1. INTRODUCTION

Absorption spectroscopy over open paths provides a means of remotely sensing changes in greenhouse gas mole fractions—a critical need for greenhouse gas transport, source, and sink studies as well as future regulatory monitoring [1–3]. It is implemented in satellite instruments, upward-looking Fourier transform spectrometers (FTS), and ground-based FTS and laser spectrometers [4–11]. Ideally, these systems should detect the dry-air mole fractions (which correct for variable dilution by water vapor) of multiple gases over long paths with high precision and reproducibility to enable mapping of small gradients in both space and time. However, absorption spectroscopy faces two distinct challenges. First, spectral databases required to convert absorption to concentrations cannot support the desired reproducibility between instruments of 0.1 ppm ${\mathrm{CO}}_2$ and 2 ppb ${\mathrm{CH}}_4$ for background greenhouse gas monitoring [12], and less than 1 ppm ${\mathrm{CO}}_2$ for urban enhancement monitoring [13–15]. Second, there is a lack of portable, long-path, multigas sensors with high reproducibility to support regional monitoring. Portable FTS is limited to subkilometer paths and $\sim 5\%–10\%$ uncertainty because of divergent sources and broad instrument lineshapes [10,16]. Therefore, regional studies use flushed-cell point sensors calibrated via a reference gas [17,18]. In contrast, accurate open-air path systems could provide continuous path-averaged mole fractions that avoid representation errors associated with point sensors [3,19].

Dual frequency-comb spectroscopy (DCS) is a promising solution to both challenges. DCS [20–31] has broadband spectral coverage for multispecies detection, a bright diffraction-limited source for high signal-to-noise ratio (SNR) over multikilometer ranges, a rapid update rate for immunity to turbulence-induced optical intensity fluctuations, and, importantly, can sample the transmission on a comb tooth-by-tooth basis for high-accuracy spectra. Here, we show that the full advantages of DCS can be applied to quantitative outdoor sensing of greenhouse gases. Our measured spectra span 80,000 comb teeth covering 5990 to $6260{\mathrm{cm}}^{-1}$ (1600 to 1670 nm) with absorbance noise below $5\times {10}^{-4}$. Data are acquired at the comb-tooth separation of $0.0033{\mathrm{cm}}^{-1}$ (100 MHz), with negligible instrument lineshape since the comb teeth are essentially delta functions in frequency. We demonstrate simultaneous retrieval of dry-air mole fractions of ${\mathrm{CO}}_2$, ${\mathrm{CH}}_4$, ${\mathrm{H}}_2\mathrm{O}$, HDO, and ${}^{13}{\mathrm{CO}}_2$ and air temperature over a 2 km turbulent air path. During well-mixed atmospheric conditions, these data enable high-resolution evaluation of spectral absorption models and, when combined with laboratory measurements, should lead to improved spectral absorption models critical for open long-path remote sensing [13,32].

Moreover, the advent of portable frequency combs [33] should enable field-deployable DCS to support verification and monitoring of emissions of distributed sources (e.g., carbon sequestration [11] and gas development sites [2,34]) and larger-scale monitoring networks. As an initial demonstration, time-resolved dry-air mole fractions were acquired continuously over three days. The DCS data compare well with a nearby point sensor for large-scale fluctuations with much lower sensitivity to local concentration spikes. One-sigma stabilities of $<1\mathrm{ppm}$ ($\mu \text{mol}/\text{mol}$) for ${\mathrm{CO}}_2$ and $<3\mathrm{ppb}$ (nmol/mol) for ${\mathrm{CH}}_4$ are reached at 5 min averaging. Absolute agreement is limited by the current spectral databases to $\sim 1\%–2\%$ and by variability in sampled regions. Future optimized systems with higher power and extended spectral coverage [29,35–40] could reach similar stabilities in seconds, over 10 km, while sensing additional species, isotopologues, and oxygen [28]. Finally, the absence of instrument lineshapes should enable direct cross comparison of retrievals between systems, times, and locations.

2. OPEN-AIR DUAL-COMB SPECTROMETER

Figure 1 shows the experimental setup. In dual-comb spectroscopy [20–30], two frequency combs are arranged to have offset repetition rates ($f_r$ and $f_r+\mathrm{\Delta }{f}_r$). When combined, the resulting heterodyne signal is an rf frequency comb, where each rf comb tooth is spaced by $\mathrm{\Delta }{f}_r$, and has a one-to-one relationship with a specific pair of optical comb teeth [see Fig. 1(a)]. Therefore, this rf spectrum is simply scaled to reproduce the optical spectrum.

