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Despite the potential and increasing interest in physically based hydrological models for streamflow forecasting applications, they are constrained in terms of agility to generate ensembles. Hence, we develop and test a Bayesian hierarchical model (BHM) to postprocess physically based hydrologic model simulations at multiple sites on a river network, with the aim to generate probabilistic information (i.e., ensembles) and improve raw model skill. We apply our BHM framework to daily summer (July–August) streamflow simulations at five stations located in the Narmada River basin in central India, forcing the Variable Infiltration Capacity (VIC) model with observed rainfall. In this approach, daily observed streamflow at each station is modeled with a conditionally independent probability density function with time varying distribution parameters, which are modeled as a linear function of potential covariates that include VIC outputs and meteorological variables. Using suitable priors on the parameters, posterior parameters and predictive posterior distributions—and thus ensembles—of daily streamflow are obtained. The best BHM model considers a gamma distribution and uses VIC streamflow and a nonlinear covariate formulated as the product of VIC streamflow and 2-day precipitation spatially averaged across the area between the current and upstream station. The second covariate enables correcting the time delay in flow peaks and nonsystematic biases in VIC streamflow. The results show that the BHM postprocessor increases probabilistic skill in 60% compared to raw VIC simulations, providing reliable ensembles for most sites. This modeling approach can be extended to combine forecasts from multiple sources and provide skillful multimodel ensemble forecasts.

The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively.

A recent period of increased precipitation over the Argentinian Pampas expanded the boundary of rain-fed agriculture. However, such changes may not be sustainable if they arose from transient climate regime shifts. Considerable research exists on trends and cycles in sub-daily to annual precipitation metrics including the frequency and intensity of extreme precipitation. However, efforts to identify wetter and drier phases (or regimes) in this region are scant. This article aims to bridge that gap and advance our understanding of the multi-annual behavior of regional precipitation extremes, which can have the greatest impacts. It is unlikely that all extreme events are drawn from a single probability distribution or generated by the same physical processes. Hence, hidden mixtures of Poisson distributions are fitted to several precipitation frequency metrics to explore whether the annual to decadal variations in extreme precipitation frequency are greater than anticipated from a single system, and representative of regime shifts. Statistically significant improvements in the fit over single distributions were found for statistical mixture models of the frequency of very wet days, and the frequency of wet spells. This supports the hypothesis that multiple weather regimes exist giving rise to wetter or drier epochs. Posterior probabilities of hidden states from the fitted mixture distributions were used to identify wetter and drier years for comparison with sea surface temperature anomalies. This confirmed the presence of two distinct regimes, supporting other research, into the dynamical influences of precipitation behavior in the Argentine Pampas.

Assessing flood risk using stationary flood frequency analysis techniques is commonplace. However, it is increasingly evident that the stationarity assumption of these analyses does not hold as anthropogenic climate change could shift a site’s hydroclimate beyond the range of historical behaviors. We employ nonstationary flood frequency models using the generalized extreme value (GEV) distribution to model changing flood risk for select seasons at twelve National Parks across the U.S. In this GEV model, the location and/or scale parameters of the distribution are allowed to change as a function of time-variable covariates. We use historical precipitation and modeled flows from the Variable Infiltration Capacity model (VIC), a land-surface model that simulates land–atmosphere fluxes using water and energy balance equations, as covariates to fit a best nonstationary GEV model to each site. We apply climate model projections of precipitation and VIC flows to these models to obtain future flood probability estimates. Our model results project a decrease in flood risk for sites in the southwestern U.S. region and an increase in flood risk for sites in northern and eastern regions of the U.S. for the selected seasons. The methods and results presented will enable the NPS to develop strategies to ensure public safety and efficient infrastructure management and planning in a nonstationary climate. Keywords: Flood risk

Recent studies have linked interannual sea level variability and extreme events along the U.S. northeast coast (NEC) to the North Atlantic Oscillation (NAO), a natural internal climate mode that prevails in the North Atlantic Ocean. The correlation between the NAO index and coastal sea level north of Cape Hatteras was weak from the 1960s to the mid-1980s, but it has markedly increased since around 1987. The causes for the decadal shift remain unknown. Yet understanding the abrupt change is vital for decadal sea level prediction and is essential for risk management. Here we use a robust method, the Bayesian dynamic linear model (DLM), to explore the nonstationary NAO impact on NEC sea level. The results show that a spatial pattern change of NAO-related winds near the NEC is a major cause of the NAO–sea level relationship shift. A new index using regional sea level pressure is developed that is a significantly better predictor of NEC sea level than is the NAO and is strongly linked to the intensity of westerly winds near the NEC. These results point to the vital importance of monitoring regional changes of wind and sea level pressure patterns, rather than the NAO index alone, to achieve more accurate predictions of sea level change along the NEC.

We investigated the space–time variability of wet (Nov–Apr) and dry (May–Oct) season rainfall over Indonesia, using monthly gridded rainfall data from the University of East Anglia Climatic Research Unit covering the period 1901–2012. Three complimentary techniques were employed—(1) principal component analysis to identify the dominant modes of variability, (2) wavelet spectral analysis to identify the spectral characteristics of the leading modes and their coherence with large scale climate variables and (3) Bayesian Dynamical Linear Model (BDLM) to quantify the temporal variability of the association between rainfall modes and climate variables. In the dry season when the Inter Tropical Convergence Zone (ITCZ) is to the north of the equator the leading two principal components (PCs) explain close to 50 % of the rainfall. In the wet season the ITCZ moves to the south and the leading PCs explain close to 30 % of the variance. El Nino Southern Oscillation (ENSO) is the driver of the leading modes of rainfall variability during both seasons. We find asymmetry in the teleconnections of ENSO to high and low rainfall years in the dry season. Furthermore, ENSO and the leading PCs of rainfall have spectral coherence in the inter-annual band (2–8 years) over the entire period of record and in the multi-decadal (8–16 years) band in post-1980 years. In addition, during the 1950–1980 period the second mode of variability in both seasons has a strong relationship with Pacific Decadal Oscillation. The association between ENSO and the leading mode of Indonesian rainfall has strengthened in recent decades, more so during dry season. These inter-annual and multi-decadal variability of Indonesian rainfall modulated by Pacific climate drivers has implications for rainfall and hydrologic predictability important for water resources management.

