Communicating Uncertainty in National Security Intelligence: Expert and Non-expert Interpretations of and Preferences for Verbal and Numeric Formats

Organizations in several domains including national security intelligence communicate judgments under uncertainty using verbal probabilities (e.g., likely) instead of numeric probabilities (e.g., 75% chance), despite research indicating that the former have variable meanings across individuals. In the intelligence domain, uncertainty is also communicated using terms such as low, moderate, or high to describe the analyst’s confidence level. However, little research has examined how intelligence professionals interpret these terms and whether they prefer them to numeric uncertainty quantifiers. In two experiments (N = 481 and 624, respectively), uncertainty communication preferences of expert (n = 41 intelligence analysts inExperiment 1) and non-expert intelligence consumers were elicited. We examined which format participants judged to be more informative and simpler to process. We further tested whether participants treated probability and confidence as independent constructs and whether participants provided coherent numeric probability translations of verbal probabilities. Results showed that whereas most non-experts favored the numeric format, experts were about equally split, and most participants in both samples regarded the numeric format as more informative.Experts and non-experts consistently conflated probability and confidence. For instance, confidence intervals inferred from verbal confidence terms had a greater effect on the location of the estimate than the width of the estimate, contrary to normative expectation. Approximately ¼ of experts and over ½ of non-experts provided incoherent numeric probability translations of best estimates and lower and upper bounds when elicitations were spaced by intervening tasks.

Sand mining far outpaces natural supply in a large alluvial river

The world's large rivers are facing reduced sediment loads due to anthropogenic activities such as hydropower development and sediment extraction. Globally, estimates of sand extraction from large river systems are lacking, in part due to the pervasive and distributed nature of extraction processes. For the Mekong River, the widely assumed estimate of basin-wide sand extraction is 50 Mt per year. This figure is based on 2013 estimates and is likely to be outdated. Here, we demonstrate the ability of high-resolution satellite imagery to map, monitor, and estimate volumes of sand extraction on the Lower Mekong River in Cambodia. We use monthly composite images from PlanetScope imagery (5 m resolution) to estimate sand extraction volumes over the period 2016–2020 through tracking sand barges. We show that rates of extraction have increased on a yearly basis from 24 Mt (17 to 32 Mt) in 2016 to 59 Mt (41 to 75 Mt) in 2020 at a rate of ∼8 Mt yr−1 (6 to 10 Mt yr−1), where values in parentheses relate to lower and upper error bounds, respectively. Our revised estimates for 2020 (59 Mt) are nearly 2 times greater than previous best estimates for sand extraction for Cambodia (32 Mt) and greater than current best estimates for the entire Mekong Basin (50 Mt). We show that over the 5-year period, only 2 months have seen positive (supply exceeds extraction) sand budgets under mean scenarios (5 months under the scenarios with the greatest natural sand supply). We demonstrate that this net negative sand budget is driving major reach-wide bed incision with a median rate of −0.26 m a−1 over the period 2013 to 2019. The use of satellite imagery to monitor sand mining activities provides a low-cost means to generate up-to-date, robust estimates of sand extraction in the world's large rivers that are needed to underpin sustainable management plans of the global sand commons.

Joint inference of CFC lifetimes and banks suggests previously unidentified emissions

Chlorofluorocarbons (CFCs) are harmful ozone depleting substances and greenhouse gases. CFC production was phased-out under the Montreal Protocol, however recent studies suggest new and unexpected emissions of CFC-11. Quantifying CFC emissions requires accurate estimates of both atmospheric lifetimes and ongoing emissions from old equipment (i.e. ‘banks’). In a Bayesian framework we simultaneously infer lifetimes, banks and emissions of CFC-11, 12 and 113 using available constraints. We find lifetimes of all three gases are likely shorter than currently recommended values, suggesting that best estimates of inferred emissions are larger than recent evaluations. Our analysis indicates that bank emissions are decreasing faster than total emissions, and we estimate new, unexpected emissions during 2014-2016 were 23.2, 18.3, and 7.8 Gg/yr for CFC-11, 12 and 113, respectively. While recent studies have focused on unexpected CFC-11 emissions, our results call for further investigation of potential sources of emissions of CFC-12 and CFC-113, along with CFC-11.

psi-Hilfer Fractional Approximations of Csiszar's f-Divergence

Here are given tight probabilistic inequalities that provide nearly best estimates for the Csiszar's f-divergence. These use the right and left psi -Hilfer fractional derivatives of the directing function f. Csiszar's f- divergence or the so called Csiszar's discrimination is used as a measure of dependence between two random variables which is a very essential aspect of stochastics, we apply our results there. The Csiszar's discrimination is the most important and general measure for the comparison between two probability measures. We give also other applications.

