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

Оценка средней скорости на 10-и метровой глубине для разрезов с высокоскоростным верхним слоем при микрорайонировании

Описываются способы оценки средней скорости поперечной волны на 10-и метровой глубине для разрезов, верхняя часть которых представлена уплотненным насыпным грунтом или мерзлыми породами

ATMODAT Standard v3.0

Within the AtMoDat project (Atmospheric Model Data), a standard has been developed which is meant for improving the FAIRness of atmospheric model data published in repositories. The ATMODAT standard includes concrete recommendations related to the maturity, publication and enhanced FAIRness of atmospheric model data. The suggestions include requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine readable landing pages are a core element of this standard, and should hold and present discipline-specific metadata on simulation and variable level. This standard is an updated and translated version of "Bericht über initialen Kernstandard und Kurationskriterien des AtMoDat Projektes (v2.4

Signal-to-noise analysis of time-dependent greenhouse warming experiments. Part 1: Pattern analysis

Results from a control integration and time-dependent greenhouse warming experiments performed with a coupled ocean-atmosphere model are analysed in terms of their signal-to-noise properties. The aim is to illustrate techniques for efficient description of the space-time evolution of signals and noise and to identify potentially useful components of a multivariate greenhouse-gas ''fingerprint''. The three 100-year experiments analysed here simulate the response of the climate system to a step-function doubling Of CO2 and to the time-dependent greenhouse-gas increases specified in Scenarios A (''Business as Usual'') and D (''Draconian Measures'') of the Intergovernmental Panel on Climate Change (IPCC). If signal and noise patterns are highly similar, the separation of the signal from the natural variability noise is difficult. We use the pattern correlation between the dominant Empirical Orthogonal Functions (EOFs) of the control run and the Scenario A experiment as a measure of the similarity of signal and noise patterns. The EOF 1 patterns of signal and noise are least similar for near-surface temperature and the vertical structure of zonal winds, and are most similar for sea level pressure (SLP). The dominant signal and noise modes of precipitable water and stratospheric/tropospheric temperature contrasts show considerable pattern similarity. Despite the differences in forcing history, a highly similar EOF 1 surface temperature response pattern is found in all three greenhouse warming experiments. A large part of this similarity is due to a common land-sea contrast component of the signal. To determine the degree to which the signal is contaminated by the natural variability (and/or drift) of the control run, we project the Scenario A data onto EOFs 1 and 2 of the control. Signal contamination by the EOF 1 and 2 modes of the noise is lowest for near-surface temperature, a situation favorable for detection. The signals for precipitable water, SLP, and the vertical structure of zonal temperature and zonal winds are significantly contaminated by the dominant noise modes. We use cumulative explained spatial variance, principal component time series, and projections onto EOFs in order to investigate the time evolution of the dominant signal and noise modes. In the case of near-surface temperature, a single pattern emerges as the dominant signal component in the second half of the Scenario A experiment. The projections onto EOFs 1 and 2 of the control run indicate that Scenario D has a large common variability and/or drift component with the control run. This common component is also apparent between years 30 and 50 of the Scenario A experiment, but is small in the 2 x CO2 integration. The trajectories of the dominant Scenario A and control run modes evolve differently, regardless of the basis vectors chosen for projection, thus making it feasible to separate signal and noise within the first two decades of the experiments. For Scenario D it may not be possible to discriminate between the dominant signal and noise modes until the final 2-3 decades of the 100-year integration

Spin versus Lattice Polaron: Prediction for Electron-Doped CaMnO3

CaMnO3 is a simple bi-partite antiferromagnet(AF) which can be continuously electron-doped up to LaMnO3. Electrons enter the doubly degenerate E_g subshell with spins aligned to the S=3/2 core of Mn^4+ (T_2g^3)$. We take the Hubbard and Hund energies to be effectively infinite. Our model Hamiltonian has two E_g orbitals per Mn atom, nearest neighbor hopping, nearest neighbor exchange coupling of the S=3/2 cores, and electron-phonon coupling of Mn orbitals to adjacent oxygen atoms. We solve this model for light doping. Electrons are confined in local ferromagnetic (FM) regions (spin polarons) where there proceeds an interesting competition between spin polarization (spin polarons) which enlarges the polaron, and lattice polarization (Jahn-Teller polarons) which makes it smaller. A symmetric 7-atom ferromagnetic cluster (Mn_7^27+) is the stable result, with net spin S=2 relative to the undoped AF. The distorted oxygen positions around the electron are predicted. The model also predicts a critical doping x_c=0.045 where the polaronic insulator becomes unstable relative to a FM metal.Comment: 9 pages with 7 embedded postscript figures and 2 table

