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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.

Timely projections of seasonal streamflow extremes can be useful for the early implementation of annual flood risk adaptation strategies. However, predicting seasonal extremes is challenging particularly under non-stationary conditions and if extremes are connected in space. The goal of this study is to implement a space-time model for projection of seasonal streamflow extremes that considers the nonstationarity and spatio-temporal dependence of high flows. We develop a space-time model to project seasonal streamflow extremes for several lead times up to 2 months using a Bayesian Hierarchical Modelling (BHM) framework. This model is based on the assumption that streamflow extremes (3-day maxima) at a set of gauge locations are realizations of a Gaussian elliptical copula and generalized extreme value (GEV) margins with nonstationary parameters. These parameters are modeled as a linear function of suitable covariates from the previous season selected using the deviance information criterion (DIC). Finally, the copula is used to generate streamflow ensembles, which capture spatio-temporal variability and uncertainty. We apply this modelling framework to predict 3-day maximum flow in spring (May-June) at seven gauges in the Upper Colorado River Basin (UCRB) with 0 to 2 months lead time. In this basin, almost all extremes that cause severe flooding occur in spring as a result of snowmelt and precipitation. Therefore, we use regional mean snow water equivalent and temperature from the preceding winter season as well as indices of large-scale climate teleconnections – ENSO, AMO, and PDO – as potential covariates for 3-day maximum flow. Our model evaluation, which is based on the comparison of different model versions and the energy skill score, indicates that the model can capture the space-time variability of extreme flow well and that model skill increases with decreasing lead time. We also find that the use of climate variables slightly enhances skill relative to using only snow information. Median projections and their uncertainties are consistent with observations thanks to the representation of spatial dependencies through covariates in the margins and a Gaussian copula. This spatio-temporal modeling framework helps to plan seasonal adaptation and preparedness measures as predictions of extreme spring flows become available 2 months before actual flood occurrence.

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.

This study introduces a Bayesian logistic regression framework that is capable of providing skillful probabilistic forecasts of Arctic sea ice cover, along with quantifying the attendant uncertainties. The presence or absence of ice (absence defined as ice concentration below 15%) is modeled using a categorical regression model, with atmospheric, oceanic, and sea ice covariates at 1‐ to 7‐month lead times. The model parameters are estimated in a Bayesian framework, thus enabling the posterior predictive probabilities of the minimum sea ice cover and parametric uncertainty quantification. The model is fitted and validated to September minimum sea ice cover data from 1980 through 2018. Results show overall skillful forecasts of the minimum sea ice cover at all lead times, with higher skills at shorter lead times, along with a direct measure of forecast uncertainty to aide in assessing the reliability.

Streamflow extremes, especially, summer seasonal streamflow in monsoon climate makes a significant contribution to the reliability of water resources and the health of ecology. The summer extreme precipitation and streamflow also cause severe floods resulting in loss of life and property. Large scale climate drivers impart strong spatial and temporal variability in the flow extremes, which needs to be modeled for use in efficient management of resources. To this end, we developed a space-time model to capture the variability of –summer season 3-day maximum streamflow. In this, the extremes at each station are assumed to be distributed as Generalized Extreme Value (GEV) distribution with non-stationary parameters. Thus, the parameters are modeled as a linear function of suitable covariates – typically, large scale climate variables and regional mean precipitation. In addition, the spatial dependence of the extremes is modeled via a Gaussian copula. The parameters of the nonstationary GEV at each location are estimated via maximum likelihood, whereas those of the Copula are estimated via the Inversion of Kendall’s tau estimator method. Ensembles of streamflow in time are based on the temporal varying covariates and from the Copula are generated, consequently, capturing the spatial and temporal variability and the attendant uncertainty. Furthermore, various return level can also be obtained from these simulations. The model is demonstrated by application to 3-day maximum summer streamflow in a representative basin from two different monsoonal climate – India and Southwest U.S. In addition to comparing the performance of the median of the simulations with the historic observations, we also compare the number of stations that exceed a specific level- say, 75th percentile which indicates the spatial performance. The model validation indicates that the model is able to capture the space-time variability, furthermore, it captures the variability in wet and dry years, consistent with observations. This framework can be applied to generate ensembles of at several lead times – week to seasonal, to provide risks of various levels of streamflow. This will be of immense use in water resources, agriculture and flood management and planning.

