Snow Assessments

02 October 2023

Northern Hemisphere Continental Snow Cover Extent: 2022/2023 Snow Season

David A. Robinson & Thomas W. Estilow (Rutgers University, Piscataway, New Jersey, USA)

Snow cover extent (SCE) over Northern Hemisphere (NH) lands for the August 2022–July 2023 period averaged 24.6 million km². This is 0.3 million km² less than the 1991-2020 mean and 0.5 million km² below the full period of record mean (Table 1). This ranks this recent snow season as having the 37th most extensive cover in the 56-year period of record. Monthly SCE during the season ranged from 46.3 million km² in January 2023 to 2.4 million km² in August 2022.

Three figures are presented to depict SCE this past season compared to past years. Figure 1 tracks weekly NH SCE over the course of the past season compared to the period-of-record mean and extremes. August SCE was, as usual, quite minimal over NH lands. September SCE was in the upper tercile due to the 6th most extensive Eurasia (EUR) cover on record, though a slow start to the snow season over North America (NA) tempered the overall NH cover. October NH cover was in the middle tercile, as it was on both continents. NH cover increased rapidly in November ranking 4th most extensive. There was quite a turnaround over EUR in December with a rank of 47th most extensive while NA covered ranked 13th most extensive. NH January SCE ranked 40th most extensive and February 31st most extensive, with EUR cover running below average and NA about average. A large inter-continental disparity arose in March with EUR 51st and NA 4th most extensive, leaving the NH with the 42nd most extensive SCE. The disparity remained but declined some in April leaving the NH with close to average SCE. A rapid May melt over NA left SCE at a record minimum with EUR ranked 34th and the NH 49th most extensive. This left limited snow cover remaining in June, which ranked 50th most extensive for the NH, with July having little SCE too.

Standardized monthly NH SCE anomalies for the 2022/2023 season were generated using Z-scores, with results shown in Figure 2. Results show that the November positive monthly anomaly was the most pronounced of the 2022/2023 season followed on the negative side of the ledger by May and June. In Figure 3, 12-month running means of NH, EUR, and NA SCE anomalies show a pronounced decline in SCE in the mid to late 1980s that has generally remained consistent since that time as declining spring SCE has been balanced by increasing fall and slightly increasing winter SCE.

SCE is calculated at the Rutgers Global Snow Lab (GSL) from daily SCE maps produced by meteorologists at the US National Ice Center, who rely primarily on visible satellite imagery to construct the maps. Maps depicting daily, weekly, and monthly conditions, anomalies, and climatologies may be viewed at the GSL website (https://snowcover.org).

Table 1: Northern Hemisphere snow cover extent for the 2022/2023 season is listed by month and year, along with departures from the 56-year (1967/1968–2022/2023) means (millions square kilometers), and the recent season’s rankings. Monthly means for the period of record are used for 9 missing months during 1968, 1969, and 1971 to create a continuous time series. Missing months fall between June and October.

Figure 1: Weekly NH SCE for 2023 (purple) plotted with the mean (grey dashed line), maximum (blue), and minimum (orange) SCE for each week. Mean weekly SCE and extremes calculated using the 56-year period from October 1966–July 2023 (excepting September, which is based on 55 years from 1967–2022). Weekly means for the period of record are used for 9 missing months during 1968, 1969, and 1971 to create a continuous time series. Missing months fall between June and October.

Figure 2: Monthly NH SCE standardized anomalies (Z-scores) for August 2022–July 2023. Mean monthly SCE calculated using the 30-year period from August 1990–July 2020. Monthly means for the period of record are used for 9 missing months during 1968, 1969, and 1971 to create a continuous time series. Missing months fall between June and October.

Figure 3: Twelve-month running anomalies of monthly SCE over NH lands as a whole and EUR and NA separately plotted on the 7th month using values from November 1966–July 2023. Mean NH SCE is 25.1 million sq. km for the full period of record. Monthly means for the period of record are used for 9 missing months during 1968, 1969, and 1971 to create a continuous series of running means. Missing months fall between June and October.

Northern Hemisphere Terrestrial Snow Mass, Winter 2022-2023

Kari Luojus (Finnish Meteorological Institute), Patricia de Rosnay and Kenta Ochi (ECMWF, UK), and Vincent Vionnet (ECCC, Canada)

The WMO GCW Snow Watch expert team has developed several trackers for the cryosphere. Terrestrial snow cover is tracked in regard to snow cover extent and water equivalent of snow cover (SWE) based on satellite data and complemented by model-based information and in-situ snow depth measurements. The trackers provide a quick look at the current state of the cryosphere relative to the mean state of the last 2-3 decades.

