Impacts of parameter uncertainties on deep chlorophyll maximum simulation revealed by the CNOP-P approach

Parameter uncertainty is a primary source of uncertainty in ocean ecosystem simulations.The deep chlorophyll maximum(DCM)is a ubiquitous ecological phenomenon in the ocean.Using a theoretical nutrients-phytoplankton model and the conditional nonlinear optimal perturbation approach related to parameters,we investigated the eff ects of parameter uncertainties on DCM simulations.First,the sensitivity of single parameter was analyzed.The sensitivity ranking of 10 parameters was obtained by analyzing the top four specifi cally.The most sensitive parameter(background turbidity)aff ects the light supply for DCM formation,whereas the other three parameters(nutrient content of phytoplankton,nutrient recycling coeffi cient,and vertical turbulent diff usivity)control nutrient supply.To explore the interactions among diff erent parameters,the sensitivity of multiple parameters was further studied by examining combinations of four parameters.The results show that background turbidity is replaced by the phytoplankton loss rate in the optimal parameter combination.In addition,we found that interactions among these parameters are responsible for such diff erences.Finally,we found that reducing the uncertainties of sensitive parameters could improve DCM simulations remarkably.Compared with the sensitive parameters identifi ed in the single parameter analysis,reducing parameter uncertainties in the optimal combination produced better model performance.This study shows the importance of nonlinear interactions among various parameters in identifying sensitive parameters.In the future,the conditional nonlinear optimal perturbation approach related to parameters,especially optimal parameter combinations,is expected to greatly improve DCM simulations in complex ecosystem models.

Recent Advances in China on the Predictability of Weather and Climate

This article summarizes the progress made in predictability studies of weather and climate in recent years in China,with a main focus on advances in methods to study error growth dynamics and reduce uncertainties in the forecasting of weather and climate.Specifically,it covers(a)advances in methods to study weather and climate predictability dynamics,especially those in nonlinear optimal perturbation methods associated with initial errors and model errors and their applications to ensemble forecasting and target observations,(b)new data assimilation algorithms for initialization of predictions and novel assimilation approaches to neutralize the combined effects of initial and model errors for weather and climate,(c)applications of new statistical approaches to climate predictions,and(d)studies on meso-to small-scale weather system predictability dynamics.Some of the major frontiers and challenges remaining in predictability studies are addressed in this context.

A New Sensitivity Analysis Approach Using Conditional Nonlinear Optimal Perturbations and Its Preliminary Application

Simulations and predictions using numerical models show considerable uncertainties,and parameter uncertainty is one of the most important sources.It is impractical to improve the simulation and prediction abilities by reducing the uncertainties of all parameters.Therefore,identifying the sensitive parameters or parameter combinations is crucial.This study proposes a novel approach:conditional nonlinear optimal perturbations sensitivity analysis(CNOPSA)method.The CNOPSA method fully considers the nonlinear synergistic effects of parameters in the whole parameter space and quantitatively estimates the maximum effects of parameter uncertainties,prone to extreme events.Results of the analytical g-function test indicate that the CNOPSA method can effectively identify the sensitivity of variables.Numerical results of the theoretical five-variable grassland ecosystem model show that the maximum influence of the simulated wilted biomass caused by parameter uncertainty can be estimated and computed by employing the CNOPSA method.The identified sensitive parameters can easily change the simulation or prediction of the wilted biomass,which affects the transformation of the grassland state in the grassland ecosystem.The variance-based approach may underestimate the parameter sensitivity because it only considers the influence of limited parameter samples from a statistical view.This study verifies that the CNOPSA method is effective and feasible for exploring the important and sensitive physical parameters or parameter combinations in numerical models.

The Influence of Arctic Sea Ice Concentration Perturbations on Subseasonal Predictions of North Atlantic Oscillation Events

