Combining stratospheric H2O data sets into longer-term ... · 2O data sets into longer-term...

Combining stratospheric H2O data sets into longer-term GOZCARDS data records

Lucien Froidevaux1, John Anderson2, Ray Wang3, and Ryan Fuller1 1. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA ([email protected]) 2. Hampton University, Hampton, VA 3. Georgia Institute of Technology, Atlanta, GA

Part II: Comparisons between GOZCARDS and SWOOSH H2O (with S. Davis, K. Rosenlof) GOZCARDS: Global OZone Chemistry And Related trace gas Data records for the Stratosphere part of NASA’s MEaSUREs program MEaSUREs: Making Earth System data records for Use in Research Environments Acknowledgments: H. Pumphrey (UARS MLS H2O data), A. Lambert (Aura MLS data), P. Bernath / K. Walker (ACE-‐FTS data), and N. Livesey for support

WAVAS II Meeting, JPL, Pasadena, Dec. 4-6, 2013

© 2013. All rights reserved. Lucien Froidevaux et al., WAVAS Meeting, JPL, Dec. 2013

Timeline of satellite missions and instruments considered for the GOZCARDS project and the creation of a stratospheric composition Earth System Data Record (ESDR).

•  Datasets for H2O – HALOE (V19) [10/91 - 11/05] – UARS MLS (V6) [10/91 - 04/93] – Aura MLS (V3.3) [03/04 onward] – ACE-FTS (V2.2) [08/04 onward] SAGE-II (V6.2) H2O was considered and

can be useful - but not included in GOZCARDS (trend robustness consideration because of channel drift issue; this was undergoing further analysis by the SAGE II team)

•  Common Grids for GOZCARDS – Mixing ratios (time, latitude, pressure)

•  Monthly zonal averages •  10 degree latitude bins •  P(i)= 1000/10(-i/6) i=0, 1, 2, ..

(same as UARS: ~ 2.7 km spacing)

•  netCDF files > include mean values, but also std. deviations, std. errors, + info on local time, SZA, days used each month,…

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GOZCARDS: Satellite/instrument timeline & datasets

1980 1985 1990 1995 2000 2005 2010 2015

SAGE I

SAGE II

UARS MLS

HALOE

ACE-FTS

EOS Aura MLS

Lucien Froidevaux et al., WAVAS Meeting, JPL, Dec. 2013

Approach for merging H2O (and HCl): iterative steps

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Iterative Merging Procedure for Water Vapor at 5oN and 22 hPaSource Data (Overlap Period)

A S O N D J2005

F M A M J J A S O N3

4

5

H2O

/ pp

mv

Merging Iteration Step 1

A S O N D J2005

F M A M J J A S O N

Merging Iteration Step 2

A S O N D J2005

F M A M J J A S O N3

4

5

H2O

/ pp

mv

Source and Merged Data

A S O N D J2005

F M A M J J A S O N

Source and Merged Data (Long Period)

91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 123

4

5

H2O

/ pp

mv

Aura MLS ACE-FTS HALOE UARS MLS Temporary Merged Series Final Merged Series Each instrument dataset carries same weight in our adjustment approach. - temporal coverage for MLS & lack of ACE-FTS v2.2 data after 2010 imply that delivered GOZCARDS record is often dominated by HALOE and Aura MLS.

Here, weights are 2/3 for orange pts. and 1/3 for blue pts.

•  For each dataset (HALOE, ACE-FTS, and Aura MLS), first calculate monthly zonal means (ppmv) for each 10° lat. bin & pressure level (~2.7 km spacing), after taking care of data screening.

•  As overlap of all 3 datasets together is small during Aug. 2004 to Nov. 2005 overlap period, we first merge (de-bias and average) MLS and one occultation dataset, and then, merge in the 2nd occultation data.

•  UARS MLS H2O strat. data are adjusted to the above result and merged in at the end.


HALOE (V19 data) - aerosol-related screening (see plot at right) - cloud screening (see Hervig and McHugh, 1999) - screen out artifacts from faulty trip angles and constant lockdown angle registration issues UARS MLS (V6 data, same as V104, Pumphrey, 1999) - follow data flag usage (see Livesey et al., 2003) - loss of sensitivity (less data) at 100 hPa in tropics Aura MLS (V3.3 data) - follow data flag usage (see Lambert et al., 2007, updates in Livesey et al., 2013) ACE-FTS (V2.2 data) - removal of occasional (~5%) outlier values needed - also use error bar criteria (per ACE-FTS team comments) SAGE II - useful to investigate (+ version 7 has now been released) -  not used for delivered GOZCARDS H2O, but we did look at V6.2 data -  LS gaps (aerosol-related) and variability in mid-1980’s à early data less useful -  data above ~ 2hPa are noisier/poorer (we do not recommend their use) -  caution is advised because of channel drift issue (possibly a small issue)

4 Lucien Froidevaux et al., WAVAS Meeting, JPL, Dec. 2013

Water Vapor Data Screening

Anomalously low HALOE H2O LS values before mid-1992 are screened out if use max. aerosol extinction value (at 3.46 µm) - thanks to S. Davis for discussions on this.

