AMTAtmospheric Measurement TechniquesAMTAtmos. Meas. Tech.1867-8548Copernicus PublicationsGöttingen, Germany10.5194/amt-9-4447-2016Recent divergences in stratospheric water vapor measurements by frost point
hygrometers and the Aura Microwave Limb SounderHurstDale F.dale.hurst@noaa.govhttps://orcid.org/0000-0002-6315-2322ReadWilliam G.VömelHolgerhttps://orcid.org/0000-0003-1223-3429SelkirkHenry B.https://orcid.org/0000-0001-9431-5385RosenlofKaren H.https://orcid.org/0000-0002-0903-8270DavisSean M.https://orcid.org/0000-0001-9276-6158HallEmrys G.https://orcid.org/0000-0001-5137-2902JordanAllen F.OltmansSamuel J.https://orcid.org/0000-0002-7390-2553Cooperative Institute for Research in Environmental Sciences,
University of Colorado, Boulder, Colorado, USAGlobal Monitoring Division, NOAA Earth System Research Laboratory,
Boulder, Colorado, USAJet Propulsion Laboratory, California Institute of Technology,
Pasadena, California, USAEarth Observing Laboratory, National Center for Atmospheric Research,
Boulder, Colorado, USALaboratory for Atmospheric Chemistry and Dynamics, NASA Goddard Space
Flight Center, Greenbelt, Maryland, USAGoddard Earth Science Technology and Research, Universities Space
Research Association, Columbia, Maryland, USAChemical Sciences Division, NOAA Earth System Research Laboratory,
Boulder, Colorado, USADale F. Hurst (dale.hurst@noaa.gov)8September201699444744575May20166June201610August201622August2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://amt.copernicus.org/articles/9/4447/2016/amt-9-4447-2016.htmlThe full text article is available as a PDF file from https://amt.copernicus.org/articles/9/4447/2016/amt-9-4447-2016.pdf
Balloon-borne frost point hygrometers (FPs) and the Aura Microwave Limb
Sounder (MLS) provide high-quality vertical profile measurements of water
vapor in the upper troposphere and lower stratosphere (UTLS). A previous
comparison of stratospheric water vapor measurements by FPs and MLS over
three sites – Boulder, Colorado (40.0∘ N); Hilo, Hawaii
(19.7∘ N); and Lauder, New Zealand (45.0∘ S) – from
August 2004 through December 2012 not only demonstrated agreement better than
1 % between 68 and 26 hPa but also exposed statistically significant
biases of 2 to 10 % at 83 and 100 hPa (Hurst et al., 2014). A simple
linear regression analysis of the FP–MLS differences revealed no significant
long-term drifts between the two instruments. Here we extend the drift
comparison to mid-2015 and add two FP sites – Lindenberg, Germany
(52.2∘ N), and San José, Costa Rica (10.0∘ N) – that
employ FPs of different manufacture and calibration for their water vapor
soundings. The extended comparison period reveals that stratospheric FP and
MLS measurements over four of the five sites have diverged at rates of 0.03
to 0.07 ppmv year-1 (0.6 to 1.5 % year-1) from ∼ 2010
to mid-2015. These rates are similar in magnitude to the 30-year (1980–2010)
average growth rate of stratospheric water vapor
(∼ 1 % year-1) measured by FPs over Boulder (Hurst et al.,
2011). By mid-2015, the FP–MLS differences at some sites were large enough
to exceed the combined accuracy estimates of the FP and MLS measurements.
Introduction
Water vapor in the Earth's atmosphere influences the radiation budget by
strongly attenuating outgoing long-wave radiation. Though the lower
troposphere holds the vast majority of atmospheric water vapor, abundance
changes in the relatively dry upper troposphere and lower stratosphere (UTLS)
can significantly impact global surface temperatures and climate (Forster and
Shine, 2002; Solomon et al., 2010). Satellite-based remote sensors have
greatly enhanced our ability to monitor UTLS water vapor on a near-global
scale. However, because of the limited operational lifetimes of satellite
sensors, an analysis of trends over decadal or longer scales requires the
merging of measurements by different instruments. Efforts to combine UTLS
water vapor data sets from different satellites have demonstrated the need to
reduce measurement biases between instruments before trend analyses are
performed (Davis et al., 2016; Hegglin et al., 2014; Froidevaux et al.,
2015). The necessity of adjusting data sets before they are merged imposes an
additional source of uncertainty on any determination of long-term trends.
