Estimating daily pan evaporation using artificial neural network in a semi-arid environment

The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (E _Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E _Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R ²?>?0.88 and RMSE?<?1.2 mm day^?1), but the ANN method gave better estimates than the calibrated Hargreaves method.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

The climatological characteristics of photosynthetically active radiation in arid and semi-arid regions of China

Photosynthetically active radiation (Q _p ) is a key variable in models of net primary productivity and carbon cycle modelling. The relationship between broadband global solar radiation (R _s) and Q _p is investigated using 6?years of radiation data collected at 9 sites in arid and semi-arid regions of China. The dependence of Q _p /R _S on aerosol optical depth (AOD) and water vapour content are also discussed. A simple and efficient all-weather empirically derived model is developed to estimate Q _p from R _s. The annual average daily Q _p in arid and semi-arid areas is 29.9?±?11.7 and 27.3?±?10.1?mol?m^-2 d^-1, respectively. The highest value (31.9?±?11.3?mol?m^-2 d^-1) appears at Linze in the arid area. The lowest value (24.3?±?9.7?mol?m^-2 d^-1) appears at Ansai in the semi-arid area. The results show that the monthly variation of the Q _p /R _s ratio ranges from 1.69?±?0.19?mol?MJ^-1 in Aksu to 1.91?±?0.08?mol?MJ^-1 in Fukang. There is a small decreasing trend of the ratio of Q _p to R _s (PAR fraction) in arid and semi-arid regions because of the recent increase in fine aerosols. A simple and efficient empirically model suit for all-weather condition was developed to estimate Q _p from R _s. The slope a and intercept b of the regression line between estimated and measured values is close to 1 and zero, respectively. The application of the model to data collected from different locations also results in reasonable estimates of Q _p .     

Simulation and Regionalization of Daily Global Solar Radiation: A Case Study in Quebec,Canada

Global solar radiation (GSR) is essential for agricultural and plant growth modelling, air and water heating analyses, and solar electric power systems. However, GSR gauging stations are scarce compared with stations for monitoring common meteorological variables such as air temperature and relative humidity. In this study, one power function, three linear regression, and three non-linear models based on an artificial neural network (ANN) are developed to extend short records of daily GSR for meteorological stations where predictors (i.e., temperature and/or relative humidity) are available. The seven models are then applied to 19 meteorological stations located across the province of Quebec (Canada). On average, the root-mean-square errors (RMSEs) for ANN-based models are 0.33–0.54?MJ?m^?2?d^?1 smaller than those for the power function and linear regression models for the same input variables, indicating that the non-linear ANN-based models are more efficient in simulating daily GSR. Regionalization potential of the seven models is also evaluated for ungauged stations where predictors are available. The power function and the three linear regression models are tested by interpolating spatially correlated at-site coefficients using universal kriging or by applying a leave-one-out calibration procedure for spatially uncorrelated at-site coefficients. Regional ANN-based models are also developed by training the model based on the leave-one-out procedure. The RMSEs for regional ANN models are 0.08–0.46?MJ?m^?2?d^?1 smaller than for other models using the same input conditions. However, the regional ANN-based models are more sensitive to new station input values compared with the other models. Maps of interpolated coefficients and regional equations of the power function and the linear regression models are provided for direct application to the study area.     

Comparison of human radiation exchange models in outdoor areas

Results from the radiation components of seven different human thermal exchange models/methods are compared. These include the Burt, COMFA, MENEX, OUT_SET* and RayMan models, the six-directional method and the new Park and Tuller model employing projected area factors (f _p) and effective radiation area factors (f _eff) determined from a sample of normal- and over-weight Canadian Caucasian adults. Input data include solar and longwave radiation measured during a clear summer day in southern Ontario. Variations between models came from differences in f _p and f _eff and different estimates of longwave radiation from the open sky. The ranges between models for absorbed solar, net longwave and net all-wave radiation were 164, 31 and 187?W?m^?2, respectively. These differentials between models can be significant in total human thermal exchange. Therefore, proper f _p and f _eff values should be used to make accurate estimation of radiation on the human body surface.     

