Observed changes in temperature extremes over Asia and their attribution

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The Tibetan Plateau (TP) is the largest and highest upland on Earth. Warming on the TP is faster than that in surrounding areas. Evaluating our understanding of the causes behind these changes provides a test of tools used for projections of future climate in the region. In this study, we analyse the observed changes in twelve extreme temperature indices and compare them with model simulations based on the Coupled Model Intercomparison Project Phase 5 (CMIP5). An optimal fingerprinting method is used to perform detection and attribution analyses on the changes in absolute intensity, percentile-based frequency, fixed threshold exceedances of temperature extremes and diurnal temperature ranges in the central and eastern TP. The results show that during 1958-2017 the TP has experienced increasing intensity and frequency of warm extremes and decreasing intensity and frequency of cold extremes, with almost all these changes larger than those in China and East China. The detection results and attribution analyses show that the anthropogenic (ANT) signal can be robustly detected in the trends for all extreme indices on the TP, and the natural (NAT) signal in some cases, too. The attributable contribution from ANT is estimated to be much larger than that from NAT for most indices. The study also indicates that the CMIP5 models may underestimate the magnitude of warming in some temperature extremes, especially the indices related to cold extremes. This should be kept in mind when informing adaptation decisions on the TP with projections based on the same models.

Section: Detection Resultssupporting

confidence: 89%

Changes in temperature extremes on the Tibetan Plateau and their attribution

Yin

Sun

Donat

2019

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“…The studies show that CMIP3 and CMIP5 models generally reproduced the observed changes in temperature extremes in the second half of the 20th century at the global scale (Sillmann et al 2013, Flato et al 2013 and regional scale in some regions (e.g. Alexander and Arblaster 2009, Morak et al 2013, Dong et al 2018. Evidence has shown that anthropogenic forcing has very likely contributed to the observed changes in the frequency of daily temperature extremes since the 1950s (e.g.…”

Section: Introductionmentioning

confidence: 90%

“…The World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI) defined a set of indices to describe changes in temperature and precipitation extremes (Zhang et al 2011). These indices, computed from observational data of different regions, show increasing number of warm extremes and decreasing number of cold extremes in past decades over global land (Donat et al 2013a, 2013b, 2016, Alexander 2016) and over continents including Asia (Dong et al 2018), Europe (Christidis and Stott 2016), Australia (Alexander and Arblaster 2017) and other continents (Donat et al 2013b, Alexander 2016. These studies also suggest that changes in nighttime indices are larger than those in daytime indices.…”

Section: Introductionmentioning

confidence: 99%

Human influence on frequency of temperature extremes

Sun

Zhang

et al. 2020

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We investigate the influence of external forcings on the frequency of temperature extremes over land at the global and continental scales by comparing HadEX3 observations and simulations from the Coupled Model Intercomparison Programme Phase 6 (CMIP6) project. We consider four metrics including warm days and nights (TX90p and TN90p) and cold days and nights (TX10p and TN10p). The observational dataset during 1951-2018 shows continued increases in the warm days and nights and decreases in the cold days and nights in most land areas in the years after 2010. The area of the so-called 'warming hole' in North America is much reduced in 1951-2018 compared with that in 1951-2010. The comparison between observation and simulations based on an optimal fingerprinting method shows that the anthropogenic forcing, dominated by greenhouse gases, plays the most important role in the changes of the frequency indices. Changes in CMIP6 multi-model mean response to all forcing need to be scaled down to best match the observations, indicating that the multi-model ensemble mean may have overestimated the observed changes. Analyses that involve signals from anthropogenic and natural external forcings confirm that the anthropogenic signal can be detected over global land as a whole and for most continents in all temperature indices. Analyses that include signals from greenhouse gas (GHG), anthropogenic aerosol (AA) and natural external (NAT) forcings show that the GHG signal is detected in all indices over the globe and most continents while the AA signal can be detected mainly in the warm extremes but not the cold extremes over the globe and most continents. The effect of NAT is negligible in most land areas. GHG's warming effect is offset partially by AA's cooling effect. The combined effects from both explain most of the observed changes over the globe and continents.

“…In this approach, the number of archived and easily obtainable station normals is far greater than that of station time series, particularly as one goes back in time, thus maximizing available station data in space and time (New et al 1999(New et al , 2000. The data set has been widely used to quantify temperature extremes over China and other regions (Cao et al 2017, Dong et al 2018, Yin and Sun 2018. For the purpose of comparing temperature extremes in different regions of China, we divided the Chinese mainland into eight climate regions based on administrative divisions and the regional characteristics of the monsoon climate of China (Shi and Xu 2007, Mao et al 2010.…”

Section: Datamentioning

confidence: 99%

Variations in start date, end date, frequency and intensity of yearly temperature extremes across China during the period 1961–2017

Han

Miao

Duan

et al. 2020

LETTERVariations in start date, end date, frequency and intensity of yearly temperature extremes across China during the period Abstract Frequent temperature extremes due to climate change have had serious effects on human society and the natural environment. Using a 0.25°×0.25°gridded Tmax (daily maximum temperature) and Tmin (daily minimum temperature) data set and 12 global climate models simulations from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we investigated variations in yearly temperature extremes in China during the past five decades with respect to four characteristics, namely, their start date, end date, frequency, and intensity. Results showed that the occurrence of nighttime extremes (the cold nights and warm nights) responded strongly to climate change. For 1961-2017, cold extremes started later (3.25 d/decade) and ended earlier (−4.58 d/decade), with decreased frequency (−6.56 d/decade), especially for cold nights, and weakened intensity (0.14°C/ decade). In the same period, warm extremes started earlier (−3.43 d/decade) and ended later (3.15 d/ decade) with increased frequency (6.79 d/decade), especially for warm nights, and enhanced intensity (0.09°C/decade). The spatial pattern of the variations was complex with anomalous regions. Multimodel ensembles (MMEs) from CMIP6 agreed well with observations regarding the average trends of temperature extremes over China, although detailed changes in spatial pattern were not captured adequately. The hazards of temperature extremes deserve close attention in the future due to the complex changes likely to occur across China for various characteristics of these temperature extremes under conditions of climate change.