The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its l...The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.展开更多
In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are revi...In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are reviewed,and the“spring predictability barrier”problem for El Nino‒Southern Oscillation events and targeted observation issues for tropical cyclone forecasts are taken as two representative examples.Nonlinear theory reveals that initial errors of particular spatial structures,environmental conditions,and nonlinear processes contribute to significant prediction errors,whereas nonlinear technology provides a pioneering approach for reducing observational and forecast errors via targeted observations through the application of the CNOP method.Follow-up research further validates the scientific rigor of the theory in revealing the nonlinear mechanism of significant prediction errors,and relevant practical field campaigns for targeted observations verify the effectiveness of the technology in reducing prediction uncertainties.The CNOP method has achieved international recognition;furthermore,its applications further extend to ensemble forecasts for weather and climate and further enrich the nonlinear technology for reducing prediction uncertainties.It is expected that this nonlinear theory and technology will play a considerably important role in reducing prediction uncertainties for high-impact weather and climate events.展开更多
Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly effi...Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.展开更多
This paper preliminarily investigates the application of the orthogonal conditional nonlinear optimal perturbations(CNOPs)–based ensemble forecast technique in MM5(Fifth-generation Pennsylvania State University–Nati...This paper preliminarily investigates the application of the orthogonal conditional nonlinear optimal perturbations(CNOPs)–based ensemble forecast technique in MM5(Fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model). The results show that the ensemble forecast members generated by the orthogonal CNOPs present large spreads but tend to be located on the two sides of real tropical cyclone(TC) tracks and have good agreements between ensemble spreads and ensemble-mean forecast errors for TC tracks. Subsequently, these members reflect more reasonable forecast uncertainties and enhance the orthogonal CNOPs–based ensemble-mean forecasts to obtain higher skill for TC tracks than the orthogonal SVs(singular vectors)–, BVs(bred vectors)– and RPs(random perturbations)–based ones. The results indicate that orthogonal CNOPs of smaller magnitudes should be adopted to construct the initial ensemble perturbations for short lead–time forecasts, but those of larger magnitudes should be used for longer lead–time forecasts due to the effects of nonlinearities. The performance of the orthogonal CNOPs–based ensemble-mean forecasts is case-dependent,which encourages evaluating statistically the forecast skill with more TC cases. Finally, the results show that the ensemble forecasts with only initial perturbations in this work do not increase the forecast skill of TC intensity, which may be related with both the coarse model horizontal resolution and the model error.展开更多
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 t...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.展开更多
The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts...The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts.For nine selected TC cases,the NFSV-tendency perturbations of the WRF model,including components of potential temperature and/or moisture,are calculated when TC intensities are forecasted with a 24-hour lead time,and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts.The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time,and their dominant energies concentrate in the middle layers of the atmosphere.Moreover,such structures do not depend on TC intensities and subsequent development of the TC.The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs,makes larger contributions to the TC intensity forecast uncertainty.Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model,even if using a WRF with coarse resolution.展开更多
Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones,namely Higos,Nangka,Saudel,and Atsani,over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weat...Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones,namely Higos,Nangka,Saudel,and Atsani,over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weather Bureau in 2020.The conditional nonlinear optimal perturbation(CNOP)method has been utilized in real-time to identify the sensitive regions for targeting observations adhering to the procedure of real-time field campaigns for the first time.The observing system experiments were conducted to evaluate the effect of dropsonde data and CNOP sensitivity on TC forecasts in terms of track and intensity,using the Weather Research and Forecasting model.It is shown that the impact of assimilating all dropsonde data on both track and intensity forecasts is case-dependent.However,assimilation using only the dropsonde data inside the sensitive regions displays unanimously positive effects on both the track and intensity forecast,either of which obtains comparable benefits to or greatly reduces deterioration of the skill when assimilating all dropsonde data.Therefore,these results encourage us to further carry out targeting observations for the forecast of tropical cyclones according to CNOP sensitivity.展开更多
This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). In Part I, it is shown that the model...This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II.Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60?S and 60?N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.展开更多
Among all of the sources of tropical cyclone(TC) intensity forecast errors, the uncertainty of sea surface temperature(SST) has been shown to play a significant role. In the present study, we determine the SST forcing...Among all of the sources of tropical cyclone(TC) intensity forecast errors, the uncertainty of sea surface temperature(SST) has been shown to play a significant role. In the present study, we determine the SST forcing error that causes the largest simulation error of TC intensity during the entire simulation period by using the WRF model with time-dependent SST forcing. The SST forcing error is represented through the application of a nonlinear forcing singular vector(NFSV)structure. For the selected 12 TC cases, the NFSV-type SST forcing errors have a nearly coherent structure with positive(or negative) SST anomalies located along the track of TCs but are especially concentrated in a particular region. This particular region tends to occur during the specific period of the TCs life cycle when the TCs present relatively strong intensity, but are still intensifying just prior to the mature phase, especially within a TC state exhibiting a strong secondary circulation and very high inertial stability. The SST forcing errors located along the TC track during this time period are verified to have the strongest disturbing effect on TC intensity simulation. Physically, the strong inertial stability of TCs during this time period induces a strong response of the secondary circulation from diabatic heating errors induced by the SST forcing error. Consequently, this significantly influences the subsidence within the warm core in the eye region, which,in turn, leads to significant errors in TC intensity. This physical mechanism explains the formation of NSFV-type SST forcing errors. According to the sensitivity of the NFSV-type SST forcing errors, if one increases the density of SST observations along the TC track and assimilates them to the SST forcing field, the skill of TC intensity simulation generated by the WRF model could be greatly improved. However, this adjustment is most advantageous in improving simulation skill during the time period when TCs become strong but are still intensifying just prior to reaching full maturity. In light of this, the region along the TC track but in the time period of TC movement when the NFSV-type SST forcing errors occur may represent the sensitive area for targeting observation for SST forcing field associated with TC intensity simulation.展开更多
