DOI: 10.5061/dryad.pk0p2ngzt
title: Atmospheric data used for calibrating the tropopause in global chemistry-climate or chemistry-transport models

description: We divide the atmosphere into distinct spheres based on their physical,
 chemical, and dynamical traits.  In deriving chemical budgets and
 climate trends, which differ across spheres, we need clearly defined
 boundaries.  Focusing on atmospheric mass and greenhouse gases,
 our primary spheres are the troposphere and stratosphere (~99.9 % by
 mass), and the boundary between them is the tropopause.  The
 standard method of locating the tropopause is based on the World
 Meteorological Organization’s lapse rate tropopause (LRT) defined from the
 vertical temperature gradient as observed by radiosonde balloons.
  Every global climate-weather model has one or more methods to
 calculate the LRT. Both involve subjective choices: expert judgment given
 meter-scale variability of sonde temperature profiles; methods for
 calculating gradients from km-thick model layers. Further, LRT and similar
 methods are consistent only in regions where gradients are primarily
 vertical (core tropics and midlatitudes) and fail in others (sub-tropical
 jets and polar regions). Age-of-air tracers clock the effective
 time-distance from the tropopause, allowing unambiguous separation of
 stratosphere from troposphere in the chaotic jet regions.  We
 apply a global model with synthetic tracer e90 (90-day e-folding),
 focusing on ozone and temperature structures about the tropopause using
 ozone sonde and satellite observations. We calibrate an
 observation-consistent tropopause for e90 using tropics-plus-midlatitudes
 and then apply it globally to calculate total tropospheric air-mass and
 tropopause ozone values.  Such calibration can identify weak
 tropospheric mixing rates.  The concept of calibrating an
 age-of-air tropopause can be readily applied to other models and even to
 observed age-of-air tracers like sulfur hexafluoride.
description: This dataset was collected from several sources as outlined in
 the table below The CTM 4-D data is published in https://doi.org/10.5061/dryad.qbzkh18qq as netcdf files (CTRL data) <strong>Table 1</strong>.  Observational data sets type source files profiles dates ozone sondes Wallops (WAL, 38°N), SHADOZV06 Ascension (ASC, 8°S) SHADOZV06 Lauder (LAU, 45°S) NIWA 1,477 831 1,973 1,477/tot 831/tot 1,973/tot 1995-2020 1998-2022 1986-2021   downloaded:  .dat's 2023-10-03 (WAL & ASC); csv's 2023-01-08 (LAU) processing:  see Table 3. OMPS OMPS-NPP_LP-L2-O3-DAILY_v2.6 3,587 ~1,160/day 2014-2023   downloaded: 2023-04-01 via wget, https://data.gesdisc.earthdata.nasa.gov/data/SNPP_OMPS_Level2/... accepted profiles: 'O3Convergence' &lt;10; ' O3Status' =2:7; ' O3Quality' =0; 'QMV' =0; 'ASI_PMCFlag' =0. processing:  'TropopauseAltitude' reported using GMAO FP-IT T profiles ACE-FTS ACEFTS_L2_v4p0_O3.nc (includes O<sub>3</sub>, p, T) 1 94,675/tot 2004-2020   downloaded: 2023-12-17 from doi:10.20383/101.0291             https://databace.scisat.ca/level2/ processing:  grid is 1 km altitude; drop all O<sub>3</sub> &lt;0 and below 6 km (k=1:6).  For tropopause, find lowest troposphere level (LR &gt; 2 K/km) with 2 stratosphere levels (LR ≤2) above it. The data was processed to identify the tropopause values of O3, T, potential temperature using several tropopause algorithms described in the table below   <strong>Table 2.</strong>  CTM and the different tropopause definitions <strong>CTM grid cells</strong> The Chemistry-Transport Model here has a regular latitude-by-longitude 160x320 grid of resolution ~1.1º and 57 eta-coordinate levels defining the pressure edges of each layer based on the T159L60N80 grid of the ECMWF IFS model. The lowest 5 IFS L60 layers are combined into 2 CTM near-surface layers.  