Spatial gradients of temperature, accumulation and δ&amp;lt;sup&amp;gt;18&amp;lt;/sup&amp;gt;O-ice in  Greenland over a series of Dansgaard–Oeschger events

Guillevic, Myriam; Bazin, Lucie; Landais, A.; Kindler, Philippe; Orsi, Anaïs; Masson‐Delmotte, Valérie; Blunier, Thomas; Buchardt, S. L.; Capron, Émilie; Leuenberger, Markus; Martinerie, Patricia; Prié, Frédéric; Vinther, Bo M.

doi:10.5194/cp-9-1029-2013

Cited by 83 publications

(109 citation statements)

References 113 publications

(191 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This sensitivity of δ 15 N has even been used to adjust temperature and/or accumulation rate scenarios (Buizert et al, 2013;Guillevic et al, 2013;Kindler et al, 2014;Landais et al, 2006). We tested the influence of the accumulation rate and temperature scenarios on the simulated δ 15 N profiles for the last deglaciation, but even with large uncertainties in the input scenarios, it is not possible to reproduce the measured Antarctic δ 15 N increase at Dome C and EDML with the old version of the LGGE model.…”

Section: Transient Run With the Old Modelmentioning

confidence: 99%

“…The uncertainty in the temperature reconstructions can be estimated to ±3 • C over the last deglaciation in Greenland . As for the Greenland accumulation rate, an uncertainty of 20 % can be associated with the Last Glacial Maximum (LGM) value (Cuffey and Clow, 1997;Guillevic et al, 2013;Kapsner et al, 1995). In Antarctica, both temperature and accumulation rate are deduced from water isotopic records except for WAIS Divide, where layer counting back to the last glacial period is possible .…”

Section: Input Scenariosmentioning

confidence: 99%

“…In Greenland ice cores, where strong and abrupt surface temperature changes occurred during the last glacial period and deglaciation, δ 15 N is also affected by strong thermal fractionation. An abrupt warming (on the order of 10 • C in less than 50 years) indeed induces a transient temperature gradient of a few degrees in the firn Guillevic et al, 2013;Kindler et al, 2014). δ 15 N is thus modified as δ 15 N therm = · T , where is the thermal fractionation coefficient (Grachev and Severinghaus, 2003), and this thermal signal is superimposed on the gravitational one (the δ 15 N therm observed is lower than 0.15 ‰) in most cases.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Modelling firn thickness evolution during the last deglaciation: constraints on sensitivity to temperature and impurities

et al. 2017

Self Cite

View full text Add to dashboard Cite

Abstract. The transformation of snow into ice is a complex phenomenon that is difficult to model. Depending on surface temperature and accumulation rate, it may take several decades to millennia for air to be entrapped in ice. The air is thus always younger than the surrounding ice. The resulting gas-ice age difference is essential to documenting the phasing between CO 2 and temperature changes, especially during deglaciations. The air trapping depth can be inferred in the past using a firn densification model, or using δ 15 N of air measured in ice cores.All firn densification models applied to deglaciations show a large disagreement with δ 15 N measurements at several sites in East Antarctica, predicting larger firn thickness during the Last Glacial Maximum, whereas δ 15 N suggests a reduced firn thickness compared to the Holocene. Here we present modifications of the LGGE firn densification model, which significantly reduce the model-data mismatch for the gas trapping depth evolution over the last deglaciation at the coldest sites in East Antarctica (Vostok, Dome C), while preserving the good agreement between measured and modelled modern firn density profiles. In particular, we introduce a dependency of the creep factor on temperature and impurities in the firn densification rate calculation. The temperature influence intends to reflect the dominance of different mechanisms for firn compaction at different temperatures. We show that both the new temperature parameterization and the influence of impurities contribute to the increased agreement between modelled and measured δ 15 N evolution during the last deglaciation at sites with low temperature and low accumulation rate, such as Dome C or Vostok. We find that a very low sensitivity of the densification rate to temperature has to be used in the coldest conditions. The inclusion of impurity effects improves the agreement between modelled and measured δ 15 N at cold East Antarctic sites during the last deglaciation, but deteriorates the agreement between modelled and measured δ 15 N evolution at Greenland and Antarctic sites with high accumulation unless threshold effects are taken into account. We thus do not provide a definite solution to the firnification at very cold Antarctic sites but propose potential pathways for future studies.

show abstract

Section: Transient Run With the Old Modelmentioning

confidence: 99%

Section: Input Scenariosmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modelling firn thickness evolution during the last deglaciation: constraints on sensitivity to temperature and impurities

et al. 2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…This isotope-temperature relationship (isotope thermometer) (Johnsen et al, 2001) has been central to the use of ice core water isotope records to reconstruct past Greenland climate variations. However, the comparison of water stable isotope measurements with past temperatures inferred either from the inversion of borehole temperature data (DahlJensen et al, 1998) or from the fingerprint of firn air fractionation in ice core air δ 15 N has revealed that (i) for a given site, the isotope-temperature relationship varies through time (e.g., Guillevic et al, 2013;Landais et al, 2004;Kindler et al, 2013;Severinghaus and Brook, 1999), and (ii) for a given stadial-interstadial event, the isotope-temperature relationship varies between sites . The reported temporal isotope-temperature relationships vary between 0.3 and 0.6 ‰ • C −1 .…”