By analyzing the outputs of the pre-industrial control runs of four models within phase 5 of the Coupled Model Intercomparison Project, the effects of initial sea temperature errors on the predictability of Indian Oce...By analyzing the outputs of the pre-industrial control runs of four models within phase 5 of the Coupled Model Intercomparison Project, the effects of initial sea temperature errors on the predictability of Indian Ocean Dipole events were identified. The initial errors cause a significant winter predictability barrier(WPB) or summer predictability barrier(SPB).The WPB is closely related with the initial errors in the tropical Indian Ocean, where two types of WPB-related initial errors display opposite patterns and a west–east dipole. In contrast, the occurrence of the SPB is mainly caused by initial errors in the tropical Pacific Ocean, where two types of SPB-related initial errors exhibit opposite patterns, with one pole in the subsurface western Pacific Ocean and the other in the upper eastern Pacific Ocean. Both of the WPB-related initial errors grow the fastest in winter, because the coupled system is at its weakest, and finally cause a significant WPB. The SPB-related initial errors develop into a La Ni ?na–like mode in the Pacific Ocean. The negative SST errors in the Pacific Ocean induce westerly wind anomalies in the Indian Ocean by modulating the Walker circulation in the tropical oceans. The westerly wind anomalies first cool the sea surface water in the eastern Indian Ocean. When the climatological wind direction reverses in summer, the wind anomalies in turn warm the sea surface water, finally causing a significant SPB. Therefore, in addition to the spatial patterns of the initial errors, the climatological conditions also play an important role in causing a significant predictability barrier.展开更多
Using the Regional Ocean Modeling System, this study investigates the simulation uncertainties in the current velocity in the low-latitude North Pacific where the Kuroshio originates [i.e., the beginning of the Kurosh...Using the Regional Ocean Modeling System, this study investigates the simulation uncertainties in the current velocity in the low-latitude North Pacific where the Kuroshio originates [i.e., the beginning of the Kuroshio(BK)]. The results show that the simulation uncertainties largely reflect the contributions of wind stress forcing errors, especially zonal wind stress errors,rather than initial or boundary errors. Using the idea of a nonlinear forcing singular vector, two types of zonal wind stress errors(but sharing one EOF mode) are identified from error samples derived from reanalysis data as having the potential to yield large simulation uncertainties. The type-1 error possesses a pattern with positive anomalies covering the two zonal bands of 0?–15?N and 25?–40?N in the Pacific Ocean, with negative anomalies appearing between these two bands; while the type-2 error is almost opposite to the type-1 error. The simulation uncertainties induced by the type-1 and-2 errors consist of both large-scale circulation errors controlled by a mechanism similar to the Sverdrup relation and mesoscale eddy-like errors generated by baroclinic instability. The type-1 and-2 errors suggest two areas: one is located between the western boundary and the meridional 130?E along 15?–20?N, and the other is located between 140?–150?E and along 15?–20?N. The reduction of errors over these two areas can greatly improve the simulation accuracy of the current velocity at BK. These two areas represent sensitive areas for targeted observations associated with the simulation of the current velocity at BK.展开更多
This paper compares data from linearized and nonlinear Zebiak-Cane model, as constrained by observed sea surface temperature anomaly （SSTA）, in simulating central Pacific （CP） and eastern Pacific （EP） E1 Nino. T...This paper compares data from linearized and nonlinear Zebiak-Cane model, as constrained by observed sea surface temperature anomaly （SSTA）, in simulating central Pacific （CP） and eastern Pacific （EP） E1 Nino. The difference between the temperature advections （determined by subtracting those of the linearized model from those of the nonlinear model）, referred to here as the nonlinearly induced temperature advection change （NTA）, is analyzed. The results demonstrate that the NTA records warming in the central equatorial Pacific during CP E1 Nino and makes fewer contributions to the structural distinctions of the CP E1 Nino, whereas it records warming in the eastern equatorial Pacific during EP E1 Nino, and thus significantly promotes EP E1 Nino during E1 Nino-type selection. The NTA for CP and EP E1 Nino varies in its amplitude, and is smaller in CP E1 Nino than it is in EP E1 Nino. These results demonstrate that CP E1 Nino are weakly modulated by small intensities of NTA, and may be controlled by weak nonlinearity; whereas, EP E1 Nino are significantly enhanced by large amplitudes of NTA, and are therefore likely to be modulated by relatively strong nonlinearity. These data could explain why CP E1 Nino are weaker than EP E1 Nino. Because the NTA for CP and EP E1 Nino differs in spatial structures and intensities, as well as their roles within different E1 Nino modes, the diversity of E1 Nino may be closely related to changes in the nonlinear characteristics of the tropical Pacific.展开更多
A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data....A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.展开更多
