scholarly journals USING GLOBAL CLIMATE INDICES TO PREDICT RAINFALL AND SUGARCANE PRODUCTIVITY IN DRYLANDS OF BANYUWANGI, EAST JAVA, INDONESIA

2020 ◽  
Vol 21 (2) ◽  
pp. 78
Author(s):  
Muhammad Rasyid Ridla Ranomahera ◽  
Bayu Dwi Apri Nugroho ◽  
Prima Diarini Riajaya ◽  
Rivandi Pranandita Putra
Keyword(s):  
Southern Oscillation ◽  
Global Climate ◽  
Saccharum Officinarum ◽  
Crop Productivity ◽  
Climate Index ◽  
Climate Indices ◽  
Sugar Mills ◽  
Index Rainfall ◽  
Using Data ◽  
Sugarcane Productivity

<p>In Indonesia, sugarcane (<em>Saccharum officinarum </em>L.) is mostly cultivated in drylands, thus depending on rainfall for crop growth and development. Rainfall is an essential factor affecting sugarcane productivity. The global climate indices can be used to investigate potential of rainfall within a given area and its relationship with crop productivity. This reserach aimed to analyze the relationship between the global climate index, rainfall, and sugarcane productivity in drylands near Glenmore sugar mill, i.e., Benculuk and Jolondoro, Banyuwangi, East Java, Indonesia. The global climate index data used were the Southern Oscillation Index (SOI) and Sea Surface Temperature (SST) between 1995 and 2014. Results of this research showed that SOI and SST can be used to predict the rainfall in both Benculuk and Jolondoro. Rainfall (y) can be predicted with SST data (x) using the equation of y = -352.49x + 7724.1 in Benculuk and y = -107.32 + 3443.4 in Jolondoro, as well as with SOI data (x) using the equation of y = 38.664x + 1555.1 in Benculuk and y = 10.541x + 1567.8 in Jolondoro. Sugarcane productivity (y) in Jolondoro can be predicted using data of total rainfall (x) between October and March with the following equation: y = -0.1672x + 1157.3. This equation can be used by sugar mills, sugarcane growers, and other sugarcane-relevant stakeholders for determining the appropriate growing season.</p>

scholarly journals Evaluating the Tourist Climate Comfortable Period of China in a Changing Climate

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Dan-Dan Yu ◽  
Shan Li ◽  
Zhong-Yang Guo
Keyword(s):  
Climate Change ◽  
Global Climate ◽  
Scientific Evidence ◽  
Climatic Conditions ◽  
Climate Index ◽  
Climate Indices ◽  
Climate Data ◽  
Climate Conditions ◽  
Positive Effects ◽  
Changing Climate

The evaluation of climate comfort for tourism can provide information for tourists selecting destinations and tourism operators. Understanding how climate conditions for tourism evolve is increasingly important for strategic tourism planning, particularly in rapidly developing tourism markets like China in a changing climate. Multidimensional climate indices are needed to evaluate climate for tourism, and previous studies in China have used the much criticized “climate index” with low resolution climate data. This study uses the Holiday Climate Index (HCI) and daily data from 775 weather stations to examine interregional differences in the tourist climate comfortable period (TCCP) across China and summarizes the spatiotemporal evolution of TCCP from 1981 to 2010 in a changing climate. Overall, most areas in China have an “excellent” climate for tourism, such that tourists may visit anytime with many choices available. The TCCP in most regions shows an increasing trend, and China benefits more from positive effects of climate change in climatic conditions for tourism, especially in spring and autumn. These results can provide some scientific evidence for understanding human settlement environmental constructions and further contribute in improving local or regional resilience responding to global climate change.

scholarly journals Sugarcane Productivity Simulation Under Different Scenarios by DSSAT/CANEGRO Model in the Western São Paulo

2020 ◽  
Vol 12 (7) ◽  
pp. 82
Author(s):  
Angela Madalena Marchizelli Godinho ◽  
Asdrubal Jesus Farias-Ramírez ◽  
Maria Alejandra Moreno-Pizani ◽  
Tadeu Alcides Marques ◽  
Franklin Javier Paredes-Trejo ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Absolute Error ◽  
Saccharum Officinarum ◽  
Crop Productivity ◽  
Yield Data ◽  
Model Based ◽  
Sugarcane Crop ◽  
Sugarcane Yield ◽  
Sugarcane Productivity ◽  
D Values

