ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-11521-2016Case studies of the impact of orbital sampling on stratospheric trend detection and derivation of tropical vertical velocities: solar occultation vs. limb emission soundingMillánLuis F.lmillan@jpl.nasa.govLiveseyNathaniel J.SanteeMichelle L.NeuJessica L.ManneyGloria L.FullerRyan A.Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, USANew Mexico Institute of Mining and Technology, Socorro, New Mexico, USANorthWest Research Associates, Redmond, Washington, USALuis F. Millán (lmillan@jpl.nasa.gov)16September20161618115211153426April20169May201616August201629August2016This 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://acp.copernicus.org/articles/16/11521/2016/acp-16-11521-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/11521/2016/acp-16-11521-2016.pdf
This study investigates the representativeness of two types of orbital
sampling applied to stratospheric temperature and trace gas fields. Model
fields are sampled using real sampling patterns from the Aura Microwave Limb
Sounder (MLS), the HALogen Occultation Experiment (HALOE) and the Atmospheric
Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS). The MLS
sampling acts as a proxy for a dense uniform sampling pattern typical of limb
emission sounders, while HALOE and ACE-FTS represent coarse nonuniform
sampling patterns characteristic of solar occultation instruments. First,
this study revisits the impact of sampling patterns in terms of the sampling
bias, as previous studies have done. Then, it quantifies the impact of
different sampling patterns on the estimation of trends and their associated
detectability. In general, we find that coarse nonuniform sampling patterns
may introduce non-negligible errors in the inferred magnitude of temperature
and trace gas trends and necessitate considerably longer records for their
definitive detection. Lastly, we explore the impact of these sampling
patterns on tropical vertical velocities derived from stratospheric water
vapor measurements. We find that coarse nonuniform sampling may lead to a
biased depiction of the tropical vertical velocities and, hence, to a biased
estimation of the impact of the mechanisms that modulate these velocities.
These case studies suggest that dense uniform sampling such as that available
from limb emission sounders provides much greater fidelity in detecting
signals of stratospheric change (for example, fingerprints of greenhouse gas
warming and stratospheric ozone recovery) than coarse nonuniform sampling
such as that of solar occultation instruments.
Introduction
Satellite
data have provided a wealth of information on the Earth system and have had a
profound impact on operational numerical weather forecasting. Unlike
ground-based instruments or airborne field campaigns, satellite data provide
continuous global coverage, which facilitates the study and assimilation of
distributions of atmospheric fields, as well as global model evaluation.
However, satellite measurements sample continuously changing atmospheric
fields only at discrete times and locations, depending on the satellite orbit
as well as the measurement technique, which can result in a biased depiction
of the atmospheric field.
Typically, the impact of orbital sampling has been evaluated by comparing a
raw model field against a satellite-sampled one. For example, many studies
have documented sampling errors for rainfall estimates
e.g., and brightness temperatures , as well as O3, CO, temperature and a few other atmospheric
parameters sampled by nadir-viewing instruments . Recently, evaluated the sampling bias in
monthly and annual mean climatologies of O3 and H2O from 16 satellite
instruments, including limb emission sounders, limb scattering sounders,
solar occultation instruments and a stellar occultation instrument. They
concluded that coarse sampling may introduce significant sampling
uncertainties in climatologies, not only through nonuniform spatial sampling
but, more importantly, through nonuniform temporal sampling, that is to say,
producing regional monthly means using measurements that do not cover the
entire month. As expected, the sampling bias was found to be the greatest in
regions with large natural variability.
In this study we further evaluate the impact of the Aura Microwave Limb
Sounder (MLS), the HALogen Occultation Experiment (HALOE) and the Atmospheric
Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) sampling
patterns using the Canadian Middle Atmosphere Model (CMAM). MLS sampling
provides a dense uniform pattern, while HALOE and ACE-FTS are representative
of coarser solar occultation sampling patterns. We use HALOE and ACE-FTS
sampling patterns because they are commonly used solar occultation datasets
and, furthermore, because their sampling patterns are significantly
different and thus representative of the range of observation patterns
obtained by solar occultation instruments.
Our study has two purposes. (1) We expand upon previous studies by
quantifying the sampling bias of these instruments affecting measurements of
upper tropospheric and stratospheric temperature and trace gas species.
