Surface albedo is a key variable controlling solar radiation absorbed at the
Greenland Ice Sheet (GrIS) surface and, thus, meltwater production. Recent
decline in surface albedo over the GrIS has been linked to enhanced snow
grain metamorphic rates, earlier snowmelt, and amplified melt–albedo feedback
from atmospheric warming. However, the importance of distinct surface types
on ablation area albedo and meltwater production is still relatively
unknown. In this study, we analyze albedo and ablation rates using in situ
and remotely sensed data. Observations include (1) a new high-quality in
situ spectral albedo data set collected with an Analytical Spectral Devices
Inc. spectroradiometer measuring at 325–1075 nm along a 1.25 km transect
during 3 days in June 2013; (2) broadband albedo at two automatic weather
stations; and (3) daily MODerate Resolution Imaging Spectroradiometer (MODIS)
albedo (MOD10A1) between 31 May and 30 August 2012 and 2013. We find that
seasonal ablation area albedos in 2013 have a bimodal distribution, with
snow and ice facies characterizing the two peaks. Our results show that a
shift from a distribution dominated by high to low albedos corresponds to an
observed melt rate increase of 51.5 % (between 10–14 July and 20–24
July 2013). In contrast, melt rate variability caused by albedo changes
before and after this shift was much lower and varied between
Surface albedo, defined as the bihemispheric reflectance integrated across the visible and near-infrared wavelengths (Schaepman-Strub et al., 2006), is a key variable controlling Greenland Ice Sheet (GrIS) surface melting. During the melt season, surface albedo modulates absorbed solar radiation at the ice surface and, consequently, the surface energy and mass balance of the ice sheet (Cuffey and Paterson, 2010). Over the last decade, an observed decline in albedo has been linked to less summer snow cover, expansion of bare ice area, and enhanced snow grain metamorphic rates from atmospheric warming, amplified by the melt–albedo feedback (Box et al., 2012; Stroeve et al., 2013; Tedesco et al., 2011). This positive feedback involves increased melting and exposure of bare ice, impurities, and meltwater ponding, reducing surface albedo and, by increasing solar radiation absorption, accelerating melt further (Box et al., 2012; Tedesco et al., 2011).
The GrIS surface has a wide range of surface types with different albedos, including snow, ice, dust and sediment-rich impurities, cryoconite holes, melt ponds, and streams. Yet, the importance of these surface types on ablation area albedos and thus, meltwater production over the melt season is still relatively unresolved (Rennermalm et al., 2013). Current state-of-the-art surface mass balance (SMB) models, such as Modèle Atmosphérique Régionale (MAR) v3.2 and Regional Atmospheric Climate MOdel (RACMO2), consider some variability in surface types by including the presence of meltwater ponding, snow, and bare ice surfaces to characterize seasonal variations in ablation area albedo (Alexander et al., 2014; Van Angelen et al., 2012). Furthermore, RACMO2 considers the presence of black carbon concentrations on snow and is capable of utilizing realistic MODerate Resolution Imaging Spectroradiometer (MODIS) background albedo data (Van Angelen et al., 2012), thereby representing the impact of surface types spatially aggregated to the MODIS resolution. However, few studies have utilized these modeling tools to understand how the distributions of surface types are changing ablation area albedo (e.g., Alexander et al. 2014). This is increasingly important due to enhanced surface melt associated with anomalously warm atmospheric circulation patterns in 2007–2012 (Hall et al., 2013; Nghiem et al., 2012; Tedesco et al., 2013) that may become more frequent in the future. Additionally, some studies suggest that a new control of ice sheet albedo is the deposition and accumulation of light-absorbing impurities advected from snow-free areas and forest fires outside of Greenland (Dumont et al., 2014; Keegan et al., 2014).