Sugarcane (Saccharum officinarum L.) is one of the most important crops in Brazil and its growth and development can be simulated through process-based models. The current study evaluated a model based on the decision support system for the transfer of Agrotechnology DSSAT/CANEGRO to simulate the sugarcane crop productivity in the western region of S&atilde;o Paulo. The DSSAT/CANEGRO model was calibrated using published yield parameters from a selection of five Brazilian sugarcane cultivars, while sugarcane yield data (tons of stems per hectare) from commercial land were used as benchmark data. Other modeling inputs were derived from the primary regional cultivar. The root mean square error (RMSE), Willmott agreement index (d), and mean absolute error (MAE) were used as performance metrics. The DSSAT/CANEGRO model resulted in a good RMSE performance. The productivity estimates were better for the cultivars SP791010 and RB835486, with RMSE equal to 2.27 and 4.48 Mg ha-1, respectively. The comparison between model-based estimates and observed data produced d values in the range from 0.86 to 0.99, and MAE values in the range of 1.84 to 4.22 Mg ha-1.

scholarly journals Development of Precise Indices for Assessing the Potential Impacts of Climate Change

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1231
Author(s):  
Vinay Kumar
Keyword(s):  
Climate Change ◽  
Southern Oscillation ◽  
Diversity Index ◽  
Region Of Interest ◽  
Heavy Precipitation ◽  
Climate Index ◽  
Climate Indices ◽  
Special Issue ◽  
Indonesian Archipelago ◽  
Weather And Climate

The Special Issue on climate indices and climate change deals with various kinds of indices exits to assess weather and climate over a region. These indices might be based on local, regional, remote variables, which may affect and define the weather and climate of a region. Climate indices are the time series used to monitor the state of the climate and its relationship with other possible causes. With indices being myriad, it is challenging to choose which one is appropriate for a region of interest. However, the relationship between the indices and the climate of a region varies. El-Nino Southern Oscillation (Southern Oscillation Index, SOI/ENSO) is one of the most robust climate signals that stimulate rainfall, temperature, and hurricanes via teleconnections. SOI has a correlation of 0.5 over the Indonesian archipelago. Here, some of the well-known indices Holiday Climate Index (HCI), Tourism Climate Index (TCI), and Simple Diversity Index (SDI) are being reconnoitered to understand the holiday-tourism, end-of-the-day (EOD) judgment. The intrusion of dry air in the middle troposphere can create unstable weather, leading to heavy precipitation. The Special Issue seeks to encourage researchers to discover new indices in multidisciplinary department of atmospheric and physical sciences.

Coincident recruitment patterns of Southern Hemisphere fishes

2016 ◽  
Vol 73 (2) ◽  
pp. 270-278 ◽  
Author(s):  
Claudio Castillo-Jordán ◽  
Neil L. Klaer ◽  
Geoffrey N. Tuck ◽  
Stewart D. Frusher ◽  
Luis A. Cubillos ◽  
...  
Keyword(s):  
South Africa ◽  
New Zealand ◽  
Southern Hemisphere ◽  
Southern Oscillation ◽  
Global Climate ◽  
Southern Annular Mode ◽  
Fish Stock ◽  
Climate Indices ◽  
Dynamic Factor ◽  
Recruitment Patterns

Three dominant recruitment patterns were identified across 30 stocks from Australia, New Zealand, Chile, South Africa, and the Falkland Islands using data from 1980 to 2010. Cluster and dynamic factor analysis provided similar groupings. Stocks exhibited a detectable degree of synchrony among species, in particular the hakes and lings from Australia, New Zealand, Chile, and South Africa. We tested three climate indices, the Interdecadal Pacific Oscillation (IPO), Southern Annular Mode (SAM), and Southern Oscillation Index (SOI), to explore their relationship with fish stock recruitment patterns. The time series of IPO and SOI showed the strongest correlation with New Zealand hoki (blue grenadier, Macruronus novaezelandiae) and Australian jackass morwong (Nemadactylus macropterus) (r = 0.50 and r = –0.50), and SAM was positively related to Australian Macquarie Island Patagonian toothfish (Dissostichus eleginoides) (r = 0.49). Potential linkages in recruitment patterns at sub-basin, basin, and multibasin scales and regional and global climate indices do account for some of the variation, playing an important role for several key Southern Hemisphere species.

