On the Dynamics and Predictability of the Atlantic Niño

This thesis seeks to broaden our understanding of the Atlantic Niño. The Atlantic Niño is the dominant mode of coupled interannual climate variability in the equatorial Atlantic. Its sea surface temperature (SST) signature is similar to the Pacific Niño, peaking in boreal summer, with a secondary Niño-like phenomenon occurring in boreal winter. Both the summer and winter Niños affect seasonal climate variability locally and in remote regions. To what extent is the Atlantic Niño driven by dynamical processes? Using multiple linear regression, SST variability in the equatorial Atlantic is decomposed into a dynamical part, and a stochastic part. When the Atlantic Niño is active, dynamical SST variability dominates stochastic SST variability, indicating that ocean dynamics are involved in establishing it. The Atlantic Niño is relatively symmetric. Does this symmetry correspond the symmetry of the Atlantic Bjerknes feedback? Decomposing the Bjerknes feedback into three feedback elements, robust regression is used to diagnose the strength of the feedback elements when they act on positive or negative anomalies (composites). In the Pacific, clear asymmetries emerge. In the Atlantic, differences between positive and negative composites are less consistent. Assessing the stationarity of the Bjerknes feedback shows that both the feedback elements and their symmetries vary on decadal time scales. A strong, coupled warm bias in the equatorial Atlantic inhibits realistic simulations of the Atlantic Niño in coupled global climate models of the current generation. A review synthesises our current understanding of the processes that create and maintain the equatorial Atlantic warm bias. Does the warm bias affect the ability of a model to predict the Atlantic Niño? Analysing two hindcasting experiments – one using a biased model, the other employing surface heat flux correction –, shows that bias alleviation enhances the predictability of SST variability in boreal summer

On the Dynamics and Predictability of the Atlantic Nino

This thesis seeks to broaden our understanding of the Atlantic Niño. The Atlantic Niño is the dominant mode of coupled interannual climate variability in the equatorial Atlantic. Its sea surface temperature (SST) signature is similar to the Pacific Niño, peaking in boreal summer, with a secondary Niño-like phenomenon occurring in boreal winter. Both the summer and winter Niños affect seasonal climate variability locally and in remote regions. To what extent is the Atlantic Niño driven by dynamical processes? Using multiple linear regression, SST variability in the equatorial Atlantic is decomposed into a dynamical part, and a stochastic part. When the Atlantic Niño is active, dynamical SST variability dominates stochastic SST variability, indicating that ocean dynamics are involved in establishing it. The Atlantic Niño is relatively symmetric. Does this symmetry correspond the symmetry of the Atlantic Bjerknes feedback? Decomposing the Bjerknes feedback into three feedback elements, robust regression is used to diagnose the strength of the feedback elements when they act on positive or negative anomalies (composites). In the Pacific, clear asymmetries emerge. In the Atlantic, differences between positive and negative composites are less consistent. Assessing the stationarity of the Bjerknes feedback shows that both the feedback elements and their symmetries vary on decadal time scales. A strong, coupled warm bias in the equatorial Atlantic inhibits realistic simulations of the Atlantic Niño in coupled global climate models of the current generation. A review synthesises our current understanding of the processes that create and maintain the equatorial Atlantic warm bias. Does the warm bias affect the ability of a model to predict the Atlantic Niño? Analysing two hindcasting experiments – one using a biased model, the other employing surface heat flux correction –, shows that bias alleviation enhances the predictability of SST variability in boreal summer

Interannual variability of wind power input to near-inertial currents in the North Atlantic

Near-inertial oscillations are an important feature of the climate system. The output of a high-resolution ocean model of the North Atlantic was used to investigate interannual variability of wind power input (WPI) to near-inertial currents with respect to the North Atlantic Oscillation (NAO). The model is forced with NCEP/NCAR reanalysis wind stress for JFM of the years 1989 (strong positive NAO-phase) and 2010 (negative NAO). Atmospheric parameters are tightly related to the NAO. The storm track in 1989 is intensified and channels storms into the subpolar North Atlantic, while it is more fanned out in 2010, allowing single storms to travel into the Mediterranean Sea. Similar patterns emerge from the distribution of near-inertial wind stress magnitude (NIWSM), i.e. the part of the wind stress spectrum that is most efficient in generating near-inertial energy (NIE). Seasonally averaged NIWSM, however, is not anchored to the storm track but is shifted to the south. This behaviour is due to the latitude-dependent inertial frequency, which decouples synoptic variability from the near-inertial frequency band. WPI for the two considered years is consistent with the different distributions of storms: While 1989 produced a total rate of WPI of 6.48 x 109 W (= GW) and a secondary centre of weakly increased WPI in the eastern subpolar North Atlantic corresponding to the intensified storm track in this region, total WPI in 2010 amounted to 9.64 GW and was associated with a strongly enhanced secondary centre of WPI in the subtropics. Although anomalies both in the storm track and mean NIWSM are more pronounced in the subpolar ocean basin, WPI prefers the subtropics. It is proposed that a mixture of atmospheric and oceanic processes is responsible for this asymmetry, chief among them the variation of the Coriolis frequency with latitude. Patterns of WPI, mixed layer NIE, and NIE in the deep ocean are similar to each other. NIE decreases drastically with depth. Mean NIWSM is a promising atmospheric proxy to WPI. Linear statistical models of WPI built from this quantity allow the estimation of total WPI for each winter (JFM) from 1980 to 2013 in low, mid-, and high-latitudes. Total WPI as well as mid-latitude WPI is only weakly correlated with the NAO. Low- and high-latitude WPI on the other hand is strongly correlated with the NAO, with the magnitude of correlation coefficients exceeding values of 0.8, suggesting that the relationship of WPI to the NAO lies in shifting the patterns of WPI. During negative NAO phases, WPI is pulled towards the subtropics (and thus intensified), whereas it shifts towards the polar ocean during positive NAO phases, both in accordance with changes in the configuration of the storm track tail. Since the response of WPI to comparable mean NIWSM is weakening with latitude, total WPI is more strongly influenced by lowlatitude WPI. It is concluded that the relationship between WPI in the North Atlantic and the NAO is of a twofold nature: While total WPI is only weakly and inversely related to the NAO, the distribution of WPI is strongly depending on it