1992

1993

1999

HALOE H2O vs aerosol extinction

Thresholds for creation of monthly zonal mean data - Use 15 or more values in general for occultation data - For ACE-FTS: Use 10 or more values in extra-tropics; 6 or more values for 25S - 25N (10-degree) bins.

Diagnostics relating to different datasets

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- Characterization of correlations/drifts > merged data will be affected by issues in original data

Example of H2O time series: Aura MLS and ACE-FTS (top panel) Middle panel: Deseasonalized anomalies Bottom: Linear fit to dif. of deseason. anomalies (ACE-FTS - Aura MLS)

Similar annual cycle patterns - See Remsberg (2010) for mesospheric study (HALOE); upper mesospheric asymmetry about equator (axis near 10S) has been noted/discussed before.


100 hPa, 55°N

100 hPa, 5°S

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Latitude/pressure contours of diagnostics (illustrated in previous slide) for H2O from ACE-FTS and Aura MLS Top plot: Correlation coefficient for the deseasonalized time series. Bottom plot: Ratio of the slope in diff. of deseasonalized series over the error in slope. - Good agreement is generally observed (as in previous slide example) - A few regions with poorer results > part of this may be due to poorer sampling from ACE-FTS for monthly zonal means (a few outliers can skew results) > in mesosphere, poorer slope differences are usually associated with slower rates of increase (or decreases) in the ACE-FTS time series, in comparison to Aura MLS.

Diagnostics relating to various datasets


Time series offsets (adjustments)

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Offsets applied to source data time series > offsets are often fairly constant with latitude (systematic differences)


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Systematic error estimates for GOZCARDS H2O Error bars provide a range within which 95% of the source data values lie. - One set of errors (left panels) is for values below the merged values - Other set of errors (right panels) is for values above the merged values Top panels: ppmv Bottom panels: % Such estimated error ranges could be useful for model comparisons

Error Bars


Merged fields for GOZCARDS H2O

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Top: Upper stratospheric H2O field (at 3 hPa) Data gaps exist (basically following the coverage from the original datasets) Ø  no fits performed to

these data records Bottom: Lower stratospheric water vapor “tape recorder” signal


Merged GOZCARDS H2O H2O exhibits significant global-scale changes on timescales > 1 or more decades - large variations on shorter timescales also (annual, QBO, ENSO) - many past studies (e.g., Rosenlof et al., 2001; Randel et al., 2004, 2006; Fueglistaler and Haynes, 2005; Scherer et al., 2008; Read et al., 2008; Solomon et al., 2010; Fujiwara et al., 2010; Hurst et al., 2011; Fueglistaler, 2012; Schoeberl et al., 2012; Fueglistaler et al., 2013; Garfinkel et al., 2013; Randel and Jensen, 2013) - What are the long-term H2O changes (given such large variability)? - How well are the connections between H2O, T, circulation, and surface/climate understood? > how big of a role does deep convection play? - Can model variations in H2O converge better (and how can they be better constrained)?

Global temporal variations in H2O


Upper mesospheric variations


- GOZCARDS H2O annual avg. fields near 0.01 hPa are strongly anti-correlated with solar flux. (Lyman α above from LASP Solar Data Center; Woods et al. (2000)) Note: annual avg. Lyman α & F10.7 agree very well. - Recent studies: Nedoluha et al., 2009; Remsberg, 2010. - R = - 0.94 and -0.82 at 0.01 hPa and 0.02 hPa. - We find even stronger anti-correlation for the second solar cycle than for the first.

