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Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years 2001 to 2013 using the identified predictors and a non-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001-2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct 9 out of 13 times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions. [ABSTRACT FROM AUTHOR]

Sea surface temperatures in the equatorial Pacific exhibited persistent warming beginning in January 2015 and by November 2015 were predicted to deliver a strong El Niño in the coming winter, perhaps the strongest on record, with wide-reaching hydroclimate impacts. As ENSO continues to evolve over the 2016 winter season, we ask the question: To what degree could ENSO-related hydroclimate impacts have been anticipated a season prior? We provide a retrospective hydroclimate outlook for the 2016 winter, accounting for the prevailing uncertainties that often pose a challenge to local resource managers seeking to make use of seasonal forecasts. [ABSTRACT FROM AUTHOR]

A multiscale-modeling framework for daily rainfall is considered and diagnostic results are presented for an application to the winter season in Northwest India. The daily rainfall process is considered to follow a hidden Markov model (HMM), with the hidden states assumed to be an unknown random function of slowly varying climatic modulation of the winter jet stream and moisture transport dynamics. The data used are from 14 stations over Satluj River basin in winter (December-January-February-March). The period considered is 1977/78-2005/06. The HMM identifies four discrete weather states, which are used to describe daily rainfall variability over study region. Each state was found to be associated with a distinct atmospheric circulation pattern, with the driest and drier states, State 1 and 2 respectively, characterized by a lack of synoptic wave activity. In contrast, the wetter and wettest states, States 3 and 4 respectively, are characterized by a zonally oriented wave train extending across Eurasia between 20N and 40N, identified with 'western disturbances' (WD). The occurrence of State 4 is strongly conditioned by the El Nino and Indian Ocean Dipole (IOD) phenomena in winter, which is demonstrated using large-scale correlation maps based on mean sea level pressure and sea surface temperature. This suggests that there is a tendency of higher frequency of the wet days and intense WD activities in winter during El Nino and positive IOD years. These findings, derived from daily rainfall station records, help clarify the sequence of Northern Hemisphere mid-latitude storms bringing winter rainfall over Northwest India, and their association with potentially predictable low frequency modes on seasonal time scales and longer. [ABSTRACT FROM AUTHOR]

The presence of rich ENSO variability in the long unforced simulation of GFDL's CM2.1 motivates the use of tools from dynamical systems theory to study variability in ENSO predictability, and its connections to ENSO magnitude, frequency, and physical evolution. Local Lyapunov exponents (LLEs) estimated from the monthly NINO3 SSTa model output are used to characterize periods of increased or decreased predictability. The LLEs describe the growth of infinitesimal perturbations due to internal variability, and are a measure of the immediate predictive uncertainty at any given point in the system phase-space. The LLE-derived predictability estimates are compared with those obtained from the error growth in a set of re-forecast experiments with CM2.1. It is shown that the LLEs underestimate the error growth for short forecast lead times (less than 8 months), while they overestimate it for longer lead times. The departure of LLE-derived error growth rates from the re-forecast rates is a linear function of forecast lead time, and is also sensitive to the length of the time series used for the LLE calculation. The LLE-derived error growth rate is closer to that estimated from the re-forecasts for a lead time of 4 months. In the 2,000-year long simulation, the LLE-derived predictability at the 4-month lead time varies (multi)decadally only by 9-18 %. Active ENSO periods are more predictable than inactive ones, while epochs with regular periodicity and moderate magnitude are classified as the most predictable by the LLEs. Events with a deeper thermocline in the west Pacific up to five years prior to their peak, along with an earlier deepening of the thermocline in the east Pacific in the months preceding the peak, are classified as more predictable. Also, the GCM is found to be less predictable than nature under this measure of predictability. [ABSTRACT FROM AUTHOR]

In this study, statistical linkages between autumn streamflow in the northeast United States and preceding summer sea surface temperatures are developed to establish predictive potential for climate-informed seasonal streamflow forecasts in this region. Predictor regions with physically plausible teleconnections to local streamflow are identified and evaluated in a multivariate and nonlinear framework using local regression techniques. Three such regions are identified, located in the Bering Sea, the tropical Pacific just west of Mexico, and the tropical Atlantic off the coast of Africa. Asymmetries in each region's univariate local regression result are apparent, and bivariate local regressions are used to attribute these asymmetries to interactions with physical mechanisms associated with the other two regions, and possibly other unaccounted for climatic predictors. A bivariate model including the tropical Pacific and tropical Atlantic regions yields the strongest local regression result, explaining 0.68 of the interannual streamflow variability. An analogous multivariate linear regression analysis is only able to explain 0.20 of the streamflow variability and thus the use of nonlinear methods' results in a marked improvement in streamflow simulation capability. Cross-validation considerably weakens the streamflow forecasts using this model; however, forecast skill may improve with a longer period of record or the inclusion of additional predictors. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

