Imperfect Sampling Research Articles

Species Delimitation under the General Lineage Concept: An Empirical Example Using Wild North American Hops (Cannabaceae: Humulus lupulus)

There is an emerging consensus that the intent of most species concepts is to identify evolutionarily distinct lineages. However, the criteria used to identify lineages differ among concepts depending on the perceived importance of various attributes of evolving populations. We have examined five different species criteria to ask whether the three taxonomic varieties of Humulus lupulus (hops) native to North America are distinct lineages. Three criteria (monophyly, absence of genetic intermediates, and diagnosability) focus on evolutionary patterns and two (intrinsic reproductive isolation and niche specialization) consider evolutionary processes. Phylogenetic analysis of amplified fragment length polymorphism (AFLP) data under a relaxed molecular clock, a stochastic Dollo substitution model, and parsimony identified all varieties as monophyletic, thus they satisfy the monophyly criterion for species delimitation. Principal coordinate analysis and a Bayesian assignment procedure revealed deep genetic subdivisions and little admixture between varieties, indicating an absence of genetic intermediates and compliance with the genotypic cluster species criterion. Diagnostic morphological and AFLP characters were found for all varieties, thus they meet the diagnosability criterion. Natural history information suggests that reproductive isolating barriers may have evolved in var. pubescens, potentially qualifying it as a species under a criterion of intrinsic reproductive isolation. Environmental niche modeling showed that the preferred habitat of var. neomexicanus is climatically unique, suggesting niche specialization and thus compliance with an ecological species criterion. Isolation by distance coupled with imperfect sampling can lead to erroneous lineage identification using some species criteria. Compliance with complementary pattern- and process-oriented criteria provides powerful corroboration for a species hypothesis and mitigates the necessity for comprehensive sampling of the entire species range, a practical impossibility in many systems. We hypothesize that var. pubescens maintains its genetic identity, despite substantial niche overlap with var. lupuloides, via the evolution of partial reproductive isolating mechanisms. Variety neomexicanus, conversely, will likely persist as a distinct lineage, regardless of limited gene flow with vars. lupuloides and pubescens because of ecological isolation--adaptation to the unique conditions of the Rocky Mountain cordillera. Thus, we support recognition of vars. neomexicanus and pubescens as species, but delay making a recommendation for var. lupuloides until sampling of genetic variation is complete or a stable biological process can be identified to explain its observed genetic divergence.

Estimating potential evapotranspiration without continuous daily data: possible errors and impact on water balance simulations

Alternative approaches to estimating monthly and annual potential evapotranspiration (PE) are explored in cases where daily climate data are not routinely recorded. A database consisting of data from 222 weather stations, representing a wide variety of climatic conditions, is used to draw general conclusions. In addition, two PE formulae with different data requirements are used: the standard FAO-56 Penman-Monteith equation, and a simple temperature-based equation. First, we tested the degree of bias introduced by using climate data averaged over long time periods instead of daily data. Second, we explored the sensitivity of PE estimation with respect to variations in sampling frequency of climate variables. The results show that using mean weather data has only a limited effect on monthly and annual PE estimates. Conversely, imperfect sampling of weather data may bias monthly and to a lesser extent annual PE estimates if the sampling period exceeds 5 and 10 days, respectively. Finally, we tested the impact of erroneous weather data on the simulations of annual actual evapotranspiration obtained with the Budyko model. The impact on the Budyko model outputs depends more on the dryness index of a given location than on annual PE; for regions under water stress, the errors in estimation of actual evapotranspiration are very limited, compared to humid regions where available energy is the dominating factor and the propagation of PE errors is important. Citation Oudin, L., Moulin, L., Bendjoudi, H. & Ribstein, P. (2010) Estimating potential evapotranspiration without continuous daily data: possible errors and impact on water balance simulations. Hydrol. Sci. J. 55(2), 209–222.

