Surface ozone-temperature relationships in the eastern US: A monthly climatology for evaluating chemistry-climate models

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We use long-term, coincident O3 and temperature measurements at the regionally representative US Environmental Protection Agency Clean Air Status and Trends Network (CASTNet) over the eastern US from 1988 through 2009 to characterize the surface O3 response to year-to-year fluctuations in weather, for the purpose of evaluating global chemistry-climate models. We first produce a monthly climatology for each site over all available years, defined as the slope of the best-fit line (mO3-T) between monthly average values of maximum daily 8-hour average (MDA8) O3 and monthly average values of daily maximum surface temperature (Tmax). Applying two distinct statistical approaches to aggregate the sitespecific measurements to the regional scale, we find that summer time mO3-T is 3e6 ppb K �1 (r ¼0.5 e0.8) over the Northeast, 3e4 ppb K �1 (r ¼0.5e0.9) over the Great Lakes, and 3e6 ppb K �1 (r ¼0.2e0.8) over the Mid-Atlantic. The Geophysical Fluid Dynamics Laboratory (GFDL) Atmospheric Model version 3 (AM3) global chemistry-climate model generally captures the seasonal variations in correlation coefficients and mO3-T despite biases in both monthly mean summertime MDA8 O3 (up to þ10 to þ30 ppb) and daily Tmax (up to þ5 K) over the eastern US. During summer, GFDL AM3 reproduces mO3-T over the Northeast (mO3-T ¼2e6 ppb K �1 ; r ¼0.6e0.9), but underestimates mO3-T by 4 ppb K �1 over the MidAtlantic, in part due to excessively warm temperatures above which O3 production saturates in the model. Combining Tmax biases in GFDL AM3 with an observation-based mO3-T estimate of 3 ppb K �1 implies that temperature biases could explain up to 5e15 ppb of the MDA8 O 3 bias in August and September though correcting for excessively cool temperatures would worsen the O3 bias in June. We underscore the need for long-term, coincident measurements of air pollution and meteorological variables to develop process-level constraints for evaluating chemistry-climate models used to project air quality responses to climate change. Published by Elsevier Ltd.