Monthly Climate Data Research Articles

Tree–ring studies of Toona ciliata from subtropical wet hill forests of Kalimpong, eastern Himalaya

We present a 180-years long (1824–2003C.E.) tree–ring width chronology of the fast growing broadleaf tree Toon (Toona ciliata Roem.) from the subtropical wet hill forests of Kalimpong, in the eastern Himalaya of India. The majority of tree ring studies conducted in the Himalaya have been based on conifers. Our analyses demonstrate that winter temperature prior to the season of growth, current year April precipitation and April–May drought (scPDSI) exhibit direct relationship with the ring–width chronology when correlated with monthly or seasonal climate data. The observation of the tree–growth and climate relationship showed that optimum temperature, precipitation and soil moisture together serve to modulate the radial growth of Toon in the region, however the mean minimum temperature of winter season (October–February) is the most significant climatic factor. Based on the expressed population signal (EPS) statistic, our record is reliable only back to 1945C.E., however this study clearly establishes the dendroclimatic potential of this tropical broadleaf species to carry out future tree–ring based long–term climate reconstruction in the sub-tropical Himalaya.

Constructing a long‐term monthly climate data set in central Asia

ABSTRACTWe compiled and merged in situ observation data from several sources, creating a comprehensive unified monthly air temperature and precipitation data set with 457 stations in central Asia (CA). Stations with a valid data rate higher than 80% were selected, and the remaining gaps in selected station time series were filled with an iterative‐principal component analysis (PCA) gap‐filling method. The result is a gap‐filled station data set for the period 1951–2010, with 369 and 381 stations for air temperature and precipitation, respectively. The cross‐validation shows that the iterative‐PCA gap‐filling algorithm provides stable and trustworthy estimations of gaps, with mean root mean squared error (RMSE) of 0.03 °C (in the range of 0.01–0.13 °C) for air temperature, and mean RMSE of 0.60 mm (in the range of 0.10–1.99 mm) for precipitation. A gridded data set was created by interpolating the gap‐filled station data set with the geographically weighted regression method. Comparison of the gridded data set with the National Centers for Environmental Prediction (NCEP) reanalysis data set shows that though both data sets present the long‐term mean climate situation similarly, the gridded data set exhibits less annual and monthly variability. And the gridded data set has stronger correlations with stations time series than the reanalysis data set (mean correlations are 0.994 vs 0.975 for air temperature, and 0.787 vs 0.515 for precipitation), especially for precipitation in high mountain stations. The gridded data set is more suitable for climate and hydrological studies in CA, especially in high mountains regions.

A spatiotemporal analysis of the relationship between near‐surface air temperature and satellite land surface temperatures using 17 years of data from the ATSR series

AbstractThe relationship between satellite land surface temperature (LST) and ground‐based observations of 2 m air temperature (T2m) is characterized in space and time using >17 years of data. The analysis uses a new monthly LST climate data record (CDR) based on the Along‐Track Scanning Radiometer series, which has been produced within the European Space Agency GlobTemperature project (http://www.globtemperature.info/). Global LST‐T2m differences are analyzed with respect to location, land cover, vegetation fraction, and elevation, all of which are found to be important influencing factors. LSTnight (~10 P.M. local solar time, clear‐sky only) is found to be closely coupled with minimum T2m (Tmin, all‐sky) and the two temperatures generally consistent to within ±5°C (global median LSTnight‐Tmin = 1.8°C, interquartile range = 3.8°C). The LSTday (~10 A.M. local solar time, clear‐sky only)‐maximum T2m (Tmax, all‐sky) variability is higher (global median LSTday‐Tmax = −0.1°C, interquartile range = 8.1°C) because LST is strongly influenced by insolation and surface regime. Correlations for both temperature pairs are typically >0.9 outside of the tropics. The monthly global and regional anomaly time series of LST and T2m—which are completely independent data sets—compare remarkably well. The correlation between the data sets is 0.9 for the globe with 90% of the CDR anomalies falling within the T2m 95% confidence limits. The results presented in this study present a justification for increasing use of satellite LST data in climate and weather science, both as an independent variable, and to augment T2m data acquired at meteorological stations.

