Seasonal Prediction Skill and Biases in GloSea5 Relating to the East Asia Winter Monsoon

The simulation and prediction of the climatology and interannual variability of the East Asia winter monsoon(EAWM),as well as the associated atmospheric circulation,was investigated using the hindcast data from Global Seasonal Forecast System version 5(GloSea5),with a focus on the evolution of model bias among different forecast lead times.While GloSea5 reproduces the climatological means of large-scale circulation systems related to the EAWM well,systematic biases exist,including a cold bias for most of China’s mainland,especially for North and Northeast China.GloSea5 shows robust skill in predicting the EAWM intensity index two months ahead,which can be attributed to the performance in representing the leading modes of surface air temperature and associated background circulation.GloSea5 realistically reproduces the synergistic effect of El Niño–Southern Oscillation(ENSO)and the Arctic Oscillation(AO)on the EAWM,especially for the western North Pacific anticyclone(WNPAC).Compared with the North Pacific and North America,the representation of circulation anomalies over Eurasia is poor,especially for sea level pressure(SLP),which limits the prediction skill for surface air temperature over East Asia.The representation of SLP anomalies might be associated with the model performance in simulating the interaction between atmospheric circulations and underlying surface conditions.

The Relationship between Model Biases in East Asian Summer Monsoon Rainfall and Land Evaporation

The East Asian Summer Monsoon(EASM)provides the majority of annual rainfall to countries in East Asia.Although state-of-the-art models broadly project increased EASM rainfall,the spread of projections is large and simulations of present-day rainfall show significant climatological biases.Systematic evapotranspiration biases occur locally over East Asia,and globally over land,in simulations both with and without a coupled ocean.This study explores the relationship between evapotranspiration and EASM precipitation biases.First,idealized model simulations are presented in which the parameterization of land evaporation is modified,while sea surface temperature is fixed.The results suggest a feedback whereby excessive evapotranspiration over East Asia results in cooling of land,a weakened monsoon low,and a shift of rainfall from the Philippine Sea to China,further fueling evapotranspiration.Cross-model regressions against evapotranspiration over China indicate a similar pattern of behavior in Atmospheric Model Intercomparison Project(AMIP)simulations.Possible causes of this pattern are investigated.The feedback is not explained by an overly intense global hydrological cycle or by differences in radiative processes.Analysis of land-only simulations indicates that evapotranspiration biases are present even when models are forced with prescribed rainfall.These are strengthened when coupled to the atmosphere,suggesting a role for land-model errors in driving atmospheric biases.Coupled atmosphere-ocean models are shown to have similar evapotranspiration biases to those in AMIP over China,but different precipitation biases,including a northward shift in the ITCZ over the Pacific and Atlantic Oceans.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks:Climatology,Interannual Variability,and Extremes

Climate models are vital for understanding and projecting global climate change and its associated impacts.However,these models suffer from biases that limit their accuracy in historical simulations and the trustworthiness of future projections.Addressing these challenges requires addressing internal variability,hindering the direct alignment between model simulations and observations,and thwarting conventional supervised learning methods.Here,we employ an unsupervised Cycle-consistent Generative Adversarial Network(CycleGAN),to correct daily Sea Surface Temperature(SST)simulations from the Community Earth System Model 2(CESM2).Our results reveal that the CycleGAN not only corrects climatological biases but also improves the simulation of major dynamic modes including the El Niño-Southern Oscillation(ENSO)and the Indian Ocean Dipole mode,as well as SST extremes.Notably,it substantially corrects climatological SST biases,decreasing the globally averaged Root-Mean-Square Error(RMSE)by 58%.Intriguingly,the CycleGAN effectively addresses the well-known excessive westward bias in ENSO SST anomalies,a common issue in climate models that traditional methods,like quantile mapping,struggle to rectify.Additionally,it substantially improves the simulation of SST extremes,raising the pattern correlation coefficient(PCC)from 0.56 to 0.88 and lowering the RMSE from 0.5 to 0.32.This enhancement is attributed to better representations of interannual,intraseasonal,and synoptic scales variabilities.Our study offers a novel approach to correct global SST simulations and underscores its effectiveness across different time scales and primary dynamical modes.

