Influence of South China Sea SST and the ENSO on Winter Rainfall over South China

The present study investigates the influence of South China Sea (SCS) SST and ENSO on winter (January--February--March; JFM) rainfall over South China and its dynamic processes by using station observations for the period 1951--2003, Met Office Hadley Center SST data for the period 1900--2008, and ERA-40 reanalysis data for the period 1958--2002. It is found that JFM rainfall over South China has a significant correlation with Nino-3 and SCS SST. Analyses show that in El Nino or positive SCS SST anomaly years, southwesterly anomalies at 700 hPa dominate over the South China Sea, which in turn transports more moisture into South China and favors increased rainfall. A partial regression analysis indicates that the independent ENSO influence on winter rainfall occurs mainly over South China, whereas SCS SST has a larger independent influence on winter rainfall in northern part of South China. The temperature over South China shows an obvious decrease at 300 hPa and an increase near the surface, with the former induced by Nino-3 and the latter SCS SST anomalies. This enhances the convective instability and weakens the potential vorticity (PV), which explains the strengthening of ascending motion and the increase of JFM rainfall over South China.     

Prediction of summer monsoon rainfall over India using the NCEP climate forecast system

The performance of a dynamical seasonal forecast system is evaluated for the prediction of summer monsoon rainfall over the Indian region during June to September (JJAS). The evaluation is based on the National Centre for Environmental Prediction’s (NCEP) climate forecast system (CFS) initialized during March, April and May and integrated for a period of 9 months with a 15 ensemble members for 25 years period from 1981 to 2005. The CFS’s hindcast climatology during JJAS of March (lag-3), April (lag-2) and May (lag-1) initial conditions show mostly an identical pattern of rainfall similar to that of verification climatology with the rainfall maxima (one over the west-coast of India and the other over the head Bay of Bengal region) well simulated. The pattern correlation between verification and forecast climatology over the global tropics and Indian monsoon region (IMR) bounded by 50°E–110°E and 10°S–35°N shows significant correlation coefficient (CCs). The skill of simulation of broad scale monsoon circulation index (Webster and Yang; WY index) is quite good in the CFS with highly significant CC between the observed and predicted by the CFS from the March, April and May forecasts. High skill in forecasting El Nino event is also noted for the CFS March, April and May initial conditions, whereas, the skill of the simulation of Indian Ocean Dipole is poor and is basically due to the poor skill of prediction of sea surface temperature (SST) anomalies over the eastern equatorial Indian Ocean. Over the IMR the skill of monsoon rainfall forecast during JJAS as measured by the spatial Anomaly CC between forecast rainfall anomaly and the observed rainfall anomaly during 1991, 1994, 1997 and 1998 is high (almost of the order of 0.6), whereas, during the year 1982, 1984, 1985, 1987 and 1989 the ACC is only around 0.3. By using lower and upper tropospheric forecast winds during JJAS over the regions of significant CCs as predictors for the All India Summer Monsoon Rainfall (AISMR; only the land stations of India during JJAS), the predicted mean AISMR with March, April and May initial conditions is found to be well correlated with actual AISMR and is found to provide skillful prediction. Thus, the calibrated CFS forecast could be used as a better tool for the real time prediction of AISMR.     

基于海气耦合环流模式的ENSO预测

Predictions of ENSO are described by using a coupled atmosphere-ocean general circulation model. The initial conditions are created by forcing the coupled system using SST anomalies in the tropical Pacific at the background of the coupled model climatology. A series of 24-month hindcasts for the period from November 1981 to December 1997 are carried out to validate the performance of the coupled system. Correlations of SST anomalies in the Nino3 region exceed 0.54 up to 15 months in advance and the rms errors are less than 0.9℃. The system is more skillful in predicting SST anomalies in the 1980s and less in the 1990s. The model skills are also seasonal-dependent, which are lower for the predictions starting from late autumn to winter and higher for those from spring to autumn in a year-time forecast length. The prediction, beginning from March, persists 8 months long with the correlation skill exceeding 0.6, which is important in predictions of summer rainfall in China. The predictions are succesful in many aspects for the 1997-2000 ENSO events.     

