Abstract

The effect of the El Niño–Southern Oscillation (ENSO) teleconnection and climate change trends on observed North American wintertime daily 2-m temperature is investigated for 1960–2022 with a quantile regression model, which represents the variability of the full distribution of daily temperature, including extremes and changes in spread. Climate change trends are included as a predictor in the regression model to avoid the potentially confounding effect on ENSO teleconnections. Based on prior evidence of asymmetric impacts from El Niño and La Niña, the ENSO response is taken to be piecewise linear, and the regression model contains separate predictors for warm and cool ENSO. The relationship between these predictors and shifts in median, interquartile range, skewness, and kurtosis of daily 2-m temperature are summarized through Legendre polynomials. Warm ENSO conditions result in significant warming shifts in the median and contraction of the interquartile range in central-northern North America, while no opposite effect is found for cool ENSO conditions in this region. In the southern United States, cool ENSO conditions produce a warming shift in the median, while warm ENSO conditions have little impact on the median, but contracts the interquartile range. Climate change trends are present as a near-uniform warming in the median and across quantiles and have no discernable impact on interquartile range or higher-order moments. Trends and ENSO together explain a substantial fraction of the interannual variability of daily temperature distribution shifts across much of North America and, to a lesser extent, changes of the interquartile range.

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.

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.

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.

Abstract

Climate variability affects sea levels as certain climate modes can accelerate or decelerate the rising sea level trend, but subseasonal variability of coastal sea levels is underexplored. This study is the first to investigate how remote tropical forcing from the MJO and ENSO impact subseasonal U.S. coastal sea level variability. Here, composite analyses using tide gauge data from six coastal regions along the U.S. East and West Coasts reveal influences on sea level anomalies from both the MJO and ENSO. Tropical MJO deep convection forces a signal that results in U.S. coastal sea level anomalies that vary based on MJO phase. Further, ENSO is shown to modulate both the MJO sea level response and background state of the teleconnections. The sea level anomalies can be significantly enhanced or weakened by the MJO-associated anomaly along the East Coast due to constructive or destructive interference with the ENSO-associated anomaly, respectively. The West Coast anomaly is found to be dominated by ENSO. We examine physical mechanisms by which MJO and ENSO teleconnections impact coastal sea levels and find consistent relationships between low-level winds and sea level pressure that are spatially varying drivers of the variability. Two case studies reveal how MJO and ENSO teleconnection interference played a role in notable coastal flooding events. Much of the focus on sea level rise concerns the long-term trend associated with anthropogenic warming, but on shorter time scales, we find subseasonal climate variability has the potential to exacerbate the regional coastal flooding impacts.

Abstract

We investigate the predictability of the sign of daily southeastern U.S. (SEUS) precipitation anomalies associated with simultaneous predictors of large-scale climate variability using machine learning models. Models using index-based climate predictors and gridded fields of large-scale circulation as predictors are utilized. Logistic regression (LR) and fully connected neural networks using indices of climate phenomena as predictors produce neither accurate nor reliable predictions, indicating that the indices themselves are not good predictors. Using gridded fields as predictors, an LR and convolutional neural network (CNN) are more accurate than the index-based models. However, only the CNN can produce reliable predictions that can be used to identify forecasts of opportunity. Using explainable machine learning we identify which variables and grid points of the input fields are most relevant for confident and correct predictions in the CNN. Our results show that the local circulation is most important as represented by maximum relevance of 850-hPa geopotential heights and zonal winds to making skillful, high-probability predictions. Corresponding composite anomalies identify connections with El Niño–Southern Oscillation during winter and the Atlantic multidecadal oscillation and North Atlantic subtropical high during summer.

ABSTRACT

The relationship between the warm water volume (WWV) ENSO precursor and ENSO SST weakened substantially after ~2000, coinciding with a degradation in dynamical model ENSO prediction skill. It is important to understand the drivers of the equatorial thermocline temperature variations and their linkage to ENSO onsets. In this study, a set of ocean reanalyses is employed to assess factors responsible for the variation of the equatorial Pacific Ocean thermocline during 1982–2019. Off-equatorial thermocline temperature anomalies carried equatorward by the mean meridional currents associated with Pacific tropical cells are shown to play an important role in modulating the central equatorial thermocline variations, which is rarely discussed in the literature. Further, ENSO events are delineated into two groups based on precursor mechanisms: the western equatorial Pacific type (WEP) ENSO, when the central equatorial thermocline is mainly influenced by the zonal propagation of anomalies from the western Pacific, and the off-equatorial central Pacific (OCP) ENSO, when off-equatorial central thermocline anomalies play the primary role. WWV is found to precede all WEP ENSO events by 6–9 months, while the correlation is substantially lower for OCP ENSO events. In contrast, the central tropical Pacific (CTP) precursor, which includes off-equatorial thermocline signals, has a very robust lead correlation with the OCP ENSO. Most OCP ENSO events are found to follow the same ENSO conditions, and the number of OCP ENSO events increases substantially since the start of the twenty-first century. These results highlight the importance of monitoring off-equatorial subsurface preconditions for ENSO prediction and to understand multiyear ENSO.

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.

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.