scholarly journals Brief communication: Interest of a regional climate model against ERA5 to simulate the near-surface climate of the Greenland ice sheet

2019 ◽  
Author(s):  
Alison Delhasse ◽  
Christoph Kittel ◽  
Charles Amory ◽  
Stefan Hofer ◽  
Xavier Fettweis
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
Near Surface ◽  
Weather Forecasts ◽  
Surface Climate ◽  
Medium Range

Abstract. The ERA5 reanalysis, recently made available by the European Centre for Medium-Range Weather Forecasts (ECMWF), is a new reanalysis product at a higher resolution which will replace ERA-Interim, considered to be the best reanalysis over Greenland until now. However, so far very little is known about the performance of ERA5 when compared to ERA-Interim over the Greenland Ice Sheet (GrIS). This study shows (1) that ERA5 improves not significantly the ERA-Interim comparison with near-surface climate observations over GrIS, (2) polar regional climate models (e.g. MAR) are still a useful tool to study the GrIS climate compared to ERA5, in particular in summer, and (3) that MAR results are not sensitive to the forcing used at its lateral boundaries (ERA5 or ERA-Interim).

scholarly journals Brief communication: Evaluation of the near-surface climate in ERA5 over the Greenland Ice Sheet

2020 ◽  
Vol 14 (3) ◽  
pp. 957-965 ◽  
Author(s):  
Alison Delhasse ◽  
Christoph Kittel ◽  
Charles Amory ◽  
Stefan Hofer ◽  
Dirk van As ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Climate Models ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
The Arctic ◽  
Homogeneous Surface ◽  
Near Surface ◽  
Surface Climate

Abstract. The ERA5 reanalysis, recently made available by the European Centre for Medium-Range Weather Forecasts (ECMWF), is a new reanalysis product at a high resolution replacing ERA-Interim and is considered to provide the best climate reanalysis over Greenland to date. However, so far little is known about the performance of ERA5 over the Greenland Ice Sheet (GrIS). In this study, we compare the near-surface climate from the new ERA5 reanalysis to ERA-Interim, the Arctic System Reanalysis (ASR) as well as to a state-of-the-art polar regional climate model (MAR). The results show (1) that ERA5 does not outperform ERA-Interim significantly when compared with near-surface climate observations over GrIS, but ASR better models the near-surface temperature than both ERA reanalyses. (2) Polar regional climate models (e.g., MAR) are still a useful tool to downscale the GrIS climate compared to ERA5, as in particular the near-surface temperature in summer has a key role for representing snow and ice processes such as the surface melt. However, assimilating satellite data and using a more recent radiative scheme enable both ERA and ASR reanalyses to represent more satisfactorily than MAR the downward solar and infrared fluxes. (3) MAR near-surface climate is not affected when forced at its lateral boundaries by either ERA5 or ERA-Interim. Therefore, forcing polar regional climate models with ERA5 starting from 1950 will enable long and homogeneous surface mass balance reconstructions.

scholarly journals Climate of the Greenland ice sheet using a high-resolution climate model – Part 2: Near-surface climate and energy balance

2010 ◽  
Vol 4 (2) ◽  
pp. 603-639 ◽  
Author(s):  
J. Ettema ◽  
M. R. van den Broeke ◽  
E. van Meijgaard ◽  
W. J. van de Berg
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Katabatic Wind ◽  
Surface Cooling ◽  
Near Surface ◽  
Surface Climate

Abstract. The near-surface climate of the Greenland ice sheet is characterized by persistent katabatic winds and quasi-permanent temperature deficit. Using a high resolution (11 km) regional climate model allows for detailed study of the spatial variability in these phenomena and the underlying atmospheric processes. The near-surface temperature distribution over the ice sheet is clearly affected by elevation, latitude, large scale advection, meso-scale topographic features and the occurrence of summer melt. The lowest annual temperatures of −30.5 °C are found north of the highest elevations of the GrIS, whereas the lowest southern margins are warmest (−3.5 °C). Over the ice sheet, a persistent katabatic wind system develops due to radiative surface cooling and the gently slope of the surface. The strongest wind speeds are seen in the northeast where the strong large scale winds, low cloud cover and concave surface force a continuous supply of cold air, which enhances the katabatic forcing. The radiative cooling of the surface is controlled by the net longwave emission and transport of heat towards the surface by turbulence. In summer this mechanism is much weaker, leading to less horizontal variability in near-surface temperatures and wind speed.

