The economics of boldness: equity,action, and hope

Equity is usually interpreted in terms of the concept of justice, such that an equitable share of the atmospheric space is understood in terms of past emissions. This emphasizes the collective nature of sharing the burden of mitigation and the duty to act for those who have emitted the most. An alternative is considered: the aggregate costs and benefits to all Parties that could result from both increasing the level of collective ambition and implementing a climate regime that supports bold actions across all Parties. The regional impacts and carbon flow costs across differentiated scenarios are assessed and it is argued that the majority of developing-country Parties would be better off if a high ambition outcome to which all contributed, but some more than others. Moreover, those with middle or low emissions would have proportionally more to gain (or lose) relative to the level of ambition compared to those that have had higher emissions. The climate regime should be built on the principle of common but differentiated responsibility and respective capabilities (CBDR&RC), in which all act early even if some do much more; one that accounts for justice but does not forget hope.

Policy relevance

Differing interpretations of equity and the principles of the United Nations Framework Convention on Climate Change (UNFCCC) are discussed, with a focus on how these can enhance or hinder collective action. Whilst the climate change negotiations are usually taken as games in which one party gains and another loses, and interactions are dogged by continuous conflict, it is explored instead how negotiation responses can be framed in terms of cooperation. This would emphasize the gains that could be achieved by common but differentiated collective action, which could result in a collective avoidance of impacts. The possibilities that this shift of perspective could bring are explored by comparing costs under global cooperation (or lack of it). It is found that cooperation reduces the total costs for these regions. Thus, thinking in terms of cooperation focuses the options for negotiation on the means and interpretations of the UNFCCC principles that spur action and avoid climate impacts through collective action.     

A modified parameterization of flux-profile relationships in the surface layer using different roughness length values for heat and momentum

The surface-layer flux-profile formulae of Louis (1979), used in many atmospheric models, are modified in a simple way to allow for different values of the roughness lengths for heat and momentum. The modified set of formulae simplifies the calculation of surface-layer fluxes over most natural land surfaces, where the roughness length for momentum can be almost an order of magnitude greater than that for heat.     

Present climate and climate change over North America as simulated by the fifth-generation Canadian regional climate model

The fifth-generation Canadian Regional Climate Model (CRCM5) was used to dynamically downscale two Coupled Global Climate Model (CGCM) simulations of the transient climate change for the period 1950–2100, over North America, following the CORDEX protocol. The CRCM5 was driven by data from the CanESM2 and MPI-ESM-LR CGCM simulations, based on the historical (1850–2005) and future (2006–2100) RCP4.5 radiative forcing scenario. The results show that the CRCM5 simulations reproduce relatively well the current-climate North American regional climatic features, such as the temperature and precipitation multiannual means, annual cycles and temporal variability at daily scale. A cold bias was noted during the winter season over western and southern portions of the continent. CRCM5-simulated precipitation accumulations at daily temporal scale are much more realistic when compared with its driving CGCM simulations, especially in summer when small-scale driven convective precipitation has a large contribution over land. The CRCM5 climate projections imply a general warming over the continent in the 21st century, especially over the northern regions in winter. The winter warming is mostly contributed by the lower percentiles of daily temperatures, implying a reduction in the frequency and intensity of cold waves. A precipitation decrease is projected over Central America and an increase over the rest of the continent. For the average precipitation change in summer however there is little consensus between the simulations. Some of these differences can be attributed to the uncertainties in CGCM-projected changes in the position and strength of the Pacific Ocean subtropical high pressure.     

Are Natural Climate Forcings Able to Counteract the Projected Anthropogenic Global Warming?

A two-dimensional global climate model is used to assessthe climatic changes associated with the new IPCC SRES emissions scenarios and to determine which kind of changes in total solar irradiance and volcanic perturbations could mask the projected anthropogenic global warming associated to the SRES scenarios. Our results suggest that only extremely unlikely changes in total solar irradiance and/or volcanic eruptions would be able to overcome the simulated anthropogenic global warming over the century. Nevertheless, in the critical interval of the next two decades the externally-driven natural climate variability might possibly confuse the debate about temperature trends and impede detection of the anthropogenic climate change signal.     