The Effects of Climate on Output per Worker: Evidence from the Manufacturing Industry in Colombia

This paper quantifies the effect of an increase in temperature and precipitation on the average output per worker in the Colombian manufacturing industry. In order to approach this issue rigorously, a methodology is developed with a theoretical model and an empirical estimation. The estimation of the empirical model is done with economic data from the Annual Survey of the Manufacturing Industry, the Monthly Manufacturing Sample and climate data from IDEAM. The results do not allow to reject a negative effect of temperature and a positive effect of precipitation on average output per worker.

The climate impact on female acute pyelonephritis in Taiwan: A population-based study

ObjectiveUrinary tract infection (UTI) is the main reason of community-acquired infection which causes large losses in social economy. The individual as well as climate factors make changes on the incidence. Acute pyelonephritis (APN) is one of the most serious UTI in female. The object of our study is to analyze whether climate factors will have effect on the incidence of female APN in Taiwan. Materials and methodsThis study consisted of 14,568 female patients with APN from 2001 to 2013 in Taiwan and patients with repeated APN were excluded. The monthly climate data was collected from the Central Weather Bureau. The available monthly climate data included highest, lowest, and average level of temperatures, humidity, rainfall, total rain days, and sunshine hours. ResultsThe total incidence of female APN was 23.44 each 10,000 populations. The incidence of APN was positively correlated with temperature (r = 0.66), sunshine hours (r = 0.45), rainfall (r = 0.42), rain days (r = 0.29), and humidity (r = 0.23) per month. There is the strongest correlation between the average monthly temperature and the incidence of APN (β = 0.54). The correlation with the incidence of APN was also followed by rain days (β = 0.28) and humidity (β = 0.27). ConclusionThere is a significant expression on the incidence of female APN affected by seasonality and climate parameters. The monthly average temperature has the strongest correlation with female APN. The results of this research may facilitate the potential preventive strategies on female APN.

RESPONCE A CONSUMPTIVE USE PROGRAM MODEL ON WHEAT UNDER EGYPTIAN CONDITIONS

The experiments were carried out at Moshtohor, Kalubia governorate [Latitude: 30o 21`N, Longitude: 31o 14`E and Elevation: 14 m] during 2015/16 growing seasons to test model application of wheat under Egyptian conditions. A computer application program has been developed as Consumptive Use Program plus (CUP plus) as is an application, can estimate crop evapotranspiration (ETc) and evapotranspiration of applied water (ETaw). A monthly climate data, the program uses daily measured weather data to estimate daily soil water balances for surfaces that account for evapotranspiration losses and water contributions from rainfall, seepage, and irrigation. Soil water-holding characteristics, effective rooting depths, and irrigation frequency were measured with rainfall and ETc data to calculate a daily water balance and determine effective rainfall and ETaw, which is equal to the seasonal cumulative ETc minus the effective rainfall. The main objective of this paper research is testing a mode for determining reference evapotranspiration (ETo), crop coefficient (Kc) values, crop evapotranspiration (ETc), and evapotranspiration of applied water (ETaw), which provides an estimate of the net irrigation water diversion needed to produce a crop. The obtained results show that ETo arrive to the maximum in May by 188.19 mm/month but ETaw arrive to the maximum in April by 110.71 mm/month. The application outputs a wide range of tables and charts that are useful for irrigation planning and decision making.

