Abstract

Two types of El Niño–Southern Oscillation (ENSO) simulated by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) model are examined. The model is found to produce both the eastern Pacific (EP) and central Pacific (CP) types of ENSO with spatial patterns and temporal evolutions similar to the observed. The simulated ENSO intensity is comparable to the observed for the EP type, but weaker than the observed for the CP type. Further analyses reveal that the generation of the simulated CP ENSO is linked to extratropical forcing associated with the North Pacific Oscillation (NPO) and that the model is capable of simulating the coupled air–sea processes in the subtropical Pacific that slowly spreads the NPO-induced SST variability into the tropics, as shown in the observations. The simulated NPO, however, does not extend as far into the deep tropics as it does in the observations and the coupling in the model is not sustained as long as it is in the observations. As a result, the extratropical forcing of tropical central Pacific SST variability in the CFS model is weaker than in the observations. An additional analysis with the Bjerknes stability index indicates that the weaker CP ENSO in the CFS model is also partially due to unrealistically weak zonal advective feedback in the equatorial Pacific. These model deficiencies appear to be related to an underestimation in the amount of the marine stratus clouds off the North American coasts inducing an ocean surface warm bias in the eastern Pacific. This study suggests that a realistic simulation of these marine stratus clouds can be important for the CP ENSO simulation.

Abstract

The realistic simulation of El Niño–Southern Oscillation (ENSO) by the University of California, Los Angeles (UCLA), coupled atmosphere–ocean general circulation model (CGCM) is used to test two simple theoretical models of the phenomenon: the recharge oscillator model of Jin and the delayed oscillator model of Schopf, Suarez, Battisti, and Hirst (SSBH). The target for the simple models is provided by the CGCM results prefiltered with singular spectrum analysis to extract the leading oscillatory mode. In its simplest form, the Jin model can be reduced to two first ordinary differential equations. If the parameters of the model are fit in this reduced form, it appears to capture the period of the CGCM oscillatory mode. If the Jin model is instead fit using the individual physical balances that are used to derive it, substantial misfits to the CGCM are encountered. The SSBH model can likewise be expressed either in a condensed form or a larger set of individual physical balances with highly analogous results.

It is shown that the misfits in both simple models can be greatly reduced by introducing a spinup timescale for wind stress relative to eastern equatorial Pacific SST. In the CGCM, this spinup time appears to be associated with a combination of atmospheric and ocean mixed layer processes in a way consistent with the “mixed mode” regime discussed by Syu and Neelin, which is not included in the Jin and SSBH models. These appear indistinguishable in this analysis, although the latter is more sensitive to fitting.

This paper provides a bridge between work on ENSO by theoreticians and numerical modelers. The CGCM results validate the conceptual framework of the simple models by demonstrating that they can provide a plausible representation of ENSO with realistic sets of parameters. The results also suggest that, in terms of realistic ENSO variability, the framework of the simple models can be made substantially more complete by including the adjustment time between wind stress and eastern Pacific SST required by the coupled spinup of the atmosphere and the ocean mixed layer processes outside this region.

Abstract

This study uses a series of coupled atmosphere–ocean general circulation model (CGCM) experiments to examine the roles of the Indian and Pacific Oceans in the transition phases of the tropospheric biennial oscillation (TBO) in the Indian–Australian monsoon system. In each of the three CGCM experiments, air–sea interactions are restricted to a certain portion of the Indo-Pacific Ocean by including only that portion of the ocean in the ocean model component of the CGCM. The results show that the in-phase TBO transition from a strong (weak) Indian summer monsoon to a strong (weak) Australian summer monsoon occurs more often in the CGCM experiments that include an interactive Pacific Ocean. The out-of-phase TBO transition from a strong (weak) Australian summer monsoon to a weak (strong) Indian summer monsoon occurs more often in the CGCM experiments that include an interactive Indian Ocean. The associated sea surface temperature (SST) anomalies are characterized by an ENSO-type pattern in the Pacific Ocean and basinwide warming/cooling in the Indian Ocean. The Pacific SST anomalies maintain large amplitude during the in-phase transition in the northern autumn and reverse their sign during the out-of-phase transition in the northern spring. On the other hand, the Indian Ocean SST anomalies maintain large amplitude during the out-of-phase monsoon transition and reverse their sign during the in-phase transition. These seasonally dependent evolutions of Indian and Pacific Ocean SST anomalies allow these two oceans to play different roles in the transition phases of the TBO in the Indian–Australian monsoon system.

