Search for: All records

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)—key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO${}_2$forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

 

In 1967, scientists used a simple climate model to predict that human-caused increases in atmospheric CO 2 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 (S 25 − 50 ). To date, however, S 25 − 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 S 25 − 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 S 25 − 50 have smaller-scale temperature changes and lack uniform sign. These pronounced spatial differences between S 25 − 50 signal and noise patterns are accompanied by large cooling of S 25 − 50 (1 to 2 ° C over 1986 to 2022) and low S 25 − 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. 

Since 1980, the Arctic surface has warmed four times faster than the global mean. Enhanced Arctic warming relative to the global average warming is referred to as Arctic Amplification (AA). While AA is a robust feature in climate change simulations, models rarely reproduce the observed magnitude of AA, leading to concerns that models may not accurately capture the response of the Arctic to greenhouse gas emissions. Here, we use CMIP6 data to train a machine learning algorithm to quantify the influence of internal variability in surface air temperature trends over both the Arctic and global domains. Application of this machine learning algorithm to observations reveals that internal variability increases the Arctic warming but slows global warming in recent decades, inflating AA since 1980 by 38% relative to the externally forced AA. Accounting for the role of internal variability reconciles the discrepancy between simulated and observed AA.

 

Arctic amplification of anthropogenic climate change is widely attributed to the sea-ice albedo feedback, with its attendant increase in absorbed solar radiation, and to the effect of the vertical structure of atmospheric warming on Earth’s outgoing longwave radiation. The latter lapse rate feedback is subject, at high latitudes, to a myriad of local and remote influences whose relative contributions remain unquantified. The distinct controls on the high-latitude lapse rate feedback are here partitioned into “upper” and “lower” contributions originating above and below a characteristic climatological isentropic surface that separates the high-latitude lower troposphere from the rest of the atmosphere. This decomposition clarifies how the positive high-latitude lapse rate feedback over polar oceans arises primarily as an atmospheric response to local sea ice loss and is reduced in subpolar latitudes by an increase in poleward atmospheric energy transport. The separation of the locally driven component of the high-latitude lapse rate feedback further reveals how it and the sea-ice albedo feedback together dominate Arctic amplification as a coupled mechanism operating across the seasonal cycle.

 

The response of upper tropospheric clouds and relative humidity (RH) to warming is important to the overall sensitivity of the Earth to increasing greenhouse gas concentrations. Previous research has shown that changes in hydrologic fields should closely track rising isotherms in a warming climate. Here we show that the distribution of tropical clouds and RH in general circulation models is approximately constant under greenhouse warming when using temperature as a vertical coordinate. By assuming that these fields are an invariant function of atmospheric temperature and that temperature change follows a dilute moist adiabat, we are able to accurately predict cloud fraction and RH changes in the tropical upper troposphere (150–400 hPa) in 27 general circulation models. Our results indicate that intermodel spread in changes of tropical upper tropospheric clouds and RH is closely related to differences in model climatology and could be substantially reduced if model ensembles reliably reproduced observed climatologies.