Ensemble Oceanic NINO Index (ENS ONI)
Figure 1. December 1877 Tropical Pacific sea surface temperature anomalies via HADISST2.1
Created in python
MAJOR UPDATES TO THE ENSEMBLE OCEANIC NINO INDEX (ENS ONI)
May 2021: ENS ONI median definitions are now presented in percent instead of raw numeric values to allow for more intuitive interpretations of the data.
April 2021: ENS ONI median definitions (described below) were added to the suite of available output.
March 2021: ENS ONI data for 1850-1864 has now been released, several datasets were added, and the juxtaposition of ENS ONI's 30-year sliding base periods were updated to be more symmetric with the 5-year periods (Dr Brian Magi, person comm). The pre-1865 extension of the ENS ONI reveals a potential El Nino event in 1855-56, which is strongly supported by recent work from Lin et al (2020) depicting this particular El Nino to be associated with the 2nd worst drought during the Qing dynasty in China. Applying a 30-year sliding median to Madras SLP (1796-2003) reconstructed by Allan et al (2002) also reveals substantial evidence of an El Nino in 1855-56 w/ 30-year median SLP anomalies exceeding +1.2σ in November 1855, which is near the top 10% of all Novembers in 1796-2003.
Experiments with pre-1850 instrumentally-based data like the aforementioned Madras SLP and available reanalysis over this period (like NOAA 20CRv3 (Slivinski et al (2019)) and corroboration with documentary sources such as
Garcia-Herrera et al (2008), Ortileb (2000), & Quinn et al (1987) reveal fairly substantial evidence of an El Nino in the winter of 1845-46.
Simple Ocean Data Assimilation version 2.2.4 (SODAv2.2.4) (1871-2010) (Giese and Ray (2011)), ECMWF's Ocean Reanalysis System 5 (ORAS5) (Zuo et al (2019)) (which is used for the operational ECMWF model), & NASA's Modern-Era Retrospective analysis for Research and Applications Version 2 (NASA MERRA2) (Gelaro et al (2017)), have been added to the ENS ONI, further improving the overall quality of the index and accompanying estimates of inter-dataset spread, especially for the 1871-1900 period when few datasets are available and most SST reconstructions (e.g. HADISST1) that rely on modern ENSO behavior to reconstruct ENSO when data coverage is sparse, may underestimate the amplitude of major ENSO events like 1877-78 (Giese and Ray (2011)).
For comparison purposes and to further validate the legitimacy of ENS ONI's first-order estimates of structural uncertainty that emerge between datasets, ENS ONI's inter-dataset spread is now also directly accompanied by other published estimates of NINO 3.4 region spread from NOAA's 20CRv3 (Slivinski et al (2019)) and HADISST2 (Titchner and Rayner (2014)) (See figure and the excel sheet below).
The previous quality control procedure used to filter out potentially spurious data was discontinued as the median used to derive the index here is fairly robust to outliers.
An additional excel sheet with peak ENSO event intensities (in °C) has been added to the list of deliverables provided below on this web page.
October 2020: HADISST2.1 was added to the ENS ONI, significantly increasing the quality of the ENS ONI prior to 1900, and the new quality control procedure used to filter out potentially spurious data in the ENS ONI is significantly less stringent. OISSTv2 was upgraded to OISSTv2.1, which may slightly impact recent ENS ONI data.
ENS ONI now uses median SSTs instead of mean SSTs from available datasets at each time step to better protect the ENS ONI against outliers and preserve the amplitude of pre-1950 ENSO events. Utilizing the median SST also more accurately depicts well known asymmetries in ENSO amplitude stemming from more efficient Bjerknes feedbacks and stronger non-linearities between SSTs, surface winds, and locally forced convection in El Ninos. To better reflect this asymmetry, ENSO event intensity definitions were altered from, with "Super La Nina" no longer being utilized. However, keeping in spirit of the original ONI, ENS ONI still utilizes 3-month running means of monthly ONI values to determine tri-monthly ENS ONI values. The ENS ONI is also now accompanied by uncertainty estimates, determined by inter-dataset spread at each monthly time step, approximated as 2σ, generally corresponding to 95% level statistical significance.
July 2019: ERA-5 was added to the analysis & interpolated to a 1x1 degree grid to make it more comparable to other datasets used in the ENS ONI. Its addition resulted in little notable change in the ENS ONI after 1964. The ENS ONI quality control procedure was reevaluated for the 1877-78 El Nino, resulting in more realistic amplitude evolution during the growth phase of this "Super" El Nino in 1877. Changes in the 1877-78 El Nino also implicated nearby portions of the record, leading to an extremely modest modification of other ONI values from 1865 to 1890.
June 2019: HADSST4 was added to the analysis, resulting in a slight increase in amplitude of most pre-1950 ENSO events due to better constraint of quality control procedures. The standard deviation of available SST datasets used in the ENS ONI is now also provided.
April 2021: ENS ONI median definitions (described below) were added to the suite of available output.
March 2021: ENS ONI data for 1850-1864 has now been released, several datasets were added, and the juxtaposition of ENS ONI's 30-year sliding base periods were updated to be more symmetric with the 5-year periods (Dr Brian Magi, person comm). The pre-1865 extension of the ENS ONI reveals a potential El Nino event in 1855-56, which is strongly supported by recent work from Lin et al (2020) depicting this particular El Nino to be associated with the 2nd worst drought during the Qing dynasty in China. Applying a 30-year sliding median to Madras SLP (1796-2003) reconstructed by Allan et al (2002) also reveals substantial evidence of an El Nino in 1855-56 w/ 30-year median SLP anomalies exceeding +1.2σ in November 1855, which is near the top 10% of all Novembers in 1796-2003.
