Browsing by Subject "Rainfall variability"
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Item Multidecadal rainfall variability in the South Pacific convergence zone using the geochemistry of stalagmites from the Solomon Islands(2016-08) Sekhon, Natasha; Quinn, Terrence M.; Partin, Judson W.; Jackson, Charles S; Shanahan, Timothy MThe instrumental record of hydroclimate variability in the South Pacific is sparse and of short duration; rainfall observations span on average 60-70 years, albeit, non-continuously. Therefore, proxy records of rainfall variability are needed to extend the hydroclimate record into the pre-instrumental period. In this study, we investigate the multidecadal timescale variability in rainfall using stable isotopic variations in three absolutely dated stalagmites (cave deposits) from the Central Province (CP) and Western Province (WP) of the Solomon Islands (SB) (~9.5°S, ~160°E). Hydroclimate variability in the SB is associated with the position and intensity of rainfall of the South Pacific Convergence Zone (SPCZ). The relatively high temporal resolution of the three stalagmites directs the focus on decadal-scale variability using oxygen isotope (δ18O) variations in the stalagmites, which are a proxy for rainfall variability in the tropics. We compare the three Solomon Island (SB) stalagmite δ18O records with previously published stalagmite δ18O records from Santo, Vanuatu and Guadalcanal, Central Province, Solomon Islands (CPSB) to assess the regional coherency amongst the various proxy rainfall records. The three new SB stalagmite δ18O records from this study are dominated by abrupt and large amplitude changes (~1-2‰) on multidecadal timescales. Similar patterns of multidecadal stalagmite δ18O variability have previously been identified in the stalagmite δ18O from Guadalcanal (CPSB) and Vanuatu and implies that multidecadal rainfall variability occurred across the South Pacific Ocean over the last millennium. However, the multidecadal changes do not occur in phase with time, which is in part due to age errors, but is also greatly influenced by the different seasonality of rainfall across the sites that bias which months of rainfall change will be recorded by a stalagmite. Our results also show that large changes in δ18O may not necessarily translate to equally large changes in rainfall amount as the modern rainfall - δ18O relationships at the various sites do not have equivalent slopes on multidecadal timescales. After deriving rainfall- δ18O transfer functions for the different sites, we observe that South Pacific stalagmite δ18O records translate to roughly similar amounts of rainfall change on multidecadal time scales for Guadalcanal, CPSB, Suku, CPSB and Santo, Vanuatu. However, Western Province, Solomon Island (WPSB) site appears to have the largest rainfall amount change (~2.0m/year) on multidecadal timescale. We also demonstrate that it is challenging to precisely date decadal and multidecadal scale variability in young stalagmites (past 1000 years) that have very low 238U concentrations and are very short in length. These characteristics lead to larger temporal uncertainty than solely analytical error. This will be of importance to future studies that may overlook the difficulties with young and short stalagmites records with low 238U concentrations.Item Understanding rainfall variability and extremes over the Amazon to improve their future projection(2015-05) Yin, Lei, Ph. D.; Fu, Rong (Professor); Dickinson, Robert E.; Jackson, Charles S.; Yang, Zong-Liang; Myneni, Ranga B.The Amazon, as the world's largest rainforest region, is important for global carbon cycle, climate change, and ecosystem. This dissertation aims to investigate rainfall variability and the related atmospheric and land processes in tropical South America using both observations and climate models. The first two studies concern the changes and variability of the dry season over the southern Amazon, a domain with vulnerable tropical rainforest. Ground-based observation from 1979 to 2011 indicates the dry season length (DSL) has increased at a rate of 6.5 ± 2.5 days per decade, mainly caused by a delay of the dry season ending (DSE). A delay of the DSE has a clear impact on fire counts in austral spring. Moreover, the DSE also has large interannual variation and thus has a strong impact on drought and flood. Three dry season conditions are crucial for determining its trend and interannual variation. (i) A poleward shift of the subtropical jet over South America (SJ[subscript SA]) can prevent cold frontal systems from moving northward into the Amazon. This delays cold air incursion and results in late DSE over. (ii) An anomalous anticyclonic center, which enhances westerly wind at 850 hPa (U850) and the South American Low Level Jets (SALLJs), leads to moisture export from the southern Amazon to La Plata basin and reduces convective systems that provide elevated diabatic heating. (iii) Smaller convective available potential energy (CAPE) and larger convective inhibition energy (CIN) limit local thermodynamically driven convection. However, global climate models can capture neither the drying trend nor the variability of the DSL and DSE. Thus, the third and fourth studies deal with understanding the reported systematic dry bias over the Amazon in climate models. Our evaluation of the Coupled Model Intercomparison Project phase 5 (CMIP5) models indicates the positive feedback between reduced cloudiness, too much surface solar radiation, high Bowen ratio, and suppression of rainfall appears to cause the dry bias. A further investigation on Geophysical Fluid Dynamics Laboratory (GFDL) models demonstrates the Amazon dry season dry bias origins from biases in complicated processes, highlights the importance of the northern Andes on moisture flux convergence over the Amazon in the dry season, and suggests using at least 1° atmosphere-only model to understand the remaining dry season dry bias. The fifth study proposed a new method to assess CMIP5 climate projections for rainfall extremes. The results indicate future heavy rainfall changes in the Amazon due to anthropogenic forcing may be underestimated by multi-model ensemble mean.