Recent trends of mean maximum and minimum air temperatures in the western half of Iran

In this study, the trends of the annual, seasonal and monthly maximum (T _max) and minimum (T _min) air temperatures time series were investigated for 20 stations in the western half of Iran during 1966?C2005. Three statistical tests including Mann?CKendall, Sen??s slope estimator and linear regression were used for the analysis. The annual T _max and T _min series showed a positive trend in 85% of the stations and a negative trend in 15% of the stations in the study region. The highest increase of T _max and T _min values were obtained over Kermanshah and Ahwaz at the rates of (+)0.597°C/decade and (+)0.911°C/decade, respectively. On the seasonal scale, the strongest increasing trends were identified in T _max and T _min data in summer. The highest numbers of stations with positive significant trends occurred in the monthly T _max and T _min series in August. In contrast, the lowest numbers of stations with significant positive trends were observed between November and March. Overall, the results showed similar increasing trends for the study variables, although T _min generally increased at a higher rate than T _max in the study period.     

Watershed-wide trend analysis of temperature characteristics in Karun-Dez watershed, southwestern Iran

Trend estimation of climatic characteristics for a watershed is required to determine developing compatible strategies related to design, development, and management of water resources. In this study, the trends of the annual maximum (T _max), minimum (T _min), and mean (T _mean) air temperature; temperature anomaly (T _anomaly); and diurnal temperature range (DTR) time series at 13 meteorological stations located in the Karun-Dez watershed were analyzed using the Mann–Kendall and linear regression trend tests. The pre-whitening method was used to eliminate the influence of serial correlation on the Mann–Kendall test. The result showed increasing trends in the T _min, T _mean, and T _anomaly series at the majority of stations and decreasing trend in the T _max and DTR series. A geographical analysis of the trends revealed a broad warming trend in most of the watershed, and the cooling trends were observed only in the southern parts. Furthermore, the geographical pattern of the trends in the T _mean and T _anomaly series was similar, and the T _max data did not show any dominant trend for the whole watershed. This study provides temperature change scenarios that may be used for the design of future water resource projects in the watershed.     

Analysis of the change in temperature trends in Subansiri River basin for RCP scenarios using CMIP5 datasets

This study focuses on changes in the maximum and minimum temperature over the Subansiri River basin for different climate change scenarios. For the study, dataset from Intergovernmental Panel on Climate Change (IPCC) fifth assessment report (AR5) (i.e., coupled model intercomparison project phase five (CMIP5) dataset with representative concentration pathway (RCP) scenarios) were utilized. Long-term (2011–2100) maximum temperature (T _max) and minimum temperature (T_min) time series were generated using the statistical downscaling technique for low emission scenario (RCP2.6), moderate emission scenario (RCP6.0), and extreme emission scenario (RCP8.5). Trends and change of magnitude in T _max, T _min, and diurnal temperature range (DTR) were analyzed for different interdecadal time scales (2011–2100, 2011–2040, 2041–2070, 2070–2100) using Mann-Kendall non-parametric test and Sen’s slope estimator, respectively. The temperature data series for the observed duration (1981–2000) has been found to show increasing trends in T _max and T _min at both annual and monthly scale. Trend analysis of downscaled temperature for the period 2011–2100 shows increase in annual maximum temperature and annual minimum temperature for all the selected RCP scenarios; however, on the monthly scale, T _max and T _min have been seen to have decreasing trends in some months.

     

Climate change scenarios for surface temperature in Emilia-Romagna (Italy) obtained using statistical downscaling models

Summary Possible changes of mean climate and the frequency of extreme temperature events in Emilia-Romagna, over the period 2070–2100 compared to 1960–1990, are assessed. A statistical downscaling technique, applied to HadAM3P experiments (control, A2 and B2 scenarios) performed at the Hadley Centre, is used to achieve this objective. The method applied consists of a multivariate regression based on Canonical Correlation Analysis (CCA), using as possible predictors mean sea level pressure (MSLP), geopotential height at 500 hPa (Z500) and temperature at 850 hPa (T850), and as predictands the seasonal mean values of minimum and maximum surface temperature (T_min and T_max), 90^th percentile of maximum temperature (T_max90), 10^th percentile of minimum temperature (T_min10), number of frost days (T_nfd) and heat wave duration (HWD) at the station level. First, the statistical model is optimised and calibrated using NCEP/NCAR reanalysis to evaluate the large-scale predictors. The observational data at 32 stations uniformly distributed over Emilia-Romagna are used to compute the local predictands. The results of the optimisation procedure reveal that T850 is the best predictor in most cases, and in combination with MSLP, is an optimum predictor for winter T_max90 and autumn T_min10. Finally, MSLP is the best predictor for spring T_min while Z500 is the best predictor for spring T_max90 and heat wave duration index, except during autumn. The ability of HadAM3P to simulate the present day spatial and temporal variability of the chosen predictors is tested using the control experiments. Finally, the downscaling model is applied to all model output experiments to obtain simulated present day and A2 and B2 scenario results at the local scale. Results show that significant increases can be expected to occur under scenario conditions in both maximum and minimum temperature, associated with a decrease in the number of frost days and with an increase in the heat wave duration index. The magnitude of the change is more significant for the A2 scenario than for the B2 scenario.     