The large-scale decline in albedos has been greatest in southwest Greenland
(
Changes in surface albedo are typically characterized from the MODIS and the Advanced Very High Resolution Radiometer (AVHRR) satellite sensors (e.g., Chandler et al., 2015; Stroeve et al., 2013; Wang et al., 2012; Wright et al., 2014) or modeled with regional climate models (RCMs) such as RACMO2 (Van Meijgaard et al., 2008) and MAR (Fettweis, 2007). Remotely sensed and modeled albedo has been validated with ground measurements from dispersed Greenland Climate Network automatic weather stations (GC-Net AWS; Knap and Oerlemans, 1996; Steffen and Box, 2001). These comparisons reveal that satellite products provide reasonable albedo estimates (Box et al., 2012; Stroeve et al., 2005, 2006, 2013), although discrepancies between different MODIS albedo products have been identified (Alexander et al., 2014). Despite this, RCM surface albedos remain represented in relatively simplistic terms, particularly in regions that frequently experience prolonged bare ice exposure like southwest Greenland (Fettweis et al, 2011; Fitzgerald et al., 2012; Rae et al., 2012; Van Angelen et al., 2012). This is attributed to a lack of surface roughness in the RCMs (Ettema et al., 2010) and relatively simplistic bare ice and impurity albedo schemes (Alexander et al., 2014), resulting in large inter-model differences in runoff (42 % variance; Vernon et al., 2013) despite the existence of spatially distributed ice albedo schemes and inclusion of black carbon contaminants on snow surfaces (Van Angelen et al., 2012). Recent surface albedo observations and snow model simulations of impurity-rich surfaces have been linked to enhanced ice sheet melt (Chandler et al., 2015; Dumont et al., 2014; Keegan et al., 2014), suggesting that incorporating seasonal changes in the albedo distribution of distinct surface types might improve accuracy of modeled meltwater runoff and GrIS sea level rise contributions. These findings point to the importance of a detailed assessment of high spectral, spatial, and temporal resolution albedo data to quantify how different surface types control ablation area albedo and therefore melt.
In this study, we report the results of an assessment of ablation area albedo along the southwestern GrIS for the 2012 and 2013 melt seasons. We use (1) a new high-quality in situ spectral albedo data set collected with an Analytical Spectral Devices Inc. (ASD) spectroradiometer measuring over a wavelength range of 325–1075 nm along a 1.25 km transect during 3 days in June 2013; (2) in situ broadband albedos at two automatic weather stations; and (3) daily MODIS albedo (MOD10A1) product (Hall et al., 2012) between 31 May and 30 August 2012 and 2013 to investigate how ice sheet surface types influence surface albedo and ablation rates; and (4) summer seasonal changes in surface type coverage reported in literature. First, we describe the collection of high-quality in situ spectral albedos, automatic weather station broadband albedos, and ablation stake measurements collected during early 2013 melt season along a fixed transect in the GrIS ablation area. Second, from the MODIS daily albedo data we estimate seasonal changes in the albedo distributions. These distributions were compared with seasonal changes in computed albedo distributions derived by using in situ and literature values of albedos for distinct surface type and fractional area of surface types from a nearby site (1030 m a.s.l.; reported by Chandler et al., 2015). Third, the impact of changing albedo and surface type coverage on surface melt was quantified and compared with transect ablation stake measurements. Finally, we compare these 2013 results with 2012 MOD10A1 data to better understand the overall frequency distribution, spatiotemporal variability, and ablation rates associated with dominant surface types in southwest Greenland's ablation area. This study presents the first high spatial, temporal, and spectral resolution albedo data set collected in the southwestern GrIS ablation area.
23 June 2013 WorldView-2 true color image (bands 5, 3, and 2 RGB) of the study site with elevation contours (m), MODIS pixel extents (yellow boxes), and location of the six albedo transects, ablation stake, and meteorological station sites. Location of three MODIS spatial extent regions overlaid on a 31 May 2013 MOD10A1 image (black box inset).
