22.214.171.124 Moisture and Precipitation
Water is fundamental to life, and if regional seasonal precipitation patterns were to change, the potential impacts could be profound. Consequently, it is of real practical interest to evaluate how well models can simulate precipitation, not only at global scales, but also regionally. Unlike seasonal variation in temperature, which at large scales is strongly determined by the insolation pattern and configuration of the continents, precipitation variations are also strongly influenced by vertical movement of air due to atmospheric instabilities of various kinds and by the flow of air over orographic features. For models to simulate accurately the seasonally varying pattern of precipitation, they must correctly simulate a number of processes (e.g., evapotranspiration, condensation, transport) that are difficult to evaluate at a global scale. Some of these are discussed further in Sections 8.2 and 8.6. In this subsection, the focus is on the distribution of precipitation and water vapour.
Figure 8.5a shows observation-based estimates of annual mean precipitation and Figure 8.5b shows the multi-model mean field. At the largest scales, the lower precipitation rates at higher latitudes reflect both reduced local evaporation at lower temperatures and a lower saturation vapour pressure of cooler air, which tends to inhibit the transport of vapour from other regions. In addition to this large-scale pattern, captured well by models, is a local minimum in precipitation near the equator in the Pacific, due to a tendency for the Inter-Tropical Convergence Zone (ITCZ) to reside off the equator. There are local maxima at mid-latitudes, reflecting the tendency for subsidence to suppress precipitation in the subtropics and for storm systems to enhance precipitation at mid-latitudes. The models capture these large-scale zonal mean precipitation differences, suggesting that they can adequately represent these features of atmospheric circulation. Moreover, there is some evidence provided in Section 8.3.5 that models have improved over the last several years in simulating the annual cycle of the precipitation patterns.
Models also simulate some of the major regional characteristics of the precipitation field, including the major convergence zones and the maxima over tropical rain forests, although there is a tendency to underestimate rainfall over the Amazon. When considered in more detail, however, there are deficiencies in the multi-model mean precipitation field. There is a distinct tendency for models to orient the South Pacific convergence zone parallel to latitudes and to extend it too far eastward. In the tropical Atlantic, the precipitation maximum is too weak in most models with too much rain south of the equator. There are also systematic east-west positional errors in the precipitation distribution over the Indo-Pacific Warm Pool in most models, with an excess of precipitation over the western Indian Ocean and over the Maritime Continent. These lead to systematic biases in the location of the major rising branches of the Walker Circulation and can compromise major teleconnection pathways, in particular those associated with El Niño (e.g., Turner et al., 2005). Systematic dry biases over the Bay of Bengal are related to errors in the monsoon simulations.
Despite the apparent skill suggested by the multi-model mean (Figure 8.5), many models individually display substantial precipitation biases, especially in the tropics, which often approach the magnitude of the mean observed climatology (e.g., Johns et al., 2006; see also the Supplementary Material, Figures S8.9 and S8.10). Although some of these biases can be attributed to errors in the SST field of the coupled model, even atmosphere-only versions of the models show similarly large errors (e.g., Slingo et al., 2003). This may be one factor leading to a lack of consensus among models even as to the sign of future regional precipitation changes predicted in parts of the tropics (see Chapter 10).
Figure 8.5. Annual mean precipitation (cm), observed (a) and simulated (b), based on the multi-model mean. The Climate Prediction Center Merged Analysis of Precipitation (CMAP; Xie and Arkin, 1997) observation-based climatology for 1980 to 1999 is shown, and the model results are for the same period in the 20th-century simulations in the MMD at PCMDI. In (a), observations were not available for the grey regions. Results for individual models can be seen in Supplementary Material, Figure S8.9.
At the heart of understanding what determines the regional distribution of precipitation over land and oceans in the tropics is atmospheric convection and its interaction with large-scale circulation. Convection occurs on a wide range of spatial and temporal scales, and there is increasing evidence that interactions across all scales may be crucial for determining the mean tropical climate and its regional rainfall distributions (e.g., Khairoutdinov et al., 2005). Over tropical land, the diurnal cycle dominates, and yet many models have difficulty simulating the early evening maximum in rainfall. Instead, they systematically tend to simulate rain before noon (Yang and Slingo, 2001; Dai, 2006), which compromises the energy budget of the land surface. Similarly, the land-sea breezes around the complex system of islands in Indonesia have been implicated in the failure of models to capture the regional rainfall patterns across the Indo-Pacific Warm Pool (Neale and Slingo, 2003). Over the oceans, the precipitation distribution along the ITCZ results from organised convection associated with weather systems occurring on synoptic and intra-seasonal time scales (e.g., the Madden-Julian Oscillation (MJO); see Section 8.4.8). These systems are frequently linked to convectively coupled equatorial wave structures (e.g., Yang et al., 2003), but these are poorly represented in models (e.g., Lin et al., 2006; Ringer et al., 2006). Thus the rain-bearing systems, which establish the mean precipitation climatology, are not well simulated, contributing also to the poor temporal characteristics of daily rainfall (e.g., Dai, 2006) in which many models simulate rain too frequently but with reduced intensity.
Precipitation patterns are intimately linked to atmospheric humidity, evaporation, condensation and transport processes. Good observational estimates of the global pattern of evaporation are not available, and condensation and vertical transport of water vapour can often be dominated by sub-grid scale convective processes which are difficult to evaluate globally. The best prospect for assessing water vapour transport processes in humid regions, especially at annual and longer time scales, may be to compare modelled and observed streamflow, which must nearly balance atmospheric transport since terrestrial water storage variations on longer time scales are small (Milly et al., 2005; see Section 126.96.36.199).
Although an analysis of runoff in the MMD at PCMDI has not yet been performed, the net result of evaporation, transport and condensation processes can be seen in the atmospheric humidity distribution. Models reproduce the large-scale decrease in humidity with both latitude and altitude (see Supplementary Material, Figure S8.11), although this is not truly an independent check of models, since it is almost a direct consequence of their reasonably realistic simulation of temperature. The multi-model mean bias in humidity, zonally and annually averaged, is less than 10% throughout most of the lower troposphere compared with reanalyses, but model evaluation in the upper troposphere is considerably hampered by observational uncertainty.
Any errors in the water vapour distribution should affect the outgoing LW radiation (see Section 188.8.131.52.2), which was seen to be free of systematic zonal mean biases. In fact, the observed differences in outgoing LW radiation between the moist and dry regions are reproduced by the models, providing some confidence that any errors in humidity are not critically affecting the net fluxes at the TOA. However, the strength of water vapour feedback, which strongly affects global climate sensitivity, is primarily determined by fractional changes in water vapour in response to warming, and the ability of models to correctly represent this feedback is perhaps better assessed with process studies (see Section 8.6).