Experiments with pre-1850 instrumentally-based data like the aforementioned Madras SLP and available reanalysis over this period (like NOAA 20CRv3 (Slivinski et al (2019)) and corroboration with documentary sources such as
Garcia-Herrera et al (2008), Ortileb (2000), & Quinn et al (1987) reveal fairly substantial evidence of an El Nino in the winter of 1845-46.
Simple Ocean Data Assimilation version 2.2.4 (SODAv2.2.4) (1871-2010) (Giese and Ray (2011)), ECMWF's Ocean Reanalysis System 5 (ORAS5) (Zuo et al (2019)) (which is used for the operational ECMWF model), & NASA's Modern-Era Retrospective analysis for Research and Applications Version 2 (NASA MERRA2) (Gelaro et al (2017)), have been added to the ENS ONI, further improving the overall quality of the index and accompanying estimates of inter-dataset spread, especially for the 1871-1900 period when few datasets are available and most SST reconstructions (e.g. HADISST1) that rely on modern ENSO behavior to reconstruct ENSO when data coverage is sparse, may underestimate the amplitude of major ENSO events like 1877-78 (Giese and Ray (2011)).
For comparison purposes and to further validate the legitimacy of ENS ONI's first-order estimates of structural uncertainty that emerge between datasets, ENS ONI's inter-dataset spread is now also directly accompanied by other published estimates of NINO 3.4 region spread from NOAA's 20CRv3 (Slivinski et al (2019)) and HADISST2 (Titchner and Rayner (2014)) (See figure and the excel sheet below).
The previous quality control procedure used to filter out potentially spurious data was discontinued as the median used to derive the index here is fairly robust to outliers.
An additional excel sheet with peak ENSO event intensities (in °C) has been added to the list of deliverables provided below on this web page.
October 2020: HADISST2.1 was added to the ENS ONI, significantly increasing the quality of the ENS ONI prior to 1900, and the new quality control procedure used to filter out potentially spurious data in the ENS ONI is significantly less stringent. OISSTv2 was upgraded to OISSTv2.1, which may slightly impact recent ENS ONI data.
ENS ONI now uses median SSTs instead of mean SSTs from available datasets at each time step to better protect the ENS ONI against outliers and preserve the amplitude of pre-1950 ENSO events. Utilizing the median SST also more accurately depicts well known asymmetries in ENSO amplitude stemming from more efficient Bjerknes feedbacks and stronger non-linearities between SSTs, surface winds, and locally forced convection in El Ninos. To better reflect this asymmetry, ENSO event intensity definitions were altered from, with "Super La Nina" no longer being utilized. However, keeping in spirit of the original ONI, ENS ONI still utilizes 3-month running means of monthly ONI values to determine tri-monthly ENS ONI values. The ENS ONI is also now accompanied by uncertainty estimates, determined by inter-dataset spread at each monthly time step, approximated as 2σ, generally corresponding to 95% level statistical significance.
July 2019: ERA-5 was added to the analysis & interpolated to a 1x1 degree grid to make it more comparable to other datasets used in the ENS ONI. Its addition resulted in little notable change in the ENS ONI after 1964. The ENS ONI quality control procedure was reevaluated for the 1877-78 El Nino, resulting in more realistic amplitude evolution during the growth phase of this "Super" El Nino in 1877. Changes in the 1877-78 El Nino also implicated nearby portions of the record, leading to an extremely modest modification of other ONI values from 1865 to 1890.
June 2019: HADSST4 was added to the analysis, resulting in a slight increase in amplitude of most pre-1950 ENSO events due to better constraint of quality control procedures. The standard deviation of available SST datasets used in the ENS ONI is now also provided.
Introduction
The Ensemble Oceanic NINO Index (ENS-ONI) is one of the longest running, real-time, instrumentally-based ENSO indices that has been developed to date, spanning from the mid 19th century to the present. The ENS ONI also arguably provides the most complete and up-to-date description of structural uncertainty amongst a multitude of SST reconstructions and reanalyses (for more on structural uncertainties see: Kennedy (2014)). The ENS ONI closely follows the methodology of the Climate Prediction Center (CPC), and is computed by using seasonally averaged sea surface temperature (SST) anomalies in the NINO 3.4 region (5S-5N, 120-170W) over the Equatorial Pacific as defined by Barnston et al (1997). Following this publication, the NINO 3.4 index became one of the most popular ENSO indices at the turn of the 21st century. The exceptional combined intensity and longevity of the 2014-16 multi-year "Super" El Nino event in the context of modern single-year modern "Super" El Nino events in 1972-73, 1982-83, & 1997-98 spurred the creation of the ENS ONI. The ENS ONI attempts to provide as long and reliable of an instrumentally-based ENSO record as possible while maintaining simplicity in its construction and being available for real-time scrutiny. While the quality and quantity of data platforms and spatiotemporal coverage is relatively less reliable, less numerous, and more sparse respectively, the number of ENSO event samples under the ENS ONI is more than double the "reliable" record (1950-present).
