Assessment of the Southern Ocean Sea Surface Temperature Biases in CMIP5 and CMIP6 Models

This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6).By combining models from the same community sharing highly similar SO SST biases and eliminating the effect of global-mean biases on local SST biases,the results reveal that the ensemble-mean SO SST bias at 70°-30°S decreases from 0.38℃ in CMIP5 to 0.28℃ in CMIP6,together with increased intermodel consistency.The dominant mode of the intermodel variations in the zonal-mean SST biases is characterized as a meridional uniform warm bias pattern,explaining 79.1% of the intermodel variance and exhibiting positive principal values for most models.The ocean mixed layer heat budget further demonstrates that the SST biases at 70°-50°S primarily result from the excessive summertime heating effect from surface net heat flux.The biases in surface net heat flux south of 50°S are largely impacted by surface shortwave radiation from cloud and clear sky components at different latitudes.North of 50°S,the underestimated westerlies reduce the northward Ekman transport and hence northward cold advection in models,leading to warm SST biases year-round.In addition,the westerly biases are primarily traced back to the atmosphere-alone model simulations forced by the observed SST and sea ice.These results disclose the thermal origin at the high latitude and dynamical origin at the low latitude of the SO SST biases and underscore the significance of the deficiencies of atmospheric models in producing the SO SST biases.

Correction for Depth Biases to Shallow Water Multibeam Bathymetric Data

Vertical errors often present in multibeam swath bathymetric data. They are mainly sourced by sound refraction, internal wave disturbance, imperfect tide correction, transducer mounting, long period heave, static draft change, dynamic squat and dynamic motion residuals, etc. Although they can be partly removed or reduced by specific algorithms, the synthesized depth biases are unavoidable and sometimes have an important influence on high precise utilization of the final bathymetric data. In order to. confidently identify the decimeter-level changes in seabed morphology by MBES, we must remove or weaken depth biases and improve the precision of multibeam bathymetry further. The fixed-interval profiles that are perpendicular to the vessel track are generated to adjust depth biases between swaths. We present a kind of postprocessing method to minimize the depth biases by the histogram of cumulative depth biases. The datum line in each profile can be obtained by the maximum value of histogram. The corrections of depth biases can be calculated according to the datum line. And then the quality of final bathymetry can be improved by the corrections. The method is verified by a field test.

Indian Ocean SST Biases in a Flexible Regional Ocean Atmosphere Land System(FROALS) Model

The authors examine the Indian Ocean sea surface temperature(SST) biases simulated by a Flexible Regional Ocean Atmosphere Land System(FROALS) model.The regional coupled model exhibits pronounced cold SST biases in a large portion of the Indian Ocean warm pool.Negative biases in the net surface heat fluxes are evident in the model,leading to the cold biases of the SST.Further analysis indicates that the negative biases in the net surface heat fluxes are mainly contributed by the biases of sensible heat and latent heat flux.Near-surface meteorological variables that could contribute to the SST biases are also examined.It is found that the biases of sensible heat and latent heat flux are caused by the colder and dryer near-surface air in the model.

Comparison of Advanced Technology Microwave Sounder Biases Estimated Using Radio Occultation and Hurricane Florence(2018) Captured by NOAA-20 and S-NPP

The second Advanced Technology Microwave Sounder(ATMS)was onboard the National Oceanic and Atmospheric Administration(NOAA)-20 satellite when launched on 18 November 2017.Using nearly six months of the earliest NOAA-20 observations,the biases of the ATMS instrument were compared between NOAA-20 and the Suomi National Polar-Orbiting Partnership(S-NPP)satellite.The biases of ATMS channels 8 to 13 were estimated from the differences between antenna temperature observations and model simulations generated from Meteorological Operational(MetOp)-A and MetOp-B satellites’Global Positioning System(GPS)radio occultation(RO)temperature and water vapor profiles.It was found that the ATMS onboard the NOAA-20 satellite has generally larger cold biases in the brightness temperature measurements at channels 8 to 13 and small standard deviations.The observations from ATMS on both S-NPP and NOAA-20 are shown to demonstrate an ability to capture a less than 1-h temporal evolution of Hurricane Florence(2018)due to the fact that the S-NPP orbits closely follow those of NOAA-20.

