Coupled Interannual Variability of Wind and Sea Surface Temperature in the Caribbean Sea and the Gulf of Mexico

<p>This work describes dominant patterns of coupled interannual variability of the 10-m wind and sea surface temperature in the Caribbean Sea and the Gulf of Mexico (CS&GM) during the period 1982–2016. Using a canonical correlation analysis (CCA) between the monthly mean anomalies of these fields, four coupled variability modes are identified: the dipole (March–April), transition (May–June), interocean (July–October), and meridional-wind (November–February) modes. Results show that El Niño–Southern Oscillation (ENSO) influences almost all the CS&GM coupled modes, except the transition mode, and that the North Atlantic Oscillation (NAO) in February has a strong negative correlation with the dipole and transition modes. The antisymmetric relationships found between the dipole mode and the NAO and ENSO indices confirm previous evidence about the competing remote forcings of both teleconnection patterns on the tropical North Atlantic variability. Precipitation in the CS and adjacent oceanic and land areas is sensitive to the wind–SST coupled variability modes from June to October. These modes seem to be strongly related to the interannual variability of the midsummer drought and the meridional migration of the intertropical convergence zone in the eastern Pacific. These findings may eventually lead to improving seasonal predictability in the CS&GM and surrounding land areas.</p>

Coupled Interannual Variability of Wind and Sea Surface Temperature in the Caribbean Sea and the Gulf of Mexico

This work describes dominant patterns of coupled interannual variability of the 10-m wind and sea surface temperature in the Caribbean Sea and the Gulf of Mexico (CS&GM) during the period 1982–2016. Using a canonical correlation analysis (CCA) between the monthly mean anomalies of these fields, four coupled variability modes are identified: the dipole (March–April), transition (May–June), interocean (July–October), and meridional-wind (November–February) modes. Results show that El Niño–Southern Oscillation (ENSO) influences almost all the CS&GM coupled modes, except the transition mode, and that the North Atlantic Oscillation (NAO) in February has a strong negative correlation with the dipole and transition modes. The antisymmetric relationships found between the dipole mode and the NAO and ENSO indices confirm previous evidence about the competing remote forcings of both teleconnection patterns on the tropical North Atlantic variability. Precipitation in the CS and adjacent oceanic and land areas is sensitive to the wind–SST coupled variability modes from June to October. These modes seem to be strongly related to the interannual variability of the midsummer drought and the meridional migration of the intertropical convergence zone in the eastern Pacific. These findings may eventually lead to improving seasonal predictability in the CS&GM and surrounding land areas.

Characterizing ENSO Coupled Variability and Its Impact on North American Seasonal Precipitation and Temperature*

Two questions are addressed in this paper: whether ENSO can be adequately characterized by simple, seasonally invariant indices and whether the time series of a single component—SST or OLR—provides a sufficiently complete representation of ENSO for the purpose of quantifying U.S. climate impacts. Here, ENSO is defined as the leading mode of seasonally varying canonical correlation analysis (CCA) between anomalies of tropical Pacific SST and outgoing longwave radiation (OLR). The CCA reveals that the strongest regions of coupling are mostly invariant as a function of season and correspond to an OLR region located in the central Pacific Ocean (CP-OLR) and an SST region in the eastern Pacific that coincides with the Niño-3 region. In a linear context, the authors explore whether the use of a combined index of these SST and OLR regions explains additional variance of North American temperature and precipitation anomalies beyond that described by using a single index alone. Certain seasons and regions benefit from the use of a combined index. In particular, a combined index describes more variability in winter/spring precipitation and summer temperature.

Observing the Galápagos–EUC Interaction: Insights and Challenges

Although sustained observations yield a description of the mean equatorial current system from the western Pacific to the eastern terminus of the Tropical Atmosphere Ocean (TAO) array, a comprehensive observational dataset suitable for describing the structure and pathways of the Equatorial Undercurrent (EUC) east of 95°W does not exist and therefore climate models are unconstrained in a region that plays a critical role in ocean–atmosphere coupling. Furthermore, ocean models suggest that the interaction between the EUC and the Galápagos Islands (∼92°W) has a striking effect on the basic state and coupled variability of the tropical Pacific. To this end, the authors interpret historical measurements beginning with those made in conjunction with the discovery of the Pacific EUC in the 1950s, analyze velocity measurements from an equatorial TAO mooring at 85°W, and analyze a new dataset from archived shipboard ADCP measurements. Together, the observations yield a possible composite description of the EUC structure and pathways in the eastern equatorial Pacific that may be useful for model validation and guiding future observation.

The Atlantic Meridional Mode and Its Coupled Variability with the Guinea Dome

Using an ocean–atmosphere coupled general circulation model, air–sea interaction processes associated with the Atlantic meridional mode are investigated from a new viewpoint of its link with the Guinea Dome in the northern tropical Atlantic. The subsurface thermal oceanic dome develops off Dakar from late spring to late fall owing to wind-induced Ekman upwelling. Its seasonal evolution is due to surface wind variations associated with the northward migration of the intertropical convergence zone (ITCZ). Since the upwelling cools the mixed layer in the Guinea Dome region during summer, it is very important to reproduce its variability in order to simulate the sea surface temperature (SST) there. During the preconditioning phase of the positive (negative) Atlantic meridional mode, the dome is anomalously weak (strong) and the mixed layer is anomalously deep (shallow) there in late fall. This condition reduces (enhances) the sensitivity of the mixed layer temperature to the climatological atmospheric cooling. As a result, the positive (negative) SST anomaly appears there in early winter. Then, it develops in the following spring through the wind–evaporation–SST (WES) positive feedback associated with the anomalous northward (southward) migration of the ITCZ. This, in turn, leads to the stronger (weaker) Ekman upwelling and colder (warmer) subsurface temperature in the dome region during summer. It plays an important role on the decay of the warm (cold) SST anomaly through entrainment as a negative feedback. Therefore, simulating this interesting air–sea interaction in the Guinea Dome region is critical in improving prediction skill for the Atlantic meridional mode.

Coupled Variability and Predictability in a Stochastic Climate Model of the Tropical Atlantic

The coupled variability and predictability of the tropical Atlantic ocean–atmosphere system were analyzed within the framework of a linear stochastic climate model. Despite the existence of a meridional dipole as the leading mode, tropical Atlantic variability (TAV) is dominated by equatorial features and the subtropical variability is largely uncorrelated between the northern and southern Atlantic. This suggests that atmospheric stochastic forcing plays a dominant role in defining the spatial patterns of TAV, whereas the active air–sea feedbacks mainly enhance variability at interannual and decadal time scales, causing the spectra distinctive from the red spectrum. Under the stochastic forcing, the useful predictive skill for sea surface temperature measured by normalized error variance is limited to 2 months on average, which is 1 month longer than the predictive skill of damped persistence, indicating that the contribution of ocean dynamics and air–sea feedbacks is moderate in the tropical Atlantic. To achieve maximum predictability, processes such as ocean dynamics, thermodynamical and dynamical air–sea feedbacks, and the delicate mode–mode interactions should be correctly resolved in the coupled models. Therefore, predicting TAV poses more challenge than predicting El Niño in the tropical Pacific.