Sources Of Uncertainty In Models Research Articles

Assessment of uncertainty in functional–structural plant models

Constructing functional-structural plant models (FSPMs) is a valuable method for examining how physiology and morphology interact in determining plant processes. However, such models always have uncertainty concerned with whether model components have been selected and represented effectively, with the number of model outputs simulated and with the quality of data used in assessment. We provide a procedure for defining uncertainty of an FSPM and how this uncertainty can be reduced. An important characteristic of FSPMs is that typically they calculate many variables. These can be variables that the model is designed to predict and also variables that give indications of how the model functions. Together these variables are used as criteria in a method of multi-criteria assessment. Expected ranges are defined and an evolutionary computation algorithm searches for model parameters that achieve criteria within these ranges. Typically, different combinations of model parameter values provide solutions achieving different combinations of variables within their specified ranges. We show how these solutions define a Pareto Frontier that can inform about the functioning of the model. The method of multi-criteria assessment is applied to development of BRANCHPRO, an FSPM for foliage reiteration on old-growth branches of Pseudotsuga menziesii. A geometric model utilizing probabilities for bud growth is developed into a causal explanation for the pattern of reiteration found on these branches and how this pattern may contribute to the longevity of this species. FSPMs should be assessed by their ability to simulate multiple criteria simultaneously. When different combinations of parameter values achieve different groups of assessment criteria effectively a Pareto Frontier can be calculated and used to define the sources of model uncertainty.

Uncertainty assessment of a model for biological nitrogen and phosphorus removal: Application to a large wastewater treatment plant

In the last few years, the use of mathematical models in WasteWater Treatment Plant (WWTP) processes has become a common way to predict WWTP behaviour. However, mathematical models generally demand advanced input for their implementation that must be evaluated by an extensive data-gathering campaign, which cannot always be carried out. This fact, together with the intrinsic complexity of the model structure, leads to model results that may be very uncertain. Quantification of the uncertainty is imperative. However, despite the importance of uncertainty quantification, only few studies have been carried out in the wastewater treatment field, and those studies only included a few of the sources of model uncertainty. Seeking the development of the area, the paper presents the uncertainty assessment of a mathematical model simulating biological nitrogen and phosphorus removal. The uncertainty assessment was conducted according to the Generalised Likelihood Uncertainty Estimation (GLUE) methodology that has been scarcely applied in wastewater field. The model was based on activated-sludge models 1 (ASM) and 2 (ASM2). Different approaches can be used for uncertainty analysis. The GLUE methodology requires a large number of Monte Carlo simulations in which a random sampling of individual parameters drawn from probability distributions is used to determine a set of parameter values. Using this approach, model reliability was evaluated based on its capacity to globally limit the uncertainty. The method was applied to a large full-scale WWTP for which quantity and quality data was gathered. The analysis enabled to gain useful insights for WWTP modelling identifying the crucial aspects where higher uncertainty rely and where therefore, more efforts should be provided in terms of both data gathering and modelling practises.

Assessing spatial patterns of disease risk to biodiversity: implications for the management of the amphibian pathogen, Batrachochytrium dendrobatidis

Summary1. Emerging infectious diseases can have serious consequences for wildlife populations, ecosystem structure and biodiversity. Predicting the spatial patterns and potential impacts of diseases in free‐ranging wildlife are therefore important for planning, prioritizing and implementing research and management actions.2. We developed spatial models of environmental suitability (ES) for infection with the pathogen Batrachochytrium dendrobatidis, which causes the most significant disease affecting vertebrate biodiversity on record, amphibian chytridiomycosis. We applied relatively newly developed methods for modelling ES (Maxent) to the first comprehensive, continent‐wide data base (comprising >10000 observations) on the occurrence of infection with this pathogen and employed novel methodologies to deal with common but rarely addressed sources of model uncertainty.3. We used ES to (i) predict the minimum potential geographic distribution of infection with B. dendrobatidis in Australia and (ii) test the hypothesis that ES for B. dendrobatidis should help explain patterns of amphibian decline given its theoretical and empirical link with organism abundance (intensity of infection), a known determinant of disease severity.4. We show that (i) infection with B. dendrobatidis has probably reached its broad geographic limits in Australia under current climatic conditions but that smaller areas of invasion potential remain, (ii) areas of high predicted ES for B. dendrobatidis accurately reflect areas where population declines due to severe chytridiomycosis have occurred and (iii) that a host‐specific metric of ES for B. dendrobatidis (ES for Bdspecies) is the strongest predictor of decline in Australian amphibians at a continental scale yet discovered.5. Synthesis and applications. Our results provide quantitative information that helps to explain both the spatial distribution and potential effects (risk) of amphibian infection with B. dendrobatidis at the population level. Given scarce conservation resources, our results can be used immediately in Australia and our methods applied elsewhere to prioritize species, regions and actions in the struggle to limit further biodiversity loss.

