There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

We upscaled FLUXNET observations of carbon dioxide, water, and energy fluxes to the global scale using the machine learning technique, model tree ensembles (MTE). We trained MTE to predict site-level gross primary productivity (GPP), terrestrial ecosystem respiration (TER), net ecosystem exchange (NEE), latent energy (LE), and sensible heat (H) based on remote sensing indices, climate and meteorological data, and information on land use. We applied the trained MTEs to generate global flux fields at a 0.5 degrees x 0.5 degrees spatial resolution and a monthly temporal resolution from 1982 to 2008. Cross-validation analyses revealed good performance of MTE in predicting among-site flux variability with modeling efficiencies (MEf) between 0.64 and 0.84, except for NEE (MEf = 0.32). Performance was also good for predicting seasonal patterns (MEf between 0.84 and 0.89, except for NEE (0.64)). By comparison, predictions of monthly anomalies were not as strong (MEf between 0.29 and 0.52). Improved accounting of disturbance and lagged environmental effects, along with improved characterization of errors in the training data set, would contribute most to further reducing uncertainties. Our global estimates of LE (158 +/- 7 J x 10(18) yr(-1)), H (164 +/- 15 J x 10(18) yr(-1)), and GPP (119 +/- 6 Pg C yr(-1)) were similar to independent estimates. Our global TER estimate (96 +/- 6 Pg C yr(-1)) was likely underestimated by 5-10%. Hot spot regions of interannual variability in carbon fluxes occurred in semiarid to semihumid regions and were controlled by moisture supply. Overall, GPP was more important to interannual variability in NEE than TER. Our empirically derived fluxes may be used for calibration and evaluation of land surface process models and for exploratory and diagnostic assessments of the biosphere.

Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations

. Flux footprint models are often used for interpretation of flux tower measurements, to estimate position and size of surface source areas, and the relative contribution of passive scalar sources to measured fluxes. Accurate knowledge of footprints is of crucial importance for any upscaling exercises from single site flux measurements to local or regional scale. Hence, footprint models are ultimately also of considerable importance for improved greenhouse gas budgeting. With increasing numbers of flux towers within large monitoring networks such as FluxNet, ICOS (Integrated Carbon Observation System), NEON (National Ecological Observatory Network), or AmeriFlux, and with increasing temporal range of observations from such towers (of the order of decades) and availability of airborne flux measurements, there has been an increasing demand for reliable footprint estimation. Even though several sophisticated footprint models have been developed in recent years, most are still not suitable for application to long time series, due to their high computational demands. Existing fast footprint models, on the other hand, are based on surface layer theory and hence are of restricted validity for real-case applications. To remedy such shortcomings, we present the two-dimensional parameterisation for Flux Footprint Prediction (FFP), based on a novel scaling approach for the crosswind distribution of the flux footprint and on an improved version of the footprint parameterisation of Kljun et al. (2004b). Compared to the latter, FFP now provides not only the extent but also the width and shape of footprint estimates, and explicit consideration of the effects of the surface roughness length. The footprint parameterisation has been developed and evaluated using simulations of the backward Lagrangian stochastic particle dispersion model LPDM-B (Kljun et al., 2002). Like LPDM-B, the parameterisation is valid for a broad range of boundary layer conditions and measurement heights over the entire planetary boundary layer. Thus, it can provide footprint estimates for a wide range of real-case applications. The new footprint parameterisation requires input that can be easily determined from, for example, flux tower measurements or airborne flux data. FFP can be applied to data of long-term monitoring programmes as well as be used for quick footprint estimates in the field, or for designing new sites.

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A simple two-dimensional parameterisation for Flux Footprint Prediction (FFP)

. With the eddy covariance (EC) technique, net fluxes of carbon dioxide (CO2) and other trace gases as well as water and energy fluxes can be measured at the ecosystem level. These flux measurements are a main source for understanding biosphere–atmosphere interactions and feedbacks through cross-site analysis, model–data integration, and upscaling. The raw fluxes measured with the EC technique require extensive and laborious data processing. While there are standard tools1 available in an open-source environment for processing high-frequency (10 or 20 Hz) data into half-hourly quality-checked fluxes, there is a need for more usable and extensible tools for the subsequent post-processing steps. We tackled this need by developing the REddyProc  package in the cross-platform language R that provides standard CO2-focused post-processing routines for reading (half-)hourly data from different formats, estimating the u* threshold, as well as gap-filling, flux-partitioning, and visualizing the results. In addition to basic processing, the functions are extensible and allow easier integration in extended analysis than current tools. New features include cross-year processing and a better treatment of uncertainties. A comparison of REddyProc  routines with other state-of-the-art tools resulted in no significant differences in monthly and annual fluxes across sites. Lower uncertainty estimates of both u* and resulting gap-filled fluxes by 50 % with the presented tool were achieved by an improved treatment of seasons during the bootstrap analysis. Higher estimates of uncertainty in daytime partitioning (about twice as high) resulted from a better accounting for the uncertainty in estimates of temperature sensitivity of respiration. The provided routines can be easily installed, configured, and used. Hence, the eddy covariance community will benefit from the REddyProc  package, allowing easier integration of standard post-processing with extended analysis. 1 http://fluxnet.fluxdata.org/2017/10/10/toolbox-a-rolling-list-of-softwarepackages-for-flux-related-data-processing/ , last access: 17 August 2018