Using GFDL CM2 p1(Geophysical Fluid Dynamics Laboratory Climate Model, version 2 p1), the effects of initial sea temperature errors on the predictability of the Indian Ocean Dipole(IOD) are explored. When initial temp...Using GFDL CM2 p1(Geophysical Fluid Dynamics Laboratory Climate Model, version 2 p1), the effects of initial sea temperature errors on the predictability of the Indian Ocean Dipole(IOD) are explored. When initial temperature errors are superimposed on the tropical Indian Ocean, a winter predictability barrier(WPB) and a summer predictability barrier(SPB) exist in IOD predictions. The existence of the WPB has a close relation with El Nin?o–Southern Oscillation(ENSO)in the winter of the growing phase of positive IOD events. That is, when ENSO exists in winter, no WPB appears in IOD predictions, and vice versa. In contrast, there is no inherent connection between the existence of the SPB and ENSO. Only the dominant spatial pattern of SPB-related initial errors is studied in this paper, which presents a significant west–east dipole pattern in the tropical Indian Ocean and is similar to that of WPB-related initial errors in previous studies. The SPB-related initial errors superimposed on the tropical Indian Ocean induce the sea surface temperature(SST) and wind anomalies in the tropical Pacific Ocean. Then, under the interaction between the Indian and Pacific oceans through the atmospheric bridge and Indonesian Throughflow, a west–east dipole pattern of SST errors appears in summer, which is further strengthened under the Bjerknes feedback and yields a significant SPB.展开更多
Initial errors in the tropical Indian Ocean(IO-related initial errors) that are most likely to yield the Spring Prediction Barrier(SPB) for La Ni?a forecasts are explored by using the CESM model.These initial errors c...Initial errors in the tropical Indian Ocean(IO-related initial errors) that are most likely to yield the Spring Prediction Barrier(SPB) for La Ni?a forecasts are explored by using the CESM model.These initial errors can be classified into two types.Type-1 initial error consists of positive sea temperature errors in the western Indian Ocean and negative sea temperature errors in the eastern Indian Ocean,while the spatial structure of Type-2 initial error is nearly opposite.Both kinds of IO-related initial errors induce positive prediction errors of sea temperature in the Pacific Ocean,leading to underprediction of La Nina events.Type-1 initial error in the tropical Indian Ocean mainly influences the SSTA in the tropical Pacific Ocean via atmospheric bridge,leading to the development of localized sea temperature errors in the eastern Pacific Ocean.However,for Type-2 initial error,its positive sea temperature errors in the eastern Indian Ocean can induce downwelling error and influence La Ni?a predictions through an oceanic channel called Indonesian Throughflow.Based on the location of largest SPB-related initial errors,the sensitive area in the tropical Indian Ocean for La Nina predictions is identified.Furthermore,sensitivity experiments show that applying targeted observations in this sensitive area is very useful in decreasing prediction errors of La Nina.Therefore,adopting a targeted observation strategy in the tropical Indian Ocean is a promising approach toward increasing ENSO prediction skill.展开更多
The ＂summer prediction barrier＂ （SPB） of SST anomalies （SSTA） over the Kuroshio--Oyashio Extension （KOE） refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summe...The ＂summer prediction barrier＂ （SPB） of SST anomalies （SSTA） over the Kuroshio--Oyashio Extension （KOE） refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summer, resulting in large prediction uncertainties. The fast error growth associated with the SPB occurs in the mature-to-decaying transition phase, which is usually during the August-September-October （ASO） season, of the KOE-SSTA events to be predicted. Thus, the role of KOE-SSTA evolutionary characteristics in the transition phase in inducing the SPB is explored by performing perfect model predictability experiments in a coupled model, indicating that the SSTA events with larger mature-to-decaying transi- tion rates （Category-l） favor a greater possibility of yielding a more significant SPB than those events with smaller transition rates （Category-2）. The KOE-SSTA events in Category-1 tend to have more significant anomalous Ekman pumping in their transition phase, resulting in larger prediction errors of vertical oceanic temperature advection associated with the SSTA events. Consequently, Category-1 events possess faster error growth and larger prediction errors. In addition, the anomalous Ekman upwelling （downwelling） in the ASO season also causes SSTA cooling （warming）, accelerating the transition rates of warm （cold） KOE-SSTA events. Therefore, the SSTA transition rate and error growth rate are both related with the anomalous Ekman pumping of the SSTA events to be predicted in their transition phase. This may explain why the SSTA events transferring more rapidly from the mature to decaying phase tend to have a greater possibility of yielding a more significant SPB.展开更多
Using the outputs from CMCC-CM in CMIP5 experiments,the authors identified sensitive areas for targeted observations in ENSO forecasting from the perspective of the initial error growth(IEG)method and the particle fil...Using the outputs from CMCC-CM in CMIP5 experiments,the authors identified sensitive areas for targeted observations in ENSO forecasting from the perspective of the initial error growth(IEG)method and the particle filter(PF)method.Results showed that the PF targets areas over the central-eastern equatorial Pacific,while the sensitive areas determined by the IEG method are slightly to the east of the former.Although a small part of the areas targeted by the IEG method also lie in the southeast equatorial Pacific,this does not affect the large-scale overlapping of the sensitive areas determined by these two methods in the eastern equatorial Pacific.Therefore,sensitive areas determined by the two methods are mutually supportive.When considering the uncertainty of methods for determining sensitive areas in realistic targeted observation,it is more reasonable to choose the above overlapping areas as sensitive areas for ENSO forecasting.This result provides scientific guidance for how to better determine sensitive areas for ENSO forecasting.展开更多
Model errors offset by constant and time-variant optimal forcing vector approaches （termed COF and OFV, respectively） are analyzed within the framework of E1 Nifio simulations. Applying the COF and OFV approaches to...Model errors offset by constant and time-variant optimal forcing vector approaches （termed COF and OFV, respectively） are analyzed within the framework of E1 Nifio simulations. Applying the COF and OFV approaches to the well-known Zebiak-Cane model, we re-simulate the 1997 and 2004 E1 Nifio events, both of which were poorly degraded by a certain amount of model error when the initial anomalies were generated by coupling the observed wind forcing to an ocean com- ponent. It is found that the Zebiak-Cane model with the COF approach roughly reproduced the 1997 E1 Nifio, but the 2004 E1 Nifio simulated by this approach defied an ENSO classification, i.e., it was hardly distinguishable as CP-E1 Nifio or EP-E1 Nifio. In hoth E1 Nifio simulations, substituting the COF with the OFV improved the fit between the simulations and obser- vations because the OFV better manages the time-variant errors in the model. Furthermore, the OFV approach effectively corrected the modeled E1 Nifio events even when the observational data （and hence the computational time） were reduced. Such a cost-effective offset of model errors suggests a role for the OFV approach in complicated CGCMs.展开更多
