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The anthropogenically intensified greenhouse effect has caused a radiative imbalance at the top of the atmosphere during the industrial period. This, in turn, has led to an energy surplus in various components of the Earth system, with the ocean storing the largest part. The land contribution ranks second with the latest observational estimates based on borehole temperature profiles, which quantify the terrestrial energy surplus to be 6 % in the last 5 decades, whereas studies based on state-of-the-art climate models scale it down to 2 %. This underestimation stems from land surface models (LSMs) having a subsurface that is too shallow, which severely constrains the land heat uptake simulated by Earth system models (ESMs). A forced simulation of the last 2000 years with the Max Planck Institute ESM (MPI-ESM) using a deep LSM captures 4 times more heat than the standard shallow MPI-ESM simulations in the historical period, well above the estimates provided by other ESMs. However, deepening the LSM does not remarkably affect the simulated surface temperature. It is shown that the heat stored during the historical period by an ESM using a deep LSM component can be accurately estimated by considering the surface temperatures simulated by the ESM using a shallow LSM and propagating them with a standalone forward model. This result is used to derive estimates of land heat uptake using all available observational datasets, reanalysis products, and state-of-the-art ESM experiments. This approach yields values of 10.5–16.0 ZJ for 1971–2018, which are 12 %–42 % smaller than the latest borehole-based estimates (18.2 ZJ). [ABSTRACT FROM AUTHOR]

An assessment of the soil and bedrock thermal structure of the Sierra de Guadarrama, in central Spain, is provided using subsurface and ground surface temperature data coming from four deep (20 m) monitoring profiles belonging to the Guadarrama Monitoring Network (GuMNet) and two shallow profiles (1 m) from the Spanish Meteorology Service (Agencia Estatal de Meteorología, AEMET) covering the time spans of 2015–2021 and 1989–2018, respectively. An evaluation of air and ground surface temperature coupling showed that soil insulation due to snow cover is the main source of seasonal decoupling, being especially relevant in winter at high-altitude sites. Temperature propagation in the subsurface was characterized by assuming a heat conductive regime by considering apparent thermal diffusivity values derived from the amplitude attenuation and phase shift of the annual cycle with depth. This methodology was further extended to consider the attenuation of all harmonics in the spectral domain, which allowed for analysis of thermal diffusivity from high-frequency changes in the soil near the surface at short timescales. For the deep profiles, the apparent thermal diffusivity ranges from 1 to 1.3×10-6 m 2 s -1 , which is consistent with values for gneiss and granite, the major bedrock components in the Sierra de Guadarrama. However, thermal diffusivity is lower and more heterogeneous in the soil layers close to the surface (0.4– 0.8×10-6 m 2 s -1). An increase in diffusivity with depth was observed that was generally larger in the soil–bedrock transition at 4–8 m depth. The outcomes are relevant for the understanding of soil thermodynamics in relation to other soil properties. Results with the spectral method suggest that changes in near-surface thermal diffusivity are related to changes in soil moisture content, which makes it a potential tool to gain information about soil drought and water resource availability from soil temperature data. [ABSTRACT FROM AUTHOR]

An assessment of the soil and bedrock thermal structure of the Sierra de Guadarrama, in Central Spain, is provided using subsurface and ground surface temperature data coming from four deep (20 m) monitoring profiles belonging to the Guadarrama Monitoring Network (GuMNet), and two shallow (1 m) from the Spanish Meteorology Service (AEMET), covering the time span of 2015–2021 and 1989–2018, respectively. An evaluation of air and ground surface temperature coupling shows soil insulation due to snow cover is the main source of seasonal decoupling, being especially relevant in winter at high altitude sites. Temperature propagation in the subsurface is characterized by assuming a heat conductive regime, by considering apparent thermal diffusivity values derived from the amplitude attenuation and phase shift of the annual cycle with depth. For the deep profiles, the apparent thermal diffusivity ranges from 1 to 1.3 10−6 m2s−1, consistent with values for gneiss and granite, the major bedrock components in the Sierra de Guadarrama. However, thermal diffusivity is lower and more heterogeneous in the soil layers close to the surface (0.4–0.8 10−6 m2s−1). An increase of diffusivity with depth is observed, being generally larger in the soil-bedrock transition, at 4–8 m depth. A new method based on the spectral attenuation of temperature harmonics allows for analyzing thermal diffusivity from high-frequency changes in the soil near the surface at short timescales. The results are relevant for the understanding of soil thermodynamics in relation to other soil properties and suggest that changes in heat diffusivity are related to soil moisture content changes, which makes this method a potential tool in soil drought and water resource availability reconstruction from soil temperature data. [ABSTRACT FROM AUTHOR]

