Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.

Concentrations of atmospheric carbon dioxide (CO2) have continued to increase whereas atmospheric deposition of sulphur and nitrogen has declined in Europe and the USA during recent decades. Using time series of flux observations from 23 forests distributed throughout Europe and the USA, and generalised mixed models, we found that forest-level net ecosystem production and gross primary production have increased by 1% annually from 1995 to 2011. Statistical models indicated that increasing atmospheric CO2 was the most important factor driving the increasing strength of carbon sinks in these forests. We also found that the reduction of sulphur deposition in Europe and the USA lead to higher recovery in ecosystem respiration than in gross primary production, thus limiting the increase of carbon sequestration. By contrast, trends in climate and nitrogen deposition did not significantly contribute to changing carbon fluxes during the studied period. Our findings support the hypothesis of a general CO2-fertilization effect on vegetation growth and suggest that, so far unknown, sulphur deposition plays a significant role in the carbon balance of forests in industrialized regions. Our results show the need to include the effects of changing atmospheric composition, beyond CO2, to assess future dynamics of carbon-climate feedbacks not currently considered in earth system/climate modelling.

An approach for analysis and modeling of global atmospheric chemistry is developed for application to measurements that provide a tropospheric climatology of those heterogeneously distributed, reactive species that control the loss of methane and the production and loss of ozone. We identify key species (e.g., O3, NOx, HNO3, HNO4, C2H3NO5, H2O, HOOH, CH3OOH, HCHO, CO, CH4, C2H6, acetaldehyde, acetone) and presume that they can be measured simultaneously in air parcels on the scale of a few km horizontally and a few tenths of a km vertically. As a first step, six global models have prepared such climatologies sampled at the modeled resolution for August with emphasis on the vast central Pacific Ocean basin. Objectives of this paper are to identify and characterize differences in model-generated reactivities as well as species covariances that could readily be discriminated with an unbiased climatology. A primary tool is comparison of multidimensional probability densities of key species weighted by the mass of such parcels or frequency of occurrence as well as by the reactivity of the parcels with respect to methane and ozone. The reactivity-weighted probabilities tell us which parcels matter in this case, and this method shows skill in differentiating among the models' chemistry. Testing 100 km scale models with 2 km measurements using these tools also addresses a core question about model resolution and whether fine-scale atmospheric structures matter to the overall ozone and methane budget. A new method enabling these six global chemistry–climate models to ingest an externally sourced climatology and then compute air parcel reactivity is demonstrated. Such an objective climatology containing these key species is anticipated from the NASA Atmospheric Tomography (ATom) aircraft mission (2015–2020), executing profiles over the Pacific and Atlantic Ocean basins. This modeling study addresses a core part of the design of ATom.

Accurate estimation of terrestrial gross primary productivity (GPP) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf-level ChlF was linked with canopy-scale solar-induced chlorophyll fluorescence (SIF) in a temperate deciduous forest at Harvard Forest, Massachusetts, USA. Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R2 = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively; P < 0.0001). We developed a model to estimate GPP from the tower-based measurement of SIF and leaf-level ChlF parameters. The estimation of GPP from this model agreed well with flux tower observations of GPP (R2 = 0.68; P < 0.0001), demonstrating the potential of SIF for modeling GPP. At the leaf scale, we found that leaf Fq’/Fm’, the fraction of absorbed photons that are used for photochemistry for a light-adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopy SIF yield (SIF/APAR, R2 = 0.79; P < 0.0001). We also found that canopy SIF and SIF-derived GPP (GPPSIF) were strongly correlated to leaf-level biochemistry and canopy structure, including chlorophyll content (R2 = 0.65 for canopy GPPSIF and chlorophyll content; P < 0.0001), leaf area index (LAI) (R2 = 0.35 for canopy GPPSIF and LAI; P < 0.0001), and normalized difference vegetation index (NDVI) (R2 = 0.36 for canopy GPPSIF and NDVI; P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.

