Pre-Observational Evolution of Surface Temperature in Romania as Inferred from Borehole Temperature Measurements

Transcription

1 Pure Appl. Geophys. Ó 211 Springer Basel AG DOI 1.1/s Pure and Applied Geophysics Pre-Observational Evolution of Surface Temperature in Romania as Inferred from Borehole Temperature Measurements CRISAN DEMETRESCU, 1 MARIA TUMANIAN, 1 VENERA DOBRICA, 1 CONSTANTIN MARES, 2 and ILEANA MARES 2 Abstract Temperature data from nine boreholes in the Carpathian orogen in Romania were used to obtain information on the ground surface temperature history (GSTH) in the last 25 years. The temperature measurements were taken with a thermistor probe (sensitivity in the 1 mk range) using the stop-and-go technique, at 1 m intervals, in the depth range of 2 5 m. The least squares inverse modelling approach of TARANTOLA and VALETTE (J Geophys 5:15 1, 12) was used to infer the GSTH. Long-term air temperature records available from the Romanian weather station network were used as a comparison term for the first 1 15 years of the GSTH, and as a forcing function in a POM-SAT model that combines borehole temperature profiles (BTPs) and meteorological time series (surface air temperature, SAT) to produce information on the so-called pre-observational mean (POM). Results from a global circulation model for the Romanian area are incorporated in the discussion as well. Key words: Borehole paleoclimatology, air-soil heat transfer, meteorological records, GCM simulation, Romania. 1. Introduction A great deal of effort has been devoted in the last decades to understanding, in connection with the present global warming, the tendencies in climate variations and past climate changes as a reference to the observed contemporary ones. Paleoclimatology became in this context a rapidly growing science. Borehole climatology took its share in this effort. Borehole temperature measurements can be used to reconstruct the ground surface temperature history (GSTH) because temperature changes at the surface, a result of variable energetic input, affect the distribution of temperature in the subsurface. Due to the relatively 1 Institute of Geodynamics, Romanian Academy, Bucharest, Romania. crisan@geodin.ro 2 Institute of Hydrology, Bucharest, Romania. low thermal diffusivity of rocks, surface temperature variations are recorded in the subsurface as transient perturbations to the steady-state temperature field that results from the outward propagation of internal heat (ČERMÁK, 11; VASSEUR et al., 13). The perturbation propagates downwards, with the depth of penetration depending on the frequency of the temperature signal (see, e.g., CLAUSER and MARESCHAL, 15). The GSTH reconstruction is estimated by inverting the temperature variations with depth. Various aspects of borehole climatology have been presented by BODRI and ČERMÁK (2). Climate studies based on borehole temperatures have some advantages as compared with proxy data (tree ring width and density, ice core isotopes, glacier lengths, plankton and pollen distributions in lacustrine and marine sediments): no uncertainty due to conversion from proxy data to temperature appears; the time interval and the spatial domain covered by borehole data (as well as the homogeneity of these data in the continental areas) are much larger (CHAPMAN, 15). The disadvantages of using borehole temperatures for GSTH reconstruction include the decrease of resolution with depth and time, and the difficulties introduced by the many sources of noise, which may produce a false climatic signal. Also, processes such as deposition of snow cover, solar insolation, the changing ground cover and soil moisture affect the relationship between air and ground temperature (LEWIS and WANG, 12; NITOIU and BELTRAMI, 25; POLLACK et al., 25). The attenuation of a thermal perturbation with depth proportional to the square root of frequency produces a loss of high frequency climatic information at shallow depths. Therefore, climate change studies based on borehole temperature profiles are adequate to determine long-term changes of surface