scholarly journals Combined analysis of temperature and rainfall variability as they relate to climate indices in Northern Algeria over the 1972–2013 period

2016 ◽  
Vol 48 (2) ◽  
pp. 584-595 ◽  
Author(s):  
Ayoub Zeroual ◽  
Ali A. Assani ◽  
Mohamed Meddi
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Coastal Region ◽  
Western Mediterranean ◽  
Seasonal Rainfall ◽  
Climate Index ◽  
Climate Indices ◽  
Mean Values ◽  
Northern Algeria

Many studies have highlighted breaks in mean values of temperature and precipitation time series since the 1970s. Given that temperatures have continued to increase following that decade, the first question addressed in this study is whether other breaks in mean values have occurred since that time. The second question is to determine which climate indices influence temperature and rainfall in the coastal region of Northern Algeria. To address these two questions, we analyzed the temporal variability of temperature and annual and seasonal rainfall as they relate to four climate indices at seven coastal stations in Algeria during the 1972–2013 period using the Mann–Kendall, Lombard, and canonical correlation (CC) analysis methods.The annual and seasonal maximum, minimum and mean temperatures increased significantly over that time period. Most of these increases are gradual, implying a slow warming trend. In contrast, total annual and seasonal rainfall did not show any significant change. CC analysis revealed that annual and seasonal temperatures are negatively correlated with the Western Mediterranean Oscillation (WeMOI) climate index that characterizes atmospheric circulation over the Mediterranean basin. On the other hand, rainfall is positively correlated with a large-scale atmospheric index such as the Southern Oscillation Index.

scholarly journals Seasonal streamflow forecasting in the upper Indus Basin of Pakistan: an assessment of methods

2018 ◽  
Vol 22 (6) ◽  
pp. 3533-3549 ◽  
Author(s):  
Stephen P. Charles ◽  
Quan J. Wang ◽  
Mobin-ud-Din Ahmad ◽  
Danial Hashmi ◽  
Andrew Schepen ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Hybrid Approach ◽  
Indus Basin ◽  
Climate Index ◽  
Climate Indices ◽  
Streamflow Forecasting ◽  
Upper Indus Basin ◽  
Probabilistic Forecasts ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

Abstract. Timely and skilful seasonal streamflow forecasts are used by water managers in many regions of the world for seasonal water allocation outlooks for irrigators, reservoir operations, environmental flow management, water markets and drought response strategies. In Australia, the Bayesian joint probability (BJP) statistical approach has been deployed by the Australian Bureau of Meteorology to provide seasonal streamflow forecasts across the country since 2010. Here we assess the BJP approach, using antecedent conditions and climate indices as predictors, to produce Kharif season (April–September) streamflow forecasts for inflow to Pakistan's two largest upper Indus Basin (UIB) water supply dams, Tarbela (on the Indus) and Mangla (on the Jhelum). For Mangla, we compare these BJP forecasts to (i) ensemble streamflow predictions (ESPs) from the snowmelt runoff model (SRM) and (ii) a hybrid approach using the BJP with SRM–ESP forecast means as an additional predictor. For Tarbela, we only assess BJP forecasts using antecedent and climate predictors as we did not have access to SRM for this location. Cross validation of the streamflow forecasts shows that the BJP approach using two predictors (March flow and an El Niño Southern Oscillation, ENSO, climate index) provides skilful probabilistic forecasts that are reliable in uncertainty spread for both Mangla and Tarbela. For Mangla, the SRM approach leads to forecasts that exhibit some bias and are unreliable in uncertainty spread, and the hybrid approach does not result in better forecast skill. Skill levels for Kharif (April–September), early Kharif (April–June) and late Kharif (July–September) BJP forecasts vary between the two locations. Forecasts for Mangla show high skill for early Kharif and moderate skill for all Kharif and late Kharif, whereas forecasts for Tarbela also show moderate skill for all Kharif and late Kharif, but low skill for early Kharif. The BJP approach is simple to apply, with small input data requirements and automated calibration and forecast generation. It offers a tool for rapid deployment at many locations across the UIB to provide probabilistic seasonal streamflow forecasts that can inform Pakistan's basin water management.