Estimation of daytime downward longwave radiation under clear and cloudy skies conditions over a sub-humid region

Downward longwave radiation (LW _↓ ) is a relevant variable for meteorological and climatic studies. Good estimates of this term are vitally important in correct determining of the net radiation, which, in turn, modulates the magnitude of the terms in the surface energy budget (e.g., evaporation). In remote sensing applications, the determination of daytime LW_↓ is required for estimation of the net radiation using satellite data. LW_↓ is not directly measured in weather stations and then is estimated using models with surface air temperature and humidity as input. In this paper, we identify the best models to estimate daytime downward longwave radiation from meteorological data in the sub-humid Pampean region. Several well-known models to estimate LW_↓ under clear and cloudy skies were tested. We use downward radiation components and meteorological data registered at Tandil (Argentina) from 2006 to 2010 (840 days). In addition, we propose two multiple linear regression models (MLRM-1 and MLRM-2) to estimate LW_↓ at the surface for all sky conditions. The new equations show better performance than the others models tested with root mean square errors between 12 and 16 W m^?2, bias close to zero and best agreements with measured data (r ²?≥?0.85).     

The characteristics of ultraviolet radiation in arid and semi-arid regions of China

Measurements of the broadband global solar radiation (R _S) and total ultraviolet radiation (the sum of UV-A and UV-B) were conducted from 2005 to 2010 at 9 sites in arid and semi-arid regions of China. These data were used to determine the temporal variability of UV and UV/R _S and their dependence on the water vapor content and clearness index. The dependence of UV/R _S on aerosol optical depth (AOD) and water vapor content was also investigated. In addition, a simple and efficient empirically model suited for all-weather conditions was developed to estimate UV from R _s. The annual average daily UV level in arid and semi-arid areas is 0.61 and 0.59 MJ m^?2 d^?1, respectively. The highest value (0.66?±?0.25 MJ m^?2 d^?1) was recorded at an arid area at Linze. The lowest value (0.53?±?0.22 MJ m^?2 d^?1) was recorded at a semi-arid area at Ansai. The highest daily value of UV radiation was measured in May, whereas the lowest value was measured in December. The monthly variation of the UV/R _s ratio ranged from 0.41 in Aksu to 0.35 in Qira. The monthly mean value of UV/R _s gradually increased from November and then decreased in August. A small decreasing trend of UV/R _s was observed in the arid and semi-arid regions due to recently increasing amounts of fine aerosol. A simple and efficient empirically model suit for all-weather condition was developed to estimate UV from R _s. The slope a and intercept b of the regression line between the estimated and measured values were close to 1 and zero, respectively. The relative error between the estimated and measured values was less than 11.5%. Application of the model to data collected from different locations in this region also resulted in reasonable estimates of UV.     

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T _mean and R _H variables.     

Estimating sunshine duration from other climatic data by artificial neural network for ET0 estimation in an arid environment

Sunshine duration data are desirable for calculating daily solar radiation (R _s) and subsequent reference evapotranspiration (ET₀) using the Penman–Monteith (PM) method. In the absence of measured R _s data, the Ångström equation has been recommended by the Food and Agriculture Organization (FAO) of the United Nations. This equation requires actual sunshine duration that is not commonly observed at many weather stations. This paper examines the potential for the use of artificial neural networks (ANNs) to estimate sunshine duration based on air temperature and humidity data under arid environment. This is important because these data are commonly available parameters. The impact of the estimated sunshine duration on estimation of R _s and ET₀ was also conducted. The four weather stations selected for this study are located in Sistan and Baluchestan Province (southeast of Iran). The study demonstrated that modelling of sunshine duration through the use of ANN technique made acceptable estimates. Models were compared using the determination coefficient (R ²), the root mean square error (RMSE) and the mean bias error (MBE). Average R ², RMSE and MBE for the comparison between measured and estimated sunshine duration were calculated resulting 0.81, 6.3 % and 0.1 %, respectively. Our analyses also demonstrate that the difference between the measured and estimated sunshine duration has less effect on the estimated R _s and ET₀ by using Ångström and FAO-PM equations, respectively.     

Comparison of artificial intelligence techniques for prediction of soil temperatures in Turkey

Soil temperature is a meteorological data directly affecting the formation and development of plants of all kinds. Soil temperatures are usually estimated with various models including the artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models. Soil temperatures along with other climate data are recorded by the Turkish State Meteorological Service (MGM) at specific locations all over Turkey. Soil temperatures are commonly measured at 5-, 10-, 20-, 50-, and 100-cm depths below the soil surface. In this study, the soil temperature data in monthly units measured at 261 stations in Turkey having records of at least 20 years were used to develop relevant models. Different input combinations were tested in the ANN and ANFIS models to estimate soil temperatures, and the best combination of significant explanatory variables turns out to be monthly minimum and maximum air temperatures, calendar month number, depth of soil, and monthly precipitation. Next, three standard error terms (mean absolute error (MAE, °C), root mean squared error (RMSE, °C), and determination coefficient (R ²)) were employed to check the reliability of the test data results obtained through the ANN, ANFIS, and MLR models. ANFIS (RMSE 1.99; MAE 1.09; R ² 0.98) is found to outperform both ANN and MLR (RMSE 5.80, 8.89; MAE 1.89, 2.36; R ² 0.93, 0.91) in estimating soil temperature in Turkey.

