Identification of empirical relationships between indices of ENSO and NAO and agricultural yields in Spain

Uncertainty in weather conditions for the forthcoming growing season influences farmers' decisions, based on their experience of the past climate, regarding the reduction of agricultural risk. Early within-season predictions of grain yield can represent a great opportunity for farmers to improve their management decisions and potentially increase yield and reduce potential risk. This study assessed 3 methods of within-season predictions of durum wheat yield at 10 sites across the Mediterranean Basin. To assess the value of within-season predictions, the model SiriusQuality2 was used to calculate wheat yields over a 9 yr period. Initially, the model was run with observed daily weather to obtain the reference yields. Then, yield predictions were calculated at a monthly time step, starting from 6 mo before harvest, by feeding the model with observed weather from the beginning of the growing season until a specific date and then with synthetic weather constructed using the 3 methods, historical, analogue or empirical, until the end of the growing season. The results showed that it is possible to predict durum wheat yield over the Mediterranean Basin with an accuracy of normalized root means squared error of < 20%, from 5 to 6 mo earlier for the historical and empirical methods and 3 mo earlier for the analogue method. Overall, the historical method performed better than the others. Nonetheless, the analogue and empirical methods provided better estimations for low-yielding and high-yielding years, thus indicating great potential to provide more accurate predictions for years that deviate from average conditions.

Section: Resultsmentioning

confidence: 99%

Monthly-to-seasonal predictions of durum wheat yield over the Mediterranean Basin

Ferrise¹,

Toscano²,

Pasqui³

et al. 2015

“…The dependence of agricultural production variability on ENSO and its application to the forecasting of food production have been investigated in Africa, America, Europe, and South Asia (e.g. Cane et al 1994, Jones et al 2000, Orlove et al 2000, Ferreyra et al 2001, Gimeno et al 2002, Mavromatis et al 2002, Phillips et al 2002, Meza & Wilks 2003, Selvaraju 2003, but few such studies are available on EAM and ENSO in SE Asia or China. Our aims in this study were, to analyze for the major agricultural regions of China, (1) seasonal climate variability associated with EAM and ENSO and (2) the dependence of agricultural production on seasonal climate variability.…”

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confidence: 99%

Variability in climatology and agricultural production in China in association with the East Asian summer monsoon and El Niño Southern Oscillation

Tao¹,

Yokozawa²,

Zhang³

et al. 2004

The East Asian monsoon (EAM) and the El Niño Southern Oscillation (ENSO) determine climate variability over much of East Asia, affecting vulnerable grain markets and food security in China. In this study, we investigated the variability of climate and of agricultural production in China in association with the East Asian summer monsoon (EASM) and ENSO. Data from China showed that a strong EASM decreased fall temperature in Gansu and Sichuan Provinces in western China, as well as winter temperature in Heilongjiang Province in NE China and in Shandong and Anhui Provinces in eastern China. Summer rainfall in Hunan Province in southern China increased in weak EASM years. Summer temperature increased in Heilongjiang in NE China and Gansu Province in NW China during the La Niña phase. Summer rainfall decreased in Gansu Province in NW China during the El Niño phase. Among staple crops in China (rice, wheat, maize), maize production was very vulnerable to a strong EASM and El Niño phase. In Henan Province in central China, seasonal climate variability associated with EASM and ENSO resulted in about 14.4 and 15.6%, respectively, of maize yield variability. Maize yield at the national scale decreased significantly by 5.2% during the El Niño phase. Cropland area affected and damaged by floods in Hunan Province in southern China increased significantly by 11.3 and 8.5%, respectively, in weak monsoon years. During the La Niña phase, total crop planting area increased significantly in Shandong, Henan and Anhui Provinces in central China, and in Heilongjiang Province in NE China; however, it decreased significantly in Sichuan Province in SW China. The large variability in seasonal climate and agricultural production in association with EASM and ENSO warrant applying EASM and ENSO information to agricultural and food market management.KEY WORDS: Agriculture · China · Climate variability · ENSO · Monsoon · Food security · Warning system Resale or republication not permitted without written consent of the publisherClim Res 28: [23][24][25][26][27][28][29][30] 2004 EAM climate. During the winter half of the year (October-March), the climate is mainly cold and dry, with cold spells and strong winds as the major extreme climate events. During the summer period, a rain belt moves gradually from the south to the north, and the climate in eastern China becomes hot and humid. The high rate of variation in the monsoon climate strongly affects the spatial and temporal variability of Asian ecosystems (Fu & Wen 2001).Although ENSO occurs within the tropical Pacific region, its effects are felt around much of the planet, so that it accounts for a substantial portion of the observed inter-annual variability in temperatures and precipitation (Hansen et al. 1999). ENSO influences the spatial patterns of the East Asian Summer Monsoon (EASM), and of drought and floods in China (Fu & Teng 1988, Huang & Wu 1989, Zhang et al. 1999, Huang & Zhou 2002.Meteorological disaster is an important limiting factor for stable food production, and ...

