Abstract The history of the Polynesian civilization on Rapa Nui (Easter Island) over the Common Era has come to exemplify the fragile relationship humans have with their environment. Social dynamics, deforestation, land degradation, and climatic shifts have all been proposed as important parts of the settlement history and societal transformations on Rapa Nui. Furthermore, climate dynamics of the Southeast Pacific have major global implications. While the wetlands of Rapa Nui contain critical sedimentological archives for reconstructing past hydrological change on the island, connections between the island’s hydroclimate and fundamental aspects of regional climatology are poorly understood. Here we present a hydroclimatology of Rapa Nui showing that there is a clear seasonal cycle of precipitation, with wet months receiving almost twice as much precipitation as dry months. This seasonal cycle can be explained by the seasonal shifts in the location and strength of the climatological south Pacific subtropical anticyclone. For interannual precipitation variability, we find that the occurrence of infrequent, large rain events explains 92% of the variance of the observed annual mean precipitation time series. Approximately one third (33%) of these events are associated with atmospheric rivers, 21% are associated with classic cold-front synoptic systems, and the remainder are characterized by cut-off lows and other synoptic-scale storm systems. As a group, these large rain events are most strongly controlled by the longitudinal position of the south Pacific subtropical anticyclone. The longitudinal location of this anticyclone explains 21% of the variance in the frequency of large rain events, while the remaining variance is left unexplained by any other major atmosphere-ocean dynamics. We find that over the observational era there appears to be no linear relationship between the number of large rain events and any other major climate phenomena. With the south Pacific subtropical anticyclone projected to strengthen and expand westward under global warming, our results imply that Rapa Nui will experience an increase in the number of dry years in the future. 
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                            Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data
                        
                    
    
            The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by −0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends. 
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                            - Award ID(s):
- 1841520
- PAR ID:
- 10398249
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 13
- Issue:
- 22
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 4629
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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