Detecting human influence on extreme temperatures in China

Abstract

[1] This study compares observed and model-simulated spatiotemporal patterns of changes in Chinese extreme temperatures during 1961-2007 using an optimal detection method. Four extreme indices, namely annual maximum daily maximum (TXx) and daily minimum (TNx) temperatures and annual minimum daily maximum (TXn) and daily minimum (TNn) temperatures, are studied. Model simulations are conducted with the CanESM2, which include six 5-member ensembles under different historical forcings, i.e., four individual external forcings (greenhouse gases, anthropogenic aerosol, land use change, and solar irradiance), combined effect of natural forcings (solar irradiance and volcanic activity), and combined effect of all external forcings (both natural and anthropogenic forcings). We find that anthropogenic influence is clearly detectable in extreme temperatures over China. Additionally, anthropogenic forcing can also be separated from natural forcing in two-signal analyses. The influence of natural forcings cannot be detected in any analysis. Moreover, there are indications that the effects of greenhouse gases and/or land use change may be separated from other anthropogenic forcings in warm extremes TXx and TNx in joint two-signal analyses. These results suggest that further investigations of roles of individual anthropogenic forcing are justified, particularly in studies of extremely warm temperatures over China. Citation:

Observational Quantification of Climatic and Human Influences on Vegetation Greening in China

“…This dataset is derived from a combination of the CRU TS3.2 and the NCEP/NCAR reanalysis. All these datasets have been widely used in climate change studies [41,42]. Socioeconomic data at the provincial level from the China Statistical Yearbooks are collected to analyze the underlying non-climate factors that influence the changes in vegetation.…”

Observed changes in temperature extremes over Asia and their attribution

“…This is typically accomplished using detection and attribution analysis, in which observations are compared with the simulated responses of climate models to natural and external forcings. Evidence of anthropogenic influence through the emission of greenhouse gases and aerosols, and sometimes evidence of influence from natural external forcings, can be clearly detected in the magnitudes of extreme temperatures, such as the highest annual values of daily maximum and minimum temperatures (TXx and TNx, respectively) and annual lowest daily maximum and minimum temperatures (TXn and TNn, respectively), on both global and regional scales (e.g., Christidis et al 2005;Zwiers et al 2011;Wen et al 2013;Yin et al 2016;Kim et al 2016). In general, climate models can accurately simulate the observed changes in the warmest night-time temperatures, but they underestimate the observed changes in the coldest temperatures (e.g., Kim et al 2016).…”

Detection and attribution analysis of annual mean temperature changes in China

“…Christidis et al (2013) showed that land use changes may have a cooling effect on temperature extremes at a global scale (especially on extremely warm days) due to the increase of albedo accompanied with deforestation. Wen et al (2013) also found a detectable effect of LU on annual maximum daily temperatures, suggesting that the impact of land use changes on extremely warm days might be detectable even at a regional scale. However, the LU effect is hard to detect in annual mean temperature changes on a global scale .…”

“…However, the use of shorter time averages also requires estimation of a larger covariance matrix, which results in larger estimation error with the limited data sample. According to Wen et al (2013), if an analysis is conducted on different multi-year non-overlapping mean series, the detection results are not sensitive to the use of time averaging. For this reason, we based our results on the analysis of a 9 yr mean series.…”

“…They detected the effect of anthropogenic forcing from greenhouse gases and sulfate aerosols in the annual mean temperature over China. Using an optimal detection method, Wen et al (2013) compared spatio-temporal patterns of 4 extreme indices (namely annual maxima of daily maximum and daily minimum temperatures and annual minima of daily maximum and daily minimum temperatures) with those observed and simulated by the Canadian Earth System Model 2 (CanESM2) over China between 1961 and 2007. They established a clear connection be tween human emission of greenhouse gases and ex treme temperature changes in China.…”