The Forecast Skills and Predictability Sources of Marine Heatwaves in the NUIST-CFS1.0 Hindcasts

Using monthly observations and ensemble hindcasts of the Nanjing University of Information Science and Technology Climate Forecast System(NUIST-CFS1.0) for the period 1983–2020, this study investigates the forecast skill of marine heatwaves(MHWs) over the globe and the predictability sources of the MHWs over the tropical oceans. The MHW forecasts are demonstrated to be skillful on seasonal-annual time scales, particularly in tropical oceans. The forecast skill of the MHWs over the tropical Pacific Ocean(TPO) remains high at lead times of 1–24 months, indicating a forecast better than random chance for up to two years. The forecast skill is subject to the spring predictability barrier of El Nino-Southern Oscillation(ENSO). The forecast skills for the MHWs over the tropical Indian Ocean(TIO), tropical Atlantic Ocean(TAO), and tropical Northwest Pacific(NWP) are lower than that in the TPO. A reliable forecast at lead times of up to two years is shown over the TIO, while a shorter reliable forecast window(less than 17 months) occurs for the TAO and NWP.Additionally, the forecast skills for the TIO, TAO, and NWP are seasonally dependent. Higher skills for the TIO and TAO appear in boreal spring, while a greater skill for the NWP emerges in late summer-early autumn. Further analyses suggest that ENSO serves as a critical source of predictability for MHWs over the TIO and TAO in spring and MHWs over the NWP in summer.

Ensemble Hindcasts of ENSO Events over the Past 120 Years Using a Large Number of Ensembles

Based on an intermediate coupled model （ICM）, a probabilistic ensemble prediction system （EPS） has been developed. The ensemble Kalman filter （EnKF） data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886-2005 using the EPS with 100 ensemble members and with initial conditions obtained by only assimilating historic SST anomaly observations. By examining the retrospective ensemble forecasts and available observations, the verification results show that the skill of the ensemble mean of the EPS is greater than that of a single deterministic forecast using the same ICM, with a distinct improvement of both the correlation and root mean square （RMS） error between the ensemble-mean hindcast and the deterministic scheme over the 12-month prediction period. The RMS error of the ensemble mean is almost 0.2℃ smaller than that of the deterministic forecast at a lead time of 12 months. The probabilistic skill of the EPS is also high with the predicted ensemble following the SST observations well, and the areas under the relative operating characteristic （ROC） curves for three different ENSO states （warm events, cold events, and neutral events） are all above 0.55 out to 12 months lead time. However, both deterministic and probabilistic prediction skills of the EPS show an interdecadal variation. For the deterministic skill, there is high skill in the late 19th century and in the middle-late 20th century （which includes some artificial skill due to the model training period）, and low skill during the period from 1906 to 1961. For probabilistic skill, for the three different ENSO states, there is still a similar interdecadal variation of ENSO probabilistic predictability during the period 1886~2005. There is high skill in the late 19th century from 1886 to 1905, and a decline to a minimum of skill around 1910-50s, beyond which skill rebounds and increases with time until the 2000s.

Improvement of wind field hindcasts for tropical cyclones

This paper presents a study on the improvement of wind field hindcasts for two typical tropical cyclones, i.e., Fanapi and Meranti, which occurred in 2010. The performance of the three existing models for the hindcasting of cyclone wind fields is first examined, and then two modification methods are proposed to improve the hindcasted results. The first one is the superposition method, which superposes the wind field calculated from the parametric cyclone model on that obtained from the cross-calibrated multi-platform （CCMP） reanalysis data. The radius used for the superposition is based on an analysis of the minimum difference between the two wind fields. The other one is the direct modification method, which directly modifies the CCMP reanalysis data according to the ratio of the measured maximum wind speed to the reanalyzed value as well as the distance from the cyclone center. Using these two methods, the problem of underestimation of strong winds in reanalysis data can be overcome. Both methods show considerable improvements in the hindcasting of tropical cyclone wind fields, compared with the cyclone wind model and the reanalysis data.