Sensitivity of complex networks measurements

Complex networks obtained from real-world networks are often characterized byincompleteness and noise, consequences of imperfect sampling as well as artifacts in theacquisition process. Because the characterization, analysis and modeling of complexsystems underlain by complex networks are critically affected by the quality andcompleteness of the respective initial structures, it becomes imperative to devisemethodologies for identifying and quantifying the effects of the sampling on the networkstructure. One way to evaluate these effects is through an analysis of the sensitivity ofcomplex network measurements to perturbations in the topology of the network. In thispaper, measurement sensibility is quantified in terms of the relative entropy ofthe respective distributions. Three particularly important kinds of progressiveperturbations to the network are considered, namely, edge suppression, additionand rewiring. The measurements allowing the best balance of stability (smallersensitivity to perturbations) and discriminability (separation between different networktopologies) are identified with respect to each type of perturbation. Such an analysisincludes eight different measurements applied on six different complex networksmodels and three real-world networks. This approach allows one to choose theappropriate measurements in order to obtain accurate results for networks wheresampling bias cannot be avoided—a very frequent situation in research on complexnetworks.

When does an imperfect sampling frame produce more efficient estimators than a perfect frame?

The issue of when imperfect sampling frames can result in more efficient estimators of population totals than perfect frames is explored. Our analysis is based on an expression we call the difference score. We show how, when properly expanded it provides an illuminating basis for comparing a weighted estimator under an imperfect frame with that of a conventional estimator assuming the frame has been corrected. Specifically, the circumstances (i.e., population and frame characteristics) under which an imperfect frame results in estimates of population totals that are more precise than those from a perfect frame can in many cases be discerned by analytically examining the terms in the expansion of this difference score. In addition, a classification tree methodology was used to further explore circumstances under which imperfect frames result in more precise estimators. The results of this analytical study complement, strengthen, and in many cases explain those discovered in an earlier empirical investigation that lead to recommendations as to when to correct a frame or when to adjust for imperfection using a weighting methodology called the arc weight estimator.

'Talking About Sexual Orientation, Teaching About Homophobia' Negotiating the Divide between Religious Belief and Tolerance for LGBT Rights in the Classroom

INTRODUCTION--SEXUALITY POLITICS, RELIGIOUS BELIEF AND THE CLASSROOM The question of whether or not people in the LGBT (2) community should be able to enjoy certain fundamental rights taken for granted by most Americans is a contentious issue. While a large number of people agree that same sex sexuality should no longer be criminalized (3) and that gay men and lesbians should be allowed to participate in civil unions, (4) a significant number believe that homosexuality is immoral (5) and that it should be illegal for gay men and lesbians to marry. (6) Greater or lesser tolerance for LGBT rights in the U.S., especially gay marriage, is directly tied to age, religious belief and political affiliation. A 2005 nation-wide survey conducted by the Boston Globe found that people thirty-five years old and younger are more pro-gay in their attitudes than plus sixty-five year olds. (7) The same survey revealed that Republicans, Protestants, regular churchgoers, and Southerners were most likely to oppose gay marriage. It was more likely to be favored by ... Democrats, and people who do not attend worship services or who attend a few times a year. (8) There are many explanations for why some people are against LGBT rights. For instance, some scholars argue that animus against gay men and lesbians, or homophobia, (9) stems from repressed feelings of attraction for people of the same sex, feelings that the holder finds repugnant. This in turn can sometimes cause the holder to lash out at gay men and lesbians. (10) Others contend that homophobia is rooted in sexist attitudes about proper gender roles, with lesbians and gay men being viewed as traitors to nature because of their same sex affiliations. (11) People who hold the kinds of views just explained, often find support for their ideas in interpretations of the teachings of some of the more conservative strains of the world's major religions. For example, in 2003, the same year that the Massachusetts Supreme Court's endorsed the legality of same sex marriage, the Vatican announced that exists solely between a man and woman ... while homosexual acts go against the natural moral law. (12) The Southern Baptist Convention, America's largest Protestant denomination with 14 million members, (13) has said that Homosexuality is not a 'valid alternative lifestyle.' The Bible condemns it as sin. (14) Furthermore, the African Methodist Episcopal Church, one of the oldest African American Christian denominations, has announced that it considers same sex marriage and the ordination of gay clergy contrary to church doctrine. (15) This is not to say that all religious denominations are against LGBT rights. The Unitarian Universalist Association endorses the ordination of openly lesbian, gay and bisexual ministers, (16) as does the United Church of Christ. (17) In an official pronouncement, the latter stated: We recognize the presence of ignorance, fear and hatred in the church and in our culture, and covenant to not discriminate on the basis of sexual orientation ..., and we seek to include and support those who, because of this fear and prejudice, find themselves in exile from a spiritual community. (18) It should not be forgotten that America, a majority Christian country, is a land of multiple religions. The following is only an imperfect sampling of the official views on homosexuality held by several non-christian religious groups with sizeable followings in the United States. Reform Judaism, the biggest Jewish denomination in the United States with 1.7 million members, welcomes openly gay rabbis and members and endorses same sex commitment ceremonies. (19) However, such ceremonies are not considered the same as heterosexual marriage ceremonies. (20) On the other hand, the Conservative Judaism movement, which has 1.4 million members in the United States, officially prohibits same sex weddings and openly lesbian or gay rabbis. …

How much is the ocean really warming?

