Low-energy electron diffraction study of potassium adsorbed on single-crystal graphite and highly oriented pyrolytic graphite

Potassium adsorption on graphite has been a model system for the understanding of the interaction of alkali metals with surfaces. The geometries of the s232d structure of potassium on both single-crystal graphite (SCG) and highly oriented pyrolytic graphite (HOPG) were investigated for various preparation conditions for graphite temperatures between 55 and 140 K. In all cases, the geometry was found to consist of K atoms in the hollow sites on top of the surface. The K-graphite average perpendicular spacing is 2.79±0.03 Å, corresponding to an average C-K distance of 3.13±0.03 Å, and the spacing between graphite planes is consistent with the bulk spacing of 3.35 Å. No evidence was observed for a sublayer of potassium. The results of dynamical LEED studies for the clean SCG and HOPG surfaces indicate that the surface structures of both are consistent with the truncated bulk structure of graphite

A Pseudoproxy Evaluation of the CCA and RegEM Methods for Reconstructing Climate Fields of the Last Millennium

Canonical correlation analysis (CCA) is evaluated for paleoclimate field reconstructions in the context of pseudoproxy experiments assembled from the millennial integration (850–1999 c.e.) of the National Center for Atmospheric Research Community Climate System Model, version 1.4. A parsimonious method for selecting the order of the CCA model is presented. Results suggest that the method is capable of resolving multiple (3–13) climatic patterns given the estimated proxy observational network and the amount of observational uncertainty. CCA reconstructions are compared to those derived from the regularized expectation maximization method using ridge regression regularization (RegEM-Ridge). CCA and RegEM-Ridge yield similar skill patterns that are characterized by high correlation regions collocated with dense pseudoproxy sampling areas in North America and Europe. Both methods also produce reconstructions characterized by spatially variable warm biases and variance losses, particularly at high pseudoproxy noise levels. RegEM-Ridge in particular is subject to significantly larger variance losses than CCA, even though the spatial correlation patterns of the two methods are comparable. Results collectively indicate the importance of evaluating the field performance of methods that target spatial climate patterns during the last several millennia and indicate that the results of currently available climate field reconstructions should be interpreted carefully

Detection of human influence on a new, validated 1500-Year temperature reconstruction

Climate records over the last millennium place the twentieth-century warming in a longer historical context. Reconstructions of millennial temperatures show a wide range of variability, raising questions about the reliability of currently available reconstruction techniques and the uniqueness of late-twentieth-century warming. A calibration method is suggested that avoids the loss of low-frequency variance. A new reconstruction using this method shows substantial variability over the last 1500 yr. This record is consistent with independent temperature change estimates from borehole geothermal records, compared over the same spatial and temporal domain. The record is also broadly consistent with other recent reconstructions that attempt to fully recover low-frequency climate variability in their central estimate. High variability in reconstructions does not hamper the detection of greenhouse gas-induced climate change, since a substantial fraction of the variance in these reconstructions from the beginning of the analysis in the late thirteenth century to the end of the records can be attributed to external forcing. Results from a detection and attribution analysis show that greenhouse warming is detectable in all analyzed high-variance reconstructions (with the possible exception of one ending in 1925), and that about a third of the warming in the first half of the twentieth century can be attributed to anthropogenic greenhouse gas emissions. The estimated magnitude of the anthropogenic signal is consistent with most of the warming in the second half of the twentieth century being anthropogenic

Impact of maximum borehole depths on inverted temperature histories in borehole paleoclimatology

A quantitative assessment is presented for the impact of the maximum depth of a temperature-depth profile on the estimate of the climatic transient and the resultant ground surface temperature (GST) reconstruction used in borehole paleoclimatology. The depth of the profile is important because the downwelling climatic signal must be separated from the quasi-steady state thermal regime established by the energy in the Earth's interior. This component of the signal is estimated as a linear increase in temperature with depth from the lower section of a borehole temperature profile, which is assumed to be unperturbed by recent changes in climate at the surface. The validity of this assumption is dependent on both the subsurface thermophysical properties and the character of the downwelling climatic signal. Such uncertainties can significantly impact the determination of the quasi-steady state thermal regime, and consequently the magnitude of the temperature anomaly interpreted as a climatic signal. The quantitative effects and uncertainties that arise from the analysis of temperature-depth profiles of different depths are presented. Results demonstrate that widely different GST histories can be derived from a single temperature profile truncated at different depths. Borehole temperature measurements approaching 500-600 m depths are shown to provide the most robust GST reconstructions spanning 500 to 1000 yr BP. It is further shown that the bias introduced by a temperature profile of depths shallower than 500-600 m remains even if the time span of the reconstruction target is shortened

Variable seasonal coupling between air and ground temperatures: A simple representation in terms of subsurface thermal diffusivity

The utility of subsurface temperatures as indicators of temperature changes at Earth's surface rests upon an assumption of strong coupling between surface air temperature (SAT) and ground surface temperature (GST). Here we describe a simple representation of this coupling in terms of a variable thermal diffusivity in the upper meter of the subsurface. The variability is tied to daily SAT, precipitation, and snow cover, but does not incorporate the physical details of these and the many other factors that influence the air-ground interface in many high-fidelity land-surface models. Our simple model reduces the difference between observed and modeled temperatures by a factor of 3 to 4 over a model with uniform diffusivity driven only by SAT. This simple representation of air-ground coupling offers a means of simulating subsurface temperatures using only archived meteorological records and creates the potential for examining the long term character of air-ground temperature coupling