The vertical resolution of model layers In the upper-troposphere/lower-stratosphere increases regularly from 0.8 km near 10 km altitude to 1.1 km at 17 km altitude. The CTM has layer numbers increasing upward. <strong>e90 tracer</strong> Synthetic chemical tracer, emitted uniformly everywhere from the surface, decaying with a 90-day e-fold time.  Calculated in the CTM for every cell and every time step. Emissions are set to give an atmospheric mean abundance of 100 ppb. This techniques has been used to study O3 seasonality at the tpp (Prather et al., 2011) <em>e/90ppbU</em> Highest altitude model layer with e90 &gt; 90 ppb is designated the uppermost tropospheric layer, and its upper boundary is the upper tropopause. The tropopause value for O3 and T is the mean value of the uppermost tropospheric layer.  No interpolation is done because of the large change in dO<sub>3</sub>/dz. <em>e/90ppbL</em> Lowest altitude model layer with e90 ≤ 90 ppb is designated the lowest stratospheric layer, and its lower boundary is the lower tropopause.  Often the same as e/90ppbU. <em>e/80ppbU</em> Highest altitude model layer with e90 &gt; 80 ppb, as above <em>e/80ppbL</em> Lowest altitude model layer with e90 ≤ 80 ppb, as above <em>e/70ppbU</em> Highest altitude model layer with e90 &gt; 70 ppb, as above <em>e/70ppbL</em> Lowest altitude model layer with e90 ≤ 70 ppb, as above <strong>Lapse Rate</strong> Lapse Rate (LR = -dT/dz, K/km) is calculated between 2 vertically aligned layers using the mean temperatures of each layer and the mid-layer altitude. The LR values and thresholds (LR &lt;2 K/km = strat) apply to the upper boundary of the lower box used to calculate it.  <em>WMO-LRTU</em> WMO LR Tropopause (Upper).  Here we try to match the WMO definition as closely while following the logic of the PTG algorithm.  The Lapse Rate (LR = -dT/dz, K/km) is calculated between 2 adjacent layers using the mean temperatures of each layer and the mid-layer altitude.  Two LRs for layer k are calculated:  LR1 = -[T(k+1)-T(k)]/[z(k+1)-z(k)]; and LR2 = -[T(k+2)-T(k)]/[z(k+2)-z(k)]. Thus, LR1 spans ~1 km above the mid-point of layer k and LR2 spans ~2 km above.  All layers below 4.4 km are set as tropospheric (trop).  All layers above 31 km are stratospheric (strat). Define strat layers as meeting the criteria LR1 &lt;2 K/km. If the first (lowermost) strat layer has 2 trop layers immediately above, then relabel it trop and keep going. Having found the first strat layer that passes this test, we check that either LR2 from the current layer or LR1 from the layer above meets the &lt;2 K/km criterion.  If these conditions are met, we have the first strat layer and the tpp is the lower boundary of that layer. Now look for a 2<sup>nd</sup> tpp above:  If there is an LR1 ≥ 2 K/km above (i.e., trop layer) then define the 2<sup>nd</sup> tpp (WMO-LRTU) as the lower boundary of the next strat layer.  Otherwise, the 2<sup>nd</sup> tpp is the same as the 1<sup>st</sup>.    WMO-LRTL WMO LR Tropopause (Lower). This is just the 1<sup>st</sup> tpp from WMO-LRTU. WMO-altU Alternate WMO Upper tpp.  Similar to WMO-LRT but easier to implement with CTM grid.  Identify trop layers from LR &gt; 2 K/km.  Set layers below 6 km altitude to trop. Set layers with θ &gt; 500 K to strat. From the bottom, find the first trop layer with a gap of 2 strat layers (~2 km thick region) above it.  This is the 2<sup>nd</sup> (Upper) tpp. Repeat looking for a trop layer with a gap of at least 1 strat layer above.  