Quantifying stochastic uncertainty in detection time of human-caused climate signals
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 f...
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| Format: | Article |
| Language: | English |
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Proceedings of the National Academy of Sciences
2020
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| Online Access: | https://hdl.handle.net/1721.1/124380 |
| _version_ | 1826209792288358400 |
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| author | Solomon, Susan |
| author2 | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences |
| author_facet | Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Solomon, Susan |
| author_sort | Solomon, Susan |
| collection | MIT |
| description | 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 td in individual ensemble members. Distributions of td are characterized by the median td{m} and range td{r}, computed for tropospheric and stratospheric temperatures over 1979 to 2018. Lower stratospheric cooling—primarily caused by ozone depletion—yields td{m} 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 td{r} 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 td{r} 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. |
| first_indexed | 2024-09-23T14:30:31Z |
| format | Article |
| id | mit-1721.1/124380 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T14:30:31Z |
| publishDate | 2020 |
| publisher | Proceedings of the National Academy of Sciences |
| record_format | dspace |
| spelling | mit-1721.1/1243802022-09-29T09:41:10Z Quantifying stochastic uncertainty in detection time of human-caused climate signals Solomon, Susan Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences Multidisciplinary 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 td in individual ensemble members. Distributions of td are characterized by the median td{m} and range td{r}, computed for tropospheric and stratospheric temperatures over 1979 to 2018. Lower stratospheric cooling—primarily caused by ozone depletion—yields td{m} 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 td{r} 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 td{r} 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. 2020-03-27T13:55:03Z 2020-03-27T13:55:03Z 2019-09-16 2020-02-12T19:23:00Z Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 https://hdl.handle.net/1721.1/124380 Santer, Benjamin D. et al. "Quantifying stochastic uncertainty in detection time of human-caused climate signals." Proceedings of the National Academy of Sciences of the United States of America 116 (2019): 19821-19827 © 2019 The Author(s) en 10.1073/pnas.1904586116 Proceedings of the National Academy of Sciences of the United States of America Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Proceedings of the National Academy of Sciences PNAS |
| spellingShingle | Multidisciplinary Solomon, Susan Quantifying stochastic uncertainty in detection time of human-caused climate signals |
| title | Quantifying stochastic uncertainty in detection time of human-caused climate signals |
| title_full | Quantifying stochastic uncertainty in detection time of human-caused climate signals |
| title_fullStr | Quantifying stochastic uncertainty in detection time of human-caused climate signals |
| title_full_unstemmed | Quantifying stochastic uncertainty in detection time of human-caused climate signals |
| title_short | Quantifying stochastic uncertainty in detection time of human-caused climate signals |
| title_sort | quantifying stochastic uncertainty in detection time of human caused climate signals |
| topic | Multidisciplinary |
| url | https://hdl.handle.net/1721.1/124380 |
| work_keys_str_mv | AT solomonsusan quantifyingstochasticuncertaintyindetectiontimeofhumancausedclimatesignals |