AIRS-Observed Interrelationships of Anomaly Time-series of Moist Process-Related Parameters and Inferred Feedback Values on Various Spatial Scales

There are some climate feedbacks, especially those associated with moist processes, which are not very well represented in GCMs, the primary tools to predict future climate changes associated with man's ever increasing influences on our planet. Here, we make use of the first 9 years of AIRS observations to evaluate interrelationships/correlations of atmospheric moist parameter anomalies computed from AIRS Version 5 Level-3 products, and demonstrate their usefulness to calculate certain feedback strength values. Note that a rather lively debate has flared up again on the possible usability of shorter-term, satellite-observed climate parameter anomalies for estimating climate sensitivity, i. e., the inferred strength of various (mostly moist processes related) feedbacks. Nevertheless, recent works, in particular analyses by Dessler, have pointed out the usefulness of these shorter term (but reliable) datasets in assessing global water vapor and cloud feedbacks. First we evaluate AIRS-observed interrelationships of anomaly time-series of water vapor, clouds, OLR and temperature on various spatial scales using 1x1 Degree resolution (a common GCM scale) 9-year long (Sept. 2002 through Aug. 2011) monthly anomaly time-series as starting points. We also find significant correlations among the 1x1 Degree average rate of change maps themselves, as well as among the deep tropical anomaly Hovmöller diagrams. We argue that for GCMs to be trusted for predicting longer-term climate variability, e. g., that due to global warming, they should be able to reproduce these observed relationships/metrics as closely as possible. Next, we evaluate the AIRS-observed water vapor feedback on global to regional scales. For cloud feedback, we demonstrate that unlike the global cloud feedback, which may require additional decades of data to compute reliably, regional cloud feedback strengths may already be assessed with sufficient accuracy to provide "benchmarks" for GCMs. The longwave cloud radiative forcing anomalies derived from AIRS observations are very similar to those determined using CERES products (see Susskind et al. at Session U16). In this presentation we address only the long-wave component of cloud forcing and the corresponding cloud feedback. We propose that since for this time period the main relevant surface temperature "forcings" were associated with the El Niño/La Niña variability, transient GCM runs with 2002-2011 prescribed sea surface temperatures should be the ones whose runs we want to compare with the results presented here.