We use a global hydrographic dataset to study the effect of instrument related biases on the estimates of long‐term temperature changes in the global ocean since the 1950s. The largest discrepancies are found between the expendable bathythermographs (XBT) and bottle and CTD data, with XBT temperatures being positively biased by 0.2–0.4°C on average. Since the XBT data are the largest proportion of the dataset, this bias results in a significant World Ocean warming artefact when time periods before and after introduction of XBT are compared. Using bias‐corrected XBT data we argue reduces the ocean heat content change since the 1950s by a factor of 0.62. Our estimate of the ocean heat content increase (0–3000 m) between 1957–66 and 1987–96 is 12.8·1022 J. Because of imperfect sampling this estimate has an uncertainty of at least 8·1022 J

Cluster Sampling: A False Economy?

The random sample survey is a commonly used method in market research and such surveys generally are considered to provide more robust results than purposive or convenience (such as quota) samples. Simple random samples (SRS), where every member of the population has an equal or predetermined chance of selection, and the sampling is single stage, are the simplest to visualise and the level of precision of results derived from them is easy to calculate. However, SRS are almost impossible to achieve in reality (due to imperfect sampling frames, nonresponse, and so on) and, in any case, they are relatively inefficient ways of obtaining a particular number of responses, in terms of fieldwork and travelling costs. Much of the theory underlying this paper is not new, and many of the concepts covered relate to important work published a number of years ago. However, the messages that this paper serves to portray are no less relevant today than they were in the past, as cluster sampling continues to be a popularly used type of survey design. It is less statistically efficient than simple random sampling, and this is as critical as ever today as researchers continue their quest to obtain data in an ever-faster and less expensive way. If one considers the situation of running a national survey of 10,000 people across the whole of Great Britain, it would be impractical and expensive to send interviewers across the whole country to addresses which are completely randomly scattered, because travelling time and interviewer costs are likely to be prohibitive. It would be more economical and realistic to focus on specific localities (or clusters) across the country (e.g. constituencies or wards) and sample from some localities (these particular localities being selected at random) but not from others. There is, however, a price to pay for this saving in administrative time and costs, namely that one would receive no information about the localities or the clusters from which one is not sampling. Correspondingly, aggregate results would be based only on the clusters from which one is sampling. Also, there may be good reasons why people's views within a small area are similar. This would lead to a reduction in precision and a widening of confidence intervals as compared to the situation of running a survey on a totally SRS basis. This paper begins by providing a brief background to the concept of the design effect, and how it can be used to see how various aspects of survey design (such as clustering, but also stratification and weighting) can impact on the level of statistical reliability of a survey. Focusing on clustering, a commonly used formula for calculating the confidence interval (CI) for clustered samples is then introduced. Using this formula on a worked example covering a specific national survey, it is shown how the level of confidence can vary with the number of clusters taken (which is controllable) and with the differences between the clusters (which cannot be controlled). At this point, what appears to be a surprising conclusion is verified by an alternative methodological approach. The paper then goes on to present a theoretical and somewhat historical example which nevertheless is useful in illustrating that the design effect is very different for different survey measures. The paper also touches on the issue of interviewer variability, which is in itself a form and extension of clustering. Finally, the report is put together with appropriate costings to look at the practical and financial implications of clustering-related losses of effective sample size.

