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  • 1
    Online Resource
    Online Resource
    Human influence on the seasonal cycle of tropospheric temperature
    American Association for the Advancement of Science (AAAS) ; 2018
    In:  Science Vol. 361, No. 6399 ( 2018-07-20)
    Details
    In: Science, American Association for the Advancement of Science (AAAS), Vol. 361, No. 6399 ( 2018-07-20)
    Abstract: We provide scientific evidence that a human-caused signal in the seasonal cycle of tropospheric temperature has emerged from the background noise of natural variability. Satellite data and the anthropogenic “fingerprint” predicted by climate models show common large-scale changes in geographical patterns of seasonal cycle amplitude. These common features include increases in amplitude at mid-latitudes in both hemispheres, amplitude decreases at high latitudes in the Southern Hemisphere, and small changes in the tropics. Simple physical mechanisms explain these features. The model fingerprint of seasonal cycle changes is identifiable with high statistical confidence in five out of six satellite temperature datasets. Our results suggest that attribution studies with the changing seasonal cycle provide powerful evidence for a significant human effect on Earth’s climate.
    Type of Medium: Online Resource
    ISSN: 0036-8075 , 1095-9203
    URL: Article
    DOI: 10.1126/science.aas8806
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: American Association for the Advancement of Science (AAAS)
    Publication Date: 2018
    detail.hit.zdb_id: 128410-1
    detail.hit.zdb_id: 2066996-3
    detail.hit.zdb_id: 2060783-0
    SSG: 11
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  • 2
    Online Resource
    Online Resource
    Quantifying stochastic uncertainty in detection time of human-caused climate signals
    Proceedings of the National Academy of Sciences ; 2019
    In:  Proceedings of the National Academy of Sciences Vol. 116, No. 40 ( 2019-10), p. 19821-19827
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 116, No. 40 ( 2019-10), p. 19821-19827
    Abstract: 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 t d in individual ensemble members. Distributions of t d are characterized by the median t d { m } and range t d { r } , computed for tropospheric and stratospheric temperatures over 1979 to 2018. Lower stratospheric cooling—primarily caused by ozone depletion—yields t d { 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 t d { 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 t d { 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.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1904586116
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2019
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    Robust Anthropogenic Signal Identified in the Seasonal Cycle of Tropospheric Temperature
    American Meteorological Society ; 2022
    In:  Journal of Climate Vol. 35, No. 18 ( 2022-09-15), p. 6075-6100
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 35, No. 18 ( 2022-09-15), p. 6075-6100
    Abstract: Previous work identified an anthropogenic fingerprint pattern in T AC ( x , t ), the amplitude of the seasonal cycle of mid- to upper-tropospheric temperature (TMT), but did not explicitly consider whether fingerprint identification in satellite T AC ( x , t ) data could have been influenced by real-world multidecadal internal variability (MIV). We address this question here using large ensembles (LEs) performed with five climate models. LEs provide many different sequences of internal variability noise superimposed on an underlying forced signal. Despite differences in historical external forcings, climate sensitivity, and MIV properties of the five models, their T AC ( x , t ) fingerprints are similar and statistically identifiable in 239 of the 240 LE realizations of historical climate change. Comparing simulated and observed variability spectra reveals that consistent fingerprint identification is unlikely to be biased by model underestimates of observed MIV. Even in the presence of large (factor of 3–4) intermodel and inter-realization differences in the amplitude of MIV, the anthropogenic fingerprints of seasonal cycle changes are robustly identifiable in models and satellite data. This is primarily due to the fact that the distinctive, global-scale fingerprint patterns are spatially dissimilar to the smaller-scale patterns of internal T AC ( x , t ) variability associated with the Atlantic multidecadal oscillation and El Niño–Southern Oscillation. The robustness of the seasonal cycle detection and attribution results shown here, taken together with the evidence from idealized aquaplanet simulations, suggest that basic physical processes are dictating a common pattern of forced T AC ( x , t ) changes in observations and in the five LEs. The key processes involved include GHG-induced expansion of the tropics, lapse-rate changes, land surface drying, and sea ice decrease.
