skip to main content

Search for: All records

Creators/Authors contains: "Butterworth, Brian J."

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available December 16, 2023
  2. Abstract. The observing system design of multidisciplinary fieldmeasurements involves a variety of considerations on logistics, safety, andscience objectives. Typically, this is done based on investigator intuitionand designs of prior field measurements. However, there is potential forconsiderable increases in efficiency, safety, and scientific success byintegrating numerical simulations in the design process. Here, we present anovel numerical simulation–environmental response function (NS–ERF)approach to observing system simulation experiments that aidssurface–atmosphere synthesis at the interface of mesoscale and microscalemeteorology. In a case study we demonstrate application of the NS–ERFapproach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balanceStudy Enabled by a High-density Extensive Array of Detectors 2019(CHEESEHEAD19). During CHEESEHEAD19 pre-field simulation experiments, we considered theplacement of 20 eddy covariance flux towers, operations for 72 h oflow-altitude flux aircraft measurements, and integration of various remotesensing data products. A 2 h high-resolution large eddy simulationcreated a cloud-free virtual atmosphere for surface and meteorologicalconditions characteristic of the field campaign domain and period. Toexplore two specific design hypotheses we super-sampled this virtualatmosphere as observed by 13 different yet simultaneous observing systemdesigns consisting of virtual ground, airborne, and satellite observations.We then analyzed these virtual observations through ERFs to yield an optimalaircraft flight strategy for augmenting a stratified random flux towernetwork in combination with satellitemore »retrievals. We demonstrate how the novel NS–ERF approach doubled CHEESEHEAD19'spotential to explore energy balance closure and spatial patterning scienceobjectives while substantially simplifying logistics. Owing to its modularextensibility, NS–ERF lends itself to optimizing observing system designs alsofor natural climate solutions, emission inventory validation, urban airquality, industry leak detection, and multi-species applications, among otheruse cases.« less
  3. Abstract. The Arctic marine environment plays an important role inthe global carbon cycle. However, there remain large uncertainties in howsea ice affects air–sea fluxes of carbon dioxide (CO2), partially dueto disagreement between the two main methods (enclosure and eddy covariance)for measuring CO2 flux (FCO2). The enclosure method has appearedto produce more credible FCO2 than eddy covariance (EC), but is notsuited for collecting long-term, ecosystem-scale flux datasets in suchremote regions. Here we describe the design and performance of an EC systemto measure FCO2 over landfast sea ice that addresses the shortcomingsof previous EC systems. The system was installed on a 10m tower onQikirtaarjuk Island – a small rock outcrop in Dease Strait located roughly35km west of Cambridge Bay, Nunavut, in the Canadian Arctic Archipelago. Thesystem incorporates recent developments in the field of air–sea gasexchange by measuring atmosphericmore »CO2 using a closed-path infrared gasanalyzer (IRGA) with a dried sample airstream, thus avoiding the known watervapor issues associated with using open-path IRGAs in low-flux environments.A description of the methods and the results from 4 months of continuousflux measurements from May through August 2017 are presented, highlightingthe winter to summer transition from ice cover to open water. We show thatthe dried, closed-path EC system greatly reduces the magnitude of measuredFCO2 compared to simultaneous open-path EC measurements, and for thefirst time reconciles EC and enclosure flux measurements over sea ice. Thisnovel EC installation is capable of operating year-round on solar and windpower, and therefore promises to deliver new insights into the magnitude ofCO2 fluxes and their driving processes through the annual sea icecycle.

    « less
  4. null (Ed.)
    Abstract The Chequamegon Heterogeneous Ecosystem Energy-Balance Study Enabled by a High-Density Extensive Array of Detectors 2019 (CHEESEHEAD19) is an ongoing National Science Foundation project based on an intensive field campaign that occurred from June to October 2019. The purpose of the study is to examine how the atmospheric boundary layer (ABL) responds to spatial heterogeneity in surface energy fluxes. One of the main objectives is to test whether lack of energy balance closure measured by eddy covariance (EC) towers is related to mesoscale atmospheric processes. Finally, the project evaluates data-driven methods for scaling surface energy fluxes, with the aim to improve model–data comparison and integration. To address these questions, an extensive suite of ground, tower, profiling, and airborne instrumentation was deployed over a 10 km × 10 km domain of a heterogeneous forest ecosystem in the Chequamegon–Nicolet National Forest in northern Wisconsin, United States, centered on an existing 447-m tower that anchors an AmeriFlux/NOAA supersite (US-PFa/WLEF). The project deployed one of the world’s highest-density networks of above-canopy EC measurements of surface energy fluxes. This tower EC network was coupled with spatial measurements of EC fluxes from aircraft; maps of leaf and canopy properties derived from airborne spectroscopy, ground-based measurements ofmore »plant productivity, phenology, and physiology; and atmospheric profiles of wind, water vapor, and temperature using radar, sodar, lidar, microwave radiometers, infrared interferometers, and radiosondes. These observations are being used with large-eddy simulation and scaling experiments to better understand submesoscale processes and improve formulations of subgrid-scale processes in numerical weather and climate models.« less