2 C. Demetrescu et al. Pure Appl. Geophys. Figure 1 Location of studied boreholes (large full circles with numbers) and of weather stations with long activity (triangles) temperature. Attempts to reduce these disadvantages were made by combining borehole data with proxy or meteorological data (e.g., BELTRAMI et al., 12, 15, BELTRAMI and TAYLOR, 15, CHAPMAN et al., 12,BODRI and ČERMÁK, 15,HARRIS and CHAPMAN, 15, 1, 25; PASQUALE et al., 25; HARRIS, 2). The latter gives a comprehensive review of the so-called POM-SAT model that allows, by combining meteorological and borehole data, to infer information on the mean temperature before the instrumental record. VERDOYA et al. (2) added a simultaneous assessment of the advective heat transfer in a study of several Italian boreholes. A comprehensive review of borehole climatology results in the context of contributions from climate modelling has been published recently by GONZÁLES- ROUCO et al. (2), where the reader can find an extensive reference list regarding the methodology of inversion, results at various spatial scales, from local and regional to hemispheric and global, comparison with proxy-based reconstructions, the role of heat storage in the crust within the global energy balance and, last but not least, the limitations of the method. The review concludes that borehole climatology is indeed capable of delivering reliable information about low-frequency climate evolution, accepted by the community of proxy reconstructions of climate. In this study we infer information on the preobservational temperature evolution in Romania going back to 15 using borehole temperature data obtained in the 1s in short boreholes, 1 5 m deep, by means of POM-SAT and inversion models Borehole Data 2. Data Our study uses data from nine boreholes located in three subdivisions of the Carpathian orogen, namely the Oas-Gutai Mountains (nos. 1 on the map in Fig. 1), Eastern Carpathians (nos., ) and Harghita Mountains (no. ). Temperature measurements were taken in the 1s, within the frame of a terrestrial heat flux determination program led by one of the authors (CD). Only boreholes drilled for ore exploration have

3 Pre-Observational Evolution of Surface Temperature in Romania Table 1 Main characteristics of the study boreholes No. Name Geografic coordinates Depth range (m) Latitude Longitude (N) (E) Interval of temperature measurements (m) Rock type Conductivity model k m /U /L (Wm -1 K -1 /%/km) Heat flux (mwm -2 ) 1 14 Camarzana Andesite 2.1/.35/ Neresen Andesite 2.1/.35/ Valea Ghiurghi Andesite 2.1/.35/ Rotunda Andesite 2.1/.35/ Rotunda Sedimentary rocks 3.4/.54/.33 5 Baia Sprie Sedimentary rocks 3.4/.54/ Brosteni Metamorphic rocks 3.4/./ Balan Metamorphic rocks 3.4/./ Fierastraie Andesite 1././.4 been chosen for the study as, unlike boreholes drilled for oil and gas exploration/exploitation, they lack heavy, surface connected casing metal constructions that could bias the temperatures measured in the first 1 m. The main characteristics of the study boreholes are listed in Table 1. At the epoch, the measurements were taken by the stop-and-go technique at 1 or 2 m intervals, with a thermistor probe having a sensitivity in the 1 mk range and an overall accuracy of ±.5 K. The vertical temperature profiles are shown in Figs. 2 and 3. For the inversion of vertical temperature profiles information on the thermal conductivity of rocks is needed (see Sect. 3.2). Results of laboratory measurements by a divided-bar (DEMETRESCU, 1; DEMETRESCU et al., 12) on water-saturated samples collected from the nine boreholes are plotted (full circles) in Figs. 2 and 3. They show an increase with depth that is more pronounced for sediments and, to some extent, for andesites too (Fig. 2a, b), and is small or negligible for the metamorphic rocks of the Eastern Carpathians and andesites of the Harghita Mountains (Fig. 3a, b). Assuming that, in general, the increase with depth of the thermal conductivity of rocks with certain porosity is a result of compaction, we chose to model the thermal conductivity-depth distribution by means of the classical relationship: k ¼ k 1 U m ku w ð1þ where m denotes matrix and w water, taking into account that the porosity-depth distribution has the form U ¼ U e z=l ð2þ where U is the surface porosity, z is the depth, and L is the porosity decay length. For each conductivity value an equation in k m, U, and L is written by merging Eqs. 1 and 2. The parameters k m, U, and L are then determined for each group of boreholes by a least squares procedure. The results are given in Table 1. The obtained models for the depth dependence of thermal conductivity are superimposed (continuous lines) on measured conductivities in Figs. 2 and 3 too. As regards the radiogenic heat production, also required by the inversion code, a value of 1. lwm -3 for the upper m of the crust was used (e.g., BECK and SHEN, 1; ČERMÁK et al., 1) Surface Temperature Data Air temperature data from the network of 14 meteorological stations with 1 15 years of activity run by the National Meteorological Administration, discussed by DOBRICA et al. (2), also marked in Fig. 1, were used either as forcing functions in the POM-SAT model or for a direct comparison with reconstructed surface temperatures by inversion of borehole data. An important issue for such comparisons, as discussed by Gonzáles-Rouco et al.(2), HARRIS (2), and references therein, is whether the ground surface temperature tracks the air temperature. According to that discussion, variations

