The Extremely Wet March of 2017 in Peru
© 2018 American Meteorological Society For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy. INTRODUCTION. Peru’s rainy season falls in the first quarter of the year, often dubbed the “landslide season,” with the direst downpours typically occurring in March (Lavado Casimiro et al. 2012). The rains of March 2017, however, were deadlier than usual and wreaked unprecedented havoc, leaving half of the country in a state of emergency. Widespread flooding and landslides affected 1.7 million people, while the death toll reached 177 and an estimated total damage of $3.1 billion was reported (EM-DAT 2017). Anomalously warm sea surface temperatures (SSTs) in the region, characteristic of El Niño conditions, favor extreme rainfall (Sanabria et al. 2018). Although a marked SST anomaly was present off the Peruvian coast at the time of the event (Fig. 1a), the strong global El Niño of 2015–16 had already come to an end, as demonstrated in the time series of the Southern Oscillation index (SOI; CPC 2017) shown in Fig. 1b. The ocean at the peak of the March rains was warmer than during the preceding El Niño (Fraser 2017), creating conditions that are commonly referred to as a “local” or “coastal” El Niño, also reflected in the anomalously high value of the Niño 1 + 2 index in March (Trenberth et al. 2016). A possible atmospheric triggering mechanism of the 2017 coastal warming was proposed by Garreaud (2018). While the presence of a warm SST anomaly increases the likelihood of extreme rainfall in the region, anthropogenic climate change may also make a contribution, for example, via a long-term warming of the ocean (Rhein et al. 2013) that could intensify the local El Niño. A similarly catastrophic event associated with the El Niño of 1998 amassed more rainfall in the region, but, as reported in the media, what made March 2017 distinct was the fact that heavier rainfall fell in shorter periods. This study employs a well-established probabilistic event attribution methodology (Stott et al. 2016) to examine the effect of man-made climate change on extreme rainfall events in Peru similar to 2017, over the entire month of March, as well as on shorter, submonthly time scales.