Subsurface Temperature Field Research Articles

Convformer: A Model for Reconstructing Ocean Subsurface Temperature and Salinity Fields Based on Multi-Source Remote Sensing Observations

Observational data on ocean subsurface temperature and salinity are patently insufficient because in situ observations are complex and costly, while satellite remote-sensed measurements are abundant but mainly focus on sea surface data. To make up for the ocean interior data shortage and entirely use the abundant satellite data, we developed a data-driven deep learning model named Convformer to reconstruct ocean subsurface temperature and salinity fields from satellite-observed sea surface data. Convformer is designed by deeply optimizing Vision Transformer and ConvLSTM, consisting of alternating residual connections between multiple temporal and spatial attention blocks. The input variables consist of sea surface temperature (SST), sea surface salinity (SSS), sea surface height (SSH), and sea surface wind (SSW). Our results demonstrate that Convformer exhibits superior performance in estimating the temperature-salinity structure of the tropical Pacific Ocean. The all-depth average root mean square error (RMSE) of the reconstructed subsurface temperature (ST)/subsurface salinity (SS) is 0.353 °C/0.0695 PSU, with correlation coefficients (R²) of 0.98663/0.99971. In the critical thermocline, although the root mean square errors of ST and SS reach 0.85 °C and 0.121 PSU, respectively, they remain smaller compared to other models. Furthermore, we assessed Convformer’s performance from various perspectives. Notably, we also delved into the potential of Convformer to extract physical and dynamic information from a model mechanism perspective. Our study offers a practical approach to reconstructing the subsurface temperature and salinity fields from satellite-observed sea surface data.

Reconstructing subsurface temperature profiles with sea surface data worldwide through deep evidential regression methods

In this paper, the combination of the deep evidential regression network (DERN) and empirical orthogonal function (EOF) methods, namely, the DERN-EOF method, was proposed to reconstruct the subsurface temperature profiles. Firstly, the DERN method was compared with the previous machine learning methods used in the subsurface temperature field reconstruction, for example the Self-organizing map method. The new network was shown to have certain advantages of higher accuracy and the capability to provide uncertainty estimation, which enables the reconstruction of the subsurface field with higher belief. Analysis of the method performance suggests that the DERN could improve reconstruction accuracy by almost 20% and that the uncertainty statistics agrees well with the data distribution. Then the DERN method combined with the EOF method was used to reconstruct subsurface temperature profiles at a global scale. Results suggest that the reconstruction error in highly dynamic regions, like the Kuroshio Extension and Gulf Stream regions, is significantly higher than that in other regions, and the uncertainties increase with the reconstruction error, suggesting that the proposed method can provide a relatively believable uncertainty estimation.

A spatiotemporal 3D convolutional neural network model for ENSO predictions: A test case for the 2020/21 La Niña conditions