4 C. Demetrescu et al. Pure Appl. Geophys. A B Sedimentary rocks Andesite Thermal Thermal conductivity (Wm -1 K -1 ) conductivity (Wm -1 K -1 ) Figure 2 Vertical temperature profiles and thermal conductivity measured values (full circles) and models (full line) for the Oas-Gutai Mountains. a, b Cases of different lithologies. Boreholes are labelled with the numbers in Fig. 1 A B 2 4 Metamorphic rocks 1 2 Andesite Thermal conductivity (Wm -1 K -1 ) Thermal conductivity (Wm -1 K -1 ) Figure 3 Vertical temperature profiles and thermal conductivity measured values (full circles) and models (full line) for (a) Eastern Carpathian and (b) Harghita Mountain boreholes. Numbers on BTPs correspond to locations in Fig. 1 in air and ground temperatures generally track each other over the time period of studies reviewed, but are adversely influenced by factors such as cold season snow ground cover, warm season solar insolation, changing ground cover, or soil moisture. Romanian data, presented by DEMETRESCU et al. (2), confirm at a local level the above general conclusion, based on a comparison made between the air and soil daily mean surface temperatures for ten automatic weather stations run by the National

5 Pre-Observational Evolution of Surface Temperature in Romania Meteorological Administration. In the time span of 2 years (23 24), the soil surface temperature tracked the air (2 m) temperature within ±2.2 K for all ten stations; the RMS differences range between ±1. and ±3. K. The mean difference between surface and air temperatures for individual stations ranges between.52 and 1. K. The overall mean difference for the ten stations is 1. K. In Fig. 4 annual means at the 14 stations of the air temperature at 2 m are displayed in the form of temperature anomalies with respect to their averages over the time interval They were superimposed to show the variation range over the Romanian territory on one hand and the similarity of the variation in spite of regional climate differences (GEICU and GHITESCU, 2) on the other. The latter is also illustrated by the reference mean temperature for 11 1 at each of the 14 stations, which ranges between.5 C (Brasov station) and 11. C (Turnu Severin). In terms of actual temperatures, the evolution of the average temperature over the Romanian territory, calculated from the 14 SAT available time series, is displayed in Fig GCM Simulated Data The GCM used in this paper consists of the atmospheric model ECHAM4 (ROECKNER et al., 1) SAT ( C) Year Figure 4 Superimposed surface air temperature anomaly at the 14 weather stations relative to the 11 1 means Temperature (ºC) Year with a horizontal resolution of degrees and 1 vertical levels, 5 of them located in the stratosphere, coupled to the ocean model HOPE-G with a horizontal resolution of approximately degrees with equator refinement and 2 vertical levels (WOLFF et al. 1). The ocean and atmosphere models are coupled through flux adjustment to avoid climate drift in long climate simulations. The coupled model was developed at the Max Plank Institute of Meteorology, and data from the Erik run (CUBASCH et al., 25), starting in the year 1, have been used. Daily values were extracted from the model in a box of four points around Bucharest. All values were averaged to get a mean value for the area, and then annual means for the time span 15 1 were obtained. They are displayed in Fig. 5, together with the time series of average temperature for the 14 stations. Comparing the simulated time series with actual data, a significant difference can be noticed in the general trend: while recorded temperatures show a broad maximum in the time interval (with two short, strong minima superimposed in 12 1 Figure 5 GCM-simulated SAT (15 1) as compared to the average SAT for the 14 weather stations of Fig. 1 (the shorter time series, right-hand axis). Note the shift of the latter. Seventh order polynomial fits are superimposed