Quality monitoring and biases estimation of BOC navigation signals

Many safety-critical applications that utilize the global navigation satellite system (GNSS) demand highly accurate positioning information, as well as highly integrity and reliability. Due to GNSS signals are easily distorted by the interferences or disturbances, the signal quality monitoring (SQM) is necessary to detect the presence of dangerous signal distortions. In this paper, we developed an SQM software for binary offset carrier (BOC) modulated navigation signals. Firstly, the models of BOC signal with ideal and distortion are presented respectively. Then the architecture of SQM software is proposed. Moreover, the effect of the white gaussian noise (WGN) and the front-end filter on the correlation peak of the receiver is analyzed. Finally, the biases induced by the signal distortion are evaluated. The experiments simulate the relationships between the code phase shift and the normalized correlation value in the case of the signal digital distortion and the analog distortion. The simulation results demonstrate that the proposed SQM method can effectively monitor the signal distortion and accurately estimate the correlation peak deviation caused by the distortion.

Sociodemographic characteristics, cultural biases, and environmental attitudes: An empirical application of grid-group cultural theory in Northwestern China

Natural resource-management studies have become increasingly attentive to the influences of human factors. Among these,cultural biases shape people’s responses to changes in natural resource systems. Several studies have applied grid-group cultural theory to assess the effects of multiple value biases among stakeholders on natural resource management. We developed and administered a questionnaire in the Heihe River Basin(n = 364) in northwestern China to investigate the appropriateness of applying this theory in the Chinese context of natural resource management. The results revealed various cultural biases among the respondents. In descending order of prevalence, these biases were hierarchism(46.98%), individualism(26.65%), egalitarianism(18.96%), and fatalism(2.78%), with the remaining respondents(4.67%) evidencing no obvious bias. Our empirical study revealed respondents’ worldviews and the influence of sociodemographic characteristics on cultural biases, as theoretically posited. Among the variables examined, age had a positive and significant effect across all biases except individualism. The correlation of income to all cultural biases was consistently negative. Only education had a negative and significant effect across all biases. Women were found to adhere to egalitarianism, whereas men adhered to individualism and hierarchism. Thus, grid-group cultural theory was found to be appropriate in the Chinese context, with gender, age, education, and income evidently accounting for cultural biases. Relationships between environmental attitudes and cultural biases conformed with the hypothesis advanced by grid-group cultural theory. This finding may be of value in explaining individuals’ environmental attitudes and facilitating the development and implementation of natural resource-management policies.

Heterogeneous individuals’behavioral biases model and numerical simulation

A model of the relationships between individual cognitive biases and individual decision-making based on the analysis of cognitive biases of bonded rationality individual,has been established in this paper by introducing a set of new variables called overconfidence coefficient and attribution bias coefficient to the sentiment model. The irrational expectation and irrational risk aversion as two inseparable aspects of bonded rationality are expressed in an unified model,and a method of measuring individual cognitive biases is proposed,which overcomes the shortcomings of traditional normative models that can not describe the differences of behaviors among heterogeneous individuals. As a result,numerical simulations show that individual cognitive risk is a positive interaction with overconfidence coefficient,and a negative interaction with attribution bias coefficient.

Stating Otherness Through Socio-Cultural Biases

Nowadays a well-defined idea of barbarians as the cause of the decline of an advanced civilization is widespread.This paper aims firstly to analyze the different meanings that the term barbarian has assumed in media outlets context in recent years,with emphasis on Italian political dimension.Secondly,it aims to examine if(and how much)it could be argued that,in these cases of manipulation,the sources of the late antique world have a crucial role in this kind of cultural biases.The comparative analysis of these two categories seems to reveal the reiteration of a stratified archive that,formed in ancient times and consolidated over time,maintains its main purpose:opposing the dominant social group(ingroup)and heterogeneous minorities(outgroups).So,what is the cultural operations that stay behind this approach?Is it possible to affirm that certain ideological identity archives are replicated over time applied to phenomena perceived as similar to each other,or would it be more correct to look at them as autonomous narratives?

Contrasting the Skills and Biases of Deterministic Predictions for the Two Types of El Nio

The tropical Pacific has begun to experience a new type of El Nio, which has occurred particularly frequently during the last decade, referred to as the central Pacific(CP) El Nio. Various coupled models with different degrees of complexity have been used to make real-time El Nio predictions, but high uncertainty still exists in their forecasts. It remains unknown as to how much of this uncertainty is specifically related to the new CP-type El Nio and how much is common to both this type and the conventional Eastern Pacific(EP)-type El Nio. In this study, the deterministic performance of an El Nio–Southern Oscillation(ENSO) ensemble prediction system is examined for the two types of El Nio. Ensemble hindcasts are run for the nine EP El Nio events and twelve CP El Nio events that have occurred since 1950. The results show that(1) the skill scores for the EP events are significantly better than those for the CP events, at all lead times;(2) the systematic forecast biases come mostly from the prediction of the CP events; and(3) the systematic error is characterized by an overly warm eastern Pacific during the spring season, indicating a stronger spring prediction barrier for the CP El Nio. Further improvements to coupled atmosphere–ocean models in terms of CP El Nio prediction should be recognized as a key and high-priority task for the climate prediction community.