A modelling study of photochemical regimes over Europe: robustness and variability

Abstract. The variability of the relative sensitivity of photochemical ozone formation to volatile organic compounds (VOC) and NOx emissions, the chemical regime, over Europe during summers 2001 to 2003 is simulated with a regional scale transport-chemistry model. The robustness and variability of chemical regimes is shown. A VOC sensitive regime over North-Western Europe and a mainly NOx sensitive regime over the Mediterranean basin and Eastern Europe are found, confirming earlier published results. The chemical regime time variability, its robustness with respect to several environmental factors (seasonality, interannual variability) and with respect to model uncertainty are thoroughly analysed. For the regions with well pronounced chemical regimes over North-Western Europe and the Mediterranean, the chemical regime occurrence only slightly depends on the ozone target considered – daily ozone or Ox (= O3 + NO2) maximum or mean, AOT's, SOMO35, .... For these regions, differences between particular years and summer months are weak, day to day variability is significant but does not change the occurrence of one or another chemical regime. On the contrary, over North-Eastern Germany, the chemical regime changes form one day to another and is also dependent on the ozone target chosen. Expected decreases in anthropogenic NOx emissions over Europe since the last and for the next few decades have shifted and will shift chemical regimes to more NOx sensitive. The predictive skill of chemical regime indicator species is made evident at continental scale, extending their spatial range of applicability with respect to earlier studies. Several sensitivity tests were performed in order to account for major sources of model uncertainty. With the exception of regions near ship tracks over the Mediterranean basin, the spatial pattern of chemical regimes appears to be robust with respect to model uncertainty for all cases tested.

Decentralized Control for Arbitrarily Interconnected Systems over Lossy Network

Abstract This paper deals with the control of arbitrarily topological interconnected systems where information communicated between subsystems may be lost due to unreliable links. First, the stochastic variable that is responsible for the communication status of lossy network is regarded as a source of model uncertainty. The system is modeled in the framework of linear fractional transformation with a deterministic nominal system and a stochastic model uncertainty. Then, the robust control theory is employed for system analysis. The largest probability of communication failure, tolerated by the interconnected systems keeping mean square stable, can be obtained by solving a μ synthesis optimization problem. Decentralized state feedback controllers are designed to ensure that the whole system is mean square stable for a given communication failure rate, based on the technique of linear matrix inequalities. An illustrative example is presented finally to verify the effectiveness of the proposed model and method.

Influence of Taxonomic Relatedness and Chemical Mode of Action in Acute Interspecies Estimation Models for Aquatic Species

Ecological risks to aquatic organisms are typically assessed using acute toxicity data for relatively few species and with limited understanding of relative species sensitivity. We developed a comprehensive set of interspecies correlation estimation (ICE) models based on acute toxicity data for aquatic organisms and evaluated three key sources of model uncertainty: taxonomic relatedness, chemical mode of action (MOA), and model parameters. Models are least-squares regressions of acute toxicity of surrogate and predicted species. A total of 780 models were derived from acute values for 77 species of aquatic organisms and over 550 chemicals. Cross-validation of models showed that accurate model prediction was greatest for models with surrogate and predicted taxa within the same family (91% of predictions within 5-fold of measured values). Recursive partitioning provided user guidance for selection of robust models using model mean square error and taxonomic relatedness. Models built with a single MOA were more robust than models built using toxicity values with multiple MOAs, and improve predictions among species pairs with large taxonomic distance (e.g., within phylum). These results indicate that between-species toxicity extrapolation can be improved using MOA-based models for less related taxa pairs and for those specific MOAs.

