Seasonal Changes in Climate Variables in Rainfed Crop Areas in the Lerma-Chapala-Santiago Basin, Mexico

This paper shows the effects of changes in the spatial-temporal behavior and phase shift of climate variables on rainfed agriculture in the Lerma-Chapala-Santiago Basin in central Mexico. Specifically, changes in rainfall (R), maximum temperature (Tmax), and minimum temperature (Tmin) were analyzed over two 25-year periods (1960 to 1985 and 1986 to 2010). Climate surfaces were generated by interpolation using the thin-plate smoothing spline algorithm in the software ANUSPLIN. Climate data were Fourier-transformed and fitted to a sinusoidal curve model, and changes in amplitude (increase) and phase were analyzed. The temporal behavior (1960–2010) indicated that rainfall was the most stable variable at the monthly level and presented no significant changes. However, Tmax increased by 2°C in the final period, and Tmin increased by 0.7°C at the end of the final period. The basin was discretized into ten rainfed crop areas (RCAs) according to the extent of changes in the amplitude and phase of the climate variables. The central and southern portions (55% of the area) presented more significant changes in amplitude, mainly in Tmin and Tmax. The remaining RCAs were smaller (14.6%) but presented greater variation: the amplitude of the Tmin decreased in addition to showing a phase shift, whereas Tmax increased in addition to showing a phase shift. These results translate into a delay in the characteristic temperatures of the spring and summer seasons, which can impact the rainfed crop cycle. Additionally, rainfall showed an annual decrease of approximately 50 mm in all RCAs, which can affect the phenological development of crops during critical stages (emergence through flowering). These changes represent a significant threat to the regional economy and food security of Mexico.

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Effects of inter-annual climate variability on grape harvest timing in rainfed hilly vineyards of Piedmont (NW Italy)

10.5194/egusphere-egu2020-10563 ◽

2020 ◽

Author(s):

Danilo Rabino ◽

Marcella Biddoccu ◽

Giorgia Bagagiolo ◽

Guido Nigrelli ◽

Luca Mercalli ◽

...

Keyword(s):

Climate Variability ◽

Weather Data ◽

Maximum Temperature ◽

Data Series ◽

Positive Trend ◽

Climate Variables ◽

Climate Data ◽

Historical Series ◽

Nw Italy ◽

Level Of Significance

Historical weather data represent an extremely precious resource for agro-meteorology for studying evolutionary dynamics and for predictive purposes, to address agronomical and management choices, that have economic, social and environmental effect. The study of climatic variability and its consequences starts from the observation of variations over time and the identification of the causes, on the basis of historical series of meteorological observations. The availability of long-lasting, complete and accurate datasets is a fundamental requirement to predict and react to climate variability. Inter-annual climate changes deeply affect grapevine productive cycle determining direct impact on the onset and duration of phenological stages and, ultimately, on the grape harvest and yield. Indeed, climate variables, such as air temperature and precipitation, affect evapotranspiration rates, plant water requirements, and also the vine physiology. In this respect, the observed increase in the number of warm days poses a threat to grape quality as it creates a situation of imbalance at maturity, with respect to sugar content, acidity and phenolic and aromatic ripeness.A study was conducted to investigate the relationships between climate variables and harvest onset dates to assess the responses of grapevine under a global warming scenario. The study was carried out in the &#8220;Monferrato&#8221; area, a rainfed hillslope vine-growing area of NW Italy. In particular, the onset dates of harvest of different local wine grape varieties grown in the Vezzolano Experimental Farm (CNR-IMAMOTER) and in surrounding vineyards (affiliated to the Terre dei Santi Cellars) were recorded from 1962 to 2019 and then related to historical series of climate data by means of regression analysis. The linear regression was performed based on the averages of maximum and minimum daily temperatures and sum of precipitation (1962&#8211;2019) calculated for growing and ripening season, together with a bioclimatic heat index for vineyards, the Huglin index. The climate data were obtained from two data series collected in the Experimental farm by a mechanical weather station (1962-2002) and a second series recorded (2002-2019) by an electro-mechanical station included in Piedmont Regional Agro-meteorological Network. Finally, a third long-term continuous series covering the period from 1962 to 2019, provided by Italian Meteorological Society was considered in the analysis.The results of the study highlighted that inter-annual climate variability, with a general positive trend of temperature, significantly affects the ripening of grapes with a progressive anticipation of the harvest onset dates. In particular, all the considered variables excepted precipitation, resulted negatively correlated with the harvest onset date reaching a high level of significance (up to P< 0.001). Best results have been obtained for maximum temperature and Huglin index, especially by using the most complete dataset. The change ratios obtained using datasets including last 15 years were greater (in absolute terms) than results limited to the period 1962-2002, and also correlations have greater level of significance. The results indicated clearly the relationships between the temperature trend and the gradual anticipation of harvest and the importance of having long and continuous historical weather data series available.

