Recent divergences in stratospheric water vapor measurements by frost point hygrometers and the Aura Microwave Limb Sounder

Abstract

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 year¹ (0.6 to 1.5 % year¹) 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 % year¹) 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.

Atmos. Meas. Tech., 9, 44474457, 2016 www.atmos-meas-tech.net/9/4447/2016/ doi:10.5194/amt-9-4447-2016 Author(s) 2016. CC Attribution 3.0 License.

Dale F. Hurst1,2, William G. Read3, Holger Vmel4, Henry B. Selkirk5,6, Karen H. Rosenlof7, Sean M. Davis1,7,

Emrys G. Hall1,2, Allen F. Jordan1,2, and Samuel J. Oltmans1,2

1Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA

2Global Monitoring Division, NOAA Earth System Research Laboratory, Boulder, Colorado, USA

3Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USA

4Earth Observing Laboratory, National Center for Atmospheric Research, Boulder, Colorado, USA

5Laboratory for Atmospheric Chemistry and Dynamics, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA

6Goddard Earth Science Technology and Research, Universities Space Research Association, Columbia, Maryland, USA

7Chemical Sciences Division, NOAA Earth System Research Laboratory, Boulder, Colorado, USA Correspondence to: Dale F. Hurst ([email protected])

Received: 5 May 2016 Published in Atmos. Meas. Tech. Discuss.: 6 June 2016

Revised: 10 August 2016 Accepted: 22 August 2016 Published: 8 September 2016

Abstract. Balloon-borne frost point hygrometers (FPs) and the Aura Microwave Limb Sounder (MLS) provide high-quality vertical prole 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 signicant biases of 2 to 10 % at 83 and 100 hPa (Hurst et al., 2014). A simple linear regression analysis of the FPMLS differences revealed no signicant 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 Jos,

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 ve sites have diverged at rates of 0.03 to 0.07 ppmv year1 (0.6 to 1.5 % year1)

from 2010 to mid-2015. These rates are similar in mag

nitude to the 30-year (19802010) average growth rate of stratospheric water vapor ( 1 % year1) measured by FPs

over Boulder (Hurst et al., 2011). By mid-2015, the FPMLS differences at some sites were large enough to exceed the combined accuracy estimates of the FP and MLS measurements.

1 Introduction

Water vapor in the Earths atmosphere inuences 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 signicantly 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 anal-

Published by Copernicus Publications on behalf of the European Geosciences Union.

Recent divergences in stratospheric water vapor measurements by frost point hygrometers and the Aura Microwave Limb Sounder

4448 D. F. Hurst et al.: Recent divergences in stratospheric water vapor measurements by FPs and MLS

yses 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 prole measurements of water vapor at high resolution from the surface to the middle stratosphere ( 28 km). Mea

surement programs with FPs typically focus on the UTLS for the purpose of long-term climate monitoring and/or studies of processes that inuence 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; Vmel et al., 2007a; Hurst et al., 2014).

Nearly every day since August 2004 the Aura MLS has provided 3500 near-global vertical prole 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 NOAAs Earth System Research Laboratory were compared: Boulder, Colorado;Hilo, Hawaii; and Lauder, New Zealand. Statistically signicant FPHMLS 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 FPHMLS agreement was better than 0.04 ppmv (0.8 %). FPHMLS differences at each of the three sites were also analyzed for temporal drifts using weighted linear regression ts to the full records. With a few minor exceptions the linear trends in FPHMLS differences through the end of 2012 were not statistically different from zero (Hurst et al., 2014).

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) (Vmel et al., 2007b; Vmel et al., 2016). The balloon-borne measurements are compared to MLS proles obtained during overpasses of Boulder, Hilo, Lauder and two additional FP sounding sites: Lindenberg, Germany, and San Jos, Costa Rica (Table 1). Note that the Hilo and Lauder FP soundings were performed exclusively with the NOAA FPH, the Lindenberg and San Jos proles

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 proles 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 gures pertain to the comparison conducted with MLS v3.3 retrievals. Tables presenting results based on MLS v3.3 and v4.2 are so specied. 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 (mol mol1 dry air)

in units of parts per million by volume (ppmv).

