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Climate reconstruction in the Urals from geothermal data
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Russian Geology and Geophysics 53 (2012) 1366–1373 www.elsevier.com/locate/rgg
Climate reconstruction in the Urals from geothermal data I.V. Golovanova a,*, R.Yu. Sal’manova a, D.Yu. Demezhko b a b
Institute of Geology, Ufimian Scientific Center of the Russian Academy of Sciences, ul. K. Marksa 16/2, Ufa, 450000, Russia Institute of Geophysics, Ural Branch of the Russian Academy of Sciences, ul. Amundsena 100, Yekaterinburg, 620016, Russia Received 5 April 2011; accepted 16 February 2012
Abstract Results of paleoclimatic analysis of geothermal data in the Middle and Southern Urals for different time intervals are presented. Climate reconstruction for the past millennium was made using data from 44 boreholes, and the magnitude of the Wurm–Holocene warming event was estimated based on data from two deep boreholes. The method of functional space inversion was used. The resolution of the method for reconstruction of various climatic events in the past was investigated. Parameters specified a priori and the required duration of the period to be reconstructed were chosen from the results of numerical modeling. According to the inversion results, the ground surface temperature at the maximum of the Medieval Warm Period in 1100–1200 was approximately the same as the present temperature, and at the minimum of the Little Ice Age around 1720, it was 1.2–3 °C lower than at present. The subsequent temperature rise was more pronounced in the past century. The magnitude of the Wurm–Holocene warming event, reconstructed using data from two deep boreholes is 10–11 °C. © 2012, V.S. Sobolev IGM, Siberian Branch of the RAS. Published by Elsevier B.V. All rights reserved. Keywords: geothermy; temperature; inversion; paleoclimate; climate reconstruction; Urals
Introduction It is known that the present distribution of the ground temperature and heat flux is distorted by the influence of past climate changes to a depth of several kilometers. Temperature measurement data obtained in ore-prospecting boreholes can be used to reconstruct the ground surface temperature history for periods ranging from a few hundred to a few thousand years. Refining knowledge of the magnitudes of past warmings is of great importance for the solution of the question about the nature of the present warming and prediction of future climate changes. In the Urals, paleoclimatic studies began almost simultaneously and independently at the Institute of Geology, Ufimian Scientific Center of the RAS (Ufa) and the Institute of Geophysics, Ural Branch of the RAS (Yekaterinburg). Subsequently, the geothermal groups of the two institutes conducted research on the history of climate change in the Urals in close contact with each other. It was supposed to test existing methods of paleoclimatic interpretation using a large body of facts and perform a comparative analysis of different methods of research. The results of paleoclimatic analysis of geothermal data for the Middle and Southern Urals obtained at the Institute of Geology, Ufimian Scientific Center. * Corresponding author. E-mail address: [email protected] (I.V. Golovanova)
Reconstruction of the surface temperature in the Urals over the past millennium by a geothermal method Method of interpretation. The geothermal method of paleoclimate reconstruction provides direct estimates of mean annual paleotemperatures of rocks at the bottom of the layer of annual fluctuations (at depths of 15–25 m) (Derevyanko, 2008). In the formulation of the problem, this temperature is taken as the ground surface temperature. Its variation at this depth reflects the main trends in surface air temperature. The difference between them can vary depending on latitude and local conditions, and its determination requires a special analysis. The mean annual surface air temperature in northern Eurasia is typically 4–5 °C lower than the rock temperature at the bottom of the layer of annual fluctuations mainly due to the warming effect of snow cover in winter. For the Southern Urals, the rock temperature at a depth of 25 m (T25) is related to the mean annual air temperature (Ta) as follows (Sal’nikov, 1984): T25 = 1.33 Ta + 2.57. Using this relation and based on information on the mean annual air temperature, the temperature at 25 m was calculated and a schematic map was constructed. In the study area, the present mean annual temperature at the bottom of the layer of
1068-7971/$ - see front matter D 201 2, V . S. S o bolev IGM, Siberian Branch of the RAS. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.rgg.2012.10.009 +
I.V. Golovanova et al. / Russian Geology and Geophysics 53 (2012) 1366–1373
Fig. 1. Location of the studied boreholes. 1, single boreholes; 2, groups of boreholes.
annual fluctuations range from 3.5 to 11 °C. The paleoclimate reconstruction method used in this study provides direct estimates of paleotemperatures or its deviation from the present temperature. The ground surface temperature in the Urals over the past millennium was reconstructed using geothermal data of the Institute of Geology, Ufimian Scientific Center of RAS (IRAS IG USC RAS) and the Institute of Geophysics, Ural Branch of RAS (IG UB RAS) obtained in studies of the heat flux distribution in 1969–1996. As a result of a preliminary analysis of more than 200 data of borehole temperature measurements, a test sample of 44 temperature logs was formed which satisfied the following conditions: the depth was not less than 700 m; the temperature logs correspond most closely to the one-dimensional heat transfer model, i.e., they were the least affected by nonclimatic factors–groundwater flows and seepage, topography, local surface anomalies, etc. The selected temperature logs were recorded in an area
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characterized by a single geological structure and a common climate history. Temperature measurements were made with a step of 10 or 20 m and an accuracy of up to 0.02 °C. Thermal conductivity of rock samples was studied in detail, except in the uppermost part composed of weathered rocks. For a number of boreholes, a layered subsurface model was used if the thermal conductivity varied greatly in its different parts. In other cases, it was assumed that the medium is homogeneous, and for inversion, the mean thermal conductivity was used. The temperature logs included in the sample uniformly characterize the meridionally elongated area (50°10′–58°22′ N, 57°40′–62°45′ E) is located mainly on the eastern slope of the Urals (Table 1, Fig. 1). The ground surface temperature history was reconstructed using the method of functional space inversion (FSI) (Shen and Beck, 1991). The problem is solved under the assumptions that the medium is one-dimensional and there is no convective heat transfer (Shen and Beck, 1991; Sukhorukova and Duchkov, 1998). An advantage of this method is that all the parameters of the medium can be determined simultaneously. To solve the problem, it is necessary to a priori define the model parameters such as the surface temperature, and the thermophysical properties of rocks, the heat flux density at the bottom, the initial temperature distribution in the borehole, and the variance of these properties. It is known that the more accurately the initial approximation is defined and the ranges of input parameters are narrowed, the closer the result of the inversion to the true value. Therefore, the best results can be obtained from boreholes for which there are fairly complete and qualitative experimental data on the temperature and thermal properties of the medium. The geothermal data used for climate reconstruction are complicated by thermal noise and measurement errors, which in the inversion can be interpreted as false climatic signals. The inversion algorithm allows the choice of the degree of influence of random factors on the results, which can be done by specifying constraints on primary data. The standard deviations for a priori defined borehole temperatures and thermal conductivities have a particularly strong influence. To suppress false signals, it is recommended (Shen et al., 1995) to relax the constraints imposed on a priori input parameters (especially temperature and thermal conductivity). This reduces the effect of noise, but at the expense of a loss of part of the climatic signal, resulting in smoothed ground surface temperature curves with underestimated magnitudes of climatic events. In contrast, inversion with highly constrained input parameters gives a ground surface temperature curve with good resolution. In this case, variations in the input data that are indeed related to climate change and noise are interpreted as a result of past climate changes. Data obtained from several boreholes in one region may differ greatly from each other. If we assume that the ground surface temperature in the study region has the same history, the noise is then due to local geological conditions, i.e., is random. Then, the effect of noise can be reduced by joint inversion of data from a group of
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Table 1. Location of the boreholes and its characteristics No.
