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Impact of El Nino-Southern Oscillation on Indian foodgrain production
International Journal of Climatology
Abstract
The impact of El Niño–southern oscillation (ENSO) on Indian foodgrain production was analysed for the period 1950–99. The inverse relationship between sea‐surface temperature (SST) anomalies from June to August (JJA) over the NINO3 sector of the tropical Pacific Ocean and Indian foodgrain production anomalies ( r = −0.50) was significant at the 1% level. During the warm ENSO phase, the total foodgrain production frequently decreased (12 out of 13 years) by 1 to 15%. In 10 out of 13 cold ENSO‐phase years, the total foodgrain production increased from normal. The relationship between the SST‐based NINO3 ENSO index and the Kharif season (June–September) foodgrain production anomalies ( r = −0.52) was greater than for the Rabi season (October–February) foodgrain production ( r = −0.27). The ENSO impact on rice production was greatest among the individual crops. The average drop in rice ( Kharif season crop) production during a warm ENSO‐phase year was 3.4 million tonnes (7%). In a cold ENSO‐phase year the average production increase was 1.3 million tones (3%). Wheat ( Rabi season crop) production was also influenced by ENSO, as it depends on the carryover soil water storage from the Kharif season. Sorghum and chickpea production are not significantly influenced by ENSO extremes.
Inter‐annual fluctuation of the gross value of Indian foodgrain production was very large, reducing up to US1251 million in a cold ENSO year. On average, a warm ENSO year costs US437 million from normal.
The cumulative probability distributions of foodgrain production anomalies during cold and warm ENSO phases are shifted positively or negatively, relative to the neutral distribution. The warm ENSO forcing significantly (1% level) reduced the probability of above‐average production. The cold ENSO forcing moderately increased the above‐average foodgrain production over the neutral ENSO phase (5% level). A simple conditional probability forecast based on annual and JJA NINO3 SST predicted the category of foodgrain production in 11 of the 14 years. The results demonstrated that the relationship between NINO3 ENSO index and foodgrain production could be used for agricultural applications and policy decisions on food security for the rapidly growing population in India. Copyright © 2003 Royal Meteorological Society.
... El Niño years resulted below normal S-W monsoon rainfall in coastal Andhra Pradesh and Rayalaseema regions with a deficit range of −55.9 to −5.4% in different districts of Andhra Pradesh [22]. The warm phase of ENSO declined the S-W monsoon rainfall by 14%, and in contrast, the cold phase of ENSO increased the S-W monsoon rainfall by 9% in India [4]. ...
... Among the different cereal crops, the impact of ENSO events was more on rice production. Out of 13 El Niño years in 6 years, the rice production declined by more than 10%, and out of 13 La Niña years in 10 years, rice production was above normal in India [4]. The spatial and temporal variability of monsoon rainfall leads to large-scale droughts or floods in one or the other part of India. ...
... duction. Out of 13 El Niño years in 6 years, the rice production declined by more than 10%, and out of 13 La Niña years in 10 years, rice production was above normal in India [4]. The spatial and temporal variability of monsoon rainfall leads to large-scale droughts or floods in one or the other part of India. ...
Monsoon fluctuation due to El Niño Southern Oscillation (ENSO) has a reflective influence on rice production, which is the major food grain crop in India. The impact of ENSO on the spatial variability of summer monsoon rainfall was analyzed from 1950 to 2018 and that on Kharif rice production for the period of 1998–2016. It was clear from the analysis that ENSO had varied influences on rainfall and rice production over different rice-growing districts of Karnataka. During El Niño (strong, moderate, and weak) years, southwest (S-W) monsoon rainfall was below normal in all the districts of Karnataka, wherein the highest negative deviation from normal was recorded in the Mysore district (−21.43%). In contrast, the rice production was higher in 15 districts out of 25, and the deviation from normal ranged from −39.73% in Bidar to 42.11% in Gulbarga district. During the La Niña (strong, moderate, and weak) years, S-W monsoon rainfall was above normal in 12 districts in which Bidar and Bengaluru urban districts have shown the highest positive deviation (19.93 and 19.82%, respectively). However, except for Udupi, Dakshina Kannada, Bidar, Davanagere, and Hassan districts, all the other major rice-growing districts have shown a positive deviation in rice production with the highest deviation of 62.39% in Tumkur district. Additionally, correlation coefficient values indicated the influence of southwest monsoon rainfall on Kharif rice production during El Niño years with a major contribution from September month rainfall. This kind of ENSO impact analysis on spatial rice production could be useful for formulating the farm-level site-specific management, planning, and policy decisions during ENSO periods in advance.
... W hen drought was used as categorical variable the coefficients turned out to be negative which implies that with the increase in drought intensity the yield reduced significantly.The sensitivity of rice yield to drought intensity was evaluated using Model 4 and the results are given in Table 7. The rice yield may reduce by 28.23% in normal dry condition to 50.45% in extreme dry condition in comparison 1975 -1987 1988 -2000 2001 -2013 No of years 1975-1987 1988-2000 2001-2013 No 1975, 1977, 2001, 1981, 1983, 1989, 1994, 2008, 13 19831989, 19912003, 20085 (0.01 to 0.99) 1985199720101975, 1977, Normal condition -dry 1978, 1982, 1988, 1995, 2002, 2003, 1978, 1981, 1994, 1995, 1986, 19871996, 1998, 2005, 2011, 15 1982, 1985-1999, 20002001 Linking Drought Intensity with Rice Yield in Nagaland to 24 year average rice yield of 1.76 MT/ha in Phek. In Dimapur, the rice yield may decline by 13.85% to 30.88% in similar drought conditions in comparison to the 14 years average yield of 2.01 MT/ha. ...
... W hen drought was used as categorical variable the coefficients turned out to be negative which implies that with the increase in drought intensity the yield reduced significantly.The sensitivity of rice yield to drought intensity was evaluated using Model 4 and the results are given in Table 7. The rice yield may reduce by 28.23% in normal dry condition to 50.45% in extreme dry condition in comparison 1975 -1987 1988 -2000 2001 -2013 No of years 1975-1987 1988-2000 2001-2013 No 1975, 1977, 2001, 1981, 1983, 1989, 1994, 2008, 13 19831989, 19912003, 20085 (0.01 to 0.99) 1985199720101975, 1977, Normal condition -dry 1978, 1982, 1988, 1995, 2002, 2003, 1978, 1981, 1994, 1995, 1986, 19871996, 1998, 2005, 2011, 15 1982, 1985-1999, 20002001 Linking Drought Intensity with Rice Yield in Nagaland to 24 year average rice yield of 1.76 MT/ha in Phek. In Dimapur, the rice yield may decline by 13.85% to 30.88% in similar drought conditions in comparison to the 14 years average yield of 2.01 MT/ha. ...
... Das et al. (2009) also reported about 25% decline in rice yield in 2009 in drought affected areas of North-eastern (NE) region of India. Many other studies found that Kharif production declines if the rainfall is lower in between June to September (Webster et al., 1998;Selvaraju, 2003;Kumar et al., 2004). Siato et al. (2006) also observed that the average rice yield declined from 2.5 MT/ ha to 1.4 MT/ha when the rainfall decreased from more than 690 mm to less than 610 mm in Laos in between the months of June to August. ...
Background: Rainfall being one of the most important factors of production for rainfed kharif rice, occurrence of drought may have serious implication on rice yield. Hence, this study is an attempt to understand the linkage between drought and yield of rice in Phek and Dimapur district of Nagaland where rice is the primary crop. Methods: Reconnaissance Drought Index was calculated using gridded daily rainfall (0.25o X 0.25o) and temperature (1o X 1o) (o=degree) data for the year of 1975-2013.Result: The annual mean temperature has increased by 0.03oC in Phek and Dimapur district during the study period. The increasing linear trends for annual temperatures are significant whereas, the linear trend for annual rainfall shows decreasing but insignificant trend. About 38.46% and 41.02% of the 39 years under study were ‘Normal condition-dry’ in Phek in Dimapur district, respectively. Moderate and severe drought occurred more frequently in Dimapur than Phek. After 1994, majority of the years were drought years and the frequency of occurrence was higher in Dimapur. The drought occurrences negatively impacted the rice yields and the rice yield may reduce by 13.85% in normal condition dry to 18.45% in extreme drought condition.
... Studies conducted by Iizumi et al. [82], Toshichika et al. [181], Anderson et al. [7], Qian et al. [153] have claimed the same ENSO event can lead to a decrease in crop yield and an increase in crop yield in a different region. [82,153] Myanmar (−) [82] Tanzania (−) [82] India (−) [153,162] Indonesia (+) [82,153] Soybean India (−) [82,153] China Southern (−) [82,153] Argentina (+) [153,144] Brazil (+) [82] Western of U.S.A. ...
... (−) [82] Argentina (+) [82] Kazakhstan (+) [82] South Africa Northestern (+) [82] 28 State of the art: ENSO and their impacts on global crops [82,153] Myanmar (+) [82] Tanzania (−) [82] India (+) [153,162] Indonesia (+) [82,153] Soybean India (+) [82,153] China Southern (+) [82,153] Argentina (−) [153,144] Brazil (−) [82] Western of U.S.A. ...
The El Niño Southern Oscillation (ENSO) phenomenon is a phenomenon of climatic variability, the effects of which can induce changes in temperature and rainfall in different countries of the world. These events impact sectors of great global relevance such as agriculture. This investigation evaluated the association between ENSO anomalies (ONI) in rice crops. As variables of reference to the crop, temperature, precipitation, soil moisture, and spectral indices such as NDVI, EVI and NDWI acquired from remote sensing image of MODIS and corroborated with Sentinel-1 images were considered for the identification of rice fields. For the identification of rice crops, the CART supervised classification algorithm was used in
three rice-growing areas located at different latitudes; The selected zones were established
in Colombia (Huila), Brazil (Rio Grande do Sul), and China (Hubei-Hunan). During the
investigation, the correlations between the ENSO anomalies and the reference variables asso�ciated with the culture were analyzed. Additionally, some vegetation indices were modeled
with respect to ONI and the other study variables using machine learning algorithms such
as neural networks and regression support machines. To measure the effect of ENSO on
rice production, the differences between the yield of the last two decades of each crop in
each phase of ENSO were compared with respect to normal climatic conditions. Most of the
estimated impacts were slightly negative, the most significant impacts being those reported
in the study region in Brazil and in Colombia during the second growing season.
... Kharif (summer) is the main crop growing season of Haryana as well as India, and variation in monsoon rainfall during this season from June to September influences crop growth as well as crop production. The years with higher ISMR are associated with higher crop productivity, and the years with lower ISMR are associated with lower crop productivity during kharif season (Guhathakurta & Rajeevan, 2008;Krishna Kumar et al., 2004;Prasanna, 2014;Selvaraju, 2003;Webster et al., 1998). A long-term understanding of vegetation dynamics and associated factors is crucial for demonstrating climate change patterns and monsoon variability (Sharma et al., 2021). ...
... A decrease in the yields of rice by 3% was observed during the year 1987 as compared to the yields of previous years witnessed at the country level (Sarma et al., 2008). Selvaraju (2003) and Krishna Kumar et al. (2004) also noticed a decline in the foodgrain production in India during the year 1987. According to Saini and Gulati (2014), the food grain production dropped by 18% and agricultural gross domestic product dropped by 7% with an approximate loss of USD 8 billion. ...
Our study has investigated the impact of El Niño–Southern Oscillation (ENSO) on spatio-temporal dynamics of Indian summer monsoon rainfall (ISMR) as well as vegetation for a period of 1980 to 2019 at regional scale in Haryana, India. The gridded rainfall datasets of India Meteorological Department (IMD) were examined on monthly and seasonal scale using various statistical methods like mean climatology, coefficient of variation, slope of linear, Sen’s slope, Mann–Kendall Z statistic, and hierarchical cluster analysis. The influence of ENSO on spatial distribution of ISMR was observed, where we found increasing and decreasing rainfall patterns during La Niña and El Niño years, respectively. We attempted to establish a link between ISMR and various teleconnections using time series of the National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory, and statistically significant and positive correlation was observed with the Southern Oscillation Index (SOI), whereas significantly negative correlations were observed with SST of Niño 3, Niño 3.4, and Niño 4 regions. The gridded datasets of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis V5 (ERA5) were used to evaluate the influence of ENSO on atmospheric dynamics using lower and upper tropospheric wind circulation (850 hPa and200 hpa), vertically integrated moisture transport (VIMT), and surface moisture flux (SMF). We have used satellite-based normalised difference vegetation index (NDVI) datasets of the Global Inventory Monitoring and Modeling System (GIMMS) to investigate the impact of ENSO on vegetation dynamics of Haryana and found that NDVI values were higher and lower in case of La Niña and El Niño years, respectively.
... Though some experimental and simulation studies demonstrate elevated CO 2 in the atmosphere help crops favorably (Baker et al. 1992;Kimball et al. 2002;Krishnan et al. 2007), related impact of increased temperature, changing patterns of rainfall and extreme weather events is likely to increase risks in crop production (Matthews et al. 1997;Parry et al. 2004;Fofana 2011;Pal and Mitra 2018;Nath and Mandal 2018;Guntukula and Goyari 2020). Apart from the physical impact of climate on crop yields (Selvaraju 2003;Gupta et al. 2014;Zhang et al. 2017), there is a fair amount of literature on monetary impact of climate on yields (Kumar and Parikh 2001;Kumar 2009;Guiteras 2009;Fishman 2012). Studies have also estimated impact of climate change on land value or net revenues (Mendelsohn et al. 1994;Massetti and Mendelsohn 2011;Mishra et al. 2016). ...
... For example, the coherence of actual rainfall with cotton, maize, and rice seems to be significant during 1985-1994 at six-year time horizon. Though rainfall pattern does not observe any specific structural change in our analysis, coherence of rainfall with crop yields is in line with the existing literature in the context of India (Parthasarathy and Pant 1985;Parthasarathy et al. 1992;Selvaraju 2003;Kumar et al. 2004). ...
Climate change is a major concern the world over more so for a predominantly agrarian country like India. In this paper we analyze the time horizon dynamics of crop and climate variables at the regional level in India. We also analyze the co-movements of crop yields with temperature and rainfall to observe the coherence across heterogenous time horizons. We employ Bai-Perron structural break and Continuous wavelet transform methods on yearly data of seven crop yields and climate variables. Observed variables are analyzed from 1956 to 2010 for the un-divided state of Andhra Pradesh, India. Breakpoint analysis shows an increase of around 1.0° temperature with two observed break points. Rainfall depicts no systematic change with fluctuations being largely random. The framework of wavelets-based time–frequency analysis employed in this study captures climate and crop dynamics at heterogeneous time horizons, allowing one to study the impact of climate and crop yields at both short and longer time-horizons. Wavelet based coherence analysis exhibited significant co-movement between climatic and crop variables. Given shifts in climate patterns and subsequent shifts in co-movements across time horizons at the regional level, policy makers and crop scientists should design time specific and locally viable adaption and mitigation policies to tackle the impact of climate change on crops and livelihoods.