Optimal Assimilation of Microwave Upper-Level Sounding Data in CMA-GFS

Various approaches have been proposed to minimize the upper-level systematic biases in global numerical weather prediction(NWP)models by using satellite upper-air sounding channels as anchors.However,since the China Meteorological Administration Global Forecast System(CMA-GFS)has a model top near 0.1 hPa(60 km),the upper-level temperature bias may exceed 4 K near 1 hPa and further extend to 5 hPa.In this study,channels 12–14 of the Advanced Microwave Sounding Unit A(AMSU-A)onboard five satellites of NOAA and METOP,whose weighting function peaks range from 10 to 2 hPa are all used as anchor observations in CMA-GFS.It is shown that the new“Anchor”approach can effectively reduce the biases near the model top and their downward propagation in three-month assimilation cycles.The bias growth rate of simulated upper-level channel observations is reduced to±0.001 K d^(–1),compared to–0.03 K d^(–1)derived from the current dynamic correction scheme.The relatively stable bias significantly improves the upper-level analysis field and leads to better global medium-range forecasts up to 10 days with significant reductions in the temperature and geopotential forecast error above 10 hPa.

Dynamics of Advantageous Mutant Spread in Spatial Death-Birth and Birth-Death Moran Models

The spread of an advantageous mutation through a population is of fundamental interest in population genetics. While the classical Moran model is formulated for a well-mixed population, it has long been recognized that in real-world applications, the population usually has an explicit spatial structure which can significantly influence the dynamics. In the context of cancer initiation in epithelial tissue, several recent works have analyzed the dynamics of advantageous mutant spread on integer lattices, using the biased voter model from particle systems theory. In this spatial version of the Moran model, individuals first reproduce according to their fitness and then replace a neighboring individual. From a biological standpoint, the opposite dynamics, where individuals first die and are then replaced by a neighboring individual according to its fitness, are equally relevant. Here, we investigate this death-birth analogue of the biased voter model. We construct the process mathematically, derive the associated dual process, establish bounds on the survival probability of a single mutant, and prove that the process has an asymptotic shape. We also briefly discuss alternative birth-death and death-birth dynamics, depending on how the mutant fitness advantage affects the dynamics. We show that birth-death and death-birth formulations of the biased voter model are equivalent when fitness affects the former event of each update of the model, whereas the birth-death model is fundamentally different from the death-birth model when fitness affects the latter event.

Attributing Analysis on the Model Bias in Surface Temperature in the Climate System Model FGOALS-s2 through a Process-Based Decomposition Method

This study uses the coupled atmosphere–surface climate feedback–response analysis method（CFRAM） to analyze the surface temperature biases in the Flexible Global Ocean–Atmosphere–Land System model, spectral version 2（FGOALS-s2）in January and July. The process-based decomposition of the surface temperature biases, defined as the difference between the model and ERA-Interim during 1979–2005, enables us to attribute the model surface temperature biases to individual radiative processes including ozone, water vapor, cloud, and surface albedo; and non-radiative processes including surface sensible and latent heat fluxes, and dynamic processes at the surface and in the atmosphere. The results show that significant model surface temperature biases are almost globally present, are generally larger over land than over oceans, and are relatively larger in summer than in winter. Relative to the model biases in non-radiative processes, which tend to dominate the surface temperature biases in most parts of the world, biases in radiative processes are much smaller, except in the sub-polar Antarctic region where the cold biases from the much overestimated surface albedo are compensated for by the warm biases from nonradiative processes. The larger biases in non-radiative processes mainly lie in surface heat fluxes and in surface dynamics,which are twice as large in the Southern Hemisphere as in the Northern Hemisphere and always tend to compensate for each other. In particular, the upward/downward heat fluxes are systematically underestimated/overestimated in most parts of the world, and are mainly compensated for by surface dynamic processes including the increased heat storage in deep oceans across the globe.