     

Hourly air temperature driven using multi-layer perceptron and radial basis function networks in arid and semi-arid regions

This study employed two artificial neural network (ANN) models, including multi-layer perceptron (MLP) and radial basis function (RBF), as data-driven methods of hourly air temperature at three meteorological stations in Fars province, Iran. MLP was optimized using the Levenberg–Marquardt (MLP_LM) training algorithm with a tangent sigmoid transfer function. Both time series (TS) and randomized (RZ) data were used for training and testing of ANNs. Daily maximum and minimum air temperatures (MM) and antecedent daily maximum and minimum air temperatures (AMM) constituted the input for ANNs. The ANN models were evaluated using the root mean square error (RMSE), the coefficient of determination (R ²) and the mean absolute error. The use of AMM led to a more accurate estimation of hourly temperature compared with the use of MM. The MLP-ANN seemed to have a higher estimation efficiency than the RBF ANN. Furthermore, the ANN testing using randomized data showed more accurate estimation. The RMSE values for MLP with RZ data using daily maximum and minimum air temperatures for testing phase were equal to 1.2°C, 1.8°C, and 1.7°C, respectively, at Arsanjan, Bajgah, and Kooshkak stations. The results of this study showed that hourly air temperature driven using ANNs (proposed models) had less error than the empirical equation.     

Estimation of evapotranspiration based on only air temperature data using artificial neural networks for a subtropical climate in Iran

This paper examines the potential for the use of artificial neural networks (ANNs) to estimate the reference crop evapotranspiration (ET₀) based on air temperature data under humid subtropical conditions on the southern coast of the Caspian Sea situated in the north of Iran. The input variables for the networks were the maximum and minimum air temperature and extraterrestrial radiation. The temperature data were obtained from eight meteorological stations with a range of latitude, longitude, and elevation throughout the study area. A comparison of the estimates provided by the ANNs and by Hargreaves equation was also conducted. The FAO-56 Penman–Monteith model was used as a reference model for assessing the performance of the two approaches. The results of this study showed that ANNs using air temperature data successfully estimated the daily ET₀ and that the ANNs with an R ² of 0.95 and a root mean square error (RMSE) of 0.41 mm day^?1 simulated ET₀ better than the Hargreaves equation, which had an R ² of 0.91 and a RMSE of 0.51 mm day^?1.     

The Influence of Selected Meteorological Parameters on the Concentration of Surface Ozone in the Central Region of Poland

Abstract?This paper presents the results of measurements of the concentration of surface ozone and concurrent standard meteorological parameters: total solar radiation, temperature, relative humidity, pressure, wind speed, and vertical and horizontal components of the wind. The data were collected from 2005 to 2010 at stations located in central Poland (Mazowieckie voivodeship): Warszawa (urban), Legionowo (suburban), Granica and Belsk (rural). Furthermore, Granica is situated in the forested area of Kampinoski National Park. Continuously measured surface ozone concentrations demonstrated the well-known diurnal cycle of surface ozone concentration with a maximum in the afternoon and a minimum in the early morning hours. The averaged diurnal variations over six years reveal that the highest concentrations appear at rural stations (Belsk: 55?µg?m^?3 and Granica: 50?µg?m^?3) and the lowest at the urban station (Warszawa: 41?µg?m^?3). The threshold for high levels of surface ozone (120?µg?m^?3 per 8?h) was exceeded most often at Granica and Belsk. The occurrence of the ozone “weekend effect,” especially at urban stations, has been identified. The difference between weekend and weekday surface ozone concentrations at urban and rural stations was as high as 6.5?µg?m^?3 and approximately 2?µg?m^?3, respectively. Using appropriate statistical tools, it has been shown that meteorological conditions have a significant influence on ozone concentration. High correlation coefficients were found between ozone concentration and solar radiation, temperature, relative humidity, and wind speed. The forward stepwise regression model explains up to 75% of the variations in daily surface ozone concentration in terms of meteorological variability in summer and up to 70% in winter. At the same time, a multilayer perceptron neural network model was used to reconstruct the concentration of surface ozone. High correlation coefficients (up to 0.89) indicate that, on the basis of standard meteorological parameters and NO₂ concentration, we can determine ozone concentration with high accuracy.     

Seasonal variation of carbon dioxide fluxes over irrigated soybean (Glycine max L.)

