Spatial patterns and intensity of the surface storm tracks in CMIP5 models

Author Posting. © American Meteorological Society, 2017. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 30 (2017): 4965-4981, doi:10.1175/JCLI-D-16-0228.1.To improve the understanding of storm tracks and western boundary current (WBC) interactions, surface storm tracks in 12 CMIP5 models are examined against ERA-Interim. All models capture an equatorward displacement toward the WBCs in the locations of the surface storm tracks’ maxima relative to those at 850 hPa. An estimated storm-track metric is developed to analyze the location of the surface storm track. It shows that the equatorward shift is influenced by both the lower-tropospheric instability and the baroclinicity. Basin-scale spatial correlations between models and ERA-Interim for the storm tracks, near-surface stability, SST gradient, and baroclinicity are calculated to test the ability of the GCMs’ match reanalysis. An intermodel comparison of the spatial correlations suggests that differences (relative to ERA-Interim) in the position of the storm track aloft have the strongest influence on differences in the surface storm-track position. However, in the North Atlantic, biases in the surface storm track north of the Gulf Stream are related to biases in the SST. An analysis of the strength of the storm tracks shows that most models generate a weaker storm track at the surface than 850 hPa, consistent with observations, although some outliers are found. A linear relationship exists among the models between storm-track amplitudes at 500 and 850 hPa, but not between 850 hPa and the surface. In total, the work reveals a dual role in forcing the surface storm track from aloft and from the ocean surface in CMIP5 models, with the atmosphere having the larger relative influence.JFB was partially supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections program (Grant NA15OAR4310094). Y-OK was supported by NSF Division of Atmospheric and Geospace Science Climate and Large-scale Dynamics Program (AGS-1355339), NASA Physical Oceanography Program (NNX13AM59G), and DOE Office of Biological and Environmental Research Regional and Global Climate Modeling Program (DE-SC0014433). RJS was supported by DOE Office of Biological and Environmental Research (DE-SC0006743) and NSF Directorate for Geosciences Division of Ocean Sciences (1419584),2017-10-0

Expression of a Cryptic Secondary Sigma Factor Gene Unveils Natural Competence for DNA Transformation in Staphylococcus aureus

It has long been a question whether Staphylococcus aureus, a major human pathogen, is able to develop natural competence for transformation by DNA. We previously showed that a novel staphylococcal secondary sigma factor, SigH, was a likely key component for competence development, but the corresponding gene appeared to be cryptic as its expression could not be detected during growth under standard laboratory conditions. Here, we have uncovered two distinct mechanisms allowing activation of SigH production in a minor fraction of the bacterial cell population. The first is a chromosomal gene duplication rearrangement occurring spontaneously at a low frequency [≤10−5], generating expression of a new chimeric sigH gene. The second involves post-transcriptional regulation through an upstream inverted repeat sequence, effectively suppressing expression of the sigH gene. Importantly, we have demonstrated for the first time that S. aureus cells producing active SigH become competent for transformation by plasmid or chromosomal DNA, which requires the expression of SigH-controlled competence genes. Additionally, using DNA from the N315 MRSA strain, we successfully transferred the full length SCCmecII element through natural transformation to a methicillin-sensitive strain, conferring methicillin resistance to the resulting S. aureus transformants. Taken together, we propose a unique model for staphylococcal competence regulation by SigH that could help explain the acquisition of antibiotic resistance genes through horizontal gene transfer in this important pathogen

Adaptation beyond the Stress Response: Cell Structure Dynamics and Population Heterogeneity in Staphylococcus aureus

Staphylococcus aureus, a major opportunistic pathogen responsible for a broad spectrum of infections, naturallyinhabits the human nasal cavity in about 30% of the population. The unique adaptive potential displayed by S. aureushas made it one of the major causes of nosocomial infections today, emphasized by the rapid emergence of multipleantibiotic-resistant strains over the past few decades. The uncanny ability to adapt to harsh environments is essentialfor staphylococcal persistence in infections or as a commensal, and a growing body of evidence has revealed criticalroles in this process for cellular structural dynamics, and population heterogeneity. These two exciting areas ofresearch are now being explored to identify new molecular mechanisms governing these adaptational strategies

The Arctic predictability and prediction on seasonal-to-interannual timescales (APPOSITE) data set version 1

This is the final version of the article. Available from the publisher via the DOI in this record. Discussion paper (published on 15 Oct 2015)Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi- 5 model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model 10 intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate vari15 ability on these timescales, such as the El Niño Southern Oscillation.This work was supported by the Natural Environment Research Council (grant NE/I029447/1). Helge Goessling was supported by a fellowship of the German Research Foundation (DFG grant GO 2464/1-1). Data storage and processing capacity was kindly provided by the British Atmospheric Data Centre (BADC). Thanks to Yanjun Jiao (CCCma) for his assistance with the CanCM4 simulations and to Bill Merryfield for his comments on a draft of the pape

Biodiversity Assessment and Conservation of Threatened Plant Species Belonging to the Unique Steppe with Trees in Tunisian Drylands

Biodiversity conservation from heavy grazing impacts, through the creation of national parks, is usually considered to sustain higher ecosystem resilience and to protect the natural plant cover as well as the threatened species. The study was carried out in Bou Hedma national park, a biosphere reserve containing the unique Acacia tortilis (Forssk.) Hayne subsp. raddiana (Savi) Brenan steppe with trees in Tunisia. Several functional traits of seven (7) rare and threatened plant species are used to highlight their adaptive strategies in order to understand the evolution of plant communities and the overall ecosystems functioning inside the park. Such results may provide many environmental benefits and maintain the flora biodiversity under harsh dryland conditions

Have Aerosols Caused the Observed Atlantic Multidecadal Variability?