The role of atmosphere feedbacks during ENSO in the CMIP3 models. Part II: using AMIP runs to understand the heat flux feedback mechanisms

Several studies using ocean–atmosphere general circulation models (GCMs) suggest that the atmospheric component plays a dominant role in the modelled El Nino-Southern Oscillation (ENSO). To help elucidate these findings, the two main atmosphere feedbacks relevant to ENSO, the Bjerknes positive feedback (μ) and the heat flux negative feedback (α), are here analysed in nine AMIP runs of the CMIP3 multimodel dataset. We find that these models generally have improved feedbacks compared to the coupled runs which were analysed in part I of this study. The Bjerknes feedback, μ, is increased in most AMIP runs compared to the coupled run counterparts, and exhibits both positive and negative biases with respect to ERA40. As in the coupled runs, the shortwave and latent heat flux feedbacks are the two dominant components of α in the AMIP runs. We investigate the mechanisms behind these two important feedbacks, in particular focusing on the strong 1997–1998 El Nino. Biases in the shortwave flux feedback, α SW, are the main source of model uncertainty in α. Most models do not successfully represent the negative αSW in the East Pacific, primarily due to an overly strong low-cloud positive feedback in the far eastern Pacific. Biases in the cloud response to dynamical changes dominate the modelled α SW biases, though errors in the large-scale circulation response to sea surface temperature (SST) forcing also play a role. Analysis of the cloud radiative forcing in the East Pacific reveals model biases in low cloud amount and optical thickness which may affect α SW. We further show that the negative latent heat flux feedback, α LH, exhibits less diversity than α SW and is primarily driven by variations in the near-surface specific humidity difference. However, biases in both the near-surface wind speed and humidity response to SST forcing can explain the inter-model αLH differences.

Modelling an encounter between a spacecraft and a cometary meteoroid trail in interplanetary space: The case of the Venus Climate Orbiter and comet 27P/Crommelin

We combine modern meteor activity predictors with classical techniques in astrodynamics to formulate a method of predicting encounters between cometary meteoroid trails and spacecraft in interplanetary trajectories. To demonstrate the method, we apply it to the upcoming encounter between the Venus Climate Orbiter spacecraft, due for launch in 2010, and the debris trail of comet 27P/Crommelin which is expected to reach perihelion in mid-2011. We find that a spacecraft utilising a Type II launch window to Venus in 2010 will approach the P/Crommelin dust trail to 0.025 AU on, or around, the 21st November 2010. Adherence to a launch constraint imposed by the current flight plan that limits the declination of the departure asymptote to values shallower than −30° increases this distance to 0.04 AU. A backup, Type-IV launch window for VCO exists in May–June 2011 with arrival at Venus in the Autumn of 2012. In that case, we find that the VCO spacecraft intercepts the trail twice, in late November 2011 and late September 2012, respectively. In the latter case, the expected dust fluence on the spacecraft is 3 × 10 −6 particles with an uncertainty of at least an order of magnitude. In the former case, which we model indirectly since our code can only predict the position of P/Crommelin dust in the vicinity of Venus itself, we find a fluence several times higher than the case in late 2012 but with a higher uncertainty. In both cases, the same declination constraint that was applied for the case of the primary launch window prevents trail interception. Instead, the spacecraft flies by the dust trail at a minimum distance of 0.0175 AU in the latter case and 0.0225 AU in the former case. We advocate studies such as the one presented here as part of mission analysis for future planetary missions. Further direct observations of cometary dust trails will address the main source of model uncertainty.

Sensitivity of polar stratospheric ozone loss to uncertainties in chemical reaction kinetics

Abstract. The impact and significance of uncertainties in model calculations of stratospheric ozone loss resulting from known uncertainty in chemical kinetics parameters is evaluated in trajectory chemistry simulations for the Antarctic and Arctic polar vortices. The uncertainty in modeled ozone loss is derived from Monte Carlo scenario simulations varying the kinetic (reaction and photolysis rate) parameters within their estimated uncertainty bounds. Simulations of a typical winter/spring Antarctic vortex scenario and Match scenarios in the Arctic produce large uncertainty in ozone loss rates and integrated seasonal loss. The simulations clearly indicate that the dominant source of model uncertainty in polar ozone loss is uncertainty in the Cl2O2 photolysis reaction, which arises from uncertainty in laboratory-measured molecular cross sections at atmospherically important wavelengths. This estimated uncertainty in JCl2O2 from laboratory measurements seriously hinders our ability to model polar ozone loss within useful quantitative error limits. Atmospheric observations, however, suggest that the Cl2O2 photolysis uncertainty may be less than that derived from the lab data. Comparisons to Match, South Pole ozonesonde, and Aura Microwave Limb Sounder (MLS) data all show that the nominal recommended rate simulations agree with data within uncertainties when the Cl2O2 photolysis error is reduced by a factor of two, in line with previous in situ ClOx measurements. Comparisons to simulations using recent cross sections from Pope et al. (2007) are outside the constrained error bounds in each case. Other reactions producing significant sensitivity in polar ozone loss include BrO + ClO and its branching ratios. These uncertainties challenge our confidence in modeling polar ozone depletion and projecting future changes in response to changing halogen emissions and climate. Further laboratory, theoretical, and possibly atmospheric studies are needed.