Sub-seasonal to Seasonal Hindcasts of Stratospheric Sudden Warming by BCC_CSM1.1(m):A Comparison with ECMWF

This study focuses on model predictive skill with respect to stratospheric sudden warming(SSW) events by comparing the hindcast results of BCC_CSM1.1(m) with those of the ECMWF's model under the sub-seasonal to seasonal prediction project of the World Weather Research Program and World Climate Research Program. When the hindcasts are initiated less than two weeks before SSW onset, BCC_CSM and ECMWF show comparable predictive skill in terms of the temporal evolution of the stratospheric circumpolar westerlies and polar temperature up to 30 days after SSW onset. However, with earlier hindcast initialization, the predictive skill of BCC_CSM gradually decreases, and the reproduced maximum circulation anomalies in the hindcasts initiated four weeks before SSW onset replicate only 10% of the circulation anomaly intensities in observations. The earliest successful prediction of the breakdown of the stratospheric polar vortex accompanying SSW onset for BCC_CSM(ECMWF) is the hindcast initiated two(three) weeks earlier. The predictive skills of both models during SSW winters are always higher than that during non-SSW winters, in relation to the successfully captured tropospheric precursors and the associated upward propagation of planetary waves by the model initializations. To narrow the gap in SSW predictive skill between BCC_CSM and ECMWF, ensemble forecasts and error corrections are performed with BCC_CSM. The SSW predictive skill in the ensemble hindcasts and the error corrections are improved compared with the previous control forecasts.

A hindcast of the Bohai Bay oil spill during June to August 2011

An operational three-dimensional oil spill model is developed by the National Marine Environmental Forecasting Center（NMEFC）, State Oceanic Administration, China, and the model has been running for 9 a. On June 4 and 17,2011, oil is spilled into the sea water from two separate oil platforms in the Bohai Bay, i.e., Platforms B and C of Penglai 19-3 oilfield. The spill causes pollution of thousands of square kilometres of sea area. The NMEFC’s oil spill model is employed to study the Penglai 19-3 oil-spill pollution during June to August 2011. The wind final analysis data of the NMEFC, which is based on a weather research and forecasting（WRF） model, are analyzed and corrected by comparing with the observation data. A corrected current filed is obtained by forcing the princeton ocean model（POM） with the corrected wind field. With the above marine environmental field forcing the oil spill model, the oil mass balance and oil distribution can be produced. The simulation is validated against the observation, and it is concluded that the oil spill model of the NMEFC is able to commendably simulate the oil spill distribution. Thus the NMEFC’s oil spill model can provide a tool in an environmental impact assessment after the event.

Interannual Variability and Scenarios Projection of Sea Ice in Bohai Sea Part Ⅰ: Variation Characteristics and Interannual Hindcast

The Bohai Sea is one of the southernmost areas for sea ice formation in the northern hemisphere.Sea ice disasters in this body of water severely affect marine activities and the safety of coastal residents.In this study,we analyze the variation characteristics of the sea ice in the Bohai Sea and establish an annual regression model based on predictable mode analysis method.The results show the following:1)From 1970 to 2018,the average ice grade is(2.6±0.8),with a maximum of 4.5 and a minimum of 1.0.Liaodong Bay(LDB)has the heaviest ice conditions in the Bohai Sea,followed by Bohai Bay(BHB)and Laizhou Bay(LZB).Interannual variation is obvious in all three bays,but the linear decreasing trend is significant only in BHB.2)Three modes are obtained from empirical orthogonal function analysis,namely,single polarity mode with the same sign of anomaly in all of the three bays and strong interannual variability(82.0%),the north–south dipole mode with BHB and LZB showing an opposite sign of anomalies to that in LDB and strong decadal variations(14.5%),and a linear trend mode(3.5%).Critical factors are analyzed and regression equations are established for all the principal components,and then an annual hindcast model is established by synthesizing the results of the three modes.This model provides an annual spatial prediction of the sea ice in the Bohai Sea for the first time,and meets the demand of operational sea ice forecasting.

