Seasonal prediction of African precipitation with ECHAM4–T42 ensemble simulations using a multivariate MOS re-calibration scheme

A 15 member ensemble of 20th century simulations using the ECHAM4–T42 atmospheric GCM is utilized to investigate the potential predictability of interannual variations of seasonal rainfall over Africa. Common boundary conditions are the global sea surface temperatures (SST) and sea ice extent. A canonical correlation analysis (CCA) between observed and ensemble mean ECHAM4 precipitation over Africa is applied in order to identify the most predictable anomaly patterns of precipitation and the related SST anomalies. The CCA is then used to formulate a re-calibration approach similar to model output statistics (MOS) and to derive precipitation forecasts over Africa. Predictand is the climate research unit (CRU) gridded precipitation over Africa. As predictor we use observed SST anomalies, ensemble mean precipitation over Africa and a combined vector of mean sea level pressure, streamfunction and velocity potential at 850 hPa. The different forecast approaches are compared. Most skill for African precipitation forecasts is provided by tropical Atlantic (Gulf of Guinea) SST anomalies which mainly affect rainfall over the Guinean coast and Sahel. The El Niño/Southern Oscillation (ENSO) influences southern and East Africa, however with a lower skill. Indian Ocean SST anomalies, partly independent from ENSO, have an impact particularly on East Africa. As suggested by the large agreement between the simulated and observed precipitation, the ECHAM4 rainfall provides a skillful predictor for CRU precipitation over Africa. However, MOS re-calibration is needed in order to provide skillful forecasts. Forecasts using MOS re-calibrated model precipitation are at least as skillful as forecast using dynamical variables from the model or instantaneous SST. In many cases, MOS re-calibrated precipitation forecasts provide more skill. However, differences are not systematic for all regions and seasons, and often small.     

Predictability of the subtropical dipole modes in a coupled ocean–atmosphere model

Predictability of the subtropical dipole modes is assessed using the SINTEX-F coupled model. Despite the known difficulty in predicting subtropical climate due to large internal variability of the atmosphere and weak ocean–atmosphere coupling, it is shown for the first time that the coupled model can successfully predict the South Atlantic Subtropical Dipole (SASD) 1 season ahead, and the prediction skill is better than the persistence in all the 1–12 month lead hindcast experiments. There is a prediction barrier in austral winter due to the seasonal phase locking of the SASD to austral summer. The prediction skill is lower for the Indian Ocean Subtropical Dipole (IOSD) than for the SASD, and only slightly better than the persistence till 6-month lead because of the low predictability of the sea surface temperature anomaly in its southwestern pole. However, for some strong IOSD events in the last three decades, the model can predict them 1 season ahead. The co-occurrence of the negative SASD and IOSD in 1997/1998 austral summer can be predicted from July 1st of 1997. This is because the negative sea level pressure anomalies over the South Atlantic and the southern Indian Ocean in September–October (November–December) that trigger the occurrence of the negative SASD and IOSD are related to the well predicted tropical Indian Ocean Dipole (El Niño/Southern Oscillation). Owing to the overall good performances of the SINTEX-F model in predicting the SASD, some strong IOSD, and El Niño/Southern Oscillation, the prediction skill of the southern African summer precipitation is high in the SINTEX-F model.     

Interdecadal changes in the relationship between Southern China winter-spring precipitation and ENSO

Winter-spring precipitation in southern China tends to be higher (lower) than normal in El Niño (La Niña) years during 1953–1973. The relationship between the southern China winter-spring precipitation and El Niño-Southern Oscillation (ENSO) is weakened during 1974–1994. During 1953–1973, above-normal southern China rainfall corresponds to warmer sea surface temperature (SST) in the equatorial central Pacific. There are two anomalous vertical circulations with ascent over the equatorial central Pacific and ascent over southern China and a common branch of descent over the western North Pacific that is accompanied by an anomalous lower-level anticyclone. During 1974–1994, above-normal southern China rainfall corresponds to warmer SST in eastern South Indian Ocean and cooler SST in western South Indian Ocean. Two anomalous vertical circulations act to link southern China rainfall and eastern South Indian Ocean SST anomalies, with ascent over eastern South Indian Ocean and southern China and a common branch of descent over the western North Pacific. Present analysis shows that South Indian Ocean SST anomalies can contribute to southern China winter-spring precipitation variability independently. The observed change in the relationship between southern China winter-spring rainfall and ENSO is likely related to the increased SST variability in eastern South Indian Ocean and the modulation of the Pacific decadal oscillation.     

