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  • 1
    Online Resource
    Online Resource
    Identifying human influences on atmospheric temperature
    Proceedings of the National Academy of Sciences ; 2013
    In:  Proceedings of the National Academy of Sciences Vol. 110, No. 1 ( 2013-01-02), p. 26-33
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 110, No. 1 ( 2013-01-02), p. 26-33
    Abstract: We perform a multimodel detection and attribution study with climate model simulation output and satellite-based measurements of tropospheric and stratospheric temperature change. We use simulation output from 20 climate models participating in phase 5 of the Coupled Model Intercomparison Project. This multimodel archive provides estimates of the signal pattern in response to combined anthropogenic and natural external forcing (the fingerprint) and the noise of internally generated variability. Using these estimates, we calculate signal-to-noise (S/N) ratios to quantify the strength of the fingerprint in the observations relative to fingerprint strength in natural climate noise. For changes in lower stratospheric temperature between 1979 and 2011, S/N ratios vary from 26 to 36, depending on the choice of observational dataset. In the lower troposphere, the fingerprint strength in observations is smaller, but S/N ratios are still significant at the 1% level or better, and range from three to eight. We find no evidence that these ratios are spuriously inflated by model variability errors. After removing all global mean signals, model fingerprints remain identifiable in 70% of the tests involving tropospheric temperature changes. Despite such agreement in the large-scale features of model and observed geographical patterns of atmospheric temperature change, most models do not replicate the size of the observed changes. On average, the models analyzed underestimate the observed cooling of the lower stratosphere and overestimate the warming of the troposphere. Although the precise causes of such differences are unclear, model biases in lower stratospheric temperature trends are likely to be reduced by more realistic treatment of stratospheric ozone depletion and volcanic aerosol forcing.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1210514109
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2013
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  • 2
    Online Resource
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    Taking climate model evaluation to the next level
    Springer Science and Business Media LLC ; 2019
    In:  Nature Climate Change Vol. 9, No. 2 ( 2019-2), p. 102-110
    Details
    In: Nature Climate Change, Springer Science and Business Media LLC, Vol. 9, No. 2 ( 2019-2), p. 102-110
    Type of Medium: Online Resource
    ISSN: 1758-678X , 1758-6798
    URL: Article
    DOI: 10.1038/s41558-018-0355-y
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2019
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  • 3
    Online Resource
    Online Resource
    Human and natural influences on the changing thermal structure of the atmosphere
    Proceedings of the National Academy of Sciences ; 2013
    In:  Proceedings of the National Academy of Sciences Vol. 110, No. 43 ( 2013-10-22), p. 17235-17240
    Details
    In: Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, Vol. 110, No. 43 ( 2013-10-22), p. 17235-17240
    Abstract: Since the late 1970s, satellite-based instruments have monitored global changes in atmospheric temperature. These measurements reveal multidecadal tropospheric warming and stratospheric cooling, punctuated by short-term volcanic signals of reverse sign. Similar long- and short-term temperature signals occur in model simulations driven by human-caused changes in atmospheric composition and natural variations in volcanic aerosols. Most previous comparisons of modeled and observed atmospheric temperature changes have used results from individual models and individual observational records. In contrast, we rely on a large multimodel archive and multiple observational datasets. We show that a human-caused latitude/altitude pattern of atmospheric temperature change can be identified with high statistical confidence in satellite data. Results are robust to current uncertainties in models and observations. Virtually all previous research in this area has attempted to discriminate an anthropogenic signal from internal variability. Here, we present evidence that a human-caused signal can also be identified relative to the larger “total” natural variability arising from sources internal to the climate system, solar irradiance changes, and volcanic forcing. Consistent signal identification occurs because both internal and total natural variability (as simulated by state-of-the-art models) cannot produce sustained global-scale tropospheric warming and stratospheric cooling. Our results provide clear evidence for a discernible human influence on the thermal structure of the atmosphere.
