Publications of Xavier Fettweis
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See detailWeather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium
El Jarroudi, Moussa ULiege; Chairi, Fadia; Kouadio, Louis et al

in Journal of Fungi (2021), 7(9), 1-21

Cercospora leaf spot (CLS; caused by Cercospora beticola Sacc.) is the most widespread and damaging foliar disease of sugar beet. Early assessments of CLS risk are thus pivotal to the success of disease ... [more ▼]

Cercospora leaf spot (CLS; caused by Cercospora beticola Sacc.) is the most widespread and damaging foliar disease of sugar beet. Early assessments of CLS risk are thus pivotal to the success of disease management and farm profitability. In this study, we propose a weather-based modelling approach for predicting infection by C. beticola in sugar beet fields in Belgium. Based on reported weather conditions favoring CLS epidemics and the climate patterns across Belgian sugar beetgrowing regions during the critical infection period (June to August), optimum weather conditions conducive to CLS were first identified. Subsequently, 14 models differing according to the combined thresholds of air temperature (T), relative humidity (RH), and rainfall (R) being met simultaneously over uninterrupted hours were evaluated using data collected during the 2018 to 2020 cropping seasons at 13 different sites. Individual model performance was based on the probability of detection (POD), the critical success index (CSI), and the false alarm ratio (FAR). Three models (i.e., M1, M2 and M3) were outstanding in the testing phase of all models. They exhibited similar performance in predicting CLS infection events at the study sites in the independent validation phase; in most cases, the POD, CSI, and FAR values were ≥84%, ≥78%, and ≤15%, respectively. Thus, a combination of uninterrupted rainy conditions during the four hours preceding a likely start of an infection event, RH > 90% during the first four hours and RH > 60% during the following 9 h, daytime T > 16 °C and nighttime T > 10 °C, were the most conducive to CLS development. Integrating such weather-based models within a decision support tool determining fungicide spray application can be a sound basis to protect sugar beet plants against C. beticola, while ensuring fungicides are applied only when needed throughout the season. [less ▲]

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See detailGreenland Ice sheet [in "State of the Climate in 2020"]
Moon, T.; Tedesco, M.; Mankoff, K. et al

in Bulletin of the American Meteorological Society (2021)

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See detailEvaluation framework for sub-daily rainfall extremes simulated by regional climate models
Van de Vyver, Hans; Van Schaeybroeck, Bert; De Troch, Rozemien et al

in Journal of Applied Meteorology and Climatology (2021), Online

Sub-daily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration ... [more ▼]

Sub-daily precipitation extremes are high-impact events that can result in flash floods, sewer system overload, or landslides. Several studies have reported an intensification of projected short-duration extreme rainfall in a warmer future climate. Traditionally, regional climate models (RCMs) are run at a coarse resolution using deep-convection parameterization for these extreme events. As computational resources are continuously ramping up, these models are run at convection-permitting resolution, thereby partly resolving the small-scale precipitation events explicitly. To date, a comprehensive evaluation of convection-permitting models is still missing. We propose an evaluation strategy for simulated sub-daily rainfall extremes that summarizes the overall RCM performance. More specifically, the following metrics are addressed: the seasonal/diurnal cycle, temperature and humidity dependency, temporal scaling and spatio-temporal clustering. The aim of this paper is: (i) to provide a statistical modeling framework for some of the metrics, based on extreme value analysis, (ii) to apply the evaluation metrics to a micro-ensemble of convection-permitting RCM simulations over Belgium, against high-frequency observations, and (iii) to investigate the added value of convection-permitting scales with respect to coarser 12-km resolution. We find that convection-permitting models improved precipitation extremes on shorter time scales (i.e, hourly or two-hourly), but not on 6h-24h time scales. Some metrics such as the diurnal cycle or the Clausius-Clapeyron rate are improved by convection-permitting models, whereas the seasonal cycle appears robust across spatial scales. On the other hand, the spatial dependence is poorly represented at both convection-permitting scales and coarser scales. Our framework provides perspectives for improving high-resolution atmospheric numerical modeling and datasets for hydrological applications. [less ▲]