Analysis of Sampling Errors for Climate Monitoring Satellites

Abstract Sampling retrievals of high-accuracy first-moment statistics constitute a central concern for climate research. Considered here is the important case of brightness temperature retrievals from a selection of possible orbits. Three-hourly global satellite brightness temperature data are used to predict the sampling error of monthly to annual mean brightness temperature retrieved by one or more satellites in low earth orbits. A true polar orbit is found to offer substantial advantages over a sun-synchronous orbit in the retrieval of annual mean brightness temperature, since the rotation of the local time of observation through two full diurnal cycles greatly reduces the error due to imperfect sampling of diurnal variations. Thus, a single polar orbiting satellite can produce annual mean, zonal mean brightness temperatures with typical sampling errors of less than 0.1 K, while even three sun-synchronous orbiters have high-latitude errors of up to 0.4 K. The error in retrievals of the annual mean diurnal cycle of brightness temperature is also discussed. In this case, high accuracy (<0.1 K) requires three cross-track scanning satellites in precessing orbits, or else a very large number (∼10) of nadir-viewing satellites in precessing orbits. The large sampling errors of sun-synchronous satellites are highly correlated from year to year, so that if equator-crossing times are held fixed, sampling errors in year-to-year differences of annual means are similar for sun-synchronous and precessing orbits.

Context-based surface completion

Sampling complex, real-world geometry with range scanning devices almost always yields imperfect surface samplings. These "holes" in the surface are commonly filled with a smooth patch that conforms with the boundary. We introduce a context-based method: the characteristics of the given surface are analyzed, and the hole is iteratively filled by copying patches from valid regions of the given surface. In particular, the method needs to determine best matching patches, and then, fit imported patches by aligning them with the surrounding surface. The completion process works top down, where details refine intermediate coarser approximations. To align an imported patch with the existing surface, we apply a rigid transformation followed by an iterative closest point procedure with non-rigid transformations. The surface is essentially treated as a point set, and local implicit approximations aid in measuring the similarity between two point set patches. We demonstrate the method at several point-sampled surfaces, where the holes either result from imperfect sampling during range scanning or manual removal.

Sampling efficiency and internal noise for motion detection, discrimination, and summation

By comparing real observers to an ideal observer, previous studies have found that the detection of static patterns is limited by internal noise and by imperfect sampling efficiency. We developed and applied ideal observer models for the detection, discrimination, and summation of oppositely drifting gratings in Gaussian white noise. The three tasks share a common source of internal noise. The sampling efficiencies were on the order of 1–2% except for much lower efficiency in direction discrimination for faster moving gratings. The efficiency of direction discrimination relative to detection systematically declines as the speed is increased from 1 to 6 Hz. These results suggest that observers use mismatched filters tuned to slow speeds regardless of the signal speed. Human visual motion sensing appears to use distorted representations of the incoming signals, and this distortion is a major limitation to visual performance.

Appropriate rather than representative sampling, based on acceptable levels of uncertainty

Appropriate sampling, that includes the estimation of measurement uncertainty, is proposed in preference to representative sampling without estimation of overall measurement quality. To fulfil this purpose the uncertainty estimate must include contribution from all sources, including the primary sampling, sample preparation and chemical analysis. It must also include contributions from systematic errors, such as sampling bias, rather than from random errors alone. Case studies are used to illustrate the feasibility of this approach and to show its advantages for improved reliability of interpretation of the measurements. Measurements with a high level of uncertainty (e.g. 50%) can be shown to be fit for some specified purposes using this approach. Once reliable estimates of the uncertainty are available, then a probabilistic interpretation of results can be made. This allows financial aspects to be considered in deciding upon what constitutes an acceptable level of uncertainty. In many practical situations “representative ”sampling is never fully achieved. This approach recognises this and instead, provides reliable estimates of the uncertainty around the concentration values that imperfect appropriate sampling causes.

Optical A/D Conversion by Time-Interweaved Sampling

A time-interweaved sampling scheme for optical A/D conversion is proposed, and to demonstrate this technique 2.5 GHz sine microwave signal is sampled at a rate of 16×2.5 GHz. 29dB signal-to-noise rate is acquired after processing the experimental data. In this paper we analyse the impacts of the imperfect optical sampling pulses, including the waveform, pulse width, and time jitter, and then discuss the way to improve the sampling precision.

Forest floor CO 2 fluxes estimated by eddy covariance and chamber-based model

An intercomparison study of two methods for estimating forest soil CO 2 efflux was conducted during a 3-week period in summer. An empirical model established from nearly 2 years of chamber measurements predicted that the soil CO 2 efflux ranged from 1.2 to 1.4 μmol m −2 s −1. This small range was due to the lack of variability in the model parameters (soil temperature and moisture) during the study period. Eddy covariance measurements showed more variability and diurnal dependence. Turbulent fluxes of CO 2 during the day apparently were influenced by the presence of a moss layer on the forest floor; the combined effects of moss photosynthesis and respiration reduced the turbulent fluxes by an average of 0.6 μmol m −2 s −1 relative to the modeled soil efflux. At night, the eddy fluxes of CO 2 agreed well with the modeled soil efflux; however, the turbulent fluxes were highly variable (standard deviation exceeding the mean), due to the imperfect sampling conditions associated with the nocturnal boundary layer. These results illustrate the different processes measured by the two methods, and highlight some of the limitations of the eddy covariance technique for estimating soil CO 2 efflux. Finally, they demonstrate the need for more long-term intercomparison studies, covering a broader range in soil temperature and moisture.