The study site is located on the southwestern GrIS approximately 30 km northeast of
Kangerlussuaq, Greenland (Fig. 1). Albedo measurements were
collected along a 1.25 km transect situated between
High spatial (
Spectral albedos were measured along the transect starting at Site E and
ending at Site A on 16, 17, 19, 21, 24, and 25 June 2013 between 10:00 and
18:00 local time (12:00–20:00 GMT). After rigorous quality control (see
Appendix A and B), only transect observations made on 16, 19, and 25 June were
used in analyses. Broadband
Daily average broadband albedos (300–1100 nm),
Daily MODIS broadband albedos (300–3000 nm) were acquired from the MOD10A1
product (Version 005) from NASA's Terra satellite (Hall et al., 2006; Klein
and Stroeve, 2002). High-quality flagged MOD10A1 albedo data (periods of
high SZA and cloudiness were excluded; Schaaf et al., 2011) from 31 May to
30 August 2012 and 2013 (when SZAs are minimized; e.g., Box et al., 2012)
were used in two analyses. First, MOD10A1 albedos for pixels overlapping with
our transect site (Fig. 1), hereafter
Broadband
Surface melting between 8 and 26 June was estimated using ablation stakes
installed at the Base Met Station, hereafter
Two types of melt season albedo distributions were constructed: (1) computed
distributions based on broadband
The computed distributions were constructed by assuming that the albedo
distribution for each distinct surface is represented by a normal
distribution
To compare with the computed distributions, high-quality 2012 and 2013
MOD10A1 data were used to construct observed albedo distributions at three
spatial extents (50
To identify possible snowfall events in our study area and MODIS spatial
extents, hourly precipitation and air temperature measurements collected by
a meteorological station, hereafter 660 Met Station, installed near the ice
sheet edge at the proglacial and ice sheet margin interface (Fig. 1), were
examined. Near-surface air temperature measurements from the shorter Base
Met Station time series (available from 8 to 26 June 2013) were also
examined to estimate temperature differences between the proglacial and ice
surfaces. Tundra near-surface air temperature < 1
To examine seasonal changes in MODIS albedos, and estimate the importance of
distinct surface types, relative surface melt rates were computed using the
net shortwave solar radiation equation, observed values of incoming solar
radiation from the Base Met Station on 16, 19, and 25 June, and broadband
albedo values for computed and observed distribution methods. The observed
incoming solar radiation values were averaged together and kept constant in
the relative melt rate calculations to isolate the effects of albedo changes
on melt. Net solar radiation
High-quality broadband
Descriptive statistics for high-quality albedo transects. SZA and CC listed for Base Met Station only. “brd” is used to abbreviate broadband.
Spatial variability of broadband
High-quality daily average broadband
Temporal variability in daily average
Average broadband
Albedos of dirty and clean ice surfaces are distinctly different for each
ablation stake site (Table 2). Broadband
Computed albedo distribution for a nearby site of Chandler et al. (2015) simulated across the melt season based on observed broadband
Seasonal evolution (%) of four surface types at five distinct time steps approximated from Chandler et al. (2015).
Computed albedo frequencies using typical albedo values for four distinct
surface types (Table 3 and Sect. 3.5) and changing area fractions of these surfaces
identified at a nearby site by Chandler et al. (2015) reveal a bimodal
distribution as the melt season progresses (Fig. 4). The relative strength
of the first and secondary modes change as the fractional area of darker
surfaces expands from “dirty ice exposure” to “melt” distributions and
onwards. At the start of the melt season, the abundance of lighter surfaces
coincides with a higher probability of high broadband
Percent difference in melt rate estimates for different albedo probability density functions and averaged incoming solar radiation conditions at Base Met Station from 16, 19, and 25 June relative to “early summer ice” (1 June) distribution.
Observed distributions of high-quality broadband
Observed 2013 MOD10A1 albedo distributions at three spatial extents (Fig. 6)
reveal that the bimodal distributions (cf. Fig. 4) are manifested in reality
at the 100
The bimodal distribution identified in the observed 100
MOD10A1 2013 seasonal average albedo probability density
distributions at three spatial extents: 50
MOD10A1 albedo at the 100
100
The bimodality seen in the 30 June–4 July 5-day average distribution
(Fig. 7) coincides with a brief period of higher MODIS albedo values
(
Percent difference in melt rate estimates for 100
While the 2013 MODIS albedo bimodal distribution shown in Figs. 6 and 7 are a
result of snow and ice albedos, analysis of MODIS 2012 data reveals a more
complex, multi-modal albedo distribution (Fig. 9). These distributions
cannot be explained by the presence or absence of snow and ice alone. The
