Surface mass balance modeling of the Greenland ice sheet : Sensitivity and uncertainty of the energy balance model BESSI

The surface mass balance is the main connection between the atmospheric climate change and the evolution of Greenland ice sheet over the next century. This thesis focuses on the development of surface mass and energy balance model for simulations of timescales above a century. The Bergen Snow SImulator (BESSI) needs to compromise between the necessary complexity to resolve the relevant physical processes and the computational costs of the model. There were three main studies published for the PhD focusing on model sensitivity, uncertainty assessment, transferability in space and time, and the modeling of the surface mass balance until the end of the current century. BESSI is an energy balance model that accounts for snow albedo decay, vapor fluxes and sub-surface water percolation and refreezing. The sensitivity of the surface mass balance towards the individual free model parameters was assessed for a cold and a warm period for the first publication of this thesis. The dominant factor during the warm period are uncertainties associated with the long-wave radiation and clouds, while during the cold climate of the last glacial maximum, sublimation and deposition cannot be neglected. BESSI provides useful SMB simulations over the entire Greenland ice sheet, but the uncertainties associated with the long-wave radiation are better reduced by relying on climate data input. The influence of the boundary climate conditions was studied next. The ice sheet is relatively stable to temporal variability changes, if the absolute range of change stays the same. Nevertheless, simulations based on a climatology instead of variable climate lead to a drastic overestimation of the surface mass balance. Climatologies have small amounts of daily snowfall, which lead to an increased snow albedo. A possible solution to obtain a good forcing for BESSI, and likely other surface mass balance models, is by distributing the precipitation based on the real temporal and spatial precipitation patterns. After the thorough sensitivity and climate dependency study, the surface mass balance of Greenland over the current century was simulated. There are multiple different climate scenarios, depending on the chosen behavior of humans over the current century. The climate projections for each scenario are available from the Climate Model Intercomparison Project (CMIP6). The surface mass balance of the Greenland ice sheet was modeled for multiple model-scenario combinations. For the majority of the simulations the surface mass balance decreases until 2100, but the uncertainty in the projected SMB value is large. The biggest contributor to the uncertainty is the climate model uncertainty and not the selected scenario.Snøens massebalanse er den viktigste sammenhengen mellom atmosfæriske klimaforandringer og evolusjonen av Grønlandske innlandsis i det kommende århundret. Denne avhandlingen beskriver utviklingen av en overflate masse- og energibalansemodell som blir brukt å simulere perioder lengre enn århundrer. Dette verktøyet, Bergen Snow SImulator (BESSI), er en modell som balanserer nødvendig kompleksitet for å inkludere viktige fysiske prosesser med mengden dataressurser som kreves av slike simuleringer. Denne avhandlingen inkluderer tre publikasjoner som fokuserer på modellsensitivitet, usikkerhetsanalyse, premisser for å overføre modellen i tid og rom, samt simulasjoner av massebalansen til Grønlands iskappe frem til år 2100. BESSI er en energibalansemodell som inkluderer albedoforandringer, vanndampprosesser som sublimasjon og rim dannelse, samt perkolering og frysing av smeltevann. Vi har undersøkt sensitiviteten av massebalansen til modellparametre under to ulike klimaepoker, en varm og en kald periode. Den dominerende komponenten for den varme perioden er usikkerhet med opphav i skydekke og varmestråling fra himmelen. Under det kalde klimaet til den siste istids maksimum, er sublimasjon og rimdannelse mye viktigere. Disse prosessene burde inkluderes når man simulerer massebalanse. BESSI gir nyttige simuleringer av massebalansen for innlandsisen på Grønland, men usikkerhetene i varmestrålingen fra himmelen burde reduseres. En mulig løsning på dette er å hente disse variablene direkte fra en klima-simulasjon. Den andre publikasjonen handler om hvordan massebalansen blir påvirket av klimatiske grensebetingelser. Innlandsisen er relativt stabil i møte med temporale endringer i variabiliteten dersom størrelsen av variabiliteten er kjent. I praksis er derimot denne variabiliteten ukjent og man antar typisk en konstant klimatologi. Dette fører til en overestimert massebalanse. Grunnen til dette er en små mengder daglig snøfall som fører til en økt albedo. Vi forslår en bedre løsning for klimatologien til BESSI, som trolig vil være nyttig for andre massebalansemodeller. Denne fordeler nedbør basert på realistiske temporale og romlige nedbørsmønstre. Den siste publikasjonen omhandler simulasjoner av massebalansen på Grønland i det kommende århundret. Simulasjonene er basert på flere klimascenarioer avhengige av menneskelig aktivitet og fremtidige utslipp. De forskjellige klimamodellene til klimascenarriene er en del av et stort modellsammenligningsprosjekt, Climate Model Intercomparison Project (CMIP6). Massebalansen for Grønlands innlandsis ble simulert for flere kombinasjoner av ulike modeller og scenarier. Flertallet av simulasjoner leder til en nedgang i massebalansen frem til 2100, men usikkerheten assosiert med prognoseverdien er stor. Det største bidraget til usikkerheten er klimamodellene og ikke det valgte scenarioet.Doktorgradsavhandlin

