Increasing occurrence of cold and warm extremes during the recent global warming slowdown.

The recent levelling of global mean temperatures after the late 1990s, the so-called global warming hiatus or slowdown, ignited a surge of scientific interest into natural global mean surface temperature variability, observed temperature biases, and climate communication, but many questions remain about how these findings relate to variations in more societally relevant temperature extremes. Here we show that both summertime warm and wintertime cold extreme occurrences increased over land during the so-called hiatus period, and that these increases occurred for distinct reasons. The increase in cold extremes is associated with an atmospheric circulation pattern resembling the warm Arctic-cold continents pattern, whereas the increase in warm extremes is tied to a pattern of sea surface temperatures resembling the Atlantic Multidecadal Oscillation. These findings indicate that large-scale factors responsible for the most societally relevant temperature variations over continents are distinct from those of global mean surface temperature

Global Temperature Trends

Are global temperatures on a warming trend? It is difficult to be certain about trends when there is so much variation in the data and very high correlation from year to year. We investigate the question using statistical time series methods. Our analysis shows that the upward movement over the last 130-160 years is persistent and not explained by the high correlation, so it is best described as a trend. The warming trend becomes steeper after the mid-1970s, but there is no significant evidence for a break in trend in the late 1990s. Viewed from the perspective of 30 or 50 years ago, the temperatures recorded in most of the last decade lie above the confidence band of forecasts produced by a model that does not allow for a warming trend.Land and ocean temperatures; deterministic and stochastic trends; persistence; piecewise linear trends

Estimating trends in the global mean temperature record

Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to deemphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate and time series methods that account for internal variability nonparametrically. However, given a limited record and the presence of internal variability, estimating radiatively forced historical temperature trends necessarily requires assumptions. Ostensibly empirical methods can involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to apply but long enough for internal variability to be accounted for. In the context of global mean temperatures, methods that deemphasize assumptions can therefore produce misleading inferences, because the twentieth century trend is complex and the scale of correlation is long relative to the data length. We illustrate how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can address a wider array of questions. The model allows one to distinguish, within a single framework, between uncertainties in the shorter-term versus longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric methods, if attuned to important characteristics of internal variability, can result in more accurate statements about trend uncertainty.Comment: 38 pages, 14 figure

Evidence for global runoff increase related to climate warming

Ongoing global climatic change initiated by the anthropogenic release of carbon dioxide is a matter of intense debate. We focus both on the impact of these climatic changes on the global hydrological cycle and on the amplitude of the increase of global and continental runoff over the last century, in relation to measured temperature increases. In this contribution, we propose an original statistical wavelet-based method for the reconstruction of the monthly discharges of worldwide largest rivers. This method provides a data-based approximation of the evolution of the annual continental and global runoffs over the last century. A consistent correlation is highlighted between global annual temperature and runoff, suggesting a 4% global runoff increase by 1 C global temperature rise. However, this global trend should be qualified at the regional scale where both increasing and decreasing trends are identified. North America runoffs appear to be the most sensitive to the recent climatic changes. Finally, this contribution provides the first experimental data-based evidence demonstrating the link between the global warming and the intensification of the global hydrological cycle. This corresponds to more intense evaporation over oceans coupled to continental precipitation increase or continental evaporation decrease. This process finally leads to an increase of the global continental runoff

The transient response of global-mean precipitation to increasing carbon dioxide levels

The transient response of global-mean precipitation to an increase in atmospheric carbon dioxide levels of 1% yr(-1) is investigated in 13 fully coupled atmosphere-ocean general circulation models (AOGCMs) and compared to a period of stabilization. During the period of stabilization, when carbon dioxide levels are held constant at twice their unperturbed level and the climate left to warm, precipitation increases at a rate of similar to 2.4% per unit of global-mean surface-air-temperature change in the AOGCMs. However, when carbon dioxide levels are increasing, precipitation increases at a smaller rate of similar to 1.5% per unit of global-mean surface-air-temperature change. This difference can be understood by decomposing the precipitation response into an increase from the response to the global surface-temperature increase (and the climate feedbacks it induces), and a fast atmospheric response to the carbon dioxide radiative forcing that acts to decrease precipitation. According to the multi-model mean, stabilizing atmospheric levels of carbon dioxide would lead to a greater rate of precipitation change per unit of global surface-temperature change

Global Temperature and Salinity Pilot Project

Data exchange and data management programs have been evolving over many years. Within the international community there are two main programs to support the exchange, management and processing of real time and delayed mode data. The Intergovernmental Oceanographic Commission (IOC) operate the International Oceanographic Data and Information Exchange (IODE) program which coordinates the exchange of delayed mode data between national oceanographic data centers, World Data Centers and the user community. The Integrated Global Ocean Services System is a joint IOC/World Meteorological Organization (WMO) program for the exchange and management of real-time data. These two programs are complemented by mechanisms that have been established within scientific programs to exchange and manage project data sets. In particular TOGA and WOCE have identified a data management requirement and established the appropriate infrastructure to achieve this. Where GTSPP fits into this existing framework is discussed