Eastern South Pacific mean state and variability in hybrid CGCMs

As underlined before, ENSO is obviously an important feature that a coupled model has to represent with the best possible acuracy (see previous section for the chosen criteria) in the perspective of regional climate change studies over the ocean off Peru and Chile. However, another aspect that needs special care when selecting the most relevant climate models for our downscaling experiments is the simulated mean state and variability at the open boundaries of the regional ocean model. Indeed, we chose in this thesis to study the impact of an idealized global warming scenario (4xCO2) in the stabilized regime. Under such scenario, different models are likely to exhibit different changes in the mean state and variability at the regional scale, relatively to their preindustrial behaviours. These simulated mean states and variabilities in the eastern South Pacific under preindustrial climate have first to be assessed and compared to observed or reanalyzed data, in order to identify the ones that ‘best’ simulate the ocean along the northern, southern and western boundaries of the regional model. As detailed in the previous section, four climate models of the hybrid group exhibit spatial and temporal ENSO characteristics (amplitude, spatial distribution, period, phase locking, modulation…) closer to the observed: INGV-ECHAM4 (Roeckner et al., 1996; Madec et al., 1998), IPSL-CM4 (Marti et al., 2009), UKMO-HadCM3 (Gordon et al., 2000; Pope et al., 2000; Johns et al., 2003) and UKMO-HadGem1 (Johns et al., 2006; Martin et al., 2006; Ringer et al ., 2006). These four coupled models are therefore analyzed at the limits of the regional model: 100°W-70°W, 15°N-40°S (fig. 2.9).

Fig. 2.9. Regional domain (dashed black lines) within the eastern South Pacific, with open boundaries (dashed red lines) at 100°W, 15°N and 40°S.

Grey colour is for land.

The analysis is conducted on the mean state and on the seasonal, interannual and intraseasonal variations of the main oceanic variables (particularly currents and temperature – salinity variations are weak over this region) at the northern, southern and especially western boundaries of the domain. Indeed, the latter crosses the equatorial waveguide and the associated current system and thereby concentrates most variability of both the thermocline and the large scale currents that is transmitted to the Peru-Chile coast (Strub et al., 1998; Kessler, 2006). To our knowledge, so far no study has been dedicated to the sensitivity of the regional ocean circulation to the eastern equatorial Pacific mean state and variability: for this reason, the modelled temperature and velocity fields are compared to those from observed data (when available), from the SODA 1.4.2 reanalysis (1958-2001) (Carton and Giese, 2008) and from the ORCA05 model (1992-2000) (Madec et al., 1998), which is used as the boundary forcing for the regional control run (see chapter three). The comparison is performed for different frequency bands (seasonal, intraseasonal and interannual) and is used to conduct the selection process. In addition, we have chosen to repeat these analyses for a model with a strongly biased ENSO cycle, dominated by the zonal advective feedback (CNRM-CM3 - Déqué et al., 1994; Madec et al., 1998; Royer et al., 2002), in order to compare the differences found between models from the same group (hybrid) with the differences found between models from two different groups. Indeed, the CNRM-CM3 model has strong mean zonal currents in the western equatorial Pacific and a dominant contribution of zonal advection to equatorial SST changes, similarly to other models dominated by the zonal advective feedback - like IAP-FGOALS or MIUB-ECHO-G (see section 1 of the present chapter). It also features a large cold bias in the warm pool, strong near-annual activity (see section 2 of the present chapter) and although its ENSO time scale is close to the observed (3.3 years vs. 3.7 years), it is interesting to note that previous versions of this model exhibit a biennial ENSO (e.g. Dewitte et al., 2007a), all of which are typical of a dominant zonal advective feedback. The time periods for each model considered in this analysis are indicated in table 2.4.

CGCM/Reanalysis INGV- ECHAM4

IPSL- CM4

UKMO- HadCM3

UKMO- HadGEM1

CNRM-

CM3 ORCA05 SODA- 1.4.2 Time span for computation

(years) 1761-1860 1860-2009 1859-1949 1859-1938 1930-2079 1992-2000 1958-2001 Length of simulation

(years) 100 150 91 80 150 9 44

Table 2.4. Time spans considered here for five CGCMs (INGV-ECHAM4, IPSL-CM4, UKMO-HadCM3, UKMO- HadGEM1, CNRM-CM3), an OGCM (ORCA05) and an ocean reanalysis (SODA-1.4.2).

