Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history,and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods.Dams along the river help to manage flood waters,and are important sources of electricity for the region.Being able to forecast high-impact events at long lead times therefore has enormous potential benefit.Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services.The teleconnection from El Ni ?no to Yangtze River basin rainfall meant that the strong El Ni ?no in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system.This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin,building on previous work demonstrating the retrospective skill of such a forecast.A simple forecasting methodology is presented,in which the forecast probabilities are derived from the historical relationship between hindcast and observations.Its performance for2016 is discussed.The heavy rainfall in the May–June–July period was correctly forecast well in advance.August saw anomalously low rainfall,and the forecasts for the June–July–August period correctly showed closer to average levels.The forecasts contributed to the confidence of decision-makers across the Yangtze River basin.Trials of climate services such as this help to promote appropriate use of seasonal forecasts,and highlight areas for future improvements.     

延伸期预报中的可预报性浅析

延伸期（10-30天）预报是无缝隙集约化预报预测业务体系的重要一环,一直以来在防灾减灾科学决策中起着重要作用。然而实际预报中,其准确率较中短期天气预报和气候预测均明显偏低,原因在于随着预报时效延长,预报结果存在明显的不确定性。因此,业务预报中需要充分考虑预报对象的可预报性。本文通过总结延伸期时效可预报性的来源、主流数值模式预报性能的现状,介绍了一线业务中的预报思路及常用的可预报性参考产品,揭示了可预报性理论在延伸期预报中的应用。同时还展望了延伸期预报未来的发展方向。     

Improved seasonal climate forecasts for the Caribbean region using the Florida State University Synthetic Superensemble

Summary Climate variations in the Caribbean, largely manifest in rainfall activity, have important consequences for the large-scale water budget, natural vegetation, and land use in the region. The wet and dry seasons will be defined, and the important roles played by the El Ni?o-Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO) in modulating the rainfall during these seasons will be discussed. The seasonal climate forecasts in this paper are made by 13 state of the art coupled atmosphere-ocean general circulation models (CGCMs) and by the Florida State University Synthetic Superensemble (FSUSSE), whose forecasts are obtained by a weighted combination of the individual CGCM forecasts based on a training period. The success of the models in simulating the observed 1989–2001 climatology of the various forecast parameters will be examined and linked to the models’ success in predicting the seasonal climate for individual years. Seasonal forecasts will be examined for precipitation, sea-surface temperature (SST), 2-meter air temperature, and 850 hPa u- and v-wind components during the period 1989–2001. Evaluation metrics include root mean square (RMS) error and Brier skill score. It will be shown that the FSUSSE is superior to the individual CGCMs and their ensemble mean both in simulating the 1989–2001 climatology for the various parameters and in predicting the seasonal climate of the various parameters for individual years. The seasonal climate forecasts of the FSUSSE and of the ensemble mean of the 13 state of the art CGCMs will be evaluated for years (during the period 1989–2001) that have particular ENSO and NAO signals that are known to influence Caribbean weather, particularly the rainfall. It will be shown that the FSUSSE provides superior forecasts of rainfall, SST, 2-meter air temperature, and 850 hPa u- and v-wind components during dry summers that are modulated by negative SOI and/or positive NAO indices. Such summers have become a feature of a twenty-year pattern of drought in the Caribbean region. The results presented in this paper will show that the FSUSSE is a valuable tool for forecasting rainfall and other atmospheric and oceanic variables during such periods of drought.     

地市级城镇天气预报制作及传输系统设计与实现

在城镇精细化预报制作和传输特点及需求分析的基础上,阐述了地市级城镇天气预报制作与传输系统的设计思路、功能实现和系统应用.系统选取PowerBuilder 7.0开发环境,PowerScript可视化编程语言,在Windows 2000操作系统上研制而成.由人机交互、预报自动传输和预报查询3个子系统构成,可制作城镇精细化、短时、评分、城区、城区上下班等多项预报,可随时查询利用该系统制作和传输到本地服务器上的各项预报,可用人工与自动两种方式向省局传输所需的预报.该系统为省级以下气象台的预报员提供了一个可靠、高效的预报制作、传输平台,为预报服务人员提供了一个方便、快捷的预报查询平台.     

