(As promised.)

Introduction and Abstract

This is a review, re-presentation, and report on the August 2019 article,

Y. Zhang, C. Song, L. E. Band, G. Sun, (2019), “No proportional increase of terrestrial gross Carbon sequestration from the greening Earth“, Journal of Geophysical Research: Biogeosciences, 124. 10.1029/2018JG004917

Note: I’m not a biogeoscientist, a biologist, a geologist, or even a geophysicist. I’m a statistician and engineer who learns a lot and borrows methods heavily from quantitative biologists, especially population biologists, and statistical ecologists. I also study climate science, and recently offered a course on it (Climate Science for Climate Activists, Summer 2019).

The Zhang-Song-Band-and-Sun Abstract reads:

Terrestrial vegetation, as the key component of the biosphere, has a greening trend since the beginning of this century. However, how this substantial greening translated to global gross carbon sequestration or gross primary production (GPP) is not clear. Here we investigated terrestrial GPP dynamics and the respective contributions of climate change and vegetation cover change (VCC) from 2000 to 2015. We adopted a remote sensing based data‐driven model, which was calibrated based on the global eddy flux data set (FLUXNET2015) and Moderate Resolution Imaging Spectroradiometer vegetation index data (Collection 6). A series of simulation experiments were conducted to disaggregate the effects of climate and VCC. We found a much weaker increase in global GPP (0.08%/year; P = 0.07) when compared with the global greening rate (0.23%/year; P < 0.001). The positive effect of VCC on GPP was reduced by 53% due to climate stress. Enhanced global GPP were largely contributed by nonforests, especially croplands. However, tropical forests, once a major driver of the global GPP increase, negatively contributed to global GPP trend due to warming‐induced moisture stress and deforestation. Given the limited potential of cropland carbon storage due to harvest and consumption, the contrasting GPP changes (i.e., cropland GPP increase vs. forest GPP reduction) may have shifted the distribution of the land carbon sink. Our study highlights the potential vulnerability of terrestrial gross carbon sequestration under climate and land use changes and has important implications in the global carbon cycle and climate warming mitigation.

I have 4 reasons for wanting to review this paper:

1. The greening of the Earth is not widely known. This would, on the face of it, seem like an encouraging sign. That is, greening would mean in principle that, because of emissions of great amounts of human-produced CO₂ greater amounts of Carbon were being removed from atmosphere and potentially sequestered by such greening.
2. Several of the initial proposals for obtaining negative emissions involve processes like altered farming practices and afforestation, processes which to one degree or another sequester Carbon. Accordingly, it’s interesting to learn more about natural vehicles for Carbon sequestration and how they respond in time.
3. The cited paper makes heavy use of statistical significance testing, a technique and method which is questionable. I want to see how they are using it and if I can take their data and obtain comparable results using proper Bayesian techniques. If the conclusions are different, that’s an interesting result in itself.
4. Should the conclusions of the paper hold, and, after all, it was peer reviewed, the implications for additional Carbon sequestration in croplands and elsewhere are noteworthy and should be shared with those who champion these options.

Background

The basic hypothesis Zhang, Song, Band, and Sun (hereafter “ZSBS”) are attempting to verify is “terrestrial [Gross Primary Productivity] increases proportionally as the Earth greens up”. Gross Primary Productivity or “GPP” is defined as “the amount of total carbon captured by plants via photosynthesis in a unit time”. Note this is not the same as Carbon sequestration, since the decayed byproducts of plants could enter the food chain and be respired. Also note their interest is terrestrial GPP, not oceanic GPP via phytoplankton and such. This is of great interest because, as ZSBS say, “terrestrial GPP is one of the most variable components in the [C]arbon [C]ycle”.

The Model

While reading this paper, I also referenced the related 2016 paper

Y. Zhang, C. Song, G. Sun, L. E. Band, S. McNulty, A. Noormets, Q. Zhang, Z. Zhang, (2016), “Development of a coupled carbon and water model for estimating global gross primary productivity and evapotranspiration based on eddy flux and remote sensing data“. Agricultural and Forest Meteorology, 223, 116–131. 10.1016/j.agrformet.2016.04

I did not read this second paper in full. It concerns the principal tool used in this study, a Coupled Carbon and Water model (CCW) for estimating GPP, software which is described in the 2016 paper just cited above.

Data Sources

Several data sources were provided to CCW:

• Local climatology including global radiation, precipitation, air temperature, air pressure, and air specific humidity obtained from CRU-NCEP and NCEP-NCAR Reanalyses, migrated and resampled to monthly. Vapor pressure deficit (VPD) was calculated from these.
• Land cover data from ESA’s Climate Change Initiative (CCI) were used to define the maximum light-use efficiency and climate constrains
  for each plant functional type in CCW, available at 300 m spatial resolution. The land cover codes were converted to those of the International Geosphere-Biosphere Programme (IGBP).
• GPP comparisons from FLUXCOM, a Light Use Efficiency-based product (LUE) from MODIS, OCO-2-produced solar-induced chlorophyll fluorescence data, an indicator of GPP, annual land sink estimates from the Global Carbon Project, process-based products like ISIMIP2a, and others.
• Global greenness trends were estimated with AVHRR-based global NDVI and LAI products, and with MODIS-C6 EVI/LAI products.

For details on the origins of these datasets and their special features, and on how they were specifically used with CCW, consult ZSBS (2019).

Simulation

A number of factors change independently with time which affect estimates of GPP. There are changes in vegetation cover. There are changes in climate. There are changes in the fraction of photosynthetically available radiation. These trends develop over time in any particular locale. They were used as time-evolving predictors, and were considered individually along with their cross-terms, both cross-terms and primaries taken as potentially explanatory factors for GPP.