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

We use long-term, coincident O-3 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 O-3 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 (m(O3-T)) between monthly average values of maximum daily 8-hour average (MDA8) O-3 and monthly average values of daily maximum surface temperature (T-max). Applying two distinct statistical approaches to aggregate the site-specific measurements to the regional scale, we find that summer time m(O3-T) is 3-6 ppb K-1 (r = 0.5-0.8) over the Northeast, 3-4 ppb K-1 (r = 0.5-0.9) over the Great Lakes, and 3-6 ppb K-1 (r = 0.2-0.8) 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 m(O3-T) despite biases in both monthly mean summertime MDA8 O-3 (up to +10 to +30 ppb) and daily T-max (up to +5 K) over the eastern US. During summer, GFDL AM3 reproduces m(O3-T) over the Northeast (m(O3-T) = 2-6 ppb K-1; r = 0.6-0.9), but underestimates m(O3-T) by 4 ppb K-1 over the Mid-Atlantic, in part due to excessively warm temperatures above which O-3 production saturates in the model. Combining T-max biases in GFDL AM3 with an observation-based m(O3-T) estimate of 3 ppb K-1 implies that temperature biases could explain up to 5-15 ppb of the MDA8 O-3 bias in August and September though correcting for excessively cool temperatures would worsen the O-3 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.