6 C. Demetrescu et al. Pure Appl. Geophys ) and then a decrease to 1 1 followed by the contemporary increase, the simulated time series shows a broad minimum in the first half of the 2th century, followed by an increase since about 13. The general trends in the two time series are also illustrated by means of th degree polynomial fits. 3. Method 3.1. The POM-SAT Model The POM-SAT model, proposed by HARRIS and CHAPMAN (1, 21, 25) and reviewed by HARRIS (2) as regards its sensitivity to various factors, makes use of meteorological time series (SAT) recorded close to the location of boreholes from which vertical temperature profiles (BTP) were taken. The surface temperature variation is diffused into the ground and compared to the BTP, allowing a socalled pre-observational mean temperature (POM) to be obtained. A thermal conduction model is used in the process. The POM-SAT model is based on the assumption that the subsurface is perfectly conductive, with no lateral propagation of the heat, and, consequently, the variations of surface temperature are propagated and recorded into the ground according to the onedimensional heat conduction equation (CARSLAW and JAEGER, 15). At any time, the temperature at depth z is given by the sum between the steady-state temperature resulting from the internal heat flux and the transient perturbation T t (z) caused by SAT changes. For each profile the steady-state temperature field is obtained by the upward continuation of the equilibrium temperature measured in the deepest part of the borehole, where the temperature is not affected by SAT changes and constant temperature gradients over intervals of constant conductivity are assumed. The difference between the measured temperatures and the steady-state ones, the so-called reduced temperature, is then considered as representing the SAT changes effect, T t (z). Expressing the SAT time series as a sequence of N steps of magnitude DT n, at time t n, n = 1 N, the transient temperature profile can be written (CARSLAW and JAEGER, 15) as: T t ðzþ ¼ XN z DT n erfc p 2 ffiffiffiffiffiffiffiffi z erfc p j t n 2 ffiffiffiffiffiffiffiffiffiffiffiffi j t n 1 n¼1 ð3þ where erfc is the complementary error function and k the thermal diffusivity. An additional parameter, called the pre-observational mean temperature (POM), written explicitly as a first step of the SAT time series: DT ¼ T 1 POM ð4þ where T 1 is the first measured value of the SAT time series, is introduced to account for the much longer sequence of surface air temperature that controls the climatic signal in BTP. DT is determined by successive trials as the value that minimises the misfit between the vertical temperature profile generated with SAT data and the reduced temperature one, in the least squares sense. The POM-SAT model is considered relatively insensitive to the choice of thermal diffusivity. In the models presented here the thermal diffusivity has values of m 2 /s for the Harghita area, of. 1 - m 2 /s for the Oas-Gutai area and m 2 /s for the Eastern Carpathian area (SCHÖN, 1) Inversion of BTP The basic steps in all inversions are to choose an appropriate physical model, to parameterise the problem and then to estimate the parameters. The physical model represents the connection between the borehole temperatures (either measured or calculated) and the surface and/or subsurface processes that produce them. The physical model is expressed in our case by the equation of one-dimensional timedependent heat transfer in a homogeneous or layered earth medium. A more complex physical model could include convective heat transfer by water flow or other transient thermal processes. In most cases it is assumed that the borehole data used in inversion have not been contaminated with noise from other

7 Pre-Observational Evolution of Surface Temperature in Romania transient processes or complex structures. A careful analysis of data is therefore needed before performing the inversion. In this study the Bayesian inverse theory of TARANTOLA and VALETTE (12) has been used to infer the GSTH (SERBAN et al., 21). The discrete least squares method (TARANTOLA and VALETTE, 12; TARANTOLA, 1; MENKE, 14) consists essentially of two steps. First, the problem is discretized, leading to a theoretical relation in algebraic form between the data d and the discretized model parameters p: d ¼ gp ð Þ ð5þ In the second step, a least squares misfit function between the observed and the calculated data, and between the a priori and the actual model parameters, is minimised to obtain the most probable estimates (and the estimated variances) for the model parameters (SHEN and BECK, 11). The model parameters consist of the unknown GSTH, the background heat flux, the thermal conductivity, the product between density and heat capacity, and the heat production. Assuming a known thermal property parameter structure (known thermal conductivity, density, heat capacity and heat production), the inverse problem is linear. For a GSTH model consisting of a series of independent temperature step changes, the system of linear equations can be written as: d ¼ Gp þ d r ðþ where G is a matrix independent of p and d r is the steady-state radiogenic contribution to the borehole temperature profile. In the linear case, the parameters p = (p 1, p 2,, p M ) are the unknown surface temperatures in the time interval B t i B t max (p i = T(, t i ), i = 1,, M - 1) and the background heat flux (p M ). M is the number of parameters. The time t max should be placed sufficiently far back in time such that the temperature for t C t max can be assumed constant. This is equivalent to assuming that the thermal regime before t max is a steady-state regime. It is important to note that borehole temperature GST reconstructions are not unique. A stable and unique solution requires a priori information, incorporated as an auto-covariance function. The a priori information on parameters represents our knowledge on the physical model. The thermal parameters of the model (thermal conductivity, the product density-heat capacity and heat production) are either measured directly or estimated with some degree of confidence. It is therefore not very difficult to assign a standard deviation for these parameters. They are not correlated with the GSTH. It is more difficult to define the a priori information on the GSTH because no direct measurements exist. In this case our a priori knowledge includes the observation that surface temperature is bounded, and it is most likely locally smooth. Generally, only the vertical heterogeneities are included in the inversion model. Other deviations from the model, such as topography effect, convective transfer of heat and other inhomogeneities of the thermal properties, are taken as noise in the data. These effects should be estimated in advance as well as possible. Another factor affecting the inversion accuracy is related to the available data for a certain borehole, constrained by the finite length of the borehole and/or missing data in the immediate subsurface. The accuracy can be tested using synthetic data (ANDERSSEN and SAULL, 13; SHEN and BECK, 13; CLOW, 12). 4. Results and Discussion 4.1. Pre-Observational Surface Temperature Based on Joint Analysis of Meteorological Time Series and BTPs In Fig. we show the results of applying the POM-SAT model to the pair 2-Ferastraie borehole (no. in Fig. 1 and Table 1) and the closest weather station, Bistrita, located 125 km NW of the borehole, namely the reduced temperature and the POM-SAT best fit in Fig. a, and the SAT used and the POM values obtained in Fig. b. Four SAT time series have been used, namely the annual means (black), the solar-cycle free data, which we call the interdecadal trend (11-year running averages of the annual means, red), the 3-year running averages of the solar cycle free data, the so-called centennial trend (blue) (see DOBRICA et al., 2) and the linear trend (straight

