IPCC Report.pdf - Adam Curry

Chapter 3Changes in Climate Extremes and their Impacts on the Natural Physical Environment(Palmer, 1965), which measures the departure of moisture balance from normal conditions using a simple water balance model (e.g., Dai,2011), as well as other indices such as the Precipitation Potential Evaporation Anomaly (PPEA, based on the cumulative differencebetween precipitation and potential evapotranspiration) used in Burke and Brown (2008) and the Standardized Precipitation-Evapotranspiration Index (SPEI, which considers cumulated anomalies of precipitation and potential evapotranspiration) described inVicente-Serrano et al. (2010). PDSI has been widely used for decades (in particular in the United States), and also in climate changeanalyses (e.g., Dai et al., 2004; Burke and Brown, 2008; Dai, 2011); however, it has some shortcomings for climate change monitoringand projection. PDSI was originally calibrated for the central United States, which can impair the comparability of the index acrossregions (and also across time periods if drought mechanisms change over time). Thus it is often of advantage to renormalize the localPDSI (Dai, 2011), which can also be done using the self-calibrated PDSI (Wells et al., 2004), but several studies do not apply these steps.Moreover, the land surface model underlying the computation of the PDSI is essentially a simple bucket-type model, which is lesssophisticated than more recent land surface and hydrological models and thus implies several limitations (e.g., Dai et al., 2004; Burke etal., 2006). Another important issue is that the parameterization of potential evapotranspiration as empirically (and solely) dependent onair temperature, which is often applied for these various indices (e.g., in the study of Dai et al., 2004) can lead to biased results (e.g.,Donohue et al., 2010; Milly and Dunne, 2011; Shaw and Riha, 2011). Temperature is only an indirect driver of evapotranspiration, via itseffect on vapor pressure deficit and via effects on vegetation phenology. Furthermore, approaches using potential evapotranspiration asa proxy for actual evapotranspiration do not consider soil moisture and vegetation control on evapotranspiration, which are importantmechanisms limiting drought development.For the assessment of soil moisture drought, simulated soil moisture anomalies also can be considered (Wang et al., 2005; Burke andBrown, 2008; Sheffield and Wood, 2008a; A.H. Wang et al., 2009; Dai, 2011; Orlowsky and Seneviratne, 2011). Simulated soil moistureanomalies integrate the effects of precipitation forcing, simulated actual evapotranspiration (resulting from atmospheric forcing andsimulated soil moisture limitation on evapotranspiration), and simulated soil moisture persistence. Although the soil moisture simulatedby (land-surface, hydrological, and climate) models often exhibits strong discrepancies in absolute terms, soil moisture anomalies can becompared with simple scaling and generally match reasonably well (e.g., Koster et al., 2009; A.H. Wang et al., 2009). Soil moisturepersistence is found to be an important component in projected changes in soil moisture drought, with some regions displaying yearrounddryness compared to reference (late 20th or pre-industrial) conditions due to the carry-over effect of soil moisture storage fromseason to season, leading to year-round soil moisture deficits compared to late 20th century climate (e.g., Wang et al., 2005, Figure 3-10).However, it should be noted that some land surface and hydrological models (used offline or coupled to climate models) suffer fromsimilar shortcomings as noted above for PDSI – that is, they use simple bucket models or simplified representations of potentialevapotranspiration. The latter issue has been suggested as being particularly critical for models used in offline mode (Milly and Dunne,2011). Nonetheless, for the assessment of soil moisture drought, using simulated soil moisture anomalies seems less problematic thanmany other indices for the reasons highlighted in the above paragraphs.The indices listed above have been used in various studies analyzing drought in the context of climate change, but with a few exceptionsmost available studies are based only on one index, which makes their comparison difficult. Nonetheless, these studies suggest thatprojections can be highly dependent on the choice of drought index. For instance, one study projected changes in drought area possiblyvarying between a negligible impact and a 5 to 45% increase depending on the drought index considered (Burke and Brown, 2008).Other drought indices are used to quantify hydrological drought (e.g., Heim Jr., 2002; Vidal et al., 2010; Dai, 2011), but are lesscommonly used in climate change studies. Further analyses or indices also consider the area affected by droughts (e.g., Burke et al.,2006; Sheffield and Wood, 2008a; Dai, 2011) or additional variables (such as snow or vegetation indices from satellite measurements,e.g., Heim Jr., 2002). As for the definition of other indices (Box 3-1), the determination of the reference period is critical for the assessmentof changes in drought patterns independently of the chosen index. In general, late 20th-century conditions are used as reference (e.g.,Figure 3-10).In summary, drought indices often integrate precipitation, temperature, and other variables, but may emphasize differentaspects of drought and should be carefully selected with respect to the drought characteristic in mind. In particular, someindices have specific shortcomings, especially in the context of climate change. For this reason, assessments of changes indrought characteristics with climate change should consider several indices including a specific evaluation of their relevanceto the addressed question to support robust conclusions. In this assessment we focus on the following indices: consecutivedry days (CDD) and simulated soil moisture anomalies (SMA), although evidence based on other indices (e.g., PDSI forpresent climate) is also considered (Section 3.5.1; Tables 3-2 and 3-3).169