Identifying the prime drivers of the twentieth-century multidecadal variability in the Atlantic Ocean is crucial for predicting how the Atlantic will evolve in the coming decades and the resulting broad impacts on weather and precipitation patterns around the globe. Recently, Booth et al. showed that the Hadley Centre Global Environmental Model, version 2, Earth system configuration (HadGEM2-ES) closely reproduces the observed multidecadal variations of area-averaged North Atlantic sea surface temperature in the twentieth century. The multidecadal variations simulated in HadGEM2-ES are primarily driven by aerosol indirect effects that modify net surface shortwave radiation. On the basis of these results, Booth et al. concluded that aerosols are a prime driver of twentieth-century North Atlantic climate variability. However, here it is shown that there are major discrepancies between the HadGEM2-ES simulations and observations in the North Atlantic upper-ocean heat content, in the spatial pattern of multidecadal SST changes within and outside the North Atlantic, and in the subpolar North Atlantic sea surface salinity. These discrepancies may be strongly influenced by, and indeed in large part caused by, aerosol effects. It is also shown that the aerosol effects simulated in HadGEM2-ES cannot account for the observed anticorrelation between detrended multidecadal surface and subsurface temperature variations in the tropical North Atlantic. These discrepancies cast considerable doubt on the claim that aerosol forcing drives the bulk of this multidecadal variability

A review of the role of the Atlantic meridional overturning circulation in Atlantic multidecadal variability and associated climate impacts

By synthesizing recent studies employing a wide range of approaches (modern observations, paleo reconstructions, and climate model simulations), this paper provides a comprehensive review of the linkage between multidecadal Atlantic Meridional Overturning Circulation (AMOC) variability and Atlantic Multidecadal Variability (AMV) and associated climate impacts. There is strong observational and modeling evidence that multidecadal AMOC variability is a crucial driver of the observed AMV and associated climate impacts and an important source of enhanced decadal predictability and prediction skill. The AMOC‐AMV linkage is consistent with observed key elements of AMV. Furthermore, this synthesis also points to a leading role of the AMOC in a range of AMV‐related climate phenomena having enormous societal and economic implications, for example, Intertropical Convergence Zone shifts; Sahel and Indian monsoons; Atlantic hurricanes; El Niño–Southern Oscillation; Pacific Decadal Variability; North Atlantic Oscillation; climate over Europe, North America, and Asia; Arctic sea ice and surface air temperature; and hemispheric‐scale surface temperature. Paleoclimate evidence indicates that a similar linkage between multidecadal AMOC variability and AMV and many associated climate impacts may also have existed in the preindustrial era, that AMV has enhanced multidecadal power significantly above a red noise background, and that AMV is not primarily driven by external forcing. The role of the AMOC in AMV and associated climate impacts has been underestimated in most state‐of‐the‐art climate models, posing significant challenges but also great opportunities for substantial future improvements in understanding and predicting AMV and associated climate impacts

Investigating the impact of CO2 on low-frequency variability of the AMOC in HadCM3

This study investigates the impact of CO2 on the amplitude, frequency, and mechanisms of Atlantic meridional overturning circulation (AMOC) variability in millennial simulations of the HadCM3 coupled climate model. Multichannel singular spectrum analysis (MSSA) and empirical orthogonal functions (EOFs) are applied to the AMOC at four quasi-equilibrium CO2 forcings. The amount of variance explained by the first and second eigenmodes appears to be small (i.e., 11.19%); however, the results indicate that both AMOC strength and variability weaken at higher CO2 concentrations. This accompanies an apparent shift from a predominant 100–125-yr cycle at 350 ppm to 160 yr at 1400 ppm. Changes in amplitude are shown to feed back onto the atmosphere. Variability may be linked to salinity-driven density changes in the Greenland–Iceland– Norwegian Seas, fueled by advection of anomalies predominantly from the Arctic and Caribbean regions. A positive density anomaly accompanies a decrease in stratification and an increase in convection and Ekman pumping, generating a strong phase of the AMOC (and vice versa). Arctic anomalies may be generated via an internal ocean mode that may be key in driving variability and are shown to weaken at higher CO2, possibly driving the overall reduction in amplitude. Tropical anomalies may play a secondary role in modulating variability and are thought to be more influential at higher CO2, possibly due to an increased residence time in the subtropical gyre and/or increased surface runoff driven by simulated dieback of the Amazon rain forest. These results indicate that CO2 may not only weaken AMOC strength but also alter the mechanisms that drive variability, both of which have implications for climate change on multicentury time scales

Seasonal to interannual Arctic sea-ice predictability in current GCMs

We establish the first inter-model comparison of seasonal to interannual predictability of present-day Arctic climate by performing coordinated sets of idealized ensemble predictions with four state-of-the-art global climate models. For Arctic sea-ice extent and volume, there is potential predictive skill for lead times of up to three years, and potential prediction errors have similar growth rates and magnitudes across the models. Spatial patterns of potential prediction errors differ substantially between the models, but some features are robust. Sea-ice concentration errors are largest in the marginal ice zone, and in winter they are almost zero away from the ice edge. Sea-ice thickness errors are amplified along the coasts of the Arctic Ocean, an effect that is dominated by sea-ice advection. These results give an upper bound on the ability of current global climate models to predict important aspects of Arctic climate