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Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous United States (CONUS) using environmental variables (convective available potential energy and precipitation) and National Lightning Detection Network data for 2003–16. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO–lightning relationship at daily resolution and the winter–spring ENSO–lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the coastal Southeast United States during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River valley during winter. Significance Statement: Cloud-to-ground lightning is dangerous for humans via direct strikes or through triggering wildfires, generating air pollution, etc. How lightning activity can be affected by climate remains unclear, and it is challenging to study their links because the data record for lightning is short. With the available lightning record, we developed a model that relates lightning flash counts over the United States to environmental factors. This model well represents observed fluctuations in daily and seasonal lightning over most of the United States. Because the model only needs environmental information to predict lightning flash counts, we were able to construct a longer record of predicted lightning based on the longer data record of environmental variables. With this dataset, we investigated the links between lightning and climate and found that the state of sea surface temperatures in the tropical Pacific (El Niño–Southern Oscillation) is linked to changes in U.S. lightning patterns in winter and spring. [ABSTRACT FROM AUTHOR]

Mesoscale convective cloud clusters develop and organize in the form of squall lines along the coastal Amazon in the afternoon hours and propagate inland during the evening hours. The frequency, location, organization into lines, and movement of the convective systems are determined by analyzing the "precipitation features" obtained from the TRMM satellite for the period 1998–2014. The convective clusters and their alignments into Amazon coastal squall lines are more frequent from December to July, and they mostly stay within 170 km of the coastline. Their development and movement in the afternoon and evening hours of about 14 m s−1 are helped by the sea breeze. Negative phase of Atlantic dipole and La Niña combined increase the frequency of convective clusters over the coastal Amazon. Composite environmental conditions of 13 large Amazon coastal squall-line cases in April show that conditional instability increases from 0900 to 1200 LT and the wind profiles show a jet-like structure at low levels of the atmosphere. The differences in the vertical profiles of temperature and humidity between the large-squall-line composites and no-squall-line composites are weak. However, appreciable increase in the mean value of CAPE from 0900 to 1500 LT is found in the large-squall-line composite. The mean mixing ratio of the mixed layer at 0900 LT in La Niña situations is significantly larger in the large-squall-line composite. Thus, CAPE and mixed-layer mixing ratio are considered to be promising indicators of the convective activity over the coastal belt of the Amazon basin. [ABSTRACT FROM AUTHOR]

The representation of tropical precipitation is evaluated across three generations of models participating in phases 3, 5, and 6 of the Coupled Model Intercomparison Project (CMIP). Compared to state-of-the-art observations, improvements in tropical precipitation in the CMIP6 models are identified for some metrics, but we find no general improvement in tropical precipitation on different temporal and spatial scales. Our results indicate overall little changes across the CMIP phases for the summer monsoons, the double-ITCZ bias, and the diurnal cycle of tropical precipitation. We find a reduced amount of drizzle events in CMIP6, but tropical precipitation occurs still too frequently. Continuous improvements across the CMIP phases are identified for the number of consecutive dry days, for the representation of modes of variability, namely, the Madden– Julian oscillation and El Niño–Southern Oscillation, and for the trends in dry months in the twentieth century. The observed positive trend in extreme wet months is, however, not captured by any of the CMIP phases, which simulate negative trends for extremely wet months in the twentieth century. The regional biases are larger than a climate change signal one hopes to use the models to identify. Given the pace of climate change as compared to the pace of model improvements to simulate tropical precipitation, we question the past strategy of the development of the present class of global climate models as the mainstay of the scientific response to climate change. We suggest the exploration of alternative approaches such as high-resolution storm-resolving models that can offer better prospects to inform us about how tropical precipitation might change with anthropogenic warming. [ABSTRACT FROM AUTHOR]

The central aim of this work is to investigate the characteristics of fog events over the United Arab Emirates (UAE) and identify the underlying physical processes responsible for fog initiation and dissipation. To achieve this, hourly meteorological measurements at eight airport stations, along with ERA5 reanalysis data (1995–2018), are utilized. The analysis indicates the dominance of radiation fog (RAD) as, on average, 70% of the observed events fall under this category. Fog in the UAE typically forms between 2000 and 0200 local time (LT) and dissipates between 0600 and 0900 LT. During a typical dense fog event recorded during 22–23 December 2017, cooling and moistening tendencies of up to 1.2 K h−1 and 0.7 g kg−1 h−1 are observed ~5–6 h before fog onset. In the vertical, a dry and warm layer above 750 hPa gradually descends from above 500 hPa to promote the development of fog. Similar conclusions are reached when analyzing composites of fog events. Further, the variability of fog occurrence associated with El Niño–Southern Oscillation (ENSO) patterns is explored. It is concluded that the El Niño (warm) and La Niña (cold) phases exhibit very different spatial characteristics with respect to surface meteorological variables. In particular, during El Niño events, the near-surface atmosphere is cooler and moister compared to La Niña events, favoring RAD fog formation over the UAE. Besides, fog events during El Niño years tend to last longer compared to La Niña years due to an earlier onset. [ABSTRACT FROM AUTHOR]