... Though some experimental and simulation studies demonstrate elevated CO2 in the atmosphere help crops favorably (Baker, Allen, & Boote, 1992;Kimball, Kobayashi, & Bindi, 2002;Krishnan, Swain, Bhaskar, Nayak, & Dash, 2007); related impact of increased temperature, changing patterns of rainfall and extreme weather events is likely to increase risks in crop production (Fofana, 2011;Matthews, Kropff, Horie, & Bachelet, 1997;Parry, Rosenzweig, Iglesias, Livermore, & Fischer, 2004). Apart from the physical impact of climate on crop yields (Lahari & Roy, 1985;Selvaraju, 2003;Gupta, Sen, & Srinivasan, 2014); there is a fair amount of literature on monetary impact of climate on yields (Kumar & Parikh, 2001;Kumar, 2009;Guiteras, 2009;Fishman, 2012. Studies have also estimated impact of climate change on land value or net revenues (Mendelsohn, Nordhaus, & Shaw, 1994;Massetti & Mendelsohn, 2011). ...
... Overall, high coherence or co-movement between average temperature and the crops under study seems to be significant at yearly (Parthasarathy and Pant, 1985;Parthasarathy et al., 1992;Selvaraju, 2003;Kumar et al. (2004). ...
Climate change is a major concern the world over; more for a predominantly agrarian country like India. In this paper we analyze the time horizon dynamics of crop and climate variables at the regional level in India. We also analyze the co-movements between crop yields and temperature and rainfall to observe the coherence across heterogenous time horizons. We employ Bai-Perron structural break and Continuous wavelet transform methods on yearly data of seven crop yields and climate variables. Observed variables are analyzed over a period of 55 years from 1956-2011 for the un-divided state of Andhra Pradesh, India. The study shows that there is a steady and constant increase of around 1.00 in annual average, maximum and minimum temperatures in the state, with two observed break points at 1978 and 2002. Rainfall depicts no systematic change with fluctuations being largely random. Wavelet based coherence analysis that map time-horizon specific dynamics of climate and crops with time periods exhibited significant co-movement between climatic and crop variable. Given shifts in climate patterns and subsequent shifts in co-movements across time horizons at the regional level, policy makers and crop scientists should design time specific and locally viable adaption and mitigation policies to tackle the impact of climate on crops and livelihoods.
JEL Classification: C13, Q10, Q15, Q50, Q54
... It changes the spatiotemporal pattern of temperature and precipitation in several regions of the world, leading to complicated responses of the ecosystem [1,2]. El Niño not only threatens human lives in a direct way by droughts and floods, but also indirectly through its impact on disease epidemics [3,4] or crop production, which leads to famine [3,[5][6][7]. Thus, the study of El Niño is of strong societal importance and its changes in frequency and intensity is the subject of intense ongoing research. ...
... Year with Strong El Niño Month 1958, 1964, 1966, 1969, 1973, 1983, 1987, 1988, 1992, 1995, 1998, 2003, 2010, 2016February 1958, 1966, 1969, 1973, 1983, 1987, 1992, 1998, 2010, 2016March 1958, 1966, 1969, 1983, 1987, 1992, 1998, 2010, 2016April 1958, 1983, 1987, 1992, 1998, 2016May 1957, 1983, 1987, 1992, 2015June 1957, 1965, 1972, 1987, 1997, 2015July 1957, 1963, 1965, 1972, 1987, 1997, 2015August 1951, 1957, 1963, 1965, 1972, 1982, 1987, 1997, 2002, 2015September 1951, 1957, 1963, 1965, 1972, 1982, 1987, 1997, 2002, 2015October 1951, 1953, 1957, 1963, 1965, 1969, 1972, 1976, 1982, 1986, 1987, 1997, 2002, 2009, 2015November 1951, 1953, 1957, 1963, 1965, 1969, 1972, 1976, 1977, 1982, 1986, 1987, 1991, 1994, 1997, 2002, 2009, 2015December 1951, 1953, 1957, 1963, 1965, 1968, 1972, 1976, 1982, 1986, 1987, 1991, 1994, 1997, 2002, 2009, 2015 The study area of this paper covered both the northern and southern hemisphere, where the seasons were opposite. In the remainder of this paper and the Supplementary Materials, the seasons are indicated by the boreal seasons. ...
El Niño influences the global climate through teleconnections that are not constant in space and time. In order to study and visualize the spatiotemporal patterns of the El Niño teleconnections, a new method inspired by the concept of attribute trajectories is proposed. The coordinates of the trajectories are the normalized anomalies of the relevant meteorological variables in El Niño. The data structures called flocks are extracted from the trajectories to indicate the regions that are subject to the same type of El Niño teleconnection for a certain period. It is then shown how these structures can be used to get a detailed, spatiotemporal picture of the dynamics of the El Niño teleconnections. The comparison between the flocks of the same temporal scale reveals the general dynamics of the teleconnection, while the analysis among the flocks of different temporal scales indicates the relationship between the coverage and their duration. As an illustration of this method, the spatiotemporal patterns of the anomalous temperature increase caused by El Niño are presented and discussed at the monthly and seasonal scales. This study demonstrates the capability of the proposed method in analyzing and visualizing the spatiotemporal patterns of the teleconnections.
... Rice requires more water than any other cereal crops (Bouman et al. 2007), which can make it more susceptible to drought. Selvaraju (2003) found that ENSO has a greater effect on rice production of India than other crops (wheat, sorghum, and legumes). Several studies have investigated the effect of ENSO on rice production in the Asian region (e.g., Zubair 2002;Selvaraju 2003;Falcon et al. 2004). ...
... Selvaraju (2003) found that ENSO has a greater effect on rice production of India than other crops (wheat, sorghum, and legumes). Several studies have investigated the effect of ENSO on rice production in the Asian region (e.g., Zubair 2002;Selvaraju 2003;Falcon et al. 2004). All these studies showed negative effects on rice production in various Asian countries with the seasonal impact of climatic variations. ...
Connecting climate-induced yield anomalies to the key climatic variables (KCVs) and large-scale atmospheric circulation index (LACI) is crucial to developing a strategic policy for food security in developing countries including Bangladesh. However, the effects of the KCVs and LACI on rice yield are still less explored in Bangladesh. This study aims to investigate daily climatic datasets from 18 sites and five LACIs during 1980–2017 to explore climate-induced yield anomalies to the KCVs and LACIs in different sub-zones of Bangladesh using the decoupling model and ensemble empirical mode decomposition (EEMD). We demarcated four sub-zones (northern, southwestern, south-central, and western) with different climate-induced yield index (CIYI) of winter Boro rice oscillations by employing principal component analysis. The CIYI time series in the northern zone was dominated by a 2–4-year oscillation, whereas the CIYI in the western zone demonstrated a prominent 6.5-year oscillation. Among the four sub-zones, south-central and northern zones had the most notable CIYI-KCV and CIYI-LACI associations, while the potential evapotranspiration (PET) in March and multivariate ENSO indices (MEI) in January were identified as the best yield prediction indicator. Wavelet coherence analysis indicated significant in-phase and out-phase coherences between KCVs and CIYI fluctuations at different time-frequency bands in these sub-zones. The random forest model also confirmed the MEI as the key driver influencing the rice yield fluctuation in Bangladesh. The isotopic signature of rainwater also demonstrated that the temperature variation is the main driver for event-based precipitation change in the south-central rice-growing zone. These outcomes can provide a scientific basis to take adaptive measures to mitigate the reduction in rice yield in western and northern Bangladesh for associated decision-makers and practitioners.
... According to Assessment Report Six (AR6) of Intergovernmental Panel on Climate Change (IPCC), monsoon rainfall trends in South Asia have been shaped by both greenhouse gas-induced warming and aerosol-driven cooling over the twentieth century (Dawadi et al. 2022). The performance of the monsoon (Parthasarathy et al. 1988), regional variations in monsoon rainfall (Krishna Kumar et al. 2004), and the overall fluctuations in monsoon pattern (Selvaraju 2003) play a crucial role in determining agricultural productivity in India. Both inter-annual and intra-seasonal variations of the southwest monsoon are influential in determining crop production and productivity throughout the kharif and rabi seasons (Bapuji Rao et al. 2014). ...
Climate variability has significant impacts on agricultural productivity in the Northeastern region (NER), especially during the monsoon season. This study investigates the trends and implications of key climate parameters — maximum temperature (Tmax), minimum temperature (Tmin), rainfall anomaly index (RAI), net shortwave radiation flux (NSWRF), soil moisture (SM), and relative humidity (RH) — on rice yield (Kharif) throughout the period from 1991–2020. Results indicate a significant increasing trend in Tmin (0.02 °C yr⁻¹ in Phase-II), which exhibits the strongest positive correlation (r = 0.70, p < 0.0001) with rice yield. A shift from a drying trend in Phase-I (-0.034 yr⁻¹) to a wetting trend in Phase-II (0.18 yr⁻¹) contributes to improved soil moisture and humidity, enhancing rice productivity. Despite an overall increasing yield trend (30.7 kg ha⁻¹ yr⁻¹), extreme dry conditions in 2006–07 results in yield anomalies of -6.5% and -4.8% respectively. Machine learning models, Random Forest (RF) and Extreme Gradient Boosting (XGBoost) are used to predict rice yield, with RF demonstrating superior accuracy (R² = 0.78, RMSE = 117.03, MAPE = 5.70%) compared to XGBoost (R² = 0.71, RMSE = 133.25, MAPE = 6.42%). Both models identify Tmin and Tmax as the most influential variables, followed by NSWRF and RAI. Overall, this study highlights the critical role of Tmin in improving rice yields and offers valuable insights for developing sustainable agricultural strategies and decision-making processes to ensure long-term food security amidst evolving climatic conditions.
... This level of variability is comparable to an absolute variation of Pan-American supply on the order of several percent. Asian rice production has also been found to correlate with ENSO events (Service 2014), with regional case studies suggesting that ENSO is a dominant source of variability for major producers like India and Indonesia (Selvaraju 2003;Naylor et al. 2001) but less so for China (Deng et al. 2010). ...
... There is prominently negative relationship between the mean rainfall and the ONI values, indicating the drought conditions under El Niño and wetter conditions under La Niña (r = 0.49, p < 0.01) (Fig. 3b). Rice yield change has been improved during the La Niña years, which are characterized by adequate rainfall in the years 1999, 2007, 2008, and 2010 [30][31][32] . These relationships also reflect the high association between the Oceanic Nino Index values and the estimated RYC (r = −0.61, ...
Climate change affects Indian agriculture, which depends heavily on the spatiotemporal distribution of monsoon rainfall. Despite the nonlinear relationship between crop yield and rainfall, little is known about the optimal rainfall threshold, particularly for monsoon rice. Here, we investigate the responses of rice yield to monsoon rainfall in India by analyzing historical rice production statistics and climate data from 1990 to 2017. Results show that excessive and deficit rainfall reduces rice yield by 33.7% and 19%, respectively. The overall optimal rainfall threshold nationwide is 1621 ± 34 mm beyond which rice yield declines by 6.4 kg per hectare per 100 mm of rainfall, while the identifiable thresholds vary spatially across 14 states. The temporal variations in rice yield are influenced by rainfall anomalies featured by El Niño-Southern Oscillation events.
... Production of Kharif and Rabi crops has been extensively explored in numerous research studies (Al Mamun et al., 2022;Bibi et al., 2021;Charjan et al., 2023;Chhabra & Haris, 2014;Gadakh, Dalvi, Nirmal, & Dudhade, 2021;Krishna Kumar, Rupa Kumar, Ashrit, Deshpande, & Hansen, 2004;Kumar, KaurSidana, & Thakur, 2023;Mujtaba et al., 2022;Nageswararao et al., 2018;S. Pal et al., 2022;Selvaraju, 2003;Shinde, Jadhav, Patil, Bavadekar, & Pawar, 2021). The objective of the study is: (1) To prepare NDVI, LST, and Rainfall map of the study region (2) To analyze the connection between NDVI and LST (3) To analyze the relation between the NDVI and Rainfall. ...
Current study aims at to analyze the environmental influence on Rabi crops and to analyze the rainfall pattern with the vegetation pattern. Despite a declining GDP contribution, agriculture remains a fundamental pillar of Pakistan's economy, supporting livelihoods, nutrition, and export earnings. Focused on Mianwali District, the research integrates data from Landsat satellites, MODIS LST, and rainfall records to untangle relationships between environmental factors and Rabi crop productivity. Analysis of the Normalized Difference Vegetation Index (NDVI) provides insights into crop health by revealing variations in vegetation cover. Land Surface Temperature (LST) data offers perspectives on thermal conditions during the Rabi season, crucial for understanding water stress. Rainfall data assists in assessing water availability and its impact on crop yield. Correlation analysis highlights the direct impact of environmental conditions on agricultural productivity. Temporal trends show diversification in crop types, with "High Crops" on an upward trajectory, while the overall crop area in Mianwali District consistently decreases, possibly linked to changing environmental patterns. Land Surface Temperature conditions suggest potential environmental adaptations, supported by a decrease in LST values from 2000 to 2022, indicating improved thermal conditions or adaptive strategies. Rainfall analysis underscores the significance of understanding climatic patterns for sustainable agriculture. The correlation between NDVI and LST emphasizes vegetation sensitivity to thermal conditions, providing valuable insights for ecological studies and precision agriculture. The positive correlation between Rainfall and NDVI highlights the crucial role of water availability in fostering vegetation health and guiding sustainable agricultural practices.
... One country in Southeast Asia, Malaysia, is vulnerable to El Nino, which results in a lack of rainfall during the dry season (Alam et al., 2020). Selvaraju (2003) states that El Nino significantly affects food grain production. According to research, El Nino is one of the most likely causes of climate change, which is directly linked to droughts and flooding, which have profound implications for agricultural production and the broader economy (Al-Amin and Alam et al., 2020;Atems and Sardar, 2021;Cashin et al., 2017). ...
Purpose
This research paper aims to empirically explore how stock market investors’ perceptions are affected by extreme climatic events like El Nino and floods in Malaysia.
Design/methodology/approach
This study uses structural equation modelling (SEM) to analyse the empirical data gathered through a questionnaire survey involving 273 individual investors from Bursa Malaysia between January and June 2019.
Findings
Results reveal that companies’ efforts, especially for agriculture and plantation-based industries, to adapt to climate change risk at the production, business and stock market levels significantly impact investors’ behaviour and investment decisions. Moreover, stock market investors’ climate change knowledge shows a significant moderating effect on corporate climate change adaptation initiatives and investors’ decisions to invest in Malaysian agricultural and plantation industry stocks.
Practical implications
This research has significant implications for practice and policy, as it measures the stock market investors’ level of awareness about climate change events and explores the companies’ strategies to reduce climatic risks to their business model.
Social implications
This study shows the way to adjust the climate change information in the stock market investment decision to improve market efficiency and sustainable stock exchanges initiative.
Originality/value
To the best of the authors’ knowledge, this paper is the pioneer one to provide a comprehensive link between climate change events and business performances at production level, business level and stock market levels by drawing inferences from empirical data on investors’ behaviours. This study also added value in investment theories and financial literature by observing the climate change as an important factor to determine the investors’ decisions in the stock market.