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A new method for classifying tropical cyclones or similar features is introduced. The cyclone track is considered as an open spatial curve, with the wind speed or power information along the curve considered to be a mass attribute. The first and second moments of the resulting object are computed and then used to classify the historical tracks using standard clustering algorithms. Mass moments allow the whole track shape, length, and location to be incorporated into the clustering methodology. Tropical cyclones in the North Atlantic basin are clustered with K-means by mass moments, producing an optimum of six clusters with differing genesis locations, track shapes, intensities, life spans, landfalls, seasonal patterns, and trends. Even variables that are not directly clustered show distinct separation between clusters. A trend analysis confirms recent conclusions of increasing tropical cyclones in the basin over the past two decades. However, the trends vary across clusters. [ABSTRACT FROM AUTHOR]

Numerous statistical and dynamical models have been developed in recent years to forecast ENSO events. However, for most of these models predictability for lead times over 10 months is limited. It has been hypothesized that the tropical Pacific thermocline structure may have critical information to permit longer lead ENSO forecasts. Models that use subsurface sea temperature information have already been known to produce better long lead forecasts. Here, a two-stage statistical ENSO forecasting model is developed and demonstrated using the spatially distributed depth of the 20°C isotherm (D20) as a proxy for the thermocline. In the first stage, a nonlinear dimension reduction method [maximum variance unfolding (MVU)] is used to decompose the D20 data into canonical modes. The leading spatial patterns as well as lagged values of Niño-3 are then used as predictors in a set of linear regression models to predict the Niño-3 index at lead times of up to 24 months. Cross-validated forecasts using this methodology are shown to have higher skill than those that use a dimension reduction of the same thermocline data using principal component analysis (PCA). The first three modes of the D20 data as revealed by MVU account for 89% of the variance of the data, as compared to only 48% of the variance if PCA is used. The spatial patterns of the MVU modes partition the data field in a different way than the PC modes, even though some similarities exist as to the main regions that are active. These patterns and their temporal structure are discussed here, with a view to understanding the possible source of the longer-range predictability of ENSO using the MVU modes. The skill of the PCA- and the MVU-based forecasts of Niño-3 varies depending on the starting month of the forecast for short lead times (5–10 months). However, for the lead times longer than 1 yr, the MVU-based forecast skill is not seasonally variable, while the PCA-based models do not provide significant skill at these lead times irrespective of the starting month of the forecast. Similar conclusions are obtained for forecast models for the Niño-3.4 and Niño-1.2 indices. The differences between the MVU- and PCA-based models are most marked for the Niño-1.2 long lead forecasts. [ABSTRACT FROM AUTHOR]

The performance of different models and procedures for forecasting aggregated May–July streamflow for the Churchill Falls basin on the Québec-Labrador peninsula is compared. The models compared have different lead times and include an autoregressive model using only past streamflow data, an autoregressive with exogenous input model utilizing both past streamflow and precipitation, and a linear regression model using the principal components of exogenous measures of atmospheric circulation inferred from the National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis project. The forecast skills of the different approaches are compared using a variety of measures of performance. The results indicate that relatively accurate forecasts using only measures of atmospheric circulation can be issued as early as in December of the prior year. A multimodel combination approach is found to be more effective than the use of a single forecast model. In addition, it is concluded that forecasting models utilizing atmospheric circulation data are useful, especially for basins where hydroclimatic observations are scarce and for basins where flows and other hydroclimatic variables are not strongly autocorrelated (do not depend on their past). [ABSTRACT FROM AUTHOR]

Comments on Kevin Trenberth and James Hurrell's criticisms on C. Wunsch's `The Interpretation of Short Climate Records.' Authors' belief that statistical inferences that may be drawn from short time series are strongly conditioned by scientific taste; Trenberth and Hurrell's misuse of the autoregressive moving average model.