Mean Monthly Air Temperature Research Articles

Innovative Engineering Solutions for Improving Operational Safety and Efficiency of Subways with Two-way Tunnels

The paper considers the problems of improving the operational safety of the two-way tunnel subway under weather conditions characteristic of Russian megapolises. A significant difference is indicated between annually mean, monthly mean and extreme outdoor air temperatures affecting thermodynamic parameters of the intra-subway atmosphere in Russia and foreign countries having subways. Comparative characteristics are given for the factors determining temperature regimes of the subway lines with single-way and two-way tunnels in case conventional ventilation schemes are used. The paper also describes alternative ventilation schemes involving constructive elements widely used for venting motor-road tunnels. Based on specific initial data, calculations have been performed whose results characterize temperature distribution in running single-way and two-way tunnels vented by using the ventilation scheme comprising vent ducts and air recirculation. The efficiency of the two-way tunnel ventilation scheme comprising a ventilation duct and air recirculation has been justified. Mathematical simulation of the dynamics of smoke propagation in case of a fire in tunnels and at stations has been accomplished. Efficiency of the emergency ventilation scheme comprising a vent duct in smoke protection of the fire evacuation passages for people and in smoke removal has been proved. The paper presents such station complex layout solutions that, contrary to historical prototypes, imply integrating the station ventilation and smoke removal systems into the general structure of the station space-planning solutions so that they are in close vicinity of all the designed passenger flows with retention of the comfortable space necessary for passengers.

Climate change impact on a mixed lowland oak stand in Serbia

Climatic changes and bad environmental conditions may lead to forests vitality loss and even mortality. This is the reason why increased sanitary felling operations were performed in mixed oak forests in northern Serbia in 2013 in order to solve the severe dieback which affected some Pedunculate oak ( Quercus robur L.) and Turkey oak ( Quercus cerris L.) stands, after the very dry years 2011 and 2012. Dendrochronological techniques were applied to both these oak species collected in a stand, to examine the impact of temperature, precipitation and ground water level on forest growth and investigate the potential causes of the dieback. Differences in tree-ring patterns between surviving and dead trees were not significant according to t-value (from 5.68 to 14.20) and Gleichlaufigkeit coefficient (from 76% to 82%), this meaning no distinctive responses of the two ecologically different oak species. As for radial increment, pedunculate and Turkey oak trees showed a similar response to environmental variables in this mixed stand. The Simple Pearson’s correlation analysis, which was conducted, showed that among three basic environmental variables (the mean monthly air temperature, the monthly sum of precipitation and the mean monthly water level, proxy of ground water level), the water level of Danube river in May and the temperature in April were statistically related to the growth of the four tree groups: (i) pedunculate oak vital, (ii) pedunculate oak dead, (iii) Turkey oak vital and (iv) Turkey oak dead trees, for the period 1961-2010 (p<0.05, n=60). Similar phenomena had already been observed in the Sava River basin for the growth of pure pedunculate oak forests. The long-term decline of the Danube River water level may be related to climate variations and to the changes of water management, river bed, as well as land-use. Together with the increase of temperature, this decline of the water level, and its potential unavailability in the soil, represents a serious challenge for the mixed oak forests silviculture in the Danube basin.

An attempt to assess the results of air temperature measurements from automatic weather stations in comparison to glass thermometer measurements in the context of weather types

Abstract In the article, an attempt was undertaken to compare the results of air temperature measurements made using automatic weather stations (AWS) to those of glass thermometers. The analysis considered the aspect of weather types. On the basis of simultaneous measurements carried out with the use of AWS and glass thermometers, the accuracy of measurements for 6 synoptic stations of IMGW-PIB was assessed. The stations represented the Lower and Opole Silesia regions. Mean differences in mean monthly and seasonal air temperature values (T) between AWS and glass results are not high. They are equal to ±0.1°C, only rarely reaching −0.2°C. In cold seasons and in particular months as well, they are negative. On an annual scale, differences hardly ever occur. No connection between mean difference for mean air temperature and weather types was found. The values of mean differences for mean monthly and seasonal maximum air temperature (Tx) are equal to ±0.1°C (except Śnieżka). The differences for T and Tx are of low significance, being within the normal range. Mean differences for mean monthly and seasonal minimum air temperature (Tn) are usually positive. In warm seasons they can reach 1.1°C. In the case of most of the stations under consideration, for positive differences for Tn, an increase in average (from +0.1°C to +0.5°C) and high (+0.5°C to +1.0°C) differences is noticed. The only exceptions are the Śnieżka and Opole stations. The differences for each category are not regular, therefore no universal corrections can be implemented.