Many coupled models are unable to accurately depict the multi-year La Niña conditions in the tropical Pacific during 2020–22, which poses a new challenge for real-time El Niño–Southern Oscillation (ENSO) predictions. Yet, the corresponding processes responsible for the multi-year coolings are still not understood well. In this paper, reanalysis products are analyzed to examine the ocean–atmosphere interactions in the tropical Pacific that have led to the evolution of sea surface temperature (SST) in the central-eastern equatorial Pacific, including the strong anomalous southeasterly winds over the southeastern tropical Pacific and the related subsurface thermal anomalies. Meanwhile, a divided temporal and spatial (TS) 3D convolution neural network (CNN) model, named TS-3DCNN, was developed to make predictions of the 2020/21 La Niña conditions; results from this novel data-driven model are compared with those from a physics-based intermediate coupled model (ICM). The prediction results made using the TS-3DCNN model for the 2020–22 La Niña indicate that this deep learning–based model can capture the two-year La Niña event to some extent, and is comparable to the IOCAS ICM; the latter dynamical model yields a successful real-time prediction of the Niño3.4 SST anomaly in late 2021 when it is initiated from early 2021. For physical interpretability, sensitivity experiments were designed and carried out to confirm the dominant roles played by the anomalous southeasterly wind and subsurface temperature fields in sustaining the second-year cooling in late 2021. As a potential approach to improving predictions for diversities of ENSO events, additional studies on effectively combining neural networks with dynamical processes and mechanisms are expected to significantly enhance the ENSO prediction capability.摘要2020–22年间热带太平洋经历了持续性多年的拉尼娜事件, 多数耦合模式都难以准确预测其演变过程, 这为厄尔尼诺-南方涛动(ENSO)的实时预测带来了很大的挑战. 同时, 目前学术界对此次持续性双拉尼娜事件的发展仍缺乏合理的物理解释, 其所涉及的物理过程和机制有待于进一步分析. 本研究利用再分析数据产品分析了热带东南太平洋东南风异常及其引起的次表层海温异常在此次热带太平洋海表温度(SST)异常演变中的作用, 并构建了一个时空分离(Time-Space)的三维(3D)卷积神经网络模型(TS-3DCNN)对此次双拉尼娜事件进行实时预测和过程分析. 通过将TS-3DCNN与中国科学院海洋研究所(IOCAS)中等复杂程度海气耦合模式(IOCAS ICM)的预测结果对比, 表明TS-3DCNN模型对2020–22年双重拉尼娜现象的预测能力与IOCAS ICM相当, 二者均能够从2021年初的初始场开始较好地预测2021年末 El Niño3.4区SST的演变. 此外, 基于TS-3DCNN和IOCAS ICM的敏感性试验也验证了赤道外风场异常和次表层海温异常在2021年末赤道中东太平洋海表二次变冷过程中的关键作用. 未来将神经网络与动力 模式模式间的有效结合, 进一步发展神经网络与物理过程相结合的混合建模是进一步提高ENSO事件预测能力的有效途径.

Inversion of Ocean Subsurface Temperature and Salinity Fields Based on Spatio-Temporal Correlation

Ocean observation is essential for studying ocean dynamics, climate change, and carbon cycles. Due to the difficulty and high cost of in situ observations, existing ocean observations are inadequate, and satellite observations are mostly surface observations. Previous work has not adequately considered the spatio-temporal correlation within the ocean itself. This paper proposes a new method—convolutional long short-term memory network (ConvLSTM)—for the inversion of the ocean subsurface temperature and salinity fields with the sea surface satellite observations (sea surface temperature, sea surface salinity, sea surface height, and sea surface wind) and subsurface Argo reanalyze data. Given the time dependence and spatial correlation of the ocean dynamic parameters, the ConvLSTM model can improve inversion models’ robustness and generalizability by considering ocean variability’s significant spatial and temporal correlation characteristics. Taking the 2018 results as an example, our average inversion results in an overall normalized root mean square error (NRMSE) of 0.0568 °C/0.0027 PSS and a correlation coefficient (R) of 0.9819/0.9997 for subsurface temperature (ST)/subsurface salinity (SS). The results show that SSTA, SSSA SSHA, and SSWA together are valuable parameters for obtaining accurate ST/SS estimates, and the use of multiple channels in shallow seas is effective. This study demonstrates that ConvLSTM is superior in modeling the subsurface temperature and salinity fields, fully taking global ocean data’s spatial and temporal correlation into account, and outperforms the classic random forest and LSTM approaches in predicting subsurface temperature and salinity fields.

Reconstruction of Subsurface Temperature Field in the South China Sea From Satellite Observations Based on an Attention U-Net Model

In this study, an Attention U-net network was proposed to reconstruct the subsurface temperature (ST) field with high temporal and spatial resolution in the South China Sea (SCS) from sea surface parameters observed by satellites. In addition to sea surface temperature, sea level anomaly and sea surface wind field, the wind stress curl, which influences three-dimensional structure of temperature through the induced Ekman pumping and transport, was also input into the model. The 5-day average vertical temperature profiles with spatial resolution of 0.5° from Simple Ocean Data Assimilation (SODA) reanalysis were used for training and evaluating the network. The results show that the Attention U-net model performs quite well in ST reconstruction in the upper 100 m layers of the SCS. The additional input of wind stress curl helps to improve the model accuracy. The average root mean square error (RMSE)/bias of ST decreases from 1.08°C/-0.21°C to 1.01°C/-0.05°C. Particularly, the RMSE near the thermocline is reduced significantly by up to 10.9%. The estimation error of the Attention U-net model is much smaller than that of some linear and tree models in the SCS, especially in shallow waters and regions with complex dynamic processes. The case study also shows that our model is capable of capturing the evolution of mesoscale processes in the SCS. The combination of satellite observations with high-precision ST reconstruction model will help us comprehensively understand the fine structure and variation of temperature and circulation in the marginal seas and open oceans.

