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  • Author or Editor: Christian Meinig x
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Christopher Sabine
,
Adrienne Sutton
,
Kelly McCabe
,
Noah Lawrence-Slavas
,
Simone Alin
,
Richard Feely
,
Richard Jenkins
,
Stacy Maenner
,
Christian Meinig
,
Jesse Thomas
,
Erik van Ooijen
,
Abe Passmore
, and
Bronte Tilbrook

Abstract

Current carbon measurement strategies leave spatiotemporal gaps that hinder the scientific understanding of the oceanic carbon biogeochemical cycle. Data products and models are subject to bias because they rely on data that inadequately capture mesoscale spatiotemporal (kilometers and days to weeks) changes. High-resolution measurement strategies need to be implemented to adequately evaluate the global ocean carbon cycle. To augment the spatial and temporal coverage of ocean–atmosphere carbon measurements, an Autonomous Surface Vehicle CO2 (ASVCO2) system was developed. From 2011 to 2018, ASVCO2 systems were deployed on seven Wave Glider and Saildrone missions along the U.S. Pacific and Australia’s Tasmanian coastlines and in the tropical Pacific Ocean to evaluate the viability of the sensors and their applicability to carbon cycle research. Here we illustrate that the ASVCO2 systems are capable of long-term oceanic deployment and robust collection of air and seawater pCO2 within ±2 μatm based on comparisons with established shipboard underway systems, with previously described Moored Autonomous pCO2 (MAPCO2) systems, and with companion ASVCO2 systems deployed side by side.

Open access
Jim Thomson
,
Joe Talbert
,
Alex de Klerk
,
Adam Brown
,
Mike Schwendeman
,
Jarett Goldsmith
,
Julie Thomas
,
Corey Olfe
,
Grant Cameron
, and
Christian Meinig

Abstract

The effects of biofouling on a wave measurement buoy are examined using concurrent data collected with two Datawell Waveriders at Ocean Station P: one heavily biofouled at the end of a 26-month deployment, the other newly deployed and clean. The effects are limited to the high-frequency response of the buoy and are correctly diagnosed with the spectral “check factors” that compare horizontal and vertical displacements. A simple prediction for the progressive change in frequency response during biofouling reproduces the check factors over time. The bulk statistical parameters of significant wave height, peak period, average period, and peak direction are only slightly affected by the biofouling because the contaminated frequencies have very low energy throughout the comparison dataset.

Full access
Jinbo Wang
,
Lee-Lueng Fu
,
Bruce Haines
,
Matthias Lankhorst
,
Andrew J. Lucas
,
J. Thomas Farrar
,
Uwe Send
,
Christian Meinig
,
Oscar Schofield
,
Richard Ray
,
Matthew Archer
,
David Aragon
,
Sebastien Bigorre
,
Yi Chao
,
John Kerfoot
,
Robert Pinkel
,
David Sandwell
, and
Scott Stalin

Abstract

The future Surface Water and Ocean Topography (SWOT) mission aims to map sea surface height (SSH) in wide swaths with an unprecedented spatial resolution and subcentimeter accuracy. The instrument performance needs to be verified using independent measurements in a process known as calibration and validation (Cal/Val). The SWOT Cal/Val needs in situ measurements that can make synoptic observations of SSH field over an O(100) km distance with an accuracy matching the SWOT requirements specified in terms of the along-track wavenumber spectrum of SSH error. No existing in situ observing system has been demonstrated to meet this challenge. A field campaign was conducted during September 2019–January 2020 to assess the potential of various instruments and platforms to meet the SWOT Cal/Val requirement. These instruments include two GPS buoys, two bottom pressure recorders (BPR), three moorings with fixed conductivity–temperature–depth (CTD) and CTD profilers, and a glider. The observations demonstrated that 1) the SSH (hydrostatic) equation can be closed with 1–3 cm RMS residual using BPR, CTD mooring and GPS SSH, and 2) using the upper-ocean steric height derived from CTD moorings enable subcentimeter accuracy in the California Current region during the 2019/20 winter. Given that the three moorings are separated at 10–20–30 km distance, the observations provide valuable information about the small-scale SSH variability associated with the ocean circulation at frequencies ranging from hourly to monthly in the region. The combined analysis sheds light on the design of the SWOT mission postlaunch Cal/Val field campaign.

Restricted access
Chidong Zhang
,
Aaron F. Levine
,
Muyin Wang
,
Chelle Gentemann
,
Calvin W. Mordy
,
Edward D. Cokelet
,
Philip A. Browne
,
Qiong Yang
,
Noah Lawrence-Slavas
,
Christian Meinig
,
Gregory Smith
,
Andy Chiodi
,
Dongxiao Zhang
,
Phyllis Stabeno
,
Wanqiu Wang
,
Hong-Li Ren
,
K. Andrew Peterson
,
Silvio N. Figueroa
,
Michael Steele
,
Neil P. Barton
,
Andrew Huang
, and
Hyun-Cheol Shin

Abstract

Observations from uncrewed surface vehicles (saildrones) in the Bering, Chukchi, and Beaufort Seas during June–September 2019 were used to evaluate initial conditions and forecasts with lead times up to 10 days produced by eight operational numerical weather prediction centers. Prediction error behaviors in pressure and wind are found to be different from those in temperature and humidity. For example, errors in surface pressure were small in short-range (<6 days) forecasts, but they grew rapidly with increasing lead time beyond 6 days. Non-weighted multimodel means outperformed all individual models approaching a 10-day forecast lead time. In contrast, errors in surface air temperature and relative humidity could be large in initial conditions and remained large through 10-day forecasts without much growth, and non-weighted multimodel means did not outperform all individual models. These results following the tracks of the mobile platforms are consistent with those at a fixed location. Large errors in initial condition of sea surface temperature (SST) resulted in part from the unusual Arctic surface warming in 2019 not captured by data assimilation systems used for model initialization. These errors in SST led to large initial and prediction errors in surface air temperature. Our results suggest that improving predictions of surface conditions over the Arctic Ocean requires enhanced in situ observations and better data assimilation capability for more accurate initial conditions as well as better model physics. Numerical predictions of Arctic atmospheric conditions may continue to suffer from large errors if they do not fully capture the large SST anomalies related to Arctic warming.

Restricted access