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Paul Block and Balaji Rajagopalan

Abstract

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.

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Balaji Rajagopalan, Michael E. Mann, and Upmanu Lall

Abstract

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.

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Balaji Rajagopalan, Upmanu Lall, and Mark A. Cane

Abstract

There has been an apparent increase in the frequency and duration of El Niño–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 Niño–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 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.

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Daniel Broman, Balaji Rajagopalan, and Thomas Hopson

Abstract

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.

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Yves M. Tourre, Balaji Rajagopalan, and Yochanan Kushnir

Abstract

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 frequency also displays an out-of-phase relationship in the SSTs north and south of the mean position of the intertropical convergence zone (ITCZ). This short-lived structure, lasting for approximately two years, supports the argument that a tropical SST dipole pattern is one of the characteristics of the quasi-decadal signal. All five frequency bands represent to some extent fluctuations of the North Atlantic oscillation and are associated with tropical Atlantic Ocean warming (cooling) with different spatial evolution. The two interannual bands show opposite SST evolution to the south of the ITCZ, that is, southeastward evolution from the western tropical Atlantic for the 3.5-yr period and westward spreading from the eastern tropical Atlantic for the 4.4-yr period. Moreover, a significant coherence (with a 1-yr phase lag) is found between the SST time series along the equatorial Atlantic obtained from the 3.5-yr period, and the SST time series in the NINO3 area in the Pacific. It is cautiously argued that the 3.5-yr period is largely associated with the global El Niño–Southern Oscillation phenomenon, while the evolution of the 4.4-yr period depends more upon Atlantic local conditions.

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Balaji Rajagopalan, Upmanu Lall, and Stephen E. Zebiak

Abstract

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 objective function are also obtained, and are found to be very similar to the likelihood-based results.

The procedure is extended to the optimal combination of forecasts from multiple GCMs. An application of the method is presented for global, seasonal precipitation and temperature forecasts in two different seasons, based on 41 yr of observational and model simulation data. The multimodel combination skill is significantly better than climatology skill in only a few regions of the globe, but is generally an improvement over individual models, and over a simple average of forecasts from different models. Limitations and possible improvements of the methodology are discussed.

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Nkrintra Singhrattna, Balaji Rajagopalan, K. Krishna Kumar, and Martyn Clark

Abstract

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.

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Katrina Grantz, Balaji Rajagopalan, Martyn Clark, and Edith Zagona

Abstract

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.

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Martyn Clark, Subhrendu Gangopadhyay, Lauren Hay, Balaji Rajagopalan, and Robert Wilby

Abstract

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 days) when there is high accuracy in the forecasts. At longer forecast lead times, the downscaled spatial correlations are close to zero. Similarly, the observed temporal persistence is only partly present at short forecast lead times.

A method is presented for reordering the ensemble output in order to recover the space–time variability in precipitation and temperature fields. In this approach, the ensemble members for a given forecast day are ranked and matched with the rank of precipitation and temperature data from days randomly selected from similar dates in the historical record. The ensembles are then reordered to correspond to the original order of the selection of historical data. Using this approach, the observed intersite correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology also has applications for recovering the space–time variability in modeled streamflow.

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Jessica S. Kenigson, Weiqing Han, Balaji Rajagopalan, Yanto, and Mike Jasinski

Abstract

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.

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