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.     

Seasonal predictions based on two dynamical models

Abstract

Two dynamical models are used to perform a series of seasonal predictions. One model, referred to as GCM2, was designed as a general circulation model for climate studies, while the second one, SEF, was designed for numerical weather prediction. The seasonal predictions cover the 26‐year period 1969–1994. For each of the four seasons, ensembles of six forecasts are produced with each model, the six runs starting from initial conditions six hours apart. The sea surface temperature (SST) anomaly for the month prior to the start of the forecast is persisted through the three‐month prediction period, and added to a monthly‐varying climatological SST field.

The ensemble‐mean predictions for each of the models are verified independently, and the two ensembles are blended together in two different ways: as a simple average of the two models, denoted GCMSEF, and with weights statistically determined to minimize the mean‐square error (the Best Linear Unbiased Estimate (BLUE) method).

The GCMSEF winter and spring predictions show a Pacific/North American (PNA) response to a warm tropical SST anomaly. The temporal anomaly correlation between the zero‐lead GCMSEF mean‐seasonal predictions and observations of the 500‐hPa height field (Z₅₀₀) shows statistically significant forecast skill over parts of the PNA area for all seasons, but there is a notable seasonal variability in the distribution of the skill. The GCMSEF predictions are more skilful than those of either model in winter, and about as skilful as the better of the two models in the other seasons.

The zero‐lead surface air temperature GCMSEF forecasts over Canada are found to be skilful (a) over the west coast in all seasons except fall, (b) over most of Canada in summer, and (c) over Manitoba, Ontario and Quebec in the fall. In winter the skill of the BLUE forecasts is substantially better than that of the GCMSEF predictions, while for the other seasons the difference in skill is not statistically significant.

When the Z₅₀₀ forecasts are averaged over months two and three of the seasons (one‐month lead predictions), they show skill in winter over the north‐eastern Pacific, western Canada and eastern North America, a skill that comes from those years with strong SST anomalies of the El Niño/La Niña type. For the other seasons, predictions averaged over months two and three show little skill in Z₅₀₀ in the mid‐latitudes. In the tropics, predictive skill is found in Z₅₀₀ in all seasons when a strong SST anomaly of the El Niño/La Niña type is observed. In the absence of SST anomalies of this type, tropical forecast skill is still found over much of the tropics in months two and three of the northern hemisphere spring and summer, but not in winter and fall.     

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.     

Seasonal prediction of the Leeuwin Current using the POAMA dynamical seasonal forecast model

The potential for predicting interannual variations of the Leeuwin Current along the west coast of Australia is addressed. The Leeuwin Current flows poleward against the prevailing winds and transports warm-fresh tropical water southward along the coast, which has a great impact on local climate and ecosystems. Variations of the current are tightly tied to El Niño/La Niña (weak during El Niño and strong during La Niña). Skilful seasonal prediction of the Leeuwin Current to 9-month lead time is achieved by empirical downscaling of dynamical coupled model forecasts of El Niño and the associated upper ocean heat content anomalies off the north west coast of Australia from the Australian Bureau of Meteorology Predictive Ocean Atmosphere Model for Australia (POAMA) seasonal forecast system. Prediction of the Leeuwin Current is possible because the heat content fluctuations off the north west coast are the primary driver of interannual annual variations of the current and these heat content variations are tightly tied to the occurrence of El Niño/La Niña. POAMA can skilfully predict both the occurrence of El Niño/La Niña and the subsequent transmission of the heat content anomalies from the Pacific onto the north west coast.     