Sensitivity of the Greenland surface mass and energy balance to uncertainties in key model parameters

We investigate the sensitivity of a distributed glacier surface mass and energy balance model using a variance-based analysis, for two distinct periods of the last glacial cycle: the present day (PD) and the Last Glacial Maximum (LGM). The results can be summarized in three major findings: the sensitivity towards individual model parameters and parameterizations is as variable in space as it is in time. The model is most sensitive to uncertainty related to atmospheric emissivity and the down-welling longwave radiation. While the turbulent latent heat flux has a sizable contribution to the surface mass balance uncertainty in central Greenland today, it dominates over the entire ice sheet during the cold climate of the LGM, in spite of its low impact on the overall surface mass balance of the Greenland ice sheet in the modern climate. We conclude that quantifying the model sensitivity is very helpful for tuning free model parameters because it clarifies the relative importance of individual parameters and highlights interactions between them that need to be considered.publishedVersio

Sources of uncertainty in Greenland surface mass balance in the 21st century

The surface mass balance (SMB) of the Greenland ice sheet is subject to considerable uncertainties that complicate predictions of sea level rise caused by climate change. We examine the SMB of the Greenland ice sheet in the 21st century with the Bergen Snow Simulator (BESSI) surface energy and mass balance model. To estimate the uncertainty of the SMB, we conduct simulations for four greenhouse gas emission scenarios using the output of a wide range of Earth system models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to force BESSI. In addition, the uncertainty of the SMB simulation is estimated by using 16 different parameter sets in our SMB model. The median SMB across ESMs and parameter sets, integrated over the ice sheet, decreases over time for every emission scenario. As expected, the decrease in SMB is stronger for higher greenhouse gas emissions. The regional distribution of the resulting SMB shows the most substantial SMB decrease in western Greenland for all ESMs, whereas the differences between the ESMs are most pronounced in the north and around the equilibrium line. Temperature and precipitation are the input variables of the snow model that have the largest influence on the SMB and the largest differences between ESMs. In our ensemble, the range of uncertainty in the SMB is greater than in previous studies that used fewer ESMs as forcing. An analysis of the different sources of uncertainty shows that the uncertainty caused by the different ESMs for a given scenario is larger than the uncertainty caused by the climate scenarios. In comparison, the uncertainty caused by the snow model parameters is negligible, leaving the uncertainty of the ESMs as the main reason for SMB uncertainty.publishedVersio

Robust uncertainty assessment of the spatio-temporal transferability of glacier mass and energy balance models