Improving the estimation of the true mean monthly and true mean annual air temperatures in Greece

AbstractThe true mean monthly/annual air‐temperature estimations are usually based on the monthly averages of meteorological observations performed at standard hours. The performance of this combination varies as it depends on local climate of the particular meteorological station; it consequently allows for small/large deviations from its exact value. This study examines the possible deviations in Greece and suggests ways of improving the derived estimates. Two cases are considered. (1) An area has one thermograph and a number of thermometric stations. (2) An area has several thermographs and many thermometric stations. Solutions for minimizing the estimation error in both cases are provided.

Air temperature changes in Svalbard and the surrounding seas from 1865 to 1920

ABSTRACTIn this article, air temperature variability in the Svalbard region (74–82°N, 6–30°E) from 1865 to 1920 is presented based on a large amount of early instrumental land and marine data. Measurements were taken during many exploratory and scientific expeditions to the study area. In addition, changes of air temperature from historical times to the present day (1981–2010) were investigated. Present‐day air temperatures for land were taken from regular meteorological stations and from campaign measurements, while for marine environment data were taken from three reanalysis products: 20CR, ERA‐20C and ERA‐Interim.Analysis reveals that Svalbard (land data) in the period 1865–1920 was markedly colder than today (1981–2010, by about 3 °C) in all seasons except summer, when air temperature was similar in both periods. However, the majority of mean monthly air temperatures in historical times still lie within two standard deviations (SDs) from the modern mean. Marine data show good correspondence with land data. The May–September average air temperature for the period 1871–1910 was slightly lower in the seas surrounding Svalbard (by 0.4 °C) than today. About 90% of these mean air temperatures lie within the range of ±3SDs from the ERA‐Interim present‐day mean.Seasonal patterns of air temperature changes in the Svalbard archipelago between historical and standard normal (1961–1990) periods show generally very good correspondence to similar changes, not only for the Atlantic region, but also for some other Arctic regions (e.g. SE Greenland, Canadian Arctic, Barents and Kara seas), as well as for the entire Arctic. All available reconstructions of annual air temperature using isolated series of early instrumental observations reveal that in historical times, air temperatures were colder than the standard normal (1961–1990) by about 0.5–1.0 °C. Compared with the new normal period (1981–2010), those differences rise to about 1.5–2.5 °C.

Satellite observations of changes in snow-covered land surface albedo during spring in the Northern Hemisphere

Abstract. Thirteen years of Moderate Resolution Imaging Spectroradiometer (MODIS) surface albedo data for the Northern Hemisphere during the spring months (March–May) were analyzed to determine temporal and spatial changes over snow-covered land surfaces. Tendencies in land surface albedo change north of 50° N were analyzed using data on snow cover fraction, air temperature, vegetation index and precipitation. To this end, the study domain was divided into six smaller areas, based on their geographical position and climate similarity. Strong differences were observed between these areas. As expected, snow cover fraction (SCF) has a strong influence on the albedo in the study area and can explain 56 % of variation of albedo in March, 76 % in April and 92 % in May. Therefore the effects of other parameters were investigated only for areas with 100 % SCF. The second largest driver for snow-covered land surface albedo changes is the air temperature when it exceeds a value between −15 and −10 °C, depending on the region. At monthly mean air temperatures below this value no albedo changes are observed. The Enhanced Vegetation Index (EVI) and precipitation amount and frequency were independently examined as possible candidates to explain observed changes in albedo for areas with 100 % SCF. Amount and frequency of precipitation were identified to influence the albedo over some areas in Eurasia and North America, but no clear effects were observed in other areas. EVI is positively correlated with albedo in Chukotka Peninsula and negatively in eastern Siberia. For other regions the spatial variability of the correlation fields is too high to reach any conclusions.