We estimate the amount of methane (CH4) emitted by the largest dairies in the southern California region by combining measurements from four mobile solar-viewing ground-based spectrometers (EM27/SUN), in situ isotopic 13/12CH4 measurements from a CRDS analyzer (Picarro), and a high-resolution atmospheric transport simulation with a Weather Research and Forecasting model in large-eddy simulation mode (WRF-LES). The remote sensing spectrometers measure the total column-averaged dry-air mole fractions of CH4 and CO2 (XCH4 and XCO2) in the near infrared region, providing information on total emissions of the dairies at Chino. Differences measured between the four EM27/SUN ranged from 0.2 to 22ppb (part per billion) and from 0.7 to 3ppm (part per million) for XCH4 and XCO2, respectively. To assess the fluxes of the dairies, these differential measurements are used in conjunction with the local atmospheric dynamics from wind measurements at two local airports and from the WRF-LES simulations at 111m resolution. Our top-down CH4 emissions derived using the Fourier transform spectrometers (FTS) observations of 1.4 to 4.8ppts-1 are in the low end of previous top-down estimates, consistent with reductions of the dairy farms and urbanization in the domain. However, the wide range of inferred fluxes points to the challenges posed by the heterogeneity of the sources and meteorology. Inverse modeling from WRF-LES is utilized to resolve the spatial distribution of CH4 emissions in the domain. Both the model and the measurements indicate heterogeneous emissions, with contributions from anthropogenic and biogenic sources at Chino. A Bayesian inversion and a Monte Carlo approach are used to provide the CH4 emissions of 2.2 to 3.5ppts-1 at Chino.

We present observations defining (i) the frequency and depth of convective penetration of water into the stratosphere over the United States in summer using the Next-Generation Radar system; (ii) the altitude-dependent distribution of inorganic chlorine established in the same coordinate system as the radar observations; (iii) the high resolution temperature structure in the stratosphere over the United States in summer that resolves spatial and structural variability, including the impact of gravity waves; and (iv) the resulting amplification in the catalytic loss rates of ozone for the dominant halogen, hydrogen, and nitrogen catalytic cycles. The weather radar observations of ∼2,000 storms, on average, each summer that reach the altitude of rapidly increasing available inorganic chlorine, coupled with observed temperatures, portend a risk of initiating rapid heterogeneous catalytic conversion of inorganic chlorine to free radical form on ubiquitous sulfate−water aerosols; this, in turn, engages the element of risk associated with ozone loss in the stratosphere over the central United States in summer based upon the same reaction network that reduces stratospheric ozone over the Arctic. The summertime development of the upper-level anticyclonic flow over the United States, driven by the North American Monsoon, provides a means of retaining convectively injected water, thereby extending the time for catalytic ozone loss over the Great Plains. Trusted decadal forecasts of UV dosage over the United States in summer require understanding the response of this dynamical and photochemical system to increased forcing of the climate by increasing levels of CO2 and CH4.

High-latitude ecosystems have the capacity to release large amounts of carbon dioxide (CO₂) to the atmosphere in response to increasing temperatures, representing a potentially significant positive feedback within the climate system. Here, we combine aircraft and tower observations of atmospheric CO₂ with remote sensing data and meteorological products to derive temporally and spatially resolved year-round CO₂ fluxes across Alaska during 2012–2014. We find that tundra ecosystems were a net source of CO₂ to the atmosphere annually, with especially high rates of respiration during early winter (October through December). Long-term records at Barrow, AK, suggest that CO₂ emission rates from North Slope tundra have increased during the October through December period by 73% ± 11% since 1975, and are correlated with rising summer temperatures. Together, these results imply increasing early winter respiration and net annual emission of CO₂ in Alaska, in response to climate warming. Our results provide evidence that the decadal-scale increase in the amplitude of the CO₂ seasonal cycle may be linked with increasing biogenic emissions in the Arctic, following the growing season. Early winter respiration was not well simulated by the Earth System Models used to forecast future carbon fluxes in recent climate assessments. Therefore, these assessments may underestimate the carbon release from Arctic soils in response to a warming climate.

Tropical forests are characterized by a warm and humid climate (Corlett 2011); however, there is currently little consensus on whether climate change will affect tropical forests. Paleoecological studies show that neotropical vegetation largely persisted after a 3 to 5 °C warming during the Paleocene–Eocene Thermal Maximum (Jaramillo et al 2010). However, this historical warming was short-lived and considerably slower than current warming and future warming predicted for the next century. A survey of the temperatures of broad-leaved forest land cover suggests that climatic warming could have severe consequences for tropical floras (Wright et al 2009). Closed-canopy forests are found in areas with a mean annual temperature below 28 °C, whereas areas with mean temperatures above 28 °C support shrubs and grasses instead of broad-leaved evergreen trees. Given that excessively high temperatures are typically associated with a high evaporative demand and dry climate, the absence of closed-canopy forests in areas with temperatures above 28 °C could also be a consequence of water limitation. This past record and the distribution of tropical forests suggest a temperature limit, and therefore the ecosystem sensitivity to this threshold needs to be further studied.

