Linda O. Mearns

Introduction to special section: Regional climate modeling revisited

This paper provides an introduction to the special issue of the Journal of Geophysical Research on “New Developments and Applications With the NCAR Regional Climate Model (RegCM).” In the first part of the paper we revisit and discuss outstanding issues in regional climate modeling in view of the progress achieved in this area of research during the last decade. We discuss issues of simulation length, spin-up, model physics, domain and resolution, lateral boundary conditions, multiple and two way nesting, and variable resolution approaches. In the second part we introduce the papers included in this issue. Among the primary model developments that occurred in the last few years are inclusions of the radiative transfer package and cumulus convection scheme from the National Center for Atmospheric Research (NCAR) global model CCM3, a simplified explicit moisture scheme including direct interaction with cloud radiation, testing of a variable resolution model configuration, improvements in the coupled lake model, and interactive coupling with radiatively active atmospheric aerosols. The papers in the issue illustrate a wide range of applications over different regions, such as the United States, East Asia, central Asia, eastern Africa. The main model limitations and areas in need of improvement are indicated.

The Importance of Land-Cover Change in Simulating Future Climates

Adding the effects of changes in land cover to the A2 and B1 transient climate simulations described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change leads to significantly different regional climates in 2100 as compared with climates resulting from atmospheric SRES forcings alone. Agricultural expansion in the A2 scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans. These and other influences on the Hadley and monsoon circulations affect extratropical climates. Agricultural expansion in the mid-latitudes produces cooling and decreases in the mean daily temperature range over many areas. The A2 scenario results in more significant change, often of opposite sign, than does the B1 scenario.

Approaches to the simulation of regional climate change: A review

The increasing demand by the scientific community, policy makers, and the public for realistic projections of possible regional impacts of future climate changes has rendered the issue of regional climate simulation critically important. The problem of projecting regional climate changes can be identified as that of representing effects of atmospheric forcings on two different spatial scales: large-scale forcings, i.e., forcings which modify the general circulation and determine the sequence of weather events which characterize the climate regime of a given region (for example, greenhouse gas abundance), and mesoscale forcings, i.e., forcings which modify the local circulations, thereby regulating the regional distribution of climatic variables (for example, complex mountainous systems). General circulation models (GCMs) are the main tools available today for climate simulation. However, they are run and will likely be run for the next several years at resolutions which are too coarse to adequately describe mesoscale forcings and yield accurate regional climate detail. This paper presents a review of these approaches. They can be divided in three broad categories: (1) Purely empirical approaches, in which the forcings are not explicitly accounted for, but regional climate scenarios are constructed by using instrumental data records or paleoclimatic analogues; (2) semiempirical approaches, in which GCMs are used to describe the atmospheric response to large-scale forcings of relevance to climate changes, and empirical techniques account for the effect of mesoscale forcings; and (3) modeling approaches, in which mesoscale forcings are described by increasing the model resolution only over areas of interest. Since they are computationally inexpensive, empirical and semiempirical techniques have been so far more widely used. Their application to regional climate change projection is, however, limited by their own empiricism and by the availability of data sets of adequate quality. More recently, a nested GCM-limited area model methodology for regional climate simulation has been developed, with encouraging preliminary results. As it is physically, rather than empirically, based, the nested modeling framework has a wide range of applications.

Climate Change and Forest Fire Potential in Russian and Canadian Boreal Forests

In this study outputs from four current General Circulation Models (GCMs) were used to project forest fire danger levels in Canada and Russia under a warmer climate. Temperature and precipitation anomalies between 1 × CO2 and 2 × CO2 runs were combined with baseline observed weather data for both countries for the 1980–1989 period. Forecast seasonal fire weather severity was similar for the four GCMs, indicating large increases in the areal extent of extreme fire danger in both countries under a 2 × CO2 climate scenario. A monthly analysis, using the Canadian GCM, showed an earlier start to the fire season, and significant increases in the area experiencing high to extreme fire danger in both Canada and Russia, particularly during June and July. Climate change as forecast has serious implications for forest fire management in both countries. More severe fire weather, coupled with continued economic constraints and downsizing, mean more fire activity in the future is a virtual certainty. The likely response will be a restructuring of protection priorities to support more intensive protection of smaller, high-value areas, and a return to natural fire regimes over larger areas of both Canada and Russia, with resultant significant impacts on the carbon budget.

Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the “Reliability Ensemble Averaging” (REA) Method

The ‘‘reliability ensemble averaging’’ (REA) method for calculating average, uncertainty range, and a measure of reliability of simulated climate changes at the subcontinental scale from ensembles of different atmosphere‐ ocean general circulation model (AOGCM) simulations is introduced. The method takes into account two ‘‘reliability criteria’’: the performance of the model in reproducing present-day climate (‘‘model performance’’ criterion) and the convergence of the simulated changes across models (‘‘model convergence’’ criterion). The REA method is applied to mean seasonal temperature and precipitation changes for the late decades of the twenty-first century, over 22 land regions of the world, as simulated by a recent set of nine AOGCM experiments for two anthropogenic emission scenarios (the A2 and B2 scenarios of the Intergovernmental Panel for Climate Change). In the A2 scenario the REA average regional temperature changes vary between about 2 and 7 K across regions and they are all outside the estimated natural variability. The uncertainty range around the REA average change as measured by 6 the REA root-mean-square difference (rmsd) varies between 1 and 4 K across regions and the reliability is mostly between 0.2 and 0.8 (on a scale from 0 to 1). For precipitation, about half of the regional REA average changes, both positive and negative, are outside the estimated natural variability and they vary between about 225% and 130% (in units of percent of present-day precipitation). The uncertainty range around these changes (6 rmsd) varies mostly between about 10% and 30% and the corresponding reliability varies widely across regions. The simulated changes for the B2 scenario show a high level of coherency with those for the A2 scenario. Compared to simpler approaches, the REA method allows a reduction of the uncertainty range in the simulated changes by minimizing the influence of ‘‘outlier’’ or poorly performing models. The method also produces a quantitative measure of reliability that shows that both criteria need to be met by the simulations in order to increase the overall reliability of the simulated changes.

Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles

Abstract A Bayesian statistical model is proposed that combines information from a multimodel ensemble of atmosphere–ocean general circulation models (AOGCMs) and observations to determine probability distributions of future temperature change on a regional scale. The posterior distributions derived from the statistical assumptions incorporate the criteria of bias and convergence in the relative weights implicitly assigned to the ensemble members. This approach can be considered an extension and elaboration of the reliability ensemble averaging method. For illustration, the authors consider the output of mean surface temperature from nine AOGCMs, run under the A2 emission scenario from the Synthesis Report on Emission Scenarios (SRES), for boreal winter and summer, aggregated over 22 land regions and into two 30-yr averages representative of current and future climate conditions. The shapes of the final probability density functions of temperature change vary widely, from unimodal curves for regions where...

An Introduction to Trends in Extreme Weather and Climate Events: Observations, Socioeconomic Impacts, Terrestrial Ecological Impacts, and Model Projections*

Weather and climatic extremes can have serious and damaging effects on human society and infrastructure as well as on ecosystems and wildlife. Thus, they are usually the main focus of attention of the news media in reports on climate. There are some indications from observations concerning how climatic extremes may have changed in the past. Climate models show how they could change in the future either due to natural climate fluctuations or under conditions of greenhouse gas-induced warming. These observed and modeled changes relate directly to the understanding of socioeconomic and ecological impacts related to extremes.

Extreme high-temperature events: changes in their probabilities with changes in mean temperature

Abstract Most climate impact studies rely on changes in means of meteorological variables, such as temperature, to estimate potential climate impacts, including effects on agricultural production. However, extreme meteorological events, say, a short period of abnormally high temperatures, can have a significant harmful effect on crop growth and final yield. The characteristics of daily temperature time series, specifically mean, variance and autocorrelation, are analyzed to determine possible ranges of probabilities of certain extreme temperature events [e.g., runs of consecutive daily maximum temperatures of at least 95°F (35°C)] with changes in mean temperature of the time series. The extreme temperature events considered are motivated primarily by agricultural concerns, particularly, the effects of high temperatures on corn yields in the U.S. Corn Belt. However, runs of high temperatures can also affect, for example, energy demand or morbidity and mortality of animals and humans. The relationships betw...

Trends in Extreme Weather and Climate Events: Issues Related to Modeling Extremes in Projections of Future Climate Change*

Projections of statistical aspects of weather and climate extremes can be derived from climate models representing possible future climate states. Some of the recent models have reproduced results previously reported in the Intergovernmental Panel on Climate Change (IPCC) Second Assessment Report, such as a greater frequency of extreme warm days and lower frequency of extreme cold days associated with a warmer mean climate, a decrease in diurnal temperature range associated with higher nighttime temperatures, increased precipitation intensity, midcontinent summer drying, decreasing daily variability of surface temperature in winter, and increasing variability of northern midlatitude summer surface temperatures. This reconfirmation of previous results gives an increased confidence in the credibility of the models, though agreement among models does not guarantee those changes will occur. New results since the IPCC Second Assessment Report indicate a possible increase of extreme heat stress events in a warm...

The North American Regional Climate Change Assessment Program: Overview of Phase I Results

The North American Regional Climate Change Assessment Program (NARCCAP) is an international effort designed to investigate the uncertainties in regional-scale projections of future climate and produce highresolution climate change scenarios using multiple regional climate models (RCMs) nested within atmosphere–ocean general circulation models (AOGCMs) forced with the Special Report on Emission Scenarios (SRES) A2 scenario, with a common domain covering the conterminous United States, northern Mexico, and most of Canada. The program also includes an evaluation component (phase I) wherein the participating RCMs, with a grid spacing of 50 km, are nested within 25 years of National Centers for Environmental Prediction–Department of Energy (NCEP–DOE) Reanalysis II. This paper provides an overview of evaluations of the phase I domain-wide simulations focusing on monthly and seasonal temperature and precipitation, as well as more detailed investigation of four subregions. The overall quality of the simulations i...