The ENS ONI is intended to be a predecessor product to the Extended MEI version 2 (MEI.extv2), which is currently in production. For more on MEI.extv2 see my 100th Annual American Meteorological Society (AMS) Conference poster presentation: "Reanalysis of the Extended Multivariate ENSO Index"
**A publication for the ENS ONI is currently in preparation with Dr. Brian Magi (UNC-Charlotte)**
** "The Ensemble Oceanic Nino Index" (Webb and Magi, 2021) **
The ENS ONI is intended to be a predecessor product to the Extended MEI version 2 (MEI.extv2), which is currently in production. For more on MEI.extv2 see my 100th Annual American Meteorological Society (AMS) Conference poster presentation: "Reanalysis of the Extended Multivariate ENSO Index"
**A publication for the ENS ONI is currently in preparation with Dr. Brian Magi (UNC-Charlotte)**
** "The Ensemble Oceanic Nino Index" (Webb and Magi, 2021) **
Figure 2. NINO SST regions
Data
The ENS ONI utilizes World Meteorological Organization (WMO) 30 year climatological base periods that are updated every 5 years (Table 1) to account for significant changes and inhomogeneities in the background climatic and observational mean state in the last few centuries. Raw data rounded to the nearest 0.01C was attained and datasets such as Kaplan's Extended SST Version 2, were converted from anomalies to raw data using their respective climatologies.
Although this index exhibits very robust correlations with the Bivariate ENSO Timeseries (BEST), Multivariate ENSO Index (MEI), Extended Multivariate ENSO Index (MEI.ext), this product is preliminary and should be used with caution, especially for data prior to 1877. The following 32 sea surface temperature, satellite, and reanalysis datasets were utilized in the creation of the ENS ONI at a grid spacing that varied between 1°x1° and 5°x5° latitude and longitude to ensure all were resolving the same types of phenomena in their respective fields. If these datasets weren't explicitly provided with this grid spacing, they were averaged over a number of grid boxes until this criteria was met.
HADISST2.1 (1850-2010)
COBE SST 2 (1850-2019)
HADSST4 (1850-2019)
NOAAs 20th Century Reanalysis Version 3 (1850-2015)
NOAAs 20th Century Reanalysis Version 2c (1854-2014)
ERSST Version 5 (1854-Present)
ERSST Version 4 (1854-2020)
Kaplan's Extended SST Version 2 (1856-Present)
HADISST (1870-Present)
NOAAs 20th Century Reanalysis Version 2 (1871-2012)
SODAv2.2.4 (1871-2010)
IOCADSv3 (1878-2020)
ERSST Version 3b (1880-2020)
COBE SST (1891-Present)
ERA-20CM (1900-2010)
ERA-20C (1900-2010)
CERA-20C (1901-2010)
NCEP/NCAR Reanalysis Version 1 (1948-Present)
HADSST2 (1950-2014)
HADSST3 (1950-2020)
ERA-5 (1950-Present)
WHOI OA Flux Version 3 (1958-2019)
ECMWF ORAS5 (1958-2019)
Climate Analysis Center (CAC) (now known as the CPC) SST (1970-March 2003)
NCEP CFSR (1979-2015)
NCEP DOE R2 (1979-Present)
ERA-Interim (1979-2019)
NASA MERRA (1979-2016)
NASA MERRA2 (1980-2016)
OISSTv1 (1 degree) (Nov 1981-March 2003)
OISSTv2.1 (1 degree) (Nov 1981-Present)
Aqua/MODIS SST (1 degree) (July 2002-Present)
Although this index exhibits very robust correlations with the Bivariate ENSO Timeseries (BEST), Multivariate ENSO Index (MEI), Extended Multivariate ENSO Index (MEI.ext), this product is preliminary and should be used with caution, especially for data prior to 1877. The following 32 sea surface temperature, satellite, and reanalysis datasets were utilized in the creation of the ENS ONI at a grid spacing that varied between 1°x1° and 5°x5° latitude and longitude to ensure all were resolving the same types of phenomena in their respective fields. If these datasets weren't explicitly provided with this grid spacing, they were averaged over a number of grid boxes until this criteria was met.
HADISST2.1 (1850-2010)
COBE SST 2 (1850-2019)
HADSST4 (1850-2019)
NOAAs 20th Century Reanalysis Version 3 (1850-2015)
NOAAs 20th Century Reanalysis Version 2c (1854-2014)
ERSST Version 5 (1854-Present)
ERSST Version 4 (1854-2020)
Kaplan's Extended SST Version 2 (1856-Present)
HADISST (1870-Present)
NOAAs 20th Century Reanalysis Version 2 (1871-2012)
SODAv2.2.4 (1871-2010)
IOCADSv3 (1878-2020)
ERSST Version 3b (1880-2020)
COBE SST (1891-Present)
ERA-20CM (1900-2010)
ERA-20C (1900-2010)
CERA-20C (1901-2010)
NCEP/NCAR Reanalysis Version 1 (1948-Present)
HADSST2 (1950-2014)
HADSST3 (1950-2020)
ERA-5 (1950-Present)
WHOI OA Flux Version 3 (1958-2019)
ECMWF ORAS5 (1958-2019)
Climate Analysis Center (CAC) (now known as the CPC) SST (1970-March 2003)
NCEP CFSR (1979-2015)
NCEP DOE R2 (1979-Present)
ERA-Interim (1979-2019)
NASA MERRA (1979-2016)
NASA MERRA2 (1980-2016)
OISSTv1 (1 degree) (Nov 1981-March 2003)
OISSTv2.1 (1 degree) (Nov 1981-Present)
Aqua/MODIS SST (1 degree) (July 2002-Present)
Table 1. ENS ONI 30-year base periods (updated every 5 years) (left) with corresponding ENS ONI values (right).