Section: Introductionmentioning

confidence: 99%

What controls the isotopic composition of Greenland surface snow?

et al. 2014

Self Cite

View full text Add to dashboard Cite

Abstract. Water stable isotopes in Greenland ice core data provide key paleoclimatic information, and have been compared with precipitation isotopic composition simulated by isotopically enabled atmospheric models. However, postdepositional processes linked with snow metamorphism remain poorly documented. For this purpose, monitoring of the isotopic composition (δ 18 O, δD) of near-surface water vapor, precipitation and samples of the top (0.5 cm) snow surface has been conducted during two summers (2011)(2012) at NEEM, NW Greenland. The samples also include a subset of 17 O-excess measurements over 4 days, and the measurements span the 2012 Greenland heat wave. Our observations are consistent with calculations assuming isotopic equilibrium between surface snow and water vapor. We observe a strong correlation between near-surface vapor δ 18 O and air temperature (0.85 ± 0.11 ‰ • C −1 (R = 0.76) for 2012). The correlation with air temperature is not observed in precipitation data or surface snow data. Deuterium excess (d-excess) is strongly anti-correlated with δ 18 O with a stronger slope for vapor than for precipitation and snow surface data. During nine 1-5-day periods between precipitation events, our data demonstrate parallel changes of δ 18 O and d-excess in surface snow and near-surface vapor. The changes in δ 18 O of the vapor are similar or larger than those of the snow δ 18 O. It is estimated using the CROCUS snow model that 6 to 20 % of the surface snow mass is exchanged with the atmosphere. In our data, the sign of surface snow isotopic changes is not related to the sign or magnitude of sublimation or deposition. Comparisons with atmospheric models show that day-to-day variations in near-surface vapor isotopic composition are driven by synoptic variations and changes in air mass trajectories and distillation histories. We suggest that, in between precipitation events, changes in the surface snow isotopic composition are driven by these changes in near-surface vapor isotopic composition. This is consistent with an estimated 60 % mass turnover of surface snow per day driven by snow recrystallization processes under NEEM summer surface snow temperature gradients. Our findings have implications for ice core data interpretation and model-data comparisons, and call for further process studies.

show abstract

“…On the other hand, uncertainties also arise from the age scales of proxy records, and from the application of transfer functions used to convert proxy records into climate variables. For instance, while δ 18 O is used as a temperature proxy in polar ice cores, the relationship between ice core δ 18 O and temperature is known to vary through time and between drilling sites (Masson-Delmotte et al, 2011a;Guillevic et al, 2013;Buizert et al, 2014). Similarly, the relationship between δ 18 O from tree-ring cellulose and climate may be impacted by several factors, including local monthly or annual temperature and precipitation, while the response of trees to climate changes may differ according to inherent physiological differences of the various tree species (Stuiver and Braziunas, 1987;McCarroll and Loader, 2004).…”

Section: Introductionmentioning

confidence: 99%

Water and carbon stable isotope records from natural archives: a new database and interactive online platform for data browsing, visualizing and downloading

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. Past climate is an important benchmark to assess the ability of climate models to simulate key processes and feedbacks. Numerous proxy records exist for stable isotopes of water and/or carbon, which are also implemented inside the components of a growing number of Earth system model. Model-data comparisons can help to constrain the uncertainties associated with transfer functions. This motivates the need of producing a comprehensive compilation of different proxy sources. We have put together a global database of proxy records of oxygen (δ 18 O), hydrogen (δD) and carbon (δ 13 C) stable isotopes from different archives: ocean and lake sediments, corals, ice cores, speleothems and tree-ring cellulose. Source records were obtained from the georeferenced open access PANGAEA and NOAA libraries, complemented by additional data obtained from a literature survey. About 3000 source records were screened for chronological information and temporal resolution of proxy records. Altogether, this database consists of hundreds of dated δ 18 O, δ 13 C and δD records in a standardized simple text format, complemented with a metadata Excel catalog. A quality control flag was implemented to describe age markers and inform on chronological uncertainty. This compilation effort highlights the need to homogenize and structure the format of datasets and chronological information as well as enhance the distribution of published datasets that are currently highly fragmented and scattered. We also provide an online portal based on the records included in this database with an intuitive and interactive platform (http://climateproxiesfinder.ipsl.fr/), allowing one to easily select, visualize and download subsets of the homogeneously formatted records that constitute this database, following a choice of search criteria, and to upload new datasets. In the last part, we illustrate the type of application allowed by our database by comparing several key periods highly investigated by the paleoclimate community.

show abstract

Spatial gradients of temperature, accumulation and δ<sup>18</sup>O-ice in Greenland over a series of Dansgaard–Oeschger events

Cited by 83 publications

References 113 publications

Modelling firn thickness evolution during the last deglaciation: constraints on sensitivity to temperature and impurities

Modelling firn thickness evolution during the last deglaciation: constraints on sensitivity to temperature and impurities

What controls the isotopic composition of Greenland surface snow?

Water and carbon stable isotope records from natural archives: a new database and interactive online platform for data browsing, visualizing and downloading

Contact Info

Product

Resources

About