Census 2000--Science Meets Politics

T he Supreme Court has ruled that it is illegal to use statistical sampling to provide population counts for congressional reapportionment. Should scientists other than statisticians, demographers, and political scientists bother to follow the strange politics that have engulfed Census 2000? Yes, as a warning about how partisanship can affect a scientific agency. The census began as the result of the great political compromise that founded the United States. If congressional power was to be apportioned geographically (satisfying small states) and proportionate to size (satisfying large states), regular measurement of the number and distribution of residents was a necessity. In the 19th century, census results regulated the pace for admitting new states to the union, as partisan interests took turns vying for advantage. In 1920, the census measured a population shift from rural to urban states so alarming to Congress that it postponed for a decade its constitutional obligation to reapportion. But past politics focused mostly on how census results were to be used. Today's battle has been broadened to focus on how data will be collected. Technical decisions (for example, on how many census forms to mail) normally made by career professionals are today routinely reviewed by an oversight apparatus intent on influencing how the census will be conducted. Regrettably, the division of views falls along partisan lines. Even the Supreme Court divided along conservative and liberal lines in its recent decision. Science by partisan vote is a risky enterprise. The Census Bureau takes as much professional pride in accurately reporting how inaccurate it is as it does in trying to be completely accurate in the first place. We know that we miss people in our decennial count, and we know that this undercount is unevenly distributed demographically and geographically. This knowledge comes from our own post-census quality survey. The quality survey we recommend for Census 2000 is under attack because of its potential use for drawing legislative districts in 2001. Such use is an altogether appropriate issue for resolution in the political-legal arena. But other critical uses should not be compromised. To discard the quality survey would take away the science by which we assess our performance; we could not then tell the public whether census procedures in 2000 improved on those used in 1990. Planning for the next census would be crippled. Moreover, for at least a decade, statistical surveys of any kind would be based on an imperfect sampling frame and error-prone sampling weights. Just as there can be reasonable differences among lawyers about the interpretation of the census statute, there can be reasonable differences among politicians and among statisticians about the operational feasibility of the sampling design recommended by the bureau. But no statistician can accept the widespread misrepresentation of modern sampling methods. There can be no national statistics on unemployment, housing, transportation, health, or education without sampling. We can accommodate the Court ruling that Census 2000 is not to include statistical adjustment for the apportionment count, but without sampling the Census Bureau cannot fulfill its broad statistical responsibilities to policy-making, to business planning, and to scientific research. Also at issue is the damaging and unfounded claim that the bureau opted for sampling so it could then manipulate numbers for partisan purposes. To seek tactical advantage in a political dispute by questioning the integrity of a scientific agency is troubling. When national leaders say that the bureau cannot be trusted, they invite public doubt about other statistics, such as the consumer price index, poverty trends, unemployment rates, even measures of gross domestic product. When partisanship intrudes into the conduct of science, when widely accepted scientific methods are deliberately misrepresented, when scientific agencies are casually accused of dishonesty, a shadow falls across all of U.S. science.

Bias and confounding in molecular epidemiological studies: special considerations.

Studies in the field of ‘molecular epidemiology’ are becoming common in the literature. This use of molecular-biological laboratory techniques in epidemiological research has some consequences that deserve specific attention, including the potential for some particular forms of bias. Most of the bias in epidemiological research, at large, occurs because of imperfect sampling or classification procedures that cause the data-set to misrepresent the true relationship that is being studied. While there has been a long history of methodological discussions about bias in mainstream (for example, questionnaire-based) epidemiology, the corresponding issues in relation to molecular measurements in epidemiology have not been systematically addressed. Bias, or systematic error, occurs when there is a difference between what the estimator (e.g. the relative risk) is actually estimating within the acquired data and the true effect of interest within the source population. Systematic error is attributable to methodological aspects of the study design or analysis other than sampling variation, in particular the selection of subjects, the quality of information obtained, and the superimposed effects of variables other than the disease and study factor (1). In the following, we propose (with examples) a categorization of bias, which is certainly not exhaustive but which can be a basis for further discussion. 1. Selection bias

