Present-day and future Greenland Ice Sheet precipitation frequency from satellite observations and an Earth System Model

Lenaerts, Jan T. M.; Camron, M. Drew; Wyburn-Powell, Christopher; Kay, J. E.

doi:10.5194/tc-2020-31

Cited by 3 publications

(5 citation statements)

References 16 publications

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“…. Two recent studies have used CloudSat's CPR to look at snowfall over the GIS in particular: Lenaerts et al (2019) focused on GIS snowfall frequency and leveraged the satellite observations to evaluate climate model output; and Bennartz et al (2019) used the radar measurements to provide the first in-depth, observationally based snowfall rate estimates of the GIS.…”

mentioning

confidence: 99%

Satellite Observations of Snowfall Regimes over the Greenland Ice Sheet

McIlhattan

Pettersen

Wood

et al. 2019

Preprint

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Abstract. The mass of the Greenland Ice Sheet (GIS) is decreasing due to surface melt and ice dynamics. Snowfall both adds mass to the GIS and has the capacity to reduce surface melt by increasing surface brightness, reflecting additional solar radiation back to space. This study leverages the synergy between two satellite instruments, CloudSat's Cloud Profiling Radar (CPR) and CALIPSO's Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), to identify snowfall cases over the GIS and partition them into two regimes: those associated with exclusively ice-phase cloud processes (IC) and those involving mixed-phase processes indicated by the presence of super-cooled liquid water (CLW). Overall, most CPR observations of snowfall over the GIS come from IC events (70 %), however, during the summer months, close to half of the snow observed is produced in CLW events (45 %). IC snowfall plays a dominant role in building the GIS, producing ~80 % of the total estimated 399 Gt yr−1 accumulation. Beyond the cloud phase that defines the snowfall regimes, the macrophysical cloud characteristics are distinct as well; the mean IC geometric cloud depth (~4 km) is consistently deeper than the CLW geometric cloud depth (~2 km), consistent with previous studies based on surface observations. Two-dimensional histograms of the vertical distribution of CPR reflectivities show that IC events demonstrate consistent growth toward the surface while CLW events do not. Analysis of ERA5 reanalyses shows that IC events are associated with cyclone activity and CLW events occur under large scale anomalous high pressure. Ground-based data is used to estimate the sensitivity of CloudSat's CPR to the two snowfall regimes, finding that the space-based radar is sensitive enough to detect ~95 % of IC snowfall cases and ~75 % of CLW snowfall cases seen at the surface.

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mentioning

confidence: 99%

Satellite Observations of Snowfall Regimes over the Greenland Ice Sheet

McIlhattan

Pettersen

Wood

et al. 2019

Preprint

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“…In Chapter 4, we described model progress towards CESM2, and presented a detailed assessment of GrIS surface energy and mass balance (SEB and SMB) using six historical ensemble members. A complementary study by Lenaerts et al [2020a] demonstrated that the Community Atmosphere Model version 6 (CAM6, the atmosphere component in CESM2) is so far the best CAM version for modelling clouds over Greenland, which further adds confidence to our results. On balance, we conclude that CESM2 is a capable tool in modelling GrIS SMB in a physically-based way, and is likely one of the few ESMs that can currently do this.…”

Section: General Conclusion From This Thesissupporting

confidence: 76%

“…This aligns with our results in Chapter 5, where we favorably compare cloud output from coupled CESM2 simulations to Cloudsat-CALIPSO. However, both our study and [Lenaerts et al, 2020a] indicate that the representation of clouds in CAM6 is not perfect, as for instance illustrated by biases in ice water path and longwave cloud forcing.…”

Section: Challenge 2: Atmospheric Forcingmentioning

confidence: 68%

“…Still, rapid progress is being made on this subject, also within CESM. A recent study by Lenaerts et al [2020a] compares atmosphere-only simulations performed with different Community Atmosphere Model (CAM) physics (CAM versions 4,5,6, and some intermediate versions) to ground and satellite observations, and finds that CAM6 compares best to observations in terms of cloud liquid water and longwave cloud forcing. This aligns with our results in Chapter 5, where we favorably compare cloud output from coupled CESM2 simulations to Cloudsat-CALIPSO.…”

Section: Challenge 2: Atmospheric Forcingmentioning

confidence: 99%

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Simulating the Greenland Ice Sheet Surface Mass Balance using an Earth System Model

Kampenhout¹

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“…Since the beginning of DO-Op, the CPR has collected data primarily during only the sunlit portion of the orbit. While many studies have so far used data from the Full Operations (Full-Op) period prior to the battery anomaly [4,7,14,15], the need for more recent CloudSat observations for comparison against new sensors such as the Global Precipitation Measurement (GPM) Core Observatory [16,17] or for the recent validation and optimization of models in a changing climate have increasingly required the use of the DO-Op observations as well [5,9,18,19]. This study compares CloudSat snowfall measurements taken during the Full-Op period against those taken during the DO-Op period to assess the impact of this change in sampling on snowfall properties estimated from the CloudSat snowfall product.…”

Section: Introductionmentioning

confidence: 99%

Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

Milani

Wood

2021

Remote Sensing

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Falling snow is a key component of the Earth’s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have used snowfall properties measured by it for evaluating and comparing against their snowfall products. Since a battery anomaly in 2011, the CPR has operated in a Daylight-Only Operations (DO-Op) mode, in which it makes measurements primarily during only the daylit portion of its orbit. This work provides estimates of biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat’s snowfall measurements during its Full Operations (Full-Op) period prior to the battery anomaly to evaluate the impact of the DO-Op mode sampling. For multi-year global mean values, the snowfall fraction during DO-Op changes by −10.16% and the mean snowfall rate changes by −8.21% compared with Full-Op. These changes are driven by the changes in sampling in DO-Op and are very little influenced by changes in meteorology between the Full-Op and DO-Op periods. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.

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Present-day and future Greenland Ice Sheet precipitation frequency from satellite observations and an Earth System Model

Cited by 3 publications

References 16 publications

Satellite Observations of Snowfall Regimes over the Greenland Ice Sheet

Satellite Observations of Snowfall Regimes over the Greenland Ice Sheet

Simulating the Greenland Ice Sheet Surface Mass Balance using an Earth System Model

Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

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