A Polynomial Prediction Filter Method for Estimating Multisensor Dynamically Varying Biases

The estimation of the sensor measurement biases in a multisensor system is vital for the sensor data fusion. A solution is provided for the estimation of dynamically varying multiple sensor biases without any knowledge of the dynamic bias model parameters. It is shown that the sensor bias pseudomeasurement can be dynamically obtained via a parity vector. This is accomplished by multiplying the sensor uncalibrated measurement equations by a projection matrix so that the measured variable is eliminated from the equations. Once the state equations of the dynamically varying sensor biases are modeled by a polynomial prediction filter, the dynamically varying multisensor biases can be obtained by Kalman filter. Simulation results validate that the proposed method can estimate the constant biases and dynamic biases of multisensors and outperforms the methods reported in literature.

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.

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.

Gender Roles: Biases or Differences?

This paper aims to probe into the two distinct gender roles—males and females—in several ways,and further answers the question"what on earth cause the salient differentiation in gender roles,biases or differences?"In conclusion,the author holds that it is the physiological and psychological differences in the two sexes that result in the differed distribution of such vari ous duties which males and females are supposed to fulfill as the current society has expected.

Latitudinal and Scan-dependent Biases of Microwave Humidity Sounder Measurements and Their Dependences on Cloud Ice Water Path

The relationship between differences in microwave humidity sounder(MHS)–channel biases which represent measured brightness temperatures and model-simulated brightness temperatures, and cloud ice water path(IWP) as well as the influence of the cloud liquid water path(LWP) on the relationship is examined. Seven years(2011–17) of NOAA-18 MHS-derived measured brightness temperatures and IWP/LWP data generated by the NOAA Comprehensive Large Array-data Stewardship System Microwave Surface and Precipitation Products System are used. The Community Radiative Transfer Model, version2.2.4, is used to simulate model-simulated brightness temperatures using European Center for Medium-Range Weather Forecasts reanalysis data as background fields. Scan-angle deviations of the MHS window channel biases range from-1.7 K to1.0 K. The relationships between channels 2, 4, and 5 biases and scan angle are symmetrical about the nadir. The latitudedependent deviations of MHS window channel biases are positive and range from 0–7 K. For MHS non-window channels,the latitudinal deviations between measured brightness temperatures and model-simulated brightness temperatures are larger when the detection height is higher. No systematic warm or cold deviations are found in the global spatial distribution of difference between measured brightness temperatures and model-simulated brightness temperatures over oceans after removing scan-angle and latitudinal deviations. The corrected biases of five different MHS channels decrease differently with respect to the increase in IWP. This decrease is stronger when LWP values are higher.

Social and gender biases in HIV /AIDS care in India

To understand the socio-psychological aspects of HIV transmission and gender and caste based AIDS care in India, 100 HIV positive patients and their spouses or sexual partners were included in this study.The individuals were interviewed for their pre-or extramarital sexual exposure with suspected HIV positive persons.They were also interviewed about the barriers,if any,they used,and change in their sexual behaviour with their spouses after they were tested HIV positive.All except three were males who were first detected HIV positive,while 3 women were found HIV positive first and subsequendy dieir male partners were included in the study.Eighty of 97(82.4%) males admittedly acquired HIV through heterosexual route.Of the 80 males,60(75%) acquired HIV infection from organised CSWs and 20 from casual sex partners.Many of these acquired the infection just before their marriages.More than half of these males were infected during only one or two sexual encounters. Sixty out of 75(80%) married males continued to have sex even after knowing their HIV positive status.One male and six female spouses did not get infected even after multiple insertive unprotected sex.The average life span after the diagnosis of HIV infection in Indian males without specific treatment was 4.5±3. 5 yr while in women it was 3.5±2.0 yr,indicating fast progression of AIDS in females,most probably due to gender bias in access to treatment,nutrition,care and also due to hormonal differences.Pulmonary tuberculosis was most common and first clinical presentation of HIV associated opportunistic infection.The study also showed that due to ignorance majority of husbands do not share their HIV positive status with their wives and continue to have unsafe sex.

