Downscaling large-scale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United States

Seasonally predicted precipitation at a resolution of 2.5° was statistically downscaled to a fine spatial scale of ~20 km over the southeastern United States. The downscaling was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for downscaling from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for downscaling, covering the period from 1987 to 2005. The primary techniques involved in downscaling are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. Downscaled precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the downscaling in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed downscaling drove a significant reduction in wet biases, and correlation increased by 0.1–0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the downscaling. For instance, domain averaged HSS for two category predictability by the downscaling are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and Alabama region.     

季度预测研究回顾

对月平均大气环流预报试验、季度预测和中国汛期降水预测进行了总结。结果表明气候预测的对象必须是要素的时间平均场。利用数值模拟进行气候预测是今后的主要发展方向,而季度预测技巧的提高依赖于对物理参数化和物理机制的研究。最后,讨论了季平均气温和季总降水的可预报性问题,即时效性和准确率。     

Climate change and infectious diseases: Can we meet the needs for better prediction?

The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed.     

Communicating Uncertainty in Climate Information for China: Recommendations and Lessons Learned for Climate Services

Uncertainty is an inherent characteristic of climate forecasts and projections. While there is an expanding body of international research on identifying what climate information users need to know about uncertainty, and how this should be communicated, very little of this has been conducted in a Chinese cultural context. In this paper, we report on the findings of interviews with climate experts(n = 28) and(potential) users of climate information in China(n =18) at seasonal and multidecadal timescales, with the objective of addressing the following research questions:(1)What information about uncertainty in climate forecasts and projections is currently provided to users in China?(2)What do climate experts believe that users need to know about uncertainty?(3) What information about uncertainty would(potential) users like to receive?(4) What challenges do providers and users perceive with respect to the communication of uncertainty? We find that while seasonal forecasts are predominantly presented deterministically, current and potential users are aware that there is uncertainty associated with them. Climate experts highlight the probabilistic nature of forecasts and the conditional nature of forecast quality, as areas for communication development. Interviews with(potential) users indicate that(1) preferences for deterministic information are not unanimous;(2) probabilities associated with conditions being above/below normal may only be considered useful for decision-making if they are 60%; and(3) forecasts that provide tailored statements on probability of user-relevant thresholds are preferred. At multidecadal timescales, we observe lower engagement with projections, and less evidence of interaction between providers and recipients, suggesting that development of climate services at multidecadal timescales will need to first highlight the added value of these. We present key recommendations for communicating uncertainty in seasonal forecasts and explore the potential value of multidecadal projections.     

Simulation of Precipitation in Monsoon Regions of China by CMIP3 Models

The output of 25 models used in the Coupled Model Intercomparison Project phase 3 （CMIP3） were evaluated, with a focus on summer precipitation in eastern China for the last 40 years of the 20th century. Most models failed to reproduce rainfall associated with the East Asian summer monsoon （EASM）, and hence the seasonal cycle in eastern China, but provided reasonable results in Southwest （SW） and Northeast China （NE）. The simulations produced reasonable results for the Yangtze-Huai （YH） Basin area, although the Meiyu phenomenon was underestimated in general. One typical regional phenomenon, a seasonal northward shift in the rain belt from early to late summer, was completely missed by most models. The long-term climate trends in rainfall over eastern China were largely underestimated, and the observed geographical pattern of rainfall changes was not reproduced by most models. Precipitation extremes were evaluated via parameters of fitted GEV （Generalized Ex- treme Values） distributions. The annual extremes were grossly underestimated in the monsoon-dominated YH and SW regions, but reasonable values were calculated for the North China （NC） and NE regions. These results suggest a general failure to capture the dynamics of the EASM in current coupled climate models. Nonetheless, models with higher resolution tend to reproduce larger decadal trends and annual extremes of precipitation in the regions studied.     

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.     

数值预报误差订正技术中相似-动力方法的发展

Due to the increasing requirement for high-level weather and climate forecasting accuracy, it is necessary to exploit a strategy for model error correction while developing numerical modeling and data assimilation techniques. This study classifies the correction strategies according to the types of forecast errors, and reviews recent studies on these correction strategies. Among others, the analogue-dynamical method has been developed in China, which combines statistical methods with the dynamical model, corrects model errors based on analogue information, and effectively utilizes historical data in dynamical forecasts. In this study, the fundamental principles and technical solutions of the analogue-dynamical method and associated development history for forecasts on different timescales are introduced. It is shown that this method can effectively improve medium- and extended-range forecasts, monthly-average circulation forecast, and short-term climate prediction. As an innovative technique independently developed in China, the analogue- dynamical method plays an important role in both weather forecast and climate prediction, and has potential applications in wider fields.     

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.     

