Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general circulation models

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Abstract

We assessed the impact of climate change on rice production in Asia in comprehensive consideration of the process/parameter uncertainty in general circulation models (GCMs). After inputting future climate scenarios based on the projections of GCMs for three Special Report on Emissions Scenarios (SRES) (18 GCMs for A1B, 14 GCMs for A2, and 17 GCMs for B1) into a crop model, we calculated the average change in production (ACP), the standard deviation of the change in production (SDCP), and the probability of a production decrease (PPD) for each SRES scenario, taking into account the effect of CO2 fertilization.
In the 2020s, PPD values were high for all SRES scenarios because the negative impacts of climate change were larger than the positive effects of CO2 fertilization in almost all climate scenarios in the near future. This suggests that it will be necessary to take immediate adaptive actions, regardless of the emission scenario, in the near future. In the 2080s, there were large differences in ACP, SDCP, and PPD among the SRES scenarios. The scenario with the highest atmospheric CO2 concentration, A2, showed a notable decrease in production and a high PPD in the 2080s compared with the other scenarios, despite having the largest CO2 fertilization effect. In addition, A2 had the largest SDCP among the SRES scenarios. On the other hand, the scenario with the lowest atmospheric CO2 concentration, B1, showed a small decrease in production, and a much smaller SDCP and a much lower PPD, than in the case of A2. These results for the 2080s suggest that a reduction in CO2 emissions in the long term has great potential not only to mitigate decreases in rice production, but also to reduce the uncertainty in these changes.

Introduction

Previous studies have revealed that climate change will have significant impact on crop yields (Gitay et al., 2001, Easterling et al., 2007). Many of these studies used future climate projections of general circulation models (GCMs), although it is well known that there are large uncertainties in the projections generated by GCMs (Meehl et al., 2007a). These uncertainties, which can be divided into initial condition uncertainty, process/parameter uncertainty, and emissions scenario uncertainty (Cox and Stephenson, 2007), entail uncertainties in impact assessments. It is important to take these uncertainties into account when assessing the impact of climate change from future climate projections based on GCMs.
Although many studies have taken into consideration the process/parameter uncertainty in GCMs by using multiple future climate projections of these models (e.g., Rosenzweig and Parry, 1994, Fischer et al., 2005), very few of these studies can be regarded as comprehensive (Takahashi et al., 1998, Lobell et al., 2008). Takahashi et al. (1998) estimated medians and ranges of changes in potential production of three main cereals (rice, wheat, and maize) in Asian countries by using the outputs of 11 GCMs. Although their study was advanced for its time, given the recent rapid improvement of GCMs we now need similar assessments using state-of-the-art GCMs. Lobell et al. (2008) prioritized regions where adaptations to climate change will be needed for food security in 2030; they used 60 climate scenarios based on the outputs of 20 state-of-the-art GCMs for three scenarios in the Special Report on Emissions Scenarios (SRES; Nakićenovi and Swart, 2000). Their study is currently considered to be the most advanced in terms of comprehensive consideration of the process/parameter uncertainty in GCMs. However, quantification of the impact of climate change, not only in the near future but also in the distant future, is a prerequisite for deliberations on both long-term and short-term countermeasures to reduce the impact of climate change.
Rice is one of the most important foods in Asia. Currently, 90% or more of the world's rice is produced in Asia. Of the three main cereals produced in this region, rice accounts for about 60% of production (FAO, 2005). The population of Asia constitutes half of the world's population and is projected to increase by approximately 30% by 2050 (United Nation, 2006). This will inevitably increase food demand in Asia. However, Matthews et al. (1997), using the climate scenarios of three GCMs under ×ばつ CO2 conditions, predicted that rice production in Asia will decline, although they did not comprehensively consider the process/parameter uncertainty in GCMs. Assessments of the impact of climate change on rice production in Asia that comprehensively consider this uncertainty would therefore be very valuable for predicting future food security in the region.
Our aim was to comprehensively consider the parameter/process uncertainty in GCMs in an assessment of the impact of climate change on rice production in Asia during this century by using a large number of future climate projections of GCMs. Emissions scenario uncertainty was also considered by using three SRES scenarios (A1B, A2, and B1); the impact of climate change for each SRES scenario was separately assessed. Initial condition uncertainty was not taken into consideration because of lack of data. In Section 4 we consider the uncertainty in the effects of CO2 fertilization, although this uncertainty was not the main focus of our study. The effects of technological development on future rice yields are also considered in Section 4, where the significance of the impact of climate change on future rice yields relative to the effects of technological development is discussed. Moreover, so that we could focus on the impact of climate change, we did not consider the effects of adaptations to climate change.

Section snippets

Methods and data

We used a large number of future climate projections of GCMs for three SRES scenarios (18 GCMs for A1B, 14 GCMs for A2, and 17 GCMs for B1) in order to comprehensively consider the process/parameter uncertainty in GCMs. By inputting climate scenarios based on the future climate projections of these GCMs (see Section 2.2) into a crop model (the M-GAEZ model; see Section 2.1), we calculated the average change in production (ACP), the standard deviation of the change in production (SDCP), and the

Spatial impact of climate change

Examination of the average changes in yield (=(YXs,model − Y1990s, model) ×ばつ 100/Y1990s, model) [%] without the CO2 fertilization effect (Fig. 4) revealed that climate change would reduce yield over a large area of Asia. The regions that showed large decreases in yield were western Japan, eastern China, the southern part of the Indochina peninsula, and the northern part of South Asia. In all these regions, increases in temperature during the growing periods were the main likely causes of the

Comprehensive consideration of process/parameter uncertainty in GCMs

We comprehensively considered the process/parameter uncertainty in GCMs by using a large number of future climate projections based on GCMs. Many studies have considered the process/parameter uncertainty in GCMs by using multiple future climate projections (e.g., Rosenzweig and Parry, 1994, Fischer et al., 2005), However, this uncertainty has not been comprehensively considered in most of these studies, which have used only small numbers of GCM-based future climate projections. One problem in

Conclusion

The impact of climate change, as estimated from future climate projections of GCMs, had large uncertainty caused by the process/parameter uncertainty in GCMs. This indicates that estimates of the impact of climate change depend significantly on the GCMs used. Comprehensive consideration of this uncertainty by using a large number of future climate projections of GCMs is thought to be a way of overcoming this problem to give reliable estimates of the impact of climate change.
The results revealed

Acknowledgements

We sincerely appreciate the many valuable comments provided by Drs. Hideo Shiogama, Naota Hanasaki, and Yasuaki Hijioka of the National Institute for Environmental Studies (NIES), Japan. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modeling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset (support for this dataset is provided by the Office of Science, United States

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