The Global Gridded Crop Model Intercomparison: data and modeling protocols for Phase 1 (v1.0)

Abstract

Abstract. We present protocols and input data for Phase 1 of the Global Gridded Crop Model Intercomparison, a project of the Agricultural Model Intercomparison and Improvement Project (AgMIP). The project includes global simulations of yields, phenologies, and many land-surface fluxes using 12-15 modeling groups for many crops, climate forcing data sets, and scenarios over the historical period from 1948 to 2012. The primary outcomes of the project include (1) a detailed comparison of the major differences and similarities among global models commonly used for large-scale climate impact assessment, (2) an evaluation of model and ensemble hindcasting skill, (3) quantification of key uncertainties from climate input data, model choice, and other sources, and (4) a multi-model analysis of the agricultural impacts of largescale climate extremes from the historical record.

Selection of a representative subset of global climate models that captures the profile of regional changes for integrated climate impacts assessment

“…AgMERRA provides a global, daily, 0.25°× 0.25°gridded climate dataset spanning 1980-2010 containing maximum and minimum temperatures, precipitation, solar radiation, wind speed, and relative humidity at the maximum temperature time of day. AgMERRA has been used in numerous AgMIP activities (Elliott et al 2015;Ruane et al 2015b). Climate sensitivities represent an equilibrium global mean surface temperature increase resulting from a doubling of pre-industrial CO 2 concentrations (from Table 9.5 of Flato et al 2013; n.a.…”

Estimating sowing and harvest dates based on the Asian summer monsoon

“…However, the Waha et al (2012) method is not really intended for use in irrigated multiple cropping regions. Elliott et al (2015) describe how sowing dates are defined in the GGCMI project. The GGCMI protocols use a combination of Sacks et al (2010), Portmann et al (2010), and model data to define sowing dates, thus highlighting the challenges in defining a complete, accurate dataset of sowing and harvest dates.…”

Implementing the Nitrogen cycle into the dynamic global vegetation, hydrology and crop growth model LPJmL (version 5)

“…The actual vegetation carbon pool is strongly limited by current N levels and increases substantially across all ecosystems, when N limitations are lifted (LPJmL5-nL). Under actual N limitations and static current fertilizer levels (Elliott et al, 2015), global GPP is relatively stable throughout the simulation period , yellow line in Fig. 4a) as the expansion of cropland into increasingly low-input areas compensates the increase in GPP in the natural vegetation (red line in Fig.…”

“…With the implementation of the nitrogen dynamics, the model simulations require new inputs, especially on atmospheric deposition, but also on fertilizer applications, where we currently use a static crop-and irrigation-specific data set, developed for the harmonization of crop models in the Agricultural Model Intercomparison and Improvement Project (AgMIP) (Elliott et al, 2015). This static fertilizer set also affects the simulation of historic carbon cycle dynamics, as high-input regions, such as large parts of Europe or northern America receive current high N inputs also in the early 20th century, whereas historic 25 land expansion mostly moves into regions with currently lower input systems.…”

“…Fertilizer data is crop-specific, but static in time. We use the data supplied by the Global Gridded Crop Model Intercomparison (GGCMI phase 1 Elliott et al, 2015), which is based on gridded mineral fertilizer data (Mueller et al, 2012) and manure data (Potter et al, 2010) from which 60% is assumed to be plant available (Elliott et al, 2015).…”