The strength of Earth's greenhouse effect depends on the fact that the temperature decreases with height in the troposphere, so that emission from water vapor and clouds in the colder upper troposphere is less than that from the surface. A stronger lapse rate (the rate of decrease of temperature with altitude) gives rise to a stronger greenhouse effect and a warmer surface, all else being equal. If the lapse rate changes systematically with the surface temperature, then a potentially strong lapse rate feedback may exist.
Radiative processes, large-scale dynamical processes, and convection determine the lapse rate. Radiative processes generally cool the atmosphere and heat the surface, and convection and large-scale motions in the atmosphere generally move heat upward. In the tropics the lapse rate generally follows the moist adiabatic lapse rate, the rate at which saturated air parcels cool with altitude as they are raised adiabatically. The moist adiabatic lapse rate decreases with increasing surface temperature, so by itself lapse rate feedback is expected to be negative in the tropics (Hansen et al., 1984; Wetherald and Manabe, 1986).
If the assumption of fixed relative humidity is a good approximation, then the water vapor feedback is partially cancelled by the lapse rate feedback (Cess, 1975). If the lapse rate is reduced, then the air at altitude is warmer. The warmer air contains more water vapor. The decreased greenhouse effect caused by a weaker lapse rate is offset by the increased greenhouse effect from larger amounts of water vapor at higher altitudes.
Patterns of vertical temperature structure change are one of the few parameters widely used to detect and attribute climate change to particular forcings, or to natural variability (e.g., Tett et al., 2002). (Surface temperature changes are the other main detection parameter.) If climate models correctly simulate climate feedback mechanisms, they should correctly reproduce the change in vertical temperature structure associated with different climate forcings. Thus, changes in lapse rate are indicators both of the strength of lapse rate feedback and of the response to climate forcings.
Climate models generally reproduce the observed lapse rate in the tropics and elsewhere through the incorporation of large-scale dynamics and parameterized convection and radiation. Some observations suggest relationships between surface temperature trends and temperature trends in the free troposphere that seem inconsistent with the behavior of current climate models (NRC, 2000a; Santer et al., 2000). It is still unclear whether these apparent inconsistencies are the result of a measurement problem or a failure of our understanding of the climate system.
Unfortunately, current upper-air temperature observations are not well suited to determining lapse rate changes. The vertical resolution of satellite observations is too coarse for accurate lapse rate computations, although newer instruments (e.g., the Advanced Microwave Sounding Unit) provide better vertical resolution than older ones (e.g., the Microwave Sounding Unit). Both satellite and radiosonde observations are hampered by time-varying biases, which are very difficult to remove (NRC, 2000a). Lapse rate trends are particularly sensitive to attempts to remove these biases (Lanzante et al., in press). Similarly, trends in measures of atmospheric instability and convection that are related to lapse rate (e.g., Convective Available Potential Energy and Convective Inhibition) are affected by radiosonde data inhomogeneities (Gettelman et al., in press). Thus, to improve our ability to diagnose lapse rate feedback and to detect changes in the vertical temperature structure of the atmosphere, improved long-term upper-air temperature soundings are required. The observations must be of sufficient precision to measure decadal trends in temperature (and water vapor) distributions and sufficient spatial resolution to test mechanisms by which those distributions are maintained. More information concerning upper-air temperature monitoring requirements can be found in NRC (2000c).
Using the improved observations that are recommended here, correlation statistics of temperature, water vapor, and clouds on various time and space scales should be employed to rigorously diagnose the ability of models to simulate the feedbacks that underpin interannual variability of the lapse rate and water vapor distributions. Extending the work of, for example, Ross et al. (2002), Sun and Held (1996), and Sun and Oort (1995), these analyses should be focused not only on improving understanding of the feedback processes and their representation in models but also on deriving new, parsimonious model representations of these processes. Several existing national and international programs (e.g., ARM and GEWEX) could be very helpful in facilitating this work.
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