Intraseasonal tropical variability has important implications for the mid- and high-latitude atmosphere, and in recent studies has been shown to modulate a number of weather processes in the Northern Hemisphere, such as snow depth, sea ice concentration, precipitation, atmospheric rivers, and air temperature. In such studies, the extratropical atmosphere has tended to respond to the tropical convection of the leading mode of intraseasonal variability, the Madden-Julian oscillation (MJO), with a time lag of approximately 7 days. However, the time lag between the MJO and the Antarctic atmosphere has been found to vary between less than 7 and greater than 20 days. This study builds on previous work by further examining the time-lagged response of Southern Hemisphere tropospheric circulation to tropicalMJO forcing, with specific focus on the latitude belt associated with the Antarctic Oscillation, during the months of June (austral winter) and December (austral summer) using NCEP-DOEReanalysis 2 data for the years 1979-2016. Principal findings indicate that the time lag with the strongest height anomalies depends on both the location of theMJO convection (e.g., theMJO phase) and the season, and that the lagged height anomalies in the Antarctic atmosphere are fairly consistent across different vertical levels and latitudinal bands. In addition, certain MJO phases in December displayed lagged height anomalies indicative of blocking-type atmospheric patterns, with an approximate wavenumber of 4, whereas in June most phases were associated with more progressive height anomaly centers resembling a wavenumber-3-type pattern. [ABSTRACT FROM AUTHOR]

The authors undertake a multiscale spectral reexamination of the variability of the Pacific-South American (PSA) pattern and the mechanisms by which this variability occurs. Time scales from synoptic to interannual are investigated, focusing on the means by which tropical variability is communicated to the midlatitudes and on in situ forcing within the midlatitude waveguides. Particular interest is paid to what fraction of the total variability associated with the PSA, occurring on interannual time scales, is attributable to tropical forcing relative to that occurring on synoptic and intraseasonal time scales via internal waveguide dynamics. In general, it is found that the eastward-propagating wave train pattern typically associated with the PSA manifests across time scales from synoptic to interannual, with the majority of the variability occurring on synoptic-to-intraseasonal time scales largely independent of tropical convection. It is found that the small fraction of the total variance with a tropical signal occurs via the zonal component of the thermal wind modulating both the subtropical and polar jets. The respective roles of the Hadley circulation and stationary Rossby wave sources are also examined. Further, a PSA-like mode is identified in terms of the slow components of higher-order modes of tropospheric geopotential height. This study reestablishes the multiscale nonlinear nature of the PSA modes arising largely as a manifestation of internal midlatitude waveguide dynamics and local disturbances. [ABSTRACT FROM AUTHOR]

Predictability of El Niño-Southern Oscillation (ENSO) is examined using ensemble hindcasts made with a seasonal prediction system based on the atmosphere and ocean general circulation model, the Model for Interdisciplinary Research on Climate, version 5 (MIROC5). Particular attention is paid to differences in predictive skill in terms of the prediction error for two prominent types of El Niño: the conventional eastern Pacific (EP) El Niño and the central Pacific (CP) El Niño, the latter having a maximum warming around the date line. Although the system adopts ocean anomaly assimilation for the initialization process, it maintains a significant ability to predict ENSO with a lead time of more than half a year. This is partly due to the fact that the system is little affected by the 'spring prediction barrier,' because increases in the error have little dependence on the thermocline variability. Composite analyses of each type of El Niño reveal that, compared to EP El Niños, the ability to predict CP El Niños is limited and has a shorter lead time. This is because CP El Niños have relatively small amplitudes, and thus they are more affected by atmospheric noise; this prevents development of oceanic signals that can be used for prediction. [ABSTRACT FROM AUTHOR]

