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Emily Becker and Huug van den Dool

Abstract

The North American Multimodel Ensemble (NMME) forecasting system has been continuously producing seasonal forecasts since August 2011. The NMME, with its suite of diverse models, provides a valuable opportunity for characterizing forecast confidence using probabilistic forecasts. The current experimental probabilistic forecast product (in map format) presents the most likely tercile for the seasonal mean value, chosen out of above normal, near normal, or below normal categories, using a nonparametric counting method to determine the probability of each class. The skill of the 3-month-mean probabilistic forecasts of 2-m surface temperature (T2m), precipitation rate, and sea surface temperature is assessed using forecasts from the 29-yr (1982–2010) NMME hindcast database. Three forecast configurations are considered: a full six-model NMME; a “mini-NMME” with 24 members, four each from six models; and the 24-member CFSv2 alone. Skill is assessed on the cross-validated hindcasts using the Brier skill score (BSS); forecast reliability and resolution are also assessed. This study provides a baseline skill assessment of the current method of creating probabilistic forecasts from the NMME system.

For forecasts in the above- and below-normal terciles for all variables and geographical regions examined in this study, BSS for NMME forecasts is higher than BSS for CFSv2 forecasts. Niño-3.4 forecasts from the full NMME and the mini-NMME receive nearly identical BSS that are higher than BSS for CFSv2 forecasts. Even systems with modest BSS, such as T2m in the Northern Hemisphere, have generally high reliability, as shown in reliability diagrams.

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Emily J. Becker and Ernesto Hugo Berbery

Abstract

The structure of the diurnal cycle of warm-season precipitation and its associated fields during the North American monsoon are examined for the core monsoon region and for the southwestern United States, using a diverse set of observations, analyses, and forecasts from the North American Monsoon Experiment field campaign of 2004. Included are rain gauge and satellite estimates of precipitation, Eta Model forecasts, and the North American Regional Reanalysis (NARR). Daily rain rates are of about the same magnitude in all datasets with the exception of the Climate Prediction Center (CPC) Morphing (CMORPH) technique, which exhibits markedly higher precipitation values.

The diurnal cycle of precipitation within the core region occurs earlier in the day at higher topographic elevations, evolving with a westward shift of the maximum. This shift appears in the observations, reanalysis, and, while less pronounced, in the model forecasts. Examination of some of the fields associated with this cycle, including convective available potential energy (CAPE), convective inhibition (CIN), and moisture flux convergence (MFC), reveals that the westward shift appears in all of them, but more prominently in the latter.

In general, warm-season precipitation in southern Arizona and parts of New Mexico shows a strong effect due to northward moisture surges from the Gulf of California. A reported positive bias in the NARR northward winds over the Gulf of California limits their use with confidence for studies of the moist surges along the Gulf; thus, the analysis is complemented with operational analysis and the Eta Model short-term simulations. The nonsurge diurnal cycle of precipitation lags the CAPE maximum by 6 h and is simultaneous with a minimum of CIN, while the moisture flux remains divergent throughout the day. During surges, CAPE and CIN have modifications only to the amplitude of their cycles, but the moisture flux becomes strongly convergent about 6 h before the precipitation maximum, suggesting a stronger role in the development of precipitation.

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Emily Becker, Huug van den Dool, and Qin Zhang

Abstract

Forecast skill and potential predictability of 2-m temperature, precipitation rate, and sea surface temperature are assessed using 29 yr of hindcast data from models included in phase 1 of the North American Multimodel Ensemble (NMME) project. Forecast skill is examined using the anomaly correlation (AC); skill of the bias-corrected ensemble means (EMs) of the individual models and of the NMME 7-model EM are verified against the observed value. Forecast skill is also assessed using the root-mean-square error. The models’ representation of the size of forecast anomalies is also studied. Predictability was considered from two angles: homogeneous, where one model is verified against a single member from its own ensemble, and heterogeneous, where a model’s EM is compared to a single member from another model. This study provides insight both into the physical predictability of the three fields and into the NMME and its contributing models.

Most of the models in the NMME have fairly realistic spread, as represented by the interannual variability. The NMME 7-model forecast skill, verified against observations, is equal to or higher than the individual models’ forecast ACs. Two-meter temperature (T2m) skill matches the highest single-model skill, while precipitation rate and sea surface temperature NMME EM skill is higher than for any single model. Homogeneous predictability is higher than reported skill in all fields, suggesting there may be room for some improvement in model prediction, although there are many regional and seasonal variations. The estimate of potential predictability is not overly sensitive to the choice of model. In general, models with higher homogeneous predictability show higher forecast skill.