Notation
Name of area, site, deposit
Coordinate
Depth of measurement (min–max), m
N
E
1
kr8802
Krasnoural’sk
58°22′
60°04′
7–907
2
ku622
Kushva
58°19′
59°52′
50–840
3
vol0477
Volkovskoe copper-pyrite deposit
58°10′
59°51′
20–965
4
vol0777
Volkovskoe copper-pyrite deposit
58°10′
59°51′
20–967
5
br3420
Berezovskii
56°52′
60°50′
20–780
6
dg2003
Degtyarsk
56°43′
60°05′
20–1260
7
ma8002
Mayak Scientific-Production Association
55°41′
60°44′
40–760
8
ma8304
Mayak Scientific-Production Association
55°41′
60°44′
20–1112
9
ma8505
Mayak Scientific-Production Association
55°41′
60°44′
18–1218
10
kuv165
Kuvatal
55°40′
60°10′
20–1340
11
kuv180
Kuvatal
55°39′
60°03′
30–848
12
kuv271
Kuvatal
55°39′
60°03′
20–1520
13
kuv283
Kuvatal
55°39′
60°03′
20–1200
14
ter27
Termenevskii
55°04′
58°42′
80–1045
15
ilmen1
Il’menskaya
55°00′
60°10′
20–1945
16
krug313
Kruglogorskaya
54°55′
59°55′
40–870
17
uch1755
Severo-Uchalinskaya
54°22′
59°24′
10–910
18
uch1754
Severo-Uchalinskaya
54°20′
59°25′
20–720
19
uvl62
Uvel’skaya
54°15′
60°50′
10–660
20
rech5869
Rechnoi
54°05′
59°22′
10–940
21
u–ch5658
Yuzhno-Chebachii
54°04′
59°24′
10–600
22
url3292
Urlyadinskaya
54°00′
59°25′
10–1135
23
sala1790
Salavat
53°40′
58°30′
20–680
24
alex5937
Severo-Aleksandrinskaya
53°35′
59°20′
10–960
25
mg10
Magnitogorsk
53°32′
59°10′
20–1050
26
kas2011
Kassel’skaya
53°33′
59°16′
10–1360
27
mag2056
Magnitogorsk
53°30′
59°05′
80–1530
28
mag2066
Magnitogorsk
53°25′
59°05′
20–1420
29
khudo184
Severo-Khudolazovskii
52°57′
58°33′
10–1420
30
khudo187
Severo-Khudolazovskii
52°57′
58°33′
120–1150
31
kzh4
KuzhaiS4
52°35′
62°45′
40–940
32
kzh8
KuzhaiS8
52°35′
62°45′
40–900
33
bg2351
Bogachevka
52°25′
58°20′
50–750
34
ul4118
Yulaly
52°25′
58°22′
30–600
35
spod812
Severo-Podol’sk
52°05′
58°28′
50–950
36
podol32
Podol’sk
52°05′
58°20′
20–800
37
podol34
Podol’sk
52°05′
58°20′
20–921
38
medn935
Mednogorskoe
51°30′
57°40′
40–850
39
medn936
Mednogorskoe
51°30′
57°40′
30–870
40
asch5107
Ashchebutak
51°05′
59°10′
20–680
41
osen4480
Osennee
50°55′
59°35′
10–1010
42
kok1124
Koktau
50°30′
59°05′
40–980
43
chrom79
Chromtau
50°10′
58°25′
20–860
44
chrom192
Chromtau
50°10′
58°25′
20–940
45
leuz1
Leuzinskaya
55°25′
59°00′
5–4512
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boreholes (Wang et al., 1994) or by averaging the results of the inversion (Pollack et al., 1996). In this case, the regional signal of ground surface temperature is more pronounced. Reliability of geothermal reconstructions of ground surface temperature histories is limited by two groups of factors (Demezhko, 2001). The first group includes the physical limitations of the method: resolution and the maximum length of the temperature history. The second group includes conditions due to the properties of experimental data, namely, the uncertainty caused by the influence of nonclimatic factors, thermal heterogeneity of the medium, and the possible influence of fluid seepage. The time resolution of the geothermal method of paleoclimate reconstruction worsens as one goes farther back in time. According to the analysis carried out by Demezhko (2001), the resolution of the method can be estimated by using the relative duration of a climatic episode. This parameter is the ratio of the duration of a climatic episode to the time elapsed from the start of the episode to the time of borehole temperature measurement. It is shown that only episodes with a relative duration of more than 0.5 leave an imprint on the geothermal field and can be reconstructed from geothermal data. Among these are the Wurm glaciation (relative duration of 0.88) and the Little Ice Age, which lasted from the 14th century until the end of the 19th century (relative duration of 0.82). It is possible to estimate the average temperature conditions for the period of the past 2300–800, including the optimum of the Roman period and the Medieval Warm Period, separated by a short-term cooling (relative duration of 0.64). The Medieval Warm Period can be reconstructed separately (relative duration of 0.33). Therefore, one can hope for the maximum reliability of temperature estimates of only some climatic events: the Wurm glaciation, the warming magnitude in the early Holocene (about 10,000 years BP) to the optimum of the Holocene, the Medieval Warm Period with Roman Optimum (2300– 800 years BP), the Little Ice Age (600–150 years BP), and temperature trend of the XXth century (Demezhko, 2001). Note that there are other ways to estimate the time resolution of the geothermal method. According to (Demezhko, 2001), the maximum depth of the anomaly are 7200 m for the Wurm glaciation, 1080 m for the Roman Optimum with the Medieval Warm Period, and 580 m for the Little Ice Age. Despite the rather high quality of the input data used, they certainly contain thermal noise, whose influence can be reduced by averaging the inversion results. Nonclimatic factors were suppressed by simultaneous reconstruction of the common temperature history of a number of temperature logs or by the functional space inversion method or by averaging individual ground surface temperature curves. Resolution of the method. Before applying the method to borehole data, we studied its resolution for the reconstruction of various climatic events in the past. For this, we simulated the influence of paleoclimate over the past 250 thousand years on the temperature distribution with depth (direct problem). Published data on deviations of the global mean surface
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air temperature in the Northern Hemisphere from the present temperature (Demezhko, 2001) were used as the boundary condition on the Earth’s surface. The obtained temperature distribution with depth was then used for inversion (inverse problem). From the results of the numerical simulation, we chose parameters that must be specified a priori and the required duration of the period to be reconstructed. The effect of the duration of the period on the inversion results (from simulation results) are presented in Fig. 2. The figure shows the ground surface temperatures reconstructed by inversion for 4000, 3000, 2000, and 1000 years BP and the ground surface temperature history over the past 4000 years used as the basis in the simulation. In the calculation, we used a priori standard deviations for borehole temperature σT = 0.01 K and thermal conductivity σλ = 0.3 W/(m⋅K). Boreholes 800 m deep, in principle, allow climatic history reconstruction back to several thousand years ago. However, the result depends on the chosen duration of the inversion period. The most reliable reconstructions are obtained for climatic events over the last half or one-third of the chosen period. This is because the more remote part is affected by the constraints imposed on the ground surface temperature in the solution. For example, inversion for up to 1000 years BP, as was often done in previous studies, provides more or less reliable reconstructions of events in the past 300–500 years, i.e., the Little Ice Age. To reliably reconstruct the Medieval Maximum, inversion should be performed for a period of at least 3000 year BP. It should however be recognized that the borehole depth is not sufficient to reconstruct the Medieval Warm Period with the Roman Optimum. Only the last part of this combined climatic period can be reconstructed. Of great importance is the choice of a priori standard deviations for borehole temperatures σT and thermal conductivities σλ (Fig. 3). Climatic signals attenuate with increasing depth. Therefore, for the reconstruction of more distant events, it is required to tighten the constraints on the quality of the input data. We chose values σT = 0.03 K and σλ =
Fig. 2. Effect of the duration of the period to be reconstructed on the inversion results (according to simulation data). Ground surface temperature reconstructed by inversion for 4000 (1), 3000 (2), 2000 (3), and 1000 (4) years BP and the ground surface temperature history over the past 4000 years used the basis for the simulation (5) (Demezhko, 2001).