For more information on the ENS ONI and 19th-21st century ENSO behavior see my 2017 AMS Conference poster presentation: "A Reanalysis of the Extended Multivariate ENSO Index (MEI.ext) and Comparison of the 1877-78 and 2015-16 El Nino Events"
Data for the ENS ONI was obtained at:
Climate Prediction Center (CPC): Monthly Atmospheric and SST Indices
ECMWF Climate Reanalysis
ECMWF Copernicus Climate Change Service
Global Climate Observing System (GCOS) Working Group on Surface Pressure (WG-SP)
International Research Institute for Climate and Society
Koninklijk Nederlands Meteorologisch Instituut (KNMI) Climate Explorer
Met Office Hadley Centre observation datasets
NASA Earth Observations
NCAR's Research Data Archive
NOAA's Earth System Research Laboratory
NOAA's Science Data Integration Group Live Access Server
Data for the ENS ONI was obtained at:
Climate Prediction Center (CPC): Monthly Atmospheric and SST Indices
ECMWF Climate Reanalysis
ECMWF Copernicus Climate Change Service
Global Climate Observing System (GCOS) Working Group on Surface Pressure (WG-SP)
International Research Institute for Climate and Society
Koninklijk Nederlands Meteorologisch Instituut (KNMI) Climate Explorer
Met Office Hadley Centre observation datasets
NASA Earth Observations
NCAR's Research Data Archive
NOAA's Earth System Research Laboratory
NOAA's Science Data Integration Group Live Access Server
Discussion and analysis
Although the ENS ONI is comprised of a multitude of datasets with disparate spatial and temporal resolutions, and only utilizes SST anomalies in a seemingly inconsequential region of the equatorial Pacific, the standard deviation amongst various available datasets appears to be somewhat reflective of global trends during the instrumental record. This very crude estimate of parametric uncertainty reflects structural uncertainties amongst available datasets and their sensitivity to data input, which occurs because most datasets in the ENS ONI are effectively not constructed the same way and many datasets include different versions of ICOADS and/or other observational sources (especially reanalysis datasets) respectively. Both are important points regarding uncertainty in the instrumental record that tend to be less frequently discussed in literature. Nonetheless, from the standard deviation of SSTs in the NINO 3.4 region, three primary periods of contention and notable disagreement emerge from the observed record, namely before the 1877-78 "Super" El Nino, and during the first and second World Wars. The period before 1877-78 and the first World War is largely explained by a lack of data with the latter data outage being associated with ships being sunk during the war. The apparent discrepancy amongst available datasets in and around World War 2 however is multi-faceted, featuring both a similar loss of potential data from ships sunk in the war in addition to paradigm shift in how SST observations were taken, with many ships switching from insulated bucket to engine room intake (ERI) measurements of SST as well as additional sampling uncertainty emanating from significant changes in the time of day most observations were collected (see figure 3 below) (Freeman et al (2017)).
These three aforementioned periods that are the source(s) of significant discrepancy in the instrumental record are highlighted in orange & labeled in figure 4 below.
These three aforementioned periods that are the source(s) of significant discrepancy in the instrumental record are highlighted in orange & labeled in figure 4 below.
Figure 3. ICOADS R3.0 Tropical Pacific [30S-20N, 100E-80W] percent of SST (black) and SLP (blue) observations made in daylight hours.
Figure 4. Ensemble Oceanic Nino Index uncertainty (annually smoothed) (1867-2019)
Created using Matlab 2019a
Created using Matlab 2019a
Figure 5. NOAA's 20th Century Reanalysis Version 3 (NOAA 20CRv3) (red), Hadley Centre Sea Ice and Sea Surface Temperature version 2.1 (HADISST2.1) (blue), & Ensemble Oceanic Nino Index spread (black) (annually smoothed) (1851-2010)
Created using Matlab 2019a
Created using Matlab 2019a
Attempts at reconstructing ENSO using multiple datasets and the Oceanic Nino Index has been attempted before as recently as Huang et al (2016), however their analysis was strictly limited to only several SST datasets and over the period 1950-present.
Following the methodology of Wolter and Timlin (2011), El Nino and La Nina tri-monthly periods are further binned into Weak, Moderate, Strong, and "Super" based on their tri-monthly ENS ONI percentiles shown below in table 2. El Ninos, Neutral, and La Nina are generally assumed to each occur roughly one-third of the time, however unlike Wolter and Timlin (2011), the ranks are flipped such that El Ninos occupy the lowest ranks and La Ninas the highest, we use all bi-monthly periods to rank ENSO instead of individual bi-monthly periods, and the El Nino and La Nina thresholds correspond to the lower tercile (33%) and uppermost tercile (67%) respectively as opposed to the end of the 3rd decile (30%) and beginning of the 7th decile (70%), which does not change the conclusions we draw here, although it increases the detectability of ENSO events ever-so-slightly in ENS ONI. The distribution of El Nino and La Ninas using the ENS ONI with NOAA CPC’s definition of 5 successive tri-monthly periods greater than or equal to +/-0.5C, found that La Nina and El Nino occur about 30% of the time. Therefore, the choice of ENSO threshold here is well within reason and similar to other popular ENSO indices and previous published studies.