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Emily J. Becker, Ernesto Hugo Berbery, and R. Wayne Higgins

Abstract

This study examines the seasonal characteristics of daily precipitation over the United States using the North American Regional Reanalysis (NARR). To help understand the physical mechanisms that contribute to changes in the characteristics of daily precipitation, vertically integrated moisture flux convergence (MFC) and precipitable water were included in the study. First, an analysis of the NARR precipitation was carried out because while observed precipitation is indirectly assimilated in the system, differences exist. The NARR mean seasonal amount is very close to that of observations throughout the year, although NARR exhibits a slight systematic bias toward more-frequent, lighter precipitation. Particularly during summer, the precipitation intensity and the probability distribution function (PDF) indicate that NARR somewhat underestimates extremes and overestimates lighter events in the eastern half of the United States. The intensity and PDF of moisture flux convergence exhibit a strong similarity to those of precipitation, suggesting a link between strong MFC and precipitation extremes. On the other hand, the relationship between the precipitable water and precipitation PDFs is weaker, based on the lack of agreement of their gamma distribution parameters.

The dependence of the precipitation PDF on the lower-frequency modulation of ENSO was examined. During El Niño winters, the Southwest and central United States, Gulf of Mexico region, and southeastern coast have greater precipitation intensity and extremes than during La Niña, and the Ohio River and Red River basins have lower intensity and fewer extreme events. During summer, the northern Rocky Mountains receive higher intensity precipitation with more extreme events. Most areas where the change in the daily mean precipitation between ENSO phases is large have greater shifts in the extreme tail of the PDF. The ENSO-related response of moisture flux convergence is similar to that of precipitation. ENSO-related shifts in the precipitation PDF do not appear to have a strong relationship to the shifts in precipitable water.

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Emily J. Becker, Ernesto Hugo Berbery, and R. Wayne Higgins

Abstract

This study examines the characteristics of cold-season (November–March) daily precipitation over the contiguous United States during active periods of the Madden–Julian oscillation (MJO). A large response in the precipitation rate anomaly is found over the eastern United States when MJO-related enhanced tropical convection is moving through the far western to central Pacific (conventionally known as phases 5, 6, and 7 of the MJO). Positive anomalies occur in the region of the eastern Mississippi River basin, and negative anomalies occur in the Southeast. The relative stability of this pattern throughout the three phases suggests that they can be considered together. During phases 5–7, the central United States has a daily precipitation rate between 110% and 150% of normal, while the precipitation rate over much of Florida is less than 70% of normal. Much of the lower Mississippi River basin region receives somewhat more frequent daily precipitation during MJO phases 5–7, but a greater increase is found in the daily precipitation intensity, suggesting more intense storms. On the other hand, Florida has substantially fewer daily precipitation events, with a smaller decrease in the intensity.

To understand the atmospheric mechanisms related to the above shifts in daily precipitation, elements of the atmospheric circulation were examined. Positive moisture flux convergence anomalies, which have been linked to increased precipitation rate and intensity, are found in the region of increased precipitation rate during MJO phases 5–7. During those phases, the North American jet stream is shifted northward, likely leading to a higher incidence of storms over the lower Mississippi River basin and fewer storms over Florida. This is supported by the fact that the storm track also shows increased activity over the central United States during MJO phases 5–7.

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Emily J. Becker, Huug van den Dool, and Malaquias Peña

Abstract

Forecasts for extremes in short-term climate (monthly means) are examined to understand the current prediction capability and potential predictability. This study focuses on 2-m surface temperature and precipitation extremes over North and South America, and sea surface temperature extremes in the Niño-3.4 and Atlantic hurricane main development regions, using the Climate Forecast System (CFS) global climate model, for the period of 1982–2010. The primary skill measures employed are the anomaly correlation (AC) and root-mean-square error (RMSE). The success rate of forecasts is also assessed using contingency tables.