2 Methods

Evaluations of biases and drifts in coincident FP and MLS measurements of water vapor require that their proles 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 proles proximate to the ve 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.

For this work a criterion of 18 h was used to identify tem

porally coincident MLS and FP proles. This enabled MLS proles to be compared with 94100 % of the FP soundings at each site. Employing the spatial and temporal criteria together, an average of 46 spatiotemporally coincident MLS overpass proles were identied per FP sounding at each of the 5 sites (Table 1). As in Hurst et al. (2014) the multiple MLS proles coincident with each FP ight were distilled into a single median coincident prole 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 FPMLS differences were computed, each FP prole 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 mix-

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D. F. Hurst et al.: Recent divergences in stratospheric water vapor measurements by FPs and MLS 4449

Table 1. Frost point hygrometer site information and coincident MLS prole statistics.

MLS MLS

Site Altitude Latitude Longitude Comparison FP FP Proles Proles

Site Code (m a.s.l.) ( N) ( E) Start datea Type Prolesb v3.3c v4.2d

Lindenberg LIN 112 52.21 14.12 2006.66 CFH 132 801 787 Boulder BLD 1743 39.95 105.20 2004.67 FPH/CFH 144/31 1000 990

Hilo HIH 10 19.72 155.05 2010.94 FPH 51 275 276

San Jos SJC 1075 9.98 84.13 2005.52 CFH 158 788 858

Lauder LDR 370 45.04 169.68 2004.65 FPH 117 463 458

a Decimal date of rst FP prole after the 2004.59 start of MLS data reporting. b Number of FP proles with at least one coincident MLS prole. c Number of MLS version 3.3 proles coincident with the FP proles. d Number of MLS version 4.2 proles coincident with the FP proles.

generated using the actual MLS a priori proles (Hurst et al., 2014). FP proles 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 10026 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 proles at each site and diminishes the value of the statistics generated by this type of comparison.

FPMLS differences were calculated for each FP sounding by subtracting the MLS median coincident prole from the convolved FP prole. Statistical outliers were identied independently for each site and pressure level by evaluating the residuals of FPMLS differences from smoothed time series of the differences. Points with absolute residuals exceeding twice the mean absolute residual were agged as outliers and excluded from further study. Approximately 10 % of the FPMLS differences were agged as outliers.

For some sites the records of FPMLS 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 signicant change-point, the point where the mean of the time series rst undergoes a structural pattern change. Such an analysis was performed on each time series of FPMLS 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 -

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Figure 1. 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 (lled 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 ve sites towards the ends of their records.

ing 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 prole and an a priori prole (Read et al., 2007). We used the MLS median proles as a priori proles because they produce convolved proles equivalent to those

4450 D. F. Hurst et al.: Recent divergences in stratospheric water vapor measurements by FPs and MLS

able 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 condence 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). Condence 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 t to the n-dependent percentiles. Condence levels for F -statistic values above the 99th percentile are reported as > 99 % (Table 2). Of the 32 changepoints identied for Lindenberg, Boulder, San Jos and Lauder, the condence 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 change-points.

Changepoints with high condence levels were successfully identied in the time series of FPMLS differences using piecewise linear regression, so this same analysis method was also used to evaluate trends in the differences. Piecewise continuous linear regression ts (i.e., perfectly connected at the changepoint) were employed instead of non-continuous ts because there is no evidence of step jumps in FPMLS differences at the changepoints. The absence of step jumps is conrmed by the lack of statistically signicant (2) differences between 1-year averages of FPMLS before and after the changepoints. The piecewise continuous linear ts included statistical weights (reciprocals of the squared uncertainties of the FPMLS 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 (/p n) of the median MLS mixing ratio and the Student t value for 95 % condence. FP uncertainties were estimated (95 % condence) 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 t slope uncertainties and the Student t values for 95 % condence.Fits of the Hilo differences were performed using weighted linear regression over the full-record period (decimal dates 2010.952015.5). The resulting period 2 trends and their uncertainties are included in Table 3 and Fig. 3.

