An official website of the United States government
Abstract Atmospheric blocking events are persistent quasi‐stationary geopotential height anomalies that divert the jet stream from its climatological path in the mid‐ to high‐latitudes. Previous studies have found that different phases of the El Niño–Southern Oscillation (ENSO) influence the characteristics of blocking, but none have considered the spatial diversity of El Niño. In this study, we examine Northern Hemisphere blocking events with respect to the “Central Pacific” (CP) and “Eastern Pacific” (EP) flavors of El Niño in 83 years of ERA5 reanalysis. The two El Niño flavors have dissimilar patterns of forcing on atmospheric circulation that impact the strength and placement of the upper‐level jet stream, thus affecting blocking event frequency and duration. Significant contrasts in blocking characteristics between CP and EP years are disregarded when a single ENSO index is used, and we emphasize that El Niño flavors should be considered in future investigations of blocking and ENSO‐related variability.
more »
« less
Large-Scale Environments of Successive Atmospheric River Events Leading to Compound Precipitation Extremes in California
Abstract Successive atmospheric river (AR) events—known as AR families—can result in prolonged and elevated hydrological impacts relative to single ARs due to the lack of recovery time between periods of precipitation. Despite the outsized societal impacts that often stem from AR families, the large-scale environments and mechanisms associated with these compound events remain poorly understood. In this work, a new reanalysis-based 39-yr catalog of 248 AR family events affecting California between 1981 and 2019 is introduced. Nearly all (94%) of the interannual variability in AR frequency is driven by AR family versus single events. Usingk-means clustering on the 500-hPa geopotential height field, six distinct clusters of large-scale patterns associated with AR families are identified. Two clusters are of particular interest due to their strong relationship with phases of El Niño–Southern Oscillation (ENSO). One of these clusters is characterized by a strong ridge in the Bering Sea and Rossby wave propagation, most frequently occurs during La Niña and neutral ENSO years, and is associated with the highest cluster-average precipitation across California. The other cluster, characterized by a zonal elongation of lower geopotential heights across the Pacific basin and an extended North Pacific jet, most frequently occurs during El Niño years and is associated with lower cluster-average precipitation across California but with a longer duration. In contrast, single AR events do not show obvious clustering of spatial patterns. This difference suggests that the potential predictability of AR families may be enhanced relative to single AR events, especially on subseasonal to seasonal time scales.
more »
« less
- Award ID(s):
- 1854761
- PAR ID:
- 10363217
- Publisher / Repository:
- American Meteorological Society
- Date Published:
- Journal Name:
- Journal of Climate
- Volume:
- 35
- Issue:
- 5
- ISSN:
- 0894-8755
- Format(s):
- Medium: X Size: p. 1515-1536
- Size(s):
- p. 1515-1536
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract The Indian and Pacific Oceans surround the Maritime Continent (MC). Major modes of sea surface temperature variability in both oceans, including the Indian Ocean Dipole (IOD) and El Niño–Southern Oscillation (ENSO), can strongly affect precipitation on the MC. The prevalence of fires in the MC is closely associated with precipitation amount and terrestrial water storage in September and October. Precipitation and terrestrial water storage, which is a measurement of hydrological drought conditions, are significantly modulated by Indian Ocean Dipole (IOD) and El Niño events. We utilize long-term datasets to study the combined effects of ENSO and the IOD on MC precipitation during the past 100 years (1900–2019) and find that the reductions in MC precipitation and terrestrial water storage are more pronounced during years when El Niño and a positive phase of the IOD (pIOD) coincided. The combined negative effects are produced mainly through an enhanced reduction of upward motion over the MC. Coincident El Niño-pIOD events have occurred more frequently after 1965. However, climate models do not project a higher occurrence of coincident El Niño-pIOD events in a severely warming condition, implying that not the global warming but the natural variability might be the leading cause of this phenomenon.more » « less
-
