Canary in a coal mine: perceptions of climate change risks and response options among Canadian mine operations

A survey documenting how climate change is perceived and responded to by Canadian mine operations was administered to a random sample of practitioners working at mine sites across Canada. Key findings include: (1) Mines are sensitive to climatic hazards; (2) There is concern about climate change among mine practitioners, but the majority have not yet noticed climate change to be affecting operations; (3) Future climate change is expected to have negative impacts for mine operations; (4) Mines are responding to climate change mainly through efforts to reduce greenhouse gas emissions but also through adaptation, although to a lesser degree; and (5) Knowledge of future climate change projections and impacts is limited, potentially constraining understanding of the nature of climate change risks. The survey compliments previous work documenting perceptions among upper level management on climate change in the Canadian mining industry. The results from both surveys are largely consistent, establishing that the mining sector perceives climate change as an emerging risk and is developing response options, but needs to invest more time and resources for adaptation to what are inevitable changes in climate. The results support the need for targeted in-depth research to assess the vulnerability of mining to climate change and to evaluate response options.     

Harmonization of land-use scenarios for the period 1500�C2100: 600?years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands

In preparation for the fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC), the international community is developing new advanced Earth System Models (ESMs) to assess the combined effects of human activities (e.g. land use and fossil fuel emissions) on the carbon-climate system. In addition, four Representative Concentration Pathway (RCP) scenarios of the future (2005?C2100) are being provided by four Integrated Assessment Model (IAM) teams to be used as input to the ESMs for future carbon-climate projections (Moss et al. 2010). The diversity of approaches and requirements among IAMs and ESMs for tracking land-use change, along with the dependence of model projections on land-use history, presents a challenge for effectively passing data between these communities and for smoothly transitioning from the historical estimates to future projections. Here, a harmonized set of land-use scenarios are presented that smoothly connects historical reconstructions of land use with future projections, in the format required by ESMs. The land-use harmonization strategy estimates fractional land-use patterns and underlying land-use transitions annually for the time period 1500?C2100 at 0.5°?×?0.5° resolution. Inputs include new gridded historical maps of crop and pasture data from HYDE 3.1 for 1500?C2005, updated estimates of historical national wood harvest and of shifting cultivation, and future information on crop, pasture, and wood harvest from the IAM implementations of the RCPs for the period 2005?C2100. The computational method integrates these multiple data sources, while minimizing differences at the transition between the historical reconstruction ending conditions and IAM initial conditions, and working to preserve the future changes depicted by the IAMs at the grid cell level. This study for the first time harmonizes land-use history data together with future scenario information from multiple IAMs into a single consistent, spatially gridded, set of land-use change scenarios for studies of human impacts on the past, present, and future Earth system.     

Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980�C2010 period

Several different inventories of global and regional anthropogenic and biomass burning emissions are assessed for the 1980?C2010 period. The species considered in this study are carbon monoxide, nitrogen oxides, sulfur dioxide and black carbon. The inventories considered include the ACCMIP historical emissions developed in support of the simulations for the IPCC AR5 assessment. Emissions for 2005 and 2010 from the Representative Concentration Pathways (RCPs) are also included. Large discrepancies between the global and regional emissions are identified, which shows that there is still no consensus on the best estimates for surface emissions of atmospheric compounds. At the global scale, anthropogenic emissions of CO, NO_x and SO₂ show the best agreement for most years, although agreement does not necessarily mean that uncertainty is low. The agreement is low for BC emissions, particularly in the period prior to 2000. The best consensus is for NO_x emissions for all periods and all regions, except for China, where emissions in 1980 and 1990 need to be better defined. Emissions of CO need better quantification in the USA and India for all periods; in Central Europe, the evolution of emissions during the past two decades needs to be better determined. The agreement between the different SO₂ emissions datasets is rather good for the USA, but better quantification is needed elsewhere, particularly for Central Europe, India and China. The comparisons performed in this study show that the use of RCP8.5 for the extension of the ACCMIP inventory beyond 2000 is reasonable, until more global or regional estimates become available. Concerning biomass burning emissions, most inventories agree within 50?C80%, depending on the year and season. The large differences between biomass burning inventories are due to differences in the estimates of burned areas from the different available products, as well as in the amount of biomass burned.     

Identifying hot spots of security vulnerability associated with climate change in Africa

Given its high dependence on rainfed agriculture and its comparatively low adaptive capacity, Africa is frequently invoked as especially vulnerable to climate change. Within Africa, there is likely to be considerable variation in vulnerability to climate change both between and within countries. This paper seeks to advance the agenda of identifying the hot spots of what we term “climate security” vulnerability, areas where the confluence of vulnerabilities could put large numbers of people at risk of death from climate-related hazards. This article blends the expertise of social scientists and climate scientists. It builds on a model of composite vulnerability that incorporates four “baskets” or processes that are thought to contribute to vulnerability including: (1) physical exposure, (2) population density, (3) household and community resilience, and (4) governance and political violence. Whereas previous iterations of the model relied on historical physical exposure data of natural hazards, this paper uses results from regional model simulations of African climate in the late 20th century and mid-21st century to develop measures of extreme weather events—dry days, heat wave events, and heavy rainfall days—coupled with an indicator of low-lying coastal elevation. For the late 20th century, this mapping process reveals the most vulnerable areas are concentrated in Chad, the Democratic Republic of the Congo, Niger, Somalia, Sudan, and South Sudan, with pockets in Burkina Faso, Ethiopia, Guinea, Mauritania, and Sierra Leone. The mid 21st century projection shows more extensive vulnerability throughout the Sahel, including Burkina Faso, Chad, Mali, northern Nigeria, Niger, and across Sudan.     

