The Benefits of Continuous Local Regression for Quantifying Global Warming

Change in global mean surface temperature (ΔGMST), based on a blend of land air and ocean water temperatures, is a widely cited climate change indicator that informs the Paris Agreement goal to limit global warming since preindustrial to “well below” 2°C. Assessment of current ΔGMST enables determination of remaining target‐consistent warming and therefore a relevant remaining carbon budget. In recent IPCC reports, ΔGMST was estimated via linear regression or differences between decade‐plus period means. We propose nonlinear continuous local regression (LOESS) using ±20 year windows to derive ΔGMST across all periods of interest. Using the three observational GMST data sets with almost complete interpolated spatial coverage since the 1950s, we evaluate 1850–1900 to 2019 ΔGMST as 1.14°C with a likely (17%–83%) range of 1.05°C–1.25°C, based on combined statistical and observational uncertainty, compared with linear regression of 1.05°C over 1880–2019. Performance tests in observational data sets and two model large ensembles demonstrate that LOESS, like period mean differences, is unbiased. However, LOESS also provides a statistical uncertainty estimate and gives warming through 2019, rather than the 1850–1900 to 2010–2019 period mean difference centered at the end of 2014. We derive historical global near‐surface air temperature change (ΔGSAT), using a subset of CMIP6 climate models to estimate the adjustment required to account for the difference between ocean water and ocean air temperatures. We find ΔGSAT of 1.21°C (1.11°C–1.32°C) and calculate remaining carbon budgets. We argue that continuous nonlinear trend estimation offers substantial advantages for assessment of long‐term observational ΔGMST.