Southern Quebec (Canada) summer-season heat spells over the 1941–2000 period: an assessment of observed changes

Summary Summer-season (May–September) daily maximum temperature (T _max) and daily minimum temperature (T _min) observations and three types of heat spells obtained from these temperature observations at seven weather stations located in southern Quebec (Canada) for the 60-year period from 1941 to 2000 are studied to assess temporal changes in their characteristics (i.e. frequency of occurrence, seasonal hot days and extremal durations of heat spells). Type-A and Type-B heat spells are obtained respectively from T _max and T _min observations and Type-C heat spells from simultaneous joint observations of T _max and T _min using suitable thresholds and spells of duration ≥1-day and ≥3-day. The results of this investigation show that the majority of the selected percentiles (i.e. 5P, 10P, 25P, 50P, 75P, 80P, 90P, 92P, 95P, and 98P) of T _max observations show a negative time-trend with statistically significant decreases (at 10% level) in some of the higher percentiles and in the maximal values at four out of seven stations. Almost all of the selected percentiles (same as for the T _max) and the maximal and minimal values of T _min observations show a positive trend, with statistically significant increases for all seven stations. Examination of frequencies of occurrence of heat spells, seasonal hot days and annual extremes of heat spell durations indicate that many of these characteristics of heat spells have undergone statistically significant changes over time at some of the stations for Type-A and Type-B heat spells as compared to Type-C heat spells. The Type-C heat spells are generally small in number and are found to be relatively temporally stable. More severe Type-C heat spells, i.e. the ones having T _max and T _min values simultaneously above very high thresholds and with duration ≥3-day have been rarely observed in southern Quebec.     

Local recent changes in extreme air temperature

Daily minimum and maximum air temperatures recorded in Naples (1872–1982) and in surrounding areas have been analysed in order to set up a statistical model for investigating climatic changes of extreme air temperature. We have analysed on various time-scales the mean values of minimum air temperature lower than the 10th percentile (Tmin₁₀) and the mean values of the maximum air temperature greater than the 90th percentile (Tmax₉₀). The results have shown for the city: (i) a significant secular trend both for yearly Tmin₁₀ and Tmax₉₀, mostly due to the process of urbanization, that is also responsible for (ii) the ascertained change in the character of the annual cycle, (iii) a reasonable ability to forecast winter Tmin₁₀ and summer Tmax₉₀ in statistical terms using a markovian model, and (iv) a significant 11-yr cycle with an amplitude of 0.5 °C directly related to solar activity which has never been succesfully determined before.     

Spatial gridding of daily maximum and minimum temperatures in Europe

We developed an operationally applicable land-only daily high-resolution (5?km?×?5?km) gridding method for station observations of minimum and maximum 2?m temperature (T _min/T _max) for Europe (WMO region VI). The method involves two major steps: (1) the generation of climatological T _min/T _max maps for each month of the year using block regression kriging, which considers the spatial variation explained by applied predictors; and (2) interpolation of transformed daily anomalies using block kriging, and combination of the resulting anomaly maps with climatological maps. To account for heterogeneous climatic conditions in the estimation of the statistical parameters, these steps were applied independently in overlapping climatic subregions, followed by an additional spatial merging step. Uncertainties in the gridded maps and the derived error maps were quantified: (a) by cross-validation; and (b) comparison with the T _min/T _max maps estimated in two regions having very dense temperature observation networks. The main advantages of the method are the high quality of the daily maps of T _min/T _max, the calculation of daily error maps and computational efficiency.     

