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Impacts of IOD, ENSO and ENSO Modoki on the Australian Winter Wheat Yields in Recent Decades.

Yuan C, Yamagata T - Sci Rep (2015)

Bottom Line: It is found that IOD plays a dominant role in the recent three decades; the wheat yield is reduced (increased) by -28.4% (12.8%) in the positive (negative) IOD years.In contrast, the ENSO Modoki may have its distinct impacts on the wheat yield variations, but they are much smaller compared to those of IOD.The present study may lead to a possible scheme for predicting wheat yield variations in Australia in advance by use of simple climate mode indices.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &Technology, Nanjing 210044, China.

ABSTRACT
Impacts of the Indian Ocean Dipole (IOD), two different types of El Niño/Southern Oscillation (ENSO): canonical ENSO and ENSO Modoki, on the year-to-year winter wheat yield variations in Australia have been investigated. It is found that IOD plays a dominant role in the recent three decades; the wheat yield is reduced (increased) by -28.4% (12.8%) in the positive (negative) IOD years. Although the canonical ENSO appears to be responsible for the wheat yield variations, its influences are largely counted by IOD owing to their frequent co-occurrence. In contrast, the ENSO Modoki may have its distinct impacts on the wheat yield variations, but they are much smaller compared to those of IOD. Both the observed April-May and the predicted September-November IOD indices by the SINTEX-F ocean-atmosphere coupled model initialized on April 1st just before the sowing season explain ~15% of the observed year-to-year wheat yield variances. The present study may lead to a possible scheme for predicting wheat yield variations in Australia in advance by use of simple climate mode indices.

No MeSH data available.


Related in: MedlinePlus

Probability distribution (%) of the mean winter wheat yield anomaly (%) in Australia in positive (red) and negative (blue) years of (a) IOD, (b) canonical ENSO and (c) ENSO Modoki after 10,000 bootstrap samplings. The positive and negative years are selected based on the 0.7 standard deviation of September-November DMI, November-January Niño3 and June-August EMI, respectively. Red and blue numbers with (without) parenthesis in each panel denote the average (standard deviation) of mean yield anomalies in the positive and negative event years. Double (single) asterisks denote the confidence level at 99% (90%).
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f2: Probability distribution (%) of the mean winter wheat yield anomaly (%) in Australia in positive (red) and negative (blue) years of (a) IOD, (b) canonical ENSO and (c) ENSO Modoki after 10,000 bootstrap samplings. The positive and negative years are selected based on the 0.7 standard deviation of September-November DMI, November-January Niño3 and June-August EMI, respectively. Red and blue numbers with (without) parenthesis in each panel denote the average (standard deviation) of mean yield anomalies in the positive and negative event years. Double (single) asterisks denote the confidence level at 99% (90%).

Mentions: The composite wheat yield anomalies in the positive/negative IOD and ENSO years are also investigated. The event years of IOD/canonical ENSO/ENSO Modoki are selected when the time series of SON DMI/NDJ Niño3/JJA EMI cross 0.7 standard deviations of the monthly indices, corresponding to 0.3/0.7/0.4 °C, respectively. A conventional technique of bootstrap sampling is applied for 10,000 times and the probability distribution of the averaged wheat yield anomalies in the event years are obtained as shown in Fig. 2. In the positive (negative) IOD years, the averaged wheat yield anomaly is −28.4% (12.8%). In the canonical El Niño (La Niña) years, the anomaly is −20.8% (6.5%). Similar results are obtained in the El Niño (La Niña) Modoki years with the averaged anomaly of −18.7% (6.9%). The smaller anomalous values in the two kinds of ENSO years compared to the IOD years are consistent with the correlation analysis discussed earlier and may be due to their different impacts on the wheat-growing season precipitations. As shown in Supplementary Fig. 3, in the two kinds of El Niño years, the precipitation anomalies are confined to a smaller portion of the wheat belt from May to September compared to those in the positive IOD years. In the canonical La Niña and La Niña Modoki years, the precipitation increases mostly in eastern Australia, and thus significant increase in the wheat yields is only found in Queensland, New South Wales and Victoria (Supplementary Figs 4). In contrast, during the negative IOD years, the region with the increased precipitations is much broader in July-September, extending from northwestern to southeastern Australia. This suggests a significant increase of the wheat yield in most of the major wheat producing provinces except Western Australia.


