To evaluate landscape N 2 O fluxes in the North Slope, the Flux Observations of Carbon from an Airborne Laboratory (FOCAL) system (shown in Fig. 1) was flown out of Deadhorse Airport, Prudhoe Bay, AK. Although its name comes from its ability to measure CO 2 and CH 4 fluxes, it can also simultaneously measure N 2 O flux. Measurements were made over five separate flights in several regions of the North Slope from 25 to 28 August 2013. The measurements entailed a cumulative path length of 884 km and approximate area coverage of 310 km2 (Fig. 2, Table 1). Flights consisted of straight flight tracks between 25 and 50 km long, some of which were upwind of a CH 4 ∕H 2 O EC flux tower. For each flight, the flux calculations were restricted to straight segments flown below 50 m a.g.l. For the present study, segment sections over the open ocean were also excised.

The low-flying aircraft flown in the campaign, a Diamond DA42 from Aurora Flight Sciences, housed the two main components for flux measurements (Fig. 1): a turbulence probe and a custom-built IR spectrometer measuring water vapor, CH 4 , and N 2 O at a rate of 10 times per second (10 Hz). These two components were used to measure the EC fluxes of N 2 O, CH 4 , and H 2 O during the 2013 campaign.

The flights near the flux tower were performed to compare the airborne CH 4 and H 2 O flux measurements with those from the EC flux tower (Dobosy et al., 2017). The CH 4 and H 2 O fluxes agreed with the ground measurement, and the CH 4 fluxes are consistent with other observed summertime permafrost CH 4 emissions reported in the scientific literature (see Sayres et al., 2017). The only difference in the airborne flux measurements between CH 4 , H 2 O, and N 2 O is the particular absorption feature used within the observed spectral region of the IR instrument, as further discussed in Sect. 2.2 (Fig. 3).

2.1 BAT probe description and calibration The three wind components were measured using the Best Airborne Turbulence (BAT) probe developed by the National Oceanic and Atmospheric Administration/Atmospheric Turbulence and Diffusion Division (NOAA/ATDD) in collaboration with Airborne Research Australia (Crawford et al., 1993; Dobosy et al., 2013). The BAT probe also recorded ambient temperature and pressure measurements, which were used to determine dry-air density. The aircraft was equipped with a radar altimeter, which in conjunction with three-component wind velocity measurements, was used for footprint calculations. These calculations were performed over 60 m segments along the flight track (Sayres et al., 2017). The footprints, representing the area from which the observed fluxes originated, were used to estimate the total area measured and identify which land classes were measured (Table 1). The BAT probe, developed in the 1990s, is a type of gust probe consisting of a hemispherical head, 15.5 cm in diameter, with ports at selected positions on the hemisphere to sample the pressure distribution. A gust probe functions similarly to a typical pitot-static system but includes additional pressure measurements to sense the direction of the incoming flow along with its speed. The direction is specified in two perpendicular components called angles of attack and sideslip, which rarely exceed ± 10 ∘ in balanced flight (Leise et al., 2013). The BAT probe differs from other gust probes in that it has a larger head to accommodate accelerometers and pressure sensors directly in the head, simplifying the physical and mathematical system needed to determine turbulent wind. It also has nine ports instead of the usual five found in traditional gust-probe systems. These additional four ports measure the ambient atmospheric pressure apart from small adjustments for nonzero attack and sideslip angles. Wind is sampled at 1000 Hz, filtered to control aliasing, and subsampled at 50 Hz. The BAT probe configured for the FOCAL campaign (with the gas inlets in place) was characterized in a wind tunnel (Dobosy et al., 2013) following on from an earlier wind-tunnel test of a similar unit in Indiana, USA (Garman et al., 2006). Its overall precision for wind is ±0.1 m s−1. With the entire instrument system assembled, standard-practice calibration maneuvers were flown in smooth air to establish the values of the tuning parameters for temperature, pressure, and wind measurement (Vellinga et al., 2013). Following the usual practice, we also made a calibration flight in smooth air on August 27 toward the end of the campaign (Sayres et al., 2017). Plots and comparison of spectra, cospectra, and time series for each flight provide tests of the quality of the data and of the processing through all intermediate steps.