The deterministic chaos in nonlinear systems was discovered by Edward N.Lorenz in 1963,marking the birth of nonlinear sciences.Since then,the sensitivity of predictions to initial conditions is characterized by the“b...The deterministic chaos in nonlinear systems was discovered by Edward N.Lorenz in 1963,marking the birth of nonlinear sciences.Since then,the sensitivity of predictions to initial conditions is characterized by the“butterfly effect”,triggering a scientific revolution that has lasted more than half a century and spans multiple disciplines.This article presents a perspective on the classic paper of the“butterfly effect”,which not only help reveal how pioneers challenged the infinite unknown under limited conditions to establish the foundational work,but also demonstrates how these seminal ideas inspired successors to transcend existing paradigms,unlock creative thinking,and achieve cross-disciplinary innovations.The Lorenz's theory of deterministic chaos reveals that even imperceptibly small errors in the initial state can grow to the extent that makes it impossible to forecast at arbitrary future times with acceptable errors.This recognition shook the classical physics view that“determinism implies complete predictability”and prompted meteorologists to shift from the pursuit of“long-term forecasts”to asking“how far into the future the atmosphere is predictable”,and from attempts at“perfect prediction”to scientifically“quantifying forecast uncertainty”.This transition has also fundamentally promoted the shift of the paradigms in weather forecast and subsequent climate prediction,exerted profound influence on mathematics,biology and economics,and even permeated literature,art,history and social governance—ultimately shaping the renowned Lorenz's chaos theory.展开更多
This paper reviews the development of ensemble weather forecast and the primary techniques employed in the main ensemble prediction systems(EPSs)designed by China and other countries.Here,the emphasis is placed on the...This paper reviews the development of ensemble weather forecast and the primary techniques employed in the main ensemble prediction systems(EPSs)designed by China and other countries.Here,the emphasis is placed on the advancements in the China Meteorological Administration(CMA)global and regional ensemble prediction systems(i.e.,CMA-GEPS and CMA-REPS),with particular attention to operational technologies such as initial and model perturbation methods and the applications of ensemble forecast.Through comparative verification with EPSs from other leading international numerical weather prediction(NWP)centers,CMA’s EPSs demonstrate forecast skills comparable to its global counterparts.As EPSs progress to convective scales and coupled systems between sea,land,air,and ice,the paper addresses some key challenges in ensemble forecast technologies across the aspects of operation,science,integration of artificial intelligence(AI),merging of weather and climate models,and challenging user requirements.Finally,a summary of conclusions and future perspectives on ensemble forecast are provided.展开更多
基金supported by the National Key R&D Program of China(Grant No.2023YFF0805100)。
文摘The predictability of a coupled system composed of a coupled reduced-order extratropical ocean-atmosphere model forced by a low-order three-variable tropical recharge-discharge model is explored with emphasis on its long-term forecasting capabilities.Highly idealized ensemble forecasts are produced taking into account the uncertainties in the initial states of the system,with specific attention to the structure of the initial errors in the tropical model.Three main types of experiments are explored:with random perturbations along the three Lyapunov vectors of the tropical model;along the two dominant Lyapunov vectors;and along the first Lyapunov vector only.When perturbations are introduced along all vectors,forecasting biases develop even if in a perfect model framework and with known initial uncertainty properties.Theses biases are considerably reduced only when the perturbations are introduced along the dominant Lyapunov vector.Furthermore,this perturbation strategy allows a reduced mean square error to be obtained at long lead times of a few years,as well as reliable ensemble forecasts across the whole time range.These very counterintuitive findings further underline the importance of appropriately controlling the initial error structure in the tropics through data assimilation.
基金sponsored by the National Natural Science Foun-dation of China(Grant No.42330111).
文摘In this article,our nonlinear theory and technology for reducing the uncertainties of high-impact ocean‒atmosphere event predictions,with the conditional nonlinear optimal perturbation(CNOP)method as its core,are reviewed,and the“spring predictability barrier”problem for El Nino‒Southern Oscillation events and targeted observation issues for tropical cyclone forecasts are taken as two representative examples.Nonlinear theory reveals that initial errors of particular spatial structures,environmental conditions,and nonlinear processes contribute to significant prediction errors,whereas nonlinear technology provides a pioneering approach for reducing observational and forecast errors via targeted observations through the application of the CNOP method.Follow-up research further validates the scientific rigor of the theory in revealing the nonlinear mechanism of significant prediction errors,and relevant practical field campaigns for targeted observations verify the effectiveness of the technology in reducing prediction uncertainties.The CNOP method has achieved international recognition;furthermore,its applications further extend to ensemble forecasts for weather and climate and further enrich the nonlinear technology for reducing prediction uncertainties.It is expected that this nonlinear theory and technology will play a considerably important role in reducing prediction uncertainties for high-impact weather and climate events.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.41930971,42330111,and 42405061)the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(Earth Lab).
文摘Orthogonal conditional nonlinear optimal perturbations(O-CNOPs)have been used to generate ensemble forecasting members for achieving high forecasting skill of high-impact weather and climate events.However,highly efficient calculations for O-CNOPs are still challenging in the field of ensemble forecasting.In this study,we combine a gradient-based iterative idea with the Gram‒Schmidt orthogonalization,and propose an iterative optimization method to compute O-CNOPs.This method is different from the original sequential optimization method,and allows parallel computations of O-CNOPs,thus saving a large amount of computational time.We evaluate this method by using the Lorenz-96 model on the basis of the ensemble forecasting ability achieved and on the time consumed for computing O-CNOPs.The results demonstrate that the parallel iterative method causes O-CNOPs to yield reliable ensemble members and to achieve ensemble forecasting skills similar to or even slightly higher than those produced by the sequential method.Moreover,the parallel method significantly reduces the computational time for O-CNOPs.Therefore,the parallel iterative method provides a highly effective and efficient approach for calculating O-CNOPs for ensemble forecasts.Expectedly,it can play an important role in the application of the O-CNOPs to realistic ensemble forecasts for high-impact weather and climate events.