Sensitivity of seasonal climate forecasts to persisted SST anomalies

Most estimates of the skill of atmospheric general circulation models (AGCMs) for forecasting seasonal climate anomalies have been based on simulations with actual observed sea surface temperatures (SSTs) as lower boundary forcing. Similarly estimates of the climatological response characteristics of AGCMs used for seasonal-to-interannual climate prediction generally rest on historical simulations using "perfect" SST forecasts. This work examines the errors and biases introduced into the seasonal precipitation response of an AGCM forced with persisted SST anomalies, which are generally considered to constitute a good prediction of SST in the first three-month season. The added uncertainty introduced by the persisted SST anomalies weakens, and in some cases nullifies, the skill of atmospheric predictions that is possible given perfect SST forcing. The use of persisted SST anomalies also leads to changes in local signal-to-noise characteristics. Thus, it is argued that seasonal-to-interannual forecasts using AGCMs should be interpreted relative to historical runs that were subject to the same strategy of boundary forcing used in the current forecast in order to properly account for errors and biases introduced by the particular SST prediction strategy. Two case studies are examined to illustrate how the sensitivity of the climate response to predicted SSTs may be used as a diagnostic to suggest improvements to the predicted SSTs.     

气候预测PS评分对业务影响

通过对中国气象局现行PS评分办法与理论PS评分的对比,发现现行评分办法对理论PS评分进行了两处修改。一处修改是扩大了预报正确的评定范围,但在PS评分办法实施后,在气候预测业务中,却出现了只预报2个等级的普遍现象,即在能获得高分的同时却降低了预报能力。另一处修改是按统一的要素距平划分等级,结果出现了预报对象的等级分布随着测站、月份变化而变化的现象。预报对象的等级分布是无技巧预报评分的决定因素,而预报技巧是由PS评分与无技巧预报评分之差决定的。在无技巧预报评分有差异的情况下,不同月份、不同区域之间的PS评分便失去了对比的基础。该文针对上述问题,对现行PS评分办法提出了修改建议。     

Seasonal Climate Forecasts – Potential Agricultural-Risk Management Tools?

The importance of anchoring seasonal climate forecasts to user needs is examined in this paper. Although it is generally accepted that seasonal climate forecasts have potential value, many constraints preclude the optimal use of these forecasts, including the way forecasts are produced, interpreted and applied in a variety of decision-making processes. In South Africa, a variety of agricultural users exists, ranging from the small-scale farmer to larger commercial farming entities. Useful seasonal are those produced and disseminated with the end user in mind. A retroactive test period during the 1990s, evaluates the perceived impact of incorporating seasonal rainfall forecasts into decisions made by commercial crop farmers in the central parts of South Africa. Although a small sample of commercial farmers was interviewed, the results show some benefits to commercial agriculture if seasonal climate forecast information is continuously and effectively applied over the long-term.     

An Ensemble Seasonal Forecast of Human Cases of St. Louis Encephalitis in Florida Based on Seasonal Hydrologic Forecasts

We present a method for the ensemble seasonal prediction of human St. Louis encephalitis (SLE) incidence and SLE virus transmission in Florida. We combine empirical relationships between modeled land surface wetness and the incidence of human clinical cases of SLE and modeled land surface wetness and the occurrence of SLE virus transmission throughout south Florida with a previously developed method for generating ensemble, seasonal hydrologic forecasts. Retrospective seasonal forecasts of human SLE incidence are made for Indian River County, Florida, and forecast skill is demonstrated for 2–4 months. A sample seasonal forecast of human SLE incidence is presented. This study establishes the skill of a potential component of an operational SLE forecast system in south Florida, one that provides information well in advance of transmission and may enable early interventions that reduce transmission. Future development of this method and operational application of these forecasts are discussed. The methodology also will be applied to West Nile virus monitoring and forecasting.     

