Does public policy support or undermine climate change adaptation? Exploring policy interplay across different scales of governance

Policy makers have now recognised the need to integrate thinking about climate change into all areas of public policy making. However, the discussion of ‘climate policy integration’ has tended to focus on mitigation decisions mostly taken at international and national levels. Clearly, there is also a more locally focused adaptation dimension to climate policy integration, which has not been adequately explored by academics or policy makers. Drawing on a case study of the UK, this paper adopts both a top-down and a bottom-up perspective to explore how far different sub-elements of policies within the agriculture, nature conservation and water sectors support or undermine potential adaptive responses. The top-down approach, which assumes that policies set explicit aims and objectives that are directly translated into action on the ground, combines a content analysis of policy documents with interviews with policy makers. The bottom-up approach recognises the importance of other actors in shaping policy implementation and involves interviews with actors in organisations within the three sectors. This paper reveals that neither approach offers a complete picture of the potentially enabling or constraining effects of different policies on future adaptive planning, but together they offer new perspectives on climate policy integration. These findings inform a discussion on how to implement climate policy integration, including auditing existing policies and ‘climate proofing’ new ones so they support rather than hinder adaptive planning.     

Stratospheric circulation in seasonal forecasting models: implications for seasonal prediction

Accurate seasonal forecasts rely on the presence of low frequency, predictable signals in the climate system which have a sufficiently well understood and significant impact on the atmospheric circulation. In the Northern European region, signals associated with seasonal scale variability such as ENSO, North Atlantic SST anomalies and the North Atlantic Oscillation have not yet proven sufficient to enable satisfactorily skilful dynamical seasonal forecasts. The winter-time circulations of the stratosphere and troposphere are highly coupled. It is therefore possible that additional seasonal forecasting skill may be gained by including a realistic stratosphere in models. In this study we assess the ability of five seasonal forecasting models to simulate the Northern Hemisphere extra-tropical winter-time stratospheric circulation. Our results show that all of the models have a polar night jet which is too weak and displaced southward compared to re-analysis data. It is shown that the models underestimate the number, magnitude and duration of periods of anomalous stratospheric circulation. Despite the poor representation of the general circulation of the stratosphere, the results indicate that there may be a detectable tropospheric response following anomalous circulation events in the stratosphere. However, the models fail to exhibit any predictability in their forecasts. These results highlight some of the deficiencies of current seasonal forecasting models with a poorly resolved stratosphere. The combination of these results with other recent studies which show a tropospheric response to stratospheric variability, demonstrates a real prospect for improving the skill of seasonal forecasts.     

Heterogeneous effects of warming and drought on selected wheat variety yields

Climate change is likely to significantly impact agricultural production in the Great Plains region of the Central United States. This study estimated the impact of changes in temperature and precipitation on wheat (triticum aestivum) variety yield distributions using the moment-based maximum entropy (MBME) model. This approach allows for quantification of potential weather impacts on the yield distribution, and allows these effects to vary across varieties. The unique data set matches wheat variety trial data for 1985 to 2011 with weather data from the exact trial site for 11 locations throughout Kansas. Ten widely-planted varieties with a range of biotic and abiotic characteristics were included for comparison. Weather scenarios were simulated for baseline, increased temperature (one-degree Celsius warming), decreased precipitation (tenth-percentile rainfall outcome), and a combination warming and drought scenario. Warming resulted in an 11 % yield reduction, drought a 22 % reduction, and warming and drought a cumulative 33 % reduction. These effects vary across varieties. Alternative measures of yield risk (e.g. yield variance and coefficient of variation) were also constructed under each scenario and a similar pattern of heterogeneous impacts emerges. The key findings are that (i) exposure to warming and drought lead to mean yield reductions coupled with increased yield risk for all varieties, and (ii) newer (post 2005) seed varieties have a yield advantage over older varieties, however this advantage is reduced under warming and drought conditions.     

