references.bib

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# journal article
@article{zhu_resolving_2020,
	title = {Resolving the {Differences} in the {Simulated} and {Reconstructed} {Temperature} {Response} to {Volcanism}},
	volume = {47},
	copyright = {©2020. American Geophysical Union. All Rights Reserved.},
	issn = {1944-8007},
	url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019GL086908},
	doi = {10.1029/2019GL086908},
	abstract = {Explosive volcanism imposes impulse-like radiative forcing on the climate system, providing a natural experiment to study the climate response to perturbation. Previous studies have identified disagreements between paleoclimate reconstructions and climate model simulations with respect to the magnitude and recovery from volcanic cooling, questioning the fidelity of climate model simulations, reconstructions, or both. Using the paleoenvironmental data assimilation framework of the Last Millennium Reanalysis, this study investigates the causes of the disagreements, using both real and simulated data. We demonstrate that discrepancies since 1600 CE can be largely resolved by assimilating tree-ring density records only, targeting growing season temperature instead of annual temperature, and performing the comparison at proxy locales. Simulations of eruptions earlier in the last millennium may also reflect uncertainties in forcing and modeled aerosol microphysics.},
	language = {en},
	number = {8},
	urldate = {2020-04-21},
	journal = {Geophysical Research Letters},
	author = {Zhu, Feng and Emile-Geay, Julien and Hakim, Gregory J. and King, Jonathan and Anchukaitis, Kevin J.},
	year = {2020},
	note = {\_eprint: https://agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2019GL086908},
	keywords = {paleoclimate data assimilation, volcanic eruptions, Last Millennium Reanalysis, simulation-reconstruction comparison, Superposed Epoch Analysis, temperature response},
	pages = {e2019GL086908},
	annote = {e2019GL086908 10.1029/2019GL086908},
}


@article{zhu_climate_2019,
	title = {Climate models can correctly simulate the continuum of global-average temperature variability},
	copyright = {© 2019 . Published under the PNAS license.},
	issn = {0027-8424, 1091-6490},
	url = {https://www.pnas.org/content/early/2019/04/09/1809959116},
	doi = {10.1073/pnas.1809959116},
	abstract = {Climate records exhibit scaling behavior with large exponents, resulting in larger fluctuations at longer timescales. It is unclear whether climate models are capable of simulating these fluctuations, which draws into question their ability to simulate such variability in the coming decades and centuries. Using the latest simulations and data syntheses, we find agreement for spectra derived from observations and models on timescales ranging from interannual to multimillennial. Our results confirm the existence of a scaling break between orbital and annual peaks, occurring around millennial periodicities. That both simple and comprehensive ocean–atmosphere models can reproduce these features suggests that long-range persistence is a consequence of the oceanic integration of both gradual and abrupt climate forcings. This result implies that Holocene low-frequency variability is partly a consequence of the climate system’s integrated memory of orbital forcing. We conclude that climate models appear to contain the essential physics to correctly simulate the spectral continuum of global-mean temperature; however, regional discrepancies remain unresolved. A critical element of successfully simulating suborbital climate variability involves, we hypothesize, initial conditions of the deep ocean state that are consistent with observations of the recent past.},
	language = {en},
	urldate = {2019-04-15},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Zhu, Feng and Emile-Geay, Julien and McKay, Nicholas P. and Hakim, Gregory J. and Khider, Deborah and Ault, Toby R. and Steig, Eric J. and Dee, Sylvia and Kirchner, James W.},
	month = apr,
	year = {2019},
	keywords = {spectral analysis, climate variability, model evaluation, scaling laws},
	pages = {201809959},
}


# book chapter
@inbook{ENSObook2020:ch05,
	Abstract = {Summary This chapter investigates ENSO variability before the instrumental era. Though generally indirect, paleoclimate observations provide information that no other source can, sampling ENSO behavior across different base states, subject to many types and intensities of external forcing, and providing a much longer statistical sample than afforded by the instrumental record. After first reviewing the nature, strengths, and caveats of the paleoclimate observations most relevant to ENSO, we outline how these observations may be used to infer changes in ENSO properties over time. The chapter then synthesizes the most robust paleoclimate inferences about ENSO over various time intervals: the Pliocene, Quaternary Ice Ages, the Holocene, the last millennium, and the anthropogenic era. ENSO appears to have operated on Earth for at least 3 million years, and the existing observations support the view that variations in ENSO amplitude and frequency arise primarily from processes internal to the climate system. However, multiple lines of evidence support the notion that ENSO properties are sensitive to large changes in mean climate, such as those seen during the anthropogenic era. Throughout these examples, a case is made that paleoclimate observations are now mature enough to offer quantitative constraints on ENSO and its representation in climate models, offering a key out-of-sample test of model predictions across a variety of climate scenarios. The chapter closes with a roadmap for furthering the relevance of paleoclimate observations to the study of ENSO.},
	Author = {Emile-Geay, Julien and Cobb, Kim M. and Cole, Julia E. and Elliot, Mary and Zhu, Feng},
	Booktitle = {El Ni{\~n}o Southern Oscillation in a Changing Climate},
	Chapter = {5},
	Date-Added = {2020-11-07 11:39:35 -0800},
	Date-Modified = {2020-11-07 11:40:04 -0800},
	Doi = {10.1002/9781119548164.ch5},
	Editor = {McPhaden, Michael J. and Santoso, Agus and Cai, Wenju},
	Isbn = {9781119548164},
	Keywords = {climate forcings, ENSO reconstruction, ENSO variability, Last Glacial Maximum, paleoclimate observations, paleodata, quantitative constraints},
	Pages = {87-118},
	Publisher = {American Geophysical Union (AGU)},
	Title = {Past ENSO Variability},
	Url = {https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/9781119548164.ch5},
	Year = {2020}}

# software
@software{pyleoclim,
	Author = {Deborah Khider and Feng Zhu and Jun Hu and Julien Emile-Geay},
	Date-Added = {2018-03-23 04:48:28 +0000},
	Date-Modified = {2018-03-23 04:51:11 +0000},
	Doi = {10.5281/zenodo.1205661},
	Month = mar,
	Title = {{LinkedEarth/Pyleoclim\_util: Pyleoclim release v0.4.7}},
	Url = {https://doi.org/10.5281/zenodo.1205661},
	Year = 2018,
	Bdsk-Url-1 = {https://doi.org/10.5281/zenodo.1205661}}

@software{feng_zhu_2019_3590258,
	Author = {Feng Zhu and Julien Emile-Geay and Gregory J. Hakim and Robert Tardif and Andre Perkins},
	Doi = {10.5281/zenodo.3590258},
	Month = dec,
	Publisher = {Zenodo},
	Title = {{LMR Turbo (LMRt): a lightweight implementation of the LMR framework}},
	Version = {0.6.3},
	Year = 2019,
	Bdsk-Url-1 = {https://doi.org/10.5281/zenodo.3590258}}