scholarly journals Melting trends over the Greenland ice sheet (1958–2009) from spaceborne microwave data and regional climate models

2011 ◽  
Vol 5 (2) ◽  
pp. 359-375 ◽  
Author(s):  
X. Fettweis ◽  
M. Tedesco ◽  
M. van den Broeke ◽  
J. Ettema
Keyword(s):  
Remote Sensing ◽  
Climate Models ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
P Value ◽  
Snow Layer ◽  
Near Surface ◽  
Microwave Data

Abstract. To study near-surface melt changes over the Greenland ice sheet (GrIS) since 1979, melt extent estimates from two regional climate models were compared with those obtained from spaceborne microwave brightness temperatures using two different remote sensing algorithms. The results from the two models were consistent with those obtained with the remote sensing algorithms at both daily and yearly time scales, encouraging the use of the models for analyzing melting trends before the satellite era (1958–1979), when forcing data is available. Differences between satellite-derived and model-simulated results still occur and are used here to identify (i) biases in the snow models (notably in the albedo parametrization, in the thickness of a snow layer, in the maximum liquid water content within the snowpack and in the snowfall impacting the bare ice appearance in summer) and (ii) limitations in the use of passive microwave data for snowmelt detection at the edge of the ice sheet due to mixed pixel effect (e.g., tundra or rock nearby the ice sheet). The results from models and spaceborne microwave sensors confirm a significant (p-value = 0.01) increase in GrIS surface melting since 1979. The melt extent recorded over the last years (1998, 2003, 2005 and 2007) is unprecedented in the last 50 yr with the cumulated melt area in the 2000's being, on the average, twice that of the 1980's.

scholarly journals Refreezing on the Greenland ice sheet: a comparison of parameterizations

2012 ◽  
Vol 6 (4) ◽  
pp. 743-762 ◽  
Author(s):  
C. H. Reijmer ◽  
M. R. van den Broeke ◽  
X. Fettweis ◽  
J. Ettema ◽  
L. B. Stap
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
Liquid Water Content ◽  
Annual Variations ◽  
Output Surface ◽  
Snow Model

Abstract. Retention and refreezing of meltwater are acknowledged to be important processes for the mass budget of polar glaciers and ice sheets. Several parameterizations of these processes exist for use in energy and mass balance models. Due to a lack of direct observations, validation of these parameterizations is difficult. In this study we compare a set of 6 refreezing parameterizations against output of two Regional Climate Models (RCMs) coupled to an energy balance snow model, the Regional Atmospheric Climate Model (RACMO2) and the Modèle Atmosphérique Régional (MAR), applied to the Greenland ice sheet. In both RCMs, refreezing is explicitly calculated in a snow model that calculates vertical profiles of temperature, density and liquid water content. Between RACMO2 and MAR, the ice sheet-integrated amount of refreezing differs by only 4.9 mm w.e yr−1 (4.5 %), and the temporal and spatial variability are very similar. For consistency, the parameterizations are forced with output (surface temperature, precipitation and melt) of the RCMs. For the ice sheet-integrated amount of refreezing and its inter-annual variations, all parameterizations give similar results, especially after some tuning. However, the spatial distributions differ significantly and the spatial correspondence between the RCMs is better than with any of the parameterizations. Results are especially sensitive to the choice of the depth of the thermally active layer, which determines the cold content of the snow in most parameterizations. These results are independent of which RCM is used to force the parameterizations.

scholarly journals Towards direct coupling of regional climate models and ice sheet models by mass balance gradients: application to the Greenland Ice Sheet

2011 ◽  
Vol 5 (4) ◽  
pp. 2115-2157 ◽  
Author(s):  
M. M. Helsen ◽  
R. S. W. van de Wal ◽  
M. R. van den Broeke ◽  
W. J. van de Berg ◽  
J. Oerlemans
Keyword(s):  
Mass Balance ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
Lapse Rate ◽  
Surface Mass ◽  
Temperature Lapse Rate

