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Clouds enhance Greenland ice sheet meltwater runoff - PubMed

  • ️Fri Jan 01 2016

Clouds enhance Greenland ice sheet meltwater runoff

K Van Tricht et al. Nat Commun. 2016.

Abstract

The Greenland ice sheet has become one of the main contributors to global sea level rise, predominantly through increased meltwater runoff. The main drivers of Greenland ice sheet runoff, however, remain poorly understood. Here we show that clouds enhance meltwater runoff by about one-third relative to clear skies, using a unique combination of active satellite observations, climate model data and snow model simulations. This impact results from a cloud radiative effect of 29.5 (±5.2) W m(-2). Contrary to conventional wisdom, however, the Greenland ice sheet responds to this energy through a new pathway by which clouds reduce meltwater refreezing as opposed to increasing surface melt directly, thereby accelerating bare-ice exposure and enhancing meltwater runoff. The high sensitivity of the Greenland ice sheet to both ice-only and liquid-bearing clouds highlights the need for accurate cloud representations in climate models, to better predict future contributions of the Greenland ice sheet to global sea level rise.

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Figures

Figure 1
Figure 1. Retrieved cloud properties over the GrIS by satellite remote sensing.

These retrievals result from joint CloudSat, CALIPSO and MODIS observations. Both (a) cloud ice and (b) cloud liquid water occur frequently over the GrIS, although strong spatial variations exist. (c) Mean IWP in all conditions and (d) mean LWP in all conditions also show spatial variability in cloud water contents. Dashed curves indicate 1,000 m height contours and the black dot represents the location of the Summit station.

Figure 2
Figure 2. CRE over the GrIS.

(a) The average relationship between mean LWP+IWP and mean annual CRE is positive at each location, with higher radiative effects from optically thicker (more liquid and/or ice) or more frequent clouds. The purple circles were derived from satellite observations, whereas the orange squares result from the SNOWPACK runs. The satellite-retrieved CRE estimates assume fixed surface conditions (albedo and surface temperature). In reality, these conditions may change in the absence of the detected clouds. The entire snowpack changes as a result of cloud warming, reducing the initial CRE (lower values in the orange curve). (b) Yearly mean CRE over the GrIS (2007–2010) from the SNOWPACK output. This estimate takes into account changing surface conditions (albedo and surface temperature) and snowpack with changing energy inputs due to the presence/absense of clouds. Dashed lines indicate 1,000 m height contours and the black dot represents the location of the Summit station.

Figure 3
Figure 3. The effects of clouds on the SMB during the period September 2007–September 2010.

(a) Evolution of GrIS SMB indicates that the cloudy simulations have a lower SMB. Uncertainties are shown by the shaded areas. The black curve represents the regional climate model RACMO2.3 SMB values and show the performance of SNOWPACK in simulating the GrIS SMB. (b) Yearly GrIS melt, refreezing and runoff. Despite negligible differences in melt, 58% of the meltwater refreezes in clear-sky conditions, whereas only 45% refreezes in all-sky conditions. Annual meltwater runoff is therefore about one-third higher in the presence of clouds, with a slightly higher contribution of liquid-bearing clouds. The whiskers indicate an inherent SNOWPACK uncertainty and the sensitivity to the amount of LWP/IWP in the SNOWPACK simulations.

Figure 4
Figure 4. Case study showing cloud impacts on melt/refreezing during 16–21 June 2008 at 67°N–49°E.

Solid curves represent all-sky conditions and dashed curves represent clear-sky conditions. Grey zones are characterized by a SZA>70°, considered to be nighttime. All variables as shown in the figure have been smoothed using a 6-hourly moving-average window. (a) Total (liquid+ice) water path, showing a cloud in the all-sky simulation. (b) Downwelling SW (green) and LW (purple) radiative fluxes. In the presence of a cloud, SW cooling (solid curve below dashed curve) and LW warming (solid curve above dashed curve) occur simultaneously during daytime, whereas LW warming dominates nighttime. (c) Internal energy change of the snowpack due to melting (positive) and refreezing (negative) processes. Melt rates are highest at solar noon when SW insolation peaks, whereas refreezing rates are highest at night when strong surface radiative cooling dominates. In the presence of clouds, this radiative cooling is reduced, impeding the refreezing mechanism. (d) Cumulative meltwater runoff in snow water equivalent (SWE) is higher under cloudy conditions, due to limited meltwater refreezing and earlier bare-ice exposure. (e) Surface albedo as simulated by SNOWPACK. Persistent warming by clouds enhances meltwater runoff, leading to an earlier exposure of bare ice and slush that have a much lower albedo than (fresh) snow (from 19 June onwards). At this point, the warming is amplified due to a much higher absorption of SW radiation in the all-sky simulation, as opposed to the clear-sky simulation.

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