Performance of the seasonal forecasting of the Asian summer monsoon by BCC_CSM1.1(m)

This paper provides a comprehensive assessment of Asian summer monsoon prediction skill as a function of lead time and its relationship to sea surface temperature prediction using the seasonal hindcasts of the Beijing Climate Center Climate System Model, BCC_CSM1.1(m). For the South and Southeast Asian summer monsoon, reasonable skill is found in the model's forecasting of certain aspects of monsoon climatology and spatiotemporal variability. Nevertheless, deficiencies such as significant forecast errors over the tropical western North Pacific and the eastern equatorial Indian Ocean are also found. In particular, overestimation of the connections of some dynamical monsoon indices with large-scale circulation and precipitation patterns exists in most ensemble mean forecasts, even for short lead-time forecasts. Variations of SST, measured by the first mode over the tropical Pacific and Indian oceans, as well as the spatiotemporal features over the Niño3.4 region, are overall well predicted. However, this does not necessarily translate into successful forecasts of the Asian summer monsoon by the model. Diagnostics of the relationships between monsoon and SST show that difficulties in predicting the South Asian monsoon can be mainly attributed to the limited regional response of monsoon in observations but the extensive and exaggerated response in predictions due partially to the application of ensemble average forecasting methods. In contrast, in spite of a similar deficiency, the Southeast Asian monsoon can still be forecasted reasonably, probably because of its closer relationship with large-scale circulation patterns and El Niño-Southern Oscillation.     

Summer monsoon circulation and precipitation over the tropical Indian Ocean during ENSO in the NCEP climate forecast system

This study investigates the El Niño Southern Oscillation (ENSO) teleconnections to tropical Indian Ocean (TIO) and their relationship with the Indian summer monsoon in the coupled general circulation model climate forecast system (CFS). The model shows good skill in simulating the impact of El Niño over the Indian Oceanic rim during its decay phase (the summer following peak phase of El Niño). Summer surface circulation patterns during the developing phase of El Niño are more influenced by local Sea Surface Temperature (SST) anomalies in the model unlike in observations. Eastern TIO cooling similar to that of Indian Ocean Dipole (IOD) is a dominant model feature in summer. This anomalous SST pattern therefore is attributed to the tendency of the model to simulate more frequent IOD events. On the other hand, in the model baroclinic response to the diabatic heating anomalies induced by the El Niño related warm SSTs is weak, resulting in reduced zonal extension of the Rossby wave response. This is mostly due to weak eastern Pacific summer time SST anomalies in the model during the developing phase of El Niño as compared to observations. Both eastern TIO cooling and weak SST warming in El Niño region combined together undermine the ENSO teleconnections to the TIO and south Asia regions. The model is able to capture the spatial patterns of SST, circulation and precipitation well during the decay phase of El Niño over the Indo-western Pacific including the typical spring asymmetric mode and summer basin-wide warming in TIO. The model simulated El Niño decay one or two seasons later, resulting long persistent warm SST and circulation anomalies mainly over the southwest TIO. In response to the late decay of El Niño, Ekman pumping shows two maxima over the southern TIO. In conjunction with this unrealistic Ekman pumping, westward propagating Rossby waves display two peaks, which play key role in the long-persistence of the TIO warming in the model (for more than a season after summer). This study strongly supports the need of simulating the correct onset and decay phases of El Niño/La Niña for capturing the realistic ENSO teleconnections. These results have strong implications for the forecasting of Indian summer monsoon as this model is currently being adopted as an operational model in India.     

Hydro-climatic forecasting using sea surface temperatures: methodology and application for the southeast US

Sea surface temperatures (SSTs) are often used for the development of hydro-climatic variable forecasts based on teleconnection methods. Such methods rely on projections or linear combinations of teleconnection indices [e.g. El Niño-Southern Oscillation (ENSO)] and other predictor fields. This study introduces a new hydro-climatic forecasting method identifying SST “dipole” predictors motivated by major teleconnection patterns. An SST dipole is defined as a function of average SST anomalies over two oceanic areas of specific sizes and geographic locations. An optimization algorithm is developed to search for the most significant SST dipole predictors of an external hydro-climatic series based on the Gerrity Skill Score. The significant dipoles are cross-validated and used to generate multiple forecast values. The new method is applied to the forecasting of seasonal precipitation over the southeast US. Hindcasting results show that significant dipoles related to ENSO as well as other prominent patterns at different lead times can indeed be identified. The dipole method also compares favorably with existing statistical forecasting schemes with respect to multiple skill measures. Furthermore, an operational forecasting framework able to produce ensemble forecast traces and uncertainty intervals that can support regional water resources planning and management is also developed.     