Oxygen and hydrogen isotopic variations between adjacent drips in three caves at increasing elevation in a temperate coastal rainforest, Vancouver Island, Canada

The interpretation of speleothem paleoenvironmental records requires understanding of spatial–temporal variations in vadose drip water chemistry and isotopic composition. This study reports on intra- and inter-cave differences in δD, δ18O and electrical conductivity, using 18 monthly water samples from three adjacent drips (<20m apart) in each of three caves at increasing elevation (0, 550, and 740m ASL) on very steep ground at the head of Tahsis Inlet fjord on the Pacific coast of Vancouver Island, British Columbia.All drips showed isotopic seasonal signals, despite varied patterns of drip hydrology. There was overlap in isotopic ranges (at 1 SD) between all three caves, in contrast with the expected δ18O depletion of −0.15 to −0.5‰/100m of ascent observed in standard precipitation. The isotopic seasonality was approximated with sine curves, and compared to a GNIP data set from Victoria ∼300km to the south. The δD and δ18O drip isotopes lagged the Victoria record by 155±26days and 165±50days respectively. The longest lag was at the slowest drip (sea level), while the shortest lag (87days for δ18O, 550m ASL) implies a short residence time, paradoxically from the drip with the highest mean electrical conductivity. Vadose residence time was less than one climatic year, reflecting a combination of negligible matrix porosity in the host rock and super-humid climatic conditions. Beneath the epikarst, drip hydrology was evidently by simple piston flow.Phase-shifted drip isotope records showed excellent agreement with sea level mean monthly air temperatures at the Tahsis meteorological station over the study period. The δD and δ18O drip amplitudes were damped on average 74% and 73% respectively compared to the Victoria data. The drips at 740m ASL are tightly aligned to the global mean meteoric water line (GMWL) and 18O-depleted; the drips at 550m ASL and at sea level plot along the GMWL, or between it and the Victoria LMWL, with the exception of the slowest drip (sea level) which displays some seasonal evaporitic enrichment.The findings quantify substantial intra- and inter-cave variation of δD and δ18O in drips having a wide range of drip type and volume of flow, sufficient to mute the altitudinal isotopic lapse rate in this local coastal rainforest environment.

Impact of autumn SST in the Japan Sea on winter rainfall and air temperature in Northeast China

We studied the impact of sea surface temperature anomaly (SSTA) in the Japan Sea and the sea area east of Japan on the winter rainfall and air temperature in Northeast (NE) China using the singular value decomposition (SVD) and empirical orthogonal function (EOF). The monthly-mean rainfall data observed at 160 stations in China, monthly-mean sea surface temperature (SST) of the Hadley Center for Climate Prediction and Research and monthly-mean air temperature from the NCEP reanalysis during 1960–2011 were used. Correlation analysis indicates that the SSTAs in the Japan Sea in September may last for three or four months and are an important index for forecasting the winter rainfall and air temperature in NE China. Positive SSTAs in the central Japan Sea and in the sea area east of Tokyo correspond to positive rainfall anomaly and negative air temperature anomaly in NE China. With the rise of SST in the Japan Sea, a weak cyclone appears over the Japan Sea. The northeasterly wind transports water vapor from the Okhotsk to NE China, resulting in more rainfall and lower air temperature. Negative SSTA years are accompanied by warmer air temperature and less snow in NE China. The 1000 hPa geopotential height anomaly and wind anomaly fields are simulated by IAP-9L model, which supports the analysis results.