Identification of biomarkers for early detection of lung cancer (LC) is important, in turn leading to more effective treatment and reduction of mortality. Serological proteome analysis (SERPA) was used to identify proteins around 34 kD as ECH1 and HNRNPA2B1, which had been recognized by serum autoantibody from 25 LC patients. In the validation study, including 90 sera from LC patients and 89 sera from normal individuals, autoantibody to ECH1 achieved an area under the curve (AUC) of 0.799 with sensitivity of 62.2% and specificity of 95.5% in discriminating LC from normal individuals, and showed negative correlation with tumor size (rs = −0.256, p = 0.023). Autoantibody to HNRNPA2B1 performed an AUC of 0.874 with sensitivity of 72.2% and specificity of 95.5%, and showed negative correlation with lymph node metastasis (rs = −0.279, p = 0.012). By using longitudinal preclinical samples, autoantibody to ECH1 showed an AUC of 0.763 with sensitivity of 60.0% and specificity of 89.3% in distinguishing early stage LC from matched normal controls, and elevated autoantibody levels could be detected greater than 2 y before LC diagnosis. ECH1 and HNRNPA2B1 are autoantigens that elicit autoimmune responses in LC and their autoantibody can be the potential biomarkers for the early detection of LC.

Observations of atmospheric trace gases in the tropical upper troposphere (UT), tropical tropopause layer (TTL), and lower stratosphere (LS) require dedicated measurement platforms and instrumentation. Here we present a new limb-scanning Differential Optical Absorption Spectroscopy (DOAS) instrument developed for NASA's Global Hawk (GH) unmanned aerial system and deployed during the Airborne Tropical TRopopause EXperiment (ATTREX). The mini-DOAS system is designed for automatic operation under unpressurized and unheated conditions at 14–18 km altitude, collecting scattered sunlight in three wavelength windows: UV (301–387 nm), visible (410–525 nm), and near infrared (900–1700 nm). A telescope scanning unit allows selection of a viewing angle around the limb, as well as real-time correction of the aircraft pitch. Due to the high altitude, solar reference spectra are measured using diffusors and direct sunlight. The DOAS approach allows retrieval of slant column densities (SCDs) of O3, O4, NO2, and BrO with relative errors similar to other aircraft DOAS systems. Radiative transfer considerations show that the retrieval of trace gas mixing ratios from the observed SCD based on O4 observations, the most common approach for DOAS measurements, is inadequate for high-altitude observations. This is due to the frequent presence of low-altitude clouds, which shift the sensitivity of the O4 SCD into the lower atmosphere and make it highly dependent on cloud coverage. A newly developed technique that constrains the radiative transfer by comparing in situ and DOAS O3 observations overcomes this issue. Extensive sensitivity calculations show that the novel O3-scaling technique allows the retrieval of BrO and NO2 mixing ratios at high accuracies of 0.5 and 15 ppt, respectively. The BrO and NO2 mixing ratios and vertical profiles observed during ATTREX thus provide new insights into ozone and halogen chemistry in the UT, TTL, and LS.

We quantify methane (CH4) emissions in California's San Joaquin Valley (SJV) by using 4 days of aircraft measurements from a field campaign during May–June 2010 together with a Bayesian inversion method and a mass balance approach. For the inversion estimates, we use the FLEXible PARTicle dispersion model (FLEXPART) to establish the source-receptor relationship between sampled atmospheric concentrations and surface fluxes. Our prior CH4 emission estimates are from the California Greenhouse Gas Emissions Measurements (CALGEM) inventory. We use three meteorological configurations to drive FLEXPART and subsequently construct three inversions to analyze the final optimized estimates and their uncertainty (one standard deviation). We conduct May and June inversions independently and derive similar total CH4 emission estimates for the SJV: 135 ± 28 Mg/h in May and 135 ± 19 Mg/h in June. The inversion result is 1.7 times higher than the prior estimate from CALGEM. We also use an independent mass balance approach to estimate CH4 emissions in the northern SJV for one flight when meteorological conditions allowed. The mass balance estimate provides a confirmation of our inversion results, and these two independent estimates of the total CH4 emissions in the SJV are consistent with previous studies. In this study, we provide optimized CH4 emissions estimates at 0.1° horizontal resolution. Using independent spatial information on major CH4 sources, we estimate that livestock contribute 75–77% and oil/gas production contributes 15–18% of the total CH4 emissions in the SJV. Livestock explain most of the discrepancies between the prior and the optimized emissions from our inversion.

