This study investigates the changes of the North Atlantic subtropical high (NASH) and its impact on summer precipitation over the southeastern (SE) United States using the 850-hPa geopotential height field in the National Centers for Environmental Prediction (NCEP) reanalysis, the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40), long-term rainfall data, and Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) model simulations during the past six decades . The results show that the NASH in the last 30 yr has become more intense, and its western ridge has displaced westward with an enhanced meridional movement compared to the previous 30 yr. When the NASH moved closer to the continental United States in the three most recent decades, the effect of the NASH on the interannual variation of SE U.S. precipitation is enhanced through the ridge's north-south movement. The study's attribution analysis suggested that the changes of the NASH are mainly due to anthropogenic warming. In the twenty-first century with an increase of the atmospheric CO 2 concentration, the center of the NASH would be intensified and the western ridge of the NASH would shift farther westward. These changes would increase the likelihood of both strong anomalously wet and dry summers over the SE United States in the future, as suggested by the IPCC AR4 models.

Although climate models have steadily improved their ability to reproduce the observed climate, over the years there has been little change to the wide range of sensitivities exhibited by different models to a doubling of atmospheric CO2 concentrations. Stochastic optimization is used to mimic how six independent climate model development efforts might use the same atmospheric general circulation model, set of observational constraints, and model skill criteria to choose different settings for parameters thought to be important sources of uncertainty related to clouds and convection. Each optimized model improved its skill with respect to observations selected as targets of model development. Of particular note were the improvements seen in reproducing observed extreme rainfall rates over the tropical Pacific, which was not specifically targeted during the optimization process. As compared to the default model sensitivity of 2.4°C, the ensemble of optimized model configurations had a larger and narrower range of sensitivities around 3°C but with different regional responses related to the uncertain choice in optimized parameter settings. These results suggest current generation models, if similarly optimized, may become more convergent in their measure of global sensitivity to greenhouse gas forcing. However, this exploration of the possible sources of modeling and observational uncertainty is not exhaustive. The optimization process illustrates an objective means for selecting an ensemble of plausible climate model configurations that quantify a portion of the uncertainty in the climate model development process.

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X-ray absorption spectroscopy and in situ electron paramagnetic resonance evidence were provided for the reduction of Cu(II) to Cu(I) species by alkynes in the presence of tetramethylethylenediamine (TMEDA), in which TMEDA plays dual roles as both ligand and base. The structures of the starting Cu(II) species and the obtained Cu(I) species were determined as (TMEDA)CuCl2 and [(TMEDA)CuCl]2 dimer, respectively.

Causal discovery seeks to recover cause–effect relationships from statistical data using graphical models. One goal of this paper is to provide an accessible introduction to causal discovery methods for climate scientists, with a focus on constraint-based structure learning. Second, in a detailed case study constraint-based structure learning is applied to derive hypotheses of causal relationships between four prominent modes of atmospheric low-frequency variability in boreal winter including the Western Pacific Oscillation (WPO), Eastern Pacific Oscillation (EPO), Pacific–North America (PNA) pattern, and North Atlantic Oscillation (NAO). The results are shown in the form of static and temporal independence graphs also known as Bayesian Networks. It is found that WPO and EPO are nearly indistinguishable from the cause–effect perspective as strong simultaneous coupling is identified between the two. In addition, changes in the state of EPO (NAO) may cause changes in the state of NAO (PNA) approximately 18 (3–6) days later. These results are not only consistent with previous findings on dynamical processes connecting different low-frequency modes (e.g., interaction between synoptic and low-frequency eddies) but also provide the basis for formulating new hypotheses regarding the time scale and temporal sequencing of dynamical processes responsible for these connections. Last, the authors propose to use structure learning for climate networks, which are currently based primarily on correlation analysis. While correlation-based climate networks focus on similarity between nodes, independence graphs would provide an alternative viewpoint by focusing on information flow in the network.