Natural variability contributes to model-satellite differences in tropical tropospheric warming.

A long-standing discrepancy exists between general circulation models (GCMs) and satellite observations: The multimodel mean temperature of the midtroposphere (TMT) in the tropics warms at approximately twice the rate of observations. Using a large ensemble of simulations from a single climate model, we find that tropical TMT trends (1979-2018) vary widely and that a subset of realizations are within the range of satellite observations. Realizations with relatively small tropical TMT trends are accompanied by subdued sea-surface warming in the tropical central and eastern Pacific.

Quantifying stochastic uncertainty in detection time of human-caused climate signals.

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.

Human influence on the seasonal cycle of tropospheric temperature.

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.

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 21 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.

Evidence for climate change in the satellite cloud record.

Clouds substantially affect Earth's energy budget by reflecting solar radiation back to space and by restricting emission of thermal radiation to space. They are perhaps the largest uncertainty in our understanding of climate change, owing to disagreement among climate models and observational datasets over what cloud changes have occurred during recent decades and will occur in response to global warming. This is because observational systems originally designed for monitoring weather have lacked sufficient stability to detect cloud changes reliably over decades unless they have been corrected to remove artefacts.

Observational constraints on mixed-phase clouds imply higher climate sensitivity.

Global climate model (GCM) estimates of the equilibrium global mean surface temperature response to a doubling of atmospheric CO2, measured by the equilibrium climate sensitivity (ECS), range from 2.0° to 4.6°C.

An observational radiative constraint on hydrologic cycle intensification.

Intensification of the hydrologic cycle is a key dimension of climate change, with substantial impacts on human and natural systems. A basic measure of hydrologic cycle intensification is the increase in global-mean precipitation per unit surface warming, which varies by a factor of three in current-generation climate models (about 1-3 per cent per kelvin). Part of the uncertainty may originate from atmosphere-radiation interactions.