Larry McDaniel

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...

Analysis of daily variability of precipitation in a nested regional climate model: comparison with observations and doubled CO2 results

Abstract We analyze daily mean, variability, and frequency of precipitation in two continuous 3 1 2 year long climate simulations over the continental U.S., one for present conditions and one for conditions under doubled carbon dioxide concentration, conducted with a regional climate model (RegCM) nested in a general circulation model (GCM). The purpose of the work is to analyze model errors and limitations in greater detail than previously done and to calculate quantities that eventually will be used to form climate change scenarios that account for changes in daily variability of precipitation. The models used are a version of the NCAR Community Climate Model (CCM) and the climate version (RegCM) of the NCAR/Penn State mesoscale model (MM4) at 60 km horizontal grid point spacing. Model output is compared with a 30-year daily observational data set for mainly two regions of the U.S.: the Northwest, and the central Great Plains. Statistics compared include mean daily precipitation, mean daily intensity, frequency, transition probabilities, quantiles of precipitation intensity, and interquartile ranges. We discuss how different measures of daily precipitation lead to different conclusions about the quality of the control run. For example, good agreement between model and observed data regarding mean daily precipitation usually results from compensating errors in the intensity and frequency fields (too high frequency and too low intensity). We analyze how detailed topographic features of the RegCM enhance the simulation of daily precipitation compared to the CCM simulation. In general, errors in all measures are smallest at the Northwest grid points, and the damping of the seasonal cycle of mean daily precipitation from the coast to inland Oregon is basically well reproduced. However, some errors in the frequency and intensity fields can be traced to inadequate representation of topography, even with a horizontal resolution of 60 km. Differences in the control and doubled CO2 runs (for both RegCM and CCM) for these regions are also presented. The most significant changes for the RegCM grid points is increased variability of daily precipitation under doubled CO2 conditions. Areas with significant changes (both increases and decreases) of precipitation frequency and intensity are found. There are some areas where frequency decreases, but precipitation mean daily amounts increase. Such changes, which would be masked by more traditional analyses of precipitation change, are important from a climate impacts point of view. The limitations on the analyses posed by small sample sizes are discussed.

Toward a Quantitative Estimate of Future Heat Wave Mortality under Global Climate Change

Background Climate change is anticipated to affect human health by changing the distribution of known risk factors. Heat waves have had debilitating effects on human mortality, and global climate models predict an increase in the frequency and severity of heat waves. The extent to which climate change will harm human health through changes in the distribution of heat waves and the sources of uncertainty in estimating these effects have not been studied extensively. Objectives We estimated the future excess mortality attributable to heat waves under global climate change for a major U.S. city. Methods We used a database comprising daily data from 1987 through 2005 on mortality from all nonaccidental causes, ambient levels of particulate matter and ozone, temperature, and dew point temperature for the city of Chicago, Illinois. We estimated the associations between heat waves and mortality in Chicago using Poisson regression models. Results Under three different climate change scenarios for 2081–2100 and in the absence of adaptation, the city of Chicago could experience between 166 and 2,217 excess deaths per year attributable to heat waves, based on estimates from seven global climate models. We noted considerable variability in the projections of annual heat wave mortality; the largest source of variation was the choice of climate model. Conclusions The impact of future heat waves on human health will likely be profound, and significant gains can be expected by lowering future carbon dioxide emissions.

Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.

Regional Nested Model Simulations of Present Day and 2 × CO2 Climate over the Central Plains of the U.S.

A nested regional climate model is used to generate a scenario of climate change over the MINK region (Missouri, Iowa, Nebraska, Kansas) due to doubling of carbon dioxide concentration (2 × CO2) for use in agricultural impact assessment studies. Five-year long present day (control) and 2 × CO2 simulations are completed at a horizontal grid point spacing of 50 km. Monthly and seasonal precipitation and surface air temperature over the MINK region are reproduced well by the model in the control run, except for an underestimation of both variables during the spring months. The performance of the nested model in the control run is greatly improved compared to a similar experiment performed with a previous version of the nested modeling system by Giorgi et al. (1994). The nested model generally improves the simulation of spatial precipitation patterns compared to the driving general circulation model (GCM), especially during the summer. Seasonal surface warming of 4 to 6 K and seasonal precipitation increases of 6 to 24% are simulated in 2 × CO2 conditions. The control run temperature biases are smaller than the simulated changes in all seasons, while the precipitation biases are of the same order of magnitude as the simulated changes. Although the large scale patterns of change in the driving GCM and nested RegCM model are similar, significant differences between the models, and substantial spatial variability, occur within the MINK region.

Climate Scenarios for the Southeastern U.S. Based on GCM and Regional Model Simulations

We analyze the control runs and 2 × CO2 projections (5-yearlengths) of the CSIRO Mk 2 GCM and the RegCM2 regional climate model, which was nested in the CSIRO GCM, over the Southeastern U.S.; and we present the development of climate scenarios for use in an integrated assessment of agriculture. The RegCM exhibits smaller biases in both maximum and minimum temperature compared to the CSIRO. Domain average precipitation biases are generally negative and relatively small in winter, spring, and fall, but both models produce large positive biases in summer, that of the RegCM being the larger. Spatial pattern correlations of the model control runs and observations show that the RegCM reproduces better than the CSIRO the spatial patterns of precipitation, minimum and maximum temperature in all seasons. Under climate change conditions, the most salient feature from the point of view of scenarios for agriculture is the large decreases in summer precipitation, about 20% in the CSIRO and 30% in the RegCM. Increases in springprecipitation are found in both models, about 35% in the CSIRO and 25% in theRegCM. Precipitation decreases of about 20% dominate in winter in the CSIRO,while a more complex pattern of increases and decreases is exhibited by the regional model. Temperature increases by 3 to 5 °C in the CSIRO, the higher values dominating in winter and spring. In the RegCM, temperature increases are much more spatially and temporally variable, ranging from 1 to 7 °C acrossall months and grids. In summer large increases (up to 7 °C) in maximum temperature are found in the northeastern part of the domain where maximum drying occurs.