Geostatistics-based spatial distribution of soil moisture and temperature regime classes in Mazandaran province, northern Iran

Soil moisture regime (SMR) and soil temperature regime (STR) classes as soil classification criterions are required by US Soil Taxonomy because they affect genesis, use, and management of soils. The lack of sufficient soil moisture and temperature data requires the characterization of the pedoclimate on the basis of climatic data processed by simulation models. This research was conducted to consider the new approach for SMR and STR mapping. The objectives of this study were to compare the four interpolation schemes including ordinary kriging (OK), cokriging (Co-K), inverse distance weighting, and conditional simulation for interpolating the monthly mean total precipitation (MMTP) and monthly mean air temperature (MMAT) and to apply the Java Newhall simulation model for the MMTP and MMAT predictive values at each node of 1 km2 grids across the Mazandaran province, northern Iran, for delineating the SMR and STR classes. The semivariogram analyses showed moderate to strong spatial dependence of data sets. The accuracy of interpolators varied within months for both MMTP and MMAT data sets. In most cases, OK and Co-K methods had the highest accuracy with lower mean error, root mean square error, and higher concordance correlation coefficient. The predictive maps show high diversity of SMR classes including Aridic, Ustic, Udic, and Xeric. The STR classes comprise Mesic, Thermic, and Cryic regimes. Results herein indicated that geostatistical approaches can potentially provide the opportunity for mapping of SMR and STR classes in data scarce regions.

A comprehensive examination of global atmospheric CO2 teleconnections using wavelet-based multi-resolution analysis

Interannual variability of the atmospheric CO2 accumulation is at a similar magnitude of decadal average CO2 accumulation in the atmosphere. Part of observed variability in the atmospheric CO2 can be interpreted by large-scale climate variability. Teleconnection between the atmospheric CO2 anomalous tendency and selected large-scale coupled oceanic and atmospheric oscillations (AO, NAO, PDO, IOD, ENSO and SAM) are examined using wavelet-based multi-resolution analysis. The results indicate that all six oscillation indices appear to be associated with monthly CO2 concentration at most of the 13 examined stations. About 70 % of interannual variability of CO2 anomalous tendency at Mauna Loa and South Pole can be interpreted by the climatic indices. The temperature effects on the CO2 anomalous tendency are overall positive. At the monthly scale, they interpret 14 % variability in CO2 anomalous tendency in 1980–2011. At two high elevation stations (Mauna Loa and South Pole) with large footprints, the teleconnection signal is stronger (interpreting 20 and 25 % variability in monthly CO2 anomalous tendency, respectively). Including global mean monthly air temperature time series, slightly but significantly in the statistical sense, improves the proportion of monthly CO2 anomalous tendency being interpreted at most of the examined stations. The signs (positive vs. negative) in the association between monthly CO2 anomalous tendency and the significant climatic indices appear to reflect physical mechanisms leading to such teleconnections.

Analysis of monthly, seasonal and annual air temperature variability trends in Junagadh (Saurashtra region) of Gujarat

Climatic change is one of the most important issues of present times, therefore, world-wide interest in global warming and climate change has led to numerous trend detection studies. Anthropogenic interference in the environment is one of the greatest causes of the process of climatic change in several regions of the world. This study focuses on the variability and trends of the mean annual, seasonal and monthly surface air temperature in Junagadh (Saurashtra region) of Gujarat, during the period 1980-2011. This study investigated monthly, seasonal and annual climatic variability in Junagadh (Saurashtra region) of Gujarat based on mean maximum, mean minimum and mean air temperatures. One of the main results of this study was the confirmation of a significant warming trend in average temperatures in Junagadh (Saurashtra region) of Gujarat. Analysis of maximum and minimum temperatures revealed a warming trend for the annual and all seasonal series. The warming trend for the summer and winter seasons was statistically significant at P < 0.01 level with a rate of increase of 0.006 C/year and less 0.055C/year. The air temperature time series were analyzed, so that the variability and trends can be described.

