Search

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]

Simulations of land-surface processes and phenomena often require driving time series of meteorological variables. Corresponding observations, however, are unavailable in most locations, even more so, when considering the duration, continuity and data quality required. Atmospheric reanalyses provide global coverage of relevant meteorological variables, but their use is largely restricted to grid-based studies. This is because technical challenges limit the ease with which reanalysis data can be applied to models at the site scale. We present the software toolkit GlobSim, which automates the downloading, interpolation and scaling of different reanalyses - currently ERA-5, ERA-Interim, JRA-55 and MERRA-2 - to produce meteorological time series for user-defined point locations. The resulting data have consistent structure and units to efficiently support ensemble simulation. The utility of GlobSim is demonstrated using an application in permafrost research. We perform ensemble simulations of ground-surface temperature for ten terrain types in a remote tundra area in northern Canada and compare the results with observations. Simulation results reproduced seasonal cycles and variation between terrain types well, demonstrating that GlobSim can support efficient land-surface simulations. Ensemble means often yielded better accuracy than individual simulation and ensemble ranges additionally provide indications of uncertainty arising from uncertain input. By improving the usability of reanalyses for research requiring time-series of climate variables for point locations, GlobSim can enable a wide range of simulation studies and model evaluations that previously were impeded by technical hurdles in obtaining suitable data. [ABSTRACT FROM AUTHOR]

The Canadian Land Surface Scheme and Canadian Terrestrial Ecosystem Model (CLASS-CTEM) together form the land surface component of the Canadian Earth System model (CanESM). Here we investigate the impact of changes to CLASS-CTEM that are designed to improve the simulation of permafrost physics. Eighteen tests were performed including changing the model configuration (number and depth of ground layers, different soil permeable depth datasets, adding a surface moss layer), and investigating alternative parameterizations of soil hydrology, soil thermal conductivity and snow properties. To evaluate these changes, outputs from CLASS-CTEM were compared to 1570 active layer thickness (ALT) measurements from 97 observation sites that are part of the Global Terrestrial Network for Permafrost (GTN-P), 105 106 monthly ground temperature observations from 132 GTN-P borehole sites, a blend of 5 observation-based snow water equivalent (SWE) datasets (Blended-5), remotely-sensed albedo, and seasonal discharge for major rivers draining permafrost regions. From the tests performed, the final revised model configuration has more ground layers (increased from 3 to 20) extending to greater depth (from 4.1m to 61.4m) and uses a new soil permeable depths dataset with a surface layer of moss added. The most beneficial change to the model parameterizations was incorporation of unfrozen water in frozen soils. These changes to CLASS-CTEM cause a small improvement in simulated SWE with little change in surface albedo but greatly improve the model performance at the GTN-P ALT and borehole sites. Compared to the GTN-P observations, the revised CLASS-CTEM ALTs have a weighted mean absolute error (wMAE) of 0.41-0.47m (depending on configuration), improved from >2.5m for the original model, while the borehole sites see a consistent improvement in wMAE for most seasons and depths considered, with seasonal wMAE values for the shallow surface layers of the revised model simulation at most 1.2°C greater than those calculated for the model driving screen-level air temperature compared to observations at the sites. Sub-grid heterogeneity estimates were derived from the standard deviation of ALT on the 1km² measurement grids at the GTN-P ALT sites, the spread in wMAE in grid cells with multiple GTN-P ALT sites, as well as from 35 boreholes measured within a 1200km² region as part of the Slave Province Surficial Materials and Permafrost Study. Given the size of the model grid cells (ca. 2.8°), sub-grid heterogeneity makes it likely difficult to appreciably reduce the wMAE of ALT or borehole temperatures much further. [ABSTRACT FROM AUTHOR]

In mountain areas, the use of coarse-grid re-analysis data for driving fine-scale models requires downscaling of near-surface (e.g. 2 m high) air temperature. Existing approaches describe lapse rates well but differ in how they include surface effects, i.e. the difference between the simulated 2 m and upper-air temperatures. We show that different treatment of surface effects result in some methods making better predictions in valleys while others are better in summit areas. We propose the downscaling method REDCAPP (REanalysis Downscaling Cold Air Pooling Parameterization) with a spatially variable magnitude of surface effects. Results are evaluated with observations (395 stations) from two mountain regions and compared with three reference methods. Our findings suggest that the difference between near-surface air temperature and pressure-level temperature (ΔT) is a good proxy of surface effects. It can be used with a spatially-variable Land-Surface Correction-Factor (LSCF) for improving downscaling results, especially in valleys with strong surface effects and cold air pooling during winter. While LSCF can be parameterized from a fine-scale digital elevation model (DEM), the transfer of model parameters between mountain ranges needs further investigation. [ABSTRACT FROM AUTHOR]