Balloon-borne frost point hygrometers (FPs) provide vertical profile
measurements of water vapor at high resolution from the surface to the middle
stratosphere (∼ 28 km). Measurement programs with FPs typically focus
on the UTLS for the purpose of long-term climate monitoring and/or studies of
processes that influence humidity in the upper atmosphere (e.g., cloud
microphysical processes that regulate dehydration). Though FP data sets are
spatially and temporally sparse compared to those produced by satellite
sensors, long-term records of UTLS water vapor – like the 36-year record
over Boulder, Colorado – are invaluable for determining long-term trends
(Oltmans and Hofmann, 1995; Oltmans et al., 2000; Rosenlof et al., 2001;
Scherer et al., 2008; Hurst et al., 2011) and for validating satellite-based
remote sensors like the Aura Microwave Limb Sounder (MLS; Vömel et al.,
2007a; Hurst et al., 2014).
Nearly every day since August 2004 the Aura MLS has provided ∼ 3500
near-global vertical profile measurements of water vapor from the UT well
into the mesosphere, and it continues to do so today. Stratospheric water
vapor measurements by the MLS and NOAA frost point hygrometers (FPHs) were
recently compared to evaluate biases and temporal drifts between them during
the period August 2004 through December 2012 (Hurst et al., 2014).
Measurements over three UTLS water vapor monitoring sites of the Global
Monitoring Division of NOAA's Earth System Research Laboratory were compared:
Boulder, Colorado; Hilo, Hawaii; and Lauder, New Zealand. Statistically
significant FPH–MLS biases ranging from -0.10 (-2.2 %) to
-0.46 ppmv (-10.3 %) were reported at 100 hPa over all three sites
and at 83 hPa over Boulder and Hilo. Higher in the stratosphere, at the six
MLS retrieval pressures from 68 to 26 hPa, the average FPH–MLS agreement
was better than 0.04 ppmv (0.8 %). FPH–MLS differences at each of the
three sites were also analyzed for temporal drifts using weighted linear
regression fits to the full records. With a few minor exceptions the linear
trends in FPH–MLS differences through the end of 2012 were not statistically
different from zero (Hurst et al., 2014).
Frost point hygrometer site information and coincident MLS profile
statistics.
a Decimal date of first FP profile after the 2004.59 start of MLS data
reporting. b Number of FP profiles with at least one coincident MLS profile. c
Number of MLS version 3.3 profiles coincident with the FP profiles.
d Number of MLS version 4.2 profiles coincident with the FP profiles.
Here we present an updated comparison of stratospheric water vapor
measurements by FPs and the MLS for the period August 2004 through June 2015.
Data from two different types of FPs are used: the NOAA FPH (Mastenbrook and
Oltmans, 1983; Hall et al., 2016) and the cryogenic frost point hygrometer
(CFH) (Vömel et al., 2007b; Vömel et al., 2016). The balloon-borne
measurements are compared to MLS profiles obtained during overpasses of
Boulder, Hilo, Lauder and two additional FP sounding sites: Lindenberg,
Germany, and San José, Costa Rica (Table 1). Note that the Hilo and
Lauder FP soundings were performed exclusively with the NOAA FPH, the
Lindenberg and San José profiles are solely from the CFH, and the Boulder
record combines soundings by both FP types. Though both FP types use the same
measurement principle, they are built from different parts, are independently
calibrated and have subtle yet important differences in their software and
frost control logic. Data from the two FP types are also independently
processed and quality assured.
FP profiles at each site are independently compared to MLS version 3.3
(v3.3) and the latest v4.2 water vapor retrievals using the same analysis
methods. MLS v3.3 water vapor was retrieved until 30 June 2015, after which
only v4.2 data are available. MLS v4.2 retrievals feature an improved cloud
detection methodology, use more spectral channels and include an improved
forward model for greater accuracy (Livesey et al., 2015). Unless otherwise
noted, the values presented in the text and figures pertain to the
comparison conducted with MLS v3.3 retrievals. Tables presenting results
based on MLS v3.3 and v4.2 are so specified. We consider it essential to
evaluate both MLS versions because many papers have been written using v3.3
retrievals and many more will be published using v4.2 retrievals. All water
vapor mixing ratios are reported as mole fractions (µmol mol-1 dry
air) in units of parts per million by volume (ppmv).