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.     

La Niña diversity and Northwest Indian Ocean Rim teleconnections

The differences in tropical Pacific sea surface temperature (SST) expressions of El Niño-Southern Oscillation (ENSO) events of the same phase have been linked with different global atmospheric circulation patterns. This study examines the dynamical forcing of precipitation during October–December (OND) and March–May (MAM) over East Africa and during December–March (DJFM) over Central-Southwest Asia for 1950–2010 associated with four tropical Pacific SST patterns characteristic of La Niña events, the cold phase of ENSO. The self-organizing map method along with a statistical distinguishability test was used to isolate La Niña events, and seasonal precipitation forcing was investigated in terms of the tropical overturning circulation and thermodynamic and moisture budgets. Recent La Niña events with strong opposing SST anomalies between the central and western Pacific Ocean (phases 3 and 4), force the strongest global circulation modifications and drought over the Northwest Indian Ocean Rim. Over East Africa during MAM and OND, subsidence is forced by an enhanced tropical overturning circulation and precipitation reductions are exacerbated by increases in moisture flux divergence. Over Central-Southwest Asia during DJFM, the thermodynamic forcing of subsidence is primarily responsible for precipitation reductions, with moisture flux divergence acting as a secondary mechanism to reduce precipitation. Eastern Pacific La Niña events in the absence of west Pacific SST anomalies (phases 1 and 2), are associated with weaker global teleconnections, particularly over the Indian Ocean Rim. The weak regional teleconnections result in statistically insignificant precipitation modifications over East Africa and Central-Southwest Asia.     

Potential predictability of Eurasian snow cover

Potential predictability and skill of simulated Eurasian snow cover are explored using a suite of seasonal ensemble hindcasts (i.e. retrospective forecasts), an ensemble climate simulation (spanning the years 1982–1998) and observations. Using remotely sensed observations of snow cover, we find significant point-wise correlation over the North Atlantic and North Pacific between winter and spring averaged sea-surface temperatures and Eurasian snow cover area. The observed correlation shows no discernible pattern related to the El Niño-Southern Oscillation (ENSO). The hindcasts show correlation patterns similar to the observations. However, the climate simulation shows an exaggerated ENSO pattern. The results underscore the importance of initialization in seasonal climate forecasts, and that the observed potential predictability of Eurasian snowcover cannot be solely attributed to ENSO.     

Influence of ENSO and the South Atlantic Ocean on climate predictability over Southeastern South America

We perform a systematic study of the predictability of surface air temperature and precipitation in Southeastern South America (SESA) using ensembles of AGCM simulations, focusing on the role of the South Atlantic and its interaction with the El Niño-Southern Oscillation (ENSO). It is found that the interannual predictability of climate over SESA is strongly tied to ENSO showing high predictability during the seasons and periods when there is ENSO influence. The most robust ENSO signal during the whole period of study (1949–2006) is during spring when warm events tend to increase the precipitation over Southeastern South America. Moreover, the predictability shows large inter-decadal changes: for the period 1949–1977, the surface temperature shows high predictability during late fall and early winter. On the other hand, for the period 1978–2006, the temperature shows (low) predictability only during winter, while the precipitation shows not only high predictability in spring but also in fall. Furthermore, it is found that the Atlantic does not directly affect the climate over SESA. However, the experiments where air–sea coupling is allowed in the south Atlantic suggest that this ocean can act as a moderator of the ENSO influence. During warm ENSO events the ocean off Brazil and Uruguay tends to warm up through changes in the atmospheric heat fluxes, altering the atmospheric anomalies and the predictability of climate over SESA. The main effect of the air–sea coupling is to strengthen the surface temperature anomalies over SESA; changes in precipitation are more subtle. We further found that the thermodynamic coupling can increase or decrease the predictability. For example, the air–sea coupling significantly increases the skill of the model in simulating the surface air temperature anomalies for most seasons during period 1949–1977, but tends to decrease the skill in late fall during period 1978–2006. This decrease in skill during late fall in 1978–2006 is found to be due to a wrong simulation of the remote ENSO signal that is further intensified by the local air–sea coupling in the south Atlantic. Thus, our results suggest that climate models used for seasonal prediction should simulate correctly not only the remote ENSO signal, but also the local air–sea thermodynamic coupling.     