This is the 1<sup>st</sup> (Lower) tpp. Both tpp can, and often are, the same, but strat-trop folds resolved by the CTM are easily identified. WMO-altL Alternate WMO Lower tpp. The 1<sup>st</sup> (Lower) tpp from WMO-altU search above. <strong>Potential Temperature Gradient</strong> The PTG (dθ/dz, K/km) is calculated like the LR between 2 vertically aligned layers using the mean θ of each layer and the mid-layer altitude. The PTG values and thresholds (PTG ≥ 10 K/km) apply to the upper boundary of the lower box used to calculate it.   PTG-tppU PTG Tropopause (Upper).  The algorithm here is similar to the original paper (Tinney et al., 2022), adapted to work efficiently with the CTM diagnostics. The PTG (dθ/dz, K/km) is calculated between 2 adjacent layers using the mean θ of each layer and the mid-layer altitude.  Two PTGs for layer k are calculated:  PTG1 = [θ(k+1)-θ(k)]/[z(k+1)-z(k)]; and PTG2 = [θ(k+2)-θ(k)]/[z(k+2)-z(k)]. Thus, PTG1 spans ~1 km above the mid-point of layer k and PTG2 spans ~2 km above.  All layers below 4.4 km are set as trop.  All layers above 31 km are strat. Define strat layers as meeting the criteria PTG ≥ 10 K/km. If the first (lowermost) strat layer has 2 trop layers immediately above, then relabel it trop and keep going. Having found the first strat layer that passes this test, we check that either PTG2 from the current layer or PTG1 from the layer above meets the ≥10 K/km criterion.  If these conditions are met, we have the first strat layer and the 1<sup>st</sup> tpp is the lower boundary of that layer. Now look for a 2<sup>nd</sup> tpp:  If we find a trop layer above the strat layer above the 1<sup>st</sup> tpp, then we search for the first strat layer above it using a revised threshold, PTG1 ≥ 15 K/km.  This identifies the 2<sup>nd</sup> tpp (PTG-tppU) as the lower boundary of the next strat layer.  Otherwise, the 2<sup>nd</sup> tpp is the same as the 1<sup>st</sup>.    PTG-tppL PTG Tropopause (Lower). This is just the 1<sup>st</sup> tpp from the PTG-tppU search above. The ozonesonde data were preprocessed to obtain stable and consisten profiles of O3 and T vs. P as described in Table 3  <strong>Table 3.</strong>  Ozonesonde processing and defining the tropopause find O<sub>3</sub> NaNs Flagged (e.g., -999); negative; too large (&gt;20 ppm); pressure (&lt; 1 hPa) find T NaNs Flagged; T &lt; 160 K collapse profile Remove: O<sub>3</sub> NaN pts; yoyo (down-up) sections; descent part of profile; all p &lt; 10 hPa. drop whole profiles With &lt;25 O<sub>3</sub> points; with top p &gt; 65 hPa = ASC(44), LAU(24), WAL(58). reduce # pts Many profiles have 6000-8000 pts & resolution &lt;10 m; create 4-pt (ASC & WAL) and 6-pt (LAU) averages to get 30-50 m spacing for p = 70-500 hPa. calculate 100-m gradients Use centered 2<sup>nd</sup>-order finite difference to get 100-m values for LR = -dT/dz and PTG = dθ/dz at each point.  These are too noisy to identify tropopause, see Fig. 2 calculate stable 1-km gradients Average z, T, O<sub>3</sub> for 20 points (~1 km) below/above each point; difference these to get a smoothed 1-km-average LR and PTG, see Figure 2.  Due to the smoothed 1-km resolution profile, we do not apply the WMO criterion that gradients must be sustained above the threshold point. lower tropopause LRT:  point below lowest LR ≤ 2 K/km, limited to 500 &gt; p &gt; 80 hPa PTG:  point below lowest dθ/dz ≥ 10 K/km, ibid upper tropopause LRT:  highest LR &gt; 2 K/km, limited to 500 &gt; p &gt; 80 hPa; PTG:  highest dθ/dz &lt; 10 K/km, ibid; Thresholds are not changed for the upper LRT as in Tinney et al., 2022.  
