A global evaluation of the regional spatial variability of column integrated CO₂ distributions

Abstract

[1] Satellites, such as the Orbiting Carbon Observatory (OCO), are expected to provide global measurements of column-averaged carbon dioxide (CO 2 ) dry-air mole fraction (X CO2 ) with the potential of improving the scientific understanding of regional carbon cycle processes and budgets. The satellite data products, however, are expected to have large data gaps due to the satellite track and geophysical limitations (e.g., clouds and aerosols). The satellite data will also be representative of the X CO2 distribution at the spatial scale of satellite footprints, which is smaller than the resolution of typical transport or process models. Assessing the ability of the retrieved soundings to capture X CO2 variability over different regions and times, evaluating the representation error associated with using the retrieved X CO2 product to represent X CO2 at typical model resolutions, and filling data gaps while providing an estimate of the associated uncertainty all require the evaluation of the spatial variability of X CO2 . In this study, the global spatial covariance structure of X CO2 is evaluated regionally using CO 2 concentrations simulated using the MATCH/CASA model. Results show that regional and temporal changes in the X CO2 distribution caused by seasonal changes in surface fluxes and transport produce a spatially and temporally variable X CO2 covariance structure. The effects of model setup and the relatively low resolution of the MATCH/CASA model on the evaluated X CO2 covariance structure are assessed by comparing the MATCH/CASA results to the spatial variability inferred from the higher-resolution PCTM/GEOS-4 global model, the SiB-RAMS regional model, and aircraft campaign point observations. The comparison with the higher-resolution models and aircraft data shows good agreement with MATCH/CASA results, thus indicating that the presented results provide an adequate representation of X CO2 variability as will be measured by satellites such as OCO.

Spatio-temporal approach to moving window block kriging of satellite data v1.0

“…Previous approaches required the user to choose spatial and temporal windows that determine which neighboring observations to use (see, for comparison, Alkhaled et al, 2008;Hammerling et al, 2012a, b). The approach proposed in this paper, by contrast, requires fewer subjective choicesonly the form of the sampling function and unit-dependent choice of normalizing coefficients A s and A t .…”

Toward autonomous surface-based infrared remote sensing of polar clouds: cloud-height retrievals

“…CO 2 errors are expected to be on the order of 0.5 %, based on the work of Alkhaled et al (2008). Such CO 2 errors were found to produce negligible errors (not shown).…”

“…The statistics of cloud-base heights used in this work are similar to those measured in the Arctic (see Cox et al, 2016, and references therein), with 79 % of the clouds below 2 km. Thus a surface-based spectrometer would be of the greatest benefit for retrieving the heights of low clouds, which are common in both the Antarctic (Bromwich et al, 2012;Mahesh et al, 2005) and the Arctic (Intrieri et al, 2002).…”

“…In the Antarctic, field stations are sparsely located, principally on the coast, and have fewer instruments for measuring cloud properties than in the Arctic. In addition to cloud measurements from existing field stations and past campaigns (Bromwich et al, 2012, and references therein), the Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) is making a broad suite of measurements from November 2015 to 2017. Nevertheless, there remains a dearth of surface-based remote sensors in the Antarctic.…”

Characterizing biospheric carbon balance using CO<sub>2</sub> observations from the OCO-2 satellite

“…We estimate V i,j in the vicinity of each observation i by fitting a local variogram model on the model-data residuals ( ) (See Alkhaled et al (2008) and Hammerling et al (2012) for a description of local variogram analysis.). The SI describes this implementation in greater detail.…”