On the relationship between Atlantic Niño variability and ocean dynamics

The Atlantic Niño is the dominant mode of interannual sea surface temperature (SST) variability in the eastern equatorial Atlantic. Current coupled global climate models struggle to reproduce its variability. This is thought to be partly related to an equatorial SST bias that inhibits summer cold tongue growth. Here, we address the question whether the equatorial SST bias affects the ability of a coupled global climate model to produce realistic dynamical SST variability. We assess this by decomposing SST variability into dynamical and stochastic components. To compare our model results with observations, we employ empirical linear models of dynamical SST that, based on the Bjerknes feedback, use the two predictors sea surface height and zonal surface wind. We find that observed dynamical SST variance shows a pronounced seasonal cycle. It peaks during the active phase of the Atlantic Niño and is then roughly 4–7 times larger than stochastic SST variance. This indicates that the Atlantic Niño is a dynamical phenomenon that is related to the Bjerknes feedback. In the coupled model, the SST bias suppresses the summer peak in dynamical SST variance. Bias reduction, however, improves the representation of the seasonal cold tongue and enhances dynamical SST variability by supplying a background state that allows key feedbacks of the tropical ocean–atmosphere system to operate in the model. Due to the small zonal extent of the equatorial Atlantic, the observed Bjerknes feedback acts quasi-instantaneously during the dynamically active periods of boreal summer and early boreal winter. Then, all elements of the observed Bjerknes feedback operate simultaneously. The model cannot reproduce this, although it hints at a better performance when using bias reduction

Seasonal prediction of equatorial Atlantic sea surface temperature using simple initialization and bias correction techniques

Due to strong mean state‐biases most coupled models are unable to simulate equatorial Atlantic variability. Here, we use the Kiel Climate Model to assess the impact of bias reduction on the seasonal prediction of equatorial Atlantic sea surface temperature (SST). We compare a standard experiment (STD) with an experiment that employs surface heat flux correction to reduce the SST bias (FLX) and, in addition, apply a correction for initial errors in SST. Initial conditions for both experiments are generated in partially coupled mode, and seasonal hindcasts are initialized at the beginning of February, May, August and November for 1981–2012. Surface heat flux correction generally improves hindcast skill. Hindcasts initialized in February have the least skill, even though the model bias is not particularly strong at that time of year. In contrast, hindcasts initialized in May achieve the highest skill. We argue this is because of the emergence of a closed Bjerknes feedback loop in boreal summer in FLX that is a feature of observations but is missing in STD

A comparison of the Atlantic and Pacific Bjerknes feedbacks: Seasonality, Symmetry, and Stationarity

The Bjerknes feedback is the dominant positive feedback in the equatorial ocean basins. To examine the seasonality, symmetry, and stationarity of the Pacific and Atlantic Bjerknes feedbacks we decompose them into three feedback elements that relate thermocline depth, sea surface temperature (SST), and western basin wind stress variability to each other. We partition feedback elements into composites associated with positive or negative anomalies. Using robust regression, we diagnose the strength of each composite. For the recent period 1993‐2012, composites of the Pacific Bjerknes feedback elements agree well with previous work. Positive composites are generally stronger than negative composites, and all feedback elements are weakest in late boreal spring. In the Atlantic, differences between positive and negative composites are less consistent across feedback elements. Specifically, wind variability seems to play a less important role in shaping atmosphere‐ocean coupling in the Atlantic when compared to the Pacific. However, a clear seasonality emerges: Feedback elements are generally strong in boreal summer and, for the negative composites, again in boreal winter. The Atlantic Bjerknes feedback is dominated by subsurface‐surface coupling. Applying our analysis to overlapping 25‐year periods for 1958‐2009 shows that the strengths of feedback elements in both ocean basins vary on decadal time scales. While the overall asymmetry of the Pacific Bjerknes feedback is robust, the strength and symmetry of Atlantic feedback elements vary considerably between decades. Our results indicate that the Atlantic Bjerknes feedback is non‐stationary on decadal time scales

Can Climate Models Simulate the Observed Strong Summer Surface Cooling in the Equatorial Atlantic?

Variability in the tropical Atlantic Ocean is dominated by the seasonal cycle. A defining feature is the migration of the inter-tropical convergence zone into the northern hemisphere and the formation of a so-called cold tongue in sea surface temperatures (SSTs) in late boreal spring. Between April and August, cooling leads to a drop in SSTs of approximately 5°. The pronounced seasonal cycle in the equatorial Atlantic affects surrounding continents, and even minor deviations from it can have striking consequences for local agricultures. Here, we report how state-of-the-art coupled global climate models (CGCMs) still struggle to simulate the observed seasonal cycle in the equatorial Atlantic, focusing on the formation of the cold tongue. We review the basic processes that establish the observed seasonal cycle in the tropical Atlantic, highlight common biases and their potential origins, and discuss how they relate to the dynamics of the real world. We also briefly discuss the implications of the equatorial Atlantic warm bias for CGCM-based reliable, socio-economically relevant seasonal predictions in the region