基金jointly sponsored by the National Key Research and Development Program of China (2018YFC1506402)the National Natural Science Foundation of China (Grant Nos.41475100 and 41805081)the Global Regional Assimilation and Prediction System Development Program of the China Meteorological Administration (GRAPES-FZZX2018)
文摘This paper preliminarily investigates the application of the orthogonal conditional nonlinear optimal perturbations(CNOPs)–based ensemble forecast technique in MM5(Fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model). The results show that the ensemble forecast members generated by the orthogonal CNOPs present large spreads but tend to be located on the two sides of real tropical cyclone(TC) tracks and have good agreements between ensemble spreads and ensemble-mean forecast errors for TC tracks. Subsequently, these members reflect more reasonable forecast uncertainties and enhance the orthogonal CNOPs–based ensemble-mean forecasts to obtain higher skill for TC tracks than the orthogonal SVs(singular vectors)–, BVs(bred vectors)– and RPs(random perturbations)–based ones. The results indicate that orthogonal CNOPs of smaller magnitudes should be adopted to construct the initial ensemble perturbations for short lead–time forecasts, but those of larger magnitudes should be used for longer lead–time forecasts due to the effects of nonlinearities. The performance of the orthogonal CNOPs–based ensemble-mean forecasts is case-dependent,which encourages evaluating statistically the forecast skill with more TC cases. Finally, the results show that the ensemble forecasts with only initial perturbations in this work do not increase the forecast skill of TC intensity, which may be related with both the coarse model horizontal resolution and the model error.
基金sponsored by the National Natural Science Foundation of China(Grant Nos.41930971,42105061 and 42030604).
文摘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.
基金jointly sponsored by the National Key Research and Development Program of China (Grant No. 2018YFC1506402)the National Natural Science Foundation of China (Grant Nos. 41930971, 41575061 and 41775061)
文摘The present study uses the nonlinear singular vector(NFSV)approach to identify the optimally-growing tendency perturbations of the Weather Research and Forecasting(WRF)model for tropical cyclone(TC)intensity forecasts.For nine selected TC cases,the NFSV-tendency perturbations of the WRF model,including components of potential temperature and/or moisture,are calculated when TC intensities are forecasted with a 24-hour lead time,and their respective potential temperature components are demonstrated to have more impact on the TC intensity forecasts.The perturbations coherently show barotropic structure around the central location of the TCs at the 24-hour lead time,and their dominant energies concentrate in the middle layers of the atmosphere.Moreover,such structures do not depend on TC intensities and subsequent development of the TC.The NFSV-tendency perturbations may indicate that the model uncertainty that is represented by tendency perturbations but associated with the inner-core of TCs,makes larger contributions to the TC intensity forecast uncertainty.Further analysis shows that the TC intensity forecast skill could be greatly improved as preferentially superimposing an appropriate tendency perturbation associated with the sensitivity of NFSVs to correct the model,even if using a WRF with coarse resolution.
基金jointly sponsored by the National Nature Scientific Foundation of China(Grant.Nos.41930971 and 41775061)the National Key Research and Development Program of China(Grant No.2018YFC1506402)。
文摘Valuable dropsonde data were obtained from multiple field campaigns targeting tropical cyclones,namely Higos,Nangka,Saudel,and Atsani,over the western North Pacific by the Hong Kong Observatory and Taiwan Central Weather Bureau in 2020.The conditional nonlinear optimal perturbation(CNOP)method has been utilized in real-time to identify the sensitive regions for targeting observations adhering to the procedure of real-time field campaigns for the first time.The observing system experiments were conducted to evaluate the effect of dropsonde data and CNOP sensitivity on TC forecasts in terms of track and intensity,using the Weather Research and Forecasting model.It is shown that the impact of assimilating all dropsonde data on both track and intensity forecasts is case-dependent.However,assimilation using only the dropsonde data inside the sensitive regions displays unanimously positive effects on both the track and intensity forecast,either of which obtains comparable benefits to or greatly reduces deterioration of the skill when assimilating all dropsonde data.Therefore,these results encourage us to further carry out targeting observations for the forecast of tropical cyclones according to CNOP sensitivity.
基金jointly supported by the National Key Research and Development Program of China (Grant. No. 2017YFC1501601)the National Natural Science Foundation of China (Grant. No. 41475100)+1 种基金the National Science and Technology Support Program (Grant. No. 2012BAC22B03)the Youth Innovation Promotion Association of the Chinese Academy of Sciences
文摘This paper investigates the possible sources of errors associated with tropical cyclone(TC) tracks forecasted using the Global/Regional Assimilation and Prediction System(GRAPES). In Part I, it is shown that the model error of GRAPES may be the main cause of poor forecasts of landfalling TCs. Thus, a further examination of the model error is the focus of Part II.Considering model error as a type of forcing, the model error can be represented by the combination of good forecasts and bad forecasts. Results show that there are systematic model errors. The model error of the geopotential height component has periodic features, with a period of 24 h and a global pattern of wavenumber 2 from west to east located between 60?S and 60?N. This periodic model error presents similar features as the atmospheric semidiurnal tide, which reflect signals from tropical diabatic heating, indicating that the parameter errors related to the tropical diabatic heating may be the source of the periodic model error. The above model errors are subtracted from the forecast equation and a series of new forecasts are made. The average forecasting capability using the rectified model is improved compared to simply improving the initial conditions of the original GRAPES model. This confirms the strong impact of the periodic model error on landfalling TC track forecasts. Besides, if the model error used to rectify the model is obtained from an examination of additional TCs, the forecasting capabilities of the corresponding rectified model will be improved.