Wind-wave hindcast in the Yellow Sea and the Bohai Sea from the year 1988 to 2002

We performed long-term wind-wave hindcast in the Yellow Sea and the Bohai Sea from the year 1988 to 2002, and then analyzed the regional wave climate. Comparisons between model results and satellite data are generally consistent on monthly mean significant wave height. Then we discuss the temporal and spatial characteristics of the climatological monthly mean significant wave heights and mean wave periods. The climatologically spatial patterns are observed as increasing from northwest to southeast and from offshore to deep-water area for both significant wave height and mean wave period, and the patterns are highly related to the wind forcing and local topography. Seasonal variations of wave parameters are also significant. Furthermore, we compute the extreme values of wind and significant wave height using statistical methods. Results reveal the spatial patterns of N-year return significant wave height in the Yellow Sea and the Bohai Sea, and we discuss the relationship between extreme values of significant wave height and wind forcing.

The spring prediction barrier in ENSO hindcast experiments using the FGOALS-g model

The Flexible Global Ocean-Atmosphere-Land System Model-gamil (FGOALS-g) was used to study the spring prediction barrier (SPB) in an ensemble system. This coupled model was developed and maintained at the State Key Laboratory of Atmospheric Sciences and Geophysical Fluid Dynamics (LASG). There are two steps in our hindcast experiments. The first is to integrate the coupled model continuously with sea surface temperature (SST) nudging, from 1971 to 2006. The second is to carry out a series of one-year hindcasts without SST nudging, by adopting initial values from the first step on January 1 st , April 1st , July 1st , and October 1st , from 1982 to 2005. We generate 10 ensemble members for a particular start date (1st ) by choosing different atmospheric and land conditions around the hindcast start date (1st through 10th ). To estimate the predicted SST, two methods are used: (1) Anomaly Correlation Coefficient and its rate of decrease; and (2) Talagrand distribution and its standard deviation. Results show that FGOALS-g offers a reliable ensemble system with realistic initial atmospheric and oceanic conditions, and high anomaly correlation (>0.5) within 6 month lead time. Further, the ensemble approach is effective, in that the anomaly correlation of ensemble mean is much higher than that of most individual ensemble members. The SPB exists in the FGOALS-g ensemble system, as shown by anomaly correlation and equal likelihood. Nevertheless, the role of the ensemble mean in reducing the SPB of ENSO prediction is significant. The rate of decrease of the ensemble mean is smaller than the largest deviations by 0.04-0.14. At the same time, the ensemble system "equal likelihood" declines during spring. An ensemble mean helps give a correct prediction direction, departing from largely-deviated ensemble members.

Preliminary assessment on the hindcast skill of the Arctic Oscillation with decadal experiment by the BCC_CSM1.1 climate model

The prediction skill of Arctic Oscillation (AO) in the decadal experiments with the Beijing Climate Center Climate System Model version 1.1 (BCC_CSM1.1) is assessed. As compared with the observations and historical experiments, the contribution of initialization for climate model to predict the seasonal scale AO and its interannual variations is estimated. Results show that the spatial correlation coefficient of AO mode simulated by the decadal experiment is higher than that in the historical experiment. The two groups of experiments reasonably reproduce the characteristics that AO indices are the strongest in winter and the weakest in summer. Compared with historical experiments, the correlation coefficient of the monthly and winter AO indices are higher in the decadal experiments. In particular, the correlation coefficient of monthly AO index between decadal hindcast and observation reached 0.1 significant level. Furthermore, the periodicity of the monthly and spring AO indices are achieved only in the decadal experiments. Therefore, the initial state of model is initialized by using sea temperature data may help to improve the prediction skill of AO in the decadal prediction experiments to some extent.