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Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

Remote Sensing ◽

10.3390/rs11070866 ◽

2019 ◽

Vol 11 (7) ◽

pp. 866 ◽

Cited By ~ 2

Author(s):

Imke Hans ◽

Martin Burgdorf ◽

Stefan A. Buehler

Keyword(s):

Time Series ◽

Radio Frequency ◽

Time Dependent ◽

Radio Frequency Interference ◽

Climate Variables ◽

Compelling Evidence ◽

Climate Data ◽

The Earth ◽

Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

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Homogeneity Test and Correction of Daily Temperature and Precipitation Data (1978–2015) in North China

Advances in Meteorology ◽

10.1155/2018/4712538 ◽

2018 ◽

Vol 2018 ◽

pp. 1-17 ◽

Cited By ~ 3

Author(s):

Lingling Shen ◽

Li Lu ◽

Tianjie Hu ◽

Runsheng Lin ◽

Ji Wang ◽

...

Keyword(s):

Large Scale ◽

North China ◽

Maximum Temperature ◽

Homogeneity Test ◽

Climate Data ◽

Average Temperature ◽

Temperature And Precipitation ◽

Daily Data ◽

Before And After ◽

Annual Average Temperature

Homogeneity of climate data is the basis for quantitative assessment of climate change. By using the MASH method, this work examined and corrected the homogeneity of the daily data including average, minimum, and maximum temperature and precipitation during 1978–2015 from 404/397 national meteorological stations in North China. Based on the meteorological station metadata, the results are analyzed and the differences before and after homogenization are compared. The results show that breakpoints are present pervasively in these temperature data. Most of them appeared after 2000. The stations with a host of breakpoints are mainly located in Beijing, Tianjin, and Hebei Province, where meteorological stations are densely distributed. The numbers of breakpoints in the daily precipitation series in North China during 1978–2015 also culminated in 2000. The reason for these breakpoints, called inhomogeneity, may be the large-scale replacement of meteorological instruments after 2000. After correction by the MASH method, the annual average temperature and minimum temperature decrease by 0.04°C and 0.06°C, respectively, while the maximum temperature increases by 0.01°C. The annual precipitation declines by 0.96 mm. The overall trends of temperature change before and after the correction are largely consistent, while the homogeneity of individual stations is significantly improved. Besides, due to the correction, the majority series of the precipitation are reduced and the correction amplitude is relatively large. During 1978–2015, the temperature in North China shows a rise trend, while the precipitation tends to decrease.

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A new empirical model of sub-daily rainfall intensity and its application in a rangeland biophysical model

The Rangeland Journal ◽

10.1071/rj10037 ◽

2011 ◽

Vol 33 (1) ◽

pp. 37 ◽

Cited By ~ 5

Author(s):

G. W. Fraser ◽

J. O. Carter ◽

G. M. McKeon ◽

K. A. Day

Keyword(s):

