ENSO phase-locking to the boreal winter in CMIP3 and CMIP5 models

In this study, the El Nino-Southern Oscillation (ENSO) phase-locking to the boreal winter in CMIP3 and CMIP5 models is examined. It is found that the models that are poor at simulating the winter ENSO peak tend to simulate colder seasonal-mean sea-surface temperature (SST) during the boreal summer and associated shallower thermocline depth over the eastern Pacific. These models tend to amplify zonal advection and thermocline depth feedback during boreal summer. In addition, the colder eastern Pacific SST in the model can reduce the summertime mean local convective activity, which tends to weaken the atmospheric response to the ENSO SST forcing. It is also revealed that these models have more serious climatological biases over the tropical Pacific, implying that a realistic simulation of the climatological fields may help to simulate winter ENSO peak better. The models that are poor at simulating ENSO peak in winter also show excessive anomalous SST warming over the western Pacific during boreal winter of the El Nino events, which leads to strong local convective anomalies. This prevents the southward shift of El Nino-related westerly during boreal winter season. Therefore, equatorial westerly is prevailed over the western Pacific to further development of ENSO-related SST during boreal winter. This bias in the SST anomaly is partly due to the climatological dry biases over the central Pacific, which confines ENSO-related precipitation and westerly responses over the western Pacific.     

Effects of the low-frequency zonal wind variation on the high frequency atmospheric variability over the tropics

Recently, there is increasing evidence on the interaction of atmospheric high-frequency (HF) variability with climatic low-frequency (LF) variability. In this study, we examine this relationship of HF variability with large scale circulation using idealized experiments with an aqua-planet Atmospheric GCM (with zonally uniform SST), run in different zonal momentum forcing scenarios. The effect of large scale circulation changes to the HF variability is demonstrated here. The HF atmospheric variability is enhanced over the westerly forced region, through easterly vertical shear. Our study also manifests that apart from the vertical wind shear, strong low-level convergence and horizontal zonal wind shear are also important for enhancing the HF variance. This is clearly seen in the eastern part of the forcing, where the HF activity shows relatively maximum increase, in spite of similar vertical shear over the forced regions. The possible implications for multi-scale interaction (e.g. MJO–ENSO interaction) are also discussed.     

Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004)

We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework.     

Tropical Pacific impacts of convective momentum transport in the SNU coupled GCM

Impacts of convective momentum transport (CMT) on tropical Pacific climate are examined, using an atmospheric (AGCM) and coupled GCM (CGCM) from Seoul National University. The CMT scheme affects the surface mainly via a convection-compensating atmospheric subsidence which conveys momentum downward through most of the troposphere. AGCM simulations—with SSTs prescribed from climatological and El Nino Southern Oscillation (ENSO) conditions—show substantial changes in circulation when CMT is added, such as an eastward shift of the climatological trade winds and west Pacific convection. The CMT also alters the ENSO wind anomalies by shifting them eastward and widening them meridionally, despite only subtle changes in the precipitation anomaly patterns. During ENSO, CMT affects the low-level winds mainly via the anomalous convection acting on the climatological westerly wind shear over the central Pacific—so that an eastward shift of convection transfers more westerly momentum toward the surface than would occur without CMT. By altering the low-level circulation, the CMT further alters the precipitation, which in turn feeds back on the CMT. In the CGCM, CMT affects the simulated climatology by shifting the mean convection and trade winds eastward and warming the equatorial SST; the ENSO period and amplitude also increase. In contrast to the AGCM simulations, CMT substantially alters the El Nino precipitation anomaly patterns in the CGCM. Also discussed are possible impacts of the CMT-induced changes in climatology on the simulated ENSO.     

Current status of ENSO prediction skill in coupled ocean–atmosphere models

The overall skill of ENSO prediction in retrospective forecasts made with ten different coupled GCMs is investigated. The coupled GCM datasets of the APCC/CliPAS and DEMETER projects are used for four seasons in the common 22 years from 1980 to 2001. As a baseline, a dynamic-statistical SST forecast and persistence are compared. Our study focuses on the tropical Pacific SST, especially by analyzing the NINO34 index. In coupled models, the accuracy of the simulated variability is related to the accuracy of the simulated mean state. Almost all models have problems in simulating the mean and mean annual cycle of SST, in spite of the positive influence of realistic initial conditions. As a result, the simulation of the interannual SST variability is also far from perfect in most coupled models. With increasing lead time, this discrepancy gets worse. As one measure of forecast skill, the tier-1 multi-model ensemble (MME) forecasts of NINO3.4 SST have an anomaly correlation coefficient of 0.86 at the month 6. This is higher than that of any individual model as well as both forecasts based on persistence and those made with the dynamic-statistical model. The forecast skill of individual models and the MME depends strongly on season, ENSO phase, and ENSO intensity. A stronger El Niño is better predicted. The growth phases of both the warm and cold events are better predicted than the corresponding decaying phases. ENSO-neutral periods are far worse predicted than warm or cold events. The skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November. This behavior, often termed the spring predictability barrier, is in part because predictions starting from February or May contain more events in the decaying phase of ENSO.     

