A statistical strategy to deduct regional-scale features from climate general circulation model (GCM) simulations has been designed and tested. The main idea is to interrelate the characteristic patterns of observed simultaneous variations of regional climate parameters and of large-scale atmospheric flow using the canonical correlation technique. The large-scale North Atlantic sea level pressure (SLP) is related to the regional, variable, winter (DJF) mean Iberian Peninsula rainfall. The skill of the resulting statistical model is shown by reproducing, to a good approximation, the winter mean Iberian rainfall from 1900 to present from the observed North Atlantic mean SLP distributions. It is shown that this observed relationship between these two variables is not well reproduced in the output of a general circulation model (GCM). The implications for Iberian rainfall changes as the response to increasing atmospheric greenhouse-gas concentrations simulated by two GCM experiments are examined with...

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Downscaling of Global Climate Change Estimates to Regional Scales: An Application to Iberian Rainfall in Wintertime

Statistical downscaling and bias correction are becoming standard tools in climate impact studies. This book provides a comprehensive reference to widely-used approaches, and additionally covers the relevant user context and technical background, as well as a synthesis and guidelines for practitioners. It presents the main approaches including statistical downscaling, bias correction and weather generators, along with their underlying assumptions, skill and limitations. Relevant background information on user needs and observational and climate model uncertainties is complemented by concise introductions to the most important concepts in statistical and dynamical modelling. A substantial part is dedicated to the evaluation of regional climate projections and their value in different user contexts. Detailed guidelines for the application of downscaling and the use of downscaled information in practice complete the volume. Its modular approach makes the book accessible for developers and practitioners, graduate students and experienced researchers, as well as impact modellers and decision makers.

Statistical Downscaling and Bias Correction for Climate Research

This study uses a model of snow-cover duration, an observed climate data set for the Australian alpine area, and a set of regional climate-change scenarios to assess quantitatively how changes in climate may affect snow cover in the Australian Alps. To begin, a regional interannual climate data set of high spatial resolution is prepared for input to the snow model and the resulting simulated interannual and spatial variations in snow-cover duration are assessed and compared with observations. The model provides a reasonable simulation of the sensitivities of snow-cover duration to changes in temperature and precipitation in the Australian Alps, although its performance is poorer at sites highly marginal for snow cover. (In a separate comparison, the model also performs well for sites in the European Alps.) The input climate data are then modified in line with scenarios of regional climate change based on the results of five global climate models run in enhanced greenhouse experiments. The scenarios are for the years 2030 and 2070 and allow for uncertainty associated with projecting future emissions of greenhouse gases and with estimating the sensitivity of the global climate system to enhanced greenhouse forcing. Attention focuses on the climate changes most favourable (‘best-case scenario’) and least favourable (‘worst-case scenario’) for snow cover amongst the range of climate changes in the scenarios. Under the best case scenario for 2030, simulated average snow-cover duration and the frequency of years of more than 60 days cover decline at all sites considered. However, at the higher sites (e.g., more than 1700 m) the effect is not very marked. For the worst case scenario, a much more dramatic decline in snow conditions is simulated. At higher sites, simulated average snow cover duration roughly halves by 2030 and approaches zero by 2070. At lower sites (around 1400 m), near zero average values are simulated by 2030 (compared to durations of around 60 days for current climate). These simulated changes, ranging between the best and worst case, are likely to be indicative of how climate change will affect natural snow-cover duration in the Australian Alps. However, note that the model does not allow directly for changes in the frequency and intensity of snow-bearing circulation systems, nor do the climate-change scenarios allow possible changes in interannual variability (particularly that due to the El Nino-Southern Oscillation) and local topographical effects not resolved by global climate models. The simulated changes in snow cover are worthy of further consideration in terms of their implications for the ski industry and tourism, water resources and hydroelectric power, and land-use management and planning.

Climate change and snow-cover duration in the Australian Alps

ATMOSPHERIC general circulation climate models are valuable tools for investigating the effects that large-scale perturbations (for example, increasing greenhouse gases1, volcanic dust in the atmosphere2, deforestation3,4 and desertification5) might have on climate. Designed for global-scale climate simulations and having coarse spatial resolutions (from ∼250 km (ref. 6) to 800 km (ref. 7)) such models are now being used to evaluate climatological and hydrological quantities at or near the land surface and at sub-continental scales8–10. They are also being used to provide input data for high-resolution simulations, predicting, for example, the effects of increasing atmospheric CO2 at regional scales11. Here we show that continental surface climatologies and climate change predictions for tropical forest ecotypes derived from general circulation models may be very sensitive to slight modifications in the land-surface/atmosphere coupling. Specifically our results indicate that improving the realism of the areal distribution of precipitation alters the balance between runoff and evaporation. In the case of a tropical forest, we find that by modifying the area over which rainfall is distributed, the surface climatology is changed from an evaporation-dominated regime to one dominated by runoff. Climate studies using near-surface output from such simulations may therefore be misleading.

Sensitivity of regional climates to localized precipitation in global models

Essential for society to function, the production and consumption of food, energy, and water (FEW) are deeply intertwined, leading to calls for a nexus approach to understand and manage the complex tradeoffs and cascading effects. What research exists to date on this FEW nexus? How have scholars conceptualized these interactions at the urban scale? What are some promising approaches? Where are the research gaps? To answer these questions, we conducted a quantitative review of the academic literature on the FEW nexus (1399 publications) over more than four decades (1973–2017), followed by in-depth analysis of the most influential papers using an evaluation matrix that examined four components: 1) modeling approach; 2) scale; 3) nexus ‘trigger’; and 4) governance and policy. Scholars in the fields of environmental science predominated, while social science domains were under-represented. Most papers used quantitative rather than qualitative approaches, especially integrated assessment and systems dynamics modeling although spatial scale was generally recognized, explicit consideration of multi-scalar interactions was limited. Issues of institutional structure, governance, equity, resource access, and behavior were also underdeveloped. Bibliometric analysis of this literature revealed six distinct research communities, including a nascent urban FEW community. We replicated the analysis for this urban group, finding it to be just emerging (80% of papers have been published since 2010) and dominated by scholars in industrial ecology. These scholars focus on quantifying FEW flows of the urban metabolism in isolation rather than as a nexus, largely ignoring the political and socio-economic factors shaping these flows. We propose the urban FEW metabolism as a boundary object to draw in diverse scholarly and practitioner communities. This will advance research on complex FEW systems in four key areas: (1) integration of heterogeneous models and approaches; (2) scalar linkages between urban consumption and trans-boundary resource flows; (3) how actors and institutions shape resource access, distribution and use; and (4) co-production of knowledge with stakeholders.

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A 40-year review of food–energy–water nexus literature and its application to the urban scale

In 1984, Environment Canada, Ontario Region, with financial and expert support from the Canadian Climate Program, initiated an interdisciplinary pilot study to investigate the potential impact, on Ontario, of a climate scenario which might be anticipated under doubling of atmospheric C02 conditions There were many uncertainties involved in the climate scenario development and the impacts modeling Time and resource constraints restricted this study to one climate scenario and to the selection of several available models that could be adapted to these impact studies The pilot study emphasized the approach and process required to investigate potential regional impacts in an interdisciplinary manner, rather than to produce a forecast of the future The climate scenario chosen was adapted from experimental model results produced by the Goddard Institute for Space Studies (GISS), coupled with current climate normals Gridded monthly mean temperatures and precipitation were then used to develop proje

S. J. Cohen

Papers

The Potential Impacts of a Scenario of C02-Induced Climatic Change on Ontafio, Canada