ALBERT

Description: Tunable laser direct absorption spectroscopy is a widely used technique for the in situ sensing of atmospheric composition. Aircraft deployment poses a challenging operating environment for instruments measuring climatologically relevant gases in the Earth's atmosphere. Here, we demonstrate the successful adaption of a commercially available continuous wave quantum cascade laser (QCL) and interband cascade laser (ICL) based spectrometer for airborne in situ trace gas measurements with a local to regional focus. The instrument measures methane, ethane, carbon dioxide, carbon monoxide, nitrous oxide and water vapor simultaneously, with high 1 s–1σ precision (740 ppt, 205 ppt, 460 ppb, 2.2 ppb, 137 ppt and 16 ppm, respectively) and high frequency (2 Hz). We estimate a total 1 s–1σ uncertainty of 1.85 ppb, 1.6 ppb, 1.0 ppm, 7.0 ppb and 0.8 ppb in CH4, C2H6, CO2, CO and N2O, respectively. The instrument enables simultaneous and continuous observations for all targeted species. Frequent calibration allows for a measurement duty cycle ≥90 %. Custom retrieval software has been implemented and instrument performance is reported for a first field deployment during NASA's Atmospheric Carbon and Transport – America (ACT-America) campaign in fall 2017 over the eastern and central USA. This includes an inter-instrumental comparison with a calibrated cavity ring-down greenhouse gas analyzer (operated by NASA Langley Research Center, Hampton, USA) and periodic flask samples analyzed at the National Oceanic and Atmospheric Administration (NOAA). We demonstrate good agreement of the QCL- and ICL-based instrument to these concurrent observations within the combined measurement uncertainty after correcting for a constant bias. We find that precise knowledge of the δ13C of the working standards and the sampled air is needed to enhance CO2 compatibility when operating on the 2227.604 cm−1 13C16O2 absorption line.

Description: Atmospheric inversions have been used to assess biosphere–atmosphere CO2 surface exchanges at various scales, but variability among inverse flux estimates remains significant, especially at continental scales. Atmospheric transport errors are one of the main contributors to this variability. To characterize transport errors and their spatiotemporal structures, we present an objective method to generate a calibrated ensemble adjusted with meteorological measurements collected across a region, here the upper US Midwest in midsummer. Using multiple model configurations of the Weather Research and Forecasting (WRF) model, we show that a reduced number of simulations (less than 10 members) reproduces the transport error characteristics of a 45-member ensemble while minimizing the size of the ensemble. The large ensemble of 45 members was constructed using different physics parameterization (i.e., land surface models (LSMs), planetary boundary layer (PBL) schemes, cumulus parameterizations and microphysics parameterizations) and meteorological initial/boundary conditions. All the different models were coupled to CO2 fluxes and lateral boundary conditions from CarbonTracker to simulate CO2 mole fractions. Observed meteorological variables critical to inverse flux estimates, PBL wind speed, PBL wind direction and PBL height are used to calibrate our ensemble over the region. Two optimization techniques (i.e., simulated annealing and a genetic algorithm) are used for the selection of the optimal ensemble using the flatness of the rank histograms as the main criterion. We also choose model configurations that minimize the systematic errors (i.e., monthly biases) in the ensemble. We evaluate the impact of transport errors on atmospheric CO2 mole fraction to represent up to 40 % of the model–data mismatch (fraction of the total variance). We conclude that a carefully chosen subset of the physics ensemble can represent the uncertainties in the full ensemble, and that transport ensembles calibrated with relevant meteorological variables provide a promising path forward for improving the treatment of transport uncertainties in atmospheric inverse flux estimates.

