Simulating future Caspian sea level changes using regional climate model outputs

We report on simulations of present-day climate (1961–1990) and future climate conditions (2071–2100, Special Report on Emissions Scenario A2) over the Caspian sea basin with a regional climate model (RCM) nested in time-slice general circulation model (GCM) simulations. We also calculate changes (A2 scenario minus present-day) in Caspian sea level (CSL) in response to changes in the simulated hydrologic budget of the basin. For the present-day run, both the GCM and RCM show a good performance in reproducing the water budget of the basin and the magnitude of multi-decadal changes in CSL. Compared to present-day climate, in the A2 scenario experiment we find an increase in cold season precipitation and an increase in temperature and evaporation, both over land and over the Caspian sea. We also find a large decrease of CSL in the A2 scenario run compared to the present-day run. This is due to increased evaporation loss from the basin (particularly over the sea) exceeding increased cold season precipitation over the basin. Our results suggest that the CSL might undergo large changes under future climate change, leading to potentially devastating consequences for the economy and environment of the region.     

Simulating multi-decadal variability of Caspian Sea level changes using regional climate model outputs

The Caspian Sea is the largest enclosed body of water on earth, covering approximately 4×10⁵ km² and sharing its coast with five countries (Iran, Azerbaijan, Kazakhstan, Russia and Turkmenistan). Because it has no outlet to the ocean the Caspian Sea level (CSL) has undergone rapid shifts in response to climatic forcings, and these have been devastating for the surrounding countries. In this paper we present the initial results of a modeling effort aimed at building a regional climate model for the Caspian Sea basin suitable to study the response of the CSL to interdecadal climate variability and anthropogenic climate change. Simulations are performed using the International Centre for Theoretical Physics (ICTP) regional climate model RegCM at a 50 km grid spacing for the period 1948–1990. During this period an abrupt shift occurred in the sea level after 1977, when the CSL rose about two meters until the early 1990s. Using a simple equation of hydrologic balance for the Caspian Sea basin to predict the CSL, we show that the model is able to reproduce the observed CSL changes at interannual to multidecadal scales. The correlation coefficient between the simulated and observed annual CSL changes is 0.91 and the model is able to reproduce the abrupt shift in CSL which occurred after 1977. Analysis of the climatologies before and after 1977 indicate that the CSL rise was mostly due to an increase in precipitation over the northern basin and a decrease in evaporation over the sea, primarily during the warm season. We plan to apply our model to the investigation of the response of the CSL to anthropogenic climate forcings.     

Long-term and intraannual changes in run-off and water levels in the Volga River delta

Data of the State Observation Network on the water run-off for the whole observation period and on water levels in the delta of the Volga River for the period of regulated run-off regime (1961–2006) are analyzed. Periods of various water content are revealed and the current tendency of long-term run-off changes is established with the help of difference-integral curve of recovered natural annual run-off. Periods of various degree of man’s impact on the run-off, entering the delta of the Volga River, are marked out. The role of irretrievable anthropogenic loss and the influence of run-off regulation on its intraannual distribution are assessed. Regularities of seasonal and long-term water level changes in the delta of the Volga River are revealed. The run-off regulation effect on the intraannual distribution of water levels is assessed. The influence of the water divider on the redistribution of the run-off and water levels in the delta is shown. The effect of the current increase in the Caspian Sea level on the penetration of the backwater into the delta of the Volga River is revealed.     

A Regression-based Assessment of the Predictability of New Zealand Climate Anomalies

Summary Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using a cross-validated linear regression procedure. Predictors are indices of the large scale circulation, sea-surface temperatures, the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials. No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonal time scale. Temperature forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10–20% in independent trials. Temperature anomalies are most skilfully predicted over the North Island, and skill is greatest in Spring and Summer in most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill, while at the seasonal time scale, skill depends strongly upon “remote” predictors defined over regions of the southern hemisphere distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors, especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal predictions. Forecast skill is more reliably estimated at the monthly time scale than at the seasonal time scale, as a result of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore, even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are discussed. Received July 18, 1997 Revised April 2, 1998     

An ensemble forecast of the South China Sea monsoon

1.IntroductionThispaperexploresanensembleforecaststrategyforthelarge--scaletropicalpredictionproblem.Thisisgeneralizedfromarecentstudyontheuseofempiricalorthogonalfunction(EOF)--basedperturbationsforhurricanetrackensembleforecasts,(ZhangandKrishnamur...     

