Improving the Estimation of Human Climate Influence by Selecting Appropriate Forcing Simulations

Abstract

The regression‐based optimal fingerprinting is a key tool for quantifying human climate influence. Most studies over the past decade used Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, limiting fingerprinting regression configuration options. The CMIP6 Detection and Attribution Model Intercomparison Project (DAMIP) provides several types of individual forcing simulations and thus greater configuration flexibility. To avoid overfitting the limited observational data, we suggest that a DAMIP‐based perfect model study is first used to best configure the fingerprinting regression prior to its application to observations. We find that a regression using all‐forcing, aerosol‐only, and natural‐only simulations is an overall best option for constraining human‐induced global terrestrial warming, which differs from choices commonly made previously. Applying this configuration to observations, we estimate that of the observed terrestrial warming of ∼1.5°C between 1850–1900 and 2011–2020, anthropogenic greenhouse gases contributed 1.4 to 2.3°C, offset by aerosol cooling of 0.2 to 1.2°C.

Deciphering China's Complex Pattern of Summer Precipitation Trends

“…We next turn to precipitation intensity, that is, changes in $${I}_{\text{CP}=i}(t)$$ in Equation 2. The global mean surface air temperature has increased about 1.1°C since the pre‐industrial period (Gillett et al., 2021) with substantially more warming over land (C. Li, Wang, et al., 2021). The atmospheric water‐holding capacity increases correspondingly following the Clausius‐Clapeyron relation (e.g., Trenberth et al., 2003).…”

“…The other two configurations further assess whether the individual influences of GHG and AER are detectable in the presence of other forcings (denoted by OGHG and OAER), respectively. As one of the two forcing responses of interest is not explicitly included in these fingerprinting regressions, such as the ANT response in the ALL + NAT configuration, we derive the needed regression coefficients by linear transformations of regression coefficients for the responses explicitly included (e.g., C. Li, Wang, et al., 2021). We conduct both separate analyses for individual CPs and joint analyses including all CPs in the same fingerprinting regression except the pattern showing no tendency of change to ensure valid estimates of the covariance matrix of the estimated responses.…”

Detectable Anthropogenic Intensification of the Summer Compound Hot and Dry Events Over Global Land Areas

“…Third, the signals of GHG, AER, and NAT forcing cannot be well separated due to the covariance of different forcing, which can lead to inaccurate estimation of the scale factor at the subcontinental scale (DelSole et al., 2018), which highlights the need for more available data sets, including observations and simulations, and more appropriate attribution methods (C. Li et al., 2021) to improve the robustness of the attribution results. Additionally, external forcing caused by the land use (LU) changes, which may be a crucial driver of the CHDEs, has not been adequately considered (Findell et al., 2017).…”

“…To examine the influence of anthropogenic factors on the severity of CHDEs at continental and subcontinental scales, we employed eight models that provide historical (ALL), GHG, AER, and NAT forcing are used (refer to Table S1 in Supporting Information S1). These models have been widely used in D&A, and previous studies have shown their ability to simulate extreme events accurately (Dong et al., 2021; C. Li et al., 2021; Seong et al., 2021). In this study, we used a total of 46 runs for external forcing simulations.…”

Attribution of differences in observed warming among subregions of China and observationally constrained projection

“…For example, although Qian and Zhang (2019) used anthropogenic aerosol forcing and land use forcing from the CMIP5 to conduct the DA analysis on seasonal temperature changes in China during the period 1950–2004, the two forcings were not detected. The CMIP6 (Eyring et al, 2016) Detection and Attribution Model Intercomparison Project (DAMIP) (Gillett et al, 2016) offer the opportunity to separately quantify the influences of anthropogenic aerosols, GHGs and NAT, considering the availability of different types of externally forcing simulations (Li et al, 2021). Compared to CMIP5 models, CMIP6 models have shown improvements in simulating climatological temperature, precipitation (Jiang et al, 2020) and extreme indices (Chen et al, 2020; Zhu et al, 2020).…”