ENSO,the IOD and the intraseasonal prediction of heat extremes across Australia using POAMA-2

The simulation and prediction of extreme heat over Australia on intraseasonal timescales in association with the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) is assessed using the Bureau of Meteorology’s Predictive Ocean Atmosphere Model for Australia (POAMA). The analysis is based on hindcasts over 1981–2010 and focuses on weeks 2 and 3 of the forecasts, i.e. beyond a typical weather forecast. POAMA simulates the observed increased probabilities of extreme heat during El Niño events, focussed over south eastern and southern Australia in SON and over northern Australia in DJF, and the decreased probabilities of extreme heat during La Niña events, although the magnitude of these relationships is smaller than observed. POAMA also captures the signal of increased probabilities of extreme heat during positive phases of the IOD across southern Australia in SON and over Western Australia in JJA, but again underestimates the strength of the relationship. Shortcomings in the simulation of extreme heat in association with ENSO and the IOD over southern Australia may be linked to deficiencies in the teleconnection with Indian Ocean SSTs. Forecast skill for intraseasonal episodes of extreme heat is assessed using the Symmetric Extremal Dependence Index. Skill is highest over northern Australia in MAM and JJA and over south-eastern and eastern Australia in JJA and SON, whereas skill is generally poor over south-west Western Australia. Results show there are windows of forecast opportunity related to the state of ENSO and the IOD, where the skill in predicting extreme temperatures over certain regions is increased.     

Impacts of large-scale teleconnections on climate variability over Southwest Asia

The atmospheric low frequency variability at a regional or global scale is represented by teleconnection. Using monthly dataset of the Climatic Research Unit (CRU) for the period 1971–2016, the impacts of four large-scale teleconnection patterns on the climate variability over Southwest Asia are investigated. The large-scale features include the El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO) and the East Atlantic (EA) teleconnection patterns, as well as western tropical Indian Ocean (WTIO) sea surface temperature anomaly index. Results indicate that ENSO and EA are the first leading modes that explain variation of Southwest Asian precipitation, with positive (negative) anomalies during El Niño (La Niña) and the negative (positive) phase of EA. Variation of Southwest Asian near-surface temperature is most strongly related to WTIO index, with above-average (below-average) temperature during the positive (negative) phase of WTIO index, although the negative (positive) phase of NAO also favours the above-average (below-average) temperature. On the other hand, temperature (precipitation) over Southwest Asia shows the least response to ENSO (WTIO). ENSO and EA individually explain 13 percent annual variance of precipitation, while WTIO index explains 36 percent annual variance of near-surface temperature over Southwest Asia. Analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis Interim (ERA-Interim) data indicated establishments of negative (positive) geopotential height anomalies in the middle troposphere over Southwest Asia during El Niño (La Niña) or the negative (positive) phase of NAO, EA and WTIO. The response of precipitation variability over Southwest Asia to NAO is opposite to that expected from the geopotential height anomalies, but the correlation between precipitation and NAO is not statistically significant. Due to predictability of large-scale teleconnections, results of this study are encouraging for improvement of the state-of-the-art seasonal prediction of the climate over Southwest Asia.     

