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Toshichika Iizumi, Yuhei Takaya, Wonsik Kim, Toshiyuki Nakaegawa, and Shuhei Maeda

Abstract

Weather and climate variability associated with major climate modes is a main driver of interannual yield variability of commodity crops in global cropland areas. A global crop forecasting service that is currently in the test operation phase is based on temperature and precipitation forecasts, while recent literature suggests that crop forecasting services may benefit from the use of climate index forecasts. However, no consistent comparison is available on prediction skill between yield models relying on forecasts from temperature and precipitation and from climate indices. Here, we present a global assessment of 26-yr (1983–2008) within-season yield anomaly hindcasts for maize, rice, wheat, and soybean derived using different types of statistical yield models. One type of model utilizes temperature and precipitation for individual cropping areas (the TP model type) to represent the current service, whereas the other type relies on large-scale climate indices (the CI model). For the TP models, three specifications with different model complexities are compared. The results show that the CI model is characterized by a small reduction in the skillful area from the reanalysis model to the hindcast model and shows the largest skillful areas for rice and soybean. In the TP models, the skill of the simple model is comparable to that of the more complex models. Our findings suggest that the use of climate index forecasts for global crop forecasting services in addition to temperature and precipitation forecasts likely increases the total number of crops and countries where skillful yield anomaly prediction is feasible.

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Yosuke Fujii, Toshiyuki Nakaegawa, Satoshi Matsumoto, Tamaki Yasuda, Goro Yamanaka, and Masafumi Kamachi

Abstract

The authors developed a system for simulating climate variation by constraining the ocean component of a coupled atmosphere–ocean general circulation model (CGCM) through ocean data assimilation and conducted a climate simulation [Multivariate Ocean Variational Estimation System–Coupled Version Reanalysis (MOVE-C RA)]. The monthly variation of sea surface temperature (SST) is reasonably recovered in MOVE-C RA. Furthermore, MOVE-C RA has improved precipitation fields over the Atmospheric Model Intercomparison Project (AMIP) run (a simulation of the atmosphere model forced by observed daily SST) and the CGCM free simulation run. In particular, precipitation in the Philippine Sea in summer is improved over the AMIP run. This improvement is assumed to stem from the reproduction of the interaction between SST and precipitation, indicated by the lag of the precipitation change behind SST. Enhanced (suppressed) convection tends to induce an SST drop (rise) because of cloud cover and ocean mixing in the real world. A lack of this interaction in the AMIP run leads to overestimating the precipitation in the Bay of Bengal in summer. Because it is recovered in MOVE-C RA, the overestimate is suppressed. This intensifies the zonal Walker circulation and the monsoon trough, resulting in enhanced convection in the Philippine Sea. The spurious positive correlation between SST and precipitation around the Philippines in the AMIP run in summer is also removed in MOVE-C RA. These improvements demonstrate the effectiveness of simulating ocean interior processes with the ocean model and data assimilation for reproducing the climate variability.

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Hiroaki Kawase, Yukiko Imada, Hiroshige Tsuguti, Toshiyuki Nakaegawa, Naoko Seino, Akihiko Murata, and Izuru Takayabu
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Yasuhiro Ishizaki, Tokuta Yokohata, Seita Emori, Hideo Shiogama, Kiyoshi Takahashi, Naota Hanasaki, Toru Nozawa, Tomoo Ogura, Toshiyuki Nakaegawa, Masakazu Yoshimori, Ai Yoshida, and Shigeru Watanabe

Abstract

A pattern scaling approach allows projection of regional climate changes under a wide range of emission scenarios. A basic assumption of this approach is that the spatial response pattern to global warming (scaling pattern) is the same for all emission scenarios. Precipitation minus evapotranspiration (PME) over land can be considered to be a measure of the maximum available renewable freshwater resource, and estimation of PME is fundamentally important for the assessment of water resources. The authors assessed the basic assumption of pattern scaling for PME by the use of five global climate models. A significant scenario dependency (SD) of the scaling pattern of PME was found over some regions. This SD of the scaling pattern of PME was mainly due to the SD and the nonlinear response of large-scale atmospheric and oceanic changes. When the SD of the scaling pattern of PME is significant in a target area, projections of the impact of climate change need to carefully take into consideration the SD. Although the SD of the anthropogenic aerosol scaling patterns tended to induce SDs of precipitation and evapotranspiration scaling patterns, the SDs of precipitation and evapotranspiration tended to cancel each other out. As a result, the SD of the PME scaling pattern tended to be insignificant over most regions where the SD of anthropogenic aerosol scaling patterns were significant. The authors could not find large impacts of land use change on PME scaling pattern, but the former may influence the latter on different time scales or spatial scales.

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