Climate-model induced differences in the 21st century global and regional glacier contributions to sea-level rise

Giesen, Rianne H.; Oerlemans, J.

doi:10.1007/s00382-013-1743-7

Cited by 81 publications

(73 citation statements)

References 37 publications

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“…Hence, there is an increasing need to estimate the mass loss of individual glaciers worldwide and assess future sea level rise, water resources, and natural hazards, at both global and regional scales. To estimate current and future 30 changes in glaciers, several global glacier models have been developed in the past decade (Raper and Braithwaite, 2006;Hirabayashi et al, 2010Hirabayashi et al, , 2013Radic and Hock, 2011;Marzeion et al, 2012;Slangen et al, 2012;Giesen and Oerlemans. 2013;Radic et al, 2014;Huss and Hock.…”

Section: Introductionmentioning

confidence: 99%

A global high-resolution map of debris on glaciers derived from multi-temporal ASTER images

Sasaki

Noguchi

Zhang

et al. 2016

Preprint

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Abstract. Supraglacial debris affects the response of glaciers to climate change by altering the reflectivity of solar radiation and conductive heat flux. To accurately assess the contribution of glacier melts to sea level rise, water resources and natural 10 hazards, it is important to account for the effects of debris. However, due to the practical difficulties of global-scale field measurements, information regarding the spatial distribution of the thickness and thermal properties of debris on glaciers is limited; hence, the effects of debris on glacier melting are not explicitly taken into account in current global glacier models. In this study, we developed a dataset of the thermal resistance of debris on glaciers at 90-m resolution derived from multi-temporal satellite images and satellite-derived radiation data at the global scale, excluding Greenland, Antarctica, and some of the Arctic. 15We found that supraglacial debris covered 16.8% of the entire analyzed glacial area. The highest debris cover percentage occurred in New Zealand, and the lowest was in Iceland. The area of thick debris (which suppresses glacier melting) was about two times that of thin debris (which accelerates glacier melting), indicating that the insulation effect of debris to inhibit glacier melting may dominate at the global scale. The distribution of debris was also related to the slope aspect of glaciers. Despite the limitations of this study, the resulting global distribution of the thermal resistance of debris can be incorporated into global 20 glacier models, and hence it provides a solid basis for evaluating the effects of debris on glacial melting.

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Section: Introductionmentioning

confidence: 99%

A global high-resolution map of debris on glaciers derived from multi-temporal ASTER images

Sasaki

Noguchi

Zhang

et al. 2016

Preprint

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“…Shannon et al, 2013) and mountain glaciers (e.g. Giesen and Oerlemans, 2013); and from changes in the terrestrial storage of water as ground water and in reservoirs (e.g. Lempérière, 2006).…”

Section: Introductionmentioning

confidence: 99%

Sources of 21st century regional sea-level rise along the coast of northwest Europe

et al. 2014

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Abstract. Changes in both global and regional mean sea level, and changes in the magnitude of extreme flood heights, are the result of a combination of several distinct contributions most, but not all, of which are associated with climate change. These contributions include effects in the solid earth, gravity field, changes in ocean mass due to ice loss from ice sheets and glaciers, thermal expansion, alterations in ocean circulation driven by climate change and changing freshwater fluxes, and the intensity of storm surges. Due to the diverse range of models required to simulate these systems, the contributions to sea-level change have usually been discussed in isolation rather than in one self-consistent assessment. Focusing on the coastline of northwest Europe, we consider all the processes mentioned above and their relative impact on 21st century regional mean sea levels and the 50-year return flood height. As far as possible our projections of change are derived from process-based models forced by the A1B emissions scenario to provide a self-consistent comparison of the contributions. We address uncertainty by considering both a mid-range and an illustrative high-end combination of the different components.For our mid-range ice loss scenario we find that thermal expansion of seawater is the dominant contributor to change in northwest European sea level by 2100. However, the projected contribution to extreme sea level, due to changes in storminess alone, is in some places significant and comparable to the global mean contribution of thermal expansion. For example, under the A1B emissions scenario, by 2100, change in storminess contributes around 15 cm to the increase in projected height of the 50-year storm surge on the west coast of the Jutland Peninsula, compared with a contribution of around 22 cm due to thermal expansion and a total of 58 cm from all of the contributions we consider. An illustrative combination of our high-end projections suggests increases in the 50-year return level of 86 cm at Sheerness, 95 cm at Roscoff, 106 cm at Esbjerg, and 67cm at Bergen. The notable regional differences between these locations arise primarily from differences in the rates of vertical land movement and changes in storminess.

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“…To extend knowledge about the mountain glacier variations to unobserved time periods or regions, more and more studies have relied on reanalysis data (e.g., Stahl et al, 2008;Kotlarski et al, 2010;Paul and Kotlarski, 2010;Mölg et al, 2012;van Pelt et al, 2012;Giesen and Oerlemans, 2013). For studies about the Cordillera Blanca and the South American Andes, the first-generation NCEP reanalyses have been a frequent choice (e.g., Garreaud et al, 2003;Vuille et al, 2008a, b;Hofer et al, 2010).…”

Section: Reanalysis Data: the Large-scale Predictorsmentioning

confidence: 99%

A statistical downscaling method for daily air temperature in data-sparse, glaciated mountain environments

2015

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Abstract. This study presents a statistical downscaling (SD) method for high-altitude, glaciated mountain ranges. The SD method uses an a priori selection strategy of the predictor (i.e., predictor selection without data analysis). In the SD model validation, emphasis is put on appropriately considering the pitfalls of short observational data records that are typical of high mountains. An application example is shown, with daily mean air temperature from several sites (all in the Cordillera Blanca, Peru) as target variables, and reanalysis data as predictors. Results reveal strong seasonal variations of the predictors' performance, with the maximum skill evident for the wet (and transitional) season months January to May (and September), and the lowest skill for the dry season months June and July. The minimum number of observations (here, daily means) required per calendar month to obtain statistically significant skill ranges from 40 to 140. With increasing data availability, the SD model skill tends to increase. Applied to a choice of different atmospheric reanalysis predictor variables, the presented skill assessment identifies only air temperature and geopotential height as significant predictors for local-scale air temperature. Accounting for natural periodicity in the data is vital in the SD procedure to avoid spuriously high performances of certain predictors, as demonstrated here for near-surface air temperature. The presented SD procedure can be applied to highresolution, Gaussian target variables in various climatic and geo-environmental settings, without the requirement of subjective optimization.

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Climate-model induced differences in the 21st century global and regional glacier contributions to sea-level rise

Cited by 81 publications

References 37 publications

A global high-resolution map of debris on glaciers derived from multi-temporal ASTER images

A global high-resolution map of debris on glaciers derived from multi-temporal ASTER images

Sources of 21st century regional sea-level rise along the coast of northwest Europe

A statistical downscaling method for daily air temperature in data-sparse, glaciated mountain environments

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