Estimation and Correction of Model Bias in the NASA/GMAO GEOS5 Data Assimilation System:Sequential Implementation

This study presents a simplified multivariate bias correction scheme that is sequentially implemented in the GEOS5 data assimilation system and compared against a control experiment without model bias correction. The results show considerable improvement in terms of the mean biases of rawinsonde observation-minus-background （OmB） residuals for observed water vapor, wind and temperature variables. The time series spectral analysis shows whitening of bias-corrected OmB residuals, and mean biases for rawinsonde observation-minus-analysis （OmA） are also improved. Some wind and temperature biases in the control experiment near the equatorial tropopause nearly vanish from the bias-corrected experiment. Despite the analysis improvement, the bias correction scheme has only a moderate impact on forecast skill. Significant interaction is also found among quality-control, satellite observation bias correction, and background bias correction, and the latter positively impacts satellite bias correction.

An analytical variational method for the biased quantum Rabi model in the ultra-strong coupling regime

An analytical variational method for the ground state of the biased quantum Rabi model in the ultra-strong coupling regime is presented. This analytical variational method can be obtained by a unitary transformation or alternatively by assuming the form of the ground state wave function. The key of the method is to introduce a variational parameter λ,which can be determined by minimizing the energy functional. Using this method, we calculate the physical observables with high accuracy in comparison with the numerical exact ones. Our method evidently improves over the widely used general rotating-wave approximation（GRWA） in both qualitative and quantitative aspects.

Antithetic Power Transformed Random Variables in Computer Simulations: An Error Correction Mechanism

A traditional method of Monte Carlo computer simulation is to obtain uniformly distributed random numbers on the interval from zero to one from a linear congruential generator (LCG) or other methods. Random variates can then be obtained by the inverse transformation technique applied to random numbers. The random variates can then be used as input to a computer simulation. A response variable is obtained from the simulation results. The response variable may be biased for various reasons. One reason may be the presence of small traces of serial correlation in the random numbers. The purpose of this paper is to introduce an alternative method of response variable acquisition by a power transformation applied to the response variable. The power transformation produces a new variable that is negatively correlated with the response variable. The response variable is then regressed on its power transformation to convert the units of the power transformed variable back to those of the original response variable. A weighted combination of these two variables gives the final estimate. The combined estimate is shown to have negligible bias. The correlations of various antithetic variates obtained from the power transformation are derived and illustrated to provide insights for this research and for future research into this method.

LICOM Model Datasets for the CMIP6 Ocean Model Intercomparison Project

The datasets of two Ocean Model Intercomparison Project(OMIP)simulation experiments from the LASG/IAP Climate Ocean Model,version 3(LICOM3),forced by two different sets of atmospheric surface data,are described in this paper.The experiment forced by CORE-II(Co-ordinated Ocean–Ice Reference Experiments,Phase II)data(1948–2009)is called OMIP1,and that forced by JRA55-do(surface dataset for driving ocean–sea-ice models based on Japanese 55-year atmospheric reanalysis)data(1958–2018)is called OMIP2.First,the improvement of LICOM from CMIP5 to CMIP6 and the configurations of the two experiments are described.Second,the basic performances of the two experiments are validated using the climatological-mean and interannual time scales from observation.We find that the mean states,interannual variabilities,and long-term linear trends can be reproduced well by the two experiments.The differences between the two datasets are also discussed.Finally,the usage of these data is described.These datasets are helpful toward understanding the origin system bias of the fully coupled model.

A review of progress in coupled ocean-atmosphere model developments for ENSO studies in China

El Nino-Southern Oscillation(ENSO) is the strongest interannual signal that is producedby basinscale processes in the tropical Pacific,with significant effects on weather and climate worldwide.In the past,extensive and intensive international efforts have been devoted to coupled model developments for ENSO studies.A hierarchy of coupled ocean-atmo sphere models has been formulated;in terms of their complexity,they can be categorized into intermediate coupled models(ICMs),hybrid coupled models(HCMs),and fully coupled general circulation models(CGCMs).ENSO modeling has made significant progress over the past decades,reaching a stage where coupled models can now be used to successfully predict ENSO events 6 months to one year in advance.Meanwhile,ENSO exhibits great diversity and complexity as observed in nature,which still cannot be adequately captured by current state-of-the-art coupled models,presenting a challenge to ENSO modeling.We primarily reviewed the long-term efforts in ENSO modeling continually and steadily made at different institutions in China;some selected representative examples are presented here to review the current status of ENSO model developments and applications,which have been actively pursued with noticeable progress being made recently.As ENSO simulations are very sensitive to model formulations and process representations etc.,dedicated efforts have been devoted to ENSO model developments and improvements.Now,different ocean-atmosphere coupled models have been available in China,which exhibit good model performances and have already had a variety of applications to climate modeling,including the Coupled Model Intercomparison Project Phase 6(CMIP6).Nevertheless,large biases and uncertainties still exist in ENSO simulations and predictions,and there are clear rooms for their improvements,which are still an active area of researches and applications.Here,model performances of ENSO simulations are assessed in terms of advantages and disadvantages with these differently formulated coupled models,pinpointing to the areas where they need to be further improved for ENSO studies.These analyses provide valuable guidance for future improvements in ENSO simulations and predictions.

