Ground-air temperature tracking and multi-year cycles in the subsurface temperature time series at geothermal climate-change observatory

Čermák, Vladimír; Bodri, L.; Šаfanda, Jan; Krešl, Milan; Dědeček, Petr

doi:10.1007/s11200-013-0356-2

Cited by 14 publications

(13 citation statements)

References 48 publications

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“…Under grassy cover, the slope of the ∆T-radiation relationship is negative. Under a "barren" surface (such as sand or rock), the slope value is positive [1]. Similarly, a generally positive linear correlation between the SGT-SAT offset and observed incident solar radiation was found at Emigrant Pass Observatory (EPO), Utah [20], where the authors attributed the observed phenomenon to local specific surface conditions, such as the lack of vegetation at the surface and the low porosity of the local granite medium.…”

Section: Mixed Orbitsmentioning

confidence: 80%

“…Cermak et al, who applied Recurrence Quantification Interval (RQI) analysis and calculated the hetero-geneity power spectra of the Sporilov temperature time series, reported the potential existence of multi-year cycles that correspond to periods of approximately 14, 8 and 2 -3 years [1]. Similar periodicities were confirmed in a number of meteorological series (see e.g.…”

Section: Detection Of Multi-periodic Dynamics In Temperature Time Sermentioning

confidence: 83%

“…On the other hand, the TO approach can be successfully applied not only to rapidly detect any inconsistencies between SAT and SGT but also to reveal the existence of long-term regularities. A periodicity of ~8 years was presumed in the Sporilov temperature time series compared with an amplitude of 0.6 K in the surface temperatures, which differs from the 8 K amplitude that corresponds to an annual wave with an insignificant phase lag of 0.2 between both waves [1]. In such a case, the theoretical temperature time series that are calculated for the one-harmonics annual wave alone and for the sum of both waves practically coincide even in an ideal noise-free case.…”

Section: ] [16])mentioning

confidence: 99%

“…[1]. The experiment began on January 1, 1994; the data that were used in this report cover from January 5, 1994 to March 23, 2010.…”

Section: Datamentioning

confidence: 99%

“…In the present study, we analyze air and shallow subsurface temperature series that were recorded during 1994-2010 at the experimental site of the Geothermal Climate Change Observatory (GCCO) at Sporilov (Prague) [1]. This experiment was completed with selected data from 2002 to 2013 from the ongoing experiment and focused on subsurface temperature studies under four different land cover types [2].…”

Section: Introductionmentioning

confidence: 99%

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Air-Ground Temperature Coupling: Analysis by Means of Thermal Orbits

Čermák¹,

Bodri²

2016

ACS

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Long-term measurements of air, near-surface (soil) and ground temperatures that were collected between 1994 and 2013 at the drill site of the Geothermal Climate Change Observatory (Prague) were analyzed to understand the relationship between these variables and to reveal the mechanisms of heat transport at the land-atmosphere boundary layer. The 2D Thermal Orbit (TO) method was applied to detect regularities that were hidden in noisy and highly variable temperature time series. The results showed that the temperatures at shallow depths were affected by surface air temperature (SAT) variations on seasonal and annual time scales and could be regarded as an accurate proxy for low frequency temperature variations at the Earth's surface. Only low-frequency/ high-amplitude surface temperature variations penetrate into the subsurface because of strong damping and the filtering effect of the ground surface. The borehole temperatures have good potential to capture temperature variations (periodicities) over long time scales that cannot be detected in the SAT series themselves because of the interference of higher frequency noise. The TO technique is a useful and powerful tool to quickly obtain diagnostics of the presence of long periodicities in borehole temperature time series.

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Section: Mixed Orbitsmentioning

confidence: 80%

Section: Detection Of Multi-periodic Dynamics In Temperature Time Sermentioning

confidence: 83%

Section: ] [16])mentioning

confidence: 99%

“…[1]. The experiment began on January 1, 1994; the data that were used in this report cover from January 5, 1994 to March 23, 2010.…”

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Air-Ground Temperature Coupling: Analysis by Means of Thermal Orbits

Čermák¹,

Bodri²

2016

ACS

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Time scales of the European surface air temperature variability: The role of the 7–8 year cycle

Jajcay

Hlinka

Kravtsov

et al. 2016

Geophysical Research Letters

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Air temperature variability on different time scales exhibits recurring patterns and quasi‐oscillatory phenomena. Climate oscillations with the period about 7–8 years have been observed in many instrumental records in Europe. Although these oscillations are weak if considering their amplitude, they might have nonnegligible influence on temperature variability on shorter time scales due to cross‐scale interactions recently observed by Paluš (2014). In order to quantify the cross‐scale influence, we propose a simple conditional mean approach which estimates the effect of the cycle with the period close to 8 years on the amplitude of the annual cycle in surface air temperature (SAT) in the range 0.7–1.4°C and the effect on the overall variability of the SAT anomalies (SATA) leads to the changes 1.5–1.7°C in the annual SATA means. The strongest effect in the winter SATA means reaches 4–5°C in central European station and reanalysis data.

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Eleven years of ground–air temperature tracking over different land cover types

Čermák

Bodri

Krešl

et al. 2016

Intl Journal of Climatology

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We have analyzed series of air, near‐surface and shallow ground temperatures under four land cover types, namely bare clayey soil, sand, short‐cut grass and asphalt; the samples were collected between 2002 and 2013 and monitored at the Geothermal Climate Change Observatory Sporilov, Prague (50°02.43′N, 14°28.54′E, 226 m a.s.l.). A comparison of all of the obtained temperature series revealed a strong dependence of the subsurface thermal regime on the respective surface cover material. The ground ‘skin’ temperature was generally warmer than the surface air temperature over all monitored surfaces; however, the temperatures over different land cover types differed significantly. Asphalt exhibited the highest temperatures, and temperatures below the grassy surface were the lowest. Special attention was paid to assessing the value of the ‘temperature offset’, the instant value of which sometimes varied dramatically, on both daily and annual scales, by up to 30+ K; however, on a long‐time scale, the temperature offset was generally constant and reflected the surface material. The characteristic 2003–2013 mean values for the individual covers are as follows: asphalt 4.1 K, sand 1.6 K, clay 1.4 K and grass 0.2 K. All four surface covers revealed typical daily and inter‐annual cycles, which were monitored and are discussed in detail. Incident solar radiation was the primary variable for determining the amount and temporal changes of the temperature offset values. A linear relationship between air–ground temperature differences and incident solar radiation was detected. The mean slope of the linear regression between both variables is clearly surface cover dependent. The greatest value, 3.3 K per 100 W m−2, was found for asphalt cover; rates of 1.0–1.2 apply to bare soil and sand cover, and a negative slope of −0.44 K per 100 W m−2 represents grass cover.

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Ground-air temperature tracking and multi-year cycles in the subsurface temperature time series at geothermal climate-change observatory

Cited by 14 publications

References 48 publications

Air-Ground Temperature Coupling: Analysis by Means of Thermal Orbits

Air-Ground Temperature Coupling: Analysis by Means of Thermal Orbits

Time scales of the European surface air temperature variability: The role of the 7–8 year cycle

Eleven years of ground–air temperature tracking over different land cover types

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