Empirical Model ◽

Rainfall Intensity ◽

Daily Rainfall ◽

Biophysical Model ◽

Maximum Temperature ◽

Validation Dataset ◽

Daily Maximum ◽

Climate Data ◽

Erosion Rates ◽

Biophysical Models

Sub-daily rainfall intensity has a significant impact on runoff and erosion rates in northern Australian rangelands. However, it has been difficult to include sub-daily rainfall intensity in rangeland biophysical models using historical climate data due to the limited number of pluviograph stations with long-term records. In this paper a new empirical model (‘Temperature I15’ model) was developed to predict the daily maximum 15-min rainfall intensity (I15) using daily minimum and maximum temperature and daily rainfall totals from 12 selected pluviograph stations across Australia. The ‘Temperature I15’ model accounted for 46% (P < 0.01) of the variation in observed daily I15 for an independent validation dataset derived from 67 Australia-wide pluviograph stations and represented both geographical and seasonal variability in I15. The model also accounted for 70% (P < 0.01) of the variation in the observed historical trend in I15 for the full record period (average record period was 37 years) of 73 Australia-wide pluviograph stations. The ‘Temperature I15’ model was found to be an improvement on a past empirical model of I15 and can be easily implemented in biophysical models by using readily available daily climate data. However, as the ‘Temperature I15’ model only represented 46% of the variation in daily observed I15, the model is best used in simulation studies on ‘timeframes’ in excess of 5 years. The new ‘Temperature I15’ model was implemented in the runoff equation of the Australia-wide spatial pasture growth model AussieGRASS, which predicts daily water balance and pasture growth for 185 different pasture communities. This resulted in an improved simulation of green cover for 71% of pasture communities but was worse for 25% of communities, with no change for 4% of communities.

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Modeling Seasonal Influenza Transmission and Its Association with Climate Factors in Thailand Using Time-Series and ARIMAX Analyses

Computational and Mathematical Methods in Medicine ◽

10.1155/2015/436495 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 14

Author(s):

Sudarat Chadsuthi ◽

Sopon Iamsirithaworn ◽

Wannapong Triampo ◽

Charin Modchang

Keyword(s):

Relative Humidity ◽

Moving Average ◽

Arima Model ◽

Southern Region ◽

Climate Variables ◽

Climate Data ◽

Average Temperature ◽

Minimum Relative Humidity ◽

Average Maximum ◽

Influenza Transmission

Influenza is a worldwide respiratory infectious disease that easily spreads from one person to another. Previous research has found that the influenza transmission process is often associated with climate variables. In this study, we used autocorrelation and partial autocorrelation plots to determine the appropriate autoregressive integrated moving average (ARIMA) model for influenza transmission in the central and southern regions of Thailand. The relationships between reported influenza cases and the climate data, such as the amount of rainfall, average temperature, average maximum relative humidity, average minimum relative humidity, and average relative humidity, were evaluated using cross-correlation function. Based on the available data of suspected influenza cases and climate variables, the most appropriate ARIMA(X) model for each region was obtained. We found that the average temperature correlated with influenza cases in both central and southern regions, but average minimum relative humidity played an important role only in the southern region. The ARIMAX model that includes the average temperature with a 4-month lag and the minimum relative humidity with a 2-month lag is the appropriate model for the central region, whereas including the minimum relative humidity with a 4-month lag results in the best model for the southern region.

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Spatially-explicit modelling of grassland classes – an improved method of integrating a climate-based classification model with interpolated climate surfaces

The Rangeland Journal ◽

10.1071/rj13103 ◽

2014 ◽

Vol 36 (2) ◽

pp. 175 ◽

Cited By ~ 4

Author(s):

Xiaoni Liu ◽

Hongxia Wang ◽

Jing Guo ◽

Jingqiong Wei ◽

Zhengchao Ren ◽

...

Keyword(s):

Environmental Management ◽

Classification Model ◽

Spatially Explicit ◽

Forest Steppe ◽

Climate Variables ◽

Climate Data ◽

Humid Forest ◽

Cool Temperate ◽

Total Study Area

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterised by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the inverse distance-weighted approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were ‘cool temperate-arid temperate zonal semi-desert’, ‘cool temperate-humid forest steppe and deciduous broad-leaved forest’, ‘temperate-extra-arid temperate zonal desert’, and ‘frigid per-humid rain tundra and alpine meadow’. The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies’ decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities, which will help to prevent overgrazing and land degradation.