Abstract. It is notoriously difficult to couple surface mass balance (SMB) results from climate models to the changing geometry of an ice sheet model. This problem is traditionally avoided by using only accumulation fields from a climate model, and deriving SMB by parameterizing the run-off as a function of temperature, which is often related to surface elevation. In this study, a new parameterization of SMB is presented, designed for use in ice dynamical models to allow a direct adjustment of SMB as a result of a change in elevation (Hs) or a change in climate forcing. This method is based on spatial gradients in the present-day SMB field as computed by a regional climate model. Separate linear relations are derived for ablation and accumulation regimes, using only those pairs of Hs an SMB that are found within a minimum search radius. This approach enables a dynamic SMB forcing of ice sheet models, also for initially non-glaciated areas in the peripheral areas of an ice sheet, and circumvents traditional temperature lapse rate assumptions. The method is applied to the Greenland Ice Sheet (GrIS). Model experiments using both steady-state forcing and more realistic glacial-interglacial forcing result in ice sheet reconstructions and behavior that compare favorably with present-day observations of ice thickness.

scholarly journals Melting trends over the Greenland ice sheet (1958-2009) from spaceborne microwave data and regional climate models

2010 ◽  
Vol 4 (4) ◽  
pp. 2433-2473 ◽  
Author(s):  
X. Fettweis ◽  
M. Tedesco ◽  
M. van den Broeke ◽  
J. Ettema
Keyword(s):  
Remote Sensing ◽  
Climate Models ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
P Value ◽  
Snow Layer ◽  
Near Surface ◽  
Microwave Data

Abstract. To study near-surface melt changes over the Greenland ice sheet (GrIS) since 1979, melt extent estimates from two regional climate models are compared with those obtained from spaceborne microwave brightness temperatures using two different remote sensing algorithms. Results from the two models are consistent with those obtained with the remote sensing algorithms, at both daily and yearly time scales, encouraging the use of the models for analyzing melting trends before the satellite era (1958–1979), when forcing data is available. Differences between satellite-derived and model-simulated results still occur and are here used to identify (i) biases in the snow models (notably in the albedo parametrization, in the thickness of a snow layer, in the maximum liquid water content within the snowpack and in the snowfall impacting the bare ice appearance in summer) and (ii) limitations in the use of passive microwave data for snowmelt detection at the edge of the ice sheet due to mixed pixel effect (e.g., tundra or rock nearby the ice sheet). Results from models and spaceborne microwave sensors confirm a significant (p-value = 0.01) increase in GrIS surface melting since 1979. Melt extent recorded over the last years (1998, 2003, 2005 and 2007) is unprecedented in the last 50 years with the cumulated melt area in the 2000's being, on the average, twice as that occurring during the 1980's.

Regional Climate Model Inter-Comparison for Antarctica within a Data Science Framework

2021 ◽  
Author(s):  
Jeremy Carter ◽  
Amber Leeson ◽  
Andrew Orr ◽  
Christoph Kittel ◽  
Melchior van Wessem
Keyword(s):  
Regional Climate Model ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate Change ◽  
Near Surface ◽  
Virtual Lab ◽  
Model Variability ◽  
The Antarctic

<p>Understanding the surface climatology of the Antarctic ice sheet is essential if we are to adequately predict its response to future climate change. This includes both primary impacts such as increased ice melting and secondary impacts such as ice shelf collapse events. Given its size, and inhospitable environment, weather stations on Antarctica are sparse. Thus, we rely on regional climate models to 1) develop our understanding of how the climate of Antarctica varies in both time and space and 2) provide data to use as context for remote sensing studies and forcing for dynamical process models. Given that there are a number of different regional climate models available that explicitly simulate Antarctic climate, understanding inter- and intra model variability is important.</p><p>Here, inter- and intra-model variability in Antarctic-wide regional climate model output is assessed for: snowfall; rainfall; snowmelt and near-surface air temperature within a cloud-based virtual lab framework. State-of-the-art regional climate model runs from the Antarctic-CORDEX project using the RACMO, MAR and MetUM models are used, together with the ERA5 and ERA-Interim reanalyses products. Multiple simulations using the same model and domain boundary but run at either different spatial resolutions or with different driving data are used. Traditional analysis techniques are exploited and the question of potential added value from more modern and involved methods such as the use of Gaussian Processes is investigated. The advantages of using a virtual lab in a cloud based environment for increasing transparency and reproducibility, are demonstrated, with a view to ultimately make the code and methods used widely available for other research groups.</p>