Abstract

Interannual sea surface temperature (SST) variability in the central equatorial Pacific consists of a component related to eastern Pacific SST variations (called Type-1 SST variability) and a component not related to them (called Type-2 SST variability). Lead–lagged regression and ocean surface-layer temperature balance analyses were performed to contrast their control mechanisms. Type-1 variability is part of the canonical, which is characterized by SST anomalies extending from the South American coast to the central Pacific, is coupled with the Southern Oscillation, and is associated with basinwide subsurface ocean variations. This type of variability is dominated by a major 4–5-yr periodicity and a minor biennial (2–2.5 yr) periodicity. In contrast, Type-2 variability is dominated by a biennial periodicity, is associated with local air–sea interactions, and lacks a basinwide anomaly structure. In addition, Type-2 SST variability exhibits a strong connection to the subtropics of both hemispheres, particularly the Northern Hemisphere. Type-2 SST anomalies appear first in the northeastern subtropical Pacific and later spread toward the central equatorial Pacific, being generated in both regions by anomalous surface heat flux forcing associated with wind anomalies. The SST anomalies undergo rapid intensification in the central equatorial Pacific through ocean advection processes, and eventually decay as a result of surface heat flux damping and zonal advection. The southward spreading of trade wind anomalies within the northeastern subtropics-to-central tropics pathway of Type-2 variability is associated with intensity variations of the subtropical high. Type-2 variability is found to become stronger after 1990, associated with a concurrent increase in the subtropical variability. It is concluded that Type-2 interannual variability represents a subtropical-excited phenomenon that is different from the conventional ENSO Type-1 variability.

Abstract

The Community Climate System Model, version 3 (CCSM3), is known to produce many aspects of El Niño–Southern Oscillation (ENSO) realistically, but the simulated ENSO exhibits an overly strong biennial periodicity. Hypotheses on the cause of this excessive biennial tendency have thus far focused primarily on the model’s biases within the tropical Pacific. This study conducts CCSM3 experiments to show that the model’s biases in simulating the Indian Ocean mean sea surface temperatures (SSTs) and the Indian and Australian monsoon variability also contribute to the biennial ENSO tendency. Two CCSM3 simulations are contrasted: a control run that includes global ocean–atmosphere coupling and an experiment in which the air–sea coupling in the tropical Indian Ocean is turned off by replacing simulated SSTs with an observed monthly climatology. The decoupling experiment removes CCSM3’s warm bias in the tropical Indian Ocean and reduces the biennial variability in Indian and Australian monsoons by about 40% and 60%, respectively. The excessive biennial ENSO is found to reduce dramatically by about 75% in the decoupled experiment. It is shown that the biennial monsoon variability in CCSM3 excites an anomalous surface wind pattern in the western Pacific that projects well into the wind pattern associated with the onset phase of the simulated biennial ENSO. Therefore, the biennial monsoon variability is very effective in exciting biennial ENSO variability in CCSM3. The warm SST bias in the tropical Indian Ocean also increases ENSO variability by inducing stronger mean surface easterlies along the equatorial Pacific, which strengthen the Pacific ocean–atmosphere coupling and enhance the ENSO intensity.

Abstract

This study uncovers an early 1990s change in the relationships between El Niño–Southern Oscillation (ENSO) and two leading modes of the Southern Hemisphere (SH) atmospheric variability: the southern annular mode (SAM) and the Pacific–South American (PSA) pattern. During austral spring, while the PSA maintained a strong correlation with ENSO throughout the period 1948–2014, the SAM–ENSO correlation changed from being weak before the early 1990s to being strong afterward. Through the ENSO connection, PSA and SAM became more in-phase correlated after the early 1990s. The early 1990s is also the time when ENSO changed from being dominated by the eastern Pacific (EP) type to being dominated by the central Pacific (CP) type. Analyses show that, while the EP ENSO can excite only the PSA, the CP ENSO can excite both the SAM and PSA through tropospheric and stratospheric pathway mechanisms. The more in-phase relationship between SAM and PSA impacted the post-1990s Antarctic climate in at least two aspects: 1) a stronger Antarctic sea ice dipole structure around the Amundsen–Bellingshausen Seas due to intensified geopotential height anomalies over the region and 2) a shift in the phase relationships of surface air temperature anomalies among East Antarctica, West Antarctica, and the Antarctic Peninsula. These findings imply that ENSO–Antarctic climate relations depend on the dominant ENSO type and that ENSO forcing has become more important to the Antarctic sea ice and surface air temperature variability in the past two decades and will in the coming decades if the dominance of CP ENSO persists.

