Arctic soil carbon insulation averts large spring cooling from surface-atmosphere feedbacks.

The insulative properties of soil organic carbon (SOC) and surface organic layers (moss, lichens, litter) regulate surface-atmosphere energy exchanges in the Arctic through a coupling with soil temperatures. However, a physical description of this process is lacking in many climate models, potentially biasing their high-latitude climate predictions. Using a coupled surface-atmosphere model, we identified a strong feedback loop between soil insulation, surface air temperature, and snowfall.

Machine learning for accelerating process-based computation of land biogeochemical cycles.

Global change ecology nowadays embraces ever-growing large observational datasets (big-data) and complex mathematical models that track hundreds of ecological processes (big-model). The rapid advancement of the big-data-big-model has reached its bottleneck: high computational requirements prevent further development of models that need to be integrated over long time-scales to simulate the distribution of ecosystems carbon and nutrient pools and fluxes. Here, we introduce a machine-learning acceleration (MLA) tool to tackle this grand challenge.

Process-oriented analysis of dominant sources of uncertainty in the land carbon sink.

The observed global net land carbon sink is captured by current land models. All models agree that atmospheric CO and nitrogen deposition driven gains in carbon stocks are partially offset by climate and land-use and land-cover change (LULCC) losses. However, there is a lack of consensus in the partitioning of the sink between vegetation and soil, where models do not even agree on the direction of change in carbon stocks over the past 60 years.

Radon measurements--discussion of error estimates for selected methods.

The main sources of uncertainties for grab sampling, short-term (charcoal canisters) and long term (track detectors) measurements are: systematic bias of reference equipment; random Poisson and non-Poisson errors during calibration; random Poisson and non-Poisson errors during measurements. The origins of non-Poisson random errors during calibration are different for different kinds of instrumental measurements. The main sources of uncertainties for retrospective measurements conducted by surface traps techniques can be divided in two groups: errors of surface (210)Pb ((210)Po) activity measurements and uncertainties of transfer from (210)Pb surface activity in glass objects to average radon concentration during this object exposure.