A recent study identified a pronounced lagged relationship between the Great Salt Lake's (GSL) elevation and the central tropical Pacific sea surface temperatures (SST) at the 10-15-year time scale. Using this relationship, a principal component analysis of historical time series of SST and local precipitation (P) was used in the construction of a lagged regression model to predict first the GSL elevation tendency and, from there, the GSL elevation. The combined principal component-lagged regression model was able to replicate and forecast turnarounds in the GSL elevation-that is, where prolonged increasing trends were followed by persistent decreases and vice versa. The coupling of the two time series is somewhat different from previous nonparametric, nonlinear time series methods developed for shorter-term (1-2 year) forecasts of the GSL volume. Moreover, by not accounting for interannual variability in the model, a forecast out to 6 years was feasible and was shown to intersect the 2009 and 2010 observations of the GSL elevation.

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Because of the geography of a narrow valley and surrounding tall mountains, Cache Valley (located in northern Utah and southern Idaho) experiences frequent shallow temperature inversions that are both intense and persistent. Such temperature inversions have resulted in the worst air quality in the nation. In this paper, the historical properties of Cache Valley’s winter inversions are examined by using two meteorological stations with a difference in elevation of approximately 100 m and a horizontal distance apart of ~4.5 km. Differences in daily maximum air temperature between two stations were used to define the frequency and intensity of inversions. Despite the lack of a long-term trend in inversion intensity from 1956 to present, the inversion frequency increased in the early 1980s and extending into the early 1990s but thereafter decreased by about 30% through 2013. Daily mean air temperatures and inversion intensity were categorized further using a mosaic plot. Of relevance was the discovery that after 1990 there was an increase in the probability of inversions during cold days and that under conditions in which the daily mean air temperature was below −15°C an inversion became a certainty. A regression model was developed to estimate the concentration of past particulate matter of aerodynamic diameter ≤ 2.5 μm (PM2.5). The model indicated past episodes of increased PM2.5 concentrations that went into decline after 1990; this was especially so in the coldest of climate conditions.

Seasonal prediction of the monsoon precipitation in Nepal has been a challenge. That is partly because Nepal's monsoon precipitation exhibits a distinct and strong quasi-decadal oscillation while not correlated with the El Nino-Southern Oscillation. The existing global and regional climate models are insufficient in deriving reliable precipitation prediction. This paper examines the prediction of Nepal's July to August (JA) mean precipitation using five different methods: three time series models in comparison with a persistence forecast (PF) and a climatology forecast (CF). The first model (P-AR) uses past precipitation data to forecast the future, based upon the recently uncovered quasi-periodic feature of the JA mean precipitation. The other two models (ARX-SST and ARX-GQ) add covariate sea surface temperature (SST) and global water vapour flux circulation (GQ) respectively. Based upon the evaluation of 1-year-ahead forecast, the three time series models performed better than PF and CF. Of those, the P-AR model has the least mean absolute error (MAE) of <1 mm day -1 . Based upon the 2-year-ahead forecast results, the P-AR model performs slightly better than ARX-SST and ARX-GQ models. The forecast ability of the time series models appears better than that of operational numerical models such as the NCEP Climate Forecast System (CFS) and so, can be used as an effective alternative in predicting monsoon precipitation for Nepal.