An assessment of the ENSO forecast skill of GEOS-5 system

The seasonal forecast skill of the NASA Global Modeling and Assimilation Office atmosphere–ocean coupled global climate model (AOGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the AOGCM consisting of the GEOS-5 AGCM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase transition of the equatorial zonal current from westerly to easterly.     

Decadal predictability and forecast skill

The “potential predictability” of the climate system is the upper limit of available forecast skill and can be characterized by the ratio p of the predictable variance to the total variance. While the potential predictability of the actual climate system is unknown its analog q may be obtained for a model of the climate system. The usual correlation skill score r and the mean square skill score M are functions of p in the case of actual forecasts and potential correlation ρ and potential mean square skill score $\mathcal{M}$ are the same functions of q in the idealized model context. In the large ensemble limit the connection between model-based potential predictability and skill scores is particularly straightforward with $q=\rho^{2}=\mathcal{M}.$ Decadal predictions of annual mean temperature produced with the Canadian Centre for Climate Modelling and Analysis coupled climate model are analyzed for information on decadal climate predictability and actual forecast skill. Initialized forecast results are compared with the results of uninitialized climate simulations. Model-based values of potential predictability q and potential correlation skill ρ are obtained and ρ is compared with the actual forecast correlation skill r. The skill of externally forced and internally generated components of the variability are separately estimated. As expected, ρ > r and both decline with forecast range τ, at least for the first five years. The decline of skill is associated mainly with the decline of the skill of the internally generated component. The potential and actual skill of a forecast of time-averaged temperature depends on the averaging period. The skill of uninitialized simulations is low for short averaging times and increases as averaging time increases. By contrast, skill is high at short averaging times for forecasts initialized from observations and declines as averaging times increase to about three years, then increases somewhat at longer averaging times. The skills of the initialized forecasts and uninitialized simulations begin to converge for longer averaging times. The potential correlation skill ρ of the externally forced component of temperature is largest at tropical latitudes and the skill of the internally generated component is largest over the North Atlantic, parts of the Southern Ocean and to some extent the North Pacific. Potential skill over extratropical land is somewhat weaker than over oceans. The distribution of actual correlation skill r is broadly similar to that of potential skill for the externally forced component but less so for the internally generated component. Differences in potential and actual skill suggest where improvements in the forecast system might be found.     

IAP-DecPreS年代际气候预测系统及其预报技巧

针对未来1~10 a气候状态的近期气候预测(年代际预测)是当前国际气候领域的研究热点。本文综述了中国科学院大气物理研究所发展的基于耦合气候系统模式的年代际气候预测系统IAP-DecPreS相关的研究进展。IAP-DecPreS系统的核心部分是耦合模式海洋分量初始化方案,“集合最优插值-分析增量更新”(EnOI-IAU)方案,该方案将集合最优插值(EnOI)和增量分析更新(IAU)结合起来,能够同化原始的海洋次表层温度廓线观测资料,对耦合模式进行初始化。系统的年代际回报试验表明,IAP-DecPreS对太平洋年代际振荡和大西洋多年代际变率的预测技巧与耦合模式比较计划第五阶段(CMIP5)技巧较高的模式相当。IAP-DecPreS系统被广泛应用于气候预测相关研究,包括火山气溶胶对年代际预测技巧的影响,全场同化和异常场同化两种不同的初始化方法对ENSO、印度洋偶极子模态和印度洋洋盆模态等的预测技巧的影响。最后,结合国际发展态势,对未来IAP-DecPreS的发展进行了讨论。     

