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The climate scenarios that form the basis for current climate risk assessments have no assigned probabilities, and this impedes the analysis of future climate risks. This paper proposes an approach to estimate the probability of carbon dioxide (CO2) concentration scenarios used in key climate change modeling experiments. It computes the CO2 emissions compatible with the concentrations prescribed by Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 experiments. The distribution of these compatible cumulative emissions is interpreted as the likelihood of future emissions given a concentration pathway. Using Bayesian analysis, the probability of each pathway can be estimated from a probabilistic sample of future emissions. The approach is demonstrated with five probabilistic CO2 emission simulation ensembles from four Integrated Assessment Models (IAM), leading to independent estimates of the likelihood of the CO2 concentration of Representative Concentration Pathways (RCP) and Shared Socioeconomic Pathways (SSP). Results suggest that SSP5‐8.5 is unlikely for the second half of the 21st century, but offer no clear consensus on which of the remaining scenarios is most likely. Estimates of likelihoods of CO2 concentrations associated with RCP and SSP scenarios are affected by sampling errors, differences in emission sources simulated by the IAMs, and a lack of a common experimental framework for IAM simulations. These shortcomings, along with a small IAM ensemble size, limit the applicability of the results presented here. Novel joint IAM and the Earth System Model experiments are needed to deliver actionable probabilistic climate risk assessments. Plain Language Summary: Climate model simulations are increasingly used to understand future trends in the severity and frequency of impactful climate hazards and associated physical and socioeconomic risks. Such simulations describe physical climate change in response to scenarios of greenhouse gases and land use change. However, the scenarios most widely used today as the basis for adaptation planning have no assigned probabilities; rather, they are explicitly intended to span a range of arbitrary climate futures. This reduces the applicability of resulting hazards projections for cost/benefit analysis of adaptation investments. To support risk‐based adaptation decision‐making, this study analyzes five sets of probabilistic carbon dioxide emissions simulated by four Integrated Assessment Models (IAM), along with Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 climate model ensemble results, to estimate the probability of future greenhouse gases concentration scenarios. The results of the five IAMs simulations collated in this work vary, and although they might not be representative of the wider literature, most of them suggest that the high‐emission scenario SSP5‐8.5 becomes unlikely as we reach the second half of the century. Based on lessons learned in this exercise, we propose that new sets of IAM and climate model experiments be jointly designed to better support probabilistic climate change risk assessments. Key Points: Probabilistic Integrated Assessment Models (IAM) simulations of future carbon dioxide emissions are used to evaluate the relative probability of greenhouse gases concentration scenariosJoint coordinated IAM and climate modeling experiments should be designed to enable probabilistic climate change risk assessments [ABSTRACT FROM AUTHOR]

Birdhouse is a collaborative project open for the community to participate. It is a software framework containing a collection of Web Processing Services (WPS). The deployed algorithms are focusing on Earth Systems and environmental data processing with the philosophy of streamlining the software development and deployment. By supporting climate, earth observation and biodiversity data and processes, Birdhouse can be used in a wide array of Earth sciences projects and workflows. The core benefit of this project is to allow the seamless use of climate services developed by a diverse network of national meteorological offices, regional climate service providers, academics, not-for-profit research centers and private industry. As governments move toward open-data policies, there will be a need for analytical services that extract value out of the deluge of information. Using an interoperable software architecture, institutions can provide both data and services allowing users to process the data remotely from a laptop, instead of having to acquire and maintain large storage infrastructures. [ABSTRACT FROM AUTHOR]

Background: Sea ice across the Arctic is declining and altering physical characteristics of marine ecosystems. Polar bears (Ursus maritimus) have been identified as vulnerable to changes in sea ice conditions. We use sea ice projections for the Canadian Arctic Archipelago from 2006 – 2100 to gain insight into the conservation challenges for polar bears with respect to habitat loss using metrics developed from polar bear energetics modeling. Principal Findings: Shifts away from multiyear ice to annual ice cover throughout the region, as well as lengthening ice-free periods, may become critical for polar bears before the end of the 21st century with projected warming. Each polar bear population in the Archipelago may undergo 2–5 months of ice-free conditions, where no such conditions exist presently. We identify spatially and temporally explicit ice-free periods that extend beyond what polar bears require for nutritional and reproductive demands. Conclusions/Significance: Under business-as-usual climate projections, polar bears may face starvation and reproductive failure across the entire Archipelago by the year 2100. [ABSTRACT FROM AUTHOR]