Description: This paper presents the physical basis of the Earth Polychromatic Imaging Camera (EPIC) cloud product algorithms and an initial evaluation of their performance. Since June 2015, EPIC has been providing observations of the sunlit side of the Earth with its 10 spectral channels ranging from the UV to the near-infrared. A suite of algorithms has been developed to generate the standard EPIC Level 2 cloud products that include cloud mask, cloud effective pressure/height, and cloud optical thickness. The EPIC cloud mask adopts the threshold method and utilizes multichannel observations and ratios as tests. Cloud effective pressure/height is derived with observations from the O2 A-band (780 and 764 nm) and B-band (680 and 688 nm) pairs. The EPIC cloud optical thickness retrieval adopts a single-channel approach in which the 780 and 680 nm channels are used for retrievals over ocean and over land, respectively. Comparison with co-located cloud retrievals from geosynchronous earth orbit (GEO) and low earth orbit (LEO) satellites shows that the EPIC cloud product algorithms are performing well and are consistent with theoretical expectations. These products are publicly available at the Atmospheric Science Data Center at the NASA Langley Research Center for climate studies and for generating other geophysical products that require cloud properties as input.

Description: Sarnia, Ontario, experiences pollutant emissions disproportionate to its relatively small size. The small size of the city limits traditional top-down emission estimate techniques (e.g., satellite) but a low-cost solution for emission monitoring is the mobile MAX-DOAS (Multi-AXis Differential Optical Absorption Spectroscopy). Measurements were made using this technique from 21 March 2017 to 23 March 2017 along various driving routes to retrieve vertical column densities (VCDs) of NO2 and SO2 and to estimate emissions of NOx and SO2 from the Sarnia region. A novel aspect of the current study was the installation of a NOx analyzer in the vehicle to allow real time measurement and characterization of near-surface NOx∕NO2 ratios across the urban plumes, allowing improved accuracy of NOx emission estimates. Confidence in the use of near-surface-measured NOx∕NO2 ratios for estimation of NOx emissions was increased by relatively well-mixed boundary layer conditions. These conditions were indicated by similar temporal trends in NO2 VCDs and mixing ratios when measurements were sufficiently distant from the sources. Leighton ratios within transported plumes indicated peroxy radicals were likely disturbing the NO–NO2–O3 photostationary state through VOC (volatile organic compound) oxidation. The average lower-limit emission estimate of NOx from Sarnia was 1.60±0.34 t h−1 using local 10 m elevation wind-speed measurements. Our estimates were larger than the downscaled annual 2017 NPRI-reported (National Pollution Release Inventory) industrial emissions of 0.9 t NOx h−1. Our lower-limit estimate of SO2 emissions from Sarnia was 1.81±0.83 t SO2 h−1, equal within uncertainty to the 2017 NPRI downscaled value of 1.85 t SO2 h−1. Satellite-derived NO2 VCDs over Sarnia from the ozone monitoring instrument (OMI) were lower than mobile MAX-DOAS VCDs, likely due to the large pixel size relative to the city's size. The results of this study support the utility of the mobile MAX-DOAS method for estimating NOx and SO2 emissions in relatively small, highly industrialized regions, especially when supplemented with mobile NOx measurements.

Description: Vertical profiles of aerosols, NO2, and SO2 were retrieved from Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements at a field site in northern Alberta, Canada, during August and September 2013. The site is approximately 16 km north of two mining operations that are major sources of industrial pollution in the Athabasca Oil Sands Region. Pollution conditions during the study ranged from atmospheric background conditions to heavily polluted with elevated plumes, according to meteorology. This study aimed to evaluate the performance of the aerosol and trace gas retrievals through comparison with data from a suite of other instruments. Comparisons of AODs from MAX-DOAS aerosol retrievals, lidar vertical profiles of aerosol extinction, and AERONET sun photometer indicate good performance by the MAX-DOAS retrievals. These comparisons and modelling of the lidar S-ratio highlight the need for accurate knowledge of the temporal variation in the S-ratio when comparing MAX-DOAS and lidar data. Comparisons of MAX-DOAS NO2 and SO2 retrievals to Pandora spectral sun photometer VCDs and Active-DOAS mixing ratios indicate good performance of the retrievals except when vertical profiles of pollutants within the boundary layer varied rapidly, temporally and spatially. Near-surface retrievals tended to overestimate Active-DOAS mixing ratios. The MAX-DOAS observed elevated pollution plumes not observed by the Active-DOAS, highlighting one of the instrument's main advantages. Aircraft measurements of SO2 were used to validate retrieved vertical profiles of SO2. Advantages of the MAX-DOAS instrument include increasing sensitivity towards the surface and the ability to simultaneously retrieve vertical profiles of aerosols and trace gases without requiring additional parameters such as the S-ratio. This complex dataset provided a rare opportunity to evaluate the performance of the MAX-DOAS retrievals under varying atmospheric conditions.