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society (IRI). In conjunction with the GLM (generalized linear modeling) weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts (the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.

International audience; Previous studies have investigated the centennial and multidecadal trends of the Pacific and Indian Ocean Walker cells (WCs) during the past century, but have obtained no consensus owing to data uncertainties and weak signals of the long-term trends. This paper focuses on decadal variability (periods of one to few decades) by first documenting the variability of the WCs and warm-pool convection, and their covariability since the 1960s, using in situ and satellite observations and reanalysis products. The causes for the variability and covariability are then explored using a Bayesian dynamic linear model, which can extract nonstationary effects of climate modes. The warm-pool convection exhibits apparent decadal variability, generally covarying with the Indian and Pacific Ocean WCs during winter (November–April) with enhanced convection corresponding to intensified WCs, and the Indian–Pacific WCs covary. During summer (May–October), the warm-pool convection still highly covaries with the Pacific WC but does not covary with the Indian Ocean WC, and the Indian–Pacific WCs are uncorrelated. The wintertime coherent variability results from the vital influence of ENSO decadal variation, which reduces warm-pool convection and weakens the WCs during El Niño–like conditions. During summer, while ENSO decadal variability still dominates the Pacific WC, decadal variations of ENSO, the Indian Ocean dipole, Indian summer monsoon convection, and tropical Indian Ocean SST have comparable effects on the Indian Ocean WC overall, with monsoon convection having the largest effect since the 1990s. The complex causes for the Indian Ocean WC during summer result in its poor covariability with the Pacific WC and warm-pool convection.

Correlations of 1° by 1° seasonal rainfall with Pacific sea surface temperatures (SSTs) reveal spatially distinct teleconnections between El Nino–Southern Oscillation (ENSO) and Indian summer monsoon rainfall over the full monsoon season, as well as three subseasons. Over the full season (June–September), Pacific SSTs correlate with rainfall in Western India more than that in Eastern India. This spatial signature shifts as the monsoon progresses through early (June), middle or peak (July–August), and late (September) subseasons. Specifically, a 1°C cooling of the central equatorial Pacific (i.e., La Nina conditions) can result in the following: ∼70–100% increase in precipitation in north central Indian and the Indo-Gangetic Plains during the early season, ∼30–80% increase peak season precipitation in south central India and northwestern Rajasthan, and ∼60–100% increase in late season precipitation in northern, northwestern, and central India. Furthermore, the spatial signatures between La Nina and El Nino are asymmetric in that for a particular location, the enhancement and suppression of rainfall associated with La Nina and El Nino conditions, respectively, are not equal. El Nino suppresses peak season rainfall in the south central and northwestern Rajasthan regions more than La Nina enhances it, but the opposite occurs during the late season in northern, northwestern, and central India. Additionally, the correspondence of minima (maxima) in anomalies of velocity potential aloft with maxima (minima) at 925 mb and with positive (negative) surface pressure anomalies suggests that anomalous subsidence (ascent) occurs in July–September during El Nino (La Nina) times. In the early season, however, patterns of velocity potential composites suggest a region of descent (ascent) over the western equatorial Indian Ocean, along with a region of ascent (descent) over the Indian subcontinent that exists only during the early season but not during the peak or late season. These patterns are consistent with the hypothesis that local Hadley cell circulation affects pressure and thus rainfall during the early season but that a larger-scale mechanism, such as eastward or westward shifts in the Walker circulation, may be more responsible for teleconnections seen throughout the remainder of the season. These findings indicate that focusing monsoon forecasting efforts on these regions and on subseasonal periods while incorporating ENSO asymmetries will yield useful and skillful regional forecasts, compared to the declining utility and skill of all-India summer monsoon rainfall.

We examine the characteristics of 3 day total extreme precipitation in the western United States. Coherent seasonal spatial patterns of timing and magnitude are evident in the data, motivating a seasonally based analysis. Using a clustering method that is consistent with extreme value theory, we identify coherent regions for extremes that vary seasonally. Based on storm back trajectory analysis, we demonstrate unique moisture sources and dominant moisture pathways for each spatial region. In the winter the Pacific Ocean is the dominant moisture source across the west, but in other seasons the Gulf of Mexico, the Gulf of California, and the land surface over the midwestern U.S. play an important role. We find the El Nino–Southern Oscillation (ENSO) to not have a strong impact on dominant moisture delivery pathways or moisture sources. The frequency of extremes under ENSO is spatially coherent and seasonally dependent with certain regions tending to have more (less) frequent extreme events in El Nino (La Nina) conditions.

The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.

The El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) are widely recognized as major drivers of inter-annual variability of the Indian monsoon (IM) and extreme rainfall events (EREs). We assess the time-varying strength and non-linearity of these linkages using dynamic linear regression and Generalized Additive Models. Our results suggest that IOD has evolved independently of ENSO, with its influence on IM and EREs strengthening in recent decades when compared to ENSO, whose relationship with IM seems to be weakening and more uncertain. A unit change in IOD currently has a proportionately greater impact on IM. ENSO positively influences EREs only below a threshold of 100 mm day−1. Furthermore, there is a non-linear and positive relationship between IOD and IM totals and the frequency of EREs (>100 mm day−1). Improvements in modeling this complex system can enhance the forecasting accuracy of the IM and EREs.

