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FRIPRO-Fri prosjektstøtte

Snow D-excess OriGin Study

Alternative title: Opprinnelsen av d-excess signalet i snø

Awarded: NOK 4.3 mill.

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Project Period:

2023 - 2026

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The chemistry of water is influenced by the climatic conditions at every step on the path throughout the water cycle, hence water “remembers” the climate history of this path. When water freezes this information is preserved throughout time. So any snow and ice sample has a climate story to tell, and researchers can decode the chemistry signal of water into the corresponding climate signal. The SnowDOGS project will study how a certain signal that we find in the snow is formed and thus identify its climatic origin– much like a dog, that can sniff out the origin of a path when it follows the trail backwards; our reason to call the project SnowDOGS. In Earth’s polar areas, snow has been accumulating for hundred thousands of years and thus polar ice cores contain invaluable information of Earth’s climate history. This information is vital to understand the complex climate system and the processes that define the average climate state, but also the oscillations around the average. Once we understand the climate system and can build climate models that can reproduce the past climate signal we find in ice cores, we can have trust in the climate model’s predictions about future climate. Currently however, the models fail to correctly reproduce the snow signal that is connected to annual and decadal climate oscillations. Hence, models might not correctly simulate the processes that form the climate signal in the snow – maybe something is missing in the model? To test this question, SnowDOGS will build on latest research and include snow processes, that happen after the snow has been accumulated, in an existing snow model. Results of the improved model will be tested against snow data obtained from different locations in Greenland and Antarctica. Thus, the SnowDOGS project will be able to identify the origin of the climate signal in snow that has been accumulating on ice sheets for many thousands of years. Eventually, this will help us to better predict future climate.

Ice core water isotope records from the polar areas are invaluable climate proxies providing information about the relationship between the climate mean state and it's variability. The secondary ice core water isotope "excess" parameters, d-excess and 17O-excess, are especially useful for climate reconstruction since they presumably contain climatic information from the precipitation source regions. However, when simulating excess records with state-of-the-art isotope-enabled climate models, simulated and observed records disagree in seasonal to decadal signal variability. Strikingly, the ability to simulate isotope variability in precipitation and isotope variability in ice cores is distinct, which suggests d-/17O-excess signal formation processes after deposition. Recently, it was demonstrated that post-depositional processes (PDP) can influence the snow isotopic composition, yet the impact of PDP on the ice core signal has not been quantified. Thus, PDP could be the missing link in our understanding of the transfer function between climate and ice core signal. SnowDOGS' hypothesis is thus that PDP, which are currently not implemented in climate models, define the ice core d-/17O-excess signals and overprint the original precipitation source region information. SnowDOGS will quantify the impact of PDP on ice core excess signals by implementing multiple PDP in an existing snow model for the first time. Simulated d-/17O-excess records will be compared against observed ice core records from various locations on both ice sheets. SnowDOGS will clarify i) if PDP help in aligning simulated and observed isotope records, ii) to what extent PDP overprint the original source signal, and iii) what role PDP played in generating the high-frequency variability in d-/17O-excess records observed during both the current warm and the last glacial period. SnowDOGS will thus combine modeling and proxy records to improve the reconstruction and prediction of climate variability.

Funding scheme:

FRIPRO-Fri prosjektstøtte

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