All-Orientation Search and All-Placement Search in Comparative Molecular Field Analysis

Three sets of molecules have been used to study the conventional CoMFA procedure. For all the three test sets, the resulting q2 values were observed to vary simply because of the change in the orientation or placement of the aligned molecules. The reason is believed to root in the imperfect sampling of the molecular field. We have introduced two new strategies, all-orientation search (AOS) and all-placement search (APS), to optimize the sampling process. By rotating and translating the molecular aggregate within the grid systematically, all the possible samplings of the molecular field are tested and subsequently the one with the highest q2 value can be picked out. We have also demonstrated that the combined application of AOS/APS with GOLPE procedure can yield results better than the ones by using them respectively.

Optimal Averaging for the Determination of Global Mean Temperature: Experiments with Model Data

Abstract Optimal averaging is a method to estimate some area mean of datasets with imperfect spatial sampling. The accuracy of the method is tested by application to time series of January temperature fields simulated by the NCAR Community Climate Model. Some restrictions to the application of optimal averaging are given. It is demonstrated that the proper choice of a spatial correlation model is crucial. It is shown that the optimal averaging procedures provide a better approximation to the true mean of a region than simple area-weight averaging does. The inclusion of measurement errors of realistic size at each observation location hardly changes the value of the optimal average nor does it substantially alter the sampling error. of the optimal average.

Statistical Effects of Imperfect Inspection Sampling III. Screening (Group Testing)

This paper, the third in a series, describes the effects of imperfect inspection on screening (group testing) acceptance sampling procedures. These procedures are based on first grouping items and testing the group as a whole for presence or absence of nonconforming (NC) items and proceeding to individual testing only if such presence is indicated. If sampling is perfect, the procedure can reduce the expected amount of testing, especially if the proportion of NC items is low. Modifications of the basic procedure—curtailed and hierarchal group testing—aimed at further reduction in the expected amount of testing are also discussed. Some new formulas are presented.

Gedankenexperimente: Assessing Field-Program Effectiveness by Numerical Simulations (Abstract)

Glaciological field programs may be regarded as imperfect sampling schemes designed to provide fundamental physical information on the dynamics and climatic sensitivity of the Antarctic ice sheet. Uncertainty arises as a result of technical and human factors such as: (i) logistic and financial constraints, (ii) measurement errors, (iii) low spatial resolution (see (i)), and (iv) (possibly!) misconceptions on the part of glaciologists who plan and execute field work. Regardless of such uncertainty, we depend on field data as the fundamental intellectual driving force of glaciology. Introspective evaluation of our field methods and program designs is thus reasonable, and perhaps necessary, to insure that our field programs are indeed satisfying their intended purpose.In our study, we conduct a variety of Gedankenexperimente (imaginary field programs), which sample an arbitrary, idealized ice shelf, subject to fluctuations and climatic changes on a variety of time and space scales. The “actual ” behavior of this ice shelf is produced by a time-dependent numerical simulation of ice-shelf evolution under specified forcings, using a model based on that of Lange and MacAyeal (1986). Each Gedankenexperiment consists of a spatially incomplete sampling of the model grid data at a particular moment in the evolution of the ice shelf (just as a real field program presently would sample the current state of an Antarctic ice shelf). The spatial sampling patterns are based on particular techniques commonly used in field programs (Kohnen 1985, Bindschadler and others 1987, Doake and others 1987, Shabtaie and Bentley 1987). Such sampling is designed to simulate field techniques such as airborne radio echo-sounding, surface geodetic measurements, aerial photography, and satellite altimetry (Fig. 1). We also add “random noise” to the sampled data, to simulate instrumental and navigational uncertainties.Having sampled the idealized ice shelf by using an imaginary field program, we “process” the supposed field data in order to test how well it reveals certain aspects of ice-shelf flow and evolution. This test is conducted by comparing the field-program results with the “known” behavior (by definition) of the numerical simulation. A variety of field-program design schemes are compared on the basis of their ability to predict: (i) the long-term growth or decay of the ice shelf, (ii) the “current” state of mass balance, (iii) the “current” partitioning of ice-stream input, and (iv) the balance of forces acting on the grounding line, and the tendency of the balance to change with time.A major aim of our study will be to point out how seriously the understanding of current ice-shelf dynamics and the ability to measure initial effects of global climatic changes (due to CO2 warming) are hampered by: (i) inability to map accurately all the regions of ice-shelf grounding, and (ii) inability to distinguish the effects of short-term variability from long-term, large-scale trends. To simulate the effects of ice-shelf grounding and ice-stream -temporal fluctuations, we specify in our idealized simulations that: (i) several ice rumples occasionally appear or disappear, and (ii) ice-stream fluxes, which feed the imaginary ice shelf, fluctuate (arbitrarily) with periods of 300 years.Since we assess the Gedankenexperimente in terms of their ability to detect long-term climatic trends, we run the ideal ice-shelf simulation forward in time until a statistically steady state is achieved (that is, all thickness and velocity patterns are stationary when averaged over the time-scale of fluctuation). At this point, we conduct the imaginary field programs in our study. Our main intention is to determine which Gedankenexperiment can best “see through” the short-term transient “noise” of the ideal ice-shelf evolution to detect the long-term condition of steady state.

