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NewsCongratulations to Zhou meng zi Passing her Ph.D. Dissertations smoothly
[2015-05-19]
Zhou meng zi successfully passed through the doctoral dissertation defense on May 8th 2015 at the 101 conference room of the IAP scientific research building. Her doctoral thesis title is the studies on the Principle Climate Factors, Mechanisms, and Prediction for the Grain Yield in Northeast China
In the past 100 years, the global-average surface temperatures have increased by 0.89°C. Against the background of global warming, the climate in Northeast China (NEC) is changing dramatically. Agriculture is one of the sectors most sensitive to climate change. Affected by climate change, agricultural production in Northeast China fluctuated strongly. Northeast China is an important region of China in terms of agricultural production and trade of grain. Hence, in the context of the current situation, accurate yield prediction ahead of the harvest for this region is critical for guiding adaptation efforts and providing information for policymakers. Here, we attempt to establish new forecasting model for maize and rice yield based on antecedent large-scale climate predictors instead of local meteorological variables. Especially, the physical mechanisms between crop yield and predictors are investigated. Furthermore, we evaluate the potential impact of future climate change on spring maize and single-crop rice in Northeast China. The main conclusions are summarized as follows.
(1) The springtime (March−May) North Atlantic Oscillation index (NAO) could potentially be applied in predictions of crop yield in Northeast China
Both maize yield and rice yield are negatively correlated with spring NAO index at the 95% confidence level, with correlation coefficients of −0.37 and −0.34, when using year-to-year increment data. The physical mechanism for this relationship is investigated. Springtime NAO can induce sea surface temperature anomalies (SSTAs) in the North Atlantic, which display a tripole pattern and are similar to the empirical mode pattern in spring. The spring Atlantic SSTA pattern that could persists to summer, can trigger a high-level tropospheric Rossby wave response in the Eurasia continent, resulting in atmospheric circulation anomalies over the Siberia-Mongolia region, which is unfavorable (favorable) for cold surges that affect NEC. Weaker (stronger) cold surges can accordingly reduce (increase) cloud amount, resulting in an increase (a decrease) in daily maximum temperature and a decrease (an increase) in daily minimum temperature, thereby leading to an increase (a decrease) in diurnal temperature range. And summer-mean daily minimum temperature and diurnal temperature range are most significantly related to NEC crop yield.
(2) Later-winter Bering Sea ice area index (BSI) could be employed to predict crop yields in Northeast China
Late winter BSI is a good indicator for yield forecasting in Northeast China with correlation coefficients of 0.42 and 0.31, significant at the 99% and 95% levels, for maize and rice, respectively. Further investigation reveals that positive late winter sea ice cover anomalies can persist until spring and that spring sea ice can strengthen North Pacific Oscillation (NPO) positive-phase patterns, and vice versa. NPO significantly affects sea surface temperature (SST) over the North Pacific due to sea–air interaction, particularly in the Kuroshio Current region, which may persist until summer. In association with the positive SST anomalies, the polar vortex weakens and the western Pacific subtropical high strengthens, resulting in the convergence of southern and northern air masses over NEC. Moreover, both the southerly flow along the western flank of western Pacific subtropical high and the easterly flow from the Japan Sea and the Central Pacific region supply more water vapor transport; thus, an anomalous water vapor convergence center appears in the NEC. With the anomalous updrafts, NEC exhibits positive precipitation anomalies. The greenhouse effect of water vapor results in an increase in minimum temperature, thereby leading to a decrease in diurnal temperature range (DTR). This increase in minimum temperature and decrease in DTR are primary factors favoring increases in rice and maize yields, respectively.
