North Atlantic Ocean Wave Climate Change Scenarios for the Twenty-First Century

Abstract

Using the observed relationships between sea level pressure (SLP) and significant wave height (SWH) as represented by regression models, climate change scenarios of SWH in the North Atlantic were constructed by means of redundancy analysis (for seasonal means and 90th percentiles of SWH) and nonstationary generalized extreme value analysis (for seasonal extreme SWH). SWH scenarios are constructed using output from a coupled climate model under three different forcing scenarios. Scenarios of future anomaly seasonal statistics of SWH are constructed using climate model projections of anomaly seasonal mean SLP while projections of seasonal extreme SWH are made using projections of seasonal mean SLP and seasonal SLP gradient index. The projected changes in SWH are assessed by means of a trend analysis.The northeast Atlantic is projected to have increases in both winter and fall seasonal means and extremes of SWH in the twenty-first century under all three forcing scenarios. These changes are generally accompanied by decreases in the midlatitudes of the North Atlantic and increases in the southwest North Atlantic. The rate and sign of the projected SWH change is not constant throughout the twenty-first century. In the Norwegian and North Seas, the projected SWH changes are characterized either by faster increases in the late decades than in the early decades, or by decreases in the early decades followed by increases, depending on the forcing scenario and the specific location. Using lower or higher rates of increase in greenhouse gases forcing generally leads to reduced or increased rates of change, respectively, in ocean wave heights. The sign and rate of future wave height changes in the North Sea in particular appear to be quite dependent on the forcing conditions. In general, global warming is associated with more frequent occurrence of the positive phase of the North Atlantic Oscillation (NAO) and strong cyclones, which leads to increases of wave heights in the northeast Atlantic.

Historical variation and trends in storminess along the Portuguese South Coast

“…Correlation significance levels were inferred with respect to the 0.01 and 0.05 levels, using the Pearson correlation coefficient. The North Atlantic Oscillation (NAO) index was the parameter most commonly used to explain phases of enhanced and reduced storminess along European Atlantic coastlines (Kushnir et al, 1997;Wang et al, 2003;Lozano et al, 2004;Matulla et al 2007;Dodet et al, 2010). Regarding winter values of the parameters, several authors suggest that the wave climate winter season corresponds to the months between December and March (e.g.…”

“…Despite the different periods of analysis, most of these studies indicate a similar pattern in the multi-decadal variability in extreme and mean significant wave heights. The pattern indicates that the northern part of the North Atlantic presents a trend of increasing storminess and mean significant wave height, and that the mid and southern latitudes show a decreasing trend (Kushnir et al, 1997;Wang and Swail, 2000;Swail et al, 2000;Wang et al, 2003;Dodet et al, 2010). Although such largerscale studies are useful in order to produce a more comprehensive interpretation of the overall pattern of variability in storminess, site-specific studies are required, particularly in locations where the specific site characteristics (e.g.…”

Uncertainties in extreme value modelling of wave data in a climate change perspective

“…This suggests that non-stationary modelling would be more appropriate, see, e.g. Wang et al (2004Wang et al ( , 2013, and Vanem (2013). Nevertheless, often these changes are assumed to be negligible over limited time periods and standard extreme value analysis methods are still applied.…”

Uncertainties

“…Both seasonal mean SLP anomalies, P, and anomalies of seasonal mean squared SLP gradients, G, have been shown to be good predictors for predicting seasonal SWH statistics (Wang and Swail 2006a, b;Wang et al 2004;Caires et al 2006). The use of predictor anomalies, instead of predictor values, diminishes the effects of 'model climate biases', i.e.…”

“…Therefore it is difficult to quantitatively assess the relative contribution between more than two factors at a time. Wang et al (2004), Wang and Swail (2006a) as well as Caires et al (2006) used one climate model (the Canadian CGCM2) for different emission scenarios. Wang and Swail (2006b), however, represented the average of three models for the emission scenarios A2 and B2.…”