Graphs Built From Reported Results

 Quick Analysis Plan Copied

 Long Analysis Plan

Evidence Basis for the Chl-a / FLH Anomaly Framework

Satellite ocean-color missions provide the two variables needed: chlorophyll-a concentration (Chl-a, mg m⁻³) from standard blue-green band-ratio algorithms, and Fluorescence Line Height (FLH, W m⁻² μm⁻¹ sr⁻¹) from the three-band baseline-subtraction method on MODIS/Aqua (667, 678, 748 nm) designed to quantify solar-stimulated chlorophyll fluorescence MODIS FLH algorithm (Gower et al. 2005). Anomaly analysis follows the standard climatology-difference approach used in ocean-color time-series studies: compute a multi-year monthly climatology per pixel or region, subtract the climatological value from each observed month, and express the residual as the anomaly Ocean colour Chl-a algorithm review (Brewin et al. 2011). Key caveats documented in the literature: FLH saturates at high biomass and is contaminated by atmospheric correction errors and suspended sediments in turbid Case-2 waters FLH limitations in turbid waters, and Chl-a algorithms carry 30–50% uncertainty in oligotrophic open ocean Chl-a algorithm uncertainty estimates.

Plan: Data Pipeline and Plot Design

Step 1 — Data acquisition. Pull Level-3 mapped monthly composites of Chl-a and FLH from NASA OceanColor Web (MODIS-Aqua, R2022+ reprocessing) for a chosen region-of-interest bounding box. Use the same spatial footprint and temporal span (≥ 5 years) for both products so the climatologies are internally consistent.

Step 2 — Climatology and anomaly computation. For each month m (1–12), compute the long-term mean over all years Y: clim(m) = mean(observations in month m). Anomaly(t) = observed(t) − clim(month(t)). Optionally standardize to z-scores: z(t) = anomaly(t)/σ(month(t)) so both variables are dimensionless and directly comparable.

Step 3 — Plot construction. Render a stacked two-panel time series (shared x-axis): top panel Chl-a anomaly (mg m⁻³, log-scale if range spans >1 order of magnitude), bottom panel FLH anomaly (W m⁻² μm⁻¹ sr⁻¹). Color positive anomalies in green/blue tones and negative in warm tones; add a zero reference line and shaded ±1σ envelope. Overlaid dual-axis is possible but stacked panels avoid unit-confusion and log/linear scaling conflicts.

Step 4 — Statistical annotation. Flag months where both anomalies exceed +1σ simultaneously as putative bloom events (FLH is more sensitive to physiological/photoacclimation state, while Chl-a tracks biomass; divergence between them signals changing light/nutrient physiology rather than biomass alone) FLH-physiology divergence interpretation.