Global uncertainty and sensitivity analysis of a food‐web bioaccumulation model

A global uncertainty and sensitivity analysis (UA/SA) of a state-of-the-art, food-web bioaccumulation model was carried out. We used an efficient screening analysis technique to identify the subset of the most relevant input factors among the whole set of 227 model parameters. A quantitative UA/SA was then applied to this subset to rank the relevance of the parameters and to partition the variance of the model output among them by means of a nonlinear regression of the outcomes of 1,000 Monte Carlo simulations. The concentrations of four representative persistent organic pollutants (POPs) in two representative species of the coastal marine food web of the Lagoon of Venice (Italy) were taken as model outputs. The screening analysis showed that the ranking was remarkably different in relation to the species and chemical being considered. The subsequent Monte Carlo-based quantitative analysis pointed out that the relationships among some of the parameters and the model outputs were nonlinear. The nonlinear regression showed that the fraction of output variance accounted for by each parameter was strongly dependent on the range of the octanol-water partition coefficient (K(OW)) values being considered. For the less hydrophobic chemicals, the main sources of model uncertainty were the parameters related to the respiratory bioaccumulation, whereas for the more hydrophobic ones, K(OW) and the other parameters related to the dietary uptake explained the largest fractions of the variance of the chemical concentrations in the organisms. The analysis highlighted that efforts are still needed for reducing uncertainty of model parameters to get reliable results from the application of food web bioaccumulation models.

Quantifying uncertainty in estimates of mineral dust flux: An intercomparison of model performance over the Bodélé Depression, northern Chad

Mineral dust aerosols play an important role in the climate system. Coupled climate‐aerosol models are an important tool with which to quantify dust fluxes and the associated climate impact. Over the last decade or more, numerous models have been developed, both global and regional, but to date, there have been few attempts to compare the performance of these models. In this paper a comparison of five regional atmospheric models with dust modules is made, in terms of their simulation of meteorology, dust emission and transport. The intercomparison focuses on a 3‐day dust event over the Bodélé depression in northern Chad, the world's single most important dust source. Simulations are compared to satellite data and in situ observations from the Bodélé Dust Experiment (BoDEx 2005). Overall, the models reproduce many of the key features of the meteorology and the large dust plumes that occur over the study domain. However, there is at least an order of magnitude range in model estimates of key quantities including dust concentration, dust burden, dust flux, and aerosol optical thickness. As such, there remains considerable uncertainty in model estimates of the dust cycle and its interaction with climate. This paper discusses the issues associated with partitioning various sources of model uncertainty.

Correcting first‐order errors in snow water equivalent estimates using a multifrequency, multiscale radiometric data assimilation scheme

A season‐long, multiscale, multifrequency radiometric data assimilation experiment is performed to test the feasibility of snow water equivalent (SWE) estimation. Synthetic passive microwave (PM) observations at Advanced Microwave Scanning Radiometer‐Earth Observing System frequencies and 25 km resolution and synthetic near infrared (NIR) narrowband albedo observations corresponding to Moderate Resolution Imaging Spectroradiometer band 5 (1230–1250 μm) and 1 km resolution are assimilated into a land surface model snow scheme using the ensemble Kalman filter. First‐order sources of model uncertainty, including error in precipitation quantity, grain size evolution, precipitation spatial distribution, and vegetation leaf area index are modeled. SWE remote sensing retrieval schemes would be of limited value for these scenarios where snow depth ranged between 1.0 and 2.0 m, grain size varied in space, significant vegetation was present, and the snowpack sometimes contained liquid water. Nevertheless, the true basinwide SWE is recovered, in general, within a root‐mean‐square error (RMSE) of approximately 2 cm. Synergy is observed between the PM and NIR measurements because of the complementary nature of the multiscale, multifrequency measurements. Results from the assimilation are compared to those from a pure modeling approach and from a remote sensing retrieval approach. The effects of model uncertainty, measurement error, and ensemble size are investigated.