Energy and mass-balance modelling of glaciers is a key tool for climate impact studies of future glacier behaviour. By incorporating many of the physical processes responsible for surface accumulation and ablation, they offer more insight than simpler statistical models and are believed to suffer less from problems of stationarity when applied under changing climate conditions. However, this view is challenged by the widespread use of parameterizations for some physical processes which introduces a statistical calibration step. We argue that the reported uncertainty in modelled mass balance (and associated energy flux components) are likely to be understated in modelling studies that do not use spatio-temporal cross-validation and use a single performance measure for model optimization. To demonstrate the importance of these principles, we present a rigorous sensitivity and uncertainty assessment workflow applied to a modelling study of two glaciers in the European Alps, extending classical best guess approaches. The procedure begins with a reduction of the model parameter space using a global sensitivity assessment that identifies the parameters to which the model responds most sensitively. We find that the model sensitivity to individual parameters varies considerably in space and time, indicating that a single stated model sensitivity value is unlikely to be realistic. The model is most sensitive to parameters related to snow albedo and vertical gradients of the meteorological forcing data. We then apply a Monte Carlo multi-objective optimization based on three performance measures: model bias and mean absolute deviation in the upper and lower glacier parts, with glaciological mass balance data measured at individual stake locations used as reference. This procedure generates an ensemble of optimal parameter solutions which are equally valid. The range of parameters associated with these ensemble members are used to estimate the cross-validated uncertainty of the model output and computed energy components. The parameter values for the optimal solutions vary widely, and considering longer calibration periods does not systematically result in better constrained parameter choices. The resulting mass balance uncertainties reach up to 1300 kg m−2, with the spatial and temporal transfer errors having the same order of magnitude. The uncertainty of surface energy flux components over the ensemble at the point scale reached up to 50 % of the computed flux. The largest absolute uncertainties originate from the short-wave radiation and the albedo parameterizations, followed by the turbulent fluxes. Our study highlights the need for due caution and realistic error quantification when applying such models to regional glacier modelling efforts, or for projections of glacier mass balance in climate settings that are substantially different from the conditions in which the model was optimized.publishedVersio

Atlantic inflow and low sea-ice cover in the Nordic Seas promoted Fennoscandian Ice Sheet growth during the Last Glacial Maximum

The Atlantic water inflow into the Nordic Seas has proven difficult to reconstruct for the Last Glacial Maximum. At that time, the Fennoscandian Ice Sheet grew potentially to its maximum extent. Sea-ice free conditions in the eastern Nordic Seas have been proposed as an essential moisture source contributing to this build-up. It has been hypothesized that the inflow of warm and saline Atlantic surface waters was important for maintaining these seasonally sea-ice free conditions in the Nordic Seas at that time. However, the difference between a perennially frozen ocean and a seasonally open ocean on ice sheet build-up remains unquantified. Here we use, tephra-constrained surface ventilation ages from a network of marine sediment cores and model experiments, to show that Atlantic inflow to the southern Nordic Seas likely occurred predominately via the Iceland-Faroe Atlantic inflow pathway helping to maintain seasonal open waters at the onset of the Last Glacial Maximum. Using a numerical snow model, we further demonstrate that such open-ocean conditions may have been a factor contributing to the Fennoscandian Ice Sheet growth with up to ~150% increase in surface mass balance over Norwegian coastal areas, compared to sea-ice covered conditions.publishedVersio

Surface mass balance modeling of the Greenland ice sheet : Sensitivity and uncertainty of the energy balance model BESSI