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Fig. 3. Influence of a priori standard deviations for borehole temperatures σT and thermal conductivities σλ on the inversion results (according to simulation results). (1) σT = 0.01 K and σλ = 0.3 W/(m⋅K); (2) σT = 0.03 K and σλ = 0.5 W/(m⋅K); (3) ground surface temperature history adopted as the basis for the simulation.
0.5 W/(m⋅K) for the reconstruction of the Little Ice Age. Most of the data satisfy this condition and allow a reliable reconstruction of this period. With these parameters, the Medieval Optimum turns out to be smoothed. To reconstruct it, we recommend to tighten the constraints on the quality of the input data and use the following standard deviations: σT = 0.01 K and σλ = 0.3 W/(m⋅K). Results of interpretation of temperature logs. Paleotemperature curves were reconstructed from each temperature log using the chosen parameters (Fig. 4). All curves show cooling and with a minimum in the period of 1700–1800 (Little Ice Age after the Medieval Optimum) and the subsequent temperature rise, which is more pronounced in the past century. Figure 5 shows the averaged results for all boreholes. Inversion results for the past millennium show that the Little Ice Age in the Urals was preceded by the Medieval Warm Period, whose maximum temperatures (~1200 AD) were about 0.4 °C lower than or similar to present temperatures. For boreholes located to the north of 54° N, this maximum is more pronounced than that for southern boreholes. The peak of the Little Ice Age was in about 1700– 1750 AD. The surface temperature at the time was to 1.2–3 °C lower than at present. Differences in estimates based on individual temperature logs are likely due to the influence of nonclimatic factors. Significant differences in the changes in the southern and northern parts of the region at that time were not detected. Among the objectives of the joint geothermal study of the IG USC RAS and IG UB RAS were to test existing methods of paleoclimatic interpretation based on a large body of facts, and to compare different methods. Therefore, the same sample of data was processed in the IG UB RAS by the other method proposed in (Demezhko, 2001) for the reconstruction of climate change over the past millennium (Demezhko et al., 2005). In this case, the temperature history is approximated by a step function of constant temperatures within arbitrarily defined time intervals. The sum of the deviations of measured temperatures from theoretical values is minimized. Stability of the solution is provided by progressively increasing the time
Fig. 4. Reconstructed ground surface temperature curves. Thin lines, individual reconstructions; thick line, averaged history; vertical lines, standard error of the mean.
intervals in going back to the past. By varying the temperature within the intervals, one chooses a series of equivalent temperature histories which can effectively explain measured temperature logs with a minimum number of time intervals. The most likely solution and the degree of its uncertainty are determined from the family of equivalent thermal histories. Some of the Southern Urals data have been used previously to study changes over the past few centuries (Golovanova, 2005; Golovanova et al., 2001; Pollack et al., 2003; Stulc et al., 1998). The investigated deep boreholes were different from each other and sometimes were not sufficient for the reconstruction of the Medieval Optimum and earlier events. In the present study, the database on boreholes was significantly enlarged and supplemented by data of the IG UB RAS for the Southern and Middle Urals. This made it possible to characterize a much larger region. To obtain comparable results for all boreholes, we used the same range of depths in the calculations. The resolution of the functional space inversion method for reconstructing events in the past was studied by model simulation. The ground surface temperature curves reconstructed by different methods are similar, but differ somewhat in the position and magnitude of extremes. In particular, the magnitude of the Medieval Warm Period obtained by the functional space inversion method is slightly smaller than that in (Demezhko et al., 2005). This is most likely due to a lack of information on the thermal properties of the subsurface rocks. Comparison of the results obtained using different methods shows that the use of the algorithm proposed by Demezhko (2001) is preferred in an extensive analysis of a number of temperature logs where unified parameters of selection are required and there is scarce information on the geological setting and the thermal properties of rocks. At the same time, in an analysis of data from individual boreholes with a good knowledge geological and thermal knowledge base, it is preferred to use the method of functional space inversion. If there is sufficient a priori information, this method better takes into account the quality of the input data and the real properties of the medium.
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Thus, the temperatures measured in boreholes in the Southern and Middle Urals were used to reconstruct climate changes over the past millennium employing different algorithms of inversion. Ideally, assessing the quality of borehole data during selection requires a large amount of additional information, which is often not available. We think that before using any inversion method, borehole data should be subjected to a rigorous selection. In the selection of high-quality input data, the results obtained by different methods are similar.
Reconstruction of the Pleistocene–Holocene warming in the Southern Urals The first estimate of the magnitude of the Pleistocene– Holocene warming was obtained in the IG USC RAS (Golovanova et al., 2000) from the Il’menskaya-1 borehole (55°00′ N, 60°10′ E), 2000 m deep, located in the Ilmen reserve (Fig. 1). The method of functional space inversion was used (Shen and Beck, 1991). It is known that the perturbations caused by the influence of paleoclimate extend to a much greater depth, so that when geothermal data from relatively shallow boreholes are use, some information is lost and the reconstructed magnitudes of warming are underestimated. To obtain more information using mathematical modeling, the resolution of the method was investigated and a correction was applied to take into account the insufficient depth of the borehole. The magnitude of warming estimated from this borehole is approximately 8.3 °C. Almost at the same time, a temperature history spanning more than 10 thousand years was reconstructed in the IG UB RAS using data from the SG-4 Ural superdeep borehole (Demezhko and Shchapov, 2001). A temperature log to a depth of 4 km was used. The magnitude of warming is estimated at 12 °C. A few more temperature histories were derived from boreholes up to 2 km deep (Demezhko, 2001). However, due to the insufficient depth of the boreholes, the magnitudes of warming reconstructed from them are significantly underestimated and the dates are reduced by several times. More reliable estimates of the magnitude of postglacial warming were derived from an analysis of the distribution of the average geothermal gradient over a larger number of boreholes (Demezhko, 2001). In this case, the magnitude of temperature fluctuations is chosen by solving the direct heat-conduction problem. A latitudinal temperature profile of the end of the Wurm glaciation along the meridian of 60° E was obtained using a large amount of data from the combined database of the IG UB RAS and IG USC RAS, The rate of increase in gradients with depth suggests that post-glacial warming in the Urals was accompanied by an average of 7.7 °C increase in the surface temperature. In the southern part of the region (50–54° N), it was 5.9 °C, and in the northern part (54–58° N), 9.4 °C. The magnitude of warming increases from south to north by about 1 °C/1° of latitude. All of the reconstructions, except for those inferred from the SG-4 Ural superdeep borehole, were made using data from
Fig. 5. Averaged reduced curves of ground surface temperature. Average for all reconstructions for the Middle and Southern Urals (1); for the southern part (2), for the northern part (3) of the study area.
