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 the GEOS-5 forecast system

A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office’s (GMAO’s) GEOS-5 Atmosphere–Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multi-variate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO’s atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 % improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the subpolar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.     

Predictability of the mid-latitude Atlantic meridional overturning circulation in a multi-model system

Assessing the skill of the Atlantic meridional overturning circulation (AMOC) in decadal hindcasts (i.e. retrospective predictions) is hampered by a lack of observations for verification. Models are therefore needed to reconstruct the historical AMOC variability. Here we show that ten recent oceanic syntheses provide a common signal of AMOC variability at 45°N, with an increase from the 1960s to the mid-1990s and a decrease thereafter although they disagree on the exact magnitude. This signal correlates with observed key processes such as the North Atlantic Oscillation, sub-polar gyre strength, Atlantic sea surface temperature dipole, and Labrador Sea convection that are thought to be related to the AMOC. Furthermore, we find potential predictability of the mid-latitude AMOC for the first 3–6 year means when we validate decadal hindcasts for the past 50 years against the multi-model signal. However, this predictability is not found in models driven only by external radiative changes, demonstrating the need for initialization of decadal climate predictions.     

Impact of initialization procedures on the predictive skill of a coupled ocean–atmosphere model

The sensitivity of the predictive skill of a decadal climate prediction system is investigated with respect to details of the initialization procedure. For this purpose, the coupled ocean–atmosphere UCLA/MITgcm climate model is initialized using the following three different initialization approaches: full state initialization (FSI), anomaly initialization (AI) and FSI employing heat flux and freshwater flux corrections (FC). The ocean initial conditions are provided by the German contribution to Estimating the Circulation and Climate of the Ocean state estimate (GECCO project), from which ensembles of decadal hindcasts are initialized every 5 years from 1961 to 2001. The predictive skill for sea surface temperature (SST), sea surface height (SSH) and the Atlantic meridional overturning circulation (AMOC) is assessed against the GECCO synthesis. In regions with a deep mixed layer the predictive skill for SST anomalies remains significant for up to a decade in the FC experiment. By contrast, FSI shows less persistent skill in the North Atlantic and AI does not show high skill in the extratropical Southern Hemisphere, but appears to be more skillful in the tropics. In the extratropics, the improved skill is related to the ability of the FC initialization method to better represent the mixed layer depth, and the highest skill occurs during wintertime. The correlation skill for the spatially averaged North Atlantic SSH hindcasts remains significant up to a decade only for FC. The North Atlantic MOC initialized hindcasts show high correlation values in the first pentad while correlation remains significant in the following pentad too for FSI and FC. Overall, for the current setup, the FC approach appears to lead to the best results, followed by the FSI and AI procedures.     

Impact of different ocean reanalyses on decadal climate prediction

We study the impact of three ocean state estimates (GECCO, SODA, [ECMWF]-ORA-S3) on decadal predictability in one particular forecast system, the Earth system model from the Max Planck Institute for Meteorology in Hamburg. The forecast procedure follows two steps. First, anomalies of temperature and salinity of the observational estimates are assimilated into our coupled model. Second, the assimilation runs are then used to initialize 10-year-long hindcasts/forecasts starting from each year between 1960 and 2001. The impact of the individual ocean state estimates is evaluated both by the extent to which climate variations from the ocean state estimates are adopted by the forecast system (‘fidelity’) and by the prediction skill of the corresponding hindcast experiments. The evaluation focuses on North Atlantic (NA) sea surface temperature (SST), upper-level (0–700?m) NA ocean heat content (OHC) and the Atlantic meridional overturning circulation (MOC). Regarding fidelity, correlations between observations and the assimilation runs are generally high for NA SST and NA OHC, except for NA OHC in the GECCO assimilation. MOC variations experience strong modifications when GECCO and SODA are assimilated, much less so when assimilating ORA-S3. Regarding prediction skill, when initializing with ORA-S3 and SODA, correlations with observations are high for NA OHC and moderate for NA SST. Correlations in case of GECCO, on the other hand, are high for NA SST and moderate for NA OHC. Relatively high MOC correlations between hindcasts and respective assimilation run appear in the first five years in GECCO in the tropics and subtropics and in ORA-S3 north of 50N. Correlations are largely reduced when the MOC signals are detrended. The trends in the assimilation runs are to some extent artifacts of the assimilation procedure. Hence, our potential predictabilities of the MOC are optimistic estimates of the upper limits of predictability. However, the ORA-S3 reanalysis gives the best results for our forecast system as measured by both overall fidelity of the assimilation procedure and predictions of upper-level OHC in the North Atlantic.     

