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Clarifying the near-term anthropogenic warming rate by filtering annual variability using a physics based Green’s function approach

Presentation Date
Tuesday, December 14, 2021 at 8:08am
Convention Center - Room 208-210



The rate of global surface warming is a crucial observable quantity for tracking progress towards global climate targets. Its near-term evolution is however also strongly influenced by interannual-to-decadal variability, which can hamper detection of the effects of emission mitigation. Hence, process-based approaches are needed that can reduce this variability, by separating interannual fluctuations from forced and longer-term changes.

We present a new such approach, based on Green’s functions that relate fluctuations in global mean surface air temperature (GSAT) to the monthly geographical pattern of sea-surface temperatures. For each month, a contribution to GSAT from internal variability is calculated, and subtracted from the total as a physics based filtering of the total GSAT anomaly relative to 1850-1900.

For near-term warming rates under differing assumptions, we show that our approach can advance separation between the climate responses to low and high emission scenarios by up to a decade.

Our filtering approach reduces the diagnosed rate of surface warming over the most recent decade (2011-2020), which was influenced by the El Nino of 2015-2016, from the observed 0.35 °C per decade in the HadCRUT5 dataset, to 0.24 °C per decade, consistent with the 50-year trend (1971-2020) from the same dataset. Conversely, the rate over the so-called “global warming hiatus” period (2001-2010), which was observed to be only 0.08 °C per decade, strengthens to 0.21 °C per decade using our method.

We suggest that such filtered warming rates could represent a strong addition to the tools used by the climate community to inform policy makers and stakeholder communities, provided an effort is made to develop, improve and validate standardized Green’s functions.

Funding Program Area(s)