8 C. Demetrescu et al. Pure Appl. Geophys. A Reduced T = +3.4 K 1 B 1 BISTRITA 2 3 SAT ( C) 5 4 T POM To (K) Year SD (mk) Figure POM-SAT model for borehole coupled with the Bistrita weather station. a Reduced temperature (points) and the best POM-SAT fit (red); b SAT time series with its various trends (interdecadal, red, centennial, blue, linear, black) and corresponding POM values; inset, standard deviation between the reduced temperatures and the modelled ones with various DT values black line). Of course, all SAT time series end in the POM-SAT model at 1, the year of borehole temperature measurements. In all figures the entire time series is however plotted. The inset illustrates the sensitivity of the method via the standard deviation (SD) between the reduced borehole temperatures and the modelled ones with various DT values for the SAT time series. Differences in the underground effect of the four SAT series mentioned above are limited to depths in the first 4 m, where no measured temperature data are available and are not shown. The four POM values of 4.3, 4.1, 3. and 4.2 C obtained for the four SAT series are in agreement with the conclusion of HARRIS (2) that changes of the POM values are negligible for periods shorter than the time series. The simulations made by HARRIS (2) showed that POM is a robust and a well-determined parameter for all periods less than or equal to the length of the forcing function of 15 year, independent of the amplitude of the forcing period. For long periods there is a balance between forcing function amplitude and the magnitude of the POM. In Fig. reduced temperatures and some POM- SAT model results using the Baia Mare weather station are compared for four Oas-Gutai boreholes drilled in andesites (nos. 1 3, and in Fig. 1 and Table 1) and two boreholes drilled in sedimentary rocks (nos. 4, 5). Data from the Baia Mare weather station (3 4 km S of the boreholes) were used. The plots reveal, in the first group, large differences between boreholes and between the reduced temperatures and POM-SAT models. Two models are shown, encompassing the range of reduced temperatures. Such discrepancies most probably originate from non-climatic factors contributing to noise in BTPs (e.g., topography, fluid flow, horizontal advection). The noise is particularly high in borehole. In the two boreholes drilled in sedimentary rocks (nos. 4 and 5), the discrepancy is even larger, with the BTPs in the first 2 3 m indicating a surface cooling, whereas the SAT time series shows warming. This is illustrated in Fig. b by comparing the downward diffused temperatures from the Baia Mare SAT time series, considering two pre-observational temperature steps mentioned in the plot, with reduced temperatures of boreholes 4 and 5. Having in view that there is no information available on the vertical or horizontal water flow, the most probable cause of the temperature disturbances apparent in the reduced temperature profiles, a more complex approach to simultaneously assessing the advective heat transfer

