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Tatsumi et al. (2011) examined the effects of systematic errors and horizontal resolution in simulated surface air temperature and precipitation on crop yield estimates. In this article, the Meteorological Research Institute’s 20-km atmospheric general circulation model (MRI-AGCM3.1S; Kitoh et al., 2009) and improved Global Agro-Ecological Zones (iGAEZ) model were used as a present-day climate simulation and a crop model, respectively. The systematic errors in simulated surface air temperature and precipitation are a major cause of considerable errors in crop yield estimates. The errors in simulated crop yields decrease as they are spatially aggregated up to the country scale. In addition, small-scale variabilities in the simulated surface air temperature and precipitation distinctively affect the simulated crop yields, suggesting that surface air temperature and precipitation inputs with 0.5-degree horizontal resolution are not sufficient for a small-scale simulation by iGAEZ. These are new findings in hydrology and water resources engineering. However, we would like to raise a question regarding a comparison in interannual variabilities of crop yields between those reported by the Food Agricultural Organization (FAO) and the simulation performed with iGAEZ forced with MRI-AGCM3.1S outputs. The AGCM simulation in the present-day climate is forced with prescribed sea surface temperatures (SST), which is called an “AMIP-type experiment” after the experimental setup of the Atmospheric Model Intercomparison Project (Gates et al., 1998). Atmospheric dynamics have strong nonlinearities and show a chaotic nature (Lorenz, 1963); in other words, midand high latitude phenomena in an AMIP-type simulation are deterministically predictable in the short and middle range (approximately less than or equal to 7 days) but unpredictable in the long range (approximately 7 days or longer~; e.g., Sugi et al., 1997). Instead, they are probabilistically predicted with multimember simulations, although their potential predictabilities are low even under the perfect model assumption (e.g., Nakaegawa et al., 2003). Figure 1a shows terrestrial distributions of temporal correlations of interannual variabilities of surface air temperature in land areas between observations and simulations in the 20-km mesh MRI-AGCM3.1S. High temporal correlations for surface air temperature are confined to the tropical region, where atmospheric phenomena are efficiently forced with SSTs, whereas low temporal correlations are widely distributed in midand high latitudes, confirming the findings of previous studies (e.g., Barnett et al., 1997; Gates et al., 1998). Most of the temporal correlations are statistically insignificant at the 90% confidence level. The temporal correlations of precipitation are much lower than those of surface air temperature because of high nonlinearity. Latitudinal dependency of the zonal mean of the temporal correlations of precipitation is weaker than that of surface air temperature. These low correlations are not due to the capabilities of MRI-AGCM3.1S, but to the chaotic nature of atmospheric phenomena. Therefore, the comparison of interannual variabilities between observations and the single-member AMIP-type simulations does not make sense. Instead, comparable variables include climatological (long-term) mean (e.g., Kitoh et al., 2008; Kim et al., 2010; Nakaegawa and Vergara, 2010) and probability density functions (Yasuda et al., 2010; Nakayama et al., 2011), which are widely recognized among researchers who perform impact studies.

hydrological research letters

Comment on “Effect of uncertainty in temperature and precipitation inputs and spatial resolution on the crop model” by Kenichi Tatsumi, Yosuke Yamashiki and Kaoru Takara