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.     

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.     

EISCAT/CRRES observations: nightside ionospheric ion outflow and oxygen-rich substorm injections

We present combined observations made near midnight by the EISCAT radar, all-sky cameras and the combined released and radiation efects satellite (CRRES) shortly before and during a substorm. In particular, we study a discrete, equatorward-drifting auroral arc, seen several degrees poleward of the onset region. The arc passes through the field-aligned beam of the EISCAT radar and is seen to be associated with a considerable upflow of ionospheric plasma. During the substorm, the CRRES satellite observed two major injections, 17 min apart, the second of which was dominated by O⁺ ions. We show that the observed are was in a suitable location in both latitude and MLT to have fed O⁺ ions into the second injection and that the upward flux of ions associated with it was sufficient to explain the observed injection. We interpret these data as showing that arcs in the nightside plasma-sheet boundary layer could be the source of O⁺ ions energised by a dipolarisation of the mid- and near-Earth tail, as opposed to ions ejected from the dayside ionosphere in the cleft ion fountain.     

Using micro-catchment experiments for multi-local scale modelling of nature-based solutions

The Q-natural flood management project has co-developed with the Environment Agency 18 monitored micro-catchments (~1 km²) in Cumbria, UK installing calibrated flumes aimed at quantifying the potential shift in observed flows resulting from a range of nature-based-solutions installed by local organizations. The small-scale reduces the influence of variability characterizing larger catchments that would otherwise mask any such shifts, which we attempt to relate to a shift in model parameters. This paper demonstrates an approach to applying donor-parameter-shifts obtained from modelling two of the paired micro-catchments to a much larger scale, in order to understand the potential for improved distributed modelling of nature-based solutions in the form of additional tree-planting. The models include a rainfall-runoff model, Dynamic Topmodel, and a 2D hydrodynamic model, JFlow, permitting analysis of changes in hillslope processes and channel hydrodynamics resulting from a range of distributed measures designed to emulate natural hydrological processes that evaporate, store or infiltrate flows. We report on attempts to detect shift in hydrological response using one of the paired-micro-catchment moorland versus forestry sites in Lorton using Dynamic Topmodel. A donor-parameter-shift approach is used in a hypothetical experiment to represent new woodland in a much larger catchment, although testing all combinations of spatial planting strategies, responses to multiple-extremes, failure-modes and changes to synchronization becomes intractable to support good decision making. We argue that the problem can be re-framed to use donor-parameter-shifts at multi-local-scale catchments above communities known to be at risk, commensurate with most of the evidence of NbS impacts being effective at the small scale (ca. 10 km²). This might lead to more effective modelling to help catchment managers prioritize those communities-at-risk where there is more evidence that NbS might be effective.     

Topographically moderated soil water seasons impact vegetation dynamics in semiarid mountain catchments: Illustrations from the Dry Creek Experimental Watershed,Idaho, USA

Water stored in soils, in part, controls vegetation productivity and the duration of growing seasons in wildland ecosystems. Soil water is the dynamic product of precipitation, evapotranspiration and soil properties, all of which vary across complex terrain making it challenging to decipher the specific controls that soil water has on growing season dynamics. We assess how soil water use by plants varies across elevations and aspects in the Dry Creek Experimental Watershed in southwest Idaho, USA, a mountainous, semiarid catchment that spans low elevation rain to high elevation snow regimes. We compare trends in soil water and soil temperature with corresponding trends in insolation, precipitation and vegetation productivity, and we observe trends in the timing, rate and duration of soil water extraction by plants across ranges in elevation and aspect. The initiation of growth-supporting conditions, indicated by soil warming, occurs 58 days earlier at lower, compared with higher, elevations. However, growth-supporting conditions also end earlier at lower elevations due to the onset of soil water depletion 29 days earlier than at higher elevations. A corresponding shift in peak NDVI timing occurs 61 days earlier at lower elevations. Differences in timing also occur with aspect, with most threshold timings varying by 14–30 days for paired north- and south-facing sites at similar elevations. While net primary productivity nearly doubles at higher elevations, the duration of the warm-wet period of active water use does not vary systematically with elevation. Instead, the greater ecosystem productivity is related to increased soil water storage capacity, which supports faster soil water use and growth rates near the summer solstice and peak insolation. Larger soil water storage does not appear to extend the duration of the growing season, but rather supports higher growing season intensity when wet-warm soil conditions align with high insolation. These observations highlight the influence of soil water storage capacity in dictating ecological function in these semiarid steppe climatic regimes.