    Type of Medium: Online Resource
    ISSN: 0027-8424 , 1091-6490
    URL: Article
    DOI: 10.1073/pnas.1305332110
    RVK:
    TA 1000
    RVK:
    WA 15000
    Language: English
    Publisher: Proceedings of the National Academy of Sciences
    Publication Date: 2013
    detail.hit.zdb_id: 209104-5
    detail.hit.zdb_id: 1461794-8
    SSG: 11
    SSG: 12
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  • 4
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    OMIP contribution to CMIP6: experimental and diagnostic protocol for the physical component of the Ocean Model Intercomparison Project
    Copernicus GmbH ; 2016
    In:  Geoscientific Model Development Vol. 9, No. 9 ( 2016-09-19), p. 3231-3296
    Details
    In: Geoscientific Model Development, Copernicus GmbH, Vol. 9, No. 9 ( 2016-09-19), p. 3231-3296
    Abstract: Abstract. The Ocean Model Intercomparison Project (OMIP) is an endorsed project in the Coupled Model Intercomparison Project Phase 6 (CMIP6). OMIP addresses CMIP6 science questions, investigating the origins and consequences of systematic model biases. It does so by providing a framework for evaluating (including assessment of systematic biases), understanding, and improving ocean, sea-ice, tracer, and biogeochemical components of climate and earth system models contributing to CMIP6. Among the WCRP Grand Challenges in climate science (GCs), OMIP primarily contributes to the regional sea level change and near-term (climate/decadal) prediction GCs.OMIP provides (a) an experimental protocol for global ocean/sea-ice models run with a prescribed atmospheric forcing; and (b) a protocol for ocean diagnostics to be saved as part of CMIP6. We focus here on the physical component of OMIP, with a companion paper (Orr et al., 2016) detailing methods for the inert chemistry and interactive biogeochemistry. The physical portion of the OMIP experimental protocol follows the interannual Coordinated Ocean-ice Reference Experiments (CORE-II). Since 2009, CORE-I (Normal Year Forcing) and CORE-II (Interannual Forcing) have become the standard methods to evaluate global ocean/sea-ice simulations and to examine mechanisms for forced ocean climate variability. The OMIP diagnostic protocol is relevant for any ocean model component of CMIP6, including the DECK (Diagnostic, Evaluation and Characterization of Klima experiments), historical simulations, FAFMIP (Flux Anomaly Forced MIP), C4MIP (Coupled Carbon Cycle Climate MIP), DAMIP (Detection and Attribution MIP), DCPP (Decadal Climate Prediction Project), ScenarioMIP, HighResMIP (High Resolution MIP), as well as the ocean/sea-ice OMIP simulations.
    Type of Medium: Online Resource
    ISSN: 1991-9603
    URL: Article
    DOI: 10.5194/gmd-9-3231-2016
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2016
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  • 5
    Online Resource
    Online Resource
    An Overview of the Results of the Atmospheric Model Intercomparison Project (AMIP I)
    American Meteorological Society ; 1999
    In:  Bulletin of the American Meteorological Society Vol. 80, No. 1 ( 1999-01), p. 29-55
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 80, No. 1 ( 1999-01), p. 29-55
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/1520-0477(1999)080〈0029:AOOTRO〉2.0.CO;2
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 1999
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  • 6
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    Online Resource
    Industrial-era global ocean heat uptake doubles in recent decades
    Springer Science and Business Media LLC ; 2016
    In:  Nature Climate Change Vol. 6, No. 4 ( 2016-4), p. 394-398
    Details
    In: Nature Climate Change, Springer Science and Business Media LLC, Vol. 6, No. 4 ( 2016-4), p. 394-398
    Type of Medium: Online Resource
    ISSN: 1758-678X , 1758-6798
    URL: Article
    DOI: 10.1038/nclimate2915
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2016
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  • 7
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    Evaluating Climate Models with the CLIVAR 2020 ENSO Metrics Package
    American Meteorological Society ; 2021
    In:  Bulletin of the American Meteorological Society Vol. 102, No. 2 ( 2021-02), p. E193-E217
    Details
    In: Bulletin of the American Meteorological Society, American Meteorological Society, Vol. 102, No. 2 ( 2021-02), p. E193-E217
    Abstract: El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability on the planet, with far-reaching global impacts. It is therefore key to evaluate ENSO simulations in state-of-the-art numerical models used to study past, present, and future climate. Recently, the Pacific Region Panel of the International Climate and Ocean: Variability, Predictability and Change (CLIVAR) Project, as a part of the World Climate Research Programme (WCRP), led a community-wide effort to evaluate the simulation of ENSO variability, teleconnections, and processes in climate models. The new CLIVAR 2020 ENSO metrics package enables model diagnosis, comparison, and evaluation to 1) highlight aspects that need improvement; 2) monitor progress across model generations; 3) help in selecting models that are well suited for particular analyses; 4) reveal links between various model biases, illuminating the impacts of those biases on ENSO and its sensitivity to climate change; and to 5) advance ENSO literacy. By interfacing with existing model evaluation tools, the ENSO metrics package enables rapid analysis of multipetabyte databases of simulations, such as those generated by the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6). The CMIP6 models are found to significantly outperform those from CMIP5 for 8 out of 24 ENSO-relevant metrics, with most CMIP6 models showing improved tropical Pacific seasonality and ENSO teleconnections. Only one ENSO metric is significantly degraded in CMIP6, namely, the coupling between the ocean surface and subsurface temperature anomalies, while the majority of metrics remain unchanged.