Spatial and temporal variability of relative humidity over the West African monsoon (WAM) region is investigated. In particular, the variability during the onset and retreat periods of the monsoon is considered. A K-means cluster analysis was performed to identify spatially coherent regions of relative humidity variability during the two periods. The cluster average of the relative humidity provides a robust representative index of the strength and timing of the transition periods between the dry and wet periods. Correlating the cluster indices with large-scale circulation and sea surface temperatures indicates that the land–ocean temperature gradient and the corresponding circulation, tropical Atlantic sea surface temperatures (SSTs), and to a somewhat lesser extent tropical Pacific SSTs all play a role in modulating the timing of the monsoon season relative humidity onset and retreat. These connections to large-scale climate features were also found to be persistent over interseasonal time scales, and thus best linear predictive models were developed to enable skillful forecasts of relative humidity during the two periods at 15–75-day lead times. The public health risks due to meningitis epidemics are of grave concern to the population in this region, and these risks are strongly tied to regional humidity levels. Because of this linkage, the understanding and predictability of relative humidity variability is of use in meningitis epidemic risk mitigation, which motivated this research.

[1] Despite recent challenges, conventional wisdom has held that heating over the Tibetan Plateau leads to increased Indian summer monsoon rainfall via enhancement of cross-equatorial circulation aloft, and a concurrent strengthening of both the Somali Jet and westerly winds that bring moisture to southern India. We show that such heating, quantified by monthly estimates of moist static energy in the atmosphere just above the surface, correlates with summer monsoon rainfall, but only in the early (20 May to 15 June) and late (September 1 to 15 October) monsoon season. Correlations during the main monsoon season (15 June to 31 August) are small and insignificant. The positive correlations with early and late monsoon season, however, allow for heating over Tibet to modulate as much as ~30% of the total rainfall. Furthermore, we demonstrate that heating over Tibet is independent of the El Nino Southern Oscillation, so that together they explain a substantial portion of variability in the early and late season rainfall, providing potential predictability. These links may also explain the wet conditions over India during early Holocene time and provide a quantitative link between a rise of Tibet and stronger Somali Jet.

Humans have substantially altered the thermal regimes of freshwater habitats worldwide, with significant environmental consequences. There is a critical need for a comprehensive modeling framework for forecasting the downstream impacts of two of the most common anthropogenic structures that alter river water temperatures: 1) dams that selectively release water from thermally stratified reservoirs, and 2) power generating stations and industrial plants that use river water for once-through cooling. These facilities change the thermal dynamics of the downstream waters through a complex interaction of water release volume and temperature and the subsequent exchange with the environment downstream. In order to stay within the downstream temperature limits imposed by regulatory agencies, managers must monitor not just release volumes and temperatures, but also need to be able to forecast the thermal impacts of their day-to-day operations on habitat which may be hundreds of kilometers downstream. Here we describe a coupled modeling framework that links mesoscale weather and ecological models to generate inputs for a physically-based water temperature model for monitoring and forecasting river temperatures downstream from these facilities at fine spatiotemporal scales. We provide an example of how this modeling framework is being applied to a water allocation decision support system (DSS) for the management of Endangered Species Act (ESA) listed salmon species in the Sacramento River in California.

An analysis of annual hydroclimatic variability in the Upper Colorado River basin (UCRB) for the period of 1906–2006 was performed to understand the dominant modes of multidecadal variability. First, wavelet-based spectral analysis was employed for streamflow at Lees Ferry, Arizona (aggregate location for UCRB flow), which identified two significant modes: a “low frequency” (~64-yr period) mode and a strong “decadal” (~15-yr period) component active only in recent decades. Subsequent investigation of temperature and precipitation data for the UCRB indicated that the low-frequency variability is associated with temperature via modulation of runoff efficiency while the decadal is strongly tied to moisture delivery. Simple hydrology and climate model experiments are also provided to support the aforementioned findings. Correlation of UCRB precipitation with global sea surface temperature (SST) anomalies showed a strong link with the equatorial and northern Pacific during periods of heightened variability of the decadal mode. The correlation of UCRB temperature with global SST anomalies showed strongest values in the Atlantic consistent with the Atlantic multidecadal oscillation mode. Wavelet spectral analysis of paleo-reconstructed streamflow at Lees Ferry shows both the low-frequency and decadal flow variability features. Furthermore, the strength of the decadal mode is modulated at an ~75-yr time scale, and these are consistent with epochal variations of overall streamflow variance.

Stochastic weather generators are commonly used to simulate time series of daily weather, especially minimum (Tmin) and maximum (Tmax) temperature and amount of precipitation. Recently, generalized linear models (GLM) have been proposed as a convenient approach to fit- ting weather generators. One limitation of weather generators is a marked tendency to underesti- mate the observed interannual variance in monthly, seasonal, or annual total precipitation and mean temperature, termed the 'overdispersion' phenomenon. In this study, aggregated statistics, consisting of seasonal total precipitation and mean Tmin and Tmax, are introduced as additional covariates into the GLM weather generator. With an appropriate degree of smoothing of these aggregated statistics, this approach is shown to virtually eliminate overdispersion when applied to 2 sites, Pergamino and Pilar, in the Argentine Pampas. The addition of these covariates does not distort the performance of the weather generator in other respects, such as annual cycles in the probability of precipitation and in the mean Tmin and Tmax. For seasonal total precipitation, the reduction in overdispersion is partially attributable to a corresponding reduction in the overdisper- sion of the frequency of precipitation occurrence, as well as to apparent temporal trends or 'regime' shifts. For seasonal mean Tmin and Tmax, the reduction in overdispersion is largely due to temporal trends on an interannual time scale.

