Slowdown of the Walker circulation at solar cycle maximum.

The Pacific Walker Circulation (PWC) fluctuates on interannual and multidecadal timescales under the influence of internal variability and external forcings. Here, we provide observational evidence that the 11-y solar cycle (SC) affects the PWC on decadal timescales. We observe a robust reduction of east-west sea-level pressure gradients over the Indo-Pacific Ocean during solar maxima and the following 1-2 y. This reduction is associated with westerly wind anomalies at the surface and throughout the equatorial troposphere in the western/central Pacific paired with an eastward shift of convective precipitation that brings more rainfall to the central Pacific. We show that this is initiated by a thermodynamical response of the global hydrological cycle to surface warming, further amplified by atmosphere-ocean coupling, leading to larger positive ocean temperature anomalies in the equatorial Pacific than expected from simple radiative forcing considerations. The observed solar modulation of the PWC is supported by a set of coupled ocean-atmosphere climate model simulations forced only by SC irradiance variations. We highlight the importance of a muted hydrology mechanism that acts to weaken the PWC. Demonstration of this mechanism acting on the 11-y SC timescale adds confidence in model predictions that the same mechanism also weakens the PWC under increasing greenhouse gas forcing.

An influence of solar spectral variations on radiative forcing of climate.

The thermal structure and composition of the atmosphere is determined fundamentally by the incoming solar irradiance. Radiation at ultraviolet wavelengths dissociates atmospheric molecules, initiating chains of chemical reactions-specifically those producing stratospheric ozone-and providing the major source of heating for the middle atmosphere, while radiation at visible and near-infrared wavelengths mainly reaches and warms the lower atmosphere and the Earth's surface. Thus the spectral composition of solar radiation is crucial in determining atmospheric structure, as well as surface temperature, and it follows that the response of the atmosphere to variations in solar irradiance depends on the spectrum. Daily measurements of the solar spectrum between 0.2 µm and 2.4 µm, made by the Spectral Irradiance Monitor (SIM) instrument on the Solar Radiation and Climate Experiment (SORCE) satellite since April 2004, have revealed that over this declining phase of the solar cycle there was a four to six times larger decline in ultraviolet than would have been predicted on the basis of our previous understanding. This reduction was partially compensated in the total solar output by an increase in radiation at visible wavelengths. Here we show that these spectral changes appear to have led to a significant decline from 2004 to 2007 in stratospheric ozone below an altitude of 45 km, with an increase above this altitude. Our results, simulated with a radiative-photochemical model, are consistent with contemporaneous measurements of ozone from the Aura-MLS satellite, although the short time period makes precise attribution to solar effects difficult. We also show, using the SIM data, that solar radiative forcing of surface climate is out of phase with solar activity. Currently there is insufficient observational evidence to validate the spectral variations observed by SIM, or to fully characterize other solar cycles, but our findings raise the possibility that the effects of solar variability on temperature throughout the atmosphere may be contrary to current expectations.

Causal networks for climate model evaluation and constrained projections.

Global climate models are central tools for understanding past and future climate change. The assessment of model skill, in turn, can benefit from modern data science approaches. Here we apply causal discovery algorithms to sea level pressure data from a large set of climate model simulations and, as a proxy for observations, meteorological reanalyses. We demonstrate how the resulting causal networks (fingerprints) offer an objective pathway for process-oriented model evaluation. Models with fingerprints closer to observations better reproduce important precipitation patterns over highly populated areas such as the Indian subcontinent, Africa, East Asia, Europe and North America. We further identify expected model interdependencies due to shared development backgrounds. Finally, our network metrics provide stronger relationships for constraining precipitation projections under climate change as compared to traditional evaluation metrics for storm tracks or precipitation itself. Such emergent relationships highlight the potential of causal networks to constrain longstanding uncertainties in climate change projections.