 

Fig. 1. Open-air path greenhouse gas sensing through dual-comb spectroscopy. (a) DCS concept: two combs with slightly different tooth spacing interfere on a detector, giving a third rf comb with a one-to-one mapping to the optical comb teeth. (The actual experiment spans $\sim {10}^5$ comb teeth.) (b) Experimental setup: two combs are amplified, pulse-compressed in large mode area (LMA) fiber, spectrally broadened in highly nonlinear fiber (HNLF), and filtered to generate light covering the spectral bands of interest. Two fibers carry the comb light to the rooftop, where the light is combined and launched in a $\sim 40\mathrm{mm}$ beam ($1/e^2$ diameter), and reflected from a 50 cm diameter plane mirror located 1 km distant. The return light is coupled to a multimode fiber and detected. The transmitted light power was limited to 1.5 mW so that the maximum received power was always below a conservative detector nonlinearity threshold of 50 μW average power (500 fJ pulse energy). (c) Location of the 2 km interrogation path (red line, ground projection represented by black line), the tower with the point sensor intake (inset), and elevation of the beam path (bottom inset). (d) Example transmitted intensity showing the smoothly varying comb intensity and abrupt dips due to absorption. (e) Expanded view of absorption features. The typical gas absorption lines have $\sim 40$ teeth across each $\sim 4\mathrm{GHz}$ wide line (top, several transitions from the $2\nu 3$ level of the ${\mathrm{CH}}_4$ tetradecad; bottom, R20 transition of the $30013\leftarrow 00001$ band of ${\mathrm{CO}}_2$).

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Here, we implement DCS with two mutually coherent erbium-doped fiber frequency combs ($f_r\sim 100\mathrm{MHz}$, $\mathrm{\Delta }{f}_r=444\mathrm{Hz}$) with relative $\sim 1\mathrm{Hz}$ comb linewidth and with each comb tooth’s frequency known to better than 1 kHz [23] (see Supplement 1). The comb spectra are centered within the atmospheric water-vapor window near 1.6 μm and further shaped to cover both a portion of the ${\mathrm{CH}}_4$ tetradecad and a ${\mathrm{CO}}_2$ combination band. The choice of $\mathrm{\Delta }{f}_r=444\mathrm{Hz}$ allows an alias-free optical bandwidth of $f_r^2/(2\mathrm{\Delta }{f}_r)=375{\mathrm{cm}}^{-1}$, which covers both absorption bands simultaneously [22]. The comb outputs are combined and transmitted over a 2 km folded open-air path on the NIST Boulder campus [see Figs. 1(b) and 1(c)]. This 2 km path length exceeds previous laboratory-based DCS using either multipass cells or resonant cavities.

Figures 1(d) and 1(e) show the resulting high SNR transmission spectrum acquired over $\sim 170\min$ under relatively constant temperature and pressure across the measurement path (see Supplement 1). The overall shape corresponds to the spectrally filtered comb light. The stronger ${\mathrm{CO}}_2$, ${\mathrm{H}}_2\mathrm{O}$, and ${\mathrm{CH}}_4$ absorption lines appear as sharp dips of up to 15% with many weaker ${\mathrm{CH}}_4$, ${\mathrm{CO}}_2$, and ${\mathrm{H}}_2\mathrm{O}$, HDO, and ${}^{13}{\mathrm{CO}}_2$ lines observed down to ${10}^{-3}$ absorbance. There are $\sim 80,000$ comb-tooth pairs across the $267{\mathrm{cm}}^{-1}$ window. With the spectral shaping, about half of these, or $\sim 40,000$ comb-tooth pairs, compose the measured spectrum. Each comb-tooth pair contributes a distinct data point in the transmitted spectrum with kilohertz-level frequency uncertainty (corresponding to a resolving power of ${10}^{11}$), spaced at $f_r=100\mathrm{MHz}$ ($0.0033{\mathrm{cm}}^{-1}$), and with SNR in the signal intensity exceeding $3000:1$ for these long time-averaged data. Even higher SNR values would be possible except that we aggressively limited the transmitted power to avoid any lineshape distortions due to detector nonlinearity [23,29]. These data were acquired with coherent summing (see Supplement 1), but continuous time data confirmed $\sim \mathrm{Hz}$ linewidth between the detected pairs of frequency-comb teeth. The quality of these data is consistent with previous ultrahigh-resolution laboratory DCS spectra and demonstrates that the fundamental properties of coherent DCS—namely high resolution, high accuracy, broad bandwidth, and high SNR—can be directly translated to field-based measurements.

