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 Changes in Climate Extremes and Catastrophic Events in the Mongolian Plateau from 1951 to 2012

2016 ◽  
Vol 55 (5) ◽  
pp. 1169-1182 ◽  
Author(s):  
Lei Wang ◽  
Zhi-Jun Yao ◽  
Li-Guang Jiang ◽  
Rui Wang ◽  
Shan-Shan Wu ◽  
...  
Keyword(s):  
Extreme Temperature ◽  
Climate Extremes ◽  
Warming Trend ◽  
Maximum Depth ◽  
Precipitation Extremes ◽  
Mongolian Plateau ◽  
Catastrophic Events ◽  
Temperature And Precipitation ◽  
Spatial Changes ◽  
Average Maximum

AbstractThe spatiotemporal changes in 21 indices of extreme temperature and precipitation for the Mongolian Plateau from 1951 to 2012 were investigated on the basis of daily temperature and precipitation data from 70 meteorological stations. Changes in catastrophic events, such as droughts, floods, and snowstorms, were also investigated for the same period. The correlations between catastrophic events and the extreme indices were examined. The results show that the Mongolian Plateau experienced an asymmetric warming trend. Both the cold extremes and warm extremes showed greater warming at night than in the daytime. The spatial changes in significant trends showed a good homogeneity and consistency in Inner Mongolia. Changes in the precipitation extremes were not as obvious as those in the temperature extremes. The spatial distributions in changes of precipitation extremes were complex. A decreasing trend was shown for total precipitation from west to east as based on the spatial distribution of decadal trends. Drought was the most serious extreme disaster, and prolonged drought for longer than 3 yr occurred about every 7–11 yr. An increasing trend in the disaster area was apparent for flood events from 1951 to 2012. A decreasing trend was observed for the maximum depth of snowfall from 1951 to 2012, with a decreased average maximum depth of 10 mm from the 1990s.

scholarly journals Probabilistic assessment of extreme temperature dynamics

2015 ◽  
Vol 56 ◽  
Author(s):  
Ilona Šeputytė ◽  
Robertas Alzbutas
Keyword(s):  
Moving Average ◽  
Uncertain Data ◽  
Extreme Temperature ◽  
Gumbel Distribution ◽  
Extreme Value Distribution ◽  
Extreme Value ◽  
Temperature Extremes ◽  
Probabilistic Assessment ◽  
Variation Analysis ◽  
Air Temperatures

In order to estimate likelihood of the annual minimum and maximum Lithuanian air temperatures (based on 1961–2014 data) the probabilistic assessment using the extreme value distributions was performed, in particular, for each sample the best extreme value distribution was identified. In addition, to the previously mentioned study of dry bulb temperature extremes, wet bulb temperature extremes study, which enables to determine the relative humidity, was also carried out. Usingthe selected Gumbel distribution, the local temperature data analysis in eastern Lithuania, i.e. in Dūkštas, region was conducted. Then temperature variation analysis using the moving average method was carried out and the extremes changes in view of the uncertain data were investigated.

scholarly journals Observed Changes in Return Values of Annual Temperature Extremes over Argentina

2008 ◽  
Vol 21 (21) ◽  
pp. 5455-5467 ◽  
Author(s):  
Matilde Rusticucci ◽  
Bárbara Tencer
Keyword(s):  
Minimum Temperature ◽  
Extreme Temperature ◽  
Value Theory ◽  
Extreme Value ◽  
Maximum Temperature ◽  
Temperature Extremes ◽  
Frequency Of Occurrence ◽  
Extreme Temperature Events ◽  
Huge Impact ◽  
Maximum Minimum

Abstract Extreme temperature events are one of the most studied extreme events since their occurrence has a huge impact on society. In this study, the frequency of occurrence of absolute extreme temperature events in Argentina is analyzed. Four annual extremes are defined based on minimum and maximum daily data: the highest maximum (minimum) temperature of the year, and the lowest maximum (minimum) temperature of the year. Applying the extreme value theory (EVT), a generalized extreme value (GEV) distribution is fitted to these extreme indices and return values are calculated for the period 1956–2003. Its spatial distribution indicates that, for warm extremes, maximum temperature (Tx) is expected to be greater than 32°C at least once every 100 yr throughout the country (reaching values even higher than 46°C in the central region), while minimum temperature (Tn) is expected to exceed 16°C (reaching 30°C in the central and northern regions). Cold annual extremes show larger gradients across the country, with Tx being lower than 8°C at least once every 100 yr, and Tn lower than 0°C every 2 yr, with values even less than −10°C in the southwestern part of the country. However, the frequency of occurrence of climatic extremes has changed throughout the globe during the twentieth century. Changes in return values of annual temperature extremes due to the 1976–77 climatic shift at six long-term datasets are then analyzed. The lowest Tx of the year is the variable in which the 1976–77 shift is less noticeable. At all the stations studied there is a decrease in the probability of occurrence of the highest Tx if the study is based on more recent records, while the frequency of occurrence of the highest Tn increases at some stations and decreases at others. This implies that in the “present climate” (after 1977) there is a greater frequency of occurrence of high values of Tn at Observatorio Central Buenos Aires and Río Gallegos together with a lower frequency of occurrence of high values of Tx, leading to a decrease in the annual temperature range. The most noticeable change in return values due to the 1976–77 shift is seen in Patagonia where the 10-yr return value for the highest Tn increases from 13.7°C before 1976 to 18.6°C after 1977. That is, values of the highest Tn that occurred at least once every 10 yr in the “past climate” (before 1976) now happened more than once every 2 yr.

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

2016 ◽  
Vol 47 (9-10) ◽  
pp. 2833-2849 ◽  
Author(s):  
Jiali Wang ◽  
Yuefeng Han ◽  
Michael L. Stein ◽  
Veerabhadra R. Kotamarthi ◽  
Whitney K. Huang
Keyword(s):  
Extreme Temperature ◽  
Extreme Value ◽  
Generalized Extreme Value

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.

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