Forest Habitat Use by White-tailed Deer in the Arkansas Coastal Plain

Forest habitat use by five radio-equipped white-tailed deer (Odocoileus virginianus) was monitored in the Arkansas Coastal Plain during 1982-84. The deer were located 821 times. Use of forest types was compared to expected use as calculated from availability. The study area was also divided into 491 two-hectare cells for which timber characteristics and number of deer locations were determined. Pine sawtimber was the most heavily used forest type in all seasons and was used more often than expected during spring. Also used more than expected were brushy areas (clearcut but not site prepared) during spring, summer and fall and openings (grass fields and a site-prepared clearcut) during summer. Hardwood stands were used less often than expected during every season. Also used less than expected were pine pulpwood stands in summer and pine-hardwood stands during spring and summer. A significant (P \u3c 0.001) discriminant function correctly classified 74% of the two-hectare cells as used (1+ locations) or not used (0 locations). Used cells often had less hardwood pulpwood and sawtimber and more pine sawtimber than nonused cells. Use by deer of cells containing stand edges did not differ from use of cells without edges

Crystallization and preliminary crystallographic analysis of the DNA gyrase B protein from B-stearothermophilus

DNA gyrase B (GyrB) from B. stearothermophilus has been crystallized in the presence of the non-hydrolyzable ATP analogue, 5'-adenylpl-beta-gamma-imidodiphosphate (ADPNP), by the dialysis method. A complete native data set to 3.7 Angstrom has been collected from crystals which belonged to the cubic space group I23 with unit-cell dimension a = 250.6 Angstrom. Self-rotation function analysis indicates the position of a molecular twofold axis. Low-resolution data sets of a thimerosal and a selenomethionine derivative have also been analysed. The heavy-atom positions are consistent with one dimer in the asymmetric unit

Emulating IPCC AR4 atmosphere-ocean and carbon cycle models for projecting global-mean, hemispheric and land/ocean temperatures: MAGICC 6.0

International audienceCurrent scientific knowledge on the future response of the climate system to human-induced perturbations is comprehensively captured by various model intercomparison efforts. In the preparation of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), intercomparisons were organized for atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models, named "CMIP3" and "C4MIP", respectively. Despite their tremendous value for the scientific community and policy makers alike, there are some difficulties in interpreting the results. For example, key radiative forcings have not been considered or standardized in the majority of AOGCMs integrations and carbon cycle runs. Furthermore, the AOGCM analysis of plausible emission pathways was restricted to only three SRES scenarios. This study attempts to address these issues. We present an updated version of MAGICC, the simple carbon cycle-climate model used in past IPCC Assessment Reports with enhanced representation of time-varying climate sensitivities, carbon cycle feedbacks, aerosol forcings and ocean heat uptake characteristics. This new version of MAGICC (6.0) is successfully calibrated against the higher complexity AOGCM and carbon cycle models. Parameterizations of MAGICC 6.0 are provided. Previous MAGICC versions and emulations shown in IPCC AR4 (WG1, Fig. 10.26, page 803) yielded, in average, a 10% larger global-mean temperature increase over the 21st century compared to the AOGCMs. The reasons for this difference are discussed. The emulations presented here using MAGICC 6.0 match the mean AOGCM responses to within 2.2% for the SRES scenarios. This enhanced emulation skill is due to: the comparison on a "like-with-like" basis using AOGCM-specific subsets of forcings, a new calibration procedure, as well as the fact that the updated simple climate model can now successfully emulate some of the climate-state dependent effective climate sensitivities of AOGCMs. The mean diagnosed effective climate sensitivities of the AOGCMs is 2.88°C, about 0.33°C cooler than the reported slab ocean climate sensitivities. Finally, we examine the combined climate system and carbon cycle emulations for the complete range of IPCC SRES emission scenarios and some lower mitigation pathways

Ocean variability and its influence on the detectability of greenhouse warming signals