An Introduction to the Integrated Climate Model of the Center for Monsoon System Research and its simulated influence of El Niño on East Asian-western North Pacific climate

This study introduces a new global climate model--the Integrated Climate Model （ICM）--developed for the seasonal prediction of East Asian-western North Pacific （EA-WNP） climate by the Center for Monsoon System Research at the Institute of Atmospheric Physics （CMSR, IAP）, Chinese Academy of Sciences. ICM integrates ECHAM5 and NEMO2.3 as its atmospheric and oceanic components, respectively, using OASIS3 as the coupler. The simulation skill of ICM is evaluated here, including the simulated climatology, interannual variation, and the influence of E1 Nifio as one of the most important factors on EA-WNP climate. ICM successfully reproduces the distribution of sea surface temperature （SST） and precipitation without climate shift, the seasonal cycle of equatorial Pacific SST, and the precipitation and circulation of East Asian summer monsoon. The most prominent biases of ICM are the excessive cold tongue and unrealistic westward phase propagation of equatorial Pacific SST. The main interannual variation of the tropical Pacific SST and EA-WNP climate E1 Nifio and the East Asia-Pacific Pattern--are also well simulated in ICM, with realistic spatial pattern and period. The simulated E1 Nifio has significant impact on EA-WNP climate, as in other models. The assessment shows ICM should be a reliable model for the seasonal prediction of EA-WNP climate.     

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.     

BCC_CSM模式对热带降水年循环模态的模拟

本文评估了国家气候中心发展的两个不同分辨率海—陆—气—冰多圈层耦合气候系统模式BCC_CSM1.1和BCC_CSM1.1 (m) 对热带降水两个年循环模态——揭示降水冬夏季节差异的季风模态和揭示过渡季节春季和秋季非对称特征的春秋非对称模态的模拟能力,讨论了模拟偏差产生的可能原因。分析结果表明,BCC_CSM1.1和BCC_CSM1.1 (m) 均能合理再现全球年平均降水的基本分布特征,也能较合理再现热带降水年循环模态的基本分布特征,尤其季风模态中降水与环流关于赤道反对称的特征;能够较合理再现春秋非对称模态与热带海洋表面温度(SST)年循环之间的关系。大气温度场、环流场以及热带SST的模拟偏差对降水季风模态有影响;热带SST年循环的偏差对降水春秋非对称模态的模拟偏差有贡献;模式分辨率对降水年循环模态的模拟也有一定影响。对比分析显示,大气模式和陆面模式水平分辨率提高之后模式在某些模拟性能上有所提高,这表现在:BCC_CSM1.1 (m) 模拟的1～12月降水气候态的空间变率更接近观测;热带海表温度年循环总体上更接近观测;模拟的热带降水年循环模态的部分特征更合理。但BCC_CSM1.1 (m) 的模拟结果相对观测仍存在较大偏差,有待进一步改进。     

The relative importance of ENSO and tropical Atlantic sea surface temperature anomalies for seasonal precipitation over South America: a numerical study

The role of tropical Atlantic sea surface temperature (SST) anomalies during ENSO episodes over northeast Brazil (Nordeste) is investigated using the CPTEC/COLA Atmospheric General Circulation Model (AGCM). Four sets of integrations are performed using SST in El Niño and La Niña (ENSO) episodes, changing the SST of the Atlantic Ocean. A positive dipole (SST higher than normal in the tropical North Atlantic and below normal in the tropical South Atlantic) and a negative dipole (opposite conditions), are set as the boundary conditions of SST in the Atlantic Ocean. The four experiments are performed using El Niño or La Niña SST in all oceans, except in the tropical Atlantic where the two phases of the SST dipole are applied. Five initial conditions were integrated in each case in order to obtain four ensemble results. The positive SST dipole over the tropical Atlantic Ocean and El Niño conditions over the Pacific Ocean resulted in dry conditions over the Nordeste. When the negative dipole and El Niño conditions over the Pacific Ocean were applied, the results showed precipitation above normal over the north of Nordeste. When La Niña conditions over Pacific Ocean were tested together with a negative dipole, positive precipitation anomalies occurred over the whole Nordeste. Using the positive dipole over the tropical Atlantic, the precipitation over Nordeste was below average. During La Niña episodes, the Atlantic Ocean conditions have a larger effect on the precipitation of Nordeste than the Pacific Ocean. In El Niño conditions, only the north region of Nordeste is affected by the Atlantic SST. Other tropical areas of South America show a change only in the intensity of anomalies. Central and southeast regions of South America are affected by the Atlantic conditions only during La Niña conditions, whereas during El Niño these regions are influenced only by conditions in the Pacific Ocean.     

