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.

Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from E1 Nifio to Yangtze River basin rainfall meant that the strong E1 Nifio in winter 2015/16 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real-time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements.

The north-east North Atlantic Tripole implicated as a predictor of the August precipitation decadal variability over north China

Monthly precipitation over north China in August(NCAP)is the second highest in the year,and it is important to understand its driving mechanisms to facilitate reliable forecasting.The NCAP displays a significant decadal variability of a cycle about 10-year and negatively correlates with the July north-east North Atlantic Tripole(NAT)over the decadal timescales.This study shows that the Eurasian decadal teleconnection(EAT)acts as a bridge that links the July NAT with NCAP decadal variability.This coupled ocean–atmosphere bridge(COAB)mechanism,through which the July NAT influences the decadal variability of NCAP,can be summarized as follows.The cumulative effect of the NAT drives the EAT to adjust atmospheric circulation over north China and the surrounding regions,and so regulates precipitation in north China by influencing local water vapor transport.When the July NAT is in a negative(positive)phase,the EAT pattern has a positive(negative)pattern,which promotes(weakens)the transmission of water vapor from the sea in the south-east to north China,thus increasing(decreasing)NCAP over decadal timescales.The decadal NCAP model established based on the July NAT can effectively predict the NCAP decadal variability,illustrating that the July NAT can be implicated as a predictor of the NCAP decadal variability.

Identification Standard for ENSO Events and Its Application to Climate Monitoring and Prediction in China

The El Ni?o–Southern Oscillation(ENSO)reflects anomalous variations in the sea surface temperature(SST)and atmospheric circulation over the tropical central–eastern Pacific.It remarkably impacts on weather and climate worldwide,so monitoring and prediction of ENSO draw intensive research.However,there is not yet a unique standard internationally for identifying the timing,intensity,and type of ENSO events.The National Climate Center of China Meteorological Administration(NCC/CMA)has led the effort to establish a national identification standard of ENSO events,which was officially endorsed by the National Standardization Administration of China and implemented operationally in NCC/CMA in 2017.In this paper,two key aspects of this standard are introduced.First,the Ni?o3.4 SST anomaly index,which is well-recognized in the international ENSO research community and used operationally in the US,has replaced the previous Ni?o Z index and been used to identify the start,end,and peak times,and intensity of ENSO events.Second,two new indices—the eastern Pacific ENSO(EP)index and the central Pacific ENSO(CP)index,based on the SST conditions in Ni?o3 and Ni?o4 region respectively,are calculated to first determine the ENSO type before monitoring and assessing the impacts of ENSO on China’s climate.With this standard,all historical ENSO events since 1950 are consistently re-identified;their distinct properties are diagnosed and presented;and the impacts of ENSO events under different types on China’s climate are re-assessed.This standard is also employed to validate the intensity,grade,and type of the ENSO events predicted by the NCC/CMA operational ENSO prediction system.The new standard and the thus derived unified set of re-analyzed historical ENSO events and associated information provide a good reference for better monitoring and prediction of future ENSO events.

Prediction of Primary Climate Variability Modes at the Beijing Climate Center

Climate variability modes, usually known as primary climate phenomena, are well recognized as the most impor- tant predictability sources in subseasonal-interarmual climate prediction. This paper begins by reviewing the re- search and development carried out, and the recent progress made, at the Beijing Climate Center （BCC） in predicting some primary climate variability modes. These include the El Nifio-Southern Oscillation （ENSO）, Madden-Julian Oscillation （MJO）, and Arctic Oscillation （AO）, on global scales, as well as the sea surface temperature （SST） modes in the Indian Ocean and North Atlantic, western Pacific subtropical high （WPSH）, and the East Asian winter and summer monsoons （EAWM and EASM, respectively）, on regional scales. Based on its latest climate and statistical models, the BCC has established a climate phenomenon prediction system （CPPS） and completed a hindcast experi- ment for the period 1991-2014. The performance of the CPPS in predicting such climate variability modes is system- atically evaluated. The results show that skillful predictions have been made for ENSO, MJO, the Indian Ocean basin mode, the WPSH, and partly for the EASM, whereas less skillful predictions were made for the Indian Ocean Dipole （IOD） and North Atlantic SST Tripole, and no clear skill at all for the AO, subtropical IOD, and EAWM. Improve- ments in the prediction of these climate variability modes with low skill need to be achieved by improving the BCC＇s climate models, developing physically based statistical models as well as correction methods for model predictions. Some of the monitoring/prediction products of the BCC-CPPS are also introduced in this paper.

