Using Sea-Level Data to Constrain the Contribution of the Greenland Ice Sheet to Contemporary and Recent Sea-Level Change

Due to the potentially wide-reaching impacts on climate and sea-level change of a declining Greenland Ice Sheet (GrIS), the mass balance of the past decade has caused concern that the ice sheet is reacting to increased temperatures of the industrial era and that the ice sheet is in the initial stages of deglaciation. Global mean sea-level has been rising at a rate of 1.8 +/- 0.5 mm/yr over the past 50 years (Bindoff et al. 2007), and this has accelerated to 3.1 +/-0.1mm/yr (Cazenave et al., 2008) over the past decade. This study shows that although the surface mass balance of the GrIS can react quickly to changes in temperature, overall the ice sheet is in near balance over the period 1866-2005. During 1866-2005, the contribution from the GrIS to eustatic sea-level change is not larger than the error attached to current estimates of global mean sea-level rise. A novel type of relative sea-level data gathered from salt marshes in the south west of Greenland cover the period from ~1200 to 1800AD and show that a major slowdown in local sea-level rise from ~3mm/yr to ~0mm/yr occurred around 1500-1600 AD, with no significant departure from a 0mm/yr trend thereafter. Large contributions to sea-level change from steric changes and cryospheric sources outside of Greenland are ruled out as major drivers of this deceleration in sea-level fall. Modelling results indicate that the slowdown in relative sea-level is most likely due to the combined contribution of dynamic-related ice loss from Jakobshavn Isbrae and a delayed earth response to mass loss during a period of elevated temperatures from ~1000-1500AD. When considering the saltmarsh sea-level data for the 20th century within the context of the complete time series, the magnitude of ice loss in west Greenland for the past decade does not appear to be anomalous. This analysis suggests that similar mass loss has been sustained for several centuries prior to 1500AD

Glacial Isostatic Adjustment (GIA) in Greenland: A Review

Using the most recently published regional and global deglaciation histories we provide updated estimates of the Glacial Isostatic Adjustment (GIA) component of present day uplift at a suite of GPS sites in Greenland. The GIA of the solid Earth beneath Greenland contributes -6 to +10 Gt/yr to the present day mass trends observed by the Gravity Recovery and Climate Experiment (GRACE), representing <5% contribution to the observed mass trends over the last decade. Although the contribution of GIA to GRACE estimates of mass imbalance is insignificant for Greenland as a whole, differences between deglacial models reviewed here and their assumed viscoelastic Earth structures result in significantly different estimates of regional patterns and magnitudes of GIA. This means that for some areas of Greenland (e.g. the north-west, south- and north-east) the use of GNSS to estimate elastic uplift patterns is more affected by the choice of GIA correction applied

Simulated single-layer forest canopies delay Northern Hemisphere snowmelt

Single-layer vegetation schemes in modern land surface models have been found to overestimate diurnal cycles in longwave radiation beneath forest canopies. This study introduces an empirical correction, based on forest stand-scale simulations, which reduces diurnal cycles of sub-canopy longwave radiation. The correction is subsequently implemented in land-only simulations of the Community Land Model version 4.5 (CLM4.5) in order to assess the impact on snow cover. Nighttime underestimations of sub-canopy longwave radiation outweigh daytime overestimations, which leads to underestimated averages over the snow cover season. As a result, snow temperatures are underestimated and snowmelt is delayed in CLM4.5 across evergreen boreal forests. Comparison with global observations confirms this delay and its reduction by correction of sub-canopy longwave radiation. Increasing insolation and day length change the impact of overestimated diurnal cycles on daily average subcanopy longwave radiation throughout the snowmelt season. Consequently, delay of snowmelt in land-only simulations is more substantial where snowmelt occurs early

Missing sea level rise in southeastern Greenland during and since the Little Ice Age

