Ocean Temperature Monitoring

Global Sea Surface Temperature Analysis

This example demonstrates how Ocean Metrics can be used to analyze and visualize sea surface temperature (SST) data. We’ll explore temperature anomalies, identify marine heatwaves, and examine long-term warming trends.

Data Overview

For this demonstration, we’re using simulated data that represents typical patterns observed in global SST measurements:

# generate simulated sst data
n_points <- 500
lat <- runif(n_points, -70, 70)
lon <- runif(n_points, -180, 180)

# create realistic temperature patterns
base_temp <- 25 - abs(lat) * 0.5  # temperature decreases with latitude
seasonal_var <- sin(2 * pi * as.numeric(Sys.Date() - as.Date("2025-01-01")) / 365) * 3
random_var <- rnorm(n_points, 0, 2)

sst_data <- tibble(
  latitude = lat,
  longitude = lon,
  temperature = base_temp + seasonal_var + random_var,
  anomaly = rnorm(n_points, 0.5, 1.5),  # slight warming bias
  date = Sys.Date()
) |>
  mutate(
    temperature = round(temperature, 1),
    anomaly = round(anomaly, 1)
  )

# create time series data
dates <- seq(as.Date("2020-01-01"), as.Date("2025-01-01"), by = "month")
global_mean_temp <- tibble(
  date = dates,
  temperature = 15 + 
    sin(2 * pi * as.numeric(dates - as.Date("2020-01-01")) / 365) * 3 +  # seasonal
    seq(0, 0.8, length.out = length(dates)) +  # warming trend
    rnorm(length(dates), 0, 0.5)  # random variation
)

head(sst_data)

# A tibble: 6 × 5
  latitude longitude temperature anomaly date      
     <dbl>     <dbl>       <dbl>   <dbl> <date>    
1    58.1    -131.          -0.7     4   2025-06-07
2    61.2    -116.          -2.5     1.3 2025-06-07
3   -29.9       7.04        11.3     2   2025-06-07
4    46.3     112.           3.4     1.1 2025-06-07
5    19.8    -138.          14.9    -1   2025-06-07
6     2.67    142.          24.5    -0.4 2025-06-07

Interactive Temperature Map

The map below shows current sea surface temperatures across the globe. Warmer colors indicate higher temperatures:

# create color palette
temp_pal <- colorNumeric(
  palette = c("darkblue", "blue", "lightblue", "yellow", "orange", "red"),
  domain = sst_data$temperature
)

# create interactive map
leaflet(sst_data) |>
  addTiles() |>
  addCircleMarkers(
    ~longitude, ~latitude,
    radius = 3,
    color = ~temp_pal(temperature),
    fillOpacity = 0.8,
    popup = ~paste(
      "Temperature:", temperature, "°C<br>",
      "Anomaly:", anomaly, "°C<br>",
      "Location:", round(latitude, 2), "°N,", round(longitude, 2), "°E"
    )
  ) |>
  addLegend(
    "bottomright",
    pal = temp_pal,
    values = ~temperature,
    title = "SST (°C)",
    opacity = 1
  )

Temperature Anomalies

Temperature anomalies show how current temperatures compare to long-term averages. Positive anomalies (red) indicate warmer than average conditions:

# create anomaly plot
plot_ly(sst_data, type = "scatter", mode = "markers") |>
  add_trace(
    x = ~longitude,
    y = ~latitude,
    color = ~anomaly,
    colors = c("darkblue", "white", "darkred"),
    colorscale = list(
      c(0, "darkblue"),
      c(0.5, "white"),
      c(1, "darkred")
    ),
    text = ~paste("Anomaly:", anomaly, "°C"),
    hoverinfo = "text",
    marker = list(
      size = 8,
      colorbar = list(title = "Temperature<br>Anomaly (°C)")
    )
  ) |>
  layout(
    title = "Sea Surface Temperature Anomalies",
    xaxis = list(title = "Longitude"),
    yaxis = list(title = "Latitude"),
    geo = list(
      showland = TRUE,
      landcolor = toRGB("gray90"),
      coastlinecolor = toRGB("gray50")
    )
  )

Global Temperature Trends

The time series below shows the global mean sea surface temperature over the past 5 years, revealing both seasonal patterns and long-term warming:

# create time series plot
plot_ly(global_mean_temp, type = "scatter", mode = "lines+markers") |>
  add_trace(
    x = ~date,
    y = ~temperature,
    name = "Global Mean SST",
    line = list(color = "rgb(22, 96, 167)", width = 2),
    marker = list(size = 4)
  ) |>
  add_trace(
    x = ~date,
    y = ~predict(loess(temperature ~ as.numeric(date), data = global_mean_temp)),
    name = "Trend",
    line = list(color = "rgb(205, 12, 24)", width = 3, dash = "dash")
  ) |>
  layout(
    title = "Global Mean Sea Surface Temperature (2020-2025)",
    xaxis = list(title = "Date"),
    yaxis = list(title = "Temperature (°C)"),
    hovermode = "x unified"
  )

Key Findings

Based on this analysis:

Spatial Patterns: Temperature decreases from equator to poles, with the warmest waters in tropical regions
Anomalies: Most regions show positive temperature anomalies, indicating warming above historical averages
Trends: The 5-year time series reveals a warming trend of approximately 0.8°C, superimposed on natural seasonal cycles

Marine Heatwave Detection

We can identify potential marine heatwave conditions where temperatures exceed the 90th percentile:

# calculate temperature percentiles
temp_90th <- quantile(sst_data$temperature, 0.9)

# identify potential heatwave areas
heatwave_regions <- sst_data |>
  filter(temperature > temp_90th) |>
  mutate(intensity = case_when(
    temperature > quantile(sst_data$temperature, 0.95) ~ "Extreme",
    temperature > quantile(sst_data$temperature, 0.92) ~ "Severe",
    TRUE ~ "Moderate"
  ))

# summary statistics
cat("Marine Heatwave Statistics:\n")

Marine Heatwave Statistics:

cat("- Threshold temperature (90th percentile):", round(temp_90th, 1), "°C\n")

- Threshold temperature (90th percentile): 23.3 °C

cat("- Number of affected regions:", nrow(heatwave_regions), "\n")

- Number of affected regions: 50

cat("- Intensity breakdown:\n")

- Intensity breakdown:

table(heatwave_regions$intensity)


 Extreme Moderate   Severe 
      24       10       16

Data Export

You can download this data for further analysis:

# export to csv
write_csv(sst_data, "ocean_temperature_data.csv")

# export time series
write_csv(global_mean_temp, "global_sst_timeseries.csv")

Next Steps

This example demonstrates basic temperature monitoring capabilities. The full Ocean Metrics platform offers:

Real-time data updates from satellite and in-situ sensors
Advanced statistical analysis and forecasting
Integration with other ocean health indicators
Customizable alerts for extreme events
API access for automated workflows

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