... The drivers of failure are persistent atmospheric subsidence, with the EI Niño phase of the Southern Oscillations (ENSO) having the strongest relationship with drought (Kumar et al., 2006;Ummenhofer et al., 2011). ENSO driven summer drought has been correlated with historic reductions in summer grain production, with flow-on reductions in A.K. Pokharia et al. winter crops requiring carryover soil moisture (Selvaraju, 2003). Historical accounts of famines in India record 14 famines between the 11th and 17th centuries, 12 between 1769 and 1858, and 20 between 1860 and 1908 (Das, 1988). ...
he relationship between historical climate change and past agricultural production contributes to a better understanding of the impacts of projected climate change by providing empirical data for resilient human responses. This study explores the periods of dynastic transitions and crop production at the urban site of Vadnagar, in semi-arid northwest India through several climate events, generally characterised by weakening summer monsoon precipitation during the Late Holocene. Artefacts from the site present an unbroken sequence of seven successive cultures from the first century BCE to the nineteenth century CE. Archaeobotanical data indicate the sufficient water availability during the Historic and Medieval periods, allowing crop production dominated large-grained cereals (C3 plants). However, during the Post-Medieval period (ca.1300−1850 CE) a resilient crop economy based on small-grained cereals (C4 plants) dominated, representing a human adaptation to prolonged weakening of monsoonal precipitation. Isotopic and phytolith data at the site present a clear signal of changing local environmental conditions over two millennia, consistent with regional palaeoclimate records, providing and interpretive context for agricultural evidence at Vadnagar. Despite long-term reduction in summer humidity, we argue that an adaptable agricultural package coupled with suitable water management systems allowed for the resilience of the urban settlement at Vadnagar.
... The impact of El Niño on global agriculture is variable, depending on location, crop type, season, and cropping practices Analysis of yield anomalies for a few major crops indicate that crop yields were negatively impacted in up to 22-24% of harvested areas worldwide (Iizumi et al., 2014) in El Niño years. Declines are usually observed in rice in southern China, Myanmar, and Tanzania; in wheat, in parts of China, the USA, Australia, Mexico, and parts of Europe; in maize, in south-eastern USA, China, East and West Africa, Mexico, and Indonesia; and in soybean, in India and parts of China (Selvaraju, 2003;Haile et al., 2021). In contrast, positive anomalies (crop yield increases) were observed in up to 30-36% of harvested areas worldwide. ...
The ongoing, 2023–2024 El Niño appears to become a strong event, possibly comparable to the other strong El Niño episodes on record. In itself, and because it coincides with grave tensions – economic, political and environmental across the world – the phenomenon could have major climatic, social, and economic consequences. This Opinion article considers the scientific information available, explores the possible climatic consequences, and assesses the impacts on global food security. The case of India, one of the main agricultural and most populated country in the world, is considered.
... However, the relationship between Indian monsoon rainfall patterns and El Niño is not as strong as it is in Australia, and only strong Niño typically has a negative impact on India (Patel et al., 2014). Selvaraju (2003) investigated the variability of Indian summer monsoon rainfall (ISMR) with ENSO phases (warm and cold) in major food grain producing sub-divisions of India from the year 1950 to 1999, concluding that warm ENSO years (El Niño years) reduce food grain production, as monsoon rainfall was considered as a critical input for both Kharif and Rabi season crops in intensive crop production systems. In 12 of the 13 warm El Niño years during 1950 to 1999, total food grain output was declined by 1.2 to 14.9%. ...
ENSO is considered as one of the most important recurring climatic pattern which have the ability to alter global atmospheric circulation. Despite being a single climate phenomenon, ENSO can exist in three states or phases. A neutral phase and two opposing phases El Niño and La Niña. El Niño is characterised by episodic warming of sea surface temperatures (SST) across the central and east-central Equatorial Pacific Ocean, whereas La Niña is the inverse event, characterised by episodic cooling of SST. El Niño events are typically associated with warm and dry conditions in Australia's southern and eastern inland areas, Indonesia, Philippines, and central Pacific islands such as Fiji, Tonga and Papua New Guinea. Indian summer monsoon rainfall (ISMR) inter-annual variability has been linked to variations in sea surface temperatures over the equatorial Pacific and Indian Oceans. ENSO events have a significant impact on Indian summer monsoon rainfall (ISMR) across India, with El Niño events causing the majority of droughts. Due to the agriculture sector's significant contribution to the Indian economy, El Niño-induced deficit rainfall can reduce summer crop production hampering food security, resulting in high inflation rates and lower GDP of the nation. This chapter examines the occurrence of El Niño and La Niña events, as well as their impact on climate in various parts of the world, with a particular emphasis on the Indian monsoon season and food security.
... Arumugam et al., 2014) [16][17][18][19][20]. ...
The significant reduction in agricultural production in Sri Lanka, compounded by the rising prices of fuel and basin food items followed by agricultural food shortages in Afghanistan and increase in food consumption worldwide carry our global concern towards food security and sustainability mere self-sufficient in production. Agriculture is extremely vulnerable to climate change. Extreme climate happens like heavy rainfall, high temperature and drought making heavy losses in agriculture regionally and significantly damaging the harnessing of better crop yields. As a major staple food crop, Paddy is selected for examining the impact of climate variability on paddy crop yield and variance thereof in Indian state of Tamil Nadu. Season and Crop Reports published by the Department of Agriculture, Government of Tamil Nadu and NASA Power Data are the secondary data sources were used for the study. Just-Pope yield function was used to determine the influence of climate variables on mean crop yield and variance. The results indicate that yield of paddy increases from the increase in temperature, however, are negatively associated with precipitation intensity. The variability in the yield of paddy also increases with increase in rainfall. The study has suggested weather-based crop insurance policy and climate-resilient farming techniques to reduce the losses to the farmers.
... Numerous experts have concluded that ENSO events have an effect on the price volatility of agricultural products, but that this effect is not just attributable to supply and demand issues but also to the expectations of future investors. There are a limited number of studies on this topic, which is also the focus of this investigation [25]. ...
Frequent occurrences of the EI-Nino phenomenon have made the influence of ENSO on the commodity market a hot research topic. To further investigate the connection between climate change and commodities markets and to offer a benchmark for future economic growth This study focuses on the impact of El Nio on the futures price yield of U.S. soybeans to give a new research direction for academics and investors. This research employs the GARCH-Mean model to estimate returns, then applies the event study approach and parametric tests to see whether there are significant differences in the futures price yield for U.S. soybeans. Test results indicate that EI Nino events have not had a substantial impact on the yield of U.S. soybean futures prices during the recent EI Nino phenomenon. This conclusion indicates that climate change may not be the primary factor influencing the future price of U.S. soybeans, and researchers must consider other factors. In addition, as a result of technological advancement, the government may predict that investors had already prepared for the volatility of the commodity market.
... Most of the drought conditions of the ISMR are associated with El Niño during the period 1901-2012; out of 18 drought years, 7 years are associated with strong El Niño and 6 are associated with El Niño years and thus about 72 % of the drought years are associated with the influence of Pacific Ocean (Varikoden et al., 2014). Several studies were undertaken to link the ISMR, droughts and El Niño and its impact of food grain production over India (Selvaraju, 2003;Shweta Saini and Gulati, 2014;Gadgil and Francis, 2016;Bhatla et al., 2019). However, there are only few/limited studies which try to analyse the El Niño effect on agricultural productivity and that also at the macro level (Rao et al., 2011;Prasad et al., 2014;Patel et al., 2014;Ajithkumar and Vysakh, 2018;Dakhore et al., 2020;Bhuvaneswari et al., 2013). ...
The spatial and temporal variability of Indian Summer Monsoon Rainfall (ISMR) during El Niño years (2002, 2004 & 2009) and its impact on district-wise yield of four important kharif crops viz., rice, maize, pearlmillet and sorghum over India were analysed. The number of districts fall under different rainfall classes are different in normal and El Niño years and found that there is a small shifting of spine curve towards left, which indicate the number of districts under lower rainfall classes increased in El Niño years. As far as National level analysis indicated that the yield of these major kharif crops grown steadily during the study period with higher rate of 43 kg/ha in maize and lower rate of 6.2 kg/ha in sorghum due to technological advancement. Identified high and medium vulnerable districts with respect to above crops in El Niño years based on yield changes > −10 per cent compared to average of previous and succeeding years. 77 and 36 districts identified as highly vulnerable with respect to rice and sorghum, respectively. Whereas 36 and 65 districts in the case of sorghum and maize, respectively. Thus, the results of this study provide an opportunity to policy makers/ researchers/other stakeholders to prepare short and long term contingency plan to cope up with the situation, if there is any forecast of El Niño in future.
... For maize, El Niños increase precipitation and reduce the incidence of damaging maximum temperatures relative to La Niñas in Southeast South America (Anderson et al., 2017a(Anderson et al., , 2018Cunha et al., 2001;Podestá et al., 1999) and the US Midwest (Anderson et al., 2017a(Anderson et al., , 2017bHandler 1984) (Fig. 6d), which translates into above-expected maize yields (Fig. 6b). El Niños, however, also force drought in South Africa (Anderson et al., 2019;Funk et al., 2018), India (Selvaraju et al., 2003), and Northern China (Liu et al., 2014), which translates to below-expected maize yields in these breadbaskets during El Niños relative to La Niñas (Fig. 6b). As a result of these teleconnections, El Niños tend to increase the probability of joint yield shocks in pairs of breadbaskets that include South Africa by 10-40%, most notably modifying the probability of joint yield shocks in South Africa and Northern China, and South Africa and India. ...
That climate variability and change can potentially force multiple simultaneous breadbasket crop yield shocks has been established. But research quantifying the mechanisms behind such simultaneous shocks has been constrained by short records of crop yields. Here we compile a dataset of subnational crop yields in 25 countries dating back to 1900 to study the frequency and trends in multiple breadbasket yield shocks and how large-scale climate anomalies on interannual timescales have affected multiple breadbasket yield shocks over the last century. We find that major simultaneous breadbasket yield shocks have occurred in at least three, four, or five of nine breadbaskets 10.3%, 2.3% and 1.1% of the time for maize and 18.4%, 4.6% and 2.3% of the time for wheat. Furthermore, we find that multiple breadbasket yield shocks decreased in frequency even as those breadbaskets experience increasingly frequent climate-related shocks. For both maize and wheat breadbaskets, there were fewer simultaneous yield shocks during the 1975-2017 time period as compared to 1931-1975. Finally, we find that interannual modes of climate variability-such as the El Niño Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the North Atlantic Oscillation (NAO)-have all affected the relative probability of simultaneous yield shocks in pairs of breadbaskets by up to 20-40% in both maize and wheat breadbaskets. While past literature has focused on the effects of ENSO, we find that at the global scale the NAO affects the overall number of wheat yield shocks most strongly despite only affecting northern hemisphere breadbaskets.
... This large inter-annual variability in rainfall and consecutive drought may have a serious impact on the agriculture system in the mid-hills of the Himalayas. Selvaraju [69] studied a relationship between Indian summer monsoon rainfall (SMR) and food grain production (FGP) in India and stated that inter-annual variability in SMR and total food grain production anomalies are closely related. Many researchers have pointed out the impact of drought on agriculture and farming system in different parts of the mid-hills of the Nepaesel Himalayas [70]. ...
Farming communities in the hills and mountains of the Himalayan region are some of the most vulnerable to the changing climate, owing to their specific biophysical and socioeconomic conditions. Understanding the observed parameters of the changing climate and the farmers’ perceptions of it, together with their coping approaches, is an important asset to making farming communities resilient. Therefore, this study aimed to explore the observed change in climatic variables; understand farmers’ perceptions of the changing climate; and document their adaptation approaches in farming systems in the mid-hills of the central Himalayas. Data on the observed change in climatic variables were obtained from the nearby meteorological stations and gridded regional products, and farmers’ perceptions and their adaptation practices were collected from household surveys and from the interviews of key informants. The analysis of temperature data revealed that there has been a clear warming trend. Winter temperatures are increasing faster than summer and annual temperatures, indicating a narrowing temperature range. Results on precipitation did not show a clear trend but exhibited large inter-annual variability. The standardized precipitation index (SPI) showed an increased frequency of droughts in recent years. Farmers’ perceptions of the changing climate are coherent with the observed changes in climatic parameters. These changes may have a substantial impact on agriculture and the livelihood of the people in the study area. The farmers are adapting to climate change by altering their farming systems and practices. Location-specific adaptation approaches used by farmers are valuable assets for community resilience.
... It is quite evident from a number of studies that in a year of lower ISMR, the food grain production during kharif season is also lower (Webster et al. 1998;Selvaraju 2003;Krishna Kumar et al. 2004). One of the most severe draughts of decade was noticed during the summers of 2009 leading to a decline of 14% in rice harvest (CCAP 2010). ...
This study examined the long-term (1980–2019) spatio-temporal trends, variability and teleconnections of Indian summer monsoon rainfall (ISMR) of all districts of Haryana, India and their impact on agricultural productivity. The gridded datasets of India Meteorological Department (IMD) were used to statistically analyse the rainfall distribution, trend, coefficient of variation and intensity of rainfall. The gridded datasets of European Centre for Medium‐Range Weather Forecasts (ECMWF) atmospheric reanalysis V5 (ERA5) were examined for lower and upper tropospheric wind circulation (850 hPa and 200hpa), vertically integrated moisture transport (VIMT) and surface moisture flux (SMF). The datasets of National Oceanic and Atmospheric Administration (NOAA) were correlated with ISMR and composite deviation of rainfall and rainfall intensity during El Niño and La Niña from neutral years was examined at district level. Our analysis revealed that districts lying in eastern agroclimatic zone (EAZ) of Haryana received more ISMR during each month of monsoon season as compared to the ones situated in western agroclimatic zone (WAZ). Trend-free pre-whitening Mann–Kendall (TFPW-MK) test revealed that Kurukshetra, Panipat, Ambala, Rohtak, Faridabad, Jhajjar, Sonipat, Fatehabad and Palwal have shown a decreasing trend while Mahendragarh and Panchkula have shown an increasing trend of rainfall. During the El Niño years, most of the locations in the state received deficient to large deficient category, whereas during the La Niña episodes, most of the locations received excess to large excess category of ISMR, which is indicative of the influence of El Niño–Southern Oscillation (ENSO) on the regional scale. The influence of ISMR on bajra productivity for the districts lying in WAZ and rice productivity for the districts lying in EAZ was undertaken. This study is beneficial for understanding the impacts of climate change and climate variability on ISMR dynamics in Haryana which may further guide the policy-makers and beneficiaries for optimising the use of hydrological resources.
... Khor et al. (2021) computed that the opportunity losses because of El Nino, beginning from 1986 (excluding 2018, 2019), were around USD 9.55 billion, while Oettli et al. (2018) discovered that La Nina was favorable for improving profit. These results are consistent with Selvaraju (2003), examining the impact of ENSO on food grain production, uncovering that total food grain production increased from normal during La Nina. ...
El Nino Southern Oscillation (ENSO) causes rainfall anomalies, which may disrupt Indonesia’s natural rubber production by interfering with the trees’ growth and affecting the export volume. This study analyzed the effect of ENSO dynamics on the monthly productivity of natural rubber and Technically Specified Natural Rubber (TSNR) 20 export. Monthly data from January 2006 to December 2019 were collected from the Statistics Indonesia, International Trade Centre (ITC), World Bank, Bank Indonesia, and National Ocean and Atmospheric Administration (NOAA). Descriptive statistics unveiled that strong La Nina increased the average of monthly productivity by 3.37% to 9.68%, while strong El Nino tended to decrease productivity by 1.30% to 9.27%. Moreover, the Vector Error Correction Model (VECM) demonstrated the negative effect of ENSO on Indonesia’s natural rubber export, both in the short and long term.
... The La Nina phenomenon, on the other hand, causes an increase in rainfall, which leads to an increase in the availability of water for production. This study is supported by a study by Selvaraju (2003) that during El Nino rice production decreased by 7% and La Nina rice production increased by 3% in India. ...