Subsurface ocean observations in the equatorial tropical Pacific Ocean dramatically increased after the 1990s because of the completion of the TAO moored array and a steady increase in Argo floats. In this analysis the question explored is whether a steady increase in ocean observations can be discerned in improvements in skill of predicting sea surface temperature (SST) variability associated with El Niño-Southern Oscillation (ENSO)? The analysis is based on the time evolution of skill of sea surface temperatures in the equatorial tropical Pacific since 1982 based on a seasonal prediction system. It is found that for forecasts up to a 6-month lead time, a clear fingerprint of increases in subsurface ocean observations is not readily apparent in the time evolution of prediction skill that is dominated much more by the signal-to-noise consideration of SSTs to be predicted. Finding no clear relationship between an increase in ocean observations and prediction skill of SSTs, various possibilities for why it may be so are discussed. This discussion is to motivate further exploration on the question of the tropical Pacific observing system, its influence on the skill of ENSO prediction, and the capabilities of the current generation of coupled models and ocean data assimilation systems to take advantage of ocean observations. [ABSTRACT FROM AUTHOR]

Hydrologic extremes in the form of flood and drought have large impacts on society that can be reduced through preparations made possible by seasonal prediction. However, the skill of seasonal predictions from global climate models is uncertain, which severely limits their practical use. In the past, the skill assessment has been limited to a single temporal or spatial resolution for a short hindcast period, which is prone to sampling errors, and noise that leads to uncertainty. In this work a framework that uses 'canonical' forecast events, or averages in space-time, to provide a more certain assessment of when and where models are skillful is developed. This framework is demonstrated by using NCEP's Climate Forecast System, version 2, hindcast dataset for precipitation and temperature over the contiguous United States (CONUS). As part of the canonical event analyses, the probabilistic predictability metric (PPM) is used to define spatial and seasonal variability of forecast skill and its attribution to El Niño-Southern Oscillation (ENSO) over the CONUS. The PPM indicates that there are clear seasonal and spatial patterns of model skill that provide a better understanding of when and where to have confidence in model predictions as compared to a skill metric based on a single temporal and spatial scale. Furthermore, the canonical event analysis also facilitates the attribution of spatiotemporal variations of precipitation predictive skill to the antecedent ENSO conditions. This work illustrates the importance of using canonical event analysis to diagnose seasonal predictions and discusses its extensions for model development. [ABSTRACT FROM AUTHOR]

A real-time statistical model based on the work of Leroy and Wheeler is developed via multiple logistic regression to predict weekly tropical cyclone activity over the Atlantic and east Pacific basins. The predictors used in the model include a climatology of tropical cyclone genesis for each ocean basin, an El Niño-Southern Oscillation (ENSO) index, and two indices representing the propagating Madden-Julian oscillation (MJO). The Atlantic model also includes a predictor representing the variability of sea surface temperature (SST) in the Main Development Region (MDR). These predictors are suggested as useful for the prediction of tropical cyclogenesis based on previous work in the literature and are further confirmed in this study using basic statistics. Univariate logistic regression models are generated for each predictor in each region to ensure the choice of prediction scheme. Using all predictors, cross-validated hindcasts are developed out to a seven-week forecast lead. A formal stepwise predictor selection procedure is implemented to select the predictors used in each region at each forecast lead. Brier skill scores and reliability diagrams are used to assess the skill and dependability of the models. Results show an increase in model skill over the time-varying climatology at predicting tropical cyclogenesis by the inclusion of the MJO out to a three-week forecast lead for the east Pacific and a two-week forecast lead for the Atlantic. The importance of ENSO and MDR SST for Atlantic genesis prediction is highlighted, and the uncertain effects of ENSO on east Pacific tropical cyclogenesis are revisited. [ABSTRACT FROM AUTHOR]

Sea level pressure (SLP) acts, on the one hand, as a 'bridge parameter' to which geophysical properties at the air-sea interface (e.g., wind stress and sea surface height) are linked, and on the other hand, as an 'index parameter' by which major atmospheric oscillations, including the well-known Southern Oscillation, are defined. Using 144 yr (1854-1997) of extended reconstructed SLP data, seasonal patterns of its variability are reinvestigated in detail. New features on fundamental structure of its annual and semiannual cycles are revealed in two aspects. First, the spatiotemporal patterns of yearly and half-yearly SLPs are basically determined by a network of 'amphidromes,' which are surrounded by rotational variations. Fourteen cyclonic and anticyclonic annual SLP amphidromes (half each and often in pair) are found in the global ocean, while the numbers of the two types of semiannual amphidrome are 11 and 9, respectively. The second dominant feature in SLP variability is the pattern of oscillation or seesaw for both annual and semiannual components. At least eight oscillation zones are identified for the annual cycle, which can be categorized into a boreal winter mode and an austral winter mode. As for the semiannual cycle, the seesaw pattern is geographically divided into three regimes: the North Pacific regime, the North Atlantic regime, and the Southern Ocean regime. These findings serve as a new contribution to characterizing and understanding the seasonality of the global ocean-atmosphere system. [ABSTRACT FROM AUTHOR]

