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TTL Upwelling Driven by Equatorial Waves

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TTL Upwelling Driven by Equatorial Waves TTL Upwelling driven by Equatorial Waves L09804 LIUWhat drives the annual cycle in TTL upwelling? ET AL.: START OF WATER VAPOR AND CO TAPE RECORDERS L09804 Liu et al. (2007) Ortland, D. and M. J. Alexander, The residual mean circula8on in the TTL driven by tropical waves, JAS, (in review), 2013. Figure 1. (a) Seasonal variation of 10°N–10°S mean EOS MLS water vapor after dividing by the mean value at each level. (b) Seasonal variation of 10°N–10°S EOS MLS CO after dividing by the mean value at each level. represented in two principal ways. The first is by the area of [8]ConcentrationsofwatervaporandCOnearthe clouds with low infrared brightness temperatures [Gettelman tropical tropopause are available from retrievals of EOS et al.,2002;Massie et al.,2002;Liu et al.,2007].Thesecond MLS measurements [Livesey et al.,2005,2006].Monthly is by the area of radar echoes reaching the tropopause mean water vapor and CO mixing ratios at 146 hPa and [Alcala and Dessler,2002;Liu and Zipser, 2005]. In this 100 hPa in the 10°N–10°Sand10° longitude boxes are study, we examine the areas of clouds that have TRMM calculated from one full year (2005) of version 1.5 MLS Visible and Infrared Scanner (VIRS) 10.8 mmbrightness retrievals. In this work, all MLS data are processed with temperatures colder than 210 K, and the area of 20 dBZ requirements described by Livesey et al. [2005]. Tropopause echoes at 14 km measured by the TRMM Precipitation temperature is averaged from the 2.5° resolution NCEP Radar (PR). The year studied is 2005. To generate geo- reanalysis data [Kistler et al.,2001;Randel et al.,2004]in seasonal distributions of deep convection near the tropical 2005. Similarly, the geo-seasonal variation of GEIA CO tropopause, the areas of VIRS cold clouds and PR echoes at surface emission rate in 2005 is averaged in the same 14 km from 10°S–10°Nareaccumulatedin10° longitude 10°N–10°Sand10° longitude boxes in each month. boxes for each month. Geo-seasonal distributions are obtained from the ratio of the accumulated area to total 3. Results area in each box. [7]Thincloudoccurrencenearthetropopauseisobtained [9]Figure2showsthegeo-seasonalvariationsofdeep from SAGE II observations. An algorithm similar to the convection, thin cloud, water vapor, CO, and tropopause SAGE II cloud detecting algorithm developed by Kent et al. temperature. The incidence of cold cloud reaching 210 K [1993] is used, which infers the presence of cloud when the (Figure 2a) shows four persistent longitudinal bands. 1 1.02 mmextinctioncoefficientisgreaterthan0.001kmÀ , These are located over the west Pacific (150°E–180°E), and the ratio of 0.525 mmextinctioncoefficienttothe western Indonesia (90°E–120°E), central Africa (10°E– 1.02 mmextinctioncoefficientisgreaterthan0.95.Subvi- 35°E) and the Amazon (50°W–80°W). The latter three sual clouds near the tropopause are identified from version locations display maximum incidence in Spring and Fall. 6.2 SAGE II data from 1985–2004, excluding the years The disproportionately small area of 20 dBZ radar echoes 1991–1995 which were contaminated by volcanic aerosol reaching 14 km over the West Pacific in Figure 2b is [McCormick et al.,1995].Meancloudoccurrencesare probably due to the relatively smaller or less particles calculated as the percentage of events with clouds in two inside the relatively weak deep convection over the region layers (14–16 km and 16–18 km) from 10°N–10°Sandin [Liu et al.,2007].Notably,thegeo-seasonalpatternin 10° longitude boxes. deep convection is similar to climatology of geo-seasonal 2of6 D06107 ROSENLOF AND REID: TROPICAL LOWER STRATOSPHERIC TRENDS D06107 Figure 10. Tropical HALOE water vapor (tape recorder), 5°S–5°N, plotted versus time. Note the change to lower values of the hygropause at the end of 2000 and the upward propagation of those lower values in subsequent years. function like decrease in temperature is not zonally uniform. the tape recorder signal noted by Mote et al. [1995, 1996]. This is demonstrated in Figure 14, which shows the lagged However, as noted by Rosenlof [1995], the annual cycle in correlation of monthly averaged tropical tropopause temper- tropical tropopause layer temperatures does not correlate atures as a function of longitude with the zonally averaged highly with an annual cycle in zonally averaged tropical HALOE water vapor time series along with a similar SSTs, in that maximum near tropopause temperatures are in anomaly correlation. Tropopause temperatures lead July-August when SSTs are at a minimum, but the minimum 2 months relative to the HALOE water time series to yield tropical tropopause layer temperatures occur in December– the maximum correlation. The correlation is longitudinally January, not in April, when SSTs are at a maximum. For the uniform and on averageWater Vapor and Cold Point Temperatures the correlations coefficient is 0.82 case of zonally averaged anomalies, SST and tropopause for