Super-resolution of subsurface temperature field from remote sensing observations based on machine learning

Subsurface ocean observations are sparse and insufficient, significantly constraining studies of ocean processes. Retrieving high-resolution subsurface dynamic parameters from remote sensing observations using specific inversion models is possible but challenging. This study proposed two kinds of machine learning algorithms, namely, Convolutional Neural Network (CNN) and Light Gradient Boosting Machine (LightGBM), to reconstruct the subsurface temperature (ST) of the ocean’s upper 1000 m with a high resolution of 0.25° based on the satellite-based sea surface parameters combined with Argo float and EN4 profile data. We managed to improve the spatial resolution of ST from 1° to 0.25°. We employed two machine learning algorithms to set up monotemporal models of the four seasons and time-series models and adopted the determination coefficient (R2) and Root Mean Squared Error (RMSE) to evaluate the models’ prediction accuracy. The results show that LightGBM outperformed CNN in the case of small training samples. By contrast, in the case of big training samples, CNN outperformed LightGBM. Meanwhile, the ST with a high resolution of 0.25° predicted by the time-series CNN model can better observe mesoscale phenomena. This study provides more useful and higher-resolution data support for further studies on the warming and variability of the ocean interior under global warming.

Development of a MetUM (v 11.1) and NEMO (v 3.6) coupled operational forecast model for the Maritime Continent – Part 1: Evaluation of ocean forecasts

Abstract. This article describes the development and ocean forecast evaluation of an atmosphere–ocean coupled prediction system for the Maritime Continent (MC) domain, which includes the eastern Indian and western Pacific oceans. The coupled system comprises regional configurations of the atmospheric model MetUM and ocean model NEMO at a uniform horizontal resolution of 4.5 km × 4.5 km, coupled using the OASIS3-MCT libraries. The coupled model is run as a pre-operational forecast system from 1 to 31 October 2019. Hindcast simulations performed for the period 1 January 2014 to 30 September 2019, using the stand-alone ocean configuration, provided the initial condition to the coupled ocean model. This paper details the evaluations of ocean-only model hindcast and 6 d coupled ocean forecast simulations. Direct comparison of sea surface temperature (SST) and sea surface height (SSH) with analysis, as well as in situ observations, is performed for the ocean-only hindcast evaluation. For the evaluation of coupled ocean model, comparisons of ocean forecast for different forecast lead times with SST analysis and in situ observations of SSH, temperature, and salinity have been performed. Overall, the model forecast deviation of SST, SSH, and subsurface temperature and salinity fields relative to observation is within acceptable error limits of operational forecast models. Typical runtimes of the daily forecast simulations are found to be suitable for the operational forecast applications.

A consistent and high-quality M4+ earthquake catalogue for Turkey 2007–2016 from two independent catalogues

SUMMARYWe describe the process of pairing M4+ earthquakes reported in the routine catalogues of two seismic networks in Turkey and present the resulting products: (i) hypocentre locations and magnitudes and (ii) minimum 1-D velocity model with station delays for the combined station network. The two networks are the Kandilli Observatory (KOERI) and the National Earthquake Department (AFAD) and the data cover the decade 2007–2016. The seismic stations of these two networks spatially overlap within Turkey, and with their average station spacing of about 60 km, they potentially detect all events of magnitude ≥4 independently. We first obtain separate AFAD and KOERI catalogues using a series of joint hypocentre inversions that allow us to detect outliers and problematic events. We establish separate minimum 1-D velocity models and station delays for each catalogue and reduce the average inversion residuals by 75 per cent compared to our first joint hypocentre inversion using the original catalogue event locations. We then pair M4+ events from AFAD (1219) and KOERI (1271) catalogues using a spatiotemporal window and test if the combined observations indeed belong to the same event. In these tests, we compare the hypocentre location residuals of the combined observations to those of the two individual events, apply epicentral distance limits between the individual and the paired events and assess qualitatively a simple probabilistic solution of the events. As a result, we get 1145 paired events. We finally obtain a minimum 1-D velocity model with delays for all seismic stations of AFAD and KOERI combined and relocate the entire set of events. These include all paired events plus M4+ earthquakes reported by AFAD or KOERI that could not be paired (500). The velocity model has a well-defined Moho at a depth of 32 km. The station delays for both networks regionally correspond very well and exhibit some correlation with the regional subsurface temperature field. The well-locatable hypocentres of the new M4+ catalogue have an epicentre uncertainty of 3 km and a depth uncertainty of 4 km. We ascribe to each paired earthquake a magnitude equal to the average of the reported magnitudes by AFAD and KOERI. The new magnitude frequency distribution is similar to those of AFAD and KOERI for M4+ events. Except for events of magnitude larger than 5.5, there is no systematic bias in the magnitudes reported by AFAD and by KOERI, and the standard deviation is 0.3.