The influence of Arctic sea ice concentration (SIC) on the subseasonal prediction of the North Atlantic Oscillation (NAO) event is investigated by utilizing the Community Atmospheric Model version 4. The optimal Arctic SIC perturbations which exert the greatest influence on the onset of an NAO event from a lead of three pentads (15 days) are obtained with a conditional nonlinear optimal perturbation approach. Numerical results show that there are two types of optimal Arctic SIC perturbations for each NAO event, with one weakening event (marked as type-1) and another strengthening event (marked as type-2). For positive NAO events, type-1 optimal SIC perturbations mainly show positive SIC anomalies in the Greenland, Barents, and Okhotsk Seas, while type-2 perturbations mainly feature negative SIC anomalies in these regions. For negative NAO events, the optimal SIC perturbations have almost opposite patterns to those in positive events, although there are some differences among these SIC perturbations due to different atmospheric initial conditions. Further diagnosis reveals that the optimal Arctic SIC perturbations first modify the surface turbulent heat flux and the temperature in the lower troposphere via diabatic processes. Afterward, the temperature in the low troposphere is mainly affected by dynamic advection. Finally, potential vorticity advection plays a crucial role in the 500-hPa geopotential height prediction in the northern North Atlantic sector during pentad 4, which influences NAO event prediction. These results highlight the importance of Arctic SIC on NAO event prediction and the spatial characteristics of the SIC perturbations may provide scientific support for target observations of SIC in improving NAO subseasonal predictions.

Preface to the Special Issue on the National Report to the 28th IUGG General Assembly by CNC-IAMAS (2019−2022)

The 28th General Assembly of the International Union of Geodesy and Geophysics(IUGG)will be held on 11-20 July 2023 in Berlin,Germany.The Chinese National Committee(CNC)for the International Association of Meteorology and Atmospheric Sciences(IAMAS)has invited leading Chinese scientists to review the progress and achievements in meteorology and atmospheric sciences in China since 2019,continuing its nearly 40 years tradition of submitting national reports to IUGG.The last four national reports were published as special issues in Advances in Atmospheric Sciences(AAS),the only associated journal of IAMAS,in 2007,2012,and 2019,respectively.These special issues have received increasing global attention and are widely cited.

Influence of Arctic Sea-ice Concentration on Extended-range Forecasting of Cold Events in East Asia

Utilizing the Community Atmosphere Model,version 4,the influence of Arctic sea-ice concentration(SIC)on the extended-range prediction of three simulated cold events(CEs)in East Asia is investigated.Numerical results show that the Arctic SIC is crucial for the extended-range prediction of CEs in East Asia.The conditional nonlinear optimal perturbation approach is adopted to identify the optimal Arctic SIC perturbations with the largest influence on CE prediction on the extended-range time scale.It shows that the optimal SIC perturbations are more inclined to weaken the CEs and cause large prediction errors in the fourth pentad,as compared with random SIC perturbations under the same constraint.Further diagnosis reveals that the optimal SIC perturbations first modulate the local temperature through the diabatic process,and then influence the remote temperature by horizontal advection and vertical convection terms.Consequently,the optimal SIC perturbations trigger a warming center in East Asia through the propagation of Rossby wave trains,leading to the largest prediction uncertainty of the CEs in the fourth pentad.These results may provide scientific support for targeted observation of Arctic SIC to improve the extended-range CE prediction skill.

成人急性病毒性心肌炎的临床表现与相关因素分析

目的:探讨成人急性病毒性心肌炎的相关因素。方法:连续入选我院心内科2006年5月至2018年3月收治的成人急性病毒性心肌炎患者169例(发病1个月以内),根据临床诊断分为重症心肌炎组(n=49)和非重症心肌炎组(n=120)。比较两组患者入院时的症状、生命体征、心电图、实验室检查及超声心动图参数。通过二分类Logistic回归模型进行单因素及多因素分析。结果:重症心肌炎组患者来诊时血压较非重症心肌炎组患者更低、心率更快,发生持续性室性心动过速(室速)/心室颤动(室颤)、Ⅱ度/Ⅲ度房室阻滞的比例均高于非重症心肌炎组(P均<0.05)。多因素Logistic回归分析显示,心电图QRS间期延长(OR=1.027,95%CI:1.004~1.050,P=0.020)、入院时肌酸激酶同工酶(CK-MB)升高(OR=1.024,95%CI:1.002~1.047,P=0.031)、低游离T3(FT3)水平(OR=0.068,95%CI:0.016~0.287,P<0.01)及入院时左心室射血分数(LVEF)降低(OR=0.912,95%CI:0.854~0.975,P=0.007)是重症心肌炎的相关因素。结论:成人急性病毒性心肌炎患者出现心电图QRS间期延长、CK-MB升高、低FT3水平及入院时LVEF降低是重症心肌炎的相关因素。

Application of the Conditional Nonlinear Optimal Perturbation Method to the Predictability Study of the Kuroshio Large Meander

