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Abstract. Detection of long-term, linear trends is affected by a number of factors, including the size of trend to be detected, the time span of available data, and the magnitude of variability and autocorrelation of the noise in the data. The number of years of data necessary to detect a trend is strongly dependent on, and increases with, the magnitude of variance (o-2•) and autocorrelation coefficient (qb) of the noise. For a typical range of values of o-2• and 4> the number of years of data needed to detect a trend of 5%/decade can vary from -10 to >20 years, implying that in choosing sites to detect trends some locations are likely to be more efficient and cost-effective than others. Additionally, some environmental variables allow for an earlier detection of trends than other variables because of their low variability and autocorrelation. The detection of trends can be confounded when sudden changes occur in the data, such as when an instrument is changed or a volcano erupts. Sudden level shifts in data sets, whether due to artificial sources, such as changes in instrumentation or site location, or natural sources, such as volcanic eruptions or local changes to the environment, can strongly impact the number of years necessary to detect a given trend, increasing the number of years by as much as 50% or more. This paper provides formulae for estimating the number of years necessary to detect trends, along with the estimates of the impact of interventions on trend detection. The uncertainty associated with these estimates is also explored. The results presented are relevant for a variety of practical decisions in managing a monitoring station, such as whether to move an instrument, change monitoring protocols in the middle of a long-term monitoring program, or try to reduce uncertainty in the measurements by improved calibration techniques. The results are also useful for establishing reasonable expectations for trend detection and can be helpful in selecting sites and environmental variables for the detection of trends. An important implication of these results is that it will take several decades of high-quality data to detect the trends likely to occur in nature. IntroductionThe impact of human intervention in a changing environment has brought about increased concern for detecting trends in various types of environmental data. A variety of studies

Methane (CH 4 ) is the most abundant organic trace gas in the atmosphere. In the distant past, variations in natural sources of methane were responsible for trends in atmospheric methane levels recorded in ice cores. Since the 1700s, rapidly growing human activities, particularly in the areas of agriculture, fossil fuel use, and waste disposal, have more than doubled methane emissions. Atmospheric methane concentrations have increased by a factor of 2 -3 in response to this increase, and continue to rise. These increasing concentrations have raised concern due to their potential effects on atmospheric chemistry and climate. Methane is important to both tropospheric and stratospheric chemistry, significantly affecting levels of ozone, water vapor, the hydroxyl radical, and numerous other compounds. In addition, methane is currently the second most important greenhouse gas emitted from human activities. On a per molecule basis, it is much more effective a greenhouse gas than additional CO 2 . In this review, we examine past trends in the concentration of methane in the atmosphere, the sources and sinks that determine its growth rate, and the factors that will affect its growth rate in the future. We also present current understanding of the effects of methane on atmospheric chemistry, and examine the direct and indirect impacts of atmospheric methane on climate. D

A standard baseline scenario 2,3 that assumes no policy intervention to limit greenhouse-gas emissions has 10 TW (10 ؋ 10 12 watts) of carbon-emission-free power being produced by the year 2050, equivalent to the power provided by all today's energy sources combined. Here we employ a carbon-cycle/energy model to ¶ Present address: Boeing, Saal Beach, California 90740-7644, USA.

We describe a simple method for evaluating the radiative forcing of surface temperature caused by changes in the vertical distribution of ozone. The method employs a parameterization based on one‐dimensional radiative‐convective equilibrium calculations; these calculations predict that the surface temperature should warm in response to both decreases in ozone above 30 km and increases in ozone below 30 km. The parameterization is used to investigate the response of surface temperature to observed changes in the vertical distribution of ozone at northern mid‐latitudes. We show that the observed ozone trends, taken at face value, suggest a cooling of the surface temperature at northern mid‐latitudes during the 1970s equal in magnitude to about half the warming predicted for CO2 for the same time period. However, the measurement uncertainty of the observed trends is large, with the best estimates for mid‐latitude cooling being −0.05±0.05°C. The surface cooling is caused by ozone decreases in the lower stratosphere, which outweigh the warming effects of ozone increases in the troposphere. The results obtained differ from predictions based on one‐dimensional photochemical model simulations of ozone trends for the 1970s, which suggest a warming of the surface temperature equal to ∼20% of the warming contributed by CO2. Also, the ozone decreases observed in the lower stratosphere during the 1970s produce atmospheric cooling by several tenths of a degree in the 12‐ to 20‐km altitude region over the northern mid‐latitudes. This temperature decrease is larger than the cooling due to CO2 and thus may obscure the expected stratospheric CO2 greenhouse signature.