Blog

I love the heatscatter() function from the LSD R package. I’ve even used it for my own publications (see Figs. 2 and 7 in Stocker et al. 2017). Since having written this paper and creating the figures I’ve (finally) moved to using ggplot.

We live in a data-rich world. Satellites are collecting information about the state of the Earth surface in ever more fine-grained resolution. CO2 and water exchange fluxes between the vegetation and the atmosphere are continuously measured at several hundred sites across the globe.

Reliable reconstructions of past land-use change are essential to address several key challenges in understanding the role of humans in the Earth’s environmental history. Here we discuss how to integrate our understanding of past carbon cycle changes within constraints of the land carbon budget.

In this blog, I’m going to argue why it is crucial to use observational data from ecosystem manipulation experiments (EME) for model evaluation and calibration. And I’m going to make this case using the example of nitrogen limitation and how EMEs guided the development of Dynamic Global Vegetation Models (DGVMs) over the last one-and-a-half decades and repeatedly exposed their deficiencies.

A general notion of “soil fertility” is commonly left unaccounted for in models of the terrestrial biosphere and has received relatively little attention in carbon cycle science. This changed since the presentation of results by Vicca et al.

To address this question, we have to start with the notion that, on the long term, N losses from ecosystems via gaseous and leaching pathways have to be compensated by N inputs.