(2) We investigate how differences in data coverage may affect the outcome of
two illustrative atmospheric studies: trend detection and quantification of
tropical vertical velocities. We assess the differences in the long-term
(> 30 years) trends in temperature, O3 and CO estimated using datasets
with different sampling patterns. Also, we characterize the impact of orbital
sampling on derived lower-stratospheric tropical vertical velocities. These
velocities are computed by correlating the lag of the water vapor “tape
recorder” signal between adjacent levels . As such, they are likely an upper bound on the actual velocity
. These vertical velocities are modulated by the
quasi-biennial oscillation (QBO), seasonal cycles and El Niño–Southern
Oscillation (ENSO) e.g.,.
This paper is organized as follows: Sect. describes the
satellite patterns and the model fields used. Section briefly
revisits sampling bias estimates, while the impact of sampling on the
estimation of long-term trends as well as on trend detection is presented in
Sect. . Section addresses the impact of
orbital sampling on derived tropical vertical velocities, and
Sect. summarizes our results. The results discussed in this
study should be considered as example cases. Whether the results shown
represent reasonable estimates of the true orbital-sampling-induced artifacts
(e.g., in the sampling bias, in the inferred magnitude of the trends or in
the derived tropical vertical velocities) may also depend on how well the
model fields represent the real atmosphere.
Data and methodologyModel fields
CMAM is used as a proxy for the real atmosphere. CMAM is an extension of the
Canadian Center for Climate Modeling and Analysis spectral general
circulation model. Detailed descriptions of its dynamical and chemical
schemes are given by and ,
respectively. The free-running version of the model has been extensively
evaluated and has been shown to agree relatively well with observations
relevant to chemistry, dynamics, transport and radiation
e.g.,.
In this study we use output from the CMAM30 Specified Dynamics (SD)
simulation in which temperature and winds have been nudged to the ERA-Interim
reanalysis. This dataset exploits the vast progress made by reanalyses in
representing the stratospheric circulation e.g., and as
such can be used to reliably predict the chemical fields. Before nudging the
temperature fields, a technique described by was used
to remove temporal discontinuities in the ERA-Interim upper-stratospheric
temperatures that occurred in 1985 and 1998. CMAM30-SD has been shown to have
a good representation of stratospheric temperature, O3, H2O and CH4; it has been used as a transfer function between
satellite datasets to construct a reliable long-term H2O data record
, and it has been shown to reproduce halogen-induced
midlatitude O3 loss sufficiently well for investigation of long-term
O3 trends . The version of CMAM30-SD used here has a
horizontal resolution of approximately 3.75∘ latitude by
3.75∘ longitude. This resolution (approximately 400 km) is
comparable to the horizontal resolution of HALOE, ACE-FTS and MLS, which is
limited by the ∼ 500 km limb-viewing path length, and, hence, no
smoothing of the model fields is necessary . This version
has 63 vertical levels up to 0.0007 hPa with a vertical resolution varying
from 100 m in the lower troposphere to about 3 km in the mesosphere. Model
results for the period between January 1979 and December 2012 are used in
this study.
We evaluate the following CMAM30-SD outputs: temperature, O3, CH3Cl,
H2O, CO, HCl, N2O and HNO3. These parameters are an intersection of
the available CMAM30-SD outputs, the measurements available for MLS and the
measurements available for ACE-FTS or HALOE.
Satellite instrument sampling patterns
In this study we analyze the representativeness of the orbital sampling of
the solar occultation instruments HALOE and ACE-FTS, as well as the limb
emission sounder MLS. Solar occultation data are extremely valuable for
atmospheric studies due to their fine vertical resolution, the excellent
precision and accuracy of their self-calibrated measurements, and their
potential for detecting many species. However, the sparsity of the
measurements makes understanding the impact of their sampling crucial.
HALOE was launched on the Upper Atmosphere Research Satellite (UARS) in 1991,
and it measured infrared spectra across eight broadband and gas filter
channels from 2.45 to 10.04 µm for 14 years. It measured vertical
profiles of temperature, pressure and several atmospheric trace gases, with
as many as 15 sunrise and 15 sunset profiles of these atmospheric parameters
observed at a given latitude each day . The HALOE sampling
sweeps through its full range of latitude coverage, ranging from ±80 to
±50∘ depending on the season, over a period of about 1 month. The
vertical resolution of this dataset is about 2–3 km.
ACE-FTS was launched in 2003 and profiles the atmosphere by using solar
occultation. It measures infrared spectra from 2.2 to 13.3 µm (750
to 4400 cm-1) with high spectral sampling (0.02 cm-1), which
allows the retrieval of temperature, pressure and concentration for several dozen
atmospheric trace gases . ACE-FTS is focused on
high-latitude science, and thus almost 50 % of its approximately 15
sunrise and 15 sunset occultations per day occur at latitudes around
60∘. Global latitude coverage is achieved over a period of
approximately 3 months. The vertical resolution of this dataset is about
3 km.