Multi-Scale Entropy Analysis as a Method for Time-Series Analysis of Climate Data

Evidence is mounting that the temporal dynamics of the climate system are changing at the same time as the average global temperature is increasing due to multiple climate forcings. A large number of extreme weather events such as prolonged cold spells, heatwaves, droughts and floods have been recorded around the world in the past 10 years. Such changes in the temporal scaling behaviour of climate time-series data can be difficult to detect. While there are easy and direct ways of analysing climate data by calculating the means and variances for different levels of temporal aggregation, these methods can miss more subtle changes in their dynamics. This paper describes multi-scale entropy (MSE) analysis as a tool to study climate time-series data and to identify temporal scales of variability and their change over time in climate time-series. MSE estimates the sample entropy of the time-series after coarse-graining at different temporal scales. An application of MSE to Central European, variance-adjusted, mean monthly air temperature anomalies (CRUTEM4v) is provided. The results show that the temporal scales of the current climate (1960–2014) are different from the long-term average (1850–1960). For temporal scale factors longer than 12 months, the sample entropy increased markedly compared to the long-term record. Such an increase can be explained by systems theory with greater complexity in the regional temperature data. From 1961 the patterns of monthly air temperatures are less regular at time-scales greater than 12 months than in the earlier time period. This finding suggests that, at these inter-annual time scales, the temperature variability has become less predictable than in the past. It is possible that climate system feedbacks are expressed in altered temporal scales of the European temperature time-series data. A comparison with the variance and Shannon entropy shows that MSE analysis can provide additional information on the statistical properties of climate time-series data that can go undetected using traditional methods.

Особливості температурного режиму Канева в сучасних кліматичних умовах

In this work the climatic features of the mean monthly air temperature at meteorological stations Kaniv are considered. The air temperature changes over the past 20 years with climatological standard norm (period 1960-1991 years) are compared. The graphs of perennial changes in air temperature are built. The years with abnormal temperatures are defined.

C-TOOL: A simple model for simulating whole-profile carbon storage in temperate agricultural soils

Soil organic carbon (SOC) is a significant component of the global carbon (C) cycle. Changes in SOC storage affect atmospheric CO2 concentrations on decadal to centennial timescales. The C-TOOL model was developed to simulate farm- and regional-scale effects of management on medium- to long-term SOC storage in the profile of well-drained agricultural mineral soils. C-TOOL uses three SOC pools for both the topsoil (0–25cm) and the subsoil (25–100cm), and applies temperature-dependent first order kinetics to regulate C turnover. C-TOOL also enables the simulation of 14C turnover. The simple model structure facilitates calibration and requires few inputs (mean monthly air temperature, soil clay content, soil C/N ratio and C in organic inputs). The model was parameterised using data from 19 treatments drawn from seven long-term field experiments in the United Kingdom, Sweden and Denmark. It was found that the initial SOC content had to be optimised for each experiment, but also that one set of values for other model parameters could be applied at all sites. With this set of parameters, C-TOOL can be applied more widely to evaluate effects of management options on SOC storage in temperate agricultural soils. C-TOOL simulates observed losses of SOC in soils under intensive agricultural use and the gain in SOC derived from large inputs of animal manure and inclusion of perennial grassland. The model simulates changes in SOC for the entire profile, but lack of data on subsoil SOC storage hampers a proper model evaluation. Experimental verification of management effects on subsoil C storage, subsoil C inputs from roots, and vertical transport of C in the soil profile remains prioritised research areas.

Temperature-based approaches for estimating monthly reference evapotranspiration based on MODIS data over North China

Reference evapotranspiration (ETo) maps play an important role in distributed hydrological modeling and are particularly useful for regional agricultural and water resource management. In the Three-North Shelter Forest Program, water requirements (i.e., ETo) of different land use types are important preconditions for afforestation program management. The Food and Agriculture Organization Penman–Monteith (FAO-PM) method is the most common method for estimating ETo, but it requires many different types of meteorological data, and few stations with adequate meteorological resources exist in the Three-North regions. In addition, the spatial distribution of ordinary meteorological stations is limited. This study employed two temperature-based ETo methods, Hargreaves and Thornthwaite. The monthly mean, maximum, and minimum air temperatures were estimated using moderate resolution imaging spectroradiometer (MODIS) data. The original coefficients of Hargreaves and mean temperatures of Thornthwaite were modified for regional calibration (with the FAO-PM method as the standard). In the comparison between the original/adjusted Hargreaves and the original/adjusted Thornthwaite methods, the adjusted Hargreaves method was appropriate for estimating ETo in the Three-North regions. The average mean bias error (MBE) was −0.21 mm, the relatively root mean square error (RRMSE) was 13.44 %, the correlation coefficient (R 2) was 0.85, and the slope (b) was 1.00 for the monthly ETo. While the MBE was 2.32 mm, the RRMSE was 7.07 %, the R 2 was 0.90, and the b was 1.00 for the annual ETo. Therefore, it is possible to estimate monthly and annual ETo values for other parts of the country or the world using adjusted Hargreaves with the estimated air temperature data instead of using the FAO-PM with observed data.