2.2 N 2 O instrument description and calibration The gas inlet for the N 2 O instrument is located on the BAT probe housing, 8 cm aft of the probe's hemispherical face, where ambient pressure and temperature measurements are made. The custom-built IR instrument uses off-axis integrated cavity output spectroscopy (OA-ICOS) to simultaneously measure H 2 O, CH 4 , and N 2 O (Figs. 4 and S1 in the Supplement). The light source is a distributed feedback (DFB) continuous-wave quantum cascade laser (QCL) (Hamamatsu, LC0349). The laser tunes from 1292.5 to 1293.3 cm−1 in 1.6 milliseconds. This region contains absorption features for H 2 O, CH 4 , and N 2 O (Fig. 3). Before the light enters the optical cavity, a beam-splitter diverts some of it through a Ge etalon. The etalon measures the rate at which the laser is tuning across the wavelength region, which is used to determine the width of the absorption lines. These components are all housed in the laser pressure vessel (Healy, 2016). Download The detection cell is a 25 cm length optical cavity composed of two high-reflectivity ZnSe mirrors (LohnStar Optics, R=0.9996), which creates an effective path length of ∼625 m. After leaving the cavity, the light enters the detector pressure vessel where it is focused onto a Stirling-cooled HgCdTe photoconductive detector (InfraRed Associates, Inc., MCT-12-2.05C). The detector system samples the light at 100 MHz and averages the readings to produce raw spectra with 1900 samples each. These spectra are then co-added to produce 1 spectrum every 0.1 s and are stored on the flight computer. Sample flow through the optical cavity is maintained in flight with a dry scroll pump that flushes the cell 17 times per second. The optical cavity is temperature- and pressure-controlled to T = 303.70 ± 0.05 K and p = 59.26 ± 0.01 Torr to allow conversion from concentration (moles cm−3) to mixing ratio. The cell temperature is measured by averaging the output of two 1 MΩ thermistors (General Electric, Type B) located within the cell. These were calibrated against a platinum primary standard. The cell is heated by polyimide thermofoil heaters, which are located along the cell exterior. The cell pressure is measured with a dual-headed absolute pressure transducer (MKS, D27D) and is controlled by a proportional solenoid valve. The valve is coupled with a pressure control board that uses the pressure transducer as feedback on the valve orifice's position (Fig. 1a) (Healy, 2016). Measurement of H 2 O was calibrated using a dry-air tank coupled with a bubbler flow system as described in Weinstock et al. (2009). The H 2 O measurements were used to account for dilution and water-broadening effects on the N 2 O absorption feature and to convert the mixing ratio from moles per mole of total air to moles per mole of dry air for flux computation (Webb et al., 1980; Gu et al., 2012). The broadening coefficients were determined using the approach described in Rella (2010). Periodic in-flight calibrations were performed to track and correct for drift over the course of the flight (two calibration cycles per flight). These were performed using a secondary standard (277 ppbv N 2 O) calibrated in the lab to a WMO standard (Sayres et al., 2017). Before and after the campaign, calibrations were also conducted in the lab using two primary WMO standards and a synthetic air tank (containing no N 2 O) to calibrate the absorption coefficient and check for linearity. The short-term precision of the ICOS instrument for N 2 O mixing ratios is determined using (1) σ = σ 1 s f s - 1 / 2 , where σ 1 s is the 1 s standard deviation for in-flight calibration data collected during that particular flight, and f s is the sampling frequency in Hz (Kroon et al., 2007). Optical alignment was occasionally adjusted between flight days resulting in an N 2 O precision range over the five flights of σ=0.27–0.58 ppb Hz - 1 / 2 (Table S1 in the Supplement). This is close to the recommended precision for N 2 O EC flux measurements as determined by previous studies evaluating the application of the EC technique to this particular trace gas; these groups also used QCL spectroscopy to measure N 2 O mixing ratios (Kroon et al., 2007; Eugster et al., 2007).

