The role of clouds in the climate of Greenland

• Main Author: Van Tricht, Kristof
• Other Authors: Van Lipzig, Nicole; U0049742;, Lhermitte, Stef; U0036095;, Gorodetskaya, Irina; U0062809;
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: 2016
• Subjects: Greenland; Groenland; Ice Sheet
• Online Access: https://lirias.kuleuven.be/handle/123456789/544771; https://lirias.kuleuven.be/bitstream/123456789/544771/1//PhD_VanTrichtKristof.pdf

The Greenland ice sheet (GrIS) is the second largest body of ice on Earth. Holding a potential of 7 m of global mean sea level rise, its rapidly increasing mass loss in response to global climate change will affect the entire planet. This mass loss is partly the result of a strongly decreasing surface mass balance (SMB), predominantly through increased meltwater runoff. Yet, the mechanisms involved in this decreasing SMB remain poorly understood. Recently, clouds have emerged as potential contributors to increased melt rates over the GrIS through their radiative warming of the surface, but Greenland-wide assessments of this effect are still largely lacking. Here we show that clouds have on average a radiative effect of 29.5 (±5.2) W m−2, using a unique combination of active satellite remote sensing, ground-based observations and a regional climate model. We develop an improved algorithm for cloud-base detection by ceilometer in polar regions, a smart sampling approach for estimating surface radiative fluxes based on CloudSat and CALIPSO satellite observations, and a hybrid satellite-climate model dataset with improved temporal resolution over the GrIS. Using snow model simulations, we show that the demonstrated radiative effect is responsible for a one-third increase in GrIS meltwater runoff compared to clear-sky conditions. Unexpectedly, this enhanced meltwater runoff is not caused by a direct increase in meltwater generation during the day, but rather by a reduction in refreezing rates of meltwater at night, when cloud warming is largest. Given the demonstrated high sensitivity of the GrIS to clouds in combination with the current inability of state-of-the-art climate models to reproduce the observed cloud properties, we conclude that only by incorporating new knowledge from observations in cloud parameterizations, we will be able to enhance the reliability of future projections of the GrIS and its contribution to global sea level rise. nrpages: 159 status: published