26-Years of Global Oceanic Cloud Liquid Water Path Estimates Derived From Multiple Orbiting Passive Microwave Sensors

Cloud liquid water path (LWP) is a crucial parameter for understanding cloud-climate interactions. Increased understanding of LWP variations, particularly those associated with low clouds, is vital for reducing climate change uncertainty. Confidence in variability and trend analyses depends heavily on the quality and total length of the observational record. Thus far, many long-term studies have relied on satellite visible/infrared derived cloud datasets. While such datasets are highly informative, long-term records of LWP derived using passive microwave observations have the potential to be equally useful, especially since microwave retrievals are not sunlight limited. Particularly for warm, boundary layer cloud fields (where rainfall is minimal, and/or clouds may be overlain by thin ice clouds), microwave retrievals of LWP have substantial value. The UWisc LWP climatology, originally made available in 2008, is one such long-term, high-quality passive microwave cloud record. Using the latest Version 7 Remote Sensing Systems LWP individual satellite records, the climatology has been updated and extended through the end of 2013, thus offering a 26-year global record of LWP.

A systematic drift of satellite equatorial overpass time superposed upon a climatological asymmetry in the LWP diurnal cycle can lead to spurious trends and/or artificial changes in LWP variability in long term records. Accounting for diurnal cycle effects is a particular strength of the methodology used to create this global LWP record. Such consideration is especially important for accurately quantifying LWP associated with warm, low clouds (e.g. stratocumulus). An overview of the methodology for constructing the record is presented here, along with plans for maintenance and potential improvements to the record. Analyses of the variability found in regional LWP associated with interseasonal/interannual climate variations is also presented, thus offering an important, complementary description of variations in cloud fields inferred using VIS/IR techniques. Such variability analyses have the potential to serve as key benchmarks for assessing the extent to which global climate models (GCMs) accurately simulate cloud trends/variability.