The Effect of Spatial Scale of Climatic Change Scenarios on Simulated Maize, Winter Wheat, and Rice Production in the Southeastern United States

We use the CERES family of crop models to assess the effect of different spatial scales of climate change scenarios on the simulated yield changes of maize (Zea mays L.), winter wheat (Triticum aestivum L.),and rice (Oryza sativa L.) in the Southeastern United States. The climate change scenarios were produced with the control and doubled CO2 runs of a high resolution regional climate model anda coarse resolution general circulation model, which provided the initial and lateral boundary conditions for the regional model. Three different cases were considered for each scenario: climate change alone, climate change plus elevated CO2, and the latter with adaptations. On the state level,for most cases, significant differences in the climate changed yields for corn were found, the coarse scale scenario usually producing larger modeled yield decreases or smaller increases. For wheat, however, which suffered large decreases in yields for all cases, very little contrast in yield based on scale of scenario was found. Scenario scale resulted in significantly different rice yields, but mainly because of low variability in yields. For maize the primary climate variable that explained the contrast in the yields calculated from the two scenarios is the precipitation during grain fill leading to different water stress levels. Temperature during vernalization explains some contrasts in winter wheat yields. With adaptation, the contrasts in the yields of all crops produced by the scenarios were reduced but not entirely removed. Our results indicate that spatial resolution of climate change scenarios can be an important uncertainty in climate change impact assessments, depending on the crop and management conditions.

Response of Soybean and Sorghum to Varying Spatial Scales of Climate Change Scenarios in the Southeastern United States

This study examines how uncertainty associated with the spatial scale of climate change scenarios influences estimates of soybean and sorghum yield response in the southeastern United States. We investigated response using coarse (300-km, CSIRO) and fine (50-km, RCM) scale climate change scenarios and considering climate changes alone, climate changes with CO2 fertilization, and climate changes with CO2 fertilization and adaptation. Relative to yields simulatedunder a current, control climate scenario, domain-wide soybean yield decreased by 49% with the coarse-scale climate change scenario alone, and by26% with consideration for CO2 fertilization. By contrast, thefine-scale climate change scenario generally exhibited higher temperatures and lower precipitation in the summer months resulting in greater yield decreases (69% for climate change alone and 54% with CO2fertilization). Changing planting date and shifting cultivars mitigated impacts, but yield still decreased by 8% and 18% respectively for the coarse andfine climate change scenarios. The results were similar for sorghum. Yield decreased by 51%, 42%, and 15% in response to fine-scaleclimate change alone, CO2 fertilization, and adaptation cases, respectively– significantly worse than with the coarse-scale (CSIRO) scenarios. Adaptation strategies tempered the impacts of moisture and temperature stress during pod-fill and grain-fill periods and also differed with respect to the scale of the climate change scenario.

The Uncertainty due to Spatial Scale of Climate Scenarios in Integrated Assessments: An Example from U.S. Agriculture

We investigate the effects of different climate scenario resolutions on estimates of the impacts of future climate change on agriculture in the United States. Climate scenarios were developed using both a coarse resolution, global scale general circulation model and a spatially more refined regional climate model, nested within the coarse model. The scenarios are similar on a very broad regional scale, but show important differences on a subregional scale. In most areas the fine scale scenario produces a more severe climate change. Simulated changes in crop yields (e.g., cotton, soybean, corn, wheat) were constructed under both the coarse and fine scale scenarios for the conterminous United States. The results demonstrate that the spatial scale of climate scenarios affects the estimates of regional changes in crop yields on several levels of spatial aggregation and the economic impact on the agricultural sector as a whole. For the elevated CO2 case, national economic welfare increased under the coarse sca...

Spatial Scale Effects of Climate Scenarios on Simulated Cotton Production in the Southeastern U.S.A.

We examine the effect of climate scenarios generated using results from climate models of different spatial resolution on yields simulated by the deterministic cotton model GOSSYM for the southeastern U.S.A. Two related climate change scenarios were used: a coarse-scale scenario produced from results of a general circulation model (GCM) which also provided the boundary conditions to a regional climate model (RCM), from which a fine-scale scenario was constructed. Cotton model simulations were performed for three cases: climate change alone; climate change and elevated CO2; climate change, elevated CO2 and adaptations to climate change. In general, significant differences in state-average projected yield changes between the coarse and fine-scale scenarios are found for these three cases. In the first two cases, different directions of change are found in some sub-regions. With adaptation, yields substantially increase for both climate scenarios, but more so for the coarse-scale scenario (30% domain-average increase). Under irrigation, yield change differences between the two climate scenarios are small in all three cases, and yields are higher under irrigation (~35% domain-average increase with adaptation case) compared to dryland conditions. For the climate change alone case, differences in summer water-stress levels explain the contrasts in dryland yield patterns between the coarse and fine-scale climate scenarios.