Aura MLS was launched in 2004 and measures limb millimeter and submillimeter
atmospheric thermal emission using heterodyne radiometers covering spectral
regions near 118, 191, 240 and 640 GHz and 2.5 THz, from which temperature,
trace gas concentrations and cloud ice are retrieved. Daily, it covers
latitudes from 82∘ S to 82∘ N with ∼ 3500 vertical
scans providing near-global observations. The vertical resolution of this
dataset varies among species; O3, H2O and HCl have a ∼ 3 km
resolution in the stratosphere, and CO, CH3Cl, HNO3 and N2O have a
4–8 km resolution in the stratosphere .
To investigate the impact of orbital sampling, the daily model fields are
linearly interpolated to the actual latitude and longitude of the satellite
measurements. For the sampling patterns, we use a typical year of measurement
locations. In particular, we use 1994 and 2005 for HALOE and ACE-FTS,
respectively; these are the years with the maximum number of measurements on
record for each dataset. For MLS, we use 2008 as a representative year. Gaps
in the measurements due to instrument problems as well as year-to-year
variations due to orbital state changes are not considered in this study. To
avoid differences attributed purely to diurnal cycles, all satellite
measurements are assumed to be made at 12:00 UT, obviating the need for
interpolation in time. Thus, we focus on spatial differences. Given that our
focus is on horizontal/temporal sampling, all satellite measurements are
assumed to have vertical resolution comparable to that of CMAM30-SD; however,
we want to emphasize that the vertical resolution of these instruments is in
general good enough to resolve the model fields. That is, although the impact
of the averaging kernels is not addressed in this study, for the parameters
studied here a 3 km averaging kernel does not significantly affect their
values in the upper troposphere/stratosphere.
Figure (left) shows monthly sampling counts for each
instrument. MLS has a dense and nearly uniform sampling over latitude and
time, while HALOE and ACE-FTS have sparser and less uniform sample densities
because they are limited to two measurements per orbit.
Figure (right) shows the zonal mean water vapor field
at 100 hPa as sampled by each instrument to highlight how much daily
variability may be missed by the HALOE and ACE-FTS sampling patterns. The
consequences of these contrasting sampling densities are the main motivation
for this study. As discussed by , mapping data into
vortex-centered coordinate systems such as those based on potential vorticity
(PV) or equivalent latitude (EqL) may alleviate some of the solar occultation
sampling density problems for polar processing studies. However, since this
study focuses on near-global trends and tropical upwelling velocities, such
vortex-centered coordinate systems are of very limited utility here.
Left: monthly sampling counts for MLS, HALOE and ACE-FTS, in
4∘ latitude bins. Note the nonuniform color bar increments. Right: zonal mean water vapor at 100 hPa as sampled by MLS, HALOE and ACE-FTS for
individual days. White regions denote a lack of measurements.
Sampling biases
We evaluate the sampling biases associated with constructing monthly zonal
means from the raw and satellite-sampled data. The raw or sampled zonal means
for a particular latitude bin for each pressure level are given by
Zlx‾=1N∑ylx,
where N is the total number of points, y, belonging to a latitude bin l
and x is a placeholder variable for either the raw data, denoted by the
superscript r, or the sampled data, denoted by the superscript s.
Figure shows examples of raw and sampled zonal means for
temperature, O3 and H2O. The difference between the satellite-sampled
zonal mean and the raw zonal mean gives the absolute sampling bias, that is
to say,
January 2005 zonal means as a function of pressure for temperature,
O3 and H2O (top to bottom) in 4∘ latitude bins. Left column is
raw CMAM30-SD model fields; other columns are CMAM30-SD as sampled by MLS,
HALOE and ACE-FTS (left to right), respectively. White regions denote a lack
of measurements.
SA=Zls‾-Zlr‾
or, in percentage,
SP=Zls‾-Zlr‾Zlr‾×100.
Figure shows examples of the sampling biases for temperature,
O3 and H2O for January 2005 CMAM30-SD fields. Relative biases are shown
for trace gas species to accommodate their strong vertical gradients. These
biases only display the impact of sampling the CMAM30-SD fields; as mentioned
before, how well these biases represent the true atmospheric sampling biases
will depend on how close the model fields are to the real atmospheric state.
January 2005 sampling bias as a function of latitude and pressure
for temperature, O3 and H2O (top to bottom) as measured using MLS,
HALOE and ACE-FTS sampling patterns (left to right). White regions denote a
lack of measurements.