The FMI/GCW SWE Tracker is a product of the Finnish Meteorological Institute (FMI), based on GlobSnow SWE. It was developed in collaboration with the GCW Snow Watch expert team. This tracker illustrates the current Northern Hemisphere snow water equivalent relative to the long-term mean and variability. The input data consist mainly of satellite-based passive microwave radiometer data, which is combined with ground-based observations in an assimilation framework (Luojus et al. 2021). The methodology based on passive microwave observations is not able to track snow conditions for the mountains, thus they are omitted from this analysis.

The FMI tracker indicated an above average snow mass for the Northern Hemisphere during winter 2022-2023. The above average conditions were driven by positive anomalies over both the Eurasian and North American sector. Winter 2022-2023 did not reach record conditions but was above average and slightly above the standard deviation of the 30-year baseline time series for 1982-2012, as seen in Figure 4.

Figure 4: The FMI/GCW SWE tracker – indicating above average Snow Mass for the winter 2022-2023.

The Environment and Climate Change Canada (ECCC) GCW Snow Water Equivalent Tracker provides an estimate of current Northern Hemisphere SWE relative to the 1998-2011 period. It is based on the Canadian Meteorological Centre operational daily snow depth analysis with SWE estimated using a density look-up table (Brasnett, 1999; Brown and Mote, 2009; Brown et al., 2010). The CMC analysis uses real-time surface snow depth observations and model-derived information and is not a satellite-derived product; it tracks also the snow conditions for the mountains.

The ECCC SWE Tracker, shown in Figure 5, is consistent with the FMI/GCW SWE Tracker and indicates significantly above average snow mass for the Northern Hemisphere for the full duration of winter 2022/2023. The average snow mass reached values above one standard deviation over the long-term average. The ECCC tracker includes the snow mass for mountains, while the FMI tracker does not, this makes for the difference between the results of the two trackers.

Figure 5: The ECCC SWE tracker – indicating significantly above average Snow Mass for the winter 2022-2023.

The ECMWF ERA-5 based NH SWE tracker is shown in Figure 6. It relies on a global reanalysis using model and data assimilation (Hersbach et al., 2020). The SWE tracker is computed for the NH, excluding glaciers areas and Greenland icesheet. It indicates slightly above average snow mass for the winter 2022-2023 compared to the reference period 2005-2020.

Figure 6: The ECMWF ERA5-based SWE tracker (averaged over the continental areas of the Northern Hemisphere excluding glaciers and Greenland ice sheet) – indicates slightly above average Snow Mass for the winter 2022-2023.

Figures 1-6 show that while NH SCE was around its average, the NH SWE was above its long term average, indicating slightly deeper snow packs during the winter season 2022/2023.

Central Europe and the Alps

Simon Gascoin (Centre d'études spatiales de la biosphère (Cesbio), France)

The snow cover area in the Alps remained well below a 30-year climatology during the 2023 spring despite late snowfalls in May, as shown in Figure 7a. The same pattern can be observed in every major alpine catchment, but the situation was worse in the Po river basin. These plots show the snow cover area not its water equivalent, but they reflect a lack of snow in the Alps, feeding the four major rivers, Rhine, Danube, Rhone, and Po.

Figure 7a: Snow Cover Fraction of each catchment (The climatology shows percentiles 0 (minimum), 25 (first quartile), 50 (median), 75 (third quartile) and 100 (maximum)); the red line shows the 2023 conditions.

Figure 7b: Catchments used for calculating the assessment for the Alpine snow conditions.

Assessment of the snow-covered area in the subtropical Andes during the last two winters

Mariano Masiokas, Leandro Cara, Ricardo Villalba (Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales, Mendoza, Argentina), René Garreaud, Duncan Christie (Center for Climate and Resilience Research, Santiago, Chile)

The snow that accumulates each winter in the subtropical Andes represents a crucial water resource for most human activities across central Chile and central-western Argentina. This snow regulates the flows of mountain rivers, is of central importance for the subsistence of glaciers and other ice masses, and provides the largest volumes of water for recharging the aquifers used in the populated lowland areas on both sides of the Andes.