Thus, going by the definition of ENSO noted here, El Ninos occur when the ENS ONI values are in the top 33% of all ENS ONI values for each tri-monthly period, while Neutral ENSO and La Nina correspond to the middle third and lower third of bi-monthlies respectively. Further distinctions to denote Weak, Moderate, Strong, and Super (very strong) ENSO events were also made here as was done with MEI.ext and MEI. Similar to Wolter and Timlin (2011), moderate El Ninos and moderate La Ninas were assumed to be ongoing when the MEI.extv2 bi-monthly percentiles were in the 2nd (10-20%) and 9th deciles (80-90%) respectively. Also similar to Wolter and Timlin (2011), the minimum threshold for Strong El Nino and Strong La Nina is defined as the 10th and 90th percentiles respectively.
A further distinction is made to identify extraordinarily powerful El Nino and La Nina events. The motivation for this is attributable to the fact that 4 very intense El Nino events (1877-78, 1982-83, 1997-98, 2015-16) are all more intense than not only any other El Nino, but all other ENSO events in the observed record. Furthermore, all of these El Ninos events have triggered exceptionally large and widespread climate anomalies across the globe, and the term “Super El Nino” has become increasingly popular and acceptable nomenclature in the atmospheric science community (Hameed et al (2018); Zhu et al (2018); Bing and Xie (2017); Chen et al (2016); Hong (2016); Latif et al (2015).
Thus, going by the definition of ENSO noted here, El Ninos occur when the ENS ONI values are in the top 33% of all ENS ONI values for each tri-monthly period, while Neutral ENSO and La Nina correspond to the middle third and lower third of bi-monthlies respectively. Further distinctions to denote Weak, Moderate, Strong, and Super (very strong) ENSO events were also made here as was done with MEI.ext and MEI. Similar to Wolter and Timlin (2011), moderate El Ninos and moderate La Ninas were assumed to be ongoing when the MEI.extv2 bi-monthly percentiles were in the 2nd (10-20%) and 9th deciles (80-90%) respectively. Also similar to Wolter and Timlin (2011), the minimum threshold for Strong El Nino and Strong La Nina is defined as the 10th and 90th percentiles respectively.
A further distinction is made to identify extraordinarily powerful El Nino and La Nina events. The motivation for this is attributable to the fact that 4 very intense El Nino events (1877-78, 1982-83, 1997-98, 2015-16) are all more intense than not only any other El Nino, but all other ENSO events in the observed record. Furthermore, all of these El Ninos events have triggered exceptionally large and widespread climate anomalies across the globe, and the term “Super El Nino” has become increasingly popular and acceptable nomenclature in the atmospheric science community (Hameed et al (2018); Zhu et al (2018); Bing and Xie (2017); Chen et al (2016); Hong (2016); Latif et al (2015).
Table 2. ENS ONI ENSO phase and intensity (left), rank (middle), and percentiles (right).
Instead of using percentiles for each tri-monthly period as previously described, another of popular way of defining ENSO intensity, as is done by Jan Null, is to use the peak intensity of the totality of a boreal winter and/or ENSO event (Table 3). This leads to slightly different numbers of events in each classification compared to Table 2 above, but both methods are arguably equally valid ways of subjectively defining ENSO, depending on the preference and intentions of the user.
Table 3. ENS ONI ENSO phase (left) peak tri-monthly intensity (in °C) (right)
ENSO Event Phase and Peak Intensity List (1850-2020)
"Super" El Nino: 1877-78, 1888-89, 1972-73, 1982-83, 1997-98, & 2015-16
Strong El Nino: 1896-97, 1899-00, 1902-03, 1918-19, 1930-31, 1940-41, 1957-58, 1965-66, 1987-88, 1991-92, & 2009-10
Moderate El Nino: 1855-56, 1868-69, 1885-86, 1904-05, 1905-06, 1911-12, 1913-14, 1923-24, 1925-26, 1939-40, 1941-42, 1951-52, 1963-64, 1968-69, 1976-77, 1977-78, 1986-87, 1994-95, & 2002-03
Weak El Nino: 1852-53, 1864-65, 1867-68, 1868-69, 1876-77, 1880-81, 1883-84, 1884-85, 1887-88, 1891, 1895-96, 1900-01, 1914-15, 1919-20, 1929-30, 1952-53, 1953-54, 1958-59, 1969-70, 1979-80, 2004-05, 2006-07, 2014-15, 2018-19, & 2019-20
Weak La Nina: 1869-70, 1870-71, 1872-73, 1873-74, 1879-80, 1894-95, 1897-98, 1898-99, 1903-04, 1906-07, 1908-09, 1910-11, 1915-16, 1922-23, 1938-39, 1944-45, 1950-51, 1954-55, 1956-57, 1964-65, 1971-72, 1974-75, 1978, 1983-84, 1989-90, 1995-96, 2000-01, 2005-06, 2008-09, 2016-17, 2017-18
Moderate La Nina: 1849-50(?), 1856-57, 1886-87, 1889-90, 1892-93, 1893-94, 1909-10, 1917-18, 1924-25, 1933-34, 1942-43, 1949-50, 1970-71, 1975-76, 1984-85, 1998-99, 2011-12, 2020-21
Strong La Nina: 1916-17, 1955-56, 1973-74, 1988-89, 1999-00, 2007-08, 2010-11
Table 3. ENSO Event Intensity List (1850-2020)
Figure 6. ENS ONI time series (1850-2021)
Created using Matlab 2019a
Created using Matlab 2019a
Data for the ENS ONI are available here in Excel format:
*Note that an additional excel file to the definitions table is hyperlinked further down on the page in the "ENSO Definitions" section*
Monthly NINO 3.4 SSTs (1850-Apr 2021)
ENS ONI tri-monthly data (1850-Apr 2021)
ENS ONI standardized tri-monthly data (1850-Apr 2021)
ENS ONI tri-monthly ranks (1850-Apr 2021)
ENS ONI Events Intensity (1850-Apr 2021)
ENS ONI, NOAA 20CRv3, & HADISST2.1 uncertainty (1 sigma spread) (1850-Apr 2021)
The following ENS ONI table (also linked above) uses a modified version of the Trenberth and Hoar (1997) definition of ENSO, wherein NINO 3.4 region SST anomalies must exceed +/-0.4C for 6 successive tri-monthly periods to be classified as El Nino or La Nina event. Here, the threshold is lowered slightly to 0.3C for 6 successive tri-monthlies mainly to identify early-mid 20th century ENSO events that may have barely missed the Trenberth & Hoar or CPC thresholds due to excessive, unrealistic damping of the ENS ONI that may be present. El Ninos are denoted in red, La Ninas in Blue.