2012 MODIS observations are characterized by generally lower albedos, with
six out of nine 5-day average albedo distributions ranging mostly between
0.2 and 0.5 compared to three out of nine 5-day average albedo
distributions in 2013 (cf. Fig. 7 and 10). These low albedos are confirmed
by the average seasonal MODIS 2012 albedo distributions, where a higher
probability of albedos are centered on
The presence of the dark-band region is confirmed by the diagonal band of
very low albedos (<
MODIS 2012 seasonal average albedo probability density
distributions at three spatial extents. The MODIS 2012 seasonal average
albedo probabilities for the 100
Observed ablation rates, derived from stake readings, are typically higher
for dark surfaces (dirty ice and streams) than light surfaces (clean ice;
Fig. 13). Clean ice surfaces have higher broadband
MODIS 100
The spread in observed clean ice broadband albedo values results in greater
variability in observed ablation rate estimates (Fig. 13). In contrast,
minimal broadband albedo variability is observed for dirty ice surfaces. Few
dirty ice albedo measurements were sampled as compared to clean ice
surfaces. Differences in observed ablation rates for streams are due to a
lack of albedo measurements taken over these surfaces. While ablation rates
were measured at several ablation stake stream sites, only occasional
MODIS 2012 seasonal average for the 100
GrIS ablation area albedos are strongly influenced by the presence or absence of impurity-rich debris on its surface. Clean ice and dust-covered, dirty ice have distinctly different albedos, resulting in a left-skewed albedo distribution in the middle and end of June (Fig. 5). This pattern is supported by computed and remotely sensed albedo distributions, revealing that a multi-modal distribution develops seasonally. A modest melt or snowfall event can trigger a sudden switch from a high to low albedo mode or vice versa, drastically changing ablation rates. These findings suggest that shifts in dominant surface type from snow to bare ice and clean ice to impurity-rich surfaces are important drivers in abruptly increasing seasonal ice sheet melt rates.
MODIS 2013 seasonal average for the 100
The first quality-controlled in situ ablation area albedo data set collected along a 1.25 km transect during 3 days in June 2013 is presented. Albedo data collected during in situ transect dates resemble an early summer ice surface classified in Chandler et al. (2015) and Knap and Oerlemans (1996; Fig. 4). Here, remaining snow cover and superimposed ice gradually melts, revealing underlying impurities and cryoconite holes. Visual assessment and continuous monitoring in the field revealed that the ice surface along the transect was snow-free from 8 to 26 June 2013. This period corresponds to a nonlinear decrease in albedos (Fig. 3). Accumulation of exposed below-surface impurities (Wientjes and Oerlemans, 2010), the gradual erosion of snow patches in local depressions on the ice surface (van den Broeke et al., 2011), as well as the activation and development of the hydrologic system and cryoconite hole coverage (Chandler et al., 2015) may mitigate the rate of change in ablation area albedos. Turbulent sensible heat fluxes from adjacent pro-glacial areas provide an additional explanation for the nonlinear decline in ground albedo measurements, serving to limit the melt–albedo feedback's influence (van den Broeke et al., 2011).
Under the assumptions that distinct surface types albedos follow a normal
distribution, a bimodal probability distribution preferentially develops as
ablation area albedo decreases rapidly over the melt season due to
development of an efficient meltwater drainage system, increase in
cryoconite hole coverage, and accumulation of debris-rich sediments (Fig. 4).
An increase in debris-rich and stream surfaces over the melting season
(Fig. 4) is likely responsible for the enhanced frequency of low albedo
values identified in the observed
Observed ablation rates and broadband
Compared to reality, the computed distribution (Fig. 4) probably overemphasizes each mode and does not account for darkening due to ice crystal growth over the melting season. The observed albedo distributions reveal abrupt and variable shifts in the seasonal albedo distribution (Figs. 7 and 10). At certain spatial extents, these albedo distributions transition from a high- to low-dominated mode (Fig. 6), enabling enhanced melt rates (Table 4 and Fig. 8). Alexander et al. (2014) also observed bimodal albedo distributions for Greenland's ablation area by analyzing MAR and MODIS products between 2000 and 2013. Alexander et al. (2014) attribute the dominant modes to the presence of snow and ice (and firn). This is in agreement with the analysis of the 2013 conditions but disagrees with 2012 conditions. This discrepancy could be due to the larger study area that includes areas unaffected by dust from deposition and outcropped ice layers and a 13-year averaging period suppressing outlier years like 2012 used in Alexander et al. (2014).