Regulation on radial position deviation for vertical AMB systems

As a source of model uncertainty, gyroscopic effect, depending on rotor speed, is studied for the vertical active magnetic bearing (VAMB) systems which are increasingly used in various industries such as clean rooms, compressors and satellites. This research applies H ∞ controller to regulate the rotor position deviations of the VAMB systems in four degrees of freedom. The performance of H ∞ controller is examined by experimental simulations to inspect its closed-loop stiffness, rise time and capability to suppress the high frequency disturbances. Although the H ∞ is inferior to the LQR in position deviation regulation, the required control current in the electromagnetic bearings is much less than that for LQR or PID and the performance robustness is well retained. In order to ensure the stability robustness of H ∞ controller, two approaches, by Kharitonov polynomials and TITO (two inputs & two outputs) Nyquist Stability Criterion, are employed to synthesize the control feedback loop. A test rig is built to further verify the efficacy of the proposed H ∞ controller experimentally. Two Eddy-current types of gap sensors, perpendicular to each other, are included to the realistic rotor-bearing system. A four-pole magnetic bearing is used as the actuator for generation of control force. The commercial I/O module unit with A/D and D/A converters, dSPACE DS1104, is integrated to the VAMB, gap sensors, power amplifiers and signal processing circuits. The H ∞ is designed on the basis of rotor speed 10 K rpm but in fact it is significantly robust with respect to the rotor speed, varying from 6.5 to 13.5 K rpm.

A method for identifying sources of model uncertainty in rainfall-runoff simulations

Summary A major goal in environmental modeling is identifying and quantifying sources of uncertainty in the modeling process. A forecast ensemble is developed in this study for a rainfall-runoff simulation system. This ensemble includes several quantitative precipitation estimates that serve as inputs to the Vflo ™ hydrologic model. The rainfall estimates are derived from rain gauges, radar, satellite, and combinations, and their probability distribution is assumed to encompass the true, but unknown, rainfall. Sensitive model parameters in the model are also perturbed within their physical bounds to create a combined input-parameter ensemble. If all major sources of uncertainty are accounted for, then observations of river discharge should fall within simulation bounds. Otherwise, there may be additional errors that lie within the model structure. Probability distributions derived from the forecast ensemble encompass streamflow observations for three hydrologic events examined during October and December on the Blue River Basin in Oklahoma. It is discovered, however, that all simulations from an ensemble created for a warm season case overforecast discharge peaks and volumes. Climatological rain gauge, discharge, and soil moisture observations are introduced to illuminate the source of uncertainty that was not accounted for in the combined input-parameter ensemble. Observations show a strong correlation between dry, deep-layer soils and significantly reduced runoff production (provided the same rainfall inputs) during the summer months. The Green and Ampt methodology is used in the model to compute soil infiltration rates. Evidence suggests additional abstractions such as interception and evapotranspiration by vegetation and deep cracks in the soil structure contribute to enhanced infiltration rates during the warm season. These effects need to be considered for future infiltration models.

Assessment of the effect of land use patterns on hydrologic landscape functions: a comprehensive GIS‐based tool to minimize model uncertainty resulting from spatial aggregation

AbstractIt is a common technique to predict hydrologic effects of land use changes by simulation models. On the scale of watersheds, hydrologic models comprise different approaches for many hydrologic components. One important source of model uncertainty results from errors of measured input data and their spatial aggregation. Whereas simulations on the basis of measured land use can be validated by measuring resulting hydrologic components, model uncertainty on land use scenarios cannot be quantified because of their virtuality. An entropy‐based algorithm is proposed and validated, which should serve for the identification of an appropriate level of spatial aggregation. This technique is statistically based and applicable for common methods of subwatershed and hydrologic response unit (HRU) delineation. The main criterion of this algorithm is the heterogeneity of data distribution within the investigated watershed, which might vary for different land use scenarios.For spatial output analysis of a long‐term hydrologic model, a reassignment technique of statistically distributed output values to certain grids was developed. It combines efficient model calculations for aggregated spatial areas without losing spatial output heterogeneity. This procedure enables model validation on a grid‐based resolution and serves for the identification of spatial differences of uncertainty. Besides these new approaches for spatial analysis, different common tools for data pre‐ and post‐processing were integrated into the comprising software package IOSWAT, linked to a GIS. The results of the new approaches prove them to be valid for different watersheds and hydrologic conditions. The application of the comprehensive software package is presented in Fohrer et al. (this issue). Copyright © 2005 John Wiley & Sons, Ltd.

