[1] Field-chamber measurements of soil respiration from 17 different forest and shrubland sites in Europe and North America were summarized and analyzed with the goal to develop a model describing seasonal, interannual and spatial variability of soil respiration as affected by water availability, temperature, and site properties. The analysis was performed at a daily and at a monthly time step. With the daily time step, the relative soil water content in the upper soil layer expressed as a fraction of field capacity was a good predictor of soil respiration at all sites. Among the site variables tested, those related to site productivity (e.g., leaf area index) correlated significantly with soil respiration, while carbon pool variables like standing biomass or the litter and soil carbon stocks did not show a clear relationship with soil respiration. Furthermore, it was evidenced that the effect of precipitation on soil respiration stretched beyond its direct effect via soil moisture. A general statistical nonlinear regression model was developed to describe soil respiration as dependent on soil temperature, soil water content, and site-specific maximum leaf area index. The model explained nearly two thirds of the temporal and intersite variability of soil respiration with a mean absolute error of 0.82 mmol m À2 s À1. The parameterized model exhibits the following principal properties: (1) At a relative amount of upper-layer soil water of 16% of field capacity, half-maximal soil respiration rates are reached. (2) The apparent temperature sensitivity of soil respiration measured as Q 10 varies between 1 and 5 depending on soil temperature and water content. (3) Soil respiration under reference moisture and temperature conditions is linearly related to maximum site leaf area index. At a monthly timescale, we employed the approach by Raich et al. [2002] that used monthly precipitation and air temperature to globally predict soil respiration (T&P model). While this model was able to explain some of the month-to-month variability of soil respiration, it failed to capture the intersite variability, regardless of whether the original or a new optimized model parameterization was used. In both cases, the residuals were strongly related to maximum site leaf area GLOBAL BIOGEOCHEMICAL CYCLES, VOL. 17, NO. 4, 1104, doi:10.1029/2003GB002035, 2003 15 -1 index. Thus, for a monthly timescale, we developed a simple T&P&LAI model that includes leaf area index as an additional predictor of soil respiration. This extended but still simple model performed nearly as well as the more detailed time step model and explained 50% of the overall and 65% of the site-to-site variability. Consequently, better estimates of globally distributed soil respiration should be obtained with the new model driven by satellite estimates of leaf area index. Before application at the continental or global scale, this approach should be further tested in boreal, cold-temperate, and tropical biomes as well as for non-woody vegetation.INDEX TERMS: 1615 Global...

Soil‐surface CO2 efflux and its spatial and temporal variations were examined in an 8‐y‐old ponderosa pine plantation in the Sierra Nevada Mountains in California from June 1998 to August 1999. Continuous measurements of soil CO2 efflux, soil temperatures and moisture were conducted on two 20 × 20 m sampling plots. Microbial biomass, fine root biomass, and the physical and chemical properties of the soil were also measured at each of the 18 sampling locations on the plots. It was found that the mean soil CO2 efflux in the plantation was 4.43 µmol m−2 s−1 in the growing season and 3.12 µmol m−2 s−1 in the nongrowing season. These values are in the upper part of the range of published soil‐surface CO2 efflux data. The annual maximum and minimum CO2 efflux were 5.87 and 1.67 µmol m−2 s−1, respectively, with the maximum occurring between the end of May and early June and the minimum in December. The diurnal fluctuation of CO2 efflux was relatively small (< 20%) with the minimum appearing around 09.00 hours and the maximum around 14.00 hours. Using daytime measurements of soil CO2 efflux tends to overestimate the daily mean soil CO2 efflux by 4–6%. The measurements taken between 09.00 and 11.00 hours (local time) seem to better represent the daily mean with a reduced sampling error of 0.9–1.5%. The spatial variation of soil CO2 efflux among the 18 sampling points was high, with a coefficient of variation of approximately 30%. Most (84%) of the spatial variation was explained by fine root biomass, microbial biomass, and soil physical and chemical properties. Although soil temperature and moisture explained most of the temporal variations (76–95%) of soil CO2 efflux, the two variables together explained less than 34% of the spatial variation. Microbial biomass, fine root biomass, soil nitrogen content, organic matter content, and magnesium content were significantly and positively correlated with soil CO2 efflux, whereas bulk density and pH value were negatively correlated with CO2 efflux. The relationship between soil CO2 efflux and soil temperature was significantly controlled by soil moisture with a Q10 value of 1.4 when soil moisture was <14% and 1.8 when soil moisture was >14%. Understanding the spatial and temporal variations is essential to accurately assessment of carbon budget at whole ecosystem and landscape scales. Thus, this study bears important implications for the study of large‐scale ecosystem dynamics, particularly in response to climatic variations and management regimes.

A nanoelectrode array based on vertically aligned multiwalled carbon nanotubes (MWNTs) embedded in SiO 2 is used for ultrasensitive DNA detection. Characteristic electrochemical behaviors are observed for measuring bulk and surface-immobilized redox species. Sensitivity is dramatically improved by lowering the nanotube density. Oligonucleotide probes are selectively functionalized to the open ends of nanotubes. The hybridization of subattomole DNA targets can be detected by combining such electrodes with Ru(bpy) 3 2+ mediated guanine oxidation.

[1] It is commonly believed that greenhouse-gas-induced global warming can weaken the east Asian winter monsoon but strengthen the summer monsoon, because of stronger warming over high-latitude land as compared to low-latitude oceans. In this study, we show that the surface wind speed associated with the east Asian monsoon has significantly weakened in both winter and summer in the recent three decades. From 1969 to 2000, the annual mean wind speed over China has decreased steadily by 28%, and the prevalence of windy days (daily mean wind speed > 5 m/s) has decreased by 58%. The temperature trends during this period have not been uniform. Significant winter warming in northern China may explain the decline of the winter monsoon, while the summer cooling in central south China and warming over the South China Sea and the western North Pacific Ocean may be responsible for weakening the summer monsoon. In addition, we found that the monsoon wind speed is also highly correlated with incoming solar radiation at the surface. The present results, when interpreted together with those of recent climate model simulations, suggest two mechanisms that govern the decline of the east Asian winter and summer monsoons, both of which may be related to human activity. The winter decline is associated with global-scale warming that may be attributed to increased greenhouse gas emission, while the summer decline is associated with local cooling over south-central China that may result from air pollution.

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