Extreme normalised residuals of daily temperatures in Catalonia (NE Spain): sampling strategies, return periods and clustering process

Extreme normalised residuals, defined as departures from the average values, of 65 daily maximum, T _max, and minimum, T _min, temperature series recorded in Catalonia (NE Spain) during 1950–2004 are analysed. Similarly to the sampling strategies applied to long dry spells, the partial duration series (PDS) offer some advantages in comparison with the annual extreme series. Instead of using a common percentile threshold for all temperature series, PDS are chosen according to the mean excess plot procedure. Series of extreme residuals are modelled, in terms of the L-moments formulation, by the generalised Pareto distribution. Extreme residuals of T _max and T _min are estimated for return periods ranging from 2 to 50 years and their spatial distribution is represented for selected return periods of 2, 5, 10, 25 and 50 years. Two daily extreme temperatures events, a hot episode (in August) and a cold episode (in February), are simulated taking into account the average T _max (T _min) for a day in August (February), their standard deviations and the extremes for a 50-year return period. Both simulations are compared with outstanding real episodes recorded on August 13th 2003 and February 11th 1956. Additionally, a spatial regionalisation of Catalonia in several clusters, in terms of the extreme residuals for return periods from 2 to 50 years, is done. A principal component analysis is applied to the extreme residual curves characterising every temperature series and, using as variables the principal components, the regionalisation is obtained by applying the average linkage clustering algorithm. Finally, each cluster is characterised by its average extreme residual curve for return periods ranging from 2 to 50 years at 1-year interval.     

Detection and attribution of anthropogenic forcing to diurnal temperature range changes from 1950 to 1999: comparing multi-model simulations with observations

Observations show that the surface diurnal temperature range (DTR) has decreased since 1950s over most global land areas due to a smaller warming in maximum temperatures (T _max) than in minimum temperatures (T _min). This paper analyzes the trends and variability in T _max, T _min, and DTR over land in observations and 48 simulations from 12 global coupled atmosphere-ocean general circulation models for the later half of the 20th century. It uses the modeled changes in surface downward solar and longwave radiation to interpret the modeled temperature changes. When anthropogenic and natural forcings are included, the models generally reproduce observed major features of the warming of T _max and T _min and the reduction of DTR. As expected the greenhouse gases enhanced surface downward longwave radiation (DLW) explains most of the warming of T _max and T _min while decreased surface downward shortwave radiation (DSW) due to increasing aerosols and water vapor contributes most to the decreases in DTR in the models. When only natural forcings are used, none of the observed trends are simulated. The simulated DTR decreases are much smaller than the observed (mainly due to the small simulated T _min trend) but still outside the range of natural internal variability estimated from the models. The much larger observed decrease in DTR suggests the possibility of additional regional effects of anthropogenic forcing that the models can not realistically simulate, likely connected to changes in cloud cover, precipitation, and soil moisture. The small magnitude of the simulated DTR trends may be attributed to the lack of an increasing trend in cloud cover and deficiencies in charactering aerosols and important surface and boundary-layer processes in the models.     

Spatial dependence of diurnal temperature range trends on precipitation from 1950 to 2004

This paper analyzes the spatial dependence of annual diurnal temperature range (DTR) trends from 1950–2004 on the annual climatology of three variables: precipitation, cloud cover, and leaf area index (LAI), by classifying the global land into various climatic regions based on the climatological annual precipitation. The regional average trends for annual minimum temperature (T _min) and DTR exhibit significant spatial correlations with the climatological values of these three variables, while such correlation for annual maximum temperature (T _max) is very weak. In general, the magnitude of the downward trend of DTR and the warming trend of T _min decreases with increasing precipitation amount, cloud cover, and LAI, i.e., with stronger DTR decreasing trends over drier regions. Such spatial dependence of T _min and DTR trends on the climatological precipitation possibly reflects large-scale effects of increased global greenhouse gases and aerosols (and associated changes in cloudiness, soil moisture, and water vapor) during the later half of the twentieth century.     