ENSO Hindcast Experiments Using a Coupled GCM

A group of seasonal hindcast experiments are conducted using a coupled model known as the Flexible Global Ocean-Atmosphere-Land System Modelgamil1.11 (FGOALS-g1.11) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG).Two steps are included in our ElNi o-Southern Oscillation (ENSO) hindcast experiments.The first step is to integrate the coupled GCM with the Sea Surface Temperature (SST) strongly nudged towards the observation from 1971 to 2006.The second step is to remove the SST nudging term.The authors carried out a one-year hindcast by adopting the initial values from SST nudging experiments from the first step on January 1st,April 1st,July 1st,and October 1st from 1982 to 2005.In the SST nudging experiment,the model can reproduce the observed equatorial thermocline anomalies and zonal wind stress anomalies in the Pacific,which demonstrates that the SST nudging approach can provide realistic atmospheric and oceanic initial conditions for seasonal prediction experiments.The model also demonstrates a high Anomaly Correlation Coefficient (ACC) score for SST in most of the tropical Pacific,Atlantic Ocean,and some Indian Ocean regions with a 3-month lead.Compared with the persistence ACC score,this model shows much higher ACC scores for the Ni o-3.4 index for a 9-month lead.

Wave Hindcast for the Neighbouring Seas of Korea Based on Loosely Coupled Wave-Tide-Surge Model

A hindcast simulation of 75 typhoons and winter monsoons which affected the coastal areas of Korean Peninsula is performed by use of a third generation ocean wave prediction model, WAM-cycle 4 model, loosely coupled with a com-bined tide and surge model. Typhoon wind fields are derived from the planetary marine boundary layer model for effective neutral winds embedding the vortical storm wind from the parameterized Rankin vortex type model in the limited areas of the overall modeled region. The hindcasted results illustrate that significant wave heights (SWH) considering the wave-tide-surge coupled process are significantly different from the results via the decoupled case especially in the region of the estuaries of the Changjiang Estuary, The Hangzhou Bay, and the southwestern tip of Korean Peninsula. This extensive model simulation is the first attempt to investigate the strong wave-tide-surge interaction for the shallow depth area along the coasts of the Yellow Sea and the East China Sea Continental shelf.

Wave hindcast under tropical cyclone conditions in the South China Sea:sensitivity to wind fields

Reliable wave information is critical for marine engineering.Numerical wave models are useful tools to obtain wave information with continuous spatiotemporal distributions.However,the accuracy of model results highly depends on the quality of wind forcing.In this study,we utilize observations from five buoys deployed in the northern South China Sea from August to September 2017.Notably,these buoys successfully recorded wind field and wave information during the passage of five tropical cyclones of different intensities without sustaining any damage.Based on these unique observations,we evaluated the quality of four widely used wind products,namely CFSv2,ERA5,CCMP,and ERAI.Our analysis showed that in the northern South China Sea,ERA5 performed best compared to buoy observations,especially in terms of maximum wind speed values at 10 m height(U10),extreme U10 occurrence time,and overall statistical indicators.CFSv2 tended to overestimate non-extreme U10 values.CCMP showed favorable statistical performance at only three of the five buoys,but underestimated extreme U10 values at all buoys.ERAI had the worst performance under both normal and tropical cyclone conditions.In terms of wave hindcast accuracy,ERA5 outperformed the other reanalysis products,with CFSv2 and CCMP following closely.ERAI showed poor performance especially in the upper significant wave heights.Furthermore,we found that the wave hindcasts did not improve with increasing spatiotemporal resolution,with spatial resolution up to 0.5°.These findings would help in improving wave hindcasts under extreme conditions.