Coupled atmosphere–ocean data assimilation experiments with a low-order climate model

A simple idealized atmosphere–ocean climate model and an ensemble Kalman filter are used to explore different coupled ensemble data assimilation strategies. The model is a low-dimensional analogue of the North Atlantic climate system, involving interactions between large-scale atmospheric circulation and ocean states driven by the variability of the Atlantic meridional overturning circulation (MOC). Initialization of the MOC is assessed in a range of experiments, from the simplest configuration consisting of forcing the ocean with a known atmosphere to performing fully coupled ensemble data assimilation. “Daily” assimilation (that is, at the temporal frequency of the atmospheric observations) is contrasted with less frequent assimilation of time-averaged observations. Performance is also evaluated under scenarios in which ocean observations are limited to the upper ocean or are non-existent. Results show that forcing the idealized ocean model with atmospheric analyses is inefficient at recovering the slowly evolving MOC. On the other hand, daily assimilation rapidly leads to accurate MOC analyses, provided a comprehensive set of oceanic observations is available for assimilation. In the absence of sufficient observations in the ocean, the assimilation of time-averaged atmospheric observations proves to be more effective for MOC initialization, including the case where only atmospheric observations are available.     

Statistical decadal predictions for sea surface temperatures: a benchmark for dynamical GCM predictions

Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO₂ concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2–5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6–9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2–5 years and 6–9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6–9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.     

Added-value from initialization in predictions of Atlantic multi-decadal variability

Identifying regions sensitive to external radiative changes, including anthropogenic (sulphate aerosols and greenhouse gases) and natural (volcanoes and solar variations) forcings, is important to formulate actionable information at multi-year time-scales. Internally-generated climate variability can overcome this radiative forcing, especially at regional level, so that detecting the areas for this potential dominance is likewise critical for decadal prediction. This work aims to clarify where each contribution has the largest effect on North Atlantic sea surface temperature (SST) predictions in relation to the Atlantic multi-decadal variability (AMV). Initialized decadal hindcasts and radiatively-forced historical simulations from the fifth phase of the Climate Model Intercomparison Project are analysed to assess multi-year skill of the AMV. The initialized hindcasts reproduce better the phase of the AMV index fluctuations. The radiatively-forced component consists of a residual positive trend, although its identification is ambiguous. Initialization reduces the inter-model spread when estimating the level of AMV skill, thus reducing its uncertainty. Our results show a skilful performance of the initialized hindcasts in capturing the AMV-related SST anomalies over the subpolar gyre and Labrador Sea regions, as well as in the eastern subtropical basin, and the inability of the radiatively-forced historical runs to simulate the horseshoe-like AMV signature over the North Atlantic. Initialization outperforms empirical predictions based on persistence beyond 1–4 years ahead, suggesting that ocean dynamics play a role in the AMV predictability beyond the thermal inertia. The initialized hindcasts are also more skilful at reproducing the observed AMV teleconnection to the West African monsoon. The impact of the start date frequency is also described, showing that the standard of 5-year interval between start dates yields the main features of the AMV skill that are robustly detected in hindcasts with yearly start date sampling. This work updates previous studies, complementing them, and concludes that skilful initialized multi-model forecasts of the AMV-related climate variability can be formulated, improving uninitialized projections, until 3–6 years ahead.     