(3) The influence of summer extreme high temperature on maize yield in China and its relationship to atmospheric general circulation and sea surface temperature
We analyze the impact of summer extreme high temperature on maize yield at provincial scales in China. Results shows that in 18 provinces, the maize yield would decrease dramatically from −1.56% to −15.06% for each standard deviation increase in extreme high temperature days, especially for the Northeast China and North China. Further analysis indicates that both of extreme high temperature days in Northeast China and North China appear jump change in the mid to late 1990s. For Northeast China and North China, when overlying geopotential height anomaly at 500hPa is positive, the weather is clear which is conductive to the increase of solar radiation and then lead to high temperature. In lower wind field, the westerly prevails in the Northeast China especially in Heilongjiang province, and zonal circulation is intensified which blocks the invasion of cold air mass from high latitude; for North China, the temperature advection by the southerly wind is beneficial to extreme high temperature. The key sea region that has significant impact on extreme high temperature in Northeast China is the Kuroshio region, while the main region for North China is the eastern Pacific of Equater. The positive anomaly in this region would lead to an eastward displace of the western Pacific subtropical high, in association with poor supply of water vapor and intense downdraft, North China is prone to experience high temperature weather.
(4) Improvement in the prediction of grain yield in Northeast China by using a new statistical forecasting model based on the spring North Atlantic Oscillation and late-winter Bering Sea ice cover
We develop prediction models for maize and rice yields based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index. The year-to-year increment is first forecasted and then the actual yield is obtained by adding the historical yield of the preceding year. The multivariate linear prediction model of maize shows good predictive ability, with a low normalized root-mean-square error (RMSE) of 13.9%, and the simulated yield accounts for 81% of the total variance of the historical data. To improve the performance of the multivariate linear model, a combined forecasting model for rice is established by considering the weight of the predictors. The RMSE of the model prediction is 12.9% and the predicted rice yield explains 71% of the total variance. The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models. We conclude that the statistical forecast models established here, based on the predictors identified in our previous work and the year-to-year increment approach, significantly improve the prediction of maize and rice yield in Northeast China.
(5) Potential impact of future climate change on crop yield in Northeast China We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China (NEC) by employing climate and crop models. Under the Representative Concentration Pathway 4.5 scenario (RCP4.5), the projected maize yield changes for three future periods (2010–2039, 2040–2069, and 2070–2099) relative to the mean yield in the baseline period (1976–2005) were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The range of uncertainties of the impact of climate change on crop yields became markedly wider when considering the uncertainties obtained from both the climate and the crop model, possibly due to the interaction between the two uncertainty sources.
In the past 100 years, the global-average surface temperatures have increased by 0.89°C. Against the background of global warming, the climate in Northeast China (NEC) is changing dramatically. Agriculture is one of the sectors most sensitive to climate change. Affected by climate change, agricultural production in Northeast China fluctuated strongly. Northeast China is an important region of China in terms of agricultural production and trade of grain. Hence, in the context of the current situation, accurate yield prediction ahead of the harvest for this region is critical for guiding adaptation efforts and providing information for policymakers. Here, we attempt to establish new forecasting model for maize and rice yield based on antecedent large-scale climate predictors instead of local meteorological variables. Especially, the physical mechanisms between crop yield and predictors are investigated. Furthermore, we evaluate the potential impact of future climate change on spring maize and single-crop rice in Northeast China. The main conclusions are summarized as follows.
(1) The springtime (March−May) North Atlantic Oscillation index (NAO) could potentially be applied in predictions of crop yield in Northeast China
Both maize yield and rice yield are negatively correlated with spring NAO index at the 95% confidence level, with correlation coefficients of −0.37 and −0.34, when using year-to-year increment data. The physical mechanism for this relationship is investigated. Springtime NAO can induce sea surface temperature anomalies (SSTAs) in the North Atlantic, which display a tripole pattern and are similar to the empirical mode pattern in spring. The spring Atlantic SSTA pattern that could persists to summer, can trigger a high-level tropospheric Rossby wave response in the Eurasia continent, resulting in atmospheric circulation anomalies over the Siberia-Mongolia region, which is unfavorable (favorable) for cold surges that affect NEC. Weaker (stronger) cold surges can accordingly reduce (increase) cloud amount, resulting in an increase (a decrease) in daily maximum temperature and a decrease (an increase) in daily minimum temperature, thereby leading to an increase (a decrease) in diurnal temperature range. And summer-mean daily minimum temperature and diurnal temperature range are most significantly related to NEC crop yield.
