 ◽  
Author(s):  
H. E. Rieder ◽  
J. Staehelin ◽  
J. A. Maeder ◽  
T. Peter ◽  
M. Ribatet ◽  
...  
Keyword(s):  
Time Series ◽  
Frequency Distribution ◽  
Extreme Events ◽  
Extreme Value Theory ◽  
Total Ozone ◽  
Volcanic Eruptions ◽  
Value Theory ◽  
Extreme Value ◽  
Data Set ◽  
Ozone Data

Abstract. In this study ideas from extreme value theory are for the first time applied in the field of stratospheric ozone research, because statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not adequately address the structure of the extremes. We show that statistical extreme value methods are appropriate to identify ozone extremes and to describe the tails of the Arosa (Switzerland) total ozone time series. In order to accommodate the seasonal cycle in total ozone, a daily moving threshold was determined and used, with tools from extreme value theory, to analyse the frequency of days with extreme low (termed ELOs) and high (termed EHOs) total ozone at Arosa. The analysis shows that the Generalized Pareto Distribution (GPD) provides an appropriate model for the frequency distribution of total ozone above or below a mathematically well-defined threshold, thus providing a statistical description of ELOs and EHOs. The results show an increase in ELOs and a decrease in EHOs during the last decades. The fitted model represents the tails of the total ozone data set with high accuracy over the entire range (including absolute monthly minima and maxima), and enables a precise computation of the frequency distribution of ozone mini-holes (using constant thresholds). Analyzing the tails instead of a small fraction of days below constant thresholds provides deeper insight into the time series properties. Fingerprints of dynamical (e.g. ENSO, NAO) and chemical features (e.g. strong polar vortex ozone loss), and major volcanic eruptions, can be identified in the observed frequency of extreme events throughout the time series. Overall the new approach to analysis of extremes provides more information on time series properties and variability than previous approaches that use only monthly averages and/or mini-holes and mini-highs.

scholarly journals Selecting Time Series Length to Moderate the Impact of Nonstationarity in Extreme Rainfall Analyses

2018 ◽  
Vol 57 (10) ◽  
pp. 2285-2296 ◽  
Author(s):  
Arthur T. DeGaetano ◽  
Christopher Castellano
Keyword(s):  
Time Series ◽  
Location Parameter ◽  
Extreme Rainfall ◽  
Extreme Value ◽  
Series Length ◽  
Limited Length ◽  
Long Term Trends ◽  
Recurrence Intervals ◽  
Stationary Location ◽  
The Impact

AbstractObserved and projected increases in the frequency of extreme rainfall complicate the extreme value analyses of precipitation that are used to guide engineering design specifications, because conventional methods assume stationarity. Uncertainty in the magnitude of the trend in future years precludes directly accounting for the trend in these analyses. While previous extreme value analyses have sought to use as long a record as possible, it is shown using stochastically generated time series that this practice exacerbates the potential error introduced by long-term trends. For extreme precipitation series characterized by a trend in the location parameter exceeding approximately 0.005% yr−1, limiting the record length to fewer than 70 years is recommended. The use of longer time periods results in partial-duration series that are significantly different from their stationary counterparts and a greater percentage of rainfall extremes that exceed the 90% confidence interval corresponding to a stationary distribution. The effect is most pronounced on the shortest (i.e., 2 yr) recurrence intervals and generally becomes undetectable for recurrence intervals of more than 25 years. The analyses also indicate that the practice of including stations with records of limited length that end several decades prior to the present should be avoided. Distributions having a stationary location parameter but trended scale parameter do not exhibit this behavior.

scholarly journals Recurrence Spectra of European Temperature in Historical Climate Simulations

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 166 ◽  
Author(s):  
M. Alvarez-Castro ◽  
Davide Faranda ◽  
Thomas Noël ◽  
Pascal Yiou
Keyword(s):  
Coupled Model ◽  
Temperature Extremes ◽  
Model Intercomparison ◽  
Model Ensemble ◽  
Climate Simulations ◽  
Ensemble Mean ◽  
Order Of Magnitude ◽  
Intercomparison Project ◽  
European Temperature Extremes ◽  
Cold Extremes