The AC, a signal-to-noise skill measure, is routinely higher for extremes in short-term climate than those when all forecasts are considered. While the RMSE for extremes also rises, especially when skill is inherently low, it is found that the signal rises faster than the noise. Permutation tests confirm that this is not simply an effect of reduced sample size. Both 2-m temperature and precipitation forecasts have higher anomaly correlations in the area of South America than North America; credible skill in precipitation is very low over South America and absent over North America, even for extremes. Anomaly correlations for SST are very high in the Niño-3.4 region, especially for extremes, and moderate to high in the Atlantic hurricane main development region. Prediction skill for forecast extremes is similar to skill for observed extremes. Assessment of the potential predictability under perfect-model assumptions shows that predictability and prediction skill have very similar space–time dependence. While prediction skill is higher in CFS version 2 than in CFS version 1, the potential predictability is not.

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Emily J. Becker, Huug van den Dool, and Malaquias Peña
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Sarah Strazzo, Dan C. Collins, Andrew Schepen, Q. J. Wang, Emily Becker, and Liwei Jia

Abstract

Recent research demonstrates that dynamical models sometimes fail to represent observed teleconnection patterns associated with predictable modes of climate variability. As a result, model forecast skill may be reduced. We address this gap in skill through the application of a Bayesian postprocessing technique—the calibration, bridging, and merging (CBaM) method—which previously has been shown to improve probabilistic seasonal forecast skill over Australia. Calibration models developed from dynamical model reforecasts and observations are employed to statistically correct dynamical model forecasts. Bridging models use dynamical model forecasts of relevant climate modes (e.g., ENSO) as predictors of remote temperature and precipitation. Bridging and calibration models are first developed separately using Bayesian joint probability modeling and then merged using Bayesian model averaging to yield an optimal forecast. We apply CBaM to seasonal forecasts of North American 2-m temperature and precipitation from the North American Multimodel Ensemble (NMME) hindcast. Bridging is done using the model-predicted Niño-3.4 index. Overall, the fully merged CBaM forecasts achieve higher Brier skill scores and better reliability compared to raw NMME forecasts. Bridging enhances forecast skill for individual NMME member model forecasts of temperature, but does not result in significant improvements in precipitation forecast skill, possibly because the models of the NMME better represent the ENSO–precipitation teleconnection pattern compared to the ENSO–temperature pattern. These results demonstrate the potential utility of the CBaM method to improve seasonal forecast skill over North America.

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Huug van den Dool, Emily Becker, Li-Chuan Chen, and Qin Zhang

Abstract

An ordinary regression of predicted versus observed probabilities is presented as a direct and simple procedure for minimizing the Brier score (BS) and improving the attributes diagram. The main example applies to seasonal prediction of extratropical sea surface temperature by a global coupled numerical model. In connection with this calibration procedure, the probability anomaly correlation (PAC) is developed. This emphasizes the exact analogy of PAC and minimizing BS to the widely used anomaly correlation (AC) and minimizing mean squared error in physical units.

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Li-Chuan Chen, Huug van den Dool, Emily Becker, and Qin Zhang

Abstract

In this study, precipitation and temperature forecasts during El Niño–Southern Oscillation (ENSO) events are examined in six models in the North American Multimodel Ensemble (NMME), including the CFSv2, CanCM3, CanCM4, the Forecast-Oriented Low Ocean Resolution (FLOR) version of GFDL CM2.5, GEOS-5, and CCSM4 models, by comparing the model-based ENSO composites to the observed. The composite analysis is conducted using the 1982–2010 hindcasts for each of the six models with selected ENSO episodes based on the seasonal oceanic Niño index just prior to the date the forecasts were initiated. Two types of composites are constructed over the North American continent: one based on mean precipitation and temperature anomalies and the other based on their probability of occurrence in a tercile-based system. The composites apply to monthly mean conditions in November, December, January, February, and March as well as to the 5-month aggregates representing the winter conditions. For anomaly composites, the anomaly correlation coefficient and root-mean-square error against the observed composites are used for the evaluation. For probability composites, a new probability anomaly correlation measure and a root-mean probability score are developed for the assessment. All NMME models predict ENSO precipitation patterns well during wintertime; however, some models have large discrepancies between the model temperature composites and the observed. The fidelity is greater for the multimodel ensemble as well as for the 5-month aggregates. February tends to have higher scores than other winter months. For anomaly composites, most models perform slightly better in predicting El Niño patterns than La Niña patterns. For probability composites, all models have superior performance in predicting ENSO precipitation patterns than temperature patterns.

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