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See detailWhat is the surface mass balance of Antarctica? An intercomparison of regional climate model estimates
Mottram, R.; Hansen, N.; Kittel, Christoph ULiege et al

in Cryosphere (2021), 15

We compare the performance of five different regional climate models (RCMs) (COSMO-CLM2, HIRHAM5, MAR3.10, MetUM, and RACMO2.3p2), forced by ERA-Interim reanalysis, in simulating the near-surface climate ... [more ▼]

We compare the performance of five different regional climate models (RCMs) (COSMO-CLM2, HIRHAM5, MAR3.10, MetUM, and RACMO2.3p2), forced by ERA-Interim reanalysis, in simulating the near-surface climate and surface mass balance (SMB) of Antarctica. All models simulate Antarctic climate well when compared with daily observed temperature and pressure, with nudged models matching daily observations slightly better than free-running models. The ensemble mean annual SMB over the Antarctic ice sheet (AIS) including ice shelves is 2329±94 Gt yr−1 over the common 1987–2015 period covered by all models. There is large interannual variability, consistent between models due to variability in the driving ERA-Interim reanalysis. Mean annual SMB is sensitive to the chosen period; over our 30-year climatological mean period (1980 to 2010), the ensemble mean is 2483 Gt yr−1. However, individual model estimates vary from 1961±70 to 2519±118 Gt yr−1. The largest spatial differences between model SMB estimates are in West Antarctica, the Antarctic Peninsula, and around the Transantarctic Mountains. We find no significant trend in Antarctic SMB over either period. Antarctic ice sheet (AIS) mass loss is currently equivalent to around 0.5 mm yr−1 of global mean sea level rise (Shepherd et al., 2020), but our results indicate some uncertainty in the SMB contribution based on RCMs. We compare modelled SMB with a large dataset of observations, which, though biased by undersampling, indicates that many of the biases in SMB are common between models. A drifting-snow scheme improves modelled SMB on ice sheet surface slopes with an elevation between 1000 and 2000 m, where strong katabatic winds form. Different ice masks have a substantial impact on the integrated total SMB and along with model resolution are factored into our analysis. Targeting undersampled regions with high precipitation for observational campaigns will be key to improving future estimates of SMB in Antarctica. [less ▲]

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See detailSensitivity of the surface energy budget to drifting snow as simulated by MAR in coastal Adelie Land, Antarctica
Le Toumelin, L.; Amory, Charles ULiege; Favier, V. et al

in Cryosphere (2021), (3595–3614), 15

In order to understand the evolution of the climate of Antarctica, dominant processes that control surface and low-atmosphere meteorology need to be accurately captured in climate models. We used the ... [more ▼]

In order to understand the evolution of the climate of Antarctica, dominant processes that control surface and low-atmosphere meteorology need to be accurately captured in climate models. We used the regional climate model MAR (v3.11) at 10 km horizontal resolution, forced by ERA5 reanalysis over a 9-year period (2010–2018) to study the impact of drifting snow (designating here the wind-driven transport of snow particles below and above 2 m) on the near-surface atmosphere and surface in Adelie Land, East Antarctica. Two model runs were performed, one with and one without drifting snow, and compared to half-hourly in situ observations at D17, a coastal and windy location of Adelie Land. We show that sublimation of drifting-snow particles in the atmosphere drives the difference between model runs and is responsible for significant impacts on the near-surface atmosphere. By cooling the low atmosphere and increasing its relative humidity, drifting snow also reduces sensible and latent heat exchanges at the surface (−5.7 W m−2 on average). Moreover, large and dense drifting-snow layers act as near-surface cloud by interacting with incoming radiative fluxes, enhancing incoming longwave radiation and reducing incoming shortwave radiation in summer (net radiative forcing: 5.7 W m−2). Even if drifting snow modifies these processes involved in surface–atmosphere interactions, the total surface energy budget is only slightly modified by introducing drifting snow because of compensating effects in surface energy fluxes. The drifting-snow driven effects are not prominent near the surface but peak higher in the boundary layer (fourth vertical level, 12 m) where drifting-snow sublimation is the most pronounced. Accounting for drifting snow in MAR generally improves the comparison at D17, especially for the representation of relative humidity (mean bias reduced from −14.0 % to −0.7 %) and incoming longwave radiation (mean bias reduced from −20.4 W m−2 to −14.9 W m−2). Consequently, our results suggest that a detailed representation of drifting-snow processes is required in climate models to better capture the near-surface meteorology and surface–atmosphere interactions in coastal Adelie Land. [less ▲]