Changes in climate extremes on the territory of Siberia by the middle of the 21st century: An ensemble forecast based on the MGO regional climate model

The results are analyzed of the ensemble forecast of temperature and precipitation extremes on the territory of Siberia by the middle of the 21st century based on the regional climate model of the Main Geophysical Observatory (MGO) with the resolution of 25 km. The results of computation of oceanic components of CMIP3 coupled models are used as the boundary conditions on the sea surface. It is demonstrated that the high resolution of the regional model enables to simulate the observed climate variability in a more realistic way as compared to the low-resolution models. The analysis of the signal-to-noise ratio for future climate changes made it possible to determine to which degree its internal variability for various time scales (from interannual to interdecennial one) bounds the potential of the ensemble to compute the statistically significant anthropogenic changes of extremes. A comparative analysis of variations of extreme and average seasonal characteristics of the Siberian climate is carried out.     

Validation of the experimental hindcasts produced by the GloSea4 seasonal prediction system

Using 14 year (1996–2009) ensemble hindcast runs produced with the Global Seasonal Forecasting System version 4 (GloSea4), this study evaluates the spatial and temporal structure of the hindcast climatology and the prediction skill of major climate variability. A special focus is on the fidelity of the system to reproduce and to forecast phenomena that are closely related to the East Asian climate. Overall the GloSea4 system exhibits realistic representations of the basic climate even though a few model deficiencies are identified in the sea surface temperature and precipitation. In particular, the capability of GloSea4 to capture the seasonal migration of rain belt associated with Changma implies a good potential for the Asian summer monsoon prediction. It is found that GloSea4 is as skillful as other state-of-the-art seasonal prediction systems in forecasting climate variability including the El-Nino/southern oscillation (ENSO), the East Asian summer monsoon, the Arctic Oscillation (AO), and the Madden-Julian Oscillation (MJO). The results presented in this study will provide benchmark evaluation for next seasonal prediction systems to be developed at the Korea Meteorological Administration.     

Evaluation of summer temperature and precipitation predictions from NCEP CFSv2 retrospective forecast over China

National Centers for Environmental Prediction recently upgraded its operational seasonal forecast system to the fully coupled climate modeling system referred to as CFSv2. CFSv2 has been used to make seasonal climate forecast retrospectively between 1982 and 2009 before it became operational. In this study, we evaluate the model’s ability to predict the summer temperature and precipitation over China using the 120 9-month reforecast runs initialized between January 1 and May 26 during each year of the reforecast period. These 120 reforecast runs are evaluated as an ensemble forecast using both deterministic and probabilistic metrics. The overall forecast skill for summer temperature is high while that for summer precipitation is much lower. The ensemble mean reforecasts have reduced spatial variability of the climatology. For temperature, the reforecast bias is lead time-dependent, i.e., reforecast JJA temperature become warmer when lead time is shorter. The lead time dependent bias suggests that the initial condition of temperature is somehow biased towards a warmer condition. CFSv2 is able to predict the summer temperature anomaly in China, although there is an obvious upward trend in both the observation and the reforecast. Forecasts of summer precipitation with dynamical models like CFSv2 at the seasonal time scale and a catchment scale still remain challenge, so it is necessary to improve the model physics and parameterizations for better prediction of Asian monsoon rainfall. The probabilistic skills of temperature and precipitation are quite limited. Only the spatially averaged quantities such as averaged summer temperature over the Northeast China of CFSv2 show higher forecast skill, of which is able to discriminate between event and non-event for three categorical forecasts. The potential forecast skill shows that the above and below normal events can be better forecasted than normal events. Although the shorter the forecast lead time is, the higher deterministic prediction skill appears, the probabilistic prediction skill does not increase with decreased lead time. The ensemble size does not play a significant role in affecting the overall probabilistic forecast skill although adding more members improves the probabilistic forecast skill slightly.     

Stratospheric circulation in seasonal forecasting models: implications for seasonal prediction

Accurate seasonal forecasts rely on the presence of low frequency, predictable signals in the climate system which have a sufficiently well understood and significant impact on the atmospheric circulation. In the Northern European region, signals associated with seasonal scale variability such as ENSO, North Atlantic SST anomalies and the North Atlantic Oscillation have not yet proven sufficient to enable satisfactorily skilful dynamical seasonal forecasts. The winter-time circulations of the stratosphere and troposphere are highly coupled. It is therefore possible that additional seasonal forecasting skill may be gained by including a realistic stratosphere in models. In this study we assess the ability of five seasonal forecasting models to simulate the Northern Hemisphere extra-tropical winter-time stratospheric circulation. Our results show that all of the models have a polar night jet which is too weak and displaced southward compared to re-analysis data. It is shown that the models underestimate the number, magnitude and duration of periods of anomalous stratospheric circulation. Despite the poor representation of the general circulation of the stratosphere, the results indicate that there may be a detectable tropospheric response following anomalous circulation events in the stratosphere. However, the models fail to exhibit any predictability in their forecasts. These results highlight some of the deficiencies of current seasonal forecasting models with a poorly resolved stratosphere. The combination of these results with other recent studies which show a tropospheric response to stratospheric variability, demonstrates a real prospect for improving the skill of seasonal forecasts.     