季度预测研究回顾

对月平均大气环流预报试验、季度预测和中国汛期降水预测进行了总结。结果表明气候预测的对象必须是要素的时间平均场。利用数值模拟进行气候预测是今后的主要发展方向,而季度预测技巧的提高依赖于对物理参数化和物理机制的研究。最后,讨论了季平均气温和季总降水的可预报性问题,即时效性和准确率。     

Seasonal Predictions of Summer Precipitation in the Middle-lower Reaches of the Yangtze River with Global and Regional Models Based on NUIST-CFS1.0

Accurate prediction of the summer precipitation over the middle and lower reaches of the Yangtze River(MLYR) is of urgent demand for the local economic and societal development. This study assesses the seasonal forecast skill in predicting summer precipitation over the MLYR region based on the global Climate Forecast System of Nanjing University of Information Science and Technology(NUIST-CFS1.0, previously SINTEX-F). The results show that the model can provide moderate skill in predicting the i...     

Improving long-lead seasonal forecasts of precipitation over Southern China based on statistical downscaling using BCC_CSM1.1m

Long-lead precipitation forecasts for 1–4 seasons ahead are usually difficult in dynamical climate models due to the model deficiencies and the limited persistence of initial signals. But, these forecasts could be empirically improved by statistical approaches. In this study, to improve the seasonal precipitation forecast over the southern China (SC), the statistical downscaling (SD) models are built by using the predictors of atmospheric circulation and sea surface temperature (SST) simulated by the Beijing Climate Center Climate System Model version 1.1 m (BCC_CSM1.1 m). The different predictors involved in each SD model is selected based on both its close relationship with the target seasonal precipitation and its reasonable prediction skill in the BCC_CSM1.1 m. Cross and independent validations show the superior performance of the SD models, relative to the BCC_CSM1.1 m. The temporal correlation coefficient of SD models could reach > 0.4, exceeding the 95 % confidence level. The SC precipitation index can be much better forecasted by the SD models than by the BCC_CSM1.1 m in terms of the interannual variability. In addition, the errors of the precipitation forecast in all four seasons are significantly reduced over most of SC in the SD models. For the 2015/2016 strong El Niño event, the SD models outperform the dynamical BCC_CSM1.1 m model on the spatial and regional-average precipitation anomalies, mostly due to the effective SST predictor in the SD models and the weak response of the SC precipitation to El Niño-related SST anomalies in the BCC_CSM1.1 m.     

Forecasts of hydrological parameters over the Mackenzie River Basin: Sensitivity to initial conditions,horizontal resolution and forecast range

Abstract

As part of the Global Energy and Water Cycle Experiment, Canadian global spectral forecast model predictions of surface water and energy fluxes over the Mackenzie River basin are examined. Two nine‐member ensemble forecasts of one month duration are produced with the operational model, for a spring and a summer case, at a horizontal resolution of about 100 km (T95). The sensitivity to initial conditions is measured by the degree to which the individual forecasts in the ensembles vary one from another. The evolution in time of this estimated error (ensemble standard deviation) is determined for the surface energy and water accumulations, averaged over the basin. For comparison the calculations are repeated for the Mississippi basin and over North America. The greatest sensitivity is found for the net accumulation of precipitation minus evaporation. The spring ensemble is redone at a coarser horizontal resolution (T47), and the results are similar. The forecast uncertainty (ensemble standard deviation) of the area‐averages over the basin appear to be unaffected by this change, although the ensemble mean values are sensitive to the change in resolution. The ensemble standard deviation makes a significant, abrupt increase toward the end of the second week into the forecasts. This investigation suggests a need for an improved model, if the forecasts’ useful range is to extend to one month. Available upgrades to the land‐surface, precipitation and evaporation schemes will be used in subsequent work, and the forecasts reported here will serve as a baseline for comparison.     