Spatial gridding of daily maximum and minimum 2 m temperatures supported by satellite observations

The usefulness of two remotely sensed variables, land surface temperature (LST) and cloud cover (CC), as predictors for the gridding of daily maximum and minimum 2 m temperature (T _min/T _max) was assessed. Four similar gridding methods were compared, each of which applied regression kriging to capture the spatial variation explained by the predictors used; however, both methods differed in the interpolation steps performed and predictor combinations used. The robustness of the gridding methods was tested for daily observations in January and July in the period 2009–2011 and in two different regions: the Central European region (CER) and the Iberian Peninsula (IP). Moreover, the uncertainty estimate provided by each method was evaluated using cross-validation. The regression analyses for both regions demonstrated the high predictive skills of LST for T _min and T _max on daily and monthly timescales (and lower predictive skills of CC). The application of LST as a predictor considerably improved the gridding performance over the IP region in July; however, there was only a slight improvement over the CER region. CC reduced the loss of spatial variability in the interpolated daily T _min/T _max values over the IP region. The interpolation skill was mainly controlled by the station density, but also depended on the complexity of the terrain. LST was shown to be of particular value for very low station densities (1 station per 50,000 km²). Analyses with artificially decreasing station densities showed that even in the case of very low station densities, LST allows the determination of useful regression functions.     

Seasonal reversal at Miryang Eoreumgol (Ice Valley), Korea: observation and monitoring

Climate change information required for impact studies is of a much finer scale than that provided by Global circulation models (GCMs). This paper presents an application of partial least squares (PLS) regression for downscaling GCMs output. Statistical downscaling models were developed using PLS regression for simultaneous downscaling of mean monthly maximum and minimum temperatures (T _max and T _min) as well as pan evaporation to lake-basin scale in an arid region in India. The data used for evaluation were extracted from the NCEP/NCAR reanalysis dataset for the period 1948?C2000 and the simulations from the third-generation Canadian Coupled Global Climate Model (CGCM3) for emission scenarios A1B, A2, B1, and COMMIT for the period 2001?C2100. A simple multiplicative shift was used for correcting predictand values. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response. The analysis of downscaling models reveals that (1) the correlation coefficient for downscaled versus observed mean maximum temperature, mean minimum temperature, and pan evaporation was 0.94, 0.96, and 0.89, respectively; (2) an increasing trend is observed for T _max and T _min for A1B, A2, and B1 scenarios, whereas no trend is discerned with the COMMIT scenario; and (3) there was no trend observed in pan evaporation. In COMMIT scenario, atmospheric CO₂ concentrations are held at year 2000 levels. Furthermore, a comparison with neural network technique shows the efficiency of PLS regression method.     

Burgundy regional climate change and its potential impact on grapevines

ARPEGE general circulation model simulations were dynamically downscaled by The Weather Research and Forecasting Model (WRF) for the study of climate change and its impact on grapevine growth in Burgundy region in France by the mid twenty-first century. Two time periods were selected: 1970–1979 and 2031–2040. The WRF model driven by ERA-INTERIM reanalysis data was validated against in situ surface temperature observations. The daily maximum and minimum surface temperature (T_max and T_min) were simulated by the WRF model at 8?×?8?km horizontal resolution. The averaged daily T_max for each month during 1970–1979 have good agreement with observations, the averaged daily T_min have a warm bias about 1–2?K. The daily T_max and T_min for each month (domain averaged) during 2031–2040 show a general increase. The largest increment (~3?K) was found in summer. The smallest increments (<1?K) were found in spring and fall. The spatial distribution of temperature increment shows a strong meridional gradient, high in south in summer, reversing in winter. The resulting potential warming rate in summer is equivalent to 4.7?K/century under the IPCC A2 emission scenario. The dynamically downscaled T_max and T_min were used to simulate the grape (Pinot noir grape variety) flowering and véraison dates. For 2031–2040, the projected dates are 8 and 12?days earlier than those during 1970–1979, respectively. The simulated hot days increase more than 50% in the two principal grapevine regions. They show strong impact on Pinot noir development.     

Evaluation of machine learning tools as a statistical downscaling tool: temperatures projections for multi-stations for Thames River Basin, Canada

Many impact studies require climate change information at a finer resolution than that provided by global climate models (GCMs). This paper investigates the performances of existing state-of-the-art rule induction and tree algorithms, namely single conjunctive rule learner, decision table, M5 model tree, and REPTree, and explores the impact of climate change on maximum and minimum temperatures (i.e., predictands) of 14 meteorological stations in the Upper Thames River Basin, Ontario, Canada. The data used for evaluation were large-scale predictor variables, extracted from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis dataset and the simulations from third generation Canadian coupled global climate model. Data for four grid points covering the study region were used for developing the downscaling model. M5 model tree algorithm was found to yield better performance among all other learning techniques explored in the present study. Hence, this technique was applied to project predictands generated from GCM using three scenarios (A1B, A2, and B1) for the periods (2046–2065 and 2081–2100). A simple multiplicative shift was used for correcting predictand values. The potential of the downscaling models in simulating predictands was evaluated, and downscaling results reveal that the proposed downscaling model can reproduce local daily predictands from large-scale weather variables. Trend of projected maximum and minimum temperatures was studied for historical as well as downscaled values using GCM and scenario uncertainty. There is likely an increasing trend for T _max and T _min for A1B, A2, and B1 scenarios while decreasing trend has been observed for B1 scenarios during 2081–2100.     