Robustness in spatial studies I: minimax prediction

We develop and test robust methods for estimation and for prediction in spatial studies. We assume that a stochastic process is measured, with error, at various locations. The variance/covariance structures of this process and of the measurement errors are only approximately known; in the face of these uncertainties one is to do robust estimation and prediction. We obtain a minimax linear predictor, in which mean squared error loss is first maximized over neighbourhoods quantifying the various sources of model uncertainty, and then minimized over the coefficients of the predictor subject to a constraint of unbiasedness. Robustifications of these methods are then introduced. These are based on generalized M-estimators, and are robust against contaminated error distributions. In a simulation study the procedures afford a substantial level of robustness when the model inadequacies are present, while being almost as efficient as more classical methods otherwise. Copyright © 2004 John Wiley & Sons, Ltd.

Remote stabilization over fading channels

In this paper, we study the problem of remote mean square stabilization of a MIMO system when independent fading channels are dedicated to each actuator and sensor. We show that the stochastic variables responsible for the fading can be seen as a source of model uncertainty. This view leads to robust control analysis and synthesis problems with a deterministic nominal system and a stochastic, structured, model uncertainty. As a special case, we consider the stabilization over Erasure or drop-out channels. We show that the largest probability of erasure tolerable by the closed loop is obtained from solving a robust control synthesis problem. In more general terms, we establish that the set of plants which can be stabilized by linear controllers over fading channels is fundamentally limited by the channel generated uncertainty. We show that, the notion of mean square capacity, defined for a single channel in the loop, captures this limitation precisely and characterizes equivalence classes of channels within the class of memoryless fading channels.

Sources of uncertainty in ecological modelling: predicting vegetation types from environmental attributes

In this paper, we use decision trees to construct models for predicting vegetation types from environmental attributes in a salt marsh. We examine a method for evaluating the worth of a decision tree and look at seven sources of uncertainty in the models produced, namely algorithmic, predictive, model, scenario, objective, context and scale. The accuracy of prediction of types was strongly affected by the scenario and scale, with the most dynamically variable attributes associated with poor prediction, while more static attributes performed better. However, examination of the misclassified samples showed that prediction of processes was much better, with local vegetation type-induced patterns nested within a broader environmental framework.

On the angular correlation function of SZ clusters: Extracting cosmological information from a 2D catalog

We discuss the angular correlation function of Sunyaev-Zel'dovich (SZ)-detected galaxy clusters as a cosmological probe. As a projection of the real-space cluster correlation function, the angular function samples the underlying SZ catalog redshift distribution. It offers a way to study cosmology and cluster evolution directly with the two-dimensional catalog, even before extensive follow-up observations, thereby facilitating the immediate scientific return from SZ surveys. As a simple illustration of the information content of the angular function, we examine its dependence on the parameter pair Om_m, sigma_8 in flat cosmologies. We discuss sources of modeling uncertainty and consider application to the future Planck SZ catalog, showing how these two parameters and the normalization of the SZ flux-mass relation can be simultaneously found when the local X-ray cluster abundance constraint is included.

Issues concerning the sea emissivity modeling at L band for retrieving surface salinity

In order to prepare the sea surface salinity (SSS) retrieval in the frame of the Soil Moisture and Ocean Salinity (SMOS) mission we conduct sensitivity studies to quantify uncertainties on simulated brightness temperatures (Tb) related to uncertainties on sea surface and scattering modeling. Using a two‐scale sea surface emissivity model to simulate Tb at L band (1.4 GHz), we explore the influence on estimated SSS of the parameterization of the seawater permittivity, of the sea wave spectrum, of the choice of the two‐scale cutoff wavelength, and of adding swell to the wind sea. Differences between Tb estimated with various existing permittivity models are up to 1.5 K. Therefore a better knowledge of the seawater permittivity at L band is required. The influence of wind speed on Tb simulated with various parameterizations of the sea wave spectrum differs by up to a factor of two; for a wind speed of 7 m s−1 the differences on estimated SSS is several psu depending on the sea wave spectral model taken, so that sea spectrum is a major source of uncertainty in models. We find no noticeable effect on simulated Tb when changing the two‐scale cutoff wavelength and when adding swell to the wind sea for low to moderate incidence angles. The dependence of the wind‐induced Tb on SST and SSS being weak, we assess the error in SSS estimated assuming that the wind speed influence is independent of SST and SSS. We find errors on estimated SSS up to 0.5 psu for 20°C variation in SST. Therefore this assumption would induce regional biases when applied to global measurements.