Seasonal Precipitation Prediction Research Articles

Understanding Pacific Ocean influence on interannual precipitation variability in the Sahel

AbstractMoisture budget decomposition is performed for the Sahel (10°–20°N and 20°W–40°E) in order to understand the processes that govern regional hydroclimate variability on interannual time scales and frame them in the context of their primary ocean driver. Results show that warm conditions in the Eastern Tropical Pacific remotely force anomalously dry conditions primarily through affecting the low‐troposphere mass divergence field. This behavior is related to increased subsidence over the tropical Atlantic and into the Sahel and an anomalous westward flow of moisture from the continent, both resulting in a coherent drying pattern. Understanding the physical processes relating remote sea surface temperature anomalies to atmospheric circulation changes and the resulting complex local convergence patterns is important for advancing seasonal prediction of precipitation over West Africa.

The effect of two types of El Niño on the southerly low‐level jets in North America

AbstractLow‐level jets (LLJs) are frequent weather phenomena in many regions of North America and have profound impacts on precipitation and wind energy. We used a 31 year (1979–2010) three‐hourly reanalysis data set to examine the teleconnection between southerly LLJ activity in North America and the two dominant patterns of the equatorial Pacific Ocean sea surface temperature anomalies characterized by El Niño and El Niño Modoki. We show that El Niño and El Niño Modoki exert different effects on the jet activities, and the results vary by region and by season. Overall, El Niño Modoki affects jet activity all year round, but El Niño's influence is limited mostly to the cold season (October–March). El Niño Modoki induces larger changes in jet frequency, but El Niño's influence extends to larger regions. A better understanding of this teleconnection can be used to improve seasonal predictions of precipitation and wind energy resources in regions of North America.

A hybrid statistical‐dynamical framework for meteorological drought prediction: Application to the southwestern United States

AbstractImproving water management in water stressed‐regions requires reliable seasonal precipitation predication, which remains a grand challenge. Numerous statistical and dynamical model simulations have been developed for predicting precipitation. However, both types of models offer limited seasonal predictability. This study outlines a hybrid statistical‐dynamical modeling framework for predicting seasonal precipitation. The dynamical component relies on the physically based North American Multi‐Model Ensemble (NMME) model simulations (99 ensemble members). The statistical component relies on a multivariate Bayesian‐based model that relates precipitation to atmosphere‐ocean teleconnections (also known as an analog‐year statistical model). Here the Pacific Decadal Oscillation (PDO), Multivariate ENSO Index (MEI), and Atlantic Multidecadal Oscillation (AMO) are used in the statistical component. The dynamical and statistical predictions are linked using the so‐called Expert Advice algorithm, which offers an ensemble response (as an alternative to the ensemble mean). The latter part leads to the best precipitation prediction based on contributing statistical and dynamical ensembles. It combines the strength of physically based dynamical simulations and the capability of an analog‐year model. An application of the framework in the southwestern United States, which has suffered from major droughts over the past decade, improves seasonal precipitation predictions (3–5 month lead time) by 5–60% relative to the NMME simulations. Overall, the hybrid framework performs better in predicting negative precipitation anomalies (10–60% improvement over NMME) than positive precipitation anomalies (5–25% improvement over NMME). The results indicate that the framework would likely improve our ability to predict droughts such as the 2012–2014 event in the western United States that resulted in significant socioeconomic impacts.

Precipitation trends and teleconnections identified using quantile regressions over Xinjiang, China

ABSTRACTPrecipitation in Xinjiang, China, was modelled with covariates, such as time and climate indices, using quantile regressions. Compared to a frequentist quantile regression, a Bayesian quantile regression tended to generate smoother and narrower band confidence intervals of quantile regression coefficients, especially at extremely high and low quantile levels. A full picture of temporal trends at quantile levels from 0.01 to 0.99 indicates that the wet season (May to August) precipitation in Northern Xinjiang and the western regions of Southern Xinjiang showed statistically significant increases with different magnitudes over all quantile levels. However, the wet season precipitation in South‐eastern Xinjiang decreased at some quantile levels. The Eastern Atlantic/Western Russia (EAWR) pattern was the most significant large‐scale climate pattern that influenced wet season precipitation when compared to other studied patterns, i.e. the El Niño–Southern Oscillation (ENSO), the Atlantic Multidecadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), the Northern Oscillation (NO), the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO). The quantile regression coefficients associated with the EAWR index positively increased from low to high quantile levels. The ENSO significantly affected the extremely high wet season precipitation in Xinjiang. El Niño increased and La Niña decreased wet season precipitation in Northern Xinjiang, with different magnitudes at different quantile levels. Other climate patterns, i.e. the AMO, PDO, NO, NAO and AO, did not evidently affect the wet season precipitation conditional on the ENSO and EAWR. These findings suggest that the predictability of seasonal precipitation over Xinjiang can be improved by incorporating indices associated with the ENSO and EAWR as model predictors.