The authors analyze and model time series of annual counts of tropical storms lasting more than 2 days in the North Atlantic basin and U.S. landfalling tropical storms over the period 1878–2008 in relation to different climate indices. The climate indices considered are the tropical Atlantic sea surface temperature (SST), tropical mean SST, the North Atlantic Oscillation (NAO), and the Southern Oscillation index (SOI). Given the uncertainties associated with a possible tropical storm undercount in the presatellite era, two different time series of counts for the North Atlantic basin are employed: one is the original (uncorrected) tropical storm record maintained by the National Hurricane Center and the other one is with a correction for the estimated undercount associated with a changing observation network. Two different SST time series are considered: the Met Office’s HadISSTv1 and NOAA’s Extended Reconstructed SST. Given the nature of the data (counts), a Poisson regression model is adopted. The selection of statistically significant covariates is performed by penalizing models for adding extra parameters and two penalty functions are used. Depending on the penalty function, slightly different models, both in terms of covariates and dependence of the model’s parameter, are obtained, showing that there is not a “single best” model. Moreover, results are sensitive to the undercount correction and the SST time series. Suggestions concerning the model to use are provided, driven by both the outcomes of the statistical analyses and the current understanding of the underlying physical processes responsible for the genesis, development, and tracks of tropical storms in the North Atlantic basin. Although no single model is unequivocally superior to the others, the authors suggest a very parsimonious family of models using as covariates tropical Atlantic and tropical mean SSTs. [ABSTRACT FROM AUTHOR]

Results are described from a large sample of coupled ocean–atmosphere retrospective forecasts during 1982–98. The prediction system is based on the National Center for Atmospheric Research (NCAR) Community Climate System Model, version 3 (CCSM3.0), and a state-of-the-art ocean data assimilation system made available by the National Oceanic and Atmospheric Administration (NOAA) Geophysical Fluid Dynamics Laboratory (GFDL). The retrospective forecasts are initialized in January, April, July, and November of each year, and ensembles of 6 forecasts are run for each initial month, yielding a total of 408 1-yr predictions. In generating the ensemble members, perturbations are added to the atmospheric initial state only. The skill of the prediction system is analyzed from both a deterministic and a probabilistic perspective, it is then compared to the operational NOAA Climate Forecast System (CFS), and the forecasts are combined with CFS to produce a multimodel prediction system. While the skill scores for each model are highly dependent on lead time and initialization month, the overall level of skill of the individual models is quite comparable. The multimodel combination (i.e., the unweighted average of the forecast), while not always the most skillful, is generally as skillful as the best model, using either deterministic or probabilistic skill metrics. [ABSTRACT FROM AUTHOR]

Seasonal climate prediction, in general, can achieve excellent results with a multimodel system. A relevant calibration of individual models and an optimal combination of individual models are the key elements leading to this success. However, this commonly used approach appears to be insufficient to remove the intermodel systematic errors (IMSE), which represent similar error properties in individual models after their calibration. A new postprocessing method is proposed to correct the IMSE and to increase the prediction skill. The first step consists of carrying out a diagnosis on the calibrated errors before constructing the multimodel ensemble. In contrast to previous studies, the calibrated errors here are treated directly as the investigation target, and temporal correlation coefficients between the calibrated errors and other meteorological variables are calculated. In the second stage, mathematical and statistical tools are applied in an effort to forecast the IMSE in individual models. Then, the IMSE are removed from the calibrated results and the new corrected data are used to construct the multimodel ensemble. The hindcast of the European Union–funded Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) multimodel system is used to test the method. The simulated Southern Oscillation index is used to diagnose and to correct the calibrated errors of the simulated precipitation. The prediction qualities of the corrected data are assessed and compared with those of the uncorrected dataset. The results show that it is feasible to improve seasonal precipitation prediction by forecasting and correcting the IMSE. This improvement is visible not only for the individual models, but also for the multimodel ensemble. [ABSTRACT FROM AUTHOR]