For FPMLS differences computed using MLS v4.2 retrievals the changepoints and condence 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 de-

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Figure 2. Differences between FP mixing ratios and spatiotempo-rally coincident MLS v3.3 water vapor retrievals at 68 hPa over the ve 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 FPHMLS differences in two distinct periods separated by a changepoint, except for Hilo where the shorter FP records show no indications of statistically signicant changepoints.

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 t in two periods (separated by the change-point) instead of one period. The maximum in the time series of F -statistics, Fmax, identies the most probable change-point in the time series.

The two-phase regression model was rst applied to time series of smoothed FPMLS differences at each site to look for conformity between the detected changepoints. Except for Hilo, nearly all of the changepoints identied 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 FPMLS 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 change-point 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.

The dates and condence levels of the changepoints for each time series of FPMLS differences (except at Hilo) are presented in Table 2. For Hilo the analysis found no discern-

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Table 2. Changepoint dates and their condence levels, MLS version 3.3.

Lindenberg Boulder San Jos Lauder

Pressure Changepoint CLa Changepoint CL Changepoint CL Changepoint CL (hPa) (year) (%) (year) (%) (year) (%) (year) (%)

26 2011.4 83 2009.1 > 99 2008.9 66 2010.4 > 99 32 2011.1 > 99 2009.0 > 99 2010.8 45 2010.7 99 38 2011.3 > 99 2009.5 > 99 2009.3 48 2010.7 > 99 46 2011.6 92 2009.5 > 99 2008.9 71 2010.6 > 99 56 2011.9 94 2009.3 > 99 2010.1 > 99 2010.6 > 99 68 2011.4 99 2009.5 > 99 2010.7 > 99 2010.4 > 99 83 2011.4 80 2009.3 > 99 2010.6 > 99 2010.7 93 100 2011.7 40 2009.1 > 99 2009.1 73 2011.1 94 Meanb 2011.5 2009.3 2009.8 2010.6SD 0.3 0.2 0.9 0.2

a Condence 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.

Table 3. Linear regression slopes of FPMLS v3.3 differences. Slopes are presented with their 95 % condence 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 % condence.

MLS Lindenberg Boulder Hilo San Jos Lauder Pressure Period (ppmv year1) (ppmv year1) (ppmv year1) (ppmv year1) (ppmv year1)

26 1 0.001 0.089 0.074 0.040 0.054 0.066 0.066 0.051

32 1 0.026 0.089 0.089 0.040 0.004 0.038 0.032 0.043

38 1 0.073 0.071 0.057 0.032 0.003 0.048 0.027 0.040

46 1 0.044 0.061 0.038 0.031 0.047 0.046 0.019 0.037

56 1 0.014 0.053 0.021 0.030 0.033 0.032 0.020 0.036

68 1 0.024 0.060 0.014 0.027 0.036 0.029 0.014 0.035

83 1 0.026 0.056 0.039 0.027 0.065 0.027 0.024 0.030

100 1 0.022 0.058 0.089 0.031 0.053 0.046 0.013 0.028

26 2 0.058 0.053 0.054 0.029 0.028 0.060 0.009 0.036 0.055 0.062

32 2 0.079 0.048 0.065 0.027 0.036 0.055 0.053 0.051 0.029 0.059

38 2 0.077 0.045 0.070 0.028 0.019 0.055 0.033 0.031 0.050 0.050

46 2 0.057 0.045 0.065 0.028 0.001 0.052 0.002 0.026 0.058 0.045

56 2 0.080 0.049 0.054 0.025 0.018 0.053 0.007 0.035 0.069 0.045

68 2 0.081 0.038 0.056 0.024 0.030 0.053 0.011 0.042 0.051 0.039

83 2 0.054 0.038 0.065 0.023 0.020 0.049 0.059 0.036 0.036 0.042

100 2 0.027 0.044 0.066 0.024 0.023 0.052 0.041 0.031 0.058 0.047

termined from weighted, piecewise continuous linear regression ts to the FPMLS v4.2 differences (Table 5) are also very similar to those for the v3.3 retrievals (Table 3).

3 Results for MLS v3.3

In the remainder of this work we report stratospheric averages of trends and changes in FPMLS differences. These are computed as weighted averages over the eight pressure levels above each site. Weights are the reciprocals of squared trend uncertainties (95 % condence), yielding uncertainties with 95 % condence. 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.