Carbon plants nutrition and global food security

To evaluate the effects of carbon nutrition on agricultural productivity, a physiological-process-based crop simulation model, driven by the 1961-1990 monthly climate data from global FAO dataset, was developed and applied to four crops (wheat, maize, rice and soybean --WMRS) which account for 64% of the global caloric consumption of humans. Five different temperatures and CO2 scenarios (current; glacial; pre-industrial; future_1 with 560 ppmv for CO2 and +2 °C for temperature; and future_2 with 800 ppmv for CO2 and +4 °C) were investigated. The relative values of WMRS global productions for past and future scenarios were, respectively, 49% of the present-day scenario for glacial, 82% for pre-industrial, 115% for future_1 and 124% for future_2. A sensitive growth of productivity of future scenarios (respectively to 117% and 134%) was observed if the northward shift of crops was allowed, and a strong increase was obtained without water limitation (from 151% to 157% for the five scenarios) and without biotic and abiotic stresses (from 30% to 40% for WMRS subject to the current scenario). Furthermore since the beginning of the Green Revolution (roughly happened between the '30s and the '50s of the twentieth century) production losses due to sub-optimal levels of CO2 and to biotic and abiotic stresses have been masked by the strong technological innovation trend still ongoing, which, in the last century, led to a strong increase in the global crop production (+400%-600%). These results show the crucial relevance of the future choices of research and development in agriculture (genetics, land reclamation, irrigation, plant protection, and so on) to ensure global food security.

Agro-climatic zoning in Egypt to improve irrigation water management

AbstractThe objective of this paper was to develop agro-climatic zones in the old cultivated lands of Egypt in the Nile Delta and Valley using climate normals from 1985–2014 to facilitate better irrigation water management under water scarcity conditions. 30-year monthly climate data were collected for 17 agricultural governorates in Egypt and yearly averages and 30-year averages were calculated. BISm model was used to calculate yearly averages of potential evapotranspiration (PET) and 30-year average for each governorate. Analysis of variance was done using one factor randomize complete block design, with number of years as replicates. Furthermore, the mean, the range and R2 were calculated to test the strength of the relationship between PET and climate elements. The means of PET for each governorate was separated and ranked in ascending order using least significant difference test (LSD0.05). The results identified 7 agro-climatic zones (LSD0.05 = 0.146). These zones were: (1) Alexandria; (2) Demiatte, Kafr El-Sheikh and Dakhlia; (3) El-Behira, and El-Gharbia; (4) El-Minofia, El-Sharkia, El-Kalubia, Giza and El-Fayom; (5) Beni Sweif, El-Minia, Assuit and Sohag; (6) Qena; and (7) Aswan. Such zoning will increase the ability of the Egyptian policy makers to prepare the appropriate water management and development policies as a result of the availability of proper information on each zone aiming at efficient use of the limited water resources.

Climate Response of Tree Radial Growth at Different Timescales in the Qinling Mountains.

The analysis of the tree radial growth response to climate is crucial for dendroclimatological research. However, the response relationships between tree-ring indices and climatic factors at different timescales are not yet clear. In this study, the tree-ring width of Huashan pine (Pinus armandii) from Huashan in the Qinling Mountains, north-central China, was used to explore the response differences of tree growth to climatic factors at daily, pentad (5 days), dekad (10 days) and monthly timescales. Correlation function and linear regression analysis were applied in this paper. The tree-ring width showed a more sensitive response to daily and pentad climatic factors. With the timescale decreasing, the absolute value of the maximum correlation coefficient between the tree-ring data and precipitation increases as well as temperature (mean, minimum and maximum temperature). Compared to the other three timescales, pentad was more suitable for analysing the response of tree growth to climate. Relative to the monthly climate data, the association between the tree-ring data and the pentad climate data was more remarkable and accurate, and the reconstruction function based on the pentad climate was also more reliable and stable. We found that the major climatic factor limiting Huashan pine growth was the precipitation of pentads 20–35 (from April 6 to June 24) rather than the well-known April–June precipitation. The pentad was also proved to be a better timescale for analysing the climate and tree growth in the western and eastern Qinling Mountains. The formation of the earlywood density of Chinese pine (Pinus tabulaeformis) from Shimenshan in western Qinling was mainly affected by the maximum temperature of pentads 28–32 (from May 16 to June 9). The maximum temperature of pentads 28–33 (from May 16 to June 14) was the major factor affecting the ring width of Chinese pine from Shirenshan in eastern Qinling.

Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.

Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data.