近期气候预测系统IAP-DecPreS对印度洋偶极子的回报技巧:全场同化和异常场同化的比较

印度洋偶极子（IOD）是热带印度洋年际变率主导模态之一,对于区域乃至全球气候有重要影响。准确预报IOD对于短期气候预测具有重要意义。中国科学院大气物理研究所最近建立了近期气候预测系统IAP-DecPreS,其初始化方案采用“集合最优插值—分析增量更新”（EnOI-IAU）方案,能够同化观测的海洋次表层温度廓线资料。本文分析了IAP-DecPreS季节回报试验对IOD的回报技巧,重点比较了全场同化和异常场同化两种初始化策略下预测系统对IOD的回报技巧。分析表明,8月起报秋季IOD,无论从确定性预报还是概率性预报的角度,基于全场同化的回报试验技巧均高于异常场同化的回报试验。对于5月起报的秋季IOD,基于两种初始化策略的回报试验技巧相当。研究发现,全场同化策略相对于异常场的优势主要源于它提高了对伴随ENSO发生的IOD的预报技巧。ENSO遥强迫触发的热带东印度洋“风—蒸发—SST”正反馈过程是IOD发展和维持的关键。采用全场同化策略的回报结果能够更好地模拟出IOD发展过程中ENSO遥强迫产生的异常降水场和异常风场的空间分布特征;而采用异常场同化策略,模拟的异常降水场和风场偏差较大。导致两种初始化策略预测结果技巧差异的主要原因是,全场同化能够减小模式对热带印度洋气候平均态降水固有的模拟偏差,从而提升了热带印度洋对ENSO遥强迫响应的模拟能力。而异常场同化由于在同化过程中保持了模式固有的气候平均态,因此模拟的热带印度洋对ENSO遥强迫的响应存在与模式自由积分类似的模拟偏差。     

An assessment of oceanic variability for 1960–2010 from the GFDL ensemble coupled data assimilation

The Geophysical Fluid Dynamics Laboratory has developed an ensemble coupled data assimilation (ECDA) system based on the fully coupled climate model, CM2.1, in order to provide reanalyzed coupled initial conditions that are balanced with the climate prediction model. Here, we conduct a comprehensive assessment for the oceanic variability from the latest version of the ECDA analyzed for 51 years, 1960–2010. Meridional oceanic heat transport, net ocean surface heat flux, wind stress, sea surface height, top 300 m heat content, tropical temperature, salinity and currents are compared with various in situ observations and reanalyses by employing similar configurations with the assessment of the NCEP’s climate forecast system reanalysis (Xue et al. in Clim Dyn 37(11):2511–2539, 2011). Results show that the ECDA agrees well with observations in both climatology and variability for 51 years. For the simulation of the Tropical Atlantic Ocean and global salinity variability, the ECDA shows a good performance compared to existing reanalyses. The ECDA also shows no significant drift in the deep ocean temperature and salinity. While systematic model biases are mostly corrected with the coupled data assimilation, some biases (e.g., strong trade winds, weak westerly winds and warm SST in the southern oceans, subsurface temperature and salinity biases along the equatorial western Pacific boundary, overestimating the mixed layer depth around the subpolar Atlantic and high-latitude southern oceans in the winter seasons) are not completely eliminated. Mean biases such as strong South Equatorial Current, weak Equatorial Under Current, and weak Atlantic overturning transport are generated during the assimilation procedure, but their variabilities are well simulated. In terms of climate variability, the ECDA provides good simulations of the dominant oceanic signals associated with El Nino and Southern Oscillation, Indian Ocean Dipole, Pacific Decadal Oscillation, and Atlantic Meridional Overturning Circulation during the whole analyzed period, 1960–2010.     

Can oceanic reanalyses be used to assess recent anthropogenic changes and low-frequency internal variability of upper ocean temperature?

A multivariate analysis of the upper ocean thermal structure is used to examine the recent long-term changes and decadal variability in the upper ocean heat content as represented by model-based ocean reanalyses and a model-independent objective analysis. The three variables used are the mean temperature above the 14°C isotherm, its depth and a fixed depth mean temperature (250?m mean temperature). The mean temperature above the 14°C isotherm is a convenient, albeit simple, way to isolate thermodynamical changes by filtering out dynamical changes related to thermocline vertical displacements. The global upper ocean observations and reanalyses exhibit very similar warming trends (0.045°C per decade) over the period 1965–2005, superimposed with marked decadal variability in the 1970s and 1980s. The spatial patterns of the regression between indices (representative of anthropogenic changes and known modes of internal decadal variability), and the three variables associated with the ocean heat content are used as fingerprint to separate out the different contributions. The choice of variables provides information about the local heat absorption, vertical distribution and horizontal redistribution of heat, this latter being suggestive of changes in ocean circulation. The discrepancy between the objective analysis and the reanalyses, as well as the spread among the different reanalyses, are used as a simple estimate of ocean state uncertainties. Two robust findings result from this analysis: (1) the signature of anthropogenic changes is qualitatively different from those of the internal decadal variability associated to the Pacific Interdecadal Oscillation and the Atlantic Meridional Oscillation, and (2) the anthropogenic changes in ocean heat content do not only consist of local heat absorption, but are likely related with changes in the ocean circulation, with a clear shallowing of the tropical thermocline in the Pacific and Indian oceans.     