The mass elevation effect of the Tibetan Plateau and its implications for Alpine treelines

ABSTRACTThe immense and towering Tibetan Plateau (TP) acts as a heating source and shapes the climate of not only the Eurasian continent but also the entire world. The mass elevation effect of the TP was first observed in the 1950s; however, due to the scarcity of meteorological observation stations and limited climatic data, little information on the mass elevation effect of the plateau and its implications for the position of Alpine treelines in the southeastern part of the TP is quantitatively known. This paper compares monthly mean air temperature differences at elevations of 4000, 4500, 5000, 5500 and 6000 m between the main plateau, the Qilian Mts. in the northeastern corner of the plateau and the Sichuan Basin to the east of the plateau to quantify the mass elevation effect of the plateau. The TP air temperature data are retrieved from Terra moderate‐resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and the free‐air temperatures over the westernmost Sichuan Basin are estimated using the measured lapse rate from Mt. Emei, which is located in the western portion of the Sichuan Basin. The results demonstrate the following important characteristics. (1) Owing to the mass elevation effect, air temperatures gradually increase from the eastern edge to the interior main TP. The monthly mean air temperature in the interior main plateau is approximately 2–7 °C higher than in the surrounding mountains and adjacent lowland areas. At an elevation of 4500 m (corresponding to the mean altitude of the TP), the monthly mean temperature differences between the plateau and the Sichuan Basin range from 3.58 °C (April) to 6.63 °C (June); the monthly temperature differences between the plateau and the Qilian Mts. range from 1.6 °C (July) to 7.7 °C (March). (2) The mass elevation effect of the plateau pushes the 10 °C isotherm upward in the warmest month and is indicative of a warmth index of 15 °C month up to elevations of 4600–4700 m, which enables the treeline altitude in the interior TP 500–1000 m higher than along the eastern edge. Therefore, mass elevation effect contributes to the occurrence of the highest treeline in the Northern Hemisphere, which is present on the southeastern TP.

Influence of temperature variations during a cold period on the conditions of life of the population in East Siberia

On the territory of East Siberia, the positive deviations of mean monthly air temperatures from long-term annual mean air temperatures for the past 30 years (1981–2010) were caused by the shortening of the low-temperature periods. The largest decrease of the number of days with a daily mean temperature below −25 °C is recorded in the southern and middle parts of the macroregion under conditions of a moderate, strong and very strong discomfort. On the other hand, the resulting range of variation in the number of days with a mean daily temperature below −25 °C and the duration of the heating period remain within the long-term values. A slight rise in mean temperature for the heating period entails a marked reduction in degree-days.

Spatio-Temporal Variation and Monsoon Effect on the Temperature Lapse Rate of a Subtropical Island