Ecosystem Modeling Adds Value to a South African Climate Forecast

Livestock production in South Africa is limited by frequent droughts. The South African Weather Service produces climate forecasts estimating the probability of low rainfall three and six months into the future. We used the ecosystem model SAVANNA applied to five commercial farms in the Vryburg region of the North-West Province, and five communal areas within the Province, to assess the utility of a climate forecast in refining drought coping strategies. Rainfall data from 1970 to 1994 were modified to represent a drought (225 mm of rainfall) in 1977/1978, and used in simulations. In a simulation on an example commercial farm we assumed a forecast was available in 1977 portending an upcoming drought, and that the owner sold 490 cattle and 70 sheep prior to the drought. Over the simulation period, the owner sold 31% more cattle when the forecast was used,versus when the forecast was ignored. Populations of livestock on both commercial and communal farms recovered more quickly following the drought when owners sold animals in response to the forecast. The economic benefit from sales is being explored using optimization techniques. Results and responses from South African livestock producers suggest that a real-time farm model linked with climate forecasting would be a valuable management tool.     

Quantifying the value of historical climate knowledge and climate forecasts using agricultural systems modelling

A well tested agricultural systems model was used together with 114 years of historical climate data to study the performance of a dryland wheat–fallow system as impacted by climate variations and nitrogen input levels in southeast Australia, and to investigate the value of: (1) historical climate knowledge, (2) a perfect climate forecast, and (3) various forecasts of targeted variables. The potential value of historical climate records increases exponentially with the number of years of data. In order to confidently quantify the long term optimal nitrogen application rate at the study site at least 30 years of climate data are required. For nitrogen management only, the potential value of a perfect climate forecast is about $54/ha/year with a reduction of excess nitrogen application of 20 kg N/ha/year. The value of an ENSO based forecast system is $2/ha/year. Perfect forecasting of three or six categories of growing season rainfall would have a value of $10–12/ha/year. Perfect forecasts of three or six categories of simulated crop yield would bring about $33–34/ha/year. Choosing integrated variables as a forecasting target, for example crop yield derived from agricultural modelling, has the potential to significantly increase the value of forecasts.     

Using climate information for supporting climate change adaptation in water resource management in South Africa

Water resources, and in particular run-off, are significantly affected by climate variability. At present, there are few examples of how the water management sector integrates information about changing intra-annual climate conditions in a systematic manner in developing countries. This paper, using the case study of Cape Town in the Western Cape, South Africa, identifies processes and products to facilitate increased uptake of seasonal climate forecasts among water resource managers. Results suggest that existing seasonal forecasts do not focus enough on specific users’ needs. In order to increase uptake, forecasts need to include information on the likely impact of precipitation variability on runoff and water availability. More opportunities are also needed for those with climate knowledge to interact with water resource managers, particularly in the developing country context where municipal managers’ capacity is strained. Although there are challenges that need to be overcome in using probabilistic climate information, seasonal forecast information tailored to the needs of water resource planners has the potential to support annual planning and is therefore a means of adapting to climate change.     

Reducing the prediction uncertainties of high-impact weather and climate events: An overview of studies at LASG

This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to reduce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limitations of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear optimal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the El Niño–Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reducedprojection- based four-dimensional variational data assimilation (DRP-4DVar) approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction, which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors (NLLVs) to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs represent a candidate for possible development as an ensemble method in operational forecasts. Despite the considerable efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events.     

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     

QBO influence on extratropical predictive skill

The quasi-biennial oscillation (QBO) in the zonal wind in the tropical stratosphere is one of the most predictable aspects of the circulation anywhere in the atmosphere and can be accurately forecast for many months in advance. If the stratospheric QBO systematically (and significantly) affects the tropospheric circulation, it potentially provides a predictable signal useful for seasonal forecasting. The stratospheric QBO itself is generally not well represented in current numerical models, however, including those used for seasonal prediction and this potential may not be exploited by current numerical-model based forecast systems. The purpose of the present study is to ascertain if a knowledge of the state of the QBO can contribute to extratropical boreal winter seasonal forecast skill and, if so, to motivate further research in this area. The investigation is in the context of the second Historical Forecasting Project (HFP2), a state-of-the-art multimodel two-tier ensemble seasonal forecasting system. The first tier, consisting of a prediction of sea surface temperature anomalies (SSTAs), is followed by the second tier which is a prediction of the state of the atmosphere and surface using an AGCM initialized from atmospheric analyses and using the predicted SSTs as boundary conditions. The HFP2 forecasts are successful in capturing the extratropical effects of sea surface temperature anomalies in the equatorial Pacific to the extent that a linear statistical correction based on the NINO3.4 index does not provide additional extratropical skill. By contrast, knowledge of the state of the stratospheric QBO can be used statistically to add extratropical skill centred in the region of the North Atlantic Oscillation. Although the additional skill is modest, the result supports the contention that taking account of the QBO could improve extratropical seasonal forecasting skill. This might be done statistically after the fact, by forcing the QBO state into the forecast model as it runs or, preferably, by using models which correctly represent the physical processes and behaviour of the QBO.     