Using hydrologic simulation to identify contributions of climate change and human activity to runoff changes in the Kuye river basin, China

Under the background of global climate change and local anthropogenic stresses, runoff has been in decline in most river basins in China over the past decades. Assessing the relative effect of climate change and human activities is essential not only for understanding the mechanism of hydrological response in the catchment, but also for local water resources management. The Kuye river, the first-order tributary of the middle Yellow river, has experienced significant runoff declines. In this study, the characteristics of hydro-climatic changes of the Kuye river catchment were analyzed based on the observed data for the period 1955–2010. Using the monthly climatic and hydrological data, the Yellow river water balance model (YRWBM) is calibrated and verified to a baseline period from 1955 to 1978. Subsequently, natural runoff for human-induced period (1979–1998) and strongly human-induced period (1999–2010) is reconstructed using the YRWBM model without considering local human impacts. Results indicate that the time series of runoff was divided into three periods at two critical years of 1979 and 1998. The YRWBM model performed well in simulating monthly discharges in the catchment, both NSES in calibration and verification were above 70 %, while RES in both periods were at less than 5 %. The absolute runoff reductions induced by climate change and human activities both have been growing. The percentage of runoff reduction attributed to climate variations from 60.56 % in human-induced periods to 43.50 % in strongly human-induced periods. At the same time the percentage of runoff reduction from human activities is from 39.44 % in human-induced periods to 56.50 % in strongly human-induced periods. Thus human activities demonstrated a dominant influence upon runoff decline gradually.

Up to 400‐year‐old Rhododendron shrubs on the southeastern Tibetan Plateau: prospects for shrub‐based dendrochronology

In harsh and treeless environments, shrubs constitute the dominant growth form of woody plants, thus offering the opportunity to extend dendrochronological networks beyond the uppermost and northernmost distribution limits of trees. However, shrub‐based dendrochronology has so far resulted in only a few long and climate‐responsive ring‐width chronologies at such stressful sites, particularly above the alpine tree line. A previous study on an alpine Rhododendron shrub species resulted in <80‐year‐long ring‐width chronologies. Here, we collected Rhododendron aganniphum var. schizopeplum stems from elevations between 4000 and 4500 m a.s.l. on the SE Tibetan Plateau and built six tree‐ring width chronologies. One of them is 401 years long and well replicated from AD 1670 to 2011 (EPS>0.85), thus representing the longest shrub chronology available to date. A principal component analysis (PCA) converted the total variability of all six site chronologies into PCs. Then, the six site chronologies and the PC1, accounting for 65.9% of the total variance of the tree‐ring width, were correlated with time series of monthly climate data. Based on this, the year‐to‐year variability of the ring‐width indices was positively correlated with July temperature, which thus turned out to be the dominant factor controlling growth. Accordingly, such long shrub‐ring chronologies may act as climatic and ecological proxies in treeless environments of the Tibetan Plateau.

Water balance of the Sudanese savannah woodland region

The annual water balance for 39 grid cells covering the savannah woodland region of Sudan (10–16°N; 21–36°E) was determined and regional maps produced. Long-term (1961–1990) mean monthly climate data, National Forest Inventory data and Harmonized World Soil Database data for arenosols and vertisols, the two dominant soil types in the region, were used. Model validation was performed using daily data from a site in one of the grid cells and inter-annual (1961–1990) variation examined for another grid cell. Rainfall varied from 147 to 732 mm and only exceeded evapotranspiration for 18 of the grid cells, resulting in a small increase in soil moisture and runoff. Evapotranspiration accounted for, on average, 96% of rainfall and there was little difference between soil types. Drainage only occurred from AR soils and for four of the grid cells. Runoff varied from 0 to 89 mm for arenosols and from 0 to 109 mm for vertisols. The study provided useful insights into the spatial variability in water balance components across the region.Editor D. Koutsoyiannis; Associate editor D. Gerten

Testing a reanalysis‐based infilling method for areas with sparse discontinuous air temperature data in northeastern Canada

AbstractThis study tests various applications of a new technique for infilling sparse monthly climate data which combines temperature anomalies from gridded observational and reanalysis data sets with baseline climatologies from short‐instrumental records. Out‐of‐sample comparisons between infilled and observed climate data for 53 stations in northeastern Canada suggests that mean absolute errors using the proposed method are ±1 and ±0.5 °C on monthly and annual timescales, respectively. Evaluation of several gridded data sets used to guide infilling suggests that ERA‐Interim (ERAI) and modern‐era retrospective reanalysis (MERRA) reanalyses are the most suitable for this purpose in the Labrador‐Ungava region.