The surface mass balance is the main connection between the atmospheric climate change and the evolution of Greenland ice sheet over the next century. This thesis focuses on the development of surface mass and energy balance model for simulations of timescales above a century. The Bergen Snow SImulator (BESSI) needs to compromise between the necessary complexity to resolve the relevant physical processes and the computational costs of the model. There were three main studies published for the PhD focusing on model sensitivity, uncertainty assessment, transferability in space and time, and the modeling of the surface mass balance until the end of the current century. BESSI is an energy balance model that accounts for snow albedo decay, vapor fluxes and sub-surface water percolation and refreezing. The sensitivity of the surface mass balance towards the individual free model parameters was assessed for a cold and a warm period for the first publication of this thesis. The dominant factor during the warm period are uncertainties associated with the long-wave radiation and clouds, while during the cold climate of the last glacial maximum, sublimation and deposition cannot be neglected. BESSI provides useful SMB simulations over the entire Greenland ice sheet, but the uncertainties associated with the long-wave radiation are better reduced by relying on climate data input. The influence of the boundary climate conditions was studied next. The ice sheet is relatively stable to temporal variability changes, if the absolute range of change stays the same. Nevertheless, simulations based on a climatology instead of variable climate lead to a drastic overestimation of the surface mass balance. Climatologies have small amounts of daily snowfall, which lead to an increased snow albedo. A possible solution to obtain a good forcing for BESSI, and likely other surface mass balance models, is by distributing the precipitation based on the real temporal and spatial precipitation patterns. After the thorough sensitivity and climate dependency study, the surface mass balance of Greenland over the current century was simulated. There are multiple different climate scenarios, depending on the chosen behavior of humans over the current century. The climate projections for each scenario are available from the Climate Model Intercomparison Project (CMIP6). The surface mass balance of the Greenland ice sheet was modeled for multiple model-scenario combinations. For the majority of the simulations the surface mass balance decreases until 2100, but the uncertainty in the projected SMB value is large. The biggest contributor to the uncertainty is the climate model uncertainty and not the selected scenario

Heterogeneous ice nucleation on mineral dust particles

Abweichender Titel laut Übersetzung der Verfasserin/des VerfassersZsfassung in dt. SpracheIn Rahmen dieser Arbeit wurden die Eisnukleationsaktivitäten von Mineralstaubpartikeln untersucht. Die Experimente wurden mit Hilfe eines Kryo-Mikroskops und der Öl-Emulsions-Technik, durchgeführt. Mit dieser Methode ist es möglich das Immersionsgefrieren von Wassertropfen zu untersuchen. Bei den untersuchten Proben handelt es sich um anorganische atmosphärische Aerosolpartikel. Es wurden sowohl einzelne Mineralien als auch natürliche Staubproben untersucht. Mineralstaubpartikel sind aktive Eiskeime im Immersionsmodus bis zu Temperaturen von 256K/-17°C. Damit sind Mineral\-stäube bei geringeren Temperaturen aktiv als einige biologische Substanzen. Kaliumfeldspat ist der aktivste Mineralstaub, der untersucht wurde, auch andere Silikatminerale waren aktive Eiskeime.Alle Naturproben, die Kaliumfeldspat enthalten, waren auch aktiv, falls der Feldspatgehalt mindesten $5\%$ betrug. Die Aktivität von Kaliumfeldspat ist eine Folge seiner Oberflächenstruktur und den dort vorhandenen Kaliumionen. Die Eisnukleation an Mineralstaubpartikeln findet an lokalen Nukleationsstellen auf der Oberfläche der Partikel statt, wo die Keimbildung ausgelöst wird. Diese lokalen Stellen sind Domänen von molekularen Gruppen, wo Wassermoleküle in einer eis-ähnlichen Struktur stabilisiert werden. Gute Nukleationsstellen sind auf Partikeln mit einer größeren Dichte an molekularen Gruppen zu finden. Daraus folgt, dass sich die Nukleations\-temperatur durch Vergrößerung der Partikeloberfläche teilweise erhöht.Die genaue Struktur einer molekularen Gruppe ist materialspezifisch und hängt von der lokalen chemischen Konfiguration und Struktur der Partikeloberfläche ab. In Kaliumfeldspat gibt es entsprechend günstige Konfigurationen von Hydroxy- and Oxy-gruppen. Die Kalium\-ionen scheinen einen positiven oder zumindest neutralen Effekt auf die Eisnukleation von Silikaten zu haben, im Gegensatz zu Ionen mit höherer Ladungsdichte wie Kalzium oder Natrium. Der Kaliumfeldspat ist reichlich in der Umwelt vorhanden und entsprechend der wichtigste mineralische Eiskeim. Die Keimbildungstemperaturen von Kaliumfeldspatpartikeln sind hoch genug um weitere meteorologische Eisbildungsprozesse in großer Höhe in Wolken zu ermöglichen.Ice nucleation activities of mineral dust particles were investigated. The experiments were carried out using cryo-microscopy which is an oil-emulsion based method. The immersion freezing mode was addressed with this experimental setup. The studied samples were common inorganic atmospheric aerosols. Single minerals and natural samples were tested. Mineral dust particles are active ice nuclei in the immersion freezing mode up to 256K/-17°C. The nucleation temperatures of mineral dusts are much lower than for biological ice nuclei. K-feldspar is the by far most active ice nuclei followed by other silicates. Natural samples which contain more than $5\%$ K-feldspar were also active. The activity of K-feldspar can be attributed to its surface structure and the presence of potassium ions in the surface. Ice nucleation on mineral dust particles takes place at certain nucleation sites. These sites are domains of molecular sites where water is stabilized in an ice-like structure. To form a good ice nucleation site, the site density of molecular sites needs to be high. More molecular sites are able to form larger domains on the surface, leading to better nucleation sites. This suggests further that the nucleation temperature of mineral dust particles scales with the surface area. The exact configuration of a molecular site is material specific and influenced by the local chemistry and structure of the dust particle surface. A favorable arrangement of the functional groups like surface hydroxyl and oxygen is proposed for the K-feldspar. Potassium ions seem to have a positive or neutral effect on the ice nucleation property of a silicate surface while cations with a higher charge density like calcium and sodium have a negative influence. K-feldspar is abundant in the environment and actually is the most important dust ice nucleus in the atmosphere. The nucleation temperatures of the K-feldspar particles are sufficient to enable further meteorological glaciation processes in high altitude clouds.8