On the dependence of ENSO simulation on the coupled model mean state

Systematic model error remains a difficult problem for seasonal forecasting and climate predictions. An error in the mean state could affect the variability of the system. In this paper, we investigate the impact of the mean state on the properties of ENSO. A set of coupled decadal integrations have been conducted, where the mean state and its seasonal cycle have been modified by applying flux correction to the momentum-flux and a combination of heat and momentum fluxes. It is shown that correcting the mean state and the seasonal cycle improves the amplitude of SST inter-annual variability and also the penetration of the ENSO signal into the troposphere and the spatial distribution of the ENSO teleconnections are improved. An analysis of a multivariate PDF of ENSO shows clearly that the flux correction affects the mean, variance, skewness and tails of the distribution. The changes in the tails of the distribution are particularly noticeable in the case of precipitation, showing that without the flux correction the model is unable to reproduce the frequency of large events. For the inter-annual variability the momentum-flux correction alone has a large impact, while the additional heat-flux correction is important for the teleconnections. These results suggest that the current forecasts practices of removing the forecast bias a-posteriori or anomaly initialisation are by no means optimal, since they can not deal with the strong nonlinear interactions. A consequence of the results presented here is that the predictability on annual time-ranges could be higher than currently achieved. Whether or not the correction of the model mean state by some sort of flux correction leads to better forecasts needs to be addressed. In any case, flux correction may be a powerful tool for diagnosing coupled model errors and predictability studies.     

Seasonal climate prediction for South America with FSU Multi-model Synthetic Superensemble algorithm

Summary Objective combination schemes of predictions from different models have been applied to seasonal climate forecasts. These schemes are successful in producing a deterministic forecast superior to individual member models and better than the multi-model ensemble mean forecast. Recently, a variant of the conventional superensemble formulation was created to improve skills for seasonal climate forecasts, the Florida State University (FSU) Synthetic Superensemble. The idea of the synthetic algorithm is to generate a new data set from the predicted multimodel datasets for multiple linear regression. The synthetic data is created from the original dataset by finding a consistent spatial pattern between the observed analysis and the forecast data set. This procedure is a multiple linear regression problem in EOF space. The main contribution this paper is to discuss the feasibility of seasonal prediction based on the synthetic superensemble approach and to demonstrate that the use of this method in coupled models dataset can reduce the errors of seasonal climate forecasts over South America. In this study, a suite of FSU coupled atmospheric oceanic models was used. In evaluation the results from the FSU synthetic superensemble demonstrate greater skill for most of the variables tested here. The forecast produced by the proposed method out performs other conventional forecasts. These results suggest that the methodology and database employed are able to improve seasonal climate prediction over South America when compared to the use of single climate models or from the conventional ensemble averaging. The results show that anomalous conditions simulated over South America are reasonably realistic. The negative (positive) precipitation anomalies for the summer monsoon season of 1997/98 (2001/02) were predicted by Synthetic Superensemble formulation quite well. In summary, the forecast produced by the Synthetic Superensemble approach outperforms the other conventional forecasts.     

Stakeholder Networks: Improving Seasonal Climate Forecasts

In order for a scientific innovation to reach a wide audience it needs to travel through diverse networks and be understandable to a variety of people. This paper focuses on networks of stakeholders involved in the diffusion of seasonal climate forecasts. It is argued that understanding stakeholder networks is key to determining the opportunities and barriers to the flow of forecast information, which could enable more focused forecast dissemination. Lesotho is used as a case study where Stakeholder Thematic Networks (STNs) are used as a novel method for investigating forecast dissemination. STNs enable qualitative information to be analysed through semi-quantitative mapping of relationships that enable the networks and scales of linkages to be visualised. This illustrates the types of nodes and channels of seasonal forecast information flow and so enables existing or emerging patterns of dissemination to be uncovered. Sub-networks that exist for purposes other than climate information dissemination are identified as salient sub-networks for focusing development of future forecast dissemination. These existing sub-networks enable stakeholder needs to be addressed and decrease the need for new networks to be established. By using these sub-networks, information relating to climate variability can be mainstreamed into existing development pathways. This is critical to recognise if innovations relating to climate information are to be used to improve climate change adaptation.     

