Variable North Pacific influence on drought in southwestern North America since AD 854

Tropical Pacific forcing of Late-Holocene hydrologic variability in the coastal southwest United States

“…Hydrogen isotopes in precipitation represent useful yet complex tracers of hydrological processes recording information about the history of a moisture parcel in terms of source region, storm track, condensation temperature, antecedent rainout, and finally postcondensation evaporation of the raindrop during descent (Gat and Airey, 2006;Buenning et al, 2013). Downcore dD values are interpreted as principally responding to changes in moisture source region, with low dD values reflecting midlatitude N Pacific moisture sources (often associated with colder condensation temperatures and less post-condensation evaporation) and high dD values reflecting subtropical N Pacific moisture sources (often associated with warmer condensation temperatures and more postcondensation evaporation) (Friedman et al, 2002;Buenning et al, 2013;McCabe-Glynn et al, 2013). The only exception to this rule is that intense rainout in Atmospheric River events (AR; also known as "pineapple express type storms") can cause low dD values (Coplen et al, 2008) that may be indistinguishable from low dD values brought by intense storms originating from the midlatitude N Pacific (McCabe-Glynn et al, 2013).…”

“…Downcore dD values are interpreted as principally responding to changes in moisture source region, with low dD values reflecting midlatitude N Pacific moisture sources (often associated with colder condensation temperatures and less post-condensation evaporation) and high dD values reflecting subtropical N Pacific moisture sources (often associated with warmer condensation temperatures and more postcondensation evaporation) (Friedman et al, 2002;Buenning et al, 2013;McCabe-Glynn et al, 2013). The only exception to this rule is that intense rainout in Atmospheric River events (AR; also known as "pineapple express type storms") can cause low dD values (Coplen et al, 2008) that may be indistinguishable from low dD values brought by intense storms originating from the midlatitude N Pacific (McCabe-Glynn et al, 2013). While these AR events do not dominate the storm tracks for the region, they do bring disproportionate amounts of rainfall, perhaps 30À50% of precipitation in the region (Dettinger, 2013).…”

“…Interannual variations in precipitation amount vary with the position of winter season storm tracks influenced by Pacific sea surface temperature (SST) patterns ( Fig. 1) (Namias et al, 1988;Seager et al, 2005a;Herweijer et al, 2006;Graham et al, 2007;Cook et al, 2011;Gan and Wu, 2013) Variations in Pacific Ocean SSTs, including those associated with El Niño-Southern Oscillation (ENSO) as well as extra-tropical conditions such as that in the Kuroshio Extension region (northwest Pacific), have been shown to play important roles in driving precipitation amount variability, including both droughts and pluvials, on interannual to multi-decadal timescales in the CSW US (Andrews et al, 2004;Castello and Shelton, 2004;Cook et al, 2011;DeFlorio et al, 2013;McCabe-Glynn et al, 2013;Wang et al, 2013).…”

Decadal predictability of soil water, vegetation, and wildfire frequency over North America

“…Contour intervals are denoted in the upper-right corner in each panel. Zero contours are omitted (McCabe-Glynn et al 2013) have also invoked statistical links between US droughts and warmer conditions in the Kuroshio/Oyashio extension region, however its dynamical connection is still unclear.…”

Speleothem stable isotope records for east-central Europe: resampling sedimentary proxy records to obtain evenly spaced time series with spectral guidance

“…However, a problem remains: the transformation of unevenly spaced data to a temporally equidistant time series is required in order for it to be suitable for the application of, for example, wavelet spectrum, multitaper analysis, and simple smoothers (Hammer, 2017). In the case of unevenly spaced sedimentary records, the required preprocessing step is most commonly performed using linear (e.g., Holmgren et al, 2003, Lachniet et al, 2004, or nonlinear interpolation techniques (e.g., Ersek et al, 2012), rescaling of the data (e.g., Deininger et al, 2017), or by insufficiently documented methods (e.g., McCabe-Glynn et al, 2013;Duan et al, 2014). A more complex approach combining spline smoothing and linear interpolation has been presented in a multi-paleoproxy study, where in addition the spectral characteristics of the data were assessed using multiple methods as well (Cosford et al, 2008).…”