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The remaining carbon stocks in wet tropical forests are currently at risk because of anthropogenic deforestation, but also because of the possibility of release driven by climate change. To identify the relative roles of CO2 increase, changing temperature and rainfall, and deforestation in the future, and the magnitude of their impact on atmospheric CO2 concentrations, we have applied a dynamic global vegetation model, using multiple scenarios of tropical deforestation (extrapolated from two estimates of current rates) and multiple scenarios of changing climate (derived from four independent offline general circulation model simulations). Results show that deforestation will probably produce large losses of carbon, despite the uncertainty about the deforestation rates. Some climate models produce additional large fluxes due to increased drought stress caused by rising temperature and decreasing rainfall. One climate model, however, produces an additional carbon sink. Taken together, our estimates of additional carbon emissions during the twenty-first century, for all climate and deforestation scenarios, range from 101 to 367 Gt C, resulting in CO2 concentration increases above background values between 29 and 129 p.p.m. An evaluation of the method indicates that better estimates of tropical carbon sources and sinks require improved assessments of current and future deforestation, and more consistent precipitation scenarios from climate models. Notwithstanding the uncertainties, continued tropical deforestation will most certainly play a very large role in the build-up of future greenhouse gas concentrations
As part of the international refraction measurements in Central Europe in the year 2000, three profiles traversed the region of earthquake swarms in West-Bohemia/Vogtland. The shots were also recorded at the permanent stations of the local seismic networks. The travel times of P-waves, observed in the West-Bohemian region, are discussed and interpreted in the present paper. In general, significantly lower P-wave velocities were found in the Saxothuringian (northern) part of the studied area than in the adjacent southern parts. The observed travel times are interpreted separately for the individual geological units, in particular for the plutons, crystallinicum, and the Marianske Lazne(Marienbad) Complex. After smoothing the selected data using rational approximations, the Wiechert-Herglotz method was used to compute vertically inhomogeneous velocity models. The characteristic features of the derived models are relatively low P-wave velocities at the surface and prominent velocity increases within the uppermost crust down to a depth of about one kilometer
We present a wavelet coherence method that is capable of displaying local coherence information between two seismic stations in the sense of a spectrogram. We have analyzed the vertical components of a 20-min-long time series from four stations that were situated in the seismic near field of Stromboli volcano. Typical volcanic seismic signals recorded in the near field of Stromboli volcano consist of continuous volcanic tremor superimposed on frequent Strombolian explosion signals. The tremor exhibits a banded and frequency-stable structure, whereas the broadband explosion signals span two or three frequency decades. We demonstrate that signals related to explosion earthquakes are strongly correlated within the network over 1.5 frequency decades. Using synthetic data, we show how coherent signal portions can be extracted out of noisy data using a coherence-filtering method. A time delay analysis using coherence information results in a coarse source location estimation that lies within the crater region. With the exception of randomly fluctuating coherence peaks, low correlations have been observed in the characteristic bands that are assumed to be generated by continuous tremor. In the low-frequency band that is related to the ocean microseisms (period approximate to 4-8 sec), we observe mostly high correlation that breaks down during the appearance of explosion earthquake signals. Based on further analysis using the inverse wavelet transformation, we propose a model that describes the breakdown phenomenon as a superposition of two independent events