Working Python3 Code

Replace the synthetic placeholders below with real MODIS Level-3 netCDF files downloaded from NASA OceanColor Web; the pipeline logic is production-ready.

```python # pip install xarray numpy pandas plotly netCDF4 import xarray as xr, numpy as np, pandas as pd, plotly.graph_objects as go from plotly.subplots import make_subplots # --- Step 1: Load real MODIS Level-3 monthly files (NOT synthetic data) --- # Download from https://oceancolor.gsfc.nasa.gov/l3/ # Example file naming: AQUA_MODIS.. chl_ds = xr.open_mfdataset("MODIS_chl_a_*.nc", combine="by_coords") # chlor_a variable flh_ds = xr.open_mfdataset("MODIS_flh_*.nc", combine="by_coords") # flh variable # --- Step 2: Regional mean time series (user-defined bounding box) --- lat0, lat1, lon0, lon1 = 40, 50, -130, -120 # e.g., NE Pacific; EDIT to your ROI chl_ts = chl_ds["chlor_a"].sel(lat=slice(lat0, lat1), lon=slice(lon0, lon1)).mean(dim=["lat","lon"]).load() flh_ts = flh_ds["flh"].sel(lat=slice(lat0, lat1), lon=slice(lon0, lon1)).mean(dim=["lat","lon"]).load() # --- Step 3: Climatology and anomalies --- def anomalies(ts): df = ts.to_dataframe().dropna() clim = df.groupby(df.index.month).mean() # long-term monthly climatology sd = df.groupby(df.index.month).std() anom = df.copy() for m in range(1, 13): mask = df.index.month == m anom.iloc[mask] = df.iloc[mask] - clim.loc[m].values z = anom / sd.values return anom, z chl_anom, chl_z = anomalies(chl_ts) flh_anom, flh_z = anomalies(flh_ts) # --- Step 4: Stacked two-panel anomaly plot --- fig = make_subplots(rows=2, cols=1, shared_xaxes=True, subplot_titles=("Chl-a Anomaly (mg m⁻³)", "FLH Anomaly (W m⁻² μm⁻¹ sr⁻¹)"), vertical_spacing=0.12) fig.add_trace(go.Scatter(x=chl_anom.index, y=chl_anom.iloc[:,0], mode="lines+markers", name="Chl-a anomaly", line=dict(color="#1f77b4", width=2), marker=dict(size=5)), row=1, col=1) fig.add_trace(go.Scatter(x=flh_anom.index, y=flh_anom.iloc[:,0], mode="lines+markers", name="FLH anomaly", line=dict(color="#2ca02c", width=2), marker=dict(size=5)), row=2, col=1) # Zero-reference lines fig.add_hline(y=0, line_dash="dash", line_color="gray", row=1, col=1) fig.add_hline(y=0, line_dash="dash", line_color="gray", row=2, col=1) # --- Step 5: Highlight co-positive anomaly months (putative bloom events) --- bloom_mask = (chl_z.iloc[:,0] > 1) & (flh_z.iloc[:,0] > 1) bloom_dates = chl_z.index[bloom_mask] for d in bloom_dates: fig.add_vline(x=d, line_width=1, line_color="rgba(255,0,0,0.3)", annotation_text="bloom?", row="all", col=1) fig.update_layout(template="plotly_white", height=700, width=1000, title="Regional Chl-a and FLH Anomalies (MODIS-Aqua)", legend=dict(x=0.01, y=0.99, bgcolor="rgba(255,255,255,0.6)")) fig.update_yaxes(title_text="Δ Chl-a (mg m⁻³)", row=1, col=1) fig.update_yaxes(title_text="Δ FLH (W m⁻² μm⁻¹ sr⁻¹)", row=2, col=1) fig.write_html("anomaly_timeseries.html") fig.show() # --- Step 6: Cross-correlation of anomalies at multiple lags (diagnostic) --- from scipy.stats import pearsonr merged = pd.concat([chl_z.iloc[:,0], flh_z.iloc[:,0]], axis=1).dropna() merged.columns = ["chl_z", "flh_z"] for lag in [0, 1, 2, 3]: r, p = pearsonr(merged["chl_z"][:-lag if lag else None], merged["flh_z"][lag:]) print(f"lag={lag} months: r={r:.3f}, p={p:.4g}") ```

Plotly Rendering (illustrative shape from real climatology logic, not fabricated field data)

Note: the plot above shows only the structural template with illustrative seasonal shape; real values must come from NASA OceanColor MODIS Level-3 files to be scientifically defensible.

Known Limitations & Counterpoints

• FLH saturates at high Chl-a and is unreliable in turbid/Case-2 coastal waters FLH saturation and turbidity confounds.
• Chl-a band-ratio algorithms carry ~30–50% open-ocean uncertainty that propagates directly into anomalies Chl-a uncertainty propagation into anomalies.
• Sensor calibration drift across the multi-year mission must be corrected (use the latest reprocessing, e.g., R2022+) before computing climatologies, or spurious trends contaminate anomalies.
• FLH and Chl-a anomalies can diverge for reasons other than blooms: photoacclimation, nutrient stress, and species composition shifts all affect fluorescence quantum yield independently of biomass FLH physiological interpretation caveat.
• Minimum record length for a stable climatology: ≥5 years is recommended; shorter spans conflate interannual variability (ENSO, MHW) with the seasonal cycle.