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Evolving patterns of droughts and wet spells in the conterminous United States (CONUS) are examined over 555 years using a tree-ring-based paleoclimate reconstruction of the modified Palmer drought severity index (PDSI). A hidden Markov model is used as an unsupervised method of classifying climate states and quantifying the temporal evolution from one state to another. Modeling temporal variability in spatial patterns of drought and wet spells provides the ability to objectively assess and simulate historical persistence and recurrence of similar patterns. The Viterbi algorithm reveals the probable sequence of states through time, enabling an examination of temporal and spatial features and associated large-scale climate forcing. Distinct patterns of sea surface temperature that are known to enhance or inhibit rainfall are associated with some states. Using the current CONUS PDSI field the model can be used to simulate the space–time PDSI pattern over the next few years, or unconditional simulations can be used to derive estimates of spatially concurrent PDSI patterns and their persistence and intensity across the CONUS. [ABSTRACT FROM AUTHOR]

Correlation networks identified from financial, genomic, ecological, epidemiological, social, and climatic data are being used to provide useful topological insights into the structure of high-dimensional data. Strong convection over the oceans and the atmospheric moisture transport and flow convergence indicated by atmospheric pressure fields may determine where and when extreme precipitation occurs. Here, the spatiotemporal relationship among sea surface temperature (SST), sea level pressure (SLP), and extreme global precipitation is explored using a graph-based approach that uses the concept of reciprocity to generate cluster pairs of locations with similar spatiotemporal patterns at any time lag. A global time-lagged relationship between pentad SST anomalies and pentad SLP anomalies is investigated to understand the linkages and influence of the slowly changing oceanic boundary conditions on the development of the global atmospheric circulation. This study explores the use of this correlation network to predict extreme precipitation globally over the next 30 days, using a logistic principal component regression on the strong global dipoles found between SST and SLP. Predictive skill under cross validation and blind prediction for the occurrence of 30-day precipitation that is higher than the 90th percentile of days in the wet season is indicated for the selected global regions considered. [ABSTRACT FROM AUTHOR]

This study examines the spatiotemporal variability of two sets of daily precipitation from ERA-Interim across the eastern United States between 1979 and 2013: 1) total precipitation and 2) precipitation originating from tropical moisture exports (TMEs), which have been linked to extremes of midlatitude precipitation. Archetypal analysis (AA) is introduced as a new method to decompose and characterize structures within the spatiotemporal climate data. AA is uniquely suited to identify extremal patterns and is a complementary method to empirical orthogonal function (EOF) analysis. The authors provide a brief comparison between AA and EOF analysis and then examine the spatiotemporal variability, circulation anomalies, and sea surface temperature teleconnections associated with the archetypes of the two precipitation variables. Markovian structure, seasonal variability, and interannual trends in archetype occurrence are explored using nonparametric generalized linear models (GLMs). Results show that the modes of precipitation variability and their associated teleconnections are very similar between total and TME precipitation, suggesting that TMEs can help explain prevailing modes of total precipitation variability. Both total and TME precipitation shift longitudinally conditional on the phase of the Pacific decadal oscillation (PDO) and sea surface temperatures in the North Atlantic, and they are inhibited during strong, negative PDO and positive Atlantic multidecadal oscillation (AMO) regimes. The GLM analysis reveals distinct seasonal cycles and decadal trends in archetypes likely associated with the strength and position of the North Atlantic subtropical high (NASH). The study concludes with a discussion of the limitations of the analysis and other promising applications of AA. [ABSTRACT FROM AUTHOR]

