Editing IPCC:AR6/WGI/Chapter-9 (section)

==== 9.4.1.2 Model Evaluation ====

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The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) stated that substantial challenges remained for modelling of the Greenland SMB and the dynamical ice sheet. Since SROCC, further insights into modelling of the Greenland ice sheet has come from model intercomparison studies of the SMB ( [[#Fettweis--2020|Fettweis et al., 2020]] ) and dynamical ice sheets ( [[#Goelzer--2020|Goelzer et al., 2020]] ; [[#Payne--2021|Payne et al., 2021]] ). Further aspects relevant to the forcing of the ice sheet from large scale global climate models and regional climate models are discussed in Box 9.3 and Section Atlas.11.2.

The SROCC stated that climate model simulations of Greenland SMB had improved since AR5, giving ''medium confidence'' in the ability of climate models to simulate changes in Greenland SMB. Since SROCC, a multi-model intercomparison study ( [[#Fettweis--2020|Fettweis et al., 2020]] ) of regional and global climate models has shown that the greatest inter-model spread occurs in the ablation zone, due to deficiencies in an accurate model representation of the ablation zone extent and processes related to surface melt and runoff, confirming SROCC statement that there is large uncertainty in the bare ice model ( [[#Ryan--2019|Ryan et al., 2019]] ). This intercomparison showed that simple, well-tuned SMB models using positive degree day melt schemes can perform as well as more complex physically based models (Figure ( [[IPCC:Wg1:Chapter:Atlas|Atlas]] 30). Furthermore, the ensemble mean of the models produced the best estimate of the present-day SMB relative to observations (particularly in the ablation zone). Further assessment of Greenland Ice Sheet regional SMB can be found in Section Atlas.11.2.3. Recent progress confirms SROCC assessment that there is ''medium confidence'' in the ability of climate models to simulate changes in Greenland SMB.

The SROCC noted increased use of coupled climate–ice sheet models for simulating the Greenland ice sheet, but it also noted that remaining deficiencies in coupling between models of climate and ice sheets (e.g., low spatial resolution) limited the adequate representation of the feedbacks between them. Some Earth system models (ESMs) now incorporate multi-layer snow models and full energy balance models ( [[#Punge--2012|Punge et al., 2012]] ; [[#Cullather--2014|Cullather et al., 2014]] ; [[#van%20Kampenhout--2017|van Kampenhout et al., 2017]] , 2020; [[#Alexander--2019|Alexander et al., 2019]] ) or use elevation classes to compensate for their coarser resolution ( [[#Lipscomb--2013|Lipscomb et al., 2013]] ; [[#Sellevold--2019|Sellevold et al., 2019]] ; [[#Gregory--2020|Gregory et al., 2020]] ; [[#Muntjewerf--2020a|Muntjewerf et al., 2020a]] , b). Resulting SMB simulations compare better with regional climate models and observations ( [[#Alexander--2019|Alexander et al., 2019]] ; [[#van%20Kampenhout--2020|van Kampenhout et al., 2020]] ), but the remaining shortcomings lead to problems reproducing a present-day ice-sheet state close to observations. In summary, there is ''medium confidence'' in quantitative simulations of the present-day state of the Greenland Ice Sheet in ESMs.

The SROCC ( [[#Meredith--2019|Meredith et al., 2019]] ) stated that there is ''low confidence'' in understanding coastal glacier response to ocean forcing because submarine melt rates, calving rates, bed and fjord geometry and the roles of ice mélange and subglacial discharge are poorly understood. Ice–ocean interactions remain poorly understood and difficult to model, with parametrizations often used for calving of marine-terminating glaciers ( [[#Mercenier--2018|Mercenier et al., 2018]] ) and submarine and plume-driven melt ( [[#Beckmann--2019|Beckmann et al., 2019]] ). Due to the difficulties of modelling the large number of marine-terminating glaciers and limited availability of high-resolution bedrock data, the majority of recent modelling work on Greenland outlet glaciers is focused on individual or a limited number of glaciers ( [[#Krug--2014|Krug et al., 2014]] ; [[#Bondzio--2016|Bondzio et al., 2016]] , 2017; [[#Morlighem--2016b|Morlighem et al., 2016b]] ; [[#Muresan--2016|Muresan et al., 2016]] ; [[#Choi--2017|Choi et al., 2017]] ; [[#Beckmann--2019|Beckmann et al., 2019]] ), or a specific region ( [[#Morlighem--2019|Morlighem et al., 2019]] ). Since SROCC, using a flowline model that includes calving and submarine melting, [[#Beckmann--2019|Beckmann et al. (2019)]] concluded that the AR5 upscaling of contributions from four of the largest glaciers ( [[#Nick--2013|Nick et al., 2013]] ) overestimated the total glacier contribution from the Greenland Ice Sheet, due to differences in response between large and small glaciers. The regional study of [[#Morlighem--2019|Morlighem et al. (2019)]] confirms that ice–ocean interactions have the potential to trigger extensive glacier retreat over decadal time scales, as indicated by observations ( [[#9.4.1.1|Section 9.4.1.1]] ). One focus of continental ice-sheet models has been the improved treatment of marine-terminating glaciers via the inclusion of calving processes and freely moving calving fronts ( [[#Aschwanden--2019|Aschwanden et al., 2019]] ; [[#Choi--2021|Choi et al., 2021]] ). An improved bedrock topographic dataset ( [[#Morlighem--2017|Morlighem et al., 2017]] ) allows for ice discharge to be better captured for outlet glaciers in continental ice-sheet models, and simulations indicate that bedrock topography controls the magnitude and rate of retreat ( [[#Aschwanden--2019|Aschwanden et al., 2019]] ; [[#Rückamp--2020|Rückamp et al., 2020]] ). Overall, although there is ''high confidence'' that the dynamic response of Greenland outlet glaciers is controlled by bedrock topography, there is ''low confidence'' in quantification of future mass loss from Greenland triggered by warming ocean conditions, due to limitations in the current understanding of ice–ocean interactions, its implementation in ice-sheet models, and knowledge of bedrock topography.

The SROCC ( [[#Oppenheimer--2019|Oppenheimer et al., 2019]] ) noted the progress made in Greenland Ice Sheet models since AR5. New since SROCC is a focus on improved representation of the present-day state of the ice sheet (Box 9.3; [[#Goelzer--2018|Goelzer et al., 2018]] , 2020). Improvements are closely linked to the growing number and quality of observations ( [[#9.4.1.1|Section 9.4.1.1]] ), new techniques to generate internally consistent input datasets ( [[#Morlighem--2014|Morlighem et al., 2014]] , 2016a), wider use of data assimilation techniques ( [[#Larour--2014|Larour et al., 2014]] , 2016; [[#Perego--2014|Perego et al., 2014]] ; [[#Goldberg--2015|Goldberg et al., 2015]] ; [[#Lee--2015|Lee et al., 2015]] ; [[#Schlegel--2015|Schlegel et al., 2015]] ; [[#Mosbeux--2016|Mosbeux et al., 2016]] ), increased model resolution ( [[#Aschwanden--2016|Aschwanden et al., 2016]] ) and tuning of key processes such as calving ( [[#Choi--2021|Choi et al., 2021]] ). A remaining challenge is ''low confidence'' in reproducing historical mass changes of the Greenland Ice Sheet (Box 9.3). However, there is ''medium confidence'' in ice-sheet models reproducing the present state of the Greenland Ice Sheet, leading to ''medium confidence'' in the current ability to accurately project its future evolution.

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