For central India and its west coast, rainfall in the early (15 May–20 June) and late (15 September–20 October) monsoon season correlates with Pacific Ocean sea-surface temperature (SST) anomalies in the preceding month (April and August, respectively) sufficiently well, that those SST anomalies can be used to predict such rainfall. The patterns of SST anomalies that correlate best include the equatorial region near the dateline, and for the early monsoon season (especially since ~1980), a band of opposite correlation stretching from near the equator at 120°E to ~25°N at the dateline. Such correlations for both early and late monsoon rainfall and for both regions approach, if not exceed, 0.5. Although correlations between All India Summer Monsoon Rainfall and typical indices for the El Nino-Southern Oscillation (ENSO) commonly are stronger for the period before than since 1980, these correlations with early and late monsoon seasons suggest that ENSO continues to affect the monsoon in these seasons. We exploit these patterns to assess predictability, and we find that SSTs averages in specified regions of the Pacific Ocean in April (August) offer predictors that can forecast rainfall amounts in the early (late) monsoon season period with a ~25% improvement in skill relative to climatology. The same predictors offer somewhat less skill (~20% better than climatology) for predicting the number of days in these periods with rainfall greater than 2.5 mm. These results demonstrate that although the correlation of ENSO indices with All India Rainfall has decreased during the past few decades, the connections with ENSO in the early and late parts have not declined; that for the early monsoon season, in fact, has grown stronger in recent decades.

A new method to quantify changes in El Niño–Southern Oscillation (ENSO) variability is presented, using the overlap between probability distributions of the wavelet spectrum as measured by the wavelet probability index (WPI). Examples are provided using long integrations of three coupled climate models. When subsets of Niño-3.4 time series are compared, the width of the confidence interval on WPI has an exponential dependence on the length of the subset used, with a statistically identical slope for all three models. This exponential relationship describes the rate at which the system converges toward equilibrium and may be used to determine the necessary simulation length for robust statistics. For the three models tested, a minimum of 250 model years is required to obtain 90% convergence for Niño-3.4, longer than typical Intergovernmental Panel on Climate Change (IPCC) simulations. Applying the same decay relationship to observational data indicates that measuring ENSO variability with 90% confidence requires approximately 240 years of observations, which is substantially longer than the modern SST record. Applying hypothesis testing techniques to the WPI distributions from model subsets and from comparisons of model subsets to the historical Niño-3.4 index then allows statistically robust comparisons of relative model agreement with appropriate confidence levels given the length of the data record and model simulation.

We examined the effects of land cover change over the Indian subcontinent during pre-monsoon season (March, April, and May—MAM) on early Indian summer monsoon (ISM) rainfall using observed Normalized Difference Vegetation Index (NDVI) and July precipitation for the period of 1982–2003. MAM NDVI anomalies have increased in the Indian subcontinent and the increases are significantly correlated with increases in the irrigated area, not preceding rainfall. July rainfall significantly decreased in central and southern India, and the decrease is statistically related to the increase in the preceding MAM NDVI anomalies. Decreased July surface temperature in the Indian subcontinent (an expected result of increased evapotranspiration due to irrigation and increased vegetation) leads to a reduced land–sea thermal contrast, which is one of the factors driving the monsoon, and therefore weakens the monsoon circulation. A weak early ISM appears to be at least partially a result of irrigation and the resultant increased vegetation and crop activity prior to the monsoon. Copyright © 2008 Royal Meteorological Society

This study investigates dominant patterns of snow variability and their relationship to large-scale climate circulations over the eastern half of the United States. Two snowfall variables—total seasonal snowfall (TSF) and number of snow days (NSD)—are examined. A principal components (PC) analysis is conducted on data from 124 snowfall stations. The leading mode of variability for both TSF and NSD is driven by the North Atlantic Oscillation (NAO). The secondary mode of variability for TSF is driven by the Pacific/North American pattern (PNA), while the secondary mode of variability for NSD is driven by a dipole pattern and is attributable to regional influences and noise. These patterns exhibit persistence, which provides prospects for seasonal predictions of snowfall variables. This research compliments and extends the work of Serreze et al(1998), who performed a PC analysis of geopotential heights during the winter season and correlated the spatial patterns of the leading modes of variability with seasonal snowfall values. Copyright © 2007 Royal Meteorological Society

We use the upper-level divergence zone at 150 hPa to define the areas of study for the East Asian summer monsoon (EASM) and to show the advances and retreats of the EASM. We find that the EASM can be subdivided into a northern and southern component with distinctly different driving mechanisms. The northern EASM (NEASM) is affected by heat sources in the tropical oceans related to El Nino events while the southern EASM (SEASM) is affected by the subtropical oceans related to a North Pacific sea surface temperature (SST) dipole mode. A stronger NEASM is related to above-normal western North Pacific (WNP) anticyclonic anomalies, while a stronger SEASM is related to below-normal WNP anticyclonic anomalies. These WNP anticyclonic anomalies are connected to SST anomalies in the tropical and subtropical Pacific during the pre-monsoon season (December∼May). We also find that NEASM precipitation can be predicted from regional oceanic heat sources, i.e. SST and ocean heat content, in the tropical Pacific and Indian Oceans during the pre-monsoon season using a linear regression model. SEASM precipitation can be predicted from pre-monsoon SST in the eastern North Pacific. The NEASM forecast model is more skillful than that for the SEASM. Copyright © 2007 Royal Meteorological Society