Heat storage within the Earth system is a fundamental metric to understand climate change. The current energy imbalance at the top of the atmosphere causes changes in energy storage within the ocean, the atmosphere, the cryosphere, and the continental landmasses. After the ocean, heat storage in land is the second largest term of the Earth heat inventory, affecting physical processes relevant to society and ecosystems, such as the stability of the soil carbon pool. Here, we present an update of the continental heat storage combining for the first time the heat in the land subsurface, inland water bodies, and permafrost thawing. The continental landmasses stored 23.9±0.4×1021 J during the period 1960-2020, but the distribution of heat among the three components is not homogeneous. The ground stores ~90 % of the continental heat storage, with inland water bodies and permafrost degradation accounting for ~0.7 % and ~9 % of the continental heat, respectively. Although the inland water bodies and permafrost soils store less heat than the ground, we argue that their associated climate phenomena justify their monitoring and inclusion in the Earth heat inventory. [ABSTRACT FROM AUTHOR]

The Earth climate system is out of energy balance and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere and the atmosphere. According to the 6th Assessment Report of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance, and allows for quantifying how much heat has accumulated in the Earth system, and where the heat is stored. Here we show that 380 ± 62 ZJ of heat has accumulated in the Earth system from 1971 to 2020, at a rate of 0.48 ± 0.1 Wm-2, with 89 ± 17 % of this heat stored in the ocean, 6 ± 0.1 % on land, 4 ± 1% in the cryosphere and 1 ± 0.2 % in the atmosphere. Over the most recent decade (2006-2020), the Earth heat inventory shows increased warming at rate of 0.48 ± 0.3 W m-2/decade, and the Earth climate system is out of energy balance by 0.76 ± 0.2 Wm-2. The Earth heat inventory is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. We call for an implementation of the Earth heat inventory into the Paris agreement's global stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al, 2020, is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations as coordinated by the Global Climate Observing System (GCOS). We also call for urgently needed actions for enabling continuity, archiving, rescuing and calibrating efforts to assure improved and long-term monitoring capacity of the relevant GCOS Essential Climate Variables (ECV) for the Earth heat inventory. [ABSTRACT FROM AUTHOR]

Estimates of the past thermal state of the land surface are crucial to assess the magnitude of current anthropogenic climate change, as well as to assess the ability of Earth System Models to forecast the evolution of the climate near the ground, not included in standard meteorological records. Subsurface temperature data are able to retrieve long-term changes in surface energy balance -from decadal to millennial time scales, thus constituting an important record of the dynamics of the climate system that contributes low-frequency information to proxy-based paleoclimatic reconstructions. A broadly used technique to retrieve past temperature and heat flux histories from subsurface temperature profiles based on a Singular Value Decomposition (SVD) algorithm was able to take into account a limited number of sources of uncertainty, with recent works attempting to increase the number of factors considered in uncertainty estimates. Nevertheless, the SVD methodology did not define a statistical framework for aggregating inversions of individual profiles to derive global results, which lead to estimates of global and regional uncertainties that are difficult to interpret. To alleviate the lack of a conceptual framework for estimating uncertainties in past temperature and heat flux histories at regional and global scales, we combine a new bootstrapping sampling strategy with the broadly used SVD algorithm, and assess its performance against the original SVD technique and another technique based on generating perturbed parameter ensembles of inversions. The new bootstrap approach is able to reproduce the prescribed surface temperature series used to derive an artificial profile. Bootstrap results are also in agreement with the global mean surface temperature history and the global mean heat flux history retrieved in previous studies. Furthermore, the new bootstrap technique provides with a meaningful uncertainty range for the inversion of large sets of subsurface temperature profiles. We suggest the use of this new approach particularly for aggregating results from a number of individual profiles, and to this end, we release the programs used to derive all inversions in this study as a suite of codes labelled CIBOR 1.0: Codes for Inverting BORholes, version 1.0. [ABSTRACT FROM AUTHOR]