Borehole monitoring of thermo-hydro-mechanical rock slope processes adjacent to an actively retreating glacier

Most natural rock slope failures result from long-term strength degradation. It is often hypothesized that glacial retreat leads to enhanced progressive damage accumulation in adjacent rock slopes, due to mechanical unloading and changes to the thermal and hydraulic boundary conditions. However, direct observations of subsurface processes in a rock slope subject to glacial retreat are rare. In this paper, we present the design, implementation, and performance of a new borehole monitoring system installed on a rock slope in fractured crystalline rock located beside the glacier tongue of the retreating Great Aletsch Glacier (Valais, Switzerland). The three vertical, 50 m deep boreholes were instrumented to continuously monitor groundwater pressure, temperature and deformation at high resolution, in order to investigate thermo-hydro-mechanical coupled slope processes that drive progressive rock mass damage. We show that the system is capable of measuring both reversible and irreversible displacements along single fractures at magnitudes ranging between 0.001 mm to >2 mm in the studied rock slope, and that it is often possible to identify drivers of these deformation signals. The transient subsurface temperature field shows clear indications of former ice occupation in form of cold temperatures preserved at depth and superimposed annual temperature cycles penetrating down to a depth of about 17 m. The variability of the pressure head in the slope is driven by annual snowmelt infiltration cycles, rainfall events, and the assumed connection to englacial water of the temperate glacier. The new and continuously growing dataset presented here will enable us to relate the changing boundary conditions caused by glacial retreat and fatigue from daily to annual thermal and hydraulic loading cycles to progressive rock mass weakening, which may ultimately result in rock slope failure.

An ensemble-based SST nudging method proposed for correcting the subsurface temperature field in climate model

An ensemble-based assimilation method is proposed for correcting the subsurface temperature field when nudging the sea surface temperature (SST) observations into the Max Planck Institute (MPI) climate model, ECHAM5/MPI-OM. This method can project SST directly to subsurface according to model ensemble-based correlations between SST and subsurface temperature. Results from a 50 year (1960???2009) assimilation experiment show the method can improve the subsurface temperature field up to 300 m compared to the quality-controlled subsurface ocean temperature objective analyses (EN4), through reducing the biases of the thermal states, improving the thermocline structure, and reducing the root mean square (RMS) errors. Moreover, as most of the improvements concentrate over the upper 100 m, the ocean heat content in the upper 100 m (OHT_100 m) is further adopted as a property to validate the performance of the ensemble-based correction method. The results show that RMS errors of the global OHT_100 m convergent to one value after several times iteration, indicating this method can represent the relationship between SST and subsurface temperature fields well, and then improve the accuracy of the simulation in the subsurface temperature of the climate model.

Submarine Permafrost Map in the Arctic Modeled Using 1‐D Transient Heat Flux (SuPerMAP)

