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...

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Documenting the spatial extent of the 8.2 ka event is essential for understanding the possible response of the climate system to a freshwater perturbation in the North Atlantic. In this research, we analyzed paleoclimate proxy records from 52 sites using a statistical test to detect anomalies associated with the 8.2 ka event. Our results show that this event occurred in many parts of the Northern Hemisphere extratropics and the tropics. Evidence from the tropics is more spotty, but detection rates are similar for the tropics and extratropics, suggesting that tropical evidence for the event will become stronger as more records are generated. There is also a particular need for new paleoclimate proxy records from Southern Hemisphere sites and/or with quantitative estimates of climate anomalies.

[1] Interannual to decadal variations in Earth global temperature estimates have often been identified with El Niño Southern Oscillation (ENSO) events. However, we show that variability on time scales of 2-15 years in mean annual global land surface temperature anomalies T avg are more closely correlated with variability in sea surface temperatures in the North Atlantic. In particular, the cross-correlation of annually averaged values of T avg with annual values of the Atlantic Multidecadal Oscillation (AMO) index is much stronger than that of T avg with ENSO. The pattern of fluctuations in T avg from 1950 to 2010 reflects true climate variability and is not an artifact of station sampling. A world map of temperature correlations shows that the association with AMO is broadly distributed and unidirectional. The effect of El Niño on temperature is locally stronger, but can be of either sign, leading to less impact on the global average. We identify one strong narrow spectral peak in the AMO at period 9.1 AE 0.4 years and p value of 1.7% (confidence level, 98.3%). Variations in the flow of the Atlantic meridional overturning circulation may be responsible for some of the 2-15 year variability observed in global land temperatures.