9 Pre-Observational Evolution of Surface Temperature in Romania A Reduced T = -1 K B T = -1 K Reduced T = K 1 3 T = +2 K Figure Reduced temperature (points) and POM-SAT model for the Oas-Gutai boreholes. a Drilled in andesites (1 3, from Fig. 1); b drilled in sedimentary rocks (4, 5). Two models (grey lines) encompassing the observed reduced temperatures are shown in each panel. (In b they refer to borehole 4 only) (VERDOYA et al., 2) was not considered in this study. Consequently, boreholes 4, 5 and were discarded from further processing. In Fig. results of the two boreholes in the Eastern Carpathians, nos. and in Fig. 1 and Table 1, and SAT data from the Roman weather station ( km east of boreholes) are presented. The Roman weather station was preferred as being representative for the climate characterising the eastern slope of the Carpathians, where the two boreholes were drilled. Another station, closer to the boreholes, namely Bistrita, would be representative of the climate inside the Carpathian arc (GEICU and GHITESCU 2). These two boreholes show another type of problem, namely the influence of older temperature steps, not accounted for by the single-step POM-SAT model, at depths below 3 m. A multi-step model for the temperature variation prior to the instrumental record, going back to 2, ybp, presented in Fig. a, would lead to a much better fit to measured temperatures, as shown in Fig. b. The extension of the surface temperature history, going back to 2, ybp (shown are only 1,25 years before present), qualitatively indicates the well-known Medieval Warm Period and Little Ice Age episodes. Such a model would, however, be poorly constrained unless a priori information is available. The well-known reconstruction by BECK (1) for mid-latitudes is also given in Fig. a as a general reference. The POM-SAT model requires that the SAT time series is representative for the area in which the study borehole is located. This is not the case for the present study. A closer look at the SAT distribution over the Romanian territory, using a recent map of the mean air temperature in the time interval 11 2, published by the National Meteorological Administration (GEICU and GHITESCU 2), shows

10 C. Demetrescu et al. Pure Appl. Geophys. Reduced A T = +. K B SAT ( C) 11 1 T = +2.3 K SD (mk) T (K) ROMAN POM T Year Figure POM-SAT models for boreholes and coupled with the Roman weather station. a Reduced temperature (points) and the best POM-SAT fit (broken and full red); b SAT time series with its various trends (interdecadal, red, centennial, blue, linear, black) and corresponding POM values in borehole ; inset, standard deviation between the reduced temperatures and the modelled ones with various DT values B Reduced A Time (years before present) 2 4 Figure A multi-step model (a black line; in the time interval 1 1, the recorded time series from the Roman weather station is plotted) and best fit of the POM-SAT model (b grey line) on reduced temperature for borehole. Broken line in (a) Beck (1) model significant differences between the mountainous areas where the boreholes are located and the weather stations used for the POM-SAT model. The differences amount to 2 3 K. An experiment we did for the pair of borehole -Bistrita weather station underlines the importance of the above condition. We

11 Pre-Observational Evolution of Surface Temperature in Romania diffused into the ground, on one hand, a time series with smaller amplitudes than the actual SAT at Bistrita, namely the average of the 14 weather station time series plotted in Fig. 5, and, on the other, the SAT at Bistrita with all values reduced by 4 K. While reducing amplitudes of oscillations in SAT time series has no influence on the determined POM, the other case showed a smaller POM by 4 K. The DT values reported in the figures of this section of the study should be increased accordingly in order to deal with POM values characteristic of the borehole location. On the other hand, GSTs are larger than SATs by 1 2 K, as discussed in Sect. 2.2, so the general temperature difference between weather stations and boreholes could be compensated for by the temperature difference between the air and ground surface. Consequently, we consider the obtained POMs as representative of the borehole areas. The results are given in Table 2 against the mean temperature recorded at the considered weather stations in the instrumental time interval. We end this section of the article by including in the analysis the simulated temperatures from the GCM presented above in Sect Having in view the small spatial resolution of the model of several hundred kilometres, a comparison of its underground effects with an average BTP was considered appropriate. In Fig. 1 all reduced temperatures are plotted together. As we discussed above, BTPs of boreholes 4, 5 and stick out and were not included in the average BTP. The latter is also shown. The underground effect of the surface temperature variation is superimposed, as represented by the mean SAT time series over the 14 long meteorological records of Sect The differences between the GCM and recorded temperature time series, mentioned in 2.3, show up in the downward diffused temperatures in the first 1 m of the average borehole. Differences between the two diffused temperatures amount to.2 K at most in the depth interval 2 3 m, but to 1 K around 5 1 m and to 1.2 K at the surface. POM values, also listed in Table 2, are, however, close to each other:. C (recorded average case) and.5 C (GCM case). These values indicate a step of 1. K between the pre-observational mean temperature and the observed mean temperature for the 14 stations between 151 and 1, close to the average of individual steps mentioned above, and a step of 1. K for surrogate SAT as produced by the GC model. The GCM simulation fails to produce a step of that magnitude in the time series that runs from 15 to 1: the difference between the pre-151 and post-151 means is only.5 K Pre-Observational Surface Temperature Based on the Inversion of BTPs In this section, results of inverting the vertical temperature profiles of the present study are presented. As some of these profiles appeared to be noisy or too short (see the previous section), only data for boreholes, and were retained for inversion. Information on the GST history in the last 25 years was obtained. In Fig. 11 the resulting GSTH is compared with the meteorological data for the last 13 years from the weather stations closest to the analysed boreholes Table 2 Main results of POM-SAT modelling Area Borehole POM ( C) Weather station Mean temperature ( C) Temperature step (K) (5 3) Oas-Gutai Mountains 1.5* Baia Mare.3 (15 1) 1.* Eastern Carpathians.1 Roman.3 (1 1) Harghita Mountains 4.3 Bistrita.1 (11 1) 3. Romanian mountains Average 1, 2, 3,,,. Average over 14 stations.5 (151 1) 1. GCM simulation Average 1, 2, 3,,,.5 Simulation Bucharest area (151 1) 1. * Average for six boreholes

