Emergent Constrained Projections of Mean and Extreme Warming in China

Abstract

Reliable regional temperature projections including heat extremes are essential for climate change adaptation and mitigation. Taking China as an example, simple averages from Coupled Model Intercomparison Project Phase 6 (CMIP6) models project high warming due to sampling many high climate sensitivities in the ensemble. Here, we develop an emergent constraint (EC) framework to obtain constrained mean and daily maximum temperature (TXx) warming over China by using observed global warming and local residual warming. The constrained annual mean and TXx warming over China (2.33°C [1.61–3.05°C] and 2.31°C [1.21–2.99°C]) are 0.65°C [0.04–1.76°C] and 0.63°C [–0.50–2.39°C], respectively, lower than raw projections (2.98°C [1.85–4.22°C] and 2.94°C [2.04–4.39°C]) for 2080–2099 under the intermediate‐emission scenario. Approximately half model uncertainty is reduced after constraint. The land area (population) experiencing temperature extremes in our metric is 78% (85%) of the raw projections. Our results imply a lower impact of extreme heat than implied by current raw CMIP6 projections.

Emergent Constraints on Future Projections of Tibetan Plateau Warming in Winter

“…First, we sought to identify an appropriate predictor for projected Albedo. Previous studies have used the temperature trend during an arbitrarily selected historical period to constrain future temperature change (e.g., Z. Chen et al., 2023), without considering the unstable nature of climate systems. A very recent study (Shen et al., 2023), however, proposed to select an optimal historical period for calibration, taking into account the potential effect of time‐varying climate over different historical periods, so that a more reasonable constrained result could be obtained.…”

Constraining Projected Changes in Rare Intense Precipitation Events Across Global Land Regions

“…Strong correlations between increases in global mean temperature and the intensity of rare precipitation events at global to regional scales have been noted in both observations and climate model simulations (e.g., Fischer & Knutti, 2016; Fowler et al., 2021; Kharin et al., 2013; Li et al., 2021; Seneviratne & Hauser, 2020; Sun et al., 2021; Westra et al., 2013), suggesting that this link might be useful in constraining projections of future changes in the intensity of rare precipitation events. A range of approaches have been proposed to constrain projections of future warming with constraints of observed warming using, for example, multi‐model reweighting (e.g., Liang et al., 2020), detection and attribution‐based methods (e.g., Ribes et al., 2021), and the method of emergent constraints (e.g., Chen et al., 2023; Liang et al., 2022; Tokarska et al., 2020).…”

Emergent constraints on the future East Asian winter surface air temperature changes

“…However, to establish convincible EC, it is essential to clearly understand the underlying physical mechanisms that drive these statistical relationships (Chen et al 2020, Schlund et al 2020, Shiogama et al 2022. Although some studies have constrained regional climate uncertainties, such as constraints on the South Asian summer monsoon (Huang et al 2020b) and annual mean and extreme warming in China using observed global warming (Chen et al 2023), limited attention is paid to the East Asian winter SAT uncertainties, excluding the effect of global warming, i.e. concerning local variations per 1 K of global warming.…”