Turbulence is a concern for high-resolution open-air path spectroscopy as it can easily cause strong (100%) and fast ($>100\mathrm{Hz}$) optical intensity modulation, potentially leading to excess noise in the measured optical transmission spectrum. The power spectral density related to turbulence-induced intensity noise falls off strongly as $f^{-8/3}$ beyond the characteristic cutoff Fourier frequency, $f_c\approx U/\sqrt{2\pi L\lambda }$, where $U$ is the wind speed, $\lambda$ is the optical wavelength, and L is the path length [41]. Here, $f_c$ is tens of hertz. In comparison, the DCS effectively acquires a single spectrum within $1/\mathrm{\Delta }{f}_r=1/444\mathrm{s}$. In other words, since $f_c<\mathrm{\Delta }{f}_r$, the turbulence intensity noise is effectively frozen during a single spectrum. Rain, light fog, or clipping of the beam at the telescope will reduce the SNR if there is significant attenuation, but should not distort the spectrum for similar reasons. Of course, some turbulence-induced fluctuations do occur on the timescale of a single interferogram, but a more rigorous discussion (see Fig. 1 of Supplement 1) shows they appear as multiplicative noise that is below the overall noise floor. Turbulence can also cause optical wavefront distortions and phase noise on the comb lines [41,42]. However, since both combs are copropagating, these effects are common mode and ultimately negligible. This relative immunity of DCS to turbulence is in strong contrast to high-resolution FTS or conventional swept laser spectroscopy, which have longer acquisition periods. Finally, as a coherent system, DCS is unaffected by collected sunlight because of the narrow heterodyne detection bandwidth.

3. MEASUREMENTS UNDER WELL-MIXED ATMOSPHERIC CONDITIONS

The DCS spectra provide a direct means to validate current and future spectral databases and absorption models since the spectra are free from instrument distortions and the DCS horizontal path avoids the atmospheric modeling that is required with up-looking total column measurements [7,14]. The transmission spectrum shown in Fig. 1(d) is converted to absorbance by normalizing out the overall comb spectrum and applying Beer’s law. Initially, we acquired a reference spectrum without the air path, but this approach introduced additional baseline etalons. Therefore, we instead normalized out the smoothly varying comb spectrum through piecewise baseline fitting (see Supplement 1), as is done for FTIR spectra. The resulting absorbance spectrum, shown in Fig. 2, can then be fit using different absorption models to both assess those models and find the path-averaged dry-air mole fractions of various greenhouse gas species.

We separately fit the lower (6050 to $6120{\mathrm{cm}}^{-1}$) and upper (6180 to $6260{\mathrm{cm}}^{-1}$) spectral windows. Within a spectral window, we fit the entire absorption spectrum at once. The only inputs to the fits are the measured atmospheric pressure (from calibrated pressure gauges at each end of the path) and the absorption models. The fitted parameters are the overall gas concentrations of ${\mathrm{CO}}_2$, ${}^{13}{\mathrm{CO}}_2$, ${\mathrm{H}}_2\mathrm{O}$, ${\mathrm{CH}}_4$, and HDO, and temperature. The fit to the upper spectral window includes a fit for temperature based on the band-wide ${\mathrm{CO}}_2$ absorption; in this way the path-averaged temperature is extracted directly instead of using colocated temperature sensors that can suffer from solar loading. The ${\mathrm{CO}}_2$ and ${}^{13}{\mathrm{CO}}_2$ mole fractions were extracted from the fit to the upper spectral region, while the ${\mathrm{CH}}_4$, ${\mathrm{H}}_2\mathrm{O}$, and HDO mole fractions were extracted from the fit to the lower spectral region (although there are ${\mathrm{CO}}_2$ lines present as well). More than 300 spectral lines are included in the lower spectral window and 400 in the upper spectral window.

