Evaluation of snow cover properties in ERA5 and ERA5-Land with several satellite-based datasets in the Northern Hemisphere in spring 1982–2018
Abstract. Seasonal snow cover of the Northern Hemisphere (NH) greatly influences surface energy balance, hydrological cycle, and many human activities, such as tourism and agriculture. Monitoring snow cover at continental scale is only possible from satellites or using reanalysis data. The aim of this study is to analyze timeseries of surface albedo, snow water equivalent (SWE), and snow cover extent (SCE) in spring in ERA5 and ERA5-Land reanalysis data and to compare the timeseries with several satellite-based datasets. As satellite data for the SWE intercomparison, we use bias-corrected SnowCCI v1 data for non-mountainous regions and the mean of Brown, MERRA-2 and Crocus v7 datasets for the mountainous regions. For surface albedo, we use the black-sky albedo datasets CLARA-A2 SAL, based on AVHRR data, and MCD43D51 based on MODIS data. Additionally, we use Rutgers and JAXA JASMES SCE products. Our study covers land areas north of 40° N and the period between 1982 and 2018 (spring season from March to May). The analysis shows that both ERA5 and ERA5-Land overestimate SWE. ERA5-Land shows larger overestimation, which is mostly due to very high SWE values over mountainous regions. The analysis revealed a discontinuity in ERA5 around year 2004, since adding IMS (Interactive Multisensor Snow and Ice Mapping System) from year 2004 onwards considerably improves SWE estimates but makes the trends less reliable. The negative NH SWE trends in ERA5 range from −249 Gt decade−1 to −236 Gt decade−1 in spring, which is two to three times larger than the trends detected by the other datasets (ranging from −124 Gt decade−1 to −77 Gt decade−1). Albedo estimates are more consistent between the datasets with a slight overestimation in ERA5 and ERA5-Land. SCE is accurately described in ERA5-Land, whereas ERA5 shows notably larger SCE than the satellite-based datasets. The negative trends in albedo and SCE are strongest in May, when albedo trend varies from −0.011 decade−1 to −0.006 decade−1 depending on the dataset. The negative SCE trend detected by ERA5 in May (−1.22 million km2 decade−1) is about twice as large as the trends detected by other datasets (ranging from 0.66 million km2 decade−1 to −0.50 million km2 decade−1). The analysis also shows that there is a large spatial variability in the trends, which is consistent with other studies.
Kerttu Kouki et al.
Status: open (until 16 Jun 2023)
- RC1: 'Comment on tc-2023-53', Anonymous Referee #1, 20 May 2023 reply
- RC2: 'Comment on tc-2023-53', Anonymous Referee #2, 22 May 2023 reply
Kerttu Kouki et al.
Kerttu Kouki et al.
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This is a worthwhile study, although it is far from the first to evaluate model simulations of SWE, SCE and albedo in comparison with satellite-based datasets. It may be that this paper is the first with this precise combination of snow properties, reanalyses and observations, but it is mostly restricted to documenting differences without adding understanding. How much of the exaggerated SCE trend in ERA5 is due to the discontinuity in IMS assimilation? How closely are SCE and albedo anomalies related, and to what extent are they masked by forest cover? Difference plots for the means in Figure 1 would be useful. What can be said about the relative contributions of assimilation and resolution to differences between ERA5 and ERA5-Land?
The abstract states that “The analysis shows that both ERA5 and ERA5-Land overestimate SWE”. The three datasets used as the reference for SWE in mountainous regions are themselves model products, two of them driven by the ERA-Interim predecessor of ERA5. All that is conclusively shown here is that ERA5 and ERA5-Land have larger SWE in mountainous regions than these other model products. Does the overestimate of SCE in ERA5, even after 2004, just show that IMS overestimates SCE? How much of the differences in SWE be attributed to differences in precipitation and temperature driving data between ERA5 and ERA-Interim?
Because discussion of hemispheric timeseries trends is followed by the same for continents, Figures 2 and 3 and much of the discussion in 3.1 could be cut.
The albedo paragraph starting at line 34 interrupts the discussion of snow cover; I think it would sit better at a later point in the introduction.
“relatively sparse” – relative to what?
Important to note here that IMS does not provide information on SWE, and it is not assimilated in ERA5 at elevations above 1500 m. Describe how assimilation of SCE is used to update SWE.
This sentence is a repeat from line 98.
Note that Equation (1) is from the HTESSEL documentation (and needs to be limited to a maximum of one).
Not all of the datasets in 2.2 are satellite based.
Mortimer et al. (2022) referenced here did not evaluate the bias-correct SnowCCI and states that v2 is an improvement relative to v1.
Availability of albedo estimates from ERA5 and ERA5-Land, and differences between them, have already been discussed in 2.1.
261-265, Figure 1
Differences in SCE dominates differences in albedo, so should be discussed and shown first.
Axis labels for the second row should show that this is change in SWE, not SWE.
“whether the positive trend”
Is this intended to say that ERA5 and ERA5-Land are well correlated with observations of annual variability? That is not very obvious in Figures 10 and 11, but could be quantified.
“the SWE values themselves”
“a considerable difference”
“uncertainties related to”
The writing is generally good. I noted a number of incorrect commas, but the Finns have a word for reviewers who pay excessive attention to commas.