Error Autocorrelation and Linear Regression for Temperature-Based Evapotranspiration Estimates Improvement

Estimates of evapotranspiration on a local scale is important information for agricultural and hydrological practices. However, equations to estimate potential evapotranspiration based only on temperature data, which are simple to use, are usually less trustworthy than the Food and Agriculture Organization (FAO)Penman-Monteith standard method. The present work describes two correction procedures for potential evapotranspiration estimates by temperature, making the results more reliable. Initially, the standard FAO-Penman-Monteith method was evaluated with a complete climatologic data set for the period between 2002 and 2006. Then temperature-based estimates by Camargo and Jensen-Haise methods have been adjusted by error autocorrelation evaluated in biweekly and monthly periods. In a second adjustment, simple linear regression was applied. The adjusted equations have been validated with climatic data available for the Year 2001. Both proposed methodologies showed good agreement with the standard method indicating that the methodology can be used for local potential evapotranspiration estimates.

SEASONALITY OF VIRAL RESPIRATORY INFECTIONS IN SOUTHEAST OF BRAZIL: THE INFLUENCE OF TEMPERATURE AND AIR HUMIDITY

Viruses are the major cause of lower respiratory tract infections in childhood and the main viruses involved are Human Respiratory Syncytial Virus (HRSV), Human Metapneumovirus (HMPV), Influenzavirus A and B (FLUA and FLUB), Human Parainfluenza Virus 1, 2 and 3 (HPIV1, 2 and 3) and Human Rhinovirus (HRV). The purposes of this study were to detect respiratory viruses in hospitalized children younger than six years and identify the influence of temperature and relative air humidity on the detected viruses. Samples of nasopharyngeal washes were collected from hospitalized children between May/2004 and September/2005. Methods of viral detection were RT-PCR, PCR and HRV amplicons were confirmed by hybridization. Results showed 54% (148/272) of viral positivity. HRSV was detected in 29% (79/272) of the samples; HRV in 23.1% (63/272); HPIV3 in 5.1% (14/272); HMPV in 3.3% (9/272); HPIV1 in 2.9% (8/272); FLUB in 1.4% (4/272), FLUA in 1.1% (3/272), and HPIV2 in 0.3% (1/272). The highest detection rates occurred mainly in the spring 2004 and in the autumn 2005. It was observed that viral respiratory infections tend to increase as the relative air humidity decreases, showing significant association with monthly averages of minimal temperature and minimal relative air humidity. In conclusion, viral respiratory infections vary according to temperature and relative air humidity and viral respiratory infections present major incidences it coldest and driest periods.

Association between particulate matter air pollution and monthly inhalation and nebulization procedures in Ribeirao Preto, Sao Paulo State, Brazil

The study was designed to investigate the impact of air pollution on monthly inhalation/nebulization procedures in Ribeirao Preto, Sao Paulo State, Brazil, from 2004 to 2010. To assess the relationship between the procedures and particulate matter (PM10) a Bayesian Poisson regression model was used, including a random factor that captured extra-Poisson variability between counts. Particulate matter was associated with the monthly number of inhalation/nebulization procedures, but the inclusion of covariates (temperature, precipitation, and season of the year) suggests a possible confounding effect. Although other studies have linked particulate matter to an increasing number of visits due to respiratory morbidity, the results of this study suggest that such associations should be interpreted with caution.

Association between particulate matter air pollution and monthly inhalation and nebulization procedures in Ribeirão Preto, São Paulo State, Brazil

The study was designed to investigate the impact of air pollution on monthly inhalation/nebulization procedures in Ribeirão Preto, São Paulo State, Brazil, from 2004 to 2010. To assess the relationship between the procedures and particulate matter (PM10) a Bayesian Poisson regression model was used, including a random factor that captured extra-Poisson variability between counts. Particulate matter was associated with the monthly number of inhalation/nebulization procedures, but the inclusion of covariates (temperature, precipitation, and season of the year) suggests a possible confounding effect. Although other studies have linked particulate matter to an increasing number of visits due to respiratory morbidity, the results of this study suggest that such associations should be interpreted with caution.