基金sponsored by the National Nature Scientific Foundations of China (Grant Nos. 41930971)the National Key Research and Development Program of China (Grant No. 2018YFC1506402)the National Nature Scientific Foundations of China (Grant No. 41575061)。
文摘Among all of the sources of tropical cyclone(TC) intensity forecast errors, the uncertainty of sea surface temperature(SST) has been shown to play a significant role. In the present study, we determine the SST forcing error that causes the largest simulation error of TC intensity during the entire simulation period by using the WRF model with time-dependent SST forcing. The SST forcing error is represented through the application of a nonlinear forcing singular vector(NFSV)structure. For the selected 12 TC cases, the NFSV-type SST forcing errors have a nearly coherent structure with positive(or negative) SST anomalies located along the track of TCs but are especially concentrated in a particular region. This particular region tends to occur during the specific period of the TCs life cycle when the TCs present relatively strong intensity, but are still intensifying just prior to the mature phase, especially within a TC state exhibiting a strong secondary circulation and very high inertial stability. The SST forcing errors located along the TC track during this time period are verified to have the strongest disturbing effect on TC intensity simulation. Physically, the strong inertial stability of TCs during this time period induces a strong response of the secondary circulation from diabatic heating errors induced by the SST forcing error. Consequently, this significantly influences the subsidence within the warm core in the eye region, which,in turn, leads to significant errors in TC intensity. This physical mechanism explains the formation of NSFV-type SST forcing errors. According to the sensitivity of the NFSV-type SST forcing errors, if one increases the density of SST observations along the TC track and assimilates them to the SST forcing field, the skill of TC intensity simulation generated by the WRF model could be greatly improved. However, this adjustment is most advantageous in improving simulation skill during the time period when TCs become strong but are still intensifying just prior to reaching full maturity. In light of this, the region along the TC track but in the time period of TC movement when the NFSV-type SST forcing errors occur may represent the sensitive area for targeting observation for SST forcing field associated with TC intensity simulation.
基金jointly sponsored by the National Natural Science Foundation of China (Grant Nos. 41506032 and 41530961)the National Programme on Global Change and Air–Sea Interaction (Grant No. GASI-IPOVAI-06)
文摘By analyzing the outputs of the pre-industrial control runs of four models within phase 5 of the Coupled Model Intercomparison Project, the effects of initial sea temperature errors on the predictability of Indian Ocean Dipole events were identified. The initial errors cause a significant winter predictability barrier(WPB) or summer predictability barrier(SPB).The WPB is closely related with the initial errors in the tropical Indian Ocean, where two types of WPB-related initial errors display opposite patterns and a west–east dipole. In contrast, the occurrence of the SPB is mainly caused by initial errors in the tropical Pacific Ocean, where two types of SPB-related initial errors exhibit opposite patterns, with one pole in the subsurface western Pacific Ocean and the other in the upper eastern Pacific Ocean. Both of the WPB-related initial errors grow the fastest in winter, because the coupled system is at its weakest, and finally cause a significant WPB. The SPB-related initial errors develop into a La Ni ?na–like mode in the Pacific Ocean. The negative SST errors in the Pacific Ocean induce westerly wind anomalies in the Indian Ocean by modulating the Walker circulation in the tropical oceans. The westerly wind anomalies first cool the sea surface water in the eastern Indian Ocean. When the climatological wind direction reverses in summer, the wind anomalies in turn warm the sea surface water, finally causing a significant SPB. Therefore, in addition to the spatial patterns of the initial errors, the climatological conditions also play an important role in causing a significant predictability barrier.
基金jointly sponsored by the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA11010303)the National Natural Science Foundation of China (Grant No. 41525017)
文摘Using the Regional Ocean Modeling System, this study investigates the simulation uncertainties in the current velocity in the low-latitude North Pacific where the Kuroshio originates [i.e., the beginning of the Kuroshio(BK)]. The results show that the simulation uncertainties largely reflect the contributions of wind stress forcing errors, especially zonal wind stress errors,rather than initial or boundary errors. Using the idea of a nonlinear forcing singular vector, two types of zonal wind stress errors(but sharing one EOF mode) are identified from error samples derived from reanalysis data as having the potential to yield large simulation uncertainties. The type-1 error possesses a pattern with positive anomalies covering the two zonal bands of 0?–15?N and 25?–40?N in the Pacific Ocean, with negative anomalies appearing between these two bands; while the type-2 error is almost opposite to the type-1 error. The simulation uncertainties induced by the type-1 and-2 errors consist of both large-scale circulation errors controlled by a mechanism similar to the Sverdrup relation and mesoscale eddy-like errors generated by baroclinic instability. The type-1 and-2 errors suggest two areas: one is located between the western boundary and the meridional 130?E along 15?–20?N, and the other is located between 140?–150?E and along 15?–20?N. The reduction of errors over these two areas can greatly improve the simulation accuracy of the current velocity at BK. These two areas represent sensitive areas for targeted observations associated with the simulation of the current velocity at BK.
文摘This paper compares data from linearized and nonlinear Zebiak-Cane model, as constrained by observed sea surface temperature anomaly （SSTA）, in simulating central Pacific （CP） and eastern Pacific （EP） E1 Nino. The difference between the temperature advections （determined by subtracting those of the linearized model from those of the nonlinear model）, referred to here as the nonlinearly induced temperature advection change （NTA）, is analyzed. The results demonstrate that the NTA records warming in the central equatorial Pacific during CP E1 Nino and makes fewer contributions to the structural distinctions of the CP E1 Nino, whereas it records warming in the eastern equatorial Pacific during EP E1 Nino, and thus significantly promotes EP E1 Nino during E1 Nino-type selection. The NTA for CP and EP E1 Nino varies in its amplitude, and is smaller in CP E1 Nino than it is in EP E1 Nino. These results demonstrate that CP E1 Nino are weakly modulated by small intensities of NTA, and may be controlled by weak nonlinearity; whereas, EP E1 Nino are significantly enhanced by large amplitudes of NTA, and are therefore likely to be modulated by relatively strong nonlinearity. These data could explain why CP E1 Nino are weaker than EP E1 Nino. Because the NTA for CP and EP E1 Nino differs in spatial structures and intensities, as well as their roles within different E1 Nino modes, the diversity of E1 Nino may be closely related to changes in the nonlinear characteristics of the tropical Pacific.