Hindcast Experiment of Extraseasonal Short-Term Summer Climate Prediction over China with RegCM3_IAP9L-AGCM

The regional climate model RegCM3 has been one-way nested into IAP9L-AGCM, the nine-level atmospheric general circulation model of the Institute of Atmospheric Physics, Chinese Academy of Sciences, to perform a 20-yr （1982-2001） hindcast experiment on extraseaonal short-term prediction of China summer climate. The nested prediction system is referred to as RegCM3_IAP9L-AGCM in this paper. The results show that hindeasted climate fields such as 500-hPa geopotential height, 200- and 850-hPa zonal winds from RegCM3_IAP9L-AGCM have positive anomaly correlation coefficients （ACCs） with the observations, and are better than those from the stand-alone IAP9L-AGCM. Except for the 850-hPa wind field, the positive ACCs of the other two fields with observations both pass the 90% confidence level and display a zonal distribution. The results indicate that the positive correlation of summer precipitation anomaly percentage between the nested prediction system and observations covers most parts of China except for downstream of the Yangtze River and north of Northeast and Northwest China. The nested prediction system and the IAP9L-AGCM exhibit different hindcast skills over different regions of China, and the former demonstrates a higher skill over South China than the latter in predicting the summer precipitation.

北京“23•7”特大洪水复盘分析

本文分别采用地面雨量站和雷达反演降雨数据,利用北京山区洪水预报模型对北京“23•7”暴雨洪水开展了复盘分析。结果表明,雷达反演降雨与地面雨量站测量结果一致性较强,可较好地反映降雨的时空变异性;同时,利用二者分别驱动水文模型后所得的预报效果也基本一致,说明在水文预报工作中雷达反演降雨可作为地面站网的可靠替代品。本文改进的考虑北京山区产流特点的水文模型可对大部分预报断面做出较高精度的模拟。北京山区水文过程具有很强的非线性,基于不同量级历史洪水率定的水文模型参数具有不确定性,为了适应产汇流和洪水演进规律的变化,提高洪水预报的可靠性,预报实践时需要结合实况数据及时优化模型参数,完善洪水预报方案。

季节-年际历史回报数据中北大西洋海温异常影响东亚气候的两种途径

本文基于1980—2005年ENSEMBLES(Ensemble-based Predictions of Climate Changes and their Impact)季节—年际多模式历史回报数据和20世纪耦合季节预测(CSF-20C)回报数据的月平均海温、地表气温、降水、风场及位势高度场资料,利用依赖月份的集合成员间奇异值分解方法研究了北大西洋海温异常影响东亚气候的两种途径。结果表明,春、夏季热带北大西洋海温暖异常与西南风异常密切耦合,暖中心由春至夏季加强西移,该暖海温异常能够激发东传的Kelvin,造成夏季西北太平洋异常反气旋显著增强,促进了中南半岛北部、日本南部及我国南方大部的降水增长。而中高纬度北大西洋海温在春季呈现东北暖、西南冷的偶极子型异常,该海温异常通过与风场的强烈耦合,激发了遥相关波列,影响乌拉尔山阻塞高压及东亚大槽,造成东北亚地区的低压异常,导致了朝鲜半岛、日本大部及我国东部的气温降低。

基于SWAN模型下的马拉博油罐区深水波浪条件分析

在深水波浪条件下,油罐区的稳定性和安全性都面临着极大的挑战,对所在海域的风与波浪耦合作用的波浪分析变得至关重要。本文基于第三代海浪数值模型SWAN,依据CCMP和CFSR数值风场资料,建立南大西洋海浪后报数据集。通过与尼日利亚拉格斯外海Bonga站和纳米比亚卡拉斯外海Kudu站的实测数据资料进行对比,验证了数据集的合理性。并对赤道几内亚马拉博外海深水波浪条件进行分析,为后续油罐区项目建设提供数据参考。

