Robust detection of forced warming in the presence of potentially large climate variability

Abstract

Forced climate warming can now be identified using statistical learning even under potentially large climate variability.

Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming

“…Internal variability is removed from the estimated forced response via negative coefficient values in areas of large internal variability relative to the magnitude of surface warming. This forced fingerprint pattern is qualitatively similar to fingerprints estimated in recent research that quantified the forced component of the global surface-temperature trend, while explicitly considering and accounting for the confounding effects of decadal internal variability ( 32 ). We note that our results are robust across different ML methods and a wide range of plausible parameter selections in the ML models ( SI Appendix , Text 1 and Figs.…”

“…Estimating the partitioning of forced and unforced changes in climate is challenging, but important for interpreting trends in climate observations and assessing model performance ( 8 , 11 , 32 , 45 ). Our study builds on recent research that applies climate-model-based learning and pattern-recognition techniques to the observational record in order to improve understanding of forced and unforced climate change ( 32 , 46 – 48 ).…”

“…The fitted PLS regression model includes coefficient maps, which we refer to as “fingerprint patterns” ( SI Appendix, Extended Methods , Text 1 , and Fig. S7 ) ( 6 , 31 , 32 ). These maps represent the patterns associated with internal variability ( Fig.…”

Physics-aware nonparametric regression models for Earth data analysis

“…However, patterns of internal variability (such as El Niño Southern Oscillation, ENSO) may nonetheless project onto the fingerprint, for example, if the fingerprint is derived from a given model (or a given set of models) but then applied to an unseen test model (or observations) with a potentially different representation of key modes of internal variability, such as ENSO. This phenomenon may lead to an inaccurate extraction of forced signals from models or observations, and could lead to over-or under-confidence in D&A statements if models systematically under-or overestimate the magnitude of internal variability [44,47]. Hence, robustness in fingerprint extraction with respect to model structural differences, or potential systematic biases in the models' representation of internal variability or of forced patterns, is an important issue that still remains a key uncertainty.…”

The proposed Anthropocene Epoch/Series is underpinned by an extensive array of mid‐20th century stratigraphic event signals

“…Human-and especially technology-driven changes are already profound as regards their rates (IPBES, 2019; IPCC, 2021) and magnitudes (Waters et al, 2016;Head et al, 2021a), producing major impacts on five great Earth spheresthe biosphere, atmosphere, hydrosphere, cryosphere and the surface of the lithosphere. Direct human perturbation of the biosphere combined with global warming are driving rapid changes in ecosystem function and biological communities (Williams et al, 2016), with increasing rates of species extinctions since the beginning of the 20 th century (Ceballos et al, 2015), severe declines in vertebrate populations from the 20 th century onwards (Ceballos et al, 2017(Ceballos et al, , 2020WWF, 2020), unprecedented and irreversible homogenization of once distinct biogeographic assemblages (Williams et al, 2022), a dramatic increase in a wide range of anthropogenically derived contaminants, and rapidly increasing global atmospheric surface temperature since 1970 (Sippel et al, 2021). The late 20 th and early 21 st centuries also saw changes in atmospheric circulation and precipitation patterns; warming of the upper ocean, rising sea level and coastal erosion; acidification of the oceans and the spread of oxygen-deficient 'dead zones'; increasingly severe extreme weather events such as heatwaves (terrestrial and marine), tropical cyclones, wildfires, and intense rainfall and flooding; a trajectory towards increasing megadroughts; and wholesale retreat of the cryosphere.…”