基金sponsored by the National Basic Research Program of China (Grant No. 2012CB955202)the China Scholarship Council under the Joint-PhD program for conducting research at CSIROsupported by the Indian Ocean Climate Initiative
文摘A timescale decomposed threshold regression （TSDTR） downscaling approach to forecasting South China early summer rainfall （SCESR） is described by using long-term observed station rainfall data and NOAA ERSST data. It makes use of two distinct regression downscaling models corresponding to the interannual and interdecadal rainfall variability of SCESR. The two models are developed based on the partial least squares （PLS） regression technique, linking SCESR to SST modes in preceding months on both interannual and interdecadal timescales. Specifically, using the datasets in the calibration period 1915-84, the variability of SCESR and SST are decomposed into interannual and interdecadal components. On the interannual timescale, a threshold PLS regression model is fitted to interannual components of SCESR and March SST patterns by taking account of the modulation of negative and positive phases of the Pacific Decadal Oscillation （PDO）. On the interdecadal timescale, a standard PLS regression model is fitted to the relationship between SCESR and preceding November SST patterns. The total rainfall prediction is obtained by the sum of the outputs from both the interannual and interdecadal models. Results show that the TSDTR downscaling approach achieves reasonable skill in predicting the observed rainfall in the validation period 1985-2006, compared to other simpler approaches. This study suggests that the TSDTR approach, considering different interannual SCESR-SST relationships under the modulation of PDO phases, as well as the interdecadal variability of SCESR associated with SST patterns, may provide a new perspective to improve climate predictions.
基金jointly sponsored by the National Key Research and Development Program of China (Grant No. 2017YFA0604201)the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA20060501)+1 种基金the National Natural Science Foundation of China (Grant Nos. 41876024 and 41530961)the National Programme on Global Change and Air–Sea Interaction (Grant No. GASI-IPOVAI-06)
文摘Using GFDL CM2 p1(Geophysical Fluid Dynamics Laboratory Climate Model, version 2 p1), the effects of initial sea temperature errors on the predictability of the Indian Ocean Dipole(IOD) are explored. When initial temperature errors are superimposed on the tropical Indian Ocean, a winter predictability barrier(WPB) and a summer predictability barrier(SPB) exist in IOD predictions. The existence of the WPB has a close relation with El Nin?o–Southern Oscillation(ENSO)in the winter of the growing phase of positive IOD events. That is, when ENSO exists in winter, no WPB appears in IOD predictions, and vice versa. In contrast, there is no inherent connection between the existence of the SPB and ENSO. Only the dominant spatial pattern of SPB-related initial errors is studied in this paper, which presents a significant west–east dipole pattern in the tropical Indian Ocean and is similar to that of WPB-related initial errors in previous studies. The SPB-related initial errors superimposed on the tropical Indian Ocean induce the sea surface temperature(SST) and wind anomalies in the tropical Pacific Ocean. Then, under the interaction between the Indian and Pacific oceans through the atmospheric bridge and Indonesian Throughflow, a west–east dipole pattern of SST errors appears in summer, which is further strengthened under the Bjerknes feedback and yields a significant SPB.
基金supported by the National Key R&D Program of China (Grant No.2019YFC1408004)together with the National Natural Science Foundation of China (Grant Nos.41930971,41805069,41606031)the Office of China Postdoctoral Council (OCPC) under Award Number 20190003。
文摘Initial errors in the tropical Indian Ocean(IO-related initial errors) that are most likely to yield the Spring Prediction Barrier(SPB) for La Ni?a forecasts are explored by using the CESM model.These initial errors can be classified into two types.Type-1 initial error consists of positive sea temperature errors in the western Indian Ocean and negative sea temperature errors in the eastern Indian Ocean,while the spatial structure of Type-2 initial error is nearly opposite.Both kinds of IO-related initial errors induce positive prediction errors of sea temperature in the Pacific Ocean,leading to underprediction of La Nina events.Type-1 initial error in the tropical Indian Ocean mainly influences the SSTA in the tropical Pacific Ocean via atmospheric bridge,leading to the development of localized sea temperature errors in the eastern Pacific Ocean.However,for Type-2 initial error,its positive sea temperature errors in the eastern Indian Ocean can induce downwelling error and influence La Ni?a predictions through an oceanic channel called Indonesian Throughflow.Based on the location of largest SPB-related initial errors,the sensitive area in the tropical Indian Ocean for La Nina predictions is identified.Furthermore,sensitivity experiments show that applying targeted observations in this sensitive area is very useful in decreasing prediction errors of La Nina.Therefore,adopting a targeted observation strategy in the tropical Indian Ocean is a promising approach toward increasing ENSO prediction skill.