Winter tornado activity (January–March) between 1950 and 2003 was analyzed to determine the possible effect of seasonally averaged sea surface temperatures in the equatorial Pacific Ocean, the ENSO phase, on the location and strength of tornado outbreaks in the United States. Tornado activity was gauged through analyses of tornadoes occurring on tornado days (a calendar day featuring six or more tornadoes within the contiguous United States) and strong and violent tornado days (a calendar day featuring five or more tornadoes rated F2 and greater within the contiguous United States). The tornado days were then stratified according to warm (37 tornado days, 14 violent days), cold (51 tornado days, 28 violent days), and neutral (74 tornado days, 44 violent days) winter ENSO phase. It is seen that during winter periods of neutral tropical Pacific sea surface temperatures, there is a tendency for U.S. tornado outbreaks to be stronger and more frequent than they are during winter periods of anomalously warm tropical Pacific sea surface temperatures (El Niño). During winter periods with anomalously cool Pacific sea surface temperatures (La Niña), the frequency and strength of U.S. tornado activity lies between that of the neutral and El Niño phase. ENSO-related shifts in the preferred location of tornado activity are also observed. Historically, during the neutral phase, tornado outbreaks typically occurred from central Oklahoma and Kansas eastward through the Carolinas. During cold phases, tornado outbreaks have typically occurred in a zone stretching from southeastern Texas northeastward into Illinois, Indiana, and Michigan. During anomalously warm phases activity was mainly limited to the Gulf Coast states, including central Florida. The data are statistically and synoptically analyzed to show that they are not only statistically significant, but also meteorologically reasonable. [ABSTRACT FROM AUTHOR]

Cloud-to-ground (CG) lightning flashes from the National Lightning Detection Network are analyzed to determine if the El Niño–Southern Oscillation (ENSO) cycle influences lighting activity along the Gulf Coast region. First, an updated climatology of lightning was developed for the region. Flash density maps are constructed from an 8-yr dataset (1995–2002) and compared with past lightning climatologies. Second, lightning variability is compared with the phases of ENSO. Winter lightning distributions are compared with one published study of ENSO and lightning days in the Southeast. Flash density patterns are, overall, consistent with past U.S. lightning climatology. However, the peak flash density for the annual mean was less than observed in previous climatologies, which could be due to the disproportionately large percentage of cool ENSO periods compared to previous lightning climatologies. The highest annual lightning counts were observed in 1997, which consisted of mostly warm ENSO seasons; the 1997–98 El Niño was one of the strongest on record. The lowest lightning counts were observed in 2000, which had mostly cool or neutral phases of ENSO including the lowest Niño-3.4 anomaly of the study period. Analysis of winter season lightning flash densities substantiated the role of the ENSO cycle in winter season lightning fluctuations. Winter lightning activity increased dramatically during the 1997–98 El Niño. The lowest winter flash densities are associated with cool ENSO phases. Although 8 yr is inadequate to establish a long-term pattern, results indicate that ENSO influences lightning and that further study is warranted. As more years of lightning data are acquired, a more complete climatology can be developed. [ABSTRACT FROM AUTHOR]

The El Niño–Southern Oscillation (ENSO) cycle is known to influence weather and climate along the Gulf Coast region, causing anomalously high precipitation during El Niño winters. This region is also known for having the highest lightning flash density in the United States. An 8-yr dataset (1995–2002) of cloud-to-ground (CG) lightning flashes was analyzed to determine if the ENSO cycle influences lighting activity along the Gulf Coast region. Simple Pearson’s correlations were computed between concurrent monthly pairings of Niño-3.4 sea surface temperature (SST) and CG lightning flash deviation values from the study area. The correlation results are mapped and analyzed for links to meteorological features. Statistically significant correlation values greater than 0.8 were noted over large swaths of the study area during each winter month. The highest correlations were arranged in banded swaths and associated with regions of low flash densities during December and February. In January, areas of high correlation were spatially coincident with areas of enhanced flash density. Both the enhanced CG flash regions and high correlation values and patterns are indicative of a southerly shift in the midlatitude storm track known to occur during warm ENSO events. During the spring and summer, most of the region has weak correlation with ENSO except for August, which has a large area of negative correlations. These findings indicate that lightning increases during La Niña summers. Correlation patterns in late fall are similar to those of winter. The ENSO–lightning relationship has implications for hazard assessment and can be a useful tool for long-term seasonal planning. [ABSTRACT FROM AUTHOR]

Previous wildfire research in the United States has been focused primarily on the western states. Much of this research has discovered relationships between wildfire variability and atmospheric teleconnections. Thus far, few published projects have addressed the effects of various teleconnections on wildfire in the southeastern United States. Index values for the El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific decadal oscillation (PDO), and Pacific–North American (PNA) pattern are all tested for relationships with fire variables in the state of Mississippi. Each of the indices displays significant correlations with wildfire occurrence and/or size in Mississippi. The findings of this research suggest that it might be feasible to create predictive fire-risk models for the southeastern United States based on the combination of these teleconnection indices. [ABSTRACT FROM AUTHOR]