The ozone (O3) dry depositional sink and its contribution to observed variability in tropospheric O3 are both poorly understood. Distinguishing O3 uptake through plant stomata versus other pathways is relevant for quantifying the O3 influence on carbon and water cycles. We use a decade of O3, carbon, and energy eddy covariance (EC) fluxes at Harvard Forest to investigate interannual variability (IAV) in O3 deposition velocities (vd,O3). In each month, monthly mean vd,O3 for the highest year is twice that for the lowest. Two independent stomatal conductance estimates, based on either water vapor EC or gross primary productivity, vary little from year to year relative to canopy conductance. We conclude that nonstomatal deposition controls the substantial observed IAV in summertime vd,O3 during the 1990s over this deciduous forest. The absence of obvious relationships between meteorology and vd,O3 implies a need for additional long-term, high-quality measurements and further investigation of nonstomatal mechanisms.

Over the past few decades, a hemlock woolly adelgid (HWA) infestation has significantly affected eastern hemlock (Tsuga canadensis) in the eastern U.S., and warmer winters are expected to promote a continued northward expansion in the future. Here we report a water yield increase due to the HWA infestation in New England, U.S. Since the first observation in 2002, peak growing season evapotranspiration over a hemlock-dominated area has decreased by 24–37% in 2012 and 2013. Over the same time period, the water yield from the study catchment significantly increased as compared to an adjacent catchment with less hemlock cover. The net increase was estimated to be as much as 15.6% of annual water yield in 2014 based on an ecohydrological modeling analysis. This study indicates that the ongoing hemlock decline is also largely altering hydrological regimes in the northeastern U.S.

Old-growth (>100 years old) boreal forests are recognized as having accumulated large inventories of terrestrial carbon, but the magnitude of annual net exchange is not well established. Eddy covariance measurements of carbon dioxide exchange were made at a flux tower at the Northern Old Black Spruce forest in Manitoba, Canada, from 1994 to 2008. We evaluated the uncertainty in estimates of annual net ecosystem production by comparing four methods of filling missing measurement periods. Three of the methods were previously developed by groups at Harvard University, the Fluxnet Canada Research Network, and the Max Planck Institute of Biogeochemistry; and the fourth method used the mean of measurements from the dataset selected for the same time and day. Two methods showed carbon losses near the beginning of the period but all four methods estimated carbon gains in the later years. Individual years and methods ranged ±90 g C m−2 y−1, demonstrating a wide range of estimates if data had been available from only a single year and a single method. When averaged over multiple years, the mean and standard error among methods gave a net forest carbon sink of 29 ± 10 g C m−2 y−1, ranging from 4 to 48 g C m−2 y−1. Only the upper (Mean Data and Max Planck Institute of Biogeochemistry methods) and lower (Harvard method) estimates were significantly different, with the Fluxnet Canada Research Network method being the same as all other methods. There was no difference among methods for the January to August period, or for daytime periods. All methods were statistically different from a carbon-neutral forest suggesting that the Northern Old Black Spruce forest was likely a small carbon sink. Methods that estimated high ecosystem respiration also estimated high gross ecosystem production, and the small net differences could not be easily attributed to differences in flux partitioning. For forests that are close to carbon neutral, the use of several gap-filling methods: increases confidence in conclusions regarding the ecosystem carbon balance.