Predictability of the East Asian winter monsoon indices by the coupled models of ENSEMBLES

The seasonal predictability of various East Asian winter monsoon （EAWM） indices was investigated in this study based on the retrospective forecasts of the five state-of-the-art coupled models from ENSEMBLES for a 46-year period of 19612006.It was found that the ENSEMBLES models predict five out of the 21 EAWM indices well,with temporal correlation coefficients ranging from 0.54 to 0.61.These five indices are defined by the averaged lower-tropospheric winds over the low latitudes （south of 30°N）.Further analyses indicated that the predictability of these five indices originates from their intimate relationship with ENSO.A cross-validated prediction,which took the preceding （November） observed Nifo3.4 index as a predictor,gives a prediction skill almost identical to that shown by the model.On the other hand,the models present rather low predictability for the other indices and for surface air temperature in East Asia.In addition,the models fail to reproduce the relationship between the indices of different categories,implying that they cannot capture the tropicalextratropical interaction related to EAWM variability.Together,these results suggest that reliable prediction of the EAWM indices and East Asian air temperature remains a challenge.     

A quantitative assessment of changes in seasonal potential predictability for the twentieth century

Changes over the twentieth century in seasonal mean potential predictability (PP) of global precipitation, 200 hPa height and land surface temperature are examined by using 100-member ensemble. The ensemble simulations have been conducted by using an intermediate complexity atmospheric general circulation model of the International Center for Theoretical Physics, Italy. Using the Hadley Centre sea surface temperature (SST) dataset on a 1° grid, two 31 year periods of 1920–1950 and 1970–2000 are separated to distinguish the periods of low and high SST variability, respectively. The standard deviation values averaged for the (“Niño-3.4”; 5°S–5°N, 170°W–120°W) region are 0.71 and 1.15 °C, for the periods of low and high SST variability, respectively, with a percentage change of 62 % during December–January–February (DJF). The leading eigenvector and the associated principal component time series, also indicate that the amplitude of SST variations have positive trend since 1920s to recent years, particularly over the El Niño Southern Oscillation (ENSO) region. Our hypothesis states that the increase in SST variability has increased the PP for precipitation, 200 hPa height and land surface temperature during the DJF. The analysis of signal and noise shows that the signal-to-noise (S/N) ratio is much increased over most of the globe, particularly over the tropics and subtropics for DJF precipitation. This occurs because of a larger increase in the signal and at the same time a reduction in the noise, over most of the tropical areas. For 200 hPa height, the S/N ratio over the Pacific North American (PNA) region is increasing more than that for the other extratropical regions, because of a larger percentage increase in the signal and only a small increase in noise. It is also found that the increase in seasonal mean transient signal over the PNA region is 50 %, while increase in the noise is only 12 %, during the high SST variability period, which indicates that the increase in signal is more than the noise. For DJF land surface temperature, the perfect model notion is utilized to confirm the changes in PP during the low and high SST variability periods. The correlation between the perfect model and the other members clearly reveal that the seasonal mean PP changed. In particular, the PP for the 31 years period of 1970–2000 is higher than that for the 31 years period of 1920–1950. The land surface temperature PP is increased in northern and southern Africa, central Europe, southern South America, eastern United States and over Canada. The increase of the signal and hence the seasonal mean PP is coincides with an increase in tropical Pacific SST variability, particularly in the ENSO region.     

The seasonal predictability of the Asian summer monsoon in a two-tiered forecast system

An extensive set of boreal summer seasonal hindcasts from a two tier system is compared with corresponding seasonal hindcasts from two other coupled ocean–atmosphere models for their seasonal prediction skill (for precipitation and surface temperature) of the Asian summer monsoon. The unique aspect of the two-tier system is that it is at relatively high resolution and the SST forcing is uniquely bias corrected from the multi-model averaged forecasted SST from the two coupled ocean–atmosphere models. Our analysis reveals: (a) The two-tier forecast system has seasonal prediction skill for precipitation that is comparable (over the Southeast Asian monsoon) or even higher (over the South Asian monsoon) than the coupled ocean–atmosphere. For seasonal anomalies of the surface temperature the results are more comparable across models, with all of them showing higher skill than that for precipitation. (b) Despite the improvement from the uncoupled AGCM all models in this study display a deterministic skill for seasonal precipitation anomalies over the Asian summer monsoon region to be weak. But there is useful probabilistic skill for tercile anomalies of precipitation and surface temperature that could be harvested from both the coupled and the uncoupled climate models. (c) Seasonal predictability of the South Asian summer monsoon (rainfall and temperature) does seem to stem from the remote ENSO forcing especially over the Indian monsoon region and the relatively weaker seasonal predictability in the Southeast Asian summer monsoon could be related to the comparatively weaker teleconnection with ENSO. The uncoupled AGCM with the bias corrected SST is able to leverage this teleconnection for improved seasonal prediction skill of the South Asian monsoon relative to the coupled models which display large systematic errors of the tropical SST’s.     