The 2013 bimodal albedo distributions (Fig. 7) shifts from higher to lower albedo modes in the melt season (Fig. 4) indicating that a switch in dominant surface type (i.e., from light to dark) during the melt season, and not solely grain size metamorphism, is largely responsible for lowering albedo in snow-free ablation areas. Furthermore, results from the MODIS data (Figs. 7 and 10) suggest that a transition from a light- to dark-dominated surface is abrupt rather than gradual, likely associated with the addition and removal of snow. The transition is more gradual in the left-skewed observed (Fig. 5) and computed albedo distributions (Fig. 4), likely reflecting changes in impurity content and different data set time sampling. Consistent with Chandler et al. (2015), the initial drop in MODIS ablation area albedos is likely due to both the transition from dry to wet and patchy snow surfaces. Successive lowering of albedos after snow melt is predominantly due to an increase ice crystal size (Box et al., 2012) and possibly also by expansion of darker surface area coverage (e.g., cryoconite holes, accumulation of impurities, and stream organization) and melting of dust-enriched ice layers. These distributions correspond to percent differences (e.g., 51.5 % between the 10–14 July and 20–24 July 5-day averages) in melt rate estimates that are substantial over the melt season (Table 4 and Fig. 8) and highlight the importance of considering the albedo of ablation area surface types. The higher melt rates associated with darker surfaces (Fig. 13) may lead to lighter surfaces becoming topographically prominent. In theory, this should enhance sensible heat transfer to the lighter surfaces, increasing their ablation. Future studies should consider quantifying the effects of surface roughness on ablation area albedos (e.g., Warren et al., 1998; Zhuravleva and Kokhanovsky, 2011).
Recent studies have proposed scenarios of future atmospheric warming, in which excess deposition of light-absorbing impurities (Dumont et al., 2014) and black carbon from increased forest fire frequency or incomplete fuel combustion (Keegan et al., 2014) will promote accumulation of impurities, contributing to amplified surface melting. If these findings are confirmed, these effects will likely be exacerbated in southwest Greenland's ablation area, where continued negative albedo trends (Stroeve et al., 2013) and increasingly warmer average summer temperatures (Keegan et al., 2014), in conjunction with bare ice, light-absorbing impurities, and cryoconite holes, are expected to dominate.
The spatial distribution of snow cover and background bare ice albedos is important for understanding temporal changes in 2012 and 2013 MODIS albedo distributions (Figs. 11 and 12). Compared to 2013, snow melt in 2012 was more pronounced and reached higher elevations (Tedesco et al., 2014), allowing the dark-band feature to be exposed, resulting in a lower seasonal albedo mode (Fig. 9).
The large albedo distribution changes from one MODIS 5-day average to another in 2012 (Fig. 10) is likely due to variability in meltwater ponding on the ice surface and perhaps deposition of wind-blown dust from tundra regions but not necessarily increases in melted-out debris from internal ice layers at such short timescales. However, exposure of dust and sediment-rich ice surfaces probably caused the high probability of considerably low 2012 MODIS albedo values relative to 2013. This is expected since it was identified as an extreme melt year with early onset snow melt (e.g., Nghiem et al., 2012; Tedesco et al., 2013; Figs. 9 and 10), while 2013 was a normal melt year in the 1979–2013 context (Tedesco et al., 2014). Given the coarse resolution of the MODIS pixel, it is likely that it averages out finer-scale details of distinct surface types (e.g., dirty ice and cryoconite hole surfaces) along the ice sheet edge. It is hypothesized that higher spatial resolution satellite imagery may be able to capture such regions closer to the ice sheet margin. We postulate that the area of these regions may grow in size over the melting season as demonstrated on local scales by Chandler et al. (2015) in situ observations.
The bimodal distribution observed in the 2013 MODIS data (Fig. 4) appears to be governed by the relative extent of clean ice and snow surfaces. This aligns with findings from current SMB models, as the majority of variability in the overall Greenland ablation area albedos is driven by the deposition, change, and removal of snow (Alexander et al., 2014; Van Angelen et al., 2012). However, 2012 MODIS albedo distributions cannot be explained by transitions from snow to ice and vice versa. Instead, the 2012 MODIS albedo distributions likely reflect abrupt shifts in ablation area albedos from the exposure of impurities on the ice surface in the so-called “dark-band” region as well as ice crystal growth and expansion of dirty ice areas, even with the presence of a few snowfall events. As such, dust and impurities on Greenland's ice sheet surface can influence surface albedos in the ablation area. The current state of SMB models are capable of simulating albedo as a function of meltwater ponding (Alexander et al., 2014) and impurities from atmospheric dust deposition on snow (Van Angelen et al., 2012). The models might be improved by incorporating the melting out of dust and sediments in outcropped ice layers, found in the dark-band region.