scholarly journals Variability in Fusarium Head Blight Epidemics in Relation to Global Climate Fluctuations as Represented by the El Niño-Southern Oscillation and Other Atmospheric Patterns

2012 ◽  
Vol 102 (1) ◽  
pp. 55-64 ◽  
Author(s):  
A. B. Kriss ◽  
P. A. Paul ◽  
L. V. Madden
Keyword(s):  
Time Series ◽  
Phase Shift ◽  
Fusarium Head Blight ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Global Climate ◽  
Climate Index ◽  
El Nino Southern Oscillation ◽  
Head Blight

Cross-spectral analysis was used to characterize the relationship between climate variability, represented by atmospheric patterns, and annual fluctuations of Fusarium head blight (FHB) disease intensity in wheat. Time series investigated were the Oceanic Niño Index (ONI), which is a measure of the El Niño-Southern Oscillation (ENSO), the Pacific-North American (PNA) pattern and the North Atlantic Oscillation (NAO), which are known to have strong influences on the Northern Hemisphere climate, and FHB disease intensity observations in Ohio from 1965 to 2010 and in Indiana from 1973 to 2008. For each climate variable, mean climate index values for the boreal winter (December to February) and spring (March to May) were utilized. The spectral density of each time series and the (squared) coherency of each pair of FHB–climate-index series were estimated. Significance for coherency was determined by a nonparametric permutation procedure. Results showed that winter and spring ONI were significantly coherent with FHB in Ohio, with a period of about 5.1 years (as well as for some adjacent periods). The estimated phase-shift distribution indicated that there was a generally negative relation between the two series, with high values of FHB (an indication of a major epidemic) estimated to occur about 1 year following low values of ONI (indication of a La Niña); equivalently, low values of FHB were estimated to occur about 1 year after high values of ONI (El Niño). There was also limited evidence that winter ONI had significant coherency with FHB in Indiana. At periods between 2 and 7 years, the PNA and NAO indices were coherent with FHB in both Ohio and Indiana, although results for phase shift and period depended on the specific location, climate index, and time span used in calculating the climate index. Differences in results for Ohio and Indiana were expected because the FHB disease series for the two states were not similar. Results suggest that global climate indices and models could be used to identify potential years with high (or low) risk for FHB development, although the most accurate risk predictions will need to be customized for a region and will also require use of local weather data during key time periods for sporulation and infection by the fungal pathogen.

scholarly journals Describing rainfall in northern Australia using multiple climate indices

2016 ◽  
Author(s):  
Cassandra Rogers ◽  
Jason Beringer
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Annual Rainfall ◽  
Climate Index ◽  
Climate Indices ◽  
Human Populations ◽  
Wet Season ◽  
Australian Monsoon ◽  
The North

Abstract. Savanna landscapes are globally extensive and highly sensitive to climate change, yet the physical processes and climate phenomena which affect them remain poorly understood and therefore poorly represented in climate models. Both human populations and natural ecosystems are highly susceptible to precipitation variation in these regions due to the implications on water and food availability and atmosphere-biosphere energy fluxes. Here we quantify the relationship between climate phenomena and historical rainfall variability in Australian savannas, and in particular, how these relationships changed across a strong rainfall gradient, namely the North Australian Tropical Transect (NATT). Climate phenomena were described by 16 relevant climate indices and correlated against precipitation from 1900 to 2010 to determine the relative importance of each climate index on seasonal, inter-annual and decadal time scales. Precipitation trends, climate index trends, and wet season characteristics have also been investigated using linear statistical methods. In general, climate index-rainfall correlations were stronger in the north of the NATT where inter-annual rainfall variability was lower and a high proportion of rainfall fell during the wet season. This is consistent with a decreased influence of the Indian-Australian monsoon from the north to the south. Seasonal variation was most strongly correlated with the Australian Monsoon Index, whereas inter-annual variability was related to a greater number of climatic phenomena (predominately the El Niño-Southern Oscillation along with Tasman Sea and Indonesian sea surface temperatures). These findings highlight the importance of rainfall variability and the need to understand the climate processes driving variability, and subsequently being able to accurately represent these in climate models in order to project future rainfall patterns in the Northern Territory.

