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

Abstract. International agreements for the limitation of ozone-depleting substances have already resulted in decreases in concentrations of some of these chemicals in the troposphere. Full compliance and understanding of all factors contributing to ozone depletion are still uncertain; however, reasonable expectations are for a gradual recovery of the ozone layer over the next 50 years. Because of the complexity of the processes involved in ozone depletion, it is crucial to detect not just a decrease in ozone-depleting substances but also a recovery in the ozone layer. The recovery is likely to be detected in some areas sooner than others because of natural variability in ozone concentrations. On the basis of both the magnitude and autocorrelation of the noise from Nimbus 7 Total Ozone Mapping Spectrometer ozone measurements, estimates of the time required to detect a fixed trend in ozone at various locations around the world are presented. Predictions from the Goddard Space Flight Center (GSFC) two-dimensional chemical model are used to estimate the time required to detect predicted trends in different areas of the world. The analysis is based on our current understanding of ozone chemistry, full compliance with the Montreal Protocol and its amendments, and no intervening factors, such as major volcanic eruptions or enhanced stratospheric cooling. The results indicate that recovery of total column ozone is likely to be detected earliest in the Southern Hemisphere near New Zealand, southern Africa, and southern South America and that the range of time expected to detect recovery for most regions of the world is between 15 and 45 years. Should the recovery be slower than predicted by the GSFC model, owing, for instance, to the effect of greenhouse gas emissions, or should measurement sites be perturbed, even longer times would be needed for detection.

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The factors responsible for rare summertime rainfall over portions of the United Arab Emirates (UAE), which have not been previously explored in detail, are elucidated with the Climate Forecast System Reanalysis and WRF mesoscale model simulations. The simulations show associations between active phases of the southwest Asian monsoon and intensification of the Arabian heat low, leading up to UAE rainfall events. Variability in the location and strength of the Arabian heat low circulation, which differs from the static portrayal in climatological minimum sea level pressure (MSLP), can affect the development of deep convection over the UAE. Analysis of the vorticity equation for a two-day case study period confirms that convergence is solely responsible for the spinup and maintenance of the primary heat low circulation. Convergence is also responsible for the spinup of a separate cyclonic circulation over the eastern UAE, which propagates offshore to the Arabian Gulf during morning hours. This cyclonic circulation advects moist air onshore over the western UAE, and deep convection follows from inland horizontal convective rolls and interaction with the approaching sea-breeze front. The development of widespread deep convection is shown to be most favorable during the decay phase of the Arabian heat low, when the preconditioned moist air is not replaced by drier continental flow, and the vertical profiles of temperature and moisture are also more favorable. Three other rainfall cases are briefly discussed to illustrate how the strength and geographic position of the Arabian heat low can affect rainfall characteristics over the UAE.