description: # **Tropopause data used for calibrating the tropopause in global
 chemistry-climate or chemistry-transport models**
 [https://doi.org/10.5061/dryad.pk0p2ngzt](https://doi.org/10.5061/dryad.pk0p2ngzt) **Description of the data and file structure** The data here focus on tropopause and near-tropopause profiles of P, T, O3 and synthetic age-of-air tracers from sondes, satellites and the UCI chemistry-transport model (CTM). **Files and variables** **File: O3sonde_ASC2.nc** **Description:**  Post-processed Ascension I. ozonesonde data. Cleaned to give a consistent ascent profile per text. Format netcdf4\ Dimensions: Levels = 1600\ Sondes = 831\ Variables:\ SondeFile\ Size: 831x1\ Dimensions: Sondes\ Datatype: string\ Attributes:\ id = 'Ascension O3 sonde files used 19980102:20220928'\ rev = 'revised Nov 2024, version 2'\ YYYY\ Size: 831x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'Year of sonde YYYY'\ MM\ Size: 831x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'Month of sonde MM'\ DD\ Size: 831x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'Day of sonde DD'\ ntop\ Size: 831x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'topmost Level of sonde used here'\ NB = 'ntop=0 means sonde not used, total of 44 out of 831'\ NB2 = 'yoyo-ing removed, pressure now monotonic, Nov 2024'\ pressure\ Size: 1600x831\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'pressure (hPa) of measurement'\ ozone\ Size: 1600x831\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'ozone (mole fraction, ppb)'\ temperature\ Size: 1600x831\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'temperature (K) of measurement'\ NB = 'Some sondes have O3 but no T, flt 614'\ potential\ Size: 1600x831\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'potential temperature (K) of measurement'\ NB = 'Some sondes have O3 but no T, flt 614' **File: O3sonde_WAL2.nc** **Description:**  Post-processed Wallops I. VA ozonesonde data. Cleaned to give a consistent ascent profile per text. Format: netcdf4\ Dimensions: Levels = 1600\ Sondes = 1477\ Variables:\ SondeFile\ Size: 1477x1\ Dimensions: Sondes\ Datatype: string\ Attributes:\ id = 'Wallops O3 sonde files used 19950725:20201110'\ rev = 'revised Nov 2024, version 2'\ YYYY\ Size: 1477x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'Year of sonde YYYY'\ MM\ Size: 1477x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'Month of sonde MM'\ DD\ Size: 1477x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'Day of sonde DD'\ ntop\ Size: 1477x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'topmost Level of sonde used here'\ NB = 'ntop=0 means sonde not used, total of 58 out of 1477'\ NB2 = 'yoyo-ing removed, pressure now monotonic, Nov 2024'\ pressure\ Size: 1600x1477\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'pressure (hPa) of measurement'\ ozone\ Size: 1600x1477\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'ozone (mole fraction, ppb)'\ temperature\ Size: 1600x1477\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'temperature (K) of measurement'\ NB = 'Some sondes have O3 but no T, flt 614'\ potential\ Size: 1600x1477\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'potential temperature (K) of measurement'\ NB = 