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See detailProbabilistic forecasting for sizing in the capacity firming framework
Dumas, Jonathan ULiege; Cornélusse, Bertrand ULiege; Fettweis, Xavier ULiege et al

in 2021 IEEE Madrid PowerTech (2021, July 10)

This paper proposes a strategy to size a grid-connected photovoltaic plant coupled with a battery energy storage device within the capacity firming specifications of the French Energy Regulatory ... [more ▼]

This paper proposes a strategy to size a grid-connected photovoltaic plant coupled with a battery energy storage device within the capacity firming specifications of the French Energy Regulatory Commission. The sizing problem is difficult to solve due to the capacity firming framework that consists in a two-phase engagement control with a day ahead nomination and an intraday control to minimize deviations from the planning. The two-phase engagement control is modeled with a deterministic and stochastic approaches. The optimization problems are formulated as mixed-integer quadratic problems with linear constraints, using a Gaussian copula methodology to generate PV scenarios, to approximate the mixed-integer non-linear problem of the capacity firming. Then, a grid search is conducted to approximate the optimal sizing for a given selling price using both the deterministic and stochastic approaches. The case study is composed of PV production monitored on site at the University of Liège (ULiège), Belgium. [less ▲]

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See detailSurface melting over the Greenland ice sheet derived from enhanced resolution passive microwave brightness temperatures (1979–2019)
Colosio, P.; Tedesco, M.; Ranzi, R. et al

in Cryosphere (2021), 15

Surface melting is a major component of the Greenland ice sheet surface mass balance, and it affects sea level rise through direct runoff and the modulation of ice dynamics and hydrological processes ... [more ▼]

Surface melting is a major component of the Greenland ice sheet surface mass balance, and it affects sea level rise through direct runoff and the modulation of ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting due to their long temporal coverage (1979–present) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, which has historically been of the order of tens of kilometers. Here, we use a newly released PMW dataset (37 GHz, horizontal polarization) made available through a NASA “Making Earth System Data Records for Use in Research Environments” (MeASUREs) program to study the spatiotemporal evolution of surface melting over the Greenland ice sheet at an enhanced spatial resolution of 3.125 km. We assess the outputs of different detection algorithms using data collected by automatic weather stations (AWSs) and the outputs of the Modèle Atmosphérique Régional (MAR) regional climate model. We found that sporadic melting is well captured using a dynamic algorithm based on the outputs of the Microwave Emission Model of Layered Snowpack (MEMLS), whereas a fixed threshold of 245 K is capable of detecting persistent melt. Our results indicate that, during the reference period from 1979 to 2019 (from 1988 to 2019), surface melting over the ice sheet increased in terms of both duration, up to 4.5 (2.9) d per decade, and extension, up to 6.9 % (3.6 %) of the entire ice sheet surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season started up to 4.0 (2.5) d earlier and ended 7.0 (3.9) d later per decade. We also explored the information content of the enhanced-resolution dataset with respect to the one at 25 km and MAR outputs using a semi-variogram approach. We found that the enhanced product is more sensitive to local-scale processes, thereby confirming the potential of this new enhanced product for monitoring surface melting over Greenland at a higher spatial resolution than the historical products and for monitoring its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modeled melt extent at high resolution and, potentially, to assimilate these data in climate models. [less ▲]

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See detailPerformance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adélie Land, East Antarctica
Amory, Charles ULiege; Kittel, Christoph ULiege; Le Toumelin, L. et al

in Geoscientific Model Development (2021)

Drifting snow, or the wind-driven transport of snow particles originating from clouds and the surface below and above 2 m above ground and their concurrent sublimation, is a poorly documented process on ... [more ▼]

