D. Famulari

Detecting the Signal of Atmospheric N Deposition in Recent National-Scale Vegetation Change Across Britain

Model estimates of NOy and NHx deposition across Britain for 1996 (5 km square resolution) were applied as explanatory variables to account for national-scale, fine-grained changes in plant species composition between 1990 and 1998. Plant species data were recorded from up to 27 fixed plots located within a stratified random sample of 596 1 km2. The response variable was a cover-weighted Ellenberg fertility score for each plot. Analyses were carried out separately for woodlands, semi-natural grasslands and heaths/bogs. Most of the variation in the botanical response variable occurred between plots within squares and so could not be explained by the model deposition data. NHx deposition estimates accounted for significant, but small components of between 1 km2 variation in the change in Ellenberg score in grasslands (5.6%) and heath/bogs (9.8%) but not woodlands. NOy deposition estimates were not significantly associated with vegetation change. Linear models provided the best fit and the slope of the relationship was lower for heath/bogs than grasslands. Further signal attribution at sub-kilometre square scales requires the development of fine-grained models of N deposition that can be generalised across regional sampling domains.

Low cost and state of the art methods to measure nitrous oxide emissions

This letter provides an overview of the available measurement techniques for nitrous oxide (N2O) flux measurement. It is presented to aid the choice of the most appropriate methods for different situations. Nitrous oxide is a very potent greenhouse gas; the effect of 1 kg of N2O is estimated to be equivalent to 300 kg of CO2. Emissions of N2O from the soil have a larger uncertainty compared to other greenhouse gases. Important reasons for this are low atmospheric concentration levels and enormous spatial and temporal variability. Traditionally such small increases are measured by chambers and analyzed by gas chromatography. Spatial and temporal resolution is poor, but costs are low. To detect emissions at the field scale and high temporal resolution, differences at tens of ppt levels need to be resolved. Reliable instruments are now available to measure N2O by a range of micrometeorological methods, but at high financial cost. Although chambers are effective in identifying processes and treatment effects and mitigation, the future lies with the more versatile high frequency and high sensitivity sensors.

Development of a low-cost system for measuring conditional time-averaged gradients of SO2 and NH3

A conditional time-averaged gradient (COTAG) system has been developed to provide direct long-term (weekly to monthly) average flux gradient measurements for a range of trace gases, between land and atmosphere. Over daily periods, atmospheric conditions can range from high stability, where the vertical gradients of ambient concentration are enhanced due to very small diffusivity, to highly unstable conditions, in which concentration gradients are small due to the intense turbulent activity of the surface layer. The large vertical gradients generated by high stability would bias the estimate of the actual flux: to avoid this, the COTAG system samples conditionally, within a carefully refined range of stability. A comparison with a continuous flux gradient system suggested that the removal of stable conditions from the sampling period does not substantially modify the evaluation of the long-term fluxes.

Estimation of cumulative fluxes of nitrous oxide: uncertainty in temporal upscaling and emission factors

Summary Nitrous oxide (N2O) is a greenhouse gas produced mainly by the microbial breakdown of agricultural fertilizer. ‘Emission factors’ (EFs, the fraction of nitrogen added that is released as N2O) are based on flux chamber measurements following the application of fertilizer. These measurements are very variable in space and time so that EFs are often uncertain, but this is rarely quantified. We developed a method that simplifies the calculation of EFs, incorporates prior knowledge and quantifies the uncertainty with a B ayesian approach to fit the parameters of a lognormal model. We compared this with the standard method for interpolating, extrapolating and integrating fluxes of N2O (trapezoidal integration). We verified both methods against process‐based model output where the true integral was known and against eddy covariance data where the integral was estimated more accurately because of the greater spatial and temporal coverage. We used the process‐based model to simulate flux chamber data and added a lognormal spatial distribution to the model output. The lognormal model performed better than the standard method, in terms of estimating the true underlying cumulative flux more accurately. Estimates based on chamber and eddy covariance data were sometimes substantially different, but with no clear systematic bias. The B ayesian approach with the lognormal model enabled us to combine both chamber and eddy covariance data to constrain cumulative fluxes. The standard trapezoidal method typically underestimates emission factors to some extent if fluxes are lognormally distributed in space. The B ayesian approach with the lognormal model is a robust method for quantifying the uncertainty in cumulative fluxes of N2O. HighlightsEmission factors for N 2 O are based on sparse and variable measurements, and so are uncertain.We use a B ayesian approach to simplify the calculation and quantify the uncertainty.No observed systematic difference between eddy covariance and chamber measurement methods.The standard trapezoidal method will typically underestimate emission factors.

Soil and Litter Exchange of Reactive Trace Gases

The soil and litter play an important role in the exchange of trace gases between terrestrial ecosystems and the atmosphere.

Aerosol and acid gases

The background for this discussion was the background document in this book entitled: “Surface/atmosphere exchange of atmospheric acids and aerosols, including the effect and model treatment of chemical interactions”.