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Abstract During the mid‐Holocene (MH: ∼6,000 years Before Present) and Last Interglacial LIG (LIG: ∼129,000–116,000 years Before Present) differences in the seasonal and latitudinal distribution of insolation drove Northern Hemisphere high‐latitude warming comparable to that projected for the end of the 21st century in low emissions scenarios. Paleoclimate proxy records point to distinct but regionally variable hydroclimatic changes during these past warm intervals. However, model simulations have generally disagreed on North American regional moisture patterns during the MH and LIG. To investigate how closely the latest generation of models associated with the Paleoclimate Model Intercomparison Project (PMIP4) reproduces proxy‐inferred moisture patterns during recent warm periods, we compare hydroclimate output from 17 PMIP4 models with newly updated compilations of moisture‐sensitive North American proxy records during the MH and LIG. Agreement is lower for the MH, with models producing wet anomalies across the western United States (US) where most proxies indicate increased aridity relative to the preindustrial period. The models that agree most closely with the LIG proxy compilation display relative wetness in the eastern US and Alaska, and dryness in the northwest and central US. An assessment of atmospheric dynamics using an ensemble of the three LIG simulations that best agree with the proxies suggests that weaker winter North Pacific pressure gradients and steeper summer North Pacific and Atlantic gradients drive LIG precipitation patterns. Our updated compilations and proxy‐model comparisons offer a tool for benchmarking climate models and their performance in simulating climate states that are warmer than present.
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Hunting for Discriminatory Proxies in Linear Regression Models
A machine learning model may exhibit discrimination when used to make decisions involving people. One potential cause for such outcomes is that the model uses a statistical proxy for a protected demographic attribute. In this paper we formulate a definition of proxy use for the setting of linear regression and present algorithms for detecting proxies. Our definition follows recent work on proxies in classification models, and characterizes a model's constituent behavior that: 1) correlates closely with a protected random variable, and 2) is causally influential in the overall behavior of the model. We show that proxies in linear regression models can be efficiently identified by solving a second-order cone program, and further extend this result to account for situations where the use of a certain input variable is justified as a business necessity''. Finally, we present empirical results on two law enforcement datasets that exhibit varying degrees of racial disparity in prediction outcomes, demonstrating that proxies shed useful light on the causes of discriminatory behavior in models.
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- Award ID(s):
- 1704845
- PAR ID:
- 10095671
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- ISSN:
- 1049-5258
- Page Range / eLocation ID:
- 4568-4578
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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