Climate change has become a global issue since it has a wide range of effects on a country's socioeconomic situations, including food security. The El Nino Southern Oscillation (ENSO) is a climatic change phenomenon in Indonesia that has three phases: El Nino, Normal, and La Nina. This phenomenon has an impact on rainfall intensity, which bring another impact on the agricultural sector, particularly the sub-sector of food crops. This study uses static panel data methods to examine the impact of climate change and other determinants on food production such as rice, corn, and soybeans. The cross-sectional data are concentrated on the provinces of Java that have become the production centers of rice, corn and soybeans, and the other four provinces with the highest yields of the above three products, and the time data series used from 2009 to 2017.The results obtained show that El Nino has a significant effect on decreasing rice and corn production and increasing soybean production. While La Nina has a significant effect on increasing the production of the three commodities studied. Fertilizer subsidy has a significant effect on increasing the production of the three commodities studied meanwhile productivity significantly impact rice and corn productions.
... Climate change impacts, such as rising temperatures, droughts, floods, plant diseases, pests, and other factors, may have a detrimental impact on net crop production. Variability in interannual monsoon rainfall causes droughts and floods on a massive scale in India, wreaking havoc on the country's agricultural production and economy (Parthasarathy et al., 1992;Selvaraju, 2003). Due to India's fast-rising population, current infrastructure, methodologies, crop selection, and cropping systems must be altered to address these climate change barriers. ...
... Climate change impacts, such as rising temperatures, droughts, floods, plant diseases, pests, and other factors, may have a detrimental impact on net crop production. Variability in interannual monsoon rainfall causes droughts and floods on a massive scale in India, wreaking havoc on the country's agricultural production and economy (Parthasarathy et al., 1992;Selvaraju, 2003). Due to India's fast-rising population, current infrastructure, methodologies, crop selection, and cropping systems must be altered to address these climate change barriers. ...
Climate change is misunderstood as the changes in the weather. There is little consensus on the definition of adapting to climate change in existing debates or on the criteria by which adaptation actions can be deemed successful or sustainable.Climate change is expected to result in significant economic losses on both the micro- and macro-levels. As the world's population rises, so does the strain on agriculture to maintain food and nutritional security, which is exacerbated by climate change. The future of climate change and its consequences are very uncertain, which makes planning for mitigation complex. This necessitates the development of climate-resilient technology based on a regional multidisciplinary approach. Suitable cultivars that can respond to environmental fluctuations, as well as planned agronomic management and crop pest control, must be created. The chapter discusses the various strategies that would help make agriculture climate resilient.
... The irony of flood and drought occurring concurrently almost every year, makes agriculture highly vulnerable and unstable. Many studies earlier have verified that scanty ISMR reflects low rice production (Webster et al. 1999;Selvaraju 2003;Krishna Kumar et al. 2004;Asada and Matsumoto 2009;Kumar et al. 2013;Singh et al. 2014;Nath et al. 2017;Mall et al. 2018). Some recent studies also pointed out the impact of anthropogenic emissions, as a possible mechanism of triggering drought events (Parth Sarthi et al. 2021). ...
The meteorological drought dynamics and its impacts on rice productivity has been evaluated for the Indian Summer Monsoon Rainfall (ISMR) season using the standardized precipitation index (SPI) over the middle Gangetic plains (MGP) of Bihar. The meteorological drought over the ISMR period was found to be a recurring phenomenon coinciding with the rice growing season over Bihar. The rice crop has an intensive water requirement; therefore, it is significantly impacted by the meteorological droughts. In the present study, spatiotemporal characteristics viz. intensity, frequency, and probability of meteorological drought has been assessed along with an investigation for significant trends and detection of regime shift points to identify the impact of drought on rice production. For the purpose, SPI-4 derived from high resolution gridded daily rainfall data (0.25° × 0.25°) from India Meteorological Department (IMD) has been considered to analyse the meteorological drought episodes over agro-climatic zones of Bihar from 1961 to 2019. The regime shifts were determined using the Rodionov test for the drought dynamics and production of rice in Bihar. A moderate to severe drought-prone zone was found over the zone BRZ3B; while zone BRZ2 and BRZ3A showed comparatively a greater number of mild drought events persisting with more than 70% probability of occurrence. An inkling of increasing dependency on groundwater is found, which is in turn governing the rice production regime. The present study shows there is a substantial need for climate resilience and food security policies incorporating the subtle linkage between SPI variability and crop production, especially over rice producing regions of the globe.
... The increasing trends in RCDD dry at many climate stations (e.g. Rangpur, Bogra, Rajshahi, Dhaka and Faridpur) have occurred in relatively recent dry seasons; the time-series in Fig 5 show that at several stations (numbers 6,7,9,10,11,15,26,27,33 in Fig 5) RCDD dry is higher on average from the year 2000 onwards than the previous years. In addition to the decreased seasonal total rainfall in the dry season, the RCDD dry also contributed to increased irrigation requirement for Boro rice cultivation. ...
Understanding the historical and future spatio-temporal changes in climate extremes and their potential risk to rice production is crucial for achieving food security in Bangladesh. This paper presents results from a study on trend analysis for 13 climate metrics that significantly influence rice production. The analysis was conducted using the non-parametric Mann-Kendall test and the Theil-Sen slope estimator methods. The study included data from all available weather stations in Bangladesh and the assessment was done for both the wet (May to October) and dry (November to April) seasons, which cover the growing seasons of the country’s three types of rice: Aus, Aman and Boro. Results show significant decreasing trends for wet season rainfall (>12 mm/season/year in some stations) in the central and north regions. In addition, dry season rainfall is decreasing significantly in many areas, whilst dry season dry spells are increasing throughout Bangladesh. Decrease in rainfall in some of these areas are of concern because of its impacts on rainfed Aus rice and in the sowing/planting of rainfed Aman rice and irrigated dry season Boro rice. The maximum temperatures in the wet season are increasing throughout the country at 0.5°C every ten years, significantly at most of the climate stations. The analysis shows that the number of days with temperature >36°C has significantly increased in 18 stations over the last three decades, which implies a serious risk to Aman rice yield. The current maximum temperatures (both in the wet and dry seasons) are higher than the optimum temperature ranges for rice production, and this will have likely adverse effects on yield in the face of climate change with increasing temperatures. The results herein have practical implications for planning appropriate adaptation policies to ensure food security in the country.
... show that the changes in kharif precipitation over time are much larger than in other seasons. The direction of the effect of the poor is in line with the broader literature that indicates that lower rainfall during the monsoon season decreases yields in India (Auffhammer et al., 2012;Kumar et al., 2004;Selvaraju, 2003;Webster et al., 1998). Rabi precipitation is positively associated with overall consumption of the non-poor in all three models and the magnitude of the effect is larger for the farming households. ...
While estimated numbers of past and future climate migrants are alarming, the growing empirical evidence suggests that the association between adverse climate-related events and migration is not universally positive. This dissertation seeks to advance our understanding of when and how climate migration emerges by analyzing heterogeneous climatic influences on migration in low- and middle-income countries. To this end, it draws on established economic theories of migration, datasets from physical and social sciences, causal inference techniques and approaches from systematic literature review. In three of its five chapters, I estimate causal effects of processes of climate change on inequality and migration in India and Sub-Saharan Africa. By employing interaction terms and by analyzing sub-samples of data, I explore how these relationships differ for various segments of the population. In the remaining two chapters, I present two systematic literature reviews. First, I undertake a comprehensive meta-regression analysis of the econometric climate migration literature to summarize general climate migration patterns and explain the conflicting findings. Second, motivated by the broad range of approaches in the field, I examine the literature from a methodological perspective to provide best practice guidelines for studying climate migration empirically. Overall, the evidence from this dissertation shows that climatic influences on human migration are highly heterogeneous. Whether adverse climate-related impacts materialize in migration depends on the socio-economic characteristics of the individual households, such as wealth, level of education, agricultural dependence or access to adaptation technologies and insurance. For instance, I show that while adverse climatic shocks are generally associated with an increase in migration in rural India, they reduce migration in the agricultural context of Sub-Saharan Africa, where the average wealth levels are much lower so that households largely cannot afford the upfront costs of moving. I find that unlike local climatic shocks which primarily enhance internal migration to cities and hence accelerate urbanization, shocks transmitted via agricultural producer prices increase migration to neighboring countries, likely due to the simultaneous decrease in real income in nearby urban areas. These findings advance our current understanding by showing when and how economic agents respond to climatic events, thus providing explicit contexts and mechanisms of climate change effects on migration in the future. The resulting collection of findings can guide policy interventions to avoid or mitigate any present and future welfare losses from climate change-related migration choices.
... El Nino is an important driver of annual temperature variation in the region, and negatively affects crops through rainfall patterns (see e.g. Selvaraju (2003) 42 ). Higher global temperatures could change El Nino patterns, but this is beyond the scope of this study. ...
The 880 million agricultural workers of the world are especially vulnerable to increasing heat stress due to climate change, affecting the health of individuals and reducing labour productivity. In this study, we focus on rice harvests across Asia and estimate the future impact on labour productivity by considering changes in climate at the time of the annual harvest. During these specific times of the year, heat stress is often high compared to the rest of the year. Examining climate simulations of the Coupled Model Intercomparison Project 6 (CMIP6), we identified that labour productivity metrics for the rice harvest, based on local wet-bulb globe temperature, are strongly correlated with global mean near-surface air temperature in the long term ( p ≪ 0.01, R ² > 0.98 in all models). Limiting global warming to 1.5 °C rather than 2.0 °C prevents a clear reduction in labour capacity of 1% across all Asia and 2% across Southeast Asia, affecting the livelihoods of around 100 million people. Due to differences in mechanization between and within countries, we find that rice labour is especially vulnerable in Indonesia, the Philippines, Bangladesh, and the Indian states of West Bengal and Kerala. Our results highlight the regional disparities and importance in considering seasonal differences in the estimation of the effect of climate change on labour productivity and occupational heat-stress.
... Additionally, the years 1979Additionally, the years , 1986Additionally, the years , 1987Additionally, the years , 1989Additionally, the years and 2002 were detected as the most critical years of drought at both time scales. Presumably, these years are linked with the failure of south-west monsoon winds because of the El Nino effect over the Indian sub-continent (Ganapuram et al., 2015;Saith & Slingo, 2006;Selvaraju, 2003). ...
Rajasthan state of India is prone to recurrent droughts; hence, exploring drought severities over the semi-arid Sahibi river basin is crucial for drought management. To investigate drought severity, the Rainfall Anomaly Index (RAI) was applied at two time spans, such as annual (January to December) and the monsoon season (June to September), using long-term daily rainfall data (1961–2017) for nine rain gauge stations. Similarly, for the examination of various drought characteristics like magnitude, duration and intensity, run theory analysis was used. Trends in rainfall, drought severity, magnitude, duration and intensity were computed by employing both parametric (simple linear regression) and non-parametric (Mann–Kendall and Sen’s slope) tests, while spatial pattern maps of rainfall and drought characteristics were prepared using geographical information system. The analysis of rainfall records revealed a declining trend in eastern and central parts, whereas remaining areas of the basin witnessed an increasing trend during two time spans. During the study period, drought occurrence varied both geographically and temporally. The extreme, severe and moderate drought events were more common during monsoon season. Amongst the stations, Tapukara, Bairath and Mundawar rain gauge stations experienced the largest number of drought events compared to other stations. At both time scales, the most extreme droughts in the Sahibi basin occurred in 1979, 1986, 1987, 1989 and 2002. At the annual time span, the basin had the longest drought duration of 300 days, with a drought magnitude of − 758.3 mm. Likewise, the Tapukara rain gauge station had the longest dry spell of 310 days, followed by Behrod and Kotkasim (306 days each), Kotputli and Tijara (305 days each) and Mundawar (303 days). Finally, the findings of this study are expected to be useful to agricultural scientists, policymakers and water resource managers.
... Enset tolerates impacts of ENSO extreme events, and it has a unique adaptation in conserving water in its biomass (Ambaw et al., 2019;Birmeta, 2004;Kilavi et al., 2018). Selvaraju (2003) showed that in the cold phase of ENSO, crop yield increased from its normal, and both wheat and Sorghum were the major crops that were seriously impacted by ENSO extremes and vulnerable to the adverse impact on the productivity of the crops. A study by Iizumi et al. (2014) indicated that El Niño likely improves the global-mean of some crops such as soybean, but appears to change the yields of maize, rice, and wheat by up to − 4.3%. ...
El Niño Southern Oscillation could likely distort the hydro climatological processes and adversely affect agricultural production at various magnitudes. This study explored the manifestations of ENSO-induced rainfall variability and its impact on selected cereal crops in Kembata Alaba Tembaro Zones of southern Ethiopia. Accordingly, precipitation, temperature, crop, and Sea Surface Temperature (SST) data were collected from the National Meteorology Agency of Ethiopia, and the National Oceanic and Atmospheric Administration (NOAA). Rainfall variability was analyzed using the Coefficient of Variation and various anomaly indices. Spatial and temporal relationships between SST and yield of selected crops were established using the person correlation method. Mann-Kendal trend test was also used for trend analysis. The results revealed a statically significant (P < 0.05) decreasing trend and highly variable spring and summer rainfall. Global SSTs strongly influence both summer and spring rainfalls. El Niño and La Niña events were shown to influence the local rainfall distribution and crop production at varying magnitude over different spaces and times. The yield reduction due to ENSO increases from wheat, barley, maize to Sorghum, respectively, while Enset (ventricosum) was found to be less influenced by ENSO-caused rainfall variability. This implies that sorghum and maize crops are more sensitive to El Niño and La Niña events in the study area compared to the other crops considered in this study. The conformity of Enset yield with rainfall variability could be attributed to its tolerance to moisture stress. From the results, one can conclude that the overall cereal crop productivity was adversely, but differentially, affected by ENSO-induced climate variability.
... El Niño has been found to create South Asian droughts [17]. Lower rice yields and production have been found under El Niño in Thailand [71], the Philippines [72,73], Indonesia [74,75], India [76], and Sri Lanka [77]. Slightly higher yield could be seen during La Niña, but El Niño has been found to have a much greater negative rice yield impact in Thailand than does La Niña [71]. ...
Ocean-atmospheric phenomena (OAP) have been found to be associated with regional climate variability and, in turn, agricultural production. Previous research has shown that advance information on OAP and its climate implications could provide valuable opportunities to adjust agriculture practices. In this study, we review OAP effects on crop yields, covering both shorter-term El Niño Southern Oscillation (ENSO) and longer-term ocean-related decadal climate variability (DCV) phenomena, such as Pacific Decadal Oscillation (PDO), the Tropical Atlantic Gradient (TAG), and the West Pacific Warm Pool (WPWP). We review both statistical approaches and simulation models that have been used to assess OAP impacts on crop yields. Findings show heterogeneous impacts across crops, regions, OAP phases, and seasons. Evidence also indicates that more frequent and extreme OAP phases would damage agriculture. However, economic gains could be achieved via adaptation strategies responding to the early release of OAP phase information. Discussions on current knowledge gaps and future research issues are included.
... during moderate and strong El Niño years [21]. Crop yield anomalies in India were revealed to be inversely correlated with Sea Surface Temperature (SST) anomalies from June to August over the Tropical Pacific Ocean (TPO) NINO 3 sector, rice being affected more than wheat with significant economic effects of up to USD 2.2 billion reductions in revenue in warm ENSO periods and up to USD 1.3 billion increases in revenue in the cold ENSO periods [22]. In the context of Pakistan, crop yields have been shown to be statistically correlated with solar radiation and temperatures [23]. ...