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.

Summary We present the application of a parametric stochastic weather generator within a nonstationary context, enabling simulations of weather sequences conditioned on interannual and multi-decadal trends. The generalized linear model framework of the weather generator allows any number of covariates to be included, such as large-scale climate indices, local climate information, seasonal precipitation and temperature, among others. Here we focus on the Salado A basin of the Argentine Pampas as a case study, but the methodology is portable to any region. We include domain-averaged (e.g., areal) seasonal total precipitation and mean maximum and minimum temperatures as covariates for conditional simulation. Areal covariates are motivated by a principal component analysis that indicates the seasonal spatial average is the dominant mode of variability across the domain. We find this modification to be effective in capturing the nonstationarity prevalent in interseasonal precipitation and temperature data. We further illustrate the ability of this weather generator to act as a spatiotemporal downscaler of seasonal forecasts and multidecadal projections, both of which are generally of coarse resolution.

Study region: Java Island, Indonesia. Study focus: The Indonesian island of Java is home to more than half of Indonesia’s population and routinely experiences water related natural disasters. This study represents a first step towards skillful hydrologic prediction and hydrologically-informed mitigation strategies. This is the first study to collate a comprehensive suite of hydrometeorological observations and systematically identify Variable Infiltration Capacity (VIC) Land Surface Model (LSM) parameters on Java to create a set of benchmark simulations. New hydrological insights for the region: Quality control procedures revealed inconsistencies between precipitation and streamflow with only five watersheds possessing data of suitable quality. Simulations and observations confirmed that both precipitation and streamflow variability increase eastward on the island and that rainfall-runoff response was most frequently dominated by baseflow, rather than surface runoff. The most sensitive VIC parameters were identified and then calibrated with an automatic calibration procedure. In the calibration period, model performance was generally deemed satisfactory with Nash Sutcliffe Efficiency (NSE) between 0.31 to 0.89, whereas the validation period exhibited poorer performance than expected (0.07

We examine the effects of regional climate model (RCM) horizontal resolution and forcing scaling (i.e., spatial aggregation of meteorological datasets) on the portrayal of climate change impacts. Specifically, we assess how the above decisions affect: (i) historical simulation of signature measures of hydrologic behavior, and (ii) projected changes in terms of annual water balance and hydrologic signature measures. To this end, we conduct our study in three catchments located in the headwaters of the Colorado River basin. Meteorological forcings for current and a future climate projection are obtained at three spatial resolutions (4-, 12- and 36-km) from dynamical downscaling with the Weather Research and Forecasting (WRF) regional climate model, and hydrologic changes are computed using four different hydrologic model structures. These projected changes are compared to those obtained from running hydrologic simulations with current and future 4-km WRF climate outputs re-scaled to 12- and 36-km. The results show that the horizontal resolution of WRF simulations heavily affects basin-averaged precipitation amounts, propagating into large differences in simulated signature measures across model structures. The implications of re-scaled forcing datasets on historical performance were primarily observed on simulated runoff seasonality. We also found that the effects of WRF grid resolution on projected changes in mean annual runoff and evapotranspiration may be larger than the effects of hydrologic model choice, which surpasses the effects from re-scaled forcings. Scaling effects on projected variations in hydrologic signature measures were found to be generally smaller than those coming from WRF resolution; however, forcing aggregation in many cases reversed the direction of projected changes in hydrologic behavior.

It is increasingly evident that large scale climate forcings such as El Nino Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO) are known to modulate the hydro climatology of Western United States at multi-decadal time scales. We developed a wavelet-based time series bootstrap simulation model to generate streamflow projections conditioned on climate indices of the aforementioned large scale climate forcings. These indices are based on sea surface temperature (SST) anomalies. The wavelet analysis is performed on each climate index and significant periodicities (components) are identified that pass the 95% significance test. Then a K-nearest neighbor (K-NN) Block bootstrap method is employed to generate ensembles of the identified significant components. The components being orthogonal by construction are summed to obtain sequences of the 'climate signal'. This is performed on each climate index separately, thus obtaining a climate signal vector,ඃ烜 ! , at each time step, t. Conditioned on ඃ烜 ! , a K-NN bootstrap is applied to simulate streamflow ensembles. We demonstrated this method by applying it for simulation of streamflow at Lees Ferry gauge on the Colorado River using two large scale climate forcings Pacific Decadal Oscillation (PDO) and Atlantic Multi-decadal Oscillation (AMO) which have been known to modulate the upper Colorado River Basin hydrology at multi-decadal time scales. The simulations reproduce skillfully all the distribution and nonstationary spectral properties in addition the method also provides good projections of decadal mean flow. All of these are crucial for water resources management.