A Homogeneous Linear Estimation Method for System Error in Data Assimilation

In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter （EnKF） based on the Weather Research and Forecasting （WRF） Model. The method is based on a homogeneous linear bias model, and the model bias is estimated using statistics at each assimilation cycle, which is different from the state augmentation methods proposed in pre- vious literatures. The new method provides a good estimation for the model bias of some specific variables, such as sea level pres- sure （SLP）. A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condi- tion. Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems, and the reduc- tion of analysis errors. The background error covarianee structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept ＇correlation scale＇ is introduced. However, the new method needs further evaluation with more cases of assimilation.

Different Asian Monsoon Rainfall Responses to Idealized Orography Sensitivity Experiments in the HadGEM3-GA6 and FGOALS-FAMIL Global Climate Models

Recent work has shown the dominance of the Himalaya in supporting the Indian summer monsoon （ISM）, perhaps by surface sensible heating along its southern slope and by mechanical blocking acting to separate moist tropical flow from drier midlatitnde air. Previous studies have also shown that Indian snmmer rainfall is largely unaffected in sensitivity experiments that remove only the Tibetan Plateau. However, given the large biases in simulating the monsoon in CMIP5 models, such results may be model dependent. This study investigates the impact of orographic forcing from the Tibetan Plateau, Himalaya and Iranian Plateau on the ISM and East Asian snmmer monsoon （EASM） in the UK Met Office＇s HadGEM3-GA6 and China＇s Institute of Atmospheric Physics FGOALS-FAMIL global climate models. The models chosen featnre oppositesigned biases in their simulation of the ISM rainfall and circulation climatology. The changes to ISM and EASM circulation across the sensitivity experiments are similar in both models and consistent with previous studies. However, considerable differences exist in the rainfall responses over India and China, and in the detailed aspects such as onset and retreat dates. In particular, the models show opposing changes in Indian monsoon rainfall when the Himalaya and Tibetan Plateau orography are removed. Our results show that a multi-model approach, as suggested in the forthcoming Global Monsoon Model Intercomparison Project （GMMIP） associated with CMIP6, is needed to clarify the impact of orographic forcing on the Asian monsoon and to fully understand the implications of model systematic error.

Development and evaluation of a regional ocean-atmosphere coupled model with focus on the western North Pacific summer monsoon simulation:Impacts of different atmospheric components

A regional ocean atmosphere coupled model (ROAM) is developed through coupler OASIS3,and is composed of regional climate model RegCM3 and CREM (Climate version of Regional Eta Model) as its atmospheric component and of a revised Princeton ocean model (POM2000) as its oceanic component.The performance of the ROAM over the western North Pacific summer monsoon region is assessed by the case simulation of warm season in 1998.Impacts of different atmospheric model components on the performance of ROAM are investigated.Compared with stand-alone simulation,CREM (RegCM3) produces more (or less) rainfall over ocean area with inclusion of the air-sea coupling.Different biases of rainfall are caused by the different biases of SST derived from the coupled simulation.Warm (or cold) SST bias simulated by CREM_CPL (RegCM3_CPL) increases (or decreases) the evaporation at sea surface,then increases (or decreases) the rainfall over ocean.The analyses suggest that the biases of vertical profile of temperature and specific humidity in stand-alone simulations may be responsible for the SST biases in regional coupled simulations.Compared with reanalysis data,the warmer (or colder) and moister (or dryer) lower troposphere simulated in CREM (RegCM3) produces less (or more) sea surface latent heat flux.Meanwhile,the more unstable (or stable) lower troposphere produces less (or more) cloudiness at low-level,which increases (or decreases) the solar radiation reaching on the sea surface.CREM (RegCM3) forced by observed SST overestimates (or underestimates) the sea surface net heat flux,implying a potential warm (or cold) heat source.After coupling with POM2000,the warm (or cold) heat source would further increase (or decrease) the SST.The biases of vertical profile of temperature and specific humidity may be ascribed to the different representation of cumulus convection in atmospheric models.

