Progress in climate change detection and attribution studies in China

doi:10.12006/j.issn.1673-1719.2024.280

Abstract

Abstract:

As a major frontier scientific field in climate change research, climate change detection and attribution aim to reveal the causes of climate change and quantify the impact of external forcing on climate change. These issues are not only the core of scientific research on climate change, but also the hotspot and focus of the international negotiations on climate change. China started relatively late in the field of detection and attribution, but in the past decade, with the efforts of Chinese scientists, we have achieved a number of breakthroughs in the understanding of regional climate change and the attribution of extreme events in China, and made remarkable research progress in the field of detection and attribution of climate change in China. This review shows that since the middle of the 20th century, human activities, mainly greenhouse gas emissions, are the major driver of regional warming and changes in the frequency, intensity and duration of temperature extremes in China. Human activities have a clear influence on changes in extreme precipitation, and signals of human activity can also be found in changes in some types of drought. At century scale, anthropogenic signals can be detected in the change in both mean and extreme temperatures. For major high-impact extreme events, anthropogenic forcing increases the probability of hot extreme events and decreases the probability of cold extreme events. The attribution conclusions of human influence on heavy precipitation events, drought events and complex events have low consistency, and are affected by event definition and attribution methods, etc. It is still very difficult to assess the general conclusions on the extent of human activities’ influence on these events. In the future, it is necessary to strengthen the detection and attribution on changes in precipitation, drought, atmospheric circulation, compound events, etc., to understand and improve the reliability of extreme event attribution results.

Key words: Human influence, Detection and attribution, Climate change, Climate extremes

SUN Ying, WANG Dong-Qian, ZHANG Xue-Bin. Progress in climate change detection and attribution studies in China[J]. Climate Change Research, 2025, 21(2): 153-168.

Figures/Tables 6

Fig. 1 An illustration of the Probability Density Functions (PDFs)

Fig. 2 Detection and attribution of mean temperature changes in China based on station observations and CMIP6 model data. The figure shows attributable changes from ALL, GHG, AA, and NAT forcings to observational trend (OBS) in annual, summer (JJA), and winter (DJF) temperature for the 1901-2018 (a) and 1951-2018 (b) periods[32]

Fig. 3 Detection and attribution of changes in extreme temperature indices based on centennial observations and CMIP6 model data in eastern China. (The figure shows attributable trends to ALL, GHG, AA, and NAT signals compared with the observed trends (OBS) for annual and seasonal indices during 1901-2020 (a) and 1951-2020 (b))[44]

Fig. 4 The best estimates of scaling factors and their 5%-95% confidence intervals of the ALL signal in single-signal analyses for China under different configurations of the analyses[48]

Fig. 5 Climatic conditions that are increasingly conducive to summer heat stress as measured by summer mean wet bulb globe temperature (WGBT) in China have human-induced origins: estimates of scaling factors for one-signal (ALL) and two-signal (ANT and NAT) ?ngerprint analyses (a). The white lines mark the scaling-factor best estimates. The width of the boxplot represents the 25%-75% un-certainty ranges of the scaling-factor estimates, and the whiskers extend to the 5%-95% uncertainties ranges. Also shown are trend histograms of the observation-constrained 1961-2010 summer mean WBGT in a climate with anthropogenic-only forcings (orange) and with natural-only forcings (blue) for western (b) and eastern (c) China. The observed trends are marked by vertical red lines[57]

Fig. 6 Fitted distributions, return periods, risk ratios, and exceedance probabilities of domain-averaged MPPA (a-e) by the empirical probability formula and Rx1day% (f-j) and Rx5day% (k-o) by a GEV distribution in September 2021 over northern China based on ALL (red), NAT (blue), GHG (purple), AA (orange), and CTL (green) ensembles. Black lines indicate the observed threshold values of the September 2021 event, i.e., 140.5%, 83.87%, and 88.82% for MPPA, Rx1day%, and Rx5day%, respectively. Panels (e), (j), and (o) are best estimates and 90% confidence intervals of risk ratios (left, gray boxes) and exceedance probabilities (right, color bars). The error bars and boxes mark 5%-95% uncertainty ranges estimated via the bootstrapping method (N=1000)[82]

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