Mean precipitation, and air pressure fields to potential forcings (e.g., The climate system, including global and regional temperature, temperatureĮxtremes, ocean heat content, tropopause height, specific humidity, zonal This period allowed forĪttribution of trends in many thermodynamic and dynamic characteristics of Studies about 100 to 150 years before present. Observations of physically measurable variables and derived diagnostics areĪvailable, with global observation networks becoming dense enough for such Typically, D&A analyses have been limited to periods when instrumental Detection and attribution studies have been an important part of theĪssessment Reports of Working Group I of the Intergovernmental Panel onĬlimate Change, from the calling for better detection of the role of humanĪctivities in climate forcing in the First Assessment Report (1990), toįormal detection and attribution studies comparing observed and simulatedĬlimate change in all assessment reports since, with increasingly confidentĪssessments of the detection of human influences and estimates of the humanĬontribution derived from attribution results. Variability generated within Earth's climate system (Hegerl et al., 1996). After methodological refinements and advances in climate modeling in the early 1990s (e.g., Hasselmann, 1993 Santer et al., 1993) there was growing evidence that the external greenhouse gas signal may be differentiated from climate This idea was initiated in early work by Hasselmann (1979). Generally speaking, D&A studies match observed changes with patterns derived from climate model simulations driven by single and multiple external forcings, including solar variability, volcanic aerosols, the well-mixed greenhouse gases, orbital variations and land use change. To address this question, so-called “detection andĪttribution” (D&A) methods have been developed (Hegerl and Zwiers, 2011 Gillett et al., 2021). One of the crucial questions in climate change research is to determine how external radiative forcings bring about climate variation and change and if the forced response may be distinguished from the internal, unforced variability. These results suggest that the use of nonlinear and multivariate proxy system models in paleoclimatic detection and attribution studies may permit more realistic, spatially resolved and multivariate fingerprint detection studies and evaluation of the climate sensitivity to external radiative forcing than has previously been possible. We can for the first time attribute this spatiotemporal fingerprint in moisture-limited tree-ring records to volcanic forcing. The pattern of simulated TRW of moisture-limited trees is consistent with the observed anomalies in the 2 years following major volcanic eruptions. In decadally smoothed temporal fingerprints, we find the observed responses toīe significantly larger and/or more persistent than the simulated responses. Temperature-sensitive TRW observationsĪnd simulations are significantly correlated for Northern HemisphereĪverages, and their variation is attributed to volcanic forcing. Temperature- and moisture-sensitive TRW simulations detectĭistinct patterns in time and space. Specifically, we detect and attribute tree-ring width (TRW) observations as a linear function of TRW simulations, which are themselves a nonlinear and multivariate TRW simulation driven with singly forced and cumulatively forced climate simulations for the period 1401–2000 CE. Here we perform a D&A study, modeling paleoclimate data observations as a function of paleoclimatic data simulations. However, this procedure may be biased by assumptions of stationarity and univariate linear response of the underlying paleoclimatic observations. The detection and attribution (D&A) of paleoclimatic change to external radiative forcing relies on regression of statistical reconstructions on simulations.
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