Methods
Evaluations of biases and drifts in coincident FP and MLS measurements of
water vapor require that their profiles are matched in space and time. The
same spatial criteria presented as “coincidence criteria set #1” in
Hurst et al. (2014), within ±2∘ latitude and ±8∘ longitude, were employed to identify MLS profiles proximate to
the five FP sounding sites. The spatially coincident MLS retrievals are
plotted as time series along with the FP mixing ratios at 68 hPa over each
site (Fig. 1). Note in Fig. 1 that, towards the end of each record, many
of the markers representing FP mixing ratios reside near the lower limits of
the MLS data envelope.
Daily average MLS version 3.3 overpass retrievals (gray markers,
smoothed black curves) and in situ frost point hygrometer (FP) data at
68 hPa for individual soundings at each site (filled circles). Data from two
types of FPs are shown: NOAA FPH at Boulder (dark blue), Hilo and Lauder, and CFH at Lindenberg,
Boulder (cyan) and San Jose. Note the emerging biases between FP and MLS
mixing ratios at all five sites towards the ends of their records.
For this work a criterion of ±18 h was used to identify temporally
coincident MLS and FP profiles. This enabled MLS profiles to be compared
with 94–100 % of the FP soundings at each site. Employing the spatial
and temporal criteria together, an average of 4–6 spatiotemporally
coincident MLS overpass profiles were identified per FP sounding at each of
the 5 sites (Table 1). As in Hurst et al. (2014) the multiple MLS profiles
coincident with each FP flight
were distilled into a single “median” coincident profile composed of the
median MLS mixing ratio at each pressure level. Our choice to use median
rather than mean mixing ratios reduces the potential for any anomalous MLS
retrievals to skew the values used for this comparison.
Before FP–MLS differences were
computed, each FP profile was convolved with the MLS averaging kernels to
degrade its high vertical resolution to the ∼ 3 km resolution of
lower-stratospheric MLS retrievals and place the FP mixing ratio
“retrievals” on the MLS pressure grid (Read et al., 2007; Lambert et al.,
2007). Each convolution employed a forward model, operating in
log(P)–log(H2O) space, that ingests both the FP profile and an a
priori profile (Read et al., 2007). We used the MLS median profiles as a
priori profiles because they produce convolved profiles equivalent to those
generated using the actual MLS a priori profiles (Hurst et al., 2014). FP
profiles were independently convolved with the MLS v3.3 and v4.2 averaging
kernels for eight MLS retrieval pressure levels: 100, 83, 68, 56, 46, 38, 32
and 26 hPa. FP mixing ratios were not retrieved at
pressures < 26 hPa because the averaging kernels require data above
the typical maximum altitude of high-quality FP measurements. Although
convolved FP retrievals at pressures > 100 hPa are feasible,
the coincidence criteria applied to FP and MLS retrievals at pressure levels
100–26 hPa produced very noisy comparison results at
> 100 hPa, presumably due to the much greater variability of
water vapor at pressures > 100 hPa, especially in the tropics.
Applying more stringent coincidence criteria to improve the spatiotemporal
matching of FP and MLS data below 100 hPa severely reduces the number of
coincident profiles at each site and diminishes the value of the statistics
generated by this type of comparison.
FP–MLS differences were calculated for each FP sounding by subtracting the
MLS median coincident profile from the convolved FP profile. Statistical
outliers were identified independently for each site and pressure level by
evaluating the residuals of FP–MLS differences from smoothed time series of
the differences. Points with absolute residuals exceeding twice the mean
absolute residual were flagged as outliers and excluded from further study.
Approximately 10 % of the FP–MLS differences were flagged as outliers.
For some sites the records of FP–MLS differences at 68 hPa visually
exhibit time-dependent changes in trends (Fig. 2). Many of the time series at
other pressure levels over the sites (not shown) also show these same
characteristics. Intuitively, full-record linear trend analyses of these time
series of differences would greatly misrepresent the data. Instead, the
time-dependent changes in these records indicate they should be evaluated for
a statistically significant “changepoint”, the point where
the mean of the time series first undergoes a structural pattern change. Such
an analysis was performed on each time series of FP–MLS differences using
the two-phase regression model described by Lund and Reeves (2002). The model
considers every data point to be a potential undocumented changepoint and
calculates an F-statistic for each. The F-statistic is a quantitative
assessment of how much the sum of squared residuals is reduced when the time
series is fit in two periods (separated by the changepoint) instead of one
period. The maximum in the time series of F-statistics, Fmax,
identifies the most probable changepoint in the time series.