Decadal prediction skill in a multi-model ensemble

Decadal climate predictions may have skill due to predictable components in boundary conditions (mainly greenhouse gas concentrations but also tropospheric and stratospheric aerosol distributions) and initial conditions (mainly the ocean state). We investigate the skill of temperature and precipitation hindcasts from a multi-model ensemble of four climate forecast systems based on coupled ocean-atmosphere models. Regional variations in skill with and without trend are compared with similarly analysed uninitialised experiments to separate the trend due to monotonically increasing forcings from fluctuations around the trend due to the ocean initial state and aerosol forcings. In temperature most of the skill in both multi-model ensembles comes from the externally forced trends. The rise of the global mean temperature is represented well in the initialised hindcasts, but variations around the trend show little skill beyond the first year due to the absence of volcanic aerosols in the hindcasts and the unpredictability of ENSO. The models have non-trivial skill in hindcasts of North Atlantic sea surface temperature beyond the trend. This skill is highest in the northern North Atlantic in initialised experiments and in the subtropical North Atlantic in uninitialised simulations. A similar result is found in the Pacific Ocean, although the signal is less clear. The uninitialised simulations have good skill beyond the trend in the western North Pacific. The initialised experiments show some skill in the decadal ENSO region in the eastern Pacific, in agreement with previous studies. However, the results in this study are not statistically significant (p?≈?0.1) by themselves. The initialised models also show some skill in forecasting 4-year mean Sahel rainfall at lead times of 1 and 5?years, in agreement with the observed teleconnection from the Atlantic Ocean. Again, the skill is not statistically significant (p?≈?0.2). Furthermore, uninitialised simulations that include volcanic aerosols have similar skill. It is therefore still an open question whether initialisation improves predictions of Sahel rainfall. We conclude that the main source of skill in forecasting temperature is the trend forced by rising greenhouse gas concentrations. The ocean initial state contributes to skill in some regions, but variations in boundary forcings such as aerosols are as important in decadal forecasting.     

Ensemble mean forecast skill and applications with the T213 ensemble prediction system

Ensemble forecasting has become the prevailing method in current operational weather forecasting. Although ensemble mean forecast skill has been studied for many ensemble prediction systems(EPSs) and different cases, theoretical analysis regarding ensemble mean forecast skill has rarely been investigated, especially quantitative analysis without any assumptions of ensemble members. This paper investigates fundamental questions about the ensemble mean, such as the advantage of the ensemble mean over individual members, the potential skill of the ensemble mean, and the skill gain of the ensemble mean with increasing ensemble size. The average error coefficient between each pair of ensemble members is the most important factor in ensemble mean forecast skill, which determines the mean-square error of ensemble mean forecasts and the skill gain with increasing ensemble size. More members are useful if the errors of the members have lower correlations with each other, and vice versa. The theoretical investigation in this study is verified by application with the T213 EPS. A typical EPS has an average error coefficient of between 0.5 and 0.8; the 15-member T213 EPS used here reaches a saturation degree of 95%(i.e., maximum 5% skill gain by adding new members with similar skill to the existing members) for 1–10-day lead time predictions, as far as the mean-square error is concerned.     

Decadal prediction skill for Eurasian surface air temperature in CMIP6 models

评估CMIP6年代际预测试验对季节平均SAT的预测技巧的结果表明:模式不能有效预测冬季和秋季SAT的年代际变率.IPSL-CM6A-LR和多模式集合平均对于春季SAT展现了预测技巧,其中对于变率的预测技巧好于振幅的结果.基于蒙古和我国华北地区的显著预测技巧,模式对于夏季SAT表现出最佳的预测水平.与外部强迫相比,模式对于SAT的预测技巧可能来自初始化.模式中的一个明显系统性误差值得注意,即模式中冬季SAT的变率可以持续到其他季节,而在观测中其他季节的SAT变化与冬季SAT相对独立.     

