Search for: All records

Abstract The variability of the Hadley circulation strength (HCS), crucial to tropical climate variability, is attributed to both oceanic and atmospheric forcings. El Niño–Southern Oscillation (ENSO) and variations in the extratropical upper-tropospheric eddies are the known drivers of the interannual HCS variability. However, the relative contributions of these oceanic and atmospheric forcings to the hemispheric HCS variability are not well understood. In particular, how much anomalous wind stress–driven ocean dynamics, including ENSO, impact HCS variability remains an open question. To address these gaps, we investigate the drivers of the interannual HCS variability using global coupled model experiments that include or exclude anomalous wind stress–driven ocean circulation variability. We find that the anomalous wind stress–driven ocean circulation variability significantly amplifies HCS variability in the Southern Hemisphere (SH). ENSO is the leading modulator of the SH HCS variability, which offers the potential to improve the predictability of Hadley circulation (HC)–related hydrological consequences. On the other hand, the Northern Hemisphere (NH) HCS variability is predominantly influenced by the eddy-driven internal atmospheric variability with little role in ocean dynamics. We hypothesize that the large eddy variability in the NH and concentrated ENSO-associated heating and precipitation in the SH lead to the hemisphere-dependent differences in the interannual HCS variability. 

Abstract Since the 1950s, observations and climate models show an amplification of sea surface temperature (SST) seasonal cycle in response to global warming over most of the global oceans except for the Southern Ocean (SO), however the cause remains poorly understood. In this study, we analyzed observations, ocean reanalysis, and a set of historical and abruptly quadrupled CO₂simulations from the Coupled Model Intercomparison Project Phase 6 archive and found that the weakened SST seasonal cycle over the SO could be mainly attributed to the intensification of summertime westerly winds. Under the historical warming, the intensification of summertime westerly winds over the SO effectively deepens ocean mixed layer and damps surface warming, but this effect is considerably weaker in winter, thus weakening the SST seasonal cycle. This wind‐driven mechanism is further supported by our targeted coupled model experiments with the wind intensification effects being removed. 

Abstract To improve understanding of ocean processes impacting monthly sea surface temperature (SST) variability, we analyze a Community Earth System Model, version 2, hierarchy in which models vary only in their degree of ocean complexity. The most realistic ocean is a dynamical ocean model, as part of a fully coupled model (FCM). The next most realistic ocean, from a mechanically decoupled model (MDM), is like the FCM but excludes anomalous wind stress–driven ocean variability. The simplest ocean is a slab ocean model (SOM). Inclusion of a buoyancy coupled dynamic ocean as in the MDM, which includes temperature advection and vertical mixing absent in the SOM, leads to dampening of SST variance everywhere and reduced persistence of SST anomalies in the high latitudes and equatorial Pacific compared to the SOM. Inclusion of anomalous wind stress–driven ocean dynamics as in the FCM leads to higher SST variance and longer persistence time scales in most regions compared to the MDM. The net role of the dynamic ocean, as an overall dampener or amplifier of anomalous SST variance and persistence, is regionally dependent. Notably, we find that efforts to reduce the complexity of the ocean models in the SOM and MDM configurations result in changes in the magnitude of the thermodynamic forcing of SST variability compared to the FCM. These changes, in part, stem from differences in the seasonally varying mixed layer depth and should be considered when attempting to quantify the relative contribution of certain ocean mechanisms to differences in SST variability between the models.