Ethiopian agriculture and Nile River flows are heavily dependent upon the Kiremt season (June–September) precipitation in the upper Blue Nile basin, as a means of rain-fed irrigation and streamflow contribution, respectively. Climate diagnostics suggest that the El Niño–Southern Oscillation phenomenon is a main driver of interannual variability of seasonal precipitation in the basin. One-season (March–May) lead predictors of the seasonal precipitation are identified from the large-scale ocean–atmosphere–land system, including sea level pressures, sea surface temperatures, geopotential height, air temperature, and the Palmer Drought Severity Index. A nonparametric approach based on local polynomial regression is proposed for generating ensemble forecasts. The method is data driven, easy to implement, and provides a flexible framework able to capture any arbitrary features (linear or nonlinear) present in the data, as compared to traditional linear regression. The best subset of predictors, as determined by the generalized cross-validation (GCV) criteria, is selected from the suite of potential large-scale predictors. A simple technique for disaggregating the seasonal precipitation forecasts into monthly forecasts is also provided. Cross-validated forecasts indicate significant skill in comparison to climatological forecasts, as currently utilized by the Ethiopian National Meteorological Services Agency. This ensemble forecasting framework can serve as a useful tool for water resources planning and management within the basin.

Analysis is performed on the spatiotemporal attributes of North American monsoon system (NAMS) rainfall in the southwestern United States. Trends in the timing and amount of monsoon rainfall for the period 1948–2004 are examined. The timing of the monsoon cycle is tracked by identifying the Julian day when the 10th, 25th, 50th, 75th, and 90th percentiles of the seasonal rainfall total have accumulated. Trends are assessed using the robust Spearman rank correlation analysis and the Kendall–Theil slope estimator. Principal component analysis is used to extract the dominant spatial patterns and these are correlated with antecedent land–ocean–atmosphere variables. Results show a significant delay in the beginning, peak, and closing stages of the monsoon in recent decades. The results also show a decrease in rainfall during July and a corresponding increase in rainfall during August and September. Relating these attributes of the summer rainfall to antecedent winter–spring land and ocean conditions leads to the proposal of the following hypothesis: warmer tropical Pacific sea surface temperatures (SSTs) and cooler northern Pacific SSTs in the antecedent winter–spring leads to wetter than normal conditions over the desert Southwest (and drier than normal conditions over the Pacific Northwest). This enhanced antecedent wetness delays the seasonal heating of the North American continent that is necessary to establish the monsoonal land–ocean temperature gradient. The delay in seasonal warming in turn delays the monsoon initiation, thus reducing rainfall during the typical early monsoon period (July) and increasing rainfall during the later months of the monsoon season (August and September). While the rainfall during the early monsoon appears to be most modulated by antecedent winter–spring Pacific SST patterns, the rainfall in the later part of the monsoon seems to be driven largely by the near-term SST conditions surrounding the monsoon region along the coast of California and the Gulf of California. The role of antecedent land and ocean conditions in modulating the following summer monsoon appears to be quite significant. This enhances the prospects for long-lead forecasts of monsoon rainfall over the southwestern United States, which could have significant implications for water resources planning and management in this water-scarce region.

Decadal trends in cloudiness are shown to affect incoming solar radiation (SWSFC) in the Krishna River basin (13–20°N, 72–82°E), southern India, from 1952 to 1997. Annual average cloudiness at 14 meteorological stations across the basin decreased by 0.09% of the sky per year over 1952–1997. The decreased cloudiness partly balanced the effects of aerosols on incoming solar radiation (SWSFC), resulting in a small net increase in SWSFC in monsoon months (0.1–2.9 W m−2 per decade). During the non-monsoon, aerosol forcing dominated over trends in cloud forcing, resulting in a net decrease in SWSFC (−2.8 to − 5.5 W m−2 per decade). Monthly satellite measurements from the International Satellite Cloud Climatology Project (ISCCP) covering 1983–1995 were used to screen the visual cloudiness measurements at 26 meteorological stations, which reduced the data set to 14 stations and extended the cloudiness record back to 1952. SWSFC measurements were available at only two stations, so the SWSFC record was extended in time and to the other stations using a combination of the Angstrom and Hargreaves-Supit equations. The Hargreaves-Supit estimates of SWSFC were then corrected for trends in aerosols using the literature values of aerosol forcing over India. Monthly values and trends in satellite measurements of SWSFC from National Aeronautics and Space Administration's (NASA's) surface radiation budget (SRB) matched the aerosol-corrected Hargreaves-Supit estimates over 1984–1994 (RMSE = 11.9 W m−2, 5.2%). We conclude that meteorological station measurements of cloudiness, quality checked with satellite imagery and calibrated to local measurements of incoming radiation, provide an opportunity to extend radiation measurements in space and time. Reports of decreased cloudiness in other parts of continental Asia suggest that the cloud-aerosol trade-off observed in the Krishna basin may be widespread, particularly during the rainy seasons when changes in clouds have large effects on incoming radiation compared with aerosol forcing. Copyright © 2007 Royal Meteorological Society

A nonparametric stochastic model is developed and tested for the simulation of tropical cyclone tracks. Tropical cyclone tracks demonstrate continuity and memory over many time and space steps. Clusters of tracks can be coherent, and the separation between clusters may be marked by geographical locations where groups of tracks diverge as a result of the physics of the underlying process. Consequently, their evolution may be non-Markovian. Markovian simulation models, as are often used, may produce tracks that potentially diverge or lose memory quicker than happens in nature. This is addressed here through a model that simulates tracks by randomly sampling track segments of varying length, selected from historical tracks. For performance evaluation, a spatial grid is imposed on the domain of interest. For each grid box, long-term tropical cyclone risk is assessed through the annual probability distributions of the number of storm hours, landfalls, winds, and other statistics. Total storm length is determined at birth by local distribution, and movement to other tropical cyclone segments by distance to neighbor tracks, comparative vector, and age of track. The model is also applied to the conditional simulation of hurricane tracks from specific positions for hurricanes that were not included in the model fitting so as to see whether the probabilistic coverage intervals properly cover the subsequent track. Consequently, tests of both the long-term probability distributions of hurricane landfall and of event simulations from the model are provided.