Description: Terrestrial photosynthesis is the basis for vegetation growth and drives the land carbon cycle. Accurately simulating gross primary production (GPP, ecosystem-level apparent photosynthesis) is key for satellite monitoring and Earth System Model predictions under climate change. While robust models exist for describing leaf-level photosynthesis, predictions diverge due to uncertain photosynthetic traits and parameters which vary on multiple spatial and temporal scales. Here, we describe and evaluate a gross primary production (GPP, photosynthesis per unit ground area) model, the P-model, that combines the Farquhar-von Caemmerer-Berry model for C3 photosynthesis with an optimality principle for the carbon assimilation-transpiration trade-off, and predicts a multi-day average light use efficiency (LUE) for any climate and C3 vegetation type. The model is forced here with satellite data for the fraction of absorbed photosynthetically active radiation and site-specific meteorological data and is evaluated against GPP estimates from a globally distributed network of ecosystem flux measurements. Although the P-model requires relatively few inputs and prescribed parameters, the R2 for predicted versus observed GPP based on the full model setup is 0.75 (8-day mean, 131 sites) – better than some state-of-the-art satellite data-driven light use efficiency models. The R2 is reduced to 0.69 when not accounting for the reduction in quantum yield at low temperatures and effects of low soil moisture on LUE. The R2 for the P-model-predicted LUE is 0.37 (means by site) and 0.53 (means by vegetation type). The P-model provides a simple but powerful method for predicting – rather than prescribing – light use efficiency and simulating terrestrial photosythesis across a wide range of conditions. The model is available as an R package (rpmodel).

Description: To recover the actual responsivity for the Ultraviolet Multi-Filter Rotating Shadowband Radiometer (UV-MFRSR), the complex (e.g., unstable, noisy, and with gaps) time series of its in situ calibration factors (V0) need to be smoothed. Many smoothing techniques require accurate input uncertainty of the time series. A new method is proposed to estimate the dynamic input uncertainty by examining overall variation and subgroup means within a moving time window. Using this calculated dynamic input uncertainty within Gaussian process (GP) regression provides the mean and uncertainty functions of the time series. This proposed GP solution was first applied to a synthetic signal and showed significantly smaller RMSEs than a Gaussian process regression performed with constant values of input uncertainty and the mean function. GP was then applied to three UV-MFRSR V0 time series at three ground sites. The method appropriately accounted for variation in slopes, noises, and gaps at all sites. The validation results at the three test sites (i.e., HI02 at Mauna Loa, Hawaii; IL02 at Bondville, Illinois; and OK02 at Billings, Oklahoma) demonstrated that the agreement among aerosol optical depths (AODs) at the 368 nm channel calculated using V0 determined by the GP mean function and the equivalent AERONET AODs were consistently better than those calculated using V0 from standard techniques (e.g., moving average). For example, the average AOD biases of the GP method (0.0036 and 0.0032) are much lower than those of the moving average method (0.0119 and 0.0119) at IL02 and OK02, respectively. The GP method's absolute differences between UV-MFRSR and AERONET AOD values are approximately 4.5 %, 21.6 %, and 16.0 % lower than those of the moving average method at HI02, IL02, and OK02, respectively. The improved accuracy of in situ UVMRP V0 values suggests the GP solution is a robust technique for accurate analysis of complex time series and may be applicable to other fields.