Evolution of the equatorial and dipole modes of the sea-surface temperature in the Tropical Atlantic at decadal scale

Summary ?One hundred and thirty six years (1856–1991) of monthly sea-surface temperature (SST) data in the Tropical Atlantic Ocean are used to investigate the propagating signals of the SST at a decadal (DD) time scale. The first and the third evolving modes show a relationship between the equatorial and the inter-hemispheric patterns, one evolving into the other mode and vice-versa. These modes describe two different evolutions of the SST at DD time-scale. The first EEOF features a 12-year period oscillatory regime with a strong 2-year duration inter-hemispheric pattern evolving into a weak 1-year duration equatorial pattern and vice-versa. This mode exhibits also a northward displacement of the anomalies in the band 15° S–15° N. The third EEOF also shows an oscillatory regime, but with a period of 10 years and with a relatively strong 2-year duration equatorial pattern evolving into a weak 1-year duration inter-hemispheric pattern and vice-versa. For this mode, the SST anomalies show a southward displacement in the band 15° S–15° N. These results have not yet been documented in previous works and explain some of the previous findings on the DD variability in the Tropical Atlantic. Received December 31, 2001; revised April 9, 2002; accepted September 4, 2002 Published online: March 20, 2003     

Decadal predictability and prediction skill of sea surface temperatures in the South Pacific region

The South Pacific Ocean is a key driver of climate variability within the Southern Hemisphere at different time scales. Previous studies have characterized the main mode of interannual sea surface temperature (SST) variability in that region as a dipolar pattern of SST anomalies that cover subtropical and extratropical latitudes (the South Pacific Ocean Dipole, or SPOD), which is related to precipitation and temperature anomalies over several regions throughout the Southern Hemisphere. Using that relationship and the reported low predictive skill of precipitation anomalies over the Southern Hemisphere, this work explores the predictability and prediction skill of the SPOD in near-term climate hindcasts using a set of state-of-the-art forecast systems. Results show that predictability greatly benefits from initializing the hindcasts beyond the prescribed radiative forcing, and is modulated by known modes of climate variability, namely El Niño-Southern Oscillation and the Interdecadal Pacific Oscillation. Furthermore, the models are capable of simulating the spatial pattern of the observed SPOD even without initialization, which suggests that the key dynamical processes are properly represented. However, the hindcast of the actual phase of the mode is only achieved when the forecast systems are initialized, pointing at SPOD variability to not be radiatively forced but probably internally generated. The comparison with the performance of an empirical prediction based on persistence suggests that initialization may provide skillful information for SST anomalies, outperforming damping processes, up to 2–3 years into the future.     

Empirical prediction of Indian summer monsoon rainfall with different lead periods based on global SST anomalies

Summary The main objective of this study was to develop empirical models with different seasonal lead time periods for the long range prediction of seasonal (June to September) Indian summer monsoon rainfall (ISMR). For this purpose, 13 predictors having significant and stable relationships with ISMR were derived by the correlation analysis of global grid point seasonal Sea-Surface Temperature (SST) anomalies and the tendency in the SST anomalies. The time lags of the seasonal SST anomalies were varied from 1 season to 4 years behind the reference monsoon season. The basic SST data set used was the monthly NOAA Extended Reconstructed Global SST (ERSST) data at 2° × 2° spatial grid for the period 1951–2003. The time lags of the 13 predictors derived from various areas of all three tropical ocean basins (Indian, Pacific and Atlantic Oceans) varied from 1 season to 3 years. Based on these inter-correlated predictors, 3 predictor sub sets A, B and C were formed with prediction lead time periods of 0, 1 and 2 seasons, respectively, from the beginning of the monsoon season. The selected principal components (PCs) of these predictor sets were used as the input parameters for the models A, B and C, respectively. The model development period was 1955–1984. The correct model size was derived using all-possible regressions procedure and Mallow’s “Cp” statistics. Various model statistics computed for the independent period (1985–2003) showed that model B had the best prediction skill among the three models. The root mean square error (RMSE) of model B during the independent test period (6.03% of Long Period Average (LPA)) was much less than that during the development period (7.49% of LPA). The performance of model B was reasonably good during both ENSO and non-ENSO years particularly when the magnitudes of actual ISMR were large. In general, the predicted ISMR during years following the El Ni?o (La Ni?a) years were above (below) LPA as were the actual ISMR. By including an NAO related predictor (WEPR) derived from the surface pressure anomalies over West Europe as an additional input parameter into model B, the skill of the predictions were found to be substantially improved (RMSE of 4.86% of LPA).     