In this study, variations in carbon dioxide (CO₂) fluxes resulting from gross primary production (GPP), net ecosystem exchange (NEE), and respiration (R _e) of soybean (Glycine max L.) were investigated by the Eddy Covariance method during the growing period from June to November 2005 on an irrigated sand field at the Arid Land Research Center, Tottori University in Tottori, Japan. Although climatic conditions were humid and temperate, the soybeans required frequent irrigation because of the low water holding capacity of the sandy soil at the field site. Finally, it has been found that the accumulated NEE, GPP, and R _e fluxes of soybean over 126 days amount to ?93, 319, and 226 gC m^?2, respectively. Furthermore, the average ratio of GPP to R _e was 1.4 and the average ratio of NEE to GPP was about ?0.29 for the growth period of soybean. Daily maximum NEE of ?3.8 gC m^?2 occurred when LAI was 1.1.     

Quantification of ecosystem carbon exchange characteristics in a dominant subtropical evergreen forest ecosystem

CO₂ fluxes were measured continuously for three years (2003?C2005) using the eddy covariance technique for the canopy layer with a height of 27 m above the ground in a dominant subtropical evergreen forest in Dinghushan, South China. By applying gapfilling methods, we quantified the different components of the carbon fluxes (net ecosystem exchange (NEE)), gross primary production (GPP) and ecosystem respiration (R_eco) in order to assess the effects of meteorological variables on these fluxes and the atmospherecanopy interactions on the forest carbon cycle. Our results showed that monthly average daily maximum net CO₂ exchange of the whole ecosystem varied from ?3.79 to ?14.24 ??mol m^?2 s^?1 and was linearly related to photosynthetic active radiation. The Dinghushan forest acted as a net carbon sink of ?488 g C m^?2 y^?1, with a GPP of 1448 g Cm^?2 y^?1, and a R_eco of 961 g C m^?2 y^?1. Using a carboxylase-based model, we compared the predicted fluxes of CO₂ with measurements. GPP was modelled as 1443 g C m^?2 y^?1, and the model inversion results helped to explain ca. 90% of temporal variability of the measured ecosystem fluxes. Contribution of CO₂ fluxes in the subtropical forest in the dry season (October-March) was 62.2% of the annual total from the whole forest ecosystem. On average, 43.3% of the net annual carbon sink occurred between October and December, indicating that this time period is an important stage for uptake of CO₂ by the forest ecosystem from the atmosphere. Carbon uptake in the evergreen forest ecosystem is an indicator of the interaction of between the atmosphere and the canopy, especially in terms of driving climate factors such as temperature and rainfall events. We found that the Dinghushan evergreen forest is acting as a carbon sink almost year-round. The study can improve the evaluation of the net carbon uptake of tropical monsoon evergreen forest ecosystem in south China region under climate change conditions.     

Seasonal and annual trends of carbonaceous species of PM<Subscript>10</Subscript> over a megacity Delhi,India during 2010–2017

PM₁₀ samples were collected to characterize the seasonal and annual trends of carbonaceous content in PM₁₀ at an urban site of megacity Delhi, India from January 2010 to December 2017. Organic carbon (OC) and elemental carbon (EC) concentrations were quantified by thermal-optical transmission (TOT) method of PM₁₀ samples collected at Delhi. The average concentrations of PM₁₀, OC, EC and TCA (total carbonaceous aerosol) were 222?±?87 (range: 48.2–583.8 μg m^?3), 25.6?±?14.0 (range: 4.2–82.5 μg m^?3), 8.7?±?5.8 (range: 0.8–35.6 μg m^?3) and 54.7?±?30.6 μg m^?3 (range: 8.4–175.2 μg m^?3), respectively during entire sampling period. The average secondary organic carbon (SOC) concentration ranged from 2.5–9.1 μg m^?3 in PM₁₀, accounting from 14 to 28% of total OC mass concentration of PM₁₀. Significant seasonal variations were recorded in concentrations of PM₁₀, OC, EC and TCA with maxima during winter and minima during monsoon seasons. In the present study, the positive linear trend between OC and EC were recorded during winter (R²?=?0.53), summer (R²?=?0.59) and monsoon (R²?=?0.78) seasons. This behaviour suggests the contribution of similar sources and common atmospheric processes in both the fractions. OC/EC weight ratio suggested that vehicular emissions, fossil fuel combustion and biomass burning could be the major sources of carbonaceous aerosols of PM₁₀ at the megacity Delhi, India. Trajectory analysis indicates that the air mass approches to the sampling site is mainly from Indo Gangetic plain (IGP) region (Uttar Pradesh, Haryana and Punjab etc.), Thar desert, Afghanistan, Pakistan and surrounding areas.     

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.     