Factor analysis for El Niño signals in sea surface temperature and precipitation

Maximum likelihood factor analysis (MLFA) is applied to investigate the variables of monthly Tropical Pacific sea surface temperatures (SST) from Niño 1+2, Niño 3, Niño 3.4, and Niño 4 and precipitation over New South Wales and Queensland of eastern Australia, Kalimantan Island of Indonesia, and California and Oregon of the west coast of the United States. The monthly data used were from 1950 to 1999. The November-February SST with time leads of 0, 1, 2, and 3 months to precipitation are considered for both El Niño warm phases and non El Niño seasons. Interpretations of the factor loadings are made to diagnose relationships between the SST and precipitation variables. For El Niño signals, the rotated FA loadings can efficiently group the SST and precipitation variables with interpretable physical meanings. When the time lag is 0 or 1 month, the November–February El Niño SST explains much of the drought signals over eastern Australia and Kalimantan. However, when the time lag is 2 or 3 months, the same SST cannot adequately explain the precipitation during January–May over the two regions. Communality results of five factors for precipitation indicate nearly 100% explanation of variances for Queensland and California, but the percentages are reduced to only about 30% for Oregon and Kalimantan. Factor scores clearly identify the strongest El Niño relevant to precipitation variations. Principal component factor analysis (PCFA) is also investigated, and its results are compared with MLFA. The comparison indicates that MLFA can better group SST data relevant to precipitation. The residuals of MLFA are always smaller than the PCFA. Thus, MLFA may become a useful tool for improving potential predictability of precipitation from SST predictors.     

Different impacts of various El Niño events on the Indian Ocean Dipole

Our early work (Wang and Wang in J Clim 26:1322–1338, 2013) separates El Niño Modoki events into El Niño Modoki I and II because they show different impacts on rainfall in southern China and typhoon landfall activity. The warm SST anomalies originate in the equatorial central Pacific and subtropical northeastern Pacific for El Niño Modoki I and II, respectively. El Niño Modoki I features a symmetric SST anomaly distribution about the equator with the maximum warming in the equatorial central Pacific, whereas El Niño Modoki II shows an asymmetric distribution with the warm SST anomalies extending from the northeastern Pacific to the equatorial central Pacific. The present paper investigates the influence of the various groups of El Niño events on the Indian Ocean Dipole (IOD). Similar to canonical El Niño, El Niño Modoki I is associated with a weakening of the Walker circulation in the Indo-Pacific region which decreases precipitation in the eastern tropical Indian Ocean and maritime continent and thus results in the surface easterly wind anomalies off Java-Sumatra. Under the Bjerknes feedback, the easterly wind anomalies induce cold SST anomalies off Java- Sumatra, and thus a positive IOD tends to occur in the Indian Ocean during canonical El Niño and El Niño Modoki I. However, El Niño Modoki II has an opposite impact on the Walker circulation, resulting in more precipitation and surface westerly wind anomalies off Java-Sumatra. Thus, El Niño Modoki II is favorable for the onset and development of a negative IOD on the frame of the Bjerknes feedback.     

