Quantifying the likelihood of a 2013-magnitude event We first quantify the likelihood of the June-2013 total precipitation in the observed climate. Given the rarity of the event in the observed record (Fig. 2a), we fit a Pareto (heavy-tailed) distribution to the 1951-2012 observations (Fig. 2a,S3a). From the Pareto distribution, we estimate the sample-quantile (Qo) and return period (Ro) of the June 2013 total precipitation in the present climate (see Supplemental Methods). We find that the 2013 event exceeds the 99th percentile in the observed distribution (Qo= 99.1th quantile), yielding a return period of 111 years (Fig. 2a). Because the Pareto is a distribution, extreme events are less likely to be found anomalous, and thus the corresponding return period can be considered a lower bound. Next, we assess the influence of anthropogenic forcings on the likelihood of extreme June precipitation using the historical (“20C”) and preindustrial (“PI”) simulations from the CMIP5 climate model archive (Taylor et al. 2012). We use the Kolmogorov-Smirnov (“K-S”) goodness-of-fit test to identify the models that most closely simulate the observed distribution of June total precipitation over the impacted region (Fig. 1a). (To control for the mean-bias in the models, we first re-center each model’s distribution so that the model mean matches the observed mean.) Because the simulated change in likelihood of extremes can be heavily influenced by biases in the simulated distribution, we restrict our analyses to 11 models whose K-S value exceeds 0.2 (Fig. S3), ensuring a comparatively good fit of the overall distribution, including in the tails. Among these 11 models that pass this goodness-of-fit criterion, 4 show greater mean and variability of June precipitation in the 20C simulations (Fig. 2b). However, 7 of
Quantifying the likelihood of a 2013-magnitude event We first quantify the likelihood of the June-2013 total precipitation in the observed climate. Given the rarity of the event in the observed record (Fig. 2a), we fit a Pareto (heavy-tailed) distribution to the 1951-2012 observations (Fig. 2a,S3a). From the Pareto distribution, we estimate the sample-quantile (Qo) and return period (Ro) of the June 2013 total precipitation in the present climate (see Supplemental Methods). We find that the 2013 event exceeds the 99th percentile in the observed distribution (Qo= 99.1th quantile), yielding a return period of 111 years (Fig. 2a). Because the Pareto is a distribution, extreme events are less likely to be found anomalous, and thus the corresponding return period can be considered a lower bound. Next, we assess the influence of anthropogenic forcings on the likelihood of extreme June precipitation using the historical (“20C”) and preindustrial (“PI”) simulations from the CMIP5 climate model archive (Taylor et al. 2012). We use the Kolmogorov-Smirnov (“K-S”) goodness-of-fit test to identify the models that most closely simulate the observed distribution of June total precipitation over the impacted region (Fig. 1a). (To control for the mean-bias in the models, we first re-center each model’s distribution so that the model mean matches the observed mean.) Because the simulated change in likelihood of extremes can be heavily influenced by biases in the simulated distribution, we restrict our analyses to 11 models whose K-S value exceeds 0.2 (Fig. S3), ensuring a comparatively good fit of the overall distribution, including in the tails. Among these 11 models that pass this goodness-of-fit criterion, 4 show greater mean and variability of June precipitation in the 20C simulations (Fig. 2b). However, 7 of
BAMS_2013_India_Main_revised_SUBMITTED.docx