Toward a seasonal precipitation prediction system for West Africa: Performance of CFSv2 and high‐resolution dynamical downscaling

AbstractSeasonal precipitation forecasts are important sources of information for early drought and famine warnings in West Africa. This study presents an assessment of the monthly precipitation forecast of the Climate Forecast System version 2 (CFSv2) for three agroecological zones (Sudan‐Sahel, Sudan, and Guinean zone) of the Volta Basin. The CFSv2 performance is evaluated for the Sahel drought 1983 and for all August months of the reforecast period (1982–2009) with lead times up to 8 months using a quantile‐quantile transformation for bias correction. In addition, an operational experiment is performed for the rainy season 2013 to analyze the performance of a dynamical downscaling approach for this region. Twenty‐two CFSv2 ensemble members initialized in February 2013 are transferred to a resolution of 10 km × 10 km using the Weather and Research Forecasting (WRF) model. Since the uncorrected CFSv2 precipitation forecasts are characterized by a high uncertainty (up to 175% of the observed variability), the quantile‐quantile transformation can clearly reduce this overestimation with the potential to provide skillful and valuable early warnings of precipitation deficits and excess up to 6 months in ahead, particularly for the Sudan‐Sahel zone. The operational experiment illustrates that CFSv2‐WRF can reduce the CFSv2 uncertainty (up to 69%) for monthly precipitation and the onset of the rainy season but has still strong deficits regarding the northward progression of the rain belt. Further studies are necessary for a more robust assessment of the techniques applied in this study to confirm these promising outcomes.

Seasonal prediction of precipitation over Nigeria

The availability of quantitative means of probing anticipated rainfall is essential for the purpose of planning and policy formulation everywhere in the world. However reliable prediction of rainfall remains a challenge over West Africa and Nigeria in particular. This paper predicts sea-sonal rainfall in Nigeria with the hope of increasing the reliability of predicted seasonal rainfall. Seasonal rainfall data (June-September) from 21 Nigerian stations and monthly Extended Re-constructed Sea Surface Temperature (ERSST) of the tropical Oceans spanning 1960-2012 con-stituted the input data for the formulation of prediction equations using regression analysis. Cross-validation was applied with training period of 1960-1985, verification period of 1986-2012 and vice versa. The correlations between the predicted and the observed seasonal rainfall of the training and verification periods of 1960-1985 were all significant at 5 % level except for one station. While for the training and verification periods of 1986-2012, correlations are not gener-ally significant although some were found to be above the significant level of 5%. For the recon-structed seasonal precipitation, correlations of ten stations were found to be significant at 5 %, but at 10 % level, their number increased to 13. The t-test analysis revealed that there is no sig-nificant difference between the means of predicted and observed seasonal rainfall amount for all stations except for only three stations (Osogbo, Calabar and Enugu). This suggests some degree of skill which is also an indication that Sea Surface Temperature (SST) is a good predictor of June, July, August and September (JJAS) seasonal rainfall in Nigeria. Keywords : Rainfall, Prediction, Tropical Ocean, Cross-validation, Sea Surface Temperature

Seasonal prediction of East Asian monsoon precipitation: skill sensitivity to various climate variabilities

The sensitivity of the East Asian Monsoon (EAM) precipitation prediction skill to the variabilities of the tropical Indian and Pacific sea surface temperature (SST), the North Atlantic Oscillation (NAO), and the heating and snow cover over the Tibetan Plateau is evaluated in the framework of a linear Markov model. It is found that the tropical Indo-Pacific SST helps to improve the prediction of EAM precipitation over oceanic regions and some localized areas over land, while the Indian Ocean alone does not show significant impact on prediction. The remote effects of NAO improve the prediction in the middle and lower reaches of the Yangtze and Huaihe River valleys in boreal spring, fall and winter, and over oceanic areas in boreal summer and fall. The predictive skill of our model is not sensitive to the inclusion of the sensible heating and snow cover over the Tibetan Plateau, probably because their effects are implicitly present in the original EAM model.