Coupled ocean-atmosphere surface variability and its climate impacts in the tropical Atlantic region

 This study examines time evolution and statistical relationships involving the two leading ocean-atmosphere coupled modes of variability in the tropical Atlantic and some climate anomalies over the tropical 120 °W–60 °W region using selected historical files (75-y near global SSTs and precipitation over land), more recent observed data (30-y SST and pseudo wind stress in the tropical Atlantic) and reanalyses from the US National Centers for Environmental Prediction (NCEP/NCAR) reanalysis System on the period 1968–1997: surface air temperature, sea level pressure, moist static energy content at 850 hPa, precipitable water and precipitation. The first coupled mode detected through singular value decomposition of the SST and pseudo wind-stress data over the tropical Atlantic (30 °N–20 °S) expresses a modulation in the thermal transequatorial gradient of SST anomalies conducted by one month leading wind-stress anomalies mainly in the tropical north Atlantic during northern winter and fall. It features a slight dipole structure in the meridional plane. Its time variability is dominated by a quasi-decadal signal well observed in the last 20–30 ys and, when projected over longer-term SST data, in the 1920s and 1930s but with shorter periods. The second coupled mode is more confined to the south-equatorial tropical Atlantic in the northern summer and explains considerably less wind-stress/SST cross-covariance. Its time series features an interannual variability dominated by shorter frequencies with increased variance in the 1960s and 1970s before 1977. Correlations between these modes and the ENSO-like Nino3 index lead to decreasing amplitude of thermal anomalies in the tropical Atlantic during warm episodes in the Pacific. This could explain the nonstationarity of meridional anomaly gradients on seasonal and interannual time scales. Overall the relationships between the oceanic component of the coupled modes and the climate anomaly patterns denote thermodynamical processes at the ocean/atmosphere interface that create anomaly gradients in the meridional plane in a way which tends to alter the north–south movement of the seasonal cycle. This appears to be consistent with the intrinsic non-dipole character of the tropical Atlantic surface variability at the interannual time step and over the recent period, but produces abnormal amplitude and/or delayed excursions of the intertropical convergence zone (ITCZ). Connections with continental rainfall are approached through three (NCEP/NCAR and observed) rainfall indexes over the Nordeste region in Brazil, and the Guinea and Sahel zones in West Africa. These indices appear to be significantly linked to the SST component of the coupled modes only when the two Atlantic modes+the ENSO-like Nino3 index are taken into account in the regressions. This suggests that thermal forcing of continental rainfall is particularly sensitive to the linear combinations of some basic SST patterns, in particular to those that create meridional thermal gradients. The first mode in the Atlantic is associated with transequatorial pressure, moist static energy and precipitable water anomaly patterns which can explain abnormal location of the ITCZ particularly in northern winter, and hence rainfall variations in Nordeste. The second mode is more associated with in-phase variations of the same variables near the southern edge of the ITCZ, particularly in the Gulf of Guinea during the northern spring and winter. It is primarily linked to the amplitude and annual phase of the ITCZ excursions and thus to rainfall variations in Guinea. Connections with Sahel rainfall are less clear due to the difficulty for the model to correctly capture interannual variability over that region but the second Atlantic mode and the ENSO-like Pacific variability are clearly involved in the Sahel climate interannual fluctuations: anomalous dry (wet) situations tend to occur when warmer (cooler) waters are present in the eastern Pacific and the gulf of Guinea in northern summer which contribute to create a northward (southward) transequatorial anomaly gradient in sea level pressure over West Africa. Received: 14 April 1998 / Accepted: 24 December 1998     