For each month, instrument and pressure level, this bias was computed for
all the latitude bins in which an instrument was able to sound the
atmosphere. To summarize the potential sampling biases, we computed
root-mean-square (RMS) biases over 1 year's worth of data. As an example,
Fig. shows these calculated RMS sampling biases for
temperature, O3 and H2O for 2005. Overall, there is a direct
correlation between the sampling biases and the variability of the
geophysical parameters. For example, as noted by , O3
sampling biases for the three instruments are smaller in the tropics and
larger at midlatitudes and in the polar regions, where variability is
low or high, respectively. However, the biases in all regions are minimized by
dense uniform sampling such as that of MLS.
Root-mean-square sampling bias for 2005 as a function of latitude
and pressure for temperature, O3 and H2O (top to bottom) as measured
using MLS, HALOE and ACE-FTS sampling patterns (left to right). White regions
denote a lack of measurements.
Figure shows the mean and maximum sampling biases over all
latitudes for the model year 2005 for all the atmospheric parameters studied.
In general, HALOE and ACE-FTS sampling patterns produce mean and maximum
sampling biases 1 order of magnitude larger than those of MLS. For example,
for the occultation sensors, the temperature maximum sampling biases are
about 10 K compared to 1 K for MLS. Similarly, in the middle stratosphere,
H2O maximum sampling biases for the solar occultation instruments can be
as large as 5 % compared to less than 1 %, and lastly, HNO3
maximum sampling biases can be as large as 50 % compared to less than
5 %.
Long-term trends
We now evaluate the impact of orbital sampling on the representation of
long-term trends. Accurate representation of long-term trends is crucial
because they are indicators of climate change, as well as ozone recovery. To
summarize the effect of the orbital sampling upon long-term trends we use
Taylor diagrams , which provide a convenient method for
visualizing statistics of how closely patterns match each other; in this
case, they are used to depict the success of the satellite-sampled data in
representing the variability found in the raw model fields. The similarity is
quantified by their correlation coefficient, their centered RMS difference
(RMSd) and their standard deviations. Simply, the centered RMSd is the RMS of
the differences between the two anomaly time series.
Mean (thin lines) and maximum (thicker lines) RMS sampling bias over
all latitudes for 2005 as a function of pressure for temperature (in Kelvin),
O3, CH3Cl, H2O, CO, HCl, N2O and HNO3 in percent. The vertical
grid indicates values of 0.5, 1, 5, 10, 50 and 100.
Taylor diagrams showing near-global (60∘ S to
60∘ N) long-term (1979–2012) pattern comparisons between the raw
(the reference point at (1,0)) and the satellite-sampled data at different
pressure levels. The green contours indicate the normalized RMS difference
values.
In the diagrams shown, all data are normalized to the raw model standard
deviation to facilitate showing different pressure levels in the same figure.
In these diagrams, there are four things to consider: (1) the azimuth angle
indicates the correlation between the satellite-sampled and raw data; (2) the
point with normalized standard deviation of 1 and correlation of 1 is the
reference point and corresponds to the raw model data; (3) the distance
between any point in the figure and the reference point indicates the ratio
of the centered RMSd and the raw model standard deviation (green contours);
and (4) the distance between other points in the plot and the origin is the
ratio between the satellite-sampled standard deviation and that of the raw
model field.
Near-global (60∘ S–60∘ N) long-term (1979–2012) patterns
are compared between satellite-sampled and raw model fields in
Fig. for all the atmospheric parameters
evaluated in this study. Means were computed by averaging all data available
between 60∘ N and 60∘ S with no effort to use only
latitudes where the satellites sampled. This approach was taken to show the
representativeness of near-global patterns. We did not expand this study to
the latitudes poleward of 60∘ N or 60∘ S because ACE-FTS
does not sample these areas for 4 months per calendar year and HALOE does
not sample for 5 and 6 months at the South and North Pole, respectively (see
Fig. ). Figure shows the raw model
standard deviations used to normalize these diagrams (black lines). Overall,
the MLS-sampled data (circles in Fig. ) for all
variables and all pressure levels are close to the reference point,
indicating high correlation coefficients, low centered RMSd and the expected
standard deviation (i.e., a standard deviation similar to that of the full
model fields). The HALOE-sampled data (triangles) show intermediate
performance, followed by the ACE-FTS-sampled data (squares), which show the
weakest correlation and the largest normalized standard deviation. For
example, this is easily seen in the CO Taylor diagram, where the MLS-sampled
points all cluster tightly at the reference point, whereas HALOE-sampled
points lie farther away and ACE-FTS-sampled points the farthest.