Understanding the SAM influence on the South Pacific ENSO teleconnection

The relationship between the El Niño Southern Oscillation (ENSO) and the Southern Hemisphere Annular Mode (SAM) is examined, with the goal of understanding how various strong SAM events modulate the ENSO teleconnection to the South Pacific (45°–70°S, 150°–70°W). The focus is on multi-month, multi-event variations during the last 50 years. A significant (p < 0.10) relationship is observed, most marked during the austral summer and in the 1970s and 1990s. In most cases, the significant relationship is brought about by La Niña (El Niño) events occurring with positive (negative) phases of the SAM more often than expected by chance. The South Pacific teleconnection magnitude is found to be strongly dependent on the SAM phase. Only when ENSO events occur with a weak SAM or when a La Niña (El Niño) occurs with a positive (negative) SAM phase are significant South Pacific teleconnections found. This modulation in the South Pacific ENSO teleconnection is directly tied to the interaction of the anomalous ENSO and SAM transient eddy momentum fluxes. During La Niña/SAM+ and El Niño/SAM? combinations, the anomalous transient momentum fluxes in the Pacific act to reinforce the circulation anomalies in the midlatitudes, altering the circulation in such a way to maintain the ENSO teleconnections. In La Niña/SAM? and El Niño/SAM+ cases, the anomalous transient eddies oppose each other in the midlatitudes, overall acting to reduce the magnitude of the high latitude ENSO teleconnection.     

Does sea surface temperature outside the tropical Pacific contribute to enhanced ENSO predictability?

In this paper we seek to identify inter-annual sea surface temperature anomalies (SSTA) patterns outside the tropical Pacific that may influence El Niño/Southern Oscillation (ENSO) through atmospheric teleconnections. We assume that a linear ENSO hindcast based on tropical Pacific warm water volume and Niño3.4 SSTA indices captures tropical Pacific intrinsic predictability inherent to recharge oscillator dynamics. This simple hindcast model displays statistically significant skill at the 95 % confidence level at leads of up to seven seasons ahead of the ENSO peak. Our results reveal that ENSO-independent equatorial wind stress anomalies only significantly improve the skill of that linear hindcast at the 95 % level in boreal spring and summer before the ENSO peak and in boreal fall, five seasons ahead of the ENSO peak. At those seasons, the robust large-scale SST patterns that provide a statistically significant enhancement of ENSO predictability are related to the Atlantic meridional mode and south Pacific subtropical dipole mode in spring, the Indian Ocean Dipole and the south Atlantic subtropical dipole mode in fall. While the first two regions display significant simultaneous correlations with western equatorial Pacific wind stress in three reanalyses (ERA-I, NCEP and NCEP2), the Indian Ocean Dipole and south Atlantic subtropical dipole mode correlation with Pacific winds is less robust amongst re-analyses. We discuss our results in view of other studies that suggest a remote influence of various regions on ENSO. Although modest, the sensitivity of our results to the dataset and to details of the analysis method illustrates that finding regions that influence ENSO from the statistical analysis of observations is a difficult task.     