Subseasonal variability during the South China Sea summer monsoon onset

Analysis of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) data for the period 1998–2007 reveals large subseasonal fluctuations in sea surface temperature (SST) of the South China Sea during the summer monsoon onset. These subseasonal SST changes are closely related to surface heat flux anomalies induced by surface wind and cloud changes in association with the summer monsoon onset. The SST changes feed back on the atmosphere by modifying the atmospheric instability. The results suggest that the South China Sea summer monsoon onset involves ocean–atmosphere coupling on subseasonal timescales. While the SST response to surface heat flux changes is quick and dramatic, the time lag between the SST anomalies and the atmospheric convection response varies largely from year to year. The spatial–temporal evolution of subseasonal anomalies indicates that the subseasonal variability affecting the South China Sea summer monsoon onset starts over the equatorial western Pacific, propagates northward to the Philippine Sea, and then moves westward to the South China Sea. The propagation of these subseasonal anomalies is related to the ocean–atmosphere interaction, involving the wind-evaporation and cloud-radiation effects on SST as well as SST impacts on lower-level convergence over the equatorial western Pacific and atmospheric instability over the Philippine Sea and the South China Sea.     

The superior influence of Darwin Sea level pressure anomalies over ENSO as a simple drought predictor for Southern Africa

Summary The dominant climatic mode responsible for seasonal rainfall variability across central southern Africa has been well-established as ENSO. Hence, the El Ni?o signal of the equatorial Pacific has been used extensively to predict droughts in this sub-region. Although this paper acknowledges that El Ni?o influences rainfall deficits over eastern southern Africa, an earlier signal of extreme positive sea level pressure (SLP) anomalies at Darwin for the averaged March to June period (MAMJ Darwin) has proved to have a superior remote connection to droughts in the sub-region. Simple linear statistical tools including composite techniques and correlation methods have been employed on century long data sets (1901–2000) to identify the emerging paramount connection between MAMJ Darwin SLP anomalies and southern African rainfall. Both MAMJ Darwin SLP anomalies and the Zimbabwe seasonal rainfall time series are significantly correlated (above the 95% significant level) with sea surface temperature anomalies. These represent the Indian Ocean Dipole mode in the tropical Indian Ocean and ENSO in the tropical Pacific for the averaged September to December period. ‘Pure’ MAMJ Darwin (that occur in the absence of El Ni?o in the Pacific) coincide with droughts more significantly (83% hit rate) than ‘pure’ El Ni?o events (not preceded by a high MAMJ Darwin) (38% hit rate). Co-occurrences (MAMJ Darwin preceded by El Ni?o) do not only have the highest hit rate of 93% but subsequent droughts are noticeably more severe. The ‘pure’ El Ni?os however, are not only poorly related to Zimbabwe seasonal rainfall deficits, but are apparently not connected to extreme droughts of the 20^th century. Thus, MAMJ Darwin is a good simple predictor of droughts associated with or without ENSO in the Pacific. The high prediction skill of these results, especially the inherent longer lead-time than ENSO, makes MAMJ Darwin SLP anomalies an ideal additional input candidate for sub-regional drought monitoring and forecasting schemes. In this way, drought early warning and disaster preparedness activities can be enhanced over the sub-region. Authors’ addresses: D. Manatsa, W. Chingombe, H. Matsikwa, Faculty of Science, Bindura University of Science Education, P. Bag 1020, Bindura, Zimbabwe; C. H. Matarira, Department of Geography and Environmental Science, National University of Lesotho, Roma 180, Lesotho.     