AbstractOffshore permafrost plays a role in the global climate system, but observations of permafrost thickness, state, and composition are limited to specific regions. The current global permafrost map shows potential offshore permafrost distribution based on bathymetry and global sea level rise. As a first‐order estimate, we employ a heat transfer model to calculate the subsurface temperature field. Our model uses dynamic upper boundary conditions that synthesize Earth System Model air temperature, ice mass distribution and thickness, and global sea level reconstruction and applies globally distributed geothermal heat flux as a lower boundary condition. Sea level reconstruction accounts for differences between marine and terrestrial sedimentation history. Sediment composition and pore water salinity are integrated in the model. Model runs for 450 ka for cross‐shelf transects were used to initialize the model for circumarctic modeling for the past 50 ka. Preindustrial submarine permafrost (i.e., cryotic sediment), modeled at 12.5‐km spatial resolution, lies beneath almost 2.5 ×106km2of the Arctic shelf. Our simple modeling approach results in estimates of distribution of cryotic sediment that are similar to the current global map and recent seismically delineated permafrost distributions for the Beaufort and Kara seas, suggesting that sea level is a first‐order determinant for submarine permafrost distribution. Ice content and sediment thermal conductivity are also important for determining rates of permafrost thickness change. The model provides a consistent circumarctic approach to map submarine permafrost and to estimate the dynamics of permafrost in the past.

The application of geologically-constrained 3D heat conduction models to geothermal exploration

Renewed interest in the thermal structure of the upper crust has led to the development of a new generation of tools capable of modelling the 3D heat conduction problem, incorporating the complex geology and physical properties of the crust. The numerical and resolution requirements of such models has necessitated a high performance performance computing approach, utilising massively parallel machine architecture to obtain ~10m resolution of models over a basin-scale. Here we demonstrate the application of the StGermain/Underworld geodynamic modelling framework to basin-scale geodynamic problems. The default code has been modified to incorporate importing of geometrically complex geological units, each with its own conductivity and heat production, and temperaturedependent thermal conductivity. Thermal models demonstrate the importance of incorporating 2 & 3D geometries, with large lateral variations in the subsurface temperature field and heat flow as a result of the heterogenous basin architecture. We explore the effect of lithological resolution, and identify a critical level of detail required to adequately represent basin-scale temperature variations. The regional temperature field play a dominant role in determining tenement-level subsurface temperatures, and highlight the importance of understanding regional temperature variations in geothermal exploration.

Reconstructing the Subsurface Temperature Field by Using Sea Surface Data Through Self-Organizing Map Method

Self-organizing map (SOM) method combined with the empirical orthogonal function was used to reconstruct the subsurface temperature field by using sea surface data in the Northwestern Pacific Ocean. In contrast to the traditional method, SOM method can extract nonlinear relations from the data and is more suitable for nonlinear dynamics in the ocean. Error statistics show that SOM method provides reconstructions of the subsurface temperature field with the majority of relative errors below 20% at 0-1000-m depth.

The Significance of Heat Transport by Shallow Fluid Flow at an Active Plate Boundary: The Southern Alps, New Zealand

AbstractFluid flow can influence fault behavior. Here we quantify the role of groundwater heat advection in establishing the thermal structure of the Alpine Fault, a major tectonic boundary in southern New Zealand that accommodates most of the motion between the Australian and Pacific Plates. Convergence on the Alpine Fault has rapidly uplifted the Southern Alps, resulting in high geothermal gradients and a thin seismogenic zone. A new equilibrium temperature profile from the 818‐m‐deep Deep Fault Drilling Project 2B borehole has been interrogated using one‐dimensional analytical models of fluid and rock advection. Models indicate a total heat flux of 720‐mW m−2 results from groundwater flow with Darcy velocities approximating to 7.8 × 10−10 m s−1. Groundwaters advect significantly more heat than rock advection in the shallow orogen (<6‐km depth) and are the major control on the subsurface temperature field.

A mathematical model and thermal performance analysis of single-well circulation (SWC) coupled ground source heat pump (GSHP) systems

Owing to the limitation of the available land in China, among various ground source heat pump (GSHP) system configurations, the single-well circulation (SWC) coupled GSHP systems are being intended to provide heating and cooling for building in recent years, especially utilized in this area with suitable hydrogeological and thermogeological conditions. This is due to the fact that the SWC system could not only substantially provide shallow geothermal energy for space heating or cooling in small-scale applications, but also reduce the number of boreholes needed for large-scale geothermal applications. In this work, a mathematical model has been established to analysis the groundwater seepage of SWC system, and analytical solution of steady drawdown was derived. Meanwhile, a numerical model was constructed to evaluate the thermal performance by using SWC coupled GSHP systems. Numerical experiments were performed to observe the evolution of outlet temperature, the distribution of subsurface temperature field, and the long-term development of outlet temperature. It was found that the thermal effective radius (TER) of SWC system is much larger than that of ground-coupled heat pump (GCHP) systems. Also, the temperature field in vertical section caused by the operation of SWC system is funnel-shaped. In addition, the outlet temperature fluctuates annually, and it rather starts a long-term decaying process, until reaching a quasi-steady state after about 8–10 years.