Soil moisture effects on seasonal temperature and precipitation forecast scores in Europe

The Second Global Land Atmosphere Coupling Experiment (GLACE2) is designed to explore the improvement of forecast skill of summertime temperature and precipitation up to 8?weeks ahead by using realistic soil moisture initialization. For the European continent, we show in this study that for temperature the skill does indeed increase up to 6 weeks, but areas with (statistically significant) lower skill also exist at longer lead times. The skill improvement is smaller than shown earlier for the US, partly because of a lower potential predictability of the European climate at seasonal time scales. Selection of extreme soil moisture conditions or a subset of models with similar initial soil moisture conditions does improve the forecast skill, and sporadic positive effects are also demonstrated for precipitation. Using realistic initial soil moisture data increases the interannual variability of temperature compared to the control simulations in the South-Central European area at longer lead times. This leads to better temperature forecasts in a remote area in Western Europe. However, the covered range of forecast dates (1986–1995) is too short to isolate a clear physical mechanism for this remote correlation.     

Characteristics of the water cycle and land–atmosphere interactions from a comprehensive reforecast and reanalysis data set: CFSv2

The behavior of the water cycle in the Coupled Forecast System version 2 reforecasts and reanalysis is examined. Attention is focused on the evolution of forecast biases as the lead-time changes, and how the lead-time dependent model climatology differs from the reanalysis. Precipitation biases are evident in both reanalysis and reforecasts, while biases in soil moisture grow throughout the duration of the forecasts. Locally, the soil moisture biases may shrink or reverse sign. These biases are reflected in evaporation and runoff. The Noah land surface scheme shows the necessary relationships between evaporation and soil moisture for land-driven climate predictability. There is evidence that the atmospheric model cannot maintain the link between precipitation and antecedent soil moisture as strongly as in the real atmosphere, potentially hampering prediction skill, although there is better precipitation forecast skill over most locations when initial soil moisture anomalies are large. Bias change with lead-time, measured as the variance across ten monthly forecast leads, is often comparable to or larger than the interannual variance. Skill scores when forecast anomalies are calculated relative to reanalysis are seriously reduced over most locations when compared to validation against anomalies based on the forecast model climate at the corresponding lead-time. When all anomalies are calculated relative to the 0-month forecast, some skill is recovered over some regions, but the complex manner in which biases evolve indicates that a complete suite of reforecasts would be necessary whenever a new version of a climate model is implemented. The utility of reforecast programs is evident for operational forecast systems.     

Seasonal prediction of African precipitation with ECHAM4–T42 ensemble simulations using a multivariate MOS re-calibration scheme

A 15 member ensemble of 20th century simulations using the ECHAM4–T42 atmospheric GCM is utilized to investigate the potential predictability of interannual variations of seasonal rainfall over Africa. Common boundary conditions are the global sea surface temperatures (SST) and sea ice extent. A canonical correlation analysis (CCA) between observed and ensemble mean ECHAM4 precipitation over Africa is applied in order to identify the most predictable anomaly patterns of precipitation and the related SST anomalies. The CCA is then used to formulate a re-calibration approach similar to model output statistics (MOS) and to derive precipitation forecasts over Africa. Predictand is the climate research unit (CRU) gridded precipitation over Africa. As predictor we use observed SST anomalies, ensemble mean precipitation over Africa and a combined vector of mean sea level pressure, streamfunction and velocity potential at 850 hPa. The different forecast approaches are compared. Most skill for African precipitation forecasts is provided by tropical Atlantic (Gulf of Guinea) SST anomalies which mainly affect rainfall over the Guinean coast and Sahel. The El Niño/Southern Oscillation (ENSO) influences southern and East Africa, however with a lower skill. Indian Ocean SST anomalies, partly independent from ENSO, have an impact particularly on East Africa. As suggested by the large agreement between the simulated and observed precipitation, the ECHAM4 rainfall provides a skillful predictor for CRU precipitation over Africa. However, MOS re-calibration is needed in order to provide skillful forecasts. Forecasts using MOS re-calibrated model precipitation are at least as skillful as forecast using dynamical variables from the model or instantaneous SST. In many cases, MOS re-calibrated precipitation forecasts provide more skill. However, differences are not systematic for all regions and seasons, and often small.     