*Note that an additional excel file to the definitions table is hyperlinked further down on the page in the "ENSO Definitions" section*
Monthly NINO 3.4 SSTs (1850-Apr 2021)
ENS ONI tri-monthly data (1850-Apr 2021)
ENS ONI standardized tri-monthly data (1850-Apr 2021)
ENS ONI tri-monthly ranks (1850-Apr 2021)
ENS ONI Events Intensity (1850-Apr 2021)
ENS ONI, NOAA 20CRv3, & HADISST2.1 uncertainty (1 sigma spread) (1850-Apr 2021)
The following ENS ONI table (also linked above) uses a modified version of the Trenberth and Hoar (1997) definition of ENSO, wherein NINO 3.4 region SST anomalies must exceed +/-0.4C for 6 successive tri-monthly periods to be classified as El Nino or La Nina event. Here, the threshold is lowered slightly to 0.3C for 6 successive tri-monthlies mainly to identify early-mid 20th century ENSO events that may have barely missed the Trenberth & Hoar or CPC thresholds due to excessive, unrealistic damping of the ENS ONI that may be present. El Ninos are denoted in red, La Ninas in Blue.
Figures 7-22. ENS ONI tables (by decade, except for 2010-2021).
ENSO Definitions
Shown below is a table with 25 ENSO definitions based on either SST anomalies, SST standardized anomalies, or percentiles. The corresponding reference from where this definition originates is hyperlinked in the far left column. While the NOAA/CPC ONI definition is arguably the most commonly used in the scientific community, it's far from the only one that's been frequently cited or utilized in real-time. Users of the ENS ONI should be aware that no matter how good any particular definition sounds or might seem, there is always some level of nuance involved, and that even slight modifications to a definition can lead to very different statistical and/or dynamical interpretations depending on the intention(s) of the individual and uncertainty in the underlying data. However, like combining a multitude of datasets, synthesizing a large ensemble of definitions from many prominent institutions and researchers increase the robustness, stability, and confidence that the output and conclusions derived from them are truly legitimate and significant.
This unique analysis of many ENSO definitions that are applicable to the ENS ONI also allows us to obtain an estimate of opinions from experts in the field on what is and is not ENSO at some time "t" based on output from the ENS ONI.
Considering all of the above, it seems reasonable to believe that when at least a majority of the 25 definitions (13 of 25) reach a consensus that an El Nino or La Nina is ongoing for a given monthly, bi-monthly, tri-monthly, and/or pentad period based on the ENS ONI, that an ENSO event is indeed probably, if not, likely ongoing. We will tentatively refer to this measure of ENSO as the ENS ONI Definitions Index. In order to ensure an apples-apples comparison between all the definitions, each one is given equivalent weight of up to 1 w/ a binary output (1 or 0/pass or fail) signaling the specified criteria in each definition was or was not met at a particular time step. To determine the time that this actually corresponds to for instances where tri-monthly or pentad averaged values of this ENS ONI are used, the middle month of that period is utilized. For ex, if we're looking at an ENSO definition that uses a pentad-averaged period to categorize ENSO and we're analyzing Oct-Feb (ONDJF), the binary output would be placed in the month of December, w/ January being used in place of NDJFM, etc. For bi-monthlies, these ENS ONI Definitions index values are binned into the first month of the bi-monthly period. To designate both El Ninos and La Ninas in this paradigm, La Nina (El Nino) values are negative (positive) and shaded blue (red).
Depicted in Figure 23 is the ENS ONI Median Definitions Index for 2010-present. Raw values (0 to (+ or -) 25) are converted to percent, with values <-49(%) signifying that a majority of ENSO definitions recognize La Nina conditions, whereas values >+49 (%) correspond to where most definitions contend that El Nino conditions are occurring. Note that this definitions index will lag in real-time by a few months, due to the inclusion of pentad-averaged data.
Excel file for the ENS ONI definitions index (evaluated on the median SST anomaly value presented here)
ENS ONI Median Definitions Index (1850-Apr 2021)
**Assessments and further applications of this ENS ONI Definitions Index (esp as it pertains to uncertainty) are currently being experimented with and may be released in the future**
This unique analysis of many ENSO definitions that are applicable to the ENS ONI also allows us to obtain an estimate of opinions from experts in the field on what is and is not ENSO at some time "t" based on output from the ENS ONI.
