A growing body of scholarship investigates the role of misinformation in shaping the debate on climate change. Our research builds on and extends this literature by (1) developing and validating a comprehensive taxonomy of climate contrarianism, (2) conducting the largest content analysis to date on contrarian claims, (3) developing a computational model to accurately classify specific claims, and (4) drawing on an extensive corpus from conservative think-tank (CTTs) websites and contrarian blogs to construct a detailed history of claims over the past 20 years. Our study finds that the claims utilized by CTTs and contrarian blogs have focused on attacking the integrity of climate science and scientists and, increasingly, has challenged climate policy and renewable energy. We further demonstrate the utility of our approach by exploring the influence of corporate and foundation funding on the production and dissemination of specific contrarian claims.

A growing body of scholarship investigates the role of misinformation in shaping the debate on climate change. Our research builds on and extends this literature by 1) developing and validating a comprehensive taxonomy of climate misinformation, 2) conducting the largest content analysis to date on contrarian claims, 3) developing a computational model to accurately detect specific claims, and 4) drawing on an extensive corpus from conservative think-tank (CTTs) websites and contrarian blogs to construct a detailed history of misinformation over the past 20 years. Our study finds that climate misinformation produced by CTTs and contrarian blogs has focused on attacking the integrity of climate science and scientists and, increasingly, has challenged climate policy and renewable energy. We further demonstrate the utility of our approach by exploring the influence of corporate and foundation funding on the production and dissemination of specific contrarian claims.

Sampling bias due to weather conditions has been anecdotally reported; however, in this analysis we demonstrate that manual lake sampling is significantly more likely to take place in "fair weather" conditions. We show and quantify how a manual lake monitoring program in Maine, USA, is biased due to wind speed, rainfall intensity, and air temperature. Emulating a manually sampled water quality (WQ) data set, we show that, on average, manual sampling recorded, depending upon depth, higher water temperature (between 0.4 C and 1.2 C), lower dissolved oxygen (DO) (between À0.8 and À0.4 mgL À1 ), and higher chlorophyll values (2.0 μgL À1 ) than average automated monitoring. By analyzing the actual manual monitoring data, we show that manually collected lake water temperatures are on average 1.0 C higher in the epilimnion and 0.5 C (corrected for sensor lag) higher in the hypolimnion compared to those collected using automated methods. We attribute these differences in WQ measurement values to the weather-induced manual sampling bias. We believe that the nature of weather bias on manual monitoring will always record higher water temperatures, higher chlorophyll, and lower DO than automatic monitoring. The methodologies presented in this study will apply to similar manually sampled lake monitoring programs and the manual sampling bias will likely affect other WQ parameters. The weatherinduced water temperature bias reported is of the same order of magnitude as the root mean square errors reported in many lake models and is therefore considered substantial. If generally applicable and not corrected for, these results will have important implications for climate models, and similar applications, where manually collected WQ data are employed.