boreholes of insufficient depth. Therefore, each new possibility of reconstructing ground surface temperature using data from deep boreholes containing more complete information is essential. New estimates of paleoclimate changes were obtained using data from the deep Leuzinskaya-1 parametric borehole located in the Bashkir part of the Yuryuzan–Sylva depression of the Cis-Ural foredeep (55°25′ N, 59°00′ E) (Figs. 1, 6). The temperature log recorded in April 2002 to a depth of 4512 m was used. Before the temperature measurement, the borehole was suspended for over one month and more than three months passed after the drilling. The investigated part of the section includes Paleozoic deposits to a depth of 3818 m and underlying Upper Riphean deposits. Climate reconstruction was performed using the method of inversion in a functional L-dimensional space (Shen and Beck, 1991). The quality of the input geothermal data from the borehole allows the use of the standard deviations for temperature σT = 0.05 °C in the inversion. Thermal conductivity of rocks from the borehole was not determined, but a detailed description of the main lithological and stratigraphic sequences of the Southern Urals and the eastern part of the East European platform was obtained in previous studies. Thermal properties of the rocks were adopted in accordance with the data from neighboring boreholes located in the same structural-tectonic zone, with the particular section of the borehole taken into account. The thermal conductivity was taken as σλ = 0.3 W/(m⋅K). From the results of calculations, the magnitude of the Pleistocene–Holocene warming is estimated at about 11 °C (Fig. 7). This result is comparable, in the order of magnitude, to a previous estimate derived from the Il’menskaya-1 borehole 2000 m deep (8.3 °C) (Golovanova et al., 2000). At the same time, in the southeast direction (from the Leuzinskaya-1 borehole to the Il’menskaya-1 borehole), the magnitude decreases slightly, confirming existing information on the spatial distribution of the magnitude of the Pleistocene–Holocene warming in northern Eurasia. According to palynological estimates confirmed by geothermal data, the center of the warming event was in the North Atlantic (Demezhko et al., 2007). Warming isolines descend by about
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Fig. 6. Geothermal borehole data on the Leuzinskaya-1 (a) and Il’menskaya-1 (b). (1) temperature log, (2) thermal conductivity distribution.
5° of latitude with a shift by 20° of longitude to the west of the meridian of the Urals. The paleoclimate reconstruction based on data from the deep boreholes of the Urals in good agreement with the model, thus confirming its validity. The independent estimate of the magnitude of post-glacial warming in the Urals derived from an analysis of the geothermal gradient averaged over a large number of boreholes using a statistical approach is also in good agreement with the reported result (Demezhko, 2001). This result is only the third estimate from geothermal data in the Urals after the estimates obtained previously from the Il’menskaya-1 borehole (temperature log to 2000 m) and the SG-4 borehole (temperature log to 4000 m).
Another possible approach to the assessment of past changes in climate is a joint version of data from groups of boreholes. The approach is based on the assumption that in a region with the same climatic history, joint inversion suppresses random “thermal noise” and reveals the common climatic signal. The functional space inversion method (Shen and Beck, 1991) used in our study allows an analysis of data from a group of boreholes to more clearly identify the regional surface temperature signal. The Leuzinskaya-1 and Il’menskaya-1 boreholes are relatively closely spaced apart from each other. Therefore, for the averaged characteristic of this part of the study region, we performed a joint inversion in the functional space of the data from these boreholes. The calculated magnitude of the postglacial warming is 10 °C (Fig. 7). The results provide a more reasonable estimate of the history of climate change in the Southern Urals.
Conclusions
Fig. 7. Results inversion borehole data Il’menskaya-1 and Leusinskaya-1. Ground surface temperature history reconstructed from the temperature log of the Leuzinskaya-1 (1) and from the temperature log of the Il’menskaya-1 (2) boreholes; (3) result of joint inversion.
Thus, the ground surface temperature history in the Middle and Southern Urals was reconstructed for various time intervals using the functional space inversion method. The resolution of the method for the reconstruction of various climatic events in the past was studied by mathematical modeling. The results of numerical modeling were used to choose parameters specified a priori and the required duration of the period to be reconstructed. Comparison of the ground surface temperature histories with the results of other inversion schemes shows that in the selection of high-quality input data, the results obtained by different methods are similar. The
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geological conditions in the Urals, with outcrops of Paleozoic crystalline rocks, make this region favorable for the study of global climate change based on geothermal data. Geothermal data obtained in the Urals may serve as a reference for assessing the possibilities of different methods of climate reconstruction. According to the inversion results, ground surface temperature in the Urals at the maximum of the Medieval Warm Period in 1100–1200 was approximately the same as the present temperature, and at the minimum of the Little Ice Age around 1720, it was 1.2–3 °C lower than at present. The subsequent rise in temperature was more pronounced in the past century. The magnitude of the Pleistocene–Holocene warming in the Southern Urals reconstructed from temperature logs from two deep boreholes, Leuzinskaya-1 and Il’menskaya-1, is 10–11 °C. The geothermal reconstructions of paleoclimate in the Urals are in fairly good agreement with the data obtained by other methods for the Northern Hemisphere as a whole and for adjacent areas (Climate Change..., 1990; Demezhko, 2003; Klimenko et al., 1996; Moberg et al., 2005; Nemkova and Klimanov, 1988) and with weather data for the entire observation period. The results are of independent significance for the study and prognosis of climate change. In addition, they can be used to take into account the distortions of the geothermal field due to features of the climatic history, and to refine existing knowledge of the heat flux distribution in the study area.
References Climate Change: The GPCC Scientific Assessment. Report of Working Group I of the Intergovernmental Panel on Climate Change, 1990. Cambridge Univ. Press, New York. Demezhko, D.Yu., 2001. Geothermal Method of Paleoclimate Reconstruction (in the Urals as an Example) [in Russian]. URO RAN, Yekaterinburg. Demezhko, D.Yu., 2003. Assessing the role of human factors in the current climate change. Geothermal data analysis, in: Nuclear Geophysics. Geophysical Studies of the Lithosphere. Geothermy. Second Scientific Conference Dedicated to The Memory of Yu.P. Bulashevich. Inst. Geofiziki, URO RAN, Yekaterinburg, pp. 34–35. Demezhko, D.Yu., Shchapov, V.A., 2001. 80.00 years ground surface temperature history inferred from the temperature-depth log measured in the superdeep hole SG-4 (the Urals, Russia). Global Planet. Change 29, 219–230. Demezhko, D.Yu., Utkin, V.I., Shchapov, V.A., Golovanova, I.V., 2005. Variations in the Earth’s surface temperature in the Urals during the last millennium based on borehole temperature data. Dokl. Earth Sci. 403 (5), 764–766. Demezhko, D.Yu., Ryvkin, D.G., Outkin, V.I., Duchkov, A.D., Balobaev, V.T., 2007. Spatial distribution of Pleistocene/Holocene warming