Simulation of Indian summer monsoon: experiments with SSTs

Summary Hindcasts for the Indian summer monsoons (ISMs) of 2002 and 2003 have been produced from an ensemble of numerical simulations performed with a global model by changing SST. Two sets of ensemble simulations have been produced without vegetation: (i) by prescribing the weekly observed SST from ECMWF (European Centre for Medium Range Weather Forecasting) analyses, and (ii) by adding weekly SST anomalies (SSTA) of April to the climatological SST during the simulation period from May to August. For each ensemble, 10 simulations have been realized with different initial conditions that are prepared from ECMWF data with five each from April and May analyses of both the years. The predicted June–July monsoon rainfall over the Indian region shows good agreement with the GPCP (observed) pentad rainfall distribution when 5 member ensemble is taken from May initial conditions. The All-India June–July simulated rainfall time series matches favourably with the observed time series in both the years for the five member ensemble from May initial condition but drifts away from observation with April initial conditions. This underscores the role of initial conditions in the seasonal forecasting. But the model has failed to capture the strong intra-seasonal oscillation in July 2002. Heating over equatorial Indian Ocean for June 2002 in a particular experiment using 29th May 12 GMT as initial conditions shows some intra-seasonal oscillation in July 2002 rainfall, as in observation. Further evaluation of the seasonal simulations from this model is done by calculating the empirical orthogonal functions (EOFs) of the GPCP rainfall over India. The first four EOFs explain more than 80% of the total variance of the observed rainfall. The time series of expansion coefficients (principal components), obtained by projecting on the observed EOFs, provide a better framework for inter-comparing model simulations and their evaluation with observed data. The main finding of this study is that the All-India rainfall from various experiments with prescribed SST is better predicted on seasonal scale as compares to prescribed SST anomalies. This is indicative of a possible useful seasonal forecasts from a GCM at least for the case when monsoon is going to be good. The model responses do not differ much for 2002 and 2003 since the evolution of SST during these years was very similar, hence July rainfall seems to be largely modulated by the other feedbacks on the overall circulation.     

Predictability of SST forced climate signals in two atmospheric general circulation models

 The predictability of atmospheric responses to global sea surface temperature (SST) anomalies is evaluated using ensemble simulations of two general circulation models (GCMs): the GENESIS version 1.5 (GEN) and the ECMWF cycle 36 (ECM). The integrations incorporate observed SST variations but start from different initial land and atmospheric states. Five GEN 1980–1992 and six ECM 1980–1988 realizations are compared with observations to distinguish predictable SST forced climate signals from internal variability. To facilitate the study, correlation analysis and significance evaluation techniques are developed on the basis of time series permutations. It is found that the annual mean global area with realistic signals is variable dependent and ranges from 3 to 20% in GEN and 6 to 28% in ECM. More than 95% of these signal areas occur between 35 °S–35 °N. Due to the existence of model biases, robust responses, which are independent of initial condition, are identified over broader areas. Both GCMs demonstrate that the sensitivity to initial conditions decreases and the predictability of SST forced responses increases, in order, from 850 hPa zonal wind, outgoing longwave radiation, 200 hPa zonal wind, sea-level pressure to 500 hPa height. The predictable signals are concentrated in the tropical and subtropical Pacific Ocean and are identified with typical El Ni?o/ Southern Oscillation phenomena that occur in response to SST and diabatic heating anomalies over the equatorial central Pacific. ECM is less sensitive to initial conditions and better predicts SST forced climate changes. This results from (1) a more realistic basic climatology, especially of the upper-level wind circulation, that produces more realistic interactions between the mean flow, stationary waves and tropical forcing; (2) a more vigorous hydrologic cycle that amplifies the tropical forcing signals, which can exceed internal variability and be more efficiently transported from the forcing region. Differences between the models and observations are identified. For GEN during El Ni?o, the convection does not carry energy to a sufficiently high altitude, while the spread of the tropospheric warming along the equator is slower and the anomaly magnitude smaller than observed. This impacts model ability to simulate realistic responses over Eurasia and the Indian Ocean. Similar biases exist in the ECM responses. In addition, the relationships between upper and lower tropospheric wind responses to SST forcing are not well reproduced by either model. The identification of these model biases leads to the conclusion that improvements in convective heat and momentum transport parametrizations and basic climate simulations could substantially increase predictive skill. Received: 25 April 1996 / Accepted: 9 December 1996     