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.

This study proposed a hidden Markov chain model-based drought analysis (HMM-DA) tool to understand the beginning and ending of meteorological drought and to further characterize typhoon-induced drought busters (TDB) by exploring spatiotemporal drought patterns in South Korea. It was found that typhoons have played a dominant role in ending drought events (EDE) during the typhoon season (July–September) over the last four decades (1974–2013). The percentage of EDEs terminated by TDBs was about 43–90% mainly along coastal regions in South Korea. Furthermore, the TDBs, mainly during summer, have a positive role in managing extreme droughts during the subsequent autumn and spring seasons. The HMM-DA models the temporal dependencies between drought states using Markov chain, consequently capturing the dependencies between droughts and typhoons well, thus, enabling a better performance in modeling spatiotemporal drought attributes compared to traditional methods.

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.

Wavelet and hidden Markov-based modeling frameworks were developed to better capture the nonstationarity and non-Gaussian characteristics of streamflow that linear models cannot. Climate-ba...

Hydroclimate time series often exhibit very low year-to-year autocorrelation while showing prolonged wet and dry epochs reminiscent of regime-shifting behavior. Traditional stochastic time series models cannot capture the regime-shifting features thereby misrepresenting the risk of prolonged wet and dry periods, consequently impacting management and planning efforts. Upper Colorado River Basin (UCRB) annual flow series highlights this clearly. To address this, a simulation framework is developed using a hidden Markov (HM) model in combination with large-scale climate indices that drive multidecadal variability. We demonstrate this on the UCRB flows and show that the simulations are able to capture the regime features by reproducing the multidecadal spectral features present in the data where a basic HM model without climate information cannot.

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.

We exploit El Nino–Southern Oscillation (ENSO) indices and moist static energy of surface air over the Indian subcontinent and surroundings as predictors of monsoon rainfall over India during early and late seasons, defined here as 20 May to 15 June and 20 September to 15 October, respectively. Although these seasons contribute only ~22% of the entire seasonal rainfall, they clearly affect planning of agriculture and water resources. A simple, nonlinear, statistical model applied to these predictors accounts for ~40% and 45% of the observed variance of early and late season rainfall, respectively, and similar fractions for 3 day maximum rainfall intensity. Forecasted average and 3 day maximum rainfall at grid points covering India show greatest success over central India during the early season and over west central, northwestern, and northern India during the late season, regions where agriculture dominates land use. These predictors, however, offer virtually no predictability of peak season rainfall.

[1] A new framework for simulating the El Nino/Southern Oscillation (ENSO) using a generalized linear model (GLM) is provided. The GLM provides a versatile and computationally inexpensive method for investigating ENSO dynamics, by conditioning an ENSO index on an arbitrary set of input variables. Here the system state (El Nino/neutral/La Nina) at previous times is combined with the first few principal components of sea surface temperature (SST) and thermocline depth. Despite having relatively few degrees of freedom, the model accurately reproduces 20th century SST time series, seasonal variance, power spectra, and autocorrelation functions for both the eastern and western Pacific. The GLM also has good overall forecast skill, especially at subyearly lead times; performance is competitive with models currently used for operational ENSO forecasting. The model is then used to examine changes to El Nino/La Nina statistics under CO2 increases, by using the GLM to represent simulations run with the National Center for Atmospheric Research (NCAR) Community Climate System Model (CCSM), versions 3.5 and 4. GLM simulations of 21st century CCSM4 changes to El Nino/La Nina magnitudes show insignificant results, despite a slight increase in El Nino persistence. GLM fits conditioned on millennial stabilized CCSM3.5 simulations with varying CO2 levels, however, show a weakening and shortening of El Nino events as CO2 concentration increases, whereas La Nina events become markedly stronger and do not change significantly in length. The reduction in El Nino persistence in CCSM3.5 is consistent with previous results showing that at higher CO2 levels, a stronger seasonal cycle creates a Southern Hemisphere “seasonal footprint” leading to more efficient El Nino termination.

