Probabilistic prediction of Indian summer monsoon rainfall using global climate models

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low.     

The role of the intra-daily SST variability in the Indian monsoon variability and monsoon-ENSO–IOD relationships in a global coupled model

The impact of diurnal SST coupling and vertical oceanic resolution on the simulation of the Indian Summer Monsoon (ISM) and its relationships with El Ni?o-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) events are studied through the analysis of four integrations of a high resolution Coupled General Circulation Model (CGCM), but with different configurations. The only differences between the four integrations are the frequency of coupling between the ocean and atmosphere for the Sea Surface Temperature (SST) parameter (2 vs. 24?h coupling) and/or the vertical oceanic resolution (31 vs. 301 levels) in the CGCM. Although the summer mean tropical climate is reasonably well captured with all the configurations of the CGCM and is not significantly modified by changing the frequency of SST coupling from once to twelve per day, the ISM–ENSO teleconnections are rather poorly simulated in the two simulations in which SST is exchanged only once per day, independently of the vertical oceanic resolution used in the CGCM. Surprisingly, when 2?h SST coupling is implemented in the CGCM, the ISM–ENSO teleconnection is better simulated, particularly, the complex lead-lag relationships between the two phenomena, in which a weak ISM occurs during the developing phase of an El Ni?o event in the Pacific, are closely resembling the observed ones. Evidence is presented to show that these improvements are related to changes in the characteristics of the model’s El Ni?o which has a more realistic evolution in its developing and decaying phases, a stronger amplitude and a shift to lower frequencies when a 2-hourly SST coupling strategy is implemented without any significant changes in the basic state of the CGCM. As a consequence of these improvements in ENSO variability, the lead relationships between Indo-Pacific SSTs and ISM rainfall resemble the observed patterns more closely, the ISM–ENSO teleconnection is strengthened during boreal summer and ISM rainfall power spectrum is in better agreement with observations. On the other hand, the ISM–IOD teleconnection is sensitive to both SST coupling frequency and the vertical oceanic resolution, but increasing the vertical oceanic resolution is degrading the ISM–IOD teleconnection in the CGCM. These results highlight the need of a proper assessment of both temporal scale interactions and coupling strategies in order to improve current CGCMs. These results, which must be confirmed with other CGCMs, have also important implications for dynamical seasonal prediction systems or climate change projections of the monsoon.     

Impact of intra-daily SST variability on ENSO characteristics in a coupled model

This paper explores the impact of intra-daily Sea Surface Temperature (SST) variability on the tropical large-scale climate variability and differentiates it from the response of the system to the forcing of the solar diurnal cycle. Our methodology is based on a set of numerical experiments based on a fully global coupled ocean–atmosphere general circulation in which we alter (1) the frequency at which the atmosphere sees the SST variations and (2) the amplitude of the SST diurnal cycle. Our results highlight the complexity of the scale interactions existing between the intra-daily and inter-annual variability of the tropical climate system. Neglecting the SST intra-daily variability results, in our CGCM, to a systematic decrease of 15% of El Ni?o—Southern Oscillation (ENSO) amplitude. Furthermore, ENSO frequency and skewness are also significantly modified and are in better agreement with observations when SST intra-daily variability is directly taken into account in the coupling interface of our CGCM. These significant modifications of the SST interannual variability are not associated with any remarkable changes in the mean state or the seasonal variability. They can therefore not be explained by a rectification of the mean state as usually advocated in recent studies focusing on the diurnal cycle and its impact. Furthermore, we demonstrate that SST high frequency coupling is systematically associated with a strengthening of the air-sea feedbacks involved in ENSO physics: SST/sea level pressure (or Bjerknes) feedback, zonal wind/heat content (or Wyrtki) feedback, but also negative surface heat flux feedbacks. In our model, nearly all these results (excepted for SST skewness) are independent of the amplitude of the SST diurnal cycle suggesting that the systematic deterioration of the air-sea coupling by a daily exchange of SST information is cascading toward the major mode of tropical variability, i.e. ENSO.     

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.     

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.     

On the recent development of simple,coupled ocean-atmosphere models of ENSO

Based on simple model results, I describe the recent progress in our understanding of physical mechanisms directly associated with El Niño-Southern Oscillation (ENSO) phenomena. In particular, I extract two extremes from recent simple coupled models in order to interprete two complementary phases of interannual ENSO events. I also discuss what is most necessary for more complete model studies useful to improve forecasting skills.     

Structure of a pair of anticyclonic vortices in northern part of the South China Sea in autumn of 1994

A pair of remarkable meso-scale anticyclonic vortices, one formed closely to another, inthe northern part of the South China Sea during the period from the later August to early September of 1994 were documented by the in situ observation data. Their spatial structures were examined in detail from the horizontal and two nearly perpendicular/vertical angles of view. It was shown that the horizontal scales of these two vortices were around 100, 50 km, and their vertical scales were about 500, 1000 m, respectively. Two "warm core" structures associated with these two vortices were found in their horizontal and vertical analyses. The closer spacing of these two vortices (namely, 60 km), which was smaller than the Rossby radius of deformation, suggested that they might merge with each other during their next evolution stages and form into a larger vortex eventually.     

Further study of synoptic variability in Wakasa Bay, Japan

Observations of synoptic variability from CTD and current meter measurements in Wakasa Bay, Japan in summer of 1980 and 1981 are compared with the results of 1979 reported by Yamagata, Umatani, Masunaga and Matsuura (1984). It is suggested that the speed and direction of propagation can basically be explained in terms of shelf wave dynamics.In the 1980 event, a dense (colder and more saline) water advanced eastward along the north coast at about 10 km day⁻¹. The lateral scale of the phenomenon was about 30 to 40 km, in agreement with the Rossby internal radius of deformation. The T-S and current data suggest that the 1980 cold event was dominated by phase propagation. In the 1981 event, a light (warmer and less saline) water area advanced eastward at the speed similar to the 1980 cold event, but the T-S and current data suggest that Lagrangian drift of water particles associated with strong eddy motions was not negligible.     

Interannual variability of the Kuroshio Extension and its relation to the Southern Oscillation/El Niño

The interannual variability of the temperature structure of the Kuroshio Extension is studied by establishing time series for the period 1950 to 1970 and then comparing it with the time series of sea level differences across the North Equatorial Current obtained by Wyrtki (1975). First, the present analysis shows a significant correlation between the interannual fluctuation of the Kuroshio Extension and the eddy activity south of the Kuroshio axis, suggesting the importance of the eddy-driven mechanism. Secondly, spectral analysis shows close connections between the Kuroshio Extension and the North Equatorial Current with a reasonable time lag of about 1.5 years. This time lag of the mid-latitude variability is also supported by other independent data. In particular, the present preliminary study strongly suggests that the bimodal behavior of the Kuroshio path south of Japan and the intensity of the Kuroshio Countercurrent are closely connected with the Southern Oscillation/El Niño.     

Long nonlinear topographic planetary waves in a rotating stratified ocean

Long nonlinear topographic waves in a continuously stratified ocean with a linear bottom slope are investigated. It is shown that odd cross-channel modes are governed by the Korteweg-de Vries (K-dV) equation. The solitary waves are those of a low pressure type. The long waves are shown to be modulationally stable because of the nonlinear effect due to irrotational motion. All these results are missed if the conventional quasi-geostrophic approximation is adopted.