A Regression-based Assessment of the Predictability of New Zealand Climate Anomalies

Summary Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using a cross-validated linear regression procedure. Predictors are indices of the large scale circulation, sea-surface temperatures, the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials. No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonal time scale. Temperature forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10–20% in independent trials. Temperature anomalies are most skilfully predicted over the North Island, and skill is greatest in Spring and Summer in most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill, while at the seasonal time scale, skill depends strongly upon “remote” predictors defined over regions of the southern hemisphere distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors, especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal predictions. Forecast skill is more reliably estimated at the monthly time scale than at the seasonal time scale, as a result of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore, even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are discussed. Received July 18, 1997 Revised April 2, 1998     

Seasonal differences of model predictability and the impact of SST in the Pacific

Both seasonal potential predictability and the impact of SST in the Pacific on the forecast skill over China are investigated by using a 9-level global atmospheric general circulation model developed at the Institute of Atmospheric Physics under the Chinese Academy of Sciences (IAP9L-AGCM). For each year during 1970 to 1999, the ensemble consists of seven integrations started from consecutive observational daily atmospheric fields and forced by observational monthly SST. For boreal winter, spring and summer,the variance ratios of the SST-forced variability to the total variability and the differences in the spatial correlation coefficients of seasonal mean fields in special years versus normal years are computed respectively. It follows that there are slightly inter-seasonal differences in the model potential predictability in the Tropics. At northern middle and high latitudes, prediction skill is generally low in spring and relatively high either in summer for surface air temperature and middle and upper tropospheric geopotential height or in winter for wind and precipitation. In general, prediction skill rises notably in western China, especially in northwestern China, when SST anomalies (SSTA) in the Nino-3 region are significant. Moreover,particular attention should be paid to the SSTA in the North Pacific (NP) if one aims to predict summer climate over the eastern part of China, i.e., northeastern China, North China and southeastern China.     

The relative influence of soil moisture and SST in climate predictability explored within ensembles of AMIP type experiments

Three ensembles of AMIP-type simulations using the Arpege-climat coupled land–atmosphere model have been designed to assess the relative influence of SST and soil moisture (SM) on climate variability and predictability. The study takes advantage of the GSWP2 land surface reanalysis covering the 1986–1995 period. The GSWP2 forcings have been used to derive a global SM climatology that is fully consistent with the model used in this study. One ensemble of ten simulations has been forced by climatological SST and the simulated SM is relaxed toward the GSWP2 reanalysis. Another ensemble has been forced by observed SST and SM is evolving freely. The last ensemble combines the observed SST forcing and the relaxation toward GSWP2. Two complementary aspects of the predictability have been explored, the potential predictability (analysis of variance) and the effective predictability (skill score). An analysis of variance has revealed the effects of the SST and SM boundary forcings on the variability and potential predictability of near-surface temperature, precipitation and surface evaporation. While in the tropics SST anomalies clearly maintain a potentially predictable variability throughout the annual cycle, in the mid-latitudes the SST forced variability is only dominant in winter and SM plays a leading role in summer. In a similar fashion, the annual cycle of the hindcast skill (evaluated as the anomalous correlation coefficient of the three ensemble means with respect to the “observations”) indicates that the SST forcing is the dominant contributor over the tropical continents and in the winter mid-latitudes but that SM is supporting a significant part of the skill in the summer mid-latitudes. Focusing on boreal summer, we have then investigated different aspects of the SM and SST contribution to climate variations in terms of spatial distribution and time-evolution. Our experiments suggest that SM is potentially an additional source of climate predictability. A realistic initialization of SM and a proper representation of the land–atmosphere feedbacks seem necessary to improve state-of-the-art dynamical seasonal predictions, but will be actually efficient only in the areas where SM anomalies are themselves predictable at the monthly to seasonal timescale (since remote effects of SM are probably much more limited than SST teleconnections).     

