Sea levels, recently updated with 10 new data-points, reinforce the hiatus described as a ‘pothole’ by Josh Willis of NASA’s Jet Propulsion Laboratory, Pasadena, Calif., who says you can blame the pothole on the cycle of El Niño and La Niña in the Pacific:
This temporary transfer of large volumes of water from the oceans to the land surfaces also helps explain the large drop in global mean sea level. But they also expect the global mean sea level to begin climbing again.
Attributing the ‘pothole’ to a La Nina and the transfer of water from the ocean to land in Australia and the Amazon seems dubious, given many land areas experienced reduced rainfall at the same times, as shown above.
A quadratic model of sea-level indicates deceleration is now well-established and highly significant, and if present conditions continue, sea level will peak between 2020 and 2050 between 10mm and 40mm above present levels, and may have stopped rising already.
Reference to a ‘pothole’ in a long-term trend caused by short-term La Nina, while ignoring statistically significant overall deceleration, is another example of bias in climate science.
lm(formula = y ~ x + I(x^2))
Min 1Q Median 3Q Max
-8.53309 -2.39304 0.03078 2.45396 9.17058
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.264e+05 3.517e+04 -6.438 7.40e-10 ***
x 2.230e+02 3.513e+01 6.348 1.21e-09 ***
I(x^2) -5.490e-02 8.772e-03 -6.258 1.98e-09 ***
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 3.448 on 222 degrees of freedom
Multiple R-squared: 0.9617, Adjusted R-squared: 0.9613
F-statistic: 2786 on 2 and 222 DF, p-value: < 2.2e-16
And the code:
x <- time(SL); y <- SL
l=lm(y ~ x+I(x^2))
new <- data.frame(x = 1993:2050)
predict(l, new, se.fit = TRUE)
pred.w.clim <- predict(l, new, interval="confidence")
matplot(new$x,pred.w.clim,lty=c(1,2,2), type="l", ylab="Sea Level",main="Quadratic Projection of Sea Level Rise",ylim=c(-10,100),lwd=3,col=c(2,2,2),xlab="Year")