Simulation of cumulus clouds under conditions observed during the Rain in Cumulus over the Ocean (RICO) field campaign. Within the gray volume, there is cloud liquid water, simulated with the PyCLES code developed by Kyle Pressel et al. Blue colors indicate rain. The simulation uses 5th-order weighted essentially non-oscillatory (WENO) schemes for discretization of all fluxes (of momentum, entropy, and total water), at a resolution of 50 m in the horizontal and 40 m in the vertical.
The upper panel of the animation shows the slow component of temperature changes between 22.5°S and 67.5°N, an area with sufficient data coverage since 1890 to allow this multivariate analysis. The indicated temperature changes are changes relative to the period 1850-1900.
The lower panel shows the time series of area-mean temperature changes (black) and the area-mean temperature change accounted for by the slow component in the main panel (red).
Gray areas in the upper panel are areas with insufficient data coverage for this analysis.
Several of the temperature changes are suggestive of human influences on climate. For example, the relatively uniform and steady warming of the ocean surfaces, the generally enhanced warming of continents, and the strong warming of high northern latitudes, particularly in the transition seasons, is consistent with expected effects of increases in greenhouse gas concentrations. The localized cooling between about 1950 and 1970 over industrial regions such as Europe and Southeast Asia, where anthropogenic sulfate aerosol loadings were high, is consistent with the expected cooling effect of sulfate aerosols. Also recognizable are numerous apparently natural climate variations, for example, strong temporary cooling in the North Atlantic from the 1950s through the 1970s, which contributed to the lack of global-mean temperature increase during that time.
The animations are produced using the methods described in Schneider and Held (2001). As in the paper, the data are from the Climatic Research Unit at the University of East Anglia (dataset HadCRUT3v). The analysis in the paper has been extended to the annual mean and to all seasons, including data through 2014. The overlapping decadal data groups used to determine the slow temperature variance are defined similarly to the analysis in the paper but are centered on the years 1858, …, 2008, with 15 years between successive group centers. The temperature changes represented in the animations are those accounted for by all discriminants with a variance ratio R corresponding to a p-value less than 0.1 determined by a bootstrap procedure (i.e., with a less than 10% chance of occuring if there is no coherent decadal variability). There are 2 to 4 such discriminants in each animations (i.e., the temperature changes in the animations have 2 to 4 spatial degrees of freedom).