Drifting snow, or the wind-driven transport of snow particles originating from clouds and the surface below and above 2 m above ground and their concurrent sublimation, is a poorly documented process on the Antarctic ice sheet, which is inherently lacking in most climate models. Since drifting snow mostly results from erosion of surface particles, a comprehensive evaluation of this process in climate models requires a concurrent assessment of simulated drifting-snow transport and the surface mass balance (SMB). In this paper a new version of the drifting-snow scheme currently embedded in the regional climate model MAR (v3.11) is extensively described. Several important modifications relative to previous version have been implemented and include notably a parameterization for drifting-snow compaction of the uppermost snowpack layer, differentiated snow density at deposition between precipitation and drifting snow, and a rewrite of the threshold friction velocity above which snow erosion initiates. Model results at high resolution (10 km) over Adélie Land, East Antarctica, for the period 2004–2018 are presented and evaluated against available near-surface meteorological observations at half-hourly resolution and annual SMB estimates. The evaluation demonstrates that MAR resolves the local drifting-snow frequency and transport up to the scale of the drifting-snow event and captures the resulting observed climate and SMB variability, suggesting that this model version can be used for continent-wide applications. [less ▲]

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See detailBrief Communication: Reduction of the future Greenland ice sheet surface melt with the help of solar geoengineering
Fettweis, Xavier ULiege; Hofer, Stefan ULiege; Séférian, R. et al

in Cryosphere (2021), 15

The Greenland Ice Sheet (GrIS) will be losing mass at an accelerating pace throughout the 21st century, with a direct link between anthropogenic greenhouse gas emissions and the magnitude of Greenland ... [more ▼]

The Greenland Ice Sheet (GrIS) will be losing mass at an accelerating pace throughout the 21st century, with a direct link between anthropogenic greenhouse gas emissions and the magnitude of Greenland mass loss. Currently, approximately 60 % of the mass loss contribution comes from surface melt and subsequent meltwater runoff, while 40 % are due to ice calving. In the ablation zone covered by bare ice in summer, most of the surface melt energy is provided by absorbed shortwave fluxes, which could be reduced by solar geoengineering measures. However, so far very little is known about the potential impacts of an artificial reduction of the incoming solar radiation on the GrIS surface energy budget and the subsequent change in meltwater production. By forcing the regional climate model MAR with the latest CMIP6 shared socioeconomic pathways (ssp) future emission scenarios (ssp245, ssp585) and associated G6solar experiment from the CNRM-ESM2-1 Earth System Model, we estimate the local impact of a reduced solar constant on the projected GrIS surface mass balance (SMB) decrease. Overall, our results show that even in case of low mitigation greenhouse gas emissions scenario (ssp585), the Greenland surface mass loss can be brought in line with the medium mitigation emissions scenario (ssp245) by reducing the solar downward flux at the top of the atmosphere by ~40 W/m2 or ~1.5 % (using the G6solar experiment). In addition to reducing global warming in line with ssp245, G6solar also decreases the efficiency of surface meltwater production over the Greenland ice sheet by damping the well-known positive melt-albedo feedback. With respect to a MAR simulation where the solar constant remains unchanged, decreasing the solar constant according to G6solar in the MAR radiative scheme mitigates the projected Greenland ice sheet surface melt increase by 6 %. However, only more constraining geoengineering experiments than G6solar would allow to maintain positive SMB until the end of this century without any reduction in our greenhouse gas emissions. [less ▲]

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See detailFuture sea level change under CMIP5 and CMIP6 scenarios from the Greenland and Antarctic ice sheets
Payne, A.; Nowicki, S.; ISMIP6 team et al

in Geophysical Research Letters (2021), 48

Projections of the sea level contribution from the Greenland and Antarctic ice sheets rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the ... [more ▼]

Projections of the sea level contribution from the Greenland and Antarctic ice sheets rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous CMIP5 effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming. [less ▲]

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See detailA Probabilistic Forecast-Driven Strategy for a Risk-Aware Participation in the Capacity Firming Market
Dumas, Jonathan ULiege; Cointe, Colin; Wehenkel, Antoine ULiege et al