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

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

宁夏3～7天天气预报逐日滚动制作系统

在建立ＭＯＳ预报系统的基础上,充分利用实时资料库中所提供的各种气象信息,开发应用多种预报方法以及有关实况、气候背景资料、对ＭＯＳ预报结果逐步进行人机交互订正,最后生成对外发布的宁夏各地３￣７天中期天气预报。     

Multi-year prediction skill of Atlantic hurricane activity in CMIP5 decadal hindcasts

Using a statistical relationship between simulated sea surface temperature and Atlantic hurricane activity, we estimate the skill of a CMIP5 multi-model ensemble at predicting multi-annual level of Atlantic hurricane activity. The series of yearly-initialized hindcasts show positive skill compared to simpler forecasts such as persistence and climatology as well as non-initialized forecasts and return anomaly correlation coefficients of ～0.6 and ～0.8 for five and nine year forecasts, respectively. Some skill is shown to remain in the later years and making use of those later years to create a lagged-ensemble yields, for individual models, results that approach that obtained by the multi-model ensemble. Some of the skill is shown to come from persisting rather than predicting the climate shift that occur in 1994–1995. After accounting for that shift, the anomaly correlation coefficient for five-year forecasts is estimated to drop to 0.4, but remains statistically significant up to lead years 3–7. Most of the skill is shown to come from the ability of the forecast systems at capturing change in Atlantic sea surface temperature, although the failure of most systems at reproducing the observed slow down in warming over the tropics in recent years leads to an underestimation of hurricane activity in the later period.     

Seasonal prediction skill of ECMWF System 4 and NCEP CFSv2 retrospective forecast for the Northern Hemisphere Winter

The seasonal prediction skill for the Northern Hemisphere winter is assessed using retrospective predictions (1982–2010) from the ECMWF System 4 (Sys4) and National Center for Environmental Prediction (NCEP) CFS version 2 (CFSv2) coupled atmosphere–ocean seasonal climate prediction systems. Sys4 shows a cold bias in the equatorial Pacific but a warm bias is found in the North Pacific and part of the North Atlantic. The CFSv2 has strong warm bias from the cold tongue region of the eastern Pacific to the equatorial central Pacific and cold bias in broad areas over the North Pacific and the North Atlantic. A cold bias in the Southern Hemisphere is common in both reforecasts. In addition, excessive precipitation is found in the equatorial Pacific, the equatorial Indian Ocean and the western Pacific in Sys4, and in the South Pacific, the southern Indian Ocean and the western Pacific in CFSv2. A dry bias is found for both modeling systems over South America and northern Australia. The mean prediction skill of 2 meter temperature (2mT) and precipitation anomalies are greater over the tropics than the extra-tropics and also greater over ocean than land. The prediction skill of tropical 2mT and precipitation is greater in strong El Nino Southern Oscillation (ENSO) winters than in weak ENSO winters. Both models predict the year-to-year ENSO variation quite accurately, although sea surface temperature trend bias in CFSv2 over the tropical Pacific results in lower prediction skill for the CFSv2 relative to the Sys4. Both models capture the main ENSO teleconnection pattern of strong anomalies over the tropics, the North Pacific and the North America. However, both models have difficulty in forecasting the year-to-year winter temperature variability over the US and northern Europe.     

A new way to predict forecast skill

Forecast skill (Anomaly Correlated Coefficient, ACC) is a quantity to show the forecast quality of the products of numerical weather forecasting models. Predicting forecast skill, which is the foundation of ensemble forecasting, means submitting products to predict their forecast quality before they are used.Checking the reason is to understand the predictability for the real cases. This kind of forecasting service has been put into operational use by statistical methods previously at the National Meteorological Center (NMC), USA (now called the National Center for Environmental Prediction (NCEP)) and European Center for Medium-range Weather Forecast (ECMWF). However, this kind of service is far from satisfactory because only a single variable is used with the statistical method. In this paper, a new way based on the Grey Control Theory with multiple predictors to predict forecast skill of forecast products of the T42L9 of the NMC, China Meteorological Administration (CMA) is introduced. The results show: (1) The correlation coefficients between “forecasted“ and real forecast skill range from 0.56 to 0.7 at different seasons during the two-year period. (2) The grey forecasting model GM(1,8) forecasts successfully the high peaks, the increasing or decreasing tendency, and the turning points of the change of forecast skill of cases from 5 January 1990 to 29 February 1992.     

