How are coastal sea-surface temperatures changing?

What you can answer

• Decadal SST trend for any coastal pixel (2002-present)
• Climatological seasonal cycle vs current-year deviation
• Marine heatwave detection (≥5 days above 90th percentile)
• Spatial gradient between coast and open ocean
• Cross-shelf vs cross-shore variability

What you can NOT answer with these alone

• Sub-surface temperature without combining with ECCO reanalysis or Argo float data
• Internal-wave dynamics at scales under 4 km without high-resolution SAR + altimetry combinations
• Causal attribution to a specific climate driver (ENSO, AMO) — requires separate analysis

Code template

import earthaccess
import xarray as xr
import numpy as np

earthaccess.login(strategy="netrc")

# Gulf of California (climate-vulnerable hotspot)
aoi = (-116, 22, -107, 32)
window = ("2002-07-01", "2025-12-31")

# 1. GHRSST L4 daily 1 km (gap-filled, multi-sensor analysis)
ghrsst = earthaccess.search_data(short_name="MUR-JPL-L4-GLOB-v4.1",
                                 bounding_box=aoi, temporal=window)
# Open all granules; extract analysed_sst (in Kelvin); subset to AOI
# Compute monthly mean per pixel → climatology
# Current year monthly mean → anomaly

# 2. Marine heatwave detection
# A pixel-day is a "heatwave day" if SST > 90th percentile of climatology
# (typically using 30-year baseline, but 20+ year MUR record works)
# A "heatwave" = ≥5 consecutive heatwave days
# Count heatwave-days per year → trend

# 3. SWOT SSH context (current eddies)
swot = earthaccess.search_data(short_name="SWOT_L2_LR_SSH_2.0",
                               bounding_box=aoi,
                               temporal=("2024-03-01", "2025-12-31"))
# Overlay SSH anomaly on SST anomaly → eddy-attributed warm spots

# 4. Plot:
#    - Decadal trend map
#    - Marine heatwave frequency per year
#    - Monthly anomaly time-series
#    - SST + SSH cross-section

Expected output

• Decadal trend map (°C/decade) for the AOI
• Marine heatwave frequency: heatwave-days per year time-series 2002-present
• Current-year anomaly map vs 20-year climatology
• Cross-section: SST + sub-surface temperature from ECCO

Caveats

• GHRSST L4 is gap-filled analysis — daily-global is possible because it interpolates across cloudy regions; consistency, not direct observation, in those pixels
• Coastal SST has skin-vs-bulk differences — satellites measure top millimeter; oceanographic in-situ measures meters down. Subtle differences for ecological work.
• Cloud bias: coastal regions are often persistently cloudy (e.g., California coast in summer); aggregated products handle this but high-resolution single-day products often have data gaps
• Marine-heatwave threshold convention varies — Hobday et al. 2016 is the most-cited; pick a published convention and document it

Cross-DAAC composition

OB.DAAC (MODIS Aqua SST, PACE OCI) + PO.DAAC (GHRSST, SWOT, ECCO) — two DAACs, uniform Earthdata Login.

Sources

• GHRSST MUR: https://podaac.jpl.nasa.gov/dataset/MUR-JPL-L4-GLOB-v4.1
• Marine heatwave convention (Hobday et al.): https://www.marineheatwaves.org/definition.html
• OceanColor (MODIS SST): https://oceancolor.gsfc.nasa.gov/