Raw model standard deviations used to normalize the Taylor diagrams
shown in Figs. and
. Note that H2O and CO are shown using a
logarithmic scale.
Time series of near-global (60∘ S to 60∘ N)
temperature at 10 hPa for the raw and satellite-sampled data (gray lines).
Orange lines display the trend computed using the linear fit, green lines
show the climatological seasonal cycle imposed upon a long-term trend and
light blue lines show the model computed using Eq. (). The
trend (K decade-1) computed using each method is specified in each
subplot; in brackets we show the percentage difference in trend magnitude
with respect to the trend found using the raw model data.
To highlight the impact of these sampling differences,
Fig. shows trend estimates for near-global
temperature at 10 hPa using the raw and satellite-sampled data. Three
methods have been used to compute the trends. The first is a simple linear
fit (an ordinary least square regression) through the points. In the second,
we deseasonalize the data (we remove the observed climatological monthly mean
at every grid point) before computing a linear fit. Lastly, we consider a
trend model of the form
Y=μ+ωt12+S+N,
where Y is the monthly raw or sampled average measurements (temperature,
CO or O3 concentration, etc.), μ is a baseline constant, ω is
the mean trend per year, t is time in months, S is a seasonal mean
component represented by
S=a1sin(2π12t+b1)+a2sin(2π6t+b2),
and N is the unexplained portion of the data assumed to follow a first-order autoregressive model [AR(1)]. That is, it satisfies
N=ϕN1+ε,
where ϕ is the autocorrelation of the noise, computed and assumed
temporally invariant, following , and ε is independent white noise variables with variance σε2. As
pointed out by , ϕ has the effect of reducing the amount
of information that would have been available in the same number of
independent data points. Similar models have been used in many previous trend
studies e.g..
As shown in Fig. , HALOE (ACE-FTS) sampling
artificially reduces (increases) the trend estimates by about 10 %
(25 %). Despite agreement on the sign of the trend, these
sampling-induced artifacts will compromise the robustness of the derived
temperature trends. We computed the trend using different methods in order to
emphasize that using models that are more geophysically realistic, such as
those that capture the seasonal component, may not have much impact on the
estimated trends or, as pointed out by , on the trend
statistical properties.
Figure shows how these sampling-induced trend
artifacts vary with altitude. To avoid clutter, this figure only shows the
differences in trend magnitude computed using Eq. (), but
the ones computed using the other trend detection methods are similar. We
show results for temperature, O3 and CO because these parameters exhibit
clear trends at most pressure levels in the CMAM30-SD simulations and also
because overall they can be accurately described by the model given by
Eq. (). For O3 we only use data starting from 2000 to
capture the expected period of O3 recovery. Overall, MLS sampling allows the estimation of the trend magnitudes to about 1 order of magnitude better than
HALOE and ACE-FTS sampling, with accuracy better than 1 % at most
pressure levels for temperature and CO and better than 10 % for O3.
Left: near-global (60∘ S to 60∘ N) long-term
(1979–2012) trends computed using Eq. () for temperature,
O3 and CO. Middle: percentage difference in the inferred magnitude of the
trends when computed using various satellite-sampled data with respect to the
one computed using the raw model fields. Right: number of years required to
detect such trends.
As Fig. but for 30 to 60∘ N.
Figure also shows the estimated number of years
required to definitively detect these trends. When using
Eq. (), the number of years, n*, needed to detect a
given trend with a 95 % confidence level with a probability of 0.90 can be
approximated by n*=3.3σN|ω|1+ϕ1-ϕ2/3,
which indicates that trend detectability depends on three factors: (1) ϕ
the autocorrelation of the residual between the data points and the trend
model computed following ; (2) σN the standard
deviation of the residual, which corresponds to the unexplained variability
of the data; and (3) the absolute magnitude of the trend. It is also noted
that σN is related to σε by
σN2=σε21-ϕ2
in this trend model (Eq. ). Note that ϕ was computed
for the raw as well as the satellite-sampled data. As shown in
Fig. , trend detection using data with HALOE or
ACE-FTS sampling will require considerably more years than using data with
MLS sampling. This is due to an increase in the magnitude of σN
resulting from the noisiness of the time series based on the HALOE or ACE-FTS
sampling patterns (e.g., Fig. ). For example,
at 1 hPa, the pressure level where the strongest temperature trend is found
in CMAM30-SD, a 15-year record of MLS-sampled observations would be required
to detect such a trend at the 95 % confidence level, while HALOE and
ACE-FTS sampling would require 25 and 30 years, respectively. For O3 at
2 hPa, the pressure level where the strongest O3 trend is found in
CMAM30-SD, the MLS sampling pattern would require about 11 years, while HALOE
and ACE-FTS would require about 20 and 30 years, respectively. In addition,
MLS sampling requires the same number of years as for the raw model fields;
that is, the required number of years is only determined by the natural
variability. We also performed this analysis using only the autocorrelation
computed for the raw model data and found no significant differences.