Differences between FP mixing ratios and spatiotemporally
coincident MLS v3.3 water vapor retrievals at 68 hPa over the five FP
sounding sites. In the top panel (a) dark blue and cyan markers for
Boulder depict soundings made with the NOAA FPH and the CFH, respectively. Lines show the trends in
FPH–MLS differences in two
distinct periods separated by a changepoint, except for Hilo where the
shorter FP records show no indications of statistically significant
changepoints.
The two-phase regression model was first applied to time series of smoothed
FP–MLS differences at each site to look for conformity between the detected
changepoints. Except for Hilo, nearly all of the changepoints identified for
the eight pressure levels above each site were within ±1 year of the
mean changepoint for the site. This intra-site consistency facilitated the
recognition of any non-conforming changepoints found when the model was
applied to time series of unsmoothed FP–MLS differences. When an anomalous
changepoint was detected in the unsmoothed differences, the time series of
F-statistics was examined for a secondary maximum nearer in time to the
consensus changepoint for that site. The value of the F-statistic at the
secondary maximum was typically only slightly less than Fmax, so
the more conforming changepoint of the secondary maximum was used instead of
the anomalous changepoint.
Changepoint dates and their confidence levels, MLS version
3.3.
a Confidence levels for the listed changepoint dates, with > 99 indicating a value between 99 and 100 %.
b Mean and standard deviation of the changepoint dates for each site.
The dates and confidence levels of the changepoints for each time series of
FP–MLS differences (except at Hilo) are presented in Table 2. For Hilo the
analysis found no discernable maxima (Fmax) in the time series of
F-statistics, probably because the record only began at the end of 2010,
after the changepoints that were determined for most other sites. Visually the time series of differences at
Hilo depict decreasing trends from the start of the record (Fig. 2b).
The confidence level of each changepoint was calculated using the 90th, 95th
and 99th percentiles of the Fmax distribution as a function of
n (time series length) presented in Table 1 of Lund and Reeves (2002).
Confidence levels for F-statistic values between the 90th and 99th
percentiles and for values below the 90th percentile were interpolated and
extrapolated, respectively, using a quadratic fit to the n-dependent
percentiles. Confidence levels for F-statistic values above the 99th
percentile are reported as > 99 % (Table 2). Of the
32 changepoints identified for Lindenberg, Boulder, San José and Lauder,
the confidence levels of 24 are ≥ 90 % and all but 4 are
> 68 %, substantiating the need to break each time series
into two separate intervals (periods 1 and 2) for trend analysis. The mean
and standard deviation of the 8 changepoints for each site are also presented
in Table 2. Dissimilarities between the mean changepoints for the four sites
are probably due in part to the disparate lengths and data populations of the
FP records prior to their changepoints.
Linear regression slopes of FP–MLS v3.3 differences. Slopes are
presented with their 95 % confidence intervals. Periods 1 and 2 refer to
the intervals before and including the changepoint (Table 2) and immediately
after the changepoint to 30 June 2015, respectively. Values in boldface type
are statistically different from zero
with 95 % confidence.
Changepoints with high confidence levels were successfully identified in the
time series of FP–MLS differences using piecewise linear regression, so
this same analysis method was also used to evaluate trends in the
differences. Piecewise continuous linear regression fits (i.e., perfectly
connected at the changepoint) were employed instead of non-continuous fits
because there is no evidence of step jumps in FP–MLS differences at the
changepoints. The absence of step jumps is confirmed by the lack of
statistically significant (2σ) differences between 1-year averages of
FP–MLS before and after the changepoints. The piecewise continuous linear
fits included statistical weights (reciprocals of the squared uncertainties
of the FP–MLS differences) determined from the combined uncertainties (in
quadrature) of the FP and MLS mixing ratios. Each MLS uncertainty was
computed as the product of the standard error (σ/√n) of the
median MLS mixing ratio and the Student t value for 95 % confidence.
