RESUMO
A suite of coupled climate model simulations and experiments are used to examine how resolved mesoscale ocean features affect aspects of climate variability, air-sea interactions, and predictability. In combination with control simulations, experiments with the interactive ensemble coupling strategy are used to further amplify the role of the oceanic mesoscale field and the associated air-sea feedbacks and predictability. The basic intent of the interactive ensemble coupling strategy is to reduce the atmospheric noise at the air-sea interface, allowing an assessment of how noise affects the variability, and in this case, it is also used to diagnose predictability from the perspective of signal-to-noise ratios. The climate variability is assessed from the perspective of sea surface temperature (SST) variance ratios, and it is shown that, unsurprisingly, mesoscale variability significantly increases SST variance. Perhaps surprising is the fact that the presence of mesoscale ocean features even further enhances the SST variance in the interactive ensemble simulation beyond what would be expected from simple linear arguments. Changes in the air-sea coupling between simulations are assessed using pointwise convective rainfall-SST and convective rainfall-SST tendency correlations and again emphasize how the oceanic mesoscale alters the local association between convective rainfall and SST. Understanding the possible relationships between the SST-forced signal and the weather noise is critically important in climate predictability. We use the interactive ensemble simulations to diagnose this relationship, and we find that the presence of mesoscale ocean features significantly enhances this link particularly in ocean eddy rich regions. Finally, we use signal-to-noise ratios to show that the ocean mesoscale activity increases model estimated predictability in terms of convective precipitation and atmospheric upper tropospheric circulation.
RESUMO
El Niño events, characterized by anomalous warming in the eastern equatorial Pacific Ocean, have global climatic teleconnections and are the most dominant feature of cyclic climate variability on subdecadal timescales. Understanding changes in the frequency or characteristics of El Niño events in a changing climate is therefore of broad scientific and socioeconomic interest. Recent studies show that the canonical El Niño has become less frequent and that a different kind of El Niño has become more common during the late twentieth century, in which warm sea surface temperatures (SSTs) in the central Pacific are flanked on the east and west by cooler SSTs. This type of El Niño, termed the central Pacific El Niño (CP-El Niño; also termed the dateline El Niño, El Niño Modoki or warm pool El Niño), differs from the canonical eastern Pacific El Niño (EP-El Niño) in both the location of maximum SST anomalies and tropical-midlatitude teleconnections. Here we show changes in the ratio of CP-El Niño to EP-El Niño under projected global warming scenarios from the Coupled Model Intercomparison Project phase 3 multi-model data set. Using calculations based on historical El Niño indices, we find that projections of anthropogenic climate change are associated with an increased frequency of the CP-El Niño compared to the EP-El Niño. When restricted to the six climate models with the best representation of the twentieth-century ratio of CP-El Niño to EP-El Niño, the occurrence ratio of CP-El Niño/EP-El Niño is projected to increase as much as five times under global warming. The change is related to a flattening of the thermocline in the equatorial Pacific.
RESUMO
High-frequency precipitation variance is calculated in 12 different free-running (non-data-assimilative) coupled high resolution atmosphere-ocean model simulations, an assimilative coupled atmosphere-ocean weather forecast model, and an assimilative reanalysis. The results are compared with results from satellite estimates of precipitation and rain gauge observations. An analysis of irregular sub-daily fluctuations, which was applied by Covey et al. (Geophys Res Lett 45:12514-12522, 2018. 10.1029/2018GL078926) to satellite products and low-resolution climate models, is applied here to rain gauges and higher-resolution models. In contrast to lower-resolution climate simulations, which Covey et al. (2018) found to be lacking with respect to variance in irregular sub-daily fluctuations, the highest-resolution simulations examined here display an irregular sub-daily fluctuation variance that lies closer to that found in satellite products. Most of the simulations used here cannot be analyzed via the Covey et al. (2018) technique, because they do not output precipitation at sub-daily intervals. Thus the remainder of the paper focuses on frequency power spectral density of precipitation and on cumulative distribution functions over time scales (2-100 days) that are still relatively "high-frequency" in the context of climate modeling. Refined atmospheric or oceanic model grid spacing is generally found to increase high-frequency precipitation variance in simulations, approaching the values derived from observations. Mesoscale-eddy-rich ocean simulations significantly increase precipitation variance only when the atmosphere grid spacing is sufficiently fine (< 0.5°). Despite the improvements noted above, all of the simulations examined here suffer from the "drizzle effect", in which precipitation is not temporally intermittent to the extent found in observations.