Uncertainty estimation of a glacier mass balance model

Oberflächen-Energiebilanzmodelle werden häufig angewandt um die Interaktion zwischen der Atmosphäre und im betrachteten Fall Eis und Gletscher, sowie den Einfluss von Klimavariabilität auf die Gletscher-Massenbilanz, zu untersuchen. Diese Modelle basieren auf physikalischen Zusammenhängen, welche zwar oft gut charakterisiert sind, aber nur durch Parametrisierungen umgesetzt werden können. Die freien Parameter müssen durch eine Kalibrierung ermittelt werden. Es wird oft durch eine Optimierung des Modells auf die kleinste Abweichung zur Messung umgesetzt. Das Parameterset der besten Lösung wird dann für die Analyse herangezogen. Die Unsicherheiten, die durch diese Vorgehensweise entstehen, wurden selten untersucht. In dieser Studie werden verschiedene innovative "`Kalibrierungstechniken" angewandt, mit dem Ziel eine bessere Schätzung der Modellunsicherheit der simulierten Massenbilanz zu erreichen. Die Studie verwendet ein etabliertes Energiebilanzmodell um die "`verteilte"' Massenbilanz auf zwei alpinen Gletschern im Abstand von 40km über drei Sommer zu simulieren. Die entscheidenden Modellparameter wurden mit einer globalen Sensitivitätsanalyse (GSA) gefunden. Die sensitiven Parameter variieren zwischen/über den beiden Gletschern und den drei Jahren. Dies zeigt, dass unterschiedliche Prozesse aktiv sind. 11 Parameter (von 23) wurden für die Monte-Carlo Optimierung ausgewählt. Es wurde eine mehrdimensionale Optimierung durchgeführt: Die optimalen Lösungen in Bezug auf fünf Gütekriterien wurden aus 20.000 Läufen für jede einzelne Sommer-Massenbilanz ausgewählt. Die optimalen Parameter dieser Lösungen decken den ganzen Parameterraum ab (verteilen sich über den ganzen Parameterraum). Die Albedo über Neuschnee und Firn sind die Parameter mit der geringsten Streuung, während die skalare und die Impuls-Rauhigkeitslänge am meisten variieren. Die Variabilität der optimalen Parameter zwischen einzelnen Jahren an einem Gletscher ist genauso stark ausgeprägt wie zwischen den beiden Gletschern. Die Optimierung zeigt, dass es keine Lösung gibt die in Bezug zu allen Kriterien optimal ist, aber es kann ein Ensemble von simulierten Massenbilanzen erzeugt werden, welches eine erste Abschätzung der Unsicherheit erlaubt. Jedoch gibt es auch bei einem Ensemble das Problem des "`over-fittings"': Die optimalen Parameter für einen trockenen Sommer sind unzureichend für feuchte Bedingungen. Dieser Effekt tritt unabhängig von den gewählten Gütekriterien auf. Wenn die Performance des Models durch Funktionen, die das Akkumulations- und Ablationsgebiet individuell bewerten bestimmt, wird, kann dieser Effekt reduziert werden. Das Bias ist ein schlechtes Gütekriterium, da es sehr leicht im Modell minimiert werden kann und daher anfälliger für "`over-fitting"' ist. Die Unsicherheit, welche durch "`over-fitting"' entsteht, kann mit Hilfe einer "`out-of-sample"' Cross-validierung geschätzt werden. Die Doppel-Cross-Validierung (Die Unsicherheit einer ersten Cross-Validierung wird gegen einen zweiten Datensatz getestet) zeigt, dass in diesem Fall mindestens fünf Sommer-Massenbilanzen modelliert werden müssen um eine gute Schätzung der Unsicherheit zu bekommen. Die so erhaltene Unsicherheit des Models beträgt 1m w.e.. Dies entspricht in etwa der jährlichen Variation der Massenbilanzen der beiden Gletscher. Die Notwendigkeit einer Modell-Rekalibrierung um mit anderen Klimabedingungen umgehen zu können, zeigt, dass aktuelle Massenbilanzmodelle noch Verbesserungspotential haben. Weitere Forschung sollten die Gründe für Unsicherheit des Modells und der Eingabedaten (Inputdaten) untersuchen. Robustere Kalibrierungsmethoden, basierend auf besseren räumlichen und zeitlichen