Satellites reveal hot spots of ocean changes in the early 21st century

Abstract

Ocean change leaves a potentially important imprint on ocean colorimetry. Here, we present an overview and current evaluation of the global ocean color variability from 1998 to 2022, and satellites observe that 36% of oceans (~122 million square kilometers, derived from valid observations) have experienced changes (P < 0.1). In this context, 25% of the area (formerly blue hue) is turning light blue or green, while the remaining 11% becomes bluer, mainly concentrating in the low-latitude oceans. This study further identifies a "direct" notable impact of both sea surface temperature (SST) and climate on ocean colorimetry tendency and anomaly, especially in the low-latitude oceans. Extreme SST events cause "distinct" ocean colorimetry anomalies, although 94% of cases involve relatively small SST fluctuations. Causal analysis reveals important impacts of climate change on equatorial ocean dynamics, particularly ENSO events. Our findings prove the low-latitude oceans as one of the core changing regions that respond to climate change in the early 21st century.

Fig. 1.. Global geospatial distribution of interannual average and interdecadal differences in water color in CIE-XYZ 1931 color space from 1998 to 2022.

(A and B) Global and latitude average hue angle [α*; unit, degrees (°)]. (C) Interdecadal differences of α* [(2020–2022) minus (1998–2000)]. (D) Latitudinal variation in interdecadal differences of α* shows the fractions of α* increasing/decreasing pixels within 3° latitude bins.

Fig. 2.. Time-series patterns of water color in the global oceans over the past 25 years.

(A) General trend distributions of hue angle (α*) estimated by the Theil-Sen Median method and Mann-Kendall test (area surrounded by black contours, P < 0.1) from global long–time-series data. (B) Latitudinal variation in the fraction of pixels with increasing/decreasing α* trends within 3° latitude bins (P < 0.1). (C) The fluctuation characteristics of the water color anomalies ( $a_{\mathrm{a}}^{*}$ , through SD to quantify amplitudes). (D) Average latitudinal distribution of fluctuations in α*.

Fig. 3.. Correlation (r) and causality (NIF, τ) analysis between water color and SST time-series components across global oceans from 1998 to 2022.

(A and B) The correlation and (C and D) the normalized information flow (NIF) signal distributions between the trend ( $a_{\mathrm{t}}^{*}$ and SST^t) and anomaly ( $a_{\mathrm{a}}^{*}$ and SST^a) components of α* and SST, respectively. The colored areas in the figure represent pixels with a P value of less than 0.1.

Fig. 4.. The fluctuations in water color (a*) and SST changes calculated from monthly average OC-CCI and OISST datasets from 1998 to 2022.

The colors of the boxes exhibit the absolute bias of α* induced by SST changes at different levels, with “n” denoting the proportion of data represented by each colored box. The absolute biases of SST and α* are determined on the basis of the respective climatology, with the absolute bias of SST further converted into a proportion of the climatological baseline.

Fig. 5.. The link between ocean color anomalies and climate forcing.

Pearson correlation coefficient (r) and NIF (τ) distribution between three typical climate indices [multivariate ENSO index (MEI), dipole mode index (DMI), and Atlantic multi-decadal oscillation (AMO)] and anomalous components of global α* time series ( $a_{\mathrm{a}}^{*}$ ) from 1998 to 2022. The colored areas in the figure represent pixels with a P value of less than 0.1.

Fig. 6.. The linkage between water color and phytoplankton biomass (proxy as Chla).

(A) Graphing the relationship between Chla and water color on the CIE-xy chromaticity diagram. The color bar represents the log-transformed Chla concentration. (B) Scatter plot of the water colorimetry (hue angle, α*) and Chla concentration. The orange solid line denotes the scatter fit line within a 95% prediction interval.

Fig. 7.. Phytoplankton community composition reflected by water color changes.

(A) The relationship between seven dominant PTGs and water color based on the CIE-xy chromaticity diagram. The color bar of the scatter points represents the fraction of PTGs. (B) The relationship between α* and the fractions of seven PTGs (in logarithmic space). The orange solid line denotes the scatter fit line within a 95% prediction interval.

Fig. 8.. Color chromaticity xy diagram in CIE-XYZ 1931 color space (CIE-xy).

The relationship between the coordinates of the target point, the equivalent white point, the dominant wavelength, and the hue angle (α*) (79, 80).