The amount and timing of early wet-season rainfall are important for the management of many agricultural industries in north Australia. With this in mind, a wet-season onset date is defined based on the accumulation of rainfall to a predefined threshold, starting from 1 September, for each square of a 1° gridded analysis of daily rainfall across the region. Consistent with earlier studies, the interannual variability of the onset dates is shown to be well related to the immediately preceding July–August Southern Oscillation index (SOI). Based on this relationship, a forecast method using logistic regression is developed to predict the probability that onset will occur later than the climatological mean date. This method is expanded to also predict the probabilities that onset will be later than any of a range of threshold dates around the climatological mean. When assessed using cross-validated hindcasts, the skill of the predictions exceeds that of climatological forecasts in the majority of locations in north Australia, especially in the Top End region, Cape York, and central Queensland. At times of strong anomalies in the July–August SOI, the forecasts are reliably emphatic. Furthermore, predictions using tropical Pacific sea surface temperatures (SSTs) as the predictor are also tested. While short-lead (July–August predictor) forecasts are more skillful using the SOI, long-lead (May–June predictor) forecasts are more skillful using Pacific SSTs, indicative of the longer-term memory present in the ocean. [ABSTRACT FROM AUTHOR]

The fidelity of the interannual variability of precipitation over Nordeste is examined using the suite of the NCEP Climate Forecast System (CFS) seasonal hindcasts. These are coupled ocean–land–atmosphere multiseasonal integrations. It is shown that the Nordeste rainfall variability in the season of February–April has relatively low skill in the CFS seasonal hindcasts. Although Nordeste is a comparatively small region in the northeast of Brazil, the analysis indicates that the model has a large-scale error in the tropical Atlantic Ocean. The CFS exhibits a widespread El Niño–Southern Oscillation (ENSO) forcing over the tropical Atlantic Ocean. As a consequence of this remote ENSO forcing, the CFS builds an erroneous meridional SST gradient in the tropical Atlantic that is known from observations to be a critical forcing for the rainfall variability over Nordeste. [ABSTRACT FROM AUTHOR]

In much of Ethiopia, similar to the Sahelian countries to its west, rainfall from June to September contributes the majority of the annual total, and is crucial to Ethiopia’s water resource and agriculture operations. Drought-related disasters could be mitigated by warnings if skillful summer rainfall predictions were possible with sufficient lead time. This study examines the predictive potential for June–September rainfall in Ethiopia using mainly statistical approaches. The skill of a dynamical approach to predicting the El Niño–Southern Oscillation (ENSO), which impacts Ethiopian rainfall, is assessed. The study attempts to identify global and more regional processes affecting the large-scale summer climate patterns that govern rainfall anomalies. Multivariate statistical techniques are applied to diagnose and predict seasonal rainfall patterns using historical monthly mean global sea surface temperatures and other physically relevant predictor data. Monthly rainfall data come from a newly assembled dense network of stations from the National Meteorological Agency of Ethiopia. Results show that Ethiopia’s June–September rainy season is governed primarily by ENSO, and secondarily reinforced by more local climate indicators near Africa and the Atlantic and Indian Oceans. Rainfall anomaly patterns can be predicted with some skill within a short lead time of the summer season, based on emerging ENSO developments. The ENSO predictability barrier in the Northern Hemisphere spring poses a major challenge to providing seasonal rainfall forecasts two or more months in advance. Prospects for future breakthroughs in ENSO prediction are thus critical to future improvements to Ethiopia’s summer rainfall prediction. [ABSTRACT FROM AUTHOR]

Many statistical forecast systems are available to interested users. To be useful for decision making, these systems must be based on evidence of underlying mechanisms. Once causal connections between the mechanism and its statistical manifestation have been firmly established, the forecasts must also provide some quantitative evidence of “quality.” However, the quality of statistical climate forecast systems (forecast quality) is an ill-defined and frequently misunderstood property. Often, providers and users of such forecast systems are unclear about what quality entails and how to measure it, leading to confusion and misinformation. A generic framework is presented that quantifies aspects of forecast quality using an inferential approach to calculate nominal significance levels (p values), which can be obtained either by directly applying nonparametric statistical tests such as Kruskal–Wallis (KW) or Kolmogorov–Smirnov (KS) or by using Monte Carlo methods (in the case of forecast skill scores). Once converted to p values, these forecast quality measures provide a means to objectively evaluate and compare temporal and spatial patterns of forecast quality across datasets and forecast systems. The analysis demonstrates the importance of providing p values rather than adopting some arbitrarily chosen significance levels such as 0.05 or 0.01, which is still common practice. This is illustrated by applying nonparametric tests (such as KW and KS) and skill scoring methods [linear error in the probability space (LEPS) and ranked probability skill score (RPSS)] to the five-phase Southern Oscillation index classification system using historical rainfall data from Australia, South Africa, and India. The selection of quality measures is solely based on their common use and does not constitute endorsement. It is found that nonparametric statistical tests can be adequate proxies for skill measures such as LEPS or RPSS. The framework can be implemented anywhere, regardless of dataset, forecast system, or quality measure. Eventually such inferential evidence should be complemented by descriptive statistical methods in order to fully assist in operational risk management. [ABSTRACT FROM AUTHOR]