scholarly journals Regional Climate Model Emulator based on Deep Learning: Concept and First Evaluation of a Novel Hybrid Downscaling Approach

2021 ◽  
Author(s):  
Antoine Doury ◽  
Samuel Somot ◽  
Sébastien Gadat ◽  
Aurélien Ribes ◽  
Lola Corre
Keyword(s):  
Climate Change ◽  
Network Architecture ◽  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Future Climate ◽  
Near Surface ◽  
The Way

Abstract Providing reliable information on climate change at local scale remains a challenge of first importance for impact studies and policymakers. Here, we propose a novel hybrid downscaling method combining the strengths of both empirical statistical downscaling methods and Regional Climate Models (RCMs). The aim of this tool is to enlarge the size of high-resolution RCM simulation ensembles at low cost.We build a statistical RCM-emulator by estimating the downscaling function included in the RCM. This framework allows us to learn the relationship between large-scale predictors and a local surface variable of interest over the RCM domain in present and future climate. Furthermore, the emulator relies on a neural network architecture, which grants computational efficiency. The RCM-emulator developed in this study is trained to produce daily maps of the near-surface temperature at the RCM resolution (12km). The emulator demonstrates an excellent ability to reproduce the complex spatial structure and daily variability simulated by the RCM and in particular the way the RCM refines locally the low-resolution climate patterns. Training in future climate appears to be a key feature of our emulator. Moreover, there is a huge computational benefit in running the emulator rather than the RCM, since training the emulator takes about 2 hours on GPU, and the prediction is nearly instantaneous. However, further work is needed to improve the way the RCM-emulator reproduces some of the temperature extremes, the intensity of climate change, and to extend the proposed methodology to different regions, GCMs, RCMs, and variables of interest.

scholarly journals Accumulation rates (2009–2017) in Southeast Greenland derived from airborne snow radar and comparison with regional climate models

2020 ◽  
Vol 61 (81) ◽  
pp. 225-233 ◽  
Author(s):  
Lynn Montgomery ◽  
Lora Koenig ◽  
Jan T. M. Lenaerts ◽  
Peter Kuipers Munneke
Keyword(s):  
Spatial Data ◽  
Climate Models ◽  
Layer Structure ◽  
Accumulation Rate ◽  
Regional Climate ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Regional Climate Models ◽  
Internal Layers ◽  
Accumulation Rates

AbstractSince the year 2000, Greenland ice sheet mass loss has been dominated by a decrease in surface mass balance rather than an increase in solid ice discharge. Southeast Greenland is an important region to understand how high accumulation rates can offset increasing Greenland ice sheet meltwater runoff. To that end, we derive a new 9-year long dataset (2009–17) of accumulation rates in Southeast Greenland using NASA Operation IceBridge snow radar. Our accumulation dataset derived from internal layers focuses on high elevations (1500–3000 m) because at lower elevations meltwater percolation obscured internal layer structure. The uncertainty of the radar-derived accumulation rates is 11% [using Firn Densification Model (FDM) density profiles] and the average accumulation rate ranges from 0.5 to 1.2 m w.e. With our observations spanning almost a decade, we find large inter-annual variability, but no significant trend. Accumulation rates are compared with output from two regional climate models (RCMs), MAR and RACMO2. This comparison shows that the models are underestimating accumulation in Southeast Greenland and the models misrepresent spatial heterogeneity due to an orographically forced bias in snowfall near the coast. Our dataset is useful to fill in temporal and spatial data gaps, and to evaluate RCMs where few in situ measurements are available.

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