Impact of vegetation feedback on the temperature and its diurnal range over the Northern Hemisphere during summer in a 2 × CO2 climate

This study examines the potential impact of vegetation feedback on the changes in the diurnal temperature range (DTR) due to the doubling of atmospheric CO₂ concentrations during summer over the Northern Hemisphere using a global climate model equipped with a dynamic vegetation model. Results show that CO₂ doubling induces significant increases in the daily mean temperature and decreases in DTR regardless of the presence of the vegetation feedback effect. In the presence of vegetation feedback, increase in vegetation productivity related to warm and humid climate lead to (1) an increase in vegetation greenness in the mid-latitude and (2) a greening and the expansion of grasslands and boreal forests into the tundra region in the high latitudes. The greening via vegetation feedback induces contrasting effects on the temperature fields between the mid- and high-latitude regions. In the mid-latitudes, the greening further limits the increase in T _max more than T _min, resulting in further decreases in DTR because the greening amplifies evapotranspiration and thus cools daytime temperature. The greening in high-latitudes, however, it reinforces the warming by increasing T _max more than T _min to result in a further increase in DTR from the values obtained without vegetation feedback. This effect on T _max and DTR in the high latitude is mainly attributed to the reduction in surface albedo and the subsequent increase in the absorbed insolation. Present study indicates that vegetation feedback can alter the response of the temperature field to increases in CO₂ mainly by affecting the T _max and that its effect varies with the regional climate characteristics as a function of latitudes.     

Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario

Assessing disease risk has become an important component in the development of climate change adaptation strategies. Here, the infection ability of leaf blast (Magnaporthe oryzae) was modeled based on the epidemiological parameters of minimum (T _min), optimum (T _opt), and maximum (T _max) temperatures for sporulation and lesion development. An infection ability response curve was used to assess the impact of rising temperature on the disease. The simulated spatial pattern of the infection ability index (IAI) corresponded with observed leaf blast occurrence in Indo-Gangetic plains (IGP). The IAI for leaf blast is projected to increase during the winter season (December–March) in 2020 (2010–2039) and 2050 (2040–2069) climate scenarios due to temperature rise, particularly in lower latitudes. However, during monsoon season (July–October), the IAI is projected to remain unchanged or even reduce across the IGP. The results show that the response curve may be successfully used to assess the impact of climate change on leaf blast in rice. The model could be further extended with a crop model to assess yield loss.     

Interrelationship of rainfall,temperature and reference evapotranspiration trends and their net response to the climate change in Central India

The monthly rainfall data from 1901 to 2011 and maximum and minimum temperature data from 1901 to 2005 are used along with the reference evapotranspiration (ET₀) to analyze the climate trend of 45 stations of Madhya Pradesh. ET₀ is calculated by the Hargreaves method from 1901 to 2005 and the computed data is then used for trend analysis. The temporal variation and the spatial distribution of trend are studied for seasonal and annual series with the Mann-Kendall (MK) test and Sen’s estimator of slope. The percentage of change is used to find the rate of change in 111 years (rainfall) and 105 years (temperatures and ET₀). Interrelationships among these variables are analyzed to see the dependency of one variable on the other. The results indicate a decreasing rainfall and increasing temperatures and ET₀ trend. A similar pattern is noticeable in all seasons except for monsoon season in temperature and ET₀ trend analysis. The highest increase of temperature is noticed during post-monsoon and winter. Rainfall shows a notable decrease in the monsoon season. The entire state of Madhya Pradesh is considered as a single unit, and the calculation of overall net change in the amount of the rainfall, temperatures (maximum and minimum) and ET₀ is done to estimate the total loss or gain in monthly, seasonal and annual series. The results show net loss or deficit in the amount of rainfall and the net gain or excess in the temperature and ET₀ amount.     