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Indices for daily temperature and precipitation based on quality controlled and homogenized data in Madagascar

10.5194/egusphere-egu2020-19970 ◽

2020 ◽

Author(s):

Luc Yannick Andréas Randriamarolaza ◽

Enric Aguilar ◽

Oleg Skrynyk

Keyword(s):

Climate Change ◽

Missing Data ◽

Extreme Temperature ◽

Maximum Temperature ◽

Daily Minimum Temperature ◽

Daily Maximum Temperature ◽

Climate Indices ◽

Low Density ◽

Climate Data ◽

Temperature And Precipitation

Madagascar is an Island in Western Indian Ocean Region. It is mainly exposed to the easterly trade winds and has a rugged topography, which promote different local climates and biodiversity. Climate change inflicts a challenge on Madagascar socio-economic activities. However, Madagascar has low density station and sparse networks on observational weather stations to detect changes in climate. On average, one station covers more than 20 000 km2 and closer neighbor stations are less correlated. Previous studies have demonstrated the changes on Madagascar climate, but this paper contributes and enhances the approach to assess the quality control and homogeneity of Madagascar daily climate data before developing climate indices over 1950 &#8211; 2018 on 28 synoptic stations. Daily climate data of minimum and maximum temperature and precipitation are exploited.Firstly, the quality of daily climate data is controlled by INQC developed and maintained by Center for Climate Change (C3) of Rovira i Virgili University, Spain. It ascertains and improves error detections by using six flag categories. Most errors detected are due to digitalization and measurement.Secondly, daily quality controlled data are homogenized by using CLIMATOL. It uses relative homogenization methods, chooses candidate reference series automatically and infills the missing data in the original data. It has ability to manage low density stations and low inter-station correlations and is tolerable for missing data. Monthly break points are detected by CLIMATOL and used to split daily climate data to be homogenized.Finally, climate indices are calculated by using CLIMIND package which is developed by INDECIS* project. Compared to previous works done, data period is updated to 10 years before and after and 15 new climate indices mostly related to extremes are computed. On temperature, significant increasing and decreasing decade trends of day-to-day and extreme temperature ranges are important in western and eastern areas respectively. On average decade trends of temperature extremes, significant increasing of daily minimum temperature is greater than daily maximum temperature. Many stations indicate significant decreasing in very cold nights than significant increasing in very warm days. Their trends are almost 1 day per decade over 1950 &#8211; 2018. Warming is mainly felt during nighttime and daytime in Oriental and Occidental parts respectively. In contrast, central uplands are warming all the time but tropical nights do not appear yet. On rainfall, no major significant findings are found but intense precipitation might be possible at central uplands due to shortening of longest wet period and occurrence of heavy precipitation. However, no influence detected on total precipitation which is still decreasing over 1950 - 2018. Future works focus on merging of relative homogenization methodologies to ameliorate the results.-------------------*INDECIS is a part of ERA4CS, an ERA-NET initiated by JPI Climate, and funded by FORMAS (SE), DLR (DE), BMWFW (AT), IFD (DK), MINECO (ES), ANR (FR) with co-funding by the European Union (Grant 690462).

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Holocene Climatic Change in the Northern Midwest: Pollen-Derived Estimates

Quaternary Research ◽

10.1016/0033-5894(84)90029-2 ◽

1984 ◽

Vol 22 (3) ◽

pp. 361-374 ◽

Cited By ~ 169

Author(s):

P. J. Bartlein ◽

T. Webb ◽

E. Fleri

Keyword(s):

Maximum Temperature ◽

Pollen Record ◽

Regression Equations ◽

Climate Data ◽

The South ◽

The North ◽

Modern Pollen ◽

Quantitative Estimates ◽

Multiple Regression Models ◽

July Temperature

Mapping of Holocene pollen data in the midwestern United States has revealed several broadscale vegetational changes that can be interpreted in climatic terms. These changes include (1) the early Holocene northward movement of the spruce-dominated forest and its later southward movement after 3000 yr B.P. and (2) the eastward movement of the prairie/forest border into southwestern Wisconsin by 8000 yr B.P. and its subsequent westward retreat after 6000 yr B.P. When certain basic assumptions are met, multiple regression models can be derived from modern pollen and climate data and used to transform the pollen record of these vegetational changes into quantitative estimates of temperature or precipitation. To maximize the reliability of the regression equations, we followed a sequence of procedures that minimize violations of the assumptions that underlie regression analysis. Reconstructions of precipitation during the Holocene indicated that from 9000 to 6000 yr B.P. precipitation decreased by 10 to 25% over much of the Midwest, while mean July temperature increased by 0.5° to 2.0°C. At 6000 yr B.P. precipitation was less than 80% of its modern values over parts of Wisconsin and Minnesota. After 6000 yr B.P. precipitation generally increased, while mean July temperature decreased in the north, and increased in the south. The time of the maximum temperature varies within the Midwest and is earlier in the north and later in the south.