FP uncertainties were estimated (95 % confidence) as 5 % of the FP
mixing ratios (see Sect. 5). Trends for periods 1 and 2 are presented with
their uncertainties in Table 3 and Fig. 3. Trend uncertainties were computed
as the products of the fit slope uncertainties and the Student t values for
95 % confidence. Fits of the Hilo differences were performed using
weighted linear regression over the full-record
period (decimal dates 2010.95–2015.5). The resulting period 2 trends and
their uncertainties are included in Table 3 and Fig. 3.
Trends in FP–MLS differences for the pre- and
post-changepoint periods at eight stratospheric pressure levels (100–26 hPa)
over the five FP sounding sites. Markers for each pressure level are
slightly offset in pressure for clarity. Horizontal error bars depict the
95 % confidence intervals of the trends. Only period 2 trends are shown
for Hilo because the shorter records show no indications of statistically
significant changepoints.
Changepoint dates and their confidence levels, MLS
version 4.2.
a Confidence levels for the listed changepoint dates, with > 99 indicating
a value between 99 and 100 %. b Mean and standard deviation of the listed changepoint dates for each
site.
Linear regression slopes of FP–MLS v4.2 differences. Slopes are
presented with their 95 % confidence intervals. Periods 1 and 2 refer to
the intervals before and including the changepoint (Table 4) and immediately
after the changepoint to 30 June 2015, respectively. Values in boldface type
are statistically different from zero
with 95 % confidence.
For FP–MLS differences computed using MLS v4.2 retrievals the changepoints
and confidence levels (Table 4) are very similar to those for v3.3 (Table 2). Mean changepoints
for each of the four sites are different by ≤ 0.3 year from those calculated in the v3.3 analysis. Many of the trends determined
from weighted, piecewise continuous linear regression fits to the FP–MLS
v4.2 differences (Table 5) are also very similar to those for the v3.3
retrievals (Table 3).
Results for MLS v3.3
In the remainder of this work we report stratospheric averages of trends and
changes in FP–MLS differences. These are computed as weighted averages over
the eight pressure levels above each site. Weights are the reciprocals of
squared trend uncertainties (95 % confidence), yielding uncertainties with
95 % confidence. Unless otherwise noted, averages reported in relative
units (%) are based on the mean stratospheric water vapor mixing ratio of
4.4 ppmv from 100 to 26 hPa.
Stratospheric average trends and changes in FP–MLS differences.
Weighted averages of trends and changes in FP–MLS differences at all eight
pressure levels (100–26 hPa) over each site. Stratospheric averages are
presented with their 95 % confidence limits. All values were computed
using the regression slopes and their uncertainties in Tables 3 and 5. Values
in boldface type are significantly different from zero with 95 % confidence.
Almost all of the period 1 trends in FP–MLS differences over Lindenberg,
Boulder and Lauder are positive, but for each of Lindenberg and Lauder these
trends are statistically different from zero (95 % confidence) at only one pressure level (Table 3). For
Boulder, period 1 trends at six pressure levels are statistically
significant, yielding a stratospheric average trend of
0.047 ± 0.011 ppmv year-1 (Table 6). The period 1
stratosphere-averaged trend over Boulder translates to a mean change of
0.22 ± 0.05 ppmv (5.0 ± 1.2 %) in FP–MLS differences over
∼ 4.6 years (August 2004 to mid-2009). Stratosphere-averaged period 1
changes at Lindenberg and Lauder were smaller: 0.14 ± 0.11 and
0.06 ± 0.08 ppmv, respectively (Table 6). Period 1 trends at seven of
the eight pressure levels above San José are negative, yielding a
stratosphere-averaged change of -0.17 ± 0.06 ppmv
(-3.8 ± 1.3 %) in FP–MLS differences from 2005.5 to
∼ 2009.9.
Period 2 trends in FP–MLS differences using MLS v3.3 (filled
circles) and v4.2 (open circles) retrievals at eight stratospheric pressure
levels (100–26 hPa) over the five FP sounding sites. Markers for each
pressure level are slightly offset in pressure for clarity. Horizontal error
bars depict the 95 % confidence intervals of the trends.
All but 4 of the 24 period 2 trends at Lindenberg, Boulder and Lauder are
negative and statistically significant (Table 3, Fig. 3).