We also investigated the effect of instrument noise, using
and :
σN=σε21-ϕ2+σI2nI,
where σI is the instrument noise and nI is the
number of measurements averaged. Typical noise estimates were taken from
for MLS; , and
for ACE-FTS; and and
for HALOE. Since HALOE does not measure CO, we assumed the same error as
given by for ACE-FTS. The effect of instrument noise was
found to be negligible due to the high number of measurements even for HALOE
and ACE-FTS (in a given month, around 70 000 for MLS, 600 for HALOE and 270
for ACE-FTS). These estimates of the length of the measurement record
required to detect trends do not take into account the effects of a
disruption of the measurements for a given period or aging of the instrument,
both of which can induce artificial trends in the data that are not
representative of the actual environmental trend studied.
As Fig. but for 30 to 60∘ N.
Both HALOE and ACE-FTS provide better coverage in the extratropics than in
the tropics (see Fig. ).
Figure therefore shows long-term pattern
comparisons between satellite-sampled and raw data for trends derived using
only data from 30 to 60∘ N. Figure also shows the
raw model standard deviations used to normalize these diagrams (purple
lines). In general, HALOE- and ACE-FTS-sampled data correlation coefficients
improved considerably over the near-global case, with a correlation coefficient
no smaller than ∼ 0.6 and with a centered RMSd better than 1 raw model
standard deviation (see Fig. ). MLS-sampled data
are still closest to the reference point. Two variables can have similar
trends but still perform poorly in Taylor diagrams due to either a lack of
correlation or different standard deviations. In both cases, this will impact
σN, resulting in an increase in the number of years required to
statistically detect such a trend.
Figure is equivalent to
Fig. but for the 30 to 60∘ N latitude
range. As for the near-global trends, ACE-FTS sampling still requires
considerably more years to confidently detect a trend than does MLS sampling.
HALOE, however, has a more uniform sampling density than ACE-FTS in this
latitude range (see Fig. ), and thus the time required
to detect a trend is more in line with that for MLS. Nevertheless, MLS
sampling allows estimation of trends to about 1 order of magnitude better
than HALOE and ACE-FTS sampling. As before, the effect of instrument noise
was found to be negligible (for this latitude range the approximate number of
measurements in a given month is 19 000, 220 and 70 for MLS, HALOE and
ACE-FTS, respectively).
As shown, the ability to detect trends depends upon the natural variability
and the correlation of the data. These in turn vary with the specific
parameter as well as the location and height being studied. Studies of
natural variability and autocorrelation of the data will help identify where
to monitor to find more readily detectable trends, but such a study is
outside the scope of this paper.
The atmospheric tape recorder (zonal mean water vapor anomalies in
the tropics, in this case for CMAM30-SD raw model fields) displays a clear
signal of the large-scale upward transport as indicated by the arrow. The
slope of this arrow, which is derived from the propagation speed of the water
vapor anomalies, represents the average tropical upwelling velocity for
8∘ S–8∘ N. This subset of years is shown as an example;
other years are similar.
Tropical vertical velocities
In this section we investigate the impact of orbital sampling upon derived
tropical vertical velocities (a key metric for atmospheric circulation). The
vertical velocities are calculated using the same approach as described by
and . In short, we use time series of
daily zonal mean water vapor data averaged between 8∘ S and
8∘ N (see Fig. ). We correlate these time series at
different pressure levels and determine the time lag for the best
correlation. The vertical velocity for the midpoint of each layer is simply
computed by dividing the distance between the pressure levels (the altitude
difference) by the lag. These calculations were performed using the raw model
CMAM30-SD simulations as well as the satellite-sampled data. The vertical
velocities derived from this method are a measure of the transport velocity
averaged over 8∘ S–8∘ N and have been shown to agree well
with the transformed Eulerian mean residual vertical velocity when in-mixing
from the extratropics and vertical diffusion are small .