Insights on multivariate updates of physical and biogeochemical ocean variables using an Ensemble Kalman Filter and an idealized model of upwelling

Effective data assimilation methods for incorporating observations into marine biogeochemical models are required to improve hindcasts, nowcasts and forecasts of the ocean's biogeochemical state. Recent assimilation efforts have shown that updating model physics alone can degrade biogeochemical fields while only updating biogeochemical variables may not improve a model's predictive skill when the physical fields are inaccurate. Here we systematically investigate whether multivariate updates of physical and biogeochemical model states are superior to only updating either physical or biogeochemical variables. We conducted a series of twin experiments in an idealized ocean channel that experiences wind-driven upwelling. The forecast model was forced with biased wind stress and perturbed biogeochemical model parameters compared to the model run representing the “truth”. Taking advantage of the multivariate nature of the deterministic Ensemble Kalman Filter (DEnKF), we assimilated different combinations of synthetic physical (sea surface height, sea surface temperature and temperature profiles) and biogeochemical (surface chlorophyll and nitrate profiles) observations. We show that when biogeochemical and physical properties are highly correlated (e.g., thermocline and nutricline), multivariate updates of both are essential for improving model skill and can be accomplished by assimilating either physical (e.g., temperature profiles) or biogeochemical (e.g., nutrient profiles) observations. In our idealized domain, the improvement is largely due to a better representation of nutrient upwelling, which results in a more accurate nutrient input into the euphotic zone. In contrast, assimilating surface chlorophyll improves the model state only slightly, because surface chlorophyll contains little information about the vertical density structure. We also show that a degradation of the correlation between observed subsurface temperature and nutrient fields, which has been an issue in several previous assimilation studies, can be reduced by multivariate updates of physical and biogeochemical fields.

Liquid Water Flow and Retention on the Greenland Ice Sheet in the Regional Climate Model HIRHAM5: Local and Large-Scale Impacts

To improve Greenland Ice Sheet surface mass balance (SMB) simulation, the subsurface scheme of the HIRHAM5 regional climate model was extended to include snow densification, varying hydraulic conductivity, irreducible water saturation and other effects on snow liquid water percolation and retention. Sensitivity experiments to investigate the effects of the additions and the impact of different parameterization choices are presented. Compared with 68 accumulation area ice cores, the simulated mean annual net accumulation bias is -5% (correlation coefficient of 0.90). Modeled SMB in the ablation area compares favorably with 1041 PROMICE observations with regression slope of 0.95-0.97 (depending on model configuration), correlation coefficient of 0.75-0.86 and mean bias -3%. Weighting ablation area SMB biases at low- and high-elevation with the amount of runoff from these areas, we estimate ice sheet-wide mass loss biases in the ablation area at -5% and -7% using observed (MODIS-derived) and internally calculated albedo, respectively. Comparison with observed melt day counts shows that patterns of spatial (correlation ~0.9) and temporal (correlation coefficient of ~0.9) variability are realistically represented in the simulations. However, the model tends to underestimate the magnitude of inter-annual variability (regression slope ~0.7) and overestimate that of spatial variability (slope ~1.2). In terms of subsurface temperature structure and occurrence of perennial firn aquifers and perched ice layers, the most important model choices are the albedo implementation and irreducible water saturation parameterization. At one percolation area location, for instance, the internally calculated albedo yields too high subsurface temperatures below 5 m, but when using an implementation of irreducible saturation allowing higher values, an ice layer forms in 2011, reducing the deep warm bias in subsequent years. On the other hand, prior to the formation of the ice layer, observed albedos combined with lower irreducible saturation give the smallest bias. Perennial firn aquifers and perched ice layers occur in varying thickness and area for different model parameter choices. While the occurrence of these features has an influence on the local-scale subsurface temperature, snow, ice and water fields, the Greenland-wide runoff and SMB are - in the model’s current climate - dominated by the albedo implementation.