Terrestrial biosphere carbon dynamics are the most uncertain elements of the global carbon budget. Carbon stocks estimated using spatially extensive remote sensing are crucial in reducing this uncertainty, and using these stocks as initial conditions to biosphere models can improve carbon flux predictions beyond the site level. Yet remote-sensing data are not always consistently available for large regions, so methods assessing carbon uncertainty using data sources in one location may not be transferable to another. This study assesses the use of multiple-source data from lidar, radar, imaging spectroscopy, and national forest inventories to derive forest structure and composition necessary to initialise the Ecosystem Demography model (ED2), and hence evaluate short-term carbon flux uncertainty over Harvard Forest, Massachusetts. ED2 was initialized using forest structure and composition derived from lidar and national forest inventories, radar and national forest inventories, lidar and imaging spectroscopy, and radar and imaging spectroscopy resulting in net ecosystem productivity uncertainty of 26.3%, 41.9%, 19.6%, and 20.2%, respectively, compared to ground-based forest inventory initializations. This study uniquely offers a multitude of methods to estimate forest ecosystem state, with resulting carbon uncertainties, transferable to regions with potentially different data availability. Furthermore, in preparation for satellite radar, lidar, and imaging spectrometer, this study highlights the importance of combining techniques deriving forest structure and composition at different scales, binding regional to potentially global carbon-fluxes with remote sensing, reducing this uncertainty source in global climate models.

Stomatal conductance influences both photosynthesis and transpiration, thereby coupling the carbon and water cycles and affecting surface–atmosphere energy exchange. The environmental response of stomatal conductance has been measured mainly on the leaf scale, and theoretical canopy models are relied on to upscale stomatal conductance for application in terrestrial ecosystem models and climate prediction. Here we estimate stomatal conductance and associated transpiration in a temperate deciduous forest directly on the canopy scale via two independent approaches: (i) from heat and water vapor exchange and (ii) from carbonyl sulfide (OCS) uptake. We use the eddy covariance method to measure the net ecosystem–atmosphere exchange of OCS, and we use a flux-gradient approach to separate canopy OCS uptake from soil OCS uptake. We find that the seasonal and diurnal patterns of canopy stomatal conductance obtained by the two approaches agree (to within ±6 % diurnally), validating both methods. Canopy stomatal conductance increases linearly with above-canopy light intensity (in contrast to the leaf scale, where stomatal conductance shows declining marginal increases) and otherwise depends only on the diffuse light fraction, the canopy-average leaf-to-air water vapor gradient, and the total leaf area. Based on stomatal conductance, we partition evapotranspiration (ET) and find that evaporation increases from 0 to 40 % of ET as the growing season progresses, driven primarily by rising soil temperature and secondarily by rainfall. Counterintuitively, evaporation peaks at the time of year when the soil is dry and the air is moist. Our method of ET partitioning avoids concerns about mismatched scales or measurement types because both ET and transpiration are derived from eddy covariance data. Neither of the two ecosystem models tested predicts the observed dynamics of evaporation or transpiration, indicating that ET partitioning such as that provided here is needed to further model development and improve our understanding of carbon and water cycling.

We report measurements of CH4 (measured in situ by the Harvard University Picarro Cavity Ringdown Spectrometer (HUPCRS) and NOAA Unmanned Aircraft System Chromatograph for Atmospheric Trace Species (UCATS) instruments), O3 (measured in situ by the NOAA dual-beam ultraviolet (UV) photometer), NO2, BrO (remotely detected by spectroscopic UV–visible (UV–vis) limb observations; see the companion paper of Stutz et al., 2016), and of some key brominated source gases in whole-air samples of the Global Hawk Whole Air Sampler (GWAS) instrument within the subtropical lowermost stratosphere (LS) and the tropical upper troposphere (UT) and tropopause layer (TTL). The measurements were performed within the framework of the NASA-ATTREX (National Aeronautics and Space Administration – Airborne Tropical Tropopause Experiment) project from aboard the Global Hawk (GH) during six deployments over the eastern Pacific in early 2013. These measurements are compared with TOMCAT/SLIMCAT (Toulouse Off-line Model of Chemistry And Transport/Single Layer Isentropic Model of Chemistry And Transport) 3-D model simulations, aiming at improvements of our understanding of the bromine budget and photochemistry in the LS, UT, and TTL. Changes in local O3 (and NO2 and BrO) due to transport processes are separated from photochemical processes in intercomparisons of measured and modeled CH4 and O3. After excellent agreement is achieved among measured and simulated CH4 and O3, measured and modeled [NO2] are found to closely agree with  ≤  15 ppt in the TTL (which is the detection limit) and within a typical range of 70 to 170 ppt in the subtropical LS during the daytime. Measured [BrO] ranges between 3 and 9 ppt in the subtropical LS. In the TTL, [BrO] reaches 0.5 ± 0.5 ppt at the bottom (150 hPa∕355 K∕14 km) and up to about 5 ppt at the top (70 hPa∕425 K∕18.5 km; see Fueglistaler et al., 2009 for the definition of the TTL used), in overall good agreement with the model simulations. Depending on the photochemical regime, the TOMCAT∕SLIMCAT simulations tend to slightly underpredict measured BrO for large BrO concentrations, i.e., in the upper TTL and LS. The measured BrO and modeled BrO ∕ Bryinorg ratio is further used to calculate inorganic bromine, Bryinorg. For the TTL (i.e., when [CH4]  ≥  1790 ppb), [Bryinorg] is found to increase from a mean of 2.63 ± 1.04 ppt for potential temperatures (θ) in the range of 350–360 K to 5.11 ± 1.57 ppt for θ  = 390 − 400 K, whereas in the subtropical LS (i.e., when [CH4]  ≤  1790 ppb), it reaches 7.66 ± 2.95 ppt for θ in the range of 390–400 K. Finally, for the eastern Pacific (170–90° W), the TOMCAT/SLIMCAT simulations indicate a net loss of ozone of −0.3 ppbv day−1 at the base of the TTL (θ  =  355 K) and a net production of +1.8 ppbv day−1 in the upper part (θ  =  383 K).