Two distinct modes of the East Asian winter monsoon (EAWM) have been identified, and they correspond to real and imaginary parts of the leading mode of the EAWM, respectively. Analyses of these modes used the National Centers for Environment Prediction (NCEP) and National Center for Atmospheric Research (NCAR) monthly mean reanalysis datasets for the period 1968–2003, as well as the Southern Oscillation index (SOI), North Atlantic Oscillation index, and eastern equatorial Pacific sea surface temperature (SST) data. Results were obtained by resolving a complex Hermite matrix derived from 850-hPa anomalous wind fields, and determining the resulting modes’ associations with several climate variables. The first distinct mode (M1) is characterized by an anomalous meridional wind pattern over East Asia and the western North Pacific. Mode M1 is closely related to several features of the atmospheric circulation, including the Siberian high, East Asian trough, East Asian upper-tropospheric jet, and local Hadley circulation over East Asia. Thus, M1 reflects the traditional EAWM pattern revealed in previous studies. The second distinct EAWM mode (M2), which was not identified previously, displays dominant zonal wind anomalies over the same area. Mode M2 exhibits a closer relation than M1 to sea level pressure anomalies over the northwestern Pacific southeast of Japan and with the SOI and equatorial eastern Pacific SST. Unlike M1, M2 does not show coherent relationships with the Siberian high, East Asian trough, and East Asian upper-tropospheric jet. Since atmospheric circulation anomalies relevant to M2 exhibit a quasi-barotropic structure, its existence cannot simply be attributed to differential land–sea heating. El Niño events tend to occur in the negative phase of M1 and the positive phase of M2, both corresponding to a weakened EAWM. The Arctic Oscillation does not appear to impact the EAWM on interannual time scales. Although the spatial patterns for the two modes are very different, the two distinct modes are complementary, with the leading EAWM mode being a linear combination of the two. The results herein therefore demonstrate that a single EAWM index may be inappropriate for investigating and predicting the EAWM. [ABSTRACT FROM AUTHOR]

Trajectory analysis using a weather prediction model is performed for five cases to interpret the formation of enhanced bands of vertically integrated water vapor (IWV) in the central and eastern Pacific that are frequently seen in satellite images from the Special Sensor Microwave Imager. The connection of these enhanced bands with poleward water vapor transport from the Tropics is also examined. It is shown that the leading end of the enhanced IWV bands (defined as the most eastward and poleward end) is the manifestation of moisture convergence in the warm conveyor belt associated with extratropical cyclones, while the bands away from the leading end result mainly from moisture convergence along the trailing cold fronts. There is evidence that some enhanced IWV bands may be associated with a direct poleward transport of tropical moisture along the IWV bands from the Tropics all the way to the extratropics. The trajectory analysis, together with the seasonal mean sea level pressure analysis, indicates that a favorable condition for the occurrence of a direct, along-IWV band transport of tropical (defined as south of 23.5°N) moisture to the U.S. West Coast in the eastern Pacific is a weakened subtropical ridge in the central Pacific with an enhanced southwesterly low-level flow. The authors hypothesize that the direct poleward transport of tropical moisture within an enhanced IWV band in the eastern Pacific is most possible in the neutral El Niño–Southern Oscillation (ENSO) phase and is least possible in the El Niño phase. [ABSTRACT FROM AUTHOR]