Interdecadal change in the Northern Hemisphere seasonal climate prediction skill: part II. predictability and prediction skill

The interdecadal change in seasonal predictability and numerical models’ seasonal forecast skill in the Northern Hemisphere are examined using both observations and the seasonal hindcast from six coupled atmosphere-ocean climate models from the 21 period of 1960–1980 (P1) to that of 1981–2001 (P2). It is shown that the one-month lead seasonal forecast skill of the six models’ multi-model ensemble is significantly increased from P1 to P2 for all four seasons. We identify four possible reasons accounting for the interdecadal change of the seasonal forecast skill. Firstly, the numerical model’s ability to simulate the mean state, the time variability and the spatial structures of the sea surface temperature and precipitation over the tropical Pacific is improved in P2 compared to P1. Secondly, an examination of the potential predictability of the atmosphere, estimated by the ratio of the total variance to the variance due to the internal dynamics of the model atmosphere, reveals that the atmospheric potential predictability is significantly increased after 1980s which is mainly due to an increased influence of El Niño-Southern Oscillation signal over the North Pacific and North American regions. Thirdly, the long-term climate trends in the atmosphere are found to contribute, to some extent, to the increased seasonal forecast skill especially over the Eurasian regions. Finally, the improved ocean observations in P2 may provide better initial conditions for the coupled models’ seasonal forecast.     

Assessing the simulation and prediction of rainfall associated with the MJO in the POAMA seasonal forecast system

We assess the ability of the Predictive Ocean Atmosphere Model for Australia (POAMA) to simulate and predict weekly rainfall associated with the MJO using a 27-year hindcast dataset. After an initial 2-week atmospheric adjustment, the POAMA model is shown to simulate well, both in pattern and in intensity, the weekly-mean rainfall variation associated with the evolution of the MJO over the tropical Indo-Pacific. The simulation is most realistic in December?CFebruary (austral summer) and least realistic in March?CMay (austral autumn). Regionally, the most problematic area is the Maritime Continent, which is a common problem area in other models. Coupled with our previous demonstration of the ability of POAMA to predict the evolution of the large-scale structure of the MJO for up to about 3?weeks, this ability to simulate the regional rainfall evolution associated with the MJO translates to enhanced predictability of rainfall regionally throughout much of the tropical Indo-Pacific when the MJO is present in the initial conditions during October?CMarch. We also demonstrate enhanced prediction skill of rainfall at up to 3?weeks lead time over the north-east Pacific and north Atlantic, which are areas of pronounced teleconnections excited by the MJO-modulation of tropical Indo-Pacific rainfall. Failure to simulate and predict the modulation of rainfall in such places as the Maritime Continent and tropical Australia by the MJO indicates, however, there is still much room for improvement of the prediction of the MJO and its teleconnections.     

ECMWF seasonal forecast system 3 and its prediction of sea surface temperature

The latest operational version of the ECMWF seasonal forecasting system is described. It shows noticeably improved skill for sea surface temperature (SST) prediction compared with previous versions, particularly with respect to El Nino related variability. Substantial skill is shown for lead times up to 1?year, although at this range the spread in the ensemble forecast implies a loss of predictability large enough to account for most of the forecast error variance, suggesting only moderate scope for improving long range El Nino forecasts. At shorter ranges, particularly 3?C6?months, skill is still substantially below the model-estimated predictability limit. SST forecast skill is higher for more recent periods than earlier ones. Analysis shows that although various factors can affect scores in particular periods, the improvement from 1994 onwards seems to be robust, and is most plausibly due to improvements in the observing system made at that time. The improvement in forecast skill is most evident for 3-month forecasts starting in February, where predictions of NINO3.4 SST from 1994 to present have been almost without fault. It is argued that in situations where the impact of model error is small, the value of improved observational data can be seen most clearly. Significant skill is also shown in the equatorial Indian Ocean, although predictive skill in parts of the tropical Atlantic are relatively poor. SST forecast errors can be especially high in the Southern Ocean.     