Accurately quantifying the timing and magnitude of respiration and photosynthesis by high-latitude ecosystems is important for understanding how a warming climate influences global carbon cycling. Data-driven estimates of photosynthesis across Arctic regions often rely on satellite-derived enhanced vegetation index (EVI); we find that satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a more direct proxy for photosynthesis. We model Alaskan tundra CO₂ cycling (2012–2014) according to temperature and shortwave radiation and alternately input EVI or SIF to prescribe the annual seasonal cycle of photosynthesis. We find that EVI-based seasonality indicates spring “green-up” to occur 9 days prior to SIF-based estimates, and that SIF-based estimates agree with aircraft and tower measurements of CO₂. Adopting SIF, instead of EVI, for modeling the seasonal cycle of tundra photosynthesis can result in more accurate estimates of growing season duration and net carbon uptake by arctic vegetation.

Methane (CH 4) is the second most important greenhouse gas but its emissions from northern regions is still poorly constrained. In this study, we analyze a subset of in situ CH 4 aircraft observations made over Alaska during the growing seasons of 2012–2014 as part of the Carbon in Arctic Reservoir Vulnerability Experiment (CARVE). Surface CH 4 fluxes are estimated using an atmospheric particle transport model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH 4 in the boundary layer. We estimate that between May and September, 2.1 ± 0.5 Tg, 1.7 ± 0.4 Tg and 2.0 ± 0.3 Tg of CH 4 were emitted from the region of interest for 2012–2014, respectively. The predominant sources of the CH 4 budget were two broadly classed eco-regions within our domain, with CH 4 from the tundra region accounting for over half of the overall budget, despite only representing 18 % of the total surface area. Boreal regions, which cover a large part of the study region, accounted for the remainder of the emissions. Simple multiple linear regression analysis revealed that overall, CH 4 flux were largely driven by soil temperature and elevation. In regions specifically dominated by wetlands, soil temperature and moisture at 10 cm depth were important explanatory variables while in regions that were not wetlands, soil temperature and moisture at 40 cm depth were more important, reflecting the depth at which methanogenesis occurs. Although similar variables have been found in the past to control CH 4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional scale CH 4 budget.

The February–March 2014 deployment of the National Aeronautics and Space Administration (NASA) Airborne Tropical Tropopause Experiment (ATTREX) provided unique in situ measurements in the western Pacific tropical tropopause layer (TTL). Six flights were conducted from Guam with the long-range, high-altitude, unmanned Global Hawk aircraft. The ATTREX Global Hawk payload provided measurements of water vapor, meteorological conditions, cloud properties, tracer and chemical radical concentrations, and radiative fluxes. The campaign was partially coincident with the Convective Transport of Active Species in the Tropics (CONTRAST) and the Coordinated Airborne Studies in the Tropics (CAST) airborne campaigns based in Guam using lower-altitude aircraft (see companion articles in this issue). The ATTREX dataset is being used for investigations of TTL cloud, transport, dynamical, and chemical processes, as well as for evaluation and improvement of global-model representations of TTL processes. The ATTREX data are publicly available online (at https://espoarchive.nasa.gov/).