Statistical relationship between two types of El Niño events and climate variation over the Korean Peninsula

Recently, a new type of El Niño (Warm-Pool El Niño) is more often observed than the conventional El Niño (Cold-Tongue El Niño); each has a distinctive spatial pattern. The two types of El Niño have different teleconnections; therefore their impacts on a specific region can be considerably different. In this study, we focus on statistical relationship between climate variation in Korea and the two types of El Niño. When the two types of El Niño are not separately considered, the statistical relation between climate variables in Korea and the El Niño events is weak in general. When the two types of El Niño are separately considered, however, each type exhibits significant relationship with climate variation in Korea. Therefore, consideration of two types of El Niño separately can potentially improve climate prediction over the Korean Peninsula.     

Winter precipitation variability over Korean Peninsula associated with ENSO

In this study, winter precipitation variability associated with the El Niño-Southern Oscillation (ENSO) over the Korean Peninsula was investigated using a 5-pentad running mean data because significant correlation pattern cannot be revealed using seasonal-mean data. It was found a considerably significant positive correlation between Niño3 sea-surface temperature and precipitation during early winter (from Mid-November to early-December), when the correlation coefficient is close to 0.8 in early-December; the correlation is distinctively weakened during late winter. It is demonstrated that such sudden intraseasonal change in relation to ENSO is associated with the presence of anticyclonic flow over the Kuroshio extension region (Kuroshio anticyclone). In early winter, there is strong southerly wind over the Korean Peninsula, which is induced by the Philippine Sea anticyclone and Kuroshio anticyclone. However, in January, although the Philippine Sea anticyclone develops further, the Kuroshio anticyclone suddenly disappears; as a result, the impact of ENSO is considerably weakened over the Korean Peninsula. These results indicate that the Kuroshio anticyclone during El Niño peak phase plays a critical role by strongly affecting Northeast Asia climate, including the Korean Peninsula. In addition, it is also found that there are distinctive interdecadal changes of the relationship between ENSO and precipitation over the Korean Peninsula. In particular, the strong correlation in early winter is clearer in the recent 30 years than that in the previous period of 1950–1979.     

Optimal initial perturbations for El Nino ensemble prediction with ensemble Kalman filter

A method for selecting optimal initial perturbations is developed within the framework of an ensemble Kalman filter (EnKF). Among the initial conditions generated by EnKF, ensemble members with fast growing perturbations are selected to optimize the ENSO seasonal forecast skills. Seasonal forecast experiments show that the forecast skills with the selected ensemble members are significantly improved compared with other ensemble members for up to 1-year lead forecasts. In addition, it is found that there is a strong relationship between the forecast skill improvements and flow-dependent instability. That is, correlation skills are significantly improved over the region where the predictable signal is relatively small (i.e. an inverse relationship). It is also shown that forecast skills are significantly improved during ENSO onset and decay phases, which are the most unpredictable periods among the ENSO events.     

Impact of diurnal atmosphere–ocean coupling on tropical climate simulations using a coupled GCM

The impacts of diurnal atmosphere–ocean (air–sea) coupling on tropical climate simulations are investigated using the SNU coupled GCM. To investigate the effect of the atmospheric and oceanic diurnal cycles on a climate simulation, a 1-day air–sea coupling interval experiment is compared to a 2-h coupling experiment. As previous studies have suggested, cold temperature biases over equatorial western Pacific regions are significantly reduced when diurnal air–sea coupling strategy is implemented. This warming is initiated by diurnal rectification and amplified further by the air–sea coupled feedbacks. In addition to its effect on the mean climatology, the diurnal coupling has also a distinctive impact on the amplitude of the El Nino-Southern Oscillation (ENSO). It is demonstrated that a weakening of the ENSO magnitude is caused by reduced (increased) surface net heat fluxes into the ocean during El Nino (La Nina) events. Primarily, decreased (increased) incoming shortwave radiation during El Nino (La Nina) due to cloud shading is responsible for the net heat fluxes associated with ENSO.