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.     

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。     

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.     

A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction

There are two main approaches for dealing with model biases in forecasts made with initialized climate models. In full-field initialization, model biases are removed during the assimilation process by constraining the model to be close to observations. Forecasts drift back towards the model’s preferred state, thereby re-establishing biases which are then removed with an a posterior lead-time dependent correction diagnosed from a set of historical tests (hindcasts). In anomaly initialization, the model is constrained by observed anomalies and deviates from its preferred climatology only by the observed variability. In theory, the forecasts do not drift, and biases may be removed based on the difference between observations and independent model simulations of a given period. Both approaches are currently in use, but their relative merits are unclear. Here we compare the skill of each approach in comprehensive decadal hindcasts starting each year from 1960 to 2009, made using the Met Office decadal prediction system. Both approaches are more skilful than climatology in most regions for temperature and some regions for precipitation. On seasonal timescales, full-field initialized hindcasts of regional temperature and precipitation are significantly more skilful on average than anomaly initialized hindcasts. Teleconnections associated with the El Niño Southern Oscillation are stronger with the full-field approach, providing a physical basis for the improved precipitation skill. Differences in skill on multi-year timescales are generally not significant. However, anomaly initialization provides a better estimate of forecast skill from a limited hindcast set.     

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.     

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.     

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.     

Hindcast skill and predictability for precipitation and two-meter air temperature anomalies in global circulation models over the Southeast United States

This paper presents an assessment of the seasonal prediction skill of current global circulation models, with a focus on the two-meter air temperature and precipitation over the Southeast United States. The model seasonal hindcasts are analyzed using measures of potential predictability, anomaly correlation, Brier skill score, and Gerrity skill score. The systematic differences in prediction skill of coupled ocean–atmosphere models versus models using prescribed (either observed or predicted) sea surface temperatures (SSTs) are documented. It is found that the predictability and the hindcast skill of the models vary seasonally and spatially. The largest potential predictability (signal-to-noise ratio) of precipitation anywhere in the United States is found in the Southeast in the spring and winter seasons. The maxima in the potential predictability of two-meter air temperature, however, reside outside the Southeast in all seasons. The largest deterministic hindcast skill over the Southeast is found in wintertime precipitation. At the same time, the boreal winter two-meter air temperature hindcasts have the smallest skill. The large wintertime precipitation skill, the lack of corresponding two-meter air temperature hindcast skill, and a lack of precipitation skill in any other season are features common to all three types of models (atmospheric models forced with observed SSTs, atmospheric models forced with predicted SSTs, and coupled ocean–atmosphere models). Atmospheric models with observed SST forcing demonstrate a moderate skill in hindcasting spring-and summertime two-meter air temperature anomalies, whereas coupled models and atmospheric models forced with predicted SSTs lack similar skill. Probabilistic and categorical hindcasts mirror the deterministic findings, i.e., there is very high skill for winter precipitation and none for summer precipitation. When skillful, the models are conservative, such that low-probability hindcasts tend to be overestimates, whereas high-probability hindcasts tend to be underestimates.     

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean–atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean–atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean–atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.     

全球海气耦合模式对中国区域年代际气候变化预测能力的评估

利用4个海气耦合模式对1960～2005年的多年代际回报结果,评估模式对中国区域年代际气候变化(温度和降水)的预测潜力,并初步给出2005～2015年的气候预测结果。与CMIP3多模式集合1960～2000年结果以及观测实况比较的结果表明:融入观测资料进行同化的年代际气候预测模式,对中国区域温度和降水的模拟能力总体好于CMIP3模式。年代际气候预测模式对温度气候场的模拟仍以"冷偏差"为主,但较之CMIP3模式已有显著改进,中国区域平均的冷偏差减少1.3°C;对降水气候场的模拟仍以"湿偏差"为主,但在华南沿海和西北内陆降水的模拟能力优于CMIP3模式,中国区域平均的湿偏差降低了20%。年代际模式和CMIP3模式都能较好地模拟出中国区域尤其是北方20世纪后期的增暖信号;但CMIP3模式对20世纪后期中国东部降水的旱涝结构演变的模拟与观测相反;而年代际气候预测模式未能再现华北偏旱的变化,但能成功地模拟江淮流域和华南沿海的旱涝演变。2005～2015年的10年预测表明中国区域将继续增暖0.3～0.7°C,且增温幅度北方大于南方,增幅中心位于西北内陆和青藏高原;而降水的变化趋势不显著,黄淮地区、西北内陆和青藏高原的降水略有增加,而西南地区降水将减少。但需要指出的是,这种预测的不确定性是相当大的。     