基金jointly sponsored by the National Natural Science Foundation of China (Grant No. 41376018)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA11010303)+2 种基金the China Meteorological Administration Special Public Welfare Research Fund (GYHY201506013)the Project for Development of Key Techniques in Meteorological Forecasting Operation (YBGJXM201705)the Open Foundation of the LASG/IAP/CAS
文摘The ＂summer prediction barrier＂ （SPB） of SST anomalies （SSTA） over the Kuroshio--Oyashio Extension （KOE） refers to the phenomenon that prediction errors of KOE-SSTA tend to increase rapidly during boreal summer, resulting in large prediction uncertainties. The fast error growth associated with the SPB occurs in the mature-to-decaying transition phase, which is usually during the August-September-October （ASO） season, of the KOE-SSTA events to be predicted. Thus, the role of KOE-SSTA evolutionary characteristics in the transition phase in inducing the SPB is explored by performing perfect model predictability experiments in a coupled model, indicating that the SSTA events with larger mature-to-decaying transi- tion rates （Category-l） favor a greater possibility of yielding a more significant SPB than those events with smaller transition rates （Category-2）. The KOE-SSTA events in Category-1 tend to have more significant anomalous Ekman pumping in their transition phase, resulting in larger prediction errors of vertical oceanic temperature advection associated with the SSTA events. Consequently, Category-1 events possess faster error growth and larger prediction errors. In addition, the anomalous Ekman upwelling （downwelling） in the ASO season also causes SSTA cooling （warming）, accelerating the transition rates of warm （cold） KOE-SSTA events. Therefore, the SSTA transition rate and error growth rate are both related with the anomalous Ekman pumping of the SSTA events to be predicted in their transition phase. This may explain why the SSTA events transferring more rapidly from the mature to decaying phase tend to have a greater possibility of yielding a more significant SPB.
基金supported by the National Natural Science Foundation of China [grant numbers 41930971,41775069,and 41975076]。
文摘Using the outputs from CMCC-CM in CMIP5 experiments,the authors identified sensitive areas for targeted observations in ENSO forecasting from the perspective of the initial error growth(IEG)method and the particle filter(PF)method.Results showed that the PF targets areas over the central-eastern equatorial Pacific,while the sensitive areas determined by the IEG method are slightly to the east of the former.Although a small part of the areas targeted by the IEG method also lie in the southeast equatorial Pacific,this does not affect the large-scale overlapping of the sensitive areas determined by these two methods in the eastern equatorial Pacific.Therefore,sensitive areas determined by the two methods are mutually supportive.When considering the uncertainty of methods for determining sensitive areas in realistic targeted observation,it is more reasonable to choose the above overlapping areas as sensitive areas for ENSO forecasting.This result provides scientific guidance for how to better determine sensitive areas for ENSO forecasting.
基金sponsored by the National Basic Research Program of China(Grant No.2012CB955202)the National Public Benefit(Meteorology)Research Foundation of China(Grant No.GYHY201306018)the National Natural Science Foundation of China(Grant Nos.41176013 and41230420)
文摘Model errors offset by constant and time-variant optimal forcing vector approaches （termed COF and OFV, respectively） are analyzed within the framework of E1 Nifio simulations. Applying the COF and OFV approaches to the well-known Zebiak-Cane model, we re-simulate the 1997 and 2004 E1 Nifio events, both of which were poorly degraded by a certain amount of model error when the initial anomalies were generated by coupling the observed wind forcing to an ocean com- ponent. It is found that the Zebiak-Cane model with the COF approach roughly reproduced the 1997 E1 Nifio, but the 2004 E1 Nifio simulated by this approach defied an ENSO classification, i.e., it was hardly distinguishable as CP-E1 Nifio or EP-E1 Nifio. In hoth E1 Nifio simulations, substituting the COF with the OFV improved the fit between the simulations and obser- vations because the OFV better manages the time-variant errors in the model. Furthermore, the OFV approach effectively corrected the modeled E1 Nifio events even when the observational data （and hence the computational time） were reduced. Such a cost-effective offset of model errors suggests a role for the OFV approach in complicated CGCMs.
基金supported by funding from the National Natural Science Foundation of China(Grant No.42330111)。
文摘The deterministic chaos in nonlinear systems was discovered by Edward N.Lorenz in 1963,marking the birth of nonlinear sciences.Since then,the sensitivity of predictions to initial conditions is characterized by the“butterfly effect”,triggering a scientific revolution that has lasted more than half a century and spans multiple disciplines.This article presents a perspective on the classic paper of the“butterfly effect”,which not only help reveal how pioneers challenged the infinite unknown under limited conditions to establish the foundational work,but also demonstrates how these seminal ideas inspired successors to transcend existing paradigms,unlock creative thinking,and achieve cross-disciplinary innovations.The Lorenz's theory of deterministic chaos reveals that even imperceptibly small errors in the initial state can grow to the extent that makes it impossible to forecast at arbitrary future times with acceptable errors.This recognition shook the classical physics view that“determinism implies complete predictability”and prompted meteorologists to shift from the pursuit of“long-term forecasts”to asking“how far into the future the atmosphere is predictable”,and from attempts at“perfect prediction”to scientifically“quantifying forecast uncertainty”.This transition has also fundamentally promoted the shift of the paradigms in weather forecast and subsequent climate prediction,exerted profound influence on mathematics,biology and economics,and even permeated literature,art,history and social governance—ultimately shaping the renowned Lorenz's chaos theory.
基金Supported by the National Natural Science Foundation of China(U2242213 and 42341209).
文摘This paper reviews the development of ensemble weather forecast and the primary techniques employed in the main ensemble prediction systems(EPSs)designed by China and other countries.Here,the emphasis is placed on the advancements in the China Meteorological Administration(CMA)global and regional ensemble prediction systems(i.e.,CMA-GEPS and CMA-REPS),with particular attention to operational technologies such as initial and model perturbation methods and the applications of ensemble forecast.Through comparative verification with EPSs from other leading international numerical weather prediction(NWP)centers,CMA’s EPSs demonstrate forecast skills comparable to its global counterparts.As EPSs progress to convective scales and coupled systems between sea,land,air,and ice,the paper addresses some key challenges in ensemble forecast technologies across the aspects of operation,science,integration of artificial intelligence(AI),merging of weather and climate models,and challenging user requirements.Finally,a summary of conclusions and future perspectives on ensemble forecast are provided.