Summer monsoon rains are a critical factor in Thailand’s water resources and agricultural planning and management. In fact, they have a significant impact on the country’s economic health. Consequently, understanding the variability of the summer monsoon rains over Thailand is important for instituting effective mitigating strategies against extreme rainfall fluctuations. To this end, the authors systematically investigated the relationships between summer monsoon precipitation from the central and northern regions of Thailand and large-scale climate features. It was found that Pacific sea surface temperatures (SSTs), in particular, El Niño–Southern Oscillation (ENSO), have a negative relationship with the summer monsoon rainfall over Thailand in recent decades. However, the relationship between summer rainfall and ENSO was weak prior to 1980. It is hypothesized that the ENSO teleconnection depends on the SST configuration in the tropical Pacific Ocean, that is, an eastern Pacific–based El Niño pattern, such as is the case in most of the post-1980 El Niño events, tends to place the descending limb of the Walker circulation over the Thailand–Indonesian region, thereby significantly reducing convection and consequently, rainfall over Thailand. It is believed that this recent shift in the Walker circulation is instrumental for the nonstationarity in ENSO–monsoon relationships in Thailand. El Niños of 1997 and 2002 corroborate this hypothesis. This has implications for monsoon rainfall forecasting and, consequently, for resources planning and management.

Analyses of streamflow, snow mass temperature, and precipitation in snowmelt-dominated river basins in the western United States indicate an advance in the timing of peak spring season flows over the past 50 years. Warm temperature spells in spring have occurred much earlier in recent years, which explains in part the trend in the timing of the spring peak flow. In addition, a decrease in snow water equivalent and a general increase in winter precipitation are evident for many stations in the western United States. It appears that in recent decades more of the precipitation is coming as rain rather than snow. The trends are strongest at lower elevations and in the Pacific Northwest region, where winter temperatures are closer to the melting point; it appears that in this region in particular, modest shifts in temperature are capable of forcing large shifts in basin hydrologic response. It is speculated that these trends could be potentially a manifestation of the general global warming trend in recent decades and also due to enhanced ENSO activity. The observed trends in hydroclimatology over the western United States can have significant impacts on water resources planning and management.

This paper describes the development of a statistical forecasting method for summer monsoon rainfall over Thailand. Predictors of Thailand summer (August-October) monsoon rainfall are identified from the large-scale ocean-atmospheric circulation variables (i.e., sea surface temperature and sea level pressure) in the Indo-Pacific region. The identified predictors are part of the broader El Nino Southern Oscillation (ENSO) phenomenon. The predictors exhibit significant relationship to the summer rainfall only during the post-1980 period when the Thailand summer rainfall also shows a relationship with ENSO. Two methods for generating ensemble forecasts are adapted. The first is the traditional linear regression, and the second is a local polynomial based non-parametric method. The associated predictive standard errors are used for generating ensembles. Both the methods exhibit significant comparable skills in a cross-validated mode. However, the nonparametric method shows improved skill during extreme years (i.e. wet and dry years). Furthermore, the models provide useful skill at 1~3 month lead time that can have strong impact on resources planning and management.

To test the accuracy of statistically downscaled precipitation estimates from numerical weather prediction models, a set of experiments to study what space and time scales are appropriate to obtain downscaled precipitation forecasts with maximum skill have been carried out. Fourteen-day forecasts from the 1998 version of the National Centers for Environmental Prediction (NCEP) Medium-Range Forecast (MRF) model were used in this study. It has been previously found that downscaled temperature fields have significant skill even up to 5 days of forecast lead time, but there is practically no valuable skill in the downscaled precipitation forecasts. Low skill in precipitation forecasts revolves around two main issues. First, the (intermittent) precipitation variability on daily and subdaily time scales could be too noisy to derive meaningful relationships with atmospheric predictors. Second, the model parameterizations and the coarse spatial resolution of the current generation of global-scale forecast models might be unable to resolve the local-scale variability in precipitation. Both of these issues may be addressed by spatial and temporal averaging. In this paper the authors present a diagnostic study using a set of numerical experiments to understand how spatial and temporal aggregations affect the skill of downscaled precipitation forecasts in the upper Colorado River basin. The question addressed is, if the same set of predictor variables from numerical weather prediction models is used, what space (e.g., station versus regional average) and time (e.g., subdaily versus daily) scales optimize regression-based downscaling models so as to maximize forecast skill for precipitation? Results in general show that spatial and temporal averaging increased the skill of downscaled precipitation estimates. At subdaily (6 hourly) and daily time scales, the skill of downscaled estimates at spatial scales greater than 50 km was generally higher than the skill of downscaled estimates at individual stations. For the 6-hourly time scale both for stations and for mean areal precipitation estimates the maximum forecast skill was found to be approximately half that of the daily time scale. At forecast lead times of 5 days, when there is very little skill at daily and subdaily time scales, useful skill emerged when station data are aggregated to 3- and 5-day averages.