In such drought-prone regions as Kazakhstan, research on regional drought characteristics and their formation conditions is of paramount importance for actions to mitigate drought risks caused by climate change. This paper presents the results of research on the spatio-temporal patterns of atmospheric droughts as one of the most important factors hindering the formation of crop yields. The influence of several potential predictors characterizing teleconnection in the coupled “atmosphere–ocean” system and cosmic-geophysical factors affecting their formation is analyzed. The spatial relationships between atmospheric aridity at the individual stations of the investigated area and the wheat yield in Kazakhstan as well as its relationships with potential predictors were determined using econometric methods. High correlation was shown between wheat yield fluctuations and Multivariate El-Niño–Southern Oscillation (ENSO), galactic cosmic radiation, solar activity, and atmospheric drought expressed through the soil moisture index, which in turn depends on precipitation levels and temperatures. The model could be modified further so that the individual components could be forecasted into the future using various time series in an ARIMA model. The resulting integration of these forecasts would allow the prediction of wheat yields in the future. The obtained results can be used in the process of creating effective mechanisms for adaptation to climate change and droughts based on their early diagnosis.
... Rice is the staple food grain and the most widely grown crop variety in many Asian countries, including Bangladesh. Many studies have examined the effects of ENSO on rice production in different Asian countries (e.g., (Zubair 2002;Selvaraju 2003;Falcon et al. 2004)). The studies showed an adverse effect of climate fluctuations on rice production. ...
This paper intends to explore rice yield fluctuations to large-scale atmospheric circulation indices (LACIs) in Bangladesh. The annual dataset of climate-derived yield index (CDYI), estimated using principal component analysis of Aus rice yield data of 23 districts, and five LACIs for the period 1980-2017 were used for this purpose. The key outcomes of the study were as follows: three sub-regions of Bangladesh, northern, northwestern, and northeastern, showed different kinds of CDYI anomalies. The CDYI time series in north and northeastern regions exhibited a substantial 6-year fluctuation, whereas a 2.75-to 3-year fluctuation predominated the northwestern region. Rice yield showed the highest sensitivity of LACIs in the northern region. Indian Ocean dipole (IOD) and East Central Tropical Pacific SST (Nino 3.4) in July and IOD index in March provide the best yield prediction signals for northern, northwestern, and northeastern regions. Wavelet coherence study demonstrated significant in-phase and out-phases coherences between vital climatic variables (KCVs) and CDYI anomalies at various time-frequencies in three sub-regions. The random forest (RF) model revealed the IOD as the crucial contributing factor of rice yield fluctuations in the country. The multifactorial model with different LACIs and year as predictors can predict rice yield, with the mean relative error (MRE) in the range of 4.82 to 5.78% only. The generated knowledge can be used to early assess rice yield and recommend policy directives to ensure food security.
... In addition, relationships between rice agronomy and climate are well documented (e.g. Lansigan et al., 2000;Naylor et al., 2001;Selvaraju, 2003;Lansigan, 2005;Dawe et al., 2006;Roberts et al., 2009). ...
While climate information services are widely available, translating climate information into actionable solutions to reduce climate risk, which are readily taken up by producers, remains a critical challenge. Here, we apply a bio-economic approach to assess the potential economic value of seasonal climate forecasts (SCFs) as a basis for
climate services for use in agricultural decision-making. We use a case study approach, quantifying the impacts of seasonal precipitation on rice cropping, a dominant farming system in the Greater Mekong Region (GMR) in Southeast Asia. We demonstrate values of seasonal precipitation forecasts for a range of forecast skill levels from low to perfect skill for three seasonal precipitation conditions (wet, normal and dry), as well as extreme conditions (extreme wet and extreme dry). Based on our integrated bio-economic assessment and seasonal variation in precipitation, we identify an optimal rice sowing window, which potentially results in improved yield and economic benefits compared with the currently applied sowing window. Applying this approach using common rice varieties grown by farmers – specifically, the medium growth duration Jasmine rice and the short duration Vietnamese long grain white rice variety OM 5451 – we find significant value in using seasonal precipitation forecasts to identify optimal sowing windows, ranging from an average of 220 ha 1 for perfect (100% accurate) precipitation forecasts.
We propose that such a framework can be used to examine the value of using seasonal climate forecasts, even at current skill levels, in farm adaptive operational decision-making. We envisage that demonstration of the value of using seasonal forecasts in crop production system decisions will build user confidence and help in upscaling the use of climate information in the region and more broadly.
... Rice is the staple food grain and the most widely grown crop variety in many Asian countries, including Bangladesh. Many studies have examined the effects of ENSO on rice production in different Asian countries (e.g., (Zubair 2002;Selvaraju 2003;Falcon et al. 2004)). The studies showed an adverse effect of climate fluctuations on rice production. ...
This paper intends to explore rice yield fluctuations to large-scale atmospheric circulation indices (LACIs) in Bangladesh. The annual dataset of climate-derived yield index (CDYI), estimated using principal component analysis of Aus rice yield data of 23 districts, and five LACIs for the period 1980-2017 were used for this purpose. The key outcomes of the study were as follows: (1) three sub-regions of Bangladesh, northern, northwestern, and northeastern, showed different kinds of CDYI anomalies; (2) the CDYI time series in northern and northeastern regions exhibited a substantial 6-year fluctuation, whereas a 2.75 to 3-year fluctuation predominated the northwestern region; (3) rice yield showed the highest sensitivity of LACIs in the northern region; (4) Indian Ocean dipole (IOD) and East Central Tropical Pacific SST (Nino 3.4) in July, and IOD index in March provide the best yield forecasting signals for northern, northwestern, and northeastern regions, respectively; (5) wavelet coherence study demonstrated noteworthy in-phase and out-phases coherences between key climatic variables (KCVs) and CDYI anomalies at various time-frequencies in three sub-regions; (6) the random forest (RF) model revealed the IOD as the vital contributing factor of rice yield fluctuations in the country; (6) the multi-factorial model with different LACIs and year as predictors can predict rice yield, with the mean relative error (MRE) in the range of 4.82 to 5.51% only. The generated knowledge can be used for an early assessment of rice yield and recommend policy directives to ensure food security.
... The impact of large scale oscillations is global and far reaching. ENSO has been found to have an impact on the East African ecosystem [7], crop yield in the United States [8], hurricane landfall probability in the Caribbean [9], global river discharges [10], drought in West Africa [11], and Indian food grain production [12]. IOD's influence has been reported to include Australian rainfall [13], East African rainfall [14], Australian drought [15], extreme European summer heat [16], Arabian monsoon variability [17] and global climate [18]. ...
In this study, the dynamical complexities associated with the global sea surface temperature (SST) was investigated. Monthly time series of global SST on a grid of 2 • × 2 • from January 1854 to June 2019 was analyzed using Hurst exponent, sample entropy, Lyapunov exponent and correlation dimension. Global ocean temperature was found to have predominantly positive Lyapunov exponent except for regions in Arctic and Antarctic oceans. This implies that SST is largely governed by chaotic dynamics. Temperature of tropical and high latitude oceans was found to have a correlation dimension between 2 and 2.8. These values were found to be significant, especially in the Pacific ocean.
... As the southwest monsoon plays a sensitive role for Kharif crop, it accounts for >50% of the food grain production and approximately 65% for the oilseeds. Since variability in interannual monsoon rainfall in India results in droughts and floods at a large scale, it leaves major effects on Indian agricultural production (Parthasarathy et al., 1992;Selvaraju, 2003;Kumar et al., 2004) and economy. Therefore, southwest and northeast monsoon both are critical for Kharif and Rabi crop yield in India. ...
The agricultural is an important sector and shares a major fraction of any nations’ gross national product and, thus, has a key role in the development. Due to the green revolution, crop production has increased to a greater extent in the past decades and simulated to be continued in the coming years. But, at present, one of the challenging issues is food security and maintaining the equitable standard of living for the coming generations. In this regard, one of the major global environmental issues in front of us is human-induced climate change, which threatens our ability to meet the demand. In the past decades, the high growth rate of industrialization, urbanization, and anthropogenic activities has resulted in adverse climate changes, significantly affecting the global temperature, increased greenhouse gases, seasonal variation, and irregular precipitation patterns. According to the Intergovernmental Panel on Climate Change (IPCC) global warming prediction report, the global mean temperature will be increased by 1.0–3.5°C by 2100, and CO2 doubling would be increased temperature by 1.5–4.5°C. Global mean surface temperature anomalies were also recorded to be increasing consistently with time since 1880 to the year 2019; 2016 ranks as the warmest on record (NASA GISS). Thus, increase in temperature and CO2 can be a big challenge in front of farmers and result in a major threat to agriculture and food security. It has the potential to affect agriculture positively as well as negatively in terms of yield, depending upon the variations in various factors. The share of agriculture in global emissions, the need for further global mitigation efforts, and continued projected agricultural emissions growth in many countries all combined together to underline the necessity for stronger and more effective policies. Thus, this chapter is focused on the impact of climate change on the agricultural sector and strategies for mitigating climate change in the agriculture system. In this context, there is an urgent need to analyze the impact of climate change on agriculture productivity and project sustainable approaches to adapt the climate change and meet the future food demand.
... The shift in Assam's climate is discernible since 1998 (Baruah, 2018) and the Brahmaputra River Valley (BRV) has been experiencing extreme regional climate change (Chakrabarty et al., 2012). Generally, variation in climatic patterns especially Indian Summer Monsoon (ISM) is attributed to El Nin͂ o Sothern Oscillation (ENSO) cycle (Gershunov et al., 2001;Prasanna, 2014;Revadekar et al., 2009;Selvaraju, 2003). However, the normal level of Indian Summer Monsoon is still existent due Eurasian warming and shift of Walker circulation anomalies toward southeast associated with ENSO (Kumar et al., 1999). ...
To better understand how farmers perceive and adapt to climate change, climate trends and a survey of farmer attitudes and behavior in the upper Brahmaputra valley zone (UBVZ) of India were analyzed. Rainfall and temperature trends were estimated in combination with the results from a detailed questionnaire of 384 farmers across 20 villages in rainfed areas of the UBVZ. From 1971 to 2007, the annual mean temperature in the UBVZ increased by 0.15 °C/decade while summer rainfall decreased markedly. Logistic regression was used for modeling the perceptions and adaptation behavior of farmers. Farmers perceptions of climate change tended to closely match those estimated from the climate data, but farmers with better access to water resources, credit, and those with higher family income, higher production, and larger farm sizes had more options to adapt and were more likely to adopt techniques to cope with climate change and variability. Factors such as age, education level, and family size of respondents were less likely to impact farmers' decisions to adapt to climate change. Download here: https://rdcu.be/cgqqA
... Different from our findings, Limsakul, (2019) reported that the relationship between MEI and rice yield was weak negative in Thailand at the country level. Related studies have found the significant relationships between ocean-atmospheric circulations and rice yield in many regions of the world including Sri Lanka (Zubair, 2002), Thailand (Limsakul, 2019), India (Selvaraju, 2003), Philippines and China (Xu et al., 2020). ...
To examine the rain-fed Aman rice yield fluctuation due to climatic anomalies overtimes in Bangladesh, we used climate-induced yield index (CIYI), ensemble empirical mode decomposition (EEMD), step-wise multiple regression, isotopic signature, wavelet transform coherence (WTC) and random forest (RF) model. In this work, daily multiple source climatic data which were collected between 1980 and 2017, from 18 weather stations and five atmospheric circulation indices were used for this purpose. The key findings were as follows; by employing principal component analysis (PCA), six temporal variability modes were identified as six corresponding sub-regions with various Aman rice CIYI fluctuations. The Aman rice CIYI in different sub-regions represented a noteworthy 3–4-year quasi-oscillation using the EEMD. The key climate variables (KCVs) including the potential evapotranspiration and cloud cover in September, the minimum temperature in August, and precipitation in July, August, and October were the best rice yield prediction signals in these sub-regions. The results suggest that Aman rice yield could likely decline by 33.59%, and 3.37% in the southwestern and southeastern regions, respectively, if KCV increased by 1 °C or 1%. The RF model suggests that the Indian Ocean Dipole (IOD) significantly influenced the rice yield. Isotopic signatures were employed to confirm the fluctuation and anti-amount effect during the Aman rice-growing period in Bangladesh. The results obtained in this study could be used as a guideline for sustainable mitigation and adaptation measures in managing agro-meteorological hazards in Bangladesh.
... The understanding of the SWM drought/flood characteristics associated with El Nino and La Nina events is of utmost importance for the planning agriculture and economic policy (M. Davis, 2001;Selvaraju, 2003). With this motivation, an attempt has been made to study the distinguishing features of ISMR during El Nino and La Nina years over the Indian subcontinents and its subregions. ...
Abstract This study presents the interannual variation in the summer monsoon rainfall during the El Nino Southern Oscillation (ENSO) phases, that is, El Nino and La Nina throughout the Indian subcontinent and its subregions during 1986–2010. The analysis includes the performance assessments of the regional climate model (RegCM4) in simulating Indian summer monsoon rainfall through comparison with India Meteorological Department rainfall data. Observation clearly demonstrates that the El Nino/La Nina produced weaker/strong monsoon rainfall over India and it increases the interannual variability of the southwest monsoon. The capability and skill of RegCM4's Grell and Mix99 cumulus parameterization scheme in simulating Indian summer monsoon rainfall associated with extreme climate are usually higher in “dry” conditions rather below average for the accurate simulation of “wet” conditions. Also, model performance was good in defining the characteristics, spatial distribution, and trend of a dry spell during ENSO phases with observation. The model‐simulated spatial and temporal value of wet spell during ENSO produces overestimated value over all India.
Climate drivers such as the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) can impact multiple sectors globally. We are currently witnessing the effects of these teleconnections against the backdrop of a changing climate. This systematic review takes stock of the available evidence on compounding and cascading effects of El Niño and the Positive IOD on health, economic, migration, conflicts, and nutrition outcomes in low‐ and middle‐income countries from the Indo‐Pacific region. The review sheds light on how effects vary between and within the considered countries and explores potential sources of heterogeneity. The search of studies was carried out in January 2024 in 12 major databases/search engines and 14 institutional websites, using English keywords, and paired by forward and backward citation tracking of the included studies. The review's inclusion criteria encompassed quantitative studies as long as they provide an estimate of relationship between the climate driver and outcome, and qualitative studies that aim to infer causation such as realist evaluation or process tracing. The analysis used a combination of meta‐analysis with random‐effects models, median effects from correlational and regression studies, and narrative synthesis. We found that El Niño is likely to decrease agricultural production and productivity at the Indo‐Pacific level, although the analysed studies are highly diverse. The absence of evidence on the effects of the considered climate drivers on migration, conflict, food security and nutrition is an important evidence gap. We found limited evidence on the differential effects by El Niño's and +IOD's magnitude and no studies examining their combined impact or qualitative effectiveness studies. The high risk of bias detected across studies calls for more thorough attention to study design, conduct, and reporting in answering questions about effects. Despite remaining evidence gaps, this review highlights potential effects of El Niño and +IOD in the Indo‐Pacific and underscores the need for context‐specific policy responses to mitigate risks at local and regional levels. Caution is warranted in interpreting the overall findings given the generally high risk of bias of evidence.