The winter snow accumulation in this portion of the Andes has declined substantially since 2009-2010, resulting in an extended dry period (locally known as the “Megadrought”) that is unprecedented in the instrumental record. In an assessment of the daily 2000-2021 MODIS snow covered area (SCA) data from this region, the winter of 2021 showed in many sectors the lowest values on record. An update to this assessment indicates that the SCA showed some improvements during the winter of 2022, reaching average to slightly above average conditions towards the northern and southern margins of the study region (Figure 8). Nonetheless, below-average conditions persisted along the central watersheds between ca. between ca. 32° and 37°S, especially along the eastern Argentinean side of the Andes. As these watersheds contain the most populated urban centres of the region, cities like Santiago de Chile and Mendoza, in Argentina, have continued with water shortages and water restriction measures implemented by local governments to face the long-lasting drought.

Figure 8: A) Map of the study area in the Andes of Chile and Argentina between ca. 27° and 39°S. The main river basins are indicated together with the cities of Santiago and Mendoza in Chile and Argentina, respectively. B) Mean cold-season (April-October) SCA in the study area, expressed as the average number of days each pixel is covered by snow during this part of the year. The SCA values were derived from daily NASA MODIS satellite images and extracted from the web platform Observatorio de Nieve en los Andes de Argentina y Chile. C) Cold-season SCA anomalies for the year 2021. D) Same as C), but for the year 2022.

Third Pole Region

Lijuan Ma (National Climate Center, China Meteorological Administration, Beijing, China)

In 2022/2023, snow cover extent (SCE) in the Third Pole region was near to normal, with SCE slightly below normal for February and March, but above normal for the other months. As a result, seasonal mean SCE for the winter of 2022/2023 and the spring of 2023 were 4.3×103 km² (0.32%) and 16.3×103 km² (2.5%) larger than normal, which were 1344.5×103 km² and 668.9×103 km², respectively.

Spatially, the number of snow cover days (NSCD) in 2022/2023 winter was dominated by negative anomalies, especially in the middle and eastern part of the core area (above 2000 m), with negative anomalies exceeded -20 days along mid-Himalayas. While in the past spring, positive NSCD anomalies extended in the middle to northeastern part of the core area, compare to winter. However, in northwestern part of the core area, the positive anomalies in spring were not as obvious as that in winter (Figure 9).

Figure 9: Anomalies of the number of snow cover days in winter of 2022/2023 (left, DJF) and spring of 2023 (right, MAM), relative to the 2005–2020 average.

Cryosphere changes and Water Management in Central Asia

Joel Fiddes (Institute for Snow and Avalanche Research, Davos, Switzerland)

Over the last decade increasing attention has been paid to monitoring of the Central Asian cryosphere due to its critical role in regional water security and vulnerability to global climate change. Several international projects supported by the international community have focused on glacier monitoring, high altitude hydro-meteorological observations, permafrost and seasonal snow cover. Building and sustaining regional and national capacity is extremely important in ensuring sustainability of these programs, to enable long-term understanding of cryospheric changes and their impacts on water resources and other environmental changes (Figure 10).

Figure 10: A comprehensive cryosphere monitoring network has been built up over the last decade within projects CATCOS, CICADA and CROMO-ADAPT, supported by the Swiss Development Corporation, and cover glacier measurements, as well as Permafrost and seasonal snow (CROMO-ADAPT).

The SAPPHIRE Central Asia Project (Smart & Precise Prognostic Hydrology in Central Asia) is an initiative developed to support the Central Asian National Hydrometeorological Services in utilizing modern automatic or remote monitoring technologies, e.g. processing and operationalizing high-frequency data from modern gauging stations. One activity on operationalizing cryosphere data is a basin scale snow tracker which compares real-time modelled snow water equivalent against climatic averages to identify anomalies over large areas (Figure 11) providing the real-time analysis of snow water equivalent over the course of the snow season, over the region.

Figure 11: Snow trackers using a mixture of models and remote sensing to give realtime analysis of snow water equivalent anomalies at basin scale.

Georgian glaciers

G. Kordzakhia, L. Shengelia, G. Tvauri (Georgian HydroMet Service)

In Georgia, on the ridge of the Greater Caucasus there are well-developed, high-elevation high glaciers (the highest elevation of glaciers 5 174 m). Since the 1960s, climate change has led to the disappearance of 29% of Georgia’s glaciers, and the areas covered by glaciers has decreased by 30.3%, with direct impacts on the water balance, the degradation of landscapes, the contribution to the increase in the level of the Black Sea, and the growth of the natural disasters frequency and intensity of glacial origin, with material and human losses.