[1] Despite the influence of hydroclimate on river ecosystems, most efforts to date have focused on using climate information to predict streamflow for water supply. However, as water demands intensify and river systems are increasingly stressed, research is needed to explicitly integrate climate into streamflow forecasts that are relevant to river ecosystem management. To this end, we present a five step risk-based framework: (1) define risk tolerance, (2) develop a streamflow forecast model, (3) generate climate forecast ensembles, (4) estimate streamflow ensembles and associated risk, and (5) manage for climate risk. The framework is successfully demonstrated for an unregulated watershed in southwest Montana, where the combination of recent drought and water withdrawals has made it challenging to maintain flows needed for healthy fisheries. We put forth a generalized linear modeling (GLM) approach to develop a suite of tools that skillfully model decision-relevant low flow characteristics in terms of climate predictors. Probabilistic precipitation forecasts are used in conjunction with the GLMs, resulting in season-ahead prediction ensembles that provide the full risk profile. These tools are embedded in an end-to-end risk management framework that directly supports proactive fish conservation efforts. Results show that the use of forecasts can be beneficial to planning, especially in wet years, but historical precipitation forecasts are quite conservative (i.e., not very “sharp”). Synthetic forecasts show that a modest “sharpening” can strongly impact risk and improve skill. We emphasize that use in management depends on defining relevant environmental flows and risk tolerance, requiring local stakeholder involvement.

[1] Observations of 408 monsoon low-pressure systems (MLPSs) including 196 monsoon depressions (MDs) that formed in the Bay of Bengal during the 1951–2007 period, and the gridded analysis of daily rainfall fields for the same period, were used to identify the association of antecedent rainfall (1 week average rainfall prior to the genesis of MLPS) with the genesis of MLPS and length of inland penetration by MDs. Prestorm rainfall is treated as a surrogate to prestorm ground wetness conditions due to unavailability of historical soil-moisture data over the monsoon region. These observations were analyzed using self-organizing maps (SOMs) to group nine different prestorm monsoon rainfall patterns into different transition states like active, active-to-break, break-to-active, break, etc. The analysis indicates that MLPS are four times more likely to form on a day during active monsoon state compared to break state. Analysis of MLPSs linked to each monsoon state represented by SOM nodes shows that MDs with higher inland penetration were associated with higher antecedent rainfall. On the other hand, there was no significant difference in low-level atmospheric circulation for MDs with shortest and longest inland penetration.

[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.

We develop a multiproxy, reduced-dimension methodology to blend magnesium-calcium (Mg/Ca) and alkenone (U 37k) paleo sea surface temperature (SST) records from the eastern and western equatorial Pacific, to recreate snapshots of full field SSTs and zonal winds from 10 to 2ka B.P. in 2000year increments. Single-proxy reconstructions (Mg/Ca only versus U 37K only) reveal differences in the timing and duration of maximum cooling across the east-central equatorial Pacific. The largest zonal temperature differences (average west Pacific SST minus average east Pacific SST) occur at 6ka B.P. for the Mg/Ca-only reconstruction (0.61 degrees C) and at 10 and 4ka for the U 37K-only reconstruction (0.55 degrees C and 0.47 degrees C, respectively). Disagreements between SST trends suggested by each proxy call for methods that can resolve the common patterns between each and have motivated the work presented in this study. In combining inferences from these proxies, we treat both Mg/Ca and U 37K reconstructions of SST as annual average values, but we recognize that they may be sensitive to different seasons. In the multiproxy reconstruction, the zonal SST difference is largest at 10ka (0.26 degrees C), with coldest SST anomalies of approximate to-0.9 degrees C in the eastern equatorial Pacific and concurrent easterly maximum zonal wind anomalies of 7ms(-1) throughout the central Pacific. From 10 to 2ka, the entire equatorial Pacific warms, but at a faster rate in the east than the west, and the average central Pacific easterly winds weaken gradually to approximately 2ms(-1). These patterns are broadly consistent with previous inferences of reduced El Nino-Southern Oscillation variability associated with a La Nina-like state during the early to middle Holocene.

