Past and Future Climate Variability: Extremes, Scaling, and Dynamics

• Main Author: Rhines, Andrew Nelson
• Other Authors: Huybers, Peter, Tziperman, Eli, Farrell, Brian
• Format: Thesis
• Language: English
• Published: 2015
• Subjects: Atmospheric Sciences; Geophysics; Mathematics; Arctic; Greenland
• Online Access: http://nrs.harvard.edu/urn-3:HUL.InstRepos:17467324

Severe impacts result from extreme events such as heat waves, droughts, cold spells, and floods. Characterizing and predicting variations in climate that give rise to these phenomena is important for mitigating their effects on human and natural systems. This thesis investigates whether climate variability is measurably changing and describes the observational basis for recent shifts in the temperature distribution. New methodology is presented that robustly estimates local distributional changes and permits for mapping them to regional or global scales, overcoming limitations of previous analyses. Contrary to the widespread view that climate variability has increased in recent decades, these analyses show that temperature variability has generally declined — albeit with important regional differences. Historical observations of temperature are crucial for long-term monitoring of Earth's climate. However, hundreds of millions of daily observations contain precision-related biases that prevent their use in distributional analyses. A new machine-learning algorithm automatically corrects for these biases, enabling their use in long-term climate studies. The algorithm increases the number of usable observations by an order of magnitude and has many applications in quality control and signal classification. As the observations sample Earth's climate sparsely in space and time, sophisticated statistical methods are used to map local signals to estimates of the full spatial field and its uncertainties. Much of the observed contraction of variability is shown to stem from decreased meridional temperature gradients due to amplified arctic warming in the northern hemisphere. Short-term extremes are also contextualized with the low frequency variability inferred from paleoclimate observations and simulations. Spectral estimates used to measure variability on different time scales are shown to be surprisingly robust to unavoidable time-uncertainty present in all proxy records. Oxygen isotope records from Greenland that are widely used as temperature proxies therefore contain reliable signals of past climate variability on 1–60,000 year time scales, though the extent to which these reliably preserve temperature signals remains uncertain. In a further study we examine the validity of this temperature proxy using a set of global paleoclimate simulations with moisture source tracking, quantifying a seasonality bias that may explain the paleothermometer's damped response during glacial periods. Engineering and Applied Sciences - Applied Math Climate dynamics; Temperature variability; Climate extremes; Precision decoding; Hidden Markov Models; Quantile Regression; Spectral Analysis; Paleoclimate