季节预测模式对东亚夏季环流的预测能力及其对热带海洋的响应分析

东亚夏季环流变化对中国夏季降水的年际变化有重要影响,因此需要进一步理解季节预测模式对东亚夏季环流的预测能力。利用1991~2013年美国国家环境预测中心（NCEP）、中国气象局国家气候中心（NCC）和日本东京气候中心（TCC）的三个季节预测模式（CFS V2、BCC_CSM V2和MRI-CGCM）以及NCEP/NCAR再分析资料,定量评估了模式对东亚夏季风（EASM）和夏季西太平洋副热带高压（WPSH）强度的预测能力。在此基础上,分析了模式预测的EASM和WPSH对热带海温异常的响应能力,以及ENSO事件对EASM和WPSH预测的影响,阐述了预测误差产生的原因。结果表明:整体而言,三个模式对EASM和WPSH的预测技巧较高,但TCC模式对WPSH的预测技巧相对较低。三个模式预测的850 hPa风场在西北太平洋存在一个异常气旋,使得预测的EASM偏强和WPSH偏弱。同时,二者的年际变率整体比观测小。三个模式预测的EASM和WPSH对热带海洋海温异常的响应随季节演变特征与观测比较接近,但NCEP模式和TCC模式预测的EASM对前期热带太平洋和前期、同期热带印度洋的海温异常响应要强于观测,NCC模式预测的EASM对前期和同期的热带太平洋的海温异常响应明显比观测强。此外,三个模式预测的WPSH对前期和同期的热带太平洋、热带印度洋和热带大西洋的海温异常响应明显强于观测。三个模式预测的EASM和WPSH在ENSO年的平均绝对误差（MAE）整体而言要比正常年的小很多,NCEP模式和NCC模式预测的EASM和WPSH的MAE在La Ni?a年和El Ni?o年差别不大,而TCC模式预测的EASM和WPSH的MAE在El Ni?o年比在La Ni?a年大很多,表明ENSO事件是东亚夏季环流重要的可预报源。     

A Non-Linear Dynamical–Statistical Model for Reconstruction of the Air–Sea Element Fields in the Tropical Pacific Ocean

Aiming at tackling the difficulty in establishing a sea surface temperature (SST) dynamical model, this study develops a non-linear dynamical–statistical model of SST fields and their correlative factors based on Genetic Algorithms (GA) and the dynamical system reconstruction idea, which greatly improves the El Niño–Southern Oscillation (ENSO) forecast model. Using Hadley SST data, sea surface wind (SSW) and sea level pressure (SLP) data from the National Centers for Environmental Prediction-National Center for Environmental Research (NCEP-NCAR), with empirical orthogonal function (EOF) time-space for reconstruction, we carry out numerical integral forecasting experiments for SST, SSW, and SLP fields. By statistical analysis of the forecasting experiments, we find that forecasts for less than 25 months perform better than longer term forecasts. Based on the model, we forecast SST, SSW, and SLP fields in September, October, and November 2014 and predict a weak La Niña event. This study explores a novel method for the complex atmosphere–ocean system.     

Favorable connections between seasonal footprinting mechanism and El Niño

Previous studies suggested that the wintertime SST in the North Pacific that are generated by the concurrent North Pacific Oscillation (NPO) are able to force El Niño during subsequent winter via the so-called ‘seasonal footprinting mechanism’ (SFM). We examine how the NPO effectively generates the El Niño via the SFM in the observations and models. The occurrence ratio for El Niño under conditions of NPO forcing during the previous winters is about 41 % for the period of 61 years (1949–2009), indicating that the atmospheric forcing from the mid-latitudes through the SFM does not always trigger an El Niño. We observed certain favorable conditions under which the SFM may effectively induce El Niño. We directly compared these observations with two cases: when the wintertime NPO leads to El Niño during the following winter through the SFM, and when the wintertime NPO is not followed by El Niño. Our analysis demonstrates that the spatial structures of the NPO, associated wind speed and net heat flux in the northeast Pacific, differ between the two cases. Such differences determine the existence of a footprint SST in the northeastern Pacific during the late spring and summer, which plays a key role in initiating the El Niño via the projection of westerly wind stress anomalies onto the equatorial Pacific during the same seasons. By conducting linear baroclinic model experiments, it is found that the positions of La Niña SST forcing during the previous winter are able to modify the spatial structures of the NPO, which produces favorable conditions for the El Niño during subsequent winter via the SFM.     