Laboratory study of the rates and products of the phototransformations of naphthalene adsorbed on samples of titanium dioxide,ferric oxide,muscovite, and fly ash

The photodegradation of naphthalene (NPH), chosen as a model of polynuclear aromatic pollutants, has been studied in the presence of a layer of four water-insoluble inorganic solids which can be found in the troposphere (TiO₂, Fe₂O₃, muscovite, and a fly ash sample). Direct photolysis of NPH is negligible at >340 nm. Dark adsorption of NPH on TiO₂ (mainly anatase, nonporous, 50 m² g^–1) at 293 K corresponds to a surface coverage ofca. 50% at equilibrium. Under these conditions (saturated surface), the stationary-state photocatalytic degradation reaches 0.4 molecule nm^–2 h^–1 (>340 nm, radiant fluxca. 22 mW cm^–2). Dioxygen is required and its partial pressure in air is such that the degradation is zero order in O₂. Water vapor markedly increases the rate. The other particulates have also an effect, less important than that of TiO₂, however quite noticeable with respect to surface area unit for the fly ash sample which contains 3.2% Fe₂O₃. Apart from 1,4-naphthoquinone, which is the main intermediate product in all cases, 2-naphthol, phthalide, phthaldialdehyde, phthalic acid, acetophenone, benzaldehyde, benzoic acid are also formed on dry TiO₂. Depending on their volatility, these compounds are transferred to the gas phase or remain principally adsorbed on the solid particles where they are further transformed. For instance, phthalic acid (or anhydride) and benzoic acid are generated from 1,4-naphthoquinone. Degradation mechanisms are briefly discussed.     

Persistence and long-term memories of daily maximum and minimum temperatures in southern South America

The persistence and long-term memories in daily maximum and minimum temperature series during the instrumental period in southern South America were analysed. Here, we found a markedly seasonal pattern both for short- and long-term memories that can lead to enhanced predictability on intraseasonal timescales. In addition, well-defined spatial patterns of these properties were found in the region. Throughout the entire region, the strongest dependence was observed in autumn and early winter. In the Patagonia region only, the temperatures exhibited more memory during the spring. In general, these elements indicate that nonlinear interactions exist between the annual cycles of temperature and its anomalies. Knowledge of the spatiotemporal behaviour of these long-term memories can be used in the building of stochastic models that only use persistence. It is possible to propose two objective forecast models based on linear interactions associated with persistence and one that allows for the use of information from nonlinear interactions that are manifested in the form of forerunners.     

Investment risk and return under renewable decarbonization of a power market

How does financial performance risk affect investments in low-carbon electricity-generating technologies to achieve climate policy targets? A detailed risk simulation of price formation in the Great Britain wholesale power market is used to show that the increasing replacement of fossil facilities with wind, ceteris paribus, may cause a deterioration of the financial risk–return performance metrics for incremental investments. Low-carbon investments appear to be high risk, low return, and as such may require a progressively higher level of support over time than envisaged by the conventional degression trajectories. The increasing riskiness of the wholesale market will to some extent offset the benefits of lower capital costs and operational efficiencies if investors need to satisfy cautious debt coverage ratios alongside positive expected returns. This increased risk is additional to the well-known ‘merit order effect’ of low-carbon investments progressively depressing wholesale prices and hence their expected investment returns.

Policy relevance

Policy support for renewable technologies such as wind is usually based upon levelized costs and is expected to reduce over time as capital costs and operational efficiencies improve. However, levelized costs do not take full account of the risk aversion that investors may have in practice. Expected policy support reductions may be moderated to some extent by the increased financial performance risk that intermittent technologies bring to the power market. The annual risk-return profiles for incremental investments deteriorate for all technologies as wind replaces fossil fuels. This extra risk premium will need to be incorporated into evaluating policy incentives for new investments in a decarbonizing power market.     