/pdf/basic-and-extensible-post-processing-of-eddy-covariance-flux-5bqu16zenm.pdf

Basic and extensible post-processing of eddy covariance flux data with REddyProc

The magnitude and evolution of parameters that characterize feedbacks in the coupled carbon–climate system are compared across nine Earth system models (ESMs). The analysis is based on results from biogeochemically, radiatively, and fully coupled simulations in which CO2 increases at a rate of 1% yr−1. These simulations are part of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CO2 fluxes between the atmosphere and underlying land and ocean respond to changes in atmospheric CO2 concentration and to changes in temperature and other climate variables. The carbon–concentration and carbon–climate feedback parameters characterize the response of the CO2 flux between the atmosphere and the underlying surface to these changes. Feedback parameters are calculated using two different approaches. The two approaches are equivalent and either may be used to calculate the contribution of the feedback terms to diagnosed cumulative emissions. The contribution of carbon–concentration feedback to...

Carbon-concentration and carbon-climate feedbacks in CMIP5 Earth system models

This study presents the systematic development of an open-source, flexible and modular eddy-covariance (EC) data processing framework. This is achieved through adopting a Development and Systems Operation (DevOps) philosophy, building on the eddy4R family of EC code packages in the R Language for Statistical Computing as foundation. These packages are community-developed via the GitHub distributed version control system and wrapped into a portable and reproducible Docker filesystem that is independent of the underlying host operating system. The HDF5 hierarchical data format then provides a streamlined mechanism for highly compressed and fully self-documented data ingest and output. This framework is applicable beyond EC, and more generally builds the capacity to deploy complex algorithms developed by scientists in an efficient and scalable manner. In addition, modularity permits meeting project milestones while retaining extensibility with time. The efficiency and consistency of this framework is demonstrated in the form of three application examples. These include tower EC data from first instruments installed at a National Ecological Observatory (NEON) field site, aircraft flux measurements in combination with remote sensing data, as well as a software intercomparison. In conjunction with this study, the first two eddy4R packages and simple NEON EC data products are released publicly. While this proof-of-concept represents a significant advance, substantial work remains to arrive at the automated framework needed for the streaming generation of science-grade EC fluxes.

/pdf/eddy4r-a-community-extensible-processing-analysis-and-2twx7bpvog.pdf

eddy4R: A community-extensible processing, analysis and modeling framework for eddy-covariance data based on R, Git, Docker and HDF5

One of the most pressing questions with regard to climate feedback processes in a warming Arctic is the regional-scale methane release from Arctic permafrost areas. The Airborne Measurements of Methane Fluxes (AIRMETH) campaign is designed to quantitatively and spatially explicitly address this question. Ground-based eddy covariance (EC) measurements provide continuous in-situ observations of the surface-atmosphere exchange of methane. However, these observations are rare in the Arctic permafrost zone and site selection is bound by logistical constraints among others. Consequently, these observations cover only small areas that are not necessarily representative of the region of interest. Airborne measurements can overcome this limitation by covering distances of hundreds of kilometers over time periods of a few hours. Here, we present the potential of environmental response functions (ERFs) for quantitatively linking methane flux observations in the atmospheric surface layer to meteorological and biophysical drivers in the flux footprints. For this purpose thousands of kilometers of AIRMETH data across the Alaskan North Slope are utilized, with the aim to extrapolate the airborne EC methane flux observations to the entire North Slope. The data were collected aboard the research aircraft POLAR 5, using its turbulence nose boom and fast response methane and meteorological sensors. After thorough data pre-processing, Reynolds averaging is used to derive spatially integrated fluxes. To increase spatial resolution and to derive ERFs, we then use wavelet transforms of the original high-frequency data. This enables much improved spatial discretization of the flux observations, and the quantification of continuous and biophysically relevant land cover properties in the flux footprint of each observation. A machine learning technique is then employed to extract and quantify the functional relationships between the methane flux observations and the meteorological and biophysical drivers in the flux footprints. Lastly, the resulting ERFs are used to extrapolate the methane release over spatio-temporally explicit grids of the Alaskan North Slope. Metzger et al. (2013) have demonstrated the efficacy of this technique for regionalizing airborne EC heat flux observations to within an accuracy of ≤18% and a precision of ≤5%. Here, we show for the first time results from applying the ERF procedure to airborne methane EC measurements, and report its potential for spatio-temporally explicit inventories of the regional-scale methane exchange. References: Metzger, S., Junkermann, W., Mauder, M., Butterbach-Bahl, K., Trancon y Widemann, B., Neidl, F., Schafer, K., Wieneke, S., Zheng, X. H., Schmid, H. P., and Foken, T.: Spatially explicit regionalization of airborne flux measurements using environmental response functions, Biogeosciences, 10, 2193-2217, doi:10.5194/bg-10-2193-2013, 2013.