The Attribution of Land–Atmosphere Interactions on the Seasonal Predictability of Drought

Abstract Drought has significant social and economic impacts that could be reduced by preparations made possible through seasonal prediction. During the convective season, when the potential of extreme drought is the highest, the soil moisture can provide a means of improved predictability through land–atmosphere interactions. In the past decade, there has been a significant amount of work aimed at better understanding the predictability of land–atmosphere interactions. One such approach classifies the interactions between the land and the atmosphere into coupling states. The coupling states have been shown to be persistent and were used to demonstrate the existence of strong biases in the coupling of the NCEP Climate Forecast System, version 2 (CFSv2). In this work, the attribution of the coupling state on the seasonal prediction of precipitation and temperature and the extent to which the bias in the coupling state hinders the prediction of drought is analyzed. This analysis combines the predictions from statistical models with the predictions from CFSv2 as a means to isolate and attribute the predictability. The results indicate that the intermountain region is a hotspot for seasonal prediction because of local persistence of initial conditions. In addition, the local persistence of initial conditions provides some level of drought prediction; however, accounting for the spatial interactions provides a more complete prediction. Furthermore, the statistical models provide more skillful predictions of precipitation during drought than the CFSv2; however, the CFSv2 predictions are more skillful for daily maximum temperature during drought. The implication, limitations, and extensions of this work are also discussed.

Improved Seasonal Prediction of Temperature and Precipitation over Land in a High-Resolution GFDL Climate Model

Abstract This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

Improvement of CGCM prediction for wet season precipitation over Maritime Continent using a bias correction method

ABSTRACTA new model output statistics method – Ensemble Selective Simple Linear Regression (E‐SSLR) – is developed based on SLR in order to increase the seasonal prediction skill of a Coupled General Circulation Model (CGCM) over the Maritime Continent (MC), a region with large model simulation errors. E‐SSLR is applied to Pusan National University (PNU) CGCM hindcast over the MC region for the period of 1981–2010 to reduce the systematic model bias in boreal winter (DJF) seasonal mean precipitation and outgoing long‐wave radiation (OLR) anomalies. Three oceanic indices (Nino 3.4, El Nino Modoki and Indian Ocean Dipole (IOD) Mode indices) and one atmospheric index (Southern Oscillation Index, SOI) produced from PNU CGCM hindcast are used as SLR predictor. E‐SSLR consists of three steps: Selection, SLR and Ensemble. The selection and ensemble steps are added to the conventional SLR step to overcome the weakness of the linear regression method. In the selection step, the grids with a temporal correlation coefficient between predictor and predictand exceeding the threshold are selected. These grids (grid‐selected) are corrected by SLR in the second step. For the grids that are grid‐not‐selected, the original CGCM results are used without further correction. This prevents insignificant statistical correction due to the application of low correlated predictors to the SLR. The correction effect of E‐SSLR is analysed in terms of deterministic and categorical analyses. The result shows that the seasonal predictability of DJF seasonal precipitation and OLR in the MC region is increased by using E‐SSLR, and this increment is statistically significant. The correction effect is larger when indices with high predictability that are closely correlated with the predictand are used as predictors.

A possible link between wildfire aerosol and North American Monsoon precipitation in Arizona–New Mexico

