Orbital to millennial-scale variations in Asian monsoon speleothem δ18O have been widely interpreted as records of monsoon intensity and/or rainfall amount. To assess the influence of these and other mechanisms on higher-frequency δ18O variability, we utilize simulations from a spectrally nudged isotope-enabled general circulation model coupled with instrumental climate data to investigate the climatic controls on interannual precipitation δ18O (δ18Op) variability at four key cave locations affected by the Asian monsoon: Qunf Cave, Oman; Mawmluh Cave, India; Tham Mai Cave, Laos; and Dongge Cave, China. Comparison with instrumental climate data shows that interannual δ18Op variations are only weakly related to local precipitation amount at the four sites and are instead controlled primarily by large-scale monsoon intensity and upstream precipitation over the tropical Indo-Pacific region, which influence the δ18O of incoming moisture. Spatial correlations with sea surface temperature and precipitation, composite analyses, and time series analyses show that the El Niño-Southern Oscillation (ENSO) also plays a key role in modulating interannual precipitation δ18Op variability in the region, especially in northern India and Southeast Asia, with positive δ18Op anomalies during El Niño years reflecting increased contribution of high δ18O moisture from the nearby Bay of Bengal. Coherent interannual to decadal δ18O variations seen in high-resolution proxy records from across the Asian monsoon region, likely record monsoon intensity and upstream rainout, whereas ENSO related variability is likely to be strongest in records from northern India and Southeast Asia, with the largest anomalies expected when weak monsoons and El Niño occur together.

We present a new event-scale catalog of stable isotopic measurements from 5 years of storm events at 4 sites in southern California, which is used to understand the storm to storm controls on the isotopic composition of precipitation and validate the event-scale performance of an isotope-enabled GCM simulation (IsoGSM) (Yoshimura et al. 2008). These analyses are motivated to improve the interpretation of proxy records from this region and provide guidance in testing the skill of GCMs in reproducing the hydrological variability in the western US. We find that approximately 40% of event-scale isotopic variability arises from the percentage of precipitation that is convective and the near surface relative humidity in the days prior to the storms landfall. The additional isotopic variability arises from the fact that storms arriving from different source regions advect moisture of distinct isotopic compositions. We show using both field correlation and Lagrangian trajectory analysis that the advection of subtropical and tropical moisture is important in producing the most isotopically enriched precipitation. The isotopic catalog is then used along with satellite-derived δD retrievals of atmospheric moisture to benchmark the performance of the IsoGSM model for the western US. The model is able to successfully replicate the observed isotopic variability suggesting that it is closely reproducing the moisture transport and storm track dynamics that drive the large storm-to-storm isotopic range. Notably, we find that an increase in moisture flux from the central tropical Pacific leads to a convergence of isotopically enriched water vapor in the subtropics and consequently an increase in δ18O of precipitation at sites along the entire west coast. Changes in poleward moisture flux from the central Tropical Pacific have important implications for both the global hydrological cycle and regional precipitation amounts and we suggest such changes can be captured through instrumental and proxy-reconstruction of the spatiotemporal isotopic patterns in the precipitation along the west coast of the US. © 2011 Springer-Verlag.

Four dynamic regional climate models (RCMs) and one statistical downscaling approach were used to downscale 10 years of historical climate in California. To isolate possible limitations of the downscaling methods, we used initial and lateral boundary conditions from the NCEP global reanalysis. Results of this downscaling were compared to observations and to an independent, fine-resolution reanalysis (NARR). This evaluation is preparation for simulations of future-climate scenarios, the second phase of this CEC scenarios project. Each model has its own strengths and weaknesses, which are reported here. In general, the dynamic models perform as well as other state-of-the-art dynamical regional climate models, and the statistical model has comparable or superior skill, although for a very limited set of meteorological variables. As is typical, the dynamical models have the most trouble simulating clouds, precipitation, and related processes, especially snow. This suggests that the weakest aspects of the models are parameterized subgrid scale processes, the hydrological cycle, and land surface processes. However, the resulting probabilistic ensemble simulations result in reduced model uncertainty and a better understanding of model spread.