Spatial variability of water vapor turbulent transfer within the boundary layer

Although the physics of evaporation within the inner region of the boundary layer is believed to be well understood, observations of mass-energy exchange processes have been hindered by the limitations of point sensors. A combination of point sensors and active remote sensing, namely, water-Raman Lidar measurements, offers new opportunities to study relatively large areas at temporal and spatial scales previously unattainable. Results from experiments over uniform canopies both confirm some traditional theories and challenge some of the underlying assumptions concerning the homogeneity of the surface-atmosphere interface and the use of point sensors to characterize large areas.This work was performed under the auspices of the U.S. Department of Energy. The authors would like to thank F. Archuleta, J. Archuleta, F. Barnes, W. Clements, K. Muller and W. Porch of LANL, R. Whitis of USDA, R. Jackson and P. Pinter of USDA-WCL, and L. Balick of EG&G for their invaluable time and support.     

Sea-ice effects on climate model sensitivity and low frequency variability

A change in a sea-ice parameter in a global coupled climate model results in a reduction in amplitude (of about 60%) and a shortening of the predominant period of decadal low frequency variability in the time series of globally averaged surface air temperature. These changes are global in extent and also are reflected in time series of area-averaged SSTs in the equatorial eastern Pacific Ocean, the principal components of the first EOFs of global surface air temperature and sea level pressure, Asian monsoon precipitations and other quantities. Coupled ocean-atmosphere-sea ice processes acting on a global scale are modified to produce these changes. Global climate sensitivity is reduced when ice albedo feedback is weakened due to the change in sea ice that makes it more difficult to melt. The changes in the amplitude and time scale of the low frequency variability in the model are traced to changes in the base state of the climate simulations as affected by modifications associated with the changes in sea ice. Making sea ice more difficult to melt results in increased sea-ice area, colder high latitudes, increased meridional surface temperature gradients, and, to a first order, stronger surface winds in most regions which strengthen near-surface currents, particularly in the Northern Hemisphere, and decreases the advection time scale in the upper ocean gyres. Additionally, in the North Atlantic there is enhanced meridional overturning due to increased density mainly in the Greenland Sea region. This also contributes to an intensified North Atlantic gyre. The changes in base state due to the sea ice change result in a more predominant decadal time scale of near 14 years and significantly reduced contributions from lower frequencies in the range of 15–40 year periods. Received: 11 December 1998 / Accepted: 4 October 1999     

Climatic change in Britain: Is SO2 more significant than CO2?

Summary Many climate scientists have suggested that anthropogenic emissions of greenhouse gases may create severe climate problems for Britain; however, the potential cooling effects of sulphur dioxide are widely acknowledged. In this investigation, we analyze British mean annual temperature, mean annual precipitation, and mean diurnal air temperature range over the period 1929–1988. Our analyses of these records reveal (a) a shift in the early 1950s away from warming and toward cooling, (b) a relative decline in maximum air temperatures when compared to minimum air temperatures, (c) a strong decline in the diurnal air temperature range and (d) a significant linkage between diurnal temperature range and precipitation. Given these signals in the observed climate record, it would appear that SO₂ rather than CO₂ has been the major anthropogenic climate influence in Britain over the past four decades.With 6 Figures     

Assessment of the soil CO2 gradient method for soil CO2 efflux measurements: comparison of six models in the calculation of the relative gas diffusion coefficient

This paper uses a refined soil gradient method to estimate soil CO₂ efflux. Six different models are used to determine the relative gas diffusion coefficient (ξ). A weighted harmonic averaging is used to estimate the soil CO₂ diffusion coefficient, yielding a better estimate of soil CO₂ efflux. The resulting soil CO₂ efflux results are then compared to the soil CO₂ efflux measured with a soil chamber. Depending on the choice of ξ model used, the estimated soil CO₂ efflux using the gradient method reasonably approximates the efflux obtained using the soil chamber method. In addition, the estimated soil CO₂ efflux obtained by this improved method is well described by an exponential function of soil temperature at a depth of 0.05 m with the temperature sensitivity ( Q ₁₀) of 1.81 and a linear function of soil moisture at a depth of 0.12 m, in general agreement with previous findings. These results suggest that the gradient method is a practical cost-effective means to measure soil CO₂ emissions. Results from the present study suggest that the gradient method can be used successfully to measure soil CO₂ efflux provided that proper attention is paid to the judicious use of the proper diffusion coefficient.     

Geometric consistency for regression model estimation and testing in climatology and meteorology

Abstract

The effects of outliers on linear regression are examined. The sensitivity of classical least‐squares (LS) procedures to outliers is shown to be associated with the geometric inconsistency between the data space and the analysis space. This is illustrated for both estimation and inference. A geometrically consistent procedure based on the Euclidean distance is proposed. This procedure involves the least absolute deviation (LAD) regression and a new permutation test for matched pairs (PTMP). Comparisons made with LS techniques demonstrate that the proposed procedure is more resistant to the existence of outliers in the data set and leads to more intuitive results. Applications and illustrations using meteorological and climatological data are also discussed.