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magnitudes in Northern Eurasia inferred from geothermal data. Climate of the Past 3, 559–568. Derevyanko, A.P. (Ed.), 2008. Global Changes in the Climate and Natural Environment of the Late Cenozoic in Siberia [in Russian]. Izd. SO RAN, Novosibirsk. Golovanova, I.V., 2005. Thermal Field of the Southern Urals [in Russian]. Nauka, Moscow. Golovanova, I.V., Valieva, R.Yu., 2006. New estimates of the magnitude of the Wurm/Holocene warming in the Southern Urals fromgeothermal data, in: Geological Collection No. 5. Information Materials [in Russian]. DizainPoligrafServis, Ufa, pp. 201–203. Golovanova, I.V., Selezniova, G.V., Smorodov, E.A., 2000. Palaeoclimate change in the Southern Urals inferred from deep borehole geothermal measurements: the climate variations during the last Pleistocene Ice-Age, in: Geothermics at the Turn of the Century: Abstracts. University of Evora, Portugal, April 3–7, 2000, p. 26. Golovanova, I.V., Harris, R.N., Selezniova, G.V., Stulc, P., 2001. Evidence of climatic warming in the southern Urals region derived from borehole temperatures and meteorological data. Global Planet. Change 29, 167– 188. Klimenko, V.V., Klimanov, V.A., Fedorov, M.V., 1996. History of the mean temperature of the Northern Hemisphere over the past 11,000 years. Dokl. Akad. Nauk, 348 (1), 111–114. Moberg, A., Sonechkin, D.M., Holmgren, K., Datsenko, N.M., Karl’en, W., 2005. Highly variable northern hemisphere temperatures reconstructed from low-and high-resolution proxy data. Nature 433, 613–617. Nemkova, V.K. Klimanov, V.A.,1988. Climate characteristics of the Bashkir Urals in the Holocene, in: Some Issues of Biostratigraphy, Paleomagnetism, and Tectonics of the Urals in the Cenozoic [in Russian]. BSC, Ural Branch of the USSR, Ufa, pp.65–71. Pollack, H.N., Shen, Y., Huang, S., 1996. Inference of ground surface temperature history from subsurface temperature data: Interpreting ensembles of borehole logs. Pure Appl. Geophys. 147 (3), 537–550. Pollack, H.N., Demezhko, D.Yu., Duchkov, A.D., Golovanova, I.V., Huang, S., Shchapov, V.A., Smerdon, J., 2003. Surface temperature trends in Russia over the past five centuries reconstructed from borehole temperatures. J. Geophys. Res. 108 (B4). doi: 10.1092/2002JB002154 Sal’nikov, V.E., 1984. Geothermal Regime of the Southern Urals [in Russian]. Nauka, Moscow. Shen, P.Y., Beck, A.E., 1991. Least squares inversion of borehole temperature measurements in functional space. J. Geophys. Res. 96 (B12), 19,965– 19,979. Shen, P.Y., Pollack, H.N., Huang, S., Wang, K., 1995. Effects of subsurface heterogeneity on the inference of climate change from borehole temperature data: Model studies and field examples from Canada. J. Geophys. Res. 100 (B4), 6383–6396. Stulc, P., Golovanova, I.V., Selezniova, G.V., 1998. Climate change record in the Earth–example of borehole data analysis in the Urals region, Russia. Phys. Chem. Earth 23 (9–10), 1109–1114. Sukhorukova, K.V., Duchkov, A.A., 1998. Reconstruction of the ground surface temperature in the last centuries by thermograms for South-Siberian boreholes. Geologiya i Geofizika (Russian Geology and Geophysics) 39 (8), 1121–1129 (1123–1131). Wang, K., Lewis, T.J., Belton, D.S., Sheen, Y., 1994. Difference in recent ground surface warming in eastern and western Canada: Evidence from borehole temperatures. Geophys. Res. Lett. (21), 2689–2692.
Editorial responsibility: A.D. Duchkov
Russian Geology and Geophysics 53 (2012) 1366–1373 www.elsevier.com/locate/rgg
Climate reconstruction in the Urals from geothermal data I.V. Golovanova a,*, R.Yu. Sal’manova a, D.Yu. Demezhko b a b
Institute of Geology, Ufimian Scientific Center of the Russian Academy of Sciences, ul. K. Marksa 16/2, Ufa, 450000, Russia Institute of Geophysics, Ural Branch of the Russian Academy of Sciences, ul. Amundsena 100, Yekaterinburg, 620016, Russia Received 5 April 2011; accepted 16 February 2012
Abstract Results of paleoclimatic analysis of geothermal data in the Middle and Southern Urals for different time intervals are presented. Climate reconstruction for the past millennium was made using data from 44 boreholes, and the magnitude of the Wurm–Holocene warming event was estimated based on data from two deep boreholes. The method of functional space inversion was used. The resolution of the method for reconstruction of various climatic events in the past was investigated. Parameters specified a priori and the required duration of the period to be reconstructed were chosen from the results of numerical modeling. According to the inversion results, the ground surface temperature at the maximum of the Medieval Warm Period in 1100–1200 was approximately the same as the present temperature, and at the minimum of the Little Ice Age around 1720, it was 1.2–3 °C lower than at present. The subsequent temperature rise was more pronounced in the past century. The magnitude of the Wurm–Holocene warming event, reconstructed using data from two deep boreholes is 10–11 °C. © 2012, V.S. Sobolev IGM, Siberian Branch of the RAS. Published by Elsevier B.V. All rights reserved. Keywords: geothermy; temperature; inversion; paleoclimate; climate reconstruction; Urals
Introduction It is known that the present distribution of the ground temperature and heat flux is distorted by the influence of past climate changes to a depth of several kilometers. Temperature measurement data obtained in ore-prospecting boreholes can be used to reconstruct the ground surface temperature history for periods ranging from a few hundred to a few thousand years. Refining knowledge of the magnitudes of past warmings is of great importance for the solution of the question about the nature of the present warming and prediction of future climate changes. In the Urals, paleoclimatic studies began almost simultaneously and independently at the Institute of Geology, Ufimian Scientific Center of the RAS (Ufa) and the Institute of Geophysics, Ural Branch of the RAS (Yekaterinburg). Subsequently, the geothermal groups of the two institutes conducted research on the history of climate change in the Urals in close contact with each other. It was supposed to test existing methods of paleoclimatic interpretation using a large body of facts and perform a comparative analysis of different methods of research. The results of paleoclimatic analysis of geothermal data for the Middle and Southern Urals obtained at the Institute of Geology, Ufimian Scientific Center. * Corresponding author. E-mail address: [email protected] (I.V. Golovanova)
Reconstruction of the surface temperature in the Urals over the past millennium by a geothermal method Method of interpretation. The geothermal method of paleoclimate reconstruction provides direct estimates of mean annual paleotemperatures of rocks at the bottom of the layer of annual fluctuations (at depths of 15–25 m) (Derevyanko, 2008). In the formulation of the problem, this temperature is taken as the ground surface temperature. Its variation at this depth reflects the main trends in surface air temperature. The difference between them can vary depending on latitude and local conditions, and its determination requires a special analysis. The mean annual surface air temperature in northern Eurasia is typically 4–5 °C lower than the rock temperature at the bottom of the layer of annual fluctuations mainly due to the warming effect of snow cover in winter. For the Southern Urals, the rock temperature at a depth of 25 m (T25) is related to the mean annual air temperature (Ta) as follows (Sal’nikov, 1984): T25 = 1.33 Ta + 2.57. Using this relation and based on information on the mean annual air temperature, the temperature at 25 m was calculated and a schematic map was constructed. In the study area, the present mean annual temperature at the bottom of the layer of
1068-7971/$ - see front matter D 201 2, V . S. S o bolev IGM, Siberian Branch of the RAS. Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.rgg.2012.10.009 +
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Fig. 1. Location of the studied boreholes. 1, single boreholes; 2, groups of boreholes.