Absorption models for species in this region are evolving. The models consist of a set of spectral parameters that describe the temperature-, pressure-, and concentration-dependent strength, location, and width of each absorption feature, along with a lineshape profile model. Figure 2 shows the results of fits using the High-Resolution Transmission Molecular Absorption Database (HITRAN) 2012 [43] and 2008 [44]. The standard deviation of the residuals is $\sim 2\times {10}^{-3}$ absorbance units for 5 min averages at the 100 MHz ($0.0033{\mathrm{cm}}^{-1}$) point spacing, dropping to $<5\times {10}^{-4}$ at 170 min. When scaled to the same resolution, this SNR is comparable to high-resolution solar, up-looking FTS spectra (but in a more compact, potentially portable instrument package without long, moving interferometer arms). For the upper spectral region, fits using the similar HITRAN 2012 [43] and 2008 [44] databases result in peak residuals below $3\times {10}^{-3}$, except for one errant line near 187.38 THz (coincident with a reported weak HDO line in HITRAN 2008). For the lower spectral region, HITRAN 2012 has methane parameters quite different from HITRAN 2008, and this difference is strongly reflected in the residuals, as well as the concentrations as discussed in Table 1.

 

Fig. 2. Baseline-corrected absorbance from the transmission spectrum of Fig. 1(d). (a) The left spectrum mainly comprises ${\mathrm{CH}}_4$ and ${\mathrm{H}}_2\mathrm{O}$ lines, with some weaker ${\mathrm{CO}}_2$ lines, while the right spectrum shows the $30013\leftarrow 00001$ ${\mathrm{CO}}_2$ band, ${\mathrm{CO}}_2$ hot bands, and ${\mathrm{H}}_2\mathrm{O}$, ${}^{13}{\mathrm{CO}}_2$, and HDO features. The data (red line), fit with HITRAN 2008 (blue line, indistinguishable from the data), and residuals (upper blue line) are shown. There are $\sim 40,000$ data points, or comb teeth, spaced at 100 MHz across the spectral windows. (b) Expanded view of a few representative lines along with residuals for fits to the HITRAN 2008 model [44], the HITRAN 2012 model [43], the line-mixing and speed-dependent model of Ref. [14], and line-by-line Voigt fits to each line. Also shown are the fit residuals for a 5 min time average, identical to those used in the time-resolved data of Fig. 3. The noise per comb tooth (in absorbance units) for the 5 min averaged spectra is $2.0\times {10}^{-3}$, $2.7\times {10}^{-3}$, and $1.7\times {10}^{-3}$ for the three windows shown, with the differences attributable to different spectral intensities. For the 170 min average, the noise decreases with the square root of time, as expected, to $3.5\times {10}^{-4}$, $5.1\times {10}^{-4}$, and $3.0\times {10}^{-4}$, respectively. However, there are clear residuals near the spectral lines due to mismatch between the measured lineshapes and absorption models.

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Table 1. Mole Fractions Retrieved from Fits of the Absorbance Spectrum of Fig. 2 with Four Absorption Models: HITRAN 2008 [44], HITRAN 2012 [43], Line-Mixing Speed-dependent Voigt (LM/SD) [14], and Toth et al. [48]^a

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The Voigt profile used with the HITRAN databases [43,44] neglects, among other things, coupling between energy states of nearby transitions (line mixing) and the effect of collisions on the Doppler contribution to the lineshape (speed dependence). Therefore, concentrations extracted using the Voigt profiles have been shown to lead to inaccurate atmospheric retrievals [45]. Figure 2(b) also shows residuals to a fit using a lineshape model and a corresponding spectral parameter database that includes line-mixing and speed-dependent Voigt profiles and was developed to support accurate satellite-based trace gas monitoring [14,46,47]. Residuals remain, with quite different structures compared to the other models; however, the fitted concentration using the line-mixing and speed-dependent model should have the highest accuracy.

Figure 2(b) also contains residuals resulting from a line-by-line Voigt profile fit where the collisional linewidth, line center, and line strength are finally allowed to vary on a line-by-line basis. Though this fit overlooks the quantum-mechanical basis of the previous spectral databases, the small residual may indicate that the largest sources of error in absorption models are the spectral parameters (line strength, line center, broadening coefficients, etc.) rather than deviations from the Voigt lineshape model.