Understanding the Global Surface-Atmosphere Energy Balance in FGOALS-s2 through an Attribution Analysis of the Global Temperature Biases

Based on an attribution analysis of the global mean temperature biases in the Flexible Global Ocean- AtmOsphere-Land System model, spectral version 2 （FGOALS-s2） through a coupled atmosphere-surface ch- mate feedback-response analysis method （CFRAM）, the model＇s global surface-atmosphere energy balance in boreal winter and summer is examined. Within the en- ergy-balance-based CFRAM system, the model temperature biases are attributed to energy perturbations resulting from model biases in individual radiative and non-radia- tive processes in the atmosphere and at the surface. The results show that, although the global mean surface tem- perature （Ts） bias is only 0.38 K in January and 1.70 K in July, and the atmospheric temperature （Ta） biases from the troposphere to the stratosphere are only around ＋3 K at most, the temperature biases due to model biases in rep- resenting the individual radiative and non-radiative proc- esses are considerably large （over -4-10 K at most）. Spe- cifically, the global cold radiative Ts bias, mainly due to the overestimated surface albedo, is compensated for by the global warm non-radiative Ts bias that is mainly due to the overestimated downward surface heat fluxes. The model biases in non-radiative processes in the lower tro- posphere （up to 5-15 K） are relatively much larger than in upper levels, which are mainly responsible for the warm Ta biases there. In contrast, the global mean cold ira biases in the mid-to-upper troposphere are mainly dominated by radiative processes. The warm/cold Ta biases in the lower/upper stratosphere are dominated by non-radiative processes, while the warm ira biases in the mid-strato- sphere can be attributed to the radiative ozone feedback process.

Cultural Difference and Cognitive Biases as a Trigger of Critical Crashes or Disasters<br/>—Evidence from Case Studies of Human Factors Analysis

On the basis of the analysis of past case studies of crashes or disasters, it has been clarified how cultural difference and cognitive biases become a trigger of serious crashes or disasters. Heuristic-based biases such as confirmation bias, groupthink, and social loafing surely appeared in the process of crash or disaster breakout. Overconfidence-bases biases such as illusion of control, fallacy of plan, and optimistic bias are also ubiquitous in the route to a critical crash or disaster. Moreover, framing biases contribute to the distorted decision making, and eventually turn into the main cause of critical crash or disaster. In this way, as well as human factors or ergonomics approaches for designing man-machine systems, the prevention and the deletion of cognitive biases are indispensable for the preventing serious crashes or disasters from occurring. Until now, the distortion of decision making has not been discussed from the cultural differences of way of thinking. As well as a variety of cognitive biases, cultural difference in behavior is expected to be important for understanding the root causes of critical crash or disaster. We found that cultural difference distorted judgment through case studies of critical crashes or disasters. It was also demonstrated that considering cultural difference, as well as cognitive biases, is important to prevent irrational and biased decision making from occurring in safety management.

How Cognitive Biases Make Innovation Difficult in Markets for Fast Moving Consumer Goods

Biases affect our judgments and decisions everywhere,because in our daily life,no matter where you are,what kind of occupation you are doing,every decision we make is more or less interfered by cognitive biases,which even determines the outcome of things.In addition,with the development of the times,the progress of science and technology,and the change of social structure,we have experienced too many processes from rejection to acceptance,from stubbornness to change.However,it often takes time;especially in the commercial field,the timing when users accept products can better reflect this point.This article mainly aims at these phenomena,through the information,examples and data from the online sources,to explore how four kinds of cognitive biases:status quo bias,loss aversion bias,mere-exposure effect,and bounded-rationality that affect the smooth progress of innovation products in the fast consumer market,and how these biases can attack the confidence of merchants,so that the originally widely favored products will eventually end in failure.At the end of the article will also discuss the heuristics that can deal with the biases.

Can ChatGPT reduce human financial analysts’optimistic biases?

This paper examines the potential of ChatGPT,a large language model,as a financial advisor for listed firm performance forecasts.We focus on the constituent stocks of the China Securities Index 300 and compare ChatGPT’s forecasts for major financial performance measures with human analysts’forecasts and the realised values.Our findings suggest that ChatGPT can correct the optimistic biases of human analysts.This study contributes to the literature by exploring the potential of ChatGPT as a financial advisor and demonstrating its role in reducing human biases in financial decision-making.

The estimation method of GPS instrumental biases

A model of estimating the global positioning system (GPS) instrumental biases and the methods to calculate the relative instrumental biases of satellite and receiver are presented. The calculated results of GPS instrumental biases, the relative instrumental biases of satellite and receiver, and total electron content (TEC) are also shown. Finally, the stability of GPS instrumental biases as well as that of satellite and receiver instrumental biases are evaluated, indicating that they are very stable during a period of two months and a half.