Exceptional stratospheric contribution to human fingerprints on atmospheric temperature.

In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO2 should warm Earth's troposphere and cool the stratosphere. This important signature of anthropogenic climate change has been documented in weather balloon and satellite temperature measurements extending from near-surface to the lower stratosphere. Stratospheric cooling has also been confirmed in the mid to upper stratosphere, a layer extending from roughly 25 to 50 km above the Earth's surface (S25 - 50). To date, however, S25 - 50 temperatures have not been used in pattern-based attribution studies of anthropogenic climate change. Here, we perform such a "fingerprint" study with satellite-derived patterns of temperature change that extend from the lower troposphere to the upper stratosphere. Including S25 - 50 information increases signal-to-noise ratios by a factor of five, markedly enhancing fingerprint detectability. Key features of this global-scale human fingerprint include stratospheric cooling and tropospheric warming at all latitudes, with stratospheric cooling amplifying with height. In contrast, the dominant modes of internal variability in S25 - 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S25 - 50 signal and noise patterns are accompanied by large cooling of S25 - 50 (1 to 2[Formula: see text]C over 1986 to 2022) and low S25 - 50 noise levels. Our results explain why extending "vertical fingerprinting" to the mid to upper stratosphere yields incontrovertible evidence of human effects on the thermal structure of Earth's atmosphere.

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

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.

Identifying human influences on atmospheric temperature.

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.

Context for interpreting equilibrium climate sensitivity and transient climate response from the CMIP6 Earth system models.

For the current generation of earth system models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), the range of equilibrium climate sensitivity (ECS, a hypothetical value of global warming at equilibrium for a doubling of CO2) is 1.8°C to 5.6°C, the largest of any generation of models dating to the 1990s. Meanwhile, the range of transient climate response (TCR, the surface temperature warming around the time of CO2 doubling in a 1% per year CO2 increase simulation) for the CMIP6 models of 1.7°C (1.3°C to 3.0°C) is only slightly larger than for the CMIP3 and CMIP5 models. Here we review and synthesize the latest developments in ECS and TCR values in CMIP, compile possible reasons for the current values as supplied by the modeling groups, and highlight future directions. Cloud feedbacks and cloud-aerosol interactions are the most likely contributors to the high values and increased range of ECS in CMIP6.

Competing influences of anthropogenic warming, ENSO, and plant physiology on future terrestrial aridity.

The 2011-2016 Californian drought illustrates that drought-prone areas do not always experience relief once a favorable phase of El Niño-Southern Oscillation (ENSO) returns. In the 21st century, such an expectation is unrealistic in regions where global warming induces an increase in terrestrial aridity larger than the aridity changes driven by ENSO variability. This premise is also flawed in areas where precipitation supply cannot offset the global warming-induced increased evaporative demand. Here, atmosphere-only experiments are analyzed to identify land regions in which aridity is currently sensitive to ENSO, and where projected future changes in mean aridity exceed the range caused by ENSO variability. Insights into the drivers of these aridity changes are obtained in simulations with incremental addition of three different factors to current climate: ocean warming, vegetation response to elevated CO2 levels, and intensified CO2 radiative forcing. The effect of ocean warming overwhelms the range of ENSO-driven temperature variability worldwide, increasing potential evapotranspiration (PET) in most ENSO-sensitive regions. Additionally, ~39% of the regions currently sensitive to ENSO receive less precipitation in the future, independent of the ENSO phase. Aridity increases consequently in 67-72% of the ENSO-sensitive area. When both radiative and physiological effects are considered, the area affected by aridity rises to 75-79% when using PET-derived measures of aridity, but declines to 41% when total soil moisture aridity indicator is employed. This reduction mainly occurs because plant stomatal resistance increases under enhanced CO2 concentrations, which results in improved plant water use efficiency, and hence reduced evapotranspiration and soil desiccation. Imposing CO2-invariant stomatal resistance may overestimate future drying in PET-derived indices.

Moving beyond the Total Sea Ice Extent in Gauging Model Biases.

Reproducing characteristics of observed sea ice extent remains an important climate modeling challenge. In this study we describe several approaches to improve how model biases in total sea ice distribution are quantified, and apply them to historically forced simulations contributed to the Coupled Model Intercomparison Project phase 5 (CMIP5). The quantity of hemispheric total sea ice area, or some measure of its equatorward extent is often used to evaluate model performance. We introduce a new approach which 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 several observational data sets, we apply several new methods to evaluate the climatological spatial distribution and the annual cycle of sea ice cover in 41 CMIP5 models. We show 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. Our results 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 fine scale structure of sea ice characteristics, however, our "sector scale" metric aids to reduce the impact of compensating errors in hemispheric integrals.