It is well known that the diversity of life appears to fluctuate during the course of the Phanerozoic, the eon during which hard shells and skeletons left abundant fossils (0-542 million years ago). Here we show, using Sepkoski's compendium of the first and last stratigraphic appearances of 36,380 marine genera, a strong 62 +/- 3-million-year cycle, which is particularly evident in the shorter-lived genera. The five great extinctions enumerated by Raup and Sepkoski may be an aspect of this cycle. Because of the high statistical significance we also consider the contributions of environmental factors, and possible causes.

A new mathematical framework is presented for producing maps and large-scale averages of temperature changes from weather station data for the purposes of climate analysis. This allows one to include short and discontinuous temperature records, so that nearly all temperature data can be used. The framework contains a weighting process that assesses the quality and consistency of a spatial network of temperature stations as an integral part of the averaging process. This permits data with varying levels of quality to be used without compromising the accuracy of the resulting reconstructions. Lastly, the process presented here is extensible to spatial networks of arbitrary density (or locally varying density) while maintaining the expected spatial relationships. In this paper, this framework is applied to the Global Historical Climatology Network land temperature dataset to present a new global land temperature reconstruction from 1800 to present with error uncertainties that include many key effects. In so doing, we find that the global land mean temperature has increased by 0.911 ± 0.042 C since the 1950s (95% confidence for statistical and spatial uncertainties). This change is consistent with global land-surface warming results previously reported, but with reduced uncertainty. 3 IntroductionWhile there are many indicators of climate change, the long-term evolution of global surface temperatures is perhaps the metric that is both the easiest to understand and most closely linked to the quantitative predictions of climate models. It is also backed by the largest collection of raw data. According to the summary provided by the Intergovernmental Panel on Climate Change (IPCC), the mean global surface temperature (both land and oceans) has increased 0.64 ± 0.13 C from 1956 to 2005 at 95% confidence (Trenberth et al. 2007).During the latter half of the twentieth century weather monitoring instruments of good quality were widely deployed, yet the quoted uncertainty on global temperature change during this time period is still ± 20%. Reducing this uncertainty is a major goal of this paper. Longer records may provide more precise indicators of change; however, according to the IPCC, temperature increases prior to 1950 were caused by a combination of anthropogenic factors and natural factors (e.g. changes in solar activity), and it is only since about 1950 that man-made emissions have come to dominate over natural factors. Hence constraining the post-1950 period is of particular importance in understanding the impact of greenhouse gases.The Berkeley Earth Surface Temperature project was created to help refine our estimates of the rate of recent global warming. This is being approached through several parallel efforts to A) increase the size of the data set used to study global climate change, B) bring additional statistical techniques to bear on the problem that will help reduce the uncertainty in the resulting averages, and C) produce new analysis of systematic effects, including data selection bias, urban hea...

China has recently made available hourly air pollution data from over 1500 sites, including airborne particulate matter (PM), SO2, NO2, and O3. We apply Kriging interpolation to four months of data to derive pollution maps for eastern China. Consistent with prior findings, the greatest pollution occurs in the east, but significant levels are widespread across northern and central China and are not limited to major cities or geologic basins. Sources of pollution are widespread, but are particularly intense in a northeast corridor that extends from near Shanghai to north of Beijing. During our analysis period, 92% of the population of China experienced >120 hours of unhealthy air (US EPA standard), and 38% experienced average concentrations that were unhealthy. China’s population-weighted average exposure to PM2.5 was 52 μg/m3. The observed air pollution is calculated to contribute to 1.6 million deaths/year in China [0.7–2.2 million deaths/year at 95% confidence], roughly 17% of all deaths in China.