As the concentration of greenhouse gases increases in the atmosphere, the atmosphere clearly must respond in some manner to accommodate the change in radiative forcing. The atmosphere may respond by warming to some degree, it may change its vertical distribution of moisture and cloud cover, or any combination of these may occur. Each of the state variables must be monitored, including their vertical distributions through the troposphere and lower stratosphere, to evaluate the nature of anthropogenic and natural changes. One of the most hotly debated topics in modern climatology is how atmospheric moisture distribution will change in response to the addition of greenhouse gases and therefore whether, or by how much, this moisture response will moderate the temperature response. Thus, it is not enough to measure temperature, simply because temperature has been the initial focus of the greenhouse debate.
Atmospheric observations must be colocated with those stations established to monitor surface conditions. This need directly follows from the earlier point that most, if not all, dec-cen atmospheric variability and change are in response to changes in slower components of the climate system, such as land, ice, and ocean. These components represent the lower boundary of the atmosphere. In many cases, as noted above, atmospheric changes strongly covary with changes at the surface. To evaluate, diagnose, and attribute dec-cen change, such covariation must be captured in a manner that facilitates analysis and evaluation of hypotheses that describe the coupled mechanisms driving and modulating long-term variability.
Process studies and related field efforts must be directed to improving our understanding and parameterization of surface-atmosphere interaction. Obviously, it is through this boundary interaction that slower-scale components communicate their influences to the atmosphere. Thus, appropriate parameterization of these phenomena are essential, since modeling efforts are the primary tool we have for forecasting future change. We also need better parameterization of clouds, including distribution and feedback processes, since their treatment in models may prove crucial in predicting long-term climate responses to changes in radiative forcing, as well as other feedback influences associated with variability and change. These parameterizations are currently a primary limitation in existing models.
The radiative effects of aerosols, direct and indirect, are poorly constrained. Cloud processes, although they occur on far shorter than decadal timescales, are a major uncertainty in predicting future radiation balances. Parameterizations need to be improved.
Carbon cycle questions require a CO2 measurement strategy that accounts for the hierarchy of scales, both temporal and spatial, inherent in ecosystem processes and their controls. Atmospheric concentration data must allow the identification and quantification of regional sources and sinks and their responses