1. CC-07: Atmospheric Carbon Dioxide Concentrations and Weather: Evidence from Hawaii Kevin F. Forbes The Catholic University of America Washington, DC EMAIL : Forbes@CUA.edu Introduction The World Meteorological Organization has noted that the incidence of extreme weather events over the past decade matches IPCC projections but qualifies this conclusion by stating that “it is impossible to say that an individual weather or climate event was “caused” by climate change….” [World Meteorological Organization, 2011, p 15]. This qualification is not an insignificant scientific caveat; it leaves the causes of extreme events open to question, allowing some to attribute the increased incidence of extreme events to so-called “natural variability.” It has undermined the political consensus necessary to adopt robust, cost-effective policies to reduce CO 2 emissions. This research project sharply questions the “impossibility” of attributing specific weather events to CO 2 concentrations. The analysis presented here focuses on the incidence of rainfall. Specifically, using day-ahead hourly forecast data, hourly CO 2 data, and hourly data on the incidence of rainfall, this research presents evidence on CO 2 ’s effect on the probability of precipitation. What does Climate Science Indicate About CO 2 and Precipitation ? The vast proportion of climate scientists believe that the atmosphere becomes warmer and more moist in response to anthropogenic increases in greenhouse gases such as CO 2. The implications of this for the frequency and intensity of precipitation has been explored by Sun et al [ 2007 ] using an ensemble of climate models. Their modeling analysis indicates that the impact of climate change on precipitation can be expected to vary by region with wet regions receiving more precipitation while dry regions becoming drier [ Sun et al, 2007, p 4817 ]. Specifically, for the IPCC’s SRES B1 projection, precipitation is expected to be less frequent in dry regions, such as the western United States, in 2080–99 than in the present. In contrast, precipitation is expected to be more frequent over the tropical Pacific, South Asian, and African monsoon regions and high latitudes as compared to the present. The fact remains that there is 4 percent more water vapor– and associated additional moist energy–available both to power individual storms and to produce intense rainfall from them. Climate change is present in every single meteorological event… Michael Mann Distinguished Professor of Meteorology Penn State Meteorologists do not explicitly take CO 2 levels into account when forecasting the probability of precipitation. This provides an opportunity to test for the effect of CO 2 while also controlling for the weather conditions expected by meteorologists. A multivariate logit model is estimated to assess the relationship between CO 2 concentrations and the probability of precipitation. The logit methodology is a specialized type of regression analysis used to analyze dichotomous outcomes. Unlike traditional correlation analysis, the modeling approach enables the researcher to control for possible confounding factors. It is used by social scientists to test hypotheses when the dependent variable is dichotomous. It is used by atmospheric scientists to make probabilistic forecasts with respect to meteorological events such as precipitation. The analysis controls for the forecasted probability of precipitation, forecasted temperature, forecasted dewpoint, forecasted humidity, forecasted visibility, forecasted wind speeds, measures of forecasted precipitation classifications, and measures of forecasted sky conditions. The model also controls for possible hour of the day effects and seasonal effects. A Method to Assess the Climate Models with Respect to Precipitation Conclusion and Future Research It is possible to assess CO 2 ’s effect on the likelihood of the occurrence of specific precipitation events. One of the next steps in the research agenda will be to examine the relationship between CO 2 and temperature at various locations. It is possible that the research could contribute to a more productive dialogue on climate policy. Data The study employs data on hourly atmospheric concentrations of CO 2 reported by the Mauna Loa Observatory (MLO) in Hawaii, hourly data on observed precipitation at the nearby Hilo International Airport, and the corresponding day-ahead forecast data for each hour. The model was estimated over the period 6 August 2009 – 29 November 2011. There are 17,271 hourly observations in the sample. Figure 1. Conditional Marginal Effects of CO 2 on the Probability of Precipitation with 95% Confidence Intervals Results The results are consistent with the ensemble of climate models employed by Sun et al [ 2007]. Specifically, the estimated coefficient on the CO 2 variable is positive and statistically different from zero at the one percent level of statistical significance. The marginal effect of CO 2 on the probability of precipitation increases with increases in CO 2 concentrations (Figure 1). The marginal effect also increases with the levels of forecasted precipitation and forecasted humidity even though the simple correlations among the variables are relatively low. A counterfactual analysis reveals that the probability of precipitation is sensitive to the CO 2 concentration level (Table 1). The analysis reveals that the probability of precipitation can be significantly affected by the level of CO 2. A number of precautions were undertaken. The standard errors of the coefficients were calculated using the Newey-West procedure and thus the findings are based on standard errors that are heteroskedastic and autocorrelation-consistent. The issue of bias in the coefficients due to possible endogeneity in the CO 2 levels was considered and rejected based on the Durbin-Wu-Hausman test for endogeneity. The possibility that the estimated CO 2 coefficient reflects “reverse causation,” i.e. the occurrence of precipitation affecting CO 2 levels instead of CO 2 affecting precipitation, was found to be without merit based on an analysis that employed the lagged levels of CO 2. Table 1. Comparison of the Probability of Precipitation based on the Actual CO 2 Level and Counterfactual Levels of CO 2 DateHour (UT) Actual Level of CO2 (ppm) Model Predicted Probability of Precipitation Based on the Actual Level of CO2 Model Predicted Probability of Precipitation Based on the Maximum Level of CO2 Reported over the Sample Period Model Predicted Probability of Precipitation Based on the Minimum Level of CO2 Reported over the Sample Period Observed Precipitation Level (mm) 10/12/200912384.450.5060.7300.43613.72 10/3/20097383.330.5190.7550.4687.11 10/3/200910382.910.5140.7560.4705.08 10/3/20099383.090.5470.7780.5004.06 10/3/200911383.010.5590.7870.5143.81 9/25/200917384.530.5070.7300.4352.79 8/20/20099385.090.5100.7240.4292.54 10/12/200911384.430.5110.7340.4412.29 10/3/20098383.280.5160.7530.4652.29 10/3/200916383.020.6020.8150.5571.78 10/3/200913383.100.5290.7650.4811.78 10/3/200918383.010.5760.7980.5311.52 8/20/200912385.110.5010.7160.4191.02 9/26/200918383.630.5500.7740.4941.02 10/3/200912383.350.5430.7720.4921.02 For Hawaii, the predicted impact of a 1 PPM increase in CO 2 on the probability of precipitation increases as CO 2 levels rise Marginal Effect on the Probability of Precipitation