Yet, before analyzing the oceanic forcing at the open boundaries of the regional domain, characteristics of the wind structure (seasonal cycle, variability) simulated by the four hybrid CGCMs over the eastern South Pacific are presented. Indeed, such analysis can be helpful to understand biases of the coupled models in the ocean, since the ocean circulation and dynamics in

the eastern South Pacific are dependent on the atmospheric forcing to a large extent, and particularly on the wind structure (see chapter one). In addition, although the fidelity of the wind forcing was not chosen as a selection criterion (see the introduction of the present chapter), it is also used to illustrate the interest of the statistical downscaling procedure (see also chapter four).

2.3.1. The wind structure over the ESP

Fig. 2.10. Surface wind stress (Pa) over the ESP from the CGCMs (INGV-ECHAM4, IPSL-CM4, UKMO-HadCM3 and UKMO-HadGEM1) and from satellite observations (QuickSCAT and ERS): climatological mean surface wind stress in (a) austral winter (April to September) and (b) austral summer (October to March). (c) Surface wind stress variability (RMS). The colour bar and contour interval for mean wind stress (resp. wind stress variability) is indicated below (b) (resp. (c)). The size (resp. direction) of the arrows on (a) and (b) is for the magnitude (resp. direction) of the wind stress.

Main characteristics of the surface winds simulated by the CGCMs are presented on figure 2.10, and compared to those from QuickSCAT (CERSAT, 2002) and ERS (Bentamy et al., 1996) satellite data. Note that there are some differences between the two observed datasets in the region, as previously noted by Croquette et al. (2007): despite similar structures, the magnitude of the winds measured by ERS is lower than that measured by QuickSCAT, which was shown to have a better agreement with in-situ data from coastal stations along the Peru coast (Croquette et al., 2007).

In addition, ERS misses a large fraction of the variability north of the equator, which is mainly related to the ITCZ. Both data sources however reproduce the structure and seasonality of the ESP anticyclone and of the associated winds (fig. 2.10 a and b), already described in the previous chapter: during the southern hemisphere winter (fig. 2.10a), the anticyclone is in its northernmost position, driving strong alongshore winds along the coasts of central Chile and central Peru. In summer (fig. 2.10b), the anticyclone tends to relax and moves about five degrees southwards. As a consequence, coastal winds are weaker off Peru but stronger off central Chile between about 30°S and 35°S, a region characterized by the presence of coastal jets. Maps of wind stress variability reflect the zone of influence of the subtropical high, with maximum variabilities offshore in the southwestern corner of the regional domain, and off central Chile in the coastal jet area. A local maximum off the Pisco-San Juan area (14-16°S) is also visible in both datasets.

The four CGCMs also feature the presence of the SEP anticyclone, as well as its relaxation and poleward migration in austral summer. However, they exhibit significant differences with the observed wind patterns. UKMO-HadCM3 and INGV-ECHAM4 are able to reproduce the magnitude of the observed winds, particularly in the northwestward branch: 0.08-0.10 Pa and 0.06- 0.08 Pa in winter, 0.06 Pa and 0.06-0.08 Pa in summer, for respectively UKMO-HadCM3 and INGV-ECHAM4; versus 0.08-0.10 Pa and 0.08 Pa in winter, 0.06-0.08 Pa and 0.04-0.06 Pa in summer for QuickSCAT and ERS, respectively (fig. 2.10a,b). However, both CGCMs have biases in the first two degrees or so from the coast (one or two grid points), where UKMO-HadCM3 greatly overestimates the alongshore winds and INGV-ECHAM4 greatly underestimates them (it is also evident in maps of wind stress variability – see fig. 2.10c). As discussed in the previous chapter, this is mostly due to the coarse resolution of the simulated atmospheric fields (see table 1 from section 1) and to the poor representation of low-level winds over the andinean region characterized by steep orography. Nevertheless, the INGV model features the presence of small- scale local maxima of the wind stress in the central Chile and central Peru coastal jet areas just like in the observations (fig. 2.10a,b,c), with the right seasonality (greatest in winter off Peru and in summer off Chile) and amplitude (0.08 Pa in winter off Peru, 0.10-0.12 Pa in summer off Chile).