E-print/Working paper (2021)

This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power ... [more ▼]

This paper addresses the energy management of a grid-connected renewable generation plant coupled with a battery energy storage device in the capacity firming market, designed to promote renewable power generation facilities in small non- interconnected grids, such as Overseas France islands. A recently developed new deep learning model named normalizing flows is used to generate quantile forecasts of renewable generation. Normalizing flows are an increasingly active and promising area of machine learning research. They provide a general mechanism for defining expressive probability distributions, only requiring the specification of a base distribution and a series of bijective transformations. Then, a probabilistic forecast-driven strategy is designed, modeled as a min-max-min robust optimization problem with recourse, and solved using a Benders decomposition. The convergence is improved by building an initial set of cuts derived from domain knowledge. Robust optimization models the generation randomness using an uncertainty set which includes the worst-case generation scenario, and protects this scenario under the minimal increment of costs. This approach improves the results over a deterministic approach with nominal point forecasts by finding a trade-off between conservative and risk-seeking policies. Finally, a dynamic risk-averse parameters selection strategy based on the quantile forecasts distribution provides an additional gain. The case study uses the photovoltaic generation monitored on site at the University of Liège (ULiège), Belgium. [less ▲]

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See detailBrief communication: CMIP6 does not suggest any atmospheric blocking increase in summer over Greenland by 2100
Delhasse, Alison ULiege; Hanna, E.; Kittel, Christoph ULiege et al

in International Journal of Climatology (2021)

The Greenland blocking index (GBI), an indicator of the synoptic‐scale circulation over Greenland, has been anomalously positive during most summers since the late 1990s. Such changes in atmospheric ... [more ▼]

The Greenland blocking index (GBI), an indicator of the synoptic‐scale circulation over Greenland, has been anomalously positive during most summers since the late 1990s. Such changes in atmospheric circulation, favouring anticyclonic conditions, have led to an increase in Greenland summer temperatures, a decrease in cloud cover and larger surface melt. The GBI is therefore a key indicator of melting and surface mass balance variability over the Greenland ice sheet. However, the models of fifth phase of the Coupled Model Intercomparison Project (CMIP5) do not represent the increase in GBI that is suggested by recent observations, and do not project any significant increase in GBI until 2100. In this study, the new generation of CMIP6 Earth‐system models is evaluated in order to analyse the evolution of the future GBI. All CMIP5 and CMIP6 projections reveal the same trend towards a decrease of the GBI until 2100 and no model reproduces the strong increase in the persistence of summer blocking events observed over the last few decades. Significant melting events related to a highly positive GBI, as observed in summer 2019, are still not considered by CMIP6 models and therefore the projected surface melt increase of the ice sheet could be underestimated if such summer circulation changes persist in the next decades. [less ▲]

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See detailInfluence of ocean tides and ice shelves on ocean–ice interactions and dense shelf water formation in the D’Urville Sea, Antarctica
Huot, Pierre-Vincent; Fichefet, T.; Jourdain, N.C. et al

in Ocean Modelling (2021), 162

The D’Urville Sea, East Antarctica, is a major source of Dense Shelf Water (DSW), a precursor of Antarctic Bottom Water (AABW). AABW is a key water mass involved in the worldwide ocean circulation and ... [more ▼]