The Shkhara glacier retreat illustrates well the accelerated melting of the Georgian glaciers over the last half of century. The data from LANDSAT and from field observations of the hydrometeorological department of Georgia show that the retreating speed of glacier Shkhara has increased from approximately 6.5 m/year to approximately 14.7 m/year. (Figure 12). A similar change was observed for other large glaciers of Georgia.

Figure 12: Graphs of the Shkhara glacier retreat: blue curve according to ground observations and orange one according to SRS data.

Asian Water Tower

Tandong Yao (Institute of Tibetan Plateau Research)

The Third Pole, encompassing the Tibetan Plateau, the Himalayas, the Karakorum, the Hindu Kush, the Pamirs, and the Tien Shan Mountains, is characterized as the Asian Water Tower (AWT) which is the most important and most vulnerable among the water towers of the world. The AWT is the planet’s largest reservoir of ice and snow after Arctic and Antarctic regions. It provides a reliable water supply to almost 2 billion people.

Glacial melting is accelerating in the region. From 2000 to 2018, total glacier mass in the AWT decreased by approximately 4.3%, with a heterogeneous spatial pattern with greatest magnitude of melting in the southeastern Tibetan Plateau and smaller retreat or even gain in mass in the Karakoram, western Kunlun, and eastern Pamir. Permafrost degradation is obvious, which is characterized by thickening of the active layer, rising of ground temperature, and shortening of frozen duration of active layer. Snow cover area has significantly decreased, and the snowmelt season has shortened. The number, total area and volume of glacier lakes have increased rapidly as a whole and the total water mass in lakes has increased by approximately 16% of the total lake volume.

During 1980-2018, annual river run-off across most of the AWT showed a significant increase in rivers such as the upper Indus (+3.9 Gt per decade) but stable status in rivers (such as the Yangtze and Salween), while a decline in run-off was observed in the Yellow River (–1.5 Gt per decade). In a warming world, climate change will further exacerbate this imbalance, with increased water scarcity in the Indus and Amu Darya River basins and decreased scarcity in the Yangtze and Yellow River basins.

Figure 13: Observed changes of glaciers, lakes and run-off over the AWT. a, Spatial patterns of glacier mass balance between 2000 and 2018 based on digital elevation models. b, Eight continuous mass balance measurements in endorheic and exorheic basins. c, Spatial pattern of basin-wide lake volume changes between 1976 and 2019. d, Time series of total lake volume changes in endorheic and exorheic basins. e, Spatial pattern of run-off trends for seven large rivers. f, Time series of run-off for three rivers in endorheic basins and four rivers in exorheic basins.

References

Brasnett, B. 1999. A global analysis of snow depth for numerical weather prediction, Journal of Applied Meteorology 38:726-740.

Brown, R., Derksen, C., and Wang, L. (2010), A multi-data set analysis of variability and change in Arctic spring snow cover extent, 1967–2008, J. Geophys. Res., 115, D16111, doi:10.1029/2010JD013975.

Brown, R. D., and P. W. Mote, 2009: The Response of Northern Hemisphere Snow Cover to a Changing Climate. J. Climate, 22, 2124–2145, https://doi.org/10.1175/2008JCLI2665.1.

Estilow, T. W., A.H. Young, and D.A. Robinson, 2015: A long-term Northern Hemisphere snow cover extent data record for climate studies and monitoring. Earth Syst. Sci. Data, 7, 137–142, doi:10.5194/essd-7-137-2015.

Reference: Gascoin, S., Monteiro, D., & Morin, S. (2022). Reanalysis-based contextualization of real-time snow cover monitoring from space. Environmental Research Letters, 17(11), 114044. https://doi.org/10.1088/1748-9326/ac9e6a.

Helfrich, S. R., D. McNamara, B. H. Ramsay, T. Baldwin, and T. Kasheta, 2007: Enhancements to, and forthcoming developments to the Interactive Multisensor Snow and Ice Mapping System (IMS), Hydrological Processes 21: 12, 1576-1586. doi:10.1002/hyp.6720.

Hersbach, H., B. Bell, P. Berrisford, et al.: The ERA5 Global Reanalysis, QJRMS, 146, 1999-2049,2020, https://doi.org/10.1002/qj.3803.

Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J., Moisander, M., Mortimer, C., Derksen, C., Hiltunen, M., Smolander, T., Ikonen, J., Cohen, J., Veijola, K., and Venäläinen, P.: "GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset". Scientific Data 8, 163 (2021). https://doi.org/10.1038/s41597-021-00939-2.