Evaluating wind extremes in CMIP5 climate models

Kumar, Dheeraj; Mishra, Vimal; Ganguly, Auroop R.

doi:10.1007/s00382-014-2306-2

Cited by 77 publications

(70 citation statements)

References 46 publications

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“…An improved ability to decompose different causes of extreme weather could also have benefits for climate modelling applications, such as for distinguishing different drivers of variability and constraining uncertainty estimates in projected changes to extreme events11151648495051, noting that many of the repercussions of a warmer world are expected to be experienced through changes to extreme weather events and associated natural hazards912131425262730.…”

Section: Discussionmentioning

confidence: 99%

“…Recent years have seen a growing interest in the socioeconomic and biophysical impacts of extreme weather events123456789 including how they relate to climate change10111213141516 and sustainable development17181920 throughout the world. However, a current knowledge gap in understanding the causes of extreme weather events is the role of concurrent phenomena, noting that different types of phenomena such as cyclones, fronts and thunderstorms can sometimes occur simultaneously in the same geographic region (i.e., concurrently)521222324.…”

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confidence: 99%

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Extreme weather caused by concurrent cyclone, front and thunderstorm occurrences

Dowdy

Catto

2017

Sci Rep

111

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Phenomena such as cyclones, fronts and thunderstorms can cause extreme weather in various regions throughout the world. Although these phenomena have been examined in numerous studies, they have not all been systematically examined in combination with each other, including in relation to extreme precipitation and extreme winds throughout the world. Consequently, the combined influence of these phenomena represents a substantial gap in the current understanding of the causes of extreme weather events. Here we present a systematic analysis of cyclones, fronts and thunderstorms in combination with each other, as represented by seven different types of storm combinations. Our results highlight the storm combinations that most frequently cause extreme weather in various regions of the world. The highest risk of extreme precipitation and extreme wind speeds is found to be associated with a triple storm type characterized by concurrent cyclone, front and thunderstorm occurrences. Our findings reveal new insight on the relationships between cyclones, fronts and thunderstorms and clearly demonstrate the importance of concurrent phenomena in causing extreme weather.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Extreme weather caused by concurrent cyclone, front and thunderstorm occurrences

Dowdy

Catto

2017

Sci Rep

111

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“…Kumar et al (2015) noted that regional models are rarely run using the full suite of global climate model (GCM) ensembles and that regional models may therefore not be able to capture the most likely changes in extreme wind speeds. To address these limitations and drawbacks, Kumar et al (2015) used data from the latest generation of global circulation models, the Coupled Model Intercomparison Project phase 5 (CMIP5), to evaluate historical and future changes in extreme wind speeds at the global scale. They found that the trends in the ERA-Interim reanalysis dataset for the historical period showed large spatial variability but no spatially consistent pattern.…”

Section: Evident Declines In Extreme Wind Speed In Some Regionsmentioning

confidence: 99%

“…Horton et al (2012Horton et al ( , 2014 investigated the occurrence and persistence of future atmospheric stagnation events, and they noted that changes in near-surface wind occurrences varied globally, with the western United States and northern India exhibiting the largest increases and with northern and southern Africa, the eastern part of South America and Australia exhibiting decreases. Furthermore, a future projection of global-scale extreme wind speeds has been performed only in the study of Kumar et al (2015) using CMIP5 datasets. Consequently, most previous studies have not made future projections regarding mean SWS and extreme wind speeds at regional scales.…”