Over the past three decades, hydrologists and water resources specialists have been concerned with the issue of nonstationarity arising from several factors. First is the effect of human intervention on the landscape that may cause changes in the precipitation–runoff relationships at various temporal and spatial scales. Second is the occurrence of natural events such as volcanic explosions or forest fires that may cause changes in the composition of the air, the soil surface, and geomorphology. Third is the low-frequency component of oceanic–atmospheric phenomena that may have significant effects on the variability of hydrological processes such as annual runoff, peak flows, and droughts. Fourth is global warming, which may cause changes to oceanic and atmospheric processes, thereby affecting the hydrological cycle at various temporal and spatial scales. There has been a significant amount of literature on the subject and thousands of research and project articles and books published in recent decades. Examples of human intrusion on the landscape are the changes in land use resulting from agricultural developments in semiarid and arid lands (e.g., Pielke et al. 2007, 2011), changes caused by large-scale deforestation (e.g., Gash and Nobre 1997), changes resulting from open-pit mining operations (e.g., Salas et al. 2008), and changes from increasing urbanization in watersheds (e.g., Konrad and Booth 2002, Villarini et al. 2009). These intrusions change hydrologic response characteristics such as the magnitude and timing of floods. In many situations, current systems and management practices will be ill equipped to cope with such changes unless adjustments are made. Large-scale landscape changes such as deforestation in the tropical regions can potentially alter atmospheric circulation patterns, and consequently affect global weather and climate (e.g., Lee et al. 2008, 2009). Major natural events, such as the volcanic explosion of Mount St. Helens in 1980 or the El Chichon volcanic explosion of 1982 induce a shock to the climate system in the form of global cooling that continues for several years. These events can also affect global circulation. Low-frequency climate drivers of the oceanic– atmospheric system such as the El Nino/Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) modulate global climate at interannual and multidecadal time scales. These drivers are the main sources of nonstationarity in global climate and hydrology. Large numbers of papers documenting the effect of these drivers on global hydroclimatology continue to emerge (e.g., Dilley and Heyman 1995; Mantua et al. 1997; Enfield et al. 2001; Akintug and Rasmussen 2005; Hamlet et al. 2005). In addition to climate variability and change due to the previously mentioned factors, anthropogenic warming of the oceans and atmosphere because of increased greenhouse gas concentrations and the ensuing changes to the hydrologic cycle are topics of serious pursuit. The international scientific community is making strides in understanding the potential warming and its effects on all aspects of climate variability [Intergovernmental Panel on Climate Change (IPCC) 2007], but the impacts on the hydrologic cycle remain debatable and inconclusive (e.g., Cohn and Lins 2005; Legates et al. 2005; Hirsch and Ryberg 2011). Based on analyses of the global mean CO2 (GMCO2) and annual flood records in the United States, no strong statistical evidence for flood magnitudes increasing with GMCO2 increases were found (Hirsch and Ryberg 2011). Although general circulation models have had success in the attribution of warming global temperatures to anthropogenic causes, their credibility and utility in reproducing variables that are relevant to hydrology and water resources applications is less clear. For example, the IPCC Report for Latin America acknowledges that “the current GCMs do not produce projections of changes in the hydrological cycle at regional scales with confidence. In particular the uncertainty of projections of precipitation remain high : : :That is a great limiting factor to the practical use of such projections for guiding active adaptation or mitigation policies” (Magrin et al. 2007; Boulanger et al. 2007). A variety of methods exist that address the concern of nonstationarity in hydrological processes and the topic remains an active research area. For example, in watersheds in which increasing urbanization has been documented causing significant effects in the flood response and magnitude, watershed modeling has been utilized to estimate the possible changes in the flood frequency and magnitude. Frequency analysis methods also have been applied when the parameters (or the moments such as the mean and variance) of a given model (e.g., the Gumbel model) may vary with time (e.g., Strupczewski et al. 2001; Clarke 2002). In addition, the role that low-frequency components of the oceanic– atmospheric system (represented, for example, by large-scale oscillations such as ENSO, PDO, and AMO) have on extreme events such as floods has been recognized. These large-scale forcing factors have been shown to exert in-phase and out-of-phase oscillations in the magnitude of floods, mean flows, and droughts

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.

