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Maritimes Ecosystem Indicator Catalogue

8 Atlantic MultiDecadal Oscillation Index

Data Type: Tabular Data

Spatial Scope: Northern Hemisphere (0-60N)

Duration 1854-2025

Source: NOAA , https://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v5/index/ersst.v5.amo.dat

8.1 Introduction to Indicator

The AMO describes long-duration changes in the sea surface temperature of the North Atlantic Ocean characterized by extended cool (negative) and warm (positive) phases that may last for 20-60 years (Knight, Folland, and Scaife 2006). Positive AMO phases are associated with warm waters in the North Atlantic, reduced precipitation in the Southern US, and increased hurricane activity in the Tropical Atlantic; negative AMO phases are associated with roughly the opposite (Knight, Folland, and Scaife 2006).

AMO values are computed from detrended sea surface temperature anomalies, such that the resultant value offers a description of large-scale temperature variation that is independent of gradual warming as a result of climate change.

AMO is linked with several oceanographic and atmospheric processes including air temperature and precipitation, storm strength, and variability in ocean currents.

8.2 View Data 

library(plotly)
plotly_df <- data@data %>%
  mutate(date = lubridate::make_date(year, month, day = 1),
         smooth_1yr = zoo::rollapply(SSTA_value, mean, width = 12, partial = TRUE),
         smooth_10yr = zoo::rollapply(SSTA_value, mean, width = 120, partial = TRUE),
         overall_mean = mean(SSTA_value))

p <- plot_ly(plotly_df, x = ~date) %>%
  add_lines(y = ~SSTA_value,
            name = "Monthly Anomaly",
            line = list(color = "lightgrey"),
            hovertemplate = "Monthly anomaly: %{y:.2f}<extra></extra>"
           ) %>%
  add_lines(y = ~smooth_1yr,
            name = "1-yr Smooth",
            line = list(color = "blue", width = 2),
            hovertemplate = "1-yr smoothed: %{y:.2f}<extra></extra>",
            ) %>%
  add_lines(y = ~smooth_10yr,
            name = "10-yr Smooth",
            line = list(color = "red", width = 2),
            hovertemplate = "10-yr smoothed: %{y:.2f}<extra></extra>",
           ) %>%
    add_lines(y = ~overall_mean,
            name = "Overall Mean",
            line = list(color = "black", width = 2, dash = "dash"),
            hovertemplate = "Overall Mean: %{y:.2f}<extra></extra>",
           ) %>%
  layout(
    title = "Atlantic Multidecadal Oscillation Index for Northern Hemisphere",
    xaxis = list(title = "Date"),
    yaxis = list(title = "AMO Index",
                 fixedrange = TRUE),
    hovermode = "x unified",
    margin = list( t = 80)
    
  ) %>%
  config(displayModeBar = FALSE) 

p

Figure 8.1: AMO Index Value; 1854-2025

8.4 Relevance to Research and Stock Assessments

AMO can both directly and indirectly affect marine ecosystem function (Nye et al. 2014).

In Canada’s maritimes region, AMO is closely linked to several ecological trends with relevance to fisheries and stock assessments. AMO has shown negative correlations with primary productivity and fish biomass in Eastern Canada (NAFO divisions 4X, 5YB, Western Scotian Shelf, and Bay of Fundy) (Araújo and Bundy 2012), and has been hypothesized a limitation on stock size despite moratoriums on fishing activity for commercially important species like Atlantic salmon (Condron et al. 2005).

Still, specific effects of AMO on marine resources can vary aross space. For example, AMO has displayed a positive relationship with catch per unit effort of bluefin Tuna in Canadian regions (Gulf of St. Lawrence and Nova Scotia), and a contrastng negative correlation in the United States (Hansell et al. 2020). Impacts of AMO on focal species are therefore likely related to thresholds of thermal performance of a focal species within an ecosystem.

8.5 Variable Definitions

Table 8.2: Column names and definitions in the AMO dataset.
variable description unit
year year of model-fit value
month month of model-fit value
SSTA_value AMO index

8.6 Additional Data

No additional data for AMO.

8.7 Get the Data 

library(marea)
data('amo')
plot(amo)

References

Araújo, Júlio Neves, and Alida Bundy. 2012. “Effects of Environmental Change, Fisheries and Trophodynamics on the Ecosystem of the Western Scotian Shelf, Canada.” Marine Ecology Progress Series 464: 51–67.
Condron, Alan, Robert DeConto, Raymond S Bradley, and Francis Juanes. 2005. “Multidecadal North Atlantic Climate Variability and Its Effect on North American Salmon Abundance.” Geophysical Research Letters 32 (23).
Hansell, A, J Walter, S Cadrin, W Golet, A Hanke, M Lauretta, and L Kerr. 2020. “Incorporating the Atlantic Mutidecadal Oscillation into the Western Atlantic Bluefin Tuna Stock Assessment.” ICCAT Collect. Vol. Sci. Pap 77: 376–88.
Knight, Jeff R, Chris K Folland, and Adam A Scaife. 2006. “Climate Impacts of the Atlantic Multidecadal Oscillation.” Geophysical Research Letters 33 (17).
Nye, Janet A, Matthew R Baker, Richard Bell, Andrew Kenny, K Halimeda Kilbourne, Kevin D Friedland, Edward Martino, Megan M Stachura, Kyle S Van Houtan, and Robert Wood. 2014. “Ecosystem Effects of the Atlantic Multidecadal Oscillation.” Journal of Marine Systems 133: 103–16.