A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander （KLM） state using the conditional nonlinear optimal parameter perturbation （CNOP-P） method. Because of their relatively large uncertainties, three model parameters were considered： the interracial friction coefficient, the wind-stress amplitude, and the lateral friction coefficient. We determined the CNOP-Ps optimized for each of these three parameters independently, and we optimized all three parameters simultaneously using the Spectral Projected Gradient 2 （SPG2） algorithm. Similarly, the impacts caused by errors in initial conditions were examined using the conditional nonlinear optimal initial perturbation （CNOP-I） method. Both the CNOP-I and CNOP-Ps can result in significant prediction errors of the KLM over a lead time of 240 days. But the prediction error caused by CNOP-I is greater than that caused by CNOP-P. The results of this study indicate not only that initial condition errors have greater effects on the prediction of the KLM than errors in model parameters but also that the latter cannot be ignored. Hence, to enhance the forecast skill of the KLM in this model, the initial conditions should first be improved, the model parameters should use the best possible estimates.

Influence of Positive and Negative Indian Ocean Dipoles on ENSO via the Indonesian Throughflow: Results from Sensitivity Experiments

The role of the Indonesian Throughflow （ITF） in the influence of the Indian Ocean Dipole （IOD） on ENSO is investigated using version 2 of the Parallel Ocean Program （POP2） ocean general circulation model. We demonstrate the results through sensitivity experiments on both positive and negative IOD events from observations and coupled general circulation model simulations. By shutting down the atmospheric bridge while maintaining the tropical oceanic channel, the IOD forcing is shown to influence the ENSO event in the following year, and the role of the ITF is emphasized. During positive IOD events, negative sea surface height anomalies （SSHAs） occur in the eastern Indian Ocean, indicating the existence of upwelling. These upwelling anomalies pass through the Indonesian seas and enter the western tropical Pacific, resulting in cold anomalies there. These cold temperature anomalies further propagate to the eastern equatorial Pacific, and ultimately induce a La Nifia- like mode in the following year. In contrast, during negative IOD events, positive SSHAs are established in the eastern Indian Ocean, leading to downwelling anomalies that can also propagate into the subsurface of the western Pacific Ocean and travel further eastward. These downwelling anomalies induce negative ITF transport anomalies, and an E1 Nifio-like mode in the tropical eastern Pacific Ocean that persists into the following year. The effects of negative and positive IOD events on ENSO via the ITF are symmetric. Finally, we also estimate the contribution of IOD forcing in explaining the Pacific variability associated with ENSO via ITE

Influence of the Arctic on the Predictability of Eurasian Winter Extreme Weather Events

The linkage between the Arctic and midlatitudes has received much attention recently due to the rapidly changing climate.Many investigations have been conducted to reveal the relationship between the Arctic and Eurasian extreme events from the perspective of climatological statistics.As a prediction source for extreme events in Eurasia,Arctic conditions are crucial for extreme event predictions.Therefore,it is urgent to explore the Arctic influence on the predictability of Eurasian extreme events due to the large uncertainties in Arctic conditions.Considering the sensitivity and nonlinearity of the atmospheric circulations in midlatitude to Arctic conditions,it is necessary to investigate the Arctic influences on Eurasian extreme weather events in case studies at weather time scales.Previous studies indicate that only perturbations in specific patterns have fast growth.Thus,the conditional nonlinear optimal perturbation approach is recommended for exploring the uncertainties in Arctic initial and boundary conditions and their synergistic effect on Eurasian extreme events.Moreover,the mechanism for extreme event formation may differ in different cases.Therefore,more extreme cases should be investigated to reach robust conclusions.

Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model

Based on the high-resolution Regional Ocean Modeling System(ROMS) and the conditional nonlinear optimal perturbation(CNOP) method, this study explored the effects of optimal initial errors on the prediction of the Kuroshio large meander(LM) path, and the growth mechanism of optimal initial errors was revealed. For each LM event, two types of initial error(denoted as CNOP1 and CNOP2) were obtained. Their large amplitudes were found located mainly in the upper 2500 m in the upstream region of the LM, i.e., southeast of Kyushu. Furthermore, we analyzed the patterns and nonlinear evolution of the two types of CNOP. We found CNOP1 tends to strengthen the LM path through southwestward extension. Conversely,CNOP2 has almost the opposite pattern to CNOP1, and it tends to weaken the LM path through northeastward contraction.The growth mechanism of optimal initial errors was clarified through eddy-energetics analysis. The results indicated that energy from the background field is transferred to the error field because of barotropic and baroclinic instabilities. Thus, it is inferred that both barotropic and baroclinic processes play important roles in the growth of CNOP-type optimal initial errors.