12 C. Demetrescu et al. Pure Appl. Geophys. 2 4 Reduced Figure 1 The POM-SAT model applied to the average borehole temperature profile (thick black) and best fit with two forcing functions: the average SAT time series for 14 weather stations (grey full line) and the GCM simulation for (grey broken line). Thin black lines, individual vertical profiles used in the previous section. As the general level of the reconstructed temperature in borehole is lower than in borehole (Fig. 11a), a better comparison can be made with temperatures recorded at another weather station, namely Brasov (Fig. 11b). A level difference can also be noted for borehole when the GSTH is compared to data from the Bistrita weather station, located rather far away at 125 km north of the borehole. The general trends are, however, similar. The reconstructed GSTHs follow very well the recorded temperature trend in the first 4 5 years before borehole measurements were taken. All reconstructions by inversion of BTPs, including the discarded boreholes (not shown), present a maximum in the 1s, followed by a sharp decrease with a minimum in the 1s, and then an increasing trend to the end of the century. The time of temperature measurements (1) prevents a better characterisation of the last increasing temperature episode. A closer look at the data shows that borehole shows a rather broad minimum in the time interval. This minimum is shifted toward the present in borehole inversion data (15 1), and so is the preceding maximum (from around 1 in no. to 13 in no. ). In Fig. 12, where the average over the 14 weather stations is plotted together with the inversion results, the above conclusions are more obvious. In the SAT time series the decadal temperature time change is enhanced by averaging out short-term variations of about 4 years present in the data (see DOBRICA et al., 2). The inversion results were superimposed in such a way that the 1 temperature value was at the same level as the 1 value of the SAT. A very good agreement is seen in the best accuracy time span of the inversion (1 1). In this time interval, after the increasing of temperature up to 1, a pronounced decrease till 1 and a sketchy increase after 1 follow. The latter episode indicates the beginning of the contemporary warming. The minimum trend at 13 15, shown by the GSTH from borehole mentioned above, is probably determined by the two pronounced minima of the SAT, at 13 and 14, as the temporal position of the inverted temperature values is close to the two epochs. Borehole provided a shorter GSTH. It shows a somewhat extended maximum between 1 and 1 in comparison with other boreholes, but the decreasing trend that follows is similar to the general trend of the SAT. The shift toward the present of maxima and minima of GSTH in borehole might be caused by a misrepresentation of thermal conductivity adopted for the deeper part of the borehole (SERBAN, 22; SERBAN et al., 21). However, the 1 maximum and the following sharp decrease till 1 can be seen in borehole too. For a general view of the results of this study, we also superimposed the other two types of information we dealt with in Fig. 12 concerning the pre-

13 Pre-Observational Evolution of Surface Temperature in Romania A Closest weather station ROMAN Temperature (ºC) 1 POM Year B C Closest weather station BRASOV Closest weather station BISTRITA Year Year Figure 11 Comparison of inversion-determined GSTHs (thick black) with SAT time series (thin black) from the closest weather stations. a GSTHs from boreholes and as compared to SAT from the Roman station; b SAT from the Brasov station; c GSTH from borehole compared to SAT from the Bistrita station observational temperature evolution, namely the POM value (broken grey line) obtained from the pair average borehole-average SAT and the GCM Year Figure 12 Synthesis of pre-observational temperatures reconstructed by means of inversion of BTPs (black with labels), SAT-POM model (dashed grey) and GCM (thin black). Numbers, borehole codes. Solid grey line, the average SAT time series over the 14 weather stations. GSTHs by inversion are offset to the temperature of the meteorological time series in 1 simulation (full grey line). The latter was processed in the same way as the SAT time series. We note the good agreement with the SAT series regarding the decadal temperature changes, but also the important discrepancy regarding the centennial trend, especially in the 13 1 time span. This makes a comparison of inverted temperature values with the GCMsimulated values less convincing. 5. Conclusions Vertical temperature profiles from nine boreholes in the 2 5 m depth range in the Carpathian orogen have been processed to obtain information on the preobservational temperatures characterising the ground surface. Two types of information were recovered using the two methods of borehole climatology, namely the POM-SAT model and the inversion. While the former provides a so-called pre-observational mean, by combining meteorological and borehole data, the latter produces a series of