Presented are investigations into the spatial structure of teleconnections between both the winter El Nino–Southern Oscillation (ENSO) and global sea surface temperatures (SSTs), and a measure of continental U.S. summer drought during the twentieth century. Potential nonlinearities and nonstationarities in the relationships are noted. During the first three decades of this century, summer drought teleconnections in response to SST patterns linked to ENSO are found to be strongest in the southern regions of Texas, with extensions into regions of the Midwest. From the 1930s through the 1950s, the drought teleconnection pattern is found to extend into southern Arizona. The most recent three decades show weak teleconnections between summer drought over southern Texas and Arizona, and winter SSTs, which is consistent with previous findings. Instead, the response to Pacific SSTs shows a clear shift to the western United States and southern regions of California. These epochal variations are consistent ...

Recent advances in understanding the role of the El Nino-Southern Oscillation (ENSO) in climate variability present opportunities for improving efficiency in agricultural production. We investigated the relationships between ENSO, climate and maize yields in the U.S. cornbelt, using both observed data and crop simulations. Using a time-series of sea-surface temperature anomalies (SSTA) from the NINO3 region of the Pacific Ocean and historical records of temperature and precipitation spatially averaged across 51 mid-western climate divisions from 1950 to 1995, we ran linear correlation tests at three different lags. Northern hemisphere wintertime SSTAs were significantly correlated with air temperature at the 95% level of confidence in both the previous (r 0.32) and following (r 0.41) summer, but had opposite signs. Correlations with precipitation were significant only in the summer preceding the ENSO event (r0.31). Detrended maize yield for the same area and time period was also significantly related to SSTAs in the winter after harvest, with a correlation coefficient of 0.39, indicating that ENSO accounts for : 15% of interannual maize yield variability in the cornbelt. Crop growth simulations at seven sites across the region suggest that water stress in July and August is the primary cause of lowered corn yield in La Nina years, but shortened grainfill period due to higher temperatures is also important. The benefits of El Nino-related rainfall and cooler temperatures are less pronounced than the negative impacts of warmer and dryer La Ninas. However, advance warning of both ENSO phases may present opportunities for improved crop management in the cornbelt. Copyright © 1999 Royal Meteorological Society.

Global analyses of monthly sea surface temperature (SST) anomalies from 1856 to 1991 are produced using three statistically based methods: optimal smoothing (OS), the Kalrnan filter (KF) and optimal interpolation (OI). Each of these is accompanied by estimates of the error covariance of the analyzed fields. The spatial covariance function these methods require is estimated from the available data; the time-marching model is a first-order autoregressive model again estimated from data. The data input for the analyses are monthly anomalies from the United Kingdom Meteorological Office historical sea surface temperature data set (MOHSST5) (Parker et al., 1994) of the Global Ocean Surface Temperature Atlas (COSTA) (Bottoraley et al., 1990). These analyses are compared with each other, with COSTA, and with an analy- sis generated by projection (P) onto a set of empirical orthogonal functions (as in Smith et al. (1996)). In theory, the quality of the analyses should rank in the order OS, KF, OI, P, and COSTA. It is found that the first four give comparable results in the data-rich periods (1951-1991), but at times when data is sparse the first three differ significantly from P and COSTA. At these times the latter two often have extreme and fluctuating values, prima facie evidence of error. The statistical schemes are also verified against data not used in any of the analyses (proxy records derived from corals and air temperature records from coastal and island stations). We also present evidence that the analysis error estimates are indeed indicative of the quality of the products. At most times the OS and KF products are close to the OI product, but at times of especially poor coverage their use of information from other times is advantageous. The methods appear to reconstruct the major features of the global SST field from very sparse data. Comparison with other indications of the E1 Nifio - Southern Oscillation cycle show that the analyses provide usable information on interannual variability as far back as the 1860s.

Guided by the increasing awareness and detectability of spatiotemporally organized climatic variability at interannual and longer timescales, the authors motivate the paradigm of a climate system that exhibits excitations of quasi-oscillatory eigenmodes with characteristic timescales and large-scale spatial patterns of coherence. It is assumed that any such modes are superposed on a spatially and temporally autocorrelated stochastic noise background. Under such a paradigm, a previously described (Mann and Park) multivariate frequency-domain approach is promoted as a particularly effective means of spatiotemporal signal identification and reconstruction, and an associated forecasting methodology is introduced. This combined signal detection/forecasting scheme exhibits significantly greater skill than conventional forecasting approaches in the context of a synthetic example consistent with the adopted paradigm. The example application demonstrates statistically significant skill at 5‐ 10-yr lead times. Applications to operational long-range climatic forecasting are motivated and discussed.

International audience; We present a comprehensive assessment of the present and expected future pulse of the Indian monsoon climate based on observational and global climate model projections. The analysis supports the view that seasonal Indian monsoon rains in the latter half of the 21th century may not be materially different in abundance to that experienced today although their intensity and duration of wet and dry spells may change appreciably. Such an assessment comes with considerable uncertainty. With regard to temperature, however, we find that the Indian temperatures during the late 21st Century will very likely exceed the highest values experienced in the 130-year instrumental record of Indian data. This assessment comes with higher confidence than for rainfall because of the large spatial scale driving the thermal response of climate to greenhouse gas forcing. We also find that monsoon climate changes, especially temperature, could heighten human and crop mortality posing a socio-economic threat to the Indian subcontinent.