Gedankenexperimente: Assessing Field-Program Effectiveness by Numerical Simulations (Abstract)

Glaciological field programs may be regarded as imperfect sampling schemes designed to provide fundamental physical information on the dynamics and climatic sensitivity of the Antarctic ice sheet. Uncertainty arises as a result of technical and human factors such as: (i) logistic and financial constraints, (ii) measurement errors, (iii) low spatial resolution (see (i)), and (iv) (possibly!) misconceptions on the part of glaciologists who plan and execute field work. Regardless of such uncertainty, we depend on field data as the fundamental intellectual driving force of glaciology. Introspective evaluation of our field methods and program designs is thus reasonable, and perhaps necessary, to insure that our field programs are indeed satisfying their intended purpose. In our study, we conduct a variety of Gedankenexperimente (imaginary field programs), which sample an arbitrary, idealized ice shelf, subject to fluctuations and climatic changes on a variety of time and space scales. The “actual ” behavior of this ice shelf is produced by a time-dependent numerical simulation of ice-shelf evolution under specified forcings, using a model based on that of Lange and MacAyeal (1986). Each Gedankenexperiment consists of a spatially incomplete sampling of the model grid data at a particular moment in the evolution of the ice shelf (just as a real field program presently would sample the current state of an Antarctic ice shelf). The spatial sampling patterns are based on particular techniques commonly used in field programs (Kohnen 1985, Bindschadler and others 1987, Doake and others 1987, Shabtaie and Bentley 1987). Such sampling is designed to simulate field techniques such as airborne radio echo-sounding, surface geodetic measurements, aerial photography, and satellite altimetry (Fig. 1). We also add “random noise” to the sampled data, to simulate instrumental and navigational uncertainties. Having sampled the idealized ice shelf by using an imaginary field program, we “process” the supposed field data in order to test how well it reveals certain aspects of ice-shelf flow and evolution. This test is conducted by comparing the field-program results with the “known” behavior (by definition) of the numerical simulation. A variety of field-program design schemes are compared on the basis of their ability to predict: (i) the long-term growth or decay of the ice shelf, (ii) the “current” state of mass balance, (iii) the “current” partitioning of ice-stream input, and (iv) the balance of forces acting on the grounding line, and the tendency of the balance to change with time. A major aim of our study will be to point out how seriously the understanding of current ice-shelf dynamics and the ability to measure initial effects of global climatic changes (due to CO2 warming) are hampered by: (i) inability to map accurately all the regions of ice-shelf grounding, and (ii) inability to distinguish the effects of short-term variability from long-term, large-scale trends. To simulate the effects of ice-shelf grounding and ice-stream -temporal fluctuations, we specify in our idealized simulations that: (i) several ice rumples occasionally appear or disappear, and (ii) ice-stream fluxes, which feed the imaginary ice shelf, fluctuate (arbitrarily) with periods of 300 years. Since we assess the Gedankenexperimente in terms of their ability to detect long-term climatic trends, we run the ideal ice-shelf simulation forward in time until a statistically steady state is achieved (that is, all thickness and velocity patterns are stationary when averaged over the time-scale of fluctuation). At this point, we conduct the imaginary field programs in our study. Our main intention is to determine which Gedankenexperiment can best “see through” the short-term transient “noise” of the ideal ice-shelf evolution to detect the long-term condition of steady state.