We propose a new method to determine the ground surface temperature under the snowpack in the Arctic area from satellite observations. The obtained ground temperature time series were evaluated over 21 reference sites in Northern Alaska and compared with ground temperatures obtained with global models. The method is extremely promising for monitoring ground temperature below the snowpack and studying the spatio-temporal variability thanks to 15 years of observations over the whole Arctic area.

Microwave L-band radiometry offers a promising tool for estimating the total surface-to-subsurface liquid water amount (LWA) in the snow and firn in polar ice sheets. An accurate modelling of wet snow effective permittivity is a key to this. Here, we evaluated the performance of ten commonly used microwave dielectric mixing models for estimating LWA in the percolation zone of the Greenland Ice Sheet to help an appropriate choice of dielectric mixing model for LWA retrieval algorithms.

Precipitation (P) and soil moisture (SM) are critical components of the climate system but poorly understood in the Arctic. Using NASA's SMAP satellite, we analyzed SM and P patterns in Finland. SM and P correlate strongly in summer and fall but weakly in spring due to snowmelt. While the area of P can be estimated from SM, estimating its intensity is more challenging. Water bodies complicate SM retrieval. The promising results suggest this method could be applied across the Arctic.

We use model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutant budgets in this area as local anthropogenic emissions drop.

We used L-band observations from the SMAP mission to quantify the surface and subsurface liquid water amounts (LWA) in the percolation zone of the Greenland ice sheet. The algorithm is described, and the validation results are provided. The results demonstrate the potential for creating an LWA data product across GrIS, which will advance our understanding of ice sheet physical processes for better projection of Greenland’s contribution to global sea level rise.