Considering all of the above, it seems reasonable to believe that when at least a majority of the 25 definitions (13 of 25) reach a consensus that an El Nino or La Nina is ongoing for a given monthly, bi-monthly, tri-monthly, and/or pentad period based on the ENS ONI, that an ENSO event is indeed probably, if not, likely ongoing. We will tentatively refer to this measure of ENSO as the ENS ONI Definitions Index. In order to ensure an apples-apples comparison between all the definitions, each one is given equivalent weight of up to 1 w/ a binary output (1 or 0/pass or fail) signaling the specified criteria in each definition was or was not met at a particular time step. To determine the time that this actually corresponds to for instances where tri-monthly or pentad averaged values of this ENS ONI are used, the middle month of that period is utilized. For ex, if we're looking at an ENSO definition that uses a pentad-averaged period to categorize ENSO and we're analyzing Oct-Feb (ONDJF), the binary output would be placed in the month of December, w/ January being used in place of NDJFM, etc. For bi-monthlies, these ENS ONI Definitions index values are binned into the first month of the bi-monthly period. To designate both El Ninos and La Ninas in this paradigm, La Nina (El Nino) values are negative (positive) and shaded blue (red).
Depicted in Figure 23 is the ENS ONI Median Definitions Index for 2010-present. Raw values (0 to (+ or -) 25) are converted to percent, with values <-49(%) signifying that a majority of ENSO definitions recognize La Nina conditions, whereas values >+49 (%) correspond to where most definitions contend that El Nino conditions are occurring. Note that this definitions index will lag in real-time by a few months, due to the inclusion of pentad-averaged data.
Excel file for the ENS ONI definitions index (evaluated on the median SST anomaly value presented here)
ENS ONI Median Definitions Index (1850-Apr 2021)
**Assessments and further applications of this ENS ONI Definitions Index (esp as it pertains to uncertainty) are currently being experimented with and may be released in the future**
Figure 23. ENS ONI Definitions Index evaluated on the median ENS ONI value (in %) (2010-present).
Institution/Reference |
Criteria Amplitude |
Criteria Length |
> +/- 0.7σ SST |
1 tri-monthly |
|
> +/- 0.8°C SST |
Any month |
|
> +/- 0.5°C SST |
6 consecutive pentads |
|
> +/- 1.0°C SST |
Any month |
|
El Nino: 33rd %ile, La Nina: 67th %ile |
1 tri-monthly |
|
> +/- 0.3°C SST |
6 consecutive tri-monthlies |
|
> +/- 0.5°C SST |
6 consecutive months |
|
El Nino: 25th %ile, La Nina: 75th %ile |
Any month |
|
> +/- 1.0σ SST |
4 consecutive months |
|
> +/- 1.2σ SST |
Any month |
|
> +/- 0.5°C SST |
2 consecutive tri-monthlies |
|
> +/- 0.5σ SST |
Any month |
|
> +/- 1.0σ SST |
1 tri-monthly |
|
> +/- 0.5°C SST |
5 consecutive tri-monthlies |
|
> +/- 1.4°C SST |
1 tri-monthly |
|
> +/- 1.0°C SST |
1 tri-monthly |
|
> +/- 0.5σ SST |
1 tri-monthly |
|
> +/- 0.5°C SST |
1 pentad |
|
> +/- 0.4°C SST |
3 consecutive months |
|
> +/- 1.0°C SST |
3 consecutive months |
|
> +/- 0.4°C SST |
6 consecutive tri-monthlies |
|
> +/- 0.6°C SST |
1 pentad |
|
El Nino: 30th %ile, La Nina: 70th %ile |
1 bi-monthly |
|
> +/- 0.5°C SST |
1 tri-monthly |
|
> +/- 0.6σ SST |
1 tri-monthly |
Table 4. ENSO Definitions, Reference/institution (left), criteria amplitude (middle), and criteria length (right)
Acknowledgements
I want to provide a special thanks to my peers at UNC-Charlotte, my advisor Dr Brian Magi (@brianmagi), and Terrell Wade (@Thunderstorm381), and so many others for their support, advice, and providing me with the necessary tools and coding skill sets to conduct this project as part of my ongoing masters thesis. Special hat tip to Terrell Wade on providing some useful insight to significantly improve the efficiency of the quality control procedure used in the ENS ONI.
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DiNezio, P. N. and C. Deser (2014). "Nonlinear Controls on the Persistence of La Niña*." Journal of Climate 27(19): 7335-7355.
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Folland, C. K. and D. E. Parker (1995). "Correction of instrumental biases in historical sea surface temperature data." Quarterly Journal of the Royal Meteorological Society 121(522): 319-367.
Freeman, E., et al. (2017). "ICOADS Release 3.0: a major update to the historical marine climate record." International Journal of Climatology 37(5): 2211-2232.
Garcia-Herrera, R., et al. (2008). "A Chronology of El Niño Events from Primary Documentary Sources in Northern Peru*." Journal of Climate 21(9): 1948-1962.
Garfinkel, C. I., et al. (2019). "The salience of nonlinearities in the boreal winter response to ENSO: North Pacific and North America." Climate Dynamics 52(7): 4429-4446.
Gelaro, R., et al. (2017). "The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)." Journal of Climate 30(14): 5419-5454.
Global Climate Observing System (GCOS) Working Group on Surface Pressure (WG-SP). (2018). www.esrl.noaa.gov/psd/gcos_wgsp/
Hameed, S. N., et al. (2018). "A model for super El Niños." Nature Communications 9(1): 2528.
Hayashi, M. and F. F. Jin (2017). "Subsurface nonlinear dynamical heating and ENSO asymmetry." Geophysical Research Letters 44(24): 12,427-412,435.