ABSTRACT

Previous studies linked the increase of the middle and low reaches of the Yangtze River (MLRYR) rainfall to tropical Indian Ocean warming during extreme El Niños’ (e.g., 1982/83 and 1997/98 extreme El Niños) decaying summer. This study finds the linkage to be different for the recent 2015/16 extreme El Niño’s decaying summer, during which the above-normal rainfalls over MLRYR and northern China are respectively linked to southeastern Indian Ocean warming and western tropical Indian Ocean cooling in sea surface temperatures (SSTs). The southeastern Indian Ocean warming helps to maintain the El Niño–induced anomalous lower-level anticyclone over the western North Pacific Ocean and southern China, which enhances moisture transport to increase rainfall over MLRYR. The western tropical Indian Ocean cooling first enhances the rainfall over central-northern India through a regional atmospheric circulation, the latent heating of which further excites a midlatitude Asian teleconnection pattern (part of circumglobal teleconnection) that results in an above-normal rainfall over northern China. The western tropical Indian Ocean cooling during the 2015/16 extreme El Niño is contributed by the increased upward latent heat flux anomalies associated with enhanced surface wind speeds, opposite to the earlier two extreme El Niños.

Abstract

With the continuous increase in computing capabilities, large-eddy simulation (LES) has recently gained popularity in applications related to flow, turbulence, and dispersion in the urban atmospheric boundary layer (ABL). Herein, we perform high-resolution building-scale LES over the Seoul, South Korea, city area to investigate the impact of inflow turbulence on the resulting turbulent flow field in the urban ABL. To that end, LES using the cell perturbation method for inflow turbulence generation is compared to a case where no turbulence fluctuations in the incoming ABL are present (unperturbed case). Validation of the model results using wind speed and wind direction observations at 3 m above ground level reveals minimal differences irrespective of the presence of incoming ABL turbulence. This is due to the high density of building structures present at the surface level that create shear instabilities in the flow field and therefore induce local turbulence production. In the unperturbed case, turbulent fluctuations are found to slowly propagate in the vertical direction with increasing fetch from the inflow boundaries, creating an internal boundary layer that separates the turbulent region near the building structures and the nonturbulent flow aloft that occupies the rest of the ABL. Analysis of turbulence quantities including energy spectra, velocity correlations, and passive scalar fluxes reveals significant underpredictions that rapidly grow with increasing height within the ABL. These results demonstrate the need for realistic inflow turbulence in building-resolving LES modeling to ensure proper interactions within the ABL.

Abstract

A quasi balance with respect to parcel buoyancy at cloud base between destabilizing processes and convection is imposed as a constraint on convective cloud-base mass flux in a modified version of the Kain–Fritsch cumulus parameterization. Supporting evidence is presented for this treatment, showing a cloud-base quasi balance (CBQ) on a time scale of approximately 1–3 h in explicit simulations of deep convection over the U.S. Great Plains and over the tropical Pacific Ocean with the Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS). With the exception of the smaller of two convective events in the Great Plains simulation, a CBQ is still observed upon restriction of the data analysis to instances where the available buoyant energy (ABE) exceeds a threshold value of 1000 J kg⁻¹. This observation is consistent with the view that feedbacks between convection and cloud-base parcel buoyancy can control the rate of convection on shorter time scales than those associated with the elimination of buoyant energy and supports the addition of a CBQ constraint to the Kain–Fritsch mass-flux closure.

Tests of the modified Kain–Fritsch scheme in single-column-model simulations based on the explicit three-dimensional simulations show a significant improvement in the representation of the main convective episodes, with a greater amount of convective rainfall. The performance of the scheme in COAMPS precipitation forecast experiments over the continental United States is also investigated. Improvements are obtained with the modified scheme in skill scores for middle to high rainfall rates.

Abstract

The time-expanded sampling (TES) method, designed to improve the effectiveness and efficiency of ensemble-based data assimilation and subsequent forecast with reduced ensemble size, is tested with conventional and satellite data for operational applications constrained by computational resources. The test uses the recently developed ensemble Kalman filter (EnKF) at the Naval Research Laboratory (NRL) for mesoscale data assimilation with the U.S. Navy’s mesoscale numerical weather prediction model. Experiments are performed for a period of 6 days with a continuous update cycle of 12 h. Results from the experiments show remarkable improvements in both the ensemble analyses and forecasts with TES compared to those without. The improvements in the EnKF analyses by TES are very similar across the model’s three nested grids of 45-, 15-, and 5-km grid spacing, respectively. This study demonstrates the usefulness of the TES method for ensemble-based data assimilation when the ensemble size cannot be sufficiently large because of operational constraints in situations where a time-critical environment assessment is needed or the computational resources are limited.