The Greenland Ice Sheet has been losing mass at an accelerating rate over the past 2 decades. Understanding ice mass and glacier changes during the preceding several hundred years prior to geodetic measurements is more difficult because evidence of past ice extent in many places was later overridden. Salt marshes provide the only continuous records of relative sea level (RSL) from close to the Greenland Ice Sheet that span the period of time during and since the Little Ice Age (LIA) and can be used to reconstruct ice mass gain and loss over recent centuries. Salt marsh sediments collected at the mouth of Dronning Marie Dal, close to the Greenland Ice Sheet margin in southeastern Greenland, record RSL changes over the past ca. 300 years through changing sediment and diatom stratigraphy. These RSL changes record a combination of processes that are dominated by local and regional changes in Greenland Ice Sheet mass balance during this critical period that spans the maximum of the LIA and 20th-century warming. In the early part of the record (1725–1762 CE) the rate of RSL rise is higher than reconstructed from the closest isolation basin at Timmiarmiut, but between 1762 and 1880 CE the RSL rate is within the error range of the rate of RSL change recorded in the isolation basin. RSL begins to slowly fall around 1880 CE, with a total amount of RSL fall of 0.09±0.1 m in the last 140 years. Modelled RSL, which takes into account contributions from post-LIA Greenland Ice Sheet glacio-isostatic adjustment (GIA), ongoing deglacial GIA, the global non-ice sheet glacial melt fingerprint, contributions from thermosteric effects, the Antarctic mass loss sea level fingerprint and terrestrial water storage, overpredicts the amount of RSL fall since the end of the LIA by at least 0.5 m. The GIA signal caused by post-LIA Greenland Ice Sheet mass loss is by far the largest contributor to this modelled RSL, and error in its calculation has a large impact on RSL predictions at Dronning Marie Dal. We cannot reconcile the modelled RSL and the salt marsh observations, even when moving the termination of the LIA to 1700 CE and reducing the post-LIA Greenland mass loss signal by 30 %, and a “budget residual” of  mm yr−1 since the end of the LIA remains unexplained. This new RSL record backs up other studies that suggest that there are significant regional differences in the timing and magnitude of the response of the Greenland Ice Sheet to the climate shift from the LIA into the 20th century

Microstructure representation of snow in coupled snowpack and microwave emission models

This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions, and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland, were used to drive the model over the 2011–2012 and 2012–2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error 0.16 to 0.24mm for MOSES and 0.14 to 0.18mm for SNICAR). No model (HUT, MEMLS, or DMRT-ML) provided a consistently good fit across all frequencies and polarisations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarisation ranged from 0.7 to 6.9 K. Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERM–empirical MEMLS model combination (2011–2012) and 3.3 for DMRT-ML in conjunction with MOSES microstructure (2012–2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack–microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11K at 36.5 GHz H-pol and 18K at V-pol when the Jules Investigation Model ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011–2012 season. The impact of snowpack parameterisation increases as the microwave scattering increases. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems

Multi-physics ensemble modelling of Arctic tundra snowpack properties

Sophisticated snowpack models such as Crocus and SNOWPACK struggle to properly simulate profiles of density and specific surface area (SSA) within Arctic snowpacks due to an underestimation of wind-induced compaction, misrepresentation of basal vegetation influencing compaction and metamorphism, and omission of water vapour flux transport. To improve the simulation of profiles of density and SSA, parameterisations of snow physical processes that consider the effect of high wind speeds, the presence of basal vegetation and alternate thermal conductivity formulations were implemented into an ensemble version of the Soil, Vegetation and Snow version 2 (SVS2-Crocus) land surface model, creating Arctic SVS2-Crocus. The ensemble versions of default and Arctic SVS2-Crocus were driven with in-situ meteorological data and evaluated using measurements of snowpack properties (SWE, depth, density and SSA) at Trail Valley Creek (TVC), Northwest Territories, Canada over 32-years (1991&ndash;2023). Results show that both default and Arctic SVS2-Crocus can simulate the correct magnitude of SWE (RMSE for both ensembles: 55 kg m-2) and snow depth (default RMSE: 0.22 m; Arctic RMSE: 0.18 m) at TVC in comparison to measurements. Wind-induced compaction within Arctic SVS2-Crocus effectively compacts the surface layers of the snowpack, increasing the density, and reducing the RMSE by 41 % (176 kg m-3 to 103 kg m-3). Parameterisations of basal vegetation are less effective in reducing compaction of basal snow layers (default RMSE: 67 kg m-3; Arctic RMSE: 65 kg m-3), reaffirming the need to consider water vapour flux transport for simulation of low-density basal layers. The top 100 ensemble members of Arctic SVS2-Crocus produced lower continuous ranked probability scores (CRPS) than default SVS2-Crocus when simulating snow density profiles. The top performing members of the Arctic SVS2-Crocus ensemble featured modifications that raise wind speeds to increase compaction in snow surface layers and prevent snowdrift and increase viscosity in basal layers. Selecting these process representations in Arctic SVS2-Crocus will improve simulation of snow density profiles, which is crucial for many applications

Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals

Spatial variability in snowpack properties negatively impacts our capacity to make direct measurements of snow water equivalent (SWE) using satellites. A comprehensive data set of snow microstructure (94 profiles at 36 sites) and snow layer thickness (9000 vertical profiles across 9 trenches) collected over two winters at Trail Valley Creek, NWT, Canada, were applied in synthetic radiative transfer experiments. This allowed robust assessment of the impact of estimation accuracy of unknown snow microstructural characteristics on the viability of SWE retrievals. Depth hoar layer thickness varied over the shortest horizontal distances, controlled by subnivean vegetation and topography, while variability of total snowpack thickness approximated that of wind slab layers. Mean horizontal correlation lengths of layer thickness were sub-metre for all layers. Depth hoar was consistently ~30% of total depth, and with increasing total depth the proportion of wind slab increased at the expense of the decreasing surface snow layer. Distinct differences were evident between distributions of layer properties; a single median value represented density and specific surface area (SSA) of each layer well. Spatial variability in microstructure of depth hoar layers dominated SWE retrieval errors. A depth hoar SSA estimate of around 7% under the median value was needed to accurately retrieve SWE. In shallow snowpacks <0.6m, depth hoar SSA estimates of ±5-10% around the optimal retrieval SSA allowed SWE retrievals within a tolerance of ±30 mm. Where snowpacks were deeper than ~30cm, accurate values of representative SSA for depth hoar became critical as retrieval errors were exceeded if the median depth hoar SSA was applied

A Theory of Change for Improving Children’s Perceptions, Aspirations and Uptake of STEM Careers

There is concern about the low numbers and diversity of young people choosing careers and study subjects in science, technology, engineering and maths (STEM) at university and beyond. Many interventions aimed at addressing this issue have focused on young people aged 14+ years old. However, these interventions have resulted in little improvement in the numbers and diversity of young people progressing into STEM careers. The aim of this study is to ask “What are the affordances of a Theory of Change (ToC) for increasing the diversity and number of young people choosing a career in STEM post-18?” An innovative ToC is introduced which provides the theoretical underpinnings and context for the complex mix of interventions necessary to lead to a significant change in the number and diversity of those choosing STEM careers. Case studies of interventions developed using the ToC are presented. This approach, and associated ToC, is widely applicable across STEM, education and public engagement fields

Impact of measured and simulated tundra snowpack properties on heat transfer

Snowpack microstructure controls the transfer of heat to, as well as the temperature of, the underlying soils. In situ measurements of snow and soil properties from four field campaigns during two winters (March and November 2018, January and March 2019) were compared to an ensemble of CLM5.0 (Community Land Model) simulations, at Trail Valley Creek, Northwest Territories, Canada. Snow micropenetrometer profiles allowed for snowpack density and thermal conductivity to be derived at higher vertical resolution (1.25 mm) and a larger sample size (n=1050) compared to traditional snowpit observations (3 cm vertical resolution; n=115). Comparing measurements with simulations shows CLM overestimated snow thermal conductivity by a factor of 3, leading to a cold bias in wintertime soil temperatures (RMSE=5.8 ∘C). Two different approaches were taken to reduce this bias: alternative parameterisations of snow thermal conductivity and the application of a correction factor. All the evaluated parameterisations of snow thermal conductivity improved simulations of wintertime soil temperatures, with that of Sturm et al. (1997) having the greatest impact (RMSE=2.5 ∘C). The required correction factor is strongly related to snow depth () and thus differs between the two snow seasons, limiting the applicability of such an approach. Improving simulated snow properties and the corresponding heat flux is important, as wintertime soil temperatures are an important control on subnivean soil respiration and hence impact Arctic winter carbon fluxes and budgets

Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site

Estimates of winter (snow-covered non-growing season) CO2 fluxes across the Arctic region vary by a factor of 3.5, with considerable variation between measured and simulated fluxes. Measurements of snow properties, soil temperatures, and net ecosystem exchange (NEE) at Trail Valley Creek, NWT, Canada, allowed for the evaluation of simulated winter NEE in a tundra environment with the Community Land Model (CLM5.0). Default CLM5.0 parameterisations did not adequately simulate winter NEE in this tundra environment, with near-zero NEE (< 0.01 gCm^-2d^-1) simulated between November and mid-May. In contrast, measured NEE was broadly positive (indicating net CO2 release) from snow-cover onset until late April. Changes to the parameterisation of snow thermal conductivity, required to correct for a cold soil temperature bias, reduced the duration for which no NEE was simulated. Parameter sensitivity analysis revealed the critical role of the minimum soil moisture threshold of decomposition (Ψmin) in regulating winter soil respiration. The default value of this parameter (Ψmin) was too high, preventing simulation of soil respiration for the vast majority of the snow-covered season. In addition, the default rate of change of soil respiration with temperature (Q10) was too low, further contributing to poor model performance during winter. As Ψmin and Q10 had opposing effects on the magnitude of simulated winter soil respiration, larger negative values of Ψmin and larger positive values of Q10 are required to simulate wintertime NEE more adequately.Natural Environment Research CouncilPeer Reviewe