scholarly journals On the interconnections among major climate modes and their common driving factors

2019 ◽  
Author(s):  
Xinnong Pan ◽  
Geli Wang ◽  
Peicai Yang ◽  
Jun Wang ◽  
Anastasios A. Tsonis
Keyword(s):  
Climate Variability ◽  
Driving Forces ◽  
Southern Oscillation ◽  
Global Climate ◽  
Regional Climate ◽  
Sunspot Cycle ◽  
Climate Indices ◽  
Intrinsic Variability ◽  
Quasi Biennial Oscillation ◽  
Climate Modes

Abstract. The variations in oceanic and atmospheric modes on various timescales play important roles in generating regional and global climate variability. Many efforts have been devoted to identify the relationships between the variations in climate modes and regional climate variability, but rarely explored the interconnections among these climate modes. Here we use climate indices to represent the variations in major climate modes and we examine the harmonic relationship among the driving forces of climate modes by the combination of Slow Feature Analysis (SFA) and wavelet analysis. We find that all of the significant peak-periods of driving-force signals in the climate indices can be represented as the harmonics of four base periods: 2.32 yr, 3.90 yr, 6.55 yr and 11.02 yr. We infer that the period of 2.32 yr is associated with the signal of Quasi Biennial Oscillation (QBO). The periods of 3.90 yr and 6.55 yr are connected with the intrinsic variability of El Niño-Southern Oscillation (ENSO), and the period of 11.02 yr arises from the sunspot cycle. Results suggest that the base periods and their harmonic oscillations linked to QBO, ENSO and solar activities act as the key connections among the climatic modes with synchronous behaviors, highlighting the important roles of these three oscillations in the variability of current climate.

scholarly journals Describing rainfall in northern Australia using multiple climate indices

2017 ◽  
Vol 14 (3) ◽  
pp. 597-615 ◽  
Author(s):  
Cassandra Denise Wilks Rogers ◽  
Jason Beringer
Keyword(s):  
Climate Models ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Annual Rainfall ◽  
Climate Index ◽  
Climate Indices ◽  
Human Populations ◽  
Wet Season ◽  
Australian Monsoon ◽  
The North

Abstract. Savanna landscapes are globally extensive and highly sensitive to climate change, yet the physical processes and climate phenomena which affect them remain poorly understood and therefore poorly represented in climate models. Both human populations and natural ecosystems are highly susceptible to precipitation variation in these regions due to the effects on water and food availability and atmosphere–biosphere energy fluxes. Here we quantify the relationship between climate phenomena and historical rainfall variability in Australian savannas and, in particular, how these relationships changed across a strong rainfall gradient, namely the North Australian Tropical Transect (NATT). Climate phenomena were described by 16 relevant climate indices and correlated against precipitation from 1900 to 2010 to determine the relative importance of each climate index on seasonal, annual and decadal timescales. Precipitation trends, climate index trends and wet season characteristics have also been investigated using linear statistical methods. In general, climate index–rainfall correlations were stronger in the north of the NATT where annual rainfall variability was lower and a high proportion of rainfall fell during the wet season. This is consistent with a decreased influence of the Indian–Australian monsoon from the north to the south. Seasonal variation was most strongly correlated with the Australian Monsoon Index, whereas yearly variability was related to a greater number of climate indices, predominately the Tasman Sea and Indonesian sea surface temperature indices (both of which experienced a linear increase over the duration of the study) and the El Niño–Southern Oscillation indices. These findings highlight the importance of understanding the climatic processes driving variability and, subsequently, the importance of understanding the relationships between rainfall and climatic phenomena in the Northern Territory in order to project future rainfall patterns in the region.

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