我国短期气候预测的物理基础及其预测思路

短期气候预测依据大气科学原理,运用气候动力学、统计学等手段,在研究气候异常成因的基础上对未来气候趋势进行预测。虽然目前我国短期气候预测的水平还不高,但短期气候预测是国家经济发展和防灾减灾的迫切需求,提高预测准确率是气象科研和业务人员的重要任务。该文从海洋、积雪等外强迫信号及大气环流大尺度变动等大气内部特性等角度概述了短期气候预测的物理基础,简要回顾了近60年来我国短期气候预测的发展历程,并介绍了作者近十几年来研制短期气候预测客观统计学及统计与动力学相结合预测模型的主要思路。     

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.     

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.     

CCM3大气环流模式月-季尺度预报初步试验

以1991和1994年NCEP再分析格点资料作初始场, 用NCAR气候模式CCM3进行了48次月、季预报, 针对500 hPa高度和中国降水资料对其预测能力进行了检验.结果表明, CCM3对月尺度的高度场的预报有一定的能力.对中国160站的降水距平进行月尺度和季节预报, 其准确程度可以和目前经验预报的水平相当.比较使用实际海温和用外推法预测的海温两种下边界条件所作的预报结果发现, 两者的效果差异不大.     

Calibration and combination of dynamical seasonal forecasts to enhance the value of predicted probabilities for managing risk

Seasonal probability forecasts produced with numerical dynamics on supercomputers offer great potential value in managing risk and opportunity created by seasonal variability. The skill and reliability of contemporary forecast systems can be increased by calibration methods that use the historical performance of the forecast system to improve the ongoing real-time forecasts. Two calibration methods are applied to seasonal surface temperature forecasts of the US National Weather Service, the European Centre for Medium Range Weather Forecasts, and to a World Climate Service multi-model ensemble created by combining those two forecasts with Bayesian methods. As expected, the multi-model is somewhat more skillful and more reliable than the original models taken alone. The potential value of the multimodel in decision making is illustrated with the profits achieved in simulated trading of a weather derivative. In addition to examining the seasonal models, the article demonstrates that calibrated probability forecasts of weekly average temperatures for leads of 2–4 weeks are also skillful and reliable. The conversion of ensemble forecasts into probability distributions of impact variables is illustrated with degree days derived from the temperature forecasts. Some issues related to loss of stationarity owing to long-term warming are considered. The main conclusion of the article is that properly calibrated probabilistic forecasts possess sufficient skill and reliability to contribute to effective decisions in government and business activities that are sensitive to intraseasonal and seasonal climate variability.     

我国现有短期气候业务预测方法综述

对我国短期气候业务预测方法的综合分析表明 :我国现有的短期气候业务预测经验和统计方法仍是主要的 ;物理因子和前兆强信号结合天气气候学分析的概念预测模型方法在业务预测中发挥重要作用 ;动力模式预测方法在业务预测中取得一定效果 .随着动力模式的进一步发展 ,短期气候业务预测将逐步走向以动力模式客观预报为指导预报的新阶段     

最优气候值方法在中国的应用

该文用最优气候值方法研究了 1 951～ 1 998年中国 1 60个站点季、月的气温和降水 .结果表明 ,在中国最优气候值方法对气温具有相当的可用性 ,尤其是对冬季气温有很好的预报效果 ;对于降水 ,只对个别时间段的个别地区有一定的可用性 .对季的预报好于对月的预报 .无论是预报值与观测值的相关系数还是最优气候值的平均年份数在每一固定时期内都呈现出有规则的连续性空间分布 ,但是空间分布随时间变化而变化     

Impact of soil moisture�Catmosphere coupling on European climate extremes and trends in a regional climate model

Processes acting at the interface between the land surface and the atmosphere have a strong impact on the European summer climate, particularly during extreme years. These processes are to a large extent associated with soil moisture (SM). This study investigates the role of soil moisture?Catmosphere coupling for the European summer climate over the period 1959?C2006 using simulations with a regional climate model. The focus of this study is set on temperature and precipitation extremes and trends. The analysis is based on simulations performed with the regional climate model CLM, driven with ECMWF reanalysis and operational analysis data. The set of experiments consists of a control simulation (CTL) with interactive SM, and sensitivity experiments with prescribed SM: a dry and a wet run to determine the impact of extreme values of SM, as well as experiments with lowpass-filtered SM from CTL to quantify the impact of the temporal variability of SM on different time scales. Soil moisture?Cclimate interactions are found to have significant effects on temperature extremes in the experiments, and impacts on precipitation extremes are also identified. Case studies of selected major summer heat waves reveal that the intraseasonal and interannual variability of SM account for 5?C30% and 10?C40% of the simulated heat wave anomaly, respectively. For extreme precipitation events on the other hand, only the wet-day frequency is impacted in the experiments with prescribed soil moisture. Simulated trends for the past decades, which appear consistent with projected changes for the 21st century, are identified to be at least partly linked to SM-atmosphere feedbacks.