The D’Urville Sea, East Antarctica, is a major source of Dense Shelf Water (DSW), a precursor of Antarctic Bottom Water (AABW). AABW is a key water mass involved in the worldwide ocean circulation and long-term climate variability. The properties of AABW in global climate models suffer from several biases, making climate projections uncertain. These models are potentially omitting or misrepresenting important mechanisms involved in the formation of DSW, such as tides and ocean–ice shelf interactions. Recent studies pointed out that tides and ice shelves significantly influence the coastal seas of Antarctica, where AABW originates from. Yet, the implications of these two processes in the formation and evolution of DSW are poorly understood, in particular in the D’Urville Sea. Using a series of NEMO-LIM numerical simulations, we assess the sensitivity of dense water formation in the D’Urville Sea to the representation of tides and ocean–ice shelf interactions during the years 2010–2015. We show that the ice shelves off Adélie Land are highly sensitive to tidal forcing, with a significant basal melt increase in the presence of tides. Ice shelf basal melt freshens and cools the ocean over significant portions of the coastal seas at the depth of the ice shelf draft. An opposite warming and increase in salinity are found in the upper layers. The influence of ice shelf basal melt on the ocean is largely increased in the presence of tides. However, the production of sea ice is found to be mostly unaffected by these two processes. Water mass transport out of polynyas and ice shelf cavities are then investigated, together with their sensitivity to tides and ocean–ice shelf interactions. Ice shelf basal melt impacts the volume of dense waters in two ways: (1) Dense Shelf Water and Modified Shelf Water are consumed to form water masses of intermediate density inside the ice shelf cavities, and (2) the freshening of the ocean subsurface makes its transformation into dense water by sea ice formation more difficult. These results suggest that ice shelf basal melt variability can explain part of the observed changes of dense water properties, and may also affect the production of dense water in a future climate. [less ▲]

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See detailDiverging future surface mass balance between the Antarctic ice shelves and grounded ice sheet
Kittel, Christoph ULiege; Amory, Charles ULiege; Agosta, Cécile et al

in Cryosphere (2021), 15(3), 1215-1236

The future surface mass balance (SMB) will influence the ice dynamics and the contribution of the Antarctic ice sheet (AIS) to the sea level rise. Most of recent Antarctic SMB projections were based on ... [more ▼]

The future surface mass balance (SMB) will influence the ice dynamics and the contribution of the Antarctic ice sheet (AIS) to the sea level rise. Most of recent Antarctic SMB projections were based on the fifth phase of the Coupled Model Intercomparison Project (CMIP5). However, new CMIP6 results have revealed a +1.3 ∘C higher mean Antarctic near-surface temperature than in CMIP5 at the end of the 21st century, enabling estimations of future SMB in warmer climates. Here, we investigate the AIS sensitivity to different warmings with an ensemble of four simulations performed with the polar regional climate model Modèle Atmosphérique Régional (MAR) forced by two CMIP5 and two CMIP6 models over 1981–2100. Statistical extrapolation enables us to expand our results to the whole CMIP5 and CMIP6 ensembles. Our results highlight a contrasting effect on the future grounded ice sheet and the ice shelves. The SMB over grounded ice is projected to increase as a response to stronger snowfall, only partly offset by enhanced meltwater run-off. This leads to a cumulated sea-level-rise mitigation (i.e. an increase in surface mass) of the grounded Antarctic surface by 5.1 ± 1.9 cm sea level equivalent (SLE) in CMIP5-RCP8.5 (Relative Concentration Pathway 8.5) and 6.3 ± 2.0 cm SLE in CMIP6-ssp585 (Shared Socioeconomic Pathways 585). Additionally, the CMIP6 low-emission ssp126 and intermediate-emission ssp245 scenarios project a stabilized surface mass gain, resulting in a lower mitigation to sea level rise than in ssp585. Over the ice shelves, the strong run-off increase associated with higher temperature is projected to decrease the SMB (more strongly in CMIP6-ssp585 compared to CMIP5-RCP8.5). Ice shelves are however predicted to have a close-to-present-equilibrium stable SMB under CMIP6 ssp126 and ssp245 scenarios. Future uncertainties are mainly due to the sensitivity to anthropogenic forcing and the timing of the projected warming. While ice shelves should remain at a close-to-equilibrium stable SMB under the Paris Agreement, MAR projects strong SMB decrease for an Antarctic near-surface warming above +2.5 ∘C compared to 1981–2010 mean temperature, limiting the warming range before potential irreversible damages on the ice shelves. Finally, our results reveal the existence of a potential threshold (+7.5 ∘C) that leads to a lower grounded-SMB increase. This however has to be confirmed in following studies using more extreme or longer future scenarios. [less ▲]

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See detailUncertainty in East Antarctic firn thickness constrained using a model ensemble approach
Verjans, Vincent; Leeson, A.A; McMillan, M. et al

in Geophysical Research Letters (2021)