Recent investigations have considered whether it is possible to achieve early detection of greenhouse-gas-induced climate change by observing changes in ocean variables. In this study we use model data to assess some of the uncertainties involved in estimating when we could expect to detect ocean greenhouse warming signals. We distinguish between detection periods and detection times. As defined here, detection period is the length of a climate time series required in order to detect, at some prescribed significance level, a given linear trend in the presence of the natural climate variability. Detection period is defined in model years and is independent of reference time and the real time evolution of the signal. Detection time is computed for an actual time-evolving signal from a greenhouse warming experiment and depends on the experiment's start date. Two sources of uncertainty are considered: those associated with the level of natural variability or noise, and those associated with the time-evolving signals. We analyze the ocean signal and noise for spatially averaged ocean circulation indices such as heat and fresh water fluxes, rate of deep water formation, salinity, temperature, transport of mass, and ice volume. The signals for these quantities are taken from recent time-dependent greenhouse warming experiments performed by the Max Planck Institute for Meteorology in Hamburg with a coupled ocean-atmosphere general circulation model. The time-dependent greenhouse gas increase in these experiments was specified in accordance with scenario A of the Intergovernmental Panel on Climate Change. The natural variability noise is derived from a 300-year control run performed with the same coupled atmosphere-ocean model and from two long (>3000 years) stochastic forcing experiments in which an uncoupled ocean model was forced by white noise surface flux variations. In the first experiment the stochastic forcing was restricted to the fresh water fluxes, while in the second experiment the ocean model was additionally forced by variations in wind stress and heat fluxes. The mean states and ocean variability are very different in the three natural variability integrations. A suite of greenhouse warming simulations with identical forcing but different initial conditions reveals that the signal estimated from these experiments may evolve in noticeably different ways for some ocean variables. The combined signal and noise uncertainties translate into large uncertainties in estimates of detection time. Nevertheless, we find that ocean variables that are highly sensitive indicators of surface conditions, such as convective overturning in the North Atlantic, have shorter signal detection times (35?65 years) than deep-ocean indicators (≥100 years). We investigate also whether the use of a multivariate detection vector increases the probability of early detection. We find that this can yield detection times of 35?60 years (relative to a 1985 reference date) if signal and noise are projected onto a common ?fingerprint? which describes the expected signal direction. Optimization of the signal-to-noise ratio by (spatial) rotation of the fingerprint in the direction of low-noise components of the stochastic forcing experiments noticeably reduces the detection time (to 10?45 years). However, rotation in space alone does not guarantee an improvement of the signal-to-noise ratio for a time-dependent signal. This requires an ?optimal fingerprint? strategy in which the detection pattern (fingerprint) is rotated in both space and time

Insights into Chi recognition from the structure of an AddAB-type helicase–nuclease complex

Homologous recombination DNA repair requires double-strand break resection by helicase–nuclease enzymes. The crystal structure of bacterial AddAB in complex with DNA substrates shows that it employs an inactive helicase site to recognize ‘Chi' recombination hotspot sequences that regulate resection

Identification of external influences on temperatures in California

We use eight different observational datasets to estimate California-average temperature trends over 1950-1999. Observed results are compared to trends from a suite of control simulations of natural internal climate variability. Observed increases in annual-mean surface temperature are distinguishable from climate noise in some but not all observational datasets. The most robust results are large positive trends in mean and maximum daily temperatures in late winter/early spring, as well as increases in minimum daily temperatures from January to September. These trends are inconsistent with model-based estimates of natural internal climate variability, and thus require one or more external forcing agents to be explained. Our results suggest that the warming of Californian winters over the second half of the twentieth century is associated with human-induced changes in large-scale atmospheric circulation. We also hypothesize that the lack of a detectable increase in summertime maximum temperature arises from a cooling associated with large-scale irrigation. This cooling may have, until now, counteracted the warming induced by increasing greenhouse gases and urbanization effects

Krakatau's long goodbye in the Ocean

State-of-the-art climate models suggest that 20th Century ocean warming and sea-level rise were substantially reduced by the 1883 eruption of Krakatau. Volcanically induced cooling of the ocean surface penetrated into deeper layers where it persisted for decades. We find that volcanic eruptions have longer lasting effects than previously suspected, sufficient to offset a large fraction of ocean warming and sea-level rise caused by anthropogenic influences over the 20th Century. We examine the latest suite of coupled ocean-atmosphere model experiments that include time-varying external forcings (e.g., changes in greenhouse gases, solar irradiance, sulfate aerosols and volcanic aerosols) for the period 1880-2000 (see Methods). These models have differences in physics, resolution, initial conditions, 'spin-up' and ocean-atmosphere coupling procedures, as well as different combinations of external forcings. Uncertainties in both the applied forcings and in the model responses to them are therefore inherent in our investigation

Variability of Ocean Heat Uptake: Reconciling Observations and Models

This study examines the temporal variability of ocean heat uptake in observations and in climate models. Previous work suggests that coupled Atmosphere-Ocean General Circulation Models (A-OGCMs) may have underestimated the observed natural variability of ocean heat content, particularly on decadal and longer timescales. To address this issue, we rely on observed estimates of heat content from the 2004 World Ocean Atlas (WOA-2004) compiled by Levitus et al. (2005). Given information about the distribution of observations in WOA-2004, we evaluate the effects of sparse observational coverage and the infilling that Levitus et al. use to produce the spatially-complete temperature fields required to compute heat content variations. We first show that in ocean basins with limited observational coverage, there are important differences between ocean temperature variability estimated from observed and infilled portions of the basin. We then employ data from control simulations performed with eight different A-OGCMs as a test-bed for studying the effects of sparse, space- and time-varying observational coverage. Subsampling model data with actual observational coverage has a large impact on the inferred temperature variability in the top 300 and 3000 meters of the ocean. This arises from changes in both sampling depth and in the geographical areas sampled. Our results illustrate that subsampling model data at the locations of available observations increases the variability, reducing the discrepancy between models and observations