Table 1 compares the mole fractions extracted using the different absorption models. There is a significant model-dependent spread. For ${\mathrm{CO}}_2$, all four absorption models rely on analysis of the same underlying laboratory FTS data, so this spread emphasizes the consequences of different lineshape models. As mentioned above, among the models considered here the line-mixing, speed-dependent Voigt profile (LM/SD) and corresponding spectral parameter database [14,46,47] is expected to yield the most accurate results for ${\mathrm{CO}}_2$. Table 1 also reports the DCS systematic uncertainty excluding the model-dependent effects. The uncertainty is based on the root-mean-square of the sensitivities of the retrieved mole fractions to several different factors: maximum pressure and temperature path inhomogeneities ($\pm 500\mathrm{Pa}$ and 6 K, respectively), uncertainty in path length ($\pm 20\mathrm{cm}$) and air pressure ($\pm 30\mathrm{Pa}$), and baseline correction. Baseline correction is the largest contributor (by a factor of 10 or more) in particular due to an etalon ripple with absorbance amplitude ${10}^{-3}$ (see Supplement 1). For ${\mathrm{CO}}_2$, the other factors contribute below 0.06 ppm uncertainty.

Table 2 compares the DCS to the tower-mounted point sensor. For ${\mathrm{CO}}_2$, there is 1.8% offset with the LM/SD fits, which increases to 2.8% for the HITRAN 2008 fits. Under the windy, well-mixed atmospheric conditions for these data, the DCS and tower-mounted point sensor should measure almost identical mole fractions. Given that the point sensor is calibrated directly against the World Meteorological Organization (WMO) reference gas (see Supplement 1), we attribute most of the offset to the DCS retrieval and specifically to the line strengths of the absorption model. For ${\mathrm{CH}}_4$, the DCS analyzed with the HITRAN 2008 database is in excellent agreement with the tower sensor (although the fits to HITRAN 2012 exhibit a 5.7% offset from the tower sensor). For ${\mathrm{H}}_2\mathrm{O}$, the two systems agree to within the uncertainty of the tower sensor.

Table 2. Comparison of the Mole Fractions Obtained with the Dual-Comb Spectrometer and the Tower-Mounted Point Sensor under the Well-Mixed, Stable Atmospheric Conditions of Fig. 2^a

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The main conclusion—that better absorption models are needed to support accurate greenhouse gas monitoring—very much echoes the significant body of work in support of satellite measurements. Subpercent uncertainties in retrieved gas concentrations will require improvements in the spectral database, possibly through laboratory frequency-comb or cavity ring-down systems [23,29,49,50]. Finally, while DCS does rely on accurate spectral databases (as with any open-air path absorption technique), it should be straightforward to reanalyze DCS spectra as the databases are refined. In fact, this feature of the dual-comb spectra is important for accurate greenhouse gas monitoring. Whereas extractive flushed-cell sampling instruments rely on reference gas calibrations that must be performed periodically to maintain accuracy and cross-instrument comparability, the measured dual-comb spectra from multiple instruments can be compared directly and indefinitely. Therefore, the retrieved mole fractions can be similarly compared when the spectra are fit with an accurate, bias-free absorption model. For example, as water-broadening coefficients for ${\mathrm{CO}}_2$ and ${\mathrm{CH}}_4$ become available, these effects can be included without the empirical corrections needed with flushed-cell point sensors [18].

4. TIME-RESOLVED MEASUREMENTS OF THE DRY-AIR MOLE FRACTIONS OVER A THREE-DAY PERIOD

The data of Fig. 2 were acquired over a windy, well-mixed period in which the mole fractions were quite stable. Normally, the mole fractions will vary significantly from nearby sources and sinks, and as the planetary boundary-layer height changes. We can analyze these time variations using the same fitting procedures described above. Figure 3 shows the results over a three-day period at 5 min averaging, analyzed with the HITRAN 2008 spectral database. The path-averaged air temperature is extracted directly from the fit to the $30013\leftarrow 00001$ ${\mathrm{CO}}_2$ spectral band, placing even greater reliance on accurate spectral parameters.