ABSTRACTPrevious research highlights the dominant role of Pacific sea surface temperatures (SSTs) and their associated large‐scale teleconnections in modulating the North American monsoon (NAM). At the regional scale, feedbacks associated with land‐surface boundary conditions have been shown to provide ‘memory’ in the system. Here, a previously unexplored second‐order linkage between aerosol generated by late‐spring wildfires and subsequent summer precipitation delivered by the NAM in the Arizona‐New Mexico (AZNM) region is proposed. Correlations between June/July organic carbon and elemental carbon (OC/EC) in Inter‐agency Monitoring of Protected Visual Environments (IMPROVE) aerosol data (Gila Wilderness and Bandelier) and NAM precipitation in southern AZNM over the period 1994–2012 are shown to be negative (r = –0.4), suggesting that active antecedent wildfire seasons tend to be immediately followed by a weak late summer monsoon. This result is consistent with a previous study linking precipitation and area burned in wildfires in AZNM. A survey of extant literature suggests a sound basis for potential mechanisms related to convective processes and cloud microphysics, and furthermore suggests that this forcing could be of similar magnitude to well‐documented land‐based second‐order NAM forcings (antecedent snowpack and soil moisture, vegetation, and mineral dust). Based on these results, we believe that the role of aerosols in modulating summer precipitation deserves further investigation both observationally and in modeling studies. If indeed wildfire smoke does contribute to the modulation of NAM intensity, by virtue of its close temporal association with NAM season, it may well represent a factor that could contribute effectively to improved seasonal prediction of summer precipitation in the NAM region.

Evaluating Skill of Seasonal Precipitation and Temperature Predictions of NCEP CFSv2 Forecasts over 17 Hydroclimatic Regions in China

Abstract Seasonal predictions of precipitation and surface air temperature from the Climate Forecast System, version 2 (CFSv2), are evaluated against gridded daily observations from 1982 to 2007 over 17 hydroclimatic regions in China. The seasonal predictive skill is quantified with skill scores including correlation coefficient, RMSE, and mean bias for spatially averaged seasonal precipitation and temperature forecasts for each region. The evaluation focuses on identifying regions and seasons where significant skill exists, thus potentially contributing to skill in hydrological prediction. The authors find that the predictive skill of CFSv2 precipitation and temperature forecasts has a stronger dependence on seasons and regions than on lead times. Both temperature and precipitation forecasts show higher skill from late summer [July–September (JAS)] to late autumn [October–December (OND)] and from winter [December–February (DJF)] to spring [March–May (MAM)]. The skill of CFSv2 precipitation forecasts is low during summer [June–August (JJA)] and winter (DJF) over all of China because of low potential predictability of the East Asian summer monsoon and the East Asian winter monsoon for China. As expected, temperature predictive skill is much higher than precipitation predictive skill in all regions. As observed precipitation shows significant correlation with the Oceanic Niño index over western, southwestern, and central China, the authors found that CFSv2 precipitation forecasts generally show similar correlation pattern, suggesting that CFSv2 precipitation forecasts can capture ENSO signals. This evaluation suggests that using CFSv2 forecasts for seasonal hydrological prediction over China is promising and challenging.

A Framework for Dynamical Seasonal Prediction of Precipitation over the Pacific Islands

Abstract A three-step approach to develop a framework for dynamical seasonal prediction of precipitation over the U.S. Affiliated Pacific Islands (USAPI) is adopted. First, guided by the climatological features of basic variables, a view that climates of the USAPI are connected by large-scale phenomena involving the warm pool, South Pacific convergence zone, tropical monsoons, and subtropical anticyclone is proposed. Second, prediction skill in ensemble hindcasts performed with the Climate Forecast System, version 2 (CFSv2), is evaluated with the hypothesis that ENSO is the leading candidate for large and persisting precipitation departures. Third, moist static energy budget diagnostics are performed to identify physical processes responsible for precipitation anomalies. At leads of 0–6 months, CFSv2 demonstrates useful skill in predicting Niño-3.4 SST and equatorial Pacific precipitation anomalies. During El Niño, positive precipitation anomalies along the central (eastern) equatorial Pacific are anchored by net radiative flux (Frad) and moist advection (evaporation and Frad). The model’s skill in predicting precipitation anomalies over South Pacific (Hawaiian) islands is highest (lowest). Over the west Pacific islands, the skill is low during the rainy season. During El Niño, skill over the USAPI, in particular predicting dryness persistence at long leads is useful. Suppressed precipitation over the Hawaiian and South Pacific (west Pacific) islands are determined by anomalous dry and cold air advection (reduced evaporation and Frad). These processes are local, but are dictated by circulation anomalies forced by ENSO. Model budget estimates are qualitatively consistent with those obtained from reanalysis, boosting confidence for societal benefits. However, observational constraints, as well as budget residuals, pose limitations.