“…Through its effects on weather conditions, the NAO also has an indirect influence on certain economic activities. Some studies have empirically demonstrated the influence of the NAO on the agricultural sector (Gimeno et al 2002, Kim & McCarl 2005, the energy sector (Cherry et al 2005), and marine transport (Woolf et al 2003). The influence of the NAO on the tourism sector has not previously been studied.…”

Section: Abstract: North Atlantic Oscillation · International Tourismentioning

confidence: 99%

“…The use of cross-correlation functions is the most common method of analysis applied in natural science research to detect connections between atmospheric variability described by the NAO index and different climatic, biological, and oceanographic variables (Hurrell 1995, Gimeno et al 2002, Orfila et al 2005, among many others). Nevertheless, this analysis should be handled with care since if each one of the analyzed series has a very high degree of autocorrelation, then the non-zero values of the cross-correlation function do not necessarily imply a true relationship between the 2 time series (Katz 1988).…”

Section: Cross-correlation Analysismentioning

confidence: 99%

“…One of the most often used indicators of NAO activity was developed by Hurrell (1995) and is formally defined as 208 Álvarez-Díaz et al: North Atlantic Oscillation and tourism demand the mean value of the difference between the normalized sea-level pressure between Lisbon (Portugal) and Stykkisholmur (Iceland) during the winter months (December, January, February, and March). The winter value is said to describe the whole year beginning that January (Gimeno et al 2002, Cherry et al 2005. The annual data for the NAO index can be directly downloaded from the web page of the National Center for Atmospheric Research at www.cgd.ucar.edu/cas/ jhurrell/indices.html.…”

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confidence: 99%

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Statistical relationships between the North Atlantic Oscillation and international tourism demand in the Balearic Islands, Spain

Álvarez‐Díaz

Otero-Giráldez²,

González‐Gómez³

2010

A large number of studies have been devoted to analyzing international tourism demand; however, even today, the impact of climate and weather on tourism receives only limited attention. We studied the empirical influence of the North Atlantic Oscillation (NAO), the most important mode of variability in northern atmospheric circulation, on international tourism demand -specifically, tourist arrivals from the UK and Germany to the Balearic archipelago (Spain). We used 2 traditional techniques usually applied in natural sciences, cross-correlation functions and the Granger causality test, as well as a novel and flexible methodology called the autoregressive distributed lag (ARDL) boundstesting approach. ARDL modeling can be a useful tool in illuminating relationships between variables. Our empirical evidence demonstrates the existence of a statistical relationship between the NAO and tourist arrivals from the UK and Germany to the Balearic Islands. The finding of a statistical relationship between the NAO and tourism suggests that this atmospheric phenomenon can be of great interest to social researchers who study international tourism flows. The NAO index can be used as an explanatory variable in tourism demand models, allowing researchers to develop parsimonious models, as well as to avoid certain economic problems (e.g. multicolinearity). KEY WORDS: North Atlantic Oscillation · International tourism demand · Granger causality test · Cross-correlation analysis · Autoregressive distributed lag · ARDL · Balearic IslandsResale or republication not permitted without written consent of the publisher 1 Climate and weather are meteorologically similar phenomena manifested and studied at different scales. De Freitas (2003) defines climate as the accumulation of daily and seasonal weather events over a long time period. On the other hand, weather is the condition of the atmosphere at a specific place and at a specific time Clim Res 43: 207-214, 2010 2007, Scott et al. 2008). The most widely used variables were temperature, rainfall, wet days, cloud cover, humidity, sunshine, and wind speed. However, the inclusion of these variables in a tourism-demand model could lead to some econometric problems since they are highly correlated to each other (multicollinearity problems). Other researchers have constructed indices that allow summarizing the significance of climate for tourism, but they were only used to evaluate and rate recreational climates in terms of user sensitivity and satisfaction (de Freitas 2003.One key variable that might be useful to summarize meteorological information and potentially useful to explain tourism is the recurrent pattern of atmospheric variability observed over the North Atlantic Ocean, known as the North Atlantic Oscillation (NAO). The phenomenon is formally defined as an anomalous difference in atmospheric pressure between a subtropical high-pressure belt (around the latitudes of 35 to 40°in the Northern Hemisphere and centered near the Azores) and a subpolar low-pressure belt (cen...