Subseasonal Prediction of Early-summer Northeast Asian Cut-off Lows by BCC-CSM2-HR and GloSea5

Northeast Asian cut-off lows are crucial cyclonic systems that can bring temperature and precipitation extremes over large areas.Skillful subseasonal forecasting of Northeast Asian cut-off lows is of great importance.Using two dynamical forecasting systems,one from the Beijing Climate Center(BCC-CSM2-HR)and the other from the Met Office(GloSea5),this study assesses simulation ability and subseasonal prediction skill for early-summer Northeast Asian cut-off lows.Both models are shown to have good ability in representing the spatial structure of cut-off lows,but they underestimate the intensity.The skillful prediction time scales for cut-off low intensity are about 10.2 days for BCC-CSM2-HR and 11.4 days for GloSea5 in advance.Further examination shows that both models can essentially capture the initial Rossby wave train,rapid growth and decay processes responsible for the evolution of cut-off lows,but the models show weaker amplitudes for the three-stage processes.The underestimated simulated strength of both the Eurasian midlatitude and East Asian subtropical jets may lead to the weaker local eddy-mean flow interaction responsible for the cut-off low evolution.

Preface to the 2nd Special Issue on Climate Science for Service Partnership China

It is a great pleasure to introduce this second special issue of Advances in Atmospheric Sciences with new highlights from the Climate Science for Service Partnership(CSSP,Scaife et al.,2021)between China and the UK.The CSSP harnesses expertise in the China Meteorological Administration’s National Climate Centre(CMA NCC),the Institute of Atmospheric Physics(IAP)at the Chinese Academy of Sciences and the Met Office,plus key UK and Chinese universities and institutes to deliver a vibrant programme of collaborative research.

第27章 认识平流层和中间层100年进步

平流层包含地球大气质量的约17%,但其存在直到1902年才被认识到。随后数十年,随着更多的平流层观测,我们的认识逐渐增加。1913年,保护生命免遭有害紫外辐射伤害的臭氧层被发现。通过臭氧和水汽观测,第一个平流层大气环流的基本概念被提出。自1950年代以来,我们对平流层和中间层的认识快速扩展,该区域在气候系统中的重要性已经清晰。随着观测更多,一些新的平流层现象被发现:准两年振荡、平流层爆发性增温、南半球臭氧洞和平流层变率的地面影响。这些现象均没有被理论预料到。理论的进步更多地源自新的平流层观测中出现的无法解释的现象。自1960年代以来,动力过程和平流层-对流层耦合环流的重要性被认识到。自大约2000年以来,平流层--甚至是中间层--更好的表示开始包括在气候和天气模式中。我们现在知道,为了制作更准确的季节天气预报和预测气候长期变化及臭氧层未来演化,模式需要具有实际动力和化学解析完好的平流层。

Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin

We demonstrate that there is significant skill in the GloSea5 operational seasonal forecasting system for predicting June mean rainfall in the middle/lower Yangtze River basin up to four months in advance.Much of the rainfall in this region during June is contributed by the mei-yu rain band.We find that similar skill exists for predicting the East Asian summer monsoon index(EASMI)on monthly time scales,and that the latter could be used as a proxy to predict the regional rainfall.However,there appears to be little to be gained from using the predicted EASMI as a proxy for regional rainfall on monthly time scales compared with predicting the rainfall directly.Although interannual variability of the June mean rainfall is affected by synoptic and intraseasonal variations,which may be inherently unpredictable on the seasonal forecasting time scale,the major influence of equatorial Pacific sea surface temperatures from the preceding winter on the June mean rainfall is captured by the model through their influence on the western North Pacific subtropical high.The ability to predict the June mean rainfall in the middle and lower Yangtze River basin at a lead time of up to 4 months suggests the potential for providing early information to contingency planners on the availability of water during the summer season.