'Some sondes have O3 but no T, flt 614' **File: O3sonde_LAU2.nc** **Description:**  Post-processed Lauder NZ ozonesonde data. Cleaned to give a consistent ascent profile per text. Format: netcdf4\ Dimensions: Levels = 1600\ Sondes = 1973\ Variables:\ SondeFile\ Size: 1973x1\ Dimensions: Sondes\ Datatype: string\ Attributes:\ id = 'Lauder O3 sonde files used 19860803:20210622'\ rev = 'revised Nov 2024, version 2'\ YYYY\ Size: 1973x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'Year of sonde YYYY'\ MM\ Size: 1973x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'Month of sonde MM'\ DD\ Size: 1973x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'Day of sonde DD'\ ntop\ Size: 1973x1\ Dimensions: Sondes\ Datatype: int32\ Attributes:\ id = 'topmost Level of sonde used here'\ NB = 'ntop=0 means sonde not used, total of 24 out of 1973'\ NB2 = 'yoyo-ing removed, pressure now monotonic, Nov 2024'\ pressure\ Size: 1600x1973\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'pressure (hPa) of measurement'\ ozone\ Size: 1600x1973\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'ozone (mole fraction, ppb)'\ temperature\ Size: 1600x1973\ Dimensions: Levels,Sondes\ Datatype: single\ Attributes:\ id = 'temperature (K) of measurement'\ potential\ Size: 1600x1973\ Dimensions: Levels,Sondes\ Datatype: single **File: tpp_*FiguresTables_v2*.xlsx** **Description:**  Data sets used to plot Figures 1-9 in the manuscript. Each figure (or pair of) has its own page in the spreadsheet. Data for the new Supporting Data Figures S1-S5 are now included. Fig01 tab column variable units, notes number\ A title and (lower down) the matlab scripts for plotting data\ B lower edge index # (1:58) 58\ C eat-a P = eta-a + eta-b*Psrf (hPa) 58\ D eta-b 58\ E\ F plot alt (km) 58\ G\ H latitude # (1:1:160) 160\ I mid-pt (degrees N) 160\ J\ K longitude # (1:1:320)\ L mid-pt (degrees E) 320\ M\ N colormap 'cmp' for Fig 1d (R)\ O (G)\ P (B) Fig02 tab sonde variable units notes\ A title and matlab scripts for plotting data\ B ASC sonde#600 z km (5:357)\ C ASC sonde#600 -dT/dz K/km 30 m difference\ D ASC sonde#600 -dT/dz K/km 1 km difference\ E ASC sonde#600 -dT/dz K/km 1 km average\ F ASC sonde#600 dTHETA/dz K/km 30 m difference\ G ASC sonde#600 dTHETA/dz K/km 1 km difference\ H ASC sonde#600 dTHETA/dz K/km 1 km average\ I\ J WAL sonde#800 z km (5:244)\ K WAL sonde#800 -dT/dz K/km 30 m difference\ L WAL sonde#800 -dT/dz K/km 1 km difference\ M WAL sonde#800 -dT/dz K/km 1 km average\ N WAL sonde#800 dTHETA/dz K/km 30 m difference\ O WAL sonde#800 dTHETA/dz K/km 1 km difference\ P WAL sonde#800 dTHETA/dz K/km 1 km average\ Q\ R LAU sonde#1100 z km (5:362)\ S LAU sonde#1100 -dT/dz K/km 30 m difference\ T LAU sonde#1100 -dT/dz K/km 1 km difference\ U LAU sonde#1100 -dT/dz K/km 1 km average\ V LAU sonde#1100 dTHETA/dz K/km 30 m difference\ W LAU sonde#1100 dTHETA/dz K/km 1 km difference\ X LAU sonde#1100 dTHETA/dz K/km 1 km average Fig03 tab see text/tables in text for tropopause criteria extended rows for each season\ column criteria/variable DJF(4:163) # MAM(166:325) # JJA(328:487) # SON(490:649) #\ A title and matlab scripts for plotting data\ B Lat # 160 160 160 160\ C PTG-tpp average upper troposphere model layer 160 160 160 160\ D