Section: Future Studymentioning

confidence: 99%

Changes in terrestrial near-surface wind speed and their possible causes: an overview

et al. 2017

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drag forces are caused by changes in the external and internal friction in the atmosphere. Changes in surface friction are mainly caused by changes in the surface roughness due to land use and cover change (LUCC), including urbanization, and changes in internal friction are mainly induced by changes in the boundary layer characteristics. Future studies should compare the spatio-temporal differences in SWS between high and low altitudes and quantify the effects of different factors on the SWS. Additionally, in-depth analysis of extreme SWS events and prediction of future mean and extreme SWS values at global and regional scales are also necessary. Abstract Changes in terrestrial near-surface wind speed (SWS) are induced by a combination of anthropogenic activities and natural climate changes. Thus, the study of the long-term changes of SWS and their causes is very important for recognizing the effects of these processes. Although the slowdown in SWS has been analyzed in previous studies, to the best of knowledge, no overall comparison or detailed examination of this research has been performed. Similarly, the causes of the decreases in SWS and the best directions of future research have not been discussed in depth. Therefore, we analyzed a series of studies reporting SWS trends spanning the last 30 years from around the world. The changes in SWS differ among different regions. The most significant decreases have occurred in Central Asia and North America, with mean linear trends of − 0.11 m s −1 decade −1 ; the second most significant decreases have occurred in Europe, East Asia, and South Asia, with mean linear trends of − 0.08 m s −1 decade −1 ; and the weakest decrease has occurred in Australia. Although the SWS in Africa has decreased, this region lacks long-term observational data. Therefore, the uncertainties in the long-term SWS trend are higher in this region than in other regions. The changes in SWS, caused by a mixture of global-, regional-, and localscale factors, are mainly due to changes in driving forces and drag forces. The changes in the driving forces are caused by changes in atmospheric circulation, and the changes in the Keywords

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“…These models are selected based on the availability of daily maximum 10‐m wind speed for both RCP4.5 and RCP8.5 scenarios for the period of 2006–2099. The multimodel ensemble mean (MME) in CMIP5 project well represents the observed patterns of extreme precipitation, temperature and wind speed, which has been evaluated in numerous researches (Kumar et al, ; Scoccimarro et al, ; Yao et al, ). Furthermore, we briefly assess the performance of these models for simulating present‐day climatology and find that the MME is able to capture the spatial pattern of these variables and their zonal distributions (Figures S1–S3).…”

Section: Methodsmentioning

confidence: 99%

Hot Spots of Climate Extremes in the Future

Wang

et al. 2019

JGR Atmospheres

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A Regional Extreme Climatic Change Index (RECCI), simultaneously considering the changes in intensity, frequency and interannual variability of three major extreme climatic variables (i.e., precipitation, temperature and wind speed), is constructed to represent regional changes of climate extremes in response to global warming. First, the daily outputs from 13 models in the Coupled Model Intercomparison Project phase 5 project in both historical and future simulations under the Representative Concentration Pathway 8.5 scenario are used to compute the extreme climatic indices. Second, the RECCI is computed on both annual and seasonal time scales during three periods (i.e., 2016–2035, 2046–2065 and 2080–2099) over 26 subregions. Finally, the spatiotemporal change of the RECCI is investigated, and then, the 26 subregions are classified into four categories for each period. The first category with the largest RECCI value is very sensitive to global warming, which is called hot spots of climate extremes. The results show that most hot spots are not time invariant on annual and seasonal time scales with some exceptions. On the annual time scale, the Amazon Basin is the only persistent hot spot in all three periods. For the seasonal time scale in March‐April‐May, the climate extremes in the Amazon Basin always display the strongest responsiveness to global warming, and the Eastern Africa is the only persistent hot spot in June‐July‐August in three periods. Similar results are also found for the other two seasons and periods. In addition, the change in extreme temperature is crucial over the East Asia with change in frequency prominent.

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Evaluating wind extremes in CMIP5 climate models

Abstract: are statistically not significant over most regions. The MME model simulates the spatial patterns of extreme winds for 25-100 year return periods. The projected extreme winds from GCMs exhibit statistically less significant trends compared to the historical reference period.

Cited by 77 publications

References 46 publications

Extreme weather caused by concurrent cyclone, front and thunderstorm occurrences

Extreme weather caused by concurrent cyclone, front and thunderstorm occurrences

Changes in terrestrial near-surface wind speed and their possible causes: an overview

Hot Spots of Climate Extremes in the Future

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