Description: The Montreal Protocol, and its subsequent amendments, has successfully prevented catastrophic losses of stratospheric ozone, and signs of recovery are now evident. Nevertheless, recent work has suggested that ozone in the lower stratosphere (95 %, 30∘ S–30∘ N) decreases dominate the quasi-global integrated decrease (99 % probability); the integrated tropical stratospheric column (1–100 hPa, 30∘ S–30∘ N) displays a significant overall ozone decrease, with 95 % probability. These decreases do not reveal an inefficacy of the Montreal Protocol; rather, they suggest that other effects are at work, mainly dynamical variability on long or short timescales, counteracting the positive effects of the Montreal Protocol on stratospheric ozone recovery. We demonstrate that large interannual midlatitude (30–60∘) variations, such as the 2017 resurgence, are driven by non-linear quasi-biennial oscillation (QBO) phase-dependent seasonal variability. However, this variability is not represented in current regression analyses. To understand if observed lower stratospheric ozone decreases are a transient or long-term phenomenon, progress needs to be made in accounting for this dynamically driven variability.

Description: As natural gas extraction and use continues to increase, the need to quantify emissions of methane (CH4), a powerful greenhouse gas, has grown. Large discrepancies in Indianapolis CH4 emissions have been observed when comparing inventory, aircraft mass-balance, and tower inverse modeling estimates. Four years of continuous CH4 mole fraction observations from a network of nine tower-based cavity ring-down spectrometers measuring atmospheric CH4 mole fractions at 39 to 136 m above ground as part of the Indianapolis Flux Experiment (INFLUX) are utilized to investigate four possible reasons for the abovementioned inconsistencies: (1) differences in definition of the city domain, (2) a highly temporally variable and spatially non-uniform CH4 background, (3) temporal variability in CH4 emissions, and (4) the presence of unknown CH4 sources. Reducing the Indianapolis urban domain size to be consistent with the inventory domain size decreases the CH4 emission estimation of the inverse modeling methodology by about 35 % and thereby lessens the discrepancy by bringing total city flux within an error range of one of the inventories. Nevertheless, the inverse modeling estimate still remains about 40 % higher than the inventory value. Hourly urban background CH4 mole fractions are shown to be heterogeneous and temporally variable. Statistically significant, long-term biases in background mole fractions of 2–5 ppb are found from single point observations from most wind directions. Random errors in single point background mole fractions observed for a few hours are 20–30 ppb, but decrease substantially when data are averaged over multiple days. Boundary layer budget estimates suggest that Indianapolis CH4 emissions did not change significantly when comparing 2014 to 2016. However, it appears that CH4 emissions may follow a diurnal cycle with daytime emissions (12–16 LST) approximately twice as large as nighttime emissions (20–5 LST). The strongest CH4 source in Indianapolis is the South Side Landfill. Other point sources, perhaps leaks from the natural gas distribution system, are localized and transient, and do not appear to be a consistently large source of CH4 emissions in Indianapolis. Long-term averaging, spatially-extensive upwind mole fraction observations, mesoscale atmospheric modeling of the regional emissions environment, and careful treatment of the times of day and areal representation of emission estimates is recommended for precise and accurate quantification of urban CH4 emissions.

Description: The Montreal Protocol has successfully prevented catastrophic losses of stratospheric ozone, and signs of recovery are now evident. Nevertheless, recent work suggests that ozone in the lower stratosphere ( 95 %, 30° S–30° N) decreases dominate the quasi-global integrated decrease (99 % probability); the integrated tropical stratospheric column (1–100 hPa, 30° S–30° N) displays a significant overall decrease, with 95 % probability. These decreases do not reveal an inefficacy of the Montreal Protocol. Rather, they suggest other effects to be at work, mainly dynamical variability on long or short timescale, counteracting the protocol's regulation of halogenated ozone depleting substances (hODS). We demonstrate that large inter-annual mid-latitude variations (30° –60° ), such as the 2017 resurgence, are driven by non-linear QBO phase-dependent seasonal variability. However, this variability is not represented in current regression analyses. To understand if observed lower stratospheric decreases are a transient or long-term phenomenon, progress needs to be made in accounting for this dynamically-driven variability.