Daten, sollten für zukünftige Modelle entwickelt werden.Surface energy and mass balance models are popular tools to study ice-atmosphere interactions or the response of glacier mass-balance to climate variability. These complex models rely on physical relationships which are well understood but also contain many unknown free parameters that need to be calibrated. The calibration procedure often minimizes a single performance measure with respect to observed mass-balance data. The parameter set leading to the best model performance in then selected for the analysis. The real uncertainty associated with this tuning procedure is rarely addressed in the literature. In this thesis various innovative calibration approaches are applied and discussed with the objective to provide more robust uncertainty estimates of the simulated mass-balance. We use a well established energy balance model to simulate the distributed mass balance of two alpine glaciers distant of 40 km for a period of three summers. A global sensitivity analysis (GSA) was performed to identify the key model parameters. The most sensitive parameters are different over the two glaciers and for the three years, illustrating the different processes at play in each case. 11 parameters (out of 23) were selected for the final Monte-Carlo optimization. Then a multi-objective calibration procedure was applied: the optimal solutions with respect to five objective functions (instead of one in a classic calibration) were identified out of 20.000 runs for each individual summer mass balances. The optimal parameters often span over the entire allowed parameter space. The fresh snow and firn albedo are the best confined parameters, while the momentum and scalar roughness length above ice are among the most variable. The variability of the optimal parameter set between two different years at one location can be as pronounced as between the two glaciers. This multi-objective optimization clearly shows that no parameter set can rank best for all objectives, but it also allows to create an ensemble of simulated mass balances, providing a first hint about the model uncertainty. Nevertheless, this ensemble is also subject to over-fitting, i.e. the optimal parameter sets for a dry summer might prove inefficient under wet conditions. This effect is always present, but it can be mitigated with the choice of an objective function considering the accumulation and the ablation area of the glacier separately. The bias turned out to be a objective function which is too easy for the model to minimize leading and thus is most prone to over-fitting. The uncertainty associated with this over-fitting can be quantified with the help of “out-of-sample” validation. With a double cross-validation procedure (in which the uncertainty computed from a first cross-validation loop is tested against a second out-of-sample set of data), our results suggest that, in this case, a minimum of 5 summers of modeled mass balance is needed to have an accurate estimation of the model uncertainty. The uncertainty of the mass balance model is estimated to be as high a 1m w.e., representing about the annual variation of the mass-balance of the studied glaciers. The necessity for a model recalibration in order to cope with various climate settings suggests that the current generation of mass-balance models could still be improved for a wider range of applications. Future research should focus on identifying the sources of uncertainty in the model and in the forcing data, as well as developing more robust calibration procedures, e.g. based on data of higher spatio-temporal resolution.by Tobias ZollesKurzfassung in deutscher SpracheUniversity of Innsbruck, Masterarbeit, 2016(VLID)165178