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.     

Dynamics of decadal variability in the Atlantic subpolar gyre: a stochastically forced oscillator

Internal variability of the Atlantic subpolar gyre is investigated in a 600 years control simulation of a comprehensive coupled climate model. The subpolar gyre shows irregular oscillations of decadal time scale with most spectral power between 15 and 20 years. Positive and negative feedback mechanisms act successively on the circulation leading to an internal oscillation. This involves periodically enhanced deep convection in the subpolar gyre center and intermittently enhanced air-sea thermal coupling. As a result, anomalies of the large-scale atmospheric circulation can be transferred to the ocean on the ocean’s intrinsic time scale, exciting the oscillator stochastically. A detailed understanding of oscillatory mechanisms of the ocean and their sensitivity to atmospheric forcing holds considerable potential for decadal predictions as well as for the interpretation of proxy data records.     

Interannual predictability of Arctic sea ice in a global climate model: regional contrasts and temporal evolution

The predictability of the Arctic sea ice is investigated at the interannual time scale using decadal experiments performed within the framework of the fifth phase of the Coupled Model Intercomparison Project with the CNRM-CM5.1 coupled atmosphere–ocean global climate model. The predictability of summer Arctic sea ice extent is found to be weak and not to exceed 2 years. In contrast, robust prognostic potential predictability (PPP) up to several years is found for winter sea ice extent and volume. This predictability is regionally contrasted. The marginal seas in the Atlantic sector and the central Arctic show the highest potential predictability, while the marginal seas in the Pacific sector are barely predictable. The PPP is shown to decrease drastically in the more recent period. Regarding sea ice extent, this decrease is explained by a strong reduction of its natural variability in the Greenland–Iceland–Norwegian Seas due to the quasi-disappearance of the marginal ice zone in the center of the Greenland Sea. In contrast, the decrease of predictability of sea ice volume arises from the combined effect of a reduction of its natural variability and an increase in its chaotic nature. The latter is attributed to a thinning of sea ice cover over the whole Arctic, making it more sensitive to atmospheric fluctuations. In contrast to the PPP assessment, the prediction skill as measured by the anomaly correlation coefficient is found to be mostly due to external forcing. Yet, in agreement with the PPP assessment, a weak added value of the initialization is found in the Atlantic sector. Nevertheless, the trend-independent component of this skill is not statistically significant beyond the forecast range of 3 months. These contrasted findings regarding potential predictability and prediction skill arising from the initialization suggest that substantial improvements can be made in order to enhance the prediction skill.     

A verification framework for interannual-to-decadal predictions experiments

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.     

Impact of in-consistency between the climate model and its initial conditions on climate prediction

We investigated the influence of dynamical in-consistency of initial conditions on the predictive skill of decadal climate predictions. The investigation builds on the fully coupled global model “Coupled GCM for Earth Simulator” (CFES). In two separate experiments, the ocean component of the coupled model is full-field initialized with two different initial fields from either the same coupled model CFES or the GECCO2 Ocean Synthesis while the atmosphere is initialized from CFES in both cases. Differences between both experiments show that higher SST forecast skill is obtained when initializing with coupled data assimilation initial conditions (CIH) instead of those from GECCO2 (GIH), with the most significant difference in skill obtained over the tropical Pacific at lead year one. High predictive skill of SST over the tropical Pacific seen in CIH reflects the good reproduction of El Niño events at lead year one. In contrast, GIH produces additional erroneous El Niño events. The tropical Pacific skill differences between both runs can be rationalized in terms of the zonal momentum balance between the wind stress and pressure gradient force, which characterizes the upper equatorial Pacific. In GIH, the differences between the oceanic and atmospheric state at initial time leads to imbalance between the zonal wind stress and pressure gradient force over the equatorial Pacific, which leads to the additional pseudo El Niño events and explains reduced predictive skill. The balance can be reestablished if anomaly initialization strategy is applied with GECCO2 initial conditions and improved predictive skill in the tropical Pacific is observed at lead year one. However, initializing the coupled model with self-consistent initial conditions leads to the highest skill of climate prediction in the tropical Pacific by preserving the momentum balance between zonal wind stress and pressure gradient force along the equatorial Pacific.

