No abstract

Assessment of spatial and temporal variation in the impacts of ozone on human health, vegetation, and climate requires appropriate metrics. A key component of the Tropospheric Ozone Assessment Report (TOAR) is the consistent calculation of these metrics at thousands of monitoring sites globally. Investigating temporal trends in these metrics required that the same statistical methods be applied across these ozone monitoring sites. The nonparametric Mann-Kendall test (for significant trends) and the Theil-Sen estimator (for estimating the magnitude of trend) were selected to provide robust methods across all sites. This paper provides the scientific underpinnings necessary to better understand the implications of and rationale for selecting a specific TOAR metric for assessing spatial and temporal variation in ozone for a particular impact. The rationale and underlying research evidence that influence the derivation of specific metrics are given. The form of 25 metrics (4 for model-measurement comparison, 5 for characterization of ozone in the free troposphere, 11 for human health impacts, and 5 for vegetation impacts) are described. Finally, this study categorizes health and vegetation exposure metrics based on the extent to which they are determined only by the highest hourly ozone levels, or by a wider range of values. The magnitude of the metrics is influenced by both the distribution of hourly average ozone concentrations at a site location, and the extent to which a particular metric is determined by relatively low, moderate, and high hourly ozone levels. Hence, for the same ozone time series, changes in the distribution of ozone concentrations can result in different changes in the magnitude and direction of trends for different metrics. Thus, dissimilar conclusions about the effect of changes in the drivers of ozone variability (e.g., precursor emissions) on health and vegetation exposure can result from the selection of different metrics.

No abstract

Three alpine and three subalpine sites were monitored for up to 4 years to acquire data on the temporal and spatial variability of CO2 flux through snowpacks. We conclude that the snow formed a passive cap which controlled the concentration of CO2 at the snow‐soil interface, while the flux of CO2 into the atmosphere was controlled by CO2 production in the soil. Seasonal variability in the flux at all sites was characterized by early winter minima followed by a rise in flux that averaged 70% above the minima over about a 1‐month period. The seasonal variability was not related to soil temperatures which remained relatively constant. Interannual variability was small, and spatial variability was smaller than previously reported. Spatial variability on a scale of 1 to 10 m was less than 30% of the average fluxes and not significantly greater than estimated error in most cases. Spatial variability on a scale of 10‐ to 100‐m was about a factor of 2 and on a scale of 100 to 1000 m was about a factor of 4. The 100‐ to 1000‐m variability was complicated by the fact that the sites were in different ecosystems, alpine and subalpine, and at different elevations. We attribute the small variability at the 1‐ to 10‐m scale to the deep snow cover, from 1.4 to 5 m. We hypothesize that horizontal diffusion under the snow cover reduced small‐scale horizontal gradients, while the insulating effect of the deep snow cover kept the soil temperature and moisture relatively constant. Equivalent annual wintertime flux averaged about 95 g C m−2 yr−1 in the alpine and about 232 g C m−2 yr−1 in the subalpine sites. Measurements of CO2 concentrations at 0.2 and 0.5 m in the soil of one of the subalpine sites indicated that production early in the snow season occurred at or below 0.5 m while production between 0.5 m, and the surface became important after the start of the melt season.

Soil surface CO(2) flux (F(s)) is the dominant respiratory flux in many temperate forest ecosystems. Snowpacks increase this dominance by insulating the soil against the low temperature to which aboveground components are exposed. However, measurement of F(s) in winter may be impeded by snow cover. Likewise, developing annual F(s) models is complicated by seasonal variation in root and microbial metabolism. We compared three methods of measuring sub-snow F(s): (1) dynamic chamber measurements at the upper snowpack surface (F(snow)), (2) dynamic chamber measurements at the soil surface via snowpits (F(soil)), and (3) static estimates based on measured concentrations of carbon dioxide ([CO(2)]) and conductance properties of the snowpack (F(diffusional)). Methods were compared at a mid-elevation forest in northeastern Washington, a mid-elevation forest in northern Idaho, and a high-elevation forest and neighboring meadow in Wyoming. The methods that minimized snowpack disturbance, F(diffusional) and F(snow), yielded similar estimates of F(s). In contrast, F(soil) yielded rates two to three times higher than F(snow) at the forested sites, and seven times higher at the subalpine meadow. The ratio F(soil)/F(snow) increased with increasing snow depth when compared across all sites. Snow removal appears to induce elevated soil flux as a result of lateral CO(2) diffusion into the pit. We chose F(snow) as our preferred method and used it to estimate annual CO(2) fluxes. The snowpack was present for 36% of the year at this site, during which time 132 g C m(-2), or 17% of the annual flux, occurred. We conclude that snowpack CO(2) flux is quantitatively important in annual carbon budgets for these forests and that the static and dynamic methods yield similar and reasonable estimates of the flux, as long as snowpack disturbance is minimized.

No abstract

No abstract

No abstract