Predictability of South China Sea Summer Monsoon Onset

Predicting monsoon onset is crucial for agriculture and socioeconomic planning in countries where millions rely on the timely arrival of monsoon rains for their livelihoods. In this study we demonstrate useful skill in predicting year-to-year variations in South China Sea summer monsoon onset at up to a three-month lead time using the GloSea5 seasonal forecasting system. The main source of predictability comes from skillful prediction of Pacific sea surface temperatures associated with El Ni?no and La Ni?na. The South China Sea summer monsoon onset is a known indicator of the broadscale seasonal transition that represents the first stage of the onset of the Asian summer monsoon as a whole. Subsequent development of rainfall across East Asia is influenced by subseasonal variability and synoptic events that reduce predictability, but interannual variability in the broadscale monsoon onset for East Asian summer monsoon still provides potentially useful information for users about possible delays or early occurrence of the onset of rainfall over East Asia.

Predictable and Unpredictable Components of the Summer East Asia–Pacific Teleconnection Pattern

The East Asia–Pacific(EAP) teleconnection pattern is the dominant mode of circulation variability during boreal summer over the western North Pacific and East Asia, extending from the tropics to high latitudes. However, much of this pattern is absent in multi-model ensemble mean forecasts, characterized by very weak circulation anomalies in the mid and high latitudes. This study focuses on the absence of the EAP pattern in the extratropics, using state-of-the-art coupled seasonal forecast systems. The results indicate that the extratropical circulation is much less predictable, and lies in the large spread among different ensemble members, implying a large contribution from atmospheric internal variability. However,the tropical–mid-latitude teleconnections are also relatively weaker in models than observations, which also contributes to the failure of prediction of the extratropical circulation. Further results indicate that the extratropical EAP pattern varies closely with the anomalous surface temperatures in eastern Russia, which also show low predictability. This unpredictable circulation–surface temperature connection associated with the EAP pattern can also modulate the East Asian rainband.

Erratum to: Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin

The article[Predicting June Mean Rainfall in the Middle/Lower Yangtze River Basin],written by[Gill M.MARTIN,Nick J.DUNSTONE,Adam A.SCAIFE,and Philip E.BETT],was originally published electronically on the publisher’s internet portal on[10 December 2019]without open access.

Seasonal Rainfall Forecasts for the Yangtze River Basin in the Extreme Summer of 2020

Seasonal forecasts for Yangtze River basin rainfall in June,May–June–July(MJJ),and June–July–August(JJA)2020 are presented,based on the Met Office GloSea5 system.The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon(EASM)index,which is transformed into regional-mean rainfall through linear regression.The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation.The forecasts verify well in terms of giving strong,consistent predictions of above-average rainfall at lead times of at least three months.However,the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period,leading to observed values that lie outside the 95%prediction intervals of the three-month forecasts.The forecasts presented here are consistent with other studies of the 2020 EASM rainfall,whereby the enhanced mei-yu front in early summer is skillfully forecast,but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured.This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors.

Predictability and Risk of Extreme Winter PM_(2.5)Concentration in Beijing

Air pollution remains a serious environmental and social problem in many big cities in the world.How to predict and estimate the risk of extreme air pollution is unsettled yet.This study tries to provide a solution to this challenge by examining the winter PM_(2.5)concentration in Beijing based on the UNprecedented Simulation of Extremes with ENsembles(UNSEEN)method.The PM_(2.5)concentration observations in Beijing,Japanese 55-yr reanalysis data,and the Met Office near term climate prediction system(DePreSys3a)large ensemble simulations are used,and 10,000proxy series are generated with the model fidelity test.It is found that in Beijing,the main meteorological driver of PM_(2.5)concentration is monthly 850-hPa meridional wind(V850).Although the skill in prediction of V850 is low on seasonal and longer timescales,based on the UNSEEN,we use large ensemble of initialized climate simulations of V850 to estimate the current chance and risk of unprecedented PM_(2.5)concentration in Beijing.We unravel that there is a 3%(2.1%–3.9%)chance of unprecedented low monthly V850 corresponding to high PM_(2.5)in each winter,within the 95%range,calculated by bootstrap resampling of the data.Moreover,we use the relationship between air quality and winds to remove the meridional wind influence from the observed record,and find that anthropogenic intervention appears to have reduced the risk of extreme PM_(2.5)in Beijing in recent years.