The Influence of Soil Moisture Upon the Geothermal Climate Signal

Estimates of regional climate warming over the past few hundred years are being obtained from profiles of borehole temperature versus depth. The assumptions in recovering mean annual Surface Air Temperature (SAT) are that the relationship between the Ground Surface Temperature (GST) and the temperature-depth profile is purely conductive, and that SAT is uniquely coupled to GST. While these assumptions have been demonstrated to be approximately valid, they ignore the role of moisture transport in soil between soil and atmosphere. In this study we examine the influence of climatic changes in precipitation upon mean annual GST with climatic SAT held constant. We use the most recent version of our Prairie SVAT model for a set of 80 years simulations. Our findings are 10 increasing precipitation reduces mean annual GST, 2) phasing maximum precipitation to occur during the warmest months reduces mean annual GST, and 3) increasing the variance of precipitation reduces mean annual GST. The amplitudes of the effects are small but potentially not insignificant fractions of the geothermal climate signal. One of the long-term objectives of this investigation is to use global EOS SAT and remotely sensed soil moisture to link region-specific, geothermal climate signal histories to evolution of regional climate

A model study of the effects of climatic precipitation changes on ground temperatures

Temperature changes at the Earth surface propagate into the subsurface and leave a thermal signature in the underlying soil and rock. Inversions of subsurface temperature measurements yield reconstructions of ground surface temperature (GST) histories that provide estimates of climatic changes. A question remaining in the interpretation of reconstructed GST histories is the extent to which GST changes reflect changes principally in surface air temperature (SAT), or whether other factors may be significant. Here we use a Land Surface Processes (LSP) model to examine the influence of precipitation changes on GST and subsurface temperature and moisture fields on annual to decadal timescales. We model soil and vegetation conditions representative of a prairie region in the southern Great Plains of North America and force the model with meteorological data synthesized from a typical year in the region. Model responses are observed after changes in the amount of daily precipitation, the intensity and frequency of daily precipitation, and the diurnal and seasonal timing of precipitation. We show that: (1) increasing daily precipitation cools mean annual GST, (2) increasing the intensity and reducing the frequency of daily precipitation, while holding the annual amount of precipitation constant, cools mean annual GST, and (3) shifting maximum precipitation to occur in the warmest months cools mean annual GST. We compare modeled results to observed precipitation changes during the 20th century and conclude that the observed precipitation changes would cause only small changes to GST within the modeled region, on the order of 0.1 K or less

Detection of Human Influence on a New, Validated 1500-Year Temperature Reconstruction

Climate records over the last millennium place the twentieth-century warming in a longer historical context. Reconstructions of millennial temperatures show a wide range of variability, raising questions about the reliability of currently available reconstruction techniques and the uniqueness of late-twentieth-century warming. A calibration method is suggested that avoids the loss of low-frequency variance. A new reconstruction using this method shows substantial variability over the last 1500 yr. This record is consistent with independent temperature change estimates from borehole geothermal records, compared over the same spatial and temporal domain. The record is also broadly consistent with other recent reconstructions that attempt to fully recover low-frequency climate variability in their central estimate. High variability in reconstructions does not hamper the detection of greenhouse gas-induced climate change, since a substantial fraction of the variance in these reconstructions from the beginning of the analysis in the late thirteenth century to the end of the records can be attributed to external forcing. Results from a detection and attribution analysis show that greenhouse warming is detectable in all analyzed high-variance reconstructions (with the possible exception of one ending in 1925), and that about a third of the warming in the first half of the twentieth century can be attributed to anthropogenic greenhouse gas emissions. The estimated magnitude of the anthropogenic signal is consistent with most of the warming in the second half of the twentieth century being anthropogenic

Climate field reconstruction uncertainty arising from multivariate and nonlinear properties of predictors

Climate field reconstructions (CFRs) of the global annual surface air temperature (SAT) field and associated global area-weighted mean annual temperature (GMAT) are derived in a collection of pseudoproxy experiments for the past millennium. Pseudoproxies are modeled from temperature (T), precipitation (P), T + P, and VS-Lite (VSL), a nonlinear and multivariate proxy system model for tree ring widths. Spatial patterns of reconstruction skill and spectral bias for the T + P and VSL-derived CFRs are similar to those previously shown using temperature-only pseudoproxies but demonstrate overall degraded skill and spectral bias for SAT reconstruction. Analysis of GMAT spectra nevertheless suggests that the true GMAT frequency spectrum is resolved by those pseudoproxies (T, T + P, and VSL) that contain some temperature information. The results suggest that mixed temperature and moisture-responding paleoclimate data may produce actual GMAT reconstructions with skill, error, and spectral characteristics like those expected from univariate and linear temperature responders, but spatially resolved CFR results should be analyzed cautiousl

Continental heat gain in the global climate system

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95084/1/grl15494.pd