annual fluctuations range from 3.5 to 11 °C. The paleoclimate reconstruction method used in this study provides direct estimates of paleotemperatures or its deviation from the present temperature. The ground surface temperature in the Urals over the past millennium was reconstructed using geothermal data of the Institute of Geology, Ufimian Scientific Center of RAS (IRAS IG USC RAS) and the Institute of Geophysics, Ural Branch of RAS (IG UB RAS) obtained in studies of the heat flux distribution in 1969–1996. As a result of a preliminary analysis of more than 200 data of borehole temperature measurements, a test sample of 44 temperature logs was formed which satisfied the following conditions: the depth was not less than 700 m; the temperature logs correspond most closely to the one-dimensional heat transfer model, i.e., they were the least affected by nonclimatic factors–groundwater flows and seepage, topography, local surface anomalies, etc. The selected temperature logs were recorded in an area
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characterized by a single geological structure and a common climate history. Temperature measurements were made with a step of 10 or 20 m and an accuracy of up to 0.02 °C. Thermal conductivity of rock samples was studied in detail, except in the uppermost part composed of weathered rocks. For a number of boreholes, a layered subsurface model was used if the thermal conductivity varied greatly in its different parts. In other cases, it was assumed that the medium is homogeneous, and for inversion, the mean thermal conductivity was used. The temperature logs included in the sample uniformly characterize the meridionally elongated area (50°10′–58°22′ N, 57°40′–62°45′ E) is located mainly on the eastern slope of the Urals (Table 1, Fig. 1). The ground surface temperature history was reconstructed using the method of functional space inversion (FSI) (Shen and Beck, 1991). The problem is solved under the assumptions that the medium is one-dimensional and there is no convective heat transfer (Shen and Beck, 1991; Sukhorukova and Duchkov, 1998). An advantage of this method is that all the parameters of the medium can be determined simultaneously. To solve the problem, it is necessary to a priori define the model parameters such as the surface temperature, and the thermophysical properties of rocks, the heat flux density at the bottom, the initial temperature distribution in the borehole, and the variance of these properties. It is known that the more accurately the initial approximation is defined and the ranges of input parameters are narrowed, the closer the result of the inversion to the true value. Therefore, the best results can be obtained from boreholes for which there are fairly complete and qualitative experimental data on the temperature and thermal properties of the medium. The geothermal data used for climate reconstruction are complicated by thermal noise and measurement errors, which in the inversion can be interpreted as false climatic signals. The inversion algorithm allows the choice of the degree of influence of random factors on the results, which can be done by specifying constraints on primary data. The standard deviations for a priori defined borehole temperatures and thermal conductivities have a particularly strong influence. To suppress false signals, it is recommended (Shen et al., 1995) to relax the constraints imposed on a priori input parameters (especially temperature and thermal conductivity). This reduces the effect of noise, but at the expense of a loss of part of the climatic signal, resulting in smoothed ground surface temperature curves with underestimated magnitudes of climatic events. In contrast, inversion with highly constrained input parameters gives a ground surface temperature curve with good resolution. In this case, variations in the input data that are indeed related to climate change and noise are interpreted as a result of past climate changes. Data obtained from several boreholes in one region may differ greatly from each other. If we assume that the ground surface temperature in the study region has the same history, the noise is then due to local geological conditions, i.e., is random. Then, the effect of noise can be reduced by joint inversion of data from a group of
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Table 1. Location of the boreholes and its characteristics No.
Notation
Name of area, site, deposit
Coordinate
Depth of measurement (min–max), m
N
E
1
kr8802
Krasnoural’sk
58°22′
60°04′
7–907
2
ku622
Kushva
58°19′
59°52′
50–840
3
vol0477
Volkovskoe copper-pyrite deposit
58°10′
59°51′
20–965
4
vol0777
Volkovskoe copper-pyrite deposit
58°10′
59°51′
20–967
5
br3420
Berezovskii
56°52′
60°50′
20–780
6
dg2003
Degtyarsk
56°43′
60°05′
20–1260
7
ma8002
Mayak Scientific-Production Association
55°41′
60°44′
40–760
8
ma8304
Mayak Scientific-Production Association
55°41′
60°44′
20–1112
9
ma8505
Mayak Scientific-Production Association
55°41′
60°44′
18–1218
10
kuv165
Kuvatal
55°40′
60°10′
20–1340
11
kuv180
Kuvatal
55°39′
60°03′
30–848
12
kuv271
Kuvatal
55°39′
60°03′
20–1520
13
kuv283
Kuvatal
55°39′
60°03′
20–1200
14
ter27
Termenevskii
55°04′
58°42′
80–1045
15
ilmen1
Il’menskaya
55°00′
60°10′
20–1945
16
krug313
Kruglogorskaya
54°55′
59°55′
40–870
17
uch1755
Severo-Uchalinskaya
54°22′
59°24′
10–910
18
uch1754
Severo-Uchalinskaya
54°20′
59°25′
20–720
19
uvl62
Uvel’skaya
54°15′
60°50′
10–660
20
rech5869
Rechnoi
54°05′
59°22′
10–940
21
u–ch5658
Yuzhno-Chebachii
54°04′
59°24′
10–600
22
url3292
Urlyadinskaya
54°00′
59°25′
10–1135
23
sala1790
Salavat
53°40′
58°30′
20–680
24
alex5937
Severo-Aleksandrinskaya
53°35′
59°20′
10–960
25
mg10
Magnitogorsk
53°32′
59°10′
20–1050
26
kas2011
Kassel’skaya
53°33′
59°16′
10–1360
27
mag2056
Magnitogorsk
53°30′
59°05′
80–1530
28
mag2066
Magnitogorsk
53°25′
59°05′
20–1420
29
khudo184
Severo-Khudolazovskii
52°57′
58°33′
10–1420
30
khudo187
Severo-Khudolazovskii
52°57′
58°33′
120–1150
31
kzh4
KuzhaiS4
52°35′
62°45′
40–940
32
kzh8
KuzhaiS8
52°35′
62°45′
40–900
33
bg2351
Bogachevka
52°25′
58°20′
50–750
34
ul4118
Yulaly
52°25′
58°22′
30–600
35
spod812
Severo-Podol’sk
52°05′
58°28′
50–950
36
podol32
Podol’sk
52°05′
58°20′
20–800
37
podol34
Podol’sk
52°05′
58°20′
20–921
38
medn935
Mednogorskoe
51°30′
57°40′
40–850
39
medn936
Mednogorskoe
51°30′
57°40′
30–870
40
asch5107
Ashchebutak
51°05′
59°10′
20–680
41
osen4480
Osennee
50°55′
59°35′
10–1010
42
kok1124
Koktau
50°30′
59°05′
40–980
43
chrom79
Chromtau
50°10′
58°25′
20–860
44
chrom192
Chromtau
50°10′
58°25′
20–940
45
leuz1
Leuzinskaya
55°25′
59°00′
5–4512
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boreholes (Wang et al., 1994) or by averaging the results of the inversion (Pollack et al., 1996). In this case, the regional signal of ground surface temperature is more pronounced. Reliability of geothermal reconstructions of ground surface temperature histories is limited by two groups of factors (Demezhko, 2001). The first group includes the physical limitations of the method: resolution and the maximum length of the temperature history. The second group includes conditions due to the properties of experimental data, namely, the uncertainty caused by the influence of nonclimatic factors, thermal heterogeneity of the medium, and the possible influence of fluid seepage. The time resolution of the geothermal method of paleoclimate reconstruction worsens as one goes farther back in time. According to the analysis carried out by Demezhko (2001), the resolution of the method can be estimated by using the relative duration of a climatic episode. This parameter is the ratio of the duration of a climatic episode to the time elapsed from the start of the episode to the time of borehole temperature measurement. It is shown that only episodes with a relative duration of more than 0.5 leave an imprint on the geothermal field and can be reconstructed from geothermal data. Among these are the Wurm glaciation (relative duration of 0.88) and the Little Ice Age, which lasted from the 14th century until the end of the 19th century (relative duration of 0.82). It is possible to estimate the average temperature conditions for the period of the past 2300–800, including the optimum of the Roman period and the Medieval Warm Period, separated by a short-term cooling (relative duration of 0.64). The Medieval Warm Period can be reconstructed separately (relative duration of 0.33). Therefore, one can hope for the maximum reliability of temperature estimates of only some climatic events: the Wurm glaciation, the warming magnitude in the early Holocene (about 10,000 years BP) to the optimum of the Holocene, the Medieval Warm Period with Roman Optimum (2300– 800 years BP), the Little Ice Age (600–150 years BP), and temperature trend of the XXth century (Demezhko, 2001). Note that there are other ways to estimate the time resolution of the geothermal method. According to (Demezhko, 2001), the maximum depth of the anomaly are 7200 m for the Wurm glaciation, 1080 m for the Roman Optimum with the Medieval Warm Period, and 580 m for the Little Ice Age. Despite the rather high quality of the input data used, they certainly contain thermal noise, whose influence can be reduced by averaging the inversion results. Nonclimatic factors were suppressed by simultaneous reconstruction of the common temperature history of a number of temperature logs or by the functional space inversion method or by averaging individual ground surface temperature curves. Resolution of the method. Before applying the method to borehole data, we studied its resolution for the reconstruction of various climatic events in the past. For this, we simulated the influence of paleoclimate over the past 250 thousand years on the temperature distribution with depth (direct problem). Published data on deviations of the global mean surface
