Oceanic mesoscale variability and atmospheric convection on 10°N in the eastern Pacific Tom Farrar Thesis committee: Bob Weller, Raf Ferrari, Jim Price, John Toole MIT: 7 Sept., 2006 Air-sea interaction mooring E. Pacific Warm Pool

Isotherms in main thermocline Blue dots are current meters ~60 days

SST Mooring (°C) Wave “crests”

Spring-intensified intraseasonal variability in dynamic height near 10°N in the eastern tropical Pacific has been previously recognized (Miller et al., 1985; Perigaud, 1990; Giese et al., 1994). Spring-intensified intraseasonal variability in dynamic height near 10°N in the eastern tropical Pacific has been previously recognized (Miller et al., 1985; Perigaud, 1990; Giese et al., 1994). Mesoscale intraseasonal variability on 10°N: background

Farrar and Weller (2006; JGR)

Spring-intensified intraseasonal variability in dynamic height near 10°N in the eastern tropical Pacific has been previously recognized (Miller et al., 1985; Perigaud, 1990; Giese et al., 1994). Many (at least 4) different hypotheses regarding generation have been proposed. (More on this later…) Many (at least 4) different hypotheses regarding generation have been proposed. (More on this later…) Mesoscale intraseasonal variability on 10°N: background

Spring-intensified intraseasonal variability in dynamic height near 10°N in the eastern tropical Pacific has been previously recognized (Miller et al., 1985; Perigaud, 1990; Giese et al., 1994). Many (at least 4) different hypotheses regarding generation have been proposed. (More on this later…) Characterization of the variability remains inadequate for understanding of causes and consequences. Characterization of the variability remains inadequate for understanding of causes and consequences. Example: Perigaud inferred eastward energy propagation, but Giese et al. argued for localized wind forcing at the eastern boundary– these two notions cannot both be correct Example: Perigaud inferred eastward energy propagation, but Giese et al. argued for localized wind forcing at the eastern boundary– these two notions cannot both be correct Mesoscale intraseasonal variability on 10°N: background

I. Quantifying the observed variability (Farrar and Weller, 2006) A. Annual modulation of the variability B. The observed dispersion characteristics (or, interpretation as Rossby waves) II. Hypotheses for the variability (Farrar and Weller, 2006)  Observed evidence for baroclinic instability III. The effect of this variability (via SST) on atmospheric convection

 The amplitude of the variability is annually modulated and is typically strongest in April.

Farrar and Weller (2006; JGR)

Black asterisks: Peak power at each frequency White asterisks: TOPEX tidal aliases (Schlax and Chelton, 1994) Spectrum of zonal slope of SSH (a proxy for northward velocity) on 10°N Westward propagationEastward propagation

Mean U=-11 cm/s Mean U=-12 cm/s (Surface currents derived from satellite and in situ measurements; Bonjean and Lagerloef, 2002) Mean climatological zonal surface current NEC NECC

Black asterisks: Peak power at each frequency White asterisks: TOPEX tidal aliases (Schlax and Chelton, 1994) Westward propagationEastward propagation Spectrum of zonal slope of SSH (a proxy for northward velocity) on 10°N U=-11 cm/s U=0 Pink lines: l=0 Black lines: l=k ω=frequency k=east-west wave number l=north-south wave number β=df/dy L D =deformation radius U= (steady) geostrophic zonal flow

Black asterisks: Peak power at each frequency White asterisks: TOPEX tidal aliases (Schlax and Chelton, 1994) Westward propagationEastward propagation Spectrum of zonal slope of SSH (a proxy for northward velocity) on 10°N U=-11 cm/s U=0 Pink lines: l=0 Black lines: l=k Signal propagation is consistent with dispersion relation for 1st baroclinic mode Rossby waves in a mean westward flow.

I. Direct wind forcing (Giese et al., 1994) II. Instability of NEC or NEC/NECC system A. Baroclinic instability (Philander, 1976) B. Barotropic instability (Perigaud, 1990  null result) III. Radiation of Rossby waves from baroclinic coastal Kelvin waves IV. NECC Retroflection (Hansen and Maul, 1990) Hypotheses for intraseasonal variability: ?  See Farrar and Weller (2006; JGR) for a more detailed discussion. Farrar and Weller (2006), Zamudio et al. (2006)

U 1 ρ 1 U 2 ρ 2 H1H1 H2H2 Necessary condition for instability of a westward surface current Since H 1 /H 2 ≈0.02, a very good approximation to the above condition is: (Or, U 1 < -20 cm/s) 2-Layer model of baroclinic instability:

U 1 ρ 1 U 2 ρ 2 H1H1 H2H2 Necessary condition for instability of a westward surface current Since H 1 /H 2 ≈0.02, a very good approximation to the above condition is: 2-Layer model of baroclinic instability:  Approach: Is there any correspondence of flow speeds meeting this condition with increased levels of intraseaonal variability? (Or, U 1 < -20 cm/s)

Pink lines: Black lines: Grey lines: (eastward)  day band-passed SSH (color contours, +/-8 cm range)  Surface currents estimated from TOPEX + Levitus dynamic height

black=-15 cm/s white=-25 cm/s (U crit ~-20 cm/s) During every year except 1998, there is a correspondence between U crit and the amplitude of the variability  Baroclinic instability likely explains the enhanced intraseasonal variability and its annual cycle