A first high-quality in situ spectral albedo data set collected along a fixed transect is presented for southwest Greenland's ablation area. Previous studies have attributed an increase in melt season duration, less snowfall accumulation, enhanced snow grain metamorphism rates and melt–albedo feedback as primary mechanisms for lowering ablation area albedos. Here, we demonstrate an additional control on albedos in the ablation area, namely the distribution of distinct surface types such as snow, clean ice, impurity-rich ice, melt ponds, and streams and also examine their modulation on surface ablation. The spatial extent of each of these surface types result in a multi-modal albedo distributions in the ablation area. Analysis of MODIS data suggests that a multi-modal distribution and, consequentially, a shift from light- to dark-dominated surfaces and sensitivity to melting of outcropped ice layers characterize seasonal changes in Greenland's ablation area and therefore melt rates.
Continued atmospheric warming coinciding with a darkening ice surface will increase the ice sheet surface meltwater production and runoff. Here, we show the importance of the distribution of dirty ice surfaces, which are likely the result of accumulation of impurities melted out from internal ice layers (at longer timescales, e.g., summer 2012) rather than contemporary deposition of atmospherically transported dust (except perhaps at short timescales). Future research should investigate the importance of surface accumulation of impurities and if its surface area can change to significantly influence GrIS albedo and surface ablation. Analysis of spatiotemporal variability in albedos using higher spatial resolution imagery is needed to adequately characterize surface types, particularly for dust and sediment-rich surfaces, to improve our understanding of the contribution of ablation area albedos to GrIS mass loss.
At the start of each transect, the ASD was calibrated to current
hemispherical atmospheric conditions by orienting the RCR skyward along a
nadir-viewing angle. Subsequent measurements were taken with the ASD rotated
180
Apparent outliers were identified using the Spectral Analysis and Management
System software to identify outliers. Outliers were defined as
physically unrealistic spectral albedo values (> 1.0) and raw
spectra that were markedly different to the other spectra across the
entire spectral range (visible and near-infrared wavelengths) taken for the
same sample. For 16 June, 20 spectra were deemed outliers (total spectra
collected
Radiative conditions during transect dates at the Base Met
Station, including incoming solar radiation (ISR, black line), outgoing
solar radiation (OSR, green line; left
To ensure a high-quality
As a proxy for cloud cover, relative cloud cover (CC) was
calculated every second as the ratio of modeled clear-sky and observed
incoming solar radiation similar to Box (1997). Clear-sky incoming
shortwave fluxes at the surface were calculated with a solar radiance model
(Iqbal, 1988). Model inputs of water vapor content, surface pressure,
aerosol optical depth at 380 and 500 nm, and area optical thickness were
estimated from the Kangerlussuaq AEROsol Robotic NETwork (AERONET) station
(Holben et al., 2001). SZA was also modeled with the solar radiance model
using latitude, longitude, time of day, and day of year at the Base Met
Station.
Cloud cover and radiative conditions varied among transects (Fig. B1). The
majority of
Broadband
A high range of CC variability, instead of consistently high CC, was found to
be responsible for saturating
By removing the majority of shortcomings and uncertainties identified in
transect radiative and surface conditions, a high-quality albedo data set was
produced. Optimal SZA, CC, and radiative conditions were observed for 16, 19,
and 25 June.
The Top Met Station was installed upon a homogeneous clean ice surface, and
the Base Met Station was installed above a heterogeneous surface of mixed
clean and dirty ice. Both stations measured solar radiation fluxes every
0.5 h at 300–1100 nm, using S-LIB-M003 silicon pyranometers and a U30 data
logger (Table C1;
Meteorological station sites and associated variables.
S. E. Moustafa, A. K. Rennermalm, L. C. Smith and J. R. Mioduszewski were funded by NASA grant NNX11AQ38G and NNX14AH93G. S. E. Moustafa was also funded by NASA Earth and Space Science Fellowship Program NNX12AN98H. Additional funding was provided by Rutgers University faculty research grant. Field logistical support was provided by CH2M Hill Polar Field Services and the Kangerlussuaq International Science Station. The authors would like to thank A. Pope M. S. Pelto, and K. Casey as well as two anonymous reviewers for valuable feedback and commentary. Edited by: M. van den Broeke