Reducing Vulnerability to Climate Variability in Southern Africa: The Growing Role of Climate Information

Since the devastating southern Africa drought of 1991/92 awareness has grown of the potential to better manage climate variability in the region through seasonal climate forecasting and monitoring of El Niño and the Southern Oscillation (ENSO). While other factors besides ENSO affect southern Africa's climate, and climate forecasting for the region is not based exclusively on ENSO, a major El Niño beginning in 1997 captured the attention of policy-makers and the public. Awareness of drought risks associated with the 1997/98 event was greater than during previous El Niños in 1991/92 and 1994/95. Mitigation and planning efforts also began earlier, with drought early warnings widely available and being taken seriously prior to the 1997/98 agricultural season. Actions taken include issuance of guidance to the public, on-going monitoring and preparedness efforts including the development of national preparedness plans in some countries, pre-positioning of food stocks, donor coordination, and greater reliance on the private-sector for meeting regional food needs. Although 1998 regional crop production was slightly below average, a major drought did not materialize. Nonetheless the experience is likely to ultimately strengthen capacity within the region to manage climate variability over the long term.     

动力-统计客观定量化汛期降水预测研究新进展

汛期降水预测是短期气候预测的重要内容之一,也是难点之一。近20年来,动力-统计相结合的预测方法在解决这一复杂的科学难题方面取得了一定进展。该文系统地介绍了近年来国家级气候预测业务中关于动力-统计客观定量化预测的原理、最优因子订正和异常因子订正两类预测方案,及动力-统计集成的中国季节降水预测系统 (FODAS1.0)。2009—2012年的汛期降水预测中,动力-统计客观定量化预测方法4年平均PS评分为73,距平相关系数为0.16,体现了较高的预报技巧。但该方法仍存在不足,需通过加强气候因子与降水之间关系的诊断分析、完善短期气候模式的物理过程、改进参数化方案及研发有针对性的区域气候模式等手段,进一步提高模式本身的预报技巧,使动力-统计预测方法在汛期降水预测中发挥更大作用。     

Using soil moisture forecasts for sub-seasonal summer temperature predictions in Europe

Soil moisture exhibits outstanding memory characteristics and plays a key role within the climate system. Especially through its impacts on the evapotranspiration of soils and plants, it may influence the land energy balance and therefore surface temperature. These attributes make soil moisture an important variable in the context of weather and climate forecasting. In this study we investigate the value of (initial) soil moisture information for sub-seasonal temperature forecasts. For this purpose we employ a simple water balance model to infer soil moisture from streamflow observations in 400 catchments across Europe. Running this model with forecasted atmospheric forcing, we derive soil moisture forecasts, which we then translate into temperature forecasts using simple linear relationships. The resulting temperature forecasts show skill beyond climatology up to 2 weeks in most of the considered catchments. Even if forecasting skills are rather small at longer lead times with significant skill only in some catchments at lead times of 3 and 4 weeks, this soil moisture-based approach shows local improvements compared to the monthly European Centre for Medium Range Weather Forecasting (ECMWF) temperature forecasts at these lead times. For both products (soil moisture-only forecast and ECMWF forecast), we find comparable or better forecast performance in the case of extreme events, especially at long lead times. Even though a product based on soil moisture information alone is not of practical relevance, our results indicate that soil moisture (memory) is a potentially valuable contributor to temperature forecast skill. Investigating the underlying soil moisture of the ECMWF forecasts we find good agreement with the simple model forecasts, especially at longer lead times. Analyzing the drivers of the temperature forecast skills we find that they are mainly controlled by the strengths of (1) the soil moisture-temperature coupling and (2) the soil moisture memory. We find a negative relationship between these controls that weakens the forecast skills, nevertheless there is a middle ground between both controls in several catchments, as shown by our results.