Continental Heat Storage and Ground Heat Storage

This dataset contains an updated estimate of the global continental heat storage for the period 1960-2020. For the first time, the continental heat storage is assessed as composed by: ground heat storage due to changes in subsurface temperatures, inland water heat storage due to the warming of inland water bodies, and permafrost heat storage due to thawing of ground ice in the Arctic.

Additionally, the datasset includes the global ground heat storage for the period 1960-2020. The ground heat storage has been estimated by inverting 1079 subsurface temperature profiles form the Xibalbá database (see below in this webpage) and a bootstrap technique to aggregate the Singular Value Decomposition (SVD) inversions of each profile (see the Research section in this webpage).

Currently, the dataset is in its second version, which includes an update of continental heat content uncertainty, where the standard deviation has been corrected from the precedent version to consider properly the value from permafrost heat storage uncertainty.

Reference:

von Schuckmann, K., Minère, A., Gues, F., Cuesta-Valero, F. J., Kirchengast, G., Adusumilli, S., Straneo, F., Allan, R., Barker, P. M., Beltrami, H., Boyer, T., Cheng, L., Church, J., Desbruyeres, D., Dolman, H., Domingues, C. M., García-García, A., Giglio, D., Gilson, J. E., Gorfer, M., Haimberger, L., Hendricks, S., Hosoda, S., Johnson, G. C., Killick, R., King, B., Kolodziejczyk, N., Korosov, A., Krinner, G., Kuusela, M., Langer, M., Lavergne, T., Lawrence, I., Li, Y., Lyman, J., Marzeion, B., Mayer, M., MacDougall, A. H., McDougall, T., Monselesan, D. P., Nitzbon, J., Otosaka, I., Peng, J., Purkey, S., Roemmich, D., Sato, K., Sato, K., Savita, A., Schweiger, A., Shepherd, A., Seneviratne, S. I., Simons, L., Slater, D. A., Slater, T., Smith, N., Steiner, A., Suga, T., Szekely, T., Thiery, W., Timmermans, M.-L., Vanderkelen, I., Wjiffels, S. E., Wu, T., and Zemp, M. (2023). Heat stored in the Earth system 1960-2020: Where does the energy go? Earth Syst. Sci. Data Discuss., [preprint], accepted.

Data:

Cuesta-Valero, F.J., Beltrami, H., García-García, A., Krinner, G., Langer, M., MacDougall, A., Nitzbon, J, Peng, J., von Schuckmann, K., Seneviratne, S.I., Thiery, W., Vanderkelen, I., Wu, T. (2023). GCOS EHI 1960-2020 Continental Heat Content (Version 2). World Data Center for Climate (WDCC) at DKRZ. https://doi.org/10.26050/WDCC/GCOS_EHI_1960-2020_CoHC_v2.

Xibalbá Profiles

Dataset containing 1079 subsurface temperature-depth profiles measured around the world. Each log has been screened to eliminate those containing non-climatic artifacts. The subsurface temperature logs comprising this database are the result of the quality-control process explained in Cuesta-Valero et al. (2021), including the following harmonization requeriments:

• Each log include at least one temperature record between 15 m and 100 m of depth.

• Each log include at least one temperature record between 250 m and 310 m of depth.

• Each log include at least three temperature records between 200 m and 300 m of depth.

• All boreholes are truncated to include temperature records between 15 m and 300 m of depth.

Reference:

Cuesta-Valero F.J., García-García A., Beltrami H., González-Rouco J.F., and García-Bustamante E. (2021). Long-Term Global Ground Heat Flux and Continental Heat Storage from Geothermal Data. Clim. Past, 17, 451-468, doi:10.5194/cp-17-451-2021. Article highlighted by the editors of CP.

Data:

Cuesta-Valero, F.J., Beltrami, H., García-García, A., González-Rouco, J. F., García-Bustamante, E. (2021). Xibalbá: Underground Temperature Database. figshare. Dataset. https://doi.org/10.6084/m9.figshare.13516487.v4.

Long-term Surface Temperature (LoST) Database

Dataset containing long-term surface absolute temperatures for the period ~1300-1700 of the Comon Era (CE) over North America and some Caribbean islands. Long-term climatology estimates from 514 subsurface temperature profiles are retrieved and interpolated into a NetCDF file in order to facilitate its use and distribution.

All subsurface temperature-depth profiles were truncated to the depth range between 199 m and 300 m in order to retrieve estimates for the same temporal period according to the equation $$ t \propto \frac{z^{2}}{4\cdot \kappa} $$ where \(t\) is time since the surface signal happend, \(z\) is depth at which the signature of the surface perturbation is recorded, and \(\kappa\) is the thermal diffusivity of the ground.

A Gradient plus Inverse Distance Squared (GIDS) interpolation to a \(0.5^{\circ}\times 0.5^{\circ}\) grid was performed considering all 514 estimates from subsurface temperature profiles. Only grid cells within a radious of 650 km around a subsurface temperature profile were considered. This distance criterium was obtained from a pseudo-proxy experiment using five CMIP5 Past Millennium simulations, and consist in the maximum distance in which the RMSE between interpolated temperatures and the entire distribution of simulated temperatures in North America is smaller than \(1\ ^{\circ}C\) for all five models. Please, see the reference for more details.

Reference:

Cuesta-Valero F.J., García-García A., Beltrami H., Zorita E. and Jaume-Santero F. (2019). Long-term Surface Temperature (LoST) database as a complement for GCM preindustrial simulations. Climate of the Past, 15, 1099-1111, doi:10.5194/cp-15-1099-2019.

Data:

Cuesta-Valero, F. J., García-García, A., Beltrami, H., Zorita, E., Jaume-Santero, F. (2019). Long-term Surface Temperature (LoST) Database. figshare. Dataset. https://doi.org/10.6084/m9.figshare.8124887.v1.