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The negative effects of human activities on the altitude pattern of atmospheric temperature

The key features of this pattern are global-scale tropospheric warming and stratospheric cooling over the 34-y satellite temperature record. We show that current climate models are highly unlikely to produce this distinctive signal pattern by internal variability alone, or in response to naturally forced changes in solar output and volcanic aerosol loadings.

These results highlight the very unusual nature of observed changes in atmospheric temperature. 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.

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.

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. External influences include human-caused changes in well-mixed greenhouse gases, stratospheric ozone, and other radiative forcing agents, as well as natural fluctuations in solar irradiance and volcanic aerosols.

We have two main scientific objectives.

To date, only one signal detection study involving hemispheric-scale surface temperature changes has relied on information 9. When fingerprint investigations use information from simulations with natural external forcing, it is typically for the purpose of ascertaining whether model-predicted solar and volcanic signals are detectable in observational climate records, and whether the amplitude of the model signals is consistent with observed estimates of signal strength 71213.

3.1 Factors affecting climate

We are addressing a different statistical question here. We seek to determine whether observed changes in the large-scale thermal structure of the atmosphere are truly unusual relative to the best current estimates of the total natural variability of the climate system.

The significance testing framework applied here is highly conservative. Our estimates incorporate variability information from 850 AD to 2005, and sample substantially larger naturally forced changes in volcanic aerosol loadings and solar irradiance than have been observed over the satellite era.

Our second objective is to examine the sensitivity of fingerprint results to current uncertainties in models and observations. With one exception 11previous fingerprint studies of changes in the vertical structure of atmospheric temperature have used information from individual models.

  • Winds that blow to Britain from inland areas such as central Europe will be cold and dry in winter;
  • Because of the large thermal inertia of the oceanic mixed layer, the recovery of tropospheric temperature from volcanically induced cooling can take up to a decade Fig;
  • Direction of prevailing winds Winds that blow from the sea often bring rain to the coast and dry weather to inland areas;
  • Each entry in the dataset included;
  • In some cold periods, glaciers grew and spread over large regions.

These limitations have raised questions regarding the reliability of fingerprint-based findings of a discernible human influence on climate 14. We use atmospheric temperature changes from simulations with estimated historical changes in these factors: We also analyze integrations with the following: We compare simulation output with observed atmospheric temperature changes inferred from satellite-based Microwave Sounding Units MSUs.

Our focus is on zonally averaged temperature changes for three broad layers of the atmosphere: We use observational MSU information from two different groups: An important aspect of our fingerprint study is its use of additional estimates of observational uncertainty provided by the RSS group 17 SI Appendix.

Human and natural influences on the changing thermal structure of the atmosphere

Two processing choices facilitate the comparison of models and observations. First, we calculate synthetic MSU temperatures from CMIP-5 simulations, so that modeled and observed layer-averaged temperatures are vertically weighted in a similar way 10. Splicing makes it possible to compare modeled and observed temperature changes over the full observed satellite record.

Global-Mean Temperature Changes Fig. The abrupt TLS warming signals Fig. Because of the large thermal inertia of the oceanic mixed layer, the recovery of tropospheric temperature from volcanically induced cooling can take up to a decade Fig.