A paper published by Anagnostopoulos et al. in volume 55 of the Hydrological Sciences Journal (HSJ) concludes that climate models are poor based on temporal correlation between observations and individual simulations. This interpretation hinges on a common misconception, that climate models predict natural climate variability. This discussion underlines fundamental differences between hydrological and climatological models, and hopes to clear misunderstandings regarding the proper use of climate simulations. Citation Huard, D. (2011) A black eye for the Hydrological Sciences Journal. Discussion of ‘A comparison of local and aggregated climate model outputs with observed data’ by G.G. Anagnostopoulos et al. (2010, Hydrol. Sci. J. 55 (7), 1094–1110). Hydrol. Sci. J. 56(7), 1330–1333. [ABSTRACT FROM PUBLISHER]

An outstanding issue of hydrological modeling is the adequate treatment of uncertainties in model calibration and prediction. The current paradigm is that the major sources of uncertainties, namely input, output and model uncertainty should be accounted for directly, instead of assuming they can be safely lumped into the output uncertainties. In this paper, Bayesian analysis is used to calibrate the conceptual hydrologic monthly model GR2M taking into account input, output, structural and initial state uncertainty through error models and priors. Calibration is performed under different error assumptions to study the influence of the initial state uncertainty, the consequences of large input errors, the impact of error assumptions on calibrated parameter posterior distributions and the definition of error models. It is shown how such an analysis can be used to separate, a posteriori, the different sources of errors, and in particular, to identify structural errors from data errors. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of taking into account the tail dependence in the context of bivariate frequency analysis based on copulas. Three nonparametric estimators of the tail-dependence coefficient are compared by simulations with seven families of copulas. We choose the two estimators most adapted to a bivariate frequency analysis of the annual maximum flows and the corresponding flow hydrograph volumes of the Loire River (France). In this example, the bivariate return period and the conditional density of the volume given that the flow exceeds a given threshold are computed. The results show, as can be expected, that out of the seven copula families tested, five overestimate the return periods of correlated extreme events. These results bring to the forefront the importance of taking into account the tail dependence in order to estimate the risk adequately. [ABSTRACT FROM AUTHOR]

The impact of input errors in the calibration of watershed models is a recurrent theme in the water science literature. It is now acknowledged that hydrological models are sensitive to errors in the measures of precipitation and that those errors bias the model parameters estimated via the standard least squares (SLS) approach. This paper presents a Bayesian uncertainty framework allowing one to account for input, output, and structural (model) uncertainties in the calibration of a model. Using this framework, we study the impact of input uncertainty on the parameters of the hydrological model 'abc.' Mostly of academic interest, the 'abc' model has a response linear to its input, allowing the closed form integration of nuisance variables under proper assumptions. Using those analytical solutions to compute the posterior density of the model parameters, some interesting observations can be made about their sensitivity to input errors. We provide an explanation for the bias identified in the SLS approach and show that in the input error context the prior on the input 'true' value has a significant influence on the parameters' posterior density. Overall, the parameters obtained from the Bayesian method are more accurate, and the uncertainty over them is more realistic than with SLS. This method, however, is specific to linear models, while most hydrological models display strong nonlinearities. Further research is thus needed to demonstrate the applicability of the uncertainty framework to commonly used hydrological models. [ABSTRACT FROM AUTHOR]

This user guide describes a Python package, PyMC, that allows users to efficiently code a probabilistic model and draw samples from its posterior distribution using Markov chain Monte Carlo techniques.

Background and Purpose: Several studies have been undertaken to validate quantitative methods of evaluating cervical spinal stenosis. This study was performed to assess the degree of interobserver and intraobserver agreement in the qualitative evaluation of cervical spinal stenosis on CT myelograms and MR images., Methods: Cervical MR images and CT myelograms of 38 patients were evaluated retrospectively. Six neuroradiologists with various backgrounds and training independently assessed the level, degree, and cause of stenosis on either MR images or CT myelograms. Unknown to the evaluators, 16 of the patients were evaluated twice to determine intraobserver variability., Results: Interobserver agreement among the radiologists with regard to level, degree, and cause of stenosis on CT myelograms showed kappa values of 0.50, 0.26, and 0.32, respectively, and on MR images showed kappa values of 0.60, 0.31, and 0.22, respectively. Intraobserver agreement with regard to level, degree, and cause of stenosis on CT myelograms showed mean kappa values of 0.69, 0.41, and 0.55, respectively, and on MR images showed mean kappa values of 0.80, 0.37, and 0.40, respectively., Conclusion: MR imaging and CT myelographic evaluation of cervical spinal stenosis by using current qualitative methods results in significant variation in image interpretation.