3.3.2 Changes in Large-scale Precipitation

3.3.2.1 Global Land Areas

Trends in global annual land precipitation were analysed using data from the GHCN, using anomalies with respect to the 1981 to 2000 base period (Vose et al., 1992; Peterson and Vose, 1997). The observed GHCN linear trend (Figure 3.12) over the 106-year period from 1900 to 2005 is statistically insignificant, as is the CRU linear trend up to 2002 (Table 3.4b). However, the global mean land changes (Figure 3.12) are not at all linear, with an overall increase until the 1950s, a decline until the early 1990s and then a recovery. Although the global land mean is an indicator of a crucial part of the global hydrological cycle, it is difficult to interpret as it is often made up of large regional anomalies of opposite sign.

Figure 3.12. Time series for 1900 to 2005 of annual global land precipitation anomalies (mm) from GHCN with respect to the 1981 to 2000 base period. The smooth curves show decadal variations (see Appendix 3.A) for the GHCN (Peterson and Vose, 1997), PREC/L (Chen et al., 2002), GPCP (Adler et al., 2003), GPCC (Rudolf et al., 1994) and CRU (Mitchell and Jones, 2005) data sets.

There are several other global land precipitation data sets covering more recent periods: Table 3.4a gives their characteristics, and the linear trends and their significance are given in Table 3.4b. There are a number of differences in processing, data sources and time periods that lead to the differences in the trend estimates. All but one data set (GHCN) are spatially infilled by either interpolation or the use of satellite estimates of precipitation. The Precipitation Reconstruction over Land (PREC/L) data (Chen et al., 2002) include GHCN data, synoptic data from the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center’s Climate Anomaly Monitoring System (CAMS), and the Global Precipitation Climatology Project (GPCP) data (Adler et al., 2003), and are a blend of satellite and gauge data. The Global Precipitation Climatology Centre (GPCC; updated from Rudolf et al., 1994) provides monthly data from surface gauges on several grids constructed using GPCC sources (including data from CRU, GHCN, a Food and Agriculture Organization (FAO) database and many nationally provided data sets). The data set designated GPCC VASClimO (Beck et al., 2005) uses only those quasi-continuous stations whose long-term homogeneity can be assured, while GPCC v.3 has used all available stations to provide more complete spatial coverage. Gridding schemes also vary and include optimal interpolation and grid-box averaging of areally weighted station anomalies. The CRU data set is from Mitchell and Jones (2005).

Table 3.4. (a) Characteristics and references of the six global land-area precipitation data sets used to calculate trends. (b) Global land precipitation trends (mm per decade). Trends with 5 and 95% confidence intervals and levels of significance (italic, 1–5%) were estimated by REML (see Appendix 3.A), which allows for serial correlation in the residuals of the data about the linear trend. All trends are based on annual averages without estimates of intrinsic uncertainties.

(a)

(b)

Notes:

^a Series ends at 2002

^b Series ends at 2000

For 1951 to 2005, trends range from –7 to +2 mm per decade and 5 to 95% error bars range from 3.2 to 5.3 mm per decade. Only the updated PREC/L series (Chen et al., 2002) trend and the GPCC v.3 trend appear to be statistically significant, but the uncertainties, as seen in the different estimates, undermine that result. For 1979 to 2005, GPCP data are added and trends range from –16 to +13 mm per decade but none is significant. Nevertheless, the discrepancies in trends are substantial, and highlight the difficulty of monitoring a variable such as precipitation that has large variability in both space and time. On the other hand, Figure 3.12 also suggests that interannual fluctuations have some overall reproducibility for land as a whole. The lag-1 autocorrelation of the residuals from the fitted trend (i.e., the de-trended persistence) is in the range 0.3 to 0.5 for the PREC/L, CRU and GHCN series but 0.5 to 0.7 for the two GPCC and the GPCP series. This suggests that either the limited sampling by in situ gauge data adds noise, or systematic biases lasting a few years (the lifetime of a satellite) are afflicting the GPCP data, or a combination of the two.