An Assessment of Improvements in Global Monsoon Precipitation Simulation in FGOALS-s2简

The performance of Version 2 of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS-s2） in simulat ing global monsoon precipitation （GMP） was evaluated. Compared with FGOALS-sl, higher skill in simulating the annual modes of climatological tropical precipitation and interannual variations of GMP are seen in FGOALS-s2. The simulated domains of the northwestern Pacific monsoon （NWPM） and North American monsoon are smaller than in FGOALS-s 1. The main deficiency of FGOALS-s2 is that the NWPM has a weaker monsoon mode and stronger negatiw,＇ pattern in spring-fall asymmetric mode. The smaller NWPM domain in FGOALS-s2 is due to its simulated colder SST over the western Pacific warm pool. The relationship between ENSO and GMP is simulated reasonably by FGOALS-s2. However, the simulated precipitation anomaly over the South African monsoon region-South Indian Ocean during La Nina years is opposite to the observation. This results mainly from weaker warm SST anomaly over the maritime continent during La Nifia years, leading to stronger upper-troposphere （lower-troposphere） divergence （convergence） over the Indian Ocean, and artificial vertical as cent （descent） over the Southwest Indian Ocean （South African monsoon region）, inducing local excessive （deficient） rainfall. Comparison between the historical and pre-industrial simulations indicated that global land monsoon precipitation changes from 1901 to the 1970s were caused by internal variation of climate system. External forcing may have contributed to the increasing trend of the Australian monsoon since the 1980s. Finally, it shows that global warming could enhance GMR especially over the northern hemispheric ocean monsoon and southern hemispheric land monsoon.     

BCC二代气候系统模式的季节预测评估和可预报性分析

本文利用国家气候中心（BCC）第二代季节预测模式系统历史回报数据,从确定性预报和概率预报两个方面系统地评估了该模式对气温、降水和大气环流的季节预报性能,并与BCC一代气候预测模式的结果进行了对比,重点分析了二代模式的季节可预报性问题。结果显示,BCC二代模式对全球气温、降水和环流的预报性能整体上优于一代模式,特别在热带中东太平洋、印度洋和海洋大陆地区的温度和降水的预报效果改进尤为明显。这些热带地区降水预报的改进,可以通过激发太平洋—北美型（PNA）、东亚—太平洋型（EAP）等遥相关波列提升该模式在中高纬地区的季节预报技巧。分析表明,厄尔尼诺和南方涛动（ENSO）信号在热带和热带外地区均是模式季节可预报性的重要来源,BCC二代模式能够较好把握全球大气环流对ENSO信号的响应特征,从而通过对ENSO预报技巧的改进有效地提升了模式整体的预报性能。从概率预报来看,BCC二代模式对我国冬季气温和夏季降水具备一定的预报能力,特别是对我国东部大部分地区冬季气温正异常和负异常事件预报的可靠性和辨析度相对较高。因此,进一步提高模式对热带大尺度异常信号和大气主要模态的预报能力、加强概率预报产品释用对提高季节气候预测水平具有重要意义。     

Cross-season relation of the South China Sea precipitation variability between winter and summer

The present study reveals cross-season connections of rainfall variability in the South China Sea (SCS) region between winter and summer. Rainfall anomalies over northern South China Sea in boreal summer tend to be preceded by the same sign rainfall anomalies over southern South China Sea in boreal winter (denoted as in-phase relation) and succeeded by opposite sign rainfall anomalies over southern South China Sea in the following winter (denoted as out-of-phase relation). Analysis shows that the in-phase relation from winter to summer occurs more often in El Niño/La Niña decaying years and the out-of-phase relation from summer to winter appears more frequently in El Niño/La Niña developing years. In the summer during the El Niño/La Niña decaying years, cold/warm and warm/cold sea surface temperature (SST) anomalies develop in tropical central North Pacific and the North Indian Ocean, respectively, forming an east–west contrast pattern. The in-phase relation is associated with the influence of anomalous heating/cooling over the equatorial central Pacific during the mature phase of El Niño/La Niña events that suppresses/enhances precipitation over southern South China Sea and the impact of the above east–west SST anomaly pattern that reduces/increases precipitation over northern South China Sea during the following summer. The impact of the east–west contrast SST anomaly pattern is confirmed by numerical experiments with specified SST anomalies. In the El Niño/La Niña developing years, regional air-sea interactions induce cold/warm SST anomalies in the equatorial western North Pacific. The out-of-phase relation is associated with a Rossby wave type response to anomalous heating/cooling over the equatorial central Pacific during summer and the combined effect of warm/cold SST anomalies in the equatorial central Pacific and cold/warm SST anomalies in the western North Pacific during the mature phase of El Niño/La Niña events.     