    Type of Medium: Online Resource
    ISSN: 0003-0007 , 1520-0477
    URL: Article
    DOI: 10.1175/BAMS-D-19-0337.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2021
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  • 8
    Online Resource
    Online Resource
    Moving beyond the Total Sea Ice Extent in Gauging Model Biases
    American Meteorological Society ; 2016
    In:  Journal of Climate Vol. 29, No. 24 ( 2016-12-15), p. 8965-8987
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 29, No. 24 ( 2016-12-15), p. 8965-8987
    Abstract: Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. This study describes several approaches to improve how model biases in total sea ice distribution are quantified, and applies them to historically forced simulations contributed to phase 5 of the Coupled Model Intercomparison Project (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent, is often used to evaluate model performance. A new approach is introduced that investigates additional details about the structure of model errors, with an aim to reduce the potential impact of compensating errors when gauging differences between simulated and observed sea ice. Using multiple observational datasets, several new methods are applied to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. It is shown that in some models, error compensation can be substantial, for example resulting from too much sea ice in one region and too little in another. Error compensation tends to be larger in models that agree more closely with the observed total sea ice area, which may result from model tuning. The results herein suggest that consideration of only the total hemispheric sea ice area or extent can be misleading when quantitatively comparing how well models agree with observations. Further work is needed to fully develop robust methods to holistically evaluate the ability of models to capture the finescale structure of sea ice characteristics; however, the “sector scale” metric used here aids in reducing the impact of compensating errors in hemispheric integrals.
    Type of Medium: Online Resource
    ISSN: 0894-8755 , 1520-0442
    URL: Article
    URL: Article
    DOI: 10.1175/JCLI-D-16-0026.1
    DOI: 10.1175/JCLI-D-16-0026.s1
    RVK:
    UA 4548
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2016
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  • 9
    Online Resource
    Online Resource
    Quantifying underestimates of long-term upper-ocean warming
    Springer Science and Business Media LLC ; 2014
    In:  Nature Climate Change Vol. 4, No. 11 ( 2014-11), p. 999-1005
    Details
    In: Nature Climate Change, Springer Science and Business Media LLC, Vol. 4, No. 11 ( 2014-11), p. 999-1005
    Type of Medium: Online Resource
    ISSN: 1758-678X , 1758-6798
    URL: Article
    DOI: 10.1038/nclimate2389
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2014
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  • 10
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    Online Resource
    Sampling strategies for the comparison of climate model calculated and satellite observed brightness temperatures
    American Geophysical Union (AGU) ; 2000
    In:  Journal of Geophysical Research: Atmospheres Vol. 105, No. D7 ( 2000-04-16), p. 9393-9406
    Details
    In: Journal of Geophysical Research: Atmospheres, American Geophysical Union (AGU), Vol. 105, No. D7 ( 2000-04-16), p. 9393-9406
    Abstract: Brightness temperatures derived from polar‐orbiting satellites are valuable for the evaluation of global climate models. However, the effect of orbital constraints must be taken into account to ensure valid comparisons. As part of the Atmospheric Model Intercomparison Project II climate model comparisons, this study seeks to evaluate the monthly mean simulated brightness temperature differences of possible model output sampling strategies with respect to the exact satellite sampling and whether they can be practically implemented to provide meaningful comparisons with these satellite observations. We compare various sampling strategies with a proxy satellite data set constructed from model output and actual TIROS operational vertical sounder orbital trajectories, rather than with the observations themselves. To a large extent, this enables isolation of the sampling error from errors caused by deficiencies in the modeled climate processes. Our results suggest that the traditional method of calculating brightness temperatures from monthly mean temperature and moisture profiles yields biases from both nonlinear effects and the removal of the diurnal cycle that may be unacceptable in many applications. However, we also find that a brightness temperature calculation every hour of the simulation provides substantially lower sampling biases provided that there are two or more properly aligned satellites. This is encouraging because it means that for many applications modelers need not accurately mimic actual satellite trajectories in the sampling of their simulations. If only one satellite is available for comparison with simulations, more sophisticated sampling seems necessary. For such circumstances, we introduce a simple procedure that serves as a useful approximation to the rather complex procedure required to sample a model exactly as a polar‐orbiting satellite does the Earth. With all sampling methods, removal of biases associated with cloud cover is problematic and deserves further study.
    Type of Medium: Online Resource
    ISSN: 0148-0227
    URL: Article
    DOI: 10.1029/1999JD901182
    Language: English
    Publisher: American Geophysical Union (AGU)
    Publication Date: 2000
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    SSG: 16,13
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