Mass balance assessments of the East Antarctic ice sheet (EAIS) are highly sensitive to changes in firn thickness, causing substantial disagreement in estimates of its contribution to sea‐level. To better ... [more ▼]

Mass balance assessments of the East Antarctic ice sheet (EAIS) are highly sensitive to changes in firn thickness, causing substantial disagreement in estimates of its contribution to sea‐level. To better constrain the uncertainty in recent firn thickness changes, we develop an ensemble of 54 model scenarios of firn evolution between 1992‐2017. Using statistical emulation of firn‐densification models, we quantify the impact of firn compaction formulation, differing climatic forcing, and surface snow density on firn thickness evolution. At basin scales, the ensemble uncertainty in firn thickness change ranges between 0.2–1.0 cm yr‐1 (15–300% relative uncertainty), with the choice of climate forcing having the largest influence on the spread. Our results show the regions of the ice sheet where unexplained discrepancies exist between observed elevation changes and an extensive set of modelled firn thickness changes estimates, marking an important step towards more accurately constraining ice sheet mass balance. [less ▲]

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See detailGreater Greenland Ice Sheet contribution to global sea level rise in CMIP6
Hofer, S.; Lang, Charlotte ULiege; Amory, Charles ULiege et al

in Nature Communications (2020), 11

Future climate projections show a marked increase in Greenland Ice Sheet (GrIS) runoff during the 21st century, a direct consequence of the Polar Amplification signal. Regional climate models (RCMs) are a ... [more ▼]

Future climate projections show a marked increase in Greenland Ice Sheet (GrIS) runoff during the 21st century, a direct consequence of the Polar Amplification signal. Regional climate models (RCMs) are a widely used tool to downscale ensembles of projections from global climate models (GCMs) to assess the impact of global warming on GrIS melt and sea level rise contribution. Initial results of the CMIP6 GCM model intercomparison project have revealed a greater 21st century temperature rise than in CMIP5 models. However, so far very little is known about the subsequent impacts on the future GrIS surface melt and therefore sea level rise contribution. Here, we show that the total GrIS sea level rise contribution from surface mass loss in our high-resolution (15 km) regional climate projections is 17.8 ± 7.8 cm in SSP585, 7.9 cm more than in our RCP8.5 simulations using CMIP5 input. We identify a +1.3 °C greater Arctic Amplification and associated cloud and sea ice feedbacks in the CMIP6 SSP585 scenario as the main drivers. Additionally, an assessment of the GrIS sea level contribution across all emission scenarios highlights, that the GrIS mass loss in CMIP6 is equivalent to a CMIP5 scenario with twice the global radiative forcing. [less ▲]

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See detailContrasting seasonal changes in total and intense precipitation in the European Alps from 1903 to 2010
Ménégoz, M.; Valla, E.; Jourdain, N.C. et al

in Hydrology and Earth System Sciences (2020), 24

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See detailGreenland liquid water discharge from 1958 through 2019
Mankoff, K.; Noël, B.; Fettweis, Xavier ULiege et al

in Earth System Science Data (2020), 12(2811–2841),

Greenland runoff, from ice mass loss and increasing rainfall, is increasing. That runoff, as discharge, impacts the physical, chemical, and biological properties of the adjacent fjords. However, where and ... [more ▼]

Greenland runoff, from ice mass loss and increasing rainfall, is increasing. That runoff, as discharge, impacts the physical, chemical, and biological properties of the adjacent fjords. However, where and when the discharge occurs is not readily available in an open database. Here we provide data sets of high-resolution Greenland hydrologic outlets, basins, and streams, as well as a daily 1958 through 2019 time series of Greenland liquid water discharge for each outlet. The data include 24 507 ice marginal outlets and upstream basins and 29 635 land coast outlets and upstream basins, derived from the 100 m ArcticDEM and 150 m BedMachine. At each outlet there are daily discharge data for 22 645 d – ice sheet runoff routed subglacially to ice margin outlets and land runoff routed to coast outlets – from two regional climate models (RCMs; MAR and RACMO). Our sensitivity study of how outlet location changes for every inland cell based on subglacial routing assumptions shows that most inland cells where runoff occurs are not highly sensitive to those routing assumptions, and outflow location does not move far. We compare RCM results with 10 gauges from streams with discharge rates spanning 4 orders of magnitude. Results show that for daily discharge at the individual basin scale the 5 % to 95 % prediction interval between modeled discharge and observations generally falls within plus or minus a factor of 5 (half an order of magnitude, or +500 %/−80 %). Results from this study are available at https://doi.org/10.22008/promice/freshwater (Mankoff, 2020a) and code is available at http://github.com/mankoff/freshwater (last access: 6 November 2020) (Mankoff, 2020b). [less ▲]

