Search for: All records

We describe a form of Atlantic Meridional Overturning Circulation (AMOC) variability that we believe has not previously appeared in observations or models. It is found in an ensemble of eddy‐resolving North Atlantic simulations that the AMOC frequently reverses in sign at ∼35°N with gyre‐wide anomalies in size and that reach throughout the water column. The duration of each reversal is roughly 1 month. The reversals are part of the annual AMOC cycle occurring in boreal winter, although not all years feature an actual reversal in sign. The occurrence of the reversals appears in our ensemble mean, suggesting it is a forced feature of the circulation. A partial explanation is found in an Ekman response to wind stress anomalies. Model ensemble simulations run with different combinations of climatological and realistic forcings argue that it is the atmospheric forcing specifically that results in the reversals, despite the signals extending into the deep ocean.

 

The strong sea‐surface temperature (SST) gradient associated with the Gulf Stream (GS) is widely acknowledged to play an important role in shaping mid‐latitude weather and climate. Despite this, an index for the GS SST gradient has not yet been standardized in the literature. This paper introduces a monthly index for the large‐scale SST gradient across the separated GS based on the time‐varying GS position detected from sea‐surface height. Analysis suggests that the variations in the monthly average SST gradient throughout the year result primarily from SST variability to the north of the GS, with little contribution from SST to the south. The index exhibits a weak periodicity at ∼2 years. Sea level pressure and turbulent heat flux patterns suggest that variability in the large‐scale SST gradient is related to atmospheric (rather than oceanic) forcing. Ocean‐to‐atmosphere feedback does not persist throughout the year, but there is some evidence of wintertime feedback.

 

Reanalysis datasets are frequently used in the study of atmospheric variability owing to their length of record and gridded global coverage. In the midlatitudes, much of the day-to-day atmospheric variability is associated with atmospheric fronts. These fronts are also responsible for the majority of precipitation in the midlatitudes, and are often associated with extreme weather, flooding, and wildfire activity. As such, it is important that identification of fronts and their associated rainfall remains as consistent as possible between studies. Nevertheless, it is often the case that only one reanalysis dataset and only one objective diagnostic for the detection of atmospheric fronts is used. By applying two different frontal identification methods across the shared time period of eight reanalysis datasets (1980–2001), it is found that the individual identification of fronts and frontal precipitation is significantly affected by both the choice of identification method and dataset. This is shown to subsequently impact the climatologies of both frontal frequency and frontal precipitation globally with significant regional differences as well. For example, for one diagnostic, the absolute multireanalysis range in the global mean frontal frequency and the proportion of precipitation attributed to atmospheric fronts are 12% and 69%, respectively. A percentage reduction of 77% and 81%, respectively, in these absolute multireanalysis ranges occurs, however, upon regridding all datasets to the same coarser grid. Therefore, these findings have important implications for any study on precipitation variability and not just those that consider atmospheric fronts.

 

The “eddying” ocean, recognized for several decades, has been the focus of much observational and theoretical research. We here describe a generalization for the analysis of eddy energy, based on the use of ensembles, that addresses two key related issues: the definition of an “eddy” and the general computation of energy spectra. An ensemble identifies eddies as the unpredictable component of the flow, and permits the scale decomposition of their energy in inhomogeneous and non‐stationary settings. We present two distinct, but equally valid, spectral estimates: one is similar to classical Fourier spectra, the other reminiscent of classical empirical orthogonal function analysis. Both satisfy Parseval's equality and thus can be interpreted as length‐scale dependent energy decompositions. The issue of “tapering” or “windowing” of the data, used in traditional approaches, is also discussed. We apply the analyses to a mesoscale “resolving” (1/12°) ensemble of the separated North Atlantic Gulf Stream. Our results reveal highly anisotropic spectra in the Gulf Stream and zones of both agreement and disagreement with theoretically expected spectral shapes. In general, we find spectral slopes that fall off faster than the steepest slope expected from quasi‐geostrophic theory.

 

Abstract High-resolution observations have demonstrated the presence of strong time-mean near-surface wind convergence (NSWC) anchored across oceanic frontal zones, such as the western boundary currents. Initial analyses appeared to show a close association between this time-mean NSWC and time-mean properties of the underlying sea surface temperature (SST), such as the gradients and second derivatives (e.g., Laplacian of SST), acting through pressure-adjustment and vertical-mixing mechanisms. However, a series of recent papers have revealed the instantaneous NSWC to be dominated by atmospheric fronts and have suggested the importance of air–sea processes occurring instead on shorter, synoptic time scales. In this paper, using the ERA5 reanalysis dataset in the Gulf Stream region, we aim to reconcile these viewpoints by investigating the spatial and temporal dependence of NSWC and its relationship to SST. It is revealed that while atmospheric frontal processes govern the day-to-day variability of NSWC, the relatively weak but persistent pressure-adjustment and vertical-mixing mechanisms provide lower-frequency modulations in conditions both with and without atmospheric fronts. In addition to their temporal characteristics, each mechanism is shown through spectral analysis to dominate on specific spatial scales. In light of recent work that has tied remote atmospheric responses to NSWC anomalies in western boundary current regions, these results emphasize the importance of oceanic frontal zones for atmospheric variability on all spatiotemporal scales. 

Abstract This study compares the spread in climatological tropical cyclone (TC) precipitation across eight different reanalysis datasets: NCEP-CFSR, ERA-20C, ERA-40, ERA5, ERA-Interim, JRA-55, MERRA-2, and NOAA-20C. TC precipitation is assigned using manual tracking via a fixed 500-km radius from each TC center. The reanalyses capture similar general spatial patterns of TC precipitation and TC precipitation fraction, defined as the fraction of annual precipitation assigned to TCs, and the spread in TC precipitation is larger than the spread in total precipitation across reanalyses. The spread in TC precipitation relative to the inter-reanalysis mean TC precipitation, or relative spread, is larger in the east Pacific than in the west Pacific. Partitioned by reanalysis intensity, the largest relative spread across reanalyses in TC precipitation is from high-intensity TCs. In comparison with satellite observations, reanalyses show lower climatological mean annual TC precipitation over most areas. A comparison of area-averaged precipitation rate in TCs composited over reanalysis intensity shows the spread across reanalyses is larger for higher intensity TCs. Testing the sensitivity of TC precipitation assignment to tracking method shows that climatological mean annual TC precipitation is systematically larger when assigned via manual tracking versus objective tracking. However, this tendency is minimized when TC precipitation is normalized by TC density. Overall, TC precipitation in reanalyses is affected by not only horizontal output resolution or any TC preprocessing, but also data assimilation and parameterization schemes. The results indicate that improvements in the representation of TCs and their precipitation in reanalyses are needed to improve overall precipitation.