(2) Later-winter Bering Sea ice area index (BSI) could be employed to predict crop yields in Northeast China
Late winter BSI is a good indicator for yield forecasting in Northeast China with correlation coefficients of 0.42 and 0.31, significant at the 99% and 95% levels, for maize and rice, respectively. Further investigation reveals that positive late winter sea ice cover anomalies can persist until spring and that spring sea ice can strengthen North Pacific Oscillation (NPO) positive-phase patterns, and vice versa. NPO significantly affects sea surface temperature (SST) over the North Pacific due to sea–air interaction, particularly in the Kuroshio Current region, which may persist until summer. In association with the positive SST anomalies, the polar vortex weakens and the western Pacific subtropical high strengthens, resulting in the convergence of southern and northern air masses over NEC. Moreover, both the southerly flow along the western flank of western Pacific subtropical high and the easterly flow from the Japan Sea and the Central Pacific region supply more water vapor transport; thus, an anomalous water vapor convergence center appears in the NEC. With the anomalous updrafts, NEC exhibits positive precipitation anomalies. The greenhouse effect of water vapor results in an increase in minimum temperature, thereby leading to a decrease in diurnal temperature range (DTR). This increase in minimum temperature and decrease in DTR are primary factors favoring increases in rice and maize yields, respectively.
(3) The influence of summer extreme high temperature on maize yield in China and its relationship to atmospheric general circulation and sea surface temperature
We analyze the impact of summer extreme high temperature on maize yield at provincial scales in China. Results shows that in 18 provinces, the maize yield would decrease dramatically from −1.56% to −15.06% for each standard deviation increase in extreme high temperature days, especially for the Northeast China and North China. Further analysis indicates that both of extreme high temperature days in Northeast China and North China appear jump change in the mid to late 1990s. For Northeast China and North China, when overlying geopotential height anomaly at 500hPa is positive, the weather is clear which is conductive to the increase of solar radiation and then lead to high temperature. In lower wind field, the westerly prevails in the Northeast China especially in Heilongjiang province, and zonal circulation is intensified which blocks the invasion of cold air mass from high latitude; for North China, the temperature advection by the southerly wind is beneficial to extreme high temperature. The key sea region that has significant impact on extreme high temperature in Northeast China is the Kuroshio region, while the main region for North China is the eastern Pacific of Equater. The positive anomaly in this region would lead to an eastward displace of the western Pacific subtropical high, in association with poor supply of water vapor and intense downdraft, North China is prone to experience high temperature weather.
(4) Improvement in the prediction of grain yield in Northeast China by using a new statistical forecasting model based on the spring North Atlantic Oscillation and late-winter Bering Sea ice cover
We develop prediction models for maize and rice yields based on the spring North Atlantic Oscillation index and the Bering Sea ice cover index. The year-to-year increment is first forecasted and then the actual yield is obtained by adding the historical yield of the preceding year. The multivariate linear prediction model of maize shows good predictive ability, with a low normalized root-mean-square error (RMSE) of 13.9%, and the simulated yield accounts for 81% of the total variance of the historical data. To improve the performance of the multivariate linear model, a combined forecasting model for rice is established by considering the weight of the predictors. The RMSE of the model prediction is 12.9% and the predicted rice yield explains 71% of the total variance. The corresponding cross-validation test and independent samples test further demonstrate the efficiency of the models. We conclude that the statistical forecast models established here, based on the predictors identified in our previous work and the year-to-year increment approach, significantly improve the prediction of maize and rice yield in Northeast China.
(5) Potential impact of future climate change on crop yield in Northeast China We evaluated the potential impact of future climate change on spring maize and single-crop rice in northeastern China (NEC) by employing climate and crop models. Under the Representative Concentration Pathway 4.5 scenario (RCP4.5), the projected maize yield changes for three future periods (2010–2039, 2040–2069, and 2070–2099) relative to the mean yield in the baseline period (1976–2005) were 2.92%, 3.11% and 2.63%, respectively. By contrast, the evaluated rice yields showed slightly larger increases of 7.19%, 12.39%, and 14.83%, respectively. The range of uncertainties of the impact of climate change on crop yields became markedly wider when considering the uncertainties obtained from both the climate and the crop model, possibly due to the interaction between the two uncertainty sources.
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