Impact of Subsurface Entrainment on ENSO Prediction: 1997-98 El Nino

Twenty-one-year hindcasts of sea surface temperature (SST) anomalies in the tropical Pacific were performed to validate the influence of ocean subsurface entrainment on SST prediction.A new hybrid coupled model was used that considered the entrainment of subsurface temperature anomalies into the sea surface.The results showed that predictions were improved significantly in the new coupled model.The predictive correlation skill increased by about 0.2 at a lead time of 9 months,and the root-mean-square (RMS) errors were decreased by nearly 0.2°C in general.A detailed analysis of the 1997-98 El Nio hindcast showed that the new model was able to predict the onset,peak (both time and amplitude),and decay of the 1997-98 strong El Nio event up to a lead time of one year,factors that are not represented well by many other forecast systems.This implies,in terms of prediction,that subsurface anomalies and their impact on the SST are one of the controlling factors in ENSO cycles.Improving the presentation of such effects in models would increase the forecast skill.     

Summer interactions between weather regimes and surface ocean in the North-Atlantic region

This study aims at understanding the summer ocean-atmosphere interactions in the North Atlantic European region on intraseasonal timescales. The CNRMOM1d ocean model is forced with ERA40 (ECMWF Re-Analysis) surface fluxes with a 1-h frequency in solar heat flux (6 h for the other forcing fields) over the 1959–2001 period. The model has 124 vertical levels with a vertical resolution of 1 m near the surface and 500 m at the bottom. This ocean forced experiment is used to assess the impact of the North Atlantic weather regimes on the surface ocean. Composites of sea surface temperature (SST) anomalies associated with each weather regime are computed and the mechanisms explaining these anomalies are investigated. Then, the SST anomalies related to each weather regime in the ocean-forced experiment are prescribed to the ARPEGE Atmosphere General Circulation Model. We show that the interaction with the surface ocean induces a positive feedback on the persistence of the Blocking regime, a negative feedback on the persistence of the NAO-regime and favours the transition from the Atlantic Ridge regime to the NAO-regime and from the Atlantic Low regime toward the Blocking regime.     

Factors that affect the amplitude of El Nino in global coupled climate models

Historically, El Nino-like events simulated in global coupled climate models have had reduced amplitude compared to observations. Here, El Nino-like phenomena are compared in ten sensitivity experiments using two recent global coupled models. These models have various combinations of horizontal and vertical ocean resolution, ocean physics, and atmospheric model resolution. It is demonstrated that the lower the value of the ocean background vertical diffusivity, the greater the amplitude of El Nino variability which is related primarily to a sharper equatorial thermocline. Among models with low background vertical diffusivity, stronger equatorial zonal wind stress is associated with relatively higher amplitude El Nino variability along with more realistic east–west sea surface temperature (SST) gradient along the equator. The SST seasonal cycle in the eastern tropical Pacific has too much of a semiannual component with a double intertropical convergence zone (ITCZ) in all experiments, and thus does not affect, nor is it affected by, the amplitude of El Nino variability. Systematic errors affecting the spatial variability of El Nino in the experiments are characterized by the eastern equatorial Pacific cold tongue regime extending too far westward into the warm pool. The time scales of interannual variability (as represented by time series of Nino3 SSTs) show significant power in the 3–4 year ENSO band and 2–2.5 year tropospheric biennial oscillation (TBO) band in the model experiments. The TBO periods in the models agree well with the observations, while the ENSO periods are near the short end of the range of 3–6 years observed during the period 1950–94. The close association between interannual variability of equatorial eastern Pacific SSTs and large-scale SST patterns is represented by significant correlations between Nino3 time series and the PC time series of the first EOFs of near-global SSTs in the models and observations. Received: 17 April 2000 / Accepted: 17 August 2000     

Links between tropical SST anomalies and precursory signalsassociated with the interannual variability of Asian summer monsoon