Interpolation was used to fill the data gaps due to the sampling patterns. In
the case of HALOE sampling, this implies linearly interpolating to fill gaps
in June and December. For ACE-FTS, gaps are filled in January, March, May,
July, September, November and December, when no measurements are made over
the tropics (8∘ S to 8∘ N); thus, we are applying the
analysis to highly interpolated data. Considering the degree of interpolation
required, we do not recommend the use of ACE-FTS to derive tropical upwelling
velocities, but we include this case merely as an illustrative example.
Top: wTR (monthly vertical velocities) derived using
daily time correlations of the water vapor tape recorder at different
pressure levels from the raw CMAM30-SD data as well as the satellite-sampled
data. wTR derived using the raw model fields and MLS-sampled
data are almost identical. The pressure levels averaged are 30, 40, 50 and
60 hPa. Bottom: wTR scatterplots (mm s-1) for
MLS, HALOE and ACE-FTS sampling, respectively, vs. the velocities derived
using raw model fields. The slopes' 95 % confidence intervals are ±
0.007, 0.06 and 0.09 for MLS, HALOE and ACE-FTS, respectively.
(a) Time series of wTR (mean monthly vertical
velocities averaged over 30, 40, 50 and 60 hPa) derived using CMAM30-SD raw
data (black), the quasi-biennial oscillation (QBO) shear index (QSI – purple)
and the multivariate ENSO index (MEI - orange dashed line). (b) Time
series of wTR for the raw model fields (black) as well as the
model fit described by Eq. () (gray). (c–e) Time
series of wTR for satellite-sampled data (color coded). The
thin black line displays the same wTR derived using raw model
fields (black line in b) for ease of comparison with the
satellite-sampled ones. The model fit for each of the satellite-sampled
wTR values, described by Eq. (), is shown in gray
for each of these time series.
Figure (top) shows the vertical velocities averaged over
60–30 hPa derived using raw model fields as well as the satellite-sampled
data. To quantify the impact of the different orbital sampling patterns,
Fig. (bottom) displays scatterplots between the raw
fields and the satellite-sampled vertical velocities. The best correlation
(R=1.00), the best line fit (1.07 x+ 0.02, obtained using an
ordinary least squares fit regression) and the smallest RMSd (0.005) are
found when using the MLS sampling. ACE-FTS and HALOE sampling lead to
non-negligible artifacts when deriving vertical velocities from the tape
recorder.
Previous studies have shown variability in middle stratospheric tropical
vertical velocities on the order of up to ±40 % associated with the
QBO and ENSO . To better
understand the impact of these sampling-induced artifacts, we fit the
following model to the monthly vertical velocities
wTR=q⋅QSI[t-tq]+e⋅MEI[t-te]+c,
where wTR is the vertical velocity derived from the tape
recorder, QSI is a QBO shear index, MEI is the
multivariate ENSO index, c is a baseline constant, q and e are
constants modifying the magnitude of the QSI or MEI, and tq and te are
the QSI or MEI time offsets, respectively. The QSI is calculated from the
difference in the Singapore zonal winds at 50 and 25 hPa
. The MEI is determined using a combination of the
principal component analysis of sea level pressure, sea surface temperature,
zonal and meridional surface winds, surface air temperature, and cloudiness as
described by .
Figure a shows the time series of the QSI and the
MEI, along with the vertical velocities averaged over 60–30 hPa derived
using raw model fields. As can be seen, these vertical velocities are clearly
correlated with the QSI but also show a strong relationship with the MEI in
some years. Figure b–e displays the results of
fitting the model described by Eq. () to the raw (panel b) and
satellite-sampled (panels c–e) derived vertical velocities. The time offsets
were fitted using the raw model fields and then imposed onto the
satellite-sampled data. We do not fit a modeled seasonal cycle, such as the
one described by Eq. (), because the methodology used
suppresses the seasonal cycle . As shown, this model is able
to capture most of the variability in the derived vertical velocities. The
fits are primarily driven by the QSI, with MLS sampling overestimating its
influence by 3.8 % (the differences in q in the equations shown in
Fig. ), while HALOE and ACE-FTS sampling
underestimate it by 30.7 and 31.5 %, respectively. The impact of the
sampling is more pronounced for the MEI (the differences in e), with MLS,
HALOE and ACE-FTS underestimating its influence by 11, 64 and 122 %. We
emphasize that these sampling-induced offsets to the strength of the
modulation effects of the QBO and ENSO on the circulation are only applicable
to CMAM30-SD fields. These fields may not accurately represent the
stratospheric tropical vertical velocities and, consequently, the actual
sampling offsets could be different. As such, they should be considered only
as potential biases.
