188.8.131.52 Ocean Precipitation
Remotely sensed precipitation measurements over the ocean are based on several different sensors in the microwave and infrared that are combined in different ways. Many experimental products exist. Operational merged products seem to perform best in replicating island-observed monthly amounts (Adler et al., 2001). This does not mean they are best for trends or low-frequency variability, because of the changing mixes of input data. The main global data sets available for precipitation, and which therefore include ocean coverage, have been the GPCP (Huffman et al., 1997; Adler et al., 2003) and the NOAA Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP; Xie and Arkin, 1997). Comparisons of these data sets and others (Adler et al., 2001; Yin et al., 2004) reveal large discrepancies over the ocean; however, there is better agreement among the passive microwave products even using different algorithms. Over the tropical oceans, mean amounts in CMAP and GPCP differ by 10 to 15%. Calibration using observed rainfall from small atolls in CMAP was extended throughout the tropics in ways that are now recognised as incorrect. However, evaluation of GPCP reveals that it is biased low by 16% at such atolls (Adler et al., 2003), also raising questions about the ocean GPCP values. Differences arise due to sampling and algorithms. Polar-orbiting satellites each obtain only two instantaneous rates per day over any given location, and thus suffer from temporal sampling deficiencies that are offset by using geostationary satellites. However, only less-accurate infrared sensors are available with the latter. Model-based (including reanalysis) products perform poorly in the evaluation of Adler et al. (2001) and are not currently suitable for climate monitoring. Robertson et al. (2001b) examined monthly anomalies from several satellite-derived precipitation data sets (using different algorithms) over the tropical oceans. The expectation in the TAR was that measurements from the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and passive TRMM microwave imager (TMI) would clarify the reasons for the discrepancies, but this has not yet been the case. Robertson et al. (2003) documented poorly correlated behaviour (correlation 0.12) between the monthly, tropical ocean-averaged precipitation anomalies from the PR and TMI sensors. Although the TRMM PR responds directly to precipitation size hydrometeors, it operates with a single attenuating frequency (13.8 GHz) that necessitates significant microphysical assumptions regarding drop size distributions for relating reflectivity, signal attenuation and rainfall, and uncertainties in microphysical assumptions for the primary TRMM algorithm (2A25) remain problematic.
The large regional signals from monsoons and ENSO that emphasise large-scale shifts in precipitation are reasonably well captured in GPCP and CMAP (see Section 3.6.2), but cancel out when area-averaged over the tropics, and the trends and variability of the tropical average are quite different in the two products. Global precipitation from GPCP (updated from Adler et al., 2003, but not shown) has monthly variability with a standard deviation of about 2% of the mean. The variability in the ocean and land areas when examined separately is larger, about 3%, and with variations related to ENSO events (Curtis and Adler, 2003). During El Niño events, area-averaged precipitation increases over the oceans but decreases over land.
Although the trend over 25 years in global total precipitation in the GPCP data set (Adler et al., 2003) is very small, there is a small increase (about 4% over the 25 years) over the oceans in the latitude range 25°S to 25°N, with a partially compensating decrease over land (2%) in the same latitude belt. Northern mid-latitudes show a decrease over land and ocean. Over a slightly longer time frame, precipitation increased over the North Atlantic between 1960 to 1974 and 1975 to 1989 (Josey and Marsh, 2005) and is reflected in changes in salinity in the oceans (Section 5.2.3). The inhomogeneous nature of the data sets and the large ENSO variability limit what can be said about the validity of changes, both globally and regionally.