Modeling monthly mean air temperature for Brazil

Air temperature is one of the main weather variables influencing agriculture around the world. Its availability, however, is a concern, mainly in Brazil where the weather stations are more concentrated on the coastal regions of the country. Therefore, the present study had as an objective to develop models for estimating monthly and annual mean air temperature for the Brazilian territory using multiple regression and geographic information system techniques. Temperature data from 2,400 stations distributed across the Brazilian territory were used, 1,800 to develop the equations and 600 for validating them, as well as their geographical coordinates and altitude as independent variables for the models. A total of 39 models were developed, relating the dependent variables maximum, mean, and minimum air temperatures (monthly and annual) to the independent variables latitude, longitude, altitude, and their combinations. All regression models were statistically significant (α?≤?0.01). The monthly and annual temperature models presented determination coefficients between 0.54 and 0.96. We obtained an overall spatial correlation higher than 0.9 between the models proposed and the 16 major models already published for some Brazilian regions, considering a total of 3.67?×?10⁸?pixels evaluated. Our national temperature models are recommended to predict air temperature in all Brazilian territories.     

Retrieval of the microphysical properties in a CASP storm by integration of a numerical kinematic model

Abstract

A realistic kinematic model of cloud microphysical processes is presented. Heat and water substances in various phases are transported in the model by specific horizontal and vertical velocities using a semi‐Lagrangian numerical transport scheme. The model is applied to a CASP case study with horizontal and vertical velocities consistent with radiosondes and radar observations. The model is shown to capture the general observed features of the cloud and precipitation fields. In particular, it is shown that for the case studied stratiform clouds extend much higher than the top of detectable precipitation. This numerical model constitutes a useful tool for understanding the life cycle of hydrometeors as a function of their position within the storm system.     

Seasonal predictions based on two dynamical models

Abstract

Two dynamical models are used to perform a series of seasonal predictions. One model, referred to as GCM2, was designed as a general circulation model for climate studies, while the second one, SEF, was designed for numerical weather prediction. The seasonal predictions cover the 26‐year period 1969–1994. For each of the four seasons, ensembles of six forecasts are produced with each model, the six runs starting from initial conditions six hours apart. The sea surface temperature (SST) anomaly for the month prior to the start of the forecast is persisted through the three‐month prediction period, and added to a monthly‐varying climatological SST field.

The ensemble‐mean predictions for each of the models are verified independently, and the two ensembles are blended together in two different ways: as a simple average of the two models, denoted GCMSEF, and with weights statistically determined to minimize the mean‐square error (the Best Linear Unbiased Estimate (BLUE) method).

The GCMSEF winter and spring predictions show a Pacific/North American (PNA) response to a warm tropical SST anomaly. The temporal anomaly correlation between the zero‐lead GCMSEF mean‐seasonal predictions and observations of the 500‐hPa height field (Z₅₀₀) shows statistically significant forecast skill over parts of the PNA area for all seasons, but there is a notable seasonal variability in the distribution of the skill. The GCMSEF predictions are more skilful than those of either model in winter, and about as skilful as the better of the two models in the other seasons.

The zero‐lead surface air temperature GCMSEF forecasts over Canada are found to be skilful (a) over the west coast in all seasons except fall, (b) over most of Canada in summer, and (c) over Manitoba, Ontario and Quebec in the fall. In winter the skill of the BLUE forecasts is substantially better than that of the GCMSEF predictions, while for the other seasons the difference in skill is not statistically significant.

When the Z₅₀₀ forecasts are averaged over months two and three of the seasons (one‐month lead predictions), they show skill in winter over the north‐eastern Pacific, western Canada and eastern North America, a skill that comes from those years with strong SST anomalies of the El Niño/La Niña type. For the other seasons, predictions averaged over months two and three show little skill in Z₅₀₀ in the mid‐latitudes. In the tropics, predictive skill is found in Z₅₀₀ in all seasons when a strong SST anomaly of the El Niño/La Niña type is observed. In the absence of SST anomalies of this type, tropical forecast skill is still found over much of the tropics in months two and three of the northern hemisphere spring and summer, but not in winter and fall.