Variability of rainfall characteristics over the South Coast region of South Africa

Thirty years of daily rainfall data are analysed for the South Coast region of South Africa, a region which experiences substantial rainfall variability and frequent severe drought and flood events, but whose climate variability has not been much researched. It is found that El Niño–Southern Oscillation (ENSO) exerts an influence since most wet years correspond to mature phase La Niña years. ENSO also influences South Coast rainfall via increases in the number of cut-off lows in southern South Africa during mature phase La Niña years. A statistically significant correlation between the Niño 3.4 index and monthly rainfall totals, and between this index and the frequency of wet days, exists for two summer months and also for June. There are also changes in the heavy rainfall day frequencies from one decade to another. Examination of NCEP re-analyses indicates that wet (dry) years result from an equatorward (poleward) shift in the subtropical jet, cyclonic (anticyclonic) pressure anomalies over the South Atlantic and South Africa, and increased (decreased) density of mid-latitude cyclonic systems.     

ENSO induced monthly oscillations of precipitation: the unique case of the south tropical Indian Ocean in austral summer

Seasonal (three-month average) climate forecasts have advanced due in large part to improved modeling of the ENSO phenomenon. Long-range monthly forecasts are more problematic because of internal atmospheric variability. Further, it is often assumed that monthly precipitation anomalies are representative of the overall seasonal anomaly. This is not always the case as, according to the Global Precipitation Climatology Project Version 2.1 data set, up to 20% of areas demonstrating some significant teleconnection to ENSO show El Ni?o minus La Ni?a differences of one sign in the middle month and the opposite sign in the adjacent months. Most interestingly, this maximum percentage occurs in December–January–February (DJF), a time when the ENSO boundary forcing is strongest. These oscillatory DJF seasons also cluster in space—with significant positive–negative-positive differences in the western South Tropical Indian Ocean (STIO) and negative–positive–negative differences in the far eastern STIO. Representative gauges confirm that these precipitation patterns have been associated with ENSO events since 1951, and pentad precipitation data confirm that they are confined to DJF and evolve at the monthly scale. The abrupt end of the Indian Ocean Dipole mode in January, an increase in the importance of local SST anomalies in February, and an ENSO-induced mid-latitude Rossby wave during austral summer combine to generate the cross-basin precipitation gradient around 15°S.     

Assessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavors of ENSO during boreal winter

Forecast skill of the APEC Climate Center (APCC) Multi-Model Ensemble (MME) seasonal forecast system in predicting two main types of El Niño-Southern Oscillation (ENSO), namely canonical (or cold tongue) and Modoki ENSO, and their regional climate impacts is assessed for boreal winter. The APCC MME is constructed by simple composite of ensemble forecasts from five independent coupled ocean-atmosphere climate models. Based on a hindcast set targeting boreal winter prediction for the period 1982–2004, we show that the MME can predict and discern the important differences in the patterns of tropical Pacific sea surface temperature anomaly between the canonical and Modoki ENSO one and four month ahead. Importantly, the four month lead MME beats the persistent forecast. The MME reasonably predicts the distinct impacts of the canonical ENSO, including the strong winter monsoon rainfall over East Asia, the below normal rainfall and above normal temperature over Australia, the anomalously wet conditions across the south and cold conditions over the whole area of USA, and the anomalously dry conditions over South America. However, there are some limitations in capturing its regional impacts, especially, over Australasia and tropical South America at a lead time of one and four months. Nonetheless, forecast skills for rainfall and temperature over East Asia and North America during ENSO Modoki are comparable to or slightly higher than those during canonical ENSO events.