GOZCARDS MERGED H2O at 0.010 hPa 60oS-60oN

91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12Year

2.0

2.5

3.0

3.5

4.0

4.5

Wat

er /

ppm

v

Work (led by John Anderson) is shown in next few slides - Collaboration with S. Davis/K. Rosenlof (SWOOSH data providers) - SWOOSH: The Stratospheric Water and OzOne Satellite Homogenized (more on SWOOSH in presentation by K. Rosenlof) GOZCARDS versus SWOOSH - Datasets for H2O > GOZCARDS uses UARS MLS, HALOE, Aura MLS, & ACE-FTS. > SWOOSH uses SAGE-II (V7), SAGE-III, UARS MLS, HALOE, & Aura-MLS. ▪ Use monthly zonal mean VMRs in 18 latitude bins (10° bins; also report data in 2.5° bins) ▪ Vertical grid: Aura MLS (v3.3) pressure levels (from ~316 hPa to 1 hPa) ▪ Merging method: use Aura-MLS as reference and calculate offsets based on collocated profile pairs

Some differences in merging approach and in datasets used - also some differences in data screening and data treatment à we do not expect a “perfect” match (GOZCARDS versus SWOOSH) > but this should be a useful cross-check; analyses of differences are ongoing.

Part II: Comparisons between GOZCARDS and SWOOSH H2O

Lucien Froidevaux et al., WAVAS Meeting, JPL, Dec. 2013 12

Slope= 0.55 + 0.46 %/decade

R=0.97

Slope= 0.58 + 0.42 %/decade

R=0.96

Top Frames: SWOOSH and GOZCARDS merged time series. Middle Frames: SWOOSH and GOZCARDS deseasonalized anomalies. Bottom Frames: Differences (SWOOSH - GOZCARDS) of the deseasonalized

anomalies and the slope (trend) of these differences (±3 σ error)

Comparisons between GOZCARDS and SWOOSH H2O


Examples

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100x(SWOOSH-GOZCARDS)/GOZCARDS

(1991 through 2012)

Correlation coefficients for differences in deseasonalized anomaly series Typically, R = 0.9 to 0.95 for most of stratosphere - degrades somewhat for p > 100 hPa. Contours are in 0.1 increments between -0.9 and 0.9 (0.03 increments outside these bounds)

Differences (contours are in 5% increments)


Slopes in diff. of deseasonalized anomalies - Yellow shading indicates slopes that are not statistically significant (at 3 σ level). - Regions with most significant differences occur at high lats. and for p > 100 hPa.

Contours are in 0.05 ppmv/decade increments. Grey shading indicates negative values.

Contours are in 1 %/decade increments. Grey shading indicates negative values.




Top Frames: SWOOSH and GOZCARDS merged time series. Middle Frames: SWOOSH and GOZCARDS deseas. anomalies. Bottom Frames: Differences (SWOOSH - GOZCARDS) of the deseasonalized anomalies and slope (trend) of diffs. (±3 σ error)

Two examples with poorer matches between the two time series - Such plots (lat./p bins) do not all look alike in terms of time periods with outliers or the potential causes > early and/or late periods can have significant leverage in terms of poorer trend diffs. > we will continue to investigate such issues > this is part of current uncertainties (dataset inclusion or exclusion, like SAGE or ACE-FTS data + approach, possibly to a lesser extent) Poor matches are not common for most of the stratosphere (including 100 hPa in the tropics, on average).

R=0.94

R=0.83

Slope= -2.6 + 0.8 %/decade

Slope= -2.7 + 0.8 %/decade


100 hPa

68 hPa 65°S

15°N

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Comparisons between GOZCARDS and SWOOSH H2O SWOOSH

GOZCARDS

Tropical tape recorder signals in H2O -  Overall good agreement between these two data records. -  Fewer data gaps for SWOOSH (as it uses SAGE II data).


Summary - We have described GOZCARDS monthly zonal mean H2O data - available online. w Use keyword GOZCARDS at http://mirador.gsfc.nasa.gov (GES-DISC search) w  Also http://gozcards.jpl.nasa.gov ; see/use the “README” documentation w  netCDF files exist for each source dataset & for merged dataset. Version ev1.01.

à2 files per year. Include offsets applied to original zonal means + std. devs., std. errors, SZA and LST information, days of month with data,…

- GOZCARDS versus SWOOSH H2O – helps to reveal areas with larger errors > For most of stratosphere: mean diffs. are within 5% (bias depends on ref. set) > Correl. Coefficients for deseason. anomalies > ~0.9 except for p >100 hPa. > Slopes of diffs. between the 2 data records à most significant issues are for p ≥ 100 hPa and at highest lats. (most heights) > some differences seem linked to which datasets are used (and when) + merging approaches are somewhat different (probably a smaller factor in general) > For long-term trends in above regions, more caution is advised when using the data records [until we better elucidate the larger GOZCARDS/SWOOSH differences]. > However, diffs. are typically small compared to observed medium-term strat. H2O variations in past 2 decades (e.g., post-2000 drop in LS H2O). Most medium-term variations agree quite well and R values are mostly > 0.9. - Future work of interest > More comparisons vs independent data (but previous work exists) and models - Feedback on data records is welcome (further assessment by the community)


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