We analyse and quantify the recurrences of European temperature extremes using 32 historical simulations (1900–1999) of the fifth Coupled Model Intercomparison Project (CMIP5) and 8 historical simulations (1971–2005) from the EUROCORDEX experiment. We compare the former simulations to the 20th Century Reanalysis (20CRv2c) dataset to compute recurrence spectra of temperature in Europe. We find that, (1) the spectra obtained by the model ensemble mean are generally consistent with those of 20CR; (2) spectra biases have a strong regional dependence; (3) the resolution does not change the order of magnitude of spectral biases between models and reanalysis, (4) the spread in recurrence biases is larger for cold extremes. Our analysis of biases provides a new way of selecting a subset of the CMIP5 ensemble to obtain an optimal estimate of temperature recurrences for a range of time-scales.

scholarly journals Seasonal and continual wind speed modelling for the coastal urban city, Karachi

MAUSAM ◽  
2021 ◽  
Vol 69 (2) ◽  
pp. 289-296
Author(s):  
NAEEM SADIQ
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Weibull Distribution ◽  
Extreme Value ◽  
Generalized Extreme Value Distribution ◽  
Cumulative Distribution ◽  
Maximum Deviation ◽  
Generalized Extreme Value ◽  
Urban City ◽  
Seasonal Wind

ABSTRACT.  Variation in wind speed not only indicates the strengthening or weakening of pressure systems but its role in wind farm in the vicinity of coastal area is also crucial. Probability distributions through time series of wind speed data serves foremost basic need for the said parameters. Exploratory data analysis revealed that for coastal city Karachi, maximum wind speed (~23 m/s) occurred during monsoon with its peak during postmonsoon with maximum deviation (~3.5 m/s). Mean / trimmed mean during spring and postmonsoon (~11.5 m/s) as well as in premonsoon and monsoon (~18.5 m/s) remain almost identical while minimum wind blowing during winter and postmonsoon are also identical (~6 m/s). Autumn and winter exhibits least standard deviations. Critical and statistical values have been compared for distribution modelling, while parametric values of different seasonal and continual distributions are also estimated. The study is supported by cumulative distribution functions and probability-probability plots. It is not uncommon to use Weibull distribution for wind speed modelling. By using daily data time series of wind speed for the coastal station Karachi, it has been explored that widely accepted Weibull distribution provides comparatively poor distribution results when compared to other more complicated models (i.e., Wakeby and generalized extreme value distributions]. It is found that annual and seasonal wind comes after the Wakeby distribution except premonsoon summer which follows the generalized extreme value distribution (GEV) for the city. No continual and / or seasonal wind speed follows the Weibull distribution, ultimately and / or more appropriately. The study may give some new insights for aviation and wind engineering purposes.

scholarly journals Extreme events in total ozone over Arosa – Part 1: Application of extreme value theory

2010 ◽  
Vol 10 (5) ◽  
pp. 12765-12794 ◽  
Author(s):  
H. E. Rieder ◽  
J. Staehelin ◽  
J. A. Maeder ◽  
T. Peter ◽  
M. Ribatet ◽  
...  
Keyword(s):  
Time Series ◽  
Frequency Distribution ◽  
Extreme Events ◽  
Extreme Value Theory ◽  
Total Ozone ◽  
Volcanic Eruptions ◽  
Value Theory ◽  
Extreme Value ◽  
Data Set ◽  
Ozone Data

Abstract. In this study ideas from extreme value theory are for the first time applied in the field of stratospheric ozone research, because statistical analysis showed that previously used concepts assuming a Gaussian distribution (e.g. fixed deviations from mean values) of total ozone data do not adequately address the structure of the extremes. We show that statistical extreme value methods are appropriate to identify ozone extremes and to describe the tails of the Arosa (Switzerland) total ozone time series. In order to accommodate the seasonal cycle in total ozone, a daily moving threshold was determined and used, with tools from extreme value theory, to analyse the frequency of days with extreme low (termed ELOs) and high (termed EHOs) total ozone at Arosa. The analysis shows that the Generalized Pareto Distribution (GPD) provides an appropriate model for the frequency distribution of total ozone above or below a mathematically well-defined threshold, thus providing a statistical description of ELOs and EHOs. The results show an increase in ELOs and a decrease in EHOs during the last decades. The fitted model represents the tails of the total ozone data set with high accuracy over the entire range (including absolute monthly minima and maxima), and enables a precise computation of the frequency distribution of ozone mini-holes (using constant thresholds). Analyzing the tails instead of a small fraction of days below constant thresholds provides deeper insight into the time series properties. Fingerprints of dynamical (e.g. ENSO, NAO) and chemical features (e.g. strong polar vortex ozone loss), and major volcanic eruptions, can be identified in the observed frequency of extreme events throughout the time series. Overall the new approach to analysis of extremes provides more information on time series properties and variability than previous approaches that use only monthly averages and/or mini-holes and mini-highs.

