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Climate change 2007 : the physical science basis, S. Solomon, D. Qin, M. Manning, M. Marquis, K. Averyt, M.M.B. Tignor, H. LeRoy Miller, Jr. and Z. Chen (Eds.) : book review

01 Jan 2010-Vol. 92, Iss: 1, pp 86-87
TL;DR: The most comprehensive assessment of climate change during the past, present and future was made in the fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC).
Abstract: This contribution to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change is the most comprehensive scientific assessments of climate change during the past (the climate during periods before the development of measuring instruments, including historic and geological time), the present (the average weather over a number of recent decades) and the future (projected long-term average weather changes due to changes in atmospheric composition or other factors). The book also provides an excellent overview on how the science of climate change has progressed, including the methods used, and also shows the recent advances made in the modelling of regional climate change over the African continent. Moreover, the scientific understanding of anthropogenic effects on global climate has improved since the Third Assessment Report, which has led to very high confidence that the global average net effect of human activities over the past 250 years has been one of warning.
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TL;DR: A new statistical explanation of MaxEnt is described, showing that the model minimizes the relative entropy between two probability densities defined in covariate space, which is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts.
Abstract: MaxEnt is a program for modelling species distributions from presence-only species records. This paper is written for ecologists and describes the MaxEnt model from a statistical perspective, making explicit links between the structure of the model, decisions required in producing a modelled distribution, and knowledge about the species and the data that might affect those decisions. To begin we discuss the characteristics of presence-only data, highlighting implications for modelling distributions. We particularly focus on the problems of sample bias and lack of information on species prevalence. The keystone of the paper is a new statistical explanation of MaxEnt which shows that the model minimizes the relative entropy between two probability densities (one estimated from the presence data and one, from the landscape) defined in covariate space. For many users, this viewpoint is likely to be a more accessible way to understand the model than previous ones that rely on machine learning concepts. We then step through a detailed explanation of MaxEnt describing key components (e.g. covariates and features, and definition of the landscape extent), the mechanics of model fitting (e.g. feature selection, constraints and regularization) and outputs. Using case studies for a Banksia species native to south-west Australia and a riverine fish, we fit models and interpret them, exploring why certain choices affect the result and what this means. The fish example illustrates use of the model with vector data for linear river segments rather than raster (gridded) data. Appropriate treatments for survey bias, unprojected data, locally restricted species, and predicting to environments outside the range of the training data are demonstrated, and new capabilities discussed. Online appendices include additional details of the model and the mathematical links between previous explanations and this one, example code and data, and further information on the case studies.

4,621 citations

Journal ArticleDOI
TL;DR: While the multimodel average appears to still be useful in some situations, the results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.
Abstract: Recent coordinated efforts, in which numerous general circulation climate models have been run for a common set of experiments, have produced large datasets of projections of future climate for various scenarios. Those multimodel ensembles sample initial conditions, parameters, and structural uncertainties in the model design, and they have prompted a variety of approaches to quantifying uncertainty in future climate change. International climate change assessments also rely heavily on these models. These assessments often provide equal-weighted averages as best-guess results, assuming that individual model biases will at least partly cancel and that a model average prediction is more likely to be correct than a prediction from a single model based on the result that a multimodel average of present-day climate generally outperforms any individual model. This study outlines the motivation for using multimodel ensembles and discusses various challenges in interpreting them. Among these challenges are that the number of models in these ensembles is usually small, their distribution in the model or parameter space is unclear, and that extreme behavior is often not sampled. Model skill in simulating present-day climate conditions is shown to relate only weakly to the magnitude of predicted change. It is thus unclear by how much the confidence in future projections should increase based on improvements in simulating present-day conditions, a reduction of intermodel spread, or a larger number of models. Averaging model output may further lead to a loss of signal— for example, for precipitation change where the predicted changes are spatially heterogeneous, such that the true expected change is very likely to be larger than suggested by a model average. Last, there is little agreement on metrics to separate ‘‘good’’ and ‘‘bad’’ models, and there is concern that model development, evaluation, and posterior weighting or ranking are all using the same datasets. While the multimodel average appears to still be useful in some situations, these results show that more quantitative methods to evaluate model performance are critical to maximize the value of climate change projections from global models.

1,056 citations

Journal ArticleDOI
TL;DR: The performance characteristics of mmen-Mg(2)(dobpdc) indicate it to be an exceptional new adsorbent for CO(2) capture, comparing favorably with both amine-grafted silicas and aqueous amine solutions.
Abstract: Two new metal–organic frameworks, M2(dobpdc) (M = Zn (1), Mg (2); dobpdc4– = 4,4′-dioxido-3,3′-biphenyldicarboxylate), adopting an expanded MOF-74 structure type, were synthesized via solvothermal and microwave methods. Coordinatively unsaturated Mg2+ cations lining the 18.4-A-diameter channels of 2 were functionalized with N,N′-dimethylethylenediamine (mmen) to afford Mg2(dobpdc)(mmen)1.6(H2O)0.4 (mmen-Mg2(dobpdc)). This compound displays an exceptional capacity for CO2 adsorption at low pressures, taking up 2.0 mmol/g (8.1 wt %) at 0.39 mbar and 25 °C, conditions relevant to removal of CO2 from air, and 3.14 mmol/g (12.1 wt %) at 0.15 bar and 40 °C, conditions relevant to CO2 capture from flue gas. Dynamic gas adsorption/desorption cycling experiments demonstrate that mmen-Mg2(dobpdc) can be regenerated upon repeated exposures to simulated air and flue gas mixtures, with cycling capacities of 1.05 mmol/g (4.4 wt %) after 1 h of exposure to flowing 390 ppm CO2 in simulated air at 25 °C and 2.52 mmol/g (9...

990 citations

Journal ArticleDOI
TL;DR: In this article, the authors analyzed the climate projections of 11 earth system models that performed both emission-driven and concentration-driven RCP8.5 simulations and found that seven out of the 11 ESMs simulate a larger CO2 (on average by 44 ppm, 985 ± 97 ppm by 2100) and hence higher radiative forcing (by 0.25 W m−2) when driven by CO2 emissions than for the concentration driven scenarios.
Abstract: In the context of phase 5 of the Coupled Model Intercomparison Project, most climate simulations use prescribed atmospheric CO2 concentration and therefore do not interactively include the effect of carbon cycle feedbacks. However, the representative concentration pathway 8.5 (RCP8.5) scenario has additionally been run by earth system models with prescribed CO2 emissions. This paper analyzes the climate projections of 11 earth system models (ESMs) that performed both emission-driven and concentration-driven RCP8.5 simulations. When forced by RCP8.5 CO2 emissions, models simulate a large spread in atmospheric CO2; the simulated 2100 concentrations range between 795 and 1145 ppm. Seven out of the 11 ESMs simulate a larger CO2 (on average by 44 ppm, 985 ± 97 ppm by 2100) and hence higher radiative forcing (by 0.25 W m−2) when driven by CO2 emissions than for the concentration-driven scenarios (941 ppm). However, most of these models already overestimate the present-day CO2, with the present-day biase...

905 citations

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