A hierarchical Bayesian regression model is presented for reconstructing the average summer streamfiow at five gauges in the Delaware River basin using eight regional tree-ring chronologies. The model provides estimates of the posterior probability distribution of each reconstructed streamfiow series considering parameter uncertainty. The vectors of regression coefficients are modeled as draws from a common multivariate normal distribution with unknown parameters estimated as part of the analysis. This leads to a multilevel structure. The covariance structure of the streamfiow residuals across sites is explicitly modeled. The resulting partial pooling of information across multiple stations leads to a reduction in parameter uncertainty. The effect of no pooling and full pooling of station information, as end points of the method, is explored. The no- pooling model considers independent estimation of the regression coefficients for each streamfiow gauge with respect to each tree-ring chronology. The full-pooling model considers that the same regression coefficients apply across all streamfiow sites for a particular tree-ring chronology. The cross-site correlation of residuals is modeled in all cases. Performance on metrics typically used by tree-ring reconstruction experts, such as reduction of error, coefficient of efficiency, and coverage rates under credible intervals is comparable to, or better, for the partial-pooling model relative to the no-pooling model, and streamfiow estimation uncertainty is reduced. Long record simulations from reconstructions are used to develop estimates of the probability of duration and severity of droughts in the region. Analysis of monotonic trends in the reconstructed drought events do not reject the null hypothesis of no trend at the 90% significance over 1754-2000. [ABSTRACT FROM AUTHOR]

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]

Using a 1951–2003 gridded daily rainfall dataset for India, the authors assess trends in the intensity and frequency of exceedance of thresholds derived from the 90th and the 99th percentile of daily rainfall. A nonparametric method is used to test for monotonic trends at each location. A field significance test is also applied at the national level to assess whether the individual trends identified could occur by chance in an analysis of the large number of time series analyzed. Statistically significant increasing trends in extremes of rainfall are identified over many parts of India, consistent with the indications from climate change models and the hypothesis that the hydrological cycle will intensify as the planet warms. Specifically, for the exceedance of the 99th percentile of daily rainfall, all locations where a significant increasing trend in frequency of exceedance is identified also exhibit a significant trend in rainfall intensity. However, extreme precipitation frequency over many parts of India also appears to exhibit a decreasing trend, especially for the exceedance of the 90th percentile of daily rainfall. Predominantly increasing trends in the intensity of extreme rainfall are observed for both exceedance thresholds. [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]

Historical variations in the equator-to-pole surface temperature gradient (EPG) and the ocean--land surface temperature contrast (OLC) based on spatial finite differencing of gridded historical sea surface and land air temperatures are analyzed. The two temperature gradients represent zonally symmetric and asymmetric thermal forcings of the atmosphere. The strength and position of the Hadley cell and of the westerlies is related to the EPG, while the strength of the eddies coupled to the mid/high-latitude quasigeostrophic flow is related to the OLC. Taking these two parameters as simple yet highly meaningful diagnostics of the low-frequency variability of the atmosphere and climate system, the authors revisit a number of timely issues in the area of diagnostic climate studies. Of particular interest are seasonality and its variations and evidence of warming expected from greenhouse gas increases. Investigations of possible effects of CO2 -induced greenhouse warming are pursued by comparing the trends in EPG and OLC estimated from the observations and by using the Geophysical Fluid Dynamics Laboratory (GFDL) general circulation model (GCM) results for control and transient-increased CO2 simulations. Significant differences are noted between the trends in EPG and OLC for observational data and the increased CO2 GCM scenario. However, the dynamical response of both EPG and OLC during subperiods with warming and cooling is consistent with that exhibited by the GFDL GCM. In this sense, the "fingerprint" of anthropogenic forcing of the climate is not clearly evident in these basic diagnostics of large-scale climate variability. [ABSTRACT FROM AUTHOR]

Zonal and meridional surface temperature gradients are considered to be determinants of large-scale atmospheric circulation patterns. However, there has been limited investigation of these gradients as diagnostic aids. Here, the twentieth-century variability in the Northern Hemisphere equator-to-pole temperature gradient (EPG) and the ocean-land temperature contrast (OLC) is explored. A secular trend in decreasing EPG and OLC is noted. Decadal and interannual (ENSO-related) variations in the joint distribution of EPG and OLC are identified, hinting at multistable climate states that may be indigenous to the climate or due to changing boundary forcings. The NH circulation patterns for cases in the tails of the joint distribution of EPG and OLC are also seen to be different. Given this context, this paper extends past efforts to develop insights into jet stream dynamics using the Lorenz-1984 model, which is forced directly and only by EPG and OLC. The joint probability distribution of jet stream and eddy energy, conditional on EPG and OLC scenarios, is investigated. The scenarios correspond to (i) warmer versus colder climate conditions and (ii) polarized ENSO phases. The latter scenario involves the use of a heuristic ENSO model to drive the Lorenz-1984 model via a modulation of the EPG or the OLC. As with GCMs, the low-order model reveals that the response to El Niño forcing is not similar to an anthropogenic warming signature. The potential uses of EPG and OLC as macro-level indicators of climate change and variability and for comparing results across GCMs and observations are indicated. [ABSTRACT FROM AUTHOR]

