A mass-weighted isentropic coordinate for mapping chemical tracers and computing atmospheric inventories

Abstract

Abstract. We introduce a transformed isentropic coordinate Mθe, defined as the dry air mass under a given equivalent potential temperature surface (θe) within a hemisphere. Like θe, the coordinate Mθe follows the synoptic distortions of the atmosphere but, unlike θe, has a nearly fixed relationship with latitude and altitude over the seasonal cycle. Calculation of Mθe is straightforward from meteorological fields. Using observations from the recent HIAPER Pole-to-Pole Observations (HIPPO) and Atmospheric Tomography Mission (ATom) airborne campaigns, we map the CO2 seasonal cycle as a function of pressure and Mθe, where Mθe is thereby effectively used as an alternative to latitude. We show that the CO2 seasonal cycles are more constant as a function of pressure using Mθe as the horizontal coordinate compared to latitude. Furthermore, short-term variability in CO2 relative to the mean seasonal cycle is also smaller when the data are organized by Mθe and pressure than when organized by latitude and pressure. We also present a method using Mθe to compute mass-weighted averages of CO2 on a hemispheric scale. Using this method with the same airborne data and applying corrections for limited coverage, we resolve the average CO2 seasonal cycle in the Northern Hemisphere (mass-weighted tropospheric climatological average for 2009–2018), yielding an amplitude of 7.8 ± 0.14 ppm and a downward zero-crossing on Julian day 173 ± 6.1 (i.e., late June). Mθe may be similarly useful for mapping the distribution and computing inventories of any long-lived chemical tracer.

Evaluating Northern Hemisphere Growing Season Net Carbon Flux in Climate Models Using Aircraft Observations

“…This corresponds to a more than 1% decrease contrary to the increase expected from Graven. However, this may not be accurate as there may not be enough data in either mission alone to fully constrain the seasonal cycle (Figure S3 in Supporting Information S1) and account for interannual variability in CO 2 fluxes, which were shown by Jin et al (2021) to be non-negligible.…”

“…Global-scale aircraft observations, such as those made during the High-Performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole-to-Pole Observations project (HIPPO, 2009(HIPPO, -2011 and the Atmospheric Tomography Mission (ATom, 2016(ATom, -2018, are representative of large regions and capture the vertical profile of atmospheric CO 2 (Thompson et al, 2022;Wofsy, 2011). These campaigns measured the vertical structure of CO 2 in the atmosphere across a range of latitudes and over the full seasonal cycle and allowed for the analysis of seasonal changes in hemispheric-scale atmospheric CO 2 (e.g., Jin et al, 2021), which are dominated by land exchange. We use the seasonal cycle of atmospheric CO 2 concentrations measured during the HIPPO and ATom flight campaigns to develop a metric for evaluating the simulation of terrestrial CO 2 exchange in prognostic ESMs.…”

Airborne measurements of oxygen concentration from the surface to the lower stratosphere and pole to pole

“…Atmospheric oxygen is quantified as the ratio of the relative abundance of O 2 to the relative abundance of N 2 in units of per meg (Keeling and Shertz, 1992;Keeling and Manning, 2014)…”

“…We have assessed the magnitude of any overall biases in Medusa by comparing our airborne observations from the HIPPO, ORCAS, and ATom campaigns to biweekly station flask samples collected and analyzed by the Scripps O 2 Program (Keeling and Manning, 2014). We use samples from all 10 stations in the Scripps O 2 Program network.…”

“…Atmospheric O 2 observations can be a powerful tool for elucidating carbon cycle processes on multiple time and space scales because of the unique relationships between O 2 and CO 2 surface exchange (e.g., Keeling and Shertz, 1992;Stephens et al, 1998;Ishidoya et al, 2013a;Keeling and Manning, 2014;Nevison et al, 2015;Morgan et al, 2019). Although measuring atmospheric O 2 is challenging because of the need to detect small variations against the large natural background, various in situ and flask-based techniques are now capable of achieving precision at the required 10 −6 relative level (Keeling, 1988;Bender et al, 1994;Manning et al, 1999;Tohjima, 2000;Stephens et al, 2003Stephens et al, , 2007a.…”