Modeling the extrinsic incubation of Dirofilaria immitis in South America based on monthly and continuous climatic data

The spread and recrudescence of dirofilariosis across several regions, either caused by Dirofilaria immitis or D. repens, responds to many factors. Building upon the temperature model by Slocombe et al. (1989), a number of studies have been performed to generate predictive models for dirofilariosis. These studies have demonstrated the length of transmission periods and the number of Dirofilaria generations supported throughout several regions of the world (either at large or at small-scale). The usual procedure have proved to be extremely time consuming, as it appears impractical when assessing potential transmission at large scale, such as at a country or large-scale level. Due to the increasing need to suggest standardized surveillance protocols and apply adequate preventive measures at national and regional levels, a model for calculation of Dirofilaria HDUs based on monthly data was adapted for large-scale regional use. The models proposed are based on both point data (meteorological stations) and interpolated climate data layers (WorldClim).Three different models (daily and monthly models based on point data, and monthly model based on continuous data) were developed and compared statistically. When compared with the results from the classical daily model, the monthly models proposed accurately predicted the locations were extrinsic incubation was possible. These models proved to be adequate for the regional analysis of the extrinsic incubation of D. immitis and, hence, the relative risk of transmission in South America. Further, these models confirm that favorable temperatures for heartworm transmission in South America are present in most of the countries. D. immitis extrinsic incubation follows a seasonal pattern in Argentina, Chile, Uruguay, eastern Paraguay and southeastern Brazil; while in northern half of South America (less than 25° S) transmission may occur year-round. Moreover, high risk areas suitable for dirofilariasis transmission are not geographically constant throughout the year. The validation procedures indicate that the predicted HDU and HG maps are good predictors of dirofilariosis potential distribution, but estimating dirofilariosis prevalences based on them might not be completely accurate.The resulting distribution and seasonal maps would be useful for heartworm prevention by chemoprophylaxis in different regions known to be endemic for canine dirofilariasis. The information here provided can be an important tool in veterinary public health, as well as a guide for future research.

Pan-tropical analysis of climate effects on seasonal tree growth.

Climate models predict a range of changes in tropical forest regions, including increased average temperatures, decreased total precipitation, reduced soil moisture and alterations in seasonal climate variations. These changes are directly related to the increase in anthropogenic greenhouse gas concentrations, primarily CO2. Assessing seasonal forest growth responses to climate is of utmost importance because woody tissues, produced by photosynthesis from atmospheric CO2, water and light, constitute the main component of carbon sequestration in the forest ecosystem. In this paper, we combine intra-annual tree growth measurements from published tree growth data and the corresponding monthly climate data for 25 pan-tropical forest sites. This meta-analysis is designed to find the shared climate drivers of tree growth and their relative importance across pan-tropical forests in order to improve carbon uptake models in a global change context. Tree growth reveals significant intra-annual seasonality at seasonally dry sites or in wet tropical forests. Of the overall variation in tree growth, 28.7% was explained by the site effect, i.e. the tree growth average per site. The best predictive model included four climate variables: precipitation, solar radiation (estimated with extrasolar radiation reaching the atmosphere), temperature amplitude and relative soil water content. This model explained more than 50% of the tree growth variations across tropical forests. Precipitation and solar radiation are the main seasonal drivers of tree growth, causing 19.8% and 16.3% of the tree growth variations. Both have a significant positive association with tree growth. These findings suggest that forest productivity due to tropical tree growth will be reduced in the future if climate extremes, such as droughts, become more frequent.

