Collaborative Research: P2C2--Fingerprinting Forced and Unforced Variability in Holocene Paleoclimate Record

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Distinguishing between natural and forced climate variability has large implications for both interpreting past variations and for making accurate predictions of the future. Climate models and paleoclimate observations disagree about the magnitude of variability at interdecadal-and-longer time scales. This creates uncertainty in estimates of equilibrium climate sensitivity (temperature increase that result from a doubling of atmospheric carbon dioxide concentration) and in model projections of long-term climate variability. The key to reconciling this contradiction lies in understanding the relative importance of forced and unforced variability in the paleoclimate records. If the paleoclimate reconstructions are correct, then the missing physics must be identified, whether it be a lack of sensitivity to external forcing (e.g. volcanic eruption, solar variability, changing concentrations of greenhouse gases and aerosols), or an underrepresentation of internal variability (internal mechanisms within the climate system). This project proposes a framework for fingerprinting forced and unforced climate variability in Holocene paleoclimate records, based on their different spatial and temporal statistics. Specifically, the researchers will develop spatio-temporal fingerprints for forced and unforced climate variability, using a combination of Global Circulation Models (GCMs) simulations and fundamental physical principles, and apply them to the multiproxy paleoclimate record of Holocene climate variability. The researchers will build the fingerprints starting from GCMs, which provide a complete representation of the climate system and have the ability to disaggregate forced and unforced variability. The proxy-system models (forward modelling of mechanistic processes either biological or chemical, by which climate variability is recorded in a climate archive such as corals or ice cores) will be used to evaluate how these fingerprints are expressed in proxy records that are sparse, noisy, and can alter the statistics of climate signals. Given well-documented deficiencies in the ability of GCMs to simulate low-frequency variability in both observations and proxies, the GCM-based fingerprints will be complemented with physical-statistical stochastic models.The potential Broader Impacts include a greater understanding of Holocene climate variability and modeled projections by (1) building a single coherent picture of the factors controlling the response of temperature, hydrology, and hydroclimate proxies in the climate system; and (2) improving the physical interpretation of the low-frequency climate variability recorded in paleoclimate records. This research will potentially illuminate both the nature and sources of climate variability over the Holocene, and the physical mechanisms responsible. This has large implications for interpreting the recent observational record, and for inferences of climate projections. The statistical frameworks, model simulations, and simple dynamical models developed in this project will serve as a conceptual framework for climate-related research across the Earth Sciences and over all geologic timescales.The project will provide scientific training and professional development for two undergraduate and two graduate students. The undergraduate students will be involved in the research and its publication as demonstrated by the track record of the researchers. Output from the model simulations, Code for setting up the simulations with the publicly available Community Earth System Model (CESM), and Code for reproducing the diagnostics using the publicly available data and model output will be all made publicly available.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.