Seasonality of viral respiratory infections in Southeast of Brazil: the influence of temperature and air humidity

Viruses are the major cause of lower respiratory tract infections in childhood and the main viruses involved are Human Respiratory Syncytial Virus (HRSV), Human Metapneumovirus (HMPV), Influenzavirus A and B (FLUA and FLUB), Human Parainfluenza Virus 1, 2 and 3 (HPIV1, 2 and 3) and Human Rhinovirus (HRV). The purposes of this study were to detect respiratory viruses in hospitalized children younger than six years and identify the influence of temperature and relative air humidity on the detected viruses. Samples of nasopharyngeal washes were collected from hospitalized children between May/2004 and September/2005. Methods of viral detection were RT-PCR, PCR and HRV amplicons were confirmed by hybridization. Results showed 54% (148/272) of viral positivity. HRSV was detected in 29% (79/272) of the samples; HRV in 23.1% (63/272); HPIV3 in 5.1% (14/272); HMPV in 3.3% (9/272); HPIV1 in 2.9% (8/272); FLUB in 1.4% (4/272), FLUA in 1.1% (3/272), and HPIV2 in 0.3% (1/272). The highest detection rates occurred mainly in the spring 2004 and in the autumn 2005. It was observed that viral respiratory infections tend to increase as the relative air humidity decreases, showing significant association with monthly averages of minimal temperature and minimal relative air humidity. In conclusion, viral respiratory infections vary according to temperature and relative air humidity and viral respiratory infections present major incidences it coldest and driest periods.

Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network

[EN] Here we present monthly, basin-wide maps of the partial pressure of carbon dioxide (pCO2) for the North Atlantic on a latitude by longitude grid for years 2004 through 2006 inclusive. The maps have been computed using a neural network technique which reconstructs the non-linear relationships between three biogeochemical parameters and marine pCO2. A self organizing map (SOM) neural network has been trained using 389 000 triplets of the SeaWiFSMODIS chlorophyll-a concentration, the NCEP/NCAR reanalysis sea surface temperature, and the FOAM mixed layer depth. The trained SOM was labelled with 137 000 underway pCO2 measurements collected in situ during 2004, 2005 and 2006 in the North Atlantic, spanning the range of 208 to 437atm. The root mean square error (RMSE) of the neural network fit to the data is 11.6?atm, which equals to just above 3 per cent of an average pCO2 value in the in situ dataset. The seasonal pCO2 cycle as well as estimates of the interannual variability in the major biogeochemical provinces are presented and discussed. High resolution combined with basin-wide coverage makes the maps a useful tool for several applications such as the monitoring of basin-wide air-sea CO2 fluxes or improvement of seasonal and interannual marine CO2 cycles in future model predictions. The method itself is a valuable alternative to traditional statistical modelling techniques used in geosciences.

Climatological analysis of temperature and salinity fields in the Mediterranean sea

A climatological field is a mean gridded field that represents the monthly or seasonal trend of an ocean parameter. This instrument allows to understand the physical conditions and physical processes of the ocean water and their impact on the world climate. To construct a climatological field, it is necessary to perform a climatological analysis on an historical dataset. In this dissertation, we have constructed the temperature and salinity fields on the Mediterranean Sea using the SeaDataNet 2 dataset. The dataset contains about 140000 CTD, bottles, XBT and MBT profiles, covering the period from 1900 to 2013. The temperature and salinity climatological fields are produced by the DIVA software using a Variational Inverse Method and a Finite Element numerical technique to interpolate data on a regular grid. Our results are also compared with a previous version of climatological fields and the goodness of our climatologies is assessed, according to the goodness criteria suggested by Murphy (1993). Finally the temperature and salinity seasonal cycle for the Mediterranean Sea is described.

