Statistical Methodology for Evaluating Process-Based Climate Models

North and South Atlantic lateral volume exchange is a key component of the Atlantic Meridional Overturning Circulation (AMOC) embedded in Earth’s climate. Northward AMOC heat transport within this exchange mitigates the large heat loss to the atmosphere in the northern North Atlantic. Because of inadequate climate data, observational basin-scale studies of net interbasin exchange between the North and South Atlantic have been limited. Here ten independent climate datasets, five satellite-derived and five analyses, are synthesized to show that North and South Atlantic climatological net lateral volume exchange is partitioned into two seasonal regimes. From late-May to late-November, net lateral volume flux is from the North to the South Atlantic; whereas from late-November to late-May, net lateral volume flux is from the South to the North Atlantic. This climatological characterization offers a framework for assessing seasonal variations in these basins and provides a constraint for climate models that simulate AMOC dynamics.

Download Full-text

County-level USA: No Robust Relationship between Geoclimatic Variables and Cognitive Ability

10.31234/osf.io/avpk7 ◽

2020 ◽

Author(s):

Bryan J Pesta ◽

John Fuerst ◽

Emil O. W. Kirkegaard

Keyword(s):

Cognitive Ability ◽

Climate Models ◽

Cold Weather ◽

Regression Equations ◽

County Level ◽

Spatial Lag ◽

Average Temperature ◽

Wrong Direction ◽

Race Ethnicity ◽

Demographic Covariates

Using a sample of ~3,100 U.S. counties, we tested geoclimatic explanations for why cognitive ability varies across geography. These models posit that geoclimatic factors will strongly predict cognitive ability across geography, even when a variety of common controls appear in the regression equations. Our results generally do not support UV radiation (UVR) based or other geoclimatic models. Specifically, although UVR alone predicted cognitive ability at the U.S. county-level (β = -.33), its validity was markedly reduced in the presence of climatic and demographic covariates (β = -.16), and was reduced even further with a spatial lag (β = -.10). For climate models, average temperature remained a significant predictor in the regression equation containing a spatial lag (β = .35). However, the effect was in the wrong direction relative to typical cold weather hypotheses. Moreover, when we ran the analyses separately by race/ethnicity, no consistent pattern appeared in the models containing the spatial lag. Analyses of gap sizes across counties were also generally inconsistent with predictions from the UVR model. Instead, results seemed to provide support for compositional models.

Download Full-text