Assessment of probability multimodel seasonal forecast based on the APCC model data

The probability multimodel forecast system based on the Asia-Pacific Economic Cooperation Climate Center (APCC) model data is verified. The winter and summer seasonal mean fields T ₈₅₀ and precipitation seasonal totals are estimated. To combine the models into a multimodel ensemble, the probability forecast is calculated for each of single models first, and then these forecasts are combined using the total probability formula. It is shown that the multimodel forecast is considerably more skilful than the single-model forecasts. The forecast quality is higher in the tropics compared to the mid- and high latitudes. The multimodel ensemble temperature forecasts outperform the random and climate forecasts for Northern Eurasia in the above- and below-normal categories. Precipitation forecast is less successful. For winter, the combination of single-model ensembles provides the precipitation forecast skill exceeding that of the random forecast for both Northern Eurasia and European Russia.     

Seasonal Prediction of Summer Precipitation over East Africa Using NUIST-CFS1.0

East Africa is particularly vulnerable to precipitation variability, as the livelihood of much of the population depends on rainfed agriculture. Seasonal forecasts of the precipitation anomalies, when skillful, can therefore improve implementation of coping mechanisms with respect to food security and water management. This study assesses the performance of Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUISTCFS1.0) on forecasting June–September(JJAS) seasonal precipitation anomalies over East Africa. The skill in predicting the JJAS mean precipitation initiated from 1 May for the period of 1982–2019 is evaluated using both deterministic and probabilistic verification metrics on grid cell and over six distinct clusters. The results show that NUIST-CFS1.0 captures the spatial pattern of observed seasonal precipitation climatology, albeit with dry and wet biases in a few parts of the region. The model has positive skill across a majority of Ethiopia, Kenya, Uganda, and Tanzania, whereas it doesn’t exceed the skill of climatological forecasts in parts of Sudan and southeastern Ethiopia. Positive forecast skill is found over regions where the model shows better performance in reproducing teleconnections related to oceanic SST. The prediction performance of NUIST-CFS1.0 is found to be on a level that is potentially useful over a majority of East Africa.     

An Ensemble Seasonal Forecast of Human Cases of St. Louis Encephalitis in Florida Based on Seasonal Hydrologic Forecasts

We present a method for the ensemble seasonal prediction of human St. Louis encephalitis (SLE) incidence and SLE virus transmission in Florida. We combine empirical relationships between modeled land surface wetness and the incidence of human clinical cases of SLE and modeled land surface wetness and the occurrence of SLE virus transmission throughout south Florida with a previously developed method for generating ensemble, seasonal hydrologic forecasts. Retrospective seasonal forecasts of human SLE incidence are made for Indian River County, Florida, and forecast skill is demonstrated for 2–4 months. A sample seasonal forecast of human SLE incidence is presented. This study establishes the skill of a potential component of an operational SLE forecast system in south Florida, one that provides information well in advance of transmission and may enable early interventions that reduce transmission. Future development of this method and operational application of these forecasts are discussed. The methodology also will be applied to West Nile virus monitoring and forecasting.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China

National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.     