BCC_CSM1.1气候模式年代际试验对北极涛动季节回报能力的初步评估

本文基于国家气候中心气候系统模式BCC_CSM1.1自1960—2004年每年起报的年代际预测试验结果,初步评估了该模式对北极涛动（AO）的预报技巧。同时,把该模式年代际预测结果与历史试验模拟比较,分析了气候模式初始化对年代际试验预测季节尺度AO及其年际变化的贡献。结果表明,年代际试验和历史试验均能反映出AO模态是北半球中高纬大气变率第一模态的特征,其中年代际预测试验回报的AO模态与观测的空间相关系数高于历史试验。两组试验基本能再现AO指数冬季最强、夏季最弱的特征。与历史试验相比,年代际预测试验回报月和冬季AO指数与观测的相关系数更高,特别是年代际试验与观测的月AO指数相关系数达到了0.1的显著性水平。年代际试验回报月、春季AO指数的变化周期更接近观测结果。因此,年代际试验中初始状态使用海温资料进行初始化,在一定程度上可以提高AO的回报能力。     

Scale-dependent regional climate predictability over North America inferred from CMIP3 and CMIP5 ensemble simulations

Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty (noise) in surface temperature predictions (represented by the spread among an ensemble of global climate model simulations) generally exceeds the ensemble mean (signal) at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.     

Decadal climate predictions with a coupled OAGCM initialized with oceanic reanalyses

We investigate the effects of realistic oceanic initial conditions on a set of decadal climate predictions performed with a state-of-the-art coupled ocean-atmosphere general circulation model. The decadal predictions are performed in both retrospective (hindcast) and forecast modes. Specifically, the full set of prediction experiments consists of 3-member ensembles of 30-year simulations, starting at 5-year intervals from 1960 to 2005, using historical radiative forcing conditions for the 1960–2005 period, followed by RCP4.5 scenario settings for the 2006–2035 period. The ocean initial states are provided by ocean reanalyses differing by assimilation methods and assimilated data, but obtained with the same ocean model. The use of alternative ocean reanalyses yields the required perturbation of the full three-dimensional ocean state aimed at generating the ensemble members spread. A full-value initialization technique is adopted. The predictive skill of the system appears to be driven to large extent by trends in the radiative forcing. However, after detrending, a residual skill over specific regions of the ocean emerges in the near-term. Specifically, natural fluctuations in the North Atlantic sea-surface temperature (SST) associated with large-scale multi-decadal variability modes are predictable in the 2–5 year range. This is consistent with significant predictive skill found in the Atlantic meridional overturning circulation over a similar timescale. The dependency of forecast skill on ocean initialization is analysed, revealing a strong impact of details of ocean data assimilation products on the system predictive skill. This points to the need of reducing the large uncertainties that currently affect global ocean reanalyses, in the perspective of providing reliable near-term climate predictions.     

An analysis of prediction skill of monthly mean climate variability

In this paper, lead-time and spatial dependence in skill for prediction of monthly mean climate variability is analyzed. The analysis is based on a set of extensive hindcasts from the Climate Forecast System at the National Centers for Environmental Prediction. The skill characteristics of initialized predictions is also compared with the AMIP simulations forced with the observed sea surface temperature (SST) to quantify the role of initial versus boundary conditions in the prediction of monthly means. The analysis is for prediction of monthly mean SST, precipitation, and 200-hPa height. The results show a rapid decay in skill with lead time for the atmospheric variables in the extratropical latitudes. Further, after a lead-time of approximately 30?C40?days, the skill of monthly mean prediction is essentially a boundary forced problem, with SST anomalies in the tropical central/eastern Pacific playing a dominant role. Because of the larger contribution from the atmospheric internal variability to monthly time-averages (compared to seasonal averages), skill for monthly mean prediction associated with boundary forcing is also lower. The analysis indicates that the prospects of skillful prediction of monthly means may remain a challenging problem, and may be limited by inherent limits in predictability.     