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.

Spatiotemporal distribution and decadal change of the monthly temperature predictability limit in China

Based on the nonlinear Lyapunov exponent and nonlinear error growth dynamics, the spatiotemporal distribution and decadal change of the monthly temperature predictability limit(MTPL) in China is quantitatively analyzed. Data used are daily temperature of 518 stations from 1960 to 2011 in China. The results are summarized as follows:(1) The spatial distribution of MTPL varies regionally. MTPL is higher in most areas of Northeast China, southwest Yunnan Province, and the eastern part of Northwest China. MTPL is lower in the middle and lower reaches of the Yangtze River and Huang-huai Basin.(2)The spatial distribution of MTPL varies distinctly with seasons. MTPL is higher in boreal summer than in boreal winter.(3) MTPL has had distinct decadal changes in China, with increase since the 1970 s and decrease since2000. Especially in the northeast part of the country, MTPL has significantly increased since 1986. Decadal change of MTPL in Northwest China, Northeast China and the Huang-huai Basin may have a close relationship with the persistence of temperature anomaly. Since the beginning of the 21 st century, MTPL has decreased slowly in most of the country, except for the south. The research provides a scientific foundation to understand the mechanism of monthly temperature anomalies and an important reference for improvement of monthly temperature prediction.

High-Resolution Projections of Mean and Extreme Precipitation over China by Two Regional Climate Models

In this study, we employ two regional climate models(RCMs or RegCMs), which are RegCM4 and PRECIS(Providing Regional Climates for Impact Studies), with a horizontal grid spacing of 25 km, to simulate the precipitation dynamics across China for the baseline climate of 1981–2010 and two future climates of 2031–2060 and 2061–2090. The global climate model(GCM)—Hadley Centre Global Environment Model version 2-Earth Systems(HadGEM2-ES) is used to drive the two RCMs. The results of baseline simulations show that the two RCMs can correct the obvious underestimation of light rain below 5 mm day^-1 and the overestimation of precipitation above 5 mm day^-1 in Northwest China and the Qinghai–Tibetan Plateau, as being produced by the driving GCM. While PRECIS outperforms RegCM4 in simulating annual precipitation and wet days in several sub-regions of Northwest China, its underperformance shows up in eastern China. For extreme precipitation, the two RCMs provide a more accurate simulation of continuous wet days(CWD) with reduced biases and more realistic spatial patterns compared to their driving GCM. For other extreme precipitation indices, the RCM simulations show limited benefit except for an improved performance in some localized regions. The future projections of the two RCMs show an increase in the annual precipitation amount and the intensity of extreme precipitation events in most regions. Most areas of Southeast China will experience fewer number of wet days, especially in summer, but more precipitation per wet day(≥ 30 mm day^-1). By contrast, number of wet days will increase in the Qinghai–Tibetan Plateau and some areas of northern China. The increase in both the maximum precipitation for five consecutive days and the regional extreme precipitation will lead to a higher risk of increased flooding. The findings of this study can facilitate the efforts of climate service institutions and government agencies to improve climate services and to make climate-smart decisions.

The Process and Benefits of Developing Prototype Climate Services——Examples in China

Changes in climate pose major challenges to society, and so decision-makers need actionable climate information to inform their planning and policies to make society more resilient to climatic changes. Climate services are being developed to provide such actionable climate information. The successful development and use of climate services benefits greatly from close engagement between developers, providers, and users of the services. The Climate Science for Service Partnership China(CSSP China) is a China–UK collaboration fostering closer engagement between climate scientists, providers of climate services, and users of climate services. We describe the process within CSSP China of co-developing climate services through trials with users to revise and improve a prototype. Examples are provided covering various scientific capabilities, user needs, and parts of China. The development process is yielding many benefits, such as increasing the engagement between providers and users, making users more aware of how climate information can be of use in their decision-making, giving the climate service providers a better understanding of the users’ requirements for climate information, and shaping future scientific research and development. In addition to the benefits, we also document some challenges that have emerged, along with ways of alleviating them. We have two key recommendations from our experiences: make the time and space for effective engagement between the users and developers of any climate service;bring the needs of the users in to the design and delivery of the climate service as early as possible and throughout the development cycle.

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.