The phenological response of vegetation to ongoing climate change may have great implications for hydrological regimes in the eastern United States. However, there have been few studies that analyze its resultant effect on catchment discharge dynamics, separating from dominant climatic controls. In this study, we examined the net effect of phenological variations on the long-term and interannual gross primary production (GPP) and evapotranspiration (ET) fluxes in a temperate deciduous forest, as well as on the catchment discharge behavior in a mixed deciduous-conifer forest catchment. First, we calibrated the spring and autumn leaf phenology models for the Harvard Forest in the northeastern United States, where the onsets of greenup and senescence have been significantly advanced and delayed, 10.3 and 6.0 days respectively, over the past two decades (1992–2011). We then integrated the phenology models into a mechanistic watershed ecohydrological model (RHESSys), which improved the interannual and long-term simulations of both the plot-scale daily GPP and ET fluxes and the catchment discharge dynamics. We found that the phenological changes amplified the long-term increases in GPP and ET driven by the climatic controls. In particular, the earlier greenup onsets resulted in increases in annual ET significantly, while the delayed senescence onsets had less influence. Consequently, the earlier greenup onsets reduced stream discharge not only during the growing season but also during the following dormant season due to soil water depletion. This study highlights the importance of understanding vegetation response to ongoing climate change in order to predict the future hydrological nonstationarity in this region.

Understanding, quantifying, and tracking atmospheric methane and emissions is essential for addressing concerns and informing decisions that affect the climate, economy, and human health and safety. Atmospheric methane is a potent greenhouse gas (GHG) that contributes to global warming. While carbon dioxide is by far the dominant cause of the rise in global average temperatures, methane also plays a significant role because it absorbs more energy per unit mass than carbon dioxide does, giving it a disproportionately large effect on global radiative forcing. In addition to contributing to climate change, methane also affects human health as a precursor to ozone pollution in the lower atmosphere.

The East Asian Summer Monsoon driven by temperature and moisture gradients between the Asian continent and the Pacific Ocean, leads to approximately 50% of the annual rainfall in the region across 20–40°N. Due to its increasing scientific and social importance, there have been several previous studies on identification of moisture sources for summer monsoon rainfall over East Asia mainly using Lagrangian or Eulerian atmospheric water vapor models. The major source regions for EASM previously proposed include the North Indian Ocean, South China Sea and North western Pacific. Based on high-precision and high-frequency 6-year measurement records of hydrofluorocarbons (HFCs), here we report a direct evidence of rapid intrusion of warm and moist tropical air mass from the Southern Hemisphere (SH) reaching within a couple of days up to 33°N into East Asia. We further suggest that the combination of direct chemical tracer record and a back-trajectory model with physical meteorological variables helps pave the way to identify moisture sources for monsoon rainfall. A case study for Gosan station (33.25°N, 126.19°E) indicates that the meridional transport of precipitable water from the SH accompanying the southerly/southwesterly flow contributes most significantly to its summer rainfall.

Systematic biases in eddy covariance measurements of net ecosystem-atmosphere carbon dioxide exchange (NEE) are ubiquitous in forests when turbulence is low at night. We propose an alternative to the conventional bias correction, the friction velocity (u*) filter, by hypothesizing that these biases have two separate, concurrent causes: (1) a subcanopy CO2 storage pool that eludes typical storage measurements, creating a turbulence-dependent bias, and (2) advective divergence loss of CO2, creating a turbulence-independent bias. We correct for (1) using a simple parametric model of missing storage (MS). Prior experiments have inferred (2) directly from atmospheric measurements (DRAINO). For sites at which DRAINO experiments have not been performed or are infeasible, we estimate (2) empirically using a PAR-extrapolated advective respiration loss (PEARL) approach. We compare u* filter estimates of advection and NEE to MS-PEARL estimates at one temperate forest and two tropical forest sites. We find that for tropical forests, u* filters can produce a range of extreme NEE estimates, from long-term forest carbon emission to sequestration, that diverge from independent assessments and are not physically sustainable. Our MS model eliminates the dependence of nighttime NEE on u*, consistent with findings from DRAINO studies that nighttime advective losses of CO2 are often not dependent on the strength of turbulence. Our PEARL estimates of mean advective loss agree with available DRAINO measurements. The MS-PEARL correction to long-term NEE produces better agreement with forest inventories at all three sites. Moreover, the correction retains all nighttime eddy covariance data and is therefore more widely applicable than the u* filter approach, which rejects substantial nighttime data—up to 93% at one of the tropical sites. The full MS-PEARL NEE correction is therefore an equally defensible and more practical alternative to the u* filter, but leads to different conclusions about the resulting carbon balance. Our results therefore highlight the need to investigate which approach's underlying hypotheses are more physically realistic.