     

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.     

CMIP6年代际气候预测计划（DCPP）概况与评述

年代际气候预测计划（DCPP）是第六次国际耦合模式比较计划（CMIP6）的子计划之一,其目标是利用多模式开展气候系统年代际预测、可预测性和变率机制研究。DCPP设计了3组试验,即年代际回报试验、预报试验以及理解年代际变率机制和可预测性的敏感性试验。目前有21个模式拟参与DCPP计划,其中包括5个来自中国的模式。DCPP将推动解决气候系统从年际到年代际尺度预测相关的多项科学问题,评估当前气候预测系统预报技巧,挖掘潜在可预报性,研究长时间尺度气候变率形成机制,提供对科学和社会有用的预测产品。     

国家气候中心短期气候预测模式系统业务化进展

该文简要介绍了国家气候中心短期气候预测模式系统的研发成果,并侧重于从海洋资料同化系统、陆面资料同化系统、月动力延伸预测模式系统、季节气候预测模式系统4个方面介绍了第2代短期气候预测模式系统的业务化进展。第2代海洋资料同化系统已初步建成,其对温盐的同化效果总体上优于第1代同化系统;陆面资料同化系统正在研发中,目前已完成其中的多源降水融合子系统的业务建设工作,可为陆面分量提供实时的大气降水强迫分析场;第2代月动力延伸预测系统基于国家气候中心大气环流模式BCC_AGCM2.2建立,已于2012年8月进入准业务运行阶段;第2代季节预测模式系统基于国家气候中心气候系统模式BCC_CSM1.1(m) 建立,将于2013年底投入准业务运行。初步评估表明:第2代月动力延伸预测模式系统和季节气候预测模式系统分别对候、旬、月和季节、年际时间尺度的气候变率体现出了一定的预测能力,其对降水、气温、环流等要素的预测技巧总体上要高于第1代预测系统。     

Real-time multi-model decadal climate predictions

We present the first climate prediction of the coming decade made with multiple models, initialized with prior observations. This prediction accrues from an international activity to exchange decadal predictions in near real-time, in order to assess differences and similarities, provide a consensus view to prevent over-confidence in forecasts from any single model, and establish current collective capability. We stress that the forecast is experimental, since the skill of the multi-model system is as yet unknown. Nevertheless, the forecast systems used here are based on models that have undergone rigorous evaluation and individually have been evaluated for forecast skill. Moreover, it is important to publish forecasts to enable open evaluation, and to provide a focus on climate change in the coming decade. Initialized forecasts of the year 2011 agree well with observations, with a pattern correlation of 0.62 compared to 0.31 for uninitialized projections. In particular, the forecast correctly predicted La Niña in the Pacific, and warm conditions in the north Atlantic and USA. A similar pattern is predicted for 2012 but with a weaker La Niña. Indices of Atlantic multi-decadal variability and Pacific decadal variability show no signal beyond climatology after 2015, while temperature in the Niño3 region is predicted to warm slightly by about 0.5 °C over the coming decade. However, uncertainties are large for individual years and initialization has little impact beyond the first 4 years in most regions. Relative to uninitialized forecasts, initialized forecasts are significantly warmer in the north Atlantic sub-polar gyre and cooler in the north Pacific throughout the decade. They are also significantly cooler in the global average and over most land and ocean regions out to several years ahead. However, in the absence of volcanic eruptions, global temperature is predicted to continue to rise, with each year from 2013 onwards having a 50 % chance of exceeding the current observed record. Verification of these forecasts will provide an important opportunity to test the performance of models and our understanding and knowledge of the drivers of climate change.