WMO-LRT average upper troposphere model layer 160 160 160 160\ E e/90ppb average upper troposphere model layer 160 160 160 160\ F e/80ppb average upper troposphere model layer 160 160 160 160\ G e/70ppb average upper troposphere model layer 160 160 160 160\ H WMO-alt average upper troposphere model layer 160 160 160 160\ I\ J PTG-tpp average lower troposphere model layer 160 160 160 160\ K WMO-LRT average lower troposphere model layer 160 160 160 160\ L e/90ppb average lower troposphere model layer 160 160 160 160\ M e/80ppb average lower troposphere model layer 160 160 160 160\ N e/70ppb average lower troposphere model layer 160 160 160 160\ O WMO-alt average lower troposphere model layer 160 160 160 160 Fig04 tab TAMF annual mean: rows 5:13\ tropopause criteria source Global-upper Global-lower folds blank 60S60N-upper 60S60N-lower blank 25S25N-upper 25S25N-lower\ A title and matlab scripts for plotting data\ B label\ C PTG-tpp CTM TAMF X X X X X X\ D WMO-LRT CTM TAMF X X X X X X\ E e/90ppb CTM TAMF X X X X X X X\ F e/80ppb CTM TAMF X X X X X X X\ G e/70ppb CTM TAMF X X X X X X X\ H WMO-alt CTM TAMF X X X X X X\ I\ J as reported OMPS TAMF X X\ \``` Global \``` B day number (5:5:365) lines 17:89\ C PTG-tpp CTM TAMF lines 17:89\ D WMO-LRT CTM TAMF lines 17:89\ E e/90ppb CTM TAMF lines 17:89\ F e/80ppb CTM TAMF lines 17:89\ G e/70ppb CTM TAMF lines 17:89\ H WMO-alt CTM TAMF lines 17:89\ \```\ 60S60N\ \```\ B CTM day number (5:5:365) lines 93:165\ C PTG-tpp CTM TAMF lines 93:165\ D WMO-LRT CTM TAMF lines 93:165\ E e/90ppb CTM TAMF lines 93:165\ F e/80ppb CTM TAMF lines 93:165\ G e/70ppb CTM TAMF lines 93:165\ H WMO-alt CTM TAMF lines 93:165\ I OMPS month DJFMAMJJASON lines 93:104\ J OMPS month CTM OMPS lines 93:104\ \```\ 25S25N\ \```\ B CTM day number (5:5:365) lines 169:241\ C PTG-tpp CTM TAMF lines 169:241\ D WMO-LRT CTM TAMF lines 169:241\ E e/90ppb CTM TAMF lines 169:241\ F e/80ppb CTM TAMF lines 169:241\ G e/70ppb CTM TAMF lines 169:241\ H WMO-alt CTM TAMF lines 169:241\ I OMPS month DJFMAMJJASON lines 169:180\ J OMPS month CTM OMPS lines 169:180 Fig05 tab A title and matlab scripts for plotting data B month JFMAMJJASOND tpp criteria\ C WAL tpp P (hPa) CTM WMO-alt lines 6:17\ D ASC tpp P (hPa) CTM WMO-alt lines 6:17\ E LAU tpp P (hPa) CTM WMO-alt lines 6:17\ F\ G WAL tpp O3 (ppb) CTM WMO-alt lines 6:17\ H ASC tpp O3 (ppb) CTM WMO-alt lines 6:17\ I LAU tpp O3 (ppb) CTM WMO-alt lines 6:17 B month JFMAMJJASOND tpp criteria\ C WAL tpp P (hPa) CTM e/90ppb lines 19:30\ D ASC tpp P (hPa) CTM e/90ppb lines 19:30\ E LAU tpp P (hPa) CTM e/90ppb lines 19:30\ F\ G WAL tpp O3 (ppb) CTM e/90ppb lines 19:30\ H ASC tpp O3 (ppb) CTM e/90ppb lines 19:30\ I LAU tpp O3 (ppb) CTM e/90ppb lines 19:30 B month JFMAMJJASOND tpp criteria\ C WAL tpp P (hPa) OMPS lines 32:43\ D ASC tpp P (hPa) OMPS lines 32:43\ E LAU tpp P (hPa) OMPS lines 32:43\ F\ G WAL tpp O3 (ppb) OMPS lines 32:43\ H ASC tpp O3 (ppb) OMPS lines 32:43\ I LAU tpp O3 (ppb) OMPS lines 32:43 B month JFMAMJJASOND tpp criteria\ C WAL tpp P (hPa) ACE lines 45:56\ D ASC tpp P (hPa) ACE lines 45:56\ E LAU tpp P (hPa) ACE lines 45:56\ F\ G WAL tpp O3 (ppb) ACE lines 45:56\ H ASC tpp O3 (ppb) ACE lines 45:56\ I LAU tpp O3 (ppb) ACE lines 45:56 B month JFMAMJJASOND tpp