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Comparison of Collocated Automated (NCECONet) and Manual (COOP) Climate Observations in North Carolina

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1873.1 ◽

2006 ◽

Vol 23 (5) ◽

pp. 671-682 ◽

Cited By ~ 19

Author(s):

Christopher Holder ◽

Ryan Boyles ◽

Ameenulla Syed ◽

Dev Niyogi ◽

Sethu Raman

Keyword(s):

North Carolina ◽

Climate Models ◽

Daily Rainfall ◽

Maximum Temperature ◽

Spatial Density ◽

Climate Data ◽

Data Resource ◽

Weather And Climate ◽

Observation Times ◽

Mean Square Errors

Abstract The National Weather Service's Cooperative Observer Program (COOP) is a valuable climate data resource that provides manually observed information on temperature and precipitation across the nation. These data are part of the climate dataset and continue to be used in evaluating weather and climate models. Increasingly, weather and climate information is also available from automated weather stations. A comparison between these two observing methods is performed in North Carolina, where 13 of these stations are collocated. Results indicate that, without correcting the data for differing observation times, daily temperature observations are generally in good agreement (0.96 Pearson product–moment correlation for minimum temperature, 0.89 for maximum temperature). Daily rainfall values recorded by the two different systems correlate poorly (0.44), but the correlations are improved (to 0.91) when corrections are made for the differences in observation times between the COOP and automated stations. Daily rainfall correlations especially improve with rainfall amounts less than 50 mm day−1. Temperature and rainfall have high correlation (nearly 1.00 for maximum and minimum temperatures, 0.97 for rainfall) when monthly averages are used. Differences of the data between the two platforms consistently indicate that COOP instruments may be recording warmer maximum temperatures, cooler minimum temperatures, and larger amounts of rainfall, especially with higher rainfall rates. Root-mean-square errors are reduced by up to 71% with the day-shift and hourly corrections. This study shows that COOP and automated data [such as from the North Carolina Environment and Climate Observing Network (NCECONet)] can, with simple corrections, be used in conjunction for various climate analysis applications such as climate change and site-to-site comparisons. This allows a higher spatial density of data and a larger density of environmental parameters, thus potentially improving the accuracy of the data that are relayed to the public and used in climate studies.

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Modeling the Impact of High Temperature on Mortality in Pakistan

Sustainability ◽

10.3390/su14010332 ◽

2021 ◽

Vol 14 (1) ◽

pp. 332

Author(s):

Mushtaq Ahmad Khan Barakzai ◽

S.M. Aqil Burney

Keyword(s):

High Temperature ◽

Geographical Location ◽

Maximum Temperature ◽

Climate Data ◽

Medium Temperature ◽

Average Rainfall ◽

Medium Level ◽

High Level ◽

The Impact ◽

Very High

The objective of this paper is to model and study the impact of high temperature on mortality in Pakistan. For this purpose, we have used mortality and climate data consisting of maximum temperature, variation in monthly temperature, average rainfall, humidity, dewpoint, as well as average air pressure in the country over the period from 2000 to 2019. We have used the Generalized Linear Model with Quasi-Poisson link function to model the number of deaths in the country and to assess the impact of maximum temperature on mortality. We have found that the maximum temperature in the country has a significant impact on mortality. The number of deaths in Pakistan increases as the maximum temperature increases. We found that, as the maximum temperature increase beyond 30 °C, mortality increases significantly. Our results indicate that mortality increases by 27% when the maximum temperature in the country increases from medium category to a very high level. Similarly, the number of deaths in the country increases by 11% when the temperature increases from medium temperature to high level. Furthermore, our study found that when the maximum temperature in the country decreases from a medium level to a low level, the number of deaths in the country decreases by 23%. This study does not consider the impact of other factors on mortality, such as age, medical conditions, gender, geographical location, as well as variability of temperature across the country.

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