This study examines the impact of interaction of El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) teleconnections on Indian Summer Monsoon Rainfall (ISMR) in Haryana state, India, from 1980 to 2023. As the second-largest contributor of food grains in India, with 86% of its cultivated area, Haryana is vital for studying the impacts of teleconnections. Results indicate that ENSO has a stronger influence on ISMR than IOD, with significant correlations ranging from −0.69 to −0.15, while IOD correlations were non-significant, ranging from −0.25 to 0.12. During El Niño years with neutral IOD, rainfall reduced by up to 50%, while reductions were less during El Niño with positive IOD. These findings align with vertically integrated moisture transport and convective available potential energy data. The normalized difference vegetation index variation closely follows ISMR variation, indicating higher rainfall benefits vegetation growth while lower rainfall hampers it. Rice (Oryza sativa) cultivation increased, whereas crops like bajra (Pennisetum glaucum), maize (Zea mays), and jowar (Sorghum vulgare) showed varying trends. Regression analysis reveals complex relationships between temperature, rainfall, and crop productivity. This research enhances understanding of climate change effects on ISMR dynamics in Haryana, offering valuable insights for policymakers and stakeholders to optimize hydrological resource utilization.
El Nino is a big ocean-atmosphere climate interaction It happens when Sea Temperatures (SST) warm up in central and eastern parts of the Equatorial Pacific. Whereas La Nina is the opposite event of it, when ocean SST cools down in those same areas. Together, El Nino and La Nina are known as the El Nino Southern Oscillation Index (O). Usually, El Nino brings warm and dry weather to southern & eastern inland regions of India, Australia, Indonesia, Philippines, Malaysia, and the central Pacific islands. The variations of Indian summer monsoon Rainfall (ISMR) are linked to changes in sea surface temperatures over the equatorial Pacific & Indian Oceans. ENSO events really affect summer rainfall in India and major droughts have faced during El Nino events. When El Nino kicks in, it often leads to weak monsoons & higher temperatures in India. This raises the chances of droughts, which could upset crop production & water supply in India. The lack of rain because of El Nino causes crop production to drop, especially short-duration Kharif crops. This situation can lead to higher inflation rates & slower GDP growth, since agriculture is such a huge part of India’s economy. Therefore, this paper provide information about how El Nino affects climate throughout various regions of India, especially regarding monsoons and growing food.
The Himalayan region is characterized by its heterogeneous topography and diverse land use/land cover types that significantly influence the weather and climatic patterns in the Indian sub-continent. Predicting future precipitation is crucial for understanding and mitigating the impacts of climate change on water resources, land degradation including soil erosion by water as well as sustainability of the natural resources. The study aimed to downscale future precipitation with Shared Socioeconomic Pathway (SSP) scenarios using machine learning methods in the Tehri Dam catchment area, located in the North-Western Himalayas, India. The study compared the performance of multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) models for statistical downscaling. During the training and testing phases, RF and ANN demonstrated reasonably satisfactory results in comparison to MLR. In general, models performed best on a monthly time scale compared to daily and yearly scales where RF model performed quite well. Therefore, the RF model was used to generate future climate scenarios for the near (2015–2040), mid (2041–2070), and far (2071–2100) future periods under the shared socioeconomic pathway (SSP) scenarios. An increasing trend in precipitation was observed across the area (grids), with varying magnitudes. The SSP1-2.6 scenario was projected the least change, ranging from 1.4 to 3.3%, while the SSP2-4.5 scenario indicated an average increase of 3.7 to 14.0%. The highest emission scenario (SSP5-8.5) predicted an increase of 8.4 to 27.5% in precipitation during the twenty-first century. In general, the increase in precipitation was higher in the far future compared to the mid and near future period. This projected increase in the precipitation may have the serious implications on food security, hydrological behaviour, land degradation, and accelerated sedimentation in the Himalayan region.
To assess drought risk, susceptibility to food security, and water resource utilization, it is crucial to comprehend dry spell patterns from a hydrological perspective. Some regional studies have noted an extension of dry spells on a global and regional scale, but it is still unclear how often dry spells occur during the summer monsoon season, which is dominated by rainfall. This study uses the Mann-Kendall trend test to examine the trend of dry spells during Bangladesh's summer monsoon from 1985 to 2022 to close this gap. Using the Frontier Atmospheric General Circulation model and remote sensing methods to examine the effects of ocean elements such as Indian Ocean Dipole (IOD), Sea Surface Temperature (SST), El Niño-Southern Oscillation (ENSO) conditions, and the zonal wind. Daily rainfall data for 34 weather stations were obtained from the Bangladesh Meteorological Department, while surface water occurrence and change intensity data were retrieved from the JRC Global Surface Water Mapping Layers, v1.3 (FAO, UN). The NOAA Physical Sciences Laboratory (PSL) and the Tokyo Climate Center/WMO Regional Climate Centre in RA II (Asia) provided the IOD, SST, ENSO, and zonal wind data. A notable dry spell anomaly over Bangladesh was also noted in this research, with the short, medium-length, and long dry spells increasing in 82.35%, 73.53%, and 50% of weather stations. When El Niño was present, there was less of a dry spell and more during La Niña. The climatic variability of IOD events and SST anomalies in the eastern and western tropical Indian Ocean were also noted by this study to be connected to these anomalous events. The correlation coefficient between summer monsoon rainfall and DMI is 0.34. Throughout the study period, there were changes in the upper atmosphere's and lower troposphere's wind circulation. The study allows the prioritization of regions for drought, effective water resource management, and food scarcity preparedness.
Global warming is continuing to occur globally as result of fossil fuel burning and no signs of decreasing concentration of greenhouse gases. The environment is highly dependent on the climate of a particular region. Any variation may negatively impact the proper functioning and processes of the ecosystem. Every sector is severely under threat of global warming and its associated impacts. The consequences include reduced agricultural productivity, forest degradation, biodiversity loss, species shift, sea level rise, habitat loss, enhanced land degradation, increased occurrences of cyclones, floods and heat waves, and so on. In recent decades the impact of climate change is observed more frequently. The preparedness against the adverse impact is vital. It indicates the development of a climate-resilient sustainable community for tackling the climate change and associated issues. Policies and proper implementation generated through site-specific impact studies will help us achieve the targets. A case study conducted at various watersheds of Himalayan region showed increase in temperature and rainfall under various IPCC emission scenarios. Hence, the fragile Himalayas necessitate the development of climate-resilient sustainable communities.KeywordsClimate changeRadiative forcingEcosystemAdaptationHimalayas
Sustainability Studies: Environmental and Energy Management is a collection of reviews on topics on sustainability with the objective of informing the reader about the environmental impact of industrialization and the ways technology can be implemented to sustain it. The book presents 11 chapters that focus on the environmental issues, waste management methods, and green chemistry for environmental-friendly production and construction. 2 chapters bring attention to important concepts that are central to sustainability, namely, environmental justice and climate change. The editors have ensured an adequate balance of theoretical concepts and practical information to give readers a broad overview of environmental sustainability. Each chapter is structured into easy-to-read sections that are suitable for readers who are learning about sustainability as part of their educational curriculum. Sustainability Studies: Environmental and Energy Management is a primer on sustainability and environmental management for students and academics in environmental science, and engineering courses.
A spectral analysis of the 1848-1995 (148 year) time series of Sontakke and Singh (1996) representing estimates of summer monsoon (June-September) precipitation amounts over six homogeneous zones (Northwest NW, North central NC, Northeast NE. West Peninsular WP, East Peninsular EP, South Peninsular SP) and the whole of India (AI) revealed significant periodicities in the QBO and QTO regions (2-3 years and 3-4 years) as also higher periodicities, some common to all zones. To study the ENSO relationship, a finer classification of years was adopted. For the All India summer monsoon rainfall as also for all the zones except NE, Unambiguous ENSOW (where El Nino existed and SOI minima and SST maxima were in the middle of the calendar year i.e., May-August), were overwhelmingly associated with droughts and the cold (C) events were associated with floods. For other types of events, the results were uncertain and a few extreme rainfalls occurred even during some Non-events.
El Ninos which occurred during 1871-1990 are divided into two categories of events. The first category, EW, consists of the El Ninos in which the equatorial southeast (ESE) Pacific region (0-10° S; 80°W-180°W) experienced a Warn1ing phase as defined by suitable objective criteria, and the second category, E, consists of El Ninos in which the ESE Pacific region did not experience the warming phase. Sea surface temperature rise as well as anomaly over the Pacific region, summer monsoon rainfall over India and over its meteorological sub-divisions, in the categories EW and E are compared. Area-averaged rainfall of India for the summer monsoon season and for each of the months July and September are significantly (at 0.1 percent level) lower in EW events in comparison to those in E events. The summer monsoon rainfall of each of the 12 sub-divisions, from northwest and central India constituting about 50 per cent of the Indian plains, is significantly lower in EW events than that in E events, the highest rainfall deficiency in EW events being in the westernmost sub-divisions, i.e., West Rajasthan and Saurashtra-Kutch. Possible causes for the same have also been discussed.
We have analysed the variation of all-India foodgrain production over the last four decades to assess the typical magnitude of year-to-year fluctuation. This has shown that the magnitude of the variation between the foodgrain production in 1996 and 1997 is by no means unusual for normal monsoon years and such fluctuations should not have a large impact on GDP if the management of the economy allows for this natural fluctuation. Much larger year-to-year changes are expected for years with large anomalies in monsoon rainfall. The problems of sustaining the growth rate in irrigated areas and enhancing that of rainfed areas are addressed. It is suggested that to attain adequate growth rates for sustaining the per capita availability, a genuinely interdisciplinary approach is required with active participation of the farmers in identifying the optimal strategies.
The influence of El Niño-Southern Oscillation (ENSO) on crop production in the southeastern US was studied to identify crops that are vulnerable to ENSO-related weather variability and therefore likely to benefit from application of ENSO-based climate forecasts. The historical (1960-1995) response of total value and its components (yield, area harvested and price) to ENSO phases and quarterly sea surface temperature anomalies (SST) in the eastern equatorial Pacific was analyzed for six crops (peanut, tomato, cotton, tobacco, corn and soybean) in four states (Alabama, Florida, Georgia and South Carolina). ENSO phase significantly influenced corn and tobacco yields, the areas of soybean and cotton harvested, and the values of corn, soybean, peanut and tobacco. ENSO phases explained an average shift of $212 million, or 25.9%, of the value of corn. Canonical correlation analysis (CCA) identified significant responses of corn, soybean and cotton yields, and peanut value to SST across the region; and of peanut and tobacco yields, and tomato and soybean values in particular states.
The Tropical Ocean-Global Atmosphere (TOGA) program sought to determine the predictability of the coupled ocean-atmosphere system. The World Climate Research Programme's (WCRP) Global Ocean-Atmosphere-Land System (GOALS) program seeks to explore predictability of the global climate system through investigation of the major planetary heat sources and sinks, and interactions between them. The Asian-Australian monsoon system, which undergoes aperiodic and high amplitude variations on intraseasonal, annual, biennial and interannual timescales is a major focus of GOALS. Empirical seasonal forecasts of the monsoon have been made with moderate success for over 100 years. More recent modeling efforts have not been successful. Even simulation of the mean structure of the Asian monsoon has proven elusive and the observed ENSO-monsoon relationships has been difficult to replicate. Divergence in simulation skill occurs between integrations by different models or between members of ensembles of the same model. This degree of spread is surprising given the relative success of empirical forecast techniques. Two possible explanations are presented: difficulty in modeling the monsoon regions and nonlinear error growth due to regional hydrodynamical instabilities. It is argued that the reconciliation of these explanations is imperative for prediction of the monsoon to be improved. To this end, a thorough description of observed monsoon variability and the physical processes that are thought to be important is presented. Prospects of improving prediction and some strategies that may help achieve improvement are discussed.
The relationship between ENSO/anti-ENSO events in the Pacific basin and simultaneous all India monsoon has been re-examined for the period 1901-1990 using Southern Oscillation Index (SOI). The result shows that there is fairly strong association between ENSO events and dry monsoon years. There exists a weak teleconnection between anti-ENSO events and wet monsoon indicating that anti-ENSO events have only a moderate impact on the Indian monsoon rainfall. Developing ENSO (anti-ENSO) episodes during the monsoon season indicates non-occurrence of simultaneous floods (droughts) with a very high degree of confidence 70 (50) percent of the droughts (floods) during the above period have occurred during ENSO (anti-ENSO) events indicating that extreme monsoon activities in the form of droughts (floods) might be important factors for the occurrence of simultaneous ENSO/anti-ENSO events.
A coupled ocean-atmosphere data assimilation procedure yields improved forecasts of El Niño for the 1980s compared with previous
forecasting procedures. As in earlier forecasts with the same model, no oceanic data were used, and only wind information
was assimilated. The improvement is attributed to the explicit consideration of air-sea interaction in the initialization.
These results suggest that EI Niño is more predictable than previously estimated, but that predictability may vary on decadal
or longer time scales. This procedure also eliminates the well-known spring barrier to EI Niño prediction, which implies that
it may not be intrinsic to the real climate system.
The International Research Institute for Climate Prediction (IRI) was
formed in late 1996 with the aim of fostering the improvement,
production, and use of global forecasts of seasonal to interannual
climate variability for the explicit benefit of society. The development
of the 1997/98 El Niño provided an ideal impetus to the IRI
Experimental Forecast Division (IRI EFD) to generate seasonal climate
forecasts on an operational basis. In the production of these forecasts
an extensive suite of forecasting tools has been developed, and these
are described in this paper. An argument is made for the need for a
multimodel ensemble approach and for extensive validation of each
model's ability to simulate interannual climate variability accurately.
The need for global sea surface temperature forecasts is demonstrated.
Forecasts of precipitation and air temperature are presented in the form
of net assessments, following the format adopted by the regional
consensus forums. During the 1997/98 El Niño,the skill of the net
assessments was greater than chance, except over Europe, and in most
cases was an improvement over a forecast of persistence of the latest
month's climate anomaly.
SOUTHERN Africa is subject to recurrent droughts which cause severe food shortages. There is considerable evidence1 that El Niño2 warm events in the Pacific Ocean are linked to below-average rainfall in southern Africa, and the 1991–92 El Niño event was accompanied by the worst drought in southern Africa this century, affecting nearly 100 million people. But although models can predict El ñ events a year in advance3–6, the drought was not anticipated, increasing relief costs. Here we present data show-ing a strong correlation between an El Niño index and both rainfall and maize yield in Zimbabwe. Surprisingly, the correlation with maize yield is stronger than that with rainfall, with more than 60% of the variance in yield accounted for by sea surface temperatures in the eastern equatorial Pacific Ocean—half-way around the world. We also show that model predictions of the El Niño index provide accurate forecasts of maize yield in Zimbabwe, with lead times of up to a year. As maize is the most important food crop for the ten-nation Southern African Development Community region7, we suggest that this approach could provide an effective early-warning system for southern African drought-induced famines.
We attempt to construct a logical framework for the deciphering of the physical processes that determine the interannual variability of the coupled climate system. of particular interest are the causes of the ‘predictability barrier’ in the boreal spring when observation‐prediction correlations rapidly decline. the barrier is a property of many models and occurs irrespective of what time of year a forecast is initiated. Noting that most models used in interannual prediction emphasize the coupled physics of the Pacific Ocean basin, with the intent of encapsulating the essential structure of the El Niño‐Southern Oscillation (ENSO) system, lagged Southern Oscillation Index (SOI) correlations are compared with the model results. the lagged SOI correlations also decrease rapidly in springtime. In that sense, the coupled ocean‐atmosphere models are behaving in a manner very similar to the real system, at least as it is defined by the SOI.