A Preliminary Application of the Differential Evolution Algorithm to Calculate the CNOP

A projected skill is adopted by use of the differential evolution (DE) algorithm to calculate a conditional nonlinear optimal perturbation (CNOP). The CNOP is the maximal value of a constrained optimization problem with a constraint condition, such as a ball constraint. The success of the DE algorithm lies in its ability to handle a non-differentiable and nonlinear cost function. In this study, the DE algorithm and the traditional optimization algorithms used to obtain the CNOPs are compared by analyzing a theoretical grassland ecosystem model and a dynamic global vegetation model. This study shows that the CNOPs generated by the DE algorithm are similar to those by the sequential quadratic programming (SQP) algorithm and the spectral projected gradients (SPG2) algorithm. If the cost function is non-differentiable, the CNOPs could also be caught with the DE algorithm. The numerical results suggest the DE algorithm can be employed to calculate the CNOP, especially when the cost function is non-differentiable.

Can Adaptive Observations Improve Tropical Cyclone Intensity Forecasts?

In order to investigate whether adaptive observations can improve tropical cyclone （TC） intensity forecasts,observing system simulation experiments （OSSEs） were conducted for 20 TC cases originating in the western North Pacific during the 2010 season according to the conditional nonlinear optimal perturbation （CNOP） sensitivity,using the fifth version of the PSU/NCAR mesoscale model （MM5） and its 3DVAR assimilation system.A new intensity index was defined as the sum of the number of grid points within an allocated square centered at the corresponding forecast TC central position,that satisfy constraints associated with the Sea Level Pressure （SLP）,near-surface horizontal wind speed,and accumulated convective precipitation.The higher the index value is,the more intense the TC is.The impacts of the CNOP sensitivity on the intensity forecast were then estimated.The OSSE results showed that for 15 of the 20 cases there were improvements,with reductions of forecast errors in the range of 0.12％-8.59％,which were much less than in track forecasts.The indication,therefore,is that the CNOP sensitivity has a generally positive effect on TC intensity forecasts,but only to a certain degree.We conclude that factors such as the use of a coupled model,or better initialization of the TC vortex,are more important for an accurate TC intensity forecast.

Preface to the Special Issue on Extreme Cold Events from East Asia to North America in Winter 2020/21

Along with anthropogenic global warming,the Northern Hemisphere(NH)has experienced more frequent cold air out-breaks and heavy snowfalls in recent decades(Petoukhov and Semenov,2010;Liu et al.,2012;Cohen et al.,2014;2020;Overland et al.,2015).The most recent example of extreme winter weather was the extreme cold events that occurred from East Asia to North America during the winter of 2020/21(Cohen et al.,2021;Zheng et al.,2022a).

Optimal Precursors Triggering the Kuroshio Extension State Transition Obtained by the Conditional Nonlinear Optimal Perturbation Approach

In this study, the initial perturbations that are the easiest to trigger the Kuroshio Extension （KE） transition connecting a basic weak jet state and a strong, fairly stable meandering state, are investigated using a reduced-gravity shallow water ocean model and the CNOP （Conditional Nonlinear Optimal Perturbation） approach. This kind of initial perturbation is called an optimal precursor （OPR）. The spatial structures and evolutionary processes of the OPRs are analyzed in detail. The results show that most of the OPRs are in the form of negative sea surface height （SSH） anomalies mainly located in a narrow band region south of the KE jet, in basic agreement with altimetric observations. These negative SSH anomalies reduce the merid- ional SSH gradient within the KE, thus weakening the strength of the jet. The KE jet then becomes more convoluted, with a high-frequency and large-amplitude variability corresponding to a high eddy kinetic energy level; this gradually strengthens the KE jet through an inverse energy cascade. Eventually, the KE reaches a high-energy state characterized by two well defined and fairly stable anticyclonic meanders. Moreover, sensitivity experiments indicate that the spatial structures of the OPRs are not sensitive to the model parameters and to the optimization times used in the analysis.

Role of Parameter Errors in the Spring Predictability Barrier for ENSO Events in the Zebiak–Cane Model

ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model.

