Addressing the pasture anomaly: how uncertainty in historical pasture data leads to divergence of atmospheric CO2 in Earth System Models

The characterization of land-use changes and activities within Earth System Models (ESMs) has evolved over the years, from inclusion of net emissions only, to dynamic maps of land-use activity. As part of the land-use harmonization (LUH) project for the 5th IPCC Assessment Report (AR5), new historical reconstructions of land-use change were developed for use in ESMs; these were formulated in terms of gridded maps of land-use activities and land-use transitions including agricultural expansion and abandonment, wood harvest, and shifting cultivation. In addition, due to the uncertainties involved in historical land-use reconstructions, the LUH data was evaluated in over 1600 different reconstructions. Here, we build upon the LUH approach and convert the LUH method into an optimization problem that allows model parameters to be varied in a systematic way to quantitatively meet a desired set of model constraints. We use these methods to address the "pasture anomaly" - an anomalous sudden increase in pasture-related emissions that occurs around 1950-1960 and which causes the simulated atmospheric CO2 in ESMs to diverge from the observed record (prior to that period, ESMs using the LUH data products are typically very successful at reproducing observed atmospheric CO2). First, we apply our optimization method while attempting to preserve as much LUH data as possible and simultaneously removing the pasture anomaly from the land-use emissions time-series. Next, we broaden the method and allow key model inputs to vary within realistic bounds (corresponding to specific ways in which the pasture reconstruction is uncertain). The result is a set of alternative land-use histories that quantify the manifold of possible solutions to the pasture anomaly problem. The most realistic reconstructions within this set can be employed by ESMs as a practical solution to closing the gap between historical atmospheric CO2 records and ESM predictions.