Application of Statistical Models to the Prediction of Seasonal Rainfall Anomalies over the Sahel

AbstractRainfall in the Sahel region of Africa is prone to large interannual variability, and it has exhibited a recent multidecadal drying trend. The well-documented social impacts of this variability have motivated numerous efforts at seasonal precipitation prediction, many of which employ statistical techniques that forecast Sahelian precipitation as a function of large-scale indices of surface air temperature (SAT) anomalies, sea surface temperature (SST), surface pressure, and other variables. These statistical models have demonstrated some skill, but nearly all have adopted conventional statistical modeling techniques—most commonly generalized linear models—to associate predictor fields with precipitation anomalies. Here, the results of an artificial neural network (ANN) machine-learning algorithm applied to predict summertime (July–September) Sahel rainfall anomalies using indices of springtime (April–June) SST and SAT anomalies for the period 1900–2011 are presented. Principal component analysis was used to remove multicollinearity between predictor variables. Predictive accuracy was assessed using repeated k-fold random holdout and leave-one-out cross-validation methods. It was found that the ANN achieved predictive accuracy superior to that of eight alternative statistical methods tested in this study, and it was also superior to that of previously published predictive models of summertime Sahel precipitation. Analysis of partial dependence plots indicates that ANN skill is derived primarily from the ability to capture nonlinear influences that multiple major modes of large-scale variability have on Sahelian precipitation. These results point to the value of ANN techniques for seasonal precipitation prediction in the Sahel.

Spatiotemporal variability of Alberta's seasonal precipitation, their teleconnection with large-scale climate anomalies and sea surface temperature

Using wavelet analysis, wavelet-based principal component analysis (WPCA), composite analysis and scale-averaged wavelet power (SAWP) of seasonal precipitation, we investigated their spatiotemporal variability, their relationships with seven large-scale climate indices and atmospheric circulation patterns, sea surface temperature (SST) of the Pacific and Atlantic oceans, and the predictability of Alberta's seasonal precipitation. June–August (JJA) precipitation showed statistically significant oscillations at about 16–20 year, while September–November (SON) precipitation at about 4–8 year time scale. Plots showing clusters of annular contour lines for positive and negative Morlet wavelet coefficients, respectively represent cycles of abundant and sparse seasonal precipitation that had occurred over 1900–2011. Wavelet power spectrum plots show that seasonal climate indices and principal component (PC1) of precipitation anomalies exhibited dominant oscillations that appeared and disappeared in an unpredictable manner. Wavelet coherence analysis shows that Alberta's precipitation had been linked more to Nino3, Pacific decadal Oscillation (PDO) and Pacific/North American (PNA) than other climate indices, and in the 8–25 year than inter-annual time scales. Similar results are found from Pearson's correlation between PC1 of band-passed, zero to three seasonal lags, of Alberta's seasonal precipitation and climate anomalies. Aggregate composites of Alberta's December–February (DJF) precipitation showed that the influences of El Nino Southern Oscillation (ENSO), PDO and PNA had been stronger in southern than in northern Alberta. El Nino (La Nina), warm PDO (cool PDO) and high PNA (low PNA) are associated mainly with sparse and abundant DJF precipitation of southern Alberta. Prevalent atmospheric circulation patterns provide physical explanations on the effects of climate anomaly on the DJF precipitation revealed by the composite analysis.

A Comparison of Statistical Approaches for Seasonal Precipitation Prediction in Pakistan

Abstract The present study focuses on two statistical approaches for improving seasonal precipitation prediction skills for Pakistan. Precipitation over Pakistan is concentrated in July–August (JA), when droughts and floods occur recurrently and cause disasters. Empirical orthogonal function (EOF) analysis is used to assess spatial patterns of precipitation, and two precipitation patterns are identified: a consistent pattern and a north–south dipole pattern. Two statistical approaches, the statistical regression method using prewinter predictors and statistical downscaling, are employed to perform rainfall predictions for JA in Pakistan. Linear regression (LR) and optimal subset regression (OSR) are used for each approach, and the regression forecast methods are compared with the raw model outputs. Historical data for large-scale variables from the NCEP–NCAR reanalysis and version 1.0 of the coupled atmosphere–ocean general circulation model from the Beijing Climate Center (CGCM1.0/BCC) outputs in 1986–2011 are used as predictors for the statistical prewinter method and statistical downscaling, respectively. In the majority of the years, the statistical prewinter method and statistical downscaling are able to correct the erroneous signs of the raw dynamical model output for the consistent pattern. The statistical prewinter method is found to provide more skillful predictions than the statistical downscaling on the prediction of the dipolelike pattern. The best prediction skills for the consistent pattern and dipolelike pattern are provided by NCEP-OSR and NCEP-LR, which have significant correlations of 0.39 and 0.40, respectively. For all the forecast methods in this study, prewinter prediction and downscaled prediction show considerable improvements when compared with model output. These statistical methods provide valuable approaches for studying local climates.