    Type of Medium: Online Resource
    ISSN: 0894-8755 , 1520-0442
    URL: Article
    URL: Article
    DOI: 10.1175/JCLI-D-21-0766.1
    DOI: 10.1175/JCLI-D-21-0766.s1
    RVK:
    UA 4548
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2022
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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  • 4
    Online Resource
    Online Resource
    Causes of differences in model and satellite tropospheric warming rates
    Springer Science and Business Media LLC ; 2017
    In:  Nature Geoscience Vol. 10, No. 7 ( 2017-7), p. 478-485
    Details
    In: Nature Geoscience, Springer Science and Business Media LLC, Vol. 10, No. 7 ( 2017-7), p. 478-485
    Type of Medium: Online Resource
    ISSN: 1752-0894 , 1752-0908
    URL: Article
    DOI: 10.1038/ngeo2973
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2017
    detail.hit.zdb_id: 2396648-8
    detail.hit.zdb_id: 2405323-5
    SSG: 16,13
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  • 5
    Online Resource
    Online Resource
    Comparing Tropospheric Warming in Climate Models and Satellite Data
    American Meteorological Society ; 2017
    In:  Journal of Climate Vol. 30, No. 1 ( 2017-01), p. 373-392
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 30, No. 1 ( 2017-01), p. 373-392
    Abstract: Updated and improved satellite retrievals of the temperature of the mid-to-upper troposphere (TMT) are used to address key questions about the size and significance of TMT trends, agreement with model-derived TMT values, and whether models and satellite data show similar vertical profiles of warming. A recent study claimed that TMT trends over 1979 and 2015 are 3 times larger in climate models than in satellite data but did not correct for the contribution TMT trends receive from stratospheric cooling. Here, it is shown that the average ratio of modeled and observed TMT trends is sensitive to both satellite data uncertainties and model–data differences in stratospheric cooling. When the impact of lower-stratospheric cooling on TMT is accounted for, and when the most recent versions of satellite datasets are used, the previously claimed ratio of three between simulated and observed near-global TMT trends is reduced to approximately 1.7. Next, the validity of the statement that satellite data show no significant tropospheric warming over the last 18 years is assessed. This claim is not supported by the current analysis: in five out of six corrected satellite TMT records, significant global-scale tropospheric warming has occurred within the last 18 years. Finally, long-standing concerns are examined regarding discrepancies in modeled and observed vertical profiles of warming in the tropical atmosphere. It is shown that amplification of tropical warming between the lower and mid-to-upper troposphere is now in close agreement in the average of 37 climate models and in one updated satellite record.
    Type of Medium: Online Resource
    ISSN: 0894-8755 , 1520-0442
    URL: Article
    URL: Article
    DOI: 10.1175/JCLI-D-16-0333.1
    DOI: 10.1175/JCLI-D-16-0333.s1
    RVK:
    UA 4548
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2017
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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  • 6
    Online Resource
    Online Resource
    Multi-Frequency Analysis of Simulated versus Observed Variability in Tropospheric Temperature
    American Meteorological Society ; 2020
    In:  Journal of Climate Vol. 33, No. 23 ( 2020-12), p. 10383-10402
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 33, No. 23 ( 2020-12), p. 10383-10402
    Abstract: Studies seeking to identify a human-caused global warming signal generally rely on climate model estimates of the “noise” of intrinsic natural variability. Assessing the reliability of these noise estimates is of critical importance. We evaluate here the statistical significance of differences between climate model and observational natural variability spectra for global-mean mid- to upper-tropospheric temperature (TMT). We use TMT information from satellites and large multimodel ensembles of forced and unforced simulations. Our main goal is to explore the sensitivity of model-versus-data spectral comparisons to a wide range of subjective decisions. These include the choice of satellite and climate model TMT datasets, the method for separating signal and noise, the frequency range considered, and the statistical model used to represent observed natural variability. Of particular interest is the amplitude of the interdecadal noise against which an anthropogenic tropospheric warming signal must be detected. We find that on time scales of 5–20 years, observed TMT variability is (on average) overestimated by the last two generations of climate models participating in the Coupled Model Intercomparison Project. This result is relatively insensitive to different plausible analyst choices, enhancing confidence in previous claims of detectable anthropogenic warming of the troposphere and indicating that these claims may be conservative. A further key finding is that two commonly used statistical models of short-term and long-term memory have deficiencies in their ability to capture the complex shape of observed TMT spectra.
    Type of Medium: Online Resource
    ISSN: 0894-8755 , 1520-0442
    URL: Article
    URL: Article
    DOI: 10.1175/JCLI-D-20-0023.1
    DOI: 10.1175/JCLI-D-20-0023.s1
    RVK:
    UA 4548
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2020
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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