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.

Climatic fluctuations have profound effects on water resources variability in the western United States. The research reported herein centers on streamflow predictability at the medium- and long-range scales in rivers that originate in Colorado. Specifically, we want to improve forecasting seasonal and yearly streamflows based on atmospheric-oceanic forcing factors, such as geopotential height, wind, and sea surface temperature, as well as hydrologic factors, such as snow water equivalent. The approach followed in the study involves searching for potential predictors, applying principal component analysis (PCA) and multiple linear regression (MLR) for forecasting at individual sites, canonical correlation analysis (CCA) for forecasting at multiple sites, and testing the forecasts using various performance measures. The analysis includes comparisons of forecasts by using various combinations of possible predictors, such as hydrologic, atmospheric, and oce- anic variables. The study brought into relevance the significant benefits of using atmospheric, oceanic, and hydrological predictors for long- range streamflow forecasting. It has been shown that forecasts based on PCA applied to individual sites give very good results for both seasonal and yearly timescales. We also found that although PCA has been applied on a site-by-site basis, the forecasts approximated well the historical cross correlations, although some underestimation was noted for two sites. Furthermore, the forecasts based on CCAwere less efficient than those based on PCA. DOI: 10.1061/(ASCE)HE.1943-5584.0000343. © 2011 American Society of Civil Engineers. CE Database subject headings: Forecasting; Streamflow; Stochastic processes; Colorado; Hydrologic data. Author keywords: Forecasting streamflows; Atmospheric/oceanic predictors; Flow prediction; Stochastic analysis; PCA; CCA.

Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested. The authors tested impact of uncertainty in daily precipitation on streamflow forecasts. In order to test the impact, a distributed hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble streamflows. The uncertainty in model inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider range of streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time lags between rainfall and runoff in this rainfall dominated basin. This ensemble streamflow generation framework was also applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method.

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.

Sea-level rise is not globally uniform. A combination of observations and climate-model simulations reveals a pattern of sea-level changes in the Indian Ocean, with a decrease in the southern tropical Indian Ocean and a rise elsewhere, that can be attributed to changes in the atmospheric overturning circulation. Global sea level has risen during the past decades as a result of thermal expansion of the warming ocean and freshwater addition from melting continental ice1. However, sea-level rise is not globally uniform1,2,3,4,5. Regional sea levels can be affected by changes in atmospheric or oceanic circulation. As long-term observational records are scarce, regional changes in sea level in the Indian Ocean are poorly constrained. Yet estimates of future sea-level changes are essential for effective risk assessment2. Here we combine in situ and satellite observations of Indian Ocean sea level with climate-model simulations, to identify a distinct spatial pattern of sea-level rise since the 1960s. We find that sea level has decreased substantially in the south tropical Indian Ocean whereas it has increased elsewhere. This pattern is driven by changing surface winds associated with a combined invigoration of the Indian Ocean Hadley and Walker cells, patterns of atmospheric overturning circulation in the north–south and east–west direction, respectively, which is partly attributable to rising levels of atmospheric greenhouse gases. We conclude that—if ongoing anthropogenic warming dominates natural variability—the pattern we detected is likely to persist and to increase the environmental stress on some coasts and islands in the Indian Ocean.

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

Multifaceted demands on western water supply, such as irrigation and mandated biological flows, coupled with climate variability are increasing the importance of supply forecasting to water managers. In this study, we develop and examine the accuracy of a seasonal ensemble streamflow forecasting model for the Yakima River Basin. The model incorporates large-scale climate information, related to the Pacific North American (PNA) pattern, with the objective of increasing the skill of the forecasts for water managers and stakeholders in the basin. Our study has found that spring runoff in the Yakima Basin is strongly correlated (correlation significant at the 5% significance level) with two of the large-scale circulation patterns associated with the PNA pattern from the preceding fall and winter seasons. Incorporating such climate information into our forecasts allowed a longer lead-time (a season in advance) planning period for water managers and stakeholders from the current practice of an April 1st forecast. The ensemble nature of our streamflow forecasts provides continuous probability distributions that will help the decision maker to objectively quantify the associated risk with selected streamflow values.

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