Comparison of extended medium-range forecast skill between KMA ensemble,ocean coupled ensemble,and GloSea5

This article describes a three way inter-comparison of forecast skill on an extended medium-range time scale using the Korea Meteorological Administration (KMA) operational ensemble numerical weather prediction (NWP) systems (i.e., atmosphere-only global ensemble prediction system (EPSG) and ocean-atmosphere coupledEPSG) and KMA operational seasonal prediction system, the Global Seasonal forecast system version 5 (GloSea5). The main motivation is to investigate whether the ensemble NWP system can provide advantage over the existing seasonal prediction system for the extended medium-range forecast (30 days) even with putting extra resources in extended integration or coupling with ocean with NWP system. Two types of evaluation statistics are examined: the basic verification statistics - the anomaly correlation and RMSE of 500-hPa geopotential height and 1.5-meter surface temperature for the global and East Asia area, and the other is the Real-time Multivariate Madden and Julian Oscillation (MJO) indices (RMM1 and RMM2) - which is used to examine the MJO prediction skill. The MJO is regarded as a main source of forecast skill in the tropics linked to the mid-latitude weather on monthly time scale. Under limited number of experiment cases, the coupled NWP extends the forecast skill of the NWP by a few more days, and thereafter such forecast skill is overtaken by that of the seasonal prediction system. At present stage, it seems there is little gain from the coupled NWP even though more resources are put into it. Considering this, the best combination of numerical product guidance for operational forecasters for an extended medium-range is extension of the forecast lead time of the current ensemble NWP (EPSG) up to 20 days and use of the seasonal prediction system (GloSea5) forecast thereafter, though there exists a matter of consistency between the two systems.     

Diagnostics of subseasonal prediction biases of the Asian summer monsoon by the NCEP climate forecast system

Biases of subseasonal prediction of the Asian summer monsoon are diagnosed using daily data from the hindcasts of 45-day integrations by the National Centers for Environmental Prediction Climate Forecast System version 2. The retrospective forecasts often show apparent systematic biases, which are mostly represented by the underestimation of the whole Asian monsoon. Biases depend not only on lead time, but also on the stage of monsoon evolution. An abrupt turning point of bias development appears around late June and early July, when ensemble spread and bias growth of winds and precipitation show a significant change over the northwestern Pacific (NWP) and the South Asian summer monsoon (SASM) region. The abrupt turning of bias development of winds, precipitation, and surface temperature is also captured by the first two modes of multivariate empirical orthogonal function analysis. Several features appear associated with the abrupt change in bias development: the western Pacific subtropical high (WPSH) begins its first northward jump and the surface temperature over the Tibetan Plateau commences a transition from warm bias to cold bias, and a reversal of surface temperature biases occurs in the eastern tropical Indian Ocean and the SASM region. The shift of WPSH position and the transition of surface thermal bias show close relationships with the formation of bias centers in winds and precipitation. The rapid growth in bias due to the strong internal atmospheric variability during short leads seems to mainly account for the weak WPSH and SASM in the model. However, at certain stages, particularly for longer-lead predictions, the biases of slowly varying components may also play an important role in bias development of winds and precipitation.     

Evaluation of precipitation prediction skill of IMD operational NWP system over Indian monsoon region

Summary The study provides a concise and synthesized documentation of the current level of skill of the operational NWP model of India Meteorological Department based on daily 24 hours forecast run of the model during two normal monsoon years 2001 and 2003 making detailed inter-comparison with daily rainfall analysis from the use of high dense land rain gauge observations. The study shows that the model, in general, is able to capture three regions of climatologically heavy rainfall domains along Western Ghats, Northeast India and over east central India, over the domain of monsoon trough. However, the accuracy in prediction of location and magnitude of rainfall fluctuates considerably. The inter-comparison reveals that performance of the model rainfall forecast deteriorated in 2003 when rainfall over most parts of the region was significantly under-predicted. These features are also reflected in the error statistics. The study suggests that there is a need to maximize the data ingest in the model with a better data assimilation scheme to improve the rainfall forecast skill.