A Novel Method for the Homogenization of Daily Temperature Series and Its Relevance for Climate Change Analysis

Instrumental daily series of temperature are often affected by inhomogeneities. Several methods are available for their correction at monthly and annual scales, whereas few exist for daily data. Here, an improved version of the higher-order moments (HOM) method, the higher-order moments for autocorrelated data (HOMAD), is proposed. HOMAD addresses the main weaknesses of HOM, namely, data autocorrelation and the subjective choice of regression parameters. Simulated series are used for the comparison of both methodologies. The results highlight and reveal that HOMAD outperforms HOM for small samples. Additionally, three daily temperature time series from stations in the eastern Mediterranean are used to show the impact of homogenization procedures on trend estimation and the assessment of extremes. HOMAD provides an improved correction of daily temperature time series and further supports the use of corrected daily temperature time series prior to climate change assessment.

Crop Yield Responses to Temperature Fluctuations in 19th Century Finland: Provincial Variation in Relation to Climate and Tree Rings

Past agricultural responses to climate variability can helps us to better understand the current and future impacts of climate change on agricultural production. We studied rye (Secale cereale) and barley (Hordeum vulgare) yield responses to temperature fluctuations in Finland during the period 1861–1913. Our analyses demonstrate the high sensitivity of non-industrialised northern agriculture to temperature anomalies. We found evidence of a strong relationship between monthly and seasonal mean temperatures and crop yields. In particular, high spring temperatures were associated with higher yields. Additionally, we tested temperature-sensitive tree-ring series for their value in indicating previous agricultural outputs. The results imply that tree-ring proxies (in particular, maximum latewood density) can provide novel material for studies of historical periods and locations where instrumentally measured climate and harvest data are not available.

Monthly Tahiti coral Sr/Ca and oxygen isotope data from IODP Hole 310-M0024A

The early last glacial termination was characterized by intense North Atlantic cooling and weak overturning circulation. This interval between ~18,000 and 14,600 years ago, known as Heinrich Stadial 1, was accompanied by a disruption of global climate and has been suggested as a key factor for the termination. However, the response of interannual climate variability in the tropical Pacific (El Niño-Southern Oscillation) to Heinrich Stadial 1 is poorly understood. Here we use Sr/Ca in a fossil Tahiti coral to reconstruct tropical South Pacific sea surface temperature around 15,000 years ago at monthly resolution. Unlike today, interannual South Pacific sea surface temperature variability at typical El Niño-Southern Oscillation periods was pronounced at Tahiti. Our results indicate that the El Niño-Southern Oscillation was active during Heinrich Stadial 1, consistent with climate model simulations of enhanced El Niño-Southern Oscillation variability at that time. Furthermore, a greater El Niño-Southern Oscillation influence in the South Pacific during Heinrich Stadial 1 is suggested, resulting from a southward expansion or shift of El Niño-Southern Oscillation sea surface temperature anomalies.

H.W.M. Olbers measurements of air temperature in Bremen from 1803 to 1822

We rediscovered a temperature time series from Heinrich W. M. Olbers. Heinrich W. M. Olbers measured in Bremen, Sandstrasse 15, in Germany from 1803 to 1821 three times a day (7 am, 1-2 pm and 10 pm) the temperature at his window of his study, which is up to 16 m above the zero marking at the Weserbrücke. The temperature values from 1814 are missing. We got the temperature values from different sources in the Olbers estate. We calculated the daily mean and digitized it in various plots. A very small trend towards cooling is apparent in the data which might be insignificant. But a clear seasonal trend was identifiable: the late winter and the early spring were becoming warmer, while the summer and early autumn became cooler. The average temperature in Bremen was 8.3606 deg C at that time. Additionally we combined the newly discovered Heinrich W. M. Olbers temperature data and the Heinemann and Bätjer data to see whether there are great differences between these two time series. Although the temperatures of Heinrich W. M. Olbers are in general cooler than the Heinemann and Bätjer data they fit together.