Dynamical seasonal prediction of Southern African summer precipitation

Prediction skill for southern African (16°–33°E, 22°–35°S) summer precipitation in the Scale Interaction Experiment-Frontier coupled model is assessed for the period of 1982–2008. Using three different observation datasets, deterministic forecasts are evaluated by anomaly correlation coefficients, whereas scores of relative operating characteristic and relative operating level are used to evaluate probabilistic forecasts. We have found that these scores for December–February precipitation forecasts initialized on October 1st are significant at 95 % confidence level. On a local scale, the level of prediction skill in the northwestern and central parts of southern Africa is higher than that in northeastern South Africa. El Nino/Southern Oscillation (ENSO) provides the major source of predictability, but the relationship with ENSO is too strong in the model. The Benguela Nino, the basin mode in the tropical Indian Ocean, the subtropical dipole modes in the South Atlantic and the southern Indian Oceans and ENSO Modoki may provide additional sources of predictability. Within the wet season from October to the following April, the precipitation anomalies in December-February are the most predictable. This study presents promising results for seasonal prediction of precipitation anomaly in the extratropics, where seasonal prediction has been considered a difficult task.

Understanding Prediction Skill of Seasonal Mean Precipitation over the Tropics

Abstract The connection between the local SST and precipitation (SST–P) correlation and the prediction skill of precipitation on a seasonal time scale is investigated based on seasonal hindcasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2). The results demonstrate that there is good correspondence between the two: precipitation skill is generally high only over the regions where SST–P correlation is positive and is low where SST–P correlation is small or weakly negative. This result has fundamental implications for understanding the limits of precipitation predictability on seasonal time scale and helps explain spatial variations in the skill of seasonal mean precipitation. Over the regions where atmospheric variability drives the ocean variability (and consequently the local SST–P correlation is weakly negative), the inherently unpredictable nature of atmospheric variability leads to low predictability for seasonal precipitation. On the other hand, over the regions where slow time scale ocean variability drives the atmosphere (and the local SST–P correlation is large positive), the predictability of seasonal mean precipitation is also high.

Potential seasonal predictability: Comparison between empirical and dynamical model estimates

Methods for estimating potential seasonal predictability from a single realization of daily data are validated against an ensemble of simulations from an atmospheric model driven by observed sea surface temperature, sea ice extent, and greenhouse gas concentration. The methods give surprisingly good estimates of potential predictability of seasonal precipitation despite the fact that the methods assume Gaussian distributions. For temperature, the methods systematically underestimate weather noise variance over land, often by a factor of 2 or more. This bias can be reduced by taking account of precipitation‐induced variability. These conclusions may be model dependent, and hence, confirmation in other models would be of interest. Nevertheless, for the state‐of‐the‐art atmospheric model used in this study, the results strongly support the validity of the single time series approach to estimating potential predictability and enhances our confidence in previous estimates of potential predictability based on observations alone.

Case Study: ENSO Events, Rainfall Variability and the Potential of SOI for the Seasonal Precipitation Predictions in Iran

The studies in recent decades show that many natural disasters such as tropical severe storms, hurricanes development, torrential rain, river flooding, and landslides in some regions of the world and severe droughts and wildfires in other areas are due to El Nino-Southern Oscillation (ENSO). This research aims to contribute to an improved definition of the relation between ENSO and seasonal (autumn and winter) variability of rainfall over Iran. The results show that during autumn, the positive phase of SOI is associated with decrease in the rainfall amount in most part of the country; negative phase of SOI is associated with a significant increase in the rainfall amount. It is also found that, during the winter time when positive phase of SOI is dominant, winter precipitation increases in most areas of the eastern part of the country while at the same time the decreases in the amount of rainfall in other parts is not significant. Moreover, with negative phase of SOI in winter season the amount of rainfall in most areas except south shores of Caspian Sea in the north decreases, so that the decrease of rainfall amount in the eastern part is statistically significant.