scholarly journals Trends in rainfall and temperature extremes in Morocco

2015 ◽  
Vol 3 (2) ◽  
pp. 1175-1201 ◽  
Author(s):  
K. Khomsi ◽  
G. Mahe ◽  
Y. Tramblay ◽  
M. Sinan ◽  
M. Snoussi
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Extreme Events ◽  
Temperature Regime ◽  
Temperature Extremes ◽  
Extreme Temperatures ◽  
Region Data ◽  
Long Time ◽  
Cold Events ◽  
Semi Arid

Abstract. In Morocco, socioeconomic fields are vulnerable to weather extreme events. This work aims to analyze the frequency and the trends of temperature and rainfall extreme events in two contrasted Moroccan regions (the Tensift in the semi-arid South, and the Bouregreg in the sub-humid North), during the second half of the 20th century. This study considers long time series of daily extreme temperatures and rainfall, recorded in the stations of Marrakech and Safi for the Tensift region, and Kasba-Tadla and Rabat-Sale for the Bouregreg region, data from four other stations (Tanger, Fes, Agadir and Ouarzazate) from outside the regions were added. Extremes are defined by using as thresholds the 1st, 5th, 90th, 95th, and 99th percentiles. Results show upward trends in maximum and minimum temperatures of both regions and no generalized trends in rainfall amounts. Changes in cold events are larger than those for warm events, and the number of very cold events decrease significantly in the whole studied area. The southern region is the most affected with the changes of the temperature regime. Most of the trends found in rainfall heavy events are positive with weak magnitudes even though no statistically significant generalized trends could be identified during both seasons.

scholarly journals Flood trends and variability in the Mekong river

2010 ◽  
Vol 14 (3) ◽  
pp. 407-418 ◽  
Author(s):  
J. M. Delgado ◽  
H. Apel ◽  
B. Merz
Keyword(s):  
Time Series ◽  
20Th Century ◽  
Value Function ◽  
Location Parameter ◽  
Ordinary Least Squares ◽  
Extreme Value ◽  
Mekong River ◽  
Monte Carlo Experiment ◽  
Generalized Extreme Value ◽  
Type Ii Errors

Abstract. Annual maximum discharge is analyzed in the Mekong river in Southeast Asia with regard to trends in average flood and trends in variability during the 20th century. Data from four gauging stations downstream of Vientiane, Laos, were used, covering two distinct hydrological regions within the Mekong basin. These time series span through over 70 years and are the longest daily discharge time series available in the region. The methods used, Mann Kendal test (MK), ordinary least squares with resampling (OLS) and non-stationary generalized extreme value function (NSGEV), are first tested in a Monte Carlo experiment, in order to evaluate their detection power in presence of changing variance in the time series. The time series are generated using the generalized extreme value function with varying scale and location parameter. NSGEV outperforms MK and OLS, both because it resulted in less type II errors, but also because it allows for a more complete description of the trends, allowing to separate trends in average and in variability. Results from MK, OLS and NSGEV agreed on trends in average flood behaviour. However, the introduction of a time-varying scale parameter in the NSGEV allowed to isolate flood variability from the trend in average flood and to have a more complete view of the changes. Overall, results showed an increasing likelihood of extreme floods during the last half of the century, although the probability of an average flood decreased during the same period. A period of enhanced variance in the last quarter of the 20th century, estimated with the wavelet power spectrum as a function of time, was identified, which confirmed the results of the NSGEV. We conclude that the absence of detected positive trends in the hydrological time series was a methodological misconception due to over-simplistic models.

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