Regional temperature change projections for the twenty-first century are generated using a multimodel ensemble of atmosphere–ocean general circulation models. The models are assigned coefficients jointly, using a Bayesian linear model fitted to regional observations and simulations of the climate of the twentieth century. Probability models with varying degrees of complexity are explored, and a selection is made based on Bayesian deviance statistics, coefficient properties, and a classical cross-validation measure utilizing temporally averaged data. The model selected is shown to be superior in predictive skill to a naïve model consisting of the unweighted mean of the underlying atmosphere–ocean GCM (AOGCM) simulations, although the skill differential varies regionally. Temperature projections for the A2 and B1 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios are presented. [ABSTRACT FROM AUTHOR]

Examines the spatial structure of teleconnections between the winter El Niño-Southern Oscillation and the global sea surface temperature in the United States. Measure of continental US summer drought in the twentieth century; Nonlinearities and nonstationarities in the relationships; Observation of drought teleconnection patterns.

The evolution of El Niño can be separated into two phases-namely, growth and mature-depending on whether the regional sea surface temperature has adjusted to the tropospheric warming in the remote tropics (tropical regions away from the central and eastern tropical Pacific Ocean). The western Sahel's main rainy season (July-September) is shown to be affected by the growth phase of El Niño through (i) a lack of neighboring North Atlantic sea surface warming, (ii) an absence of an atmospheric column water vapor anomaly over the North Atlantic and western Sahel, and (iii) higher atmospheric vertical stability over the western Sahel, resulting in the suppression of mean seasonal rainfall as well as number of wet days. In contrast, the short rainy season (October-December) of tropical eastern Africa is impacted by the mature phase of El Niño through (i) neighboring Indian Ocean sea surface warming, (ii) positive column water vapor anomalies over the Indian Ocean and tropical eastern Africa, and (iii) higher atmospheric vertical instability over tropical eastern Africa, leading to an increase in the mean seasonal rainfall as well as in the number of wet days. While the modulation of the frequency of wet days and seasonal mean accumulation is statistically significant, daily rainfall intensity (for days with rainfall > 1 mm day−1), whether mean, median, or extreme, does not show a significant response in either region. Hence, the variability in seasonal mean rainfall that can be attributed to the El Niño-Southern Oscillation phenomenon in both regions is likely due to changes in the frequency of rainfall. [ABSTRACT FROM AUTHOR]

Six water emergencies have occurred since 1981 for the New York City (NYC) region despite the following: 1) its perhumid climate, 2) substantial conservation of water since 1979, and 3) meteorological data showing little severe or extreme drought since 1970. This study reconstructs 472 years of moisture availability for the NYC watershed to place these emergencies in long-term hydroclimatic context. Using nested reconstruction techniques, 32 tree-ring chronologies comprised of 12 species account for up to 66.2% of the average May-August Palmer drought severity index. Verification statistics indicate good statistical skill from 1531 to 2003. The use of multiple tree species, including rarely used species that can sometimes occur on mesic sites like Liriodendron tulipifera, Betula lenta, and Carya spp., seems to aid reconstruction skill. Importantly, the reconstruction captures pluvial events in the instrumental record nearly as well as drought events and is significantly correlated to precipitation over much of the northeastern United States. While the mid-1960s drought is a severe drought in the context of the new reconstruction, the region experienced repeated droughts of similar intensity, but greater duration during the sixteenth and seventeenth centuries. The full record reveals a trend toward more pluvial conditions since ca. 1800 that is accentuated by an unprecedented 43-yr pluvial event that continues through 2011. In the context of the current pluvial, decreasing water usage, but increasing extra-urban pressures, it appears that the water supply system for the greater NYC region could be severely stressed if the current water boom shifts toward hydroclimatic regimes like the sixteenth and seventeenth centuries. [ABSTRACT FROM AUTHOR]