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.     

An analysis of seasonal predictability in coupled model forecasts

In the recent decade, operational seasonal prediction systems based on initialized coupled models have been developed. An analysis of how the predictability of seasonal means in the initialized coupled predictions evolves with lead-time is presented. Because of the short lead-time, such an analysis for the temporal behavior of seasonal predictability involves a mix of both the predictability of the first and the second kind. The analysis focuses on the lead-time dependence of ensemble mean variance, and the forecast spread. Further, the analysis is for a fixed target season of December?CJanuary?CFebruary, and is for sea surface temperature, rainfall, and 200-mb height. The analysis is based on a large set of hindcasts from an initialized coupled seasonal prediction system. Various aspects of predictability of the first and the second kind are highlighted for variables with long (for example, SST), and fast (for example, atmospheric) adjustment time scale. An additional focus of the analysis is how the predictability in the initialized coupled seasonal predictions compares with estimates based on the AMIP simulations. The results indicate that differences in the set up of AMIP simulations and coupled predictions, for example, representation of air?Csea interactions, and evolution of forecast spread from initial conditions do not change fundamental conclusion about the seasonal predictability. A discussion of the analysis presented herein, and its implications for the use of AMIP simulations for climate attribution, and for time-slice experiments to provide regional information, is also included.     

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。     

Decadal prediction of Sahel rainfall: where does the skill (or lack thereof) come from?

Previous works suggest decadal predictions of Sahel rainfall could be skillful. However, the sources of such skill are still under debate. In addition, previous results are based on short validation periods (i.e. less than 50 years). In this work we propose a framework based on multi-linear regression analysis to study the potential sources of skill for predicting Sahel trends several years ahead. We apply it to an extended decadal hindcast performed with the MPI-ESM-LR model that span from 1901 to 2010 with 1 year sampling interval. Our results show that the skill mainly depends on how well we can predict the timing of the global warming (GW), the Atlantic multidecadal variability (AMV) and, to a lesser extent, the inter-decadal Pacific oscillation signals, and on how well the system simulates the associated SST and West African rainfall response patterns. In the case of the MPI-ESM-LR decadal extended hindcast, the observed timing is well reproduced only for the GW and AMV signals. However, only the West African rainfall response to the AMV is correctly reproduced. Thus, for most of the lead times the main source of skill in the decadal hindcast of West African rainfall is from the AMV. The GW signal degrades skill because the response of West African rainfall to GW is incorrectly captured. Our results also suggest that initialized decadal predictions of West African rainfall can be further improved by better simulating the response of global SST to GW and AMV. Furthermore, our approach may be applied to understand and attribute prediction skill for other variables and regions.

     

CMIP5多模式对东亚地区地面气温年际变率的回报研究

对CMIP5全球气候模式中年代际回报试验的气温资料及其简单集合平均(Multi-model ensemble mean,EMN)和贝叶斯模式平均的结果(Bayesian Model Averaging,BMA)进行经验正交函数(Empirical Orthogonal Function,EOF)分解和Morlet小波分析,检验评估各个模式及其EMN和BMA对东亚地面气温的方差、气温时空分布特征及周期变化的回报能力。结果表明,10个模式、EMN、BMA都能很好地回报出1981—2010年东亚地面气温的方差分布,其中BMA回报效果最好。EOF分析表明,BMA能较好地回报出东亚地面气温第一模态的时空分布。MIROC5能较好地回报出第二模态的趋势变化,但却不能回报出气温的年际变率。绝大多数模式和EMN、BMA虽然能回报出东亚地面气温的变化趋势,但是对气温年际变率的回报仍然是比较困难的。CMCC-CM对气温变化主模态的3~5 a的周期变化特征回报效果最好,和NCEP资料的结果最为接近。     

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.