Quantification of exploitable shallow geothermal energy by using Borehole Heat Exchanger coupled Ground Source Heat Pump systems

In previous studies, the amount of exploitable shallow geothermal energy was estimated by assuming a uniform temperature drop of 2–6°C in the aquifer. In this work, a more comprehensive numerical model has been employed to evaluate the available amount of shallow geothermal energy by using Borehole Heat Exchanger coupled Ground Source Heat Pump systems. Numerical experiments have been performed by simulating the long-term evolution of the subsurface temperature field, which is subject to the operation of borehole heat exchangers and varying parameters like subsurface thermal conductivity and groundwater flow velocity. The concept of equivalent temperature drop is proposed as an auxiliary quantity for the subsurface. With the help of this parameter, a procedure has been established to quantify the amount of shallow geothermal potential. Following this approach, a realistic equivalent temperature reduction is found to be from −1.8 to −4.4°C in the subsurface over a period of 30years. This can be translated to an annual extractable geothermal energy value in a unit surface area, and it ranges from 3.5 to 8.6kWhm−2a−1. The exact value is site specific and heavily depends on the soil thermal conductivity, groundwater velocity, and borehole arrangement.

Benefits of CMIP5 Multimodel Ensemble in Reconstructing Historical Ocean Subsurface Temperature Variations

Abstract A complete map of the ocean subsurface temperature is essential for monitoring aspects of climate change such as the ocean heat content (OHC) and sea level changes and for understanding the dynamics of the ocean/climate variation. However, global observations have not been available in the past, so a mapping strategy is required to fill the data gaps. In this study, an advanced mapping method is proposed to reconstruct the historical ocean subsurface (0–700 m) temperature field from 1940 to 2014 by using ensemble optimal interpolation with a dynamic ensemble (EnOI-DE) approach and a multimodel ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) historical and representative concentration pathway 4.5 simulations. The reconstructed field is a combination of two parts: a first guess provided by the ensemble mean of CMIP5 models and an adjustment by minimizing the analysis error with the assistance of error covariance determined by the CMIP5 models. The uncertainty of the field can also be assessed. This new approach was evaluated using a series of tests, including subsample tests by using data from the Argo period, idealized tests by specifying a truth field from the models, and withdrawn-data tests by removing 20% of the observations for validation. In addition, the authors showed that the ocean mean state, long-term trends, and interannual and decadal variability are all well represented. Furthermore, the most significant benefit of this method is to provide an improved estimate of the long-term historical OHC changes since 1940, which have important implications for Earth’s energy budget.

Improving the temperature predictions of subsurface thermal models by using high-quality input data. Part 1: Uncertainty analysis of the thermal-conductivity parameterization

The subsurface temperature field and the geothermal conditions in sedimentary basins are frequently examined by using numerical thermal models. For those models, detailed knowledge of rock thermal properties are paramount for a reliable parameterization of layer properties and boundary conditions. Despite the state-of-the-art in other research fields (e.g. hydrogeological ground-water models) where the spatial permeability variations within geological layers is often considered, parameterization of the major rock thermal properties (in particular thermal conductivity, to minor extent radiogenic heat production and specific heat capacity) is almost always conducted by applying constant parameters for each modelled layer. Moreover, initial parameter values are usually obtained from only few core measurements and/or literature values, which raise questions for their representativeness. Only some rare studies have considered detailed lithological composition or well log information, still with constant layer properties.This study presents a thermal-modelling scenario analysis in which we demonstrate how the use of both different parameter input type (from literature, lithology and well logs) and parameter input style (constant or spatially varying layer values) affects model temperature predictions in sedimentary basins. It is a case study located in the Danish-German border region at the northern margin of the North German Basin. To conduct the scenario analysis, rock thermal properties are determined from lithological descriptions and standard petrophysical well logs for several boreholes in the area of study. Statistical values of rock thermal properties are derived for each geological formation at each well location and, furthermore, for the entire dataset. The thermal model is validated against known observed temperatures of good quality.Results clearly show that the use of location-specific well-log derived rock thermal properties and the integration of laterally varying input data (reflecting changes of lithofacies) significantly improves the temperature prediction. The parameterization from boreholes always prevails over the parameterization based on literature values, and it allows for reducing uncertainty of model temperatures by up to 80%.