1998年中国大洪水时期的水汽收支研究

文中首先通过水汽通量的势函数和流函数的计算 ,分析了 1998年中国大洪水时期的全球水汽背景 ,然后从雨情分析入手 ,将 1998年 5～ 8月长江、松花江流域洪水期分为 7个降水阶段、11个区域 ,对各时段、各区域的水汽收支作了诊断分析 ,得到中国大洪水时期部分水汽收支图像 ,揭示了水汽循环的一些规律 ,主要结果如下 :( 1) 1998年 5～ 8月 ,中国东部地区是全球最强的水汽汇区 ,这与 1991年夏季的情况相似。水汽通量的势函数极小值区 (最大辐合区 )对应强降水区 ,并且暴雨区的水汽辐合是由半球尺度的水汽输送造成 ,这表明 ,即使对于区域性大洪水 ,它必须从极大范围地区获得水汽供应。分析还表明 ,南海季风的爆发及其区域内西南方向水汽流的增强与印度洋势函数 (水汽辐散 )的增强关系密切。( 2 )大气的水汽收支表明 ,降水主要来自水汽的辐合项 ,辐合主要发生在大气低层 ;用余差法计算出的局地蒸发项一般为降水量的 13 ～ 12 ,因而水汽的再循环过程也十分重要 ;垂直输送项把低层的水汽向中上层输送 ,增加高层的水汽积累 ,为积云的发展和潜热释放提供条件。( 3 )南海地区的水汽输送情况与中国强降水密切相关 ,南海季风爆发后 ,其强劲南风气流输送水汽的区域往往是强降水发生区。对于整个中国东部大陆区而言 ,来     

Seasonal climate prediction for South America with FSU Multi-model Synthetic Superensemble algorithm

Summary Objective combination schemes of predictions from different models have been applied to seasonal climate forecasts. These schemes are successful in producing a deterministic forecast superior to individual member models and better than the multi-model ensemble mean forecast. Recently, a variant of the conventional superensemble formulation was created to improve skills for seasonal climate forecasts, the Florida State University (FSU) Synthetic Superensemble. The idea of the synthetic algorithm is to generate a new data set from the predicted multimodel datasets for multiple linear regression. The synthetic data is created from the original dataset by finding a consistent spatial pattern between the observed analysis and the forecast data set. This procedure is a multiple linear regression problem in EOF space. The main contribution this paper is to discuss the feasibility of seasonal prediction based on the synthetic superensemble approach and to demonstrate that the use of this method in coupled models dataset can reduce the errors of seasonal climate forecasts over South America. In this study, a suite of FSU coupled atmospheric oceanic models was used. In evaluation the results from the FSU synthetic superensemble demonstrate greater skill for most of the variables tested here. The forecast produced by the proposed method out performs other conventional forecasts. These results suggest that the methodology and database employed are able to improve seasonal climate prediction over South America when compared to the use of single climate models or from the conventional ensemble averaging. The results show that anomalous conditions simulated over South America are reasonably realistic. The negative (positive) precipitation anomalies for the summer monsoon season of 1997/98 (2001/02) were predicted by Synthetic Superensemble formulation quite well. In summary, the forecast produced by the Synthetic Superensemble approach outperforms the other conventional forecasts.     

Hydro-climatic forecasting using sea surface temperatures: methodology and application for the southeast US

Sea surface temperatures (SSTs) are often used for the development of hydro-climatic variable forecasts based on teleconnection methods. Such methods rely on projections or linear combinations of teleconnection indices [e.g. El Niño-Southern Oscillation (ENSO)] and other predictor fields. This study introduces a new hydro-climatic forecasting method identifying SST “dipole” predictors motivated by major teleconnection patterns. An SST dipole is defined as a function of average SST anomalies over two oceanic areas of specific sizes and geographic locations. An optimization algorithm is developed to search for the most significant SST dipole predictors of an external hydro-climatic series based on the Gerrity Skill Score. The significant dipoles are cross-validated and used to generate multiple forecast values. The new method is applied to the forecasting of seasonal precipitation over the southeast US. Hindcasting results show that significant dipoles related to ENSO as well as other prominent patterns at different lead times can indeed be identified. The dipole method also compares favorably with existing statistical forecasting schemes with respect to multiple skill measures. Furthermore, an operational forecasting framework able to produce ensemble forecast traces and uncertainty intervals that can support regional water resources planning and management is also developed.