Indian Ocean SST modes and Their Impacts as Simulated in BCC_CSM1.1(m) and HadGEM3

The sea surface temperature anomalies(SSTAs) in the tropical Indian Ocean(TIO) show two dominant modes at interannual time scales,referred to as the Indian Ocean basin mode(IOBM) and dipole mode(IOD).Recent studies have shown that the IOBM and IOD not only affect the local climate,but also induce remarkable influences in East Asia via teleconnections.In this study,we assess simulations of the IOBM and IOD,as well as their teleconnections,using the operational seasonal prediction models from the Met Office(Had GEM3) and Beijing Climate Center [BCC CSM1.1(m)].It is demonstrated that the spatial patterns and seasonal cycles are generally reproduced by the control simulations of BCC CSM1.1(m) and Had GEM3,although spectra biases exist.The relationship between the TIO SSTA and El Nio is successfully simulated by both models,including the persistent IOBM warming following El Nio and the IOD–El Nio interactions.BCC CSM1.1(m) and Had GEM3 are capable of simulating the observed local impact of the IOBM,such as the strengthening of the South Asian high.The influences of the IOBM on Yangtze River rainfall are also captured well by both models,although this teleconnection is slightly weaker in BCC CSM1.1(m) due to the underestimation of the northwestern Pacific subtropical high.The local effect of the IOD on East African rainfall is reproduced by both models.However,the remote control of the IOD on rainfall over southwestern China is not clear in either model.It is shown that the realistic simulations of TIO SST modes and their teleconnections give rise to the source of skillful seasonal predictions over China.     

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.     

BCC_CSM模式夏季关键区海温回报评估

利用国家气候中心气候系统模式(Beijing Climate Center Climate System Model, BCC_CSM)的汛期回报试验数据集, 评估了夏季中低纬度海表面温度(Sea Surface Temperature, SST)的预测能力。结果表明:该模式对夏季中低纬海温具有一定的预测能力, 且在低纬地区的预测技巧尤为出色。对太平洋、热带印度洋和北大西洋这三个关键区进一步分析发现, 该模式对不同海区海温的预测能力有所不同。其中, 模式对夏季北太平洋海温及Ni?o 3.4指数表现出显著的预测技巧, 对热带印度洋、北大西洋海温及热带印度洋全区一致海温模态(Indian Ocean Basin-wide Warming, IOBW)也表现出一定的预测技巧, 而对北大西洋海温三极子模态(North Atlantic Tripole, NAT)的技巧相对较低。研究发现, 预测技巧与前冬的ENSO状态密切相关, 当前冬位于ENSO异常位相时, BCC_CSM模式对于三大海区夏季海温的预测技巧要高于前冬位于ENSO正常位相时, 且对NAT指数也具有更高的预测技巧。前冬ENSO所处的位相对于该模式对夏季Ni?o 3.4指数及IOBW指数的预测技巧影响不明显。此外, 该模式对夏季海温的预测技巧依赖于超前时间, 预测技巧在大部分情形下超前1个月的预测技巧相对更高。     

Nonlinear precipitation response to El Niño and global warming in the Indo-Pacific

Precipitation changes over the Indo-Pacific during El Niño events are studied using an Atmospheric General Circulation Model forced with sea-surface temperature (SST) anomalies and changes in atmospheric CO₂ concentrations. Linear increases in the amplitude of the El Niño SST anomaly pattern trigger nonlinear changes in precipitation amounts, resulting in shifts in the location and orientation of the Intertropical Convergence Zone (ITCZ) and the South Pacific Convergence Zone (SPCZ). In particular, the maximum precipitation anomaly along the ITCZ and SPCZ shifts eastwards, the ITCZ shifts south towards the equator, and the SPCZ becomes more zonal. Precipitation in the equatorial Pacific also increases nonlinearly. The effect of increasing CO₂ levels and warming SSTs is also investigated. Global warming generally enhances the tropical Pacific precipitation response to El Niño. The precipitation response to El Niño is found to be dominated by changes in the atmospheric mean circulation dynamics, whereas the response to global warming is a balance between dynamic and thermodynamic changes. While the dependence of projected climate change impacts on seasonal variability is well-established, this study reveals that the impact of global warming on Pacific precipitation also depends strongly on the magnitude of the El Niño event. The magnitude and structure of the precipitation changes are also sensitive to the spatial structure of the global warming SST pattern.