Impacts of IOD, ENSO and ENSO Modoki on the Australian Winter Wheat Yields in Recent Decades.

Yuan C, Yamagata T - Sci Rep (2015)

Probability distribution (%) of the mean winter wheat yield anomaly (%) in Australia in positive (red) and negative (blue) years of (a) IOD, (b) canonical ENSO and (c) ENSO Modoki after 10,000 bootstrap samplings. The positive and negative years are selected based on the 0.7 standard deviation of September-November DMI, November-January Niño3 and June-August EMI, respectively. Red and blue numbers with (without) parenthesis in each panel denote the average (standard deviation) of mean yield anomalies in the positive and negative event years. Double (single) asterisks denote the confidence level at 99% (90%).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4663488&req=5

f2: Probability distribution (%) of the mean winter wheat yield anomaly (%) in Australia in positive (red) and negative (blue) years of (a) IOD, (b) canonical ENSO and (c) ENSO Modoki after 10,000 bootstrap samplings. The positive and negative years are selected based on the 0.7 standard deviation of September-November DMI, November-January Niño3 and June-August EMI, respectively. Red and blue numbers with (without) parenthesis in each panel denote the average (standard deviation) of mean yield anomalies in the positive and negative event years. Double (single) asterisks denote the confidence level at 99% (90%).
Mentions: The composite wheat yield anomalies in the positive/negative IOD and ENSO years are also investigated. The event years of IOD/canonical ENSO/ENSO Modoki are selected when the time series of SON DMI/NDJ Niño3/JJA EMI cross 0.7 standard deviations of the monthly indices, corresponding to 0.3/0.7/0.4 °C, respectively. A conventional technique of bootstrap sampling is applied for 10,000 times and the probability distribution of the averaged wheat yield anomalies in the event years are obtained as shown in Fig. 2. In the positive (negative) IOD years, the averaged wheat yield anomaly is −28.4% (12.8%). In the canonical El Niño (La Niña) years, the anomaly is −20.8% (6.5%). Similar results are obtained in the El Niño (La Niña) Modoki years with the averaged anomaly of −18.7% (6.9%). The smaller anomalous values in the two kinds of ENSO years compared to the IOD years are consistent with the correlation analysis discussed earlier and may be due to their different impacts on the wheat-growing season precipitations. As shown in Supplementary Fig. 3, in the two kinds of El Niño years, the precipitation anomalies are confined to a smaller portion of the wheat belt from May to September compared to those in the positive IOD years. In the canonical La Niña and La Niña Modoki years, the precipitation increases mostly in eastern Australia, and thus significant increase in the wheat yields is only found in Queensland, New South Wales and Victoria (Supplementary Figs 4). In contrast, during the negative IOD years, the region with the increased precipitations is much broader in July-September, extending from northwestern to southeastern Australia. This suggests a significant increase of the wheat yield in most of the major wheat producing provinces except Western Australia.

Bottom Line: It is found that IOD plays a dominant role in the recent three decades; the wheat yield is reduced (increased) by -28.4% (12.8%) in the positive (negative) IOD years.In contrast, the ENSO Modoki may have its distinct impacts on the wheat yield variations, but they are much smaller compared to those of IOD.The present study may lead to a possible scheme for predicting wheat yield variations in Australia in advance by use of simple climate mode indices.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Meteorological Disaster of Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science &Technology, Nanjing 210044, China.

ABSTRACT
Impacts of the Indian Ocean Dipole (IOD), two different types of El Niño/Southern Oscillation (ENSO): canonical ENSO and ENSO Modoki, on the year-to-year winter wheat yield variations in Australia have been investigated. It is found that IOD plays a dominant role in the recent three decades; the wheat yield is reduced (increased) by -28.4% (12.8%) in the positive (negative) IOD years. Although the canonical ENSO appears to be responsible for the wheat yield variations, its influences are largely counted by IOD owing to their frequent co-occurrence. In contrast, the ENSO Modoki may have its distinct impacts on the wheat yield variations, but they are much smaller compared to those of IOD. Both the observed April-May and the predicted September-November IOD indices by the SINTEX-F ocean-atmosphere coupled model initialized on April 1st just before the sowing season explain ~15% of the observed year-to-year wheat yield variances. The present study may lead to a possible scheme for predicting wheat yield variations in Australia in advance by use of simple climate mode indices.

No MeSH data available.


Related in: MedlinePlus