Sensitivity of the Greenland surface mass and energy balance to uncertainties in key model parameters

We investigate the sensitivity of a distributed glacier surface mass and energy balance model using a variance-based analysis, for two distinct periods of the last glacial cycle: the present day (PD) and the Last Glacial Maximum (LGM). The results can be summarized in three major findings: the sensitivity towards individual model parameters and parameterizations is as variable in space as it is in time. The model is most sensitive to uncertainty related to atmospheric emissivity and the down-welling longwave radiation. While the turbulent latent heat flux has a sizable contribution to the surface mass balance uncertainty in central Greenland today, it dominates over the entire ice sheet during the cold climate of the LGM, in spite of its low impact on the overall surface mass balance of the Greenland ice sheet in the modern climate. We conclude that quantifying the model sensitivity is very helpful for tuning free model parameters because it clarifies the relative importance of individual parameters and highlights interactions between them that need to be considered

Sources of uncertainty in Greenland surface mass balance in the 21st century

The surface mass balance (SMB) of the Greenland ice sheet is subject to considerable uncertainties that complicate predictions of sea level rise caused by climate change. We examine the SMB of the Greenland ice sheet in the 21st century with the Bergen Snow Simulator (BESSI) surface energy and mass balance model. To estimate the uncertainty of the SMB, we conduct simulations for four greenhouse gas emission scenarios using the output of a wide range of Earth system models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to force BESSI. In addition, the uncertainty of the SMB simulation is estimated by using 16 different parameter sets in our SMB model. The median SMB across ESMs and parameter sets, integrated over the ice sheet, decreases over time for every emission scenario. As expected, the decrease in SMB is stronger for higher greenhouse gas emissions. The regional distribution of the resulting SMB shows the most substantial SMB decrease in western Greenland for all ESMs, whereas the differences between the ESMs are most pronounced in the north and around the equilibrium line. Temperature and precipitation are the input variables of the snow model that have the largest influence on the SMB and the largest differences between ESMs. In our ensemble, the range of uncertainty in the SMB is greater than in previous studies that used fewer ESMs as forcing. An analysis of the different sources of uncertainty shows that the uncertainty caused by the different ESMs for a given scenario is larger than the uncertainty caused by the climate scenarios. In comparison, the uncertainty caused by the snow model parameters is negligible, leaving the uncertainty of the ESMs as the main reason for SMB uncertainty