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See detailGrSMBMIP: intercomparison of the modelled 1980–2012 surface mass balance over the Greenland Ice Sheet
Fettweis, Xavier ULiege; Hofer, S.; Krebs-Kanzow, U. et al

in Cryosphere (2020), 14

Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will ... [more ▼]

Observations and models agree that the Greenland Ice Sheet (GrIS) surface mass balance (SMB) has decreased since the end of the 1990s due to an increase in meltwater runoff and that this trend will accelerate in the future. However, large uncertainties remain, partly due to different approaches for modelling the GrIS SMB, which have to weigh physical complexity or low computing time, different spatial and temporal resolutions, different forcing fields, and different ice sheet topographies and extents, which collectively make an inter-comparison difficult. Our GrIS SMB model intercomparison project (GrSMBMIP) aims to refine these uncertainties by intercomparing 13 models of four types which were forced with the same ERA-Interim reanalysis forcing fields, except for two global models. We interpolate all modelled SMB fields onto a common ice sheet mask at 1 km horizontal resolution for the period 1980–2012 and score the outputs against (1) SMB estimates from a combination of gravimetric remote sensing data from GRACE and measured ice discharge; (2) ice cores, snow pits and in situ SMB observations; and (3) remotely sensed bare ice extent from MODerate-resolution Imaging Spectroradiometer (MODIS). Spatially, the largest spread among models can be found around the margins of the ice sheet, highlighting model deficiencies in an accurate representation of the GrIS ablation zone extent and processes related to surface melt and runoff. Overall, polar regional climate models (RCMs) perform the best compared to observations, in particular for simulating precipitation patterns. However, other simpler and faster models have biases of the same order as RCMs compared with observations and therefore remain useful tools for long-term simulations or coupling with ice sheet models. Finally, it is interesting to note that the ensemble mean of the 13 models produces the best estimate of the present-day SMB relative to observations, suggesting that biases are not systematic among models and that this ensemble estimate can be used as a reference for current climate when carrying out future model developments. However, a higher density of in situ SMB observations is required, especially in the south-east accumulation zone, where the model spread can reach 2 m w.e. yr−1 due to large discrepancies in modelled snowfall accumulation. [less ▲]

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See detailDeep learning-based multi-output quantile forecasting of PV generation
Dumas, Jonathan ULiege; cointe, colin; Fettweis, Xavier ULiege et al

in 2021 IEEE Madrid PowerTech (2020, November 10)

This paper develops probabilistic PV forecasters by taking advantage of recent breakthroughs in deep learning. A tailored forecasting tool, named encoder-decoder, is implemented to compute intraday multi ... [more ▼]

This paper develops probabilistic PV forecasters by taking advantage of recent breakthroughs in deep learning. A tailored forecasting tool, named encoder-decoder, is implemented to compute intraday multi-output PV quantiles forecasts in order to efficiently capture the time correlation. Quantile regression, a non-parametric approach, is selected as it assumes no prior knowledge of the probabilistic forecasting distribution. The case study is composed of PV production monitored on site at the university of Liège (ULiège), Belgium. The weather forecasts from the regional climate model provided by the Laboratory of Climatology of the university of Liège are used as inputs of the deep learning models. The quality of the forecasts are quantitatively assessed by the continuous ranked probability and winkler scores. The results indicate this architecture improves the forecast quality and is computationally efficient to be incorporated in an intraday decision making tool for robust optimization. [less ▲]

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