Impact of crude distillation unit model accuracy on refinery production planning

In this work, we examine the impact of crude distillation unit(CDU) model errors on the results of refinery-wide optimization for production planning or feedstock selection. We compare the swing cut + bias CDU model with a recently developed hybrid CDU model(Fu et al., 2016). The hybrid CDU model computes material and energy balances, as well as product true boiling point(TBP) curves and bulk properties(e.g., sulfur% and cetane index, and other properties). Product TBP curves are predicted with an average error of 0.5% against rigorous simulation curves. Case studies of optimal operation computed using a planning model that is based on the swing cut + bias CDU model and using a planning model that incorporates the hybrid CDU model are presented. Our results show that significant economic benefits can be obtained using accurate CDU models in refinery production planning.

Adaptive algorithm for estimating excavation-Induced displacements using field performance data

Empirical models provide a practical way to estimate the displacements induced by excavations.However,there are uncertainties associated with the predictions of empirical models owing to:(a)the imperfect knowledge of the model and(b)the uncertainties of the input variables.The uncertainties of these models can be characterized by a bias factor which is defined as the ratio of the actual displacement to the predicted displacement.The bias factors associated with the C&O method and the KJHH model are evaluated using the Bayesian method and a database of 71 excavations in Shanghai.To improve the predictions of the maximum displacement,an adaptive algorithm is proposed using field performance data.The performance of the proposed algorithm is demonstrated by an example in which excavation-induced displacements are generated by finite element method in normally consolidated clays.The example shows that the developed algorithm can significantly improve the predictions by incorporating the field performance data.

A hierarchical path-segmentation movement ecology framework

This paper lays out a hierarchical,appropriate-complexity framework for conceptualizing movement-path segments at different spatiotemporal scales in a way that facilitates comparative analyses and bridges behavior and mathematical concepts.It then outlines a process for generating a multimode,multiscale stochastic simulation model that can be used to test animal movement hypotheses and make predictions of movement responses to management and global change.Many methods for analyzing movement data begin by generating step-length(SL)and turning-angle(TA)distributions from relocation time-series data,some of which are linked to ecological,landscape,and environmental covariates.The frequency at which these data are collected may vary from sub-seconds to several hours.The kinds of questions that may be asked of these data,however,are very much scale dependent.The hierarchical path-segmentation(HPS)framework presented here clarifies how the scale at which SL and TA data are collected relates to other sub-and super-diel scales.Difficulties arise because the information contained in SL and TA time series are often not directly relatable to the physiological,ecological,and sociological factors that drive the structure of movement paths at longer scales.These difficulties are overcome by anchoring the classification of movement types around the concept of fixed-period(24 h)diel activity routines and providing a bridge between behavioral/ecological and stochastic-walk concepts(means,variances,correlations,individual-state and local environmental covariates).This bridge is achieved through the generation of relatively short segments conceived as characteristic sequences of fundamental movement elements.These short segments are then used to characterize longer canonical-activity-mode segments that emerge through movement at behaviorally relevant sub-diel scales.HPS thus provides a novel system for integrating sub-minute movement sequences into canonical activity modes(CAMs)that,in turn,can be strung together into various types of diel activity routines(DARs).These DARs both vary among individuals within a given day,and for any given individual across time and under the influence of landscape factors.An understanding of how DARs are influenced by environmental inputs will help us predict the response of supra-diel lifetime movement phases(LiMPs)of individuals,as well as their complete lifetime tracks(LiTs),to anthropogenically induced global change.