Almost all of the period 1 trends in FPMLS differences over Lindenberg, Boulder and Lauder are positive, but for each of Lindenberg and Lauder these trends are statistically different from zero (95 % condence) at only one pressure level (Table 3). For Boulder, period 1 trends at six pressure levels are statistically signicant, yielding a stratospheric average trend of 0.047 0.011 ppmv year1

(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 FPMLS differences over 4.6 years (Au

gust 2004 to mid-2009). Stratosphere-averaged period 1

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4452 D. F. Hurst et al.: Recent divergences in stratospheric water vapor measurements by FPs and MLS

Figure 3. Trends in FPMLS differences for the pre- and post-changepoint periods at eight stratospheric pressure levels (10026 hPa) over the ve FP sounding sites. Markers for each pressure level are slightly offset in pressure for clarity. Horizontal error bars depict the 95 % condence intervals of the trends. Only period 2 trends are shown for Hilo because the shorter records show no indications of statistically signicant changepoints.

Table 4. Changepoint dates and their condence levels, MLS version 4.2.

Lindenberg Boulder San Jos Lauder

Pressure Changepoint CLa Changepoint CL Changepoint CL Changepoint CL (hPa) (year) (%) (year) (%) (year) (%) (year) (%)

26 2011.4 96 2009.1 > 99 2008.9 69 2010.6 > 99 32 2011.4 99 2009.0 > 99 2010.8 39 2010.7 > 99 38 2011.3 > 99 2010.0 > 99 2010.8 76 2010.7 > 99 46 2011.7 92 2009.6 > 99 2009.5 86 2010.6 > 99 56 2011.6 87 2009.3 > 99 2010.1 96 2010.6 > 99 68 2011.4 > 99 2009.5 > 99 2010.6 > 99 2011.1 > 99 83 2011.4 83 2010.2 > 99 2010.9 > 99 2010.7 97 100 2011.4 73 2009.1 > 99 2009.1 78 2010.7 > 99 Meanb 2011.4 2009.5 2010.1 2010.7SD 0.1 0.4 0.8 0.2

a Condence 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.

ences. 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 FPMLS differences at these three extra-tropical sites changed signicantly 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 signicant due to the shorter FP measurement record. The stratosphere-averaged trend in FPMLS differences at Hilo, 0.015 0.019 ppmv year1,

also lacks statistical signicance (95 % condence). Period 2

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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 Jos are negative, yielding a stratosphere-averaged change of

0.17 0.06 ppmv (3.8 1.3 %) in FPMLS differences

from 2005.5 to 2009.9.

All but 4 of the 24 period 2 trends at Lindenberg, Boulder and Lauder are negative and statistically signicant (Table 3, Fig. 3). Stratosphere-averaged trends are 0.064 0.016, 0.062 0.009 and 0.052 0.017 ppmv year1, respec

tively (Table 6), demonstrating relatively consistent rates of change (1.2 to 1.5 % year1) in the FPMLS differ-

D. F. Hurst et al.: Recent divergences in stratospheric water vapor measurements by FPs and MLS 4453

Table 5. Linear regression slopes of FPMLS v4.2 differences. Slopes are presented with their 95 % condence 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 % condence.

MLS Lindenberg Boulder Hilo San Jos Lauder Pressure Period (ppmv year1) (ppmv year1) (ppmv year1) (ppmv year1) (ppmv year1)