The changes in tropical upwelling associated with QBO and ENSO assessed here
have been shown to alter O3 transport to the midlatitude lower
stratosphere and to account for approximately half the interannual
variability in midlatitude tropospheric O3. It has been
hypothesized that this observed relationship between stratospheric upwelling
changes and changes in tropospheric O3 may provide an emergent constraint
on the tropospheric O3 response to long-term strengthening of the
circulation associated with greenhouse gas increases. If so, accurate
quantification of the variability in tropical vertical velocities is crucial
to reducing uncertainties in estimating this response.
Summary
In this paper we evaluate the effect of orbital sampling on satellite
measurements of stratospheric temperature and several trace gases. In
particular, we quantify the impact of sampling in terms of the sampling bias.
To illustrate the impact of orbital sampling on the outcome of representative
atmospheric studies, we also quantify the induced differences in the inferred
magnitude of trends and their detectability, as well as the induced
differences in derived tropical vertical velocities. We calculate these
sampling-induced artifacts by interpolating CMAM30-SD model fields (used as a
proxy for the real atmosphere) to the real sampling patterns of three
satellite instruments – Aura MLS, HALOE and ACE-FTS – to allow us to
compare a dense uniform sampling pattern characteristic of limb emission
sounders to the coarse nonuniform sampling patterns characteristic of solar
occultation instruments.
The results suggest that overall
coarse nonuniform sampling patterns, such as the ones from HALOE and
ACE-FTS, can introduce sampling biases about 1 order of magnitude greater
than those from dense uniform sampling patterns, such as the one from MLS.
For example, we found a temperature maximum sampling bias of about 10 K
compared to 1 K and H2O maximum sampling biases as large as 5 % as
opposed to less than 1 % in the middle stratosphere. These results
corroborate the results of and .
dense uniform sampling patterns accurately reproduce the magnitude of
the model trends with only small errors. Records based on such sampling
patterns will require the same number of years as when using the raw model
fields, that is to say, trend detection is limited only by the natural
variability. In contrast, coarse nonuniform sampling patterns may introduce
non-negligible errors to the inferred magnitude of trends, with considerably
more years of data thus required to conclusively detect a given trend. This
is because the sparse nonuniform sampling leads to an increase in the
standard deviation of the total noise in the time series. For example, for
near-global temperature trends (60∘ S–60∘ N) at 10 hPa,
HALOE and ACE-FTS sampling patterns artificially bias the trend estimates by
about -10 and 25 %, respectively. Also, at 1 hPa, the pressure level at which the strongest temperature trend was found in CMAM30-SD, an MLS sampling
pattern will require 15 years to detect this particular trend, while the
HALOE and ACE-FTS sampling will require 25 and 30 years, respectively.
coarse nonuniform sampling patterns may lead to an over- or underestimation
of the modulation effects of the controlling mechanisms of the tropical
vertical velocities. For example, with respect to CMAM30-SD estimates, HALOE
and ACE-FTS sampling patterns underestimate the QBO modulation strength by
30.7 and 31.5 %, and the ENSO modulation strength by 64 and 122 %,
respectively. Dense uniform sampling patterns are considerably better suited
to deriving tropical vertical velocities; for example, MLS sampling only
overestimates the QBO influence by 3.8 % and underestimates the ENSO
influence by 11 %.
Stratospheric changes such as a possible increase in the circulation and
trends in temperature and O3 are signatures of greenhouse gas warming and
stratospheric O3 recovery. Thus, our ability to accurately measure these
changes is crucial for detecting anthropogenic influences on climate.
Data availability
All the data used in this study are publicly available. CMAM30-SD fields can
be found in the Canadian Centre for Climate Modelling and Analysis webpage
(http://www.cccma.ec.gc.ca/data/cmam/output/CMAM/CMAM30-SD/index.shtml).
MLS data are available from the NASA Goddard Space Flight Center Earth
Sciences (GES) Data and Information Services Center
(http://disc.sci.gsfc.nasa.gov/Aura/data-holdings/MLS/index.shtml).
HALOE data are available from the HALOE GATS webpage
(http://haloe.gats-inc.com/download/index.php). ACE-FTS data are
available from the ACE Public Datasets webpage
(http://www.ace.uwaterloo.ca/public.html).
Acknowledgements
Work at the Jet Propulsion Laboratory, California Institute of Technology,
was done under contract with the National Aeronautics and Space
Administration. We thank David Plummer of Environment Canada for his
assistance in obtaining the CMAM30-SD dataset.
Edited by: B. Funke Reviewed by: two anonymous referees
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