Three-Dimensional Structure of Optimal Nonlinear Excitation for Decadal Variability of the Thermohaline Circulation

The decadal variability of the North Atlantic thermohaline circulation(THC) is investigated within a three-dimensional ocean circulation model using the conditional nonlinear optimal perturbation method. The results show that the optimal initial perturbations of temperature and salinity exciting the strongest decadal THC variations have similar structures: the perturbations are mainly in the northwestern basin at a depth ranging from 1500 to 3000 m. These temperature and salinity perturbations act as the optimal precursors for future modifications of the THC, highlighting the importance of observations in the northwestern basin to monitor the variations of temperature and salinity at depth. The decadal THC variation in the nonlinear model initialized by the optimal salinity perturbations is much stronger than that caused by the optimal temperature perturbations, indicating that salinity variations might play a relatively important role in exciting the decadal THC variability. Moreover, the decadal THC variations in the tangent linear and nonlinear models show remarkably different characteristics, suggesting the importance of nonlinear processes in the decadal variability of the THC.

Lessons Learned from the Tragedy during the 100 km Ultramarathon Race in Baiyin,Gansu Province on 22 May 2021

Twenty-one runners died of hypothermia during the 100 km Ultramarathon Mountain race in Baiyin,Gansu Province on 22 May 2021.The hypothermia was caused by a combination of low temperatures,precipitation,and high winds associated with a typical large-scale cold front passing by the race site that morning.Based on historical hourly records of 13 meteorological surface stations over the past six years,temperature(3.0°C)and apparent temperature(−5.1°C)at 1200 LST as well as gust wind speed(11.2 m s^(−1))at 1100 LST on the day of the tragedy were found to be within the top or bottom 5th percentile for the month of May.The precipitation was only moderate at this time,but when temperature lower than 3.0°C,gust wind speed greater than 11.2 m s^(−1),and precipitation greater than 0.1 mm for any adjacent three hours were combined together,1200 LST 22 May fell within the top 0.1%of cases.The European Centre for Medium-range Weather Forecasting model produced reasonably good forecasts of the low temperature and high wind one day and seven days before the event,respectfully.Based on this study,lessons that can be learned from this tragedy are summarized from an academic perspective:Hazard and impact forecasts of high-impact weather events should be developed to increase the value of weather forecasts.Probability forecasts should be issued by government weather agencies and communicated well to the public.And more importantly,knowledge of how to evaluate the impact of weather should be delivered to the public in the future.We would like to extend our deepest condolences to the families and loved ones of the people who lost their lives in this tragedy,including 21 runners and one officer.May our efforts honor those who lost their lives by highlighting the value of weather forecasting and calling for greater action in the future.

诚信的价值内涵与培育路径研究

诚信理念是社会主义核心价值观要义之一,它既有人类诚信所蕴含的一般的内容,也有社会主义社会制度所赋予的特色。我们不仅要把诚信作为国家层面的价值观来理解,同时还要明白诚信是社会与公民发展的价值目标。培育诚信价值观的基本路径包括:树立正确的诚信观、培育诚信意识、践行诚信理念、开拓诚信视野等。

Using the Lund-Potsdam-Jena Model to Understand the Different Responses of Three Woody Plants to Land Use in China

In this study, the approach of conditional nonlinear optimal perturbation related to initial perturbation （CNOP-I） was employed to investigate the maximum variations in plant amount for three main woody plants （a temperate broadleaved evergreen, a temperate broadleaved summergreen, and a boreal needleleaved evergreen） in China. The investigation was conducted within a certain range of land use intensity using a state-of-the-art Lund-Potsdam-Jena dynamic global vegetation model （LPJ DGVM）. CNOP-I represents a class of deforestation and can be considered a type of land use with respect to the initial perturbation. When deforestation denoted by the CNOP-I has the same intensity for all three plants, the variation in plant amount of the boreal needleleaved evergreen in northern China is greater than the variation in plant amount of both the temperate broadleaved evergreen and temperate broadleaved summergreen in southern China. As deforestation intensity increases, the plant amount variation in the three woody plant functional types carbon changes, in a nonlinear fashion. The impact of land use on plant functional types is minor because the interaction between climate condition and land use is not considered in the LPJ model. Finally, the different impacts of deforestation on net primary production of the three plant functional types were analyzed by modeling gross primary production and autotrophic respiration. Our results suggest that the CNOP-I approach is a useful tool for exploring the nonlinear and different responses of terrestrial ecosystems to land use.