Agricultural production responses to climate variability require salient information to support decisions. We coupled a new hybrid stochastic weather generator (combining parametric and nonparametric components) with a crop simulation model to assess yields and economic returns relevant to maize production in two contrasting regions (Pergamino and Pilar) of the Pampas of Argentina. The linked models were used to assess likely outcomes and production risks for seasonal forecasts of dry and wet climate. Forecasts involving even relatively small deviations from climatological probabilities of precipitation may have large impacts on agricultural outcomes. Furthermore, yield changes under alternative scenarios have a disproportionate effect on economic risks. Additionally, we show that regions receiving the same seasonal forecast may experience fairly different outcomes: a forecast of dry conditions did not change appreciably the expected distribution of economic margins in Pergamino (a climatically optimal location) but modified considerably economic expectations (and thus production risk) in Pilar (a more marginal location). Fil: Apipattanavis, Somkiat. State University Of Colorado Boulder; Estados Unidos Fil: Bert, Federico Esteban. Universidad de Buenos Aires. Facultad de Agronomia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Podestá, Guillermo. University of Miami; Estados Unidos Fil: Rajagopalan, Balaji. State University Of Colorado Boulder; Estados Unidos

Airborne particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) has been linked to a wide range of adverse health effects and as a result is currently regulated by the U.S. Environmental Protection Agency. PM2.5 originates from a multitude of sources and has heterogeneous physical and chemical characteristics. These features complicate the link between PM2.5 emission sources, ambient concentrations and health effects. The goal of the Denver Aerosol Sources and Health (DASH) study is to investigate associations between sources and health using daily measurements of speciated PM2.5 in Denver. The datxa set being collected for the DASH study will be the longest daily speciated PM2.5 data set of its kind covering 5.5 years of daily inorganic and organic speciated measurements. As of 2008, 4.5 years of bulk measurements (mass, inorganic ions and total carbon) and 1.5 years of organic molecular marker measurements have been completed. Several techniques were used to reveal long-term and short-term temporal patterns in the bulk species and the organic molecular marker species. All species showed a strong annual periodicity, but their monthly and seasonal behavior varied substantially. Weekly periodicities appear in many compound classes with the most significant weekday/weekend effect observed for elemental carbon, cholestanes, hopanes, select polycyclic aromatic hydrocarbons (PAHs), heavy n-alkanoic acids and methoxyphenols. Many of the observed patterns can be explained by meteorology or anthropogenic activity patterns while others do not appear to have such obvious explanations. Similarities and differences in these findings compared to those reported from other cities are highlighted.

[1] Plausibility and reproducibility of the relationship and physical mechanisms proposed between the northern East Asian summer monsoon (NEASM) and El Nino are verified and illuminated using the Community Climate System Model (CCSM) with the Community Atmosphere Model–Community Land Model version 3 (CAM-CLM3). The climate responses over East Asia and western North Pacific to El Nino are simulated in the CCSM3 using dynamic atmosphere and land with prescribed climatological and El Nino sea surface temperature (SST) anomalies. A significantly intensified NEASM is simulated for the El Nino experiments, which validates the positive correlation between NEASM precipitation and El Nino found in recent observational analysis. Analysis of lower level wind vector and vorticity in the model experiments elucidates the physical mechanism behind the positive correlation between NEASM precipitation and El Nino, and shows that the western North Pacific anticyclone plays an important role in the connection between the NEASM and the tropical SST anomalies.

Dominant spatiotemporal patterns of joint sea surface temperature (SST) and sea level pressure (SLP) variability in the Atlantic Ocean are identified using a multivariate frequency domain analysis. Five significant frequency bands are isolated ranging from the quasi biennial to the quasi decadal. Two quasi-biennial bands are centered around 2.2- and 2.7-yr periods; two interannual bands are centered around 3.5- and 4.4-yr periods; the fifth band at the quasi-decadal frequency is centered around 11.4-yr period. Between 1920 and 1955, the quasi-decadal band is less prominent compared to the quasi-biennial bands. This happens to be the period when SLP gradually increased over the Greenland–Iceland regions. The spatial pattern at the quasi-decadal frequency displays an out-of-phase relationship in the SLP in the vicinity of the subtropical anticyclones in both hemispheres (indicative of an out-of-phase quasi-decadal variability in the North and South Atlantic Hadley circulation). The quasi-decadal fr...

A Bayesian methodology is used to assess the information content of categorical, probabilistic forecasts of specific variables derived from a general circulation model (GCM) forecast ensemble, and to combine a “prior” forecast (climatological probabilities of each category) with a categorical probabilistic forecast derived from a GCM ensemble to develop posterior, or “regularized” categorical probabilities. The combination algorithm assigns a weight to a particular model forecast and to climatology. The ratio of the sample likelihood of the model based on the posterior categorical probabilities, to that based on climatological probabilities, computed over the period of record of historical forecasts, provides a measure of the skill or information content of a candidate model. The weight given to a GCM forecast serves as a secondary indicator of its information content. Model weights are determined by maximizing the likelihood ratio. Results using the so-called ranked probability skill score as an...

A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5...

There has been an apparent increase in the frequency and duration of El Nino-Southern Oscillation events in the last two decades relative to the prior period of record. Furthermore, 1990-95 was the longest period of sustained high Darwin sea level pressure in the instrumental record. Variations in the frequency and duration of such events are of considerable interest because of their implications for understanding global climatic variability and also the possibility that the climate system may be changing due to external factors such as the increased concentration of greenhouse gases in the atmosphere. Nonparametric statistical methods for time series analysis are applied to a 1882 to 1995 seasonal Darwin sea level pressure (DSLP) anomaly time series to explore the variations in El Nino-like anomaly occurrence and persistence over the period of record. Return periods for the duration of the 1990-95 event are estimated to be considerably smaller than those recently obtained by Trenberth and Hoar using a linear ARMA model with the same time series. The likelihood of a positive anomaly of the DSLP, as well as its persistence, is found to exhibit decadal- to centennial-scale variability and was nearly as high at the end of the last century as it has been recently. The 1990-95 event has a much lower return period if the analysis is based on the 1882-1921 DSLP data. The authors suggest that conclusions that the 1990-95 event may be an effect of greenhouse gas-induced warming be tempered by a recognition of the natural variability in the system.