Search

Current global reanalyses show marked discrepancies in snow mass and snow cover extent for the Northern Hemisphere. Here, benchmark snow datasets are produced by driving a simple offline snow model, the Brown Temperature Index Model (B-TIM), with temperature and precipitation from each of three reanalyses. B-TIM offline snow performs comparably to or better than online (coupled land-atmosphere) reanalysis snow when evaluated against in situ snow measurements. Sources of discrepancy in snow climatologies, which are difficult to isolate when comparing online reanalysis snow products amongst themselves, are partially elucidated by separately bias-adjusting temperature and precipitation in B-TIM. Interannual variability in snow mass and snow spatial patterns is far more self-consistent amongst offline B-TIM snow products than amongst online reanalysis snow products, and specific artifacts related to temporal inhomogeneity in snow data assimilation are revealed in the analysis. B-TIM, released here as an open-source, self-contained Python package, provides a simple benchmarking tool for future updates to more sophisticated online and offline snow datasets. [ABSTRACT FROM AUTHOR]

We assess and rank 23 gridded snow water equivalent (SWE) products by implementing a novel evaluation strategy using a new suite of reference data from two cross-validated sources and a series of product inter-comparisons. The new reference data combines in situ measurements from both snow courses and airborne gamma measurements. Compared to previous evaluations of gridded products, we have substantially increased the spatial coverage and sample size across North America, and we are able to evaluate product performance across both mountain and non-mountain regions. The evaluation strategy we use ranks overall relative product performance while still accounting for individual differences in ability to represent SWE climatology, variability, and trends. Assessing these gridded products fills an important gap in the literature since individual gridded products are frequently chosen without prior justification as the basis for evaluating land surface and climate model outputs, along with other climate applications. The top performing products across the range of tests performed are ERA5-Land followed by the Crocus snow model. Our evaluation indicates that accurate representation of hemispheric SWE varies tremendously across the range of products. While most products are able to represent SWE reasonably well across Northern Hemisphere non-mountainous regions, the ability to accurately represent SWE in mountain regions and to accurately represent historical trends are much more variable. Finally, we demonstrate that for the ensemble of products evaluated here, attempts to assimilate surface snow observations and/or satellite measurements lead to a deleterious influence on regional snow mass trends, which is an important consideration for how such gridded products are produced and applied in the future. [ABSTRACT FROM AUTHOR]

We use snow course and airborne gamma data available over North America to compare the validation of gridded snow water equivalent (SWE) products when evaluated with one reference dataset versus the other. We assess product performance across both non-mountainous and mountainous regions, determining the sensitivity of relative product rankings and absolute performance measures. In non-mountainous areas, product performance is insensitive to the choice of SWE reference dataset (snow course or airborne gamma): the validation statistics (bias, unbiased root mean squared error, correlation) are consistent with one another. In mountainous areas, the choice of reference dataset has little impact on relative product ranking but a large impact on assessed error magnitudes (bias and unbiased root mean squared error). Further analysis indicates the agreement in non-mountainous regions occurs because the reference SWE estimates themselves agree up to spatial scales of at least 50 km, comparable to the grid spacing of most available SWE products. In mountain areas, there is poor agreement between the reference datasets even at short distances (< 5 km). We determine that differences in assessed error magnitudes result primarily from the range of SWE magnitudes sampled by each method, although their respective spatiotemporal distribution and elevation differences between the reference measurements and grid centroids also play a role. We use this understanding to produce a combined reference SWE dataset for North America, applicable for future gridded SWE product evaluations and other applications. [ABSTRACT FROM AUTHOR]

Snow water equivalent (SWE) is a valuable characteristic of snow cover, and it can be estimated using passive spaceborne radiometer measurements. The radiometer based GlobSnow SWE retrieval methodology, which assimilates weather station snow depth observations into the retrieval, has improved reliability and accuracy of SWE retrieval when compared to stand-alone radiometer SWE retrievals. To further improve the GlobSnow SWE retrieval methodology, we investigate implementing spatially and temporally varying snow densities into the retrieval procedure. Thus far, the GlobSnow SWE retrieval has used a constant snow density throughout the retrieval despite differing locations, snow depth or time of winter. This constant snow density is a known source of inaccuracy in the retrieval. Three different versions of spatially and temporally varying snow densities are tested over a 10-year period (2000-2009). These versions use two different spatial interpolation techniques, ordinary Kriging interpolation and inverse distance weighted regressing (IDWR). All versions were found to improve the SWE retrieval compared to the baseline GlobSnow v3.0 product although differences between versions are small. Overall, the best results were obtained by implementing IDWR interpolated densities into the algorithm, which reduced RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) by about 4 mm and 5 mm when compared to the baseline GlobSnow product, respectively. Furthermore, implementing varying snow densities into the SWE retrieval improves the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product and a product post-processed with varying snow densities. [ABSTRACT FROM AUTHOR]

The recent Chevron case1 raised the issue of retrospectivity of legislation. While this issue is not new, it has been argued in the past that there are limits on when governments can resort to enacting retrospective laws. These limits centre on the ability of government to protect the revenue in the public interest. This paper explores the history of retrospective taxation legislation in Australia, and analyses whether such legislation was justified in the circumstances to achieve this goal. The authors argue that the Chevron case not only entrenches the right of governments to enact retrospectively with respect to taxation laws, but unjustifiably extends that right in the name of 'protecting the revenue'. This will have serious implications for taxation practitioners and their clients. The authors contend that retrospective legislation should only be considered in the most egregious circumstances, and that it is incumbent upon governments to acknowledge deficiencies in legislation promptly, and amend such legislation quickly, in order to provide certainty and maintain public confidence in the taxation system. [ABSTRACT FROM AUTHOR]