Drivers of the Severity of the Extreme Hot Summer of 2015 in Western China

Western China experienced an extreme hot summer in 2015, breaking a number of temperature records. The summer mean surface air temperature (SAT) anomaly was twice the interannual variability. The hottest daytime temperature (T_Xx) and warmest night-time temperature (T_Nx) were the highest in China since 1964. This extreme hot summer occurred in the context of steadily increasing temperatures in recent decades. We carried out a set of experiments to evaluate the extent to which the changes in sea surface temperature (SST)/sea ice extent (SIE) and anthropogenic forcing drove the severity of the extreme summer of 2015 in western China. Our results indicate that about 65%–72% of the observed changes in the seasonal mean SAT and the daily maximum (T_max) and daily minimum (T_min) temperatures over western China resulted from changes in boundary forcings, including the SST/SIE and anthropogenic forcing. For the relative role of individual forcing, the direct impact of changes in anthropogenic forcing explain about 42% of the SAT warming and 60% (40%) of the increase in T_Nx and Tmin (T_Xx and T_max) in the model response. The changes in SST/SIE contributed to the remaining surface warming and the increase in hot extremes, which are mainly the result of changes in the SST over the Pacific Ocean, where a super El Niño event occurred. Our study indicates a prominent role for the direct impact of anthropogenic forcing in the severity of the extreme hot summer in western China in 2015, although the changes in SST/SIE, as well as the internal variability of the atmosphere, also made a contribution.     

Examining evapotranspiration trends in Africa

Surface temperatures are projected to increase 3–4°C over much of Africa by the end of the 21st century. Precipitation projections are less certain, but the most plausible scenario given by the Intergovernmental Panel on Climate Change (IPCC) is that the Sahel and East Africa will experience modest increases (~5%) in precipitation by the end of the 21st century. Evapotranspiration (E_a) is an important component of the water, energy, and biogeochemical cycles that impact several climate properties, processes, and feedbacks. The interaction of E_a with climate change drivers remains relatively unexplored in Africa. In this paper, we examine the trends in E_a, precipitation (P), daily maximum temperature (T_max), and daily minimum temperature (T_min) on a seasonal basis using a 31?year time series of variable infiltration capacity (VIC) land surface model (LSM) E_a. The VIC model captured the magnitude, variability, and structure of observed runoff better than other LSMs and a hybrid model included in the analysis. In addition, we examine the inter-correlations of E_a, P, T_max, and T_min to determine relationships and potential feedbacks. Unlike many IPCC climate change simulations, the historical analysis reveals substantial drying over much of the Sahel and East Africa during the primary growing season. In the western Sahel, large increases in daily maximum temperature appear linked to E_a declines, despite modest rainfall recovery. The decline in E_a and latent heating in this region could lead to increased sensible heating and surface temperature, thus establishing a possible positive feedback between E_a and surface temperature.     

Comprehensive modeling of monthly mean soil temperature using multivariate adaptive regression splines and support vector machine

Soil temperature (T _s) and its thermal regime are the most important factors in plant growth, biological activities, and water movement in soil. Due to scarcity of the T _s data, estimation of soil temperature is an important issue in different fields of sciences. The main objective of the present study is to investigate the accuracy of multivariate adaptive regression splines (MARS) and support vector machine (SVM) methods for estimating the T _s. For this aim, the monthly mean data of the T _s (at depths of 5, 10, 50, and 100 cm) and meteorological parameters of 30 synoptic stations in Iran were utilized. To develop the MARS and SVM models, various combinations of minimum, maximum, and mean air temperatures (T _min, T _max, T); actual and maximum possible sunshine duration; sunshine duration ratio (n, N, n/N); actual, net, and extraterrestrial solar radiation data (R _s, R _n, R _a); precipitation (P); relative humidity (RH); wind speed at 2 m height (u ₂); and water vapor pressure (V_p) were used as input variables. Three error statistics including root-mean-square-error (RMSE), mean absolute error (MAE), and determination coefficient (R ²) were used to check the performance of MARS and SVM models. The results indicated that the MARS was superior to the SVM at different depths. In the test and validation phases, the most accurate estimations for the MARS were obtained at the depth of 10 cm for T _max, T _min, T inputs (RMSE = 0.71 °C, MAE = 0.54 °C, and R ² = 0.995) and for RH, V _p, P, and u ₂ inputs (RMSE = 0.80 °C, MAE = 0.61 °C, and R ² = 0.996), respectively.