Oak decline in a southern Finnish forest as affected by a drought sequence

We investigated the decline of a pedunculate oak (Quercus robur L.) forest growing on shallow soil at the northern distributional limit of the species in southern Finland, using the dendroclimatic approach. About 200-year-old trees in three vigour classes — healthy, declining and dead — were sampled in 2008. Annual tree-ring, earlywood and latewood widths were measured and chronologies were established. The tree-ring data were correlated with monthly and seasonal climate data. Radial increment of oaks was positively related to the June and July precipitations. This was expressed especially in total ring width and latewood width, whereas the earlywood was more influenced by the warmer winter and spring. Furthermore, the correlation between the current year earlywood width and the preceding year latewood width was higher than between the earlywood and latewood of the same year. The analyses showed that the dead oaks and part of the declining oaks had ceased growing during 2005–2007 after a decadelong summer drought series. This indicates a time lag in the oak dieback. The radial growth of the declining and the dead oaks had dropped already since the 1990s, while the healthy oaks had better longterm growth and higher adaptive capacity to climate variation.

Investigation of a simple approach to predict rainscreen wall ventilation rates for hygrothermal simulation purposes

Hygrothermal simulation programs are commonly used by design engineers to analyse moisture performance of building envelopes. For ventilated assemblies, programs typically require the user to enter cavity ventilation rates. If such input data are not prepared on physical grounds, it can impair the quality of the simulation results. In earlier papers: (1) estimations of ventilation rates in experimental walls based on cavity air velocity measurements; (2) comparative calculations based on monthly and annual tabular climate data, a simple driving force model and models of cavity airflow and heat balance, have been presented. In this study, the models and hourly climate field data were used to perform calculations of transient cavity ventilation rates for comparison with previous experimental results. For 13 different time periods extending from 24 to 91 h, the calculated average ventilation rates were within or very close to the experimentally estimated limits for the ventilation rate. Additionally, the calculations captured the temporal variability and the physical cause of ventilation airflow in the cavities reasonably well. The applied calculation methodology can be developed into a user friendly approach to estimate realistic ventilation rate input data for hygrothermal purposes. Limitations and possible improvements of the methodology are discussed. The influence of the accuracy and resolution of the ventilation rate input data in simulations is demonstrated in a case study.

Estimation of Monthly Mean Reference Evapotranspiration in Turkey

Monthly mean reference evapotranspiration (ET0) is estimated using adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN) models. Various combinations of long-term average monthly climatic data of wind speed, air temperature, relative humidity, and solar radiation, recorded at stations in Turkey, are used as inputs to the ANFIS and ANN models so as to calculate ET0 given by the FAO-56 PM (Penman-Monteith) equation. First, a comparison is made among the estimates provided by the ANFIS and ANN models and those by the empirical methods of Hargreaves and Ritchie. Next, the empirical models are calibrated using the ET0 values given by FAO-56 PM, and the estimates by the ANFIS and ANN techniques are compared with those of the calibrated models. Mean square error, mean absolute error, and determination coefficient statistics are used as comparison criteria for evaluation of performances of all the models considered. Based on these evaluations, it is found that the ANFIS and ANN schemes can be employed successfully in modeling the monthly mean ET0, because both approaches yield better estimates than the classical methods, and yet ANFIS being slightly more successful than ANN.

Dendrochronological potential of Ephedra equisetina from Zaravshan Mountains (Tajikistan) in climate change studies

Abstract Samples from Mongolian Ephedra (Ephedra equisetina Bunge) was collected in the Zaravshan Mountains (the Fann Mountains), Tajikistan. The wood of Ephedra is ring-porous with well-defined growth rings. Annual ring widths were measured, individual series were first cross-dated and then averaged as a standard chronology. Correlations were calculated between the standard ring-width chronology and monthly climate data recorded in the weather station Iskanderkul. Dendroclimatological analysis showed that July temperature is the growth limiting factor of this species. Our study has shown high dendrochronological potential of Ephedra.