Abstract:In the absence of widespread snowfall observations over the Arctic Ocean, reanalysis products provide a wide range of estimates of time‐evolving snowfall rates over Arctic sea ice, and it can be difficult to determine which product is most representative. In this work, Arctic snowfall rates retrieved from 2006 to 2016 CloudSat observations and snowfall products from three reanalyses are assessed. The products can be brought into encouraging agreement over the region on interannual time scales once differences … Show more
“…The declining trend in summer snowfall is statistically significant (p < 0.05) in JRA55 and MERRA2 but is not statistically significant in ERA5. Consistent with previous studies (Barrett et al, 2020;Boisvert et al, 2018;Cabaj et al, 2020), Arctic summer snowfall in MERRA2 is generally higher than other reanalysis products. Likewise, the numbers of snowstorm events in these three reanalyzes show similar interannual variations and long-term trends (red lines in Figures 1a, 1c, and 1e).…”
The Arctic near-surface atmosphere has warmed at more than twice the rate of the global average over recent decades, while summer sea ice coverage has reduced by around 50% (Walsh, 2014). The positive ice-albedo feedback along with several other feedback mechanisms have contributed to these rapid changes (Goosse et al., 2018;Serreze & Barry, 2014). Because the Arctic climate is generally dry, where column-integrated water vapor on seasonal time scales is less than ∼20 mm even in the wettest summer (Rinke et al., 2019;Serreze & Barry, 2014;Serreze et al., 1995), winter snow accumulation is generally limited to ∼20-30 cm in the Eurasian and the Pacific Seas (
“…The declining trend in summer snowfall is statistically significant (p < 0.05) in JRA55 and MERRA2 but is not statistically significant in ERA5. Consistent with previous studies (Barrett et al, 2020;Boisvert et al, 2018;Cabaj et al, 2020), Arctic summer snowfall in MERRA2 is generally higher than other reanalysis products. Likewise, the numbers of snowstorm events in these three reanalyzes show similar interannual variations and long-term trends (red lines in Figures 1a, 1c, and 1e).…”
The Arctic near-surface atmosphere has warmed at more than twice the rate of the global average over recent decades, while summer sea ice coverage has reduced by around 50% (Walsh, 2014). The positive ice-albedo feedback along with several other feedback mechanisms have contributed to these rapid changes (Goosse et al., 2018;Serreze & Barry, 2014). Because the Arctic climate is generally dry, where column-integrated water vapor on seasonal time scales is less than ∼20 mm even in the wettest summer (Rinke et al., 2019;Serreze & Barry, 2014;Serreze et al., 1995), winter snow accumulation is generally limited to ∼20-30 cm in the Eurasian and the Pacific Seas (
“…Although ERA5 is a reanalysis product, it has been shown to have good representation of precipitation over the Arctic relative to buoy and satellite data, and is superior to its predecessor, ERA-Interim. Two recent studies conclude that ERA5 is the best data set currently available to represent precipitation across the Arctic region 29 , 30 . Fig.…”
As the Arctic continues to warm faster than the rest of the planet, evidence mounts that the region is experiencing unprecedented environmental change. The hydrological cycle is projected to intensify throughout the twenty-first century, with increased evaporation from expanding open water areas and more precipitation. The latest projections from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) point to more rapid Arctic warming and sea-ice loss by the year 2100 than in previous projections, and consequently, larger and faster changes in the hydrological cycle. Arctic precipitation (rainfall) increases more rapidly in CMIP6 than in CMIP5 due to greater global warming and poleward moisture transport, greater Arctic amplification and sea-ice loss and increased sensitivity of precipitation to Arctic warming. The transition from a snow- to rain-dominated Arctic in the summer and autumn is projected to occur decades earlier and at a lower level of global warming, potentially under 1.5 °C, with profound climatic, ecosystem and socio-economic impacts.
“…We currently use ERA-Interim snowfall to force the NESOSIM model, which has an approximately 2-month data latency; however, ERA Interim is being replaced by ERA5, which has a data latency of only ~2 days, similar to the CDR ice conentration (Meier et al, 2017) and OSI-SAF ice drift (Lavergne et al, 2010) product latencies that are also needed to produce NESOSIM snow depths. ERA5 exhibits a high bias in snowfall compared to ERA-I (Cabaj et al, 2020;Wang et al, 2019), so updated ERA5/NESOSIM model calibration is currently ongoing in preparation for the 2019/2020 winter. Both the NESOSIM and ICESat-2 source codes are open source and publicly available (links provided below).…”
Section: Toward An Icesat-2 Sea Ice Thickness Productmentioning
National Aeronautics and Space Administration's (NASA's) Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System, is now providing routine, very high-resolution, surface elevation data across the globe, including the Arctic and Southern oceans. In this study, we demonstrate our new processing chain for converting the along-track ICESat-2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion, we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model. The coarse resolution (~100 km) snow data are redistributed onto the high-resolution (approximately 30-100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA's Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from European Space Agency's CryoSat-2 satellite mission, with ICESat-2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19 February to 21 March) 2008 ice thickness estimates using the same input assumptions, which show an~0.37 m or~20% thinning across an inner Arctic Ocean domain in this 11-year time period. Plain Language Summary NASA's ICESat-2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard is a highly precise laser, which is now providing routine, very high-resolution, surface height measurements across the globe, including over the Arctic and Southern oceans. In this study, we show new estimates of Arctic sea ice thickness from the first winter season of data collected by ICESat-2. Sea ice thickness is calculated by combining the measured ICESat-2 freeboards-the extension of sea ice above sea level-with a new snow on sea ice model. Our derived thicknesses are consistently lower than the thicknesses calculated from ESA's CryoSat-2 data and the original ICESat mission, which ended in 2008. More work is needed to verify these new thickness estimates.
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