Summary ?The interannual variability of broad-scale Asian summer monsoon was studied using a general circulation model (GCM) and NCEP (National Center for Environmental Prediction) data set during 1979–95. In the GCM experiment, the main emphasis was given to isolate the individual role of surface boundary conditions on the existence of winter-spring time circulation anomalies associated with the interannual variability of Asian summer monsoon. In order to understand the role of sea-surface temperatures (SSTs) alone on the existence of precursory signals, we have conducted 17 years numerical integration with a GCM forced with the real-time monthly averaged SSTs of 1979 to 1995. In this experiment, among the many surface boundary conditions only SSTs are varying interannually. The composite circulation anomalies simulated by the GCM have good resemblance with the NCEP circulation anomalies over subtropical Asia. This suggests that the root cause of the existence of winter-spring time circulation anomalies associated with the interannual variability of Asian summer monsoon is the interannual variability of SST. Empirical Orthogonal Functions (EOFs) of 200-mb winds and OLR were constructed to study the dynamic coupling between SST anomalies and winter-spring time circulation anomalies. It is found that the convective heating anomalies associated with SST anomalies and stationary eddies undergo systematic and coherent interannual variations prior to summer season. We have identified Matsuno-Gill type mode in the velocity potential and stream function fields. This suggests the existence of dynamic links between the SST anomalies and the precursory signals of Asian summer monsoon. Received June 9, 1999/Revised April 7, 2000     

Pacific Ocean sea-surface temperature variability and predictability of rainfall in the early and late parts of the Indian summer monsoon season

For central India and its west coast, rainfall in the early (15 May–20 June) and late (15 September–20 October) monsoon season correlates with Pacific Ocean sea-surface temperature (SST) anomalies in the preceding month (April and August, respectively) sufficiently well, that those SST anomalies can be used to predict such rainfall. The patterns of SST anomalies that correlate best include the equatorial region near the dateline, and for the early monsoon season (especially since ~1980), a band of opposite correlation stretching from near the equator at 120°E to ~25°N at the dateline. Such correlations for both early and late monsoon rainfall and for both regions approach, if not exceed, 0.5. Although correlations between All India Summer Monsoon Rainfall and typical indices for the El Ni?o-Southern Oscillation (ENSO) commonly are stronger for the period before than since 1980, these correlations with early and late monsoon seasons suggest that ENSO continues to affect the monsoon in these seasons. We exploit these patterns to assess predictability, and we find that SSTs averages in specified regions of the Pacific Ocean in April (August) offer predictors that can forecast rainfall amounts in the early (late) monsoon season period with a ~25% improvement in skill relative to climatology. The same predictors offer somewhat less skill (~20% better than climatology) for predicting the number of days in these periods with rainfall greater than 2.5?mm. These results demonstrate that although the correlation of ENSO indices with All India Rainfall has decreased during the past few decades, the connections with ENSO in the early and late parts have not declined; that for the early monsoon season, in fact, has grown stronger in recent decades.     

Forecasting the equatorial Pacific sea surface temperatures by neural network models

We used neural network models to seasonally forecast the tropical Pacific sea surface temperature anomalies (SSTA) in the Ni?o 3.4 region (6 ^°S–6 ^°N, 120 ^°W–170 ^°W). The inputs to the neural networks (i.e., the predictors) were the first seven wind stress empirical orthogonal function (EOF) modes of the tropical Pacific (20 ^°S–20 ^°N, 120 ^°E–70 ^°W) for four seasons and the Ni?o 3.4 SSTA itself for the final season. The period of 1952–1981 was used for training the neural network models, and the period 1982–1992 for forecast validation. At 6-month lead time, neural networks attained forecast skills comparable to the other El Ni?o-Southern Oscillation (ENSO) models. Our results suggested that neural network models were viable for ENSO forecasting even at longer lead times of 9 to 12 months. We hypothesized that at these longer leads, the underlying relationship between the wind stress and Ni?o 3.4 SSTA became increasingly nonlinear. The neural network results were interpreted in light of current theories, e.g., the role of the “off-equatorial” Rossby waves in triggering the onset of an ENSO event and the delayed-oscillator theory in the development and termination of an ENSO event. Received: 31 October 1995 / Accepted: 25 July 1996