Methane (CH4) is the second most important greenhouse gas but its emissions from northern regions are still poorly constrained. In this study, we analyze a subset of in situ CH4 aircraft observations made over Alaska during the growing seasons of 2012-2014 as part of the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE). Net surface CH4 fluxes are estimated using a Lagrangian particle dispersion model which quantitatively links surface emissions from Alaska and the western Yukon with observations of enhanced CH4 in the mixed layer. We estimate that between May and September, net CH4 emissions from the region of interest were 2.2 ± 0.5 Tg, 1.9 ± 0.4 Tg, and 2.3 ± 0.6 Tg of CH4 for 2012, 2013, and 2014, respectively. If emissions are only attributed to two biogenic eco-regions within our domain, then tundra regions were the predominant source, 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, CH4 fluxes 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, suggesting deeper methanogenesis in drier soils. Although similar environmental drivers have been found in the past to control CH4 emissions at local scales, this study shows that they can be used to generate a statistical model to estimate the regional-scale net CH4 budget.

Development of greenhouse gas (GHG) emission inventories is necessary for estimating the relative significance of emissions from various sources and evaluating the effects of mitigation efforts. Inventories are developed for specific purposes and can provide emission estimates at many scales, from facility level, to urban, regional, national, and global scales.

This report outlines actions to improve estimates of the amounts of anthropogenic methane emitted in the United States and to improve the utility and usability of methane emission inventories to governments, industries, academia, nongovernmental organizations, and the general public. Atmospheric observations are fed into atmospheric inverse models to compute emission fluxes, called the “top-down” approach to emission estimation. The more familiar “bottom-up” approach is based on scaling up of data for emissions by individual components or facilities.Top-down and bottom-up approaches can be tested against each other to improve the application of both approaches.

In the United States, methane emission data are generated by various entities including the U.S. Environmental Protection Agency (EPA), state and local governments, industry, and researchers from academia, national laboratories, and nongovernmental organizations (NGOs). The stakeholder community using these emission data is even broader and includes policymakers at various levels of government, industry, scientific communities, and the general public. The needs of these stakeholders are diverse, as are their academic backgrounds and their understanding of the generation and reporting of methane emission information. Careful consideration of the audience for any published methane emission data is a key step in generating products that will be scientifically valid and properly used. For the scientific community, presentation of results in peer-reviewed literature would be expected to facilitate dissemination of key technical findings to fellow researchers. Similarly, various governmental agencies, research institutes, industries, and NGOs also publish methane results and reports, but they may not have undergone the peer-review process. As a broader audience attempts to understand and apply the research findings, there is increasing potential for misinterpreting and incorrectly using the results.

Measurements of emissions and monitoring of methane are essential for the development of robust emission inventories as described in Chapter 2. Field measurement of emissions from various sectoral sources can provide improved understanding of processes that lead to emissions, which contributes to the development of process-based emission models as well as regional- and urban-scale mitigation strategies. Furthermore, atmospheric monitoring of methane concentrations is also needed to detect regional trends in emissions and enable rigorous comparisons to bottom-up approaches.

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.