Surface Energy Balance Measurements Above an Exurban Residential Neighbourhood of Kansas City,Missouri

Previous measurements of urban energy balances generally have been limited to densely built, central city sites and older suburban locations with mature tree canopies that are higher than the height of the buildings. In contrast, few data are available for the extensive, open vegetated types typical of low-density residential areas that have been newly converted from rural land use. We made direct measurements of surface energy fluxes using the eddy-covariance technique at Greenwood, a recently developed exurban neighbourhood near Kansas City, Missouri, USA, during an intensive field campaign in August 2004. Energy partitioning was dominated by the latent heat flux under both cloudy and near clear-sky conditions. The mean daytime Bowen ratio (β) values were 0.46, 0.48, and 0.47 respectively for the cloudy, near clear-sky and all-sky conditions. Net radiation (R _n ) increased rapidly from dawn (−34 and −58W m⁻²) during the night to reach a maximum (423 and 630W m⁻²) after midday for cloudy and near clear-sky conditions respectively. Mean daytime values were 253 and 370W m⁻², respectively for the cloudy and near clear-sky conditions, while mean daily values were 114 for cloudy and 171W m⁻² for near clear-sky conditions, respectively. Midday surface albedo values were 0.25 and 0.24 for the cloudy and near clear-sky conditions, respectively. The site exhibited an angular dependence on the solar elevation angle, in contrast to previous observations over urban and suburban areas, but similar to vegetated surfaces. The latent heat flux (Q _E ), sensible heat flux (Q _H ), and the residual heat storage ΔQ _s terms accounted for between 46–58%, 21–23%, and 18–31% of R _n , respectively, for all-sky conditions and time averages. The observed albedo, R _n , and Q _E values are higher than the values that have been reported for suburban areas with high summer evapotranspiration rates in North America. These results suggest that the rapidly growing residential areas at the exurban fringe of large metropolitan areas have a surface energy balance that is more similar to the rural areas from which they were developed than it is to the older suburbs and city centres that make up the urban fabric to which they are being joined.     

On triangular tomography

Long range acoustic soundings with triads of transducers can determine accurately the vorticity integrated over the corresponding triangular areas of the ocean. However, for a given number n of transducers not all such observations are independent. In this note we show, first, that the number N of possible independent observations is in fact N = (n ? 1) (n ? 2)/2 Secondly, we shall show such observations are in principle able to provide estimates of the vorticity field and its derivatives up to order m = (n ? 3). Thus four transducers yield an accurate estimate of the scalar vorticity ω and its horizontal gradient δ ω, while five transducers will yieldalso the Laplacian δ²ω.     

Evaluation of Mediterranean Sea water and heat budgets simulated by an ensemble of high resolution regional climate models

Air-sea heat and freshwater water fluxes in the Mediterranean Sea play a crucial role in dense water formation. Here, we compare estimates of Mediterranean Sea heat and water budgets from a range of observational datasets and discuss the main differences between them. Taking into account the closure hypothesis at the Gibraltar Strait, we have built several observational estimates of water and heat budgets by combination of their different observational components. We provide then three estimates for water budget and one for heat budget that satisfy the closure hypothesis. We then use these observational estimates to assess the ability of an ensemble of ERA40-driven high resolution (25 km) Regional Climate Models (RCMs) from the FP6-EU ENSEMBLES database, to simulate the various components, and net values, of the water and heat budgets. Most of the RCM Mediterranean basin means are within the range spanned by the observational estimates of the different budget components, though in some cases the RCMs have a tendency to overestimate the latent heat flux (or evaporation) with respect to observations. The RCMs do not show significant improvements of the total water budget estimates comparing to ERA40. Moreover, given the large spread found in observational estimates of precipitation over the sea, it is difficult to draw conclusions on the performance of RCM for the freshwater budget and this underlines the need for better precipitation observations. The original ERA40 value for the basin mean net heat flux is ?15 W/m² which is 10 W/m² less than the value of ?5 W/m² inferred from the transport measurements at Gibraltar Strait. The ensemble of heat budget values estimated from the models show that most of RCMs do not achieve heat budget closure. However, the ensemble mean value for the net heat flux is ?7 ± 21 W/m², which is close to the Gibraltar value, although the spread between the RCMs is large. Since the RCMs are forced by the same boundary conditions (ERA40 and sea surface temperatures) and have the same horizontal resolution and spatial domain, the reason for the large spread must reside in the physical parameterizations. To conclude, improvements are urgently required to physical parameterizations in state-of-the-art regional climate models, to reduce the large spread found in our analysis and to obtain better water and heat budget estimates over the Mediterranean Sea.