Abstract Variability in the El Niño‐Southern Oscillation (ENSO) has global impacts on seasonal temperatures and rainfall. Current detection methods for extreme phases, which occur with irregular periodicity, rely upon sea surface temperature anomalies within a strictly defined geographic region of the Pacific Ocean. However, under changing climate conditions and ocean warming, these historically motivated indicators may not be reliable into the future. In this work, we demonstrate the power of data clustering as a robust, automatic way to detect anomalies in climate patterns. Ocean temperature profiles from Argo floats are partitioned into similar groups utilizing unsupervised machine learning methods. The automatically identified groups of measurements represent spatially coherent, large‐scale water masses in the Pacific, despite no inclusion of geospatial information in the clustering task. Further, spatiotemporal dynamics of the clusters are strongly indicative of El Niño events, the east Pacific warming phase of ENSO. The fitting of a cluster model on a collection of ocean profiles identifies changes in the vertical structure of the temperature profiles through reassignment to a different group, concisely capturing physical changes to the water column during an El Niño event, such as thermocline tilting. Clustering proves to be an effective tool for analysis of the irregularly sampled (in space and time) data from Argo floats and may serve as a novel approach for detecting anomalies given the freedom from thresholding decisions. Unsupervised machine learning could be particularly valuable due to its ability to identify patterns in data sets without user‐imposed expectations, facilitating further discovery of anomaly indicators.more » « less
-
Abstract In around 1990, significant shifts occurred in the spatial pattern and temporal evolution of the El Niño‐Southern Oscillation (ENSO), with these shifts showing asymmetry between El Niño and La Niña phases. El Niño transitioned from the Eastern Pacific (EP) to the Central Pacific (CP) type, while La Niña's multi‐year (MY) events increased. These changes correlated with shifts in ENSO dynamics. Before 1990, El Niño was influenced by the Tropical Pacific (TP) ENSO dynamic, shifting to the Subtropical Pacific (SP) ENSO dynamic afterward, altering its spatial pattern. La Niña was influenced by the SP ENSO dynamic both before and after 1990 and has maintained the CP type. The strengthened SP ENSO dynamic since 1990, accompanied by enhanced precipitation efficiency during La Niña, make it easier for La Niña to transition into MY events. In contrast, there is no observed increase in precipitation efficiency during El Niño.more » « less
-
Application of Clustering Algorithms to TRMM Precipitation over the Tropical and South Pacific OceanUnderstanding multiscale rainfall variability in the South Pacific convergence zone (SPCZ), a southeastward-oriented band of precipitating deep convection in the South Pacific, is critical for both the human and natural systems dependent on its rainfall, and for interpreting similar off-equatorial diagonal convection zones around the globe. A k-means clustering method is applied to daily austral summer (December–February) Tropical Rainfall Measuring Mission (TRMM) satellite rainfall to extract representative spatial patterns of rainfall over the SPCZ region for the period 1998–2013. For a k = 4 clustering, pairs of clusters differ predominantly via spatial translation of the SPCZ diagonal, reflecting either warm or cool phases of El Niño–Southern Oscillation (ENSO). Within each of these ENSO phase pairs, one cluster exhibits intense precipitation along the SPCZ while the other features weakened rainfall. Cluster temporal behavior is analyzed to investigate higher-frequency forcings (e.g., the Madden–Julian oscillation and synoptic-scale disturbances) that trigger deep convection where SSTs are sufficiently warm. Pressure-level winds and specific humidity from the Climate Forecast System Reanalysis are composited with respect to daily cluster assignment to investigate differences between active and quiescent SPCZ conditions to reveal the conditions supporting enhanced or suppressed SPCZ precipitation, such as low-level poleward moisture transport from the equator. Empirical orthogonal functions (EOFs) of TRMM precipitation are computed to relate the “modal view” of SPCZ variability associated with the EOFs to the “state view” associated with the clusters. Finally, the cluster number is increased to illustrate the change in TRMM rainfall patterns as additional degrees of freedom are permitted.more » « less