the monthly averageare causally related on many =mescales time series correlations. This high temperatures are also not well correlated; the coefficient is correlation is a consequence of the strong annual cycle in 0.23 using NCEP 10°S–10°Ntropopausetemperatures both tropopause temperatures and water vapor and results in À Rosenlof & Reid [2008] • Causes of the annual cycle in water vapor, temperature, and upwelling in the TTL “remain to be clarified” [Randel & Jensen, 2013]. • Causes of recent decadal- scale changes in stratospheric water vapor Figure 11. TheSolomon et al. [2010] 10°N–10°SwatervapormixingratiofromHALOEatthealtitudeoftheaverageprofile minimum in the tropics (black solid, scale on left) and NCEP/NCAR reanalysis zonal average tropopause are poorly understood, yet temperatures (grey dashed, scale on right). The correlation maximizes with a 2-month shift, with water vapor lagging. these changes have 10 of 15 resulted in significant decadal-scale changes in surface warming. • Likely related to changes in TTL upwelling. M. Abalos et al.: Variability in tropical upwelling and correlations with tracers 11509 Upwelling rates at 100hPa • Schoeberl et al. [2008]: Upwelling rates in TTL based on H2O observaons ~0.4-0.5 mm/s • Dima and Wallace [2007] from ERA-40 ~ 0.55 mm/s M. Abalos et al.: Variability in tropical upwelling and correlations with tracers 11509 • Abalos et al [2012]: ERA-Interim; three different methods: Fig. 3. Time series and mean seasonal cycles of the three upwelling estimates averaged over 18◦ N–S at 70, 80 and 100 hPa (top to bottom w w w panels). Green: residual circulation ( ⇤), blue:Annual Mean ~ 0.4-0.5 mm/s momentum balance estimate ( m⇤ ), and red: thermodynamic estimate ( Q⇤ ). 11-days running 1 means are applied to the time series. The annualAnnual Cycle ~ a factor of two cycles are calculated as monthly means over 2005–2010 (mm s− ). agreement among these estimates reflects a reasonably good understanding of the seasonal and sub-seasonal variability in tropical upwelling. 3 Co-variations of upwelling, temperatures and tracers Fig. 3. Time series and mean seasonal cycles of the three upwelling estimates averaged over 18A◦ N–S simple at explanation 70, 80 and for100 the hPa strong (top to correlations bottom between panels). Green: residual circulation (w⇤), blue: momentum balance estimate (wm⇤ ), and red: thermodynamictemperatures estimate and tracers (w inQ⇤ ). the 11-days tropical running lower stratosphere means are applied to the time series. The annual cycles are calculated as monthly means over 2005–2010(Fig. 1) is that (mm they s 1 result). primarily from forcing by tropical upwelling. The origin− of this coupling can be appreciated by examining the zonal mean thermodynamic and tracer mixing ratio continuity equations in the TEM formalism (Andrews agreement among theseet al., estimates 1987): reflects a reasonably good understanding of the seasonal and sub-seasonal variability in @T 1 @T v⇤ w⇤S Q (4) tropical upwelling. @t = − a @ − + Fig. 4. Linear correlations among the time series of the three up- 1 @ z/H @T @ welling estimates as a function of pressure. e− v T w T −e z/H @z 0 0 a S + 0 0 3 Co-variations of upwelling,− " temperatures · and tracers!# @χ 1 @χ @χ v⇤ w⇤ M P L (5) the entire data record. Figure 3 showsA overall simple good explanation agree- @t for= − thea @ strong− correlations@z + r · + between− ment among the time series and the meantemperatures seasonal variation and tracersIn the continuity in the tropical Eq. (5), χ lowerrepresents stratosphere the zonal mean mix- of the three upwelling estimates, especially(Fig. between 1) isw thatm⇤ and they resulting ratio primarily of the tracer, from forcingM is the by eddy tropical transport term (as w . Inspection of the variability in the time series reveals in Andrews et al., 1987,r Eq.· 9.4.13) and P L is the chem- Q⇤ upwelling. The origin of this coupling can be appreciated− by strong similarities among the three estimates,examining showing the nu- zonalical mean production thermodynamic minus loss and rate. tracer Averaging mixing over the tropics merous common fluctuations on a wide range of timescales. and for a given pressure level, these equations state that the ratio continuity equations in the TEM formalism (Andrews Note that the good agreement between wm⇤ and wQ⇤ suggests changes in tropical mean temperature or tracer concentra- that wm⇤ may be accurately calculatedet from al., resolved 1987): eddy tion arise from the combined effects of meridional and verti- fluxes alone. Correlations between the different estimates are cal advection by the residual mean circulation (that is, mean shown in Fig. 4. The correlations among@wT, w and w1 @,T in meridional transport to/from the extra-tropics and upwelling ⇤ m⇤ v⇤ Q⇤ w⇤S Q (4) the tropical lower stratosphere are around@t 0.64–0.76.= − a These@ − acting+ on the background vertical gradient), eddy transport fairly high correlations between the estimates are encourag- and diabatic heating in the case of temperature or chemical Fig.
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