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air temperature in the Northern Hemisphere from the present temperature (Demezhko, 2001) were used as the boundary condition on the Earth’s surface. The obtained temperature distribution with depth was then used for inversion (inverse problem). From the results of the numerical simulation, we chose parameters that must be specified a priori and the required duration of the period to be reconstructed. The effect of the duration of the period on the inversion results (from simulation results) are presented in Fig. 2. The figure shows the ground surface temperatures reconstructed by inversion for 4000, 3000, 2000, and 1000 years BP and the ground surface temperature history over the past 4000 years used as the basis in the simulation. In the calculation, we used a priori standard deviations for borehole temperature σT = 0.01 K and thermal conductivity σλ = 0.3 W/(m⋅K). Boreholes 800 m deep, in principle, allow climatic history reconstruction back to several thousand years ago. However, the result depends on the chosen duration of the inversion period. The most reliable reconstructions are obtained for climatic events over the last half or one-third of the chosen period. This is because the more remote part is affected by the constraints imposed on the ground surface temperature in the solution. For example, inversion for up to 1000 years BP, as was often done in previous studies, provides more or less reliable reconstructions of events in the past 300–500 years, i.e., the Little Ice Age. To reliably reconstruct the Medieval Maximum, inversion should be performed for a period of at least 3000 year BP. It should however be recognized that the borehole depth is not sufficient to reconstruct the Medieval Warm Period with the Roman Optimum. Only the last part of this combined climatic period can be reconstructed. Of great importance is the choice of a priori standard deviations for borehole temperatures σT and thermal conductivities σλ (Fig. 3). Climatic signals attenuate with increasing depth. Therefore, for the reconstruction of more distant events, it is required to tighten the constraints on the quality of the input data. We chose values σT = 0.03 K and σλ =
Fig. 2. Effect of the duration of the period to be reconstructed on the inversion results (according to simulation data). Ground surface temperature reconstructed by inversion for 4000 (1), 3000 (2), 2000 (3), and 1000 (4) years BP and the ground surface temperature history over the past 4000 years used the basis for the simulation (5) (Demezhko, 2001).
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Fig. 3. Influence of a priori standard deviations for borehole temperatures σT and thermal conductivities σλ on the inversion results (according to simulation results). (1) σT = 0.01 K and σλ = 0.3 W/(m⋅K); (2) σT = 0.03 K and σλ = 0.5 W/(m⋅K); (3) ground surface temperature history adopted as the basis for the simulation.
0.5 W/(m⋅K) for the reconstruction of the Little Ice Age. Most of the data satisfy this condition and allow a reliable reconstruction of this period. With these parameters, the Medieval Optimum turns out to be smoothed. To reconstruct it, we recommend to tighten the constraints on the quality of the input data and use the following standard deviations: σT = 0.01 K and σλ = 0.3 W/(m⋅K). Results of interpretation of temperature logs. Paleotemperature curves were reconstructed from each temperature log using the chosen parameters (Fig. 4). All curves show cooling and with a minimum in the period of 1700–1800 (Little Ice Age after the Medieval Optimum) and the subsequent temperature rise, which is more pronounced in the past century. Figure 5 shows the averaged results for all boreholes. Inversion results for the past millennium show that the Little Ice Age in the Urals was preceded by the Medieval Warm Period, whose maximum temperatures (~1200 AD) were about 0.4 °C lower than or similar to present temperatures. For boreholes located to the north of 54° N, this maximum is more pronounced than that for southern boreholes. The peak of the Little Ice Age was in about 1700– 1750 AD. The surface temperature at the time was to 1.2–3 °C lower than at present. Differences in estimates based on individual temperature logs are likely due to the influence of nonclimatic factors. Significant differences in the changes in the southern and northern parts of the region at that time were not detected. Among the objectives of the joint geothermal study of the IG USC RAS and IG UB RAS were to test existing methods of paleoclimatic interpretation based on a large body of facts, and to compare different methods. Therefore, the same sample of data was processed in the IG UB RAS by the other method proposed in (Demezhko, 2001) for the reconstruction of climate change over the past millennium (Demezhko et al., 2005). In this case, the temperature history is approximated by a step function of constant temperatures within arbitrarily defined time intervals. The sum of the deviations of measured temperatures from theoretical values is minimized. Stability of the solution is provided by progressively increasing the time
Fig. 4. Reconstructed ground surface temperature curves. Thin lines, individual reconstructions; thick line, averaged history; vertical lines, standard error of the mean.
intervals in going back to the past. By varying the temperature within the intervals, one chooses a series of equivalent temperature histories which can effectively explain measured temperature logs with a minimum number of time intervals. The most likely solution and the degree of its uncertainty are determined from the family of equivalent thermal histories. Some of the Southern Urals data have been used previously to study changes over the past few centuries (Golovanova, 2005; Golovanova et al., 2001; Pollack et al., 2003; Stulc et al., 1998). The investigated deep boreholes were different from each other and sometimes were not sufficient for the reconstruction of the Medieval Optimum and earlier events. In the present study, the database on boreholes was significantly enlarged and supplemented by data of the IG UB RAS for the Southern and Middle Urals. This made it possible to characterize a much larger region. To obtain comparable results for all boreholes, we used the same range of depths in the calculations. The resolution of the functional space inversion method for reconstructing events in the past was studied by model simulation. The ground surface temperature curves reconstructed by different methods are similar, but differ somewhat in the position and magnitude of extremes. In particular, the magnitude of the Medieval Warm Period obtained by the functional space inversion method is slightly smaller than that in (Demezhko et al., 2005). This is most likely due to a lack of information on the thermal properties of the subsurface rocks. Comparison of the results obtained using different methods shows that the use of the algorithm proposed by Demezhko (2001) is preferred in an extensive analysis of a number of temperature logs where unified parameters of selection are required and there is scarce information on the geological setting and the thermal properties of rocks. At the same time, in an analysis of data from individual boreholes with a good knowledge geological and thermal knowledge base, it is preferred to use the method of functional space inversion. If there is sufficient a priori information, this method better takes into account the quality of the input data and the real properties of the medium.
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Thus, the temperatures measured in boreholes in the Southern and Middle Urals were used to reconstruct climate changes over the past millennium employing different algorithms of inversion. Ideally, assessing the quality of borehole data during selection requires a large amount of additional information, which is often not available. We think that before using any inversion method, borehole data should be subjected to a rigorous selection. In the selection of high-quality input data, the results obtained by different methods are similar.