We propose that (i) the springtime is a period where errors may grow most rapidly in a coupled ocean‐atmosphere forecast model or (ii) there are other influences on the system that are not included in the simple coupled‐model formulations. Both propositions are based on observations. By examining the period of correlation decrease, it is noticed that the equatorial pressure gradients tend to be a minimum at the time of the correlation decrease, suggesting that the ocean‐atmosphere system may be least robust during the spring and, thus, subject to error growth. At the same time the south Asian summer monsoon is growing very rapidly. As the monsoon circulation is highly variable in both phase and amplitude from year to year, the ocean‐atmosphere system may be subject to variable and impulsive forcing each spring.
A monsoon intensity index, based on the magnitude of the mean summer vertical shear in the ‘South Asia’ region, was defined for the broad‐scale monsoon. ‘Strong’ and ‘weak’ monsoon seasons were determined by the index and were shown to be consistent with the independent broad‐scale outgoing long‐wave‐radiation fields. Associated with the anomalous monsoons were global scale, coherent summer circulation patterns. of particular importance was that stronger (weaker) than average summer trade winds were associated with strong (weak) monsoon periods. Thus, a signal of the variable monsoon was detected in the low‐level wind fields over the Pacific Ocean that would be communicated to the Pacific Ocean through surface stresses.
A longer‐period context for the anomalous summer monsoon circulation fields was sought. Based on the summer monsoon index, annual cycles for the years in which there were strong and weak monsoon seasons were composited. Large‐scale coherent differences were apparent in the circulation fields over most of the globe including south Asia and the tropical Indian Ocean as far as the previous winter and spring. Although the limited data period renders the absoluteness of the conclusions difficult to confirm, the results indicate that the variable monsoon (and hence the signal in the Pacific Ocean trade regime) are immersed in a larger scale and slowly evolving circulation system. Based on the observation that the monsoon and the Walker circulation appear to be in quadrature, it is proposed that these two circulations are selectively interactive. During the springtime, the rapidly growing monsoon dominates the near‐equatorial Walker circulation. During autumn and winter, the monsoon is weakest with convection fairly close to the equator; the Walker circulation is then strongest and may dominate the winter monsoon. During the summer the monsoon may dominate. Numerical experiments are proposed to test both propositions.
The National Weather Service intends to begin routinely issuing long-lead forecasts of three-month mean US temperature and precipitation by the beginning of 1995. The ability to produce useful forecasts for certain seasons and regions at projection times of up to one year is attributed to advances in data observing and processing, computer capability, and physical understanding-particularly, for tropical ocean-atmospheric phenomena. Because much of the skill of the forecasts comes from anomalies of tropical SST related to ENSO, we highlight here long-lead forecasts of the tropical Pacific SST itself, which have higher skill than the US forecasts that are made largely on their basis. The performance of five ENSO prediction systems is examined: two are dynamical; and two are statistical (canonical correlation analysis and constructed analogs, used at the Climate Prediction Center of NCEP). -from Authors
The magnitude of spatial and lagged correlation between the sea-surface temperature anomalies of the eastern tropical Pacific Ocean (El Nino) and corn yield in the USA is presented. The record is not homogeneous in that the correlation level varies quite strongly over time. The period between 1910 and 1050 shows completely uncorrelated behaviour. In the most recent 40 years the correlation is found to be strongest in the region just south of the Great Lakes in the states of Illinois, Iowa, and Indiana. Correlation maps are presented that show the degree of association for the corn yield in those 41 states for which records are available. For the entire record the highest correlations occur with the sea-surface temperature anomalies appearing after the corn is harvested in September to February of the following year. Since corn yields are a proxy for summer agricultural drought in the central part of the USA, the droughts during the period 1910 to 1950 must have had some other cause.
One of the most significant advances in the atmospheric sciences in recent years has been the development of an ability to predict ocean-atmosphere variability in the equatorial Pacific Ocean. Occasional basin-wide warming or cooling of equatorial sea-surface temperatures, known as El Niño and La Niña events, together with an associated oscillation of atmospheric pressure over the South Pacific Ocean, known as the Southern Oscillation, can have global climate repercussions. Both extremes of the El Niño - Southern Oscillation (ENSO) phenomenon have been associated with temperature and rainfall anomalies around the world. The influence of the tropical oceans on the atmosphere forms the theoretical basis of seasonal climate forecasts. The International Research Institute for climate prediction (IRI) was formed in late 1996 with the aim of fostering the improvement, production and use of global forecasts of seasonal-to-interannual climate variability for the explicit benefit of society. The development of the 1997/98 El Niño event provided an ideal impetus to the IRI Experimental Forecast Division (IRI-EFD) to generate seasonal climate forecasts on an operational basis. In the production of these forecasts an extensive suite of forecasting tools has been developed, and these are described in this paper.
Using multiple regression, several statistical models were developed to predict Indian monsoon rainfall from conditions antecedent to the start of the monsoon season (defined as the period June-September). The predictors were selected in a stepwise fashion from a set of 11 predictors. New predictors, such as the preceeding winter to spring sea level pressure change at Bombay, India, which was a leading predictor in all the models, could alone account for 50-60% of the predictand variance. The latitudinal position of the 500-mbar ridge axis in April along 75°E longitude was generally the second predictor to be entered. The regression model based on the earliest development sample (1951-1970), however, had the Southern Oscillation Index of Tahiti-Darwin winter-to-spring sea level pressure tendency as its number two predictor. -from Authors
Since the 1970s, the inverse relationship between the Indian monsoon rainfall and the El Niño-Southern Oscillation (ENSO) has weakened considerably. The cause for this breakdown is shown to be most likely the strengthening and poleward shift of the jet stream over the North Atlantic. These changes have led to the recent development of a significant correlation between wintertime western European surface air temperatures and the ensuing monsoon rainfall. This western Europe winter signal extended eastward over most of northern Eurasia and remained evident in spring, such that the effect of the resulting meridional temperature contrast was able to disrupt the influence of ENSO on the monsoon.
Weather and crop yields in the Midwest have exhibited wide fluctuations during the past 10 yr. Sea surface temperatures (SST, El Nino and its counterpart, La Nina) have been related to or blamed for these weather abnormalities via teleconnections. Yield, weather, and El Nino related (southern oscillation index - SO) data beginning in 1900 were assembled to determine if significant relationships could be ascertained. Midwestern weather and corn (Zea mays L.) yield data were coded relative to the SO. The SO groupings were <-8.0 (El Nino like, low phase), >.0.8 (La Nina like, high phase), and in between. Yields were grouped >10% or <10% relative to expected, or in between. Corn yields exhibited wide variation when the SO was in between, indicating that weather factors other than SO influenced corn yield during those oceanic conditions. However, when summer SO was in the high phase (low phase), there was a statistical tendency for corn yields to be lower (higher) than expected, respectively for all Corn Belt states studied, except Missouri. The low (high) phase of the SO is generally related to El Nino (La Nina). During the low (high) phase of the SO, much of the Corn Belt received more (less) rainfall in July, August, and September. At the same time, high temperatures-or heat stress-were generally lower (higher) during the low (high) phase of the SO. Both high (but not excessive) precipitation and lower temperatures are associated with good corn yield. If El Nino forecasts improve as is expected, Midwestern corn yield forecasts should similarly improve.
Surface marine observations, satellite data, and station observations of surface pressure and precipitation are used to describe the evolution of sea surface temperature (SST) anomalies, surface wind fields, and precipitation anomaly patterns during major warm episodes in the eastern and central tropical Pacific. The sequence of events is described in terms of composite SST and wind fields (30o N-30oS) for six warm episodes since 1949, and time series and cross-spectral analyses of mean monthly data along six shipping lanes which cross the equator between the South American coast and 170oW.-from Authors
If events run true to form, the present El Niño is approaching a climax — which is why, acknowledging a Christmas connection, the phenomenon was so named. Predicting these events and their consequences is a daunting task, but there is progress to report.
A coupled ocean-atmosphere data assimilation procedure yields improved forecasts of El Nino for the 1980s compared with previous forecasting procedures. As in earlier forecasts with the same model, no oceanic data were used, and only wind information was assimilated. The improvement is attributed to the explicit consideration of air-sea interaction in the initialization. These results suggest that El Nino is more predictable than previously estimated, but that predictability may vary on decadal or longer time scales. This procedure also eliminates the well-known spring barrier to El Nino prediction, which implies that it may not be intrinsic to the real climate system. 24 refs., 5 figs., 1 tab.
The yearly variation of corn yield above or below the trend in the United States is shown to be closely associated with the occurrence of warm sea-surface temperature anomalies in the equatorial Pacific Ocean. Data for the past 115 years indicate that warmer than normal sea-surface temperatures in the equatorial Pacific Ocean are likely to coincide with above average corn yields in the United States. In the period 1868–1982 there have been fifty warm sea-surface temperature years as identified by Quinn et al. (1978). Examination of the yields on a state-by-state basis shows that Illinois has the strongest association and Iowa is second. For the 115-year record, Illinois and Iowa show a statistical level of significance in a chi square test at the 0.1% level. The adjacent states are at a much lower level. For the total U.S. corn yield which includes a much larger region the results are still significant at the 1.0% level. The results of this paper suggest that during the onset of a warm sea-surface temperature event there is a southerly displacement of the storm tracks during the growing season which modifies the climate so as to produce above average growth of the corn crop. It should be emphasized that these two climate anomalies occur concurrently. In fact, the maximum deviation from normal of the sea-surface temperatures occurs in the winter season after the corn crop has grown. Therefore, these results represent a correlation. The timing of the events suggests that the cause lies elsewhere.
There has been a resurgence of studies relating to Sea Surface Temperature (SST) variations in the equatorial Pacific and variations in atmospheric flow patterns over tropical as well as over middle latitudes. In this note, the SST variations over the equatorial eastern Pacific and the variations in the Indian monsoon rainfall (June-September) are re-examined with particular reference to drought and flood years. Our analysis reveals that major drought (flood) years are associated with warmer (cooler) than normal SST values before and after the monsoon season. A possible connection with a feedback mechanism between the SST variation in the equatorial Pacific and the monsoon rainfall over India and vicinity is hypothesized.
Monsoon season (June-September) precipitation data from 31 Indian subdivisions and mean monthly precipitation data from 35 Indian and Sri Lanka stations, spanning the period 1875-79, were analyzed to determine the relationship between equatorial Pacific warm episodes (El Nino events) and interannual fluctuations in precipitation over India and Sri Lanka. The data reveal a strong tendency for a below normal summer monsoon during the 25 moderate/strong warm episodes which occurred during the period.-Authors
A review is given of the meaning of the term El Niño and how it
has changed in time, so there is no universal single definition. This
needs to be recognized for scientific uses, and precision can only be
achieved if the particular definition is identified in each use to
reduce the possibility of misunderstanding. For quantitative purposes,
possible definitions are explored that match the El Niños
identified historically after 1950, and it is suggested that an El
Niño can be said to occur if 5-month running means of sea surface
temperature (SST) anomalies in the Niño 3.4 region
(5°N-5°S, 120°-170°W) exceed 0.4°C for 6 months or
more. With this definition, El Niños occur 31% of the time and La
Niñas (with an equivalent definition) occur 23% of the time. The
histogram of Niño 3.4 SST anomalies reveals a bimodal character.
An advantage of such a definition is that it allows the beginning, end,
duration, and magnitude of each event to be quantified. Most El
Niños begin in the northern spring or perhaps summer and peak
from November to January in sea surface temperatures.
This presentation attempts to describe in very qualitative terms a theory of production of high energy radiation (soft and hard X-rays) in magnetoactive plasmas of astrophysical interest. Special emphasis has been placed on the application of our model to extars and in particular to Sco X-1. More rigorous arguments may be found elsewhere [1] and the interested reader is urged to consult that reference for more details.
Detailed correlation analysis of the all-India monsoon rainfall and mean
sea-level seasonal pressure at Bombay (19°N, 73°E) up to three
lags on either side of the monsoon wren during the last 30 years
(1951-80) indicates a systematic relationship. The winter-to-premonsoon
(March, April, May-Deceinber, January, February; MAM-DJF) seasonal
pressure tendency at Bombay shows a correlation coefficient (CC) of 0.70
(significant at 0.1% level) with the Indian monsoon rainfall.Further
examination of this relationship over a long period of 144 years
(1847-1990), using sliding correlation analysis, reveals some
interesting features. The sliding CCs were positive before 1870,
negative during 1871-1900, positive in the years 1901-40, and again
negative later on, showing systematic turning points around the years
1870, 1900, and 1940. In light of other corroborative evidence, these
climatic regimes can be identified as `meridional monsoon' periods
during 1871-1900 and after 1940, and as `zonal monsoon' periods before
1870 and during 1901-40, similar to the observation of Fu and Fletcher.
It is also observed that the relationship between Bombay pressure and
Indian monsoon rainfall becomes dominant when the ENSO variance in
Bombay pressure is high and falls apart when the ENSO variance is
small.The paper contains a listing of the long homogeneous data series
on all-India monsoon rainfall and monthly MSL pressure at Bombay for the
period 1847-1990.
Sea surface temperature (SST) variations in the equatorial eastern Pacific (0-10oS, 180-90oW) are compared with variations in atmospheric temperature, circulation, rainfall and trace-constituent amount. Significant at the 99.9% level is the zero-lag correlation of -0.62 between this SST and the Southern Oscillation Index during 1932-79, the correlation of 0.72 between this SST and the zonally averaged temperature in the tropical troposphere 2 seasons later during 1958-79, and the correlation of -0.62 between this SST and Indian summer monsoon rainfall 1-2 seasons earlier during 1868-1977. -from Author NOAA, Air Resources Laboratories, Silver Spring, MD 20910, and CSIRO Division of Atmospheric Physics, Mordialloc, Victoria 3195, Australia.
The National Weather Service intends to begin routinely issuing
long-lead forecasts of 3-month mean U.S. temperature and precipitation
by the beginning of 1995. The ability to produce useful forecasts for
certain seasons and regions at projection times of upto 1 yr is
attributed to advances in data observing and processing, computer
capability, and physical understanding-particularly, for tropical
ocean-atmosphere phenomena. Because much of the skill of the forecasts
comes from anomalies of tropical SST related to ENSO, we highlight here
long-lead forecasts of the tropical Pacific SST itself, which have
higher skill than the U.S forecasts that are made largely on their
basis.The performance of five ENSO prediction systems is examined: Two
are dynamical [the Cane-Zebiak simple coupled model of Lamont-Doherty
Earth Observatory and the nonsimpie coupled model of the National
Centers for Environmental Prediction (NCEP)]; one is a hybrid coupled
model (the Scripps Institution for Oceanography-Max Planck Institute for
Meteorology system with a full ocean general circulation model and a
statistical atmosphere); and two are statistical (canonical correlation
analysis and constructed analogs, used at the Climate Prediction Center
of NCEP). With increasing physical understanding, dynamically based
forecasts have the potential to become more skillful than purely
statistical ones. Currently, however, the two approaches deliver roughly
equally skillful forecasts, and the simplest model performs about as
well as the more comprehensive models. At a lead time of 6 months
(defined here as the time between the end of the latest observed period
and the beginning of the predictand period), the SST forecasts have an
overall correlation skill in the 0.60s for 1982-93, which easily
outperforms persistence and is regarded as useful. Skill for extra-
tropical surface climate is this high only in limited regions for
certain seasons. Both types of forecasts are not much better than local
higher-order autoregressive controls. However, continual progress is
being made in understanding relations among global oceanic and
atmospheric climate-scale anomaly fields.1t is important that more
real-time forecasts be made before we rush to judgement. Performance in
the real-time setting is the ultimate test of the utility of a long-lead
forecast. The National Weather Service's plan to implement new
operational long-lead seasonal forecast products demonstrates its
effectiveness in identifying and transferring "cutting edge"
technologies from theory to applications. This could not have been
accomplished without close ties with, and the active cooperation of, the
academic and research communities.