Cite as: Author(s) (2013), Title, Abstract B43G-07 presented at 2013 Fall Meeting, AGU, San Francisco, Calif., 9-13 Dec.

The Airborne Measurements of Methane Fluxes (AirMeth) Arctic Campaign

One of the most pressing questions with regard to climate feedback processes in a warming Arctic is the regional-scale greenhouse gas release from Arctic permafrost areas. Ground-based eddy covariance (EC) measurements provide continuous in-situ observations of the surface-atmosphere exchange of energy and matter. However, these observations are rare in the Arctic permafrost zone and site selection is bound by logistical constraints among others. Consequently, these observations cover only small areas that are not necessarily representative of the region of interest. Airborne measurements can overcome this limitation by covering distances of hundreds of kilometers over time periods of a few hours. The Airborne Measurements of Methane Fluxes (AIRMETH) campaigns are designed to quantitatively and spatially explicitly address this question. During the AIRMETH-2012 and AIRMETH-2013 campaigns aboard the research aircraft POLAR 5 we measured turbulent exchange of energy, methane, and (in 2013) carbon dioxide along thousands of kilometers covering the North Slope of Alaska and the Mackenzie Delta, Canada. Time-frequency (wavelet) analysis, footprint modeling, and machine learning techniques are used to (i) determine spatially resolved turbulence statistics, fluxes, and contributions of biophysical surface properties, and (ii) extract regionally valid functional relationships between environmental drivers and the observed fluxes. These environmental response functions (ERF) are used to explain spatial flux patterns and – if drivers are available in temporal resolution – allow for spatio-temporal scaling of the observations. This presentation will focus on 2012 methane fluxes on the North Slope of Alaska and the relevant processes on the regional scale and provide an updated 100 m resolution methane flux map of the North Slope of Alaska.

Low Permafrost Methane Emissions from Arctic Airborne Flux Measurements

A Gas Analyzer Sampling System Optimized for Eddy-Covariance Applications

. A fast response (10 Hz) chemiluminescence detector for ozone (O3) was used to determine O3 fluxes using the eddy covariance technique at the Penlee Point Atmospheric Observatory on the south coast of the UK during April and May 2018. The median O3 flux was −0.132 mg m−2 h−1 (0.018 ppbv m s−1) corresponding to a deposition velocity of 0.037 cm s−1 (interquartile range 0.017–0.065 cm s−1) – similar to the higher values previously reported for open ocean flux measurements, but not as high as some other coastal results. Eddy covariance footprint analysis of the site indicates that the flux footprint was predominantly over water (> 96 %), varying slightly with tide. At moderate-to-high wind speeds, ozone deposition increased with wind speed, and showed a linear dependence with friction velocity of comparable magnitude to predictions from the one-layer model of (Fairall et al., 2007). Deposition was also elevated at very low wind speeds, most likely because the footprint contracted to include a greater land contribution in these conditions.

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Stefan Metzger

Papers

eddy4R: A community-extensible processing, analysis and modeling framework for eddy-covariance data based on R, Git, Docker and HDF5

The Airborne Measurements of Methane Fluxes (AirMeth) Arctic Campaign

Low Permafrost Methane Emissions from Arctic Airborne Flux Measurements

A Gas Analyzer Sampling System Optimized for Eddy-Covariance Applications

Measurements of Ozone Deposition to a Coastal Sea by EddyCovariance