 

Fig. 3. Time-resolved mole fractions. (a) Time dependence of the dry-air mole fraction for ${\mathrm{CO}}_2$ (green), ${\mathrm{CH}}_4$ (light blue), and water (dark blue) retrieved from the DCS and the corresponding values from the tower-mounted point sensor (black line) at 5 min periods over three days. (b) Time dependence of ${\mathrm{CO}}_2$ and ${\mathrm{CH}}_4$ with an expanded y axis. Also shown are the time dependence of HDO (pink) and the retrieved path-averaged temperature (orange). All data are from the fit with the absorption model based on HITRAN 2008. The DCS ${\mathrm{CO}}_2$ mole fraction has been scaled to remove the 2.8% offset between the DCS and the tower-mounted sensor over the initial $\sim 3\mathrm{h}$ period (shaded region) where the atmosphere was well-mixed and the measurement path was upwind from the NIST central utility plant.

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During periods of wind and daytime thermal turbulence, the dry-air mole fractions reported by both the DCS and the tower-mounted point sensor are relatively flat. During low wind and nighttime boundary-layer settling, both show strong variations in time. However, as expected, the point sensor is much more susceptible to short spikes from local plumes. (For comparison purposes, the tower-sensor data are averaged to 5 min here; at shorter timescales the spikes are even more pronounced.) Moreover, statistically significant offsets are common between the point sensor and the path-averaged DCS results, which is not surprising given the presence of localized emissions from vehicles, the NIST central utility plant (just south of the air path), and a nearby roadway. The comparison emphasizes the quantitative differences that can arise between a single point sensor and a path-averaged system. One expects the path-averaged system to connect more directly to kilometer-scale regional transport models. Moreover, with the addition of multiple reflectors the same DCS system could interrogate multiple displaced paths [11], providing even greater utility to regional models.

The overall sensitivity, or precision, of the DCS is calculated directly as the Allan deviation over a period of relative stability, as shown in Fig. 4. The optimal averaging time period depends on the timescale of the atmospheric fluctuations; we selected 5 min for Fig. 3, which also leads to a precision comparable to the systematic uncertainty (Table 2). However, operation over longer distances, at the full eye-safe power level of 9.6 mW, or at longer wavelengths where the absorption is tenfold stronger, will all dramatically improve the precision. In addition, stronger absorption lines will reduce the systematic uncertainty that is dominated by baseline ripple (etalons).

 

Fig. 4. Precision (Allan deviation) from data acquired during high-wind periods with a stable, well-mixed atmosphere. (a) ${\mathrm{CO}}_2$ precision (green) versus averaging time, $\tau$, which follows $15\sqrt{\tau }/\mathrm{ppm}$, where $\tau$ is in seconds (gray line), or 0.86 ppm at 5 min. At longer times, actual atmospheric mole fraction variations cause a decrease in the slope. A modest increase of the launched power up to the eye-safe limit of 9.6 mW (or alternatively an increased receive aperture) would yield the improved precision of $3\sqrt{\tau }/\mathrm{ppm}$ (dashed gray line). (b) ${\mathrm{CH}}_4$ precision (blue) follows a scaling of $40\sqrt{\tau }/\mathrm{ppb}$ (gray line) or 2.3 ppb at 5 min. Again, modest power increases, or increased receive aperture, would yield the improved precision of $8\sqrt{\tau }/\mathrm{ppb}$ (dashed gray line).

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5. CONCLUSION

We demonstrate that DCS is ideally suited to the challenges associated with accurate sensing of atmospheric trace gases across open-air paths; it combines broadband spectral coverage for accurate multispecies detection with a diffraction-limited laser output for high sensitivity over multikilometer air paths. Equally important, its high acquisition speed achieves immunity to turbulence-induced intensity noise, and its negligible instrument lineshape enables high accuracy and ultimately accurate cross comparison of spectra (and therefore concentrations) acquired with different systems, at different times and locations. We demonstrate these capabilities over a 2 km open-air path with an initial system that measures ${\mathrm{CO}}_2$, ${}^{13}{\mathrm{CO}}_2$, ${\mathrm{CH}}_4$, ${\mathrm{H}}_2\mathrm{O}$, HDO, and air temperature. Future broader bandwidth systems will detect more species, while higher power output will further improve the sensitivity. DCS data can support the development of accurate absorption models used in global, satellite-based greenhouse gas monitoring. Moreover, with the recent advancement of portable, high-performance frequency combs [33], there is no technological barrier to regional deployment of fielded DCS systems that have costs comparable to high-performance point sensors and are capable of autonomous, eye-safe, around-the-clock monitoring of multiple gas species over multiple optical paths.