一个全球海气耦合模式跨季度汛期预测能力的初步检验和评估

文中利用一个全球大气 海洋耦合模式 ,对中国汛期气候异常进行了 1991～ 2 0 0 1年共 11a的跨季度回报试验和检验研究。采用一套多指标的评估方法 ,对该模式的预报性能进行系统的定量评估。结果表明 ,该模式对中国汛期降水和温度及夏季北半球大尺度环流场等都有一定的跨季度预报能力。模式对中国不同区域夏季降水的预测能力有所不同。总的来说 ,模式对中国东部和西部的降水趋势回报较好 ,模式预报好于气候预报和持续性预报。从相关系数指标来看 ,模式跨季度预测夏季温度的技巧在中国西部比中国东部高。

BCC_CSM模式夏季关键区海温回报评估

利用国家气候中心气候系统模式(Beijing Climate Center Climate System Model,BCC_CSM)的汛期回报试验数据集,评估了夏季中低纬度海表面温度(Sea Surface Temperature,SST)的预测能力。结果表明:该模式对夏季中低纬海温具有一定的预测能力,且在低纬地区的预测技巧尤为出色。对太平洋、热带印度洋和北大西洋这三个关键区进一步分析发现,该模式对不同海区海温的预测能力有所不同。其中,模式对夏季北太平洋海温及Nio 3.4指数表现出显著的预测技巧,对热带印度洋、北大西洋海温及热带印度洋全区一致海温模态(Indian Ocean Basin-wide Warming,IOBW)也表现出一定的预测技巧,而对北大西洋海温三极子模态(North Atlantic Tripole,NAT)的技巧相对较低。研究发现,预测技巧与前冬的ENSO状态密切相关,当前冬位于ENSO异常位相时,BCC_CSM模式对于三大海区夏季海温的预测技巧要高于前冬位于ENSO正常位相时,且对NAT指数也具有更高的预测技巧。前冬ENSO所处的位相对于该模式对夏季Nio 3.4指数及IOBW指数的预测技巧影响不明显。此外,该模式对夏季海温的预测技巧依赖于超前时间,预测技巧在大部分情形下超前1个月的预测技巧相对更高。

应用九层全球大气格点模式进行跨季度短期气候预测系统性试验

利用中国科学院大气物理研究所 9层大气环流模式 (IAP9L AGCM)对夏季气候进行了 30年 ( 1970～ 1999年 )集合回报试验 ,并采用统计学分析方法对跨季度夏季短期气候的可预测性问题进行了初步探讨 .结果表明 ,该模式对对流层中、高层大气环流的预测能力强于低层 ,位势高度场和表面气温的可预测性最大 ,而降水的可预测性则相对较小 .对流层中、高层位势高度场的可预测性基本呈带状分布 ,越靠近赤道可预测性越高 ;而降水的可预测性基本局限于赤道东太平洋及热带个别区域 .由此可见 ,降水的预测极为困难和复杂 。

区域气候模式对中国夏季降水的10年回报试验及其评估分析

该文使用国家气候中心全球海气耦合模式嵌套区域气候模式 (RegCM- NCC)对 1 991～ 2 0 0 0年中国夏季降水进行了数值回报试验。从模式回报的降水 1 0年平均状况来看 ,模式基本上能够反映这 1 0年夏季的平均状况。用国家气候中心预报评分P、技巧评分SS、距平相关系数 (ACC)和异常气候评分TS 4种评估参数对模式的回报试验进行了总体评估分析 ,结果表明 :该模式对我国汛期降水具有一定的跨季度预报能力 ,对部分地区 (西部 ,东北 ,长江下游等 )有较强的预报能力。从相关系数来看 ,预报准确率较高 (即相关系数较高 )的地区是 :中国东北地区的北部 (内蒙古的北部和黑龙江的西北部 ) ,内蒙古—河套—长江中游地区 ,新疆的西北部 ,西藏的东部和四川的西部 ,江南部分地区 ,广西部分地区。这些地区的中心一般均超过 0 .90的信度检验。 1 60个站中相关系数高于 0 .2 0的有 5 4个 ,约占 33.73%。