The E⊗eE\otimes e Jahn-Teller Polaron in Comparison with the Holstein Polaron

Based on an exact expression for the self-energy of the Jahn-Teller polaron, we find that symmetry of pseudospin rotation makes the vertex correction much less effective than that for the Holstein polaron. This ineffectiveness brings about a smaller effective mass m^* and a quantitatively differenent large-to-small polaron crossover, as examined by exact diagonalization in a two-site system. In the strong-coupling and antiadiabatic region, a rigorous analytic expression is found for m^*

Genome-wide identification of Ago2 binding sites from mouse embryonic stem cells with and without mature microRNAs

MicroRNAs (miRNAs) are 19–22-nucleotide noncoding RNAs that post-transcriptionally regulate mRNA targets. We have identified endogenous miRNA binding sites in mouse embryonic stem cells (mESCs), by performing photo-cross-linking immunoprecipitation using antibodies to Argonaute (Ago2) followed by deep sequencing of RNAs (CLIP-seq). We also performed CLIP-seq in Dicer[superscript −/−] mESCs that lack mature miRNAs, allowing us to define whether the association of Ago2 with the identified sites was miRNA dependent. A significantly enriched motif, GCACUU, was identified only in wild-type mESCs in 3′ untranslated and coding regions. This motif matches the seed of a miRNA family that constitutes ~68% of the mESC miRNA population. Unexpectedly, a G-rich motif was enriched in sequences cross-linked to Ago2 in both the presence and absence of miRNAs. Expression analysis and reporter assays confirmed that the seed-related motif confers miRNA-directed regulation on host mRNAs and that the G-rich motif can modulate this regulation.Leukemia & Lymphoma Society of AmericaUnited States. Public Health Service (Grant R01-GM34277)United States. Public Health Service (Grant R01-CA133404)National Cancer Institute (U.S.) (Grant P01-CA42063)National Cancer Institute (U.S.) Cancer Center Support (Grant P30-CA14051

Plio-Pleistocene climatic change had a major impact on the assembly and disassembly processes of Iberian rodent communities

Comprehension of changes in community composition through multiple spatio-temporal scales is a prime challenge in ecology and palaeobiology. However, assembly, structuring and disassembly of biotic metacommunities in deep-time is insufficiently known. To address this, we used the extensively sampled Iberian Plio-Pleistocene fossil record of rodent faunas as our model system to explore how global climatic events may alter metacommunity structure. Through factor analysis, we found five sets of genera, called faunal components, which co-vary in proportional diversity over time. These faunal components had different spatio-temporal distributions throughout the Plio-Pleistocene, resulting in non-random changes in species assemblages, particularly in response to the development of the Pleistocene glaciations. Three successive metacommunities with distinctive taxonomic structures were identified as a consequence of the differential responses of their members to global climatic change: (1) Ruscinian subtropical faunas (5.3–3.4 Ma) dominated by a faunal component that can be considered as a Miocene legacy; (2) transition faunas during the Villafranchian–Biharian (3.4–0.8 Ma) with a mixture of different faunal components; and (3) final dominance of the temperate Toringian faunas (0.8–0.01 Ma) that would lead to the modern Iberian assemblage. The influence of the cooling global temperature drove the reorganisation of these rodent metacommunities. Selective extinction processes due to this large-scale environmental disturbance progressively eliminated the subtropical specialist species from the early Pliocene metacommunity. This disassembly process was accompanied by the organisation of a diversified metacommunity with an increased importance of biome generalist species, and finally followed by the assembly during the middle–late Pleistocene of a new set of species specialised in the novel environments developed as a consequence of the glaciations