A new global coupled atmosphere–ocean forecast system model (CFS03) has recently been developed at the National Centers for Environmental Prediction (NCEP). The new coupled model consists of a T62L64 version of the operational NCEP Atmospheric Global Forecast System model and the Geophysical Fluid Dynamics Laboratory Modular Ocean Model version 3, and is expected to replace the current NCEP operational coupled seasonal forecast model. This study assesses the performance of the new coupled model in simulating El Niño–Southern Oscillation (ENSO), which is considered to be a desirable feature for models used for seasonal prediction. The diagnoses indicate that the new coupled model simulates ENSO variability with realistic frequency. The amplitude of the simulated ENSO is similar to that of the observed strong events, but the ENSO events in the simulation occur more regularly than in observations. The model correctly simulates the observed ENSO seasonal phase locking with the peak amplitude near the end of the year. On average, however, simulated warm events tend to start about 3 months earlier and persist longer than observed. The simulated ENSO is consistent with the delayed oscillator, recharge oscillator, and advective–reflective oscillator theories, suggesting that each of these mechanisms may operate at the same time during the ENSO cycle. The diagnoses of the simulation indicate that the model may be suitable for real-time prediction of ENSO. [ABSTRACT FROM AUTHOR]

Numerical and statistical predictions of simplified models are linearly combined in a sensitivity study to identify the crucial parameters that are needed to enhance predictability of the El Niño–Southern Oscillation (ENSO) phenomenon. The results indicate that the ENSO prediction skill of the simplified models can be improved. The profit achieved strongly depends on the phase information that is utilized by the forecast combination and is inherent in predictions of a quasi-periodic process such as ENSO. The simplest forecast combination that still yields useful forecasts at longer lead times of about half of the ENSO period (18–24 months) is the combination of two persistence forecast schemes. For the prediction period 1982–2003, that is the persistence of a sea surface temperature anomaly (SSTA) index area at 60°S, 180°W and the Niño-3 index SSTA. The level of skill improvement critically depends on the prediction schemes and prediction period, as well as on the period from which the combination weights are derived. Differences between combination forecast and hindcast are minimized if the statistical weights are derived from a time period that is characterized by an ENSO statistic that is close to the prediction period. In this study the prediction period has simply been halved for the sake of simplicity to derive the statistical weights, which is sufficient for predicting the 1980s and 1990s (with each other), but not for predicting, for example, the 1970s. The suppressed 1976/77 El Niño event makes the periodic occurrence less regular compared to the other decades. However, this forecast combination technique can be applied in a much more elaborate way. [ABSTRACT FROM AUTHOR]

Hindcasts for the period 1972–92 have been made using the two-tiered method. This involved forcing a global atmospheric model with sea surface temperature anomalies for the low-latitude Pacific Ocean generated with the Zebiak and Cane intermediate coupled model. Outside of this Pacific Ocean domain sea surface temperatures were specified from climatology. All hindcasts were for a duration of 12 months, with each hindcast commencing on 1 January of the individual years. An ensemble of 10 hindcasts was made for each year using different initial atmospheric conditions obtained from a long control run. In addition, a four-member ensemble simulation was made with the same global atmospheric model forced with the annually varying Global Sea Ice and Sea Surface Temperature (GISST) distribution for the period 1971–91. This provided a criterion against which the limited sea surface temperature forcing for the hindcasts could be compared. In the analysis of the results no attempt was made to remove systematic errors or to minimize other possible model deficiencies. The hindcasts reproduced the observed temporal variability of the Southern Oscillation index, with transitions between ENSO events particularly well defined. The interannual variability of the anomalous zonal wind stress over the equatorial Pacific Ocean was hindcast satisfactorily, but an index of the Pacific–North America oscillation was poorly represented in the hindcasts. This deficiency may have implications for predictability in regions influenced by this oscillation. Rainfall hindcasts are presented in some detail, particularly time series for individual model grid boxes or averages over regions. The results are presented as monthly (rather than seasonal) totals for individual years in order to provide some indication of their potential temporal limits. The highest accuracy for rainfall was achieved over the low-latitude Pacific Ocean where anomaly correlation coefficients with... [ABSTRACT FROM AUTHOR]

The skill of a coupled ocean--atmosphere model in predicting ENSO has recently been improved using a new initialization procedure in which initial conditions are obtained from the coupled model, nudged toward obser-vations of wind stress. The previous procedure involved direct insertion of wind stress observations, ignoring model feedback from ocean to atmosphere. The success of the new scheme is attributed to its explicit consideration of ocean--atmosphere coupling and the associated reduction of "initialization shock" and random noise. The so-called spring predictability barrier is eliminated, suggesting that such a barrier is not intrinsic to the real climate system. Initial attempts to generalize the nudging procedure to include SST were not successful; possible explanations are offered. In all experiments forecast skill is found to be much higher for the 1980s than for the 1970s and 1990s, suggesting decadal variations in predictability. [ABSTRACT FROM AUTHOR]