Simulation of the effect of doubled atmospheric CO2 on the climate of northern and central California

A Local Climate Model (LCM) is described that can provide a high-resolution (10 km) simulation of climate resulting from a doubling of atmospheric CO₂ concentrations. A canonicalregression function is used to compute the monthly temperature (mean of daily-maximum-temperature) and precipitation for any point, given a set of predictor variables. Predictor variables represent the influence of terrain, sea-surface temperature (SST), windfields, CO₂ concentration, and solar radiation on climate. The canonical-regression function is calibrated and validated using empirical windfield, SST, and climate data from stations in the western U.S. To illustrate an application of the LCM, the climate of northern and central California is simulated for a doubled CO₂ (600 ppmv) and a control scenario (300 ppmv CO₂). Windfields and SSTs used to compute predictor variables are taken from general circulation model simulations for these two scenarios. LCM solutions indicate that doubling CO₂ will result in a 3 C° increase in January temperature, a 2 C° increase in July temperature, a 16 mm (37%) increase in January precipitation, and a 3 mm (46%) increase in July precipitation.     

Coasts, water levels, and climate change: A Great Lakes perspective

The North American Laurentian Great Lakes hold nearly 20 % of the earth’s unfrozen fresh surface water and have a length of coastline, and a coastal population, comparable to frequently-studied marine coasts. The surface water elevations of the Great Lakes, in particular, are an ideal metric for understanding impacts of climate change on large hydrologic systems, and for assessing adaption measures for absorbing those impacts. In light of the importance of the Great Lakes to the North American and global economies, the Great Lakes and the surrounding region also serve as an important benchmark for hydroclimate research, and offer an example of successful adaptive management under changing climate conditions. Here, we communicate some of the important lessons to be learned from the Great Lakes by examining how the coastline, water level, and water budget dynamics of the Great Lakes relate to other large coastal systems, along with implications for water resource management strategies and climate scenario-derived projections of future conditions. This improved understanding fills a critical gap in freshwater and marine global coastal research.     

Evolving Turbulence Realizations of Atmospheric Flow

A significant difference exists between estimates of contaminant atmospheric transport and dispersion calculated by an ensemble-averaged model and the turbulent details of any particular atmospheric transport and dispersion realization. In some cases, however, it is important to be able to make inferences of these realizations using ensemble-averaged models. It is possible to make such inferences if there are sensors in the field to report contaminant concentration observations. Any information determined about the atmospheric transport and dispersion realization can then be assimilated into a forecast model. This approach can enhance the accuracy of the atmospheric transport and dispersion forecast of a particular event. This work adopts that approach and reports on a genetic algorithm used to optimize the variational problem. Given contaminant sensor measurements and a transport and dispersion model, one can back-calculate unknown source and meteorological parameters. In this case, we demonstrate the dynamic recovery of unknown meteorological variables, including the transport variables that comprise the “outer variability” (wind speed and wind direction) and the dispersion variables that comprise the “inner variability” (contaminant spread). The optimization problem is set up in an Eulerian grid space, where the comparison of the concentration field variable between the predictions and the observations forms the cost function. The transport and dispersion parameters, which are determined from the optimization, are in Lagrangian space. This calculation is applied to continuous and instantaneous releases in a horizontally homogeneous wind field such as that observed during traditional transport and dispersion field experiments. The method proves to be successful at recovering the unknown transport and dispersion parameters for a numerical experiment.     

Carbon fluxes resulting from U.S. private timberland management

A carbon budget model was developed to examine the effects of forest management practices on carbon storage in U.S. private timberlands. The model explicitly incorporates the demand for wood products and its impact on harvesting and other management decisions. Forest carbon is divided into four components: carbon stored in trees, soils, forest litter, and understory vegetation. Changes in the forest carbon inventory result from tree growth and management activities, in particular harvesting. Harvesting of timber for wood products is determined by demand and supply forces. The model then tracks carbon in timber removals through primary and secondary processing and disposal stages. Harvesting also has effects on carbon in soils, forest litter, and understory vegetation. A base-run scenario projects increases in carbon storage in U.S. private timberlands by 2040; however, this increase is offset by carbon emissions resulting from harvesting.