Quantifying stochastic uncertainty in detection time of human-caused climate signals
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States). Program for Climate Model Diagnosis and Intercomparison
- Environment and Climate Change Canada, Victoria, BC (Canada). Canadian Centre for Climate Modelling and Analysis
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Earth, Atmospheric, and Planetary Sciences
Large initial condition ensembles of a climate model simulation provide many different realizations of internal variability noise superimposed on an externally forced signal. They have been used to estimate signal emergence time at individual grid points, but are rarely employed to identify global fingerprints of human influence. Here we analyze 50- and 40-member ensembles performed with 2 climate models; each was run with combined human and natural forcings. We apply a pattern-based method to determine signal detection time in individual ensemble members. Distributions of are characterized by the median and range , computed for tropospheric and stratospheric temperatures over 1979 to 2018. Lower stratospheric cooling—primarily caused by ozone depletion—yields values between 1994 and 1996, depending on model ensemble, domain (global or hemispheric), and type of noise data. For greenhouse-gas–driven tropospheric warming, larger noise and slower recovery from the 1991 Pinatubo eruption lead to later signal detection (between 1997 and 2003). The stochastic uncertainty is greater for tropospheric warming (8 to 15 y) than for stratospheric cooling (1 to 3 y). In the ensemble generated by a high climate sensitivity model with low anthropogenic aerosol forcing, simulated tropospheric warming is larger than observed; detection times for tropospheric warming signals in satellite data are within ranges in 60% of all cases. The corresponding number is 88% for the second ensemble, which was produced by a model with even higher climate sensitivity but with large aerosol-induced cooling. Whether the latter result is physically plausible will require concerted efforts to reduce significant uncertainties in aerosol forcing.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA)
- Grant/Contract Number:
- AC52-07NA27344; SCW1295
- OSTI ID:
- 1562311
- Alternate ID(s):
- OSTI ID: 1625037; OSTI ID: 1635090
- Report Number(s):
- LLNL-JRNL-770081
- Journal Information:
- Proceedings of the National Academy of Sciences of the United States of America, Vol. 116, Issue 40; ISSN 0027-8424
- Publisher:
- National Academy of SciencesCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
A pause in Southern Hemisphere circulation trends due to the Montreal Protocol
|
journal | March 2020 |
Climate model synthetic satellite brightness temperature data
|
dataset | January 2023 |
Similar Records
Modeling and analysis of the tritium fuel cycle for ARC- and STEP-class D-T fusion power plants
Marine boundary layer aerosol in the eastern North Atlantic: seasonal variations and key controlling processes