Hennermann, K. and Berrisford, P.: What are the changes from ERA-Interim to ERA5? (2018). https://confluence.ecmwf.int/pages/viewpage.action?pageId=74764925
Hersbach, H. and Dee, D.: ERA5 reanalysis is in production, ECMWF Newsletter, Vol. 147, p. 7, available at: https://www.ecmwf.int/en/newsletter/147/news/era5-reanalysis-production (last access: 10 Jun 2019), 2016
Hersbach, H., et al. (2015). "ERA-20CM: a twentieth-century atmospheric model ensemble." Quarterly Journal of the Royal Meteorological Society 141(691): 2350-2375.
Hirahara, S., et al. (2014). "Centennial-Scale Sea Surface Temperature Analysis and Its Uncertainty." Journal of Climate 27(1): 57-75.
Hong, L.-C. (2016). Super El Niño, Springer.
Hu, Z.-Z., et al. (2014). "Why were some La Niñas followed by another La Niña?" Climate Dynamics 42(3): 1029-1042.
Huang, B., et al. (2015). "Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4). Part I: Upgrades and Intercomparisons." Journal of Climate 28(3): 911-930.
Huang, B., et al. (2016). "Further Exploring and Quantifying Uncertainties for Extended Reconstructed Sea Surface Temperature (ERSST) Version 4 (v4)." Journal of Climate 29(9): 3119-3142.
Huang, B., et al. (2016). "Ranking the strongest ENSO events while incorporating SST uncertainty." Geophysical Research Letters 43(17): 9165-9172.
Huang, B., et al. (2017). "Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons." Journal of Climate 30(20): 8179-8205.
Huang, B., et al. (2020). "How Significant Was the 1877/78 El Niño?" Journal of Climate 33(11): 4853-4869.
Ishii, M., et al. (2005). "Objective analyses of sea-surface temperature and marine meteorological variables for the 20th century using ICOADS and the Kobe Collection." International Journal of Climatology 25(7): 865-879.
Kanamitsu, M., et al. (2002). "Ncep–doe amip-ii reanalysis (r-2)." Bulletin of the American Meteorological Society 83(11): 1631-1644.
Kaplan, A., et al. (1998). "Analyses of global sea surface temperature 1856–1991." Journal of Geophysical Research: Oceans 103(C9): 18567-18589.
Karnauskas, K. B., et al. (2012). "A Pacific Centennial Oscillation Predicted by Coupled GCMs*." Journal of Climate 25(17): 5943-5961.
Keeley, S. (2018). “ECMWF Climate Reanalysis” www.ecmwf.int/en/research/climate-reanalysis.
Kennedy, J. J., et al. (2011). "Reassessing biases and other uncertainties in sea surface temperature observations measured in situ since 1850: 1. Measurement and sampling uncertainties." Journal of Geophysical Research: Atmospheres 116(D14).
Kennedy, J. J. (2014). "A review of uncertainty in in situ measurements and data sets of sea surface temperature." Reviews of Geophysics 52(1): 1-32.
Kennedy, J. J., et al. (2019). "An ensemble data set of sea-surface temperature change from 1850: the Met Office Hadley Centre HadSST.4.0.0.0 data set." Journal of Geophysical Research: Atmospheres 0(ja).
Kent, E. C., et al. (2013). "Global analysis of night marine air temperature and its uncertainty since 1880: The HadNMAT2 data set." Journal of Geophysical Research: Atmospheres 118(3): 1281-1298.
Kessler, W. S. (2002). "Is ENSO a cycle or a series of events?" Geophysical Research Letters 29(23): 40-41-40-44.
Kiladis, G. N. and H. F. Diaz (1986). "An Analysis of the 1877–78 ENSO Episode and Comparison with 1982–83." Monthly Weather Review 114(6): 1035-1047.
Ku, H. H. (1966). "Notes on the use of propagation of error formulas." Journal of Research of the National Bureau of Standards 70(4): 263-273.
Laloyaux, P., et al. (2018). "CERA-20C: A Coupled Reanalysis of the Twentieth Century." Journal of Advances in Modeling Earth Systems 10(5): 1172-1195.
Latif, M., et al. (2015). "Super El Niños in response to global warming in a climate model." Climatic Change 132(4): 489-500.
Lin, K. H. E., et al. (2020). "Historical droughts in the Qing dynasty (1644–1911) of China." Clim. Past 16(3): 911-931.
Liu, W., et al. (2015). "Extended Reconstructed Sea Surface Temperature Version 4 (ERSST.v4): Part II. Parametric and Structural Uncertainty Estimations." Journal of Climate 28(3): 931-951.
NASA. (2018). “NASA Earth Observations” neo.sci.gsfc.nasa.gov/view.php?datasetId=MYD28M.
Null, J. (2018) “El Nino and La Nina Years and Intensities”. ggweather.com/enso/oni.htm
NOAA’s Climate Prediction Center. “Cold and Warm Episodes by Season”. origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php
NOAA’s Earth System Research Laboratory. (2020). https://esrl.noaa.gov/gmd/
NOAA’s National Centers for Environmental Information. (2018). “Equatorial Pacific Sea Surface Temperatures” www.ncdc.noaa.gov/teleconnections/enso/indicators/sst/
Oldenborgh, G., KNMI Climate Explorer. Retrieved from: https://climexp.knmi.nl/about.cgi?id=someone@somewhere
Okumura, Y. M. and C. Deser (2010). "Asymmetry in the Duration of El Niño and La Niña." Journal of Climate 23(21): 5826-5843.
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