Seasonal Rainfall Forecasts for the Yangtze River Basin of China in Summer 2019 from an Improved Climate Service

Rainfall forecasts for the summer monsoon season in the Yangtze River basin(YRB) allow decision-makers to plan for possible flooding, which can affect the lives and livelihoods of millions of people. A trial climate service was developed in 2016, producing a prototype seasonal forecast product for use by stakeholders in the region, based on rainfall forecasts directly from a dynamical model. Here, we describe an improved service based on a simple statistical downscaling approach. Through using dynamical forecast of an East Asian summer monsoon(EASM) index, seasonal mean rainfall for the upper and middle/lower reaches of YRB can be forecast separately by use of the statistical downscaling, with significant skills for lead times of up to at least three months. The skill in different sub-basin regions of YRB varies with the target season. The rainfall forecast skill in the middle/lower reaches of YRB is significant in May–June–July(MJJ), and the forecast skill for rainfall in the upper reaches of YRB is significant in June–July–August(JJA). The mean rainfall for the basin as a whole can be skillfully forecast in both MJJ and JJA. The forecasts issued in 2019 gave good guidance for the enhanced rainfall in the MJJ period and the near-average conditions in JJA. Initial feedback from users in the basin suggests that the improved forecasts better meet their needs and will enable more robust decision-making.

Verification of the 2019 GloSea5 Seasonal Tropical Cyclone Landfall Forecast for East China

A prototype climate service was developed and trialled in early 2019 to provide seasonal forecast of the June–July–August(JJA) tropical cyclone(TC) landfall risk for the East China region ahead of the forthcoming typhoon season.Test forecasts were produced in both March and April 2019 and a final forecast was released to the China Meteorological Administration(CMA) on 1 May 2019. The trial service was produced by using the Met Office Global Seasonal forecast system(GloSea5), and a forecast of the western Pacific subtropical high(WPSH) index was used to infer the TC landfall risk based on a simple linear regression between historical model WPSH indices and observed TC landfalls in East China. The forecast method shows significant skill for forecasting the JJA TC landfall risk in East China with up to three-month lead time, with the greatest skill for predictions initialized in May. The 2019 forecast provided good guidance of the near-average TC activity observed in East China in JJA 2019. Success of the forecast adds confidence to an improved climate service ahead of the 2020 typhoon season.

Representation and Predictability of the East Asia-Pacific Teleconnection in the Beijing Climate Center and UK Met Office Subseasonal Prediction Systems

Based on the empirical orthogonal function(EOF) analysis, the East Asia–Pacific(EAP) teleconnection is extracted as the leading mode of the subseasonal variability over East Asia in summer, with a meridional tripole structure and significant periods of 10–30 and 50–70 days. A two-dimensional phase–space diagram is established for the EAP index and its time tendency so as to monitor the real-time state of EAP events. Based on the phase composite analysis, the general circulation anomalies first occur over the high-latitude area of Europe centered near Novaya Zemlya at the beginning of EAP events. These general circulation anomalies then influence rainfall over Northeast China,North China, and the region south of the Yangtze River valley(YRV) as the phases of EAP event progress. The representation, predictability, and prediction skill of the EAP teleconnection are examined in the two fully coupled subseasonal prediction systems of the Beijing Climate Center(BCC) and UK Met Office(UKMO GloSea5). Both models are able to simulate the EAP meridional tripole over East Asia as the leading mode and its characteristics of evolution as well, except for the weaker precursors over Novaya Zemlya and an inconspicuous influence on precipitation over Northeast China. The actual prediction skill of the EAP teleconnection during May–September(MJJAS) is about 10 days in the BCC model and 15 days in the UKMO model based on correlation measures, but is higher when initialized from the EAP peak phases or when targeted on strong EAP scenarios. However, both of the ensemble prediction systems are under-dispersive and the predictable signals extend to 18 and 30 days in BCC and UKMO models based on signal-to-error metrics, indicating that there may be further scope for enhancing the capability of these models for the EAP teleconnection prediction and the associated impacts studies.