26 1 0.007 0.089 0.077 0.041 0.054 0.065 0.053 0.047

32 1 0.026 0.082 0.081 0.040 0.015 0.038 0.026 0.043

38 1 0.076 0.071 0.052 0.029 0.009 0.034 0.033 0.040

46 1 0.046 0.061 0.038 0.030 0.013 0.039 0.011 0.038

56 1 0.023 0.059 0.022 0.030 0.026 0.033 0.017 0.035

68 1 0.032 0.059 0.018 0.027 0.019 0.031 0.005 0.029

83 1 0.030 0.058 0.029 0.024 0.028 0.025 0.017 0.030

100 1 0.064 0.066 0.079 0.030 0.037 0.047 0.023 0.030

26 2 0.062 0.053 0.056 0.030 0.015 0.070 0.011 0.035 0.044 0.063

32 2 0.079 0.051 0.064 0.027 0.032 0.057 0.029 0.051 0.024 0.060

38 2 0.071 0.045 0.082 0.031 0.014 0.055 0.055 0.047 0.059 0.050

46 2 0.054 0.046 0.063 0.029 0.005 0.052 0.007 0.031 0.060 0.045

56 2 0.065 0.044 0.055 0.026 0.030 0.055 0.002 0.036 0.075 0.043

68 2 0.079 0.039 0.054 0.024 0.038 0.053 0.013 0.042 0.064 0.047

83 2 0.058 0.038 0.083 0.028 0.059 0.052 0.043 0.041 0.025 0.045

100 2 0.037 0.039 0.077 0.023 0.001 0.050 0.022 0.031 0.064 0.041

Table 6. Stratospheric average trends and changes in FPMLS differences. Weighted averages of trends and changes in FPMLS differences at all eight pressure levels (10026 hPa) over each site. Stratospheric averages are presented with their 95 % condence limits. All values were computed using the regression slopes and their uncertainties in Tables 3 and 5. Values in boldface type are signicantly different from zero with 95 % condence.

Period 1 Period 2 Full record

MLS Trend Change Trend Change Change Site Version (ppmv year1) (ppmv) (ppmv year1) (ppmv) (ppmv)

Lindenberg 3.3 0.029 0.023 0.14 0.11 0.064 0.016 0.25 0.06 0.11 0.13

Lindenberg 4.2 0.039 0.023 0.19 0.11 0.062 0.015 0.25 0.06 0.07 0.13

Boulder 3.3 0.047 0.011 0.22 0.05 0.062 0.009 0.38 0.06 0.16 0.08

Boulder 4.2 0.044 0.011 0.22 0.05 0.066 0.010 0.40 0.06 0.18 0.08

Hilo 3.3 0.015 0.019 0.07 0.09

Hilo 4.2 0.025 0.019 0.11 0.09

San Jos 3.3 0.039 0.013 0.17 0.06 0.006 0.012 0.04 0.07 0.12 0.09

San Jos 4.2 0.020 0.012 0.10 0.06 0.002 0.013 0.02 0.07 0.11 0.09

Lauder 3.3 0.009 0.013 0.06 0.08 0.052 0.017 0.25 0.08 0.19 0.11

Lauder 4.2 0.006 0.012 0.04 0.07 0.054 0.017 0.26 0.08 0.21 0.11

trends at San Jos 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 Jos is small and not statistically different from zero.

Changes in FPMLS 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, respec

tively (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 pres-

sure levels, not from the stratospheric averages in Table 6.

Remarkably the stratosphere-averaged full-record change of

0.12 0.09 ppmv at San Jos is similar to those at the other

sites despite the period 1 changes at San Jos being mostly negative.

4 Results for MLS v4.2

Trends in FPMLS 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 ver-

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4454 D. F. Hurst et al.: Recent divergences in stratospheric water vapor measurements by FPs and MLS

Figure 4. Period 2 trends in FPMLS differences using MLS v3.3 (lled circles) and v4.2 (open circles) retrievals at eight stratospheric pressure levels (10026 hPa) over the ve FP sounding sites. Markers for each pressure level are slightly offset in pressure for clarity. Horizontal error bars depict the 95 % condence intervals of the trends.

sion 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 intensies the stratosphere-averaged trend from 0.015 0.019

to 0.025 0.019 ppmv year1. Interestingly the choice of

MLS retrieval version also makes a signicant difference in the period 1 trends at San Jos, with v3.3 yielding a stronger stratosphere-averaged trend of 0.039 0.013 ppmv year1

than v4.2 (0.020 0.012 ppmv year1). The choice of

MLS version makes very little difference to the stratosphere-average period 2 trends at San Jos even though v4.2 reduces the number of pressure levels with signicant trends from four to two.