Reconstruction of the Pleistocene–Holocene warming in the Southern Urals The first estimate of the magnitude of the Pleistocene– Holocene warming was obtained in the IG USC RAS (Golovanova et al., 2000) from the Il’menskaya-1 borehole (55°00′ N, 60°10′ E), 2000 m deep, located in the Ilmen reserve (Fig. 1). The method of functional space inversion was used (Shen and Beck, 1991). It is known that the perturbations caused by the influence of paleoclimate extend to a much greater depth, so that when geothermal data from relatively shallow boreholes are use, some information is lost and the reconstructed magnitudes of warming are underestimated. To obtain more information using mathematical modeling, the resolution of the method was investigated and a correction was applied to take into account the insufficient depth of the borehole. The magnitude of warming estimated from this borehole is approximately 8.3 °C. Almost at the same time, a temperature history spanning more than 10 thousand years was reconstructed in the IG UB RAS using data from the SG-4 Ural superdeep borehole (Demezhko and Shchapov, 2001). A temperature log to a depth of 4 km was used. The magnitude of warming is estimated at 12 °C. A few more temperature histories were derived from boreholes up to 2 km deep (Demezhko, 2001). However, due to the insufficient depth of the boreholes, the magnitudes of warming reconstructed from them are significantly underestimated and the dates are reduced by several times. More reliable estimates of the magnitude of postglacial warming were derived from an analysis of the distribution of the average geothermal gradient over a larger number of boreholes (Demezhko, 2001). In this case, the magnitude of temperature fluctuations is chosen by solving the direct heat-conduction problem. A latitudinal temperature profile of the end of the Wurm glaciation along the meridian of 60° E was obtained using a large amount of data from the combined database of the IG UB RAS and IG USC RAS, The rate of increase in gradients with depth suggests that post-glacial warming in the Urals was accompanied by an average of 7.7 °C increase in the surface temperature. In the southern part of the region (50–54° N), it was 5.9 °C, and in the northern part (54–58° N), 9.4 °C. The magnitude of warming increases from south to north by about 1 °C/1° of latitude. All of the reconstructions, except for those inferred from the SG-4 Ural superdeep borehole, were made using data from
Fig. 5. Averaged reduced curves of ground surface temperature. Average for all reconstructions for the Middle and Southern Urals (1); for the southern part (2), for the northern part (3) of the study area.
boreholes of insufficient depth. Therefore, each new possibility of reconstructing ground surface temperature using data from deep boreholes containing more complete information is essential. New estimates of paleoclimate changes were obtained using data from the deep Leuzinskaya-1 parametric borehole located in the Bashkir part of the Yuryuzan–Sylva depression of the Cis-Ural foredeep (55°25′ N, 59°00′ E) (Figs. 1, 6). The temperature log recorded in April 2002 to a depth of 4512 m was used. Before the temperature measurement, the borehole was suspended for over one month and more than three months passed after the drilling. The investigated part of the section includes Paleozoic deposits to a depth of 3818 m and underlying Upper Riphean deposits. Climate reconstruction was performed using the method of inversion in a functional L-dimensional space (Shen and Beck, 1991). The quality of the input geothermal data from the borehole allows the use of the standard deviations for temperature σT = 0.05 °C in the inversion. Thermal conductivity of rocks from the borehole was not determined, but a detailed description of the main lithological and stratigraphic sequences of the Southern Urals and the eastern part of the East European platform was obtained in previous studies. Thermal properties of the rocks were adopted in accordance with the data from neighboring boreholes located in the same structural-tectonic zone, with the particular section of the borehole taken into account. The thermal conductivity was taken as σλ = 0.3 W/(m⋅K). From the results of calculations, the magnitude of the Pleistocene–Holocene warming is estimated at about 11 °C (Fig. 7). This result is comparable, in the order of magnitude, to a previous estimate derived from the Il’menskaya-1 borehole 2000 m deep (8.3 °C) (Golovanova et al., 2000). At the same time, in the southeast direction (from the Leuzinskaya-1 borehole to the Il’menskaya-1 borehole), the magnitude decreases slightly, confirming existing information on the spatial distribution of the magnitude of the Pleistocene–Holocene warming in northern Eurasia. According to palynological estimates confirmed by geothermal data, the center of the warming event was in the North Atlantic (Demezhko et al., 2007). Warming isolines descend by about
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Fig. 6. Geothermal borehole data on the Leuzinskaya-1 (a) and Il’menskaya-1 (b). (1) temperature log, (2) thermal conductivity distribution.
5° of latitude with a shift by 20° of longitude to the west of the meridian of the Urals. The paleoclimate reconstruction based on data from the deep boreholes of the Urals in good agreement with the model, thus confirming its validity. The independent estimate of the magnitude of post-glacial warming in the Urals derived from an analysis of the geothermal gradient averaged over a large number of boreholes using a statistical approach is also in good agreement with the reported result (Demezhko, 2001). This result is only the third estimate from geothermal data in the Urals after the estimates obtained previously from the Il’menskaya-1 borehole (temperature log to 2000 m) and the SG-4 borehole (temperature log to 4000 m).
Another possible approach to the assessment of past changes in climate is a joint version of data from groups of boreholes. The approach is based on the assumption that in a region with the same climatic history, joint inversion suppresses random “thermal noise” and reveals the common climatic signal. The functional space inversion method (Shen and Beck, 1991) used in our study allows an analysis of data from a group of boreholes to more clearly identify the regional surface temperature signal. The Leuzinskaya-1 and Il’menskaya-1 boreholes are relatively closely spaced apart from each other. Therefore, for the averaged characteristic of this part of the study region, we performed a joint inversion in the functional space of the data from these boreholes. The calculated magnitude of the postglacial warming is 10 °C (Fig. 7). The results provide a more reasonable estimate of the history of climate change in the Southern Urals.
Conclusions
Fig. 7. Results inversion borehole data Il’menskaya-1 and Leusinskaya-1. Ground surface temperature history reconstructed from the temperature log of the Leuzinskaya-1 (1) and from the temperature log of the Il’menskaya-1 (2) boreholes; (3) result of joint inversion.
Thus, the ground surface temperature history in the Middle and Southern Urals was reconstructed for various time intervals using the functional space inversion method. The resolution of the method for the reconstruction of various climatic events in the past was studied by mathematical modeling. The results of numerical modeling were used to choose parameters specified a priori and the required duration of the period to be reconstructed. Comparison of the ground surface temperature histories with the results of other inversion schemes shows that in the selection of high-quality input data, the results obtained by different methods are similar. The
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geological conditions in the Urals, with outcrops of Paleozoic crystalline rocks, make this region favorable for the study of global climate change based on geothermal data. Geothermal data obtained in the Urals may serve as a reference for assessing the possibilities of different methods of climate reconstruction. According to the inversion results, ground surface temperature in the Urals at the maximum of the Medieval Warm Period in 1100–1200 was approximately the same as the present temperature, and at the minimum of the Little Ice Age around 1720, it was 1.2–3 °C lower than at present. The subsequent rise in temperature was more pronounced in the past century. The magnitude of the Pleistocene–Holocene warming in the Southern Urals reconstructed from temperature logs from two deep boreholes, Leuzinskaya-1 and Il’menskaya-1, is 10–11 °C. The geothermal reconstructions of paleoclimate in the Urals are in fairly good agreement with the data obtained by other methods for the Northern Hemisphere as a whole and for adjacent areas (Climate Change..., 1990; Demezhko, 2003; Klimenko et al., 1996; Moberg et al., 2005; Nemkova and Klimanov, 1988) and with weather data for the entire observation period. The results are of independent significance for the study and prognosis of climate change. In addition, they can be used to take into account the distortions of the geothermal field due to features of the climatic history, and to refine existing knowledge of the heat flux distribution in the study area.
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Editorial responsibility: A.D. Duchkov