2.3 Airborne EC flux calculations The airborne EC method relies on the fact that gases like H 2 O, CH 4 , and N 2 O emitted from the surface are transported upward into the atmospheric boundary layer by turbulent eddies. On average, upward flux occurs when updrafts are, more often than not, enriched in the transported gas relative to downdrafts. Thus, the covariance of vertical wind velocity with gas concentration is positive (negative) for upward (downward) flux. To determine the covariance between vertical wind velocity w and N 2 O mixing ratios c, we first separate each variable into changes associated with large-scale air motion (i.e., advection) and small-scale air motion, i.e., turbulence (e.g., w = w ‾ + w ′ ). We separated the two scales by fitting fourth-order polynomials to the measurements of w and c made along each individual straight leg of each flight (Fig. 2). The fit itself incorporates the larger-scale trends (e.g., w ‾ ), which are subtracted from the data. The remaining residuals from this fit are the turbulent quantities of interest (e.g., w′) (Foken, 2008). By multiplying w by the density of dry air ρ d and extracting the residual as discussed above, one obtains the turbulent dry-air mass flux. The covariance of this dry-air flux (ρ d w)′ with the turbulent mixing ratio c′ then yields the trace-gas flux of interest by the general EC approach (Webb et al., 1980; Gu et al., 2012): (2) F = ( ρ d w ) ′ c ′ ‾ . As previously mentioned, airborne EC measurements average over space instead of time. Accordingly, we compute the N 2 O fluxes (along with CH 4 and H 2 O fluxes) using the general equation for airborne EC flux calculations: (3) F = ∑ k = 1 N ρ d w ′ k c ′ k V k ∑ k = 1 N V k , where V is the airspeed of the aircraft, and the other variables are defined as in Eq. (2) (Sayres et al., 2017; Dobosy et al., 2017). The true airspeed V = d l / d t is included in Eq. (3) to convert the variable of integration from time to space because the raw data are recorded at uniformly spaced time intervals (every 0.1 s) (Crawford et al., 1993). A number N of samples is averaged, with the denominator yielding their cumulative path length through the air. Air density and vertical wind velocity w from the BAT probe are filtered and then subsampled at 10 Hz to match the measurement frequency for c N 2 O , c CH 4 , and c H 2 O . Because the BAT probe observes a specific packet of air before the spectrometer does, a correction for the lag is applied to the data. The lag time from the gas inlet to the optical cavity was measured in the laboratory to be around 0.55 s. The lag between the BAT-probe measurements and those of the ICOS instrument in flight were determined by a cross-correlation analysis of w and c CH 4 . They varied between 0.4 and 1.2 s. Methane was used as a proxy for N 2 O to determine the lag because of its stronger signal. Computation of dry-air density uses the measured dry-air mixing ratio of H 2 O. Turbulent quantities required for the footprint model are then computed by summing Eq. (3) over each flight segment. The mean N 2 O fluxes displayed in Table 2 are computed by summing Eq. (3) over the multiple segments of each flight depicted in Fig. 2, excluding flight-path sections over coastal waters.

2.4 Flux uncertainty analysis All confidence intervals reported in Tables 2 and S1 are derived using bootstrap resampling (Dobosy et al., 2017), not from w and c N 2 O individually but from flux fragments (Sayres et al., 2017; Dobosy et al., 2017). These fragments are short (typically 1 s) blocks of integrated data that include the integrals of the three wind components, the height above ground, and the cross products of turbulent departure quantities from Eq. (3). All are integrated as above – over the path through the air rather than time. They are each about 60 m long and vary slightly due to small airspeed changes. These measurements, and therefore the corresponding confidence intervals, contain both environmental variability and variability arising from instrumental noise. The fragments are serially correlated, and their means, trends, and variances are heterogeneous on scales greater than the 6 km found by ogive analysis to belong to turbulence. A procedure described by Mudelsee (2010) decomposes such partially determined, autocorrelated, and variably spaced data streams using the equation (4) X S = T S + σ S R S . Here X(S) is a random-variable function over the path length S defined at irregular intervals (Mudelsee, 2010). The T(S) and σ(S) are the respective deterministic trend and variance of X(S) for each S, and R(S) is a serially correlated random-variable series with a zero mean and unit variance. The T(S) and σ(S) for the current analysis are estimated as overlapping averages and variances of the measured fragments taken over 6 km, as determined by ogive analysis. They are evaluated at intervals of 1 km along the track and treated as fixed throughout the rest of the process. These larger scales can be treated as determinable from some (mesoscale) model. The serial correlation of the random series R(S) is removed by a first-order Markov model, the inverse of a first-order causal filter (Dobosy et al., 2017). The resulting decorrelated series is (ideally) independent and “weakly” homogeneous (i.e., it has zero mean and unit variance). As such, it is suitable for bootstrap resampling. A resample size of 80 000 random decorrelated sequences, each the same length as the original set of fragments was drawn. The explained portion of the variance was then reapplied to each new resample using a process that is the reverse of the process of its removal; this process provided an ensemble of 80 000 new potential outcomes of the original experiment. The confidence intervals for N 2 O flux were determined from the distribution of this population of reconstituted potential outcomes. A Student's t test was also used to evaluate whether the Pearson correlation coefficient for w′ and c N 2 O ′ , and hence the N 2 O flux, differs significantly from a random (zero) correlation (Eugster and Merbold, 2015). Because the atmospheric data stream is serially correlated, as noted above, the N samples do not represent the total number of independent samples n. The number of independent samples is determined by (5) n ≅ N 1 - ρ 1 1 + ρ 1 , where ρ 1 is the lag-1 autocorrelation coefficient (Eugster and Merbold, 2015). The conclusions of this test are incorporated into Table 2.