5 Discussion

The magnitudes of statistically signicant stratosphere-averaged trends in FPMLS differences (0.6 to 1.5 % year1) from 2010 to mid-2015 are similar

in magnitude to the 1 % year1 average stratospheric

water vapor increase reported from FP measurements over

Boulder during 19802010 (Hurst et al., 2011). Negative trends in FPMLS 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 46 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 FPMLS 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). Vmel 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 (1) to < 5 % (Vmel et al., 2016). A recent evaluation of the NOAA FPH (Hall et al., 2016) demonstrates that the stratospheric measurement uncertainties (2) are < 6 % (< 0.3 ppmv). Employing 3 and 5 % as 1 and 2 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 from5.0 to 8.5 % (0.23 to 0.34 ppmv) and 6.4 to 9.4 % (0.29 to0.40 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 FPMLS 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 ts 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 sitepressure level combinations, and another 5 were within 0.05 ppmv of the ACCFP5.

Six of the end point differences exceeding the ACCFP5 were

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D. F. Hurst et al.: Recent divergences in stratospheric water vapor measurements by FPs and MLS 4455

Figure 5. FPMLS v3.3 differences at the starting points (open circles), changepoints (asterisks) and ending points (lled circles) of the time series as dened by piecewise continuous linear ts. 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 signicant changepoints were detected in the shorter records.

at 100 and 83 hPa, pressure levels for which FPMLS biases of up to 10 % have already been reported (Hurst et al., 2014).

By mid-2015 the FPMLS 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 FPMLS differences during period 2. At Boulder, six and four end point differences exceeded or were within0.05 ppmv of the ACCFP3 and ACCFP5, respectively, with all but one (100 hPa) caused by strong negative period 2 trends. At Lauder and San Jos, 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 FPMLS 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 FPMLS 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 extra-tropical sites are similar to one another but disparate from those at tropical San Jos. The differences at Hilo have also drifted downward since late 2010, but the FPH record is too short to permit the detection of statistically signicant trends. We deliberately compared MLS retrievals with ve different records of in situ, balloon-borne measurements compiled using two independent FPs with different manufacturers, calibration, frost control parameters and data processing. Our nding 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 ve 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.

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4456 D. F. Hurst et al.: Recent divergences in stratospheric water vapor measurements by FPs and MLS

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 of2.5 K year1 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.

6 Conclusions

Recent, signicant 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 year1 (0.6 to 1.5 % year1), similar in magnitude to the 1 % year1 average growth rate

of stratospheric water vapor observed over Boulder during 19802010 (Hurst et al., 2011). By mid-2015, the FPMLS differences at some sites were large enough to exceed the 5 8 % (1) 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 proles per day by MLS (Mller 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 proles 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 Mller 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 2030 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.

7 Data availability

NOAA FPH data for Boulder, Hilo and Lauder are available via anonymous ftp at ftp://ftp.cmdl.noaa.gov/data/ozwv/WaterVapor

Web End =ftp://ftp.cmdl.noaa.gov/data/ ftp://ftp.cmdl.noaa.gov/data/ozwv/WaterVapor

Web End =ozwv/WaterVapor . FP data for all ve sites will be made available through the GCOS Reference Upper Air Network (http://www.gruan.org

Web End =http://www.gruan.org ) and the Network for the Detection of Atmospheric Composition Change (http://www.ndsc.ncep.noaa.gov

Web End =http:// http://www.ndsc.ncep.noaa.gov

Web End =www.ndsc.ncep.noaa.gov ). MLS version 3.3 data for over-passes of the ve FP sites are available at the Aura Validation Data Center (http://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/MLS/V03/L2GPOVP/H2O

Web End =http://avdc.gsfc.nasa.gov/pub/data/ http://avdc.gsfc.nasa.gov/pub/data/satellite/Aura/MLS/V03/L2GPOVP/H2O

Web End =satellite/Aura/MLS/V03/L2GPOVP/H2O ). For version MLS4.2 overpass data please substitute V04 for V03 in the URL.

Acknowledgements. The NOAA frost point hygrometer network is supported in part by NOAAs Climate Program Ofce, the US Global Climate Observing System Program and NASAs 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 Daviss participation in this study was supported by NOAA resources targeted for water vapor research in the upper troposphere.

Edited by: S. BuehlerReviewed by: H. C. Pumphrey and one anonymous referee

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