Stratosphere-averaged trends are -0.064 ± 0.016, -0.062 ± 0.009 and -0.052 ± 0.017 ppmv year-1, respectively (Table 6),
demonstrating relatively consistent rates of change (-1.2 to -1.5 % year-1) in the FP–MLS differences. These mean trends translate to
stratosphere-averaged changes of -0.25 ppmv (-5.8 %), -0.38 ppmv
(-8.7 %) and -0.25 ppmv (-5.7 %) over the period 2 lengths of roughly
4.0, 6.2 and 5.1 years, respectively. This is compelling evidence that
FP–MLS differences at these three extra-tropical sites changed
significantly during the 4–6 years prior to mid-2015.
All but one of the period 2 trends at Hilo are negative, but none are
statistically significant due to the shorter FP measurement record. The
stratosphere-averaged trend in FP–MLS differences at Hilo,
-0.015 ± 0.019 ppmv year-1, also lacks statistical
significance (95 % confidence). Period 2 trends at San José are split
between positive and negative, with two of each being statistically different
from zero (Table 3). The resulting
stratosphere-averaged period 2 trend for San José is small and not
statistically different from zero.
Changes in FP–MLS differences over the entire comparison period are
calculated by summing the changes for periods 1 and 2 at each pressure
level. For Lindenberg, Boulder and Lauder the stratosphere-averaged full-record
changes are -0.11 ± 0.13, -0.16 ± 0.08 and -0.19 ± 0.11 ppmv,
respectively (Table 6). Uncertainties in the full-record
changes were calculated from the combined (in quadrature) uncertainties of
the period 1 and 2 changes at each of the eight pressure levels, not from the
stratospheric averages in Table 6. Remarkably the stratosphere-averaged
full-record change of -0.12 ± 0.09 ppmv at San José is similar to
those at the other sites despite the period 1 changes at San José being
mostly negative.
Results for MLS v4.2
Trends in FP–MLS v4.2 differences (Table 5) are, for the most part, very
similar to those determined for v3.3 (Table 3). Period 2 trends calculated
using v3.3 and v4.2 retrievals (Fig. 4) demonstrate that the choice of MLS
version makes little difference to the results. An exception is at Hilo,
where the switch from v3.3 to v4.2 strengthens the negative period 2 trends
at 83 and 100 hPa, and intensifies the stratosphere-averaged trend from
-0.015 ± 0.019 to -0.025 ± 0.019 ppmv year-1. Interestingly
the choice of MLS retrieval version also makes a significant difference in
the period 1 trends at San José, with v3.3 yielding a stronger
stratosphere-averaged trend of -0.039 ± 0.013 ppmv year-1 than
v4.2 (-0.020 ± 0.012 ppmv year-1). The choice of MLS version makes
very little difference to the stratosphere-average period 2 trends at San
José even though v4.2 reduces the number of pressure levels with
significant trends from four to two.
FP–MLS v3.3 differences at the starting points (open circles),
changepoints (asterisks) and ending points (filled circles) of the time
series as defined by piecewise continuous linear fits. Colored vertical
curves join the ending points to serve as visual guides. Black vertical
curves depict the combined accuracy estimates for FP and MLS measurements of
stratospheric water vapor based on FP accuracy values of 3 % (dotted)
and 5 % (dashed). Note that many of the ending point values lie near or
outside the combined accuracy estimates. For Hilo only the starting and
ending point differences are presented because no significant changepoints
were detected in the shorter records.
Discussion
The magnitudes of statistically significant stratosphere-averaged trends in
FP–MLS differences (-0.6 to -1.5 % year-1) from ∼ 2010
to mid-2015 are similar in magnitude to the ∼ 1 % year-1
average stratospheric water vapor increase reported from FP measurements over
Boulder during 1980–2010 (Hurst et al., 2011). Negative trends in FP–MLS
differences imply that MLS measurements have biased high, that FP
measurements have biased low or that some combination of both has occurred
over the last 4–6 years. Given these scenarios, an increasing trend in
stratospheric water vapor would be exaggerated by MLS measurements that have
biased high and underestimated or undetected by FP measurements that have
biased low. For a decreasing water vapor trend the effects of these
temporally changing biases would be reversed.