Global surface temperature response to 11-year solar cycle forcing consistent with general circulation model results

The 11-year solar cycle is associated with a roughly 1Wm-2 trough-to-peak variation in total solar irradiance and is expected to produce a global temperature response. The sensitivity of this response is, however, contentious. Empirical best estimates of global surface temperature sensitivity to solar forcing range from 0.08 to 0.18 K [W m-2 ]-1. In comparison, best estimates from general circulation models forced by solar variability range between 0.03-0.07 K [W m-2]-1, prompting speculation that physical mechanisms not included in general circulation models may amplify responses to solar variability. Using a lagged multiple linear regression method, we find a sensitivity of globalaverage surface temperature ranging between 0.02-0.09 K [W m-2]-1, depending on which predictor and temperature datasets are used. On the basis of likelihood maximization, we give a best estimate of the sensitivity to solar variability of 0.05 K [W m-2]-1 (0.03-0.09 K, 95% c.i.). Furthermore, through updating a widely-used compositing approach to incorporate recent observations, we revise prior global temperature sensitivity best estimates of 0.12 to 0.18 K [W m-2]-1 downwards to 0.07 to 0.10 K [W m-2]-1. The finding of a most-likely global temperature response of 0.05 K [W m-2]-1 supports a relatively modest role for solar cycle variability in driving global surface temperature variations over the 20th century and removes the need to invoke processes that amplify the response relative to that exhibited in general circulation models.

Facilitating Sender-Receiver Agreement in Communicated Probabilities: Is it Best to Use Words, Numbers or Both?

Organizations tasked with communicating expert judgments couched in uncertainty often use numerically bounded linguistic probability schemes to fix the meaning of verbal probabilities. An experiment (N=1,202 after exclusions) was conducted to ascertain whether agreement with such a scheme was better when probabilities were presented verbally, numerically or in a hybrid “verbal + numeric” format. Across three agreement measures, the numeric and hybrid formats outperformed the verbal format and also yielded better discrimination between low and high probabilities. The hybrid format did not confer any advantage over the purely numeric format. Agreement with the standard was directly related to numeracy, verbal reasoning ability and an actively open-minded thinking style, all of which also were inversely related to incoherence (expressed as best estimates that fell outside one’s credible interval). The findings indicate that numerically bounded linguistic probability schemes are not an effective means of communicating information about probabilities to others.

A survey of proximal methods for monitoring leaf phenology in temperate deciduous forests

Tree phenology is a major driver of forest-atmosphere mass and energy exchanges. Yet tree phenology has historically not been recorded at flux measurement sites. Here, we used seasonal time-series of ground-based NDVI (Normalized Difference Vegetation Index), RGB camera GCC (Greenness Chromatic Coordinate), broad-band NDVI, LAI (Leaf Area Index), fAPAR (fraction of Absorbed Photosynthetic Active Radiation), CC (Canopy Closure), fRvis (fraction of Reflected Radiation) and GPP (Gross Primary Productivity) to predict six phenological markers detecting the start, middle and end of budburst and of leaf senescence in a temperate deciduous forest. We compared them to observations of budburst and leaf senescence achieved by field phenologists over a 13-year period. GCC, NDVI and CC captured very well the interannual variability of spring phenology (R2 > 0.80) and provided the best estimates of the observed budburst dates, with a mean absolute deviation (MAD) less than 4 days. For the CC and GCC methods, mid-amplitude (50%) threshold dates during spring phenological transition agreed well with the observed phenological dates. For the NDVI-based method, on average, the mean observed date coincides with the date when NDVI reaches 25% of its amplitude of annual variation. For the other methods, MAD ranges from 6 to 17 days. GPP provides the most biased estimates. During the leaf senescence stage, NDVI- and CC-derived dates correlated significantly with observed dates (R2 =0.63 and 0.80 for NDVI and CC, respectively), with MAD less than 7 days. Our results show that proximal sensing methods can be used to derive robust phenological indexes. They can be used to retrieve long-term phenological series at flux measurement sites and help interpret the interannual variability and decadal trends of mass and energy exchanges.HighlightsWe used 8 indirect methods to predict the timing of phenological events.GCC, NDVI and CC captured very well the interannual variation of spring phenology.GCC, NDVI and CC provided the best estimates of observed budburst dates.NDVI and CC derived-dates correlated with observed leaf senescence dates.