Conclusions regarding 10°N Intraseasonal Signal 1. Signal propagation is consistent with dispersion relation for 1 st baroclinic mode Rossby waves in a mean westward flow. 2. The timing and location of the variability is consistent with baroclinic instability as a generation mechanism. Air-sea interaction mooring SST (°C) Not shown (but important for what follows): horizontal advection by this mesoscale variability drives local SST variations

Band-pass filtered buoy data  Surface solar radiation and mixed-layer temperature appear roughly out of phase  Suggests SST may be modulating convection (but this record is too short)

Visible reflectivity Visible reflectivity from GOES-9 satellite from GOES-9 satellite <6 months of data <6 months of data Columnar cloud liquid water (CLW) Columnar cloud liquid water (CLW) from TMI and 3 SSM/I satellites from TMI and 3 SSM/I satellites 8 years of data 8 years of data Surface solar radiation Surface solar radiation from ISCCP (Rossow and Zhang, 1995) from ISCCP (Rossow and Zhang, 1995) 20 years of data 20 years of data Examination of the relationship between mesoscale intraseasonal variability on 10°N and atmospheric convection Warm SST Heat Incident solar Reflected Surface solar

SST (°C) To start, we’ll inspect the temporal evolution of SST and some of these “cloud properties” along this line

SST (°C) on 10°N (TMI) SST, zonal high-pass

SST (°C) on 10°N (TMI) SST, zonal high-pass 21-d average visible reflectivity (GOES)

SST (°C) on 10°N (TMI) SST, zonal high-pass 21-d average visible reflectivity (GOES) Contours are warm SST anomalies (from figure on left)

SST (°C) on 10°N (TMI) SST, zonal high-pass 21-d average visible reflectivity (GOES) Contours are warm SST anomalies (from figure on left)

SST (°C) on 10°N (TMI) SST, zonal high-pass Log 10 of cloud liquid water (mm; from TMI and 2 SSM/I satellites) Contours are warm SST anomalies (from figure on left)

Coherence amplitude of logarithm of CLW and SST on 10°N (8 yrs of data) Peak power in sea surface slope (white) Doppler-shifted Rossby wave dispersion curve Black asterisks and black contours indicate statistically significant coherence SST and log(CLW) are coherent in the Rossby wave band

Coherence amplitude of surface solar radiation and SST on 10.5°N (20 yrs of data) Peak power in sea surface slope (white) Black asterisks and black contours indicate statistically significant coherence SST and solar radiation are coherent in the Rossby wave band But, SST and solar radiation are expected to be coherent, in general…

Coherence phase of surface solar radiation and SST on 10.5°N (20 yrs of data) Phase angle (deg)  In the Rossby wave band, the coherence phase supports the idea that SST variations drive variations in solar radiation (i.e. clouds)  Outside the Rossby wave band, the phase is consistent with an SST response to solar forcing.

Coherent variability in SST and cloud properties detected in 3 pairs of satellite data sets suggests that oceanic mesoscale variability modulates atmospheric convection in the region. Coherent variability in SST and cloud properties detected in 3 pairs of satellite data sets suggests that oceanic mesoscale variability modulates atmospheric convection in the region % of the variance in the logarithm of cloud liquid water at time scales of days and zonal scales of ~10° can be attributed to mesoscale SST variability % of the variance in the logarithm of cloud liquid water at time scales of days and zonal scales of ~10° can be attributed to mesoscale SST variability. Mesoscale intraseasonal variability on 10°N: relationship to atmospheric convection

Coherent variability in SST and cloud properties detected in 3 pairs of satellite data sets suggests that oceanic mesoscale variability modulates atmospheric convection in the region % of the variance in the logarithm of cloud liquid water at time scales of days and zonal scales of ~10° can be attributed to mesoscale SST variability. A few previous studies have related cloud signals to oceanic mesoscale SST signals: Tropical instability waves (Deser et al., 1993; Hashizume et al., 2001) Near the Southern Ocean (O’Neill et al., 2005) Many recent studies have detected signals in surface winds related to mesoscale SST signals e.g., Hayes et al. (1989); Nonaka and Xie (2003); Xie (2004); Small et al. (2005) One mechanism for the wind signal is destabilization of the atmospheric boundary layer by the SST field  The cloud signal is consistent with this mechanism. Mesoscale intraseasonal variability on 10°N: relationship to atmospheric convection

Conclusion The energetic mesoscale variability in the E. Pacific warm pool appears to result in part from baroclinic instability of the NEC Thus, the variability is likely important to the mean heat and momentum balance of this climatically important region In addition, the variability has a more direct effect on atmospheric convection by modulating SST E. Pacific Warm Pool

Supplementary slides:

Dispersion curves in 2-layer and 1.5-layer models Barotropic mode Baroclinic mode Growth rate

Pink lines: Black lines: Grey lines: (eastward)  day band-passed SSH (color contours, +/-8 cm range)  Surface currents estimated from TOPEX + Levitus dynamic height

Pink lines: Black lines: Grey lines: (eastward)  day band-passed SSH (color contours, +/-8 cm range)  Surface currents estimated from TOPEX + Levitus dynamic height