An objective numerical drought/flood index has been used to obtain, on the dryness side, the Drought Area Index (DAI) and on the wetness side, the Flood Area Index (FAI) for India for the period 1891-1979. The DAI for a given year is the percentage area of India corresponding to a mean monsoon index with drought intensity equal to or less than -2 (moderate drought or worse). Likewise, on the wetness side, the Flood Area Index (FAI) for the given year is the percentage area of India corresponding to a mean monsoon index with flood intensity equal to or greater than +2 (moderate flood or worse), where the mean monsoon index of an area is the mean drought/flood index for the four monsoon months (June-September). -from Authors
An attempt is made to obtain a coherent picture of the extent and mode of operation of the ‘southern oscillation.’ This term is used here, following Sir Gilbert Walker, to describe a standing fluctuation of opposed pressure anomalies in both eastern and western hemispheres. The existence of this opposition has been verified, using more recent data, for stations in the Indian and Pacific Ocean regions; results show the oscillation was less marked in recent decades.
The representativeness and physical meaning of the index devised by Walker to characterize the state of the oscillation are considered. The geographical extent of the phenomenon is examined using correlation and regression charts of pressures with Walker's index. The temperature and rainfall anomalies associated with it may be derived qualitatively from the pressure anomalies.
Recent data are used to verify persistence and lag correlations between station pressures; while there has again been some decline, the lag correlations of elements with previous South America pressures still hold good. The decline in these various quantities is indicative of a minor secular change commencing in the 1920's, which is also evident in a decrease in the variability of pressure.
What ‘periodicities’ appear to exist in elements affected by the southern oscillation may well be an outcome of sampling fluctuations in (often persistent) random series. This is suggested by the variety of supposed ‘periods’ reported, and their evanescence in space and time. An example of this evanescence in time is provided from the Darwin pressure record.
A mechanism for the oscillation is proposed in terms of variations in a direct toroidal circulation between warmer eastern and cooler western hemispheres. These variations are attributed, (following a model by Palmer of the synoptic climatology of the tropical Pacific) to variations in the south-east trades in the South Pacific and the consequent variations in cyclonic vortex generation in the West Pacific. The persistence of anomalies is then due to the extent of ocean areas in the south-east Pacific where the sea temperature is lower than the air temperature. The lag correlations observed may be due to this persistence and to a transmission of anomalies along the trades through air-sea interaction.
Association between the all-India summer monsoon (June to September) rainfall and an index of the Southern Oscillation (SO) is studied in relation to the vagaries of the monsoon rainfall and the seasonal characteristics of the SO. The Southern Oscillation index (SOI) used is the difference of normalized sea surface pressure between Tahiti and Darwin, two stations located in the core regions of the circulation systems associated with the SO. The data length of 46 years from 1935 to 1980 is used in the detailed examination of the nature of association between these parameters.
The SOI values of different months and standard seasons show opposite tendencies during deficient and excess years of all-India monsoon rainfall. The correlation coefficients (CC) between the all-India monsoon rainfall series and the SOIs of summer monsoon (JJA), autumn (SON) and winter (DJF) minus spring (MAM) seasons are significant at the 1 per cent level. The correlations have been examined by dividing the series into two equal halves of 23 years and different sliding window widths of 10, 20 and 30 years. The least squares fit line is represented by the equation y (all-India monsoon rainfall) =85.9 - 2.7x (DJF minus MAM, SOI), the variance explained by this line is about 13 per cent. In view of the large spatial variability of the summer monsoon rainfall the correlations are examined for the rainfall series of various meteorological subdivisions of the country. The CC between the monsoon rainfall of the subdivisions north of 16°N and west of 80°E and the SOI series of DJF minus MAM is significant at the 5 per cent level or above.
Potentialities of the SOI of DJF minus MAM, an important premonsoon circulation parameter, are examined for the seasonal prediction of Indian summer monsoon rainfall, and the limitations of single parameter prediction models are discussed.
A case is made for regional-scale Indian summer monsoon rainfall data and analysis, intermediate between the widely used all-India and meteorological subdivision data sets. Macro-regional units are constructed from the data provided by Parthasarathy et al., 10 being defined using principal components analysis and a classification algorithm. The temporal changes of summer monsoon rainfall over the period 1871–1985 are analysed and described for each of these regions, and the marked diversity of fluctuations between the regions is emphasized by a variety of methods. The degree of relationship is considered for each region between drier and wetter conditions and (i) El Niño and non-El Niño years, and (ii) SST anomalies in the eastern tropical Pacific. Clear regional differences are apparent, but even statistically highly significant relationships are not large in any absolute sense. The need for explanatory analyses at the regional scale, in addition to those at the more common all-India scale, is stressed.
The interannual fluctuations in the Southern Oscillation indices (Wright, 1975) and their relations to the Indian monsoon (June-September) rainfall have been examined for the period of 106 years from 1875 to 1980. The monsoon rainfall is significantly (99.9 per cent level) correlated with the Southern Oscillation indices for the seasons: MJJ (0.59), ASO (0.67), NDJ (0.53), and FMA of the following year (0.38). The fluctuations in the Southern Oscillation index for the ASO season appear strongly related to the nearly simultaneous monsoon rainfall of India. This implies that the large positive (negative) value of the Southern Oscillation index, signifying strengthening (weakening) of the Walker circulation coincides with large excess (deficient) monsoon rainfall over India. The coherence spectrum reveals that the Southern Oscillation index and the monsoon rainfall are highly correlated in the period range of about 2–2.5 years and 4–6 years. The first of these periods corresponds with the Quasi-Biennial Oscillation and the latter agrees with the features of the Southern Oscillation, suggesting a strong link between Indian monsoon rainfall and these two phenomena. The striking feature of the composites of the Southern Oscillation index averaged for all the drought years and for all the flood years is the simultaneous occurrence of low (high) Southern Oscillation index and droughts (floods) in India. However, this association has limited use in long-range prediction. A preliminary study suggests that a nearly simultaneous occurrence of major climatic anomalies of the tropics, such as droughts in India and El Niño off the coast of Peru, are linked to the Southern Oscillation, indicating some kind of time dependent zonal east-west circulation, i.e. Walker circulation.
Climate plays a major role in determining variations in Australian crop production. The El Niño-Southern Oscillation (ENSO) phenomenon is an important mode of climatic variation affecting the Australian region. Interannual fluctuations of Australian crop production and ENSO might thus be expected to be quite closely related. Analysis of the limited data available at present suggests that fluctuations in some Australian crops are in fact associated with ENSO and may be predictable, well in advance of harvest, from observed sea-surface temperatures around northern Australia.
The total volume of rain-water over India has been computed for each of the years during the period 1871-1978 on the basis of the rainfall data for a fixed network of 306 raingauges. The mean, the standard deviation and the coefficient of variation of the series of annual rain-water volume are 3143 km3, 300 km3 and 9.5 per cent, respectively. The series is homogeneous and random. The serial correlation is 0.013 which is too low to suggest any relationship between successive terms. Gaussian distribution gives a good fit to the series.
The series giving the percentage area of the country with a deficiency of 20 per cent or more, as well as the percentage area with an excess of 20 per cent or more have been obtained. These bring out the variability in the deficiency and excess of rain-water over the country during the period. The years of well-marked rain-water deficiency/excess over India have been identified by utilizing the criterion of 40 per cent or more area under rain-water deficiency/excess. The occurrence of such years has been found to be random and the number of such years in five-year periods appears to be distributed in accordance with the Binomial and the Poisson Probability models.
The impact of marked deficiency/excess on the All India Index of food grain prices and All India Index of food grain production has been examined. It is found that the impact of well-marked deficiency can generally be seen clearly but that of well-marked excess is seen clearly in some cases only. This is understandable. Overall excess in a year may or may not result in floods which have the potentiality to damage crops. The impact of the well-marked deficiency of 1918 has been maximum.
Remedial measures which would make the economy of the country less dependent on the variability of the annual rain-water have been discussed.
The magnitude of spatial and lagged correlation between the sea-surface temperature anomalies of the eastern tropical Pacific Ocean (El NiñMo) and corn yield in the USA is presented. The record is not homogeneous in that the correlation level varies quite strongly over time. The period between 1910 and 1950 shows completely uncorrelated behaviour. In the most recent 40 years the correlation is found to be strongest in the region just south of the Great Lakes in the states of Illinois, Iowa, and Indiana. Correlation maps are presented that show the degree of association for the corn yield in those 41 states for which records are available. For the entire record the highest correlations occur with the sea-surface temperature anomalies appearing after the corn is harvested in September to February of the following year. Since corn yields are a proxy for summer agricultural drought in the central part of the USA, the droughts during the period 1910 to 1950 must have had some other cause.
Despite advances in the capacity to predict the evolution of the El Niño–southern oscillation (ENSO) phenomenon and advances in understanding the influence of ENSO on rainfall in tropical regions such as Sri Lanka, there has been limited use of climate predictions for agricultural decision-making. Climatic fluctuations have a profound influence on the cultivation of crops such as rice, which is the staple food in Sri Lanka. Here, the relationship between the sea-surface temperature-based ENSO index of NINO3.4, rainfall and the departure of Sri Lankan rice production from long-term trends, is analysed for the ‘Maha’ (October to March) and ‘Yala’ (April to September) cultivation seasons between 1952 and 1997.
During the El Niño phase, the Maha rice production frequently increased (10 out of 15 seasons) and the Yala production frequently decreased (10 out of 14 seasons). Conversely, during the La Niña phase, the Maha production decreased (seven out of ten seasons) and Yala production increased (six out of eight seasons). Floods, state interventions, civil disturbances, fertilizer price hikes and extreme anomalies in the previous season were noted in the majority of seasons in which these ENSO–production linkages were violated.
The correlation of the Maha rice production anomaly with the average NINO3.4 from October to December was significant at the 5% level and that with the aggregate October to December rainfall was significant at the 1% level. Yala rice production showed a significant relationship with concurrent NINO3.4 and a strong correlation (r = 0.60) with the previous season's rainfall. Yala cultivation is water constrained, and carryover storage from the previous season is often used to determine the extent of planting.
The relationships between ENSO and seasonal rice production and the relationship between Yala rice production and previous Maha rainfall could be used for agricultural management and policy formulation. Copyright
This review paper presents an assessment of the current state of knowledge and capability in seasonal climate prediction at the end of the 20th century. The discussion covers the full range of issues involved in climate forecasting, including (1) the theory and empirical evidence for predictability; (2) predictions of surface boundary conditions, such as sea surface temperatures (SSTs) that drive the predictable part of the climate; (3) predictions of the climate; and (4) a brief consideration of the application of climate forecasts. Within this context, the research of the coming decades that seeks to address shortcomings in each area is described. Copyright © 2001 Royal Meteorological Society
A significant correlation between the seasonal rainfall over India and the southern oscillation index (SOI) illustrated by Walker is examined on the basis of a long series of 100 year data. The relationship is discussed in the light of recent applications of global interactions in the regional climatic change models through feedback processes. The years of large scale deficient/excess rainfall over India during June–August season are examined in relation to the concurrent SOI regimes. This aspect may be of importance in examining the large scale circulation features in association with severe droughts/floods over India.Eine signifikante Korrelation zwischen dem jahreszeitlichen Regen in Indien und dem Index der sdlichen Oszillation (SOI) nach Walker wird aufgrund einer hundertjhrigen Reihe von Beobachtungsdaten untersucht. Die Beziehung wird im Hinblick auf neue Anwendungen globaler Wechselwirkungen durch Rckkoppelungsprozesse in regionalen Klimaschwankungsmodellen besprochen. Die Jahre mit grorumigem Mangel oder berschu an Regen in Indien in der Zeit von Juni bis August wird in Beziehung zum gleichzeitigen SOI-Verlauf untersucht. Diese Gesichtspunkte knnen bei der Untersuchung grorumiger Zirkulationsmerkmale in Verbindung mit starken Trockenheiten oder berschwemmungen in Indien von Bedeutung sein.
A hierarchy of ENSO (El Nio/Southern Oscillation) prediction schemes has been developed which includes statistical schemes and physical models. The statistical models are, in general, based on advanced statistical techniques and can be classified into models which use either low-frequency variations in the atmosphere (sea level pressure or surface wind) or upper ocean heat content as predictors. The physical models consist of coupled ocean-atmosphere models of varying degrees of complexity, ranging from simplified coupled models of the shallow water-type to coupled general circulation models. All models, statistical and physical, perform considerably better than the persistence forecast on predicting typical indices of ENSO on lead times of 6 to 12 months. The most successful prediction schemes, the fully physical coupled ocean-atmosphere models, show significant prediction abilities at lead times exceeding one year period. We therefore conclude that ENSO is predictable at least one year in advance. However, all of this applies to gross indices of ENSO such as the Southern Oscillation Index. Despite the demonstrated predictability, little is known about the predictability of specific features known to be associated with ENSO (e.g. Indian Monsoon rainfall, Southern African drought, or even off-equatorial sea surface temperature). Nor has the relative importance for prediction of different regional anomalies or different physical processes yet been established. A seasonal dependence in predictability is well established, but the processes responsible for it are not fully understood.
The associations between strong to moderate El Nino events and the all-India and subdivisional summer monsoon rainfall is examined for the period 1871 to 1978. The significance of the association is assessed by applying the Chi-square test to the contingency table.
The analysis indicates that during 22 El Nino years the Indian monsoon rainfall was mostly below normal over most parts of the country. However, the association between El Nino and deficient rainfall or drought is statistically significant over the subdivisions west of longitude 80°E and north of 12°N. During the five strong El Nino years—1877, 1899, 1911, 1918, and 1972—many areas of India suffered large rainfall deficiencies and severe droughts. There are four moderate El Nino years—1887, 1914, 1953, and 1976—when the suffering was marginal.
The relationship between El Nino and the Indian monsoon rainfall is expected to be useful in forecasting large-scale anomalies in the monsoon over India.
Year-to-year fluctuations of summer monsoon (June–September) rainfall of India are studied in relation to planetary and regional scale features. Anomalous epochs in the monsoon rainfall have been found to coincide with the epochs having anomalous patterns of temperature distribution in the northern hemispheric extratropics as well as with the spells of years having anomalous patterns of sea surface temperature distribution in the equatorial Pacific Ocean (EL-Nino phenomenon).
Relationship between monsoonal rainfall and regional atmospheric circulation features is studied by compositing data of five good and five bad monsoon rainfall years over India. A comparison of the two data sets yields interesting relationships between the anomalous patterns of rainfall on the one hand and atmospheric parameters on the other. On the average parameters of monsoon depressions are more or less the same among the two types of composites. The most important distinguishing feature of good monsoon years is the greater frequency of cyclogenesis (monsoon lows included) on the regional scale which keeps the monsoon trough near its normal position and with concomitant higher cyclonic vorticity in the trough zone contributes to greater seasonal rainfall on the regional scale during good monsoon years.