criteria\ C WAL tpp P (hPa) sondes lines 59:70\ D ASC tpp P (hPa) sondes lines 59:70\ E LAU tpp P (hPa) sondes lines 59:70\ F\ G WAL tpp O3 (ppb) sondes lines 59:70\ H ASC tpp O3 (ppb) sondes lines 59:70\ I LAU tpp O3 (ppb) sondes lines 59:70 Fig06&07 tab A title and matlab scripts for plotting data B month JFMAMJJASOND source\ C WAL tpp P (hPa) sonde lines 6:17\ D ASC tpp P (hPa) sonde lines 6:17\ E LAU tpp P (hPa) sonde lines 6:17\ F\ G WAL tpp O3 (ppb) sonde lines 6:17\ H ASC tpp O3 (ppb) sonde lines 6:17\ I LAU tpp O3 (ppb) sonde lines 6:17 B month JFMAMJJASOND tpp criteria\ C\ D\ E\ F\ G WAL tpp O3 (ppb) sonde +-2km lines 19:30\ H ASC tpp O3 (ppb) sonde +-2km lines 19:30\ I LAU tpp O3 (ppb) sonde +-2km lines 19:30 B month JFMAMJJASOND CTM criteria\ C WAL tpp P (hPa) WMO-LRT lines 34:45\ D ASC tpp P (hPa) WMO-LRT lines 34:45\ E LAU tpp P (hPa) WMO-LRT lines 34:45\ F\ G WAL tpp O3 (ppb) WMO-LRT lines 34:45\ H ASC tpp O3 (ppb) WMO-LRT lines 34:45\ I LAU tpp O3 (ppb) WMO-LRT lines 34:45 B month JFMAMJJASOND CTM criteria\ C WAL tpp P (hPa) WMO-alt lines 47:58\ D ASC tpp P (hPa) WMO-alt lines 47:58\ E LAU tpp P (hPa) WMO-alt lines 47:58\ F\ G WAL tpp O3 (ppb) WMO-alt lines 47:58\ H ASC tpp O3 (ppb) WMO-alt lines 47:58\ I LAU tpp O3 (ppb) WMO-alt lines 47:58 B month JFMAMJJASOND CTM criteria\ C WAL tpp P (hPa) PTgrad lines 60:71\ D ASC tpp P (hPa) PTgrad lines 60:71\ E LAU tpp P (hPa) PTgrad lines 60:71\ F\ G WAL tpp O3 (ppb) PTgrad lines 60:71\ H ASC tpp O3 (ppb) PTgrad lines 60:71\ I LAU tpp O3 (ppb) PTgrad lines 60:71 B month JFMAMJJASOND CTM criteria\ C WAL tpp P (hPa) e/70ppb lines 73:84\ D ASC tpp P (hPa) e/70ppb lines 73:84\ E LAU tpp P (hPa) e/70ppb lines 73:84\ F\ G WAL tpp O3 (ppb) e/70ppb lines 73:84\ H ASC tpp O3 (ppb) e/70ppb lines 73:84\ I LAU tpp O3 (ppb) e/70ppb lines 73:84 B month JFMAMJJASOND CTM criteria\ C WAL tpp P (hPa) e/80ppb lines 86:97\ D ASC tpp P (hPa) e/80ppb lines 86:97\ E LAU tpp P (hPa) e/80ppb lines 86:97\ F\ G WAL tpp O3 (ppb) e/80ppb lines 86:97\ H ASC tpp O3 (ppb) e/80ppb lines 86:97\ I LAU tpp O3 (ppb) e/80ppb lines 86:97 B month JFMAMJJASOND CTM criteria\ C WAL tpp P (hPa) e/90ppb lines 99:110\ D ASC tpp P (hPa) e/90ppb lines 99:110\ E LAU tpp P (hPa) e/90ppb lines 99:110\ F\ G WAL tpp O3 (ppb) e/90ppb lines 99:110\ H ASC tpp O3 (ppb) e/90ppb lines 99:110\ I LAU tpp O3 (ppb) e/90ppb lines 99:110 Fig08 tab tpp O3 (ppb) for 4 seasons\ A title and matlab scripts for plotting data\ B index # lines 5:164\ C latitude (deg N)\ D DJF upper tpp PTG-tpp\ E WMO-LRT\ F e/90ppb\ G e/80ppb\ H e/70ppb\ I WMO-alt\ J\ K lower tpp PTG-tpp\ L WMO-LRT\ M e/90ppb\ N e/80ppb\ O e/70ppb\ P WMO-alt\ Q\ R MAM upper tpp PTG-tpp\ S WMO-LRT\ T e/90ppb\ U e/80ppb\ V e/70ppb\ W WMO-alt\ X\ Y lower tpp PTG-tpp\ Z WMO-LRT\ AA e/90ppb\ AB e/80ppb\ AC e/70ppb\ AD WMO-alt\ AE\ AF JJA upper tpp PTG-tpp\ AG WMO-LRT\ AH e/90ppb\ AI e/80ppb\ AJ e/70ppb\ AK WMO-alt\ AL\ AM lower tpp PTG-tpp\ AN WMO-LRT\ AO e/90ppb\ AP e/80ppb\ AQ e/70ppb\ AR WMO-alt\ AS\ AT SON upper tpp PTG-tpp\ AU WMO-LRT\ AV e/90ppb\ AW e/80ppb\ AX e/70ppb\ AY WMO-alt\ AZ\ BA lower tpp PTG-tpp\ BB WMO-LRT\ BC e/90ppb\ BD e/80ppb\ BE e/70ppb\ BF WMO-alt Fig09 tab A title and matlab scripts for plotting data\ column variable range units number\ B e90 60:2:98 ppb 20\ C WAL Altitude (km) of e90 layer 20\ D ASC Altitude (km) of e90 layer 20\ E LAU Altitude (km) of e90 layer 20\ F\ G WAL Age (days per km) 20\ H ASC Age (days per km) 20\ I LAU Age (days per km) 20\ J\ K e90 61:2:99 ppb 20\ L WAL Brunt-Väisälä: N^2 (1e-3 /sec^2) 20\ M ASC Brunt-Väisälä: N^2 (1e-3 /sec^2) 20\ N LAU Brunt-Väisälä: N^2 (1e-3 /sec^2) 20\ O\ P WAL -dln(theta)/dln(e90) (unitless) 20\ Q ASC -dln(theta)/dln(e90) (unitless) 20\ R LAU -dln(theta)/dln(e90) (unitless) 20 FigS123 tab A title\ rows 4:14\ B jj, 1:10\ C mean latitude for band jj (deg N)\ D title for band jj\ rows 16:56\ B jj, 1:10, latitude belt\ C e90 value, 1:4, for 60, 70, 80, & 90 ppb\ D 16 %ile of PV (PVU)\ E 50 %ile of PV (PVU)\ F 84 %ile of PV (PVU)\ G mean PV value\ rows 58:98\ B jj, 1:10, latitude belt\ C e90 value, 1:4, 50, 70, 80, & 90 ppb\ D 16 %ile of O3 (ppb)\ E 50 %ile of O3 (ppb)\ F 84 %ile of O3 (ppb)\ G mean O3 value FigS45 tab A title\ B jj, 1:10, latitude ranges about 61S, 51S, 41S, 31S, 21S, 21N, 31N, 41N, 51N, 61N.\ C mon, 1:12, months JFMAMJJASOND\ D median O3 (ppb) at e90=60ppb\ E median O3 (ppb) at e90=70ppb\ F median O3 (ppb) at e90=80ppb\ G median O3 (ppb) at e90=90ppb\ H\ I median PV (PVU) at e90=60ppb\ J median PV (PVU) at e90=70ppb\ K median PV (PVU) at e90=80ppb\ L median PV (PVU) at e90=90ppb **Code/software** Any netcdf reader will do for the .nc files. The Figure/Table data sets are given in a .xlsx file with separate page tabs for each Figure. **Access information** Data was collected from the following sources: * OMPS OMPS-NPP_LP-L2-O3-DAILY_v2.6 downloaded: 2023-04-01 via wget, [https://data.gesdisc.earthdata.nasa.gov/data/SNPP_OMPS_Level2/](https://data.gesdisc.earthdata.nasa.gov/data/SNPP_OMPS_Level2/)... accepted profiles: 'O3Convergence' &lt;10; ' O3Status' =2:7; ' O3Quality' =0; 'QMV' =0; 'ASI_PMCFlag' =0. processing:  'TropopauseAltitude' reported using GMAO FP-IT T profiles * ACE-FTS ACEFTS_L2_v4p0_O3.nc (includes O3, p, T) downloaded: 2023-12-17 from doi:10.20383/101.0291             [https://databace.scisat.ca/level2/](https://databace.scisat.ca/level2/) processing:  grid is 1 km altitude; drop all O3 &lt;0 and below 6 km (k=1:6).  For tropopause, find lowest troposphere level (LR &gt; 2 K/km) with 2 stratosphere levels (LR ≤2) above it. * ozonesondes Wallops (WAL, 38°N), SHADOZV06; [https://tropo.gsfc.nasa.gov/shadoz/Wallops.html](https://tropo.gsfc.nasa.gov/shadoz/Wallops.html) Ascension (ASC, 8°S) SHADOZV06: [https://tropo.gsfc.nasa.gov/shadoz/Ascension.html](https://tropo.gsfc.nasa.gov/shadoz/Ascension.html) Lauder (LAU, 45°S) NIWA: [https://woudc.org/data/explore.php?dataset=ozonesonde](https://woudc.org/data/explore.php?dataset=ozonesonde) downloaded:  .dat's 2023-10-03 (WAL & ASC); csv's 2023-01-08 (LAU) **Version changes** **7-Apr-2025:**  In response to review of the paper in AGU Advances (2025AV001651), I added a new figure 1b (similar to figure 1a) as well as Supporting Information (figures S1-5) on the use of ozone (O3) and potential vorticity (PV) as tropopause indicators.  The large 3D data set for figures 1abcd is now included as a netcdf file, and the scripts to read the netcdf file as well as make the all the plots are now in each tab of the spreadsheet.