Temperature lapse rate (TLR) has been widely used in the prediction of mountain climate and vegetation and in many ecological models. The aims of this paper are to explore the spatio-temporal variations and monsoon effects on the TLR in the subtropical island of Taiwan with its steep Central Mountain Region (CMR). A TLR analysis using the 32-year monthly mean air temperatures and elevations from 219 weather stations (sea level to 3852 m a.s.l.) was performed based on different geographical regions and monsoon exposures. The results revealed that the average TLR for all of Taiwan is -5.17°C km^(-1), with a general tendency to be steeper in summer and shallower in winter. The results are also shallower than the typical or global average TLR of -6.5°C km^(-1). During the prevailing northeast monsoon season (winter), the TLR exhibits a contrast between the windward side (steeper, -5.97°C km^(-1)) and the leeward side (shallower, -4.51°C km^(-1)). From the diagnosis on spatial characteristics of monthly cloud amount and vertical atmospheric profiles, this contrasting phenomenon may be explained by the warming effect of onshore stratus clouds (500 - 2500 m depth) on cold and dry Siberian monsoon air on the windward side of the CMR. On the southwestern leeward side of the CMR, the low-level (1500 m), the weak ventilation atmosphere and temperature inversion make the TLR shallower than on the windward side.

Homogeneity of Monthly Mean Air Temperature of the United Republic of Tanzania with HOMER

The long-term climate datasets are widely used in a variety of climate analyses. These datasets, however, have been adversely impacted by inhomogeneities caused by, for example relocations of meteorological station, change of land use cover surrounding the weather stations, substitution of meteorological station, changes of shelters, changes of instrumentation due to its failure or damage, and change of observation hours. If these inhomogeneities are not detected and adjusted properly, the results of climate analyses using these data can be erroneous. In this paper for the first time, monthly mean air temperatures of the United Republic of Tanzania are homogenized by using HOMER software package. This software is one of the most recent homogenization software and exhibited the best results in the comparative analysis performed within the COST Action ES0601 (HOME). Monthly mean minimum (TN) and maximum (TX) air temperatures from 1974 to 2012 were used in the analysis. These datasets were obtained from Tanzania Meteorological Agency (TMA). The analysis reveals a larger number of artificial break points in TX (12 breaks) than TN (5 breaks) time series. The homogenization process was assessed by comparing results obtained with Correlation analysis and Principal Component analysis (PCA) of homogenized and non-homogenized datasets. Mann-Kendal non-parametric test was used to estimate the existence, magnitude and statistical significance of potential trends in the homogenized and non-homogenized time series. Correlation analysis reveals stronger correlation in homogenized TX than TN in relation to non-homogenized time series. Results from PCA suggest that the explained variances of the principal components are higher in homogenized TX than TN in relation to non-homogenized time series. Mann-Kendal non-parametric test reveals that the number of statistical significant trend increases higher with homogenized TX (96%) than TN (67%) in relation to non-homogenized datasets.

Monthly Air Temperatures over Northern China Estimated by Integrating MODIS Data with GIS Techniques

AbstractThe Three-North Shelter/Protective Forest Programme (TNSFP), the largest ecological afforestation program in the world, was launched in 1978 and will last until 2050 to improve ecological conditions in the Three-North regions of China. To manage the shelter forests sustainably, it is necessary to accurately estimate air temperature on a large scale, but the spatial distribution of ground meteorological stations is limited. A hybrid method was established by combining stepwise regression modeling and spatial interpolation techniques (SRMSIT) to construct the monthly mean, minimum, and maximum air temperatures (Tmean, Tmin, and Tmax, respectively) at a 1 km × 1 km grid size in the Three-North regions. Stepwise regression modeling was applied to construct the relationship between air temperatures (Tmean, Tmin, and Tmax—the dependent variables) and geographical and Moderate Resolution Imaging Spectroradiometer (MODIS) variables (the independent variables). Spatial interpolation techniques were used to correct the residual values. According to the factor analysis, three geographic (altitude, latitude, and continentality) and two MODIS variables [nighttime land surface temperature (LST) and normalized difference vegetation index] were selected in stepwise regression modeling, and nighttime LST was the most powerful remote sensing variable. The SRMSIT method, in which the spatial interpolation of the residuals was done with inverse distance weighting, achieved average root-mean-square error values at 0.86°, 1.10°, and 1.13°C for Tmean, Tmin, and Tmax, respectively. Therefore, the simple regression algorithms derived from the combination of remote sensing and geographical variables, together with residual interpolation techniques, have the potential to accurately estimate monthly air temperature in large regions.