Inertial oscillations near the coast of Nova Scotia during CASP

Abstract

Analysis of current, temperature and salinity records in the nearshore region of the Scotian Shelf during the Canadian Atlantic Storms Program (CASP), reveals that the inertial wave field is highly intermittent, with comparable amplitudes in the surface and deep layers. Clockwise current energy in the surface layer is concentrated at a frequency slightly below inertial, consistent with Doppler shifting by the strong mean current and/or straining by the mean flow shear, whereas the spectral peak in deep water is at the local inertial frequency. Clockwise coherence is high (γ² ≥ 0.8) horizontally over the scale of the array (60 km × 120 km) and in the vertical, with upward phase propagation rates of 0.15–0.50 × 10^?12 ms^?1, inversely proportional to the local value of the Brunt Väisälä frequency. Clockwise current energy decreases in the onshore direction and appears to be completely inhibited on the 60‐m isobath.

A case study of the response to the CASP IOP 14 storm indicates that the inertial waves may be generated by a strong wind shift propagating onshore at a speed of 10 ms^?1. On the eastern side of the array (Liscomb line), clockwise current oscillations propagate onshore in the surface layer at a rate (8.1 ± 0.9 m s^?1) comparable with the speed of the atmospheric front, while waves in the pycnocline move offshore at a lower (internal wave) speed (1.8 m s^?1). Furthermore the temperature and salinity fluctuations are in (out) of phase with longshore current in the deep (surface) layer. However, on the western side of the array (Halifax line), the inertial waves are more complex. A sharp steepening of phase lines at the coast indicates that the phase speed of clockwise current oscillations is considerably reduced and the evidence for offshore propagation of internal waves is less clear. The discrepancies between observations on the two lines suggest that the internal wave field is three‐dimensional.

Results of simple mixed‐layer models indicate that the inertial response near the surface is sensitive to the accurate definition of the local wind field, but not to certain model physics, such as the form of the decay term. The observations also show some qualitative similarities with models for two‐dimensional response to a moving front (e.g. Kundu, 1986), but the actual forcing terms are more complicated, based on IOP 14 wind measurements.     

A verification framework for interannual-to-decadal predictions experiments

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.     

Rancher and farmer perceptions of climate change in Nevada, USA

Farming and ranching communities in arid lands are vulnerable to the adverse impacts of climate change. We surveyed Nevada ranchers and farmers (n?=?481) during 2009–2010 to assess climate change related knowledge, assumptions, and perceptions. The large majority of this group agreed that we are in a period of climate change; however, only 29 % of them believed that human activity is playing a significant role. Female ranchers and farmers hold more scientifically accurate knowledge about climate change than do their male counterparts, regardless of Democratic or Republican affiliation. Partisan affiliation, political ideology, and gender have strong impacts on climate change knowledge and perceptions. Republican, conservative and male rural residents view climate change as a low national priority, less important to themselves, and less harmful to their communities. Female ranchers and farmers are more concerned about the negative impacts of climate change. We found that only 4 % of our subjects (n?=?299) attribute local environment changes to climate change or global warming. The knowledge gained from this study will help researchers and natural resource managers understand how to best communicate about climate change with rural communities, and support policy makers in identifying potentially effective adaptation and mitigation policies and outreach programs.     

ORTHOPHOTOGRAPHY FOR RURAL MAP REVISION

The Ordnance Survey (OS) has recently investigated the potential of orthophotography for revision of 1:2500 scale mapping following advances in technology since an earlier investigation in 1973. The results have been compared with stereoplotting on an automated workstation. The background to the investigation and the existing revision methods used for rural areas are described. Orthophotography was produced on a Zeiss 22 Orthocomp at 1:2500 scale using digital height model data supplied from contours (at a 5 m vertical interval) on 1:10000 scale mapping. A pilot block of 20 km² was revised at Kingsbridge, south Devon to assess three levels of revision (local fit, part remedial, and full remedial), using three techniques (orthophotograph overlay, Stereo-orthophoto Plotter and stereoplotting instrument). The quality of the image of the orthophotographs was below that expected for rectified enlargements and the cost of orthophotograph production made the technique uneconomic compared with existing techniques. Orthophotographs were capable of improving the accuracy of revision mapping, but the most cost effective and accurate method was full remedial revision using a Kern PG2 automated workstation.     

The effects of spatial autoregressive dependencies on inference in ordinary least squares: a geometric approach

There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce “spurious correlation” that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.