Snow on Arctic Sea Ice in a Warming Climate as Simulated in CESM

Earth system models are valuable tools for understanding how the Arctic snow-ice system and the feedbacks therein may respond to a warming climate. In this analysis, we investigate snow on Arctic sea ice to better understand how snow conditions may change under different forcing scenarios. First, we use in situ, airborne, and satellite observations to assess the realism of the Community Earth System Model (CESM) in simulating snow on Arctic sea ice. CESM versions one and two are evaluated, with V1 being the Large Ensemble experiment (CESM1-LE) and V2 being configured with low- and high-top atmospheric components. The assessment shows CESM2 underestimates snow depth and produces overly uniform snow distributions, whereas CESM1-LE produces a highly variable, excessively-thick snow cover. Observations indicate that snow in CESM2 accumulates too slowly in autumn, too quickly in winter-spring, and melts too soon and rapidly in late spring. The 1950-2050 trends in annual mean snow depths are markedly smaller in CESM2 (-0.8 cm decade-1) than in CESM1-LE (-3.6 cm decade-1) due to CESM2 having less snow overall. A perennial, thick sea-ice cover, cool summers, and excessive summer snowfall facilitate a thicker, longer-lasting snow cover in CESM1-LE. Under the SSP5-8.5 forcing scenario, CESM2 shows that, compared to present-day, snow on Arctic sea ice will: (1) undergo enhanced, earlier spring melt, (2) accumulate less in summer-autumn, (3) sublimate more, and (4) facilitate marginally more snow-ice formation. CESM2 also reveals that summers with snow-free ice can occur ∼30-60 years before an ice-free central Arctic, which may promote faster sea-ice melt.

Long-Term Performance Assessment of Low-Cost Atmospheric Sensors in the Arctic Environment

The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m-3 for PM2.5 and ≈3 µg m-3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m-3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment.

Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective

Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth's atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global precipitation Measurement (GPM) Core Observatory satellite and CloudSat's Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow-rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow-rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM's Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)-snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR-DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z-S approach.

Feedbacks and Interactions: From the Arctic Cryosphere to the Climate System

Changes in the Arctic’s climate are a result of complex interactions between the cryosphere, atmosphere, ocean, and biosphere. More feedbacks from the cryosphere to climate warming are positive and result in further warming than are negative, resulting in a reduced rate of warming or cooling. Feedbacks operate at different spatial scales; many, such as those operating through albedo and evapotranspiration, will have significant local effects that together could result in global impacts. Some processes, such as changes in carbon dioxide (CO₂) emissions, are likely to have very small global effects but uncertainty is high whereas others, such as subsea methane (CH₄) emissions, could have large global effects. Some cryospheric processes in the Arctic have teleconnections with other regions and major changes in the cryosphere have been largely a result of large-scale processes, particularly atmospheric and oceanic circulation. With continued climate warming it is highly likely that the cryospheric components will play an increasingly important climatic role. However, the net effect of all the feedbacks is difficult to assess because of the variability in spatial and temporal scales over which they operate. Furthermore, general circulation models (GCMs) do not include all major feedbacks while those included may not be accurately parameterized. The lack of full coupling between surface dynamics and the atmosphere is a major gap in current GCMs.