14 C. Demetrescu et al. Pure Appl. Geophys. temperatures, the so-called GSTH, covering, in our case, the last 25 years. A simulation of temperatures between 15 and 1 using a global circulation model was included in the discussion of the results as well. The POM-SAT model applied to BTP-SAT pairs of this study indicates lower mean ground temperatures for the *1 years preceding the meteorological record than for the 1 15 years with meteorological data by K. The surrogate SAT time series provided by the GCM simulation for the time interval 151 1, the same interval as used for meteorological SAT information, led to a close value of 1. K. However, the actual mean in the GCM time series prior to 151 is lower than the mean by only.5 K. The GCM time series also fails when compared to the meteorological record regarding the long-term trend, especially after 13, when the GCM time series indicates a lasting temperature increase. In the meteorological record the increase of temperatures only starts in the 1s. It is interesting to note that the decadal time change, obtained after averaging out frequencies of about 4 years present in the data, is very similar in both time series, meteorological and GCM. Three of the nine vertical temperature profiles available for this study were inverted to give the GSTH. A very good agreement with a 1 15-yearlong meteorological time series is seen in the best accuracy time span of the inversion, 1 1. All GSTHs show in this time interval, after the increasing of temperature to 1, a pronounced decrease till 1 and followed by a sketchy increase after 1. The latter episode indicates the beginning of the contemporary warming. A minimum trend at 13 15, shown by the GSTH from borehole, is probably determined by the pronounced minimum of the SAT decadal variation at *14 as the temporal position of the inverted temperature values is close to the two epochs. For one borehole, no., a shift toward the present of that minimum and of the preceding maximum is seen, probably caused by inappropriate thermal conductivity values used in the inversion process. A comparison to a GCM time series calculated for the 15 1 time span was irrelevant, as that time series does not reproduce either the long-term trend in the measured SAT in the 2th century on one hand or the lower pre-observational temperature as determined by a POM-SAT model using measured SAT on the other. This study presents information on the surface temperature evolution for *1 years before the instrumental record, going back to 15, thus filling in a gap left by a previous study (SERBAN et al., 21) on nine 5 1,4-m-deep boreholes that indicated the presence of Weichselian glaciation and climate warming following the Weichselian period, but could give no information on climate changes in the last 2 3 years, as measurements from the first 5 15 m of the boreholes were discarded because of casing-induced disturbances. The new information for the Romanian territory is important in the context of climate evolution in a larger area, Europe, as the variability of the long-term change has been shown to be similar all over the continent (DOBRICA et al., 2; DOBRICA et al., 21). Acknowledgements The study was supported by the project PNII-PC Paleoclim no. 31-3/2. Delia Zemira Serban (Institute of Geodynamics and Aarhus University) provided codes for BTP inversion. We thank the two anonymous reviewers whose comments helped improve the manuscript. REFERENCES ANDERSSEN, R.S., and SAULL, V. A. (13), Surface temperature history determination from borehole measurements, Math. Geol. 5, BECK, A.E. (1), Climatically perturbed temperature gradients and their effect on regional and continental heat flow means, Tectonophysics 41, 1 3. BECK, A.E., and SHEN, P.Y. (1), On a more rigorous approach to geothermic problems, Tectonophysics 14, 3 2. BELTRAMI, H., and TAYLOR, A.E. (15), Records of climatic change in the Canadian Arctic: towards calibrating oxygen isotope data with geothermal data, global planet. Change 11, 5. BELTRAMI, H., JESSOP, A.M., and MARESCHAL, J.C. (12), Ground temperature histories in eastern and central Canada from geothermal measurements: evidence of climatic change, Palaeogeogr., Palaeoclimatol., Palaeoecol. (Global Planet. Change Sect.), BELTRAMI, H., CHAPMAN, D.S., ARCHAMBAULT, S., and BERGERON, Y. (15), Reconstruction of high resolution ground temperature

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