Here we assess the recent changes in FP–MLS differences in relation to the
estimated accuracies of stratospheric water vapor measurements by the MLS and
FPs. Accuracy estimates for MLS v3.3 and v4.2 retrievals are identical and
range from 4 to 8 % (0.18 to 0.32 ppmv) over the pressure levels of
interest (Livesey et al., 2013; Livesey et al., 2015). Vömel et
al. (2007a) assessed the stratospheric measurement uncertainties of the CFH
and estimated the accuracy to be < 10 % (< 0.5 ppmv),
but a recent reassessment lowers the uncertainty estimate (1σ) to
< 5 % (Vömel et al., 2016). A recent evaluation of the NOAA
FPH (Hall et al., 2016)
demonstrates that the stratospheric measurement uncertainties (2σ)
are < 6 % (< 0.3 ppmv). Employing 3 and 5 % as
1σ and 2σ accuracy estimates for the FPs, the combined (in
quadrature) accuracy estimates of FP and MLS measurements of stratospheric
water vapor at the eight retrieval pressures range from 5.0 to 8.5 %
(0.23 to 0.34 ppmv) and 6.4 to 9.4 % (0.29 to 0.40 ppmv), respectively.
From here forward the combined accuracy estimates for FPs and MLS based on
FP measurement uncertainties of 3 and 5 % are denoted
ACCFP3 and ACCFP5, respectively.
Figure 5 displays the values of FP–MLS differences (v3.3) at the start of
each record, at the changepoint and at the end of each record for the eight pressure levels,
as determined by the piecewise linear fits described above.
By the end of the comparison period in mid-2015, 18 of the 40 differences
exceeded the ACCFP3 and another 5 were within 0.05 ppmv of the
ACCFP3. End point differences surpassed the more conservative
ACCFP5 estimates for 11 site–pressure level combinations, and another 5
were within 0.05 ppmv of the ACCFP5. Six of the end point differences
exceeding the ACCFP5 were at 100 and 83 hPa, pressure levels for which
FP–MLS biases of up to 10 % have already been reported (Hurst et al.,
2014).
By mid-2015 the FP–MLS differences at seven pressure levels over Lindenberg
exceeded the ACCFP5 (Fig. 5). However, the starting point differences
for four of these seven levels also exceeded or nearly exceeded the ACCFP5
(Fig. 5), indicating that the large differences in mid-2015 resulted from
the continuation of long-term biases rather than recent drifts. At the other
three pressure levels over Lindenberg the end point differences exceeded
ACCFP5 because of large decreases in FP–MLS differences during period 2.
At Boulder, six and four end point differences exceeded or were within
0.05 ppmv of the ACCFP3 and ACCFP5, respectively, with all but one
(100 hPa) caused by strong negative period 2 trends. At Lauder and San
José, one and three end point differences exceeded or were within 0.05 ppmv of
the ACCFP5, respectively, all of which resulted from strong declines.
At Hilo the starting point and end point differences at 100 and 83 hPa
exceeded or were within 0.05 ppmv of the ACCFP5, consistent with the
long-term biases already reported for these pressure levels (Hurst et al.,
2014).
Very similar results were obtained when MLS v4.2 retrievals were employed
(not shown). By mid-2015, 44 and 25 % of the FP–MLS differences (both
MLS versions) exceeded the ACCFP3 and ACCFP5, respectively.
Likewise, 57 and 40 % of the end point differences exceeded or were
within 0.05 ppmv of the ACCFP3 and ACCFP5 estimates, respectively.
If the recent divergences between FPs and MLS continue, they will inevitably
push FP–MLS differences at most pressure levels to exceed the combined
accuracy estimates of the two instruments.
It is intriguing that the period 2 trends at the three extratropical sites
are similar to one another but disparate from those at tropical San José.
The differences at Hilo have also drifted downward since late 2010, but the
FPH record is too short to permit the detection of statistically significant trends. We deliberately
compared MLS retrievals with five different records of in situ, balloon-borne
measurements compiled using two independent FPs with different
manufacturers, calibration, frost control parameters and data processing. Our
finding of similar divergences (not step changes) in FP and MLS measurements
over the three extratropical sites suggests a positive drift in MLS
retrievals over these locations, primarily because it is highly unlikely that
the two different types of FPs are drifting at similar rates at the three
sites. We plan to continue closely comparing MLS and FP measurements over
these five sites to ascertain if they continue to diverge, settle into a
stable bias or start to reconverge.
The causes of the recent divergences in stratospheric water vapor
measurements by FPs and MLS at Lindenberg, Boulder and Lauder are currently
unknown. The MLS team is actively exploring multiple avenues in their
investigation of possible instrumental behaviors that might lead to water
vapor measurement drifts of the magnitudes documented here. For example, the
relationship between the MLS “standard” O3 product, measured in the
240 GHz region and shown to be very stable (Hubert et al., 2016), and a
secondary MLS O3 product obtained from the same 190 GHz spectral region
used for the water vapor measurements is being closely examined. At this
stage it is premature to offer conclusions from these studies.
Given the known sensitivities of MLS retrievals to atmospheric temperature
changes, an annual drift of 1 % in water vapor retrievals would require a
steep temperature trend of 2.5 K year-1 that is not observed in the
temperature retrievals of MLS or other instruments. Such a temperature trend
would also manifest itself as drifts in the MLS retrievals of other
atmospheric constituents, like ozone, that are absent from the measurement
records. Frost point hygrometers are stable over a wide range of atmospheric
temperatures (-80 to 30 ∘C) because their electronics
are well insulated and their measurements are independent of atmospheric
temperatures. It is therefore highly unlikely that atmospheric temperature
changes are driving the observed drifts in MLS retrievals or FP measurements
of water vapor.
Conclusions
Recent, significant divergences in stratospheric water vapor measurements by
balloon-borne frost point hygrometers and the Aura Microwave Limb
Sounder are reported for four globally distributed FP sites: Lindenberg,
Germany; Boulder, Colorado; Hilo, Hawaii; and Lauder, New Zealand. These
sites employ two types of FPs with different manufacturers, calibration,
frost control parameters and data processing. The rates of
divergence from ∼ 2010 to mid-2015 range from 0.03 to
0.07 ppmv year-1 (0.6 to 1.5 % year-1), similar in magnitude to the
∼ 1 % year-1 average growth rate of stratospheric water
vapor observed over Boulder during 1980–2010 (Hurst et al., 2011). By
mid-2015, the FP–MLS differences at some sites were large enough to exceed
the 5–8 % (1σ) combined accuracy estimates of the FP and MLS
measurements.
These divergences should prompt serious discussions about our future
capabilities to monitor UTLS water vapor around the globe. Currently there
is no comprehensive, long-term plan for a monitoring program that even
approaches the 3500 near-global profiles per day by MLS (Müller et al.,
2016). A third-generation Stratospheric Aerosol and Gas Experiment (SAGE
III) spectrometer is ready to be deployed in late 2016 on the International
Space Station, where it will provide an average of 32 vertical profiles of
UTLS water vapor each day. Ultimately, when Aura MLS fails, there will be an
immediate 99 % reduction in the spatiotemporal density of measurements
because there is no plan to replace MLS with a satellite sensor of similar
capabilities. For this reason Müller et al. (2016) have proposed the
creation of a large network of FPs covering the globe and funded in a
committed way that would make the network sustainable for many decades.
Towards this goal, a network of 20–30 globally distributed FP sounding sites
is in development as part of the Global Climate Observing System (GCOS)
Reference Upper Air Network (GRUAN; Bodeker et al., 2016). However, even
with a FP network of 100 sites performing weekly soundings the
spatiotemporal density of UTLS water vapor measurements would be only
0.4 % of what MLS is currently providing.
Data availability
NOAA FPH data for Boulder, Hilo and Lauder are available via anonymous ftp at
ftp://ftp.cmdl.noaa.gov/data/ozwv/WaterVapor. FP data for all five
sites will be made available through the GCOS Reference Upper Air Network
(http://www.gruan.org) and the Network for the Detection of Atmospheric
Composition Change (http://www.ndsc.ncep.noaa.gov). MLS version 3.3
data for overpasses of the five FP sites are available at the Aura Validation
Data Center
(http://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/MLS/V03/L2GPOVP/H2O).
For version MLS 4.2 overpass data please substitute “V04” for “V03 ” in
the URL.
Acknowledgements
The NOAA frost point hygrometer network is supported in part by NOAA's
Climate Program Office, the US Global Climate Observing System Program and
NASA's Upper Atmosphere Research Program. The FPH soundings used in this study were carefully conducted
at Hilo by David Nardini and Darryl Kuniyuki, and at Lauder by